Economics of the Food Processing Industry Lessons from Bihar, India 9789811385537, 9789811385544

Table of contents :
Acknowledgements
Contents
About the Author
Acronyms
1 Introduction
References
2 Food Processing: Understanding Common Threads
2.1 Introduction: Why Food Processing?
2.2 General Food Processing Characteristics
2.2.1 Agro-Based Versus Food Processing Industries
2.2.2 What Is Processed Food?
2.2.3 Defining Food Processing as an Industry
2.3 Sub-sectors/Product Networks in Food Processing
2.4 Common Product Networks in Food Processing
2.4.1 Meat Processing Product Networks
2.4.2 Fish Processing Product Networks
2.4.3 Fruit and Vegetable (F&V) Product Networks
2.4.4 Dairy-Based Product Networks
2.4.5 Grain-Based Product Networks
2.4.6 Miscellaneous Groupings
2.5 Product Networks and Region Specificity in Food Processing
2.5.1 Financing Options in Food Processing
2.6 The Way Forward: Embedding Product Networks Within the Regional Context
References
3 Identifying Trajectories in Food Processing
3.1 Food Processing: A Regional Perspective
3.2 Trajectory 1: Benchmark Stylized Facts About Food Processing
3.3 Trajectory 2: The Indian Experience with Food Processing
3.3.1 Stylized Facts and Trajectory 2
3.3.2 Working Capital Constraint in Trajectory 2
3.3.3 Other Constraints in Trajectory 2
3.3.4 Performance of Private Companies in Food Processing in India
3.3.5 National Policies and Subnational Outcomes for Food Processing in India
3.4 Summary
References
4 Food Processing: The Bihar Trajectory
4.1 Bihar: A Short History of Regional Attributes
4.1.1 Pre-2000 Bihar: Economy, Industries and Food Processing
4.1.2 Up to 2006: Leading to Bifurcation of Bihar
4.1.3 2006 Onward: The Bihar Turnaround with Focus on Food Processing
4.2 Relative Performance Post-2008: Trajectory 3 (Bihar) Versus Trajectory 2
4.2.1 Sub-sectoral Inefficiency
4.2.2 Missing Middle Size in Registered Manufacturing Post-2008
4.2.3 Perception of the Business Environment
4.2.4 Business Intentions
4.2.5 Summarizing Observations About Bihar's Manufacturing Pre-2016
4.2.6 Industrial Policy in Bihar
4.3 Identifying Product Networks in Bihar's Food Processing Industries
4.4 Summarizing Across Trajectories: Potential Lessons from Bihar
References
5 Food Processing in Bihar: Industrial Ecosystem
5.1 Introduction: Government as a Stakeholder in Industrialization
5.1.1 Industrial Policy: What Is Its Role in Food Processing?
5.2 Government Policy in Food Processing in Bihar
5.2.1 Pattern of Entry into Food Processing in Bihar
5.3 Evaluating Policy Effects for Food Processing in Bihar
5.3.1 Project-Level Analysis: PMA as Unit of Account
5.3.2 Sub-sectoral Spillover Effects: Factory as the Unit of Account
5.4 Policy Networks: Going Beyond IP for Food Processing
5.4.1 Policy Networks for Bihar Industries
5.4.2 Other Incentive Policies
5.4.3 Recent Indirect-Tax Policy Changes: Introduction of GST in India
5.4.4 Land Policies and Institutions
5.4.5 Industrial Credit Institutions
5.5 Electricity Infrastructure
5.6 Targeting of Policies and Design of Institutions for Food Processing
5.6.1 Green Industrial Policy
References
6 Food Processing in Bihar: Efficiency in Physical Costs
6.1 Introduction
6.2 Theory: Role of the Missing Middle, Physical Costs and Firm Performance
6.2.1 Limited Credit for Small Firms
6.2.2 Limited Credit for Larger Firms
6.3 Efficiency Analysis of Food Processing in Bihar
6.3.1 Sub-sectoral Focus in the Bihar Trajectory: Why Grain Milling and Dairy?
6.4 Empirical Estimation of the Production Process for Grain Milling and Dairy in Bihar
6.4.1 Size Distribution in the Population of Grain Milling and Dairy
6.4.2 Nature of the Production Process: Grain Milling in Bihar
6.4.3 Nature of Dairy Processing in Bihar
6.5 Efficiency Analysis of Grain Milling and Dairy in Bihar
6.5.1 Traditional and Modern Approaches to Measure Efficiency
6.5.2 Two-Stage Network DEA (NDEA): A Brief Description
6.6 Government Policy and Firm Efficiency: Rice Mills in Bihar
6.6.1 Subsidy and Rice Mills in Bihar
6.6.2 Efficiency in Rice Milling: Two-Stage Network DEA (NDEA)
6.6.3 Benchmarking Efficiency Scores
6.6.4 Efficiency Differences Between SFC and Non-SFC Mills: Simar–Zelenyuk-Adapted Li Test
6.6.5 Quantile Regression for Explaining Inefficiency in Rice Mills in Bihar
6.7 Conclusion
References
7 Food Processing in Bihar: Entrepreneurial Perceptions
7.1 Physical Versus Non-physical Costs in Food Processing
7.2 Existing Literature on Entrepreneurial Identity and Behaviour
7.2.1 Motivation for Non-physical Costs and rCFT
7.3 Theoretical Model: Non-physical Costs of Business and rCFT
7.3.1 Post-entry Behaviour: Types of rCFT
7.3.2 Policy Targeting of Entrepreneurial Mindsets
7.4 Empirical Observations from Primary Survey in Bihar
7.4.1 Survey Method: Snowball Sampling
7.4.2 Sample Description
7.4.3 Empirical Measurement of Risk Perception and rCFT
7.4.4 Empirically Testable Hypotheses
7.4.5 Results
7.4.6 Limits to Theory or Limits to Empirical Testing of Theory?
7.5 Going Forward: What Should Industrial Policy Focus On?
7.6 Benchmarking Results: World Bank Enterprise Survey
References
8 Comparing Lessons Across Trajectories
8.1 Bihar and Food Processing: Central Features
8.2 Successful and Not-So-Successful Regional Trajectories in Food Processing
8.2.1 Enjera in Ethiopia
8.2.2 Warehouse Receipts Program in Tanzania
8.2.3 Dairy Initiatives
8.3 Technology-Based Food Start-Ups
8.4 Going Forward: Viable Strategy in Processed Food for Bihar
References
9 Conclusion: Lessons From Bihar's Food Processing
9.1 Lessons From Bihar
9.2 Alternative Theories
9.3 Data Issues
9.4 So What?..
References
Appendix Glossary
Index

Citation preview

Themes in Economics Theory, Empirics, and Policy

Debdatta Saha

Economics of the Food Processing Industry Lessons from Bihar, India

Themes in Economics Theory, Empirics, and Policy

Series Editors Satish Kumar Jain, Jawaharlal Nehru University, New Delhi, India Karl Ove Moene, Max Planck Institute, Munich, Germany Anjan Mukherji, Jawaharlal Nehru University, New Delhi, India

The main objective of the series is to publish volumes dealing with topics in economic theory and empirics with important policy implications. The series aims to publish monographs, both theoretical and empirical, on topics of contemporary interest. While topics that are important from a policy perspective are preferable, volumes in this series are one of the following types: (i) Research dealing with important economic theory topics; (ii) Rigourous empirical work on issues of contemporary importance; and (iii) Edited volumes of selected papers, either of individual authors, or those presented in Economic Theory and Policy conferences which will be conducted annually. Some of the topics that explored in this series are: poverty, income inequality, eminent domain (land acquisition in particular), some theoretical aspects of the functioning of the market mechanism, economic and social implications of affirmative action.

More information about this series at http://www.springer.com/series/15590

Debdatta Saha

Economics of the Food Processing Industry Lessons from Bihar, India

123

Debdatta Saha Faculty of Economics South Asian University New Delhi, Delhi, India

Themes in Economics ISBN 978-981-13-8553-7 ISBN 978-981-13-8554-4 https://doi.org/10.1007/978-981-13-8554-4

(eBook)

© Springer Nature Singapore Pte Ltd. 2020 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

Dedicated to the entrepreneurial spirit of Bihar

Acknowledgements

Many individuals have helped and inspired me in various capacities in writing this book. First and foremost, I thank Prof. Anjan Mukherji for encouraging me to study the food processing industry in Bihar through the International Growth Centre (IGC)-sponsored project of 2016. He provided valuable insights in the earlier stages of the draft as well. I sincerely acknowledge his contributions to improving the book. I thank the IGC, London and IGC, Bihar for financing our project on food processing in Bihar in 2016–2017. Some sections of this book draw on the final report of this project. I also take this opportunity to thank Prof. Maitreesh Ghatak of the London School of Economics and Political Science, London for reviewing our report to the IGC on the Bihar project and providing a very encouraging feedback. It was his comment that the report should be converted into a monograph that seeded the idea for this book. Many individuals at the Asia Development Research Institute (ADRI, Patna), which was the nodal institution for the project, helped me during the survey in Bihar in 2016–2017. Among them, special thanks are due to Shaibal Gupta (Member-Secretary of ADRI), Prabhat P. Ghosh, (Professor at ADRI), Sunita Lall (Administrator at ADRI) and Suryakant Kumar (Finance Officer, ADRI). I also thank Barna Ganguli, my co-investigator for the IGC Project on Bihar’s food processing for co-authoring a section of Chap. 4 in this book as well. A number of other individuals in Bihar have also played a significant role in the development of this book: officers at the Department of Industries and the Udyog Mitra, BIADA; office bearers at the Bihar Industries Association (BIA) and the Bihar Chamber of Commerce (BCC), the entrepreneurs we interviewed in the state and Project Management Agency (PMA) officers. This list is too large to enumerate individually. However, their contribution to this book is immense. They helped me not only collect relevant data but also understand the ground reality of prospects and challenges of the food processing industry in Bihar. In particular, I gratefully acknowledge the support from Shri S. Siddharth, currently Principal Secretary, Government of Bihar (GoB), Mr. Ganesh Khetriwal at the Bihar Chamber of Commerce, Mr. Vijay Goenka (Hebe Ispat) and Mr. Sanjay Goenka (BIA Vice President) for facilitating a number of interviews with these entrepreneurs.

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Acknowledgements

I take this opportunity to thank my colleague Prof. Sunil Kumar at the Faculty of Economics, South Asian University (SAU) for co-authoring a section of Chap. 6. Thanks are also due to some of my students at the South Asian University (SAU) and my research assistants in the IGC Project for collecting the data used for conducting the analysis in Chap. 6. A large thank is due to my doctoral student Jessica Thacker, Faculty of Economics, (SAU) for arranging the ASI unit-level data for our analysis for dairy and grain milling at the 3-digit NIC 2008 classification for Bihar. I would like to thank Rajeev Verma of the Chandragupt Institute of Management, Patna (CIMP) for sharing their report on the physical verification of rice mills in Bihar (project number 34 of 2016 published by the CIMP: further details are at http://www.cimp.ac.in/p/projects), which we use in our analysis in Chap. 6 of this book. Thanks are also due to Barna Ganguli for helping me access this report and to my ex-student Pratyoosh Kashyap, Faculty of Economics, (SAU) for alerting me the Livestock Census data. Last but not the least, a big thank you to research assistants Amresh Kumar and Prabhat Kumar for aiding me in conducting the survey among the entrepreneurs. This volume, for me, was simultaneously discovering Bihar through food as well as food through Bihar. The process of finding an appropriate language to deliver what I learnt from Bihar required a strong degree of optimism in the face of various oddities. These included floods in the state, when our vehicle had to detour a large territory to reach the destination for entrepreneur interviews. In the process, I discovered the incredible Dashrath Manjhi Road, named after Dashrath Manjhi ‘the Mountain Man’, who single-handedly hewed a pathway through a mountain so that residents on the remote side of this monolithic structure could access public facilities on the other side. Each time we hit a roadblock in the survey, I would remember this incredible man and continue with the work at hand. Similarly inspiring were lectures on yoga, including techniques of relaxation, taught by Swami Niranjanananda Saraswati of the Bihar School of Yoga, Munger, which I experimented with during long hours of working on this manuscript. I owe my gratitude to all of these interesting and inspiring people from Bihar. More personally, I thank my son Abhay, husband Abheek and parents (Tushar Kanti Majumdar and Dipika Majumdar) for ensuring that I get the time to pen down my experience with food processing in Bihar in the form of this book. A special thanks to Abhay for ‘renting’ his comfortable room to me for the final editing process. Last but most importantly, I thank Nupoor Singh at Springer Nature for helping me throughout the process of writing, editing and manuscript submission. On a lighter note, her constant reminders regarding submission deadlines, though extremely worrisome during the writing process, are highly appreciated in hindsight. Thank you Nupoor for ensuring the completion of this book. Thanks are also due to Jayanthi Narayanaswamy at Scientific Publishing Services, Chennai, India for helping in the final publication stage of the book and to my doctoral student, Ms. T.M. Vasuprada for proof-reading the final version of the draft.

Contents

1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1 11

2 Food Processing: Understanding Common Threads . . . . . . . . . . 2.1 Introduction: Why Food Processing? . . . . . . . . . . . . . . . . . . . 2.2 General Food Processing Characteristics . . . . . . . . . . . . . . . . 2.2.1 Agro-Based Versus Food Processing Industries . . . . . . 2.2.2 What Is Processed Food? . . . . . . . . . . . . . . . . . . . . . . 2.2.3 Defining Food Processing as an Industry . . . . . . . . . . . 2.3 Sub-sectors/Product Networks in Food Processing . . . . . . . . . 2.4 Common Product Networks in Food Processing . . . . . . . . . . . 2.4.1 Meat Processing Product Networks . . . . . . . . . . . . . . . 2.4.2 Fish Processing Product Networks . . . . . . . . . . . . . . . 2.4.3 Fruit and Vegetable (F&V) Product Networks . . . . . . . 2.4.4 Dairy-Based Product Networks . . . . . . . . . . . . . . . . . . 2.4.5 Grain-Based Product Networks . . . . . . . . . . . . . . . . . . 2.4.6 Miscellaneous Groupings . . . . . . . . . . . . . . . . . . . . . . 2.5 Product Networks and Region Specificity in Food Processing . 2.5.1 Financing Options in Food Processing . . . . . . . . . . . . 2.6 The Way Forward: Embedding Product Networks Within the Regional Context . . . . . . . . . . . . . . . . . . . . . . . . . Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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3 Identifying Trajectories in Food Processing . . . . . . . . . . . . . . 3.1 Food Processing: A Regional Perspective . . . . . . . . . . . . . . 3.2 Trajectory 1: Benchmark Stylized Facts About Food Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3 Trajectory 2: The Indian Experience with Food Processing .

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3.3.1 3.3.2 3.3.3 3.3.4

Stylized Facts and Trajectory 2 . . . . . . . . . . . . . . . . . Working Capital Constraint in Trajectory 2 . . . . . . . . . Other Constraints in Trajectory 2 . . . . . . . . . . . . . . . . Performance of Private Companies in Food Processing in India . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3.5 National Policies and Subnational Outcomes for Food Processing in India . . . . . . . . . . . . . . . . . . . 3.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Food Processing: The Bihar Trajectory . . . . . . . . . . . . . . . . . . 4.1 Bihar: A Short History of Regional Attributes . . . . . . . . . . . 4.1.1 Pre-2000 Bihar: Economy, Industries and Food Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1.2 Up to 2006: Leading to Bifurcation of Bihar . . . . . . . 4.1.3 2006 Onward: The Bihar Turnaround with Focus on Food Processing . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Relative Performance Post-2008: Trajectory 3 (Bihar) Versus Trajectory 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.1 Sub-sectoral Inefficiency . . . . . . . . . . . . . . . . . . . . . 4.2.2 Missing Middle Size in Registered Manufacturing Post-2008 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.3 Perception of the Business Environment . . . . . . . . . . 4.2.4 Business Intentions . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.5 Summarizing Observations About Bihar’s Manufacturing Pre-2016 . . . . . . . . . . . . . . . . . . . . . . 4.2.6 Industrial Policy in Bihar . . . . . . . . . . . . . . . . . . . . . 4.3 Identifying Product Networks in Bihar’s Food Processing Industries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4 Summarizing Across Trajectories: Potential Lessons from Bihar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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5 Food Processing in Bihar: Industrial Ecosystem . . . . . . . . . . . . . . 5.1 Introduction: Government as a Stakeholder in Industrialization . 5.1.1 Industrial Policy: What Is Its Role in Food Processing? . 5.2 Government Policy in Food Processing in Bihar . . . . . . . . . . . . 5.2.1 Pattern of Entry into Food Processing in Bihar . . . . . . . 5.3 Evaluating Policy Effects for Food Processing in Bihar . . . . . . . 5.3.1 Project-Level Analysis: PMA as Unit of Account . . . . . 5.3.2 Sub-sectoral Spillover Effects: Factory as the Unit of Account . . . . . . . . . . . . . . . . . . . . . . . . .

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5.4 Policy 5.4.1 5.4.2 5.4.3

Networks: Going Beyond IP for Food Processing Policy Networks for Bihar Industries . . . . . . . . . Other Incentive Policies . . . . . . . . . . . . . . . . . . . Recent Indirect-Tax Policy Changes: Introduction of GST in India . . . . . . . . . . . . . . . . . . . . . . . . . 5.4.4 Land Policies and Institutions . . . . . . . . . . . . . . . 5.4.5 Industrial Credit Institutions . . . . . . . . . . . . . . . . 5.5 Electricity Infrastructure . . . . . . . . . . . . . . . . . . . . . . . . 5.6 Targeting of Policies and Design of Institutions for Food Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.6.1 Green Industrial Policy . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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6 Food Processing in Bihar: Efficiency in Physical Costs . . . . . . . . 6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2 Theory: Role of the Missing Middle, Physical Costs and Firm Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2.1 Limited Credit for Small Firms . . . . . . . . . . . . . . . . . . 6.2.2 Limited Credit for Larger Firms . . . . . . . . . . . . . . . . . 6.3 Efficiency Analysis of Food Processing in Bihar . . . . . . . . . . 6.3.1 Sub-sectoral Focus in the Bihar Trajectory: Why Grain Milling and Dairy? . . . . . . . . . . . . . . . . . . 6.4 Empirical Estimation of the Production Process for Grain Milling and Dairy in Bihar . . . . . . . . . . . . . . . . . . . . . . . . . . 6.4.1 Size Distribution in the Population of Grain Milling and Dairy . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.4.2 Nature of the Production Process: Grain Milling in Bihar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.4.3 Nature of Dairy Processing in Bihar . . . . . . . . . . . . . . 6.5 Efficiency Analysis of Grain Milling and Dairy in Bihar . . . . . 6.5.1 Traditional and Modern Approaches to Measure Efficiency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.5.2 Two-Stage Network DEA (NDEA): A Brief Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.6 Government Policy and Firm Efficiency: Rice Mills in Bihar . 6.6.1 Subsidy and Rice Mills in Bihar . . . . . . . . . . . . . . . . . 6.6.2 Efficiency in Rice Milling: Two-Stage Network DEA (NDEA) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.6.3 Benchmarking Efficiency Scores . . . . . . . . . . . . . . . . . 6.6.4 Efficiency Differences Between SFC and Non-SFC Mills: Simar–Zelenyuk-Adapted Li Test . . . . . . . . . . . 6.6.5 Quantile Regression for Explaining Inefficiency in Rice Mills in Bihar . . . . . . . . . . . . . . . . . . . . . . . .

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6.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 191 Appendix: Table Describing Efficiency Scores and Other Features of SFC and Non-SFC Mills . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 193 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 195 7 Food Processing in Bihar: Entrepreneurial Perceptions . . . . . . . 7.1 Physical Versus Non-physical Costs in Food Processing . . . . . 7.2 Existing Literature on Entrepreneurial Identity and Behaviour . 7.2.1 Motivation for Non-physical Costs and rCFT . . . . . . . 7.3 Theoretical Model: Non-physical Costs of Business and rCFT 7.3.1 Post-entry Behaviour: Types of rCFT . . . . . . . . . . . . . 7.3.2 Policy Targeting of Entrepreneurial Mindsets . . . . . . . 7.4 Empirical Observations from Primary Survey in Bihar . . . . . . 7.4.1 Survey Method: Snowball Sampling . . . . . . . . . . . . . . 7.4.2 Sample Description . . . . . . . . . . . . . . . . . . . . . . . . . . 7.4.3 Empirical Measurement of Risk Perception and rCFT . 7.4.4 Empirically Testable Hypotheses . . . . . . . . . . . . . . . . 7.4.5 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.4.6 Limits to Theory or Limits to Empirical Testing of Theory? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.5 Going Forward: What Should Industrial Policy Focus On? . . . 7.6 Benchmarking Results: World Bank Enterprise Survey . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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8 Comparing Lessons Across Trajectories . . . . . . . . . . . . . . . . . . 8.1 Bihar and Food Processing: Central Features . . . . . . . . . . . . 8.2 Successful and Not-So-Successful Regional Trajectories in Food Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.2.1 Enjera in Ethiopia . . . . . . . . . . . . . . . . . . . . . . . . . . 8.2.2 Warehouse Receipts Program in Tanzania . . . . . . . . . 8.2.3 Dairy Initiatives . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.3 Technology-Based Food Start-Ups . . . . . . . . . . . . . . . . . . . . 8.4 Going Forward: Viable Strategy in Processed Food for Bihar Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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239 240 241 241 244 246 248 248

9 Conclusion: Lessons From Bihar’s Food Processing . 9.1 Lessons From Bihar . . . . . . . . . . . . . . . . . . . . . . 9.2 Alternative Theories . . . . . . . . . . . . . . . . . . . . . . 9.3 Data Issues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.4 So What?.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Glossary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 257 Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 261

About the Author

Debdatta Saha is an Assistant Professor at the Faculty of Economics, South Asian University (SAU), India. Having worked as an economist at a leading public policy think-tank and as an economist with the competition regulator in India (Competition Commission of India) prior to her appointment at SAU, she has an abiding interest in issues concerning industrial structure, competition and public policy; industrial policy in particular. Her doctoral research at the Indian Statistical Institute, New Delhi on applied game theory in industrial organization gave her valuable insights into the micro-foundation of market structures and industrial outcomes. She has presented her work on industrial policies on various international platforms, and is currently collaborating with the UN Environment Programme (UNEP) to design lecture modules on Green Industrial Policy. This book is the outcome of her analysis of Bihar’s food processing industry in the context of an IGC-sponsored project (with Barna Ganguli, ADRI, Bihar as co-investigator) that explored the drivers of industrial growth for a state with a low industrial base using a lead sector: food processing.

xiii

Acronyms

AP APMC ASICC ASI

CII CMIE COMFED cr. CSO DCS DIPP DPR

FAO FDA FDI FICCI F&V FVC GoB GoI GDDP GHG

Andhra Pradesh Agricultural Produce Marketing Committee A Standard Industrial Commodity Classification Annual Survey of Industries is the primary source for industrial statistics in India, conducting annual surveys at the census level and for state-level samples for registered manufacturing factories, which come under the Factories Act, 1948 Confederation of Indian Industries Centre for Monitoring of Indian Economy Bihar State Milk Co-operative Federation Ltd. crore, an Indian unit of account; see Glossary Central Statistical Organization, Government of India Dairy Co-operative Societies Department of Investment Promotion and Planning, GoI “Detailed Project Report regular basis” is termed as establishment. Paid or unpaid apprentices, paid household member/servant/resident worker in an enterprise are considered as hired workers. Short representation: estab. Food and Agriculture Organization, United Nations Food and Drugs Administration, USA Foreign Direct Investment Federation of Indian Chambers of Commerce & Industry Fruit and Vegetable Food Value Chains Government of Bihar Government of India Gross District Domestic Product is the GDP at the district level for a state in a country Greenhouse Gas

xv

xvi

GSDP

GVA HACCP HSN INR IP IQF KVIC MoFPI MITI MSME MW NDEA NVA OAE PMA rCFT ROI RTE SFC TFP WRS

Acronyms

Gross State Domestic Product is the value of consumption, investment and net government expenditure and is the equivalent of GDP at the sub-national level Gross Value Added Hazard Analysis and Critical Control Points standard for food safety Harmonized System of Nomenclature Indian Rupees: the national currency of India Industrial Policy Individual Quick Freezing: A processing technique for frozen foods Khadi and Village Industries Commission Ministry of Food Processing Industry, GoI Ministry of International Trade and Industry, Japan Ministry of Micro, Small and Medium Enterprises, GoI Megawatt Network Data Envelopment Analysis Net Value Added Own Account Enterprise with no hired workers Project Management/Monitoring Agency: for preparing DPRs and assisting entrepreneurs in Bihar Region-based counterfactual thinking Return on Investment defined as net profit as a percentage of total physical cost (TC) Ready-To-Eat State Food Corporation, Bihar Total Factor Productivity Warehouse Receipts Scheme

Chapter 1

Introduction

This volume is concerned about the manner in which industrialization takes place in a region with a very low industrial base. What are the factors that facilitate the entry of sustainable businesses, generate employment and create wealth in such a region? What ensures their growth? Should particular types of enterprises that intensely use local resources be preferred over others? These questions are important from a regional industrial policy perspective as well as other macroeconomic objectives, such as employment generation and the larger and more nebulous issue of regional economic development. Before we begin to answer them, one needs to address the obvious: what are the reasons due to which this region has a low industrial density in the first place? For understanding this, one will have to engage with the realities of the region under study. This type of region-specific analysis runs into the problem of generalization. How likely is it that there will be other regions that have the same reasons for lack of industries? If there are not many, then the policy lessons drawn from this exercise will remain limited to this region alone. Specificity in the research agenda has marked recent advances in empirical industrial organization studies since the late 1980s, which Prof. Sutton refers to as a very productive ‘ultramicro’ research agenda (Sutton 2007). He points out that way back in June 1987, the Journal of Industrial Economics symposium included ‘influential’ contributions on narrowly defined issues with regional idiosyncrasies, such as the auctioning of offshore oil leases in the US (Hendricks and Porter 1988) or the more (regionally)specific problem of gasoline price wars in Vancouver, Canada (Slade 1992).1 It was, in fact, the lack of generalization that motivated Prof. Sutton’s search for a unified theory to explain the evolution of concentration parsimoniously (using only the two dimensions of endogenous sunk costs of advertising and intensity2 of price competition) for a class of food processing industries across six developed countries 1 These

papers were later published in The Review of Economic Studies in different years as mentioned in the references at the end of this chapter. 2 Prof. Sutton refers to this as ‘toughness’ of price competition. © Springer Nature Singapore Pte Ltd. 2020 D. Saha, Economics of the Food Processing Industry, Themes in Economics, https://doi.org/10.1007/978-981-13-8554-4_1

1

2

1 Introduction

(US, UK, France, Germany, Italy and Japan) in his classic volume (Sutton 2007). The core argument is that product characteristics determine the nature of market concentration and to a large extent does not need a region-specific filter.3 Note, however, that Prof. Sutton had selected developed countries (some of them part of the European Union, sharing similar legal framework and institutions) with a large industrial base and eschewed from any policy and welfare analysis in his thesis. Conducting a similar exercise, keeping in mind a policy imperative, for developing countries with low levels of industrialization requires a sacrifice in terms of a general theory underpinning the evolution of an industry. Tolstoy’s novel Anna Karenina contained an apt quote: “Happy families are all alike; every unhappy family is unhappy in its own way.” Industrial and economic backwardness, similarly arises due to different factors, resulting in unique regional problems that are difficult to generalize. We conduct a similar exercise as Prof. Sutton and that too in the same food processing industry. However, region-specificity is at the forefront of our discussion. We do not apologize for this specificity in our approach, as we intend to pick out policy lessons that we might be or more importantly, not be able to apply to other regions within the same industrial space of processed food. Despite its undoubted elegance, we expend with theoretical generalization. We argue that an ‘ultra-micro’ study of food processing, using Bihar4 as the context, educates policy by highlighting what is more likely to work and what policy-levers are unlikely to deliver outcomes by comparing regional specificities and bottlenecks. To this extent, our work is closer in spirit to Prof. Sutton’s Enterprise Map for Ethiopia (joint with Nebil Kellow; see Sutton and Kellow (2011)), where the authors provide a sketch of enterprises in an industrially and economically backward country. The discussion there is embedded within the regional boundaries of Ethiopia and there is no attempt at generalization. The authors investigate the Ethiopia-specific research issue of the source of capabilities of middle and large firms across several sectors (including food processing) in the country. Our comparison with Sutton and Kellow (2011) is not an accident. First, the analysis in Sutton and Kellow (2011) demonstrates that the same author changes the paradigm for research (from the one in Sutton (2007)) for the same industry to address concerns of a developing country. Second, we consider Ethiopia as a close parallel to Bihar and compare outcomes in food processing in these two regions. Note here a problem in comparison: Ethiopia is an independent country whereas Bihar is a subnational region in India.5 At the same time, Bihar, as a region, shares some uncanny similarities with Ethiopia6 and has much less in common with the states of Karnataka or Maharashtra or even West Bengal, which are subnational states in India. 3 Prof.

Sutton’s classic text ‘Sunk Costs and Market Structure: Price Competition, Advertising and the Evolution of Concentration.’ however, does point out several limitations of this exercise for analysing processed food sub-sectors like Ready-To-Eat (RTE) cereals, packaged soups, frozen foods, baby foods, etc. 4 A subnational region (state) in the northern part of India. 5 Chapter 8 elaborates on this issue. 6 BBC country profile page for Ethiopia (https://www.bbc.com/news/world-africa-13349398) mentions the state of civil unrest and droughts up until 1991. Bihar, too, has had a history of political

1 Introduction

3

This points to the difficulty of a purely India-specific focus for industrial analysis, as discussed in Chap. 2. We now revert to our original question: ‘why is a region industrially backward?’. Our research methodology to address this, as the long-winded explanation above indicates, is to study this from the vantage of a particular region as well as a particular industry (an ‘industrial sub-sector × region’-tuple that we refer to as ‘trajectory’ in food processing in Chap. 2). Our choice of region, as is obvious by now, is Bihar and the chosen industry is food processing. What ails Bihar’s economy, in general, and industries, in particular, has been of academic and policy interest in India in recent times (for instance, see the discussion in Chakrabarti (2013), Mukherji and Mukherji (2015), Dasgupta (2010)). The immediate spotlight on this region is due to its recent spectacular performance, referred to as the ‘Bihar miracle’, as measured by some economic indicators such as the state GDP (SGDP) growth rate. The contrast in outcomes for the state is clear from its lacklustre condition prior to 2006, a brief sketch of which is provided in Chap. 4. However, Bihar continues to be almost without any significant industrial density, despite major improvements in electricity, roads, law and order. Bihar Economic Survey (2018-19) notes that while this state reports a share of 5.4% of all enterprises in India in 2018, most of these are informal and tiny in size. Bihar’s share in India’s industries is lower than its population share of 8.6% of the country. One potential reason for the low firm density due to its history of several years of political misrule which led to a breakdown of public institutions (Witsoe 2013), de-urbanization and flight of capital (Chakrabarti 2013), which continued till 2006.7 Weather-based uncertainties are another bane for Bihar. Despite abundant natural riverine and agri-resources, this region has one of the most unpredictable weather patterns. The state has witnessed years of simultaneous floods and droughts. These natural calamities, which increase uncertainties in agricultural production, create problems for ensuring certainties in supply-lines for the manufacturing of processed food products. Fragmented and small-size landholdings, as is common to much of eastern India (including Bihar) increases the problem of aggregating produce from individual small farmers for achieving minimum scale for processing in a factory. For a state which lacks a common agricultural trading market like an APMC (Agricultural Produce Marketing Committee), establishing backward linkages with multiple small farmers is a big challenge. These are potential explanators for the low industrial base. Not only is the industrial base low in Bihar, but it is also marked by a significant duality in firm size. This is evident in both formal and informal manufacturing, as Chap. 4 elucidates. We refer to this as the ‘missing middle’ problem, as there is a clear absence of middle-sized firms in manufacturing (including food processing). This phenomenon has been studied in the literature on the misallocation of resources for developing countries. The contention is that resource misallocation leads to this kind of highly skewed distribution of firm sizes in the population of existing firms in the industry, as efficient mid-sized firms are not allocated the inputs/resources of misrule and alternates between droughts and floods. Both are land-locked regions, limiting easy access to trade using sea-routes. 7 Chapter 4 discusses this in detail.

4

1 Introduction

production. Another strand of the literature related to regulations also discusses the role of cut-off-based regulations in developing countries which create such distortions in the distribution of firm size. For instance, labour laws and tax policies which apply more rigorously for firms above a certain cut-off size can act as a disincentive for firm expansion from small to middle sizes. Note that a missing middle is not an uncommon phenomenon for food processing industries even for developed countries, such as the US. Sutton (2007) studies the frozen food sub-sector in the US, which showed marked dualism with a missing middle size in the distribution of firm size in the 1980s. He cites competition in advertising expenditure, which squeezed the profits of mid-size firms. For Bihar, intense competition is not an issue given the overall low density of firms. It has to be a different set of issues that lead to this missing middle in firm size distribution. However, isolating the exact cause for this phenomenon is still a complicated identification exercise. As we discuss in Chap. 4, it is nearly impossible to untangle the policy events in the state alongside other national-level factors to address issues of endogeneity that are inevitable in econometric analyses of this phenomenon. To avoid these complications, we appeal to the historical time-line of the denouement of important events in the state and use ‘history-as-causality’ type of argumentation to explain the missing middle phenomenon in Bihar.8 While this is not the only reason, it is a compelling argument supported by the political economy commentaries on Bihar, such as Witsoe (2013) and Chakrabarti (2013). Most of the academic literature on the ‘missing middle’ investigate its causes, such as Restuccia and Rogerson (2013), Restuccia and Rogerson (2017) and Tybout (2000). Industry studies show that these causes are specific to a region, such as the US frozen foods example we mentioned earlier. This does not necessarily generalize to contexts of low industrial density. We feel that studying the effect of the ‘missing middle’ is as important as its causes, and there is very little academic investigation along this dimension. This is also related to our other queries regarding successful interventions to industrialize Bihar. The existence of the ‘missing middle’ increases the fragility of existence for new small-scale entrants and can result in high-risk assessment among entrepreneurs thereby dissuading business expansion, as Chaps. 6 and 7 explain. Non-expansion of existing small firms and no entry of middle-sized firms are likely to exacerbate the skewness in firm size distribution in the long-run. What we show is that this is likely to be caused by the existing extremity in the distribution of firm sizes in food processing in Bihar. More specifically, our explorations in these chapters show that policy interventions attracted small-scale entry (and a few large firms) thereby accentuating the skewness in the distribution in firm size, rather than correcting for the missing middle size. We embed our policy discussions using an industrial ecosystem framework, which includes all stakeholders in the industrialization process. However, Industrial Policy (IP) within such a system is of central importance. This is true not only of Bihar, but also other states in India. There is a stiff inter-state competition in providing subsidies through these policies in order to attract investments in industrial sectors 8 Chapter

4 provides further details.

1 Introduction

5

(see Chap. 5). The Bihar government has imperatives of generating employment, given its dense population. The state has a precariously high dependence on the tertiary sector as well as agriculture. Bihar Economic Survey (2018-19) reports that as of 2018, an 8.6% share of the total population of the country is in Bihar, resulting in a tremendous population density of 1106 persons per square kilometre roughly three times that of the national average. Generating productive employment in the state, by reducing the burden on agriculture and tertiary sectors needs strong support from the sustainable development of the secondary sector, with manufacturing in focus. At present (2017–2018), the state has a minimal contribution of the secondary sector (which includes manufacturing) at 17.5% of state GSDP, whereas the primary sector (including agriculture and other allied sectors) contributes 20% and the bulk comes from the service-oriented tertiary sector at 62.3%. It is not for employment generation alone that we focus on registered manufacturing in food processing.9 The tertiary sector is also capable of producing employment. One might argue that Bihar, in line with India, has bypassed the manufacturing stage in its structural transition from agriculture-dependence to tertiary sectors. As an industrial sector, food processing has strong backward linkages with agriculture and therefore, is capable of absorbing not only output and excess labour from agriculture, it is also capable of reducing wastage of agri-produce and enhancing food security. In times of changing climate and uncertainties about food production, this is an important element of government policy. A different motivation for our study of food processing in Bihar is that the Government of Bihar (GoB), since 2008 till 2016, had put in place very generous incentive schemes for attracting investments into food processing in Bihar. Food processing in Bihar, therefore, allows us to study the effects of policy for fostering industrial development. The argument, motivating the special incentives for this sector, was that Bihar is a state with an abundance of agri-produce, such as staples (such as maize and rice), multiple vegetables10 and fruits such as banana, litchi, pineapple11 as well as dairy and poultry output. Therefore, it was showcased as an attractive investment destination for manufacturing units in food processing. That an input advantage is a strong argument in favour of the location of processing units in perishable food products is undeniable, as the time-to-processing-outlet matters for these products. This argument stresses that for perishable products, such as agri-produce (including dairy and poultry items), industrial processing units should locate near input sources. By this logic, as long as a region satisfies the input abundance criterion, it should be an automatic sink for attracting industrial investments in food processing. However, the optimism presented in this line of reasoning requires a reality-check. 9 Our

formal analysis restricts itself to registered manufacturing, as these units have been treated with policy incentives in recent years, in particular, those in food processing. The policy issues are discussed in detail in Chap. 5. 10 The Yes Bank and CII (Confederation of Indian Industries) 2016 Report entitled ‘Make in India: Opportunities in the Food Processing Sector’ mentions potato, eggplant, cauliflower, tomato as the most abundantly grown crops in Bihar. It is available at https://www.yesbank.in/pdf/make_in_ india-_opportunities_in_food_processing_sector.pdf. 11 ibid.

6

1 Introduction

If this argument was correct, Bihar should have been one of the hubs for industrial processing of food products in India by 2008,12 as this agri-abundance is not new for this region located in the fertile Gangetic plain.13 It is an empirical fact that prior to 2008, there was hardly any significant food processing activity in Bihar, other than state-owned initiatives in dairy and sugarcane and the odd tiny rice-milling unit using primitive technology. It was the policy announcements in 2008 and the continued subsidization of food processing as a lead sector for industrialization that resulted in a slew of entry of new units into this sector in Bihar. This points to some weaknesses in the argument that Bihar is a ‘natural’ destination for locating industrial units in food processing in the sense that unfettered market-based incentives would lead to the development of this industry in the state. That this did not happen without policy support needs us to examine the earlier claim about input-advantage-based manufacturing location decisions in processed food. The problem is the partial definition of ‘input’ in this claim: while raw materials for processing is undoubtedly the bulk input for this industry, successful manufacturing requires other inputs, such as land, capital, manpower and entrepreneurial skill (in making appropriate technology choices as well marketing decisions). This brings us back to our initial claim that regional-specificities, which we identify as part of the ‘industrial ecosystem’ in Chap. 5, have the potential to significantly constrain the feasibility for industrialization in a particular sector. Therefore, a narrowly defined industrial trajectory (in our case, this is food processing in Bihar) is the appropriate unit of analysis, and that all the elements of the ecosystem should be studied in minutiae to be able to address our research objective of deciding which factors aid or constrain industrial development. In this book, Chaps. 4 to 7 identify the following constraints: skewed firm size distribution with a missing middle size of firms, financial market imperfections and lack of entrepreneurial skills (most importantly, the ‘entrepreneurial mindset’ as defined in Chap. 7) as well as missing markets in expert services and skilled manpower. These, though specific to the Bihar trajectory, is not a rare phenomenon in many other parts of the world. Chapter 8 attempts to compare across similar (and dissimilar) trajectories to draw out policy lessons that will work (or will not) in other regions. There is no simple policy fix for the problem of the low and skewed density of firms, even for a sector for which raw material input is abundantly available in the region. The sharp correction in the state’s Industrial Policy (IP) in 2016 is, possibly, reflective of the frustration of policymakers with the outcomes in food processing that belied initial expectations. The vertical and horizontal dimensions of IP, discussed 12 We use 2008 as a reference year, as special incentives for industrial investments in food processing were announced by the GoB in 2008. 13 This holds despite the region’s proneness to floods and droughts. Soil fertility and abundant water resources have ensured that Bihar produces a large bounty of agricultural output. There are other problems, such as the small size of landholdings and yield issues in agriculture for Bihar. However, in comparison with many other states in India, it is undeniable that Bihar does produce a significant amount of maize, rice, milk and many fruits and vegetables that can sustain a viable processing industry in the state. It has a major presence in crops like maize and makhana, though there are other states with higher agricultural yield than Bihar.

1 Introduction

7

in detail in Chap. 5, are needed in a delicate balance. Horizontal elements of the policy address issues that affect all industrial sectors, such as infrastructure, property rights and other such indicators of ‘ease-of-doing’ business. Vertical instruments in the policy favour one sector over others, so that investments are channelled to the chosen sector (the controversial ‘picking-up-winners’ aspect of IP). Our thesis about the use of policy rests on how the twin problems of financial market imperfections work in tandem with a lack of entrepreneurial skill in the presence of the ‘missing middle’ in firm size. Mid-sized firms perform an important function for small-scale entrants, which is the most common entry size in food processing.14 However, the strategy to market the processed product can differ for different small-scale entrants. We tie this choice of marketing to the skewness of the distribution of firm size. At one extreme, an entrant can cater to the ‘non-retail’ segment, which does not require advertising expenditure on own-brand sales, either through unbranded wholesale or industrial sales. At the other extreme, the entrant engages in the retail of own-branded products. An example of the former is basic processing products like polished rice (using milling technology) from raw paddy. This output can be sold in non-branded packets of different sizes to other intermediate buyers or in the unbranded unpackaged form in general stores (which is very common in India). Sutton (2007) also mentions catering as a potential market segment to sell this product. We claim that middle-sized firms sub-contract and purchase unbranded products (through ‘buy-ins’) from smallsized firms, thus creating a market to place the product for entrants.15 Large-sized firms integrate vertically through the entire supply chain, due to the reputation and quality standards necessary for maintaining brand share in the marketplace. The absence of middle-sized firms adds to the struggle of new entrants with product placement in the market. They now have to incur additional expenditure to invest in own brands and struggle to find shelf-space in the retail market alongside large firms or are forced to sell with extremely low margins through unbranded wholesale. Lack of access to working capital due to financial market imperfections and novice skill set in entrepreneurship now exacerbates the problem of placing own-label products 14 We found this empirical regularity in our survey in Bihar in 2016 for food processing. Gebrewolde and Rockey (2018) mentions a similar pattern for most industries in Ethiopia. Small-scale entry is present in developed country food processing, as noted by Vyas (2015) for a number of sub-sectors in the Scottish food and drinks industry as well as many instances mentioned in Sutton (2007) for the six countries of USA, UK, Japan, Germany, France and Italy. 15 Our argument is in contrast with Sutton and Kellow (2011), who argue that the only viable form of entry into an industry in Ethiopia is for a firm to be mid-sized. We place a more relaxed condition: we find that successful entry requires entrepreneurial experience and access to appropriate information. Even small-scale entry is likely to result in productive firms being established in food processing in Bihar. Small-scale success stories are present for many countries, e.g. the Khadi example in India, food processing units in the US (see Sutton (2007)) and the UK, as discussed in Vyas (2015). Note that Sutton and Kellow (2011) generalize their findings across a diverse set of industries, from leather, textiles to pharmaceuticals and food processing. The possibility of successful small-scale entry is more likely for most sub-sectors in processed food than in others. Big firm failure is, in fact, seen in many instances such as Ruchi Sofa Industries Pvt. Ltd., which used to be India’s largest oil processing firm.

8

1 Introduction

directly in the retail market for the small entrants. This, we identify, is the primary cause of why, despite handsome subsidies to this sector from 2008–2016, small firms have not been able to expand. Using this theory in the backdrop, Chap. 6 studies growth constraints for firms in registered manufacturing across grain milling and dairy, as well as rice mills. Note that the latter is a part of grain milling. It is treated differently as the data sources for grain and rice milling are different. Our theory regarding marketing inefficiency as a major constraint finds support in our two-stage Network Data Envelopment Analysis (NDEA) analysis. We find that the extremely low-efficiency scores for these units are mainly due to marketing rather than technical inefficiency. This finding has a clear implication about policy design. Assume that the government policy from 2008–2016 is a contract between the monopoly principal (GoB) and agents (entrants in food processing). The contract is low-powered because of its inability to attract middle and large-sized investments. While we do find small-scale entry, the subsidy part of the contract incentivizes investments in the start-up capacity of production. This is likely to improve technical efficiency, but its impact on improving marketing efficiency is likely to be limited. This explains why the overall profitability of new businesses is very low. Imperfect availability of industrial finance, particularly collateralized working capital, work against small-sized businesses. They are undercapitalized and unable to undertake the kind of last-mile retailing expenditure that large firms do in processed food. Another way of examining our argument is to focus on the entrepreneurial perception of risks and its relationship with firm performance. We now focus on the nature of the constraint on entrepreneurial skills, using the construct of the region-based counterfactual thinking (rCFT). Whereas our earlier explanation was centred on efficiency arguments based on minimization of physical costs of production (sunk/fixed and variable costs in production, distribution and marketing), this perspective examines the non-physical costs of entrepreneurship and this is captured using costs due to inexperience and financial market imperfections. These costs are difficult to measure empirically, but their pervasive presence was evident in our discussions with entrepreneurs in food processing in Bihar. Intermingled with descriptions of difficulties in resource mapping for inputs for bulk processing in a factory (as was the case with a makhana processing unit) to product placement in retail markets (multiple rice milling units expressed their concern regarding this), we observed frequent references to these non-physical costs costs in the qualitative component of the interviews. We use the theory of regret-based decision-making to develop the concept of rCFT, which provides a link between the region and regret-based decision-making. We motivate rCFT in our primary survey among entrepreneurs in Bihar in 2016 (as explained in detail in Chap. 7) by asking the entrepreneurs: Would you do business in Bihar had you not been a native of the state?

We contend that rCFT helps us understand the extent and dynamics of the entrepreneurial skill constraint in regions like Bihar. The low industrial base indicates that prior to 2008, entrepreneurship was not a preferred profession. Most of

1 Introduction

9

the new businesses (mostly rice mills) that were attracted to the policy announcements up to 2016 were first-generation entrepreneurs, and not external mid-sized investments. A few large firms did start business in food processing, such as Godrej Agrovet producing poultry feed in Hajipur Industrial Area and ITC’s dairy unit in Munger, but the majority were small local investments, who perceived the government’s upfront capital subsidies in the manufacturing business of food processing as a good opportunity. Our primary survey in Bihar in 2016 revealed that most of these businesses would start undercapitalized to minimize delays and cost overruns. This kind of hurried entry would run into financial problems very soon. Given inadequate access to working capital, poor financial outcomes would not only deter these entrepreneurs from further plans for expansion, but would also be reflected in their ‘mentalizing’ about poor performance. This form of cognitive dissonance, whereby the entrepreneur explains the reason for unpalatable outcomes due to her/his regional identity and lack of outside options, is captured by the rCFT. While counterfactual thinking (CFT) has been discussed in the literature in entrepreneurial behavioural economics, we add the dimension of the region to it to explain the feedback loop of how entrepreneurial perceptions work in the specific industrial ecosystem under study. rCFT, for inexperienced first-generation entrepreneurs, is likely to be significantly negative; we should expect to see abundant responses of the kind: We would never have done business in Bihar had we not been native to Bihar.

Second, for rCFT to reflect the extent of the constraint placed on a region due to lack of entrepreneurship skills, we would expect it to be negatively correlated with a variable that captures the perception of risk of an entrepreneur. Negative rCFT should be correlated with high-risk perception and vice versa. The higher this correlation between rCFT and risk perception, the larger is the entrepreneurial skill constraint. Note here that the World Bank Enterprise Survey, through its ‘Ease-ofDoing Business’ index, tries to capture this using data on environmental variables, like the presence (or absence) of single-window clearance facility, enforcement of property rights, etc. We, instead, use directly a measure of ‘entrepreneurial mindset’, as reflected by the rCFT. This measure captures the extent to which an entrepreneur thinks a region has innate business potential, which the Ease-of-Doing Business index tries to assess using pre-specified indicators. Third, rCFT is closely associated with the ‘missing middle’ problem in firm size distribution. Larger is the negative value of rCFT, lower is the possibility that the entrepreneur will harbour plans for business expansion. With the mid-size firms missing, small new entrants have to struggle to find appropriate retail outlets. Our contention is that the inability of finding efficient alternatives to industrial sales to the mid-sized firms results in this type of rCFT, dampening the entrepreneur’s appetite for further expansion. Our data of a sample of firms in various sub-sectors in food processing reveals that there is significant negative rCFT among novice entrepreneurs and this is correlated with a high risk perception. Additionally, it is only with prior experience of the

10

1 Introduction

entrepreneur in the trading business16 and access to diverse information sources (captured by membership in trade associations) that risk perception goes down. This allows for the possibility that negative rCFT changes to positive rCFT, i.e. the entrepreneur asserts that they would do business in Bihar even if they were not native to the state. This exercise shows that for a region to successfully exploit its raw material resource advantage, a necessary ingredient is entrepreneurial experience, which helps the entrepreneur form positive expectations about business expansion and capability creation. Additionally, we find that front-loaded capital subsidies from the government are unlikely to screen effectively between entrepreneurs with different mindsets, just as it cannot proactively discriminate against small-scale entry. Note that an abundance of ‘reluctant’ entrepreneurs would not be able to take the kind of risks that the literature on entrepreneurship assigns to these agents for achieving growth of a firm in any industry. Hence, by this alternative channel of entrepreneurial behaviour and skill constraint, we show that policy incentives would not be very successful in engendering the growth of firms in Bihar. The policy implications of both of these explanations, viz. marketing difficulties and limited entrepreneurial skills, is clear. The vertical arm of the IP targeting food processing needs to be padded with additional horizontal investments in region-based brand creation as well as entrepreneurial coaching programs. In terms of its chapters, this volume starts with a description of some possible trajectories and product networks in food processing, arguing why a narrowly defined regional one of food processing in Bihar bears merit for exclusive study in Chaps. 2 and 3. The Bihar trajectory is discussed in detail in Chap. 4, where we discuss how history has shaped industrial outcomes in the state and why food processing is of special relevance for the state. Chapter 5 begins an in-depth analysis of the industrial ecosystem in Bihar, with the government as an important stakeholder in the process of industrialization. The empirical assessment of policy incentives from 2008–2016 for Bihar indicates only modest success in terms of outcomes. Despite a potential policy bias against small firm entry, it is precisely these firms that entered the sub-sector of rice milling in large numbers due to the subsidies. The next two chapters provide answers to the reason for the lack of policy success from two different perspectives. Chapter 6 picks up the discussion on physical costs and highlights the constraints to the growth of entrants due to the absence of industrial sales to mid-sized firms. Chapter 7 follows up with another line of analysis: constraints in entrepreneurial skill and correct ‘entrepreneurial mindset’. Chapter 8 compares trajectories in food processing, providing evidence of successes as well as failures. Chapter 9 concludes. A Glossary is provided at the end of this volume to help readers understand the names and characteristics of a number of local delicacies from Bihar. One has to admit that it is not possible to do justice to them in the written form. The best manner to experience them would be to indulge in them in person in eateries in Bihar, as the author had occasion for during the field survey in 2016. Apart from 16 Sutton

and Kellow (2011) observe the same pattern for Ethiopia: trader experience of managerowners of mid- to large-sized firms help them expand the capabilities of the firm better than novice entrepreneurs.

1 Introduction

11

this, we have to apologize for some deliberate exclusions of specific details from the survey in Bihar, as they are not directly relevant in the context of this volume.17 Doing so would make the book unusually lengthy and meandering.

References Bihar Economic Survey (2018-19), Finance Department, Government of Bihar Chakrabarti R (2013) Bihar breakthrough: the turnaround of a Beleaguered State. Rupa Publications Dasgupta C (2010) Unraveling Bihar’s ’growth miracle’. Econ Polit Wkly XLV 52:50–62 Gebrewolde TM, Rockey J (2018) The effectiveness of industrial policy in developing Countries: causal evidence from ethiopian manufacturing firms. University of Leicester Working Paper No. 16/07 Hendricks K, Porter RH (1988) An empirical study of an auction with asymmetric information. Am Econ Rev 78(5):865–883 Mukherji A, Mukherji A (2015) Bihar: what went wrong? And what changed? In: Pangariya A, Rao MG (eds) The making of miracles in Indian States. Oxford University Press, New York Restuccia D, Rogerson R (2013) Misallocation and productivity. Rev Econ Dyn 16(1):1–10 Restuccia D, Rogerson R (2017) The causes and costs of misallocation. J Econ Perspect 31(3):151–174 Slade ME (1992) Vancouver’s gasoline-price wars: an empirical exercise in uncovering supergame strategies. Rev Econ Stud 59(2):257–276 Sutton J (2007) Sunk costs and market structure: price competition, advertising and the evolution of concentration. The MIT Press, Cambridge Sutton J, Kellow N (2011) An enterprise map of Ethiopia. International Growth Centre, London, UK. http://eprints.lse.ac.uk/36390/.ISBN9781907994005 Tybout JR (2000) Manufacturing in developing Countries: how well do they do and why? J Econ Lit 38(1):11–44 Vyas V (2015) Low-cost, low-tech innovation: new product development in the food industry. Routledge, New York Witsoe J (2013) Bihar in Kohli A, Singh P ed. Routledge Handbook of Indian Politics. https://doi.org/10.4324/9780203075906

17 Some of these include engaging anecdotes from an official at Udyog Mitra, who described the reluctance of a local entrepreneur in repaying the bank-loan that he had taken for his food processing venture. In a most entertaining conversation, he recounted how the entrepreneur offered to go to jail rather than part with any liquidity to repay the loan to the perplexed collection agent sent by the bank. Though this underscores the problem of finance in doing business in regions like Bihar, we have not been able to include a number of these details, as this problem is true for all kinds of business, not only food processing.

Chapter 2

Food Processing: Understanding Common Threads

2.1 Introduction: Why Food Processing? We live in the age of the Anthropocene (see the discussion in Kress and Stine (2017); Domanska (2014); Zalasiewicz et al. (2008)). The history of man, relative to our planet’s timeline, is actually not very long. However, the effects of human activity have changed the destiny of earth in major ways. While the debate on global warming might very well be an ongoing engagement for us, mounting scientific evidence in recent times make it very hard to deny the complicity of human activity in the warming of the planet, as discussed in Cook et al. (2016), Fischer and Knutti (2015), Ring et al. (2012) and Satterthwaite (2008). Changing climate and unpredictable weather patterns have raised questions about food security (refer to Lal (2004); Lobell et al. (2008); Wheeler and Von Braun (2013)). At this point, the effect of climate risks on the food security is not unanimous, with some evidence showing differential regional impacts (see Parry et al. (1999) for effects on Africa and Brown and Funk (2008) for effects on the Americas, Africa and Asia), some others1 argue that the problem of food security till 2050 is not a matter of quantity of production from agriculture, such as Hunter et al. (2017). At one level, there is some evidence that total output is not threatened much under modern systems of agriculture as most countries are producing more than the necessary quantities. And yet, the issue of post-harvest loss as well as problems of distribution and infrastructure remain as major reasons for food shortage and malnutrition worldwide today, as discussed in the report of FAO (2015). It is in this context of reduction of post-harvest losses and improving food distribution that we motivate our study of food processing. This industry bears the promise of achieving these goals for better food security, despite climatic shocks. As its name suggests, this industry is derived from agro-based activities, with multiple possibilities in different contexts and times.

1 http://theconversation.com/we-dont-need-to-double-world-food-production-by-2050-heres-

why-74211. © Springer Nature Singapore Pte Ltd. 2020 D. Saha, Economics of the Food Processing Industry, Themes in Economics, https://doi.org/10.1007/978-981-13-8554-4_2

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In its most primitive avatar, processing of food is done with the intention of extending the shelf life of perishables and reducing the seasonality of agri-output (see Food Processing Handbook (2012)). The most expansive outlook for processed food, on the other hand, links it with the creation of historical–cultural–regional identities, as Pilcher (2017) mentions. Recent innovations in the food industry, unsurprisingly, is related to unique labelling and marketing regional identities in food. For instance, most beverages derived from fresh produce have the purpose of transforming the raw produce to another form that has a longer shelf life. At the same time, it has given rise to distinct geographically tagged products, such as the Scotch whisky from Scotland, cognac and champagne from France, the Japanese rice wine ‘sake’, Spanish version of red wine with chopped fruits (sangria), Cuban cocktail ‘daiquiri’, the primarily Russian vodka, Tibetan rice beer ‘chang’ or the local Indian toddy. Among non-alcoholic beverages is the national (or regional) tag such as the Darjeeling or Assam tea from India. This shows the power of processed food to create local identities globally. However, some caveats should be raised upfront. Food processing is a doubleedged sword. On the one hand, it might be a victim of climatic shocks and reduced inputs from agri-output. On the other, industrial activity in the preparation of processed food is also a contributor to Greenhouse Gas (GHG) emissions that lead to globally higher temperatures, as Foley et al. (2011) mentions. Globally, direct emissions from agriculture represent 10–12% of overall GHG emissions as per estimates of Heller and Keoleian (2014). Their study claims that agri-activities is the thirdlargest contributor for carbon footprints in a typical U.S. household after transportation and housing. Intensity of usage of water is another factor: the United Nations World Water Development Report of 20122 notes that while 1 kg of rice production requires approximately 3500 L of water, the same quantity of beef production requires about four times more (around 15,000 L of water). A single cup of coffee itself, according to these UN estimates, requires 140 L of water. Among other waterintensive food crops are sugarcane (with an estimated usage of 1500–3000 L of water to produce 1 kg in Indian conditions3 ), soya and wheat (each with a requirement of 900 L to produce 1 kg in Indian conditions4 ). The trade-off here is this: food processing contributes to GHG emission and environmental degradation and at the same time, this sector can contribute to increasing agricultural incomes in developing countries due to its requirements of primary agri-produce as a raw input. There are multiple paths that the literature seems to identify for the income enhancement potential of food processing. Consider the case of Thailand. Watanabe et al. (2009) finds food processing as a pro-poor activity here as both the channels of employment generation and input usage from agriculture have worked effectively. At a global level, this industry comes with the promise of increased trade flows between the Northern and Southern countries, as is the case with organic processed foods. Raynolds (2004) notes that though the global North 2 United

Nations World Water Development Report 4. UNESCO, UN-Water, WWAP. March 2012. of Claro Energy available at https://claroenergy.in/5-most-water-intensive-crops/.

3 Estimates 4 ibid.

2.1 Introduction: Why Food Processing?

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dominates the market for this food category, a South–North network has opened up linking major markets in the USA and Europe with processed food production regimes in the South.5 Argentina, Mexico, Dominican Republic and other Latin American countries are in the lead in this market with commodities such as coffee, cocoa, sugar, soya and meat. In the fiscal year 2017, roughly 53% of Argentina’s organic produce was exported to the US and another 28% to the EU.6 Another possibility is that the manufacturing part of this industry will create an ecosystem with an appropriate density of manufacturing firms ensuring the delivery of infrastructure (roads, power, credit lines, etc.) that will benefit other sectors of the economy. This is a line of argument that we trace out in this book: the possibility of using food processing as a lead sector to jump-start industrialization in an industrially backward area. So, while there is a cost to the environment, proactive measures to develop food process promise multiple benefits for a region with high population density. This does not imply that we should underestimate the environmental constraints in the process. With deteriorating air quality and falling water tables, sustainability in businesses has become a priority for countries like India. Government policies promoting industries need to proactively account for the environmental cost of promoting manufacturing activity. In particular, the structure of subsidies and financial incentives can be made contingent on environmental goals, such as increased usage of solar energy, rainwater harvesting, water recycling, etc. In recent times, ‘Green’ Industrial Policies (or Green IPs, discussed later in this book) directly address this trade-off. At another level, it is possible to see the retail connection from manufactured processes in food, which will also lead to employment generation (through supermarkets, restaurants and other food-related services). However, the core focus of this volume is the development of manufacturing activity in processed food and not the service-based catering or retail segments. It is manufacturing in food processing that has received special incentives from Bihar’s government, and our interest is to see how these interventions have worked out. However, an investigation of the marketing of processed food products is an integral part of our discussions. Note here that for any industrial enterprise to develop in processed food in any region (not only Bihar), there is a requirement of a general economic principle of viability to work, independent of any additional policy support. In the context of food processing, as elucidated by Dorfman (2014), this principle works through a generalized form of arbitrage. For instance, when an entrepreneur decides to enter into the industry of fruit juice manufacturing, she does so because there are positive returns in arbitrage over time by transforming the raw fruit product to the edible juice form. However, there is a technological limit to this kind of arbitrage. Food Processing Handbook (2012) clearly mentions that severe processing techniques such as heat preservation, freezing or drying might significantly change the chemical composition of the raw product. Quality changes through processing might imply that consumers 5 This

means not a mere transfer of agricultural raw material, but the movement of final processed goods from the South to the global North. 6 For Argentina, the data is from statista.com.

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might prefer the raw product rather than the processed product. For instance, excessive drip loss during thawing of frozen strawberries often results in consumer bias in favour of the fresh produce. Essentially, if processing creates a completely new product by drastically changing the physicality of the product, then the principle of arbitrage fails to apply as we end up with an entirely different processed product. Even if we assume that extreme distortion of the product characteristics does not happen for much of basic processing activity in food, there might be other factors that act as constraints. In Bihar, for example, there is a strong preference for fresh food products, as we found out during our primary survey in the state in 2016–2017. The extent of this preference is seen in the difficulties that cold storage products face: consumers prefer fresh agri-produce and not output from cold storages. Rather than frozen processed desserts, consumers prefer freshly cooked local sweets (such as ramdana lai or khaja), constraining the market for frozen desserts to urban centres like the state capital of Patna. In regions like Bihar, to what extent should we stand by the generalized principle of arbitrage to determine the viability of processing activities in food? We have to be certain that once the conditions of generalized arbitrage hold, unfettered market forces can sustain an industry in manufacturing processed food. Any application of this principle assumes that there should be a willingness to pay for the transformed product (post-processing) such that costs of production are covered. Hypothetically, suppose this is the case. This assumption removes the demand constraint from the discussion. Alongside, we limit ourselves to the category of ‘non-controversial’ processed food products, like polished rice, for which demand is unfettered in the region.7 In this scenario, what exactly is the role of government policy? Is it to constrain monopolistic exploitation of that willingness to pay among suppliers, which also squeezes out the potential competition in the market? Or, is it to compensate for some other factors in the industrial ecosystem that we have not yet discussed, that is nonetheless essential for the production of processed food? Our arguments are in favour of the latter. We contend that simply satisfying a viability condition in terms of profitable arbitrage is not sufficient to ensure the development of a sustainable business in food processing in a region. This is a purely raw material input-based argument, which suppresses other ingredients necessary for the production of manufactured items in food processing. By this logic, a region with abundance in agricultural output (the primary raw material in food processing) is naturally suited for the location of processing industries, as the nearness to inputs would reduce transportation costs, making arbitrage across different forms of food more profitable. Nonetheless, this has not been the case for many regions, especially Bihar. While it has an abundance of agri-inputs, the bulk of this produce is exported out of the state for processing in other regions. It was not prior to 2008, when special policy incentives were set aside for developing a manufacturing base in processed food, that there was any significant activity in this sector. For food processing industries, input-based location advantage might seem compelling due to the perishability of 7 See the discussion on product networks in Sect. 2.3 later to link ‘non-controversial’ processed food

items with basic processing.

2.1 Introduction: Why Food Processing?

17

raw items. However, the importance of other inputs, such as finance, entrepreneurial skills, infrastructure as well as consumer tastes and preferences more often than not emerge as major constraints. We take the liberty, at this point, of providing an extremely concise history of food processing. This is, in part, to develop on this theme of what factors have aided or constrained manufacturing activities in food processing in different parts of the world at different time points. To a large extent, the global North has dominated the manufacturing activities in processed food, despite agricultural output abundance in many regions of the South, such as India. This exercise, therefore, clearly demonstrates the limits to the generalized arbitrage argument for addressing issues of viability in food processing. A Brief History of Food Processing As an activity, food processing has a long history since Egyptian times. Simple processes such as sun-drying raw produce have been commonly practised by humans for many centuries for storing food items post-harvest. While food product choices and diets have multiplied ubiquitously, post-industrial societies have a more homogeneous understanding of what constitutes food material, as Pilcher (2017) states The rise of modern, industrial societies during the past 300 years has increased personal choice while narrowing the overall diversity of human food supplies, just as novelty diets and individualistic eating habits have undermined the sense of community formed over the dinner table.

Processing technologies evolved to keep in tandem with changing human sensibilities towards how food is served. Nowhere is this more clearly exemplified than the technologies that have evolved for processed meat. Fitzgerald (2010) mentions that in the Middle Ages, it was a common cultural norm to ‘not only carve meat at the table, but also present various animals, such as pigs, calves, and hares, with their heads attached’. However, the emergence of the slaughterhouse in the early nineteenth century (around the time of emergence of the industrial era) was largely driven by a shift in sensibilities and a concern about public hygiene. Simple processing technologies for meat preservation using salt evolved rapidly to semi-cooked processed meat-based dishes manufactured using assembly-line techniques. Post-industrial food narratives were marked with a strong productionist bias, putting total output and efficiency issues in the forefront. From this perspective, the role of processing in reducing post-harvest losses is its dominant function, particularly in highly perishable items. The advent of modern warehousing techniques such as cold chains, Individual Quick Freezing (IQF) processes and warehouse-stores (see Connor and Schiek (1997) for developments in the US) fundamentally changed the simple sun-drying preservation techniques that were pre-industrial history. Some cottage industry, particularly in jams, pickles, F&V juices and savouries remain in many parts of the world, especially developing countries. The march

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of processed Ready-To-Eat (RTE) and convenience food items is, nonetheless, a reality in most countries. Instant noodles, fast food as well as pre-packaged meals have become common in most urban markets with the rise of international air-travel that has globalized food tastes to some extent. With globalization, newer strands have crept into the narrative with food processing marching far beyond its original scope of extending shelf life of perishable edibles to the arena of the discovery of new food items manufactured in factories (such as nutrient-fortified foods). In the global North, the focus has shifted towards specialized packaging to reduce nutrient loss and moisture-induced damage, certification and standards for hygiene in content and processing and standards for organic food products. The discussion in Sanderson and Schweigert (1985) for the future of R&D investments in food processing in the US around the 1980s indicates an increased scope for research in the discovery of new packaging materials and biotechnology advances for enhancing food safety and standards for quality. Private as well as public standards for quality have become ubiquitous in food. Consider, for instance, the HACCP (Hazard Analysis and Critical Control Points) standard for food safety and hygiene, which is mandatory for meat and juice products in the US and is regulated by the FDA and the Department of Agriculture in the USA. The seven principles of the HACCP began in the 1960s with a collaboration between the erstwhile US grains major Pillsbury and NASA (as mentioned by Sperber and Stier (2009)). It became a worldwide standard by 1997 with the incorporation in the comprehensive international standard ISO 22000 FSMS 2011 for food safety and quality. A different perspective on the historical timelines of food processing uses the notion of ‘food regimes’ as mentioned in McMichael (1992). This literature considers the history of the entire value chain from farm to food in one single complex through two time zones: the 1870–1914 order and the 1947– 1973 one. The former period coincides with colonial systems determining the agri-food relationship, with commercialization in agriculture directed at mass consumption in metropolitan areas. The national focus of the early era was challenged in the second part of the food regime, with the advent of transnational food processing companies searching globally for agri-inputs and markets. The industrial era in food processing, which coincides with the second regime, has extended itself in multiple dimensions: through contract farming in agriculture, capital-intensive automated processing technologies, new product development in food items and different platforms for retailing: online as well as offline brick-and-mortar outlets for processed food. At present, different regions of the world have specialized in different sub-sectors of manufactured food items. For example, frozen food products are common in countries such as the US, UK and some other countries in the European Union. These are not that common in large parts of the developing world, such as India. Prepared meals (such as dried and powdered soups) and Ready-To-Eat (RTE) cereals

2.1 Introduction: Why Food Processing?

19

also have a similar geographical distribution, partly due to historically defined preferences in terms of food items and partly due to the industrial ecosystem that supports the development of these sub-sectors in food processing. On the other hand, many types of ‘namkeen’ snacks and sweets are commonly available in South Asia that are not processed in the global North. We begin now with our attempt to arrive at a working definition for food processing as an industrial activity engaged in the manufacture of food products. This seemingly simple task has to deal with many issues related to definitions that do not necessarily follow value chain perspectives and which vary over time and place. The core issue we highlight through this discussion is the choice of an appropriate food processing development ‘trajectory’, a concept we develop in greater depth in the next chapter.

2.2 General Food Processing Characteristics What constitutes food processing as an industry for a region? Quoting the Ministry of Food Processing Industries (MoFPI, India) definition of 2012, Ghosh (2014) points out that there is “no systematic and scientific data pertaining to food processing activities and their demand for agricultural products based on harmonized concepts, definitions and classifications is ... available.” This depends upon some hazy boundaries between food versus agro-processing, the usage of Industrial Classification standards and also to items considered as edible processed food in the region. As mentioned earlier, culture often rigidly determines food consumption in many regions. We explicitly mention the difficulties in the definition of food processing as an industry in what follows.

2.2.1 Agro-Based Versus Food Processing Industries Consider the distinction between agro-based as opposed to food processing industries. Both of these industries depend on agricultural and animal husbandry inputs for raw materials. Further, the basic characteristic of these industries is that they transform these raw inputs in some manner (using processes defined by the existing technology as well as market demand). One way to differentiate between them is through the final output. Food processing industries cater to the final demand for what is considered edible items or food (human as well as animal consumption), whereas the output of agro-based industries extends to non-edibles such as cotton or jute. In this sense, food processing is a narrower version of agro-processing industries, the product basket from the latter includes a diverse range from food to feed (cattle

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2 Food Processing: Understanding Common Threads

or poultry feed8 ), fibre, fuel to industrial raw material. For example, sheep farming produces inputs of processed meat as well as wool, the former being a part of the output basket of food processing, whereas the latter would come under agro-based activities. To this extent, we exclude items like non-edible fibre agri-produce such as cotton, jute, mesta and coir in our analysis of processed food. Perhaps, a terminology such as agro-food (as used in Nesvadba et al. (2004); Raynolds (2004) or Bojnec and Fertõ (2009)) might reduce some confusion. Nonetheless, the prefix of ‘agro’ does not do justice to the diversity of upstream primary sectors from which food processing industries sources its inputs: horticulture, agriculture, forestry, fisheries, animal husbandry and plantation crops such as tea. Narrowing the output basket from agro-processing to food processing does not necessarily reduce the complexity of this activity. While Sect. 2.2.3 discusses in some detail the nature of consumer demand for food, we discuss some preliminaries regarding what constitutes ‘processed food’ at this point.

2.2.2 What Is Processed Food? In common parlance, food processing is an intermediate stage between raw agriinputs (including animal husbandry, such as dairy, poultry, etc.) and the food retail sector. Processed food, therefore, is a transformation of the raw input to a final edible form, that is acceptable to consumers as food items. Food preferences, habits and tastes are shaped by human culture and is climate–region–time-specific and this poses a large demand-side constraint on what is accepted and marketed as processed food. Most of the literature on food processing, such as Murthy and Dasaraju (2011), cites two fundamental stages of processing: first, basic or primary processing and then, the advanced stage which we term secondary processing. Basic processing is minimalistic. It involves cleaning and preparing the raw agri-input into an edible format. Most sub-sectors of food processing, such as grain milling, meat and fish processing, F&V or dairy, require basic cleaning, sorting, grading, dehusking (for grains) or pasteurization (for milk). These processes use relatively simple technologies that aid in food preparation and provide less value-addition than the secondary processing stage. Modern nutrition theory, however, indicates that this stage ensures that the nutritive properties of food are retained and that higher orders of processing add chemicals harmful to health.9 Advanced processing involves a major transformation of the raw agri-inputs from the primary production stage. Oftentimes, this results in the discovery of new food 8 Both

food and feed are included in food processing. in the popular press, such as https://www.medicalnewstoday.com/articles/318630.php as well as academic literature, such as https://www.cambridge.org/core/journals/public-healthnutrition/article/increasing-consumption-of-ultraprocessed-foods-and-likely-impact-on-humanhealth-evidence-from-brazil/C36BB4F83B90629DA15CB0A3CBEBF6FA indicate the harmful effects of highly processed foods. Though there is value-addition, with advanced processing it is likely that health benefits of food will be lower. 9 Articles

2.2 General Food Processing Characteristics

21

products like instant noodles or soups or frozen desserts. Despite health concerns, the convenience created for the consumer through advanced processing is undeniable and is a major reason for its current demand. Take, for instance, oatmeal for breakfast. In the traditional cooked form, it requires significant preparation time with overnight soaking and boiling as well as garnishing. Pre-cooked oatmeal, such as the Quaker’s Quick Oats brand, can be prepared in minutes in a microwave. Modern urban lifestyles have created significant demand for convenience foods with advanced processing. This covers a vast diversity of edibles: among bakery items, there are breads, cakes, etc. Among farinaceous products such as pasta, noodles and cereal-based by-products, food items like flakes, crisps or pops (made from corn, rice or wheat) and various oatmeal creations are marketed. Miscellaneous other process items vary from prepared meals in the form of sandwiches, salads or soups to processed dairy items such as yoghurts, flavoured yoghurts, ice-creams, cheese of various kinds. F&V derivatives are marketed in diverse forms such as jams, jellies and sauces.

2.2.3 Defining Food Processing as an Industry The standard procedure to define an industry requires some kind of ASICC (A Standard Industrial Commodity Classification, as discussed in Ghosh (2014)). Commonly used ASICCs are national ones, such as the National Industrial Classification (NIC), published by the CSO, India, as well as the International Standard Industrial Classification of All Economic Activities (ISIC), published by the UN Statistical Commission. Note that these standards classify industrial activities in different digit classifications, the broadest ones starting at the 2-digit level. While food products as an industrial activity have a presence at the 2-digit level, one problem is that these classifications are not time-invariant. While the ISIC Rev. 3, under Section D (Manufacturing), covered a combined category ‘Manufacture of food products and beverages’ under division 15, it is now re-organized and bifurcated under divisions 10 and 11 (‘Manufacture of food products’ and ‘Manufacture of beverages’ respectively under ISIC Rev. 4 in 2008). Table 2.1 in the Appendix summarizes the description of processed food items under these categories disaggregated to the 4-digit level. Alongside this, we present the NIC 2008 (the current national classification standard in India) for concordance. However, despite this broad coverage, some ubiquitous products from the processed food industry and crucial inputs in the production process, such as warehousing, are not included. We include the latter in Table 2.1. Many sectors claim products from food processing as inputs into their production process. There is no end to the extent to which broadening the definition of processed food is possible. Many sub-sectors with a minor contribution to food processing would be eligible for inclusion as processed food, as Ghosh (2014) mentions. Not only should we include agriculture, hunting and agro-services (NIC 2008 codes 017 and 016, respectively), but other products such as tobacco (NIC 2008: 120), chemical products (NIC 2008: 202), pharmaceuticals, medicinal chemicals and botanical products

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(NIC 2008: 210) and bio-gas energy and non-conventional electricity generation (NIC 2004: 401). A working definition for processing food comes from the MoFPI, GoI: ...food processing is a collection of industries which include items pertaining to two processes: (a) manufactured: if any raw product of agriculture or fisheries is transformed through a process [involving employees, power, machines or money] in such a way that its original physical properties undergo a change and if the transformed product is edible and has commercial value, then it comes under the domain of Food Processing Industries and (b) other : if there is a significant value-addition (increased shelf life, shelled and ready for consumption, etc.) such activity also comes under food processing.

The latter allows the inclusion of cold chains and warehouses in the ambit of manufacturing processes in food. However, this definition is not without problems, particularly from an entire supply chain-based view of a food product, despite the attempt made by the second part of the definition to allow for including value-added services such as cold storages in the production process. This definition misses out on many other upstream and downstream components that are important in this industry. While poultry feed finds inclusion in processed food, the related sector of commercial layer poultry is excluded. Similarly, bee-keeping which gives rise to high-quality unprocessed honey is also technically excluded from the definition of what qualifies as a honey processing unit. A broader definition of food processing as an industry is to include all activities that lead to the discovery of edible items fit for human and animal consumption. This would be a very large and somewhat vague set for analytical purposes. We narrow down our definition to manufacturing activities related to the production of food products (which loosely maps to the 2-digit NIC 2008 division 10). We do not include the production of beverages (NIC 2008 division 11) or tobacco (NIC 2008 division 12) in our analysis. The industrial classification for beverages and tobacco also treats them as separate categories from food products, despite the multiple presences of many firms across these divisions.10 Ghosh (2014) draws a similar boundary as ours for processed foods. Despite its limitations in accounting for multi-firm presence across sectors, this isolation of food products allows a simple bifurcation of processed food into the primary and advanced processing stages. To the extent that majority of the units that we discuss later belong to primary processing within food products and do not have a multi-sectoral presence, we are satisfied that this distinction does not do injustice to our analysis. We now take this definition to study product networks in food processing and address the nature of industrial trajectories later.

10 The best example of this is the ITC (Indian Tobacco Company) Pvt. Ltd. in India, which began its existence during the British era as a tobacco product (cigarettes, etc.) manufacturer and at present has diversified into the production of food products, such as wheat flour (one of the largest selling brands being ITC’s Aashirvaad atta), various dairy items like liquid milk (under the Aashirvaad Svasti brand) and ghee as well as fruit juices under the B Natural brand.

2.3 Sub-sectors/Product Networks in Food Processing

23

2.3 Sub-sectors/Product Networks in Food Processing Food processing has many subcomponents, such as grain processing or dairy or beverages. In common parlance, these are referred to as sub-sectors. We refer to these instead as product networks. Nicholson et al. (2011) discusses something close to our definition of product networks, with their example of dairy processing. The main raw material: fresh milk is processed into fat, protein concentrate, cream, whey, dry milk and skim milk. These materials are further processed to develop derivatives with a higher value-addition, such as condensed and evaporated milk, nutritional products, butter milk and milk powder. Note that our approach of product networks is different from that of Kim et al. (2012), where the product network is discussed in the context of a market basket analysis. There, the perspective on product networks is from the vantage of the consumption basket of a consumer, whereas ours is devoted to understanding the product network from the supply-side, i.e. understanding product configuration possibilities, given costs and technology. Hence, we define a product network in processed food as follows: Definition 2.1 Product Network: is a hierarchical network, where the nodes are food products (whose primary ingredient is a particular agri-produce, like wheat11 ) and the edges/links in the network are value-additions. The direction of the edges indicates the flow of value-addition, from basic to advanced processed products. Consider a wheat processing product network in Fig. 2.1, which presents a stylized depiction of what we mean by a typical product network. Note that this depiction does not show all the possible products that are discoverable from basic wheat processing,

Fig. 2.1 Stylized product network in wheat processing (Author’s creation) 11 Note,

however, that the network has to include ingredients like salt/sugar, etc. for any higher value-addition products.

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as the collection is vast and cumbersome to show in a single network diagram. Note that a number of agricultural crops give us flour for advanced processing, such as maize and cornflour. Akkerman et al. (2010) mentions how different types of starchy items can be milled from a variety of grains for further ‘blending’ into flour products targeted at bakeries and industrial manufacturers. In our wheat example, we simplify the process, by keeping the source of the product network fixed for a particular agricultural crop, such as wheat. One reason for this is technological specificity: for many starchy products such as wheat and maize, the flour processing machines are different.12 In our general stylization, we allow for a more general description of grain-based product networks. The purpose of this figure is to show how the different nodes relate with each other in the hierarchical structure that is characteristic of food processing. All the nodes in the network are various products that are derived from wheat, except for some essential ingredients like salt, sugar, additives like nuts, fats and preservatives. These exceptions are present in each and every product network that we discuss in Sect. 2.4. The links in the network are value-addition connections, with directional arrows showing the direction in which value-addition takes place, moving from basic to advanced processing that completely denatures the original product. The network is hierarchical because these nodes are arranged in different layers. The latter represent the different segments within which the entrepreneur can operate. The first layer relates to basic processing. In the context of wheat processing, this part of the product network starts with products developed by transforming the agri-produce to wheat flour and other by-products like bran or wholegrain wheat or flakes and reaches the retail market either directly (own-brand) sales or unbranded wholesale or indirectly (by sales to other processors, who then use their own branding to market the product). In this layer, therefore, the entrepreneur has the choice of not only which products to produce, but also the layer within which it can operate. The last layer in this network links both the first layer and the second layer of processed items such as wheat flour or bran or bakery edibles to the catering/service segment where these products can be served either in the form of further processed/cooked form or in the packaged form as available in the retail segment. One example is restaurants serving pastry items that are available in retail markets in a packaged format. Once again, the entrepreneur engaged in processing has the choice of who to sell the product: the retail market (directly or indirectly) and/or to the catering segment. There is a subtle difference between a supply chain and product network analysis, as we have defined the latter. A typical supply chain definition is (from Beamon (1999)): ... a one way, integrated manufacturing process wherein raw materials are converted into final products, then delivered to customers. Under this definition, the supply chain includes only those activities associated with manufacturing, from raw material acquisition to final product delivery. 12 For instance, see the description of different machinery for flour milling provided by ABC Machinery (Anyang Best Complete Machinery Engineering Co., Ltd.), which supplies milling equipment in Africa, South America and Southeast Asia. Further details are at: http://www.bestflourmill.com/.

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25

Supply chains focus on the process that converts raw material to final products, whereas in our network, the key focus is on all products (the nodes) that are derived from a raw agri-produce through subsequent value-additions. At every intermediate stage are products, as well as market mechanisms, that an entrant into food processing can decide to specialize in. Knowledge of the supply chain is not sufficient. It matters up to the stage of manufacturing of processed food. Beyond that, a clear idea of the product network matters. In fact, the real challenge for a successful entrepreneur in food processing is to find the correct product niche or the collection of products as well as the marketing mechanism to place these products. It goes without saying that moving up the value-addition links in a network (from primary to secondary processing) fetches higher retail margins. Branded sales in more value-added products also have the potential to reduce the effect of competition. However, expanding the business into all segments and product categories is not easy. Even for the US, Sutton (2007) notes the difficulty that market leaders have in the frozen foods market in expanding scope across the entire network, such as the problems experienced by Minute Maid in moving out of frozen juice and that of General Foods using its brand Birds Eye in the frozen potatoes market in the early 1980s. Nonetheless, the US has the highest product variety and the most comprehensive coverage of the product network, while the European markets are a close second (Sutton 2007). For developing countries in the global South, operating in only a part of the product network is often the option, as is most evident for the Bihar trajectory in our later discussions. Ideally, one would like to study sub-sectors in food processing from a general value chain perspective, of which product networks are a part. Barrett et al. (2011) provides a concise definition of a Food Value Chain (FVC) as: ... all activities required to bring farm products to consumers, including agricultural production, processing, storage, marketing, distribution, and consumption.

We do not indulge in a comprehensive FVC approach here. Rather, we limit our focus to the product network part of the FVC for our study. Our method, therefore, is to investigate the networks of products that emerge from input–output relations between less to advanced processing for different sub-sectors. The intention is to see the possibility of the emergence of higher value-added products from minimally processed products. Advanced processing, particularly in the 1990s and especially those related to fish and fruits & vegetables (F&V), has attracted a higher share in world agricultural trade (as mentioned in Maertens and Swinnen (2006)). This is coupled with a marked transition away from traditional tropical exports from developing countries, such as coffee, cocoa and tea, towards high-value-added exports (see Aksoy (2005)). Westernization of Asian diets (as mentioned in Pingali (2006)), with a large demand for highly processed products, indicates that there is a compulsion, for a thriving supply-side in the processing of food, to move up the ladder in these product networks towards higher processed products, despite concerns regarding potential harmful effects on health. We should note three caveats with our definition. First, creating these networks requires the inclusion of products, like additives, sugar or salt that are common to many networks. These networks can, therefore, have overlapping products. Starting

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with some basic processed food item, it is possible to diversify into a number of these networks. That is a part of entrepreneurial choice and they end up characterizing the trajectories in food processing with a set of regionally produced items.13 This leads to the second caveat: these regional variations in the product network will make generalizations impossible. Connor et al. (1985)’s discussion of forces shaping the manufactured food industry in the US underscores this problem. While they start with a generalization of processed foods in terms of income elasticities: Processed foods with relatively high income elasticities are frozen Fruits and Vegetables (F&V), meat substitutes, cheese, nuts, alcoholic beverages, and Food-Away-From-Home (FAFH). Relatively unprocessed foods with similar income elasticities are veal, shellfish, lamb and vitamin C-rich non-citrus fruits. Processed foods with negative income elasticities ... include processed milk, cooking oil, cereals, lunch meats, canned fruits and vegetables.

they move onto to refine this classification in terms of demographics (different family sizes), female labour force participation and information revelation through labelling of food products. Therefore, even for a single region, it is difficult to draw general conclusions about manufactured food products. We bypass this difficulty in a two-step manner: first, we discuss the product networks in isolation and then, we embed them in the regional context in Chaps. 3 and 4. However, there remains a third issue: what about the other factors that are needed in the process of value-addition in discovering newly processed food items? Simply discussing food processing using the product network metric does not help us couch the discussion in an inclusive framework with all the inputs of production. This limits the discussion to the generalized arbitragebased logic for developing the processing sector. Further enrichment is needed for understanding what factors aid sectoral growth in food processing in a region and what is the precise role for government policy.14 To address this issue, we embed the product network discussion by including industrial finance in Chap. 6, entrepreneurial skill constraints in Chap. 7 and government policy and the industrial ecosystem in Chap. 5. There is a close connection with these product networks and the necessity of coordination, planning and finance. The higher is the role of coordination, more imperative is third-party involvement, like the government. Among observable patterns in the structure of this industry, one development is through the independent choice of firms, which requires the least amount of collaboration and coordination among various stakeholders. Here, the spirit of entrepreneurial drive directs private investment after appropriate risk-adjusted profit-based calculations. Post-production, the firm has the options of retailing through the wholesale (unbranded) or the ownbrand retail or through industrial sales (and/or engage with the catering segment), as discussed earlier. Second, and more commonly, industrial food processing might involve the proactive engagement of national or local governments. Here, the govern13 This

analysis is silent about imports of processed food items, as the investigation is about manufacturing regionally available raw materials from agriculture and animal husbandry. 14 Though Barrett et al. (2011) mention reduced government presence in many parts of the FVC in developing and transition economies, we find evidence of significant involvement of government policy, particularly the MoFPI, in India’s food processing industries.

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27

ment appears as the owner of processing units (such as the dairy initiative COMFED in Bihar), alongside other private firms: regional, national or global. A third alternative not only subsumes the government’s role as a manufacturer, but also as a decision-maker in the policy space. Here, the government uses various types of incentives (discussed in detail in Chap. 5) through its industrial and investment policies to attract specific kinds of private capital to jump-start food processing as an industrial sector. Other than subsidies, the preferential purchase policy of the government also acts as a lever to enhance the capabilities of small firms (Yülek 2018). The subnational trajectory that is the mainstay of this volume, has the government as an agent in all the avatars: manufacturer, a preferential contractor with private units, policy driver and financier and is at the centre of our discussion on policy networks. Along with the varying degrees of government involvement, different structures in various sub-sectors of food processing result from different levels of coordination requirements. I. Spatially dispersed development of sub-sectors independently II. Spatially concentrated development of individual sub-sectors (commodityspecific clusters, such as a rice milling cluster) III. Spatially concentrated development of a bouquet of sub-sectors in food processing (food parks) The first category is seen in the ubiquitous presence of various kinds of manufacturing units in industrially advanced nations: from across the spectrum of dairy to modern cold chains with Individually Quick Frozen (IQF) processes. The second category is related to localized clusters of manufacturing units in a single commodity, such as a rice or maize milling cluster (present in different states of India such as Bihar, Haryana, West Bengal). These manufacturing units are regionally concentrated, but specialize in the production of a single food item. As opposed to this, the recent development in food processing is that of food parks, which is the third option. Here, we find a collection of different processed food items collectively produced in a single location. In India, at present there are 33 different fully functional food parks, built with financial support from the MoFPI, in states such as Haryana, West Bengal, Karnataka, Kerala, Madhya Pradesh, etc. Option [III] is the most intensive in the usage of planning and other support resources and option [I] the least. Option [II] is intermediate between these two. The largest requirement in option [III] is land of a minimum size and coordination among various kinds of manufacturers of processed food. These options, therefore, vary in the proactive engagement with the government: option [I] typically works with the least interference of the government, whereas various degrees of government presence is present in the other options in developing countries. The economics of agglomeration shows that clustering and geographical concentration might emerge as an equilibrium outcome even without government intervention. Some underlying conditions fostering this are identified as either (i) externalities in perfect competition or (ii) increasing returns under monopolistic competition or (iii) spatial competition under strategic interaction (see Fujita and Thisse (1996)).

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Note that, however, this theory does not include transaction costs in land acquisition and other infrastructure constraints as is the case with states like Bihar. Government intervention, at least in the development of industrial processing zones and land banks, is essential for entrants to start establishing manufacturing units. While the first and the third options cut across multiple networks, whereas the second option applies to a single product network. Which of these is ideal for a region depends not only on coordination constraints, but also on demand-side constraints on the product network: not all the products in a network will have a regional market. This will, then, affect the business viability of arrangements like food parks. Keeping these caveats in mind, we proceed with the exercise of mapping out some standard product networks. Note that we only include the basic processing segment and the retail segment: selling own-brands, unbranded wholesale as well as industrial sales. What we do not discuss is the catering/service segment, as it has a very different character.15 As our focus will mostly be on the subnational development of an industry in India, we link our product networks to the NIC 2008 classifications in our discussion. We also provide some examples of new product developments in India since the 1990s, when the country liberalized and reformed its policies towards manufacturing.

2.4 Common Product Networks in Food Processing We present a brief description of a collection of product networks. This discussion sets out all the possible products that can be developed through processing in any region. For Bihar, though, we shall focus on the grain-based and dairy-based ones in subsequent chapters. Developed countries have a larger diversity in the set of product networks in processed food than regions like Bihar. The reason for studying them is that the potential for developing any single or a multiplicity of product networks is present in any region. This enumeration exercise collects a large variety of possible products in one place for convenient reference.

2.4.1 Meat Processing Product Networks Primary Processing Primary processing of meat at the five-digit level of aggregation (following NIC 2008) include16 slaughtering and preparation of mutton (10101), beef (10102), pork (10103), poultry and other slaughtering (10104), processing and canning of meat 15 Sutton

(2007) treats the service segment similarly. that though this category is supposed not to include non-edible by-products, it does include the subclass ‘production of hides and skins originating from slaughterhouses’ (10107). We exclude this from our definition. 16 Note

2.4 Common Product Networks in Food Processing

29

(10105), rendering of lard and other edible fats of animal origin (10107), production and processing of animal offal (10108) and other meat production, processing and preserving of other meat and meat products (10109) as well as by-products, such as the manufacture of edible animal oils and fats (10403). Even within basic processing, there are different channels to market the processed product: the small butcher shop or through capital-intensive establishments like abattoirs. For instance, FutureBeef, a collaborative project including the Department of Agriculture and Fisheries, Government of Australia,17 provides the following interesting account of strategic pricing by butchers, given expensive competing meat cuts from abattoirs (measured in 2009 prices): ... beef from a 500 kg live animal, sold to the abattoir at $3/kg for the hot carcase,18 will end up retailing for an average of $11.28/kg just to cover costs....However, up to 40% of overthe-counter sales (mince, sausages, etc) retail for less than the average cost of $11.28. This means the butcher must cover costs and extract the profit margin from the more expensive sweet cuts.

A modern abattoir includes the processes of dressing, slaughter, shrinkage (transforming from hot weight to cold weight), boning and delivery to retail outlets. The largest cost for an abattoir are its set-up costs as well as the variable costs of electricity, but they are capable of attaining much higher economies of scale than small establishments like butcher shops. In informal markets, these shops slaughter, dress and sell processed meat mostly with manual labour. Among its overhead costs are wages, rent and power. FutureBeef estimates of the retail cost difference between abattoir and butcher-processed comparable meat products at $0.18 per kg, which leads to an average retail cost of $11.28 per kg to break-even at 2009 prices for Australia. In countries like India, with informal sector wages being much lower than the estimates of FutureBeef, these price differentials are much larger. Hence, the attraction of investments in large-scale abattoirs much lower than in small-scale butcher shops. However, a large number of abattoirs have been established in India in recent times. MoFPI data (May 2016) on abattoir projects funded by the GoI reveal a total project cost worth INR 784 crore have been sanctioned, with a large concentration (about one-fifth of the total number of projects) in a single state: West Bengal. Its neighbouring state of Bihar has not seen such large-scale abattoir investments. Among the many challenges of establishing capital-intensive abattoirs, one is the uncertainty of supply of livestock at a large enough scale to break-even on the costs of establishment. With small-scale supply, as is common in most regions of Bihar, informal butcher shops are the more efficient forms of primary processors. Second, though livestock can travel to any location, marketing margins of the abattoir require the development of cold storage facilities for distribution to distant places. The historical account of how abattoir development is linked with the strategic behaviour of a 17 FutureBeef provides support on research and information for the North Australian Beef Industry and is linked with the North Australia Beef Research Council. Further details are at https://futurebeef.com.au/about-us/. 18 The alternative spelling is carcass, with the same meaning in the Merriam-Webster dictionary.

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monopoly cold storage is clearly shown in the 1931 Southern Rhodesia case study in Hubbard (1981). In India, and in Bihar, note that most cold storages are used for storing vegetables like potatoes and not for processed meat.

Advanced Processing From simple processing, more value-added output can be produced in the form of non-vegetarian food items ranging from semi-cooked meat products (which require minimal cooking time) to fully prepared frozen dishes that can be served after heating. Margarine, derived from animal fat, derives its industrial processing to the meatpacking industry. In the US, Sutton (2007) notes that the early entrants in margarine were meat-packers themselves, as a by-product of animal fat happens to be margarine. Therefore, an unrelated product traces its history to the meat industry. The subclass of ‘manufacture of prepared meals and dishes (10750)’ in NIC 2008 is a part of advanced meat processing. Note that this class does not include the preparation of meals for immediate consumption, such as in restaurants. Therefore, this class excludes wholesale and retail (in-store) sale of prepared meals and dishes (4630 for wholesale and 4711 plus 4721 for retail) and activities of food service contractors (5629). Among those included are semi-cooked products, such as corn beef, meatloaf, sausages, curries, bacon, ham, cutlet-mix, chicken-n-ham and salami. Sausages themselves come in multiple varieties: cocktail, pork sausage, chicken pepperoni sausage, masala sausage and the Australian ‘kransky’ or the continental sausage. In India, a large number of processed meat dishes in the semi-cooked form exist, starting with kebabs and wraps right up to the tandoor-cooked chicken sold by large retail outlets, such as the Arambagh Hatcheries.19 Recent trends in online shopping in India has also brought in new businesses, such as the online meat shop Licious based in Bengaluru,20 which serves multiple cities in the national capital region around Delhi, Hyderabad, Pune, Mumbai, Panchkula, Mohali, Chandigarh and Bengaluru. Once again the menu lists a range of products from raw eggs, fish, meat to semi-cooked to fully prepared non-vegetarian dishes. Online retail challenges the distinctions in NIC classification, as what would be served in a restaurant is also available as pre-packed meals through online services. Despite regional and technological variations, one can conclude the following characteristic about the nature of the meat processing product network: M. Meat processing can be done at a small scale with low set-up costs through butchers or at a large-scale using capital-intensive abattoirs. Adherence to private and public standards determines access to lucrative export markets, largely in the global North, South America and Africa. Live animals can be transported to location, reducing the necessity of abattoirs to be located near regions with surplus animals 19 Arambagh Hatcheries is a large private hatchery initiative in West Bengal. Their online menu at https://arambagh.com/price/ itself lists 22 different raw chicken items and many more cooked food products. 20 https://www.licious.in/.

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31

for meat processing. Cold storage facilities, raw material availability and input price risks matter for profitability.

2.4.2 Fish Processing Product Networks Primary Processing The following subclasses of NIC 2008 describe basic processing for fish: sun-drying of fish (10201), artificial dehydration of fish and seafood (10202), radiation preservation of fish and similar food (10203), processing and preserving of fish crustacean and similar foods (10204), processing and canning of fish (10205), frog legs (10206) and other fish products (10209). Much like meat processing, basic cleaning, sorting and grading can be done either using primitive techniques at a low cost to more capital-intensive advanced radiation and artificial dehydration-based methods.

Advanced Processing Advanced processing of fish can generate new products like fishmeal. NIC 2008 subclass of the production of fishmeal for human consumption or animal feed’ (10207) includes this. An early 1975 FAO document21 describes the history of the fishmeal industry in this way: The fishmeal and oil industry, which started in northern Europe and North America at the beginning of the 19th century, was based mainly on surplus catches of herring from seasonal coastal fisheries. This was essentially an oil production activity; the oil finding industrial uses in leather tanning and in the production of soap and glycerol and other non-food products. The residue was originally used as fertilizer, but since the turn of this century it has been dried and ground into fishmeal for animal feeding. In fact, one definition of fishmeal is that it is a solid product, ground, that has been obtained by removing most of the water and some or all of the oil from fish or fish waste... Its main use is in the diets of poultry, pigs and fish which need higher quality protein than does other farm stock, such as cattle and sheep....Small oily fish are the mainstay of the fishmeal and oil industry. Even in frozen storage these fish turn rancid rapidly unless special and expensive precautions are taken. With present knowledge they can be used best by reducing them to fishmeal for animal feeding and using the oil for direct human consumption in products such as margarine. There is a good demand for high quality fishmeal and oil and production can be highly remunerative if suitable raw material is available. The industry can also utilize the offal - from filleting, gutting and other fish processing operations - which often poses disposal problems.

The process of preparation of fishmeal is quite lengthy and requires careful hygiene standards. Traditional methods begin with heating, pre-straining, pressing or centrifugation, separation, oil polishing and finally lead to evaporation, drying and milling and storage of fishmeal. New techniques such as solvent extraction, fish silage or simplified wet processing (with ethanol or isopropanol) have replaced older techniques. 21 Available

at http://www.fao.org/3/x6899e/X6899E01.htm.

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This has led to the discovery of another fish-based product for human consumption: fish protein concentrates. A version of this is sold in the form of fish-based protein capsules in the health supplements industry. Omega-oil-enriched vitamin capsules from cod-liver (marketed by companies such as GNC or Amway) is yet another product that derives its source from fish processing. Thus, from fishmeal to margarine and health supplements, there exists an interconnected product network in fish-based products. The entire product network is difficult to map directly to the subclass categorization of NIC 2008. Apart from the subclass 10207, we can think of ‘manufacture of fish oil (10404)’ as well as the subclass ‘manufacture of prepared meals and dishes (10750)’22 as elements of secondary processing in the fish-based product network. The general characterization for fish processing product network, like that of meat processing is: F. Fish processing technologies vary from simple low-cost sun-drying processes to capital-intensive factory-based production of derivative products such as fishmeal. Adherence to international certification standards determine access to export markets. Processing units are ideally located near input-availability due to high perishability of fresh catch, with input price risk affecting profitability.

2.4.3 Fruit and Vegetable (F&V) Product Networks Primary Processing of F&V Like fish processing, NIC 2008 identifies simple F&V preservation as either sundrying (10301) or advanced techniques of either artificial dehydration (10303) or radiation-based preservation (10304). It also includes processes of canning of F&V (10308) and other items (10309). Primary processing requires the presence of cold chains and warehouses (NIC 2008 subclass refrigerated warehousing/cold storages (52101)). This function of processing is directly aimed at reducing post-harvest losses and aiding temporal arbitrage in perishable agri-produce. F&V processing, thereby, creates a strong backward linkage with agriculture.

Secondary Processing of F&V The supply chain, starting with primary processed F&V items, includes a large variety of products. Higher value-additions for F&V are mapped to the NIC 2008 subclass titled ‘manufacture of fruit or vegetable juices and their concentrates, squashes and powder’ (10305) and other F&V derivative food products, such as manufacture of sauces, jams, jellies and marmalades (10306), pickles, chutney, etc. (10307), potato 22 Many

online sellers, such as Licious, serve processed meat as well as fish-based meals.

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flour and meals as well as prepared meals of vegetables (10308). Note that this category is not inclusive of non-alcoholic beverages, which is included in group 11, along with wines, malt, liquors and soft drinks. Additionally, hydrogenated oil and vanaspati ghee (10401), vegetable oils and fats excluding corn oil (10402), non-defatted flour or meals of oilseeds, oilnuts or kernels (10407), miscellaneous vegetable oils (10409), vegetable milling (production of flour or meal of dried leguminous vegetables (except dal), of roots or tubers, or of edible nuts) (10615) and corn oil (0626) are part of the higher value-end of the product network. Alongside these, prepared animal feeds (NIC 2008: 108) require a significant input of maize and some soya. Maize, along with potato, itself is a very versatile crop, with many processed food products (corn in various forms) sold in retail markets, such as corn flakes, crisps, nachos, etc. A separate market segment has opened up with certified organic produce, which command a premium over non-organic agri-output. The general product network supply-side characterization of F&V processing is: F&V. Informal sector low-cost products of jams, jellies, juices and pickles co-exist with large capital-intensive factory production in F&V processing, the latter linked with cold storage and refrigerated vans. International certification standards (including organic production) matter for accessing export markets. High perishability of the product necessitates nearness to locations with agri-input abundance, given nonnegligible transport costs. Agricultural pricing determines input price risk, affecting the profitability of processing units.

2.4.4 Dairy-Based Product Networks Basic Processing Basic dairy processing starts with pasteurized milk23 (10501). Pilcher (2017) notes that adult human consumption of milk, which is entirely absent in the animal world post-weaning of babies, resulted from a genetic mutation (the appearance of enzyme amylase) such that human digestion could tolerate milk. Plain milk itself is sold in various kinds of packaging and product categories: low fat, toned and whole milk. In informal markets, this differentiation is absent, but milk prices vary depending on the type of milch animal from which milk is sourced. Note here that we include plain as well as flavoured milk, to retain concordance with the NIC codes. However, adding flavours is a higher level of processing compared to plain milk. Milk-derivatives in the health and lifestyle segment are the largest value-added products from milk. Consider, for instance, flavoured and unflavoured probiotics. The Indian market has

23 This

includes all differentiated milk varieties such as plain or flavoured sold in different packagings, such as bottles or cardboard packs.

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seen a lot of developments in this product category recently. The first milk-based probiotic, under the brand name Yakult, was launched in Indian markets in 2007. A 50:50 joint venture between probiotic leaders Yakult Honsha (Japan) and Groupe Danone (France), the drink contains the probiotic strain LcS (Lactobacillus casei strain Shirota).24 This is a natural lactic acid culture and aids digestion. Competition in the probiotics market has opened up since then, with Mother Dairy launching its own flavoured fermented milk probiotic drink, Nutrifit, in 2008. Unlike Yakult, the Mother Dairy probiotic comes in mango, strawberry and other flavours. Nonetheless, these are not simply flavoured milk. For want of better alternatives, we club them under 10509 as other milk-based products. Another health supplement, whey protein (the common name for milk serum proteins), is also derived from milk by removing casein from skim milk using any precipitation technique, such as the addition of mineral acid (see Maubois (1984)). In India, whey protein powder retail sales are sold through dedicated stores as well as online through the e-commerce portals of Amazon and BigBasket, which stock items such as Danone’s ‘Protinex’ powder or those marketed internationally by GNC.

Advanced Processing Dairy provides many value-added products. The NIC 2008 list contains some of them: ice-cream powder and condensed milk except baby milk food (10502), baby milk foods (10503), cream, butter, cheese, curd, ghee, khoya, etc. (10504), icecream, kulfi, etc. (10505), other dairy products (10509), this product network extends to dairy products (which use butter as an input), such as bread (10711), biscuits, cakes, pastries, rusks, etc. (10712). This segment also includes multiple differentiated products under the category curd (yoghurt): plain, flavoured, probiotic, low fat. The general characterization for supply-side fundamentals of the dairy product network is D. The challenge for a dairy processor is to aggregate enough raw milk from different milk farmers in a single processing plant, which is necessary even for basic milk pasteurization. There is a large presence of co-operative-based initiatives to solve the aggregation problem. Production of dairy items, such as milk powder, requires large fixed capital expenditure. Simple dairy processing plants with manual handling and with less capital intensity co-exist with fully automated highly capital-intensive plants. International certification standards determine export potential. Input price risk matters for profitability.

24 This

strain is trademarked by Yakult Honsha, Japan.

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35

2.4.5 Grain-Based Product Networks Basic Processing Just as F&V, there are a large variety of grains. Basic processing is associated with post-harvest storage processes: cleaning, sorting, grading, polishing and are included in NIC 2008 subclasses of flour (10611), rice (10612) and pulses (dal) (10613) milling. Note here that depending on the milling technique, this stage itself can generate multiple other outputs. Consider rice milling, for instance.25 A modern rice mill, apart from producing the main product of polished unbroken rice, also produces flakes, puffs, rice bran (which can be used to make rice bran oil), Ready-To-Eat (RTE) cereals, rice straw and rice husk or hull according to the International Rice Research Institute (IRRI, Philippines). The latter two items are used in fertilizers or animal feed. Therefore, part of the output from grain milling is an input for animal feeds without further preparation, such as grain milling residues (10619). Modern processors can also generate power from the husk for operating the plant itself. Other grains, such as wheat or pulses also have multiple by-products, many of which belong to the breakfast cereals category. In India, a large number of pulse-based edibles (locally called ‘badi/bari’ or ‘papad’ (10796)) are produced by the informal sector or by micro co-operatives, such as the Khadi Village Industries Commission (KVICs) and Grameen Mahila Griha Udyog.

Advanced Processing Grain-based valued-added processed products comprise the vast plethora from cereal breakfast foods (10616), flour mixes and blended flour and dough for bread, cakes and biscuits (10617), ready-made powders for making items like ‘idli’ (10619), miscellaneous grain-milled products (10619), starches from rice, potatoes, maize, etc. (10621), farinaceous products such as macaroni, noodles, couscous, etc. (10740). The supply characteristics for grain processing product network is G. Despite a plethora of edible grains, most milling activity around the world is concentrated around a few, particularly rice. Input price risk determines profitability, certification standards matter for the export market, organic produce has a higher price-premia. Storage in silos might reduce the necessity of location of manufacturing unit near production zones.

25 This

is discussed in detail in Chap. 6.

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2.4.6 Miscellaneous Groupings Note that there are some other product networks, primarily driven by regional produce and diets. Keeping in mind the special case of Bihar, we mention two additional product networks and an infrastructural necessity in food processing. Honey processing is important because of its relationship with the minimally land intensive bee-keeping activity. For a state with limited land resources for industries, honey processing acquires special significance. Makhana is exciting due to its nutritive properties that should appeal to health-conscious consumers. It is grown through aquaponics extensively in Bihar and is also not land intensive. Warehousing and cold storages are essential inputs for developing food processing. Government policy in Bihar and other states, as well as the national policy in India, have provided special subsidies for their development.

Honey-Based Product Networks Consider, for instance, honey and its derivatives (NIC 2008: 01492). Bee-keeping enhances agricultural productivity through cross-pollination due to the presence of bees. It also gives rise to a number of products starting with unprocessed but filtered honey. This variety of honey has many nutrients (bee pollen contains vitamins, lipids, carbohydrates and proteins). Bee propolis in unprocessed honey has antiseptic properties. However, special care has to be taken for unprocessed filtered honey to eliminate bacteria that can potentially cause botulism in infants. Processed honey, though safer, loses some of these nutritive elements. Organic honey is a separate commodity, with a premium over its non-organic variant, as is honey fortified with other fruits and beeswax.

Makhana-Based Product Networks Gorgon nut/fox nut or ‘makhana’ is a regional produce from Bihar (90% of all makhana produced in India comes from Bihar26 ). In its primary processed form, makhana can be clubbed with NIC 2008 subclass 10615 which includes milling of edible nuts. Advanced processing, which includes flavoured makhana, can be classified under 10793 (processed edible nuts) and 10736 (sugar-based preservations of nuts and some other items). Being an aquatic produce (it grows freely in stagnant pond water), its cultivation is not land intensive. Makhana is naturally well-fortified with iron and calcium and is a part of most vegetarian diets in the northern part of India. The recent trend of healthy snack food items has seen a revolution of sorts in Indian supermarkets, with the inclusion of a large number of makhana-based 26 Details are available on the makhana project profile for MSMEs at http://www.msmedinewdelhi. gov.in/PDF2016-17/Project%20Profile/Project%20profile%20on%20Makhana%20Processing. pdf.

2.4 Common Product Networks in Food Processing

37

food products, such as unflavoured and flavoured pops and flakes. That the ‘healthy’ and the ‘organic’ labelling is now yielding large premiums in Indian metros is most obvious in the case of makhana. A 2016 Livemint report on makhana27 comes with the heading ‘How a heartland winter favourite was reinvented as the millennial snack of choice’. As Bihar does not have an organic certification, it is not possible for makhana to be organic. And yet, some of the makhana-based packaged snacks in the national capital region (NCR) around Delhi come with the organic tag and command a price premium of over 400% compared to the loose unbranded product. The online retailer Indiamart states that the loose or packed wholesale makhana rate in Bihar is 400 per kg INR, whereas a packet of the brand Divinutty, which comes in multiple flavours such as wasabi, peri-peri, mint, cheese and tomato, smoky barbecue or chaat masala, is priced at 150 INR for 100 g on Indiamart. This is four times the price of wholesale makhana in Bihar. Despite multiple brands, such as Sattviko, Fitminis, Woke and Wonderland Foods fighting in the retail space for makhana-based snacks, the price difference between unflavoured wholesale and flavoured packaged retail variants in makhana remain around 400%. For the makhana farmer in Bihar selling to the wholesale market, price realizations are a far cry from the premiums that the product is commanding in urban centres in recent times. Misc. A general characterization of these miscellaneous groupings is not sensible, as these are region–culture–diet-specific products that are not common to processing activity worldwide. Hence, we do not provide any overall summary for this grouping.

Warehousing and Cold Chains We include warehousing, particularly cold storages, in this discussion. Though this sub-sector does not directly provide a unique consumable product, it is at the heart of the logic of temporal arbitrage for processed food, as warehousing extends the shelf life of the agri-produce, including those from animal husbandry such as dairy or poultry products. Developing a successful processing industry is not possible without the presence of various types of warehousing, such as grain silos, cold storages for perishables and more advanced integrated cold chains. MoFPI provides a succinct definition of an integrated cold chain as one which provides services: ... without any break, from the farm gate to the consumer. It covers creation of infrastructure facility along the entire supply chain viz. pre-cooling, weighing, sorting, grading, waxing facilities at farm level, multi-product/ multi-temperature cold storage..., packing facility, IQF, blast freezing in the distribution hub and reefer vans, mobile cooling units for facilitating distribution of horticulture, organic produce, marine, dairy, meat and poultry etc.

The basic function of these storages is to facilitate re-sales of raw agri-produce and semi-finished products to processors, giving farmers an opportunity for better price realization. Chilling facilities at the collection point, along with refrigerated 27 Available

moment.html.

at

https://www.livemint.com/Leisure/mw1f95qKNTlU9WFJAsbw7L/Makhanas-

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2 Food Processing: Understanding Common Threads

vans, are essential for raw milk and fruit and vegetable juices. For grains, without storage facilities, post-harvest losses would be very large. Typically, a multi-storage cold chain has the flexibility to store different perishables at different temperatures in a containerized form. These multi-chamber modern units are not only versatile, they also reduce the risk of underutilization of the facility. Though modern cold storage technology has evolved to use solar power to minimize the expenditure on the running cost of the unit, electricity consumption remains the largest cost component in operations. The same consumption of electricity can facilitate the preservation of different types of perishables (including flowers and medicine28 ) in modern multichamber storage units. However, if there are demand-side constraints on the range of products that can be stored, then the problem of underutilization of the facility due to cyclical availability of fresh produce becomes a challenge for attaining financial viability. Different ownership structures (farmer producer co-operatives, government-run facilities, private partnerships, proprietorships and private limited companies) are present in warehousing of food and non-food products. Dorfman (2014) notes that storage, which is essentially arbitrage over time, becomes a profitable business proposition if the following condition between prices at date t and t + 1 ( pt+1 and pt , respectively) hold, given the costs of storage Ct,t+1 between t and t + 1: E t ( pt+1 ) − Ct,t+1 > pt

(2.1)

However, Brennan (1958) shows the possibility of viable storage when Eq. 2.1 does not hold, i.e. stocks are carried from one period to another even when the price expected to prevail in the future is below the current price. Brennan (1958) contextualizes his discussion using the example of ‘inverse carrying charges’ in futures markets for storage (with futures price below spot price or the prices of deferred futures below or almost the same as futures prices). Though dated, this publication delves deeply into the constituents of storage costs in a comprehensive manner. The answer to this puzzle does not lie in a trivial response that uncertainty is not accounted for in total costs in Eq. 2.1. Rather, Brennan (1958) provides an equilibrium theory to show this. According to this paper, the three marginal costs of storage are due to (i) physical storage (ii) convenience yield on stocks and (iii) a risk premium. Assuming that m t (St ) is the total net marginal cost of storage for St quantity of stock carried out of period t, Brennan (1958) argues that m t (St ) = ot (St ) + rt (St ) − ct (St ),

(2.2)

where ot (St ) is the marginal cost for physical storage, rt (St ) is the marginal risk aversion factor determining the risk premium, whereas ct (St ) is the marginal convenience yield. Brennan (1958) assumes that o > 0, o ≥ 0, r  > 0, r  ≥ 0, whereas c > 0 but c ≤ 0. The convenience yield on stock is assumed to be declining with 28 We found some examples of this kind of cross-diversification in storage items for some cold storages in Nalanda in the southern part of Bihar during our survey in 2016.

2.4 Common Product Networks in Food Processing

39

an increasing quantum of stock, with an upper cap which is large enough such that the marginal convenience yield is zero but is large and positive for small stock sizes. This is the key to the puzzle as to why stocks with futures prices lower than the present are carried forward for storage. With a relatively small stock size St , the last term in Eq. 2.2 is positive and large leading to the possibility that the overall marginal cost from storage is not positive at that level of stock. Equating net marginal revenue u t (St ) with marginal cost (in the presence of perfect competition and no externalities in storage), the equilibrium storage condition is u t (St ) = m t (St ) = E t ( pt+1 ) − pt = ot (St ) + rt (St ) − ct (St )

(2.3)

which will turn out negative for small values of St . Note that one difficulty with this theory is that the signs of the components of marginal cost are restricted by definitions used by Brennan (1958). He mentions that ...suppliers of storage are mostly engaged in production, processing or merchandising with storage as an adjunct...

He also makes the assumptions of perfection in competition and lack of externality in storage. These observations are not necessarily true for many of the cold storage initiatives, particularly private stand-alone and co-operative ventures in regions like Bihar in recent times. Cold storage operations are full-time work for many firms and are not adjunct functions to some other primary line of business. Externalities and imperfect competition are very common. Instead of this classification, we can also classify the costs of storage in another manner, though that is not free from problems. Consider the categories of fixed costs of establishing the unit29 as opposed to costs that vary by the nature of the stored product, such as opportunity costs and insurance premia (as mentioned in Dorfman (2014) and Coulter et al. (2000)). While there is some uniformity in fixed costs across different types of storage, variable costs depend on the nature of material stored. This gives rise to a difficulty of providing a general theory of costs in warehousing. There is also a diversity in business models for warehouse operations. Many storages operate on a rental model, where they store the product on behalf of the farmer and issue receipts in return of acceptance of goods for storage. This allows the farmer the flexibility to sell the produce at a date when the future price covers costs of production as well as storage. This model of storage operations passes the temporal price risk of the product to the farmer, though the possibility remains that the farmer will not claim the product from the storage at the end of the rental period in case of a price crash of the produce. Another possibility of operating a cold storage is to acquire the farm produce from the farmer, take ownership of the price risk and sell the product at a suitable future date. Here, the entire price risk is borne by the storage operator. Academic discussions on cold storage have been centred around their role in improving returns to the farmer through either 29 These

include handling costs, licence fees and clearances which are independent of the size of operations.

40

2 Food Processing: Understanding Common Threads

better price risk management (see Reardon et al. (2009)) or through access to credit through warehouse receipts programs (refer to Coulter and Shepherd (1995)). The issue of necessary conditions for viability of stand-alone cold storages operated under different types of management is under-researched. This is very important for storage operations in Bihar. Here, most storages operate with primitive storage technology. This runs into high costs: the organic agri-produce in the warehouse ‘breathes’ and decomposes, which raises the temperature at the storage premises automatically. This requires additional costs on refrigeration and cooling. Added with this is the price risk which the storage operator has to face. This makes stand-alone storage operations of the kind in Bihar often highly unviable. The scope of storage facilities is very basic in Bihar and far from the ideal definition of MoFPI. The central issue is income diversification for stand-alone storages. Standalone storages need to find alternatives to diversify incomes. As a possible road map, we provide a list of the options mentioned in Coulter et al. (2000) for Ghana, Zambia, Mozambique and Kenya (as ‘the way forward’ for these African countries from the late 1990s), in a decreasing order of scope of diversification: i. Linking storage operations with commodity exchanges: In this value-added service provided by warehouses/ cold storage operators, depositors of agri-produce can find a market for his/ her output by using the warehouse receipts as document delivery against contracts. Hence, this lets the farmer not only achieve storage and potentially higher price realizations through temporal arbitrage, but also access larger markets for selling the produce. This should, therefore, be reflected in better returns for the storage operator as well. ii. Linking storage operations with brokerages: Coulter et al. (2000) mentions that this alternative is similar to that of grain marketing co-operatives in the UK or regulated warehouses in Brazil with Banco do Brasil providing brokerage services to farmers. In a sense, this option provides increased market access for the farmer, limited only by the size of the network that the broker can provide. The end result is similar in terms of higher returns to the warehousing service provider as the earlier option. iii. Combining storage with freight-forwarding: This option allows the storage operator additional incomes from freight-forwarding services. iv. Combining storage with trading: This allows additional returns from trading in grains for the warehouse operator (which does happen with some storage operations in West Bengal, India). However, it is legally restricted for public warehouses in some countries (till recently, this was the case with Brazil). v. Allowing storage operators to act as collateral managers with banks: As Coulter and Shepherd (1995) discusses, warehouse receipts programs and management of collateral for banks is an additional source of revenue for storage operators, while ensuring better credit access to farmers. This is the least value-added service that the storage can offer a farmer and can get additional returns on. Each of these alternatives expands the scope of the storage operator beyond the sole task of preserving a perishable product over a time horizon. They mitigate the extent of price risk that the stand-alone business faces in different degrees.

2.4 Common Product Networks in Food Processing

41

While an overall generalization in terms of cost of operations is not easy, one can propose the following for warehousing and cold storage: C. A large component of operational costs is electric power in operations and a large source of risk is price differentials between current and future prices.

2.5 Product Networks and Region Specificity in Food Processing We first collect the common threads through all the generalizations across product networks in food processing. First, the manufacturing process can be undertaken at two levels differing in terms of capital intensity: the basic and advanced processing. The former sees a significant presence of informal manufacturing establishments in developing countries, whereas formal sector-registered manufacturing units dominate the latter. Second, the location choice of the manufacturing unit depends upon the perishability of raw agri-inputs, transport costs and links with retail outlets. Typically, agri-inputs are located in rural areas, whereas retailing of advanced processed products are at urban centres. Intensity of local resource usage is not guaranteed in food processing, as our Nigerian example for milk, later in this section, shows. Conventional wisdom indicates that a processing unit will locate near the source of input, as agri-produce is perishable and non-negligible transport cost creates financial incentives to locate at source. However, we have examples in meat processing, such as the discussion in Hubbard (1981). As live cattle move from one location to another, processing units need not be close to the availability of livestock. Tax and other industrial policy incentives might also distort location choices for units, if the net gains from locating further away from the input source is outweighed by policy dole-outs. Additionally, if the processing unit is vertically integrated with retail outlets, such as restaurant chains, then nearness to retail markets might be of greater interest than nearness to inputs. Third, there are constraints in both stages of the product network. For primary processing, ensuring a minimum scale of supplies of raw material is a challenge. Low scales of aggregation end up with informal arrangements in food processing, as is common in India and Bihar. We have discussed this in our comparison of butcher shops versus abattoirs in the primary processing of meat. Secondary processing faces several challenges: first, the marketing of new food products is costly. Additionally, labelling and certification have become necessary requirements in the present-day global competition in processed food at the secondary level. However, a fourth general observation is that margins in secondary processing are much higher than in primary processing, as we discussed for makhana. Actual outcomes in food processing need a regional filter as well as a reference to product networks described above. We turn to the regional dimension in the following Chap. 3. Before we do that, we briefly explain an important issue of regional

42

2 Food Processing: Understanding Common Threads

aspirations in industrial development: what are the implications of jump-starting industrialization with food processing as the lead sector? As we mentioned earlier, industrial arrangements in processed food typically follow three patterns: [I] independent random growth of firms, [II] product-specific clusters or [III] food parks. Which of these are more likely to develop in a region depend on some features of the product network. Consider advertising, standardization, certification and labelling. For earning high profits through value-addition in secondary manufacturing, such as the organic label, significant expenditure is necessary. Implementing these quality standards, through technology upgradation and marketing expenses, is very costly for small informal sector processing units without substantial financial support. In India, for instance, there is a very large informal vendor business at the processed retail food stage, but the quality of such edibles is suspect and sometimes the cause of gastric diseases. See the press-release (Livemint on 9 January 2018) below: Food-borne diseases cost India about USD 28 billion (1,78,100 crore INR) or around 0.5% of the country’s gross domestic product (GDP) every year, revealed a study by Food for All partnership of the World Bank Group and The Netherlands government. To reduce the economic burden, India needs to invest in ensuring food safety for the masses. Indians are moving from simple staples to more nutritious food. While this should have positive impacts, according to the study, the transformation is currently leading to “risky foods”....‘If no investments in food safety are made, there could be an adverse impact with potentially large costs due to increase in poverty and undernutrition...

Those large horizontal investments have to be made by the government for ensuring food quality standards (standards formulation and more importantly, implementation) is clear from the prescription of this World Bank study. Many certification standards, such as organic certification, depend on government initiatives. The US Department of Agriculture (USDA) has set very specific standards for organic produce from livestock and agriculture.30 India, in 2000, established the National Program on Organic Production (NPOP), which provided the standards and certification procedures through the Foreign Trade and Development Act of 2001. It promotes the ‘India Organic’ logo for instant recognition of a product as organic under the Indian standard. National governments also have additional objectives when they make horizontal investments in the industrial sector. For instance, employment generation for a billion-plus population is imperative for the Indian government, which has started initiatives like Make in India and Skill India for achieving this. The informal food sector is highly labour intensive and promises employment. However, implementing quality standards for the ubiquitous small establishment might entail a much higher cost than accomplishing safety and quality through competition among formal establishments. Purely from a standards-implementation point of view, which involves monitoring, constant checks and punishments as well as some financial incentives, options [II] and [III] might be attractive for the government. These are a part of formal registered manufacturing, and are easier to monitor. Option [III] is the 30 Refer

to https://www.ams.usda.gov/grades-standards/organic-standards.

2.5 Product Networks and Region Specificity in Food Processing

43

Fig. 2.2 Stylization of food park logistics network (Author’s creation)

obvious choice in the presence of economies of scope in production, such as the case of food parks. This option allows multiple investments across product lines (dairy, fruit juices, high-end cereals, etc.) in one location. The necessary condition for viability of a food park, according to us, is a connected hub-and-spoke (a wheel-shaped network) logistics relationship between a core infrastructure category, such as a cold chain (the hub) with different food products with each other through linked peripheral nodes (located through the spokes; see the stylized depiction in Fig. 2.2). Food parks are customizable to particular regional diets and can serve retail markets with a bouquet of processed products while minimizing transport costs using refrigerated vans. The real constraint for developing a food park is aggregating different product lines, acquisition of a significant area of land and developing the critical node, the cold chain. For developing economies, this is likely to hit a financial constraint first (in terms of the burden of the total food park budget that the government can bear). Finance also is interlinked with issues of strategic standards, giving rise to problems in regional development in food processing. We discuss this in the following Sect. 2.5.1.

2.5.1 Financing Options in Food Processing Apart from the government’s own sources of funds (through policy-planned expenditure on industrial development), most developing countries have used the route of Foreign Direct Investments (FDI), as well as public–private joint funding (through Public–Private Partnerships (PPPs)) for industrialization. In the particular case of food processing, options [II] and [III] have mostly used either the government’s own funds or Foreign Direct Investment (FDI). Private–Public Partnerships (PPPs) are more common for large-scale infrastructure project finance, rather than sub-sectoral development within an industry. We show, now, that the FDI route might lead to some misallocations due to its interaction with food standards.

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2 Food Processing: Understanding Common Threads

2.5.1.1

Financing Constraints: FDI and Food Standards

A possibility for national governments in developing countries is to attract FDI in food processing to pool investments for industrial development, particularly options [II] and [III]. Many national governments in developing countries do not have the same standards for food products as those ascribed to by the international community, leading to a plethora of private standards in processed food (Pingali 2006). Investments through FDIs to establish these standards to regulate food quality might act as a barrier rather than an enabling factor in the development of a local ecosystem for food processing industries in developing countries. First, the difficulty in maintaining these standards might result in reducing input usage from local areas. A stark instance of this is in an interview excerpt of a German official of an MNC mentioned in Oculi (1984). In response to a question regarding investments in a milk processing plant in the Kaduna State of Nigeria in the 1980s, he responds as follows: What does it help if you have livestock here if you import milk? There is no milk processing company here in Nigeria which can depends on Nigerian milk. They all depend on imported milk (Interview 14.10.82).

Substitution of local inputs with foreign stock to develop advanced processing deprives the natural-resource advantage and local backward linkages with agriculture and animal husbandry. Oculi (1984) provides examples of chicken livestock and cattle imports to Nigeria by American investors. Second, standards can reduce the export market potential for poor countries. An inter-country econometric study of processed food exports by Jongwanich (2009) finds that food safety standards imposed by developed countries reduce exports of processed food from developing countries. Matters are more complicated if one takes other forms of certification, such as fair trade or private labelling for organic foods. Giovannuccia and Ponte (2005) provides examples of price-premia reflected in coffee through different types of certification: fair trade, shade-grown (SBMC and Rainforest Alliance), organic or Utz Kapeh. While they do find that the fair trade certification comes with the highest premium certifications, strategic usage of these certifications by developed nations against the imports from the developing countries cannot be ruled out: Most standards and their certification procedures are not sufficiently transparent. Smaller producers may find it difficult to understand or meet certain standards, particularly those that are geared toward plantations.....As larger buyers increasingly mainstream these standards, some may become de-facto entry barriers that require considerable resources in order to be met. While some sustainability standards do pay a premium, their levels are highly disparate and, with the exception of Fair Trade, never assured...This effect has been particularly noticeable with organics for which premiums have steadily eroded over the last few years.

For national governments in poor countries, these standards raise questions about the extent to which food processing can act as an engine of industrial and economic development. The large presence of dominant firms from the global North in the premium processing value chains in food demonstrates this. Take the case of the USA.

2.5 Product Networks and Region Specificity in Food Processing

45

Gopinath et al. (1999) mentions a conundrum about processed foods exports from the USA: from 1962 to 1994, though less than 40% of its total agricultural exports were from the US, this country alone accounted for 6 out of 10 of the world’s largest food processing multinational firms (MNCs). It is not surprising that developing countries, such as India, have benefited only marginally from newer engagements with food such as organic processed varieties. A large part of sales in premium markets is captured by global MNCs,31 despite food processing being considered as a relatively less technology-intensive industry (see Vyas (2015)). This is borne out by the sales made by the International Food and Beverage Alliance (IFBA), which is a consortium established in 2008 by the CEOs of ‘leading food and non-alcoholic beverage companies’.32 IFBA, in 2016, reported a combined annual revenue of over 410 billion USD or roughly 17% of global retail trade in food.33 These MNCs expend a very large portion of their revenues in product development (IFBA Progress Report (2008–2018) report that 7 of its members spent roughly 54.5% of their sales on product formulation and innovation). Additionally, a large exercise conducted by these MNCs is advertising and marketing of food, regarding which IFBA member-firms are bound by pledges of healthy and nutritional product targeting, particularly for children.34 Hence, while technology costs are low, quality certification and advertising costs can act as large sunk costs leading to concentration of a few firms at the top end of the market. Another concern about FDI in food is the export of nutrient-rich food products outward from poor countries in return for technology transfer to set-up units that would produce ‘junk food’. The best example of this debate is that of the entry of the large cola manufacturer Pepsico in the Indian state of Punjab with a large investment (mentioned in Dogra (1988)). At present, despite a strong presence of the FSSAI (Food Safety and Standards Authority of India) promulgating policy on standards in India, exports and FDI account for multiple international public and private standards in food processing. This reduces the leverage that the national governments like India have in directing investments into this sector.

2.6 The Way Forward: Embedding Product Networks Within the Regional Context Food processing, as an industrial enterprise, holds immense potential for agriresource rich regions. Not only does this industry intensively use agri-produce as 31 For

instance, Caroli et al. (2010) notes that there is a significant presence of MNCs in the UK and French processed food markets, despite a relatively higher domestic protection provided to this sector than the automobile industry in these countries. 32 Refer to https://ifballiance.org/ about mentions Coca-Cola, Unilever, Mars, Danone, Ferrero, Kellogg’s, McDonalds, Mondelez, Nestle, Pepsico, General Mills and Grupo Bimbo as its members. 33 statista.com quotes a figure of 2.47 trillion USD for the latter. 34 https://info.foodprocessing.com/top-100-2017.

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raw material and enhances farm incomes through backward linkages, it also has the promise of generating meaningful employment which can draw out surplus unproductive labour from agriculture. As far as basic processing is concerned, most countries have a co-existence of small and large players. Most developing countries, such as Thailand, Philippines and India have a large informal retail sector in the form of street foods. While questions of hygiene standards in their production stigmatize their output, the produce from small and large formal MNCs also is not without debate regarding its nutritive content, as we saw in the case of carbonated drink manufacturer Pepsico’s entry into Punjab. Around the 1980s, snack foods had emerged as a distinct food category, many of which were considered ‘junk’ due to their low nutritive content. The large expenditures by the IBFA to investigate and inform public opinion about the health impact of soft drinks stands testimony for this. A large concentration of big players and heavy advertising expenditure is present in the US market (refer to Connor and Schiek (1997)), as well as in the OECD. The questionable nutritive content of these ‘junk’ snacks (due to their strong correlation with lifestyle diseases like diabetes) has in recent times raised consumer concerns about these processed foods. An interesting conundrum is that, in the global North, industrial concentration and large-scale advertising is present in the other extreme of junk snack foods, viz. health foods and nutritional supplements. Some global players, such as GNC, have a significant share of the market. Like junk food items, healthy snack bars, protein and vitamin-enriched drinks, etc. are also highly processed and using Sutton (2007)’s classification, have a high advertising to sales ratio. The mix of primary and secondary processed products from different product networks depend on multiple factors. Access to finance, particularly working capital, is a necessity for the food processing industry. Regional disparities in the infrastructure supporting institutional credit is an important factor influencing firm survival and growth. Second, entrepreneurial skill matters. Successful individual initiatives in processed food have to discover the appropriate combination of products from this network, and diversify into advanced stages from basic processing to realize higher margins. Market forces and rigidities in consumer tastes influence these private investments. With the increasing importance of certification and labelling standards, government intervention has become common in processed food. Additionally, the development of product-specific clusters or food parks with product diversity requires government intervention for achieving coordination and facilitation. Which of these options is ideal for a region depends on these factors specific to that region. The discussion in this chapter throws only a partial light, as we mostly discuss food product networks and their features in isolation. We extend our analysis now to incorporate regional issues in the following chapters.

Appendix

47

Appendix (See Tables 2.1 and 2.2) Table 2.1 ISIC versus NIC 2008 Activity ISIC Rev. 3; 2002 (ISIC Rev. 4; 2008)

NIC (2008)

Manufacture of food products (includes beverages for ISIC Rev. 3) –Processing and preserving of meat –Processing and preserving of fish, crustaceans and molluscs –Processing and preserving of fruit and vegetables (F&V) –Manufacture of vegetable and animal oils and fats –Manufacture of dairy products Manufacture of grain mill products, starches and starch products* (includes prepared animal feeds for ISIC Rev. 3) –Manufacture of grain mill products –Manufacture of starch and starch products Manufacture of other food products

15 (10)

10

1511 (1010)

1010

1511 (1020)

1020

1513 (1030)

1030

1514 (1040)

1040

1515 (1050)

1050

153 (106)

106

1531 (1061)

1061

1532 (1062)

1062

154 (107)

107

Activity

ISIC Rev. 3; 2002 (ISIC Rev. 4; 2008)

NIC (2008)

–Manufacture of bakery products –Manufacture of sugar –Manufacture of cocoa, chocolate and sugar confectionery –Manufacture of macaroni, noodles, couscous and similar farinaceous products –Manufacture of prepared meals and dishes

1541 (1071)

1071

1542 (1072) 1543 (1073)

1072 1073

1544 (1074)

1074

N.A. (1075)

1075 (continued)

48 Table 2.1 (continued) Activity –Manufacture of other food products n.e.c.** (prepared meals and dishes included here in ISIC Rev. 3) *Manufacture of prepared animal feeds Manufacture of beverages –Distilling, rectifying and blending of spirits; ethyl alcohol production from fermented materials –Manufacture of wines –Manufacture of malt liquors and malt –Manufacture of soft drinks; production of mineral waters and other bottled waters Warehousing and support activities for transportation –Warehousing and storage

2 Food Processing: Understanding Common Threads

ISIC Rev. 3; 2002 (ISIC Rev. 4; 2008)

NIC (2008)

**1549 (1079)

1079

1533 (108)

108

15 (11) 1551 (1101)

11 1101

1552 (1102) 1553 (1103)

1102 1103

1554 (1104)

1104

63 (52)

52

6302 (5210)

5210

Source ISIC Revised Schedules from UNStats and CSO, GoI

Appendix

49

Table 2.2 HSN 2-digit classification and net exports from India of processed food items 2-digit HSN Description code Chapter 02 Chapter 03 Chapter 04 Chapter 07 Chapter 08 Chapter 09 Chapter 10 Chapter 11 Chapter 12 Chapter 13 Chapter 15 Chapter 16 Chapter 18 Chapter 19 Chapter 20 Chapter 21 Chapter 22 Chapter 23

Meat and edible meat offal Fish and crustaceans, molluscs and other aquatic invertebrates Dairy produce; birds eggs; natural honey; edible products of animal origin, not elsewhere specified or included Edible vegetables and certain roots and tubers Edible fruit and nuts; peel of citrus fruits or melons Coffee, tea, mate and spices Cereals Products of the milling industry; malt; starches; insulin; wheat gluten Oil seeds and oleaginous fruits; miscellaneous grains, seeds and fruit; industrial or medicinal plants; straw and fodder Lac; gums, resins and other vegetable saps and extracts Animal or vegetable fats and oil and their cleavage products; prepared edible fats; animal or vegetable waxes Preparation of meat, of fish or of crustaceans, molluscs or other aquatic invertebrates. Chapter 17 Sugars and sugar confectionery Cocoa and cocoa preparations Preparations of cereals, flour, starch or milk; pastry cooks products Preparations of vegetables, fruit, nuts or other parts of plants Miscellaneous edible preparations Beverages, spirits and vinegar Residues and waste from the food industries; prepared animal fodder

Source MoFPI Data

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References Akkerman R, Van Der Meer D, Van Donk DP (2010) Make to stock and mix to order: choosing intermediate products in the food-processing industry. Int J Prod Res 48(12):3475–3492 Aksoy MA (2005) The evolution of agricultural trade flows. In. (eds) Aksoy MA, Beghin JC, Global agricultural trade and developing countries. The World Bank, Washington DC Barrett CB et al (2011) Research principles for developing country food value chains. Science 332:1154–1155 Beamon BM (1999) Designing the green supply chain. Logist Inf Manag 12(4):332–342 ˆ Fert˜o I (2009) Agro-food trade competitiveness of Central European and Balkan countries. Bojnec S, Food Policy 34(5):417–425 Brennan JG, Grandison AS (eds) (2012) Food processing handbook. Wiley-VCH, New Jersey Brennan MJ (1958) The supply of storage. Am Econ Rev 48(1):50–72 Brown ME, Funk CC (2008) Food security under climate change. NASA Publications. 131. http://digitalcommons.unl.edu/nasapub/131 Caroli E, Gautié J, Lloyd C, Lamanthe A, James S (2010) Delivering flexibility: contrasting patterns in the French and the UK food processing industry. Br J Ind Relat 48(2):284–309 Connor JM, Schiek WA (1997) Food processing: an industrial powerhouse in transition. Wiley, New Jersey Connor JM, Heien D, Kinsey J, Wills R (1985) Economic forces shaping the food-processing industry. Am J Agric Econ 67(5):1136–1142 Cook J, Oreskes N, Doran PT, Anderegg WR, Verheggen B, Maibach EW, Carlton JS, Lewandowsky S, Skuce AG, Green SA, Nuccitelli D (2016) Consensus on consensus: a synthesis of consensus estimates on human-caused global warming. Environ Res Lett 11(4):048002 Coulter JP, Shepherd AW (1995) Inventory credit: an approach to developing markets. FAO Agric Serv Bull 120:107 Coulter J, Sondhi J, Boxall R (2000) The economics of grain warehousing in Sub-Saharan Africa. African Review of Money Finance and Banking, pp 97–116 Desai BM, Namboodiri NV (1992) Development of food processing industries. Econ Polit Wkly 27(13):A37–A42 Dogra B (1988) Food processing: why MNCs? Econ Polit Wkly 23(38):1936 Domanska E (2014) The new age of the Anthropocene. J Contemp Archaeol 1(1):98–103 Dorfman JH (2014) Economics and management of the food industry. Routledge, London FAO Report on the State of Food Insecurity in the World (2015) Meeting the 2015 international hunger targets: taking stock of uneven progress Fischer EM, Knutti R (2015) Anthropogenic contribution to global occurrence of heavyprecipitation and high-temperature extremes. Nat Clim Chang 5(6):560 Fitzgerald AJ (2010) A social history of the Slaughterhouse: from inception to contemporary implications. Hum Ecol Rev 17(1):58–69 Foley JA, Ramankutty N, Brauman KA, Cassidy ES, Gerber JS, Johnston M, Mueller ND et al (2011) Solutions for a cultivated planet. Nature 478(7369):337–342 Fujita M, Thisse J-F (1996) Economics of agglomeration. J Jpn Int Econ 10:339–378 Gebrewolde TM, Rockey J (2018) The effectiveness of industrial policy in developing countries: causal evidence from Ethiopian manufacturing firms. University of Leicester Working Paper No. 16/07. https://www.le.ac.uk/economics/research/RePEc/lec/leecon/dp16-07.pdf Ghosh N (2014) An assessment of the extent of food processing in various food sub-sectors, Revised Report from the Institute of Economic Growth to the Ministry of Agriculture, GoI Giovannuccia D, Ponte S (2005) Standards as a new form of social contract? Sustainability initiatives in the coffee industry. Food Policy 30:284–301 Gopinath M, Pick D, Vasavada U (1999) The economics of foreign direct investment and trade with an application to the U.S. Food Processing Industry. Am J Agric Econ 81:442–52 Heller MC, Keoleian GA (2014) GHG emissions of U.S. dietary choices and food loss. J Ind Ecol 19:391–401

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Henson S, Humphrey J (2010) Understanding the complexities of private standards in global agrifood chains as they impact developing countries. J Dev Stud 46(9):1628–1646 Hubbard M (1981) Desperate games: Bongola Smith, the Imperial Cold Storage Company and Beuchanaland’s Beef, 1931 The Botswana Society 13:19–24 Hunter MC, Smith RG, Schipanski ME, Atwood LW, Mortensen DA (2017) Agriculture in 2050: recalibrating targets for sustainable intensification. BioScience 67(4):386–391 Jongwanich J (2009) Impact of food safety standards on processed food exports from developing countries. ADB Economics Working Paper Series No. 154. https://EconPapers.repec.org/RePEc: ris:adbewp:0154 Kim HK, Kim JK, Chen QY (2012) A product network analysis for extending the market basket analysis. Expert Syst Appl 39(8):7403–7410 Kress JW, Stine JK (2017) Living in the anthropocene: earth in the age of humans. The Smithsonian Institution, USA Lal R (2004) Soil carbon sequestration impacts on global climate change and food security. Science 304(5677):1623–1627 Lobell DB, Burke MB, Tebaldi C, Mastrandrea MD, Falcon WP, Naylor RL (2008) Prioritizing climate change adaptation needs for food security in 2030. Science 319(5863):607–610 MacDonald R, Reitmeier C (2017) Understanding food systems, agriculture, food science, and nutrition in the United States. Academic Press, New York Maertens M, Swinnen JFM (2006) Globalization, privatization, and vertical coordination in food value chains in developing and transition countries. In: Plenary paper prepared for presentation at the international association of agricultural economists conference, Gold Coast, Australia, August 12–18, 2006. https://ageconsearch.umn.edu/record/25626/files/pl06sw01.pdf Maubois JL (1984) Separation, extraction and fractionation of milk protein components. Le Lait INRAEditions 64:485–495, McMichael PD (1992) Tensions between national and international control of the world food order: contours of a new food regime. Sociol Perspect 35(2):343–365 Murthy KS, Dasaraju H (2011) Food processing industry in India: fruit processing industry in Andhra Pradesh. LAP LAMBERT Academic Publishing, GmbH & Co. KG, India Nesvadba P, Houška M, Wolf W, Gekas V, Jarvis D, Sadd PA, Johns AI (2004) Database of physical properties of agro-food materials. J Food Eng 61(4):497–503 Nicholson CF, Gomez MI, Gao OH (2011) The costs of increased localization for amultiple-product food supply chain: dairy in the united states. Food Policy 36(2):300–310 Oculi O (1984) Multinationals in Nigerian Agriculture in the 1980s. Review of African Political Economy, 31 (Capital vs. Labour in West Africa): 87–91 Parry M, Rosenzweig C, Iglesias A, Fischer G, Livermore M (1999) Climate change and world food security: a new assessment. Glob Environ Chang 9:S51–S67 Pilcher JM (2017) Food in world history, 2nd edn. Routledge (Taylor and Francis Group), New York Pingali P (2006) Westernization of Asian diets and the transformation of food systems: implications for research and policy. Food Policy 32:281–298 Raynolds LT (2004) The globalization of organic agro-food networks. World Dev 32(5):725–743 Reardon T, Farina EM (2001) The rise of private food quality and safety standards: illustrations from Brazil. Int Food Agribus Manag Rev 4(4):413–421 Reardon T, Barrett J, Berdegué J, Swinnen JFM (2009) Agrifood industry transformation and small farmers in developing countries. World Dev 37(11):1717–1727 Ring MJ, Lindner D, Cross EF, Schlesinger ME (2012) Causes of the global warming observed since the 19th century. Atmos Clim Sci 2(04):401 Sanderson GW, Schweigert BS (1985) Technical forces shaping the U.S. Food-Processing Industry. Am J Agric Econ 67(5):1143–1148 Satterthwaite D (2008) Cities’ contribution to global warming: notes on the allocation of greenhouse gas emissions. Environ Urban 20(2):539–549

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Singh SP, Tegegne F, Ekenem E (2012) The food processing industry in India: challenges and opportunities. J Food Distrib Res 43(1):81–89 Sperber WH, Stier RF (2009) Happy 50th Birthday to HACCP: Retrospective and Prospective. Food Saf Mag 42–46 Sutton J (2007) Sunk costs and market structure: price competition, advertising and the evolution of concentration. The MIT Press, Cambridge Vyas V (2015) Low-cost, low-tech innovation: new product development in the food industry. Routledge, New York Watanabe M, Jinji N, Kurihara M (2009) Is the development of the agro-processing industry propoor?: The case of Thailand. J Asian Econ 20(4):443–455 Wheeler T, Von Braun J (2013) Climate change impacts on global food security. Science 341(6145):508–513 Yülek MA (2018) How nations succeed: manufacturing, trade, industrial policy and economic development. Palgrave McMillan Zalasiewicz J, Williams M, Smith A, Barry TL, Coe AL, Bown PR, Brenchley P, Cantrill D, Gale A, Gibbard P, Gregory FJ (2008) Are we now living in the Anthropocene? Gsa Today 18(2):4

Chapter 3

Identifying Trajectories in Food Processing

3.1 Food Processing: A Regional Perspective The discussion in Chap. 2, using a product network analysis, points out the criticality of profitable arbitrage (through cheaply available raw materials) in developing food processing as an industry. At the same time, it also mentions that an abundance of raw materials alone does not determine whether industrial density in food processing will be achieved. This requires support from other institutions that enforce contracts and market functioning, working capital finance, skilled manpower as well as favourable government policy. For developed countries, Connor et al. (1985) mentions the triumvirate economic forces shaping the food processing industry as: (i) the structure of food demand; (ii) production and supply costs and (iii) the nature of competition between firms in the industry. However, such a simplification is possible if other supporting institutions are functioning perfectly. We argue that a regional perspective sheds more light, as it allows us to bring into the discussion regional imperfections in the supply of other inputs. Region now adds a second dimension to our product network-based1 understanding of food processing, resulting in two-dimensional ‘region × product network’ constructs we call ‘trajectories’. Why do we use the term ‘trajectories’? This is because a trajectory is associated with a path across time. A region has a specific history, constraints and advantages that define the development of product networks on that path. The first trajectory we discuss is our benchmark. It works for a region with perfect capital markets for accessing finance for starting or expanding business in food processing as well as no frictions in doing business, such as infrastructure bottlenecks or a lack of property rights. This applies to the production realities of the 1 Note here that we use the term sub-sector interchangeably with product network. The latter is tied

to the challenges in the placement of new food products, which are derived from basic products with minimal processing in the market. On the other hand, the sub-sector notion applies to the entire business support system that applies when we talk of a particular manufactured processed food product. As these are two ways of looking at a food product, we have used them interchangeably in the following discussion. © Springer Nature Singapore Pte Ltd. 2020 D. Saha, Economics of the Food Processing Industry, Themes in Economics, https://doi.org/10.1007/978-981-13-8554-4_3

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developed industrial ecosystems in the global North, such as the OECD countries, and is the mainstay of the discussion in Sutton (2007). Using this benchmark, we first discuss some stylized facts about the processed foods industry (including the retail and the procurement stages). We, then, investigate these facts from a different regional trajectory: the national food processing scenario in India. As a region, India has emerged as one of the largest economies in recent times. However, like some other emerging economies, it suffers from imperfections in capital markets, marked duality in industries between the formal and informal sectors and concomitantly, large variations in quality and difficulties in the standardization of food. Some other facts, such as food preferences, show significant differences between the first and this trajectory. The region, therefore, poses a large constraint on the uni-dimensional (product network-specific) food processing narrative so far. We use this to motivate further investigations from the perspective of a third trajectory: the subnational scenario of food processing with Bihar as the proxy for a region in Chap. 4. There, we indicate the difficulty of inferring the regional from the national trajectory that we describe here. Despite being limited to a specific subnational region, a large part of the food manufacturing successes in emerging economies like India has been driven by local government initiatives. In this chapter, we override these regional peculiarities and treat the nation homogeneously in the India trajectory. We intend to study the characteristics of successful food processing firms and national policy initiatives in the Indian market. We shall use this as a benchmark in the case of the Bihar trajectory in the next Chap. 4.

3.2 Trajectory 1: Benchmark Stylized Facts About Food Processing Figure 3.1 shows a general description of the nature of linkages in food processing, with a special focus on the industrial infrastructure that support manufacturing and are seamlessly present in this stylized trajectory. We use this to compare across trajectories2 later.

Connecting Agri-Inputs with Retailing in Food One of the basic attributes of manufacturing activities in food is that it creates backward linkages with inputs from various upstream sectors such as agriculture, horticulture, animal husbandry, etc. Referring to Fig. 3.1, we find that these linkages are supported by multiple institutional arrangements. For instance, between upstream farm-gate and the relatively downstream primary processing stages, one institutional link (Link I) is that of cold chain systems, which includes warehousing, refrigerated transportation system, etc. This link creates the necessary conditions for profitable 2 Table

4.10 in the following Chap. 4 provides a comparison across trajectories.

3.2 Trajectory 1: Benchmark Stylized Facts About Food Processing

55

Fig. 3.1 Infrastructure support links in food processing (Author’s creation)

arbitrage among perishable items in food processing, as mentioned earlier, along with a reduction in the seasonality of input availability. The second link of contract farming and co-operatives exists between the agri-input stage (inclusive of animal husbandry) and the primary as well as secondary processing layers, which we refer to as Link II in Fig. 3.1. In many parts of the world, a degree of corporatization in farming, with the private sector processing units directly purchasing inputs from farmers through various kinds of contracting mechanisms (referred to as contract farming) has become standard. Given asymmetric bargaining power, this institution is generally biased in favour of the processors and not the farmers. Additionally, the institution of co-operatives, through which small individual farmers aggregate their output collectively, also creates a different kind of linkage between the upstream input and the downstream processing sectors. The White Revolution in dairy is largely a co-operative driven success. Link III is between the secondary processing stage and retail food markets (including restaurants) through various kinds of contracting. Relating this discussion with our earlier description of product networks, note that upstream and downstream linkages depend on the extent of primary as opposed to secondary processing in a region. While primary processing requires marketing as well as secondary stage processing outputs, a major fraction of the former is sold through wholesale markets/unbranded retail, such as for polished and unpolished rice, wheat and other cereals. Items such as bread, which use primary processed output such as wheat, are prepared through the secondary value addition stage of bakeries, as we discussed in Chap. 2. S1. Presence of interlinkages between upstream and downstream through contracts. Concentration and competition among formal enterprises with advertising and certification as sunk costs. There is a moderate concentration of firms in food processing, but it is not uniform across sub-sectors. For sub-sectors in food processing with high endogenous sunk costs in advertising, such as soups, the market has high levels of concentration in Germany, Japan, USA, UK, Italy and France (see Sutton (2007)). Interestingly, Sutton (2007) finds a dual structure in the frozen foods market in the US. There are significant economies of scale in distribution, advertisements and selling for this

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product. Connor and Schiek (1997) also notes the difficulties faced by middle-sized firms in competing against branded majors in frozen foods like General Foods (with their brand Birds Eye) in the US and the UK. However, Sutton (2007) finds evidence of a fragmented market structure and lack of concentration in frozen foods in other European markets. For Denmark, where processed food and dairy exports are second only to pharmaceuticals, Festerling (2008) finds that it is small firms that have the largest value-added shares in processed food between 1995 and 2003, as does Vyas (2015) for the Scottish food and drinks industry3 Most of the evidence points to the simultaneous co-existence of large firms with small businesses, as the theory of endogenous sunk costs (in advertising) of Sutton (2007) predicts What the theory implies....is that some small set of firms must at some point emerge as high advertisers, whose combined market share exceeds some lower bound, however large the market becomes. A remaining fringe consisting of an indefinite number of firms that do little or no advertising may coexist with these market leaders at equilibrium.

A core characteristic of processed foods industries is its segmentation between different retailing mechanisms. With basic processing, own-label sales along with industrial sales (unbranded sales to other processors who then brand and sell the product) and unbranded wholesale are possible distribution channels.4 Links are also formed with the catering/restaurant segment of cooked or packaged retail products. Ownbrand retail dominates secondary processing. Large transnationals, which operate at the basic as well as branded retail end of food products, mostly integrate backwards into the agricultural supply chain. One of the largest sources of expenditure for them is advertising, as we discussed in Chap. 2 for the IFBA, which is a major food and non-alcoholic beverage alliance. Sutton (2007) characterizes advertising as an endogenous sunk cost that is a choice variable for firms incurred prior to engaging in retail competition and explains market concentration using this. Entry costs in the own-branded sales of RTE cereals in the US in the 1960s have been studied in Scherer (1982), Stern (1966) and Headen and McKie (1966). During this time, there were a number of anti-trust cases against cereal majors such as Kellogg’s for abuse of monopoly power. A counter to this advertising-led concentration in ownbrand retail is the comprehensive survey of food processing industries in the US (1958–1997) by Rogers (2001). He finds evidence of concentration among firms which are attributable to advertising intensity in the US prior to 1977. After this, there has been an increase in concentration, but that is not attributable to advertising alone. Moreover, there is evidence of small firm entry and survival in the unbranded retail segments, which often supply to larger firms as co-processors/subcontractors. A case in point is General Foods in the US (Sutton 2007). In the decade of the 1930s, when it was growing, it sustained itself as a specialist only in marketing, ‘buying in’ most of the frozen food from subcontractors who operated the freezing plants 3 Overall,

there is a concentration of MNCs in the global North, many of them in the US. In fact, Connor and Schiek (1997) finds evidence of significant concentration in the US markets. 4 Note that preferential purchase by the government from new entrants is another retailing mechanism. We do not put this as an option here, as this is a part of government policy and not an independent choice for the entrepreneur.

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in agricultural areas. In fact, General Foods had licensed its patented freezers with its brand names to these subcontractors. Due to the depth of the product network, different firms can find niche markets to sell their products in this trajectory, particularly RTE breakfast cereals. A continuum of large, medium and small firms are all present. The US frozen foods market in the 1960s was marked by this co-existence of all firm sizes. Sutton (2007) notes Most own-label product was supplied by small firms who supplied the larger retail chains. The non-retail segment remained more fragmented, and the smaller firms specializing in retail sales remained relatively profitable compared to those firms of similar size that faced severe competition from the majors in the retail segment, where profit rates were strongly and positively related to firm size.

The overall summary for Trajectory 1 regarding competition and concentration in food processing markets is S2. Lack of deep market segmentation of the market, with own-branded retail co-existing with unbranded and non-retail sales. The own-branded retail segment is intensive in advertising expenditure, which has the potential to deter entry resulting in concentrated markets.

Low Technology Intensity and Marginal Innovation Food processing, unlike other industries like electronics and pharmaceuticals, has a relatively low technological intensity (see Galizzi and Venturini (1996) for a general overview, Vyas (2015) for Scotland and Suwannaporn and Speece (1998) for Thailand). While secondary processing requires some technological inputs, primary processing is, even to this date, driven by rather rudimentary technologies. Marginal innovation by small firms has been a hallmark of food processing industries, as Mueller et al. (1982) discusses. New product development (as mentioned in Vyas 2015), as well as food packaging and equipment for processing, are some of the low-tech innovations in food (see Ettlie (1983)). Investments in product standards, safety certifications and quality metrics for organic foods are some of the other dimensions where technology plays a role. Biotechnology, in recent times, has made some inroads into the processed foods industry. It helps processing companies understand the biochemical properties of food and methods of packaging them in improved aseptic conditions, as Sanderson and Schweigert (1985) mentions. S3. Food processing has low technology intensity and marginal innovations.

Stability in Consumer Tastes The notion of what constitutes food has relative stability over time. Culture, history and environment determine diets and therefore, what consumers consider food.

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Despite evidence of changing diets, particularly for Asia (Pingali 2006), most research indicates that at the national level, food concepts have been fairly stable over time within national boundaries (Vyas 2015). Consider the example of the US. Connor et al. (1985) mentions that despite an average increase in per capita personal disposable income in the range of 2% per annum from 1950 to 1985, food demand overall was stable. What has changed is consumer preferences in favour of higher quality and convenience. Note that these two attributes of food can be enhanced in the same product through better packaging and labelling without introducing new foods. The quality of the food product itself does not change. Introducing new foods is a risky business strategy. Nonetheless, some food companies experiment with this. Vyas (2015) finds evidence of this in Scotland. One recent development that has changed retail competition in food is the organics segment. Raynolds (2004) notes that global organic food sales in the early 2000s was approximately USD 20 billion per year with an annual growth rate of around 20% in major North American and European markets. This has gone hand-in-hand with a preference in the global North for exotic products, which may or may not be organic. Raynolds (2004) mentions tropical products, counter-seasonal fresh produce and commodities produced locally but in insufficient quantities in this category. While this is of some importance,5 this is a nascent market.6 Another relatively new development is the convenience food category. Packed frozen food and Food-Away-From-Home (FAFH) create a new food category altogether, which has happened in the global North with sharp demographic changes (falling family sizes) and increased female labour force participation (Connor et al. 1985). Prepared dinners, ready-to-eat packaged meals produced at processing factories, pre-cooked food items requiring minimal processing, such as soups and fast food noodles are some products in retail stores worldwide in recent times due to demographic shifts and hurried urban lifestyles. The stability in food tastes that we refer to is more in terms of regional cuisine-specific diet (vegetarian vs. non-vegetarian) dimensions in the global North rather than through transitions within the different product networks in processed foods. S4. Consumer tastes in food, in terms of dietary patterns and regional cuisines, have been stable in the global north.

High Working Capital Intensity Relative to many other industries, food processing is more intensive in its use of working capital. This is due to large raw material input cost and the seasonal nature 5 Klonsky

(2000) notes that the estimated growth in retail sales of organic foods has averaged over 20% year-on-year for eight years between 1990 and 1998 compared to only two per cent in the food industry overall. 6 statista.com finds that while global sales of organic foods amounted to about 90 billion USD in 2016, non-organic food sales accounted for 2.47 trillion USD in the same year (roughly 4% of non-organic sales).

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59

of agriculture. Benet and Berend (1969) finds evidence of this for Hungary. Their study finds that food processing required one hundred per cent more ‘circulating assets’ than other industries between 1964 and 1966, though in terms of overall capital intensity food industries were no different from others. Apart from seasonality of output, they point out the different levels of imports: food products require much lower levels of imports than other industries. This feature is shared by all sub-sectors in food processing. Modern advanced silos and warehouses store seasonal produce and can reduce the intense requirement of working capital. However, the quality of processing fresh produce command a higher premium in retail markets. There is also a finite time limit to which storage is possible, making working capital an important prerequisite for this industry. There remains the issue of spillover effects of agricultural pricing onto the processing industry. Any increase in agricultural prices raises input prices and working capital requirements for the processing industries. S5. Regular availability of working capital is a necessity for the processed food industry. Fluctuations in agricultural prices is a large source of risk for food processing industries.

Food Processing Clusters in Manufacturing The trajectories we mention in Chap. 1 discuss the issue of geographical clustering, either through product-specific clusters or through a collection of different units across sub-sectors co-locating, as is the case with food parks. As we pointed out, there is a tension in choice of location for food processing units: nearness to input sources or nearness to retail markets for the end product. This happens due to the presence of agri-resource inputs in rural areas whereas most retailing zones are present in urban areas. This trade-off is typically resolved in favour of input-driven location choices for most countries. Location-based clusters for many sub-sectors in food processing, such as grain milling, animal feed and dairy, are a common feature of industrial agglomeration in the manufacturing end of food products. Additionally, the presence of forward and backward linkages and economies of scope for some products have given rise to the notion of food parks in recent times, where units across different sub-sectors in food processing co-locate at some mutually feasible region. S6. Formal manufacturing in food processing has regional clustering based on inputs and more recently, co-location in food parks due to economies of scope.

Involvement of the Retail Sector As discussed in Chap. 2, the retail sector has important links with primary and secondary processing. Even if an entrant decides to not engage in the sale of own-branded products, another option is to sell its produce (as subcontractors) for large retailers, such as Tesco or Sainsbury’s in the UK. These large retail superstores procure the

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bulk product, like grains or bakery items from co-producers and brand them for distribution and sales through their outlets. In recent times, online sellers also adopt such a strategy. This option reduces the scale of entry-level expenditure (as brand-creation is not necessary). The downstream retailers, being in close contact with the market, can provide critical inputs about product quality and packaging to unbranded upstream manufacturers. Many studies in developed countries, particularly in the context of innovation in food, show the importance of the downstream food retail sector in determining quality and types of products in the upstream manufacturing sector. However, as Vyas (2015) points out, this might not work out unilaterally in the favour of small upstream manufacturers due to their low bargaining power relative to large retailers. The latter might impose conditionalities on the co-processors which are costly and difficult for small entrants to undertake.7 Hence, the presence of retailers in manufacturing and their contribution might be a mixed blessing from small upstream entrants. S7. The manufacturing aspect of food processing is a part of the overall food business, which includes food retailing that often dictates production choices at the processing stage. Many of these stylized facts are true of the food manufacturing business across the world. For instance, most urban cities in India have the presence of large retailers and e-commerce sellers, who often procure food items for resale with their own packaging and branding (S7.). One example is Future Group Retail, with their Big Bazaar brand, which sells (often with bulk discounts and special offers) a large variety of processed food products, such as oils, pulses, rice and wheat products etc.8 Hence, what do we gain from filtering these through the lens of a regional context? We argue that even when these facts are valid in countries like India, there are several qualifications that we need to add to them. In the case of India, despite a large agri-input base and a relatively large food processing industry (albeit mostly informal: small-scale cottage industry initiatives), it’s export penetration in this segment is minimal. Hence, most industrial units cater to domestic consumption and their production basket is limited by what is considered food regionally. India boasts of distinct food preparations in different states. Some types of food products are specific to some states and these are driven purely by local demand. The core focus of this book studies food processing through the narrower regional lens of Bihar. There are studies, such as Ghosh (2014) and Singh et al. (2012), not to mention national government policies treating the food processing industry at the aggregate national level. There are many difficulties in this treatment: inputs such as land and labour come under the purview of state laws and not central legislation. Our foray into the second national trajectory is twofold only: about successful entry by private enterprises and regional variation in responses to national-level policies.

7 Note that for the Bihar trajectory, we argue the opposite. In fact, it is the presence of co-processing

possibilities that can facilitate small firm entry and survival. See Chaps. 6 and 7 for more details. details are available at https://www.futuregroup.in/aboutus.

8 Further

3.3 Trajectory 2: The Indian Experience with Food Processing

61

3.3 Trajectory 2: The Indian Experience with Food Processing At the national aggregate level for India, how large is the food processing sector? A 2017 Grant Thornton estimate values the Indian food processing industry between USD 121 and 130 billion,9 which is not a small number standalone. However, relative to the size of its agricultural output, India’s level of processing is surprisingly low across food sub-sectors, accounting for roughly 14% of the manufacturing GDP. This is minuscule in comparison even with some countries in Asia. Consider the figures quoted in the A.T. Kearney-FICCI report of 2013 titled ‘Feeding a Billion: Role of the Food Processing Industry’: the share of India for processing in fruits and vegetables was at a mere 2% in 2005, when the comparable figure for Thailand was 30% and for Malaysia, it was 80%. The per cent share of Food Processing Industries (FPI) in total Gross Value Added (GVA) from all activity for India has been less than 2 over the five-year horizon 2011–2012 to 2016–2017. Table 3.1 gives a cursory overview of the smallness of the Indian market, despite impressive growth rates hovering around 20% for different food groups for different time horizons. If we compare the RTE breakfast cereal and the frozen food segments10 for India and the US, the Indian market has a minuscule presence. The difference is sharp, particularly in frozen food products. This difference is all the more surprising, given that the US market has been stagnant in recent years. Even with predicted negative growth rates, the US market outweighs the size of the Indian frozen food market by a very large margin.

Table 3.1 Market for frozen foods: comparing India against the world Food product Country Market size Expected market CAGR (%) ’billion USD size ’billion USD (year) (year) Frozen food products RTE breakfast cereals

India

1.07 (2019)

2.71 (2024)

20.42

USA India

22.00 (2016) 0.17 (2012)

21 (2021) 0.36 (2018)

–1.20 20.18

USA

15.60 (2019)

16.36 (2023)

1.20

Source For frozen foods: Imarc Group Report (India), PR Newswire (USA); Statista.com for rest

9 Grant

Thornton report for Assocham entitled ‘Food processing sector: Challenges and growth enablers’, 22 February 2017; available at https://www.grantthornton.in/globalassets/1.-memberfirms/india/assets/pdfs/food_processing_sector.pdf. 10 Frozen food is a collection of items including frozen meats, fully prepared meals, seafood, soups and fruits and vegetables.

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3.3.1 Stylized Facts and Trajectory 2 Most of the stylized facts, which we have derived by drawing on experiences of industrialized nations, are not valid for India. Consider, for instance, the US example where despite falling share in agricultural trade, there is a large concentration of large food processing MNCs. The exact opposite is true for India: despite a very large agricultural base, the processed food segment is woefully small. Additionally, the food processing basket is very narrow. There is a predominance of primary processing of grains and pulses (around 35% of all processing), followed by beverages (roughly 30%) and dairy (around 15%), for 2011 as the A. T. Kearney-FICCI report (2013) notes. Table 3.4 shows that this has been the trend by studying the number of registered manufacturing plants at the 5-digit level of aggregation in food processing using ASI data (MoFPI website) from 2007–2008 to 2015–2016. The largest number of processing units belong to grain milling. Additionally, India has a minuscule presence in the world trade in processed foods, which several authors have commented on. Estimates of Rais et al. (2013) and Singh et al. (2012) mention a meagre 1.5% share for India in 2012–2013 in world trade of processed food products, despite being the world’s largest producer of tea, milk, fruits, cashew nuts and coconuts. Two of the six stylized facts (S1. and S6.) presented in Sect. 3.2 require significant modification, as borne out by recent empirical research. Particularly for S1., Murthy and Dasaraju (2011) notes that the appropriate stages in food processing should be threefold and not twofold (basic/ primary and final/secondary). Given the nature of informality in both the primary and secondary stages, they identify three stages: (i) basic or primary, (ii) unorganized and cottage industries and (iii) processed food industries. While the first and the third map to the primary and secondary stages introduced earlier, the second item is a mixture of these two processes. Some basic primary produce is sold through formal retail markets (such as branded packets of cereals, sugar etc.11 ) For instance, Singh et al. (2012) and Rais et al. (2013) point out the high processing costs in food which reduce the export potential for firms in India despite a very large agri-output. This number has not changed much in recent years. This indicates a lack of infrastructure support (cold chains), which reduces wastage and improves shelf-life of perishables, thereby making the possibility of arbitrage between raw and processed stages of food feasible. Additionally, linkages with the retail segment for marketing agricultural produce is also at a nascent stage. There is a large dichotomy between the large firms and small enterprises in unorganized retail and cottage industries. The existence of firms of all sizes, as discussed in S2., is absent for most regions in India. We shall be discussing the ‘missing middle’ size phenomenon in the following Chap. 4 for Bihar. This is true of the overall Indian trajectory as well.

11 Examples

of these for India apply to ITC’s Aashirvaad brand for wheat flour, Dhampure brand for sugar or to KRBL’s India Gate brand for basmati rice. In the nascent organic foods segment, there are some firms such as Organic India, which also sell organic but primary processed foods such as brown rice, wheat, various kinds of other flours etc.

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Stability in consumer demand (S4.) has been challenged in recent times in India, which Pingali (2006) maintains for Asia as a whole through westernization of diets. Traditionally, Indian diets have been biased towards unprocessed and fresh cooked foods, which is potentially a demand-side constraint for the industry. However, research in recent times (we mention Chengappa et al. (2005)) indicates that there have been significant changes in consumption patterns, particularly in middle- and high-income groups, demonstrating an ample opportunity for processed food segments in the country. Goyal and Singh (2007) finds that several factors, such as rising incomes, increased urbanization, changing lifestyle, a greater willingness to experiment with new products, an increase in the number of working women, etc., have led to strong growth in consumption of processed food products in recent times. Coming to the observation regarding high capital intensity (S5.), this is probably one of the largest constraints for developing the food processing industry. For a region with imperfections in the capital market, smooth access to working capital is difficult. Achieving the minimum efficient scale (m.e.s.) in production is, in itself, a challenge. Then, if working capital loans are collateralized against the existing stock, this works against small businesses which cannot expand easily into the high-value addition products in the product network. Incurring necessary advertising expenses becomes a large bottleneck for expansion.

3.3.2 Working Capital Constraint in Trajectory 2 One of the few empirical studies that analyse the viability of the food processing sector in India, keeping in mind working capital intensity, is that of Desai and Namboodiri (1992). For the period 1980–1981 to 1984–1985, Desai and Namboodiri (1992) mentions the existence of high working capital for multiple sub-sectors in food processing in India. We extend the analysis of Desai and Namboodiri (1992) to the year 2015–2016 using factory-level data from the Annual Survey of Industries (ASI). Note that the variables that are used in this analysis are largely financial and technological. Desai and Namboodiri (1992) calculates various ratios to rank the subsectors in terms of their financial viability. One problem with the ratio-based analysis is that intensity and productivity measures are partial. For instance, the productivity of working capital in the paper is calculated as the ratio of the value of output to working capital. This is a partial measure of productivity and would work fine if working capital was the only single factor of production. We know that that is not the case with food processing. Partial productivity measures give a biased estimate in the presence of multiple inputs, as we discuss in Chap. 6. However, as far as inferring the intensity of working capital usage goes, one can work with the ratio given in Desai and Namboodiri (1992). We intend to characterize the path of working capital intensity in food processing in this chapter for India. For understanding efficiency, we anyways have a detailed commentary on selected sub-sectors of food processing in Chap. 6. Let us clarify some definitions regarding the measurement of working capital. In the ASI data, working capital is the sum of physical working capital and in-hand plus

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bank-held cash deposits as well as land and the net balance of amounts receivable over amounts payable at the end of the accounting year. The latter includes the value of credit items on revenue account, such as sums due to the factory for goods sold, amounts advanced in connection with normal factory work, bills of exchange payable to the factory, payments made in advance such as fire insurance, telephone charges, rates and taxes, call deposits and security deposits having a normal life of less than one year, etc. It excludes any unused overdraft facility, fixed deposits irrespective of duration, advances for acquisition of fixed assets, long-term loans including interest thereon and investment. What is physical working capital? The ASI manual defines it as all physical inventories owned, held or controlled by the factory as on the closing day of the accounting year such as the materials, fuels and lubricants, stores, etc. that enter into products manufactured by the factory itself or supplied by the factory to others for processing. Physical working capital also includes the value of the stock of materials, fuels and stores etc. purchased expressly for resale, semi-finished goods and goods-in-process on account of others and goods made by the factory which are ready for sale at the end of the accounting year. However, it does not include the stock of the materials, fuels, stores, etc. supplied by others to the factory for processing. Finished goods processed by others from raw materials supplied by the factory and held by them are included and finished goods processed by the factory from raw materials supplied by others, are excluded.

Using these definitions, we extend the analysis of Desai and Namboodiri (1992) for two sub-sectors (dairy and grain milling) at the 3-digit level of classification at the all-India level for the year 2015–2016. We have already noted that for the India trajectory, most of the processing activity is limited to basic processing in grain milling, beverages and some in dairy in Sect. 3.3. As we have kept beverages aside for this volume, we retain our focus on the sub-sectors of grain milling and dairy. These two sub-sectors are of importance for Bihar as well. Hence, this exercise is to be treated as a preamble to our further exploration of these sub-sectors in a subnational context in Chap. 6. Table 3.2 presents our findings alongside those of Desai and Namboodiri (1992). The latter use average figures from the same data source for the years 1980–1981 to 1984–1985. Desai and Namboodiri (1992) uses ratios measuring of resource intensity, partial factor productivity and profitability to understand the performance of these subsectors. Resource intensity is captured through the ratios: 1. Raw material intensity: is defined as material consumed relative to total inputs; 2. Working capital intensity: is defined as working capital relative to total capital. Desai and Namboodiri (1992) comments that, in conjunction with labour intensity (measured similarly through a ratio), raw material and working capital intensity are indicative of the potential for resource use in food processing according to Desai and Namboodiri (1992). Rather than potential, we interpret this as a measure of the extent to which a factor can act as a constraint in the development of the sub-sector. The higher the intensity, the more abundantly required is the factor. Therefore, a high raw material and working capital intensities are likely to be major growth constraints if the capital market is imperfect.

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Table 3.2 Comparing resource intensity, efficiency and profitability for dairy products and grain mills 2015–2016 1980–1981 to 1984–1985 calculations of Desai and Namboodiri (1992) Dairy (105) Grains (106) Dairy (105) Grains (106) Factor intensity Raw material intensity Working capital intensity Partial factor efficiency Raw material efficiency Working capital efficiency Profitability Net profit as % of GVA ROI SOI

82.82 23.04

79.00 52.28

84.30 40.60

84.90 45.80

1.31 20.93

1.35 7.24

1.19 10.01

1.20 12.05

0.29 0.11 0.27

0.14 0.03 0.10

0.30 1.25 8.09

1.46 8.05 19.27

Source Author’s calculations for 2015–2016 using ASI data; 1980–1981 to 1984–1985 values from Desai and Namboodiri (1992)

Table 3.2 reveals some interesting patterns. For the period 1980–1981 to 1984– 1985, Desai and Namboodiri (1992) found a relatively low working capital intensity in dairy compared to grain milling. Both the figures were around half of that of raw material intensity. In 2015–2016, the ASI data shows a drastically low figure for working capital intensity for dairy and a much higher figure for grain milling in comparison with the earlier figures. While this might be a product of the fact that this is an observation for a single year, which might be an outlier, note that the behaviour of raw material intensity in both sub-sectors are roughly the same as the earlier figures. Presumably there was no change in technology. It is the high working capital intensity in grain milling that is of concern, as this is the most important sub-sector in the Bihar trajectory. Desai and Namboodiri (1992) combines factor intensities with the efficiency and profitability ratios.12 Desai and Namboodiri (1992) argues that high values for partial factor efficiency and profitability facilitate the viability of developing a sub-sector in food manufacturing, while intensity in raw material resource usage shows the ‘strength’ that the sub-sector promises. Presumably, this strength is the potential of the sub-sector to create backward linkages with agriculture. The partial factor efficiency ratios are defined by Desai and Namboodiri (1992) as 1. Raw material efficiency: the value of output as a ratio of material consumed. 2. Working capital efficiency: the value of output as a ratio of working capital. whereas, profitability is proxied by the following three measures: 12 As

the efficiency ratios are calculated without any reference to a production function, ideally these should be considered as partial productivity measures and not measures of efficiency. We retain the efficiency term here to retain concordance with Desai and Namboodiri (1992) despite our reservation about terminology.

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1. Net profit as a per cent of Gross Value Added (GVA). 2. Return on Investment (ROI): Net profit as a per cent of total capital. 3. Surplus on Investment (SOI): Net operating surplus as a per cent of total capital. We find that in 2015–2016, relative to the earlier calculations of Desai and Namboodiri (1992), there is a sharp increase in working capital efficiency (rather, partial productivity) coupled with a decrease in its intensity in dairy. This is the opposite of the scenario in grain milling. Once again, there is relative stability in raw material efficiency figures, as was the case with raw material intensity. If output and total capital did not change appreciably over these two periods, then one possible explanation is a drop in working capital for dairy relative to grain milling. Without further data and more robust efficiency analysis, it is not possible to comment on this. One fact stands out: both sectors are raw material intensive. We do not have a clear picture of working capital efficiency from this exercise. However, what we do get is a sharply reduced number for profitability in both these sub-sectors (as measured by ROI13 ) relative to earlier figures. Once again, 2015– 2016 is a single year’s observation, whereas Desai and Namboodiri (1992) present an average figure over four years. The year 2015–2016 is an important reference year for our Bihar analysis, as there was a sharp policy change in the state in 2016,14 so we limit our exploration to this year for the all-India trajectory. Is it possible that the policy change was a result of poor profitability of these two sub-sectors in food processing, which are primarily present in Bihar? The all-India picture shows 2015– 2016 to be a bad year across all states in these two sub-sectors. One cannot rule out this macroeconomic externality in the policy choice of the state: that poor outcomes at the state level are due to overall depressed market conditions, but that they act as a motivator to implement a change in policy that the state government must have contemplated for some time. Desai and Namboodiri (1992) uses another measure to comment on the nature of constraints in doing business. This is the difference between the values of ROI and SOI (return on investment and surplus on investment respectively as defined above). Paraphrasing Desai and Namboodiri (1992), a very low ROI coupled with a higher value of SOI (as we see for dairy and grain milling in 2014–2015) is attributable to interest costs and wage bill. As these costs are part of running expenses for an establishment, they would require a large amount of working capital and once again, its intense usage might create constraints in the absence of seamless access to this kind of capital input. Now, comparing ROI against SOI in Table 3.2, we find a consistent pattern for all the years: that ROI is lower than SOI for both grain milling and dairy. While a simple comparison of ROI/SOI is not a fool-proof measure of the importance of working capital, it does indicate what we have claimed is a likely 13 Note that there is no appreciable difference between the two time periods for net profits as a per cent of Gross Value Added (GVA) for dairy but the value for grain milling is significantly lower in 2015–2016 compared to the Desai and Namboodiri (1992)-calculated average in the early 1980s. 14 The special incentives given to food processing in Bihar were removed in 2016. The policy treatment of the sector is now at par with that of other sectors. More details on this are in Chaps. 4 and 5.

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constraint for growth: lack of access to working capital. We do not decompose here which of the two factors (interest cost or wage bill) that make this ratio of ROI/SOI low.15 We limit the discussion here to the overall pattern indicated by the data from ASI. We assume interest costs arise due to continuous financing of debt incurred for starting a business and is separate from working capital requirements. This might or might not be a good assumption and we need more micro-economic evidence to decide the matter. The data indicates that there are issues with cash-flow management for food processing in India, as already noted by Desai and Namboodiri (1992). We found this to be the case for most small entrepreneurs in our primary survey in Bihar food processing in 2016–2017. In particular, units in grain milling have to sell on credit and purchase inputs with instantaneous payments. The smaller the firm, the smaller is the collateral for a financial institution loan for working capital. Thus, though firms might be able to start as a small unit with loans and government subsidies, its ability to continue in business and expand business operations become difficult. Tantalizingly, smaller the start-up cost of the factory, the easier it might be for a unit to start with equity investments from the entrepreneur and subsidies from the government. However, in the absence of perfect capital markets that can finance loans for working capital, achieving efficiency in operations run into a financial constraint, as we shall show with the Bihar trajectory.

3.3.3 Other Constraints in Trajectory 2 Processed foods in India are expensive relative to the rest of the world. Consider, for instance, an observation from the MoFPI-sponsored report on the ‘Vision, Strategy and Action Plan for Food Processing in India’ by M/s Rabo India Finance Private Limited (2005)16 : Domestically, affordability is the key issue. The price differential between fresh and processed food in India is very high relative to the convenience, hygiene and health values of the processed food.

Exhibit 2.4.2 K in the same report provides an idea of the markups in pricing from the farm-gate to the retail maximum retail price (MRP) stage for branded wheat flour (‘atta’), packaged fruit juice, jam, potato chips, branded chicken nuggets, skimmed milk powder and clarified butter (‘ghee’) at 2005 prices. Consider the price jump for potato chips: from the stage of potatoes (at 0.5 INR for a 35 g packet of chips) to a 50% increase to the processing stage which involves a variable cost of 0.85 INR to a final MRP of 68 INR, after adding the retailer’s margin (0.83 INR), distributor’s margin (0.44 INR), sales tax (0.94 INR), sales discount for marketing (0.41 INR) and a very 15 Desai

and Namboodiri (1992) conduct a financial ratio analysis for this studying this issue. report is available online on the MoFPI website at http://mofpi.nic.in/sites/default/files/ volume1.pdf_0.pdf. 16 The

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large processor’s margin (2.22 INR) as well packaging cost (2 INR). Comparing across commodities is difficult, given a lack of homogeneity in measurement units (different kinds of packaging as well). However, this pattern is present in many processed items: makhana, for instance, as discussed in Chap. 2. The MoFPI report identifies multiple reasons which vary from cost and quality of farm produce to ‘infrastructure, credit, processing, packaging, long and fragmented supply chain, taxes, regulations and lack of scale’. The existence of high costs of processing is a catch-all covering pretty much all possibilities on the supply-side, and is likely to be present in many countries of the world. Among the specific issues relevant to India are financial constraints limiting firm expansion and segmentation among big and small firms. Note that cash-flow issues can be overcome with professional management of enterprises in formal registered manufacturing. It should not be surprising to find this problem in the large informal sector of the food processing industry in India. Some estimates put the informal sector output close to 90% of the total output from processed food manufacture. What is interesting is that cash-flow mismanagement and high physical working capital (indicating primitive technologies) shows up at the level of 3-digit aggregation for registered manufacturing in ASI data for India. High interest cost of debt for industries (for Bihar we found rates going up to 14% per annum) due to financial market imperfections is another problem. One of the most shocking firm exits in recent times in India is from the food processing industries: the case of Ruchi Soya Industries Limited (RSIL). Till 2015, RSIL was India’s largest edible oil and soya product processor. The firm is now up for bankruptcy proceedings due to its inability to finance a 200 crore INR debt in 2017. This is mentioned in Chap. 4, as it was one of the largest entrants in Bihar post-2008. Not only the small, fragility of large firms, in the presence of financial market imperfections in a working capital-intensive industry, is present. Note here that this problem is not uniform across all states of India, which are themselves at different stages of industrial development. The situation with industrially backward states like Bihar is probably more severe (as our survey revealed; more details are in Chap. 7). This again indicates that an appropriate frame of reference for understanding food processing should be at the state level. We shall shift focus to the subnational trajectory of Bihar after a short digression on the performance of private sector firms in processed food in India and the role of the national government, through the aegis of the Ministry of Food Processing Industries (MoFPI) in terms of national policies in food processing.

3.3.4 Performance of Private Companies in Food Processing in India Consider the recent best-performing companies in this industry in India in terms of their financials. Table 3.3 provides the details of the top four listed companies on the

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Bombay Stock Exchange (BSE), according to their net profits in 2018. Note here that the rank of these firms based on profit figures in the last four financial years is unchanged, though there is some churning in their ranks if we consider their sales turnover figures. To clarify this further, these four firms have consistently reported the highest net profits, with some minor change in their relative ranks. The year 2015 is an exception when Nestle’s position in terms of its competitors dipped to the third position from the first rank (presumably due to the controversy around its instant noodles brand Maggi, as discussed later). Thus, though there are some minor shifts in market shares, the identities of dominant firms in processed food in India are stable. For the year 2018, note the extent of this dominance: the market capitalization of the bottom two firms in Table 3.3 is individually roughly two-fifths of the market capitalization of the market leader, Nestle. The dominance of the market leader is unquestioned. Is this reflecting the standard ‘concentration-at-the-top’ type of result that is present in trajectory 1 (as discussed in Sutton (2007))? To decide this, we should first check if these firms are all in the same markets, as is common in anti-trust analysis. Deciding the market of operation is a big problem for multi-product firms. GSK, for instance, has a major presence in not only processed food with popular brands like Horlicks and Boost, it also has a major presence in pharmaceuticals with offerings such as the analgesic brand Crocin. In order to classify these firms within food processing itself requires some strong assumptions, as their profits are derived not only from this sector. One can use a yardstick that the firm should derive at least 50% of its profits from the sale of food products. This is an arbitrary criterion to overcome the problem of slotting multi-market firms into a single market. A more nuanced analysis (such as applying a SSNIP test17 ) can be conducted to determine whether or not we would like to include a firm like GSK in our list. For the time being, we include GSK because it has marketed some very successful products with significant brand-based campaigns in India and potentially provides some lessons that can be drawn upon for other firms in food processing. This caveat apart, we find another striking observation in Table 3.3: the diversity in the product offerings sharply declines with market capitalization. While KRBL specializes in rice alone (particularly, basmati rice), Nestle has a diverse portfolio from dairy products to confectionery and chocolates, prepared meals as well as beverages. While the number of observations is too limited to draw a general conclusion, we can treat this as anecdotal evidence that the market leader has a more diverse portfolio of products than smaller firms. How do we interpret these observations? Is it the case that large transnationals enter at a scale of operations that help them sustain themselves in niche developing country markets like India or is it the case that any firm which reaches a certain maturity (and are also large), they perform well? A perusal of firm identity from Table 3.3 reveals 17 SSNIP

is an acronym for the Small but Significant Non-Transitory Increase in Prices test for a hypothetical monopolist that is commonly (and often erroneously) applied in anti-trust analysis to establish dominance of a firm in a market. We do not engage further with this, as it would be outside the scope of this volume.

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Table 3.3 Recent competition among top five food companies (2018–2019), India Company # factory Products Sales turnover Market Net profits name locations (INR cr.) capitalization (INR cr.) (Yr.estd.) (INR cr.) Nestle (1912)

7 states

Britannia (1892)

9 states*

GSK 2 states Consumer Healthcare Ltd. (1924) KRBL (1988) 3 states

Dairy, chocolates, prepared dishes, beverages Biscuits, bread, dairy, cakes Nutritional supplements, biscuits

1,12,980.42

11,292.27

1,606.93

68,107.41

10,482.45

1,122.20

32,443.53

4,782.01

982.80

Rice processing

7,292.38

4,119.57

503.27

Source Website of respective companies and https://www.moneycontrol.com

that two among the top four, i.e. Nestle and GlaxoSmithKline (GSK) Consumer Healthcare Ltd. are large transnationals, whereas the others are Indian firms. It is possible that Nestle and GSK, having a transnational character, were able to exploit economies of scope by using the existing umbrella of their international branding and quality certification expenses? Reverting to the characterization presented earlier, this would be a reflection of the advertising intensity and the effect on competition in food processing (S2.). By creating brand awareness among consumers internationally, it can be argued that these firms have been able to leverage this goodwill effect in the Indian market. To some extent, the recent controversy regarding Nestle’s brand Maggi (packaged instant noodles)18 and the resulting drop in Nestle’s profits stands testimony for the success of a marketing strategy using a star product, bolstered with large-scale advertising expenditure. If the sales of the star product (in this instance, Maggi) suffers, then the bottom lines of the company are hurt. Hence, maintaining a brand image by continuous expenses in advertising and quality control have to be strategies for survival and growth for such firms. However, some Indian firms are also among the top performers, and the common link between these five firms is firm age. The year of establishment (yr. estd. for brevity in Table 3.3) reveals the length experience of the multinational firms in Indian markets (since the 1900s). The Indian firms are not young either, with Britannia being the oldest (established in 1892 in Kolkata, West Bengal). Apart from these, we also find other instances, such as MTR Foods Pvt. Ltd. (Orkla). It is based in Bengaluru and was established in 1924. Now, it has an international presence in the RTE dishes 18 There was recent controversy in Indian markets regarding the extent of lead content in Nestle’s Maggi, which affected the sales of Nestle’s largest selling brand in India; see https://www.ndtv. com/india-news/why-should-we-eat-maggi-with-lead-in-it-supreme-court-to-nestle-1972383.

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and spices segment, through its association with the Norwegian company M/s Orkla in 2007. This seems to lend merit to the claim that firm age is an important determinant of its success. Exposure to Indian markets and consumer preferences help them come up with the appropriate mix of products from among the potential product networks, as well as the appropriate segment (branded retail and/or industrial sales). However, the relationship with age (and even size, for that matter) and firm performance, in general, is unclear. The Indian market also presents instances of relatively new and small firms in food processing which have achieved quite spectacular sales turnover results, such as the impressive Shri Mahila Griha Udyog Lijjat Papad (further details at http://www.lijjat.com/). This women’s co-operative, specializing in the food item ‘papad’, was established in 1959 in Mumbai with a seed capital as small as USD 15 by seven women. At present, it has an ISO 9001:2015 certification, a turnover of more than INR 800 crore in 2018, and employs more than 43,000 women across India. There is considerable controversy about the relationship between firm age (and size) with firm growth. At one end is the classic Gibrat’s Law (see Sutton (1997)), which claims that there is no association between the proportional rate of growth of a firm and its absolute size.19 On the other, empirical investigation of this law around the 1980s by Evans (1987) fails to corroborate its claim across a set of 100 manufacturing firms. The recent review of literature in the Journal of Evolutionary Economics (see Coad et al. (2018)) summarizes the different perspectives across countries. While lack of an empirical regularity between firm age (and size) and its growth holds promise for new entrants, there is some evidence of the necessity of a minimum scale of entry from the experience of developing countries, such as the evidence of Sutton and Kellow (2011) for Ethiopia. Across a set of industries, including food processing, they find that entry by firms at the middle- and largesize is important for survival and growth. Small-scale entry is often unviable, given constraints in the industrial ecosystem that makes expansion difficult. For the Indian food processing trajectory, while we do find examples of successful niche small-scale entry, it is a fact that large MNCs market a diverse basket of products. Expanding in size along the same product line is one kind of growth. Discovering and marketing a larger variety of products using the network approach that we present in Chap. 2 is a more difficult yardstick of success. MNCs like Nestle or GSK have achieved this over time in the Indian market, but there are very few examples of Indian companies. Among these very few would be ITC, originally the Imperial Tobacco Company of India Limited. Originally, it was a British operation in India under colonial, established in 1924 and was ‘indianized’ over the years. It now has a presence across a large cross-section of product networks in processed food staples, spices, biscuits, confectionery and chocolates, snacks (noodles and pasta), beverages, dairy, RTE meals, coffee and frozen foods, apart from other industries, such as hospitality.

19 Of

course, this claim comes with the standard caveats about there is no accepted norm of how ‘size’ is to be measured and its relationship with firm age.

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Does that imply that success through expansion in the product basket requires ‘additional’ factors, that the MNCs already have? This would be an unduly pessimistic conclusion, that some firms like MTR Foods Pvt. Ltd. (Orkla) would prove incorrect, as we discussed earlier. This firm, established in 1924, has leveraged its regional identity by starting out first as a family-run restaurant in Bengaluru. The success of its cuisine (commonly referred to as ‘South Indian dishes’ such as idli, dosa, vada, etc.) prompted it to diversify into the convenience food and instant mixes segment in 1975. With the collaboration of the Norwegian firm Orkla Pvt. Ltd. since 2007, it has an international presence now and it experiments across the product networks of spices, semi-cooked meals and dairy-based drinks. Rather than an MNCfactor, the manner of entry into the market and the choice of the product network, as well as the region of operation, seems to be relevant. Consider a short history of Britannia for that matter. Registered in 1913 as a Public Limited Company under the Indian Companies Act, VII of 1913, it is India’s first biscuit manufacturing company. Around 1924, the company gained its experience in this line of business as a subsidiary of Peek, Frean & Co.Ltd., a leading biscuit manufacturing company in the UK and later expanded operations by opening factories in Kolkata, West Bengal, which had a large population of British expatriates. The English habit of ‘tea with biscuits’ provided a ready market for the firm, and it was only in 1955 that it launched its own-label Bourbon biscuit. Given these examples, and the lack of evidence regarding minimum efficient scale (m.e.s) of entry in food processing in India, consider an optimistic entry route. A potential entrepreneur in food processing may start as an employee/ sub-contractor with one of the top firms and after gaining sufficient experience (and minimum capital necessary to start a business), she/he starts a new enterprise in food processing. The choice of location of the new unit can be driven by raw material advantages or it can also have other pull-factors, such as a large market or even Industrial Policy (IP) incentives of the region. Nonetheless, it is the experience with these successful enterprises which forms the inspiration behind entry for potential entrants. A certain density of these firms might be critical for this path of entrepreneurial journey in food processing to take place. In this book, we investigate the possibility of this form of entry in Chap. 7. Large firms have manufacturing operations in different regions in India, as Table 3.3 demonstrates for the top four firms in food processing. Nestle’s production facilities, just like its product basket, is regionally diverse. It has factories in 7 states of India: Tamil Nadu, Karnataka, Haryana, Punjab, two facilities in Goa, Uttarakhand and Himachal Pradesh. Britannia has factories in 9 states, one of which is in the Hajipur Industrial Area, Bihar. One reason for this is that the company began operations in the neighbouring state West Bengal. Geographical proximity with Bihar is one possibility. However, one cannot rule out the specific role of Bihar’s handsome incentive policy, which was utilized by the Hajipur plant of Britannia. It was established in 2011, during which a special set of incentives were given to entrants in food

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processing in Bihar.20 In comparison, KRBL has a smaller radius of locations: Delhi, Haryana and Punjab. GSK has factories in Andhra Pradesh and Haryana. We present the uneven spread of firms by geographical location in the next Chap. 4. It is hard to relate these location choices to an input-advantage based generalized arbitrage logic alone, without bringing to the fore regional factors, including policy initiatives of state governments. Coming to the role of government policy, at the national level, food processing comes under the Ministry of Food Processing Industries (MoFPI), which administers national policies and administers them. Among its many objectives, the all-round development of the sector, particularly the possibility of expanding the processing of multiple food products at a given location is of immediate relevance. The concept of mega ‘food parks’, as explained in Chap. 2, addresses this issue. The central logic behind a food park is that it exploits these economies of scope across product networks by locating multiple firms with individual specializations at a single location. This would mimic a Nestle-like phenomenon without the entry of an individual MNC with a diversified product portfolio. Not only does it respect the principles of competition, but it also gives a chance to multiple small units to coalesce together their individual offerings into a diverse product basket. However, these schemes require a significant amount of coordination among different private players as well as a minimum scale of land. These coordination issues, as well as land acquisition, require third-party intermediation, and in general, the role of the government in facilitating the development of food parks is critical as highlighted in the following Sect. 3.3.5.

3.3.5 National Policies and Subnational Outcomes for Food Processing in India In recent times, India has acknowledged the lack of infrastructure and logistics for developing its food processing sector. An important development has been the central government’s permission for 100% FDI for retail trading, including through e-commerce, for food products manufactured and produced in India in 2016. Since this watershed year, increased entry, competition and retailing development (particularly online) have taken centre stage for Indian food processing. In terms of FDI numbers, the country has mopped up significant FDI in food processing since 2016. Official figures from the MoFPI indicate a 24% increase in FDI in food in India for 2017–2018 (amounting to USD 904.9 million) compared to USD 727.22 million during the 2016–2017 financial year. 2017 saw the entry of the American e-commerce major Amazon with a proposal of USD 500 million investment in retailing of food products in the country. Food retail is big business in the country, and the India Brand Equity Foundation (IBEF) estimates the size of food and grocery market combined 20 The

Hajipur plant of Britannia is mentioned among the success stories of Bihar in the Udyog Mitra publication on Success Stories in Bihar’s industries. The report is available at http://www. udyogmitrabihar.in/docs/pub/success-stories-print.pdf.

74

3 Identifying Trajectories in Food Processing

for India to be the sixth largest in the world.21 Online retailing is a segment that has seen a large growth (150% year-on-year growth in 2016, IBEF estimates), and the organics market is expected to see a threefold growth by 2020 (IBEF estimates). The export performance of this industry has not been so spectacular as other developments. IBEF estimates show that the contribution of food processing in total exports for India is around 13% in 2017–2018. Table 3.5 in the Appendix shows the earnings in USD per quintal for India from different agricultural items from 2008–2009 to 2017–2018. The largest revenue earner is not processed food items, but floriculture and seeds. As we discussed earlier, there are numerous infrastructural issues, particularly a severe lack of cold chains and warehousing facilities and increasingly stringent quality norms for exports. This lack is felt at the central policy level also and is possibly a driver for the 2016 comprehensive Pradhan Mantri Kisan SAMPADA Yojana (Scheme for Agro-Marine Processing and Development of Agro-Processing Clusters), which is being implemented by the MoFPI and addresses infrastructure bottlenecks holistically. Pradhan Mantri Kisan SAMPADA Yojana (PMKSY) The MoFPI website declares the PMKSY as ...a comprehensive package which will result in (the) creation of modern infrastructure with efficient supply chain management from farm gate to retail outlet. It will not only provide a big boost to the growth of food processing sector in the country but also help in providing better returns to farmers and is a big step towards doubling of farmers income, creating huge employment opportunities especially in the rural areas, reducing wastage of agricultural produce, increasing the processing level and enhancing the export of the processed foods.

The GoI has set aside an allocation of INR 6,000 crore for the period 2016–20 for the PMKSY and predicts that the scheme will leverage investments of INR 31,400 crore for handling of 334 lakh MT agro-produce valued at INR 1,04,125 crore, benefiting 20 lakh farmers and generating 5,30,500 direct/indirect employment in the country by the year 2019–20. In essence, the PMKSY is a collection of institutions: mega food parks, integrated cold chain and value-added infrastructure, capacity creation scheme for units, agroprocessing cluster scheme, creation of forward and backward linkages, food safety and quality assurance infrastructure, human resource development and the new Operation Greens.a The latter is an interesting focus on the tomato, onion and potato (with the catchy acronym TOP) value-chain, where the GoI is investing INR 500 crore to promote ‘Farmer Producers Organizations (FPOs), agri-logistics, processing facilities and professional management’ to strengthen the farmer-processing linkages. a Further

details are at http://mofpi.nic.in/Schemes/operation-greens.

21 https://www.ibef.org/industry/indian-food-industry.aspx.

3.3 Trajectory 2: The Indian Experience with Food Processing

75

Consider, now, the state-wise performance for these institutional developments under the umbrella of PMKSY. First comes the implementation of the mega food park scheme, 42 of which have been sanctioned22 as of March 2019. Among these, most major states have at least two parks allotted to them, other than Jharkhand and Rajasthan. Although Bihar has claimed two mega food parks since 2014 (the Pristine Mega Food Park at Khagaria and Buddha Food Park at Jehanabad, Gaya), none of these are functional. Note that the Jharkhand mega park received final approval in 2009 and it is now functional after 10 years. Comparing timelines for Bihar and Rajasthan shows the differential inter-state differences in their ability to absorb investments and facilitate industries. The functional Rajasthan mega park received its approval in February 2014, while Bihar got the final approval in August of the same year. Yet, neither park is functional at present in Bihar. Moving onto cold chain projects, Bihar has 1 completed and 2 ongoing projects in dairy and F&V only. In contrast, Maharashtra has 66 projects. Bihar has attracted 64 crore INR worth investments for these projects, which stands as minuscule in comparison with Maharashtra’s INR 1796 crore. Other initiatives, such as Make in India and Skill India, are recent national-level measures to foster entrepreneurship in sectors like food processing using local skill and raw materials. There are, nonetheless, large regional variations in the implementation of these centrally sponsored schemes, which reinforces the necessity of looking at the food processing sub-sectors in India from a state-level perspective.

3.4 Summary This chapter introduces our notion of trajectories in food processing, which are twodimensional (region × product network) constructs to discuss the nature of the food processing industry across different geographical locations. We start with a benchmark trajectory that applies to frictionless markets in developed countries and draw out some general characterizations of the industry, such as high working capital intensity, low levels of R&D, simultaneous co-existence of large and small units, involvement of large retailers in manufacturing, strong backward linkages with agriculture, advertising intensity at the own-brand retail segment, stability in consumer tastes and preferences as well as cluster-based processing. Regional trajectories, particularly the national trajectory for India (trajectory 2), are significantly different from the benchmark. The extent of processing in food as a formal manufacturing activity itself is at a much smaller scale at present, with 90% of output coming from informal manufacturing. The product basket of manufactured items is limited to grain milled items and dairy (Table 3.4 shows that the bulk of registered manufacturing units are in these two sectors) and a minimal presence in the export market. A small empirical exercise shows the importance of raw material intensity as well as working capital

22 Further

details are at http://mofpi.nic.in.

Meat

Fish

F&V

Oils and fats

Dairy products

Grain mill products

Starches and starch products

Bakery products

Sugar

Macaroni, noodles, couscous and other farinaceous products

Prepared meals and dishes

Prepared animal feeds

Spirits and ethyl alcohol

Wine

Malt (incl liquor)

Soft drinks and packaged water

Others

1010

1020

1030

1040

1050

1061

1062

1071

1072

1074

1075

1080

1101

1102

1103

1104

1079

1,096

2,515

735

340

77

26,219

4,091

887

120

64

280

555

45

73

778

955

442

27,220

4,290

896

96

79

291

547

139

61

733

993

589

13,464

1,100

2,429

709

352

90

27,479

4,225

834

117

69

296

606

343

51

744

1,056

670

13,397

1,112

2,421

832

359

85

35,840

5,114

1,264

154

74

325

677

416

83

895

1,450

757

17,792

1,493

3,307

1,052

436

115

36,880

5,101

1,401

141

77

378

755

352

75

906

1,399

766

18,244

1,653

3,394

1,078

390

146

37,175

5,251

1,483

154

78

365

873

298

129

859

1,519

723

18,131

1,695

3,312

1,110

462

140

37,450

5,546

1,520

143

71

369

820

277

105

791

1,498

744

18,272

1,753

3,300

1,101

466

148

38,608

5,765

1,597

153

74

395

881

364

91

763

1,613

699

18,953

1,783

3,240

1,133

427

170

39,319

5,983

1,624

150

70

376

918

89

780

1,626

670

19,141

1,943

3,147

1,192

534

148

2007–2008 2008–2009 2009–2010 2010–2011 2011–2012 2012–2013 2013–2014 2014–2015 2015–2016

12,807

Source MoFPI data sourced from ASI

Total

Description

NIC2008

Table 3.4 Number of units in registered food processing sub-sectors at the 5-digit level of aggregation, India

76 3 Identifying Trajectories in Food Processing

3.4 Summary

77

requirements in the sub-sectors of grain milling and dairy, using factory-level data from the ASI at the all-India level over time. If the industrial ecosystem, as reflected in the industrial layer is without imperfections, then it does not impose any constraints. One can characterize the different product networks along the lines of Sutton (2007). There, the trade-offs between the sunk costs of advertising and the gains in profits from larger market shares determine the nature of market concentration in different product networks in food processing. For homogeneous products like sugar and salt, Sutton (2007) finds that increases in market size reduce concentration, whereas sub-sectors like frozen foods, RTE cereals and soups where advertising matters due to product differentiation, this does not hold. However, if the region has imperfections within the industrial layer, be it finance or skilled manpower or policy interference, the market outcomes need to be filtered through a regional as well as sub-sectoral lens. The primary difference between the three trajectories that we have presented till now is in the industrial ecosystem of the region. With high interest rates and difficulty in accessing working capital, the difference in the nature of operations of the small and the large is expected to be large. This difference is expected to be sharper than for trajectory 1, where these financial constraints are absent. We find this to be true by comparing trajectories 1 and 2: the small unit in food processing in India from the informal sector is very different from a small firm in food processing in developed countries. Vyas (2015) shows that the small food and drinks companies are at the forefront of small-scale innovation in Scotland. However, the small informal establishment in India, which markets its product regionally, relies on traditional technologies, often functions without hired labour and as well as quality certifications and marketing expenses. On the other hand, the large units in food processing in India, that this chapter investigates, are either MNCs or established private limited companies with exports as well as a panIndia diversification of manufacturing facilities. These firms engage in brand-based advertising, like the large and middle-sized firms in trajectory 1, and present a stark contrast to the tiny operations which are the bulk manufacturers of processed foods in India. Government policy at the national level has provided incentives for food processing units to be developed across the country, but this chapter finds that there are regional differences in the absorptive capacity of these incentives. State-level trajectories become important to understand these constraints at work along with an assessment of state-level government policies. This motivates our discussion on one of the most industrially backward states of India, its advantage in food processing being its agricultural abundance and recent state-level policy initiatives in the sector: the Bihar trajectory.

Appendix See Table 3.5

80.55

270.21

Processed fruits and vegetables

Floriculture and seeds 65.19

259.76

82.22

36.35

96.88

Source Author’s calculations based on data from DGCIS

57.30

29.57

Total

78.57

80.47

260.35

91.79

40.85

121.69

87.44

295.27

98.39

39.64

211.89

188.95

Fresh fruits and vegetables

172.59

72.05

319.47

101.63

35.81

260.56

208.57

75.68

355.14

97.17

48.57

150.15

251.53

79.20

410.05

108.97

47.28

135.03

250.17

80.18

432.14

113.47

49.82

114.09

221.56

76.22

480.88

122.33

36.13

116.14

232.76

51.28

Other processed foods

93.45

51.64

90.76

51.87

Animal products

50.08

53.27

43.68

2011–2012 2012–2013 2013–2014 2104–2015 2015–2016 2016–2017

58.72

50.55

Cereals

59.08

APEDA product export (USD per quintal) 2008–2009 2009–2010 2010–2011

Table 3.5 Exports from India for major food items in the last decade

83.33

519.70

121.81

47.77

122.43

237.75

58.16

2017–2018

78 3 Identifying Trajectories in Food Processing

References

79

References Benet I, Berend I (1969) Relative capital intensity of food production and industry. Acta Oecon 4(4):379–402 Chengappa PG, Achoth L, Rashmi P, Dega V, Reddy BMR, Joshi PK (2005) Emergence of organized retail chains in India during post liberalization era. In: Paper presented at the South Asia regional conference of the international association of agricultural economists, globalization of agriculture in South Asia at Hyderabad Coad A, Holm JR, Krafft J, Quatraro F (2018) Firm age and performance. J Evol Econ 28(1):1–11 Connor JM, Schiek WA (1997) Food processing: an industrial powerhouse in transition. Wiley, USA Connor JM, Heien D, Kinsey J, Wills R (1985) Economic forces shaping the food-processing industry. Am J Agric Econ 67(5):1136–1142 Desai BM, Namboodiri NV (1992) Development of food processing industries. Econ Polit Wkly 27(13):A37–A42 Ettlie JE (1983) Organizational policy and innovation among suppliers to the food processing sector. Acad Manag J 26(1):27–44 Evans DS (1987) The relationship between firm growth, size, and age: estimates for 100 manufacturing industries. J Ind Econ 567–581 Feeding a Billion: Role of the Food Processing Industry. Report by A. T. Kearney submitted to FICCI (Federation of Indian Chambers of Commerce and Industry) in 2013. http://ficci.in/spdocument/ 20312/Feeding-a-Billion_Role-of-the-Food-Processing-Industry.pdf Festerling P (2008) Value-added in the danish food processing industries. Acta Agric Scand Sect C-Food Econ 5(1):24–43 Galizzi G, Venturini L (eds) (1996) Economics of innovation: the case of the food industry. Contribution to economics series. Springer, Berlin Ghosh N (2014) An assessment of the extent of food processing in various food sub-sectors, Revised Report from the Institute of Economic Growth to the Ministry of Agriculture, GoI Goyal A, Singh NP (2007) Consumer perception about fast food in India: an exploratory study. Br Food J 109(2):182–195 Headen RS, McKie JW (1966) The structure, conduct and performance of the breakfast cereal industry: 1954–64. Arthur D. Little Inc, Cambridge Klonsky K (2000) Forces impacting the production of organic foods. Agric Hum Values 17:233 Mueller WF, Culbertson JD, Peckham, B (1982) Market structure and technological performance in the food manufacturing industries. Monograph 11, N.C. Project 117, University of WisconsinMadison, USA Murthy KS, Dasaraju H (2011) Food processing industry in India: fruit processing industry in Andhra Pradesh. LAP LAMBERT Academic Publishing, GmbH & Co. KG, India Pingali P (2006) Westernization of Asian diets and the transformation of food systems: implications for research and policy. Food Policy 32:281–298 Rais M, Acharya S, Sharma N (2013) Food processing industry in India: S&T capability, skills and employment opportunities. J Food Process Technol 32(4):451–478 Raynolds LT (2004) The globalization of organic agro-food networks. World Dev 32(5):725–743 Rogers RT (2001) Structural change in U.S. food manufacturing, 1958–1997. Agribusiness 17:3–32 Sanderson G, Schweigert B (1985) Technical forces shaping the U.S. food-processing industry. Am J Agric Econ 67(5):1143–1148 Scherer FM (1982) The breakfast cereal industry in Adams W. edited the structure of American industry, 6th ed. Macmillan, New York Singh SP, Tegegne F, Ekenem E (2012) The food processing industry in India: challenges and opportunities. J Food Distrib Res 43(1):81–89 Stern LW (1966) Studies of organization and competition in grocery manufacturing. Technical Study No. 6: National Commission on Food Marketing, Washington DC Sutton J (1997) Gibrat’s legacy. J Econ Lit XXXV:4059

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Sutton J (2007) Sunk costs and market structure: price competition, advertising and the evolution of concentration. The MIT Press, Cambridge Sutton J, Kellow N (2011) An enterprise map of Ethiopia. International Growth Centre, London, http://eprints.lse.ac.uk/36390/. ISBN 9781907994005 Suwannaporn P, Speece M (1998) Organization of new product development in Thailand’s food processing industry. Int Food Agribus Manag Rev 1(2):195–226 Vyas V (2015) Low-cost, low-tech innovation: new product development in the food industry. Routledge, New York

Chapter 4

Food Processing: The Bihar Trajectory

4.1 Bihar: A Short History of Regional Attributes The central focus of this book is to understand the1 development of food processing as an industry in Bihar, which we call the Bihar trajectory. While we have argued in favour of following regional trajectories for analysing industrial narratives, they need to generalize to some degree to have some takeaways for industrial development in other contexts. A single-sentence generalization drawn from the Bihar trajectory is: this provides a platform to study the optimal choices for a regional government in a region with agri-resource abundance locally relative to other resources in the state, but not necessarily relative to other regions, and also a low industrial base. Should food processing be the natural choice for the state to start industrialization? First, note the centrality of the government in industrial development: the history of the region is such that private industries in food processing have not developed spontaneously. Second, the denouement of the Bihar trajectory in food processing has an interesting historical timeline: there are certain dates when the state’s history and overall industrial outcomes changed dramatically. The development of food processing as an industry in Bihar also changes at these watershed years, which mark out four distinct phases as shown in Fig. 4.1 and is discussed in detail in Sect. 4.1.1. This temporal study of the Bihar trajectory helps us understand the causality of outcomes. Food processing development in Bihar is driven by the actions of the state government. However, these are temporally clustered along with the simultaneous policies of the national government. This makes an econometric appraisal of causality very challenging. As Morck and Yeung (2011) mentions, the search for causality using econometrics runs into the problem of ...the tragedy of the commons: each successful use of an instrument creates an additional latent variable problem for all other uses of that instrument.

They highlight the historical approach towards causality, which uses temporality intelligently to infer about the drivers of economic outcomes. For this, the detailed 1 This

section has been co-authored with Barna Ganguli, ADRI, Patna. © Springer Nature Singapore Pte Ltd. 2020 D. Saha, Economics of the Food Processing Industry, Themes in Economics, https://doi.org/10.1007/978-981-13-8554-4_4

81

82

4 Food Processing: The Bihar Trajectory

Fig. 4.1 Timeline for food processing trajectory in Bihar (Author’s creation)

context matters, alongside external consistency, the plausibility of alternative narratives as well as the ‘recognition that free will makes human decisions intrinsically exogenous’ (see Morck and Yeung (2011)). Using these criteria, we hope to draw out inferences regarding policy actions in this chapter as well as in other parts of the book.

4.1.1 Pre-2000 Bihar: Economy, Industries and Food Processing Bihar has a long history of suffering, starting from the colonial period and continuing even after the independence of India from British rule in 1947 (see the discussions in Mathew and Moore (2011); Phillips (2017); Mukherji and Mukherji (2015)). During the colonial era, the overall Indian economy had stagnated. Exceptions were a few enclaves as these were port cities or defence centres. Unfortunately, being a land-locked state and without any major defence station, Bihar remained one of the poorest regions of colonial India. The state has always been predominantly agricultural. Despite vast mineral deposits, heavy industries in the pre-2000 Bihar had a rudimentary presence. There were a few mineral-based industries and some development of heavy industries in the mineral belt of the erstwhile state (pre-2000), there has not been any significant growth of industries that cover later stages of mineral-based industrial production. Industrial growth in Bihar has been stunted greatly by severe infrastructural constraints. This is coupled with non-delivery of services expected of both national and state governments as well as para-statal agencies. While the overall industrial sector in the country saw a surge in output and productivity during the economic reform period since 1991–1992, Bihar did not benefit precisely on account of its weak infrastructure and dysfunctional support institutions. Industrial contribution to GSDP was a meagre 14.7% in 1999–2000, in contrast with the national average of 25.4%.

4.1 Bihar: A Short History of Regional Attributes

83

4.1.2 Up to 2006: Leading to Bifurcation of Bihar In 2000, the erstwhile state of Bihar was bifurcated into its present-day avatar and Jharkhand. The impact of the bifurcation resulted in a 2.2% point drop in industrial contribution to GSDP to a low of 12.5% by 2000–2001. A further consequence of bifurcation was that the share of Net Value Added (NVA) of the industrial units in the residual Bihar in 2002–2003 remained only 17.9%, with Jharkhand getting the remaining share of 82.1%. This was obviously on account of nearly the entire mineral producing region becoming a part of the Jharkhand state, leaving only a limited number of large-, medium- and small-scale enterprises for the present-day Bihar. Even prior to 2000, it lagged much behind other states with respect to industrialization. After division, however, the situation became worse with the loss of large and medium industries and their ancillaries to the newly created Jharkhand state. By the year 2005–2006, Bihar had the smallest enterprises sector in India (accounting for only 1.07% of the country’s share). It is also a fact that the overall industrial sector of the state was dominated by unregistered units, accounting for half of its total income. After the division of the state, practically no mineral-based industries were left in Bihar. Agro-based industries including textiles, leather, wood and paper accounted for nearly 43% of the Gross Value Added (GVA) in the state around the year 2000. In terms of nature of businesses, between 1998 and 2005, a 17% growth rate occurred for informal establishments, two-thirds of which were rural and Own Account Enterprises (OAEs) (family-run enterprises not hiring any external workers.2 ) Majority of these units were non-agricultural (around 97%). That, however, is no indicator of the manner of production or services they provided, as 80% of them had no power connection, 11% of these enterprises were without any premises of their own, though mostly these were perennial businesses (only 4% were nonperennial3 ). Most of these units engaged in retail trading activities (around 55%), but one-fifth of the 12.25 lakh establishments were in manufacturing. Majority of these were in food processing surprisingly: small-scale cottage industry-type establishments in food processing producing jams, jellies, pickles and other food condiments and backyard rice processing. Their status as industrial manufacturing units in food processing stand challenged, even by regional, let alone national or international standards. In particular, three features stand out for these units: 13% of the 20% non-agricultural manufacturing units were OAEs and only 6% employed external workers. Second, most of these units were not only informal, but also tiny in size. Using size class of employment as a proxy for size, Table 4.1 reveals that 82% of total workers were engaged in approximately 98% of all non-agricultural establishments employing less than 6 workers. This leads us to the third feature, that has been present in the size distribution for Bihar from the early 2000s: that of a missing middle but few large-sized establishments. Using the size class of employment as a proxy for establishment size, we find that the smallest (8% of the total) 2 Fifth 3 ibid.

Economic Census of Bihar, 2005.

84

4 Food Processing: The Bihar Trajectory

Table 4.1 Missing middle size in informal establishments, Bihar (2005) Size class of employment % of workers % of establishments 1–5 6–9 ≥ 10 Total

82 8 10 100

97.56 1.99 0.45 100.00

Source Fifth Economic Census of Bihar, 2005

share of the workers in these establishments was in the middle-sized ones (almost 2% of all establishments), as opposed to the 10% in the larger establishments (which amounted to 0.45% of all establishments). The majority of workers were absorbed in a large number of small units with less than five workers, as mentioned earlier. A relatively larger number of establishments in the middle category were employing lesser workers than the relatively small number of large units. A possible explanation for the relatively higher absorptive capacity of large units (comparing to the middle size) is a higher capacity utilization than the middle-sized operations. Relatively larger units potentially had more stable contracts with the market and stability in demand than the middle-sized operations. If we are to accept a positive relationship in market strength and capacity utilization with size, the ubiquitous small units then would reflect disguised unemployment and marked inefficiency. This observation is very closely linked to the “missing middle” size debate in the distribution of firm size in developing countries, as originally discussed in Tybout (2000). What we see here is that the employment share of the tiny and the large-sized establishments is more than the middle-sized ones in the pre-2006 era. The middle and large-sized manufacturing units (measured by size class of employment) were present in the minority across the industrial spectrum, not only the informal establishments (amounting to only 15% of the total number of units). Chakrabarti (2013), for instance, mentions spatial clustering of the medium and large units: in the Patna, Magadh and Tirhut divisions. Remarkably, 10 of the 38 districts had no medium or large-scale units and another 11 districts with fewer than 5 units in each. One possibility of this skewness in firm size distribution is due to disturbed law and order conditions in the state during this period. By 2005, Bihar was making headlines as the ‘jungle raj’4 of India. Some of the entrepreneurs we surveyed (discussed in Chap. 7) were targets of threats. There was significant capital flight from the state due to political misrule and mayhem in law and order. For the decade prior to 2005, Chakrabarti (2013) estimates the loss of capital from Bihar to be around 10–12 thousand crore INR. In this scenario, business survival was possible either for very large registered firms or absolutely tiny informal units: the former with the ability to arrange for personal security while the latter not wealthy enough to be threatened. The historical timeline of the state indicates that poor law and order is a good reason 4 This

term refers to a state with the rule of the jungle which prevails in the absence of appropriate law and order.

4.1 Bihar: A Short History of Regional Attributes

85

for the flight of middle-sized firms from the state, leading to the ‘missing middle’ size in industries. This period was also marked by significant industrial sickness, given the malfunctioning industrial ecosystem in the state. The Economic Survey of 2006–2007 noted that 18 of the 259 medium and large industrial units were sick leading to the closure of 17 of these (see Chakrabarti (2013)). Even among small-scale units, the closure percentage had reached 28.3 by 2001–2002, as noted in the 3rd All-India Census of Small-Scale Industries. Chakrabarti (2013) notes that 40% of units in rural areas and a massive 60% of units in urban areas were closed due to industrial sickness. The state’s refinancing institution for industrial credit, the Bihar State Credit and Investment Corporation (BICICO) and the Bihar State Finance Corporation (BSFC) had themselves turned into sick institutions due to bad loans. The refinancing possibilities with NABARD for loans to MSME units dried up with the death of BICICO, which is now a historical institution in the state. Industrial backwardness pre-2005 was to the extent that a minuscule 3.2% of GSDP was attributable to industry as opposed to the national average of 20%, as mentioned in Chakrabarti (2013). Existing units had low margins and financial fragility.5 The effects of mal-governance show up in other statistics as well. Around 2004–2005, Phillips (2017) notes that at least 36 million Biharis lived below the official poverty line, 85% of the population lived in rural areas, and 48% were illiterate.

4.1.3 2006 Onward: The Bihar Turnaround with Focus on Food Processing Post-2006, Bihar finds a new status: from an ex-BIMARU6 state to a miracle.7 Around 2010–2011, Bihar was experiencing a record growth rate in GSDP (the secondhighest among all Indian states, after Gujarat). Though there is some controversy,8 the official statistics reveal this to be between 11 and 12%, against a national average of around 7–8%. Chakrabarti (2013) brings out the sharp structural break in the growth rates in pre- and post-2006 Bihar. In terms of the rate of growth of GSDP, Bihar was at one of the lowest rates of growth of 3.5% per year between 2000 and 2005, when the national average was around 7%. While the per capita income of the undivided Bihar in 1985 was around 59%, it was at an abysmal 26% in 5 Chakrabarti

(2013) mentions that the industrial net value added was below 7% and profits less than 6% with a very high debt–equity ratio of 3:1 in industrial units prior to 2006. 6 This acronym (bimaru) is a pun on the Hindi word “bimaru” which means unhealthy. It was formed with the first letter of the 4 Indian states: Bihar, Madhya Pradesh, Rajasthan and Uttar Pradesh in the 1980s. The indebtedness of these states would inflate the total internal debt of India due to bad fiscal management and low performance on almost all indicators of human development till the early 2000s. 7 There is substantial literature now on the “Bihar miracle”; see, for instance, Mukherji and Mukherji (2015), Thakur (2014) and Chakrabarti (2013). 8 Dasgupta (2010) discusses this in detail.

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4 Food Processing: The Bihar Trajectory

2005. Additionally, Bihar had the dubious distinction of being the only Indian state that experienced de-urbanization: from 13% share of urban to total population in 1981 to 10% in 2001. Despite the concerns raised by Dasgupta (2010) regarding the consistency of the GSDP estimates, the Bihar turnaround can be read off from other dimensions of economic activity as well. Dasgupta (2010) cites figures on new investment proposals that Bihar saw between 2008 and 2010, mostly in food processing. The total proposed new investment figure to the tune of INR 91,750 crore generated from 145 proposals with the SIPB in December 20089 had jumped to 245 proposals worth INR 1,33,841 crore by November 2009. Whether or not actual economic turnaround had taken place, the notion of what it means to do business in Bihar had undergone a radical shift in 2006. Consider this article in the India Today magazine in June 201210 regarding the new investment climate in Bihar: Big Business Houses Queue Up to Establish Industries in Bihar Until a decade ago, big business ignored Bihar when it came to new initiatives. Safety was an issue. Now, though, it’s on the verge of a belated industrial revolution. Stable law and order and a proactive government have done an image makeover for the state. The government claims a double-digit growth rate while admitting that it has been possible because of a lower base of the state’s economy...“Of the 755 business proposals approved by the State Investment Promotion Board (SIPB), 59 are already functional with an investment of over INR 3,712 crore,” Bihar Deputy Chief Minister Sushil Kumar Modi told India Today.

Chakrabarti (2013) notes this change in the business environment with similar figures: whereas proposed investments in Bihar accounted for a meagre 0.1% of the national total in 2004, this had jumped 15 times to 1.5% within six years in 2010. Improved law and order figures, though difficult to infer from reported crime statistics, has been cited in the literature as the primary reason for the turnaround (Chakrabarti (2013); Dasgupta (2010)). While not all types of crimes went down, the reported crime statistics for ‘kidnapping for ransom’ saw the largest decline in the period 2004–2008. Consider the figures noted by Dasgupta (2010): between 1998 and 2004, the overall category of ‘kidnapping’ had a negative trend of −0.09%, whereas in the period 2004–2008 it was positive (0.66%). However, the subcategory of ‘kidnapping for ransom’, saw a much higher negative trend of −37.47% in 2004– 2008 relative to previous −0.43% trend of 1998–2004.

9 As

explained later, this institution was established in 2006 to act as the single-window clearance facility for investment proposals worth INR 1 crore, lower value proposals (less than INR 1 crore) were to be cleared by the District Industry Clusters (DICs) established at the level of 38 districts in Bihar. 10 This article is available at https://www.indiatoday.in/magazine/nation/story/20120625-biharnitish-kumar-big-business-houses-queue-up-to-establish-industries-758796-2012-06-16.

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To read more into the ‘turnaround’ effect requires a strong anchoring in the low base effect of the prior-to-2006 story of Bihar. Chakrabarti (2013) mentions that despite an annual average growth rate of 86% in proposed investments for Bihar, as against the national average of 31% between 2004 and 2010, their value around 2010 amounted to less than half of that in West Bengal, slightly over two-thirds of Jharkhand, one-fifth of that in Madhya Pradesh and one-thirteenth of that earmarked for Odisha. Up to 2011, the proportion of industry in net GSDP was 4.63% compared to a national average of 20.16 (Reserve Bank of India, 2015). Additionally, per capita income and the potential for a domestic consumption-led development of food processing was suspect. Most districts in Bihar were poorer than comparables in other states. Figure 4.5 in the Appendix shows the distribution of per capita Gross District Domestic Product (GDDP at constant prices) in the state in 2006–2007. The figure is below 6000 INR for the majority, with only 4 districts recording above 10,000 INR per capita. The growth decomposition of the 12.8% average GSDP figure for Bihar around 2009–2010 shows a narrow base: a public-investment driven constructionsector heavy scenario. Dasgupta (2010) mentions a 38% trend growth rate and 39.1 per CAGR for construction between 2004–2005 and 2008–2009. This should be read as a positive effect of public investments, particularly for roads. This is an element of horizontal policy with multiple positive externalities that any benevolent state should invest in to drive the growth agenda in the long run (see Yülek (2018); Rodrik (2008)). Public investments in roads were concurrent with growth in communication and trade and services such as hotels, restaurants, etc. Building roads automatically encourages the establishment of the odd-road-side eatery and trading business for passenger vehicles that will ply on the road. Bihar had already picked up on these by 2009–2010. Note that the rate of growth of manufacturing is not spectacular, though the revival in institutions of credit and lending and reforms in electricity and other utilities such as water supply is evident from Table 4.2. Manufacturing growth trends had not been reversed yet, with some dents only in the perception of doing business (with new business proposals beginning in the state) and a largely unregistered manufacturing-led growth pattern leading to increased informality in the industrial segment. A historical baggage of very low industrial base is not easy to overturn easily: both in 2005–2006 and 2014–2015, the share of the state was 1.55% of India’s total number of functional registered factories. This was, despite, a 3.1% growth year-on-year from 2013–2014 to 2014–2015 (from 3530 in 2013– 2014 to 3420 units in 2014–2015). However, given the low base, these figures are in themselves are not unimpressive. Table 4.11 in the Appendix shows the time series of the contribution of industries to the GSDP of the state, which increased from a low of 14.7% (in 1999–2000) to around 18.5% in more recent times (2014–2015). In between, this contribution had crossed 20% around 2010–2011. Correcting for structural imbalances, which led to industrial capital flight in the first place, require a longer time horizon and policy support along other dimensions of the industrial ecosystem, as discussed in Yülek (2018). The state had inherited historical problems other than law and order problems. The British freight equalization policy (see the discussions in Mukherji and Mukherji (2015); Dasgupta (2010)) and fragmented landholding patterns, as mentioned in Dasgupta (2010), have been

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Table 4.2 Decomposition of growth rate of GSDP Variable Trend growth rate (1999–2000 Trend growth rate (2004–2005 to 2004–2005) to 2008–2009) GSDP Manufacturing -of which registered -of which unregistered Banking/Insurance Electricity/Water supply/Gas

4.4 −0.4 −6.5 1.8 5.2 −3.0

12.8 8.4 1.6 10.0 16.6 6.8

Source Part of Table 3.1 in Dasgupta (2010) using CSO data

cited in the literature as important reasons for the lack of industries in Bihar. The Planning Commission Task Force to understand the reason for industrial sickness in large units in Bihar prior to 2006, mentioned lack of working capital, unavailability of raw materials, very bad roads, inadequate communication facilities and delays in the granting of loans by banks and other financial institutions (refer to Chakrabarti (2013)). Infrastructural hurdles included lack of electricity for industries, with Bihar recording the lowest per capita power consumption of power among Indian states around 2010–2011. Chakrabarti (2013) mentions that this was around one-seventh of the national average, with no new state-level capacity generation added to the state in twenty-five years. The developments in the state around 2005–2006 have big implications for the food processing industry. Our discussion earlier underscores the importance of working capital availability and entrepreneurial ingenuity in marketing food products as necessary ingredients for success in food processing. Despite changes, Bihar was yet to see improvements in industrial finance and entrepreneurial skill by 2006. It was in this environment that the new government implemented a special policy targeting food processing with a slew of incentives for new or existing units expanding operations in this industry in 2008, as we discuss in Chap. 5 in detail. Food processing has long been hailed as the sunrise sector for Bihar. To what extent is this premise correct for Bihar? What are the likely outcomes for the food processing policy, given this background? We turn to these questions now.

4.2 Relative Performance Post-2008: Trajectory 3 (Bihar) Versus Trajectory 2 The contention that Bihar has a natural advantage in agro-based and food processing industries is based on its large agri-base and agri-dependence (Chakrabarti 2013). A large agri-base does not naturally translate itself into a regionally thriving processing industry. For instance, take the case of sugar industries. Bihar was one of the early

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success stories in sugar manufacturing. The text box below describes its subsequent failure in Bihar. Sugar Industries in Bihar: Historical Perspective Chakrabarti (2013) and Shrivastava et al. (2011) provide a brief history of sugar industries in Bihar. As a crop, Bihar was the original leader in the production (with almost a quarter of the country’s entire production of sugarcane) in India. This was much prior to the dominance of Uttar Pradesh and Maharashtra, presumably given Bihar’s abundance of water and the high water intensity of sugarcane. However, by 2005, of its 28 sugarcane crushing plants, only 9 were operational. Industrial sickness had affected the sugar mills badly, mostly the government-owned mills. Compared to the dominance of cerealbased factories in food processing, sugar has dwindled in Bihar as an industry at present. The Government publication on industrial success stories in Bihar, available at http://www.udyogmitrabihar.in/success-stories-industrialinvestments-bihar/, mentions the large private initiative: Riga Sugars Pvt. Ltd. hailed as one of the ‘oldest’ sugar factories in India, it started operations in its Sitamarhi, Bihar plant in 1933 by the Dhanuka Group of Kolkata, West Bengal. Listed on the Bombay Stock Exchange, it has become a loss-making unit for the past seven years. The quantum of its losses amounts to a cumulative of 56 crore INR as per its 2017–2018 Annual Report. Look at the statement in the Annual Report in explaining the miserable financial status of the company in 2017–2018: Due to floods in August, 2017 the recovery of sugarcane retarded... increased the cost of production of sugar substantially. On the interference of Bihar State government the cane price for the season 2017– 2018 (was) increased on the presumption of sale price of 3,800 to 3,900 INR per quintal with an assurance from the state that if sugar price (fell), the government will extend cane price subsidy. However, in spite of drastic fall in sugar prices, the state government has not so far announced any subsidy to state sugar factories. Bihar Sugar Mills Association has been demanding cane price subsidy of at least 50 INR per quintal which has not so far been extended.

Another 77-year old mill in Bihar (Tirupati Sugars Pvt. Ltd. at Bagaha, West Champaran, currently owned by the Yadu Corporations) has also hurt from the low profitability in the caning season of 2017. Inability to retain pricing flexibility, these operations have seen their financial viability badly affected due to agricultural price fluctuations.

What we highlight above is the spillover effects from agricultural pricing and other externalities on the financial viability of processing industries in food. This has been the case for the soybean industry in India, with the spectacular demise of Ruchi Soya Industries Limited. The sugar sub-sector in Bihar has suffered in a different way. In the face of a much larger production of sugarcane in the neighbouring state

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Table 4.3 Comparative descriptives for food processing industries (2010–2011) Bihar AP India Total no. of factories (# total) No. of operational factories (# operational) Fixed capital/# total (INR lakh) Investments (GFCF/#operational) (INRlakh) Profits (INR lakh)

506

1487

34,023

457

1415

28,749

152.95

827.08

2.98

23.55

182.20

0.67

−19,284

1,15,962

18,809

Source Authors’ calculations based on ASI data for 2010–2011

of Uttar Pradesh, the water intensity of the crop as well as the current search for healthy alternatives, such as coconut-based sugar, has resulted in Bihar receding from the space of refined sugar. Hence, we do not dwell further on this sub-sector in this volume. Note, however, that the issue of agricultural pricing spillovers is a clear and present problem for the financial viability of processing industries. This is underscored in our stylized fact S5. in Chap. 3. We shall touch upon this issue in our discussion of rice mills in Bihar. We now compare the number of registered establishments in food processing in Bihar with other states in India in recent times. Table 4.3 provides some descriptives for food processing industries (at the 2-digit level of aggregation from ASI data) for Bihar relative to Andhra Pradesh (AP, henceforth) and all-India figures for 2010– 2011. The comparator of AP is kept to compare outcomes of Bihar against that of a state with the largest concentration of food processing industries. Compared to a non-existent number, by 2010–2011, Bihar had 457 operational units out of a total of 506 registered establishments in food processing. However, this is about one-third of the number of operational units in AP, and a mere 1.6% of all the registered and operational food- processing units in India. A rough idea about the insignificant size of the units in Bihar can be estimated from its minuscule fixed capital per registered factory in Table 4.3. On an average, food processing manufacturing plants in Bihar are less than one-fifth in size compared to AP, although they are larger than the allIndia average. Using Gross Fixed Capital Formation (GFCF) as a measure of the capacity for future expansion through capital formation at the plant-level, we find that food processing units in Bihar in 2010–2011 invested less than one-fifth of the amount in AP. A further troublesome feature is the economic viability of this sector. If we consider the figure of plant-level profits, as given by ASI data in Table 4.3, Bihar’s food processing factories were in a net loss, though the industry as a whole in India (and certainly in AP) was in net profits.

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4.2.1 Sub-sectoral Inefficiency Let us unpack the results in Table 4.3 at the sub-sectoral level for dairy and grain milling at the 3-digit level of aggregation from ASI data and compare again the results for Bihar and AP for 2010–2011. Table 4.4 reveals the source of inefficient performance at the sub-sectoral level for Bihar: 98% of the total losses in food processing is attributable to units in grain milling. Of the total 457 operational factories in Bihar in that year, 401 are in that loss-making sub-sector. One might argue that competition among many milling units drives down profit margins for individual factories in grain milling. Then, this should be the scenario for all regions with a large concentration of grain milling units. However, the national trajectory 2 also has a preponderance of grain milling units, as discussed in the previous Chap. 3. However, Table 4.4 shows that they are not loss-making on average in 2010–2011. Neither are they making losses in AP. The poor financial performance of these units is characteristic of the Bihar trajectory. For Bihar, around 2006–2008, formal registered food processing meant a handful of small backyard rice mills with traditional technology among formal registered firms.11 Unregistered, informal sector manufacturing units were ubiquitously producing jams, jellies, pickles, the local delicacy of ‘litti chokha’ and regional sweet desserts such as ram dana lai, belgrami, anarsa, peda, khaja,12 etc. Despite its rich agri-base, the state was not a serious player in processed food. Table 4.11 in the Appendix presents the time series of industrial density in Bihar. Though most of the

Table 4.4 Sources of inefficiency in food processing industries (2010–2011) Bihar AP India Variables Dairy Grains Dairy Grains Dairy (105) (106) (105) (106) (105) # Total # Operational Fixed Capital/# total (INR lakh) Working Capital intensity (INR lakh) Gross Value Added/# operational (INR lakh) Profits (INR lakh)

Grains (106)

18 15 199.78

401 361 37.35

316 272 230.54

6293 5149 63.05

1493 1309 538.19

18,549 15,612 95.99

2440

18,958

30,760

7,58,230

4,82,711

36,54,390

305.53

−39.25

91.81

42.98

328.83

71.75

233

−18,965

−5113

58,896

57098

366313

Source Authors’ calculations based on ASI data for 2010–2011 11 Among

the handful of exceptions are some units in dairy, sugar, honey and confectionery units. to the Glossary for the definition of theses local food items from Bihar and other parts of the world. 12 Refer

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4 Food Processing: The Bihar Trajectory

factories were in food processing, their share in the GSDP of the state has been very low. It seems, therefore, that five years of subsidization favouring processing units by the government resulted in an increase of grain milling units, which were clearly underperforming with respect to other states such as AP. Other sub-sectors such as dairy shows a relatively better performance in Bihar than AP. However, Bihar had only 18 registered establishments, of which only 15 were functional. This unpacking of performance of units at the sub-sectoral level raises an important question: what is the reason for the underperformance of the entire food processing sector even after significant subsidization since 2008 in the Bihar trajectory? Before we investigate that, we present some micro empirical regularities in overall manufacturing as well as for food processing for Bihar. The following Sect. 4.2.2 deals with skewness in firm size distribution, Sect. 4.2.3 details business expectations and Sect. 4.2.4 presents some observations regarding the industrial ecosystem in Bihar.

4.2.2 Missing Middle Size in Registered Manufacturing Post-2008 There is a large literature, such as Restuccia and Rogerson (2013), Restuccia and Rogerson (2017) and Krueger (2013), discussing misallocation of resources in manufacturing in developing countries. Misallocation refers to an incorrect distribution of productive resources: more productive firms have less access or are constrained in using these resources than less productive firms. Government policy has been blamed to a large extent for distorting outcomes in favour of less efficient firms, who access more resources than the more productive firms. This academic discourse identifies resource misallocation as the main cause for low total factor productivity (TFP) and challenges an earlier line of thinking, which causally linked low levels of accumulation of labour and capital to low productivity in manufacturing. See Kathuria et al. (2014), which provides an in-depth coverage of issues of productivity in Indian manufacturing, for more details. An empirical regularity of this misallocation problem is supposed to be reflected in the “missing middle” firm size in developing countries. A precise definition of the missing middle is in Hsieh and Olken (2014): The idea of the missing middle is that there are a large number of small firms, some large firms, but very few medium-sized firms.

The theoretical underpinning of this observation, as detailed in Hsieh and Olken (2014), is that the more productive small size firms do not get access to productive resources and therefore, cannot expand into the middle size. The industrial ecosystem, this theory presumes, is biased against the small-sized units, both through access to formal institutional credit as well as through the explicit choices made by industrial policy by the government. Though the small firms would like to borrow a larger amount at the prevailing interest rate, as Hsieh and Olken (2014) explains, they are

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prevented from doing by the institutional lenders. This leads to credit being rationed for them, whereas it is not so for the large firms. De Soto (1989) mention another kind of policy bias in favour of the large: better protection of property rights. Note here that the misallocation is due to the fact that the small firms which cannot expand are presumed efficient. Hsieh and Olken (2014) also mention that medium and large firms have larger costs than small firms. These costs can be spread over a large volume of sales by the large firms, which middle-sized firms cannot. This results in the missing middle size. Expensive sunk costs for technology as well as cut-off-based regulations that raise costs for firms above a certain size threshold are potential causes. Rather than the inability of the efficient small firm to expand, here the story is that of the efficient middle-sized firms that are thwarted from continuing operation. These two channels have different conclusions for policy. Hsieh and Olken (2014) points out that policies which enable small firms access credit easily (microcredit, for instance) or pro-small tax policies can be counterproductive if the second reason holds. There will now be a perverse incentive for small firms to stay small. The differential constraints faced by the different firm sizes need to be addressed very carefully through policy. Note another important point: the criticism of the entire “missing middle” phenomenon by Hsieh and Olken (2014). To explain this further, we enter the discussion on the measurement of firm size in the following discussion.

4.2.2.1

Measurement of Firm Size for Addressing the Missing Middle

The first observation of the missing middle in developing country manufacturing was in Tybout (2000). It has found resonance in other empirical studies for countries such as Co et al. (2017) for Vietnam. This paper used the employment share for manufacturing plants in 19 countries grouped into three employment bins: those with 1–9 employees, those with employees between 10–49 and those with 50+ workers for most of the countries.13 Mapping the share of employment against these bins generated the missing middle phenomenon, which (Hsieh and Olken 2014) demonstrates clearly. There is a relatively smaller share of employment in the middle 10–49 sized bin than in the small and large categories. This is one perspective on the size debate, which has become popular in the development debate since 2000. What (Hsieh and Olken 2014) claims is that this perspective does not directly address the size of the firm issue, and the missing middle is a creation of superimposing the employment share data on the fixed employment size classes. Instead, Hsieh and Olken (2014) use exogenous thresholds for employment directly (for India) and revenue (for Indonesia and Mexico) and find a right skew in the distribution, but not a missing middle in the firm size. The employment threshold for India is based on the 100 employee cut-off, beyond which formal sector labour laws apply. The revenue cut-offs for Indonesia and Mexico are based on the thresholds above which firms have to pay a value-added tax (for Indonesia) or a higher tax 13 For

some countries, (Tybout 2000) created five or six size classes.

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rate (for Mexico). The absence of significant clustering around these thresholds is taken to imply that these policy measures do not create any meaningful distortions in firm size distribution. Additionally, the paper indicates that constraints seem to operate on large enterprises rather than the small and middle-sized ones. The average and marginal products of labour and capital for small and middle firms are lower than the large firms. With the assumption that firms which are credit-constrained should face a higher marginal cost of capital, and given that average and marginal costs co-move, Hsieh and Olken (2014) argue that it seems that the larger firms face constraints and not the small- and the middle-sized firms. There are several counterclaims to this and we focus on the missing middle size for Bihar’s food processing (and the general firm size distribution) to build on our theory explaining policy outcomes in Chap. 7. Note that our analysis in Table 4.1 for the missing middle follows the tradition of Tybout (2000). However, the usage of revenue or employment, as Hsieh and Olken (2014) does, is not the best alternative for our purpose. Firm size in the industrial policy (IP) of most states in India is calculated using plant & machinery cost, including Bihar. The standard norm for measuring firm size internationally has been in terms of employment. Standard international classification of firm size, particularly those in manufacturing, is done using some measure of labour used in production. The World Bank Enterprise Survey uses the number of employees. To the extent that there are complementarities in employment and other variables of size, such as plant & machinery cost, there should be no contradiction in these definitions. However, as technology makes large automated projects labour saving, there are likely to be inconsistencies, as we shall see in Table 4.5. Most of the large enterprises are labour saving (using mechanized forms of production, particularly dairy in Bihar). In terms of permanent staff, a fully mechanized dairy unit, such as the COMFED unit at Nalanda, uses far less labour than a non-mechanized processing unit. Instead of hanging our hat on a single measure to decide firm size, we experiment with multiple measures. We start with the most general definition of size: from the definition of what constitutes a factory under the Registered Factories Act, 1948. Three types of manufacturing plants in registered manufacturing are identified: (a) plants registered under section 2 m(i) of the Registered Factories Act, 1948, (b) plants registered under section 2 m(ii) of the same Act and (c) factories registered under section 85 of the Act. Now, consider the significance of these definitions and their relationship to size. We first note their definitions verbatim: • Section 2 (m) declares “factory” means any premises including the precincts thereof – Type 1: (i) whereon ten or more workers are working, or were working on any day of the preceding twelve months, and in any part of which a manufacturing process is being carried on with the aid of power, or is ordinarily so carried on, or – Type 2: (ii) whereon twenty or more workers are working, or were working on any day of the preceding twelve months, and in any part of which a manu-

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facturing process is being carried on without the aid of power, or is ordinarily so carried on,– Type 3: Section 85 of the Act allows the State Government (GoB, in this instance) to apply the same rules to premises (establishments) where – (i) the number of persons employed therein is less than ten, if working with the aid of power and less than twenty if working without the aid of power, or – (ii) the persons working therein are not employed by the owner thereof but are working with the permission of, or under agreement with, such owner: Provided that the manufacturing process is not being carried on by the owner only with the aid of his family. Given the nature of manufacturing for modern manufacturing processes in processed food, electricity is an essential input (definitely so for cold storages and the other sub-sectors such as dairy, grain milling and poultry feed that we consider in some detail for Bihar). Units which operate without power in food manufacture are typically small producers of unprocessed and informal sector products such as honey, jams, jellies, juices and pickles (which are a part of cottage or backyard industries). Table 4.5 shows the distribution of these types of factories for the top “industrial” districts as well as for entire Bihar using this definition using ASI data for 2013. How do we identify the top “industrial” districts? Given that the informal establishments far outweigh registered manufacturing units, we consider a district ‘industrial’ by using its share of informal establishments out of the state’s total number of establishments. Therefore, we restrict our study to five districts with the highest concentration of informal establishments in 2013, which happen to be Patna (the state’s capital), Samastipur, Darbhanga, Muzaffarpur and West Champaran. At the aggregate, for all-Bihar in 2013, the percentage of type 1 factories are higher in number than type 2. If we make the assumption that type 1 is the largest ‘size’, type 2 is the middle ‘size’, whereas type 3 is the smallest due to the nature of technology and capital cost,14 it will become apparent that there is a missing middle size in the population of manufacturing units for Bihar post-2006 for most districts, other than Samastipur and Darbhanga. These two districts account for only 7% of total registered factories. Patna alone accounts for close to 18% of these factories, and there is clear evidence of the missing middle there.15 Therefore, if we avoid any employment-based cut-offs and simply use definitions of what is a factory or enterprise-type with reasonable assumptions about the nature of technology and size, we are not able to deny the “missing middle” phenomenon altogether as Hsieh and Olken (2014) does. For food processing, in particular, there is a marked duality around this period (2013–2014), with the simultaneous existence of the small informal and the registered 14 Informal

units of type 3 are typically smaller in size and use a more primitive labour-intensive technology than registered factories. 15 The largely informal nature of manufacturing shows up in the last column of Table 4.5, with registered manufacturing exhibiting hardly any respectable density in comparison with the vast numbers of small informal establishments.

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Table 4.5 District-wise distribution of factories in registered manufacturing, Bihar (2013) District % of type 1 % of type 2 % type 3 (Sec. Proportion of Ratio of reg. factories factories 85 reg.) total reg. factories to factories establishments Patna Samastipur Darbhanga Muzaffarpur West Champaran Total

0.89 0.09 0.11 0.31 0.13

0.46 0.11 0.17 0.05 0.10

2.05 0.80 0.72 0.63 0.77

0.177 0.052 0.025 0.039 0.019

0.010 0.005 0.003 0.005 0.003

0.21

0.08

0.71

1.000

0.089

Source Sixth Economic Census of Bihar, 2013 Table 4.6 Duality in food processing (2013) Year KVIC output as % of reg GVA KVIC employment as % of reg employment 2011–2012 2012–2013 2013–2014

2.05 1.12 1.43

31.73 33.50 36.19

Source Sixth Economic Census of Bihar, 2013 & ASI data for respective years

formal manufacturing establishments. Small informal establishments are larger sinks of employment than registered units, making employment-based measures suspect for classifying size. Table 4.6 shows the output of informal sector units (which are financed and assisted by the Khadi and Village Industries Commission (KVIC), which has a mandate to aid handloom crafts and rural artisan-made products largely in food and textiles) relative to registered formal factory output and their relative employment share compared to registered manufacturing. While their employment share is more than 30% between 2011 and 2014, KVIC units produce only around 2% of the value of output generated formally. This again shows the labour-intensive production processes of the absolutely tiny enterprises as opposed to even small registered manufacturing, which are inherently labour-saving processes. The Micro, Small and Medium Scale Enterprises (MSME) Ministry of the Government of India (GoI) uses a definition based on the cost of plant & machinery alone to determine size of a manufacturing plant. The Ministry is considering a shift towards a turnover-based definition of firm size since 2018. This has not yet materialized for India. Using the plant and machinery costs at book value, we can define the following categories. 1. micro units: with cost less than 25 lakh INR 2. small units: with cost between 25 lakh and 5 crore INR

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3. medium units: with cost between 5 crore and 10 crore INR 4. large units: with cost above 10 crore INR From a time series point of view, using a plant and machinery cost based measure of size runs into the difficulty of measurement of the capital series. Many studies use the book value or historical value of project size, as Sutton (2007). This has its own problems, as does using the current value of capital used in the project. There is no unanimity in how to calculate depreciation. There are some studies that work with the Perpetual Inventory Method for creating the capital series (Kathuria et al. 2014), but its actual implementation has a vast variation in the literature. We should add that our primary survey in Bihar in 2016 resulted in a large number of missing values: for book value of project cost, land as well as number employed in the firm. Given the nascent industrial developments in the state since 2008 in food processing, entrepreneurs were reluctant to share much hard information regarding these variables. The World Bank Enterprise survey also discusses the problem of information non-revelation in developing countries without a culture of formal businesses. We explore another dimension to comment on the missing middle problem: for firms issuing share capital. Registered manufacturing in Bihar has various types of incorporations, from private limited companies to proprietorships and partnerships. What we can find easily is the quantum of issuance of shares by these firms from the Registrar of Companies (ROC), Patna. Our measure of firm size in food processing16 is based on two financial parameters for firms issuing share capital in Bihar: authorized and paid-up capital. While the former is the maximum amount of capital for which shares can be issued by a company, paid-up capital is the actual amount against which shares have been issued by it. A firm has to inform the ROC any change in the structure of these two types of capital, which is then updated in its master records. The Companies Amendment Act, 2015 has removed the requirement of a minimum paid-up capital in India. However, there are many controversies about companies with no paid-up capital despite having a notionally large ability to issue shares through authorized capital, as examples in Douglas and Shanks (1929) show. We should consider both these financial variables for an understanding of the capability of the firm to raise share capital, and therefore, determine the size of operations. For the post-2008 scenario in food processing in Bihar, we consider the years 2014–2015 and 2015–2016 which straddles the year of policy change in 2016. Table 4.7 shows the average and standard deviation of these two financials for these two years for a set of sub-sectors in food processing. We find that for a collection of sub-sectors in food processing, the average value of these variables is almost consistently smaller than the sample standard deviation. The coefficient of variation (defined as sample standard deviation normalized by the mean) in almost all the cases exceeds one. This statistical observation is indicative of large skewness in the data. This shows that the distribution of these financials is not normal. That there 16 We

use the data from the Registrar of Companies (ROC, Patna) for 2014–2015 and 2015–2016 on financials for the registered firms in Bihar by their principal line of business. The core line of business is decided by the Ministry of Corporate Affairs (MCA) criteria. Their codes at the 5-digit level are not in line with the NIC 2008. Rather it follows the coding system of NIC 2004.

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Table 4.7 Coefficient of variation for financials in selected sectors Sub-sector Authorized captial Paid-up capital 2014–2015 2015–2016 2014–2015 Dairy Cold storage Rice mills Animal feeds

2.82 0.99 2.04 1.27

2.53 1.56 1.94 1.70

4.35 0.91 2.82 1.49

2015–2016 3.40 1.80 2.41 1.77

Source Authors’ calculations based on ROC (Patna) data

were a large number of small units in food processing even in the post-2008 scenario has been discussed earlier. As all the values of authorized and paid-up capital are positive, and yet the mean is significantly less than the standard deviation, there must be significant bunching of firms at the extreme values of the size distribution with missing observations in the middle. This is regarding firms in all registered manufacturing. Coupled with our earlier observation about the missing middle size in informal establishments in the pre-2008 time frame (refer to Table 4.1 which, in fact, replicates the result of Tybout (2000)) and our post-2008 description of all manufacturing as well as food processing, we conclude that there is a continuous missing middle size in the population of firms and establishments in Bihar.

4.2.2.2

Alternative Reasons for Missing Middle Firm Size and Its Effects

In the case of Bihar, we propose that uncertain law and order and property rights that are part of the history of the state have generated a situation of missing middle size firms. The resulting distribution of firms in registered manufacturing for food processing is a plenitude of small units and a few large units. The latter are not very large in most regions, as discussed earlier, and this might arise due to endogenous sunk costs in advertising branded products at the retail end of food processing. However, the missing middle phenomenon has also been observed in other contexts, which we briefly touch upon in Chap. 2, such as the frozen foods sub-sector as well as coffee in the US. For this market, Sutton (2007) claims that it was an intense advertising competition between middle- and large-sized firms, which squeezed the profits of the middle-sized firms. The resultant distribution of firms was a few large firms like General Foods and many fringe competitors. Sutton (2007) also mentions the theoretical possibility of differences of consumer taste and preferences which can generate a dual structure of the market: with a large and fragmented non-retail sector (which does not engage in branded sales with advertising) and a concentrated retail end of the market. Hsieh and Olken (2014) point out two possibilities for the missing middle: constraints on small firms that prevent their growth and second, constraints on middle-

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and large-sized firms for continuing in business. While the former has to do largely with imperfections in input markets, particularly finance; the latter might be generated due to cut-off based government policies that create a dis-incentive for large firms. In fact, for developing countries, the missing middle phenomenon has often been attributed to political economy reasons, such as corruption and bureaucratic inefficiency (see Friedman et al. (2000); Djankov et al. (2002)). Policy biases, of the kind we mentioned before, are discussed in Rauch (1991). These policies are designed such that only formal sector firms come under their purview. Corporate taxes kick in and labour laws apply only beyond a certain size of operations. This selective application of regulations act as a deterrent to the growth of small firms to the middle size, creating the missing middle phenomenon. Gordon and Li (2005) point out a puzzle about taxation in countries like India. Contrary to theoretical predictions, governments in these countries rely on capital taxes more heavily than taxes on labour income. Much of the government revenue comes from a narrow base of a few industrial firms, leading to tax-induced misallocation of capital. Firms which use formal financial intermediaries like banks come under the tax net easily and this type of tax structure provides incentives for financial dis-intermediation: operating at a scale of production such that informal finance will work might outweigh the benefits of accessing bank finance. In this case, there would be an incentive for firms to stay unbanked and small due to taxation policies of the government. In this case, it would not be a case of inadequate supply of formal finance that would constrain the growth of small firms to the middle size; rather it would be policy-induced misallocation of capital. Policy biases are present in Bihar as well as other states of India. It is the history of flight of capital prior to 2006 that is unique to Bihar. It is possible that policy biases, financial market imperfections as well as political misrule complemented each other to produce the missing middle size. Historical events in the state are probably not the sole cause for the missing middle size in firm distribution. In any case, our intention is not to explore the causes of this phenomenon. Rather, our interest lies in the consequences of this distortion in the firm size. We propose a mechanism that aids firm expansion from small to larger sizes using the missing middle as a mechanism as follows: How Does the ‘Missing Middle’ Matter for Bihar’s Food Processing? The typical new entrant in a region like Bihar is likely to be a small firm with a narrow product basket. One particular survival strategy for this firm is to start with the production of an unbranded product for industrial sales to middle-sized firms through subcontracting. This helps the firm in avoiding costly branding expenditure to market the product. In the presence of deep market segmentation between large and small firms, with very little subcontracting connections between them. Stylized fact S2., in Chap. 3, showed that a lack of this type of segmentation allows small firms to have connections with large ones in Trajectory 1. It is a more profitable strategy for small firms to be present in the unbranded non-retail segment rather than in own-brand

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retail. As long as the products meet standard specifications for the industrial buyer, the firm finds a ready market. One important assumption that we are making here is that the large firms are not the target industrial buyers for small firms, as we discussed earlier in Chap. 3. Large firms in India, and in Bihar certainly, have very different management styles from small firms. More often than not, they have multiple plant locations and are integrated into the entire supply chain of product manufacture, as we discussed for the India trajectory. As customer loyalty and diversity in the product basket matter for these firms, they are very particular about product quality, certification, labelling and advertising. The large noise in the values of authorized and paid-up capital, discussed above, stands testimony for deep market segmentation and a missing middle size around 2014–2016. Our 2016–17 primary survey in Bihar among entrepreneurs also illuminated us regarding this observation. While many small units complained of the difficulties in input-resource mapping, i.e. finding the source from which to procure raw materials, the few large units we encountered did not report this as a problem at all. We had interviewed the manager of the large ITC dairy unit in Munger, whose response regarding this matter was most illuminating. He clearly stated that ITC’s in-house R&D division in Bengaluru helped them decide the appropriate product network and input-resource mapping. Their dependence on in-house intelligence regarding the supply chain is in line with what our theory predicts. A missing middle size in firm distribution, then, implies the loss of a ready market for selling the unbranded produce by small units. Due to this kind of market segmentation, the medium (and large) firms do not pose a competition challenge for the small firms. Rather, they provide an opportunity for small units to become co-processors through subcontractors and reduce marketing costs. Therefore, the effect of the missing middle is an increase in marketing costs for the small units, which have to now place branded products to the retail market or to the extremely low-margin wholesale or catering segment. This theory helps explains why a large number of small units, as we shall describe in Chap. 6, have their own brands in rice milling in Bihar.

4.2.3 Perception of the Business Environment A second observation of the Bihar trajectory is related to the perception of the business environment. Around 2006, Bihar had enacted some changes in the regulatory framework for industries, by establishing the single window clearing facility (SIPB for projects worth more than 1 crore INR and DICs for projects below that threshold). While the promise of good governance is important, establishing single-window clearance is also very important, particularly for serious-minded businesses, who intend to reduce the time-to-market in production. What was the impact of these

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institutional changes in terms of business perception among entrepreneurs? Direct causal analysis of this important development is difficult, as it is simultaneous with major changes in law and order as well as a new industrial policy (2006–2011). We again appeal to our approach of history-as-causality approach, rather than the econometric test for causality given the lack of appropriate instruments to control for these policy changes. We comment on the effect of the changes in the post-2006 (up to 2016) developments in two ways. First, we conduct an inter-state comparison of the Ease-of-DoingBusiness Index of the World Bank in 2014 and second, we compare the number of ‘intentions for doing business’ forms filed with the state regulators across states in different times. The World Bank Enterprise Survey (2009) provides a cross-sectional overview of city-level entrepreneurial perceptions for doing business. As we see for Bihar, there is a large concentration of industrial (formal and informal) firms concentrated in the capital city of Patna (refer to Table 4.5). We compare Bihar’s capital city Patna with Jharkhand’s capital Ranchi to comment on the performance.

4.2.3.1

Ease-of-Doing Business

Large- and middle-sized formal businesses (mostly mineral-based) were concentrated in Jharkhand in the undivided state of Bihar. The loss of these units in 2000 is the beginning of what we see as the missing middle size phenomena for the divided Bihar. Additionally, the state’s troubled political history led to capital flight. This mayhem in the state’s law and order left an imprint in business expectations regarding Bihar. The startling effect of this is reflected in the ease-of-doing business rank of the World Bank for the separated twin states within a decade of the bifurcation year 2000. We find that Jharkhand’s capital city Ranchi vastly outstripped Bihar’s capital city Patna in ease-of-doing business rank in a comparison across 17 Indian cities. In 2009, Ranchi ranked 9 whereas Patna was ranked at a modest 14 by the World Bank survey on the overall ease-of-doing business indicator (refer to Table 4.8). While the aggregate rank presents a stark contrast (much lower performance for Patna than Ranchi), the ranks on disaggregated individual elements of the index reveal a non-homogeneous picture. While starting a business and enforcing contracts see a much higher rank for Patna than Ranchi, registering property and paying taxes (both of which are specific government department functions) are much worse ranked for Patna than Ranchi. The performance of Patna in starting a business is potentially attributable to the single-window facility, while contract enforcement can reasonably be assessed to be due to the improved law and order conditions of the state since 2006. The lag effect of being a lawless state with depleted industries continues till 2014–2015. A subnational exercise carried out by the Department of Investment Promotion and Planning (DIPP), Government of India notes the relative performance

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Table 4.8 Ease-of-doing business subnational ranks for 17 Indian cities (2009) Parameter rank Patna (Bihar) Ranchi (Jharkhand) Top ranked city Overall Starting a business Dealing with construction permits Registering property Paying taxes Trading across borders Enforcing contracts Resolving insolvency

14 2 9

9 15 9

Ludhiana New Delhi (Delhi) Bengaluru (Karnataka)

15 15 10

6 4 8

2

11

15

13

Gurgaon (NCR) Ludhiana (Punjab) Bhubaneshwar (Odisha) Hyderabad (Andhra Pradesh) Hyderabad (Andhra Pradesh)

Source The World Bank Database (http://www.doingbusiness.org/data/exploreeconomies/india)

of different states in implementing policy reforms in its publication entitled Assessment of State Implementation of Business Reforms (released on 14 September 2015). Based on a 98-point action plan for business reforms agreed between the DIPP and State/UTs, this report ranks Jharkhand in the third position among 32 states/UTs (with an overall score of 63.09%), while Bihar is ranked a modest 21 with a low score of 16.41%.

4.2.4 Business Intentions A different micro indicator of business intentions is captured by figures of either IEM (Industrial Entrepreneur Memoranda) or LOIs (Letter of Intent) or DILs (Direct Industrial Licence) filed and issued by entrepreneurs with the Registrar of Companies (ROCs) of these states in Table 4.9 for the pre-2006 and post-2006 scenarios. Note that these are intentions for starting business, not actual figures for firms in operation. Therefore, these figures are more suitable for discussing the nature of the business environment in a region. While neither Bihar nor Jharkhand compare with states like Karnataka as potential destinations where entrepreneurs see themselves in action, Jharkhand has had an upper hand in this by the number of IEMs filed. Bihar, despite significant reforms, since 2006 was yet to become as industrialized as its neighbour Jharkhand or other states of India, going by these figures.

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Table 4.9 State-wise investment intentions (IEMs/LOIs/DILs) in India States 1991–2006 2007–2013 Bihar Jharkhand Karnataka

277 772 3029

180 318 1139

Source GoI data available at https://data.gov.in/

4.2.5 Summarizing Observations About Bihar’s Manufacturing Pre-2016 The overall picture leading up to 2016 is one of guarded optimism. Relative to its dismal state pre-2006, the state had embarked on some industrialization with food processing at the forefront. However, that is true if we compare the region in a time series, i.e. Bihar in 2006–2016 to Bihar pre-2006. If we compare Bihar across states in the 2006–2016 time frame, the baggage of history with the low industrial base weighs heavy. Towards the end of this period (2014–2015), the share of Bihar in the total number of industries in India was only 1.6 percent, while its share in the population of the country was 8.6%. As regards the size of operational industries, it is again smaller than the national average, as indicated by their capital base, employment base and value of output. In 2014–2015, the size of fixed capital of all the industries in Bihar (INR 9.94 thousand crores) is only 0.4% of the all-India figure (INR 2474.45 thousand crores). For Bihar, the ratios for other indicators are: working capital (0.5%), persons engaged (1.1%), value of output (0.9%) and net value added (0.6%). Once again, our guarded optimism kicks in: the fact that there were a majority of smaller industrial units is, however, an ubiquitous phenomenon, even for developed countries like the UK. Vyas (2015) finds a vast majority of small units in the Scottish food and drinks industry, which are also fairly innovative. For Bihar, 2015 is a time when the process of industrialization has only began and is still at a nascent stage. One strong ray of hope in this otherwise bleak scenario is that the growth rate of factories in Bihar during the past decade has been 11.1%, compared to the national growth rate of 4.3%. The expectation would be a continuation of this growth trend in the most recent scenario, which we comment on now.

4.2.6 Industrial Policy in Bihar As mentioned earlier, the government has been an important agent for fostering firm entry in Bihar, particularly using policy incentives through successive Industrial Policies (IPs). Recently in 2016, there has been a sharp change of the Bihar government’s policy towards food processing, as well as for other industries. The special sub-sectoral focus on processed foods was removed, and front-loaded capital subsidies removed for all industries. Now, the treatment of food processing is at par with

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a collection of other industries, such as tourism, textiles, etc.17 The incentive scheme now comes in the form of an interest subvention for institutional credit. A large concern of the state government was that despite a continued special incentive scheme for processed foods, the resultant increase in manufactured units was very narrowly based: majority in rice milling. Second, these rice milling units, with enhanced competition from each other were underperforming financially, as we discuss in the next Chap. 6. It is not surprising that initial entrants into industries, following a front-loaded capital incentive scheme will draw in many inefficient units, as Gebrewolde and Rockey (2018) observe for Ethiopia. We interpret inefficient entry from an entrepreneurialmindset point of view in Chap. 7. Market forces should foster learning among the entrants leading them to eventually successful entrepreneurship. This is highlighted in Rodrik (2008)’s mentions of failure-tolerance in government policy that must accompany the ‘policy-mindset’ as well. While it is too early to comment on the post-2016 outcomes, the policy change demonstrates the limits of fiscal leverage for Bihar. There is a necessity of incorporating elements in its policy space that can provide incentives other than those purely pecuniary in nature. Some amount of pessimism with respect to the success of the sector-specific targeting through incentives is understandable: Figs. 4.2, 4.3 and 4.4 in the Appendix show the lack of diversification across sub-sectors and geographical concentration for registered manufacturing in 2017–2018 (ASI sampling frame data for Bihar). However, for a state which has seen resurgence economically,18 there is reason to believe in future positive outcomes with some policy corrections.

4.3 Identifying Product Networks in Bihar’s Food Processing Industries Using the historical timeline discussed above, we shall follow closely developments in two sub-sectors: rice milling and dairy. We shall also discuss developments in Bihar-specific output such as makhana (fox nut) in subsequent chapters. This helps us narrow down the scope of study. Even within these sub-sectors, we can use a complete value-chain approach from farm-gate to retail. However, the entire supply chain of processed food entails the inclusion and in-depth study of heterogeneous agents: those in the procurement stage, then those in the manufacturing stage and lastly, those involved in food retail. These stages are distinct in their characteristics, and from an industrial policy point of view attracts different types of incentives and tax treatments. Much of our discussion is on the manufacturing stage for registered units (including firms of various kinds of incorporation: private limited companies, 17 Further

details are at: http://industries.bih.nic.in/Acts/AD-01-01-09-2016.pdf. the prosperity differences across districts as shown in the gross district domestic product (GDDP) maps in Figs. 4.5 and 4.6 for 2006–2007 and 2011–2012 (at 2011–2012 constant prices) in the Appendix. 18 See

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co-operatives, partnerships, proprietorships, etc.) in food processing, though we do discuss marketing issues of processed foods. Following the discussion in Chap. 2, we eschew any analysis of the food services segment, such as hotels, restaurants, etc. In the context of Bihar, a manufacturing lens makes sense. The subsidies that were offered by the state government of Bihar, for fostering growth in processed food as an industry, catered exclusively to the manufacturing stage. The policy angle also restricts us from addressing issues with the unorganized sector in processed food, which has a very different character (much lower scales of operation and lower standards for food safety). We show that despite its ubiquitous character, the bulk of processed food comes from registered manufacturing, so that this deliberate exclusion is not too harmful. For manufacturing, we explore arbitrage-based reasons and expand to industrial ecosystem factors specific to this stage of food processing. However, the latter requires us to touch upon the links between the manufacturing and retail stages, particularly through the channels of advertising and unbranded retail. Nonetheless, our study of the retail stage is not as detailed as the manufacturing stage. It is studied at the level of entrepreneurs through our primary survey, as well as secondary data on manufacturing units. Second, we do not cover certain food categories in detail, such as beverages like tea, coffee, alcoholic drinks as well as edibles such as tobacco, which, though a part of processed food, are officially treated in a different industrial category from food. This is true both in India and by international standards. While processed food is covered under the 2-digit classification standard (for NIC 2008 and ISIC Rev.4) under class 10, beverages come under class 11, whereas tobacco is grouped under class 12. Note further, that within the large sub-set of category 10, we do not focus on meat and fish processing for two reasons: first, these industries are shown to create a larger environmental damage than the other food categories and second, their presence in Bihar is minuscule. These industries are the most capital intensive among the food group of manufacturing units and require significantly higher set-up costs than cereal-based units. We thus draw a sharp boundary for what we consider as processed food manufacturing: rice processing units, dairy processing and regionally unique food items, such as makhana (fox nut) for Bihar.

4.4 Summarizing Across Trajectories: Potential Lessons from Bihar We now attempt an integration of our analyses from Chap. 1 to the present one to discuss existing results on food processing on a single platform. The movement from the first chapter to the current one involves a perusal of food processing from a purely technical and sub-sectoral focus to a nuanced description of the sector through region-sub-sector trajectories. We have considered three such spatial × sub-sectoral trajectories: the general (which applies mostly to industrialized countries or regions),

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Table 4.10 Variation in the food processing industry characterization across trajectories Stylized fact Trajectory I Trajectory II Trajectory III General Country (India) Region (Bihar) S1. Infrastructure interlinkages upstream & downstream S2. Deep market segmentation between large and small S3. Low technological intensity S4. Stability in consumer tastes S5. High working capital intensity S6. Manufacturing clusters S7. Manufacturing dependence on retailing





×

×













×

















×

×

Source Authors’ calculations

the national trajectory for India and the regional one for Bihar. Our contention is that the difference in the industrial ecosystem, particularly access to formal institutional finance, cost of compliance with international standards, ‘export-oriented’-ness and the extent of the missing middle and dualism (formal and informal establishments in mutual cohabitation) differentiate them. We have presented a collection of sub-sector × region descriptions here that we refer to as trajectories. Keeping Trajectory 1 as the benchmark, we now try to collect a summary of observations across the other two trajectories. The results are presented in Table 4.10. We use differently weighted tick-marks and crosses to show how the extent to which these observations work for the three trajectories, based on our earlier discussion as well as an additional review of the literature and our primary survey in Bihar in 2016–2017 for food processing industries. A thicker marks indicate that the condition holds more strongly for that trajectory. The same pattern of tick-marks or crosses do not follow through uniformly across trajectories for any parameter other than low technological intensity. This reinforces our earlier claim that regional trajectories shed light on industrial outcomes, and not sub-sectoral studies which are agnostic to space. The sharpest difference, as we have discussed earlier, is in S2. Bihar presents a scenario of the deepest market segmentation between large and small firms and this segmentation is progressively smaller for trajectory 2 and 1, respectively. Though there are differences between firms of different sizes in trajectory 1 in some sub-sectors like frozen foods, it is not as stark as in Bihar. The tiny informal units and the small registered proprietorship firms, which are the new entrants in food processing since 2006–2008 in Bihar, are no competition challenge for the exist-

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ing large units, with multiple plant locations, professional management and large advertising budgets (like ITC or Britannia). These firms are typically integrated into the entire value chain for products in which they specialize in a region and do not use co-processing as an option, as would be done by relatively smaller middle size units. This has been the experience with the growth trajectories of many successful middle size firms: in order to expand, many firms such as General Foods in the US have ‘bought in’ produce from co-processors. Their focus was on developing distribution and retail sales networks. For Bihar, a lack of the missing middle size in food manufacture has special implications. Due to deep market segmentation, absence of co-processing opportunities with mid-size firms is likely to increase marketing costs for small units. Co et al. (2017) note a similar missing middle size for Vietnamese firms and is one of the few papers in current literature to discuss the issue of subcontracting to deal with this size distortion. Note however their contention is across a range of industries, not food processing in isolation. It also relates to the export sector in terms of subcontracting. We focus on production for regional consumption within Bihar and not the export market. Units in Bihar are mostly non-exporters and the issue of the missing middle size of firms and subcontracting has a different connotation than that in Co et al. (2017). Knowledge of customs laws turns out to be significant explanators of firm performance in their study. This does not apply to the Bihar trajectory. Let us pause and reflect on the differences between India and Bihar trajectories. While consumer tastes have changed in urban centres in many states of India, our primary survey in Bihar showed the stagnation in tastes (continued preference for freshly cooked food over processed food, other than a few pockets of the state capital, Patna). This is reflected in the manner in which cold storages are used in Bihar: mostly for storing potatoes, even today (Minten et al. 2014), whereas multi-item storages have become a regular feature in states like Gujarat, UP and the National Capital Region (NCR) bordering Delhi. These states see a much larger footfall of international tourist traffic, whose preferences for processed food helps sustain capacity utilization of multi-commodity storages. Second, there are major differences in the manner in which clustering works out, depending on whether we concentrate on option II (clustering in a specific commodity) or option III (food parks) for the government as development alternatives in food processing. As the last Chap. 3 discusses, the mega food park scheme has seen substantial development in most major states of India, other than Bihar. The latter has been limited to option II (clusters in rice milling, makhana and maize processing), for multiple reasons that are discussed in Chap. 5. Though the quantum of processing of food is low for both trajectories,19 the scale is much lower for Bihar. The export orientation is also different: while there are states, such as Gujarat and Maharashtra, that have large exporting units in different sub-sectors of food processing, this is almost negligible in Bihar, limiting the market for sales of manufacturing units to local demand. 19 MoFPI data indicates that less than 10% of total agri-output produced in India is processed into valued-added products. This is in sharp contrast with processing rates worldwide: 65% for the US, 23% for China, 78% for Philippines, 30% for Thailand and 70% for Brazil.

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The addition of the regional (Bihar) angle to the product network highlights that what is feasible for the latter faces a constraint from the former: while some secondary processed products are part of the network generated from grains (rice, in our example in the next chapter), they are excluded with the filter of Bihar. Similarly, some other food products like makhana (fox nut) are meaningfully discussed only within the regional context of Bihar. Acknowledgements I sincerely acknowledge the effort of Barna Ganguli, friend and co-investigator in the IGC project on Food Processing Sector in Bihar, for contributing in this chapter and collaborating with her colleagues at ADRI (Mr. Nilay Singh, Database Administrator, Mr. Prakash Kumar, Computer Assistant, Mr. Chandan Nath Yadav, User Interface Developer and Mr. Syed Shakir Ali, Software Developer) for all the figures in the Appendix to this chapter.

Appendix See Table 4.11 and Figs. 4.2, 4.3, 4.4, 4.5, 4.6. Table 4.11 Share of industries in Bihar’s GSDP Year GSDP (2004–2005 Industry (INR Crore) prices) (INR Crore) 1999–2000 2000–2001 2005–2006 2009–2010 2010–2011 2011–2012 2012–2013 2013–2014 2014–2015

59157 67942 77912 114480 130171 143560 158909 173409 189789

8684 8503 11738 21536 27653 28516 28786 32203 35123

Source Data sourced by Barna Ganguli from CSO records

Share in GSDP (in%) 14.7 12.5 15.1 18.8 21.2 19.9 18.1 18.6 18.5

Appendix

109

Fig. 4.2 Distribution of food processing units in Bihar district-wise (sourced from the ASI frame of registered manufacturing (2017–2018))

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4 Food Processing: The Bihar Trajectory

Fig. 4.3 Distribution of food processing units in Bihar district-wise (sourced from the ASI frame of registered manufacturing (2017–2018))

Appendix

111

Fig. 4.4 Distribution of food processing units in Bihar district-wise (sourced from the ASI frame of registered manufacturing (2017–2018))

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4 Food Processing: The Bihar Trajectory

Fig. 4.5 District-wise Gross Domestic Product (GDDP) Bihar, 2006–2007 sourced from ADRI

Fig. 4.6 District-wise Gross Domestic Product (GDDP) Bihar, 2011–2012 sourced from ADRI

References

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References Chakrabarti R (2013) Bihar breakthrough: the turnaround of a beleaguered state. Rupa Publications, New Delhi Co CY, Nguyen TK, Nguyen TN, Tran QN (2017) The missing middle: growing and strengthening Viet Nam’s micro, small, and medium-sized enterprises. WIDER working paper no. 2017/72. https://www.econstor.eu/bitstream/10419/163043/1/88372510X.pdf Dasgupta C (2010) Unraveling Bihar’s ’growth miracle’. Econ Polit Wkly XLV 52:50–62 De Soto H (1989) The other path: the invisible revolution in the third world. Harper and Row, New York Djankov S, La Porta R, Lopez-de-Silanes F, Andrei Shleifer A (2002) The regulation of entry. Q J Econ 117(1):1–37 Douglas W, Shanks C (1929) Insulation from liability through subsidiary corporations. Yale Law J 39(2):193–218 Friedman E, Johnson S, Kaufmann D, Zoido-Lobaton P (2000) Dodging the grabbing hand: the determinants of unofficial activity in 69 countries. J Public Econ 76(3):459–493 Gebrewolde TM, Rockey J (2018) The effectiveness of industrial policy in developing countries: causal evidence from ethiopian manufacturing firms. Working paper no. 16/07, University of Leicester. https://www.le.ac.uk/economics/research/RePEc/lec/leecon/dp16-07.pdf Gordon R, Li W (2005) Tax structure in developing countries: many puzzles and a possible explanation. NBER working paper 11267. https://www.nber.org/papers/w11267 Hsieh C-T, Olken BA (2014) The missing “missing middle”. J Econ Perspect 28(3):89–108 Kathuria V, Rajesh Raj SN, Sen K (eds) (2014) Productivity in Indian manufacturing: measurements, methods and analysis. Routledge, Taylor and Francis Group Krueger AO (2013) The missing middle. In: Hope NC, Kochar A, Noll R, Srinivasan TN (eds) Economic reform in India: challenges, prospects, and lessons. Cambridge University Press, Cambridge Mathew S, Moore M (2011) State incapacity by design: understanding the Bihar story. IDS working paper no. 366 Minten B, Reardon T, Singh KM, Sutradhar R (2014) The new and the changing roles of cold storages in the potato supply chain in Bihar. Econ Polit Wkly 49(52) Morck R, Yeung B (2011) Economics, history and causation. Bus Hist Rev 85(1):39–63 Mukherji A, Mukherji A (2015) Bihar: what went wrong? and what changed? In: Pangariya A, Rao MG (eds) The making of miracles in Indian states. Oxford University Press, New York Phillips J (2017) Can Bihar break the clientelist trap? the political effects of programmatic development policy. IGC working paper no. S-34311-INB-1 Rauch JE (1991) Modeling the informal sector formally. J Dev Econ 35:33–47 Restuccia D, Rogerson R (2013) Misallocation and productivity. Rev Econ Dyn 16(1):1–10 Restuccia D, Rogerson R (2017) The causes and costs of misallocation. J Econ Perspect 31(3):151– 174 Rodrik D (2008) Normalizing industrial policy. Working paper no. 3, Harvard University. http://j.mp/2o6K6Ye Shrivastava AK, Solomon S, Sawnani A, Shukla SP (2011) Sugarcane cultivation and sugar industry in India: historical perspectives. Sugar Technol 13:266 Sutton J (2007) Sunk costs and market structure: price competition, advertising and the evolution of concentration. MIT Press, Cambridge Thakur S (2014) Single man: the life and times of Nitish Kumar of Bihar. Harper Collins Publishing, New York Tybout JR (2000) Manufacturing in developing countries: how well do they do and why? J Econ Lit 38(1):11–44

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Vyas V (2015) Low-cost, low-tech innovation: new product development in the food industry. Routledge, New York Yülek MA (2018) How nations succeed: manufacturing, trade, industrial policy and economic development. Palgrave McMillan, London

Chapter 5

Food Processing in Bihar: Industrial Ecosystem

5.1 Introduction: Government as a Stakeholder in Industrialization The preceding chapters sketched out three trajectories for food processing: in developed countries, in India and the one for Bihar. This chapter onward, we intend to comment on the performance of formal sector registered firms in the third trajectory. Before we do so, consider existing results for the US, the EU and separately for Spain. Regarding the latter, Zouaghi et al. (2017) is one of the few studies with a specific regional focus. While firm characteristics like size matter in Spain, locational characteristics also matter along with industry-specific factors. In the case of the EU, Hirsch et al. (2014) finds that firm-level characteristics drive profitability in food processing and have more relevance than industry or even year or country-specific effects. There is no mention of policy effects. While firm-level characteristics matter, Hirsch et al. (2014) finds that there are some country-specific differences between the US and EU food processing ventures. While large firms perform better due to their better bargaining power, growth of the firm positively influences profits in the US and not the EU. Why should this be the case in the same industry in two different locations? We need region-specific variables to address this. Government policy is an obvious starting point. The history of Bihar discussed in Chap. 4 demonstrates the vital role of the government, particularly the incentive scheme for food processing since 2008. In most countries, industrialization is not imaginable without taking into account the agency of the government (see Bianchi and Labory (2006)). Even in the most developed nations, government policies and interventions have changed industrial outcomes. Integrating the government into a discussion of the development of the food processing industry in states like Bihar requires an inclusive context. Consider the concept of the industrial layer as discussed in Yülek (2018). This includes not only industrial firms, entrepreneurs, skilled management and finance, but also the government as well as external experts and research institutions. Typically, any industrial narrative for any particular sector should take the form of a detailed description of the nature © Springer Nature Singapore Pte Ltd. 2020 D. Saha, Economics of the Food Processing Industry, Themes in Economics, https://doi.org/10.1007/978-981-13-8554-4_5

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of technology and costs,1 profit margins and other financials (exclusively dealt with in Desai and Namboodiri (1992)) and market structure and competition.2 While the act of investment is driven by the private-profit motive of the industrial firms and entrepreneurs, their incentives are interacted upon by government policy. The latter often has its own agenda, such as providing employment, enhancing output and productivity (see Gebrewolde and Rockey (2018) for Ethiopia, Chaurey (2017) for India and Criscuolo et al. (2012) for the UK among many recent papers). Apart from policy framing, the government also has a proactive presence through manufacturing units it owns, which compete with private players. Implementation of the policy, therefore, has an in-built problem of conflict of interest: the government has a regulatory role and it is in direct competition with private agents.3 For the India and the Bihar trajectories, we have discussed the deep market segmentation between the small and large firms. The large private initiatives in food processing in India or Bihar are comparable with those in developed countries (some of them in fact, are MNCs which operate in different regions), but the small units are very different in these trajectories. The bulk of processing activity in India, as well as Bihar (as Chaps. 3 and 4 discuss) are done by small units. The expectations of these firms in terms of government support are very different from large firms (we discuss this in Chap. 7). Policy towards a particular sector has to account for these differences. Additionally, sectoral policies are inevitably a part of the overall Industrial Policy (IP) in the state. While all kinds of government policy should matter,4 most of the academic debate has narrowed down on IP and its manifold effects (Bianchi and Labory 2006). In the immediate context of Bihar, we shall limit ourselves to a discussion of IP, sectoral IP and government as a producer in our description of the government as a stakeholder in food processing. In Chap. 6, we shall discuss the potential overlaps between agricultural pricing policy and its effects on IPs with reference to rice mills. These overlaps are crop specific, as we showed for the sugar-milling industry in Bihar, and therefore the discussion has a narrower focus. In general, even dealing with IPs as a stand-alone policy is not an easy task. As Rodrik (2008) mentions, IP is the most contentious of public policies and is the hardest to quantify in terms of a cost–benefit analysis. We first need to provide a concise definition of IP and its role in industrialization to understand its notoriety.

1 Sutton

(2007) does not discuss policy implications. finds exclusive focus in Connor et al. (1985). 3 This is apparent in the nature of competition in dairy in Bihar. 4 One can think of macroeconomic policies which affect corporate tax rates, fiscal policies which determine the amount of finances that the government will have in financing industrial subsidies or education policies affecting the skill-levels of industrial labour. They all have a role to play for industries. 2 This

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5.1.1 Industrial Policy: What Is Its Role in Food Processing? We define IP as an instrument with the government to channel industrial investments and development in a preferred direction. Note that we are explicitly integrating the government’s own agenda for industrialization in this definition. The concerns of IP are primarily targeted towards the registered manufacturing units, the implicit direction of policy focusing on the move towards formalization of the informal sector and growth of the formal sector. Simply put, these policies are in the nature of ‘carrots-and-sticks’ packages delivered in the form of various conditions for accessing pecuniary and non-pecuniary incentives. Inherently, there are more carrots than sticks in these incentive schemes. Second, we are focusing on a very narrow aspect of IP: Bianchi and Labory (2006) describes IP in the more general context of industrial development measures. They identify IP with incentives that aim at favouring firms’ structural adjustment and helping the development of new sectors.

These measures include rules, such as anti-trust regulation to foster a competitive environment in the market, sectoral regulation particularly for network industries or utilities, protection of intellectual property rights, regulation of products and labour markets as well as capabilities. The latter include actions to facilitate tangible assets, such as finance, infrastructure and intangibles such as knowledge and human capital. We focus on the capabilities part of this definition, as most rules have a national coverage, whereas capabilities can be studied from a subnational perspective. Our quest is to understand the manner in which the state government (GoB) can productively intervene to provide a supporting and sustainable environment for a state that lacks industries historically. For this purpose, IP takes the form of direct intervention in various industrial sub-sectors by the government by providing capital subsidies, interest subventions on loans, tax breaks, reductions in land and stamp duties as well as access to infrastructure and simplifications in rules to aid start or expansion of business, such as the creation of the single window clearance window clearance facilities: State Investment Promotion Board (SIPB) for large projects and District Industries Centres (DICs) for small projects. In its most interventionist avatar, this policy takes the form of a ‘picking-upwinners’ type of action by the government, which cherry-picks among alternative sectors and settles for a basket of a few to jump-start development. This is the ‘vertical’ dimension of the policy. Asymmetric information between the policy-maker and firms, with the former having less information about the industrial layer than the other stakeholders, plays a large role in the failure of ‘picking-the-winner’ style IP. On the other hand, industrial development can also be fostered by incentivizing investments in the industrial ecosystem, such as infrastructure (roads, electricity, water connection), streamlining procedures for starting a business, strengthening avenues of industrial finance and integrating goals of environmental sustainability (or green industrial policy), etc. This dimension of the policy is ‘horizontal’, as it affects all industrial actors, as discussed in Bianchi and Labory (2006). The most vicious critiques of IP point out that it distorts outcomes for the worse

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(see Bianchi and Labory (2006)). This is with respect to mostly the vertical dimensions of the IP. Milder criticisms, such as Rodrik (2008) include discussions of problems of asymmetric information, due to which the government can rarely pick winners. The government is also accused of financing projects which the private sector would have done on its own in the absence of this support. More importantly, rent-seeking in policy implementation implies a lot of wasted expenditure to access subsidies. Internationally, the evidence on IP is mixed. The Japanese MITI (Ministry of International Trade and Industry)-driven industrialization5 and the miraculous development of the East Asian economies are some examples of this strategy succeeding in the long run. India’s history of planned industrial development up to the 1990s shows the opposite case. State-led choices in investments in heavy industries, without addressing market imperfections, such as the ‘license raj’,6 which has been been linked with the low growth and stagnation of the Indian economy prior to reforms in the 1990s. The regional dimension in the ‘old-style’ IPs, generally seen as a collection of pecuniary incentives, has been, in recent times, an explicit component of policy through area-based policies. Kline and Moretti (2013) provides a detailed survey of these area-based policies which try to reduce regional inequalities. Gebrewolde and Rockey (2018) and Chaurey (2017) mention a plethora of these: Federal Empowerment Zone (EZ) programme in the US or the EU Regional Selective Assistance, Italy’s Law 488/1992 or the French EZ programme. Busso et al. (2013)’s evaluation of the US EZ programme is that it increased employment without affecting efficiency in costs or prices. Criscuolo et al. (2012) for the UK and Gobillon et al. (2012) for France also find similar evidence for the EU. Among other negative results, Neumark and Kolko (2010), Greenbaum and Engberg (2004) and Bondonio and Greenbaum (2007) find no effects of enterprise zones on growth in employment. Similarly, Gebrewolde and Rockey (2018) find that area-based IP was not at all effective for Ethiopia. The exception is Chaurey (2017) for two states in India: Himachal Pradesh and Uttarakhand, where they find a positive impact on regional industrial development indicators due to area-based IPs. Bihar presents a case where government policy specifically targeted food processing through its vertical arm. We evaluate this intervention along the following lines: 1. When is food processing to be selected as a target sector for policy incentives? i. Under what conditions should food processing be targeted specifically for vertical policy incentives? 5 The

Ministry of International Trade and Industry (MITI), established in 1949 in Japan, has often been credited for the planned development and success of hi-tech industries in Japan. See the discussion in Chap. 8. 6 License raj is the term set aside to describe the pre-1990 era of planning in India when acquiring licenses for manufacturing and businesses involved massive rent-seeking. With the advent of reforms in 1990–1991, this era came to an end, marking the phase of growth for India. See Forbes (2001) for a detailed discussion.

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ii. How do we evaluate policy outcomes? iii. Are horizontal policies sufficient to stimulate growth in food processing? The rest of the chapter attempts to answer these questions. In sum, we find that a ‘nearness-to-inputs’ kind of logic for targeting the manufacturing component of food processing through subsidies is unlikely to be successful. Other elements of the ecosystem, such as infrastructure and ease-of-access to working capital as well as intelligent entrepreneurship are critical. Bihar has some agri-resources in abundance, relative to neighbouring states (such as maize, dairy and makhana), but that alone is not sufficient argument for using vertical policy instruments. However, answering what exactly is sufficient is the most difficult question, and this relates to policy evaluation. We attempt to do this in multiple ways, using different data sources as they capture different angles of policy focus. Though we do not find very encouraging outcomes in terms of policy performance, we need to qualify this finding with a few thoughts. First is, of course, the measure of policy performance itself. Ideally, a policy that reduces the skewness in firm size distribution in Bihar should be considered a success. In order to address this, we measure the effect of the food processing policy in terms of the number and size of operations of entrants, scale expansion, non-substitution of investments from other sub-sectors and product basket diversification as the primary indicators to measure policy success in this chapter. Financial performance of the entrants is an issue that we discuss only in brief, leaving a detailed discussion of this for some specific sub-sectors such as grain milling, rice and dairy for the following Chap. 6. Policy interventions in manufacturing units in the food processing industry are likely to influence some other variables that we cannot measure, and therefore this is only a partial evaluation of the policy. Second, we might argue that the effect of the policy is to be seen from a longer time horizon and not within the short span that we work with. For most of the variables, we have data only over broad time ranges and not yearly time-series data. This limits the scope of our analysis. There is no reason to rule out more positive results with more granular as well as longer time-series data on these variables for Bihar. Third, our conclusions rest on the assumptions that a part of the standard Difference-In-Difference (DID) methodology for evaluating policy outcomes. Any fragility in these assumptions will affect our results as well. We provide a short description of the Difference-In-Difference (DID) technique for an easy understanding of the policy evaluations we conduct in this chapter. Difference-in-Difference (DID) Evaluation of Policy Effects The Difference-In-Difference (DID) estimation technique is a preferred method for evaluating effects of policy, when sharp policy discontinuities are absent along with a paucity of good instrumental variables (as Angrist and Pischke (2015) points out). Policy evaluation requires a counterfactual ‘group/region’, which is untreated with the policy effect and is the benchmark against which

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the effect of the policy is tested. Hence, two distinct groups/regions emerge: the ‘treated’ and the ‘untreated/control’. What the DID exploits is an assumption that there is a common trend that the treated and the control groups follow in the absence of the policy intervention. As this trend has a time dimension, we need observations on both the treatment and control groups in at least two time ranges: ‘before’ and ‘after’ the policy intervention. Now consider any variable which the researcher believes is affected by the policy. The theory, then, predicts that the effect of the policy on that variable can be read off as a double difference between the treatment and the control groups as well as over time (‘before’ and ‘after’ the policy event). Thus, the DID measures the average difference across regions and over time on a variable of interest y, which is presumably a target for policy. The same analysis can be done using a regression predicting the average effect of the variable y on a DID term and other controls. For this, it is standard practice to create a DID term, which is a multiplication of group and year dummies. Assume that the group dummy takes value 1 for the treatment group and 0 for the control group. Assume also that the time dummy takes value 1 for the ‘after’ time period and 0 for ‘before’. Then, the DID term is a multiplication of these two dummy variables, which captures the effect of the average y in the treatment group after the intervention. Now, the regression uses the controls of this DID term and other independent factors that are likely to affect y. The coefficient of the DID term captures the DID effect of the policy on the average level of y. Note that the common trend assumption between the groups in the absence of the treatment is crucial for the analysis to go through. One can also run more complicated Differencein-Difference-in-Differences (DDD) regressions for finer controls and more accurate assessment of policy, where another third dimension is used to difference out average effects. We use the region–sub-sector–time triple for our DDD exercise in this chapter. Among some recent examples of applications of this technique for policy evaluation is Chaurey (2017), which discusses area-based policies for India. However, note that there are multiple overlapping state-specific and national schemes for many subsectors in processed food. Chapter 3 discusses some of the important recent policy initiatives for India as a whole with MoFPI at the forefront for policy implementation. There are other agencies, such as the NHB (National Horticultural Board) for specific subsidies for cold storages and the MSME for special subsidies applicable for small units in food manufacturing in almost every sub-sector of food processing in states like Bihar and Jharkhand. There is a competition among subnational governments in India to attract investments into their states, which collide with national-level policies. While this gives the entrepreneur a bouquet of choices, identification of an individual policy’s performance is nearly impossible. This problem has to be circumvented for using DID or DDD-type of causal analysis. In the rest of the chapter, we describe the food processing policy of the government of Bihar from 2008 to 2016 in Sect. 5.2, as well as some observable patterns of entry

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from government data. Then, in Sect. 5.3, we discuss various ways of evaluating the effects of this policy. Section 5.4 then goes on to explore other necessary elements of the industrial ecosystem, such as land, finance and ‘business environment’ or ‘ease-of-doing-business’ factors in Bihar.

5.2 Government Policy in Food Processing in Bihar As our timeline in Chap. 4 depicts, Bihar’s revival in 2006 was concomitant with a new industrial policy with a five-year tenure (2006–2011), which was enhanced to the second policy cycle from 2011 to 2016. Post-2006, Bihar had witnessed three cycles of incentive schemes: between 2006 and 2011, 2011–2016 and the currently ongoing 2016–2021 IP. The first two of these policy cycles competed with each other in terms of state largesse given to new and existing units. The state did not stop at this. During the first IP cycle itself (in 2008, to be precise), the GoB also introduced a specific policy targeting food processing. This, as the following textbox summarizes, was an exceptionally handsome package of incentives. Food Processing Policy, Bihar (2008–2016) This policy offered an integrated scheme for developing the food processing sector in Bihar. The detailed set of incentives were 1. Grant support for new units: up to 40 (or 35)% of the project cost subject to a maximum of INR 10 (or 5) crore for common cluster infrastructure (or individual investor). 2. Specific Entrepreneur Targeting: 5% subsidy for SC/ST/Handicapped/Women entrepreneurs. 3. Project-specific targeting: i. Food Parks: expanded the upper limit of subsidy for food parks from INR 15 crore to INR 50 crore project cost, at a subsidy rate of 30%. ii. Cold Storage: for capacity between 5,000 and 10,000 MT, 30% subsidy against project cost whereas for capacities above 10,000 metric ton (MT), the subsidy rate was 35% of project cost upper capped at INR 5 crore. iii. Maize Silos: with a single silo unit cost capped at INR 2 crore with an average capacity of 5000 MT, a subsidy rate of 35% with a maximum subsidy of INR 70 lakh. iv. Dry Warehouse: excluding the value of land, project cost of paddy and wheat-based dry warehouse with one cycle of storage up to 3300 MT was kept at INR 4 crore, whereas for two cycles with a capacity of 2000 MT, it was capped at INR 0.8 crore. The subsidy was kept at 25%

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against the capital expenditure of the project cost with a maximum limit of INR 5 crore. 4. Interest Subvention: 3% for project cost between INR 50 and 100 crore and 6% for projects costing more than INR 100 crore. 5. Expanding Units: For units with approval as ‘under expansion’ with availed subsidy less than INR 5 crore, an additional 35% subsidy was allowed on the expanded portion capped at INR 5 crore. Note here the definition of project size: it does not include the cost of land and is in line with the MSME definition of plant size we mentioned in the previous Chap. 4. The 2016 Industrial Incentive policy has removed front-loaded capital subsidies and has merged the treatment of food processing at par with other industrial sectors. While the 2006 policy set into motion the standard set of pecuniary and non-pecuniary incentives for industries, the 2011–2016 one was more comprehensive and discriminatory. Not only were front-loaded capital subsidies introduced, but also interest subsidies as well as sales tax and registration and stamp duty exemptions, along with a special status for the food processing industry and a slew of non-pecuniary incentives were put in place. While the 2006–2011 policy transition was smooth, 2016 marked a departure, with the removal of front-loaded capital subsidies as well as the special package for food processing industries. While this sharp policy correction augurs badly from a policy continuity point of view, the government was presumably sending a signal for the next five years. The question is: what was the rationale for this reversal in policy treatment of food processing? Were the outcomes, as we discussed earlier, not in line with policy objectives? This can only be answered through some form of evaluation of the food sector-specific policy of Bihar from 2008 to 2016. Before attempting that, let us first examine the nature of entrants that the policy attracted to Bihar during this period in the following Sect. 5.2.1.

5.2.1 Pattern of Entry into Food Processing in Bihar Prior to 2008, there was no significant industrial density in Bihar. During the period 2008–2016, many small modern or semi-modern rice mills started business along with some others in sectors such as dairy and animal feeds. Consider the rate of registration of manufacturing units with the ROC, Patna. This is one of the indicators of the intention of entry within a sub-sector in the state of Bihar. Note that a firm registered in Delhi or some other ROC can also operate a manufacturing unit in the state and that a firm registered with the Patna ROC might also operate a unit outside Bihar. Hence, this rate of registration at a particular ROC is an imperfect measure of entry intentions for manufacturing in a region. However, given the history of Bihar, it is unlikely that a unit would register with the Patna ROC without manufacturing intentions within the state. Figure 5.1 compares the number of units registering with

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Fig. 5.1 Year-wise registration of manufacturing units in selected sub-sectors up to 2014, Bihar source Author’s creation using data from the ROC, Patna

the ROC at Patna in different years for the three sub-sectors, viz. rice mills, dairy units as well as cold storages. As is obvious, post-2009, rice mills dominate the registration numbers. Some dairy units have also followed suit, but the same cannot be said of cold storages. There were some old cold storage units registered with the ROC around the 1980s, but a commensurate increase in the number of cold storage registration rate is not seen as we find for rice mills. Between 2006 and 2015 (December), the SIPB had approved 2311 projects, out of which 1422 were engaged in food processing.7 The state government, through various schemes, has released approximately 390 crore INR for these units,8 of which the majority has been taken up by rice-milling projects (around 156 crore INR for 169 rice-milling units, 89 of which were functional as on September 2015). Other than rice projects, most other project submissions to the government are in Rural Agri Business Centres (RABC) (includes cold storages), wheat and maize projects (together accounting for 48% of 379 applications as on September 2015). In terms of the nature of entrants, majority were small rice-milling units established by local first-generation entrepreneurs (more details in Chap. 7). However, there were also a few large units entering Bihar, with pan-India operations and even 7 The source for these figures is Udyog Mitra, Government of Bihar. However, the Economic Survey

of Bihar (2015–2016) mentions 379 food processing units, of which 210 were functional as of September 2015. The Hindustan Times e-newspaper on 14 July 2016, however, reports a total subsidy burden of more than 3000 crore INR for the government. According to the article, out of the 8000 crore INR that has come to the state as investment, 60% is in food processing. 8 Economic Survey of Bihar (2015–2016). Hindustan Times e-newspaper on 14 July 2016 notes that 250 functional units are in food processing out of a total of 400 odd projects, with an employment of over 25,000. It also claimed that the Food Vision Policy of the government has fetched it around 4500 crore INR investment.

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one MNC in the poultry feed sub-sector. This pattern of entry, nonetheless, exaggerates the ‘missing middle’ and deep segmentation between the small and the large. There is no significant expansion of firm size from small to medium, nor is there significant entry of mid-sized firms. Table 5.1 provides a classification of the type of entry for a selection of manufacturing units in food processing in Bihar. The entrytype is grouped according to ownership and the nature of prior experience of the unit. Multinationals are treated as a separate category, followed by companies with an all-India presence. Within these, there are two distinct groups: those who enter into a particular sub-sector in food processing in Bihar with prior experience in other sub-sectors (cross-entry) and those which have been operating in the same sub-sector (same entry). These groups/sub-categories are also present for the last category of firms with only a Bihar presence. A few comments about the firms we have included in these categories are due. For our categorization, we use two different data sources from the government: first, SIPB data providing micro details of entry and nature of operations for 2040 units from 2006 to 2015 and second, the Udyog Mitra compilation on success stories of industries in Bihar.9 The latter focuses on mostly large units, which is why the table includes mostly very large firms, which the government saw as ‘successful entry’. This is indicative of two things: one, the commonly known policy-bias in favour of large units in manufacturing and second, the importance of scale of entry. The latter point has been stressed by Sutton and Kellow (2011) as a crucial factor for success for manufacturing units in Ethiopia: entry at either the middle or the large size of operations. This is presumably because of imperfections in the industrial ecosystem that create barriers for expansion of scale for small firms. However, we must read in a disclaimer here: two entries in Table 5.1 are doublestarred. These are Ruchi Soya Industries Limited (RSIL), which was one of the largest edible oil manufacturers in India till recently and Amrapali Biotech India Pvt. Ltd. Around 2015, these companies would amount to spectacular performers. However, in 2017, RSIL filed for bankruptcy under the Insolvency and Bankruptcy Code (IBC) of 2016 due to non-repayment of its INR 200 crore debt.10 Amrapali Biotech India Pvt. Ltd. is part of the Amrapali Group of the NCR region, which started out in the real estate construction business and as of 2019, the directors have been imprisoned due to a case of embezzlement of funds.11 Though Amrapali Biotech Pvt. Ltd. is a separate smaller and newer company, its inclusion in Bihar’s success chronicles by Udyog Mitra was primarily due to its association with the large builder group. RSIL is a classic example of the fact that large firms in food processing also face problems with the management of working capital and debt servicing. Among others in Table 5.1, take the case of Vindhyabasini Rice Mill Cluster Pvt. Ltd., which consists of a collection of rice mills in Rohtas. Though the promoters have more than 9 Available

at http://www.udyogmitrabihar.in/success-stories-industrial-investments-bihar/. company’s website (http://www.ruchisoya.com/) shows the uncertainty with the company’s status as of today. 11 https://economictimes.indiatimes.com/wealth/personal-finance-news/30000-home-buyers-ata-dead-end-in-noida/articleshow/70272279.cms. 10 The

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Table 5.1 Nature of entrants into food processing in Bihar (2006–2015) Firm type Firm name and Sub-sector Year of entry experience (Location) MNC

(1) Japfa Comfeed Pvt. Ltd., Indonesia

All-India (cross-entry)

(1) Pashupati Grain milling Roller Flour Mill (part of JDB Pvt. Ltd. Group, Guwahati; 1990: cement, plywood, lime, coke etc.) (2) Amrapali Snacks, RTE Biotech India Pvt. cereals, candies, Ltd. confectionary (2007) (1) Britannia Biscuits (100 Industries Ltd. years+) (2) ITC Ltd. Dairy (Co. age: 100 years+) (3) Godrej Animal feed (28 Agrovet Ltd. years)

All-India (same entry)

(4) Ruchi Soya Industries Ltd. (5) Ugraya Food and Feeds Pvt. Ltd.

Bihar (cross-entry)

(6) Jhunjhunwala Oil Mills, Varanasi (1) Lavanya Finvest Pvt. Ltd.

(2) Shakti Sudha Industries

Bihar (same entry)

Animal feed (1975)

2013 (Vaishali)

2011 (Muzaffarpur)

(1) Mahajan Rice Rice mill (30 Mills Pvt. Ltd. years)

Comfeed, Benefeed, Ultrafeed, Japfa Ultra Feed Pashupati brand atta, maida, bran, suji

2012 (Nalanda)

Mums brand

2011 (Hajipur)

Multiple: Tiger, 50-50, Good Day Milk and ghee

2015 (Munger) 2012 (Hajipur)

Refined oil (60 2012 (Kaimur) years, ex-leader in Indian market) Broiler feed 2014 (2001 across 9 (Muzaffarpur) states) Rice mill (40 years in oil milling) Biscuits (promoter: Milk distributor for COMFED) Makhana

Products/Brands

Multiple brands for cattle, poultry, aqua and specialty feed Ruchi Gold and No.1 Vanaspati Super Saver, Nutritech and Prime gold brands

2011 (Kaimur)

2012 (Muzaffarpur)

Partnership with Parle-G

2012 (Patna)

Kheer mix, pop, flakes, flour, Nutrimake (milk additive), natural Parmal mansori variety rice exporter Rice and 2.2 MW power plant

2012 (Rohtas)

(2) Siddhashram Rice mills Expansion in Rice Mill Cluster (management: 20 2014 (Buxar) Pvt. Ltd.(2006) years)

(continued)

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Table 5.1 (continued) Firm type Firm name and experience (3) Vindhyabasini Rice Mill Cluster Pvt. Ltd. (4) Kunwar Apiary Pvt. Ltd.

Sub-sector

Rice mills (promoters:30 years) Honey processing (Shashi Kumar’s beekeeping experience) (5) Golden Dairy Ice-cream and Pvt. Ltd. frozen dessert (1948) (6) Ganga Dairy Dairy (1997) Ltd.

Year of entry (Location)

Products/Brands

2009 (Rohtas)

Parboiled rice and flour (2015)

2009 (Gaya)

Shiva brand

New unit in 2009 Golden brand (Hajipur) Expansion: 2011 (Begusarai)

Ghee, milk, curd, dairy whitener, dairy creamers

Source Author’s compilation based on data from the SIPB and Udyog Mitra, Government of Bihar

thirty years experience in rice milling, the rating agency CRISIL has downgraded the long-term rating of its debt to category D from B+/Stable in 2017 citing the following reason,12 which we reproduce verbatim: Weakness: Delays in servicing instalment on term loan Low cash accrual and sizeable working capital debt led to weak liquidity, which in turn resulted in delays in servicing instalment on term loan and in meeting interest obligation on cash credit facility.

Similar is the situation with Siddhashram Rice Mill Cluster Pvt. Ltd., with a rating of D in April 2018 from the bond rating agency SMERA due to delays in servicing long term debt by more than 30 days.13 This problem of financing working capital loans as well as long-term debt is more acute with smaller units. At least, the units in Table 5.1 are private limited companies. Most of the other entrants in food processing are either proprietorships or partnerships companies. Also, there is a lack of diversification in the product network among entrants. Out of 2040 proposals in the SIPB data, roughly one-third (around 583) were from rice mills alone. For every large unit like the MNC Japfa Comfeed of Indonesia entering animal feed manufacturing at Vaishali in 2013 or the CSR-sponsored 120 crore INR dairy initiative of ITC Ltd. at Munger in 2015, there were at least one hundred rice-milling units with minimal capacity of 2 MT per hour (called a mini rice mill) around Rohtas and Kaimur. Additionally, most of these units specialized in only one type of rice production (either arwa or parboiled rice). It should be noted that these varieties of rice have a large local consumption market in the eastern part of India alone: the basmati variety of rice is popular in the 12 Rating details available at https://www.crisil.com/mnt/winshare/Ratings/RatingList/RatingDocs/

Vindhyabasini_Rice_Mills_Cluster_Private_Limited_February_15_2017_RR.pdf. 13 Rating details are at https://www.acuite.in/documents/ratings/revised/26127-RR-20180418.pdf.

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northern and western parts of India, as well as in the export market. This choice of production indicates that local demand will act as a constraint for selling the output of the manufacturing units, and, therefore, in its expansion. Very few firms entered with a diversified product basket and a minuscule number of rice mills expanded their size of operations during this period. Of the 583 rice mills in the SIPB data, only 21 units report expansion of size as the reason for accessing government subsidy. Note that there are some large units that we have not mentioned, like Shrawasthi Agrotech Pvt. Ltd. in poultry feed or AFP Manufacturing Company Pvt. Ltd. for snacks. These firms entered Bihar in 2010 and 2011–2012, respectively. They are only a handful and this has led to high concentration among large units at the top-end of the size distribution. What additionally stands out is that, at the end of 2015– 2016, the resulting difference between the small and the large units in registered manufacturing is vast, in almost every dimension of structure and operations. It is almost as though the sample of firms among the large units come from a different population compared to small units. Analysing them together for policy evaluation is inherently problematic. We attempt the treatment-control type of analysis in the following sections, keeping this caveat in mind.

5.3 Evaluating Policy Effects for Food Processing in Bihar This discussion relates to how we should evaluate the food processing policy in Bihar. One standard method is the Difference-In-Difference (DID) estimation of policy effects, as discussed earlier. There are multiple problems of identification, extreme size distribution and measurement issues for employing the DID estimation for Bihar. We have already pointed out the problems of the skewed distribution of firm size. Identification and measurement issues are also very important. Regarding the identification of the effects of policy, note that the entrepreneur, who is the decision-maker, has a choice in deciding the source of subsidy: be it the national or the MSME schemes or the state-level IP. A naive assumption is that the entrepreneur responds to the state-level IP alone (as Chaurey (2017) effectively does). This is not a correct assumption, as our entrepreneurial survey in Bihar in 2016 revealed. Some entrepreneurs in our data picked up the MSME incentives while one changed his subsidy-provider from the national one to the subnational scheme during the project’s implementation. Second is the issue of measurement of performance. An easy measure is to consider the entire income from operations for this. However, only a part of total income comes from the actual sale of manufactured goods. With the presence of other sources of income, a firm’s expansion and entry decision strategy are no longer based solely on the provision of subsidies for manufacturing. Third is the more complicated issue of what constitutes business income, particularly for registered but unlisted manufacturing units which are aplenty in Bihar. For firms with incorporations such as proprietorships, the accounting standards are weak and the entrepreneur more often than not treats business profits as personal income.

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Regarding the effectiveness of the policy, we conduct several DID and DDD estimations using CMIE CapEx data, ASI data as well as data from the Project Management Agencies (PMAs). In the CapEx data, we measure entrants using announced, new as well as completed projects in food processing, whereas for the ASI data, the number of entrants is captured by the number of registered factories in food processing as opposed to those in overall agri-business in Bihar over different time periods. To check for whether the policy could reduce the skewness in the firm size distribution, we calculate the actual figures on expansion and size of entry without conducting a DID or DDD estimation.

5.3.1 Project-Level Analysis: PMA as Unit of Account The GoB in 2007 had formulated a Vision document on food processing, which was the precursor to the 2008 policy. This document created the institution of the PMA (Project Management Agency), which is an external technical expert which would help the government to screen potential projects and aid in the uniform reporting of business plans of selected projects in the form of a Detailed Project Report (DPR). Additionally, the PMA was envisaged as the link between the entrepreneur and the bank as well as the government, helping the project access institutional finance and subsidies along the entire timeline until its completion. The PMA was also an advisory for information on input purchases and other project management related issues. The GoB bore the cost of this service (at 2% of the approved project cost for the PMA) and it was free for the entrepreneur. In this sense, the government created some capacity for appropriate project selection to address the issue of adverse selection and moral hazard inherent in this programme. Note that the commission to the PMA builds in a bias towards large projects, as the commission for the PMAs was positively correlated with project size. Between 2008 and 2016, four PMAs (IL&FS, Hebe & SPA Pvt. Ltd., Darashaw Pvt. Ltd. and SREI Pvt. Ltd.) were assigned the task of preliminary project selection, DPR creation and streamlining the process for the application for bank loans and subsidies. Given that the state was considering a critical policy turn in 2016, the PMA data presented in 2016 is an indication of the quantity, quality and diversification in projects related to food processing. Table 5.2 reveals that the policy did result in starting a large number of projects at a significant cost to the GoB. IL&FS, the largest functioning PMA in food processing in Bihar, had worked out 324 Detailed Project Reports (DPRs) till February 2016 with a success rate of 91%.14 This PMA noted that the total mobilization of investment in food processing was to the tune of 14 IL&FS had the longest experience among the other recognized PMAs in Bihar, having entered the state in March 2007. Of the 169 rice-milling proposals, 117 have had their DPRs prepared by this PMA. Note here that IL&FS has recently been hit with a huge debt crisis in recent times: see the 2018 news article https://economictimes.indiatimes.com/industry/banking/finance/banking/everythingabout-the-IL&FS-crisis-that-has-india-in-panic-mode/articleshow/66026024.cms. However, during the 2006–2008 time period, this was the largest infrastructure financing company in India.

5.3 Evaluating Policy Effects for Food Processing in Bihar Table 5.2 Distribution of projects across PMAs in Bihar, 2016 PMA Rice MaizeMilkRABC Others based based

IL&FS

117 (29%) 6 (21%)

23 (8%)

Hebe & 5 (26%) Spa Darashaw 26 (59%) 6 (15%) SREI 24 (74%) 7 (13%)

129

Total(Rs.cr.)

GoB sanctioned subsidy(INR crore)

10 (3.23%) 2 (31%)

46 (12%) 102 (67.77%) – (-) 11 (22%)

298 (3492)

680

24 (394.1)

58

3 (3%) – (-)

4 (7%) 2 (6%)

45 (395) 36 (366)

95 84

6 (16%) 5 (7%)

Source PMA data sourced from Dept. of Industries, GoB

1800 crore in 2016,15 which gives a decent multiplier of 4.6 to the investment through grants made by the government in this sector.16 Within this, the private stakeholder’s equity burden is approximately 599 crore INR, which is slightly less than double the government’s direct contribution to this sector.17 Banks, through long-term loans, had invested double the size of government grants, as per IL&FS data (February 2016).18 However, the 2011 policy does not seem to have encouraged the expansion of small units into medium-sized units. Out of 24 units handled by one of the PMAs, for instance, only one was in expansion mode. Second, the power of the incentives has failed to attract significant investment from outside the state. Again, there is a paucity of data to make exact calculations. In our primary survey data of 2016, only one unit (which is the largest pulses processing plant in the eastern part of India with two separate plants located in Gaya) have continued their operations without availing the subsidy scheme accorded to food processing units. All other units have tried to access subsidies (either in three tranches or four tranches) and are at various levels of disbursal. Even the large ITC dairy initiative at Munger drew on a 5 crore INR subsidy against its project cost of 120 crore INR. Assuming that attracting mid- to large-sized investments and fostering the growth of small firms in food processing was priority (correcting for the skew in the size distribution), the precursors to the present policy (2016) seems to have failed to achieve their objectives. This is a surprising outcome as the comprehensive subsidy coverage 15 Presentation

by the IL&FS to the Department of Industries, February 2016.

16 IL&FS noted that 340 crore INR have been released as grants to food processing as opposed to 390

crore INR reported in the Economic Survey (2015–2016). Using the IL&FS figure, the multiplier to government investment would increase to 5.3. However, if the total subsidy burden is in excess of 3000 crore INR on an incoming investment of 4500 crore INR (as mentioned in the Hindustan Times 14 July 2016 news), then the multiplier crashes to 1.5. 17 Presentation by the IL&FS to the Department of Industries, February 2016. 18 ibid.

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of the 2011 policy received an unanimous thumping support from entrepreneurs in our survey. The BIA members expressed their concern that a policy ‘which was probably most industry-friendly’ could not encourage desirable entrepreneurship (in the form of entry of medium and large projects domestically and from outside). Informally, some entrepreneurs have opined that many non-entrepreneurs took advantage of the front-loaded capital subsidies (with no intention of carrying their business forward). In fact, at the district level, the percentage of such entry was quoted to be as large as 60%. This is not a surprising development, given the strong connection of the twin asymmetric information problems of adverse selection as well as a moral hazard associated with front-loaded capital subsidies. A possible explanation for the 2016 policy correction is presumably to remove this perverse incentive with front-loaded subsidies. This analysis is rudimentary and does not comment on the relative policy performance in Bihar in comparison with other states. As we mentioned earlier, there is a strong inter-state rivalry in policy to attract investments, particularly in the less industrialized states of India. Just when Bihar was about to step out of special sectoral targeting of food processing, Jharkhand entered this policy regime in 2015 with a special policy for food processing. The policy19 is very similar in content with the 2008 food processing policy in Bihar. How do we account for this inter-state policy rivalry in food processing? The following section develops on this.

5.3.1.1

Project-Level Analysis: Comparative Performance Across States

We now approach the problem from a different unit of account that allows a crossstate comparison for manufacturing in food processing. We see the problem now at the project level: announcement, implementation and cost of new food processing projects for different states. Consider announcements first. Using data from the CapEx CMIE database,20 we compare the time series of announcements of food projects from pre-2000 to 2017–2018 in Fig. 5.2. Note that these are announcements of new projects, which might or might not result in actual functioning projects in a state. Nonetheless, as we view policy incentives as signals by a subnational government regarding the preferred direction of investments, we are checking the response from all investors over a long time series across states to see the pattern of intended investments. What comes out from this picture is the clear cyclicality of announcements: the moment a state announces an IP promising incentives, announcements jump up and taper down in the expectation of a new announcement typically in fiveyear cycles. Despite this cyclicality, West Bengal, neighbouring Bihar, outperforms 19 Further details are at http://momentumjharkhand.com/wp-content/uploads/2016/08/JharkhandFood-Processing-Policy-2015.pdf. 20 CapEx is a database maintained by the CMIE (Centre for Monitoring of Indian Economy Pvt. Ltd.), which tracks capacity addition through projects in companies across India, with disaggregation at the state and district levels. The definition of food projects in the CapEx data follows the classification given in NIC 2008.

5.3 Evaluating Policy Effects for Food Processing in Bihar

131

Fig. 5.2 Announcement of investments in food projects state-wise (Author’s creation based on CapEx data)

Jharkhand, Bihar and Andhra Pradesh in terms of its ability to attract new investments in food processing over this period. Note that the series for Bihar and Jharkhand track each other and are overlapping. Let us concentrate on the period 2008–2015, when Bihar was the state with a special incentive policy targeting food processing. West Bengal had an IP, but nothing comparable in food processing. The question we ask is, how did this policy perform in terms of attracting new investments (through announced and new projects) relative to West Bengal during this period? We appeal to the Difference-In-Differences (DID) estimate to answer this question using CapEx data. Our method is to use two time periods: regime 1 (2008–2015) and regime 2 (2015–2018) and compare new announcements in food processing across two regions: Bihar and West Bengal. The mean difference in the number of announced food processing projects across states and regimes is the DID estimate of the 2008-food processing policy in Bihar. Note that a crucial assumption for applying this technique is the common trends assumption, which states that the two regions must have similar trends prior to 2008. We find that this holds in our data. The DID regression equation 5.1 is estimated and the results are summarized in Table 5.3. ykt = δ0 + αk + βt + ψ(timet × tr eatedk ) + γ xkt + εkt

(5.1)

where, the dependent variable ykt is the estimated value of announced (and new) projects in food processing in region k = Bihar, West Bengal and time period t = either regime 1 or regime 2. αk is region (treatment) dummy and takes value 1 for Bihar (and 0 for West Bengal), whereas βt is a time dummy taking value 1 for regime 1 (and 0 for regime 2). The DID estimate of policy effect on announcements of food projects is ψ, which is the interaction term of the treatment and the time dummies. This essentially controls for Bihar in the special policy regime of 2008–2015. We also control for state-level GSDP captured through xkt . Table 5.3 shows that this

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5 Food Processing in Bihar: Industrial Ecosystem

Table 5.3 Difference-in-difference estimation for announced food projects in Bihar and West Bengal, 2008–2015 and 2015–2018 Linear regression Number of obs = 19 F(4, 18) = 11.47 Prob > F = 0.0001 Root MSE = 6896.5 Announced Coefficient Robust Std. t P > |t| 95% Conf. interval project Err. Time Treated Did GSDP Constant

14374.67 12111.35 −8210.135 0.0498915 −22263.75

4566.199 5598.941 4096.363 0.0159936 11006.08

3.15 2.16 −2.00 3.12 −2.02

0.006 0.044 0.060 0.006 0.058

4781.437 348.408 −16816.27 0.0162901 −45386.68

23967.89 23874.28 396.004 0.0834928 859.1746

Source Author’s own calculations based on CapEx data Table 5.4 Difference-in-difference estimation for new food projects in Bihar and West Bengal, 2008–2015 and 2015–2018 Linear regression Number of obs = 20 F(4, 19) = 8.63 Prob > F = 0.0004 R-squared = 0.3881 New project Coefficient Robust Std. t P > |t| 95% Conf. interval Err. Time Treated Did GSDP Constant

–409.7749 –6827.685 2574.185 0.0056555 7638.708

3345.531 3713.695 3503.971 0.0091227 6302.107

−0.12 −1.84 0.73 0.62 1.21

0.904 0.082 0.472 0.543 0.240

–7412.051 –14600.54 –4759.71 –0.0134385 –5551.753

6592.501 945.1685 9908.081 0.0247494 20829.17

Source Author’s calculations based on CapEx data

estimate of ψ is negative, and significant, but only at 6%. The negative sign is a cause for worry: despite special schemes favouring food processing, West Bengal was attracting more food projects without any food sector-specific policy in place during regime 1. A counter to this pessimistic outcome for policy is that this is merely the effect on policy announcements. What about the arrival of new projects on the ground in the two states over these two time periods? Table 5.4 replicates the earlier exercise now for new projects in food processing using CapEx data. Though the sign of the DID estimate changes from negative to positive, the effect is statistically insignificant. In terms of inter-state competition, front-loaded capital subsidies as well as other measures targeted at food processing in Bihar did start a process of inflow of investments, but in comparison with other states, we cannot identify any significant positive policy effect in the number of new projects in food processing in Bihar. While we find an inflow of new projects into food processing in Bihar, we have not disaggregated them by ownership. This is necessary to tie up this discussion with our

5.3 Evaluating Policy Effects for Food Processing in Bihar

133

Table 5.5 Aggregate estimates of completed food projects in Bihar by ownership (INR million) Ownership

2008– 2009

2010– 2011

Government

6000.00

Central Government

6000.00

2011– 2012

2012– 2013

2013– 2014

2014– 2015

2015– 2016

2016– 2017

2017– 2018

120.00

Government State

120.00

Private Sector

720

550

400

469

3,923.50 2,250.00 3400.00 250

Indian Private Sector

720

550

400

469

3923.50 750

Foreign Private Sector All owners

3400.00 250

1,500.00 720

6000.00 550

400

469

3923.50 2370.00 3400.00 250

Source CMIE CapEx data on investment projects Table 5.6 Difference in new project costs in Bihar versus Jharkhand in regimes 1 and 2 Period Bihar Jharkhand Difference 2008–2015 (FP in Bihar alone) 2015–2018 (FP in Jharkhand alone) Difference

4460.27

1400

3060.27

2565.43

14,103.20

–11,537.77

1894.84

–12,703.2

Source Author’s calculations based on CapEx data

later classification of types of new entrepreneurs from our 2016 survey in Bihar. We mention in Chap. 7 that the policy could not target effective entrepreneurship. The proprietors of these new projects, 76 of whom we interviewed through our survey in 2016, largely saw the 2008 policy as the reason for entering Bihar. Such high dependence indicates that any delays in subsidy disbursal would have negative effects on the profitability of the business. That significant outside investment did not come in is also seen from Table 5.5. It was the Indian private sector and as Chap. 7 finds, private businesses from among those who were domiciled in Bihar. It was not foreign private investments which pumped in investments into Bihar’s food processing. We also compare total project cost in food projects in Bihar and Jharkhand across regime 1 (2008–2015) and regime 2 (2015–2018) in Table 5.6. As mentioned earlier, there was a special food processing policy in Bihar alone in regime 1, whereas this occurs in Jharkhand in regime 2 and not in Bihar. While regime 1 saw larger investments (measured through total project cost in food processing) in Bihar relative to Jharkhand, there is a sharp decline in regime 2 in Bihar. This result shows the sharp interstate competition between Bihar and Jharkhand. However, Jharkhand has an upper hand in this. With a similar food processing policy, Jharkhand attracted a much larger scale of investments, as captured by project cost in processed foods, than Bihar in regime 2. This reveals the limited leverage of the government’s incentives in Bihar relative to Jharkhand.

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Incentives targeting food processing in Bihar, therefore, run into this difficulty: without it, investments do not take off in the state, whereas with the policy, the relative performance of industrial outcomes is not better than those of neighbouring states. In fact, the withdrawal of the incentive scheme in Bihar is associated with declines in investment as per the results in Table 5.6. Note here that the time period of regime 2 is much shorter than regime 1. Hence, whether the change in policy will reverse outcomes for Bihar is difficult to conclude at this stage. Nonetheless, the policy leverage of Jharkhand, which was once a part of Bihar, is much larger than that of the current state of Bihar comes out clearly. Given this overall negative prognosis of the success of the food processing policy, we need to investigate what happened on the ground after the policy was announced, in terms of cross-sectoral industrial outcomes within the state of Bihar itself. Did the policy attract new entry into food processing or did it displace investments from other related sectors in Bihar? We proceed along this line of enquiry next, by changing the unit of account from projects to factories in operation in Bihar during these two regimes.

5.3.2 Sub-sectoral Spillover Effects: Factory as the Unit of Account The food processing policy (2008–2016) was running alongside the Industrial Incentive Schemes from 2006–2011 to 2011–2016. Hence, one concern for the food sector policy is whether it was diverting resources from other sectors into food processing and its related sectors rather than encouraging new investments into the latter. In this case, it will lead to an overestimation of the positive effects of the policy. We now shift the unit of analysis to the factory or manufacturing plant, as a large component of the subsidies were front-loaded capital subsidies based on plant/project cost. To conduct this analysis, we consider the overall agri-business as an industry, of which food processing is a part (discussed in Chap. 2). It is difficult to distinguish policy effects between food processing and agri-business, as they are closely linked with each other. However, it is easier to measure this difference between non-agri and agri-businesses. Therefore, agri-business is the treatment group and non-agribusiness is the control group. Using factory level data from the ASI, we now consider again two different regimes: regime I, which is prior to the food processing policy: (2005–2008) and regime II during the tenure of the policy: (2008–2015). The first regime is with reference to change in government in 2006 and rectification of mal-functioning state regulation, but prior to special incentives given to food processing. The second regime coincides with the special incentives scheme for food processing. Along the lines of the initial analysis in Chaurey (2017), we consider the number of plants in agribusiness vs. non-agri-business in two different regions, Bihar and the rest of India. Hence, we have three dimensions across which we control for the number of manufacturing plants: region, industry (agri vs. non-agri) and regimes.

5.3 Evaluating Policy Effects for Food Processing in Bihar

135

Fig. 5.3 Total number of factories in Bihar and rest of India (Source ASI data)

Our empirical strategy is to conduct a difference-in-difference-in-difference (DDD) estimation for understanding the causal effect of the food processing policy in isolation. Figure 5.3 shows the time series of total number of factories in agriand non-agri-businesses in Bihar and the rest of India. Note that during 2008–2015, no other state had a similar food processing policy, though they all had access to the national-level schemes for this industry (which is true for Bihar as well). With the DDD identification strategy of the effect of food processing policy in Bihar, we consider the following regression equation for our analysis: y jkt = δ0 + αk + βt + γ j + ψ( postt × tr eatedk × tr eated j ) + ε jkt

(5.2)

where k, j and t index region (Bihar and rest of India), industry (agri- and non-agri-) and time regimes (pre-2008 and post 2008), respectively, y jkt is the outcome variable of the log of number of factories, which varies at the state, industry and time regime levels, δ0 represents the average effect on the log of number of plants for non-agri units in regime 1 in the rest of India, αk represents the difference in the average effect on the log of number of plants between Bihar and the rest of India, βt represents the difference in the average effect on the log of number of plants between the two time regimes, γ j represents the difference in the average effect on the log of number of plants between the two industry types and ε jkt is the error term. The DDD estimate of the food processing policy is captured by the coefficient ψ on the interaction term ( postt × tr eatedk × tr eated j ), where  postt =

0 if time t = 2005−2008 (r egime I ) 1 if time t = 2008−2015 (r egime I I ) 

treatk =

1 if region k = Bihar 0 if region k = Rest of India

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5 Food Processing in Bihar: Industrial Ecosystem

Table 5.7 DDD estimation of the effect of food processing policy on agri-business in Bihar Dependent variable: log of number of factories Estimated causal effect ψˆ −0.098 Robust standard errors p-value

0.209 0.641

Source Author’s own calculations based on ASI data summary provided by Barna Ganguli

 treat j =

1 if industry j = Agri-business 0 if industry j = Non-agri-business

We find that the estimated DDD coefficient ψˆ is negative but not significant from Table 5.7. In terms of the number of factories, the estimated average differencein-difference-in-differences estimate is 10,275 factories, i.e. this estimated number of factories is less in Bihar in regime II for agri-business relative to non-agribusinesses in regime I across the two regions. The estimated values of δ0 , αk=Bi har and γ j=Agri-business are 11.35, –4.38 and –0.59, respectively. The negative value of the average number of plants for agri-business in Bihar is in line with our earlier discussion of policy ineffectiveness. Stitching these three lines of investigation together, we conclude that policy leverage in Bihar is very low in comparison to other states. Second, most entrants are small and do not seem to be able to expand in their lines of business, despite access to subsidies. Hence, there is no sign of correction of the missing middle firm size. We, like Gebrewolde and Rockey (2018) for Ethiopia, fail to find a very successful outcome of this kind of targeting of food processing in Bihar. On the other hand, the counterfactual outcome of no targeting seems to be even more bleak, as our comparison with Jharkhand shows. For regions like Ethiopia and Bihar, the version of IP which has either area-based targeting or sectoral targeting runs into constraints of past distortions in the industrial ecosystem and market failure that reduce policy leverage. This brings us back to Yülek (2018)’s contention that developing regions starting on the industrial process must first focus on horizontal aspects of IP and perhaps come to the vertical part of policy later. For the food processing policy to work, other bottlenecks to industrial performance have to be resolved. If fiscal constraints permit, these policies can be simultaneous rather than staggered.

5.4 Policy Networks: Going Beyond IP for Food Processing The policy framework, in a broader sense, is not just about IP. Wright (1988) suggests a policy network perspective for a nuanced comment on industrialization in any country, which goes beyond the evaluation of IPs alone. This literature explicitly accounts for the set of agents involved in the policy formation and delivery processes in the context of institutional structures with which these agents interact. Extensions

5.4 Policy Networks: Going Beyond IP for Food Processing

137

of this methodology, such as Dunn and Perl (1994), Bressers and O’toole (1998), Howlett (2002), have enriched the debate on policy effectiveness and targeting. Given the structural complexity and atypical industrial structure in Bihar, this line of inquiry is most suitable. In what follows, we describe the set of institutions, agents and policies that directly or indirectly affect outcomes and form the policy network structure for the food processing sector in Bihar. We draw many details from the responses of entrepreneurs in our primary survey in Bihar in 2016–2017, which we describe later in Chap. 7.

5.4.1 Policy Networks for Bihar Industries The defining moment for Bihar’s industries was the 2006 Single Window Clearance Act. Coupled with the State Investment Promotion Board (SIPB) as the implementing agency, it outlined the regulatory boundaries and provided a single platform for government approvals for new projects above 1 crore INR. For smaller projects, the single window authority was the District Industrial Centres (DICs) at the district level. The Udyog Mitra (loosely translated as ‘industry buddy for businesses’), an institution that helps industrial units coordinate among several institutions (banks, electricity and land authorities), is another noteworthy development. It helps track, document and facilitate smooth entry and sustained functioning of units in all industrial sectors in the state. Since 2006, the institutions of the SIPB and the Udyog Mitra have to some extent eased the entry of large units, such as Godrej Agrovet or ITC Munger dairy. These benefited from a centralized process of registration and clearances. However, the process of single window clearance was not entirely smooth across all firm sizes. Entrepreneur responses in our 2016 survey revealed that clearance for large projects at the level of the SIPB was functioning much better than that for small projects at the DIC level. The recently launched Udyog Samwad is an online portal designed specifically for entrepreneurs in Bihar. Similarly, the Udyog Adalat has been launched as a platform for entrepreneurs to interact with government officials in the industries department. At present, new project application has an online interface due to these initiatives, reducing the direct contact between investors and the government at the time of initial project approval. This reduces the chances of rent-seeking. Mechanisms which introduce these interfaces alleviate concerns of corruption, as investigated by the World Bank Entrepreneurship Survey (WBES) for multiple countries. However, the last mile of project approval and disbursement of subsidy does not have this transparency, though it is much desired by some entrepreneurs in our survey. Despite gaps in implementation,21 21 Entrepreneurs who have set up units outside Patna (Gaya, for instance) have not expressed a positive opinion about their experiences with some of these institutions which require their physical presence. They have to travel and invest time in representing their business interests at meetings in Patna. Institutions which require face-to-face interactions between individuals create a trade-off in terms of time investment by entrepreneurs: whether or not to focus on their own business operations or to travel to Patna for governmental interactions.

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these institutions have the potential to coordinate efficiently between the two arms of industrialization: the government and individual entrepreneurs.

5.4.2 Other Incentive Policies State-level incentive policies interact with other central government schemes, such as those announced and administered by the Ministry of Food Processing Industries (MoFPI). For instance, our survey revealed that cold storage units faced a choice: either opt for an existing back-loaded central government subsidy scheme or for the front-loaded scheme of the Bihar government in 2008. The latter staggered disbursal of subsidy from the beginning of operations, but the former would only give subsidies upon project completion. Many projects switched from the central scheme to the Bihar government scheme, as it eased the burden of set-up costs for cold storage firms. Similarly, the state government from time to time declared sub-sector-specific policies such as the Maize Silo Policy (July 2014), Modernization of Rice Mills Policy (July 2014), etc. These granular and highly specific vertical policies create imbalances in investments, by creating distortions in incentives from a supply chain point of view. An entrepreneur starting a poultry feed unit in Bihar opined that many operations such as his would like to integrate backwards to commercial layer farming in poultry if it was similarly subsidized as poultry feed. The problem in drawing industry boundaries, as noted in Chap. 2, is that only a part of the entire value chain gets included. In this case, only the maize-based poultry feed sub-sector gets incentivized, as it is a part of the processed food industry but not the rest of the value chain in poultry.

5.4.3 Recent Indirect-Tax Policy Changes: Introduction of GST in India We have mentioned earlier that the presence of a multiplicity of taxes and tariffs is a potential reason for the high cost of processed food in India. In a move to simplify the tax structure, there have been some changes in recent times. The Goods and Service Tax (GST) was introduced in India in July 2017, bringing about a structural transformation in the indirect taxation regime in the country at the national as well as subnational level. Due to its dual structure, this tax is administered by both the national and the subnational governments. Therefore, though the GST removed some multiplicities in existing taxes such as the central excise duty, services tax, additional customs duty, surcharges, state-level value-added tax and Octroi, it has not resulted in a single tax rate applicable to the entire country. Within the geographical confines of a particular state, the national government charges the Central GST (CGST), whereas the state government charges the State GST (SGST). Addition-

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Table 5.8 Appendix: GST rates for processed food products Rate Item Zero Fresh milk, pasteurized milk but not concentrated, sweetened; Curd, lassi, buttermilk; Chena or paneer (except in unit container with brand name); Natural honey (no container-no brand); All cereals (no container-no brand); Cereal grains hulled; Flour: Atta, maida, besan (no container-no brand), wheat or meslin flour, cereal flour, groats and meals (no container-no brand); Dried makhana, whether or not shelled or peeled other than those put up in unit container and, (a) bearing a registered brand name; or (b) bearing a brand name on which an actionable claim or enforceable right in a court of law is available [other than those where any actionable claim or enforceable right in respect of such brand name has been foregone voluntarily] 5% Milk and cream including skimmed milk powder but excluding condensed milk; Yoghurt and other fermented milk and cream; Chena or paneer in unit container and branded; Egg yolk, fresh or dried; Natural honey in branded unit container; Cereal groats, meal and pellets in branded unit container. Cereal grains worked upon (hulled, rolled, flaked); Puffed rice; Sugar makhana, Gajak, Groundnut sweets; Desserts such as Khaja, Khajuli, Anarsa 12% Butter, ghee, butter oil, cheese; Condensed milk; Starches 18% Ice-cream and other edible ice; All preparations of cereals, flour, starch or milk for infant use and sold retail; Corn flakes and other cereal flakes Source Author’s creation using MoFPI data

ally, inter-state transactions attract an Integrated GST (IGST) administered by the national government. While the GST is levied on all transactions such as sale, transfer, purchase, barter, lease, or import of goods and/or services, it has a differential rate structure for different commodities and services. The MoFPI provides a summary of the GST rates applicable to the processed food category, from which we have culled out the rates applicable on items relevant for Bihar’s food processing industry in Table 5.8 in the Appendix. As is evident, different segments of the dairy and cereal industry now attract different rates, depending on the level of value-addition. For instance, in dairy, pasteurized milk now has a zero GST rate, but higher dairy-derivatives such as butter (12%) or ice-cream (18%) attracts higher GST rates. A difference is also made for the packaged variant of the same product in the case of cereals: those without containers and brands attract a zero rate, whereas all such products with retail sales attract an 18% rate. Note here that majority industrial product mix (cereals, mostly loose polished rice and milk) in food processing for Bihar, at present, comes in the zero GST rate category. What are the implications of the GST for expanding operations in the product network in food, from low value-added items to higher value-added ones? Primary processed foods have a low GST rate, as there is a lower value-addition. With its provision of the reimbursements for input cost (through input tax credit), the GST is not supposed to provide any disincentives to the producers in expanding the scope of production from low value-added items to those with a higher value-addition. However, in a state with rigidities in tastes and a strong preference for ‘fresh food’ rather than the processed variant, the sharp jumps in the tax slabs from 0 to 28% are likely to

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enhance incentives to stay within a narrow production basket. Wholesale availability of cereals, without branding and packing, sees a much higher geographical spread of consumption, particularly in the poorer districts of north Bihar. Unpacked makhana (sold loose in wholesale markets), rice by-products like ‘choora’ and ‘muri’, along with other region-specific items like ‘sattu’ (powdered gram flour)22 are sold ubiquitously in the state. Consumption of their packed and branded variants as well as high-value-added products, like bakery items, is limited to the state capital Patna and some other richer districts like Munger. The significantly higher tax slab on the high-value-added products relative to unbranded wholesale is unlikely to provide incentives for expansion through the product network, given existing local demand. Expansion into the export market will be based on the trade-off of additional costs of certification and other standard-compliance and the benefit of export earnings for individual firms. Note also the complicated procedures for filing with the GSTN (GST Network) using an internet connection acts as a disincentive for doing business for many small registered units. The cost of compliance to the GST regime is much smaller for large formal units in food processing than those which are small-scale operations, as is the case with Bihar. For instance, consider the following query posted on the MoFPI website, which is typical of the Bihar rice-milling scenario: 90 per cent of my turnover will be of unbranded rice, while 10 per cent only will be of branded one can I sell both of them in one invoice? Answer: As per invoice rules, a registered person supplying taxable goods is required to issue a tax invoice and in case of exempted goods, he is required to issue a bill of supply. As all the contents of bill of supply are included in the tax invoice, a separate bill of supply need not be issued in case of the exempt component. Thus, both branded and unbranded rice can be included in one invoice.

As is evident from the query, it is not clear how the tax will treat branded and unbranded rice from the published guidelines. Given the low literacy and numeracy levels in the state, filing returns in the format required by the GST has given rise to the importance of the chartered accountant. This implies an additional service cost which diminishes with the scale of operations. Filings have to be done monthly as well as quarterly, which is a time-consuming and cumbersome affair. Therefore, the GST is likely to be a smooth transition for large firms with a diversified consumption base (exports as well as out-of-state sales) than middle-sized or small firms in registered manufacturing of processed food. In the short run, it is unlikely to correct for the skewness in the size distributions for registered firms in Bihar. At present, collections of Bihar through the GST scheme is at a revenue shortfall, along with most other major Indian states with the exception of Andhra Pradesh.23 GST collections are supposed to benefit consumption-heavy states with a significant production base. Bihar’s consumption basket is concentrated in the zero tax-rate 22 These

terms are explained in the Glossary.

23 Press Trust of India news for January 2019 available at https://www.firstpost.com/business/gom-

on-revenue-shortfall-to-study-gst-collection-on-services-by-states-bihar-deputy-cm-sushilmodi-5946541.html.

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bracket of GST and there is a significant lack of production facilities, which possibly explains the current shortfall. With only a two-year existence since 2017, the future of the GST is difficult to predict on the basis of present outcomes.

5.4.4 Land Policies and Institutions The Bihar Industrial Area Development Authority (BIADA) was instituted in 1974. It is present in the four zones of Patna, Darbhanga, Muzaffarpur and Bhagalpur with 20, 10, 8 and 12 Industrial Areas under their respective jurisdiction. The Hajipur industrial area is one of the most successful ones in Bihar.24 The challenges that BIADA faces is reduced land availability for new industrial units, pushing up the lease rates for BIADA land significantly over the years. For instance, in the Patliputra industrial area, the per-acre land rate is above 1000 INR per acre at present. Nearness to the state capital, Patna, is a big attraction for this industrial area. One of the existing ice-cream units in the state, which wants to expand, has expressed the inability to acquire further land at the current rate in Patliputra. According to the entrepreneur, a new unit (if it comes into Patliputra industrial area) at present would find breaking even very hard over even a ten-year horizon. Additionally, the immense rate variation across industrial areas25 shows the regional disparity in industrial infrastructure and demand distribution. Most units would like to locate in and around Patna, which has the most inflated prices of all surrounding industrial areas. For food processing units, this defeats the logic that production facilities should be based near agri-produce centres in rural areas. Here, the pattern is that of consumption-centric location. It is not only a pull of the retail market that skews factory location choice towards Patna and Hajipur. The lack of infrastructure and poor connectivity in far-flung districts also have a role to play in depressing their land rates relative to the capital city. Despite its success, the Hajipur industrial area has seen a significant number of factory closures: during our survey in 2016, around 40% of the units were nonfunctional. What requires immediate attention is the drainage around units, as was observed not only by us during our field visit, but one of the rice mill owners with operations in Hajipur. This entrepreneur was less excited by government subsidies than steps to remedy the drainage issue in the industrial area by the authorities. The quality of roads and overflowing waste water from units leave a lot of scope for improvement. Effluent treatment plants (ETPs) are not installed in most units other than the government-owned dairy unit of COMFED at Hajipur. Untreated waste water pooling up outside the unit is not only a health hazard, it also raises concerns

24 One of the entrepreneurs have favourably compared Hajipur against other industrial areas such as Moradabad at Uttar Pradesh. 25 The BIADA website data shows that the per-acre rate for Aurangabad is only 30 INR, whereas it is a whopping 1105 INR for the Patliputra industrial area.

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about the potential effect of this kind of pollution on the processed edibles coming out of the factory. The allocation of land for food parks has been subject to delays resulting in Bihar falling behind neighbouring states like West Bengal, as discussed earlier. As food parks allow the effective exploitation of economies of scope in production among various sub-sectors, the lack of functional food parks reduces Bihar’s chances of being recognized as the leading state in food processing. Among recent measures, one is the Private Industrial Park Scheme (announced in 2014), whereby landowners collectively owning more than 25 acres land can set up private industrial areas through special purpose vehicles (SPVs) registered under the Companies Act/Society Registered Act. This is likely to ease some pressure on industrial land from BIADA. The Aao Bihar scheme also aims to create a pool of potential land bank for industries, by incentivizing landowners with more than two acres of land to sell to the government. Problems with land are interconnected with industrial credit. A PMA dealing with project sizes less than one crore INR mentioned that the steep conversion rates of agricultural land for industrial purposes (under the Bihar Land Conversion Act, 2010) has been a challenge for small units in starting a business in Bihar. Note that successive IPs in the state have provided 100% reimbursement of land conversion fees. However, this reimbursement comes after the unit commences commercial production. Without ready finance forthcoming to accommodate land conversion charges, this becomes a large entry cost for small businesses.

5.4.5 Industrial Credit Institutions One of the main institutional challenges for Bihar is the situation with respect to institutional finance. Small and micro units have a dedicated MSME division set up by the central government (Ministry of Micro, Small and Medium Enterprises), which also has offices at the state level. Nonetheless, tiny units, which have very low creditworthiness, negligible collateral and high degrees of risk aversion among entrepreneurs, struggle to access institutional credit, as our 2016 survey revealed. The BICICO (Bihar State Credit and Investment Corporation) has been non-functional and there is no new development to take up the role of institutional credit provision in this vacuum. Larger firms in formal registered manufacturing also do not have easy access to credit. The typical risk exposure of firms in the formal sector in food processing is around 65% equity and 35% debt, according to a member of the BIA. This large dependence on internal financing reduces the scale of operations and hampers the efficient functioning of units to a large extent. PMA interviews in 2016 also revealed another dimension to the credit problem. The project size in the technical document for a starting business, the Detailed Project Report (DPR) as drawn up by the PMA, is almost always downgraded to a smaller size by the bank to reduce its risk exposure. This reduction in size becomes a problem for accessing working capital (which uses the size of stock as collateral) for units that later want to expand the business. Smaller the initial size, smaller will be the existing

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stock, and therefore lower will be the amount of working capital credit granted by a bank. To ensure accountability, the State Level Banking Committees (SLBCs) have been assigning a lead bank for each industrial sector. The public sector bank State Bank of India (SBI) has fulfilled this role for food processing for many districts, followed by the Punjab National Bank (PNB), another public sector bank. Our survey in 2016 did not reveal any unanimity in responses in terms of the banking experience of entrepreneurs. While some blame corrupt officials and inefficiency in the banking system as the sole bane, many have had a very smooth interface with banks such as the SBI and PNB for accessing term as well as working capital loans. Typically, the older and larger enterprises have had less trouble with accessing finance than new businesses. Existing relationship with a bank has been a reputation capital for some long-running units in Bihar. The typical term loan rate has been around 8–9%, with some units reporting an exorbitant 14%. We found similar rates for working capital loans.

5.5 Electricity Infrastructure The World Bank Enterprise Survey of 2014 has noted that one of the biggest strides that Bihar has made in recent years is in the availability of electricity. However, there are quality issues for industrial use. While most entrepreneurs in our 2016 survey have opined that availability of industrial power has improved significantly over the years, quality of supply and delays in getting connections as well as the removal of electric poles from industrial land has been a challenge for setting up industrial units.26 The quality of electricity supply has a large variation at the district level, with better delivery in and around the state capital of Patna. This feature is common to most urban infrastructure and not peculiar to electricity alone.

5.6 Targeting of Policies and Design of Institutions for Food Processing The policy network consists of all actors (agents and institutions) involved in policymaking, implementation and delivery of outcomes. However, there are two distinct categories of agents: government agents (mostly represented by officials from the Industries Department and sector-specific departments such as land, electricity, etc. as well as government firms) as opposed to private agents (unregistered as well as registered units with various kinds of incorporation, business lobbies/chambers of commerce, private consultants and specialists). The interacting web of these agents 26 The

120 crore INR impressive dairy enterprise of ITC at Munger faced this issue of electricity pole removal from its allotted industrial land, leading to a two-month delay in the start of operations. The same problem has beset two other units in our survey.

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determine the effectiveness and the direction of Industrial Policies (IPs): for instance, the business lobbies try to bargain for weak labour laws and easier norms for environmental regulation, which is balanced by the government agents through the policy contract. While discussing policy targeting and institution design, Howlett (2002) brings up the issue of network cohesiveness. This relates to the extent to which various network agents share a common goal. The typical network structure for Bihar would be that of a star-shape, with the government at the centre of the network. By its very nature, the government agencies are at the centre of the star, with each of the private actors connected directly with it through their requirement of government clearances and subsidies. The higher the connections (which can happen through virtual interfaces as well as face-to-face meetings) and higher the ‘similarity’27 among nodes connected with the star in the centre of the network, the higher is the likelihood of increased network cohesiveness. This leads to a common perception of the government’s agenda across peripheral nodes (which are the individual firms). There are various degrees of connections between the private actors, through lateral or vertical supply chain interlinkages. If agents have a link with each other (membership association/direct or indirect business deals), then they are connected with each other, even if they are heterogeneous in character. Saha (2017) notes compares the degree of cohesiveness in the policy networks for Jharkhand and Bihar. Cohesiveness is much higher in the former than in the latter network. We have noted the deep segmentation between small and large firms in Bihar. This heterogeneity along with the missing middle size results in no interconnections between the peripheral nodes. The private firms have different aspirations, with large variations between the small and the large-sized firms. There is no shared objective in the network in Bihar. In contrast, Jharkhand has a more homogeneous set of mineral-based industries. Note, however, that increased cohesiveness among the entrepreneurs raises concerns for collusion and cartelization of the industry. However, with a concentration at the top-end of the firm size distribution and fringe competition among the small firms, the industrial landscape in food processing in Bihar is anyways far from the competitive ideal. The values of the large size firms dominate results, as we see in our efficiency analysis in Chap. 6. Lack of cohesiveness in the policy network makes the targeting of policy very difficult. Expectations from the government differ widely between firms of different sizes. Government policy in Bihar has to ensure that it provides incentives to both the large as well as capital infusions for the small firms. Most of the large units do not require financial aid, but depend on the state to provide enabling infrastructural conditions. In our survey in 2016, we found that units with sizes less than one crore INR were desirous of capital subsidies while units above 100 crore INR in size demanded urbanization, better infrastructure and law and order. Bihar has taken some steps towards encouraging industrial density in the state. And yet, the comparative outcomes relative to states like West Bengal are poor. 27 This type of similarity is possible if entrepreneurs are in a similar line of business or are connected with each other through co-processing and credit linkages.

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Despite some positive responses regarding the conditions of the Hajipur Industrial Area, there are issues of drainage around industrial units that need to be addressed. Small hindrances like these add up to an overall impression of a state that is yet to benefit from resource advantages in food processing. In terms of institutional design in Bihar, multiple cross-checks for accessing subsidies from the government: starting with the PMA, to the SIPB meetings right up to the approval of the chief minister, lead to unnecessary delays. The changes in the 2016 IP have reduced this, as firms are now entitled to an interest subsidy on loans alone and food processing units are treated at par with other enterprises. Nonetheless, given that large units have more debt than smaller units, this strategy incentivizes only the former. The latter in our data were looking forward to capital subsidies, which have been removed. Given low cohesiveness among firms in the overall network for Bihar, as discussed in Saha (2017), the evolution of a ‘common business culture’ and unanimous requirements of government support is unlikely in the near future.

5.6.1 Green Industrial Policy The government in Bihar also needs to work proactively towards a Green Industrial Policy that we mentioned in Chap. 2. While successive policies have focused on solar power and provided incentives for its adoption, water conservation and recycling has not received adequate attention. In fact, the outlook is that the state has an abundance of water resources, and that water is not a constraint. Consider this description from the Industrial Policy (IP) document for 2016: The state has a total water area of 10,71,000 ha or 26,77,000 acres. This includes ponds (95,000 ha), oxbow lake (9,000 ha), reservoirs (26,000 ha) and wetland (9.41 lakh hectares). The total river length in the state is approximately 3,200 km. The huge water wealth of the state presents a a gainful opportunity for fresh fish processing facilities/units...

With the intensity of water usage in food processing units, water is likely to become an environmental constraint in the growth of the industry unless proper steps are taken for its management. As we mentioned earlier, most units in the Hajipur Industrial Area do not have ETPs. Small industrial units do not want to incur the additional costs of establishing water treatment plants, though flooding of factory surroundings with untreated water poses a challenge for these establishments. Environmental pollution due to untreated factory water is an externality as of now. A comprehensive framework, including all environmental resources, has been suggested by Rodrik (2014). We also prescribe a holistic framework, not a piecemeal focus separately for solar energy and water. Additionally, the government of Bihar has to retain a balance between both horizontal and vertical arms of the policy to attract investments into the state. While the former is necessary for creating a level playing field among different-sized units, the latter is essential so that Bihar does not lose out in the competition between states in attracting industrial investments. This is

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evident from our comparison of the food processing policies in Bihar and Jharkhand in this chapter. Acknowledgements I would like to thank the participants of the Blended Finance and Industrial Policy Conference at IHEID, Geneva held in November 2018 for their inputs, particularly Mark Plant of Centre for Global Development, USA and Murat A. Yülek of the Istanbul Commerce University, Turkey. Discussions with officials at the Udyog Mitra and the Department of Industries in 2016 enriched my understanding of government policy towards industries in Bihar.

References Angrist JD, Pischke J-S (2015) Mastering metrics: the path from cause to effect. Princeton University Press, Princeton Bianchi P, Labory S (2006) International handbook on industrial policy. Edward Elgar, Cheltenham Bondonio D, Greenbaum RT (2007) Do local tax incentives affect economic growth? What mean impacts miss in the analysis of enterprise zone policies. Reg Sci Urban Econ 37(1):121–136 Bressers HTA, O’toole LJ Jr (1998) The selection of policy instruments: a network-based perspective. J Public Policy 18(3):213–239 Busso M, Gregory J, Kline P (2013) Assessing the incidence and efficiency of a prominent place based policy. Am Econ Rev 103(2):897–947 Chaurey R (2017) Location-based tax incentives: evidence from India. J Public Econ 156:101–120 Connor JM, Heien D, Kinsey J, Wills R (1985) Economic forces shaping the food-processing industry. Am J Agric Econ 67(5):1136–1142 Criscuolo C, Martin R, Overman H, Reenen J (2012) The causal effects of an industrial policy. Working Paper 17842, National Bureau of Economic Research Desai BM, Namboodiri NV (1992) Development of food processing industries. Econ Polit Wkly 27(13):A37–A42 Dunn JA Jr, Perl A (1994) Policy networks and industrial revitalization: high speed rail initiatives in France and Germany. J Public Policy 14(3):311–343 Forbes N (2001) Doing business in India: what has liberalization changed? Stanford Center for International Development Working Paper No. 93. http://kingcenter.stanford.edu/sites/default/ files/publications/93wp.pdf Gebrewolde TM, Rockey J (2018) The effectiveness of industrial policy in developing countries: causal evidence from Ethiopian manufacturing firms. University of Leicester Working Paper No. 16/07. https://www.le.ac.uk/economics/research/RePEc/lec/leecon/dp16-07.pdf Gobillon L, Magnac T, Selod H (2012) Do unemployed workers benefit from enterprise zones? The French experience. J Public Econ 96(9–10):881–892 Greenbaum RT, Engberg JB (2004) The impact of state enterprise zones on urban manufacturing establishments. J Policy Anal Manag 23:315–339 Hirsch S, Schiefer J, Gschwandtner A, Hartmann M (2014) The determinants of firm profitability differences in EU food processing. J Agric Econ 65:703–721 Howlett M (2002) Do networks matter? Linking policy network structure to policy outcomes: evidence from four Canadian policy sectors 1990–2000. Can J Polit Sci 35(2):235–267 Kline P, Moretti E (2013) Place based policies with unemployment. Am Econ Rev 103(3):238–243 Neumark D, Kolko J (2010) Do enterprise zones create jobs? Evidence from California’s enterprise zone program. J Urban Econ 68(1):1–19 Rodrik D (2008) Normalizing industrial policy. http://j.mp/2o6K6Ye Rodrik D (2014) Green industrial policy. Oxf Rev Econ Policy 30(3):469–491

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Saha D (2017) Bihar and Jharkhand: shared history to shared vision. In: Conference paper presented at the ADRI conference ‘Bihar and Jharkhand: shared history to shared vision’ in 24–28 March 2017 in Patna. Forthcoming as a chapter in the conference volume. Ane Publishers Sutton J (2007) Sunk costs and market structure: price competition, advertising and the evolution of concentration. The MIT Press, Cambridge Sutton J, Kellow N (2011) An enterprise map of Ethiopia. International Growth Centre, London, UK. ISBN 9781907994005. http://eprints.lse.ac.uk/36390/ Wright M (1988) Policy community, policy network and comparative industrial policies. Polit Stud 36(4):593–612 Yülek MA (2018) How nations succeed: manufacturing, trade, industrial policy and economic development. Palgrave McMillan, London Zouaghi F, Sanchez-Garcia M, Hirsch S (2017) What drives firm profitability? A multilevel approach to the Spanish agri-food sector. Span J Agric Res 15(3):e0117. http://revistas.inia.es/index.php/ sjar/article/view/10713. ISSN 2171-9292

Chapter 6

Food Processing in Bihar: Efficiency in Physical Costs

6.1 Introduction Firm performance in food processing depends both on firm-level characteristics as well as the industrial ecosystem or policy network in which the firm is embedded. For processed foods, there is some literature dealing with business strategies of individual firms regarding product quality, diversity (Sutton (2007)), innovation (Vyas (2015)) etc., for developed countries. Our narrative of the food processing industry investigates this issue for an industrially backward region: that of Bihar. In the previous chapter, we investigated the role of policy interventions and the policy network on outcomes at the industrial level. To control for the effect of policy (multiple such policies were implemented in a short span of time between 2006 and 2008), we started with a before–after type of analysis: what was the firm and industry performance in food processing in Bihar prior to 2006, between 2006 and 2008 and post 2008. However, there is not much to comment on prior to 2008, as there was hardly any processing activity before this year. It is 2008 onward that we have a slew of new entry and enhanced private interests in food processing in Bihar. Policy effects are integrated into the entry and functioning of firms and should be identified for policy targeting. While we do conduct such an exercise through our difference-in-difference estimations in the previous chapter, we are also alert to a number of problems in conducting them. Nonetheless, we get a rough picture of the overall food processing scenario in Bihar, but not at the level of an individual firm. When it comes to an individual firm’s performance, we have to take into account firm-specific characteristics. Our discussion of performance is in terms of a firm’s ability to earn positive net profits, without which the manufacturing unit becomes financially unviable. Hence, what is important is the management of costs in operation. We discuss, in this chapter, the management of a class of physical costs for firms in processed food. The term physical has been assigned to them as these are readily measurable. The following chapter extends the discussion to another set of costs, which we term non-physical. Among physical costs, first is the set of fixed costs for starting a manufacturing unit. This was being subsidized for food processing © Springer Nature Singapore Pte Ltd. 2020 D. Saha, Economics of the Food Processing Industry, Themes in Economics, https://doi.org/10.1007/978-981-13-8554-4_6

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units through a targeted state policy from 2008 to 2016, as discussed in the previous chapter. However, the stylized fact S5. in Chap. 3 mentions the importance of managing variable costs, due to the high raw material intensity in this industry. Therefore, working capital cost management becomes crucial for the financial viability of a firm. Apart from that, the marketing of the final product, due to varying tastes and preferences of consumers, require large branding and advertising costs. We discuss this through the stylized fact S2. Note that Sutton (2007) mentions that these marketing costs can be modelled as sunk costs, which determine the nature of competition among firms in the food processing industry. We model marketing costs as a part of physical costs that is undertaken in the second stage of a two-stage production process, where the first stage is about production efficiency. While there are scale effects in the second stage marketing costs, there are none in the first stage technical costs of production. Note that the size of the firm as well as ownership structure matters in the management of these costs. Achievement of a minimum efficient scale (m.e.s.) is necessary for firms whose scale of entry is below that m.e.s. Small-scale entry, in itself, is not a problem, unless scaling up is fraught with institutional difficulties, like the lack of access to working capital. Certain sub-sectors in Trajectory 1 such as RTE breakfast cereals like muesli, are marked by small-scale entry (Sutton 2007). In fact, the latter points out that successful entry in food processing happens in sub-sectors where achieving the scale of operations is easy. This holds for those product networks where setup costs are low and new entrants can scale up quickly. More generally, for food processing, there is no clear empirical regularity that links enhanced profitability with firm size, with many examples of higher profits being achieved by small-sized firms in some sub-sectors.1 In particular, if economies of scale are not very large, then small-scale operations can be efficient. For the India trajectory, we presented some examples of successful small firm entry, such as Lijjat Papad. However, there are some caveats: typically these units coalesce together through a co-operative, as was the case with Lijjat or the Anand dairy initiative in Gujarat. The best example for this is Bihar’s own COMFED (Bihar State Milk Co-operative Federation Ltd.), the state-owned dairy unit that is present in multiple states and is one of Bihar’s spectacular successes in food processing. The difference between co-operatives and investor-owned firms, which is the structure of the typical private limited incorporated firm, brings up the issue of ownership and its effect on the financial viability of firms in processed food. An early influential study by Lerman and Parliament (1990) for F&V processing and dairy in the US (1986–1987) showed that co-operatives performed at par or better than investor-owned firms in terms of financial performance (measured by profitability, leverage and interest coverage). This, the paper points, is contrary to theory, which expects profit-maximizing entities like privately incorporated firms to outperform co-operative structures. However, we cannot ignore the necessity of aggregating agriresources from individual farmers in the input supply chain to achieve a minimum 1 Chapter 3 briefly discusses the effect of firm size and successful entry into the food processing industry.

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scale for processing (as discussed in our first stylized fact S1. in Chap. 3). Farmer co-operatives are likely to be able to do this aggregation better than the investorowned firm, as the latter requires significant subcontracting efficiency between the buyer and multiple farmer-sellers. What our theory indicates is that mid-sized firms mimick the aggregation behaviour of co-operatives and try to achieve profitability by concentrating on scale-intensive marketing and branding activities. Even if we keep the issue of ownership structure aside, achievement of an m.e.s. is important for the small-scale entrant for its survival. The first few years of existence for a firm is fraught with uncertainty. Debt repayments along with a lack of experience with the market, and therefore, the absence of a loyal clientele adds to business risks. Most of the literature on firm exit show how fragile new entrants are: most industrial sectors experiencing new entry also experience simultaneous exit (see Dunne et al. (1988); Eriksson (1984)). Establishing a market existence is not easy for any of the trajectories we have discussed. Take the case of Unilever’s entry to the French ice-cream segment. Sutton (2007) mentions the failure of Unilever in this market in 1964, and it was not until 1970 that the firm tried to re-enter the market. Later, the firm expanded in the French ice-cream market by acquiring Motta in the 1980s. This is all the more reason for small firms to focus on less scale-intensive technical production and not marketing from the beginning. As far as ownership is concerned in the Bihar trajectory, we have pointed out in the previous Chap. 5 that units in food processing are mostly proprietorship/partnerships operating in a very narrow product space of mostly rice milling rice milling and in dairy. Interestingly for Bihar, there were very few such co-operative firm structures among the entrants post 2006–2008, the concentration of ownership structures being proprietorships or partnerships. Regarding entry size, there is a dominance of smallsized firms. That entry size is typically small has been documented for many other trajectories. Gebrewolde and Rockey (2018) finds this for Ethiopia. Hence, this is nothing exceptional for Bihar. What is unique in the Bihar trajectory is the channel through which small firms face survival issues. We claim that it is the absence of the middle size firms resulting in deep segmentation between small and large firms which creates a problem for the former. As noted in the previous Chap. 5, the difference between the large and the small firms is extremely high in all dimensions of operations in Bihar’s food processing. There are no interlinkages between the large and small units. The former are fully integrated into the supply chain. The latter have three options: either to engage in costly marketing of own-brand sales2 or to sell as unbranded products in the wholesale segment or co-process for middle-sized firms. Note here that we are not including preferential sales to the government as an option for entry, as this is a part of government policy and is unlikely to work as a general entry strategy in an industrially backward area. If this preferential purchase policy is present in the region 2 In

that case, we claim middle-sized firms would buy-in the product from small units through subcontracts/industrial sales, which would reduce the marketing costs for small firms. These costs are treated as very large fixed or sunk costs by Sutton (2007). We, on the other hand, assume that these are substantial costs in the second stage of a two-stage production process.

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and there is complete information and capital access to be a seller to the government, then only can we integrate this into the set of selling strategies of the entrant. For this analysis, we keep this aside, though we do mention later that incumbents engage in selling to the government in Bihar. This has its own issues, such as low bargaining power relative to the government in pricing matters and its attractiveness is uncertain. See our discussion on SFC (custom-milled sales to the government) versus non-SFC (open market) rice mills in Bihar in this chapter for more details. The unbranded wholesale option yields very low, often negative returns relative to input costs. The first option comes with the added costs of marketing. Coprocessing for mid-sized firms without entering into direct competition with larger firms, according to our analysis, would be a viable strategy for the small entrant, under some assumptions. Mid-sized firms would market the product of small firms through own-branded retail, incurring additional fixed costs of marketing. In the absence of a middle size of firms, this low-cost entry strategy is also ‘missing’ for the small entrant. We label this co-processing strategy for new entrants as the benchmark scenario. Unlike the existing literature, which investigates causes for the missing middle size of firms, this line of reasoning discusses its impact on the performance of smallscale entrants in food processing. We assume explicitly imperfect financial markets restricting credit access to small and middle-sized firms. This constraint is not likely to loosen unless the firm becomes large and can engage in all the parts of the supply chain: from production to marketing. This analysis provides a potential explanation of the inability of policy to thicken the density of the middle size of firm distribution, as discussed in the previous Chap. 5.

6.2 Theory: Role of the Missing Middle, Physical Costs and Firm Performance There are two possibilities contributing to a missing middle: (i) middle-sized firms cannot sustain themselves in competition, and/or (ii) small firms do not expand in size. In the industry of food processing, imperfections in the financial market can give rise to either or both these possibilities. Consider working capital access first. Stylized fact S5. indicates that there is an intensive requirement for it in this industry. Difficulties in cash-flow management and problems in accessing working capital can give rise to both these situations, albeit in different degrees. If access to working capital is conditioned upon the collateral of the existing stock in operations, then it should affect small-sized firms more than larger sizes of firms. On the other hand, working capital access can be equally difficult for small- and middle-sized firms. Next, consider imperfect access to institutional credit for starting a business. This will constrain the growth of firms in any industry, not only food processing. In recent times in India, the spectacular performance of very small manufacturing units under the government-controlled Khadi and Village Industries Commission (KVIC) has

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shown the potential for small firm survival and growth.3 This is a compelling example of the role that co-processing plays for small firms in the presence of financial market imperfections, as we show through our following short description of the KVIC success mechanism. Strength in Numbers: The Case of KVIC Units in India The ‘Khadi’ brand has yielded a large premium in the past two years in sales of processed food items like ‘papad’ and honey, as well as textiles. In fact, this industry has its roots in the homespun rough fabric native to India, called ‘khaddar’ and later renamed ‘khadi’. It was a symbol of the freedom movement in the country spearheaded by Mahatma Gandhi prior to India’s independence from colonial rule in 1947 by symbolically opting for homespun khadi cloth. The Khadi and Village Industries Commission (KVIC), established in 1956, has been tasked with the development of an umbrella of small businesses in textiles and processed food concentrated in rural areas. The processed food basket of the KVIC includes village-level oil production, F&V processing, jaggery (gur), pulses and cereal processing to mention a few. Further details are at http://www.kvic.org.in/kvicres/Village1.php. The operations of these units come under the rubric of ‘village industries’, which the KVIC defines as Any industry located in a rural area which produces any goods or renders any service with or without the use of power and in which the fixed capital investment per head of an artisan or a worker does not exceed one lakh rupees...

As the definition shows, these units are very small. However, in 2018–2019, the combined sales of KVIC units is twice as much as that of Hindustan Unilever (HUL), which is India’s largest consumer goods company. Sales have gone up 25% year on year at a time when big consumer companies are reporting low revenues due to slow demand growth. In terms of annual revenue, KVIC now compares with the top 25 listed companies in India. The chairman of KVIC has attributed this success to ...partnerships with private and public sector enterprises, aggressive marketing and government push.

The Economic Times report on KVIC performance in August 2019 also mentions that the KVIC established links with retail majors like Arvind Mills and Raymonds ...to supply khadi material that is subsequently marketed and branded by them.

This is precisely the co-processing channel for small firms we discuss in this chapter. Our theory, however, contends that this marketing strategy requires a minimum density of mid-sized firms, rather than only large firms. The second

3 Refer

to the Economic Times article dated 16 August 2019 at https://economictimes.indiatimes. com/industry/cons-products/garments-/-textiles/papad-cosmetics-honey-push-khadi-commsales-up-25-to-rs-75000-crore/articleshow/70696469.cms.

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point illustrated through this example is the collective strength of the small, which we will refer to in our policy conclusions in Chap. 8. A third point is the role of the brand of ‘khadi’ that has been leveraged during a time of uncertain demand, which is again of relevance in our discussions in the penultimate chapter of this book.

6.2.1 Limited Credit for Small Firms Consider, first, the case where only the small firms that are affected by lack of access to finance: be it set-up costs or working capital. In the benchmark scenario, there are no missing middle-sized firms, but small firms are now credit-constrained. Due to imperfect access to institutional credit, breaking even on costs is difficult for small firms. Without the missing middle, our logic about the behaviour of small-sized firms run along the following lines: suppose that the per-unit cost of production is ci and the fixed retailing costs for marketing the product is Cir in sub-sector i (such as grain milling, etc.) in food processing. Note that these costs are specific to the product network or sub-sector. In reality, these costs are not only specific to product networks, but also to the particular product or brand being launched, such as Horlicks in nutritional drinks (newly acquired by Hindustan Unilever from GlaxoSmithKline Consumer Healthcare (GSK)) or Kwality Walls in ice-creams (a well-known brand of Hindustan Unilever). However, some firms like Amrapali Biotech (discussed in the previous Chap. 5) market a range of products such as cornflakes to pickles and jams under the same brand name of Mums. Our logic works even if we increase the specificity of the costs from the sub-sector to a specific product. These costs, Cir and ci , we label as the physical costs as these are easily quantified. Now, margins in operation are determined by the relationship of these costs with market prices. For the small entrant, this price is threefold, depending on the entry strategy: either (i) the final retail price of own-brand sales or (ii) the wholesale price of unbranded retail or (iii) the price of industrial sales from selling unbranded items through co-processing to mid-sized firms. Note that we assume these strategies to be mutually exclusive. The entrant’s bargaining power in each of these strategies determine the margins they earn, as suggested by Vyas (2015) and Sutton (2007). With some minimal assumptions, it is possible to show that the third option is the least-cost entry strategy for the new small entrant. Let the size of any entrant in food processing be s, where size is measured as per our discussion in Chap. 4. For the small firm, size is s = S0 , which is the minimum size of entry in any sub-sector in registered manufacturing of processed food. For each of the entry strategies, let the per-unit price be: (i) pir (s) from own-brand sales, (ii) piw (s) from unbranded wholesale and (iii) pi (s) from unbranded industrial sales through co-processing contracts.

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We first consider the second per-unit price piw (s). Casual empiricism in processed foods shows that for this entry option, while Cir = 0, the margins are extremely low or even negative as piw (s) ≤ ci for all entry sizes s. There is no scope for product differentiation through branded sales and the laws of competition pulls the wholesale price down to marginal costs ci . This type of product sales is common in homogeneous price competition in cereals, like parboiled rice or wheat flour available loose without packaging at general stores. Additionally, there are input price risks, particularly for items like rice (such as when the Minimum Support Price (MSP) for paddy is raised such that marginal costs of acquiring inputs shoot up, which we discuss in this chapter). As we have discussed in Chap. 3, given the high raw material intensity of the food processing industry, such input price risks run into the possibility of negative margins when the already low piw (s) (due to forces of competition among sellers) is pulled below ci . Given this, unbranded wholesale results in a very unprofitable mode of entry for all sizes of entrants. The entrant has to diversify out of this distribution channel to earn higher margins. Now, we come to a comparison between the first and third options, where the size of the firm makes a difference to outcomes. The first one creates the possibility of earning margins through product differentiation created via branding. The lowering of competitive pressure on prices comes at a large cost though: marketing costs Cir . The third option does not require this cost Cir , but its viability for small entrants rests crucially on the following assumption: Assumption 6.1 For size S0 , unbranded industrial (or B2B) sales yield higher net profits than unbranded wholesale. Note that unbranded industrial sales, through contracts, ensure the regularity of supply to mid-sized firms. Instead of purchasing the product from the wholesale market, the exercise of the option of unbranded purchases from small entrants indicates that there is some value-addition that industrial sales provide for the buyer. This is mostly in the form of security of supply, apart from some basic quality checks and standardization. This is the economic basis for why the per-unit price for industrial sales is higher than that in the wholesale market. Additionally, industrial sales require small entrants to sell only to finitely many mid-sized firms, whereas unbranded wholesale requires selling to a much larger mass of consumers. This can raise the bargaining power of the small entrant in the realm of unbranded sales relative to the wholesale. For Bihar, Assumption 6.1 is not unrealistic. Note also that we are discussing subcontracts that exist between formal registered units, albeit of different sizes. There are some case studies for processed foods in India, such as that of Desai and Gopalan (1983), where subcontracting results in a profit squeeze for the outsourced party. However, this kind of subcontracting is between firms and informal sector units, mostly operated by women. For the formal argument to go through, consider the size S0 entrant and an output level qi . Industrial sales are more profitable than branded retail if net profits from the former are larger than net profits from branded retail. This is, the incentive

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compatibility constraint ensuring that size S0 prefers industrial sales (without the costs of marketing) to branded retail (with marketing costs): ( pi (S0 ) − ci )qi ≥ ( pir (S0 ) − ci )qi + Cir

(6.1)

To uniquely assign this entry strategy to S0 size alone requires that larger firms of size j > S0 do not find it incentive compatible to engage in industrial sales (which does not create the capital of reputation through branding) relative to own-brand sales: ( pi ( j) − ci )qi < ( pir ( j) − ci )qi + Cir ∀ j > S0

(6.2)

Combining Eqs. 6.1 and 6.2, our claim implies pi (S0 ) − pir (S0 )) ≥ pi ( j) − pir ( j) ∀ j > S0

(6.3)

This indicates that the price differential for the small firm in industrial sales relative to own-branded retail is at least as large as the same price differential for a larger sized firm j > S0 . Assumption 6.1 is necessary for this condition to go through. For developed countries, such as the UK, Vyas (2015) and Sutton (2007) mention a different channel, where small firms are hurt in their bargaining with larger firms. Our framework has the opposite implication: different firm sizes co-exist through supply-chain linkages and mid-size firms do not pose a competitive challenge to small entrants. This is realistic for the Bihar trajectory. However, we also find mention of this for some developed countries as well. Sutton (2007) mentions the difficulty of expansion of frozen food producers in the early development of the UK market. It was hampered by a lack of retail display space and inadequate distribution channels. As a result, this market itself remained small till 1956. However, we point to a different source of marketing problems for Bihar’s small entrants in processed food.

6.2.2 Limited Credit for Larger Firms When financial market imperfections affect firms larger in size than S0 , these firms also face higher uncertainty regarding profitability. The examples of large firms in sugarcane facing large losses or bankruptcy in processed foods, such as Riga Sugars Pvt. Ltd. or Ruchi Soya Industries Limited (RSIL), stand testimony for this possibility. The implication for marketing strategies of small firms of size S0 is the same as we discussed above. We simply need to assume that the firms of size larger than S0 have the liquidity to pay for co-processing services to S0 rather than centralize production and marketing on their own. In sum, in the presence of financial market imperfections and the ‘missing middle’ problem, small entrants will either have to engage in unprofitable wholesale or engage in own-brand sales, which requires heavy advertising expenses. In either case, the profitability of this type of firm will

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be eroded. This explains why small firms could not expand post-entry, despite the policy support from the government. Note that we make three other assumptions for this analysis: Assumption 6.2 All firm sizes have the same costs of retailing own brands Cir . These are specific to the product network. If these costs are higher for smaller firms, our claim is strengthened. Assumption 6.3 The output level qi is held constant for all firm sizes. Note that we are not considering the size-specific profit-maximizing output levels, just any arbitrary feasible level of production for all firm sizes. Assumption 6.4 Subcontracting costs for middle-sized firms are not significant. In the presence of significant subcontracting costs, the mid-sized firms would not find it profitable to subcontract with firms of size S0 . That contracting costs can be low is demonstrated by the case study of Bertolini and Giovannetti (2006) for meat processing in the Industrial District of Modena, Italy. As mentioned in our meat processing product network in Chap. 2, there is a stage of cleaning and sectioning cuts prior to final sales of processed meat. Bertolini and Giovannetti (2006) mentions that originally the sectioning of cuts was done by final producers in Italy. In recent times, there are a number of small units that specialize in sectioning of meat cuts and exist as subcontracting outfits. They provide specialist skills in the creation of specific cuts. In the case of the Italian study of Bertolini and Giovannetti (2006), co-processing accounted for 36% of domestic output in 2006. At present, these small firms co-exist with large firms that do their own sectioning of meat cuts. These small units have strong international connections, with contractual links to supply Dutch and Danish pork. In fact, Bertolini and Giovannetti (2006) ascribe the success of the Modena cluster to the existence of subcontracted small outfits. Not only does outsourcing help small firms reduce marketing costs, it also provides them an opportunity to specialize in different parts of the product network, which helps them survive in competition. Given that different small units can specialize in different functions of primary processing, as the meat processing cluster of Modena, Italy exemplifies, they can create horizontal niches of product differentiation to earn profits. This rests on the theories of divisibilities in the production process and the nature of economies of scale, as Bertolini and Giovannetti (2006) discusses. Chapter 2 mentions divisions in the product network of processed food, with a separation between primary and secondary processing. Outsourcing allows small units to focus on one part, typically upstream primary processing. This helps them learn about production processes in detail in that part of the network through this kind of specialization. Our theory, therefore, applies to product networks with strong divisibility. Economies of scale are not a necessity, as Bertolini and Giovannetti (2006) also mentions. Small units can exploit all the economies of scale at the primary-processing level, without having to engage in other parts of the product network and without incurring any marketing expenditure, if they can get co-processing contracts from middle-sized firms.

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With these assumptions, our benchmark scenario (without a missing middle) predicts that small entrants would be specialized in unbranded industrial sales, whereas larger sized firms would be marketing their own-branded products. When this benchmark fails, as has happened for the Bihar trajectory, small entrants have to engage in either branded sales to market their products or in unbranded wholesale. For own-brand sales, the small entrant will have to cover both per-unit costs of production ci as well as fixed costs of retail Cir , given a per-unit retail price pir (S0 ). With pir (S0 ) < pir ( j), j > S0 . Both inadequate starting capital and constraints in working capital access now reduce the profitability of small-sized firms. They are now placed on an unequal footing to compete against larger firms. This, more likely than not, is likely to lead to the exit of small entrants rather than their expansion. We have already discussed that unbranded wholesale entails low or negative margins, leading to similar outcomes. We raise a caveat here about the new distribution channel of e-commerce. Is it possible that e-commerce contracts can mimic the mid-sized subcontracts for small units in food processing? E-commerce and Distribution Costs for Small Food Processing Firms A small food manufacturing firm can reach the retail market not through the mechanisms we outline earlier, but also through e-commerce. However, this option does not save branding and packaging costs for the small firm. It will have to incur these expenses, unless the online sales is routed through a different firm which does the branding and packaging for the firm. We discuss application-based food delivery services lodged on smartphones in Chap. 8 later. There are very few small firms in Bihar which have an internet presence. In our survey of entrepreneurs in Bihar in 2016, only a handful of firms mentioned e-commerce as an option. The advantage of selling through the online platform is the much larger outreach in comparison with brick-andmortar sales. Note that the narrow product basket in Bihar is meant mainly for local consumption in the state, which reduces the attraction of e-commerce. As such, e-commerce cannot fulfill the role of the mid-size firms in terms of the benefits of reduced marketing costs and specialization for small firms.

Note that the type of front-loaded subsidies in manufacturing in Bihar reduces the burden on financing plant and machinery of operations, which are set-up costs. They do not subsidize the costs of own-branded retail Cir . Additionally, there are problems of policy overlaps. Subsidies through the industrial policy might be rendered ineffective due to other dimensions of public policy, as we discuss in this chapter. To address these issues, we now focus on the issue of firm efficiency in physical costs and survival issues in the sub-sectors of dairy and grain milling, with special focus on rice mills in the following sections.

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6.3 Efficiency Analysis of Food Processing in Bihar We propose to study the firm survival issue from the perspective of efficiency in operations of units in Bihar’s food processing. Efficiency measures the quantum of output produced for a given level of inputs into the production process, ceteris paribus technology and other market characteristics (see Kathuria et al. (2014)). It is a more holistic concept than factor productivity. While productivity measures can be described without any reference to the underlying production process, efficiency measures are anchored with respect to specific production functions. Note that our discussion on working capital intensity of the food processing industry in Chap. 3 was with reference to partial factor productivity. We did not discuss the underlying production process there and simply worked out the output produced per-unit of an individual factor of production. With multiple factors of production, partial productivity measures, such as average labour productivity, make little sense. Changes in both the relative price of the different factors of production and the ratio in which they are employed distort these partial measures. To account for the interaction effects of different inputs on the production process, we now focus on measures of efficiency. A caveat with this kind of analysis is that it is not interested in other aspects of firm performance, such as quality of the product produced. Competition in quality rather than quantity is of relevance for brand-based products in processed food that incur heavy advertising. While this would be true of much of the product networks in trajectory 1, Bihar’s narrow product basket of primary processed foods raises a different question: the role of the missing middle in small firm survival in a limited number of sub-sectors of processed food. To understand this, we first investigate the nature of the production function empirically for two sub-sectors, rice milling and dairy, for Bihar. We explain in the following section why we focus only on these two sub-sectors. In a nutshell, these have attracted investments in recent years and also have the potential for evolving into buoyant industries with export possibilities. The characteristic feature of the production process, for these sub-sectors, is that all the variables in the production function display large noise. The mean is smaller than the standard deviation for inputs as well as outputs, such that the coefficient of variation is strictly greater than one. This is more marked for grain milling than dairy. We contend that this resonates with our earlier claim that food processing has a missing middle size in Bihar. This exercise shows that to be a reality at the sub-sectoral level as well for this industry. Second, we do not find significant economies of scale in production in either sub-sector, when we use factory-level variables to estimate the production functions. Therefore, there is no a priori reason to believe that small size units will be less efficient in converting inputs to physical output than larger ones. However, that is the case for the first stage of production, not the final marketing stage. The empirical implication of our theory about the effects of the missing middle is that efficiency in production is unlikely to translate into an overall ability to convert the production process into a profitable enterprise due to costs in the marketing stage.

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In order to decide whether size matters for profitability requires a holistic model that accounts for all the stages of processing: from an initial input acquisition through to an intermediate physical output stage to a final realization of net profits. The main challenge to achieving efficiency in the first stage of converting agri-inputs to physical output faces two challenges: (i) that of managing set-up costs and (ii) working capital costs for acquiring the seasonal agri-produce. The second stage of the production process converts physical output into net profits through marketing costs. The empirical treatment of this model is best done using the non-parametric two-stage network DEA (NDEA) analysis. The production process is treated as an integrated network that can be segmented into different interrelated stages accounting for production and marketing efficiencies individually. In sum, this technique provides a decomposition of the production process into two interlinked stages. The first stage allows for inputs to produce an intermediate output: physical quantity. The second stage is about converting that physical quantity into net profits. Thus, inputs for the second stage come from the output from the first stage. Through this decomposition, we are able to study the mechanism that leads to the discovery of profits in the production process. The first stage has to do with technology; efficiency at this stage requires the production of the maximum possible physical output, given the level of inputs like labour, capital as well as government subsidy. Note that we model government subsidy as an input in the first stage for two reasons. First, firms can start operations even without subsidies and there are time- and effort-costs for accessing them. Second, the subsidy is an investment in the firm by the government and ideally should not be considered a free dole by the firm. The second stage is concerned with marketing processes, which now converts physical output to net profits. If a firm has an overall low-efficiency score, it would be because net profits relative to inputs would be low relative to a benchmark efficient profit level of that manufacturing unit. We can also determine, through a decomposition of the overall inefficiency into operating and marketing inefficiency, which of these stages is responsible for this. Our theory predicts that for small new entrants, in the face of the missing middle size, it will be marketing inefficiency that will be the determinant of overall inefficiency. For a manufacturing sector with intense working capital requirement, it is natural to expect inefficiency at the first stage of operating efficiency for food processing in general. However, for the Bihar trajectory, it is our contention that marketing inefficiency will be as, if not more, important than operating inefficiency. In sum, our empirical claim for the Bihar trajectory is Empirical Claim regarding Physical Costs (Set-up, Variable and Marketing Costs): In the presence of the ‘missing middle’, additional costs of retailing in own brand (Cir ) and the unfavourable terms relative to industrial sales pir (S0 )/ pi (S0 ) for the small firm of size S0 in any sub-sector i, i ={grain milling, dairy} would be the core reason for low profitability. Hence, for a given retail price pir and physical costs of production, such as set-up costs, ci and Cir , low net profits for the small entrant will be due to the absence of co-processing opportunities in the Bihar trajectory. This will show up in the data as marketing inefficiency, which implies an inability to convert the processed output into profits. This inefficiency should be present along with

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inefficiency in working capital management, which relates to translating raw inputs into physical output. We have not explicitly modelled other forms of inefficiencies. Relative to these, our theory places central importance on marketing inefficiency. In terms of the product network, we cover the broad sub-sectors of dairy, grain milling as well as rice milling, which is a subcomponent of grain processing. Many dairy units in Bihar produce a range varying from pasteurized homogenized milk (the least processed output) to cheese, yoghurts (unflavoured and flavoured), ghee (clarified butter), butter and ice-creams of various kinds. The typical product basket of a large-scale dairy in Bihar, such as that of the publicly owned COMFED (Bihar State Milk Co-operative Federation Ltd.)4 or the large private initiative of Ganga Dairy5 includes not only these items, but also dairy-based products such as sweets of various kinds like ‘peda’.6 Note that grain milling also gives rise to many secondary processed food items, such as cereal-based breakfast items as well as rice and wheatbased flakes, crisps, puffs (or ‘muri’ in the local parlance), other products such as flattened processed rice (or ‘choora’ in the local language). Modern grain milling, which we discuss in some detail in the following section, includes a whole array of value-added edibles that make its inclusion purely as a primary-processing sub-sector in food manufacture incorrect by international standards. However, we are focused on the Bihar trajectory. Here, factory-based grain milling, particularly rice milling, is restricted to processing and marketing of raw rice even at the present date. Other products are not marketed as independent brands to the extent of processed polished rice, which is the main revenue generator for mills in Bihar. Apart from the choices of own-brand retail or unbranded sale (through wholesale or industrial sales), our exploration of rice mills helps us identify another mechanism of product placement: selling to the government at prices fixed by the latter. We find two types of distribution mechanisms for rice mills, depending upon the nature of the mill’s relationship with the government as a supplier of finished products. Some mills procure paddy (the raw agri-input) and sell the processed rice mostly to the state government food corporation as custom milling operations. The output from these mills is typically referred to as custom-milled rice or CMR. These we term the SFC (State Food Corporation) mills. Others that purchase inputs and sell most of their output to the open market are termed non-SFC mills. While trading with the government gives the former certainty of off-take of output, thereby reducing marketing risks, their profit margins depend upon their bargaining power with respect to the government. Lower is their influence over government pricing, the lesser is their autonomy in pricing and earning profits. In terms of the organization of the empirical results on these issues, we start with the following Sect. 6.3.1 to spell out the reasons for investigating grain milling (including rice milling) and dairy sub-sectors for the Bihar trajectory. 4 The product basket of COMFED under the brand name of Sudha is available at http://www.sudha.

coop/topics.aspx?mid=Products. product basket of Ganga Dairy at Begusarai with the brand of Tulsi is described at http://www.gangadairy.com/products/. 6 Refer to the Glossary for an explanation. 5 The

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Section 6.4 provides an empirical characterization of these two sub-sectors, whereas Sect. 6.5 extends the analysis to the NDEA analysis. Section 6.6 follows up with the NDEA analysis exclusively for rice mills, which dominate grain milling in Bihar. This section also gives us the scope to explore the role of subsidies in the profitability of rice mills. We conclude with Sect. 6.7 where we explore further issues with the nature of marketing risks for rice mills.

6.3.1 Sub-sectoral Focus in the Bihar Trajectory: Why Grain Milling and Dairy? The historical time-line of the development of industries in Chap. 4 shows that by 2006, Bihar had a majority of primitive rice-milling initiatives and a few large dairy operations. By 2005–2006, Bihar constituted a very nominal share of only 0.8% in the total production of agro-based industries at the all-India level. In absolute terms, Bihar’s value of output in 2005–2006, was only 16.78 thousand crore INR, as against India’s 1908.36 thousand crore INR. With improved law and order since then, the state has seen the entry of many new rice mills and a few dairy units. This is not to mention an isolated large Godrej Agrovet factory in the Hajipur Industrial Area that produces animal feed. It is a maize-based processing industry that also started in the 2006–2016 phase. However, no other sub-sector other than rice-milling saw a large concentration in numbers. Most of the policy incentive recipients were rice mills and this narrow base in processing industries continues, with some sporadic exceptions. Successive Bihar Economic Surveys of the state7 shows that most of the policy incentives were mostly absorbed by rice mills. Between 2008 and 2016, the majority of the applicants for the subsidy were rice millers. Why do we study diary in detail for Bihar? Processing activity in dairy promises diversification of farm-based incomes, reducing some of the economic pressures on agriculture. With small sizes of landholdings, as is the case with Bihar and much of eastern India, achieving efficient scales of operation in processing agri-produce like F&V is limited. Sectors such as dairy, where modern innovations reduce the necessity for pastoral land for grazing milch animals, show much promise for Bihar. In terms of supply of raw material, we find that Bihar has a large livestock population as a whole as per the 19th Livestock Census Report of 2012.8 The state has the fourth highest stock among 35 Indian states with 6.43% share of all-India livestock. In itself, this figure does not have much meaning for dairy industries. It is the population of milch cattle that matters. On this front, Bihar has an impressive 6.41% of total milch cattle in India, as per this Census. Thus, it has the supply base of raw materials to start a dairy industry in the state. In terms of organization of a typical dairy unit, worldwide as well as in India, dairy is largely co-operative-based. This is potentially 7 For the years 2015–2016, 2016–2017 and 2017–2018, this publication is available at http://finance.

bih.nic.in/. report is available at http://dahd.nic.in/documents/statistics/livestock-census.

8 This

6.3 Efficiency Analysis of Food Processing in Bihar

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due to the problems of aggregation and inability to achieve scale in processing, as raw milk has to be collected from multiple dairy farmers. Bihar is a peculiar example of the successful co-existence of both private and government ownership structures. COMFED is a large government-owned dairy initiative whereas Ganga Dairy and ITC Pvt. Ltd. are private firms. Another reason for analysing dairy is that manufacturing units have to necessarily locate near the source of inputs, i.e. raw milk collection points. Unprocessed milk has a short lifespan and has to be transported from point of collection to the processing centre typically within a few hours for tropical humid conditions, as in Bihar. This necessarily forces processing location (leading to manufacturing-led job creation and off-farm income diversification) in the local region, reducing incentives for strategic manufacturing location choice based on government policy interventions, such as taxes and subsidies. This, as we argue later, is an essential condition for selecting an industry for horizontal investments for further development. However, a natural question arises: why not vegetable and fruit processing? We have addressed this in passing in our description of the Bihar trajectory in Chap. 4. Though fruits and vegetables have the similar perishability as dairy and has a large product network including juices, jams, jellies and pickles, vitamin-fortified beverages, vegetable crisps, ready-to-eat vegetable meals and curries, Bihar does not have a relative advantage in fruits and vegetables in comparison with some states like Karnataka. Additionally, the state lacks the ‘organic produce’ tag as Uttarakhand, from where produce attracts the organic premium. This is not to say that Bihar does not have an abundance of fruits and vegetables. It produces some of the most exotic tropical fruits like litchis, particularly ‘shahi litchi’, which has a larger pulp content than the ordinary variety. However, given the recent controversy regarding the excessive usage of pesticides and harmful chemicals for increasing crop output,9 the product mix from Bihar has to rest on standard items with positive consumer perception. Commercialization of items like litchi juice requires additional expenditure on certification, labelling and more importantly, marketing to create consumer awareness about the benefits of the product. Declining net returns from such expenditure, given the shrinking pulp content of the fruit in recent times, has also made juice extraction commercially unviable. It is for these reasons that we limit ourselves to dairy and rice milling. As the latter outweigh dairy in sheer number of processing units, the following Sect. 6.3.1.1 provides a short history of rice milling in Bihar.

9 The recent May–June 2019 encephalitis outbreak in Bihar’s Muzaffarpur that has claimed the lives

of 150 children has been controversially linked with litchi. Though medical professionals point out malnutrition as one of the important causes for this disease, news items such as https://timesofindia. indiatimes.com/india/aes-outbreak-toxins-behind-litchi-deaths/articleshow/69920018.cms blame the consumption of this fruit by children on an empty stomach for these deaths. Litchi farmers themselves admit to using strong chemicals to protect their crop and this has not helped the case for litchi. This, all the more, points to the necessity of farmer education, training, certification and labelling of exotic products like litchi.

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6.3.1.1

6 Food Processing in Bihar: Efficiency in Physical Costs

Rice Milling in Bihar: Brief History

Bihar has had a long history of rice production and its small-scale processing. Despite stiff competition from the neighbouring state of West Bengal, Bihar has been a paddy producer and was milling rice at a small scale prior to 2000. Domestic demand and locally conducive conditions provided incentives for small backyard rice polishing mills, which did not use modern milling methods nor did they generate electricity from husk. Government data10 shows that most of the functional units in food processing from 2008 to the present are rice mills, most of which are tiny to small in size. We find that out of the 413 functional units in food processing from the Department of Industries Udyog Mitra data for May 2017, close to half of them (174) are rice mills. It is not surprising, therefore, that most of the applicants for subsidies for either establishing or modernizing or expanding units under the 2008 special package for food processing units were rice millers. There are multiple techniques ranging from hand-pounding (stone-ground, woodground, ‘dhenki’,11 etc.) to older huller (small- and mid-sized machines) to fully modern plants for processing rice. The latter also convert rice husk to electricity for running factory operations, thus saving on electricity costs. The traditional ‘dhenki’ in many countries of South Asia, as commented upon by Harriss-White (2005), used to be operated mostly by women. This was particularly so in Bangladesh, with small backyard processing units. The introduction of semi-modern rice mills (the ‘kiskisan’ rice mills in Philippines) moved ownership of assets from women to men, without improving productivity but adding to the profitability in operations (see the discussion in Hayami et al. (1999)). Evidence from Niger (refer Diarra et al. (1999)) shows that the large gains in output and productivity started with the introduction of modern rice-milling units, with integrated huskers, rollers, polishers and packing facilities. Marginalized ‘dhenki’ operations continue to co-exist with small rice mills (semi-modern) alongside some fully modern rice mills in Bihar. Modern methods of milling ensure a 60–70% rate of extraction of unbroken rice from raw paddy, whereas it is less than 50% for some of the older techniques. For instance, Singha (2013) estimates 63% as the milling ratio for modern mills as opposed to 58% for traditional mills in Karnataka averaged over 2007–2008 to 2009–2010. In terms of outputs, an old-style or a modern rice mill produces not only the main by-product (unbroken polished rice) but some other commercially viable items such as broken rice (or ‘brokens’) used as fodder rice bran, rice husk used for power generation, etc. Lim et al. (2013) mentions that in many developing countries, the standard practice is to sell these by-products at subsistence prices to downstream industries and is not seen as a major source of revenue. In terms of price differences, the main product of

10 Successive monthly progress reports of established units are available at Udyog Mitra website (http://www.udyogmitrabihar.in/category/mpr-food). 11 This traditional wooden plank for de-husking paddy is the lowest cost technique of producing rice from paddy. We provide a definition for this in the Glossary of this book.

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polished unbroken rice, (with branded packaging) commands a large margin above the other products of lesser value, such as brokens, husk, bran, etc.12 Note that for India in the pre-1991 planning era, policy biased industrial outcomes in this sector in favour of traditional techniques at the cost of factory-based modern rice-milling methods. Bhalla (1965) chronicles this legacy, where the policy objective was that of promoting industrial outcomes so that employment is maximized (assuming that labour-intensive traditional techniques such as hand-pounding would outperform on the employment benchmark relative to modern milling). However, the history of India’s rice mills saw a slow technological intensification. Slow modernization of milling technique is seen in Bihar’s rice-milling experience as well. These units have come up from humble beginnings with single hullerbackyard production facilities to somewhat modern units. Very few of the currently functional rice mills are fully modernized units, as the report of CIMP (2016) reveals. Regarding sourcing of inputs, most of the paddy is locally produced, which is one of the major crops in Bihar, as well as neighbouring West Bengal, Odisha and Assam. However, there is a difference among mills in their input and output supply channels. One line of supply is linked to government-based paddy procurement systems (mills dealing directly with the State Food Corporation, which is a government agency of Bihar dealing with all issues related to food security (SFC mills)) during various agri-marketing seasons (Kharif Marketing Season (KMS) or Rabi Marketing Season (RMS)). Another set of mills mostly engage in open market sales and purchases of paddy (non-SFC mills). The distinctive process of input acquisition continues to the point of marketing. Essentially, SFC rice mills (though owned by private equity owners) engage in government operations, by selling their end-produce to the government and purchasing paddy from government-owned paddy procurement mechanism of DCP (Decentralized Purchase) in Bihar. We identify SFC mills by turnover: units which get more than fifty per cent of their turnover from dealings with the SFC whereas the non-SFC mills sell more than 50% to the open market. Most of the risk in the supply chain is absorbed by the government for SFC mills as the vendor off-loads the input and output price risks to the government. On the other hand, the risk of marketing and price risks are entirely borne by the private mill owner for non-SFC mills. Due to the natural correlation in DCP and open market prices of paddy, any hardening of paddy prices through government channels spill over to the open market increasing the risk to profitability for private rice mills not engaged in government procurement. Though SFC trading is likely to mitigate the price risk (input and output), delays in payment for produce and lack of control over pricing reduce the space of managerial decision-making. Of the 61 mills in our data, 48 units are engaged in procuring raw inputs and selling the final product (unbroken rice) in the open market (non-SFC activities), 13 are SFC units. These mills are spatially concentrated around the paddy-growing districts of Bhojpur, Rohtas, Buxar, Aurangabad and Bhabhua accounting for 30% of the total paddy production 12 For

instance, Singha (2013) estimates that the share of by-products (broken rice, bran and husk) in value terms is 5.5% of gross returns whereas it is 94.5% of gross returns for the main product of unbroken rice in the Indian state of Karnataka between 2007–2008 and 2009–2010.

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6 Food Processing in Bihar: Efficiency in Physical Costs

in the state, as the data from CIMP (2016) reveals. Around 2012–2013, Bihar was ranked 5th at the all-India level in terms of paddy output (accounting for 10% of mostly KMS variety rice), but received a much humbler 19th rank in terms of rice productivity, as mentioned in CIMP (2016).

6.4 Empirical Estimation of the Production Process for Grain Milling and Dairy in Bihar The way we conduct this exploration is by first discussing the nature of grain milling and dairy in Bihar in Sects. 6.4 and 6.4.3. This lends continuity to our empirical examination of these sectors for the India trajectory in Chap. 3. We mentioned that intensity of working capital requirements is a constraint for the growth of the food processing industry. The same argument goes through for Bihar, as the bulk of the expenditure in the production process generating total output is that for raw materials. Our empirical strategy is to start with a single year. The natural choice is the year 2012–2013, as it is midway between 2008 and 2016.13 This allows significant time lapse for the incentive policy to have worked into expectations of entrepreneurs and in the financial performance of production facilities. Using factory-level data for food processing units in registered manufacturing at the three-digit level of aggregation (105 for dairy and 106 for grain milling, as per NIC 2008) from the ASI dataset, we start with an empirical characterization of the first stage of the production process: from inputs to physical output, where c is the relevant per-unit physical cost. For this analysis, we assume that technology uses the standard inputs of invested capital, wages (as a proxy for labour input) and materials consumed to produce physical output in the first production stage. As per the ASI definitions, invested capital is the sum of fixed and working capital. In the second stage of the production process, these two subcomponents of capital have different effects on profits. A high working capital intensity in the first stage is a challenge to earning profits in the second stage for an industry that intensely uses raw agricultural inputs. This is particularly so if finance is not readily available. Working capital requirements are cyclical in nature and generally perishable, necessitating a high expenditure on its timely acquisition. This dampens the profit potential of the manufacturing units, especially in the face of financial market imperfections. This effect is likely to increase with the size of operations of the firm. Larger firms should have higher working capital intensities. On the other hand, the survival of the firm is likely to be a positive function of its size, as measured by fixed capital. Larger firms can pre-empt the requirement of working capital by building capital-intensive storage options for raw materials, as well as other fixed costs for expansion. These constructs enable a firm in food processing to create buffers against the uncertainties of agricultural supply, and therefore volatility in working capital infusions. However, these require large fixed capital infusions. Satisfying financial requirements for investments in 13 2016

food.

is the year for a drastic policy change for the sectoral policy change in Bihar for processed

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167

storage through access to financial markets, supply chain integration and defraying marketing costs might be easier for large firms. If profits respond to fixed capital rather than working capital, this should be considered the fixed capital effect on profitability. Three features stand out in our empirical. First, the plethora of rice mills outweighs other grain mills as well as dairy units in the sheer number of manufacturing units. These units are much smaller in size than those in dairy. However, the phenomenon of the missing middle size in the population of operating factories is present markedly in dairy as well as grain milling. Second, significant economies of scale are absent in the production process for manufacturing units across these sub-sectors, though there is clear evidence of high raw material intensity in both sub-sectors. Third, plant size (proxied by fixed capital) has a strong positive correlation with profits across these sub-sectors. Hence, we find support for our theory that predicts small size firms will have lower profitability. In terms of the trade-off between fixed and working capital, the technology in grain milling works differently from dairy. For the former, working capital is not significantly correlated with size or profits, but fixed capital is. Hence, the fixed capital effect dominates. This is not the case with dairy.

6.4.1 Size Distribution in the Population of Grain Milling and Dairy Recall our discussion on the difficulties of measuring the size of a manufacturing unit in Chap. 4. Of the various alternatives, we stick with the definition given by the Ministry of Micro, Small and Medium Enterprises (MSME) for India in terms of the fixed cost of plant and machinery (measured at book value). As a quick recap, the classification was as follows: tiny factories have a fixed capital requirement less than 25 lakh INR (or 0.25 crore INR), small enterprises between 0.25 and 5 crore INR, medium units between 5 and 10 crore INR whereas large establishments have more than 10 crore INR in fixed capital cost. Table 6.1 reports the size distribution in the population of manufacturing factories in Bihar for diary, grain milling, with a special reference to rice mills. Most of the units are in grain-milling operations, totalling 427 in 2012–2013. Of these, the majority were rice mills: around 92% of all grain mills. The number of middle-sized operations is markedly smaller for dairy and grain-milling units other than rice. For the latter, it is more of a case of a missing middle as well as large size relative to tiny and small sizes.

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Table 6.1 Size distribution by fixed capital for grain milling, rice mills and dairy, Bihar (2012– 2013) Fixed capital Rice mills Other grain mills Dairy Tiny and small (up to 5 crore INR) 307 Medium (5–10 crore INR) 53 Large (above 10 crore INR) 31 Total 391

22 3 13 36

12 1 4 17

Source Author’s calculations based on ASI data

6.4.2 Nature of the Production Process: Grain Milling in Bihar The State of Agriculture Report (2015–16), published by the Ministry of Agriculture & Farmers Welfare, shows that Bihar has a significant share between 20 and 29% share of agriculture and allied sectors in its State Gross Domestic Product (GSDP) at constant 2004–2005 prices. If we look crop-wise, particularly paddy, the state produced 8242 kilotonnes of paddy alone in 2014–2015 and was among one of the nine Indian states with a production above 5000 kilotonnes, accounting for 8% of the all-India production. This indicates a large raw material base for establishing formal processing units in paddy and other grains like maize. It is, therefore, not a surprise that most units in Bihar are in grain milling. The latest sampling frame for ASI in Bihar’s food processing (2017–2018), shown in Fig. 4.4 in the Appendix of Chap. 4 shows 662 units in grain milling. This is (potentially) an increase of 65%, from 427 units in the 2012–2013 ASI data for Bihar. The largest district-wise concentration of these units is in Rohtas (174 units) followed by Kaimur (77 units) with Patna at a close third rank (76 units). Some of these units are in wheat processing. However, given the district-wise concentration of mills, it is evident that the majority are rice mills. Rohtas and Kaimur are the rice-growing districts of Bihar, and has the largest concentration of rice mills. We proceed with the 2012–2013 ASI data to explore the descriptive statistics in grain milling (with special reference to rice processing) and dairy. Table 6.2 shows the summary statistics of the main input and output variables in the first stage of the production process in the population of manufacturing plants in Bihar for 2012–2013. The largest expenditure item on inputs is materials consumed. ASI defines it as the value of raw materials, components, chemicals, packing materials and stores which are used in the production process of the factory during the accounting year. The cost of all the materials used in the production of fixed assets, including construction work for own use, as well as components and accessories added to the finished product during the accounting year are also included. Intermediate products are excluded. The characteristic feature in Bihar’s manufacturing is present in the data for all sub-sectors: mean values of the input, size and output variables are much smaller than their standard deviations, such that the coefficient of variation is mostly greater than one. Relate this with the missing middle result we discussed earlier. What is

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169

Table 6.2 Descriptive statistics for grain milling overall in Bihar at the 3-digit level (106), (in lakh INR for 2012–2013) Sub-sector Variable Output Material Fixed Wages Profits consumed capital Grain milling overall

Rice mills

Dairy

Mean (I)

5421

4800

567

81

127

Standard deviation (II) Coefficient of variation (II/I) Mean (I) Standard deviation (II) Coefficient of variation (II/I) Mean (I) Standard deviation (II) Coefficient of variation (II/I)

14700

13400

1630

103

297

2.71

2.79

2.87

1.27

2.34

2890 4620

2500 4260

294 410

68 64

83 136

1.60

1.70

1.39

0.94

1.63

8774 12755

6862 9965

517 684

346 532

161 1130

1.45

1.45

1.32

1.53

7.02

Source Author’s calculations based on ASI data

interesting is that this skew in plant size continues even after the end of the incentive policy cycle (2006–2011) and well into the second one from 2011 to 2016. The correlation matrix in Table 6.3 shows the correlations between output (as well as profits) and the inputs into the production process. Material consumption intensity in grain milling is obvious from the almost-perfect positive correlation coefficient between output and material consumption. This is the first stage of the production process. Total output produced is significantly and positively correlated with profits in the final stage of the production process. As in our data, each unit represents single and not multiple factories, we can read off firm from the plant-level profits. There are strong positive correlations between the size of the unit (measured by fixed capital) with output and profits. This relationship holds for wages (as a measure of labour input) and materials consumed as well. Noteworthy is the insignificant relationship between working capital and profits. What explains this, to some extent, is the negative correlation coefficient of −0.37 between working capital and fixed capital. Higher fixed capital can reduce their working capital requirement through the construction of capital-intensive grain silos (which have been subsidized by the Bihar government for maize), so that this immunizes their profits from the challenges of working capital requirements. We have termed this the fixed capital effect. There is also an insignificant but negative correlation between materials consumed and working capital. Size, as measured by fixed capital, has a positive correlation with output and profits and can outweigh the working capital effect through storage

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6 Food Processing in Bihar: Efficiency in Physical Costs

Table 6.3 Correlation matrix for production process variables in grain milling (2012–2013) Profit Output Fixed Working Wages Materials capital capital consumed Profit Output Fixed capital Working capital Wages Materials consumed

1.00 0.95 0.88

1.00 0.93

1.00

−0.03

−0.16

−0.37

0.86 0.95

0.88 0.99

0.80 0.93

1.00 0.07 −0.16

1.00 0.88

1.00

Source Author’s calculations based on ASI data, 2012–2013

mechanisms in milled grain. This is in line with our theory that predicts the fragility of small firms and not the large ones. Given the skewness in the data, it is of interest to us to see what explains output production for the very small and tiny sizes as opposed to larger firm ones. The fact that almost all variables have a coefficient of variation greater than one indicates that the values of the large firms will drown out the contribution of small firms. The Jarque–Bera test for normality for total output rejects the null hypothesis that the data is normally distributed. Hence, there is, a priori, a possibility that estimating the mean of the output produced in grain milling using the Ordinary Least Squares (OLS) technique will not yield meaningful results. We, therefore, predict the output in the first stage of the production process using both the OLS and quantile regressions, where we predict the behaviour of the 25th, 50th (or the median) and the 75th quantiles in Table 6.4. As our correlation matrix indicates, it is the fixed capital part of total invested capital that matters in determining total output and not working capital. We check for this in our data and find that it is only fixed capital that is significant. All variables are measured using their log values. Post-estimation checks for heteroskedasticity and autocorrelation reveal no abnormalities. In Table 6.4, we find the difference between the OLS and the quantile regression estimates. We run two specifications of the OLS: models I and II. OLS model I includes the log of fixed capital as an explanator of the log of output, along with the logarithms of wages and materials consumed. OLS model II, on the other hand, drops this measure of capital and introduces the log of working capital. As is evident, the latter is not significant in explaining the logarithm of output from grain milling, whereas the measure of size (log of fixed capital) in model I is highly significant. Note that the fixed-capital elasticity of average output in model I is 0.26. However, the quantile estimates show a diminishing effect of fixed capital on output: this elasticity falls from a highly significant 0.41 at the lower 25th quantile to 0.34 at the median. For the 75th quantile, the effect of fixed capital on output is not significant, though raw materials continue to be significant at all quantiles. This non-linearity in the effect of fixed capital on output is as our theory predicts: for small firms, a marginal increase

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171

Table 6.4 Explaining total output generation in grain milling (106), Bihar (2012–2013) Dependent variable: log(total output) Independent OLS I OLS II 0.25 0.5 0.75 variables log(material consumed) log(wages) log(fixed capital) log(working capital) Constant

0.34***

0.32***

0.35**

0.36***

0.76***

0.48*** 0.26***

0.62*** ×

0.24** 0.41***

0.33** 0.34**

0.12 0.10

×

0.11

×

×

×

0.99*

1.54**

1.31***

1.05

0.96

***indicates p-value is significant at 1%, **for 5% and *for 10% Source Author’s calculations based on ASI data

in fixed capital/size reduces growth constraints and helps in output expansion. This is also true of the mid-sized firms, but not at the top end of the size distribution. If there are any unexploited economies in the expansion of scale of production, then it rests with small and mid-sized firms. As there is a large variation in size in the data, the mean regressions of the OLS mask the effect of fixed capital as averages are very sensitive to extreme values. Due to the logarithmic specification of all dependent and independent variables, we can read off the returns to scale from the sum of the coefficients of the regressors. For grain milling, the sum of the coefficients for the explanatory variables is almost one, indicating constant returns to scale. Overall, our result bears evidence in support of grain milling in Bihar being a raw material intensive industry roughly characterized by constant returns to scale.

6.4.3 Nature of Dairy Processing in Bihar Tables 6.1 and 6.2 show the missing middle size and the large noise in the data for the 17 dairy factories/firms in Bihar in 2012–2013. Compared to grain milling, we have much fewer observations. Therefore, we refrain from any regression analysis here. The correlation matrix in Table 6.5 shows a similar pattern as in grain milling, except for a few important differences. First, output has a much stronger positive relationship with profit in dairy than in grain milling. Second, the behaviour of fixed and working capital is different. While working capital continues to have an insignificant negative correlation with profit, it is now positively correlated with total output as well as materials consumed. Fixed capital, which is our proxy for plant size, has a minor negative correlation with profits, unlike grain milling where the association is strongly positive. The relationship between fixed and working capital is strongly positive, unlike the trade-off we see for grain milling. These differences

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6 Food Processing in Bihar: Efficiency in Physical Costs

Table 6.5 Correlation matrix for production process variables in dairy (2012–2013) Profit Output Fixed Working Wages Materials capital capital consumed Profit Output Fixed capital Working capital Wages Materials consumed

1.00 0.33 −0.01

1.00 0.83

1.00

−0.31

0.53

0.63

1.00

0.37 0.32

0.92 0.99

0.83 0.79

0.32 0.52

1.00 0.90

1.00

Source Author’s calculations based on ASI data, 2012–2013

are attributable to sub-sectoral differences due to technology as well as distribution costs. The larger product basket in dairy entails larger retailing costs of brands than in grain milling in Bihar, as discussed earlier. This explains why both size and working capital are negatively correlated with profits. Storage of milk is also much shorter term than grain in silos, hence intense usage of working capital cannot be reduced by a priori investments in storage. In fact, the larger the size of the plant, the larger should be the requirement of working capital. This is reflected in the positive correlation between fixed and working capital in dairy but not in grain milling. Hence, we do not find the dominance of the fixed capital effect. A final point is worth mentioning about the size of grain-milling units, relative to dairy, in Bihar. For the 71 grain-milling units in our data, the average total physical output in stage 1 is 2944 lakh INR, which is roughly one-third of that for 17 dairy processing units. Therefore, the per-unit average output for grain milling is 41 lakh INR for 2012–2013, whereas this same figure for dairy is 516 lakh INR: this number is 12 times larger than that for grain milling! This gives an indication of the tiny production capacity and therefore, factory sizes in grain milling relative to dairy in Bihar in 2012–2013.

6.5 Efficiency Analysis of Grain Milling and Dairy in Bihar As discussed in Chap. 3 and earlier sections of this chapter,14 all the physical costs: fixed costs in starting operations, variable costs as well as marketing costs matter for efficiency in operations. We visualize the production process through physical costs of set-up and marginal costs of production in the first stage of production (producing the physical output of the processed product) and a final stage with marketing 14 This

versity.

section has been co-authored with Sunil Kumar, Faculty of Economics, South Asian Uni-

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173

costs that convert the physical output to profits. Of all the available techniques of estimating efficiency extant in the literature, we find the two-stage network Data Envelopment Analysis (NDEA) most suitable for our analysis. It assesses the overall efficiency of operations, from the input to the profit stages, by treating the production of physical processed output as an intermediate stage. Before giving further details on this technique, we briefly discuss other methods of efficiency estimation. This discussion below highlights the relevance of the NDEA method for our stage-wise model of physical production costs in processed foods.

6.5.1 Traditional and Modern Approaches to Measure Efficiency Since the seminal work of Farrell (1957), two broad frontier approaches of efficiency measurement have evolved, one of which is parametric and the other non-parametric.15 Data envelopment analysis (DEA) is non-parametric, whereas stochastic frontier analysis (SFA) is parametric. It is worth noting here that there is vast theoretical and empirical literature on these approaches. Interested readers can refer to the excellent book by Fried et al. (2008), as well as consult the discussion in Kathuria et al. (2014) on the relative merits of these approaches. Each of these approaches has its own pros and cons and no consensus has emerged in the literature on which one is better. Both approaches measure the efficiency performance of each firm in an industry relative to the efficient frontier consisting of best-practice firms in the industry. In SFA, a specific functional form of the production function (like Cobb–Douglas or transcendental logarithmic) has to be specified a priori. Efficiency is then assessed in relation to this function with constant parameters. Depending on the chosen functional form, results will differ. On the other hand, in DEA, we do not specify a functional form, but require certain assumptions about the structure of production technology to be met, such as free disposability and convexity. In DEA, a separate mathematical programming problem has to be solved for obtaining the efficiency scores for individual firms included in the sample. Further, DEA is deterministic in nature since this postulates that all the distances from the efficient frontier are assumed to be caused by inefficiency. Over the years, DEA has emerged as a potent tool for efficiency measurement with wide-ranging applications in diverse areas (for example, banking (Wanke et al. (2017)), health care and hospitals (Chowdhury and Zelenyuk (2016)), microfinance (Wijesiri et al. (2017)) etc.). The standard DEA models can be classified into two

15 Other

than these, there is also the semi-parametric technique of Olley and Pakes (1996). As it is much more cumbersome to implement, we do not refer to it here. For further reading, one can refer to Table 2.1 on page 51 of Kathuria et al. (2014) which provides a summary and comparison of these methods.

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categories: radial (like CCR16 and BCC17 models) and non-radial (slack-based and range adjusted models) DEA models. Radial models treat the problem of reduction of inefficiency in a proportional manner or along a radial from the origin: given output (or inputs), inputs (or output) are (is) reduced (increased) proportionately. Non-proportional adjustments are accounted for in non-radial models. These models assume a ‘black box’ production structure, where inputs are assumed to flow into a black box and are transformed into outputs (Gulati and Kumar 2016). There is no mechanism in a standard DEA model to explain the internal functioning of the black box. Simply, in the standard DEA models, the inputs are transformed into outputs, but the transformation process is implicit and unknown (Matthews 2010). These models typically ignore any synergies between various stages of production or divisions within a decision-making unit (DMU) (Fukuyama and Weber 2012). Consequently, it becomes difficult to provide individual DMU managers with specific information regarding the sources of inefficiency within their DMUs (Holod and Lewis 2011). Kathuria et al. (2014) points out another problem with the standard DEA: inaccurate inefficiency assessment due to the presence of outliers. Despite its ability to be able to accommodate small sample sizes, sensitivity to outliers and the problem of lack of transparency in the production process are a drawback for the DEA method. Recently, Färe and Grosskopf (1996, 2000) have introduced the network DEA or NDEA, which has the capability to assess the efficiency of DMUs when their inputs and outputs form a network structure. This method opens the ‘black box’ by explicitly stating the transformation process of converting input resources into outputs, as mentioned in Yang and Liu (2012). The network structures range from a simple two-stage process to a complex system, where multiple divisions are linked together with intermediate measures (see the discussion in Chen et al. (2013)). Different NDEA models require significant modifications to the standard DEA models and provide a decomposition of the efficiency of the whole process into the efficiencies of the subprocesses. It is possible to model the production system as a closed one, with outputs from one stage becoming the inputs for the next stage. This is the variant of NDEA that we work with. It is also possible to work with open NDEA structures, where not only the first stage output but also other inputs enter the second production stage. The main advantage of NDEA approach over the standard DEA technique is that it opens up the black box into component processes and measures the (in)efficiency of each process as well as that of the system as a whole. Deconstruction of the black box provides a better understanding of the inefficiency at the process level as well as the overall level. We envisage the network structure in production in a two-stage fashion: stage 1 relates to the operating stage, where the production of physical output(s) from raw inputs take place in the factory and stage 2 converts the physical output to profits through the marketing stage. To the best of our knowledge, there are no other comparable techniques to the NDEA method for this two-stage decomposition of 16 This

is an acronym for the Charnes, Cooper and Rhodes model of 1978. is an acronym for the Banker, Charnes and Cooper model of 1984. Unlike the CCR model, the BCC model allows for variable returns to scale. 17 This

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175

cross-sectional data that we have on grain milling, dairy as well as rice mills. However, a drawback is that the literature around NDEA is still very new and evolving. We do not have enough papers in the empirical literature to benchmark our results against.

6.5.2 Two-Stage Network DEA (NDEA): A Brief Description To compute the efficiency scores of individual mills at the production and marketing stages, we employ the two-stage network DEA model developed by Kao and Hwang (2008). The overall operational efficiency is defined as the product of the efficiency scores at the production and marketing stages. Whereas some authors treat the NDEA process as a multiplicative decomposition of the different stages in the production process (e.g. Wanke et al. (2017)), others have treated the network structure in an additive fashion (e.g. Majiwa et al. (2018) or Kahi et al. (2017)) so that the efficiency scores in the subprocesses are added with suitable weights to derive overall efficiency scores. We use the multiplicative decomposition of the stage-wise efficiency scores as studied by Kao and Hwang (2008). The mathematical formulation of the model in Kao and Hwang (2008) model is discussed below in brief. We start with a general production process producing multiple outputs in the first stage of production, which feed into the second stage as in a closed system. Suppose there are n Decision-Making Units (DMUs) and the whole production process is composed of a series of two subprocesses. Assume that in stage 1, each DMU j , ( j = 1, . . . , n) uses m inputs X i j , (i = 1, . . . , m) to produce two types of output: Z g j , (g = 1, . . . , h) and Yr j with (r = 1, . . . , s). The h outputs labelled Z are known as intermediates, which then become the inputs in stage 2 to produce s final outputs labelled Y . Assume that u r , vi and wg are the multipliers for final stage output, inputs in the first stage and intermediate stage outputs, respectively. The efficiency of the kth DMU for the whole system, denoted as E ks , is the solution to the following problem, which we label Model 1: E ks = max

s 

u r Yr k

r =1

subject to

m 

vi X ik = 1

i=1

System constraints: s  r =1

u r Yr j −

m  i=1

vi X i j ≤ 0 ∀ j = 1, . . . , n

(6.4)

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6 Food Processing in Bihar: Efficiency in Physical Costs

Division constraints: h 

wg Z g j −

g=1 s 

m 

vi X i j ≤ 0 ∀ j = 1, . . . , n

i=1

u r Yr j −

r =1

h 

wg Z g j ≤ 0 ∀ j = 1, . . . , n

g=1

u r , vi , wg ≥ 0; r = 1, . . . , s; i = 1, . . . , m; g = 1, . . . , h After obtaining the optimal multipliers u r∗ , vi∗ and wg∗ , the system efficiency (E ks ) and division efficiencies (E k1 and E k2 ) are obtained as s u ∗ Yr k = rm=1 ∗r i=1 vi X ik h ∗ g=1 wg Z gk E k1 = m ∗ v X ik si=1 i∗ u Yr k E k2 = hr =1 r ∗ g=1 wg Z gk E ks

(6.5) (6.6) (6.7)

Note here that the system efficiency is the product of the two division efficiencies: h E k1

×

E k2

=

∗ g=1 wg Z gk m ∗ i=1 vi X ik

s

× hr =1

u r∗ Yr k

∗ g=1 wg Z gk

= E ks

(6.8)

The objective is to obtain the highest efficiency score for a DMU (the highest possible output for a given level of inputs), by optimizing with respect to the multipliers. The system constraint operates on the entire network: from initial inputs to final output(s). It is a feasibility condition which ensures that the final level of output can be produced with a given level of inputs. This condition is also applied to the intermediate stages of the problem: from initial inputs to intermediate output(s) through the first division constraint and from intermediate to final output(s) through the second division constraint. The multipliers are constrained to be non-negative. Note also the equivalence of this problem with a standard linear programming problem with a linear objective function and linear constraints. For the kth firm, let λ to be the vector of the multipliers or shadow prices us×1 , vm×1 and wh×1 such that λT =[u T vT wT ]1×(s+m+h) . X m×1 , Z h×1 and Y s×1 are, similarly, vectors for inputs, intermediate and final outputs respectively. Now, the linear programming version of the aforementioned problem is T [ Y 0 0](s+m+h)×1 Max λ1×(s+m+h) T subject to λ1×(s+m+h) [0 X 0] (s+m+h)×1 = 1

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177

System constraint: λT [Y −X 0] ≤ 0

Division constraints: λT [0 −X Z] ≤ 0 λT [Y 0 −Z] ≤ 0 u, v, w ≥ 0. In order to circumvent the problem of multiple solutions, Kao and Hwang (2008) and Kao (2017) have suggested that the first division efficiency E k1 be maximized while keeping maintaining the overall efficiency score E ks calculated from Model 1. The efficiency of the first division is measured through model 2 as follows: E 1k = max

h 

wg Z gk

g=1

subject to

m 

vi X ik = 1

i=1 s 

u r Yr k = E ks ·

r =1 h 

vi X ik

i=1

wg Z g j −

g=1

m 

m 

vi X i j ≤ 0 ∀ j = 1, . . . , n

(6.9)

i=1 s  r =1

u r Yr j −

h 

wg Z g j ≤ 0 ∀ j = 1, . . . , n

g=1

u r , vi , wg ≥ 0; r = 1, . . . , s; i = 1, . . . , m; g = 1, . . . , h After obtaining E k1 from (6.9), the efficiency of the second division E k2 is residually obtained as E k2 = E ks /E k1 . Using this method, we work out the two-stage NDEA efficiency scores for grain milling and dairy plants in Bihar from ASI data (at the 3-digit level) for 2012–2013. Table 6.6 presents the results. While the average total efficiency scores are very low (1 being fully efficient and 0 reflecting no efficiency) for both grain milling and dairy, there are three features worth mentioning:

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Table 6.6 NDEA scores for overall, technical and marketing stages in dairy (105) and grain milling (106), 2012–2013 Overall score Technical score Marketing score Grain milling (106) Mean Standard deviation Dairy processing (105) Mean Standard error

0.16 0.22

0.70 0.23

0.22 0.25

0.19 0.26

0.76 0.38

0.21 0.26

Source Author’s calculations based on ASI data

Observations about NDEA Scores in Grain Milling and Dairy i. The average efficiency scores are smaller than the standard deviation of these scores for both sub-sectors, reflecting the large variation in efficiency in the data. ii. Grain-milling units have a lower average efficiency score than dairy units. The latter are much larger, on average, in size than units in grain milling. Hence, we find a positive relationship between size and efficiency scores at the average, but across sub-sectors and not within a sub-sector. iii. Marketing, and not operating inefficiency, is responsible for the overall low-efficiency scores. While the average operating efficiency is 70% or higher in both these sectors, marketing inefficiency brings it down to around 20%. Technical inefficiency can be understood for new entrants, if we use technological inexperience as a reason. However, as we mention in the stylized fact S3. in Chap. 3, grain milling and dairy are fairly low tech and have standardized technologies. Hence, it is not surprising to find an absence of technical inefficiency. Marketing inefficiency requires a different explanation. At the heart of entrepreneurial inefficiency at the plant/factory level is the inability to market the processed product profitably, which we term marketing inefficiency. This is indicative of problems with the industrial ecosystem, as well as potentially intense competition among firms. Dairy processing has only 17 units in the ASI data, including the presence of a large number of plants owned and operated by the state-owned monopoly, COMFED. It is much more concentrated than grain milling which has 71 observations and no large state-owned monopoly firm. Therefore, intense price competition among firms reducing bottom lines is more likely to be present in grain milling than dairy. The fact that marketing efficiency scores are uniformly low for both dairy and grain milling indicate a structural problem with marketing processed products (be it simple raw rice processed from paddy or multiple milk-based derivatives such as curds, sweets, cottage cheese (‘paneer’ in the local language), butter, various forms of milk, etc.). The most plausible explanation is our claim of increased costs of marketing own-brand products for entrants in the presence of a missing middle size of firms.

6.5 Efficiency Analysis of Grain Milling and Dairy in Bihar

179

One lacuna with this analysis is that we cannot tie it up with the industrial policy variables, as the ASI data does not give us information on government subsidies. For this, we need information on the performance of units, along with the quantum of subsidies that it received. We find that it is possible for only a subset of grain-milling firms: those engaged in rice milling for the year 2014–2015 in Bihar by using data collected by the Chandragupt Institute of Management, Patna (CIMP). As most of the grain mills are, in fact, rice mills, this exploration of the impact of subsidies on production efficiency enriches the analysis presented in this section. This dataset also allows us to investigate the effect of agricultural pricing policy on efficiency. Paddy prices, through the Minimum Support Price (MSP) scheme, have recently been sharply increased in Bihar in relation to other neighbouring states. Is it possible that the effect of subsidies is outweighed by the impact of higher input prices in this raw material-intensive industry, leading to a failure of subsidies to raise profitability? We investigate this in the following Sect. 6.6.

6.6 Government Policy and Firm Efficiency: Rice Mills in Bihar Multiple government policies simultaneously act upon a firm, and their impact is not always in the same direction. While the government did provide a significant amount of front-loaded subsidies (as discussed in the previous Chap. 5), it also raised the input cost for rice mills through its agricultural pricing policy. The latter raised the MSP for paddy significantly relative to neighbouring states and for a raw material intensive industry, financial outcomes are likely to be impacted significantly by the latter through their impact on variable costs and working capital requirements. Hence, while government subsidies would defray part of the fixed cost of plant and machinery, operational costs would be high due to agricultural pricing policy. In this backdrop, we ask the question of what is the role of subsidies in the efficiency of rice-milling operations? Our approach tackles this question head-on by directly including sanctioned subsidy as an input into the production process, along with raw paddy, capital employed and land. Our direct inclusion of subsidies in the input stage is an innovation over the standard two-step procedure, as mentioned in Minviel and Latruffe (2017), Simar and Wilson (2007) and Banker and Natarajan (2008). The standard treatment of subsidy18 is either to treat subsidies as part of environmental variables in the second step, after estimating the efficiency scores through a DEA in the first step or to treat subsidies as part of the set of outputs, as discussed in Hadley (2006), Rasmussen (2010) and Silva and Marote (2013). As Minviel and Latruffe (2017) point out, treating subsidy in the output space is clearly problematic, as it artificially inflates efficiency figures. We bypass this criticism by including subsidies as inputs, as this is a realistic assumption for the Bihar trajectory. 18 This

is mostly in the context of input, output, environmental and disadvantaged area subsidies in the context of crop, dairy and other livestock farms.

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6 Food Processing in Bihar: Efficiency in Physical Costs

Note that our results are beholden to this modelling assumption as Berg (2010) has shown the sensitivity of the choice of inputs on DEA scores. To ensure that we use a meaningful measure of subsidy, we use the figure of total sanctioned subsidy, and not the exact amount in receipt of the mill. The sanctioned amount can signal the firm’s strength to its supply chain. Government support in a state like Bihar matters for the success of enterprises. Further, sanctioned subsidy is likely to be strongly correlated with the size of the plant, as we have defined earlier. Size of plant and machinery determined the quantum of sanctioned subsidies. However, Kathuria et al. (2014) mentions that an advantage of the DEA technique is that it can handle correlations among inputs. While we discuss the results in detail in what follows, here we provide a quick overview of our results. For 61 rice-milling units operating in Bihar in the year 2014–2015, our empirical claim holds: marketing inefficiency is the driver of overall inefficiency in these mills. When we benchmark our results against existing efficiency studies for rice mills in other regions, what is startling is the lack of profitability for Bihar’s rice mills. As marketing inefficiency is the driver of overall low poor profitability, one explanation comes from own-brand sales of entrants. This supports our claim regarding the role of the missing middle in the difficulties of small firm survival. The skewness in firm size distribution, we argue, is the reason why small entrants have to engage in the sale of own brands rather than through co-processing. We also explore the different incentives provided by the two alternative marketing channels for final output in the state: the government procurement route (SFC mills) as opposed to ‘non-SFC’ mills. Our result is that SFC mills have a lower average marketing efficiency than non-SFC mills. We find that almost all mills of different sizes and types (SFC or non-SFC) have own brands. Also, on average, SFC mills are smaller than non-SFC mills. This seems to be exactly as our theory predicts: smaller units should have a lower marketing efficiency. However, in order to extend this explanation to this case, we need to understand the trade-off between assurance in product placement and bargaining power in determining retail prices. As per our theory, the channel of marketing inefficiency of small firms is the requirement of expenses in branded sales. Our assumptions ensure that this logic goes through without addressing this trade-off in greater detail. Nonetheless, we have to do so for this double classification of an SFC mill, which is not only small but also engages in trade mostly with the government. Presumably, these mills/firms have a more assured channel of product placement than non-SFC mills. Therefore, uncertainty in branded sales should be larger for the latter and not the former. The government is in charge of retail pricing for SFC outlets. The bargaining power of the SFC mills vis-a-vis the government regarding final retail prices provides the answer to this trade-off. A low bargaining power with respect to the government would result in worse profit realizations for SFC mills than non-SFC mills, which can now explain the lower efficiency scores of the former relative to the latter.

6.6 Government Policy and Firm Efficiency: Rice Mills in Bihar

181

6.6.1 Subsidy and Rice Mills in Bihar For this analysis, we have culled out from the data from the ‘Report on the Physical Verification of Rice Mills in Bihar’ CIMP (2016) conducted by the CIMP in 2014– 2015 for 98 units in Bihar (under commercial production). We use the smaller subset of 61 units for which there is data on all variables of interest for our analysis. As the previous Chap. 5 shows, the immediate result of Bihar’s special food processing policy was the operationalization of a slew of rice mills in districts such as Buxar, Ara, etc. In our dataset, whereas only 2 mills had been cleared for approval for receiving this subsidy in 2009, a total of 35 mills received subsidy clearance between 2009 and 2012. The analysis period of 2014–2015 is roughly three years from the date of subsidy approval. Note that the subsidy expenditure presented by the government was not entirely on actuals. This was based on the basis of the Detailed Project Report (DPR) of the rice mill and did not reflect the actual expenditure of the government. Different mills were in different stages of subsidy disbursal.19 The scheme had a staggered timeline, with four instalments made at different stages of completion of mill shed and operations to presumably avoid opportunistic investment behaviour by mill owners. The first two instalments (amounting to around 40% of the total project cost) was disbursed early on after land was acquired and construction started. The third instalment necessitated proof completion of construction and the last stage was further delayed to ensure the start of operations. There is no identifiable pattern by firm size (total assets), age or debt to determine the stage of disbursement of the subsidy. All that the data reveals is that relatively more SFC mills have received the second subsidy instalment (40% of all SFC mills) than the first or all instalments (30% each), whereas very few non-SFC units have received the first instalment (13% of all non-SFC mills). A more striking feature of the set of mills we analyse is the large dispersion in size of operations. For instance, the average size of non-SFC firms, as measured by the DPR approved project cost is 8 crore INR, the standard deviation is as large as 114 crore INR. Some very large mills (32 crore INR) were cleared alongside projects costing less than one crore (23 lakh INR). While this problem is absent for SFC mills (mean size at 4 crore INR and standard deviation of size at 3 crore INR), this represents a very small set of mills. We find that in our data, there is a plenitude of the ‘small’ and a ‘missing middle and large’ in the size distribution of mills. The MSME criterion, mentioned in Chap. 4 yields no mills in the micro category, 2 small, 30 medium and 17 large. Superficially, the missing middle problem vanishes, but 12 mills greater than 10 crore INR cannot be categorized. Therefore, the MSME categorization is inappropriate to determine firm size. Unlike the ASI data on grain and rice mills, the CIMP data has observations on land. Using a more comprehensive definition of capital (plant, machinery and land measured at book value), Ganguli and Saha (2017) have worked out the categories for small, middle and large units in 19 In

terms of actuals, the subsidy amount was 11.68 crore INR for the 13 SFC mills and 1432.86 crore INR for the 48 non-SFC mills in 2014–2015.

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6 Food Processing in Bihar: Efficiency in Physical Costs

the food processing industry in Bihar.20 Using these cut-offs,21 we can categorize all the rice-milling units as per the following distribution: 49 small, 11 medium and 1 large rice mill. Relative to the small, it is a case of missing middle and large as we find in the case of grain milling from the ASI data in the last section. The average age of the units was five years up to December 2015 when the CIMP report was published. The range is 1–0.25 years, with the co-existence of some old and many very new unit units. Regarding debt, of the 52 mills that revealed information about the status and quantum of loans (and principal outstanding), only two SFC and eight non-SFC mills have fully repaid the principal amount, which is less than approximately 20% of the sample. The scenario, therefore, represents a large density of new small entrants with outstanding debt. The uncertainty of existence and competition with each other and neighbouring states is somewhat reflected in the product basket and capacity utilization. Mills that have sunk in a relatively larger capacity in the production of the parboiled variety of rice also diversify their output basket with the production of raw rice (17 of the 61 mills produce both varieties though the parboiled variety has a large market share in the eastern part of India around Bihar and West Bengal).22 Most mills/firms in our data are not exporters, catering to domestic demand alone. For SFC mills, the average capacity is 6.00 MT/hour and 4.08 MT/hour respectively for parboiled and raw polished varieties. Non-SFC mills have slightly higher capacity on average: 6.38 MT/hour and 4.97 MT/hour, respectively, for parboiled and raw rice. There is evidence of underutilization of capacity across all categories. This is present mostly in the case of raw rice for 16 non-SFC mills and 1 SFC mill. This is a reflection of marketing inefficiencies in selling the less popular variety in the regional market, parboiled or arwa with a higher local demand.

6.6.2 Efficiency in Rice Milling: Two-Stage Network DEA (NDEA) We work with the following structure of inputs and outputs in the two-stage network DEA model as shown in Fig. 6.1.

20 This is mentioned in the IGC project Report on the Study of Food Processing Industries in Bihar, which the author had co-written with Barna Ganguli in 2017. The GoI is now considering changing the definition of size from the current one based on investments in plant and machinery (excluding land and buildings). The 2018 proposal to classify the size of MSMEs on the basis of turnover is yet to be finalized. 21 Small units have project sizes between 1 and 10 crore INR, medium between 11 and 49 crore and large greater than or equal to 50 crore INR. 22 For instance, https://www.business-standard.com/article/markets/in-bengal-a-rice-economyrots-away-on-food-habits-subsidy-115090100437_1.html notes the high demand for parboiled rice in West Bengal.

6.6 Government Policy and Firm Efficiency: Rice Mills in Bihar

183

Fig. 6.1 Stylized two-stage network DEA model demarcating operating and marketing efficiency. Source Authors’ elaboration

We assume that the operation of rice mills consists of two distinct stages. The first stage corresponds to the production of unbroken rice and the second stage corresponds to the sales of the rice produced in the first stage.23 The inputs in the first stage are land (measured in square metre), capital employed (in money units (INR)), raw paddy input (measured in metric tonnes) and subsidy from government (measured in INR)24 with the output being unbroken rice (measured in metric tonnes). The latter acts as an input in the second stage, resulting in the output of profit after tax (PAT, measured in rupees). We assume a 68% recovery rate of unbroken rice from paddy.25 We do not include other inputs (husk, brokens, etc.) as they are not the major source of revenue for these mills, as mentioned earlier. All nominal variables are measured at 2013–2014 prices.

6.6.2.1

Summary of Results: Decomposition of Efficiency Scores

Table 6.11 in the Appendix summarizes the individual mill scores alongside millcharacteristics like age of enterprise, the district of location of the mill and debt– equity ratios for SFC and non-SFC mills for the baseline model 1, which includes paddy, land, capital and subsidy as inputs into the first stage of production. A general observation is that overall efficiency scores are very low (ranging from a minimum of 0.001–0.725), with marketing inefficiency showing up as the real reason for the small magnitudes for efficiency. For instance, the average overall efficiency score for

23 This break-up of the stages of production allows us to understand the technology and marketing drivers of efficiency separately. This is particularly so for the SFC as opposed to the non-SFC mills. Marketing risks are more likely to differ between these mills rather than operational factors, given that milling technology is standardized for most mills. 24 Note that we could not include labour input in the first stage as there was no information about employment or even wage data in the CIMP Report. 25 Note that the Government of India report on rice-paddy profile available at https://agmarknet.gov. in/Others/rice-paddy-profile_copy.pdf mentions that the recovery rate of rice on an average from cleaned paddy is 72% in modern mills and around 65% in traditional mills. Given the estimates of Singha (2013), which are also in a similar range, we settle for a conservative 68% recovery rate of rice from paddy.

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6 Food Processing in Bihar: Efficiency in Physical Costs

the SFC mills is as low as 0.009, the average operational efficiency score is as high as 0.921. The scaling down of the overall efficiency score is due to the marketing efficiency score (average for SFC mills is 0.011). Three SFC mills have operational efficiency scores of 1 (indicating that they are operationally fully efficient), their marketing efficiency scores are below 1 by a large margin. This pattern is present for the non-SFC mills as well, with the average operational efficiency score of 0.84 pulled down multiplicatively by the much lower average marketing efficiency score of 0.08 resulting in the average overall efficiency score of 0.06. Seven of the 48 nonSFC mills have an overall efficiency score of 1, their average marketing efficiency score is as low as 0.013. No mill is fully efficient overall in our data, the highest score being 92% (Mangaldeep Rice Mills; a non-SFC mill). Only two mills achieving full marketing efficiency, both of which are non-SFC; whereas we find 10 mills are fully operationally efficient, seven of which are non-SFC mills. None of the SFC mills achieve full marketing efficiency. While size is significantly positively correlated with overall efficiency for SFC firms (coefficient of 0.57), non-SFC mills reveal no pattern with a negligible coefficient of −0.004. However, non-SFC mills are, on an average, of a larger size than SFC mills. We also find that there is no strong correlation between the overall efficiency scores and outstanding loans for these firms, though the signs are flipped between SFC and non-SFC mills. While the coefficient of correlation is 0.20 between efficiency and outstanding loans for SFC mills, it is −0.10 for non-SFC mills. What is the pattern for the components of overall efficiency? Marketing efficiency is positively correlated with outstanding loans for the SFC mills, but negative for the non-SFC mills. The opposite is true for operating efficiency. High debt might not increase efficiency, particularly for small young firms where debt is more of a liability than a signal of efficiency. That financing of debt is a large source of uncertainty for the existence of small firms is well-understood. As these correlations are not significant, we do not press this issue further. To interpret our results better, we create two hypothetical scenarios: Model 2 which removes subsidy from stage 1 inputs, ceteris paribus and Model 3, which studies the efficiency scores and its break-up while maintaining paddy, land and subsidy in the input set for stage 1, but without capital. A unique pattern emerges along two dimensions, as evidenced in Table 6.7.

6.6 Government Policy and Firm Efficiency: Rice Mills in Bihar Table 6.7 Efficiency score descriptives for SFC and Non-SFC mills Mean Median Std. Dev. Model 1

Model 2

Model 3

Overall SFC efficiency Non-SFC Operating SFC efficiency Non-SFC Marketing SFC efficiency Non-SFC Overall SFC efficiency Non-SFC Operating SFC efficiency Non-SFC Marketing SFC efficiency Non-SFC Overall SFC efficiency Non-SFC Operating SFC efficiency Non-SFC Marketing SFC efficiency Non-SFC

185

Skewness Kurtosis

0.00922

0.00704

0.00990

1.93203

4.58387

0.06003 0.92112

0.01661 0.95543

0.16454 0.07739

4.57325 −0.25578

20.92014 −1.90661

0.85720 0.01058

0.88064 0.00752

0.14279 0.01206

−2.61829 2.12159

10.68332 5.45605

0.07245 0.00215

0.02056 0.00173

0.19934 0.00187

4.46443 1.45831

19.48118 2.00057

0.00964 0.45068

0.00286 0.32062

0.02191 0.38295

4.13988 0.50272

17.56711 −1.54078

0.28094 0.01058

0.16179 0.00752

0.26717 0.01206

1.44652 2.12159

1.37201 5.45605

0.07245 0.00911

0.02056 0.00664

0.19934 0.00990

4.46444 1.96069

19.48118 4.68256

0.05888 0.90378

0.01583 0.87725

0.16158 0.07138

4.61318 0.10487

21.00881 −1.90774

0.83602 0.01058

0.84803 0.00752

0.14919 0.01206

−2.86821 2.12158

12.56636 5.45605

0.07245

0.02056

0.19934

4.46444

19.02942

Source Authors’ calculations

i. Subsidy is more critical for operational efficiency than capital employed: The average overall efficiency scores (for both SFC and non-SFC mills) is marginally lower in Model 3 (without capital) than in the baseline Model 1. However, Model 2 (without subsidy) reports significantly lower overall efficiency scores for both types of mills. This, in itself, is not surprising given the observation of Berg (2010) that the DEA efficiency scores blow up with a larger number of inputs. Reducing the set of inputs is expected to reduce the overall efficiency scores. However, what is surprising is that the drop in the average overall scores is driven by a drop in the average operating efficiency while the average marketing efficiency scores are unaffected in Model 2. The average operating efficiency scores in Models 1 and 3 are very similar, indicating that subsidy mimics capital and is a very important input in the production stage determining the operational efficiency of these mills.

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6 Food Processing in Bihar: Efficiency in Physical Costs

ii. Mean efficiency score is lower than standard deviation: which indicates the large noise in the data arising due to the bi-modality and ‘missing middle’ problem discussed earlier. Coefficient of variation, which is greater than one for most observations, supports the diversity of efficiency that exists due to the size dispersion with large bunching at the left tail.

6.6.3 Benchmarking Efficiency Scores Our data reveals that though the overall efficiency scores are around 1% for SFC mills and approximately 6% for non-SFC ones, the operating efficiency scores are around 92% for SFC versus 86% for non-SFC ones on an average (refer to Table 6.7 Model 1). Marketing efficiency scores, being very low for both these types of mills (1% for SFC mills and 7% for non-SFC ones), pull down multiplicatively the overall efficiency scores. How do these results compare with other empirical estimates of rice-milling efficiency? Unfortunately, there are very few studies employing a DEA measurement for rice-milling efficiency. For Indian data, consider Kumar and Basu (2008) and Ali et al. (2009). Though they discuss efficiency of rice milling in Indian conditions, they do not conduct the NDEA analysis that we do. Similarly, Shwetha et al. (2011) discusses characteristics of rice milling in Karnataka, India, without engaging in any estimations of efficiency of those mills. They find that the final product prices are raised due to hikes in the input price through taxes and middlemen commissions. Similarly, Singha (2013) provides descriptives of rice milling, such as marketing margins and out-turn ratios (in converting paddy to rice) in their comprehensive report for three states in India (Karnataka, West Bengal and Punjab), without discussing any measures of the efficiency of these rice mills. Among international comparators for rice-milling efficiency, Wongkeawchan et al. (2004) compute the average total efficiency score as 84% for Thailand and 87% for Taiwan in the year 2000 using the DEA technique. Three of 36 mills in Thailand and 4 out of 35 mills in Taiwan are fully efficient. In contrast, none of the mills in our data are fully efficient. Methodologically, we are closest to Majiwa et al. (2018). Employing the NDEA model in the additive form, they comment on the technical efficiency scores for 26 rice mills in the Mwea region of Kenya in June 2014. Compared to their average score of 66%, our efficiency scores are surprisingly low. Operating efficiency scores in Bihar’s rice milling, which is between 85 and 90% on average, gets pulled to zero due to the multiplicative input of extremely low marketing efficiency scores. Some possible explanations for differences in results are that their stage-wise decomposition and linking function is different from ours. Unlike our decomposition of the stages of the milling process into operational and marketing stages as shown in Fig. 6.1, they decompose the milling process into stages of drying (with intermediate output paddy) and milling (with output as rice). Further, our linking function between the two stages is multiplicative, so that our overall efficiency scores are a product of

6.6 Government Policy and Firm Efficiency: Rice Mills in Bihar

187

the scores from operation and marketing. Majiwa et al. (2018) uses an additive link, assigning an equal weight of 0.5 to each stage of drying and milling. One issue with their results is the extremely small size of data, which renders the DEA estimates potentially unreliable. Second, even if we use the additive method with 0.5 weight on the two stages, our overall efficiency scores would be much lower than those in Majiwa et al. (2018) (almost half the value of the operational efficiency scores in their paper). Wongkeawchan et al. (2004) questions the two-stage decomposition of Majiwa et al. (2018). They point out, a rice mill should be viewed as an integrative unit performing two crucial functions: milling of rice and marketing of the final product. They clearly define the second role and its importance in the following manner ... as a marketing unit, it purchases paddy from farmers and forms part of the total distributive chain of milled rice to consumers, commercial merchants and government agencies.

Though they mention this, their analysis is restricted to a simple DEA and not a two-stage analysis. We carry through the logic of Wongkeawchan et al. (2004) in our two-stage decomposition of efficiency scores for Bihar. Note that operational efficiency scores in Bihar, which are quite competitive relative to international standards, are closely linked with the input of subsidy. A possibility is that these efficiency scores would be different, if we remove this subsidy and retain labour as input instead. Given the sensitivity of NDEA to the choice of inputs, one robustness check of the empirical validation of our theory is to remove subsidies and instead include labour input in the production process. Absence of any kind of labour data in the CIMP publication prevents this cross-check. We do, however, investigate the possible channels why marketing risks appear in our data, by looking at the differential impacts on SFC versus non-SFC mills in the following section.

6.6.4 Efficiency Differences Between SFC and Non-SFC Mills: Simar–Zelenyuk-Adapted Li Test To ascertain whether the observed differences in efficiency across the SFC and nonSFC mills are statistically significant, we use the Simar–Zelenyuk-adapted Li test. This is a bootstrap-based test suggested by Li (1996) for testing the equality of distributions and adapted to the DEA context by Simar and Zelenyuk (2006). The Li test measures the lack of overlap between the masses of any two distributions, and therefore it can detect differences in all the moments simultaneously. For this reason, the Li test is generally superior to using other moment-based criteria (see Curi et al. 2015). In particular, we adopt Algorithm II from Simar and Zelenyuk (2006). This algorithm is based on bootstrapping the Li test statistic using the sample of efficiency estimates, where those equal to unity are ‘smoothed’ away from the boundary

188

6 Food Processing in Bihar: Efficiency in Physical Costs

Table 6.8 Simar–Zelenyuk-adapted Li test statistics for equality of distribution of efficiency scores between SFC and Non-SFC mills Null hypothesis (H0 ) Simar–ZelenyukBootstrapped p-value Decision adapted Li test statistic Panel A: baseline model pdf (OE of SFC) = −0.7867 pdf (OE of non-SFC) pdf (OPE of SFC) = −0.1602 pdf (OPE of non-SFC) pdf (ME of SFC) = 0.3445 pdf (ME of non-SFC) Panel B: counterfactual model without subsidy pdf (OE of SFC) = −1.4663 pdf (OE of non-SFC) pdf (OPE of SFC) = 0.2997 pdf (OPE of non-SFC) pdf (ME of SFC) = 0.3445 pdf (ME of non-SFC) Panel C: counterfactual model without capital pdf (OE of SFC) = −0.9992 pdf (OE of non-SFC) pdf (OPE of SFC) = 0.5358 pdf (OPE of non-SFC) pdf (ME of SFC) = 0.3445 pdf (ME of non-SFC)

0.01

Reject H0

0.114

Do not reject H0

0.007

Reject H0

0.077

Reject H0

0.837

Do not reject H0

0.007

Reject H0

0.012

Reject H0

0.053

Reject H0

0.007

Reject H0

Source Authors’ calculations

by adding a small noise of an order of magnitude smaller than the noise of estimation (suggested by the rate of convergence).26 Note that OE stands for overall efficiency, OPE represents operating/technical efficiency and ME stands for marketing efficiency in Table 6.8. As is obvious from Table 6.8, the Li test rejects the equality of distribution of efficiency scores for overall and marketing efficiencies for the baseline Model 1 (refer to Panel A) for the two types of mills: SFC and non-SFC. However, it does not reject the null hypothesis of similarity in operating efficiency for both types of firms. The two-stage network break-up shows that while the two types of mills are significantly different in their overall and marketing efficiencies, operationally their efficiency scores are comparable. As we mentioned earlier, given low tech standardized technology in operating a rice mill, the technical stage of production is likely to be the same for the two types of mills. The difference is in marketing efficiency, which is likely to be specific to a mill’s business environment. 26 We

use the MATLAB codes developed by Simar and Zelenyuk (2006) for computing the Li-test statistic.

6.6 Government Policy and Firm Efficiency: Rice Mills in Bihar

189

Model 2 (without subsidy) reflects the same result. This is in line with our earlier result that subsidy mimics capital for operational efficiency of the two types of mills, but does not affect marketing risk. Model 3 (without capital) results in the rejection of the null hypothesis of similarity of the two types of firms on all counts of efficiency. On an average, the SFC projects cost around 4 crore INR whereas the non-SFC projects is twice that, but the variance in size is much larger for non-SFC projects than their SFC counterparts. The SFC mills employ, on an average, 112 lakh INR worth capital (26% return on capital employed) while the non-SFC counterparts on an average working with an average of 226 lakh INR worth capital (16% return on capital employed). The average behaviour of SFC mills (smaller project size with less capital employed) is masked by the larger usage of capital employed for the larger sized average non-SFC mill. Therefore, we get similar operating efficiency score distributions when capital is included as input, but the distributions differ when capital is removed in Model 3.

6.6.5 Quantile Regression for Explaining Inefficiency in Rice Mills in Bihar Consider, first, the histogram of overall efficiency scores for the rice mills that we analyse, as shown in Fig. 6.2. It is clear that 90% of the scores are bunched near zero, with a clear ‘missing middle’ set of scores. With such a large concentration of extremely low values in our data, our hunch is that any causal analysis, particularly those models attempting to explain the empirical mean/average score in the data will give erroneous results. Refer to the concerns raised by Koenker and Bassett (1978) in discussing quantile regressions. The main purpose of a quantile regression, particularly predicting the median, is an exercise of finding a solution to the problem of minimizing the sum of absolute residuals; rather than minimizing the sum of squares of residuals as ordinary OLS estimates do (see the discussion in Koenker and Hallock (2001)). Quantile regressions are not affected by extreme values, which is an advantage over OLS estimates. It is appropriate in our dataset which has extreme values, making prediction of the median efficiency scores more meaningful than the mean. However, the lack of clarity around the interpretation of a multivariate median, as mentioned in Small (1990), compels us to conduct a causal analysis with a single regressor.27

27 Margaritis and Psillaki (2007) conduct a multivariate quantile regression to explain firm leverage

using DEA efficiency scores and other controls as explanators. Their motivation for using the multivariate quantile regression is that their dependent variable (firm leverage) exhibits excessive kurtosis and is skewed to the right, as we have for our dependent variable (efficiency scores). They, however, do not provide a precise interpretation of the marginal effects for that regression.

6 Food Processing in Bihar: Efficiency in Physical Costs

4 0

2

Density

6

8

190

0

.2

.4

.6

.8

overalleff

Fig. 6.2 Histogram for overall efficiency scores. Source Author’s calculations

We experimented with several independent variables, such as debt, age of enterprise and firm size captured either by the logarithm of total assets or an SFC dummy28 to explain the dependent variable of overall efficiency scores from our NDEA analysis. Of all the specifications, we find that the size variable captured by the logarithm of total assets as significant. Table 6.9 contrasts the OLS estimates against the estimates from the quantile regressions. What we find is in support of our theory: that size matters for overall efficiency. Larger the size, more profitable is the rice mill. This is the case with both the OLS and the quantile estimates. The constant in the quantile regression is the predicted value of the median of overall efficiency scores when the logarithm of total assets takes a value of zero. At the median, the coefficient of the size variable indicates that for every unit change in the logarithm of total assets, there is a positive and significant increase of approximately 0.013 units in the overall efficiency for the rice mills in our data. Note that higher quantiles behave like the median and that the average effect on efficiency (the OLS estimate) overstates the impact of size on profitability. For rice milling, unlike the grain milling result we see earlier, the lower quantiles show lesser sensitivity to size than the median quantile. On the whole, the impact of size is seen to positively influence the ability of a rice-milling unit in converting inputs to final profits.

28 We expect the logarithm of total assets to be positively correlated with the SFC dummy and do not use them simultaneously. We find that the value of the correlation coefficient between them is 0.36 in our data.

6.6 Government Policy and Firm Efficiency: Rice Mills in Bihar

191

Table 6.9 Quantile regression with overall efficiency as the dependent variable Regressor OLS Quantile regression coefficients 0.25 0.5 (Median) 0.75 log (total assets) constant

0.035** (0.0157) −0.577 (0.282)

0.006* (0.001) −0.105 (0.029)

0.013*** (0.003) −0.204 (0.052)

0.013*** (0.006) −0.2 (0.113)

Source Author’s calculations Note *** means significance at 1%, ** means significance at 5% and * means significance at 10%

6.7 Conclusion The two-stage NDEA we conduct in our research on rice mills in Bihar points to the challenges in marketing, reflected in the extremely low marketing efficiency scores which dampen the overall efficiency estimates. We use the SFC mills as the yardstick to measure the extent of this marketing problem faced by non-SFC mills. We find that small and large mills both market own brands in processed rice from our data. Consider the correlation between capital employed by the mill (as a measure of the size of operations) and the number of brands. It is an insignificant 0.25, indicating no association between plant size with the number of own brands marketed. There is also no significant correlation between the number of brands and the type of mill (SFC/ non-SFC).29 In fact, one of the mills marketing the brand ‘Tractor’ in our data specifically mentions that low retail price realization is its major growth challenge in the CIMP report. Our empirical exercises show that the smaller SFC mills have lower profitability than larger non-SFC mills. To what extent can we apply our theory regarding the role of the missing middle in raising marketing expenses for small firms? We find that marketing inefficiency mars the performances of all our mills. To answer this question for SFC separately from non-SFC mills, we have to do some more work. Not only are the SFC mills smaller in size on an average, but they also deal with a different agent: the government and not the open market. Due to this supply positioning of SFC mills, we have to relate the discussion with the agricultural pricing policy of the government. This also has implications for non-SFC mills. In recent times, Bihar has seen a sharp increase in the input price of paddy. Around the time when the 2008 food processing policy was announced, the procurement rates for paddy set by the government of Bihar was comparable with that of the neighbouring state of West Bengal (one of the leaders in processed rice). However, to increase agricultural incomes, the Bihar government has progressively raised the procurement rates (relative to West Bengal), as Table 6.10 testifies. Whereas in 2014–2015 the procurement rate for raw paddy was lower in Bihar than in West

29 The

value of this correlation coefficient is 0.4 and is not significant.

192

6 Food Processing in Bihar: Efficiency in Physical Costs

Table 6.10 Price differential year-wise between Bihar and West Bengal for paddy procurement KMS Difference of Bihar from West Bengal rates rates per quintal Central pool operations DCP operations Raw rice Parboiled rice Raw rice Parboiled rice Common Grade A Common Grade A Common Grade A Common Grade A 2014– 2015 2015– 2016 2016– 2017

−10.06

−10.22

−9.58

−10.03

50.50

50.54

49.98

50.01

24.35

25.03

24.02

24.71

50.69

50.65

50.54

50.09

40.09

40.72

40.64

41.26

52.82

52.53

53.42

53.14

Source Author’s calculations based on data from the Food Corporation of India

Bengal, by 2016–2017, the price differential for Bihar relative to its neighbour was between 40 INR to 53 INR per quintal. First, input price shock might result in reducing marketing margins for non-SFC mills, which face a much larger input price risk and cannot raise output prices in the face of stiff competition from local and neighbouring state (West Bengal) mills. Second, lack of incentives to invest in marketing channels, given low margins from trading as SFC units (mentioned in CIMP (2016)), can lead to low marketing efficiency scores for SFC mills relative to non-SFC mills.30 If the former is the reason for marketing inefficiency, then we would have seen lower average marketing efficiency scores for non-SFC units as well. However, as we observe exactly the opposite pattern in our data, we conclude that it is the second channel (low incentives to invest in marketing channels by SFC mills) which is the prime driver for marketing inefficiency which multiplicatively reduces overall efficiency scores. This channel can have two alternative reasons: 1. Moral hazard for SFC mill owners: assuming that government contracts provide assured returns, the mill owners have no incentives to invest in marketing expenditure due to the secured government distribution channel for output. 2. No moral hazard but low bargaining power of SFC mill owners: the government has monopoly power in setting the price of SFC milled rice. Assuming that the SFC mill owner has very little bargaining power, the government can squeeze their profits through low pricing. This would be done to ensure security of food through a low milled price of rice. Now, the low price realization will not cover expenditure for achieving efficiency in marketing. It is not possible, given current information, to distinguish between these two options. The truth probably lies midway. This discussion shows the role of exter30 One

reason for this is the low bargaining power of the SFC units against state agencies.

6.7 Conclusion

193

nalities in different elements of public policies. The effect of industrial subsidies on profits can be wiped out if other policies, like agricultural pricing, raise input prices significantly. Though this is not the case for Bihar’s rice mills as non-SFC mills perform better than SFC mills, this angle that should be kept in mind in designing public policies. For Bihar, subsidies have played a critical role in the operational efficiency of rice mills. This is apparent from our counterfactual exercises with Models 2 and 3, where we remove subsidy (as well as capital) from the inputs entering the production process for both SFC and non-SFC mills. Subsidy mimics capital and affects operational efficiency. Subsidies seem to matter more for setting up operations in the first stage of the production process than in achieving marketing efficiency for Bihar’s rice mills. That grain milling and dairy also face the same issues with achieving profitability as we see in the case of rice mills indicate problems with the industrial ecosystem in Bihar. However, two comments are due regarding the robustness of our results. First is the method that we are using in this chapter, particularly the multiplicative version of the NDEA model to decompose efficiency scores. As discussed earlier, the overall efficiency scores will be slightly higher if we used an additive rather than a multiplicative NDEA model. However, these numbers for dairy and grainmilling units (rice mills in particular) would still not be comparable with international standards. Second, the discussions in this chapter are limited to physical costs, that are readily measurable. Other non-physical costs that can explain industrial outcomes from a different perspective: that of entrepreneurial perceptions and behaviour. We turn to this in the next chapter in this book. Acknowledgements I thank my colleague Sunil Kumar, Professor, Faculty of Economics, South Asian University for collaborating with me for conducting the NDEA analysis in this chapter.

Appendix: Table Describing Efficiency Scores and Other Features of SFC and Non-SFC Mills See Table 6.11.

194

6 Food Processing in Bihar: Efficiency in Physical Costs

Table 6.11 Efficiency scores for all mills in Model 1 (with subsidy and capital) DMU

Overall efficiency

Operating Marketing District effiefficiency ciency

Debt-equity ratio

Age

Panel A: SFC firms 1. Kanhayi Jee Rice Mill Pvt. Ltd.

0.00914

0.84472

0.01082

Aurangabad

14.04

4.50

2. Bhaskar Rice mill

0.00718

0.95543

0.00752

Bhojpur

6.04

6.00

3. Bhojpur Rice Mill

0.00704

0.81177

0.00867

Bhojpur

1.78

7.00

4. Lakho Agriculture and Food Products

0.03669

0.81583

0.04497

Buxar

0.71

5.50

5. Jagdamba Bhawanni Enterprises

0.01395

0.96392

0.01447

E. Champaran

2.98

7.00

6. Kishan Rice Industries

0.00244

0.99364

0.00246

Gopalganj

1.88

3.00

7. Hari Rice Udyog

0.00676

1.00000

0.00676

Kaimur

3.76

3.00

8. Kaimur Kohinur Rice Mill

0.01495

0.84640

0.01766

Kaimur

1.97

4.75

9. Maa Saraswati Modern Rice Mill

0.00110

1.00000

0.00110

Patna

2.75

6.00

10. Rameshwaram Rice Mills

0.01645

0.88555

0.01857

Rohtas

1.02

5.75

11. Sri Jagdish Industries

0.00021

0.99614

0.00021

Vaishali

1.80

4.00

12. Gauri Shankar Agriproducts

0.00217

0.86121

0.00252

W. Champaran

0.56

3.00

13. Nirmal Agriproducts

0.00173

1.00000

0.00173

W. Champaran

2.00

3.50

Source: Authors’ calculations Panel B: Non-SFC firms 1. Arwal Food Products

0.00069

0.89574

0.00078

Arwal

5.93

4.50

2. Ayush Rice Mill

0.00440

0.81007

0.00543

Aurangabad

11.70

2.50

3. Narmadeshwar Rice Mill

0.03391

0.85010

0.03989

Aurangabad

2.17

5.50

4. Kumar Commodity and Food Management

0.01305

0.89602

0.01456

Begusarai

16.34

3.50

5. Alok Harsh Rice Mill

0.00309

0.17652

0.01751

Bhojpur

5.23

4.50

6. Ananya Agro Tech

0.02111

0.92997

0.02270

Bhojpur

22.70

4.00

7. Sri Shivam Rice Mill

0.00077

0.92638

0.00083

Bhojpur

4.53

4.50

8. Lal Chinta Rice Mill

0.00651

0.81465

0.00799

Bhojpur

7.91

4.50

9. Shahabad Agro

0.00424

0.94355

0.00449

Bhojpur

4.79

4.00

10. Siddhashram Rice Mills Cluster

0.09005

0.88783

0.10143

Buxar

0.97

8.00

11. Ripuraj Agro

0.05139

0.77030

0.06671

E. Champaran

1.77

5.50

12. Tridev Rice Mill

0.00717

0.98706

0.00726

E. Champaran

3.41

7.50

13. Shivam Modern Rice Mill

0.04530

0.75633

0.05989

E. Champaran

3.80

4.50

14. Shiv Shakti Agro Products and Rice Industries

0.01598

0.73029

0.02188

E. Champaran

0.86

6.50

15. Mangaldeep Rice Mills

0.91553

0.91553

1.00000

E. Champaran

3.14

5.25

16. Nilkanth Chawal mills

0.03437

0.82125

0.04185

Gaya

0.64

5.50

17. Nilkanth Chawal Mills Expansion Project

0.02893

0.86400

0.03348

Gaya

0.64

1.50

18. Pawan Rice Industries

0.01397

0.72568

0.01925

Gopalganj

1.65

6.25

19. Mega Rice Mills

0.01724

0.92198

0.01870

Jahanabad

1.34

8.00

20. Kalp Srijan Rice Mill

0.00912

0.84074

0.01085

Kaimur

2.40

4.00

21. Vina Paras Modern Rice Mill

0.04206

0.79331

0.05302

Kaimur

2.01

6.00

22. Sriji Enterprises

0.00512

0.48734

0.01051

Kaimur

3.11

4.00

23. Shobnath Rice Mills

0.00419

0.93824

0.00447

Kaimur

3.92

5.75

(continued)

References

195

Table 6.11 (continued) DMU

Overall Operating Marketing efficiency efficiency efficiency

District

Debt-equity ratio

Age

Panel A: SFC firms 24. Krishna Rice and Polisher Mill 0.00088

1.00000

0.00088

Kaimur

4.74

7.00

25. Manjusha Mini Rice Plant

0.01362

0.91502

0.01488

Kaimur

6.29

4.00

26. Hentech Agrovet

0.03947

0.87345

0.04518

Muzaffarpur

2.47

4.00

27. Budh Bihar Rice Mill

0.02073

0.79211

0.02617

Nalanda

2.53

3.50

28. Kisan Rice Industries

0.00938

1.00000

0.00938

Nalanda

4.84

4.25

29. Gajanan Siddhi Vinayak Foods 0.00888

0.98581

0.00900

Rohtas

2.27

4.50

30. Shanti Modern Rice Mill

0.02611

1.00000

0.02611

Rohtas

1.84

5.75

31. Guptaji Brothers Rice Mill

5.75

0.01554

0.81461

0.01907

Rohtas

1.11

32. Om Shivam Modern Rice Mill 0.03553

0.82091

0.04328

Rohtas

0.94

10.25

33. Pawapuri Rice Mills

0.01141

0.78975

0.01445

Nalanda

0.40

7.50

34. Shivsagar Rice Mill

0.02595

0.96365

0.02693

Nalanda

5.19

6.50

35. Kusum Agro Endeavor

0.04300

0.81097

0.05302

Patna

1.48

3.00

36. Krrish Rice Mills

0.04274

1.00000

0.04274

Patna

1.43

4.50

37. Aarna Foods

0.00847

1.00000

0.00847

Patna

7.44

9.00

38. Sonamoti Agrotech

0.02182

0.91542

0.02383

Patna

6.26

6.00

39. Vindhyabasini Rice Mills Cluster

0.06256

0.83629

0.07481

Rohtas

1.69

6.00

40. Bhagwan Agro

0.00876

0.90978

0.00963

Rohtas

1.29

7.50

41. Jai Kisan Food Products

0.00582

1.00000

0.00582

Rohtas

2.18

8.00

42. Shivjee Modern Rice Mill

0.04783

0.99595

0.04802

Rohtas

1.11

7.50

43. Jhunjhunwala Oil Mills

0.11471

0.90598

0.12662

Rohtas

1.56

5.25

44. Kartika Rice Mill

0.17690

0.77751

0.22752

Samastipur

2.22

5.00

45. Green Earth Fertilizer

0.00095

0.79516

0.00120

Sitamadhi

2.42

3.50

46. Loma Rice Mill

0.72478

0.72478

1.00000

Vaishali

5.29

4.25

47. Shree Riddhi Siddhi Agriproducts

0.00045

1.00000

0.00045

W. Champaran

1.31

4.00

48. Mahalaxmi Industries

0.04719

0.83559

0.05647

W. Champaran

0.33

5.50

Source Authors’ calculations

References Ali J, Singh SP, Ekanem EP (2009) Efficiency and productivity changes in the indian food processing industry: determinants and policy implications. Int Food Agribus Manag Rev 1:1–24 Banker RD, Natarajan R (2008) Evaluating contextual variables affecting productivity. Oper Res 56(2):48–58 Berg S (2010) Water utility benchmarking: measurement methodologies and performance incentives. International Water Association Publishing, London Bertolini P, Giovannetti E (2006) Industrial districts and internationalization: the case of the agrifood industry in Modena. Italy, Entrep Reg Dev 18(4):279–304 Bhalla A (1965) Choosing techniques: handpounding v. machine-milling of rice: an indian case. Oxf Econ Pap 17(1):147–157 Campbell D (1967) Choosing techniques; an indian case: a comment. Oxf Econ Pap 19(1):133–135

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Chen Y, Cook WD, Kao C, Zhu J (2013) Network DEA pitfalls: divisional efficiency and frontier projection under general network structures. Eur J Oper Res 226(3):507–515 Chowdhury H, Zelenyuk V (2016) Performance of hospital services in ontario: DEA with truncated regression approach. Omega 63:111–122 CIMP (2016) Physical verification of rice mills in Bihar. Third Party Evaluation Project # 22, Chandragupt Institute of Management (CIMP), Patna, Bihar Curi C, Lozano-Vivas A, Zelenyuk V (2015) Foreign bank diversification and efficiency prior to and during the financial crisis: does one business model fit all? J Bank Financ 61:S22–S35 Desai N, Gopalan P (1983) Changes in the food processing industry from tradition to modern forms and its impact on women’s role and status. ICSSR (mimeo) Diarra SB, Staatz JM, Bingen RJ, Dembele NN (1999) The reform of rice-milling and marketing in the office du niger: catalyst for an agricultural success story in mali. Staff Paper Series 11729, Michigan State University, Department of Agricultural, Food, and Resource Economics, USA Dunne T, Roberts MJ, Samuelson L (1988) Patterns of firm entry and exit in U.S. Manufacturing Industries. RAND J Econ 19(4):495–515 Eriksson G (1984) Growth, entry and exit of firms. Scand J Econ 52–67 Färe R, Grosskopf S (1996) Productivity and intermediate products: a frontier approach. Econ Lett 50(1):65–70 Färe R, Grosskopf S (2000) Network DEA. Socio-Econ Plann Sci 34(1):35–49 Farrell MJ (1957) The measurement of productive efficiency. J R Stat Soc Ser A (General) 120(3):253–290 Fried HO, Lovell C, Schmidt SS (eds) (2008) The measurement of productive efficiency and productivity growth. Oxford University Press, Oxford Fukuyama H, Weber WL (2012) Estimating two-stage network technology inefficiency: an application to cooperative shinkin banks in Japan. Int J Oper Res Inf Syst 3:1–23 Ganguli B, Saha D (2017) Study of the food processing sector in Bihar. Technical Report 34307, International Growth Centre Project, London School of Economics. https://www.theigc.org/ project/study-of-the-food-processing-sector-in-bihar/ Gebrewolde TM, Rockey J (2018) The effectiveness of industrial policy in developing countries: causal evidence from Ethiopian manufacturing firms. University of Leicester Working Paper No. 16/07. https://www.le.ac.uk/economics/research/RePEc/lec/leecon/dp16-07.pdf Gulati R, Kumar S (2016) Assessing the impact of the global financial crisis on the profit efficiency of Indian banks. Econ Modell 58(C):167–181 Hadley D (2006) Patterns in technical efficiency and technical change at the farm-level in england and wales, 1982–2002. J Agric Econ 57(1):81–100 Harriss-White B (2005) Commercialisation, commodification and gender relations in post harvest systems for rice in South Asia. Technical Report No. 128, QEH Working Paper Series, University of Oxford, UK Hayami Y, Kikuchi M, Marciano E (1999) Middlemen and peasants in rice marketing in the Philippines. Agric Econ 20(2):79–93 Holod D, Lewis HF (2011) Resolving the deposit dilemma: a new DEA bank efficiency model. J Bank Financ 35(11):2801–2810 Kahi VS, Yousefi S, Shabanpour H, Saen RF (2017) How to evaluate sustainability of supply chains? A dynamic network DEA approach. Ind Manag Data Syst 117(9):1866–1889 Kathuria V, Rajesh Raj SN, Sen K (eds) (2014) Productivity in Indian manufacturing: measurements, methods and analysis. Routledge, London Kao C (2017) Network data envelopment analysis: foundations and extensions. Springer International Publishing, Switzerland Kao C, Hwang SN (2008) Efficiency decomposition in two-stage data envelopment analysis: an application to non-life insurance companies in Taiwan. Eur J Oper Res 185(1):418–429 Koenker R, Bassett G (1978) Regression quantiles. Econometrica 46(1):33–50 Koenker R, Hallock KF (2001) Quantile regression. J Econ Perspect 15(4):143–156

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Kumar M, Basu P (2008) Perspectives of productivity growth in Indian food industry: a data envelopment analysis. Int J Product Perform Manag 57(7):503–522 Lerman Z, Parliament C (1990) Comparative performance of cooperatives and investor-owned firms in US food industries. Agribusiness 6(6):527–540 Li Q (1996) Nonparametric testing of closeness between two unknown distribution functions. Econom Rev 15(3):261–274 Lim JS, Manan ZA, Alwi SRW, Hashim H (2013) A multi-period model for optimal planning of an integrated, resource-efficient rice mill. Comput Chem Eng 52:77–89 Majiwa E, Lee BL, Wilson C, Fujii H, Managi S (2018) A network data envelopment analysis (ndea) model of post-harvest handling: the case of Kenya’s rice processing industry. Food Secur 10(3):631–648 Margaritis D, Psillaki M (2007) Evaluating contextual variables affecting productivity. J Bus Financ Account 34(9 & 10):1447–1469 Matthews K (2010) Risk management and managerial efficiency in Chinese banks: a network DEA framework. Omega 41(2):207–215 Minviel JJ, Latruffe L (2017) Effect of public subsidies on farm technical efficiency: a meta-analysis of empirical results. Appl Econ 49(2):213–226 Olley GS, Pakes A (1996) The dynamics of productivity in the telecommunications equipments industry. Econometrica 64(6):1263–97 Rasmussen S (2010) Scale efficiency in danish agriculture: an input distance-function approach. Eur Rev Agric Econ 37(3):335–367 Shwetha MK, Mahajanashetti SB, Kerur NM (2011) Economics of paddy processing: a comparative analysis of conventional and modern rice mills. Karnataka J Agric Sci 24(3):331–335 Silva E, Marote E (2013) The importance of subsidies in azorean dairy farms efficiency. In: Santos JMA (ed) Mendes A, Soares da Silva ELDG. Efficiency measures in the agricultural sector. Springer, Dordrecht Simar L, Wilson P (2007) Estimation and inference in two-stage, semi-parametric models of production processes. J Econom 136(1):31–64 Simar L, Zelenyuk V (2006) On testing equality of distributions of technical efficiency scores. Econom Rev 25(4):497–522 Singha K (2013) Hulling and milling ratio of major paddy growing states: all- India consolidated report. Project No. 20, Agricultural Development and Rural Transformation Centre, Institute for Social and Economic Change, Bangalore, India Small CG (1990) A survey of multidimensional medians. Int Stat Rev/Revue Internationale de Statistique 58(3):263–277 Sutton J (2007) Sunk costs and market structure: price competition, advertising and the evolution of concentration. The MIT Press, Cambridge Vyas V (2015) Low-cost, low-tech innovation: new product development in the food industry. Routledge, New York Wanke P, Maredza A, Gupta R (2017) Merger and acquisitions in South African banking: a network DEA model. Res Int Bus Financ 41:362–376 Wijesiri M, Yaron J, Meoli M (2017) Assessing the financial and outreach efficiency of microfinance institutions: Do age and size matter? J Multinatl Financ Manag 40:63–76 Wongkeawchan J, Wiboonpongse A, Sriboonchitta S, Huang W (2004) Comparison of technical efficiency of rice mill systems between Thailand and Taiwan. Chiangmai Univ J Econ 8(3):92–108 Yang C, Liu HM (2012) Managerial efficiency in Taiwan bank branches: a network dea. Econ Modell 29(2):450–461

Chapter 7

Food Processing in Bihar: Entrepreneurial Perceptions

7.1 Physical Versus Non-physical Costs in Food Processing Chapters 5 and 6 have dealt with the issue of firm performance in the Bihar trajectory, keeping in mind the government’s active role in the industrial ecosystem. The central hypothesis that we have been working with is that a missing middle in firm size distribution in food processing in the state has resulted in additional marketing costs for small entrants. We argue that this is so because fresh entrants lack an outlet for industrial sales: the mid-sized firms. Large firms do not have co-processing contracts with these entrants as they are integrated into the supply chain. The lack of experience of new entrants results in poor management of retailing expenditure. Branding costs are significant in food processing (see our discussion in Chap. 3). Extremely low marketing efficiency for dairy and grain milling, particularly the discussion on rice milling in the previous Chap. 6, provide additional evidence for this. However, that discussion of the industrial layer in the Bihar trajectory models only the physical costs of operation, setup costs partly funded by the state government, marginal costs c and retailing costs C r (as explained in Chap. 6). While undoubtedly important, this in itself does not complete the picture. The role of entrepreneurs and their perceptions of costs affect results as well, but has been sidelined in our discussion till now. In this chapter, we integrate the third element in the industrial ecosystem, the entrepreneur, into the narrative of Bihar’s trajectory. The central point of investigation is the characterization of the mindset of the entrant and its implications for government policy in food processing in the state. We introduce a class of non-physical costs for this purpose. The literature on entrepreneurial behaviour has, for quite some time, insisted that it is at the level of this decision-making unit that a large part of firm performance rests. For instance, Fornahl (2003) argues that ‘(regionally shared) cognitive representations and information processes’ affect regional outcomes through entrepreneurial activities. Assuming that agents are boundedly rational with limited information and cognitive computation abilities, this framework eschews an optimal behaviour of entrepreneurs. Rather they try to decide which information to ‘act upon’. Those that © Springer Nature Singapore Pte Ltd. 2020 D. Saha, Economics of the Food Processing Industry, Themes in Economics, https://doi.org/10.1007/978-981-13-8554-4_7

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fit into existing patterns and ‘can be linked to known elements’ catch the attention of the entrepreneur. These pieces of information can disseminate to the population of entrepreneurs through social networks or proximity, as Fornahl (2003) argues. If it is to be argued that successful industrialization is a function of capable entrepreneurship, then one possibility is to attract entrepreneurs who ‘pick the right information’ and subsequently undertake the appropriate investments through policy measures. Note that this line of argument implies that irrespective of other aspects of the industrial ecosystem, i.e. government policy, institutions and infrastructure, one needs a set of agents who can undertake risks to start and expand businesses. Are these agents predisposed with these ‘entrepreneurial skills’, in the sense of possessing an ‘innate ability’? The psychological literature on entrepreneurship has pointed out some ‘traits’ like profit orientation, individualism, optimism, alertness, self-confidence, need for achievement to name a few, from the list in Fornahl (2003) that entrepreneurs exhibit. We refer to this as the ownership of the correct ‘entrepreneurial mindset’. Increasingly, however, it is accepted that the influence of the social and regional context within which such agents are embedded informs their behaviour to a large extent. Entrepreneurship is also born out of ‘non-entrepreneurs’; either due to positive role models and a nurturing environment (Simonton 1975) or due to regional unemployment and frustrations in the current occupation (Brockhaus 1982). In this sense, it is possible for any individual to learn about the ‘entrepreneurial mindset’ and succeed in business, through appropriate support systems. Whether innate or acquired, policy has to target the agent with the ‘correct’ entrepreneurial mindset. Let us assume that Industrial Policy (IP) is a contract between the policy planner (as the principal) and the entrepreneurial agent (as the agent to whom work is outsourced as per the terms of the contract). IP determines these terms, whereby entrepreneurial performance is identified and mentioned through specific targets that the agent should achieve. With this setup, the problem is similar to the standard treatment of asymmetric information between a policy planner and agent with private information about her/his type. The planner has to choose the ‘right type of agent’ to achieve an optimal allocation of resources. Optimality implies that the targeted entrepreneur achieves efficiency in operations for a given level of subsidy support from policy.1 The solution to this is solving the adverse selection problem through the provision of correct incentives in the industrial policy contract. If the wrong agent type is selected by the policy, then the target of achieving efficient resource allocation would be defeated. IP will fail to achieve its targets, and this kind of argument can potentially explain the somewhat modest gains from policy intervention in food processing in Bihar. An implication would be that the 2008 policy failed to attract the ‘right’ entrepreneur in the first place, through incorrect design of policy that failed to provide the correct incentives. Additionally, with the possibility of learning to become an entrepreneur, there is a role for horizontal policy investments for the nurturing of entrepreneurial skills in the 1 Note

that we are not arguing why external subsidy support is needed in the first place. Given the regional trajectory that we are addressing, as we mention in Chap. 5, the role of the policymaker is central to the process of industrialization.

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region. Both right targeting as well as fostering new entrepreneurship by providing an appropriate environment matter for Bihar. Policy and governance reform in Bihar around 2006–2008 would have been self-defeating had it not been possible for these changes in the overall environment in the state to encourage new entrepreneurs. The state lacked a substantial density of entrepreneurs and ‘ready-made’ entrepreneurs in food processing were few. At that same time, the policy hoped to target the correct ‘entrepreneurial mindset’: capable of taking the appropriate investments and leverage the subsidies for business expansion. We bring out the role of the entrepreneur across the sub-sectors like grain milling, dairy, etc. Costs are specific to the sub-sector within food processing that an entrepreneur enters. We refer to them as the physical costs of operation. Second, there are non-physical costs that are specific to the entrepreneur and influences the decision-making process just as significantly as the first physical set of costs. We term these costs non-physical as there is no easy way to put a monetary value to them, though they are significant and make a difference in the entrepreneurial mindset. This discussion is more abstract than that for physical costs but it provides another explanation of how the ‘missing middle’ in the distribution of firm size in the Bihar trajectory affects firm performance and policy outcomes. The modelling of these costs is based on the findings of our primary survey among entrepreneurs in Bihar in 2016–2017. In order to discuss the theory which motivated our survey questions, we need an alternative to Bihar as an entrepreneur can exert her/his free will and can choose either to be in Bihar or outside Bihar. Consider a comparison of trajectories for two different regions: Bihar and not Bihar, i.e. region k ={B, NB}. In our survey, we found that most entry into food processing was by entrepreneurs native to Bihar. Hence, our reference point is an entrepreneur j belonging to k = B. The two components of non-physical costs are the following: i. Costs due to inexperience: χ j , which is a function of the distribution of firm sizes in the two regions B, N B and local experience. ii. Costs due to financial market imperfections: ψ j , which is a function of an entrepreneur’s background. We elaborate on this in detail in Sect. 7.3 and provide only a brief explanation at this point. Note that inexperience costs, as the name suggests, is high for novice entrepreneurs, and its very presence allows for the possibility of lowering these costs through learning and experience. To this extent, this is not in line with the assumption of ‘innate ability’ in entrepreneurship. These costs are also related to our earlier claim about firm performance in a region with a missing middle, as discussed below. Relationship Between Physical and Non-physical Costs We have discussed earlier that it is a more profitable entry strategy for a smallsized new firm, under some assumptions, to specialize in co-processing through industrial sales and economize on own-brand retailing. This minimizes physical costs of operation. We also assume that it is the middle-sized firms that

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use co-processing services from small entrants. Hence, it is these middle-sized firms which provide the relevant experience to small firms through the channel of non-retail sales. If a region has a missing middle in the distribution of firm size, not only are they forced to enter into costly own-brand retailing, they are also deprived of gaining valuable experience about market conditions, consumer tastes and preferences and other parameters relevant for future firm expansion. Hence, in region k, the density of middle-sized firms (lk ), which we call δ(lk ), reduces not only physical costs of operations (entrants can avoid a costly own-brand marketing expenses Cir for entering sub-sector i), it also reduces inexperience costs for an entrepreneur j. Note that for an entrepreneur, who belongs to region B, these costs can be minimized by subcontracting with middle-sized firms in either region B or N B. However, if there are specificities in regional knowledge, i.e. ‘information on the ground’ matters significantly more than technology-specific information that is agnostic to region, then it will be the density of middle-sized firms in region B that alone will matter. Note also that there is a component in these costs that is independent of the distribution of firm size. A local entrepreneur can have lower inexperience costs as he does business in a familiar environment, not as an outsider. Therefore, local knowledge, which is linked to the missing middle problem, can reduce these costs. The other component of non-physical costs, as per our theory, is not related to the distribution of firm size either. Rather, the ease with which the entrepreneur can find information about sources of finance and can access channels of working capital influences the costs due to financial market imperfections. We label them ψ j for the jth entrepreneur. Belonging to a business family and membership in trade associations are its determinants. These factors can ease access to finance, particularly working capital, which is of central importance in the food processing industry (as mentioned in Chaps. 3, 4 and 6). We assume that the entry decision for an entrepreneur is based on the trade-off between costs (both physical and non-physical costs) and outside options. We use this as a thumb-rule-based decision-making as is commonly used for boundedly rational agents (for instance, Fornahl (2003) and Kahneman and Tversky (1973)). Using physical and non-physical costs as well as a notion of outside options,2 we build a theoretical framework of entrepreneur ‘types’ using entrepreneurial mindsets. Note here that we integrate uncertainty in output, and therefore in total costs of production: physical as well as non-physical costs, in the entry decision itself. In the existing literature, for instance, Sutton (2007), mention sunk costs as the relevant cost variable for the decision regarding entry. This is because this branch of the literature treats the total costs in a two-stage fashion. In the first stage, sunk costs matter regarding entry. In the second stage, these costs no longer matter in decisions of output or pricing: rather the other variable components of costs matter. Our model of 2 Munshi (2010) shows the relevance of outside option in entrepreneurship. Our treatment of outside

option in Sect. 7.3 is very specific and slightly different from Munshi (2010).

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total costs is very different: rather than distinguish between sunk and other costs, we distinguish between those costs which are easily measurable (all fixed and variable costs discussed in the previous chapter) and those which are psychological in nature (non-physical costs) that we model in this chapter. The decision to enter business in an industrially backward state is more realistically done with the incorporation of the latter type of costs in the entry decision itself. All physical costs matter at the time of the entry, due to uncertainty regarding output, and hence collaterals for working capital loans for post-entry operational expenses. The distinction between sunk and other costs in the treatment of entry decisions is more suitable in more industrially developed contexts with less imperfections in access to finance and more developed industrial ecosystems. Another innovation in this chapter is the modelling of a region-specific behavioural attitude of the entrepreneur to capture entrepreneurial mindsets. This we term regionbased counterfactual thinking (rCFT). This is an insight from this chapter and an addition to the existing behavioural economics literature on counterfactual thinking (CFT) by entrepreneurs. The central idea here is that the entrant in Bihar’s food processing has to face all the risks that are present in a less-industrialized state as well as normal business risks (captured through the physical costs in the previous chapter), despite a supportive government policy. We investigate the mindset with which they enter business, as this has the potential to explain their future behaviour once they start production. Expansion of small business requires a certain kind of mindset to face uncertainties. CFT models a particular way of thinking that defines entrepreneurial mindsets and rCFT enriches that framework with a regional filter. CFT is essentially a coping mechanism for the entrepreneur to deal with unpleasant outcomes in doing business and takes the form of a rationalization of the entrepreneur’s actions by creating alternative scenarios (different from actual events). The term counterfactual stems from the notion of integrating situations which did not happen or ‘are counter to facts’ in the process of decision-making. While it is commonly argued that entrepreneurs regularly underestimate the actual risk in operations in order to be able to start or expand business, they are also capable of very complicated counterfactual thinking (CFT). Existing empirical evidence on CFT is ambivalent: Baron (2008) and Baron and Markman (1999) find a lack of CFT among entrepreneurs relative to non-entrepreneurs, whereas Gaglio and Katz (2001) finds the presence of complicated CFT among entrepreneurs. What we bring on board is an integration of CFT with the regional identity of the entrepreneur, which is relevant for a region-specific trajectory in food processing. Essentially, we ask the identity-based question: how much does it matter to the entrepreneur that she/he belongs to Bihar?

in a more pointed counterfactual manner that is relevant for entrepreneurship: would the entrepreneur do business in Bihar had she/he not been native to Bihar?

Note that ‘not being native to Bihar’ is the counterfactual that is important here and this has a clear regional dimension. In the literature on entrepreneurial behaviour, the standard treatment of CFT is that either there is no CFT or there is positive or

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negative CFT. In our application, rCFT captures the implications of ‘belonging to a region’ or ‘constrained to operate in a geographical area’. A positive value of rCFT would imply that the entrepreneur would express willingness to do business in Bihar even if had she/he not been native to Bihar, whereas negative rCFT would mean that the entrepreneur would opine that she/he would never do business in Bihar had they not been native to the state. A negative response would imply that the entry decision was largely due to the entrepreneur’s ‘belonging-ness’ to Bihar, and not other factors in favour of doing business in Bihar. This is a direct test of the purported input advantage that Bihar has in food processing in the perceptions of entrepreneurs. Ceteris paribus, the larger is the perceived input advantage, the larger should be the positive value of rCFT. In-depth knowledge of the local input supply conditions can lead to positive rCFT. The lack of an rCFT would show up in an ambiguous response like ‘uncertain’ or ‘do not know’. Our framework shows that entrepreneur types with low outside option and less ability to leverage local knowledge exhibit negative rCFT. This implies significant regret in having to do business in Bihar. It is only the local entrepreneur, capable of leveraging local knowledge, who can exhibit positive rCFT). The latter should, in fact, be the best policy bet as these entrepreneurs would have future plans for business expansion. However, to come to this conclusion, we need to link rCFT with the perception of risk in doing business and future expectations. We assume that an entrant entrepreneur’s attitude to risk in operations, and therefore her/his plans for further business expansion is strongly correlated with rCFT. This is our first testable hypothesis: that negative rCFT is associated with a high perception of risk, whereas positive rCFT is linked with a low perception of doing business. An entrepreneur with positive rCFT is more likely to have plans for expansion of business, leveraging the input and policy advantages for food processing in Bihar. Negative rCFT would have the opposite implications, whereas no rCFT will yield neutral expectations about business prospects. We test for this using our survey data among entrepreneurs, and find support for our claim. We find that 40 out the 71 valid responses show negative rCFT with only 21 responses with positive rCFT. Our theory indicates that this negative rCFT comes from entrepreneur types whose local knowledge is below a cut-off, such that the non-physical costs net of benefits is higher in Bihar relative to another region. Only those local types with a high ability to leverage local information, who see the advantage of engaging in food processing in Bihar. They have a positive rCFT. Additionally, negative rCFT is found to be correlated with a high perception of risk and vice versa. We also find that for entrepreneurs with more experience and access to multiple sources of information, risk perception is low, whereas it is high for those with less experience and information access. With minimal exports outside the state (both nationally and internationally), an entrant without much experience in food processing in Bihar, is restricted to operate within the constraints of the state. Bihar’s industrial ecosystem is not perfect, despite its abundant agri-resources. Hence, it is unsurprising that such an individual will have a negative rCFT in their mindset. This result is due to the incorporation of the regional dimension into CFT. The core focus on region, though, is not unique to this chapter alone. This entire book argues for the salience of the region in the discussions

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on industry and its development trajectories. Note that the pure physical cost based efficiency arguments we presented in the last chapter, are related to the technology that determines output and does not account directly for human behaviour. That kind of efficiency analysis, we feel, falls short of providing a full account of what drives industrial outcomes in a region, particularly from an industrial layer point of view that integrates human agency along with policy and technology in the same platform. A second takeaway of this chapter is the possibility of targeting entrepreneurial mindsets using IP. We find that for regions like Bihar, with an industrially backward ecosystem, incentive schemes like the one discussed in Chap. 5 are likely to attract entry of the wrong entrepreneurial mindset: entrepreneurs with negative rCFT and high perception of risk of doing business will enter Bihar. The screening property of these subsidies is limited. This strengthens our observation on policy-induced announcement of entry cycles in states like Bihar and Jharkhand in Chap. 5. Every time a new Industrial Policy (IP) is announced, the number of proposed investments of different sizes go up and start declining sharply prior to the next policy announcement. This is a comment on the nature of IP itself: as a contract between the government and the entrepreneur, it is unlikely to have a strong screening property that selects ‘correct’ projects for partial financing.3 The rest of the discussion in this chapter develops on the themes we mention and validates the theory we present using data from a primary survey in Bihar among entrepreneurs engaged in the food processing industry in 2016–2017.

7.2 Existing Literature on Entrepreneurial Identity and Behaviour A large body of literature exists on entrepreneurship, starting with seminal work by Schumpeter (1934) in the neo-classical mainstream of economics and has continued for a long period of time (see, for instance, Kihlstrom and Laffont (1979) and Baumol (1993)).4 Entrepreneurial motivation and behaviour have also been studied from the vantage of organizational ecology (for instance, Gartner (1988); Gartner et al. (1994)) and social psychology (such as McClelland (1965); McGrath and MacMillan (2000)). The issue of entrepreneurial identity and more importantly the notion of ‘belongingness’ to a region is not very common. There is some literature on entrepreneurial identity from structural identity theorists, such as Burke and Stets (2009), where identity is internalized by the entrepreneur as his/her role. In many situations, this internalized identity is a self-concept (not influenced by external influences), so that attitudes such as perception of risk and resultant investment behaviour is left to the agent himself/herself. For instance, Gimeno et al. (1997) points out that entrepreneurs 3 We

have not defined yet what we mean by ‘correct’ project. We use entrepreneurial mindsets in this chapter and identify that type which has positive rCFT as the correct entrepreneur type and hence his/her project is the ‘correct’ project. 4 This part of the discussion draws heavily from the author’s recent publication (Saha 2019).

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are motivated from within. There is no direct superimposition from social norms or network standards regarding this identity. The entrepreneur tries to live up to his or her own self-created standard. Other definitions of entrepreneurial identity presume a set of beliefs ‘commonly’ held by the population and internalized through the cognitive processes of the entrepreneur (Murnieks and Mosakowski 2007). In most of this literature, identity becomes defined narrowly in terms of the roles assigned to it. For instance, Shane and Venkataraman (2000) defines an entrepreneur as a person engaged in the discovery, examination and exploitation of opportunities. Our proposition is that roles alone are not sufficient in themselves to provide a meaningful interpretation of entrepreneurial identity. We argue for the incorporation of a regional context in the definition of who is an entrepreneur. This kind of extension beyond roles is not without criticism. Gartner (1988) comments that research in entrepreneurial identity is unlikely to yield high dividends. We, along with Murnieks and Mosakowski (2007), argue otherwise. Our contention is that entrepreneurs develop their identities in two ways: one, by absorbing socially held behaviour expectations attached to positions. By definition, these are external to the individual and are assimilated into the entrepreneur’s notion of self through a process of absorption as discussed in Gecas (1982). Second is a set of ideas held internally by the entrepreneur. For instance, Baum et al. (2001), Baum and Locke (2004), Chen et al. (2009), Cardon et al. (2009) as well as Murnieks et al. (2014) identify passion as a driving force in entrepreneurship, a factor that is innate to the entrepreneur and potentially empowers her/him to fight external resistance and problems in doing business. These difficulties have a regional dimension, and in our theory, we add this to identity to explain behaviour. What is the relationship of entrepreneurial identity with the behaviour? We are referring to a large body of literature on non-rational factors such as heuristics which has been successfully applied to explain decision-making by entrepreneurs. Among these are reasoning fallacies such as planning errors (Baron and Markman 1999), overconfidence (Russo and Schoemaker 1992; Kahneman and Tversky 1973), overoptimism (Dawson and Henley 2013) and counterfactual thinking (Hmieleski and Corbett (2008); Markman et al. (2005); Zhao et al. (2005)). This branch of the literature does not connect at all with the entrepreneurial identity literature, as pointed out by Sánchez et al. (2011). One exception in the entrepreneurship identity literature is Burke (2004). By likening the process of entrepreneurship to control systems, he forcefully links identity with behaviour. He defines the process of entrepreneurial identity as A cyclical process occurs when an individual takes some action, views that action, evaluates the results in comparison with the standards embodied within an identity, and then incorporates this new information to modify his or her behaviour to improve the expected results.

Behaviour is continually altered until the feedback matches the identity standard (as defined in Burke (1991a)). A perfect match results in self-verification leading to a variety of emotions (ranging from satisfaction to elation) depending upon the context. When the match fails to take place, self-verification fails leading to cog-

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nitive dissonance and varying levels of distress (see Burke (1991b)). The in-built identity(ies) then motivate behaviour modification until the feedback is in line with the yardstick of identity, so that the individual can avoid the distress associated with the lack of self-verification and enjoy the positive outcomes where self-verification is achieved, as (Burke 1991b) discusses. However, an important aspect of the identity creation process is that it is understood by its relation to other identities, which it does not encompass as stressed by Burke (2004). Murnieks and Mosakowski (2007) point out, the identity of ‘wife’ gets defined associationally with that of ‘husband’. Hence, an entrepreneurial identity rests heavily on how one views non-entrepreneurial identities. This has special relevance for an entrant who finds its operations limited by local market size, has limited exposure to external markets and has very sparse business networks to interact with, as is characteristic of the Bihar trajectory. Entrepreneurship here gets tightly associated with the regional context within which the business is confined. Essentially, in the case of Bihar, the entrepreneur considers that he is not only engaged in doing business, but also that she/he is ‘doing business in Bihar’. This link of regional identity has a direct link with the heuristic of CFT and has not been pointed out by either the cognitive or the identity aspects of the entrepreneurship literature. We borrow the definition of entrepreneurial identity from Haynie et al. (2009), who define entrepreneurship as a process of envisioning the future. This process necessarily entails CFT of the kind ‘what might have been had different actions been undertaken or had circumstances been different’. CFT is a broad collection of cognitions that an entrepreneur engages in situations of stress, as pointed out earlier by Baron (2000) and Markman et al. (2005). Wadeson (2006) mentions that CFT is a display of cognitive dissonance and allows the entrepreneur to explain away unpalatable outcomes and situations to themselves in order to move ahead. There are many kinds of CFT. The empirical evidence about entrepreneurial CFT is not conclusive. While some researchers, such as Baron (2008) and Baron and Markman (1999), show that entrepreneurs do not engage in as much CFT as nonentrepreneurs, Gaglio and Katz (2001) and Gaglio (2004) find that entrepreneurs have very complex thinking processes and engage in significant CFT. With the regional dimension, we propose the rCFT as an expression of the realization of constraints by the entrepreneur due to the industrial ecosystem in a region. The heuristic of rCFT has the potential to explain behaviour as we can link it with entrepreneurial risk perception. Existing literature, such as Sitkin and Pablo (1992) and Sitkin and Weingart (1995) show that perception of risk matters crucially for risk-taking behaviour of entrepreneurs.

7.2.1 Motivation for Non-physical Costs and rCFT The central research question we investigate is: how does the locational context influence entrepreneurial behaviour? We answer this in two ways: first by introducing non-physical costs of production and second, by incorporating the region into the

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standard counterfactual thinking literature on entrepreneurial behaviour. The reason for doing this for the Bihar trajectory is that it presents some peculiarities that present potential constraints for entrepreneurs. These idiosyncrasies, which are relevant for this discussion, are the following: i. Relatively low density of middle-sized firms: Till now we have highlighted that Bihar has a missing middle in firm size distribution. What really matters for the discussion in this chapter is the density of these middle-sized firms relative to other regions. We characterize Bihar as a region where the density of middlesized firms is low in relation to other regions: the crux of industrial backwardness. ii. Underdeveloped industrial ecosystem and local knowledge: The underdeveloped industrial ecosystem implies that successful entrepreneurship requires indepth local knowledge that might not be available to a newcomer to Bihar. In the context of food processing, note that Bihar lacks an Agricultural Produce Marketing Committee (APMC) for centralized collection of agri-inputs. This implies that an individual entrepreneur has to establish the supply lines herself/ himself through multiple contracts with the agri-input suppliers. This enhances the importance of local knowledge. In our interpretation, a developed industrial ecosystem implies that the importance of local knowledge would not matter as much: infrastructure and basic information regarding supply lines would be standardized and common knowledge. This is not the case for Bihar. These two factors are integrated into our model of non-physical costs. First, a new entrepreneur faces the challenges that are common to experienced entrepreneurs but not to the entrant. This is the reason for the first set of non-physical costs due to inexperience: χ j for the jth entrant. Location makes a difference to these costs, as we show in our discussion in the following Sect. 7.3. The second set of non-physical costs arise due to financial market imperfections (such as credit rationing), which compel an entrepreneur to search for informal sources of capital. These non-physical costs have a regional context and important variables for an entrant’s decision of whether or not to enter. Post-entry, the entrepreneur has to decide on how to expand on the initial scale of operations. Typically, entry-level size in most sectors is small. It is after entry that firms try to expand in size. It is at this stage where the entrepreneur’s attitude to risk matters, as business expansion requires the entrepreneurs to bear the additional expenditure in stride. Unless the entrepreneur has a positive outlook towards the future, expansion is unlikely to be a lucrative option for the entrepreneur. We have discussed in Chap. 5 the problems with labour and tax policies based on cut-offs that discourage firm expansion above these cut-offs. Hence, unless the expectations of the entrepreneur are positive enough to compensate for the additional costs of expansion (policy costs as well as operational expenses), small-scale entry size will not lead to expanded mid-sized operations. Non-expansion by the entrant (which we assumed will be of a small scale for Bihar) will exacerbate the problem of the already existing ‘missing middle’ in firm size distribution for Bihar. Our argument runs along the following lines: Given that industrial outcomes have been stymied for a long period of time and that many businesses have not been able to expand in Bihar, a novice expresses her/his frustration of low business outcomes

7.2 Existing Literature on Entrepreneurial Identity and Behaviour

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by ascribing the blame to being tied to the immediate context of Bihar rather than personal non-performance. As an entrepreneur gains experience despite being constrained to operate in the same region with low industrial achievements, his/her rCFT changes from negative to positive and risk perception goes down. This is exhibited by the behaviour of becoming members of more business associations. Potentially, entrepreneurial identity starts out as a self-concept for a novice and over time assumes an identity mitigated by social norms and expectations, such that a non-entrepreneur (with negative rCFT and risk perception) becomes an entrepreneur. Now, the same individual has experience along with access to many business networks and positive rCFT and low-risk perception. We find Podoynitsyna et al. (2012) has explored the same issue from the dimension of mixed emotions in cognitive appraisal, whereas we explore this through rCFT. Their research also concludes that negative emotions change to positive ones along with changing risk perception as an entrepreneur gains experience. We surmise that it is incorrect to conclude either that entrepreneurs systematically disengage with rCFT or otherwise. This cognitive process varies over the life cycle of entrepreneurship, leaving clear implications for industrial policy that we discuss later. One important question that remains is: why do we only insist on a regional dimension to the identity issue for entrepreneurs? The identity issue can be discussed on multiple dimensions, not only on the basis of nativity to a particular region. Why not a gender or, more importantly, a caste dimension? Note that there is a strong case for rCFT in Bihar. Limited outside options (as was the case for many of the entrepreneurs we interviewed in Bihar and is also discussed in Munshi (2010)), can generate a strong sense of identity and generate rCFTs: either positive or negative, depending on the interaction of rCFT with the industrial ecosystem. That still leaves unanswered why other dimensions of identity do not matter for the entrepreneur. We provide an explanation from the observations we made during our survey. Our comment is on caste as a contender for identity among entrepreneurs. Caste as an Entrepreneurial Identity: The Surprising Finding from Bihar Bihar has long been associated as a region with deep fragmentation along caste lines historically (see the discussion in Prasad (1979) and Srinivasan and Kumar (1999)). Our initial expectations, at the start of our survey in Bihar for the IGC- sponsored project on food processing in the state in 2016, was also coloured by the received wisdom from academic literature. What came as a surprise was during our pilot survey of a few entrepreneurs and business association (Bihar Industries Association (BIA)) officials was that when we discussed entrepreneurial identity, the response was almost always couched in terms of either positive or negative outlook due to domicile in Bihar. One entrepreneur was very clear: The state government has given very handsome incentives for doing business (2008 onward) in food processing. However, I would never have done business here had I not been from Bihar and with family obligations, I have limited outside options....

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We found a consistent absence of a mention of caste and class or even religion as a bottleneck in entrepreneurship in various regions of the state during our interviews which ranged from 15 min to more than 2 h. While there were many complaints about the lack of institutional finance, inadequate access to working capital, delays in accessing subsidies from the government, perceived policy biases favouring large businesses (project cost over 1 crore INR in 2016) by small entrepreneurs (those with project costs less than 1 crore INR), infrastructure problems in the state, the steep cost of acquiring land or even the rental cost of land, inadequate information about regular supply of raw material and even the rigidity of tastes of consumers (biased in favour of fresh and traditional foods rather than processed items). However, never did we encounter a mention of caste and class in the numerous interactions with entrepreneurs in trade fairs, business associations, at their residences or telephonic conversations. This surprising observation is true for first-generation entrepreneurs excited by the subsidy provision in the incentive scheme for food processing provided by the state government as well as those who have been doing business through family connections for many generations. We, therefore, perceived of modelling entrepreneurial identity along the dimension of ‘belonging’-ness to a region rather than caste and class. We checked separately that we do not have only one or two castes in our data: there is a mixture of castes, and yet there is no mention of this as an important decision factor for engaging in business. This rather surprising finding gives us hope that adding the regional dimension is a sufficient statistic and should work for other industrial spaces, where the history matters and skewed distribution of firm size are present.

7.3 Theoretical Model: Non-physical Costs of Business and rCFT For understanding the mechanism in which distorted industrial outcomes in developing countries influence entrepreneurial decision-making, we provide a brief theoretical construct here. Assume that each potential entrepreneur j in sub-sector i of food processing observes the set of financial incentives in Bihar and decides whether or not to enter. What are the possible outcomes at the level of the industry? As mentioned earlier, we assume that any entrepreneur in food processing encounters two kinds of costs: physical costs related to costs of inputs, land, plant and machinery and labour and non-physical costs, which arise out of various regional factors. The first set of costs are sector-specific, whereas the second set of costs are not, as these are related to the inexperience of the entrepreneur and lack of access to financial markets. Financial market imperfections result in limited pockets of liquidity revolving through business networks accessed easily through family connections.

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Access to exclusive networks is often reflected in the pattern of membership in business associations. Additionally, lack of access to reasonably priced skilled labour and managerial talent is a bottleneck. As discussed in Chap. 6, let total physical costs in production be defined as Ci j = ( pir − ci )xi j + Cir − τi j ,

(7.1)

where τi j is the total amount of subsidization for entrepreneur j in sub-sector i of food processing, pir the per unit price for ith sub-sector, ci is per unit variable cost of production and Cir stands for all kinds of fixed costs of production, particularly those of marketing own-brand mentioned in Chap. 6. xi j is the level of physical output for j, which is a random variable and a source of uncertainty for the entrepreneur.5 Note our simplistic modelling of government subsidies. We bundle all kinds of subsidies together, so that we can study their combined effect. The drawback is that it is not possible to pick on each type of intervention individually, as is the case with the formulation in Chaurey (2017). Note another important assumption that we are making with respect to our modelling of policy incentives: we keep them independent of output xi j . For us, output is a measure of size of the firm that the entrepreneur is considering investments in. A perusal of the industrial incentive policies of the GoB will make it abundantly clear that a part of the subsidies is linked to firm size and another part is not. Now, even for the segment of subsidies clearly linked with the size of operations (capital subsidies, interest subvention, etc.), the incentive schemes in Bihar devised a timeline, with a part (depending on whether the subsidy was granted in three or four tranches) was given upfront. An entrepreneur faced significant uncertainty regarding disbursement of subsidy even after approval. The typical new entrant was always functioning in the hope that the rest of the subsidy would be made available, after the bureaucratic delays in approval and sanction. Building this into the expectations of the entrepreneur, we keep the subsidies as independent of output of the firm as our intention is to model the effect of providing these subsidies in the perception of riskiness of doing business in Bihar. Note that the results will go through with a more standard formulation of total physical costs without the market price (that subsidizes per-unit variable costs of production). This formulation is kept to retain continuity with the previous chapter. The first term in the total physical cost equation is allowed to be negative.

5 We

have used similar but slightly different notation from the previous Chap. 6. There, we used qi to denote the output of an entrant in sub-sector i of food processing, but we use xi j here for two reasons. First, in this chapter, we bring in the entrepreneur j in the discussion in addition to the sub-sector i. Second, we build in the uncertainty in the production process by making xi j random, whereas qi was deterministic. Our efficiency analysis using NDEA was deterministic in nature. We extend the analysis here with uncertainty, as we discuss risk perceptions which matter in an environment of uncertainty. We also do not mention the size s to characterize retail price pir in this chapter. Our understanding here is that the typical entrant size in the Bihar trajectory is small (see Chap. 5) and the relevant size is that of mid-sized firms l that informs the theory regarding non-physical costs and rCFT in this chapter.

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The second set of non-physical costs has two parts: (i) arising from a lack of entrepreneurial experience, χ j and (ii) due to financial market imperfections, ψ j . These are independent of sector i for any entrepreneur j and we assume that the entrepreneur encounters them in the pre-planning stage prior to entry itself. Hence, the entrepreneur knows these costs at the time of entry into food processing. Before proceeding with our theory, we first present some of the qualitative information from entrepreneurs in our survey, which informs our model.6 Makhana, Dairy and Animal Feed Examples from Bihar: The Nature of Inexperience Costs Consider the case of Mr. Satyajit Singh, CEO, Shakti Sudha Industries in the Patliputra Industrial Area of Patna, Bihar. A first-generation entrepreneur, Mr. Singh provided a detailed account of his struggle to establish his unit which processes makhana (fox nut). This is a spectacular success story as his initiative has received press coverage from multiple national dailies. In our detailed conversation in 2016 (during the IGC-sponsored survey of Bihar’s food processing industries), the one point that Mr. Singh made abundantly clear was the difficulty he faced in aggregating the individual makhana farmers and creating a database of the available supply for setting up and running his unit. A processing unit, to be profitable, has to be operated at some minimum scale of operations. Achieving this for Mr. Singh, as a novice entrepreneur (without any exposure to trading or the industry), was the hardest challenge. As opposed to this, our interview of the manager of the large dairy unit of ITC at Munger revealed the relative ease with which this private firm could put together the milk farmers from nearby areas. Dairy operations of ITC Pvt. Ltd. grew out of its existing tobacco operations in Munger as a CSR (Corporate Social Responsibility) initiative. Having a presence in the state and more experience with doing business clearly makes a difference in the level of difficulty in establishing supply lines for manufacturing in food processing industries. A different angle emerges from the example of Henraajh Feeds India Pvt. Ltd. The detailed interview of the entrepreneur, Mr. Jaydeep Narayan Srivastava, revealed how his prior experience as a trader of poultry feed and his hatcheries business led him to establish the processing unit for animal (poultry) feed with relative ease. Prior experience as a trader and the hatchery business helped him in his entrepreneurial venture with poultry feed, which comes under the food processing industries. Another notable feature of entrepreneurial behaviour is that Mr. Satyajit Singh is a member of business and commerce associations, which have a presence in the state as well

6 Note that these entrepreneurs gave us permission to reveal their identities. A collection of interviews

are available on the IGC page which has a summary of our survey at https://www.theigc.org/project/ study-of-the-food-processing-sector-in-bihar/.

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as nationally, whereas Mr. Srivastava was still considering membership in a state-level association. While we note above only a subset of the responses we received from entrepreneurial interviews during our 2016 survey in Bihar, the qualitative information gleaned reveal similar responses. We generalize these observations into two important institutions that foster entrepreneurship: • membership in business associations: provides information about supply lines, market conditions and sources of finance. • trader experience in a related business: provides information about cheap sources of inputs and marketing possibilities. Both these factors aid entrepreneurial experience, and possibly there is some substitutability in them, as we find from the different responses of the CEOs of Shakti Sudha Industries and Henraajh Feeds. A new entrant with trader experience might consider minimizing the cost of membership in associations if they have significant experience with trading and supply chain of the commodity that he/she is considering for manufacturing. We use these observations to structure the model for non-physical costs of entrepreneurship now. Consider two regions: Bihar (k = B) and outside Bihar (k = N B). We assume that the inexperience costs χ j (measured per unit of output) for an entrepreneur operating in region k = B is a function of • the density δ(lk ) of the set of mid-sized firms lk in food processing (any subsector) in region k. Inexperience costs are decreasing in the density of firms of size lk in region k. This is due to the opportunity of co-processing specific to the region that mid-sized firms provide as experience formation to the entrepreneur. Our assumption is that δ(l B ) < δ(l N B ). • local knowledge t. We assume that there is a distribution among entrepreneurs in terms of local knowledge for region k = B: t ∼ [0, t¯]. The highest value of this local knowledge variable is t¯ and the lowest is 0. The longer an individual has spent her/his time in Bihar, she/he gets to know ways of doing business that is independent of whether or not she/he has co-processing experience with midsized firms. Higher is the local experience, lower will be inexperience costs. As mentioned earlier, t matters for region k = B alone and not for k = N B, which we assume has a more developed industrial ecosystem. Definition 7.1 Inexperience costs with region specificity and local knowledge. The total inexperience costs in region k is  χ j (δ(l B ), δ(l N B ), t) =

χ j (δ(l B )) − t if k = B χ j (δ(l N B )) if k = N B

Inexperience costs inform the perception of risk in doing business in a region. One of the sources of perception of low cost of doing business in a region is related to

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experience in co-processing with mid-sized firms. Higher the missing middle size, higher is this component of inexperience costs. The second source of perception of risk in doing business does not have anything to do with the missing middle size of firms. Rather, familiarity with local conditions and a knowledge of low-cost input sources (which someone who is native to a region is likely to have) determine this component of inexperience costs. Note, however, that more developed the industrial ecosystem, the lower is the importance of local conditions, as information about sources of input becomes standardized. We assume that Bihar is a region where t matters more than other regions. For Bihar, therefore, there is a trade-off in inexperience costs, a ‘local’ type of entrepreneur can have high t coupled with high inexperience costs due to the missing middle size of firms in Bihar. Our model works out the importance of t relative to the other components of non-physical costs in determining post-entry behaviour of the entrepreneur by including another component of non-physical costs: that related to financial market imperfections. These, we model, are a function of individual entrepreneurial characteristics. At the margin, we assume that these costs labelled ψ j per unit of output7 are independent of the sector i. The arguments for ψ j are the following: i. whether the entrepreneur j belongs to a business family or is a first-generation entrepreneur; and ii. the number of memberships of business associations of the entrepreneur (#m). Hence, market imperfection costs are a function of f j and #m, such that these costs are lower for an entrepreneur belonging to a business family and for a higher number of association memberships. The former assumption is based on the literature on lending among business families through social networks (a good example is Munshi (2010) for community-based lending in the diamond business) and the latter shows the dual role that business associations have: both in providing information about supply channels that can mimic actual experience and in helping the entrepreneur access more sources of finance.

7.3.1 Post-entry Behaviour: Types of rCFT To put further structure to our analysis, assume that output for sub-sector i for entrepreneur j is stochastic: xi j ∼ U [0, 1], reflecting supply uncertainty. The entry decision that we discuss below requires the notion of an outside option for j. This is labelled ω j (δ(l B ), δ(l N B )) and is related to the missing middle issue we discussed earlier. Definition 7.2 Missing middle and outside options. Consider region B (of interest to us here). An entrepreneur j has low outside option if 7 Note

that we use non-physical costs in the per-unit sense.

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215

ω j (δ(l B ), δ(l N B )) = ω j (min{δ(l B ), δ(l N B )}) = ω j (δ(l B )) as δ(l N B ) > δ(l B ) (7.2) whereas an entrepreneur has high outside option if: ω j (δ(l B ), δ(l N B )) = ω j (max{δ(l B ), δ(l N B )}) = ω j (δ(l N B )) as δ(l N B ) > δ(l B ) (7.3) Note here that specificity in inexperience costs and local knowledge are modelled to indicate how much ‘doing business in Bihar’ matters as opposed to just ‘doing business’. Second, Bihar is identified as a region not only with a missing middle size of firms: δ(l B ) is low, but ‘how low is low?’ is answered by comparing the density of these firms in region B with N B: Bihar is a region with δ(l B ) < δ(l N B ). This outside option, as we mention earlier, is specific to a region: it enumerates the choices that an entrepreneur has if she/he was not an entrepreneur in Bihar, but is restricted to Bihar nonetheless. A low outside option, therefore, implies that an entrepreneur’s choices outside of the current profession are limited within the region itself. In our case, we consider other professions, such as trading or co-processing as the valid alternatives. Hence, if the entrepreneur was not doing business in Bihar, a low outside option would mean that she/he would have limited choices in trading or co-processing. The reason for this comes from our theory in the previous chapter, where we discuss the effect of a missing middle size of firms in the industrial ecosystem. What determines entry by an entrepreneur? We assume that the following thumbrule: Definition 7.3 An entrepreneur j decides to enter sub-sector i in region k if the total physical and non-physical costs of operation is lower than her/his outside options. The incentive compatibility constraint for j to enter the business in food processing in the presence of subsidies by the government in B is then given by Ci j + [χ j (δ(l B ), δ(l N B ), t) + ψ j ( f j , #m)]xi j ≤ ω j (δ(l B ), δ(l N B ))

(7.4)

Now, consider post-entry behaviour first, before we discuss the issue of who enters Bihar for doing business.8 We assume that post-entry, any type of entrepreneur will consider expansion of business only if his/her perception of the risk of doing business is low. Now, risk perception is related to costs of business. We use our definition of non-physical costs to create the construct of rCFT, which reflects this perception of risk. As this type of counterfactual thinking is based on regret-based decision-making, we put non-physical costs as the driver of rCFT. Now, for any entrepreneur type, we formally define her/his rCFT as follows: to capture the sense of regret, we start with Eq. 7.4, which works out the condition for entry into region B by any entrepreneur type. In essence, this condition implies that the entrepreneur would not enter a business if the sum of physical and non-physical costs is higher than type j’s outside option. rCFT is about post-entry regret due to poor financial performance and is a mental construct to deal with this sense of 8 This

is in line with the nature of backward induction solutions for dynamic games.

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frustration. Physical costs are easily quantifiable, and therefore, if the entrepreneur has to mentalize about regret, she/he must do so about her or his outside options relative to non-physical costs. Type j will regret his/her entry decision into region B in the face of bad outcomes post-entry (such as a negative output shock). A possible way to rationalize this would be outside options ‘being tied to region B’ was low relative to non-physical costs in region B. The entrepreneur rationalizes that would never be the case had she/he derived her/his outside options and non-physical costs from region N B. It is through this comparison of the difference of outside options given non-physical costs between regions B and N B that we get a measure for rCFT. Definition 7.4 Region-based counterfactual thinking (rCFT) for entrepreneur type j for doing business in region k = B is the difference between outside options (net of non-physical costs) if he/she belongs to region k = B as opposed to region k = N B. For any type j, rCFT (ρ( j, k = B)) is ρ( j, k = B) = [ω j (δ(l B )) − {χ j (δ(l B )) − t} − ψ j ] − [ω j (δ(l N B )) − {χ j (δ(l N B ))} − ψ j ] ⇒ ρ( j, k = B) = [ω j (δ(l B )) − ω j (δ(l N B ))] − [χ j (δ(l B )) − χ j (δ(l N B )) − t] = Δ1 − Δ2

where, Δ1 = ω j (δ(l B )) − ω j (δ(l N B )) reflects the difference in outside options between regions B and N B for entrepreneur j, while Δ2 = χ j (δ(l B )) − χ j (δ(l N B )) − t measures the difference between inexperience costs for entrepreneur j between regions B and N B. Note that we have characterized region B as one with δ(l B ) < δ(l N B ). The overall value of ρ j for type j depends on the difference between Δ1 and Δ2 . Depending on the value of the parameter for local knowledge t, we get different values of rCFT. To work out these effects, we first note our assumptions regarding outside options and inexperience costs for any type j: Assumption 7.1 ω j (.) is increasing in δ(lk ) ∀k = B, N B. The lesser the density of mid-sized firms in a region, the lesser is the possibility that the entrepreneur will find contractual work alternatives. Assumption 7.2 χ j (.) is decreasing in δ(lk ) ∀K = B, N B. As the density of midsized firms increases in a region, inexperience costs go down as a novice entrepreneur has a higher probability of finding experience as a trader through subcontracts prior to starting out on her own. Given these definitions, it is obvious that χ j (δ(l B )) > χ j (δ(l N B )), i.e. the inexperience costs for an entrepreneur j in region B will be higher if we neglect the effect of local knowledge. Now, the latter itself is distributed in [0, t¯]. Let tˆ be a cut-off level of t, such that for a. t > tˆ, χ j (δ(l B )) − χ j (δ(l N B )) − t < 0 such that Δ2 < 0 b. t = tˆ, χ j (δ(l B )) − χ j (δ(l N B )) − t = 0 such that Δ2 = 0 c. t < tˆ, χ j (δ(l B )) − χ j (δ(l N B )) − t > 0 such that Δ2 > 0.

7.3 Theoretical Model: Non-physical Costs of Business and rCFT

217

Given our definitions, we can now identify three types of rCFT, depending on the relative strengths of Δ1 and Δ2 as follows: Remark 7.1 Negative rCFT. For this class of entrepreneurs, ρ( j, k = B) = Δ1 − Δ2 < 0 reflecting negative rCFT. This happens when Δ2 ≥ 0 (cases [b] and [c] described above), as Δ1 < 0 given our assumptions. Remark 7.2 No rCFT. This occurs when Δ2 < 0 (case [a]) and its magnitude is such that |Δ1 | = |Δ2 |. Hence, ρ( j, k = B) = Δ1 − Δ2 = 0. Remark 7.3 Positive rCFT. Our model allows for a positive rCFT for entrepreneur j for case [a] when Δ2 |Δ1 |. In this case, rCFT ρ( j, k = B) = Δ1 − Δ2 > 0. This leads us to claim a general observation about the role that local knowledge plays in industrially backward regions like B: Claim 7.1 Missing middle, local knowledge and rCFT. For a given level of δ(l B ), lower is the level of local knowledge of an entrepreneur, higher is the possibility for negative rCFT in her/his mindset. Negative rCFT, we claim, is associated with a high perception of risk of doing business and therefore, low probability for expansion plans of business. Here, we do not refer to an objective measurement of risk, rather the focus is on perceived risk. We define the perception of risk in doing business as a combination of production, marketing and security of property challenges that adversely impact profits. We do not segregate between different types of business risk. Our focus is on the aggregate perception of risk in operations (as a combination of production, marketing and protection of investment). Our theory predicts that, for a given level of non-physical costs, an entrepreneur with low t will have a negative rCFT and tend to ‘blame’ the industrial ecosystem in order to manage their cognitive dissonance. Instead of taking risks like the classical notion of an entrepreneur, his or her reluctance in doing business will be present. Note here that the role of the ‘missing middle’, which is a historical baggage of the region B that leads to this. There is, however, the possibility of the mitigating influence of experience and more information on rCFT. Experience is a very important parameter and has a close link with product networks discussed in Chap. 2. Successful entrepreneurship in food processing has to do with finding the appropriate linkages within the product networks (from low-value-added primary processing to high-value-added ones). This requires some level of experience with the product network, local experience and/or skill or training. This we find is possible for entrepreneurs with positive rCFT. They can appropriately build on the agri-resource abundance of Bihar by expanding business in food processing. Our theory indicates that entrepreneurs with a negative rCFT do not have the correct entrepreneurial mindsets. If the policy has to target anyone, it has to be those entrepreneurs with a positive rCFT. Hence, the relevant question to ask is, given our model, which types of entrepreneurial mindsets find it incentive compatible to enter Bihar to do business. Does the policy target the correct type of entrepreneur?

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7.3.2 Policy Targeting of Entrepreneurial Mindsets To address the issue of policy targeting of entrepreneurial mindsets, consider the thumb-rule that determines entry of an entrepreneur j to region B, as given in Eq. 7.4. It states that an entrepreneur will enter if the sum of all total costs (physical as well as non-physical) is less than the entrepreneur’s outside options. Equations 7.1 and 7.4 together imply [ pir − ci + χ j (.) + ψ j (.)]xi j ≤ ω j (.) − Cir + τi j Given that we have assumed total output xi j ∼ U [0, 1],9 we now get: 

 ω j (.) − Cir + τi j Pr ob xi j ≤ r ≤1 pi − ci + χ j (.) + ψ j (.) ω j (.) + τi j ≤ pir − ci + Cir + χ j (.) + ψ j (.)

(7.5) (7.6)

Thus, we find the following result regarding the targeting power of subsidies of the type τi j for a region k = B: Claim 7.2 Adverse Selection of Entrepreneurs through IP in Region B relative to NB. Policy incentives in a region with relatively large missing middle size of firms (like region B) is more likely to attract entrepreneurs with negative rCFT than other regions with a smaller missing middle (like region N B). To see the reasoning behind this claim, fix τi j . For k = B, the inequality in Eq. 7.5 is τi j ≤ pir − ci + Cir − ω j (δ(l B )) + χ j (δ(l B )) − t + ψ j whereas for region k = N B, this is: τi j ≤ pir − ci + Cir − ω j (δ(l N B )) + χ j (δ(l N B )) + ψ j Now, i. lower is δ(l B ) relative to δ(l N B ): such that χ j (δ(l B )) − ω j (δ(l B )) > χ j (δ(l N B )) − ω j (δ(l N B )) > 0 ; and ii. lower is the value of t in region B, the easier it is to satisfy the entry condition for an entrepreneur in region B relative to region N B for the same level of subsidy τi j . Therefore, policy incentives in a region with a large missing middle of the kind δ(l B ) < δ(l N B ) is likely to attract the entry of entrepreneurs with low local knowledge, i.e. t very low. This is an adverse selection problem: in a region like Bihar with low δ(l B ), incentive policy will encourage entry 9 Note

here that the uniform distribution has been assumed for its ease in working out theoretical results.

7.4 Empirical Observations from Primary Survey in Bihar

219

of types with low t, who are then more likely to have a negative rCFT, as our earlier Claim 1 shows. In the following Sect. 7.4, we test for this relationship of experience of entrepreneurs with risk perception using data from the primary survey among entrepreneurs in food processing in Bihar in 2016–2017.

7.4 Empirical Observations from Primary Survey in Bihar Our empirical exercise of testing the nature of relationship between rCFT and risk perception among entrepreneurs in food processing in Bihar is based on a primary survey among formally registered units (partnerships as well as limited liability companies for a total of 76 units) in food processing in Bihar in 2016–2017.10 There is no published literature attempting this exercise for Bihar, presumably given the numerous difficulties in contacting individual entrepreneurs through surveys. We conducted a mixture of telephonic and face-to-face interviews of entrepreneurs from across different districts in Bihar for answering our survey questionnaire.

7.4.1 Survey Method: Snowball Sampling Our sampling method was based on snowball sampling, as discussed in Noy (2008), Goodman (1961) and Handcock and Gile (2011). This sampling technique is purposive (non-random) and is considered appropriate when the sampling frame is not known with certainty. This property of uncertainty in sample size is commonly found in hidden samples, for example, when research is on taboo subjects such as drug addiction. Then, the nature of the survey becomes non-random. Sampling starts from an initial contact point and then snowballs to other sampling units based on information provided by the initial contact. While non-revelation of information is a problem for most surveys (including ones with a randomized technique), it is much higher in the context of these hidden populations. The central idea is to develop trust by the surveyor, and unless the latter gets an introduction to new sampling units, there will be a complete lack of any response to the survey. Apart from hidden populations, recent empirical work on networks also use snowball sampling for surveying nodes in a network (see Newman (2018)). When different agents are connected with each other in a network (the connections between them can be social, economic or oth-

10 We have used our final report on Food Processing Industries in Bihar, written on the basis of our IGC project and submitted to the IGC in 2018 for a part of this chapter. Further details are at https://www.theigc.org/project/study-of-the-food-processing-sector-in-bihar/ and a short note on the project is available at https://www.ideasforindia.in/topics/trade/the-study-of-the-foodprocessing-industry-in-bihar.html.

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erwise,11 one has to ensure that all units in the network are surveyed. This criterion is not met by random sampling, as there is an underlying structure of connections among the various sampling units that the survey must capture. For our survey, all these reasons apply. So, we use this sampling technique. First, the population of functional firms at a point in time is not publicly available. For registered firms, the Ministry of Corporate Affairs maintains information about firms in operation. However, this is as per information filed by the firm. There is no exit database, like the Dunn and Bradstreet in the US, so that one can neatly identify which firms are functional in an industrial sector. The ASI (Annual Survey of Industries) collects information at the factory level of manufacturing, but due to anonymity reasons, this survey does not reveal the identity of the firm which owns the factory. Given continuous entry and exit, exact data on the population of firms in any industrial sector in any state of India is, at best, a rough estimate. The primary reason is that there is no legal restriction on an exiting firm to inform any authority that it is leaving the business. Lack of an industrial exit policy has exacerbated this problem. Hence, the population of active firms is as good as a ‘hidden population’. Regarding the network effect, we expect low interconnections among firms, given deep market segmentation in food processing in Bihar in Chap. 5. However, there are issues of trust and willingness to be surveyed for entrepreneurs. In our initial interactions with the Bihar Industries Association (BIA), we realized that no entrepreneur would accept to be interviewed by us, unless we provided a valid introduction. Munshi (2010) mentions a similar problem for interviewing diamond traders, due to the secrecy of their business. Rather than the network effect, it is this problem of entrepreneurial non-responses that prompted us to use snowball sampling. It was not an easy exercise to track down entrepreneurs from different districts of Bihar. We had to mention the reference contact to gain the trust of the interviewees. This is possibly an expression of the lack of cohesion in the policy network, as we discussed in Chap. 5, that there is a significant degree of mistrust among entrepreneurs towards the rest of the ecosystem for industries. With the permission of the state government and the Program Management Agencies (PMA12 ) we snowballed our sample from an initial source (in our case, the PMA contacts and BIA members) to their contacts, ensuring a decent sample size. We mention here the trade-off that we faced: with random sampling techniques, the econometric analysis would be much richer, as we would be able to engage with causal analysis (in the nature of regressions) to infer results about the population from our sample. On the other hand, given the reticence among entrepreneurs, randomized sampling would result in excessive non-responses defeating the purpose of the survey in the first place. It is the cost of non-response that drove our empirical strategy of sampling and analysis. An additional filter was needed for our sampling units as our interest was to study the indigenous firms of Bihar with production facilities in the state and not simply 11 Newman

(2018) provides a number of examples of these networks such as co-authorship or friendship or even buyer–seller networks. 12 These agencies are referred to Project Management Agencies in Bihar.

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marketing their products, such as G.D. Foods marketing Tops jams and jellies. These marketing firms, many of which are present in Bihar, are large listed enterprises with manufacturing facilities outside the state.13 Essentially, we want to understand the mindset of the entrant who sets up manufacturing facilities in the state with the subsidies from the government. The large marketing firms, with production facilities in other states, are examples of units which have very little connections with the small entrant: due to the deep segmentation of the market as discussed in Chap. 4. Including these large firms in our sample would bypass the assessment of risk in the production process. Additionally, units specializing in only marketing and not production were not eligible for government subsidies. We wanted to ensure that there was significant functional diversity in the sample, as there is no reason to believe a priori that entrants will function poorly post-entry in food processing in Bihar. They can be at various stages of functional performance. For this purpose, we included in our sample not only mature units across sub-sectors, but non-functional units as well (closed and yet-to-start-operations). We also have a variation in terms of maturity (units less than one-year old we term nascent and more than one year as mature). This reduces sampling bias to some extent. Of the total 76 units, the majority 61 are functional and mature, whereas 8 units are nascent (less than a year old), 2 units are in a planning stage (production yet to begin) and 5 units are non-functional and in various stages of exit. Note that none of these businesses are exporters and their marketing is limited to consumers within the state of Bihar.

7.4.2 Sample Description Our final sample size from the survey was 76 responses for entrepreneurs across sub-sectors in food processing. Our first observation is to find out the percentage of entrepreneurial types (with negative, zero/uncertain and positive rCFT) in the data. In our informal conversations with the entrepreneurs, the majority observed that they were in business in Bihar because they were native to the state. Their specific knowledge, experience and nativity in Bihar was evident, including the two potential entrants and the non-functional units. As noted earlier, to measure outside options we asked entrepreneurs what they would have done, if not engaged in the current profession. To this question, other than 21 responses, all others indicated that if they were not in the current occupation, they would be in some other business or trading activity. Of the 71 valid responses on the rCFT question in our survey, 40 respondents expressed negative rCFT, which is about 57% of all respondents. This dominance of negative rCFT validates our theory about why policy is likely to be ineffective when entrepreneurs have negative rCFT. On the other hand, 21 of the total 71 express positive rCFT and 10 have no rCFT.

13 These

firms do not form the necessary co-processing linkages that we discuss in the previous chapter. The government has also kept them out of the purview of subsidies in its policy.

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Table 7.1 Distribution of various units in primary survey Enterprise type No. in sample Cattle/poultry feed (maize-based) Cold storage Rice mills Honey Dairy Others Total

9 15 19 8 4 21 76

Source Author’s calculations based on primary survey in Bihar, 2016–2017

The sub-sectoral distribution of units in our sample is shown in Table 7.1.14 As with the population of firms in the state, the sample reflects a majority of rice mills (19 out of 76 units). However, there are a number of other units in other sub-sectors, such as dairy, honey, poultry, cattle feed, etc. We describe below from our IGC report further descriptions of the sample: Project Size Around 60% report an initial cost less than INR 5 crore. These are small units by the Ministry of Micro, Small & Medium Enterprises, Government of India criteria. The remaining 40% of the sample have project sizes less of than 50 crore INR. Only two functional units (both in dairy processing) have a project size of 120 crore INR. The pattern of many small and few large comes out through other measures as well, such as land size (majority with less than one acre of land and a minuscule with over 50 acres). While this comforts us that our sampling strategy (though non-random) does reflect the pattern in secondary data, this is anything but the picture of a healthy industrial scenario. Education and Age Our data has significant variation built in for parameters such as age and education. The average age of the entrepreneur in our sample is 45.4 years, with a large age variation from 21 to 72 years. The lowest education qualification is a Class X (4 individuals) ranging right up to doctoral degrees (3 entrepreneurs) and professional qualifications such as Chartered Accountancy (1 observation) and MBA from universities in India and the UK (total 10). Entrepreneur Origin However, there is a much lower variation for origin of the entrepreneur. We define an indigenous entrepreneur as one whose last three generations have been in Bihar. 64 of the 76 sample points are indigenous entrepreneurs. Being in the local conditions 14 Two of the units surveyed resulted in managerial interviews and not that of the original entrepreneurs and hence were not included in the final sample for understanding the perception of risk.

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matter, as majority interviewed mentioned that they were doing business in Bihar because ‘they were from Bihar’ and in fact, as our later observations show, they displayed skepticism as to whether they would do business in the state had they been outsiders. Women Entrepreneurship Similarly, we have very few women entrepreneurs (only eight women with relatively small projects (other than a wheat milling plant)). Low female entrepreneurship, despite special incentives in successive industrial policies of the state, is reflective of the overall low human capital outcomes (education, skilling, workforce participation). For most of the women interviewed, their entrepreneurial income is seen as an additional input to total family income and not an essential component of financial buoyancy of their families. This possibly translates to lower risk perceptions among women in our sample, as they have an easier outside option. Most Important Physical Cost Component The main cost component for the enterprises was both the variable and the fixed cost of plant, machinery and civil works (for more than fifty per cent of the sample) followed by the running cost of electricity. Only six enterprises mentioned the fixed cost of land as the most important component of setting up business operations. While there is some sub-sectoral pattern in the cost component (cold storages mention electricity as the main source of expenditure, whereas some rice mills consider the variable cost of paddy input as the highest cost component), the pattern is to be understood with the age of the unit, as many of the older establishments have already amortized their fixed costs, and are more concerned about variable costs of operations. Significant marketing costs are present due to own-brand sales. Marketing Other than for cold storages, almost all units market their own products under their own brand names. Almost all cold storages in Bihar operate on a rental model, where the owner lets farmers store their produce in the cold storage against a rental. Most of these sell the final produce as a part of the business policy; only when they are forced to when the farmer refuses to collect the stored product at the going rental due to price reductions in the market.15 However, in terms of using modern retail marketing channels such as online platforms, only two entrepreneurs mentioned that they were considering these options to diversify marketing risks. Father’s Occupation Exposure to business activities through father’s profession helps in forming attitude to business prospects and risks. In our sample, there is some variation regarding this, with 27 entrepreneurs reporting that their fathers were either in same/unrelated business or involved in trading activities and 16 others mentioning that their fathers 15 A few exceptions are in Nalanda, where the cold storages store flowers which they sell to tourists

and in Hajipur, where the owner sells unbranded apples in cartons to buyers in Delhi.

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were either farmers owning land or zamindars (landowners). The remaining 33 entrepreneurs report diverse professions for their fathers (ranging from professors to government employees to ‘mukhiya’ or the village chief) mostly in the service sector. It is interesting to note that the majority of the sample mention self-interest as the reason for doing business and majority of the father’s professions are unrelated to business. Type and Size of Land Related to the earlier variable is ownership of land or rather, the type of land on which the unit is established. Half the sample have put up their manufacturing on their own land, which they have leased to the unit. Among the remaining 38, the majority of 28 have leased land from the BIADA (Bihar Industrial Area Development Authority). Given the increasing circle rates due to pressure from the demand side, the latter option has become extremely expensive. According to some of the older establishments, setting up operations on BIADA land with current lease rates would make operations financially unviable. Land size is a difficult variable, which reflects the problem of the extreme distribution of project sizes in the sample (as is the case with the population of firms in Bihar). The mean size holding is 27.88 acres, which is small by industrial standards in India. However, the standard deviation of 199.04 acres is much larger than the mean and is resonant of the large variation in unit sizes. Information Sources Exactly half the sample do not have a membership with any of the business associations within the state, such as the Bihar Chamber of Commerce or the Bihar Industries Association. Nonetheless, they do have contacts with members of such associations, who referred them to us (due to our sampling strategy). Only six members have multiple memberships (these are old established units). As these associations, which have a representation with the government, are located in Patna, units in the distant districts do not find membership with these associations improving their lobbying power or information sources. Most of the demand for small units is concentrated in local areas. They do not market their product in all districts of Bihar and hence, it is rational not to invest in these memberships. Experience We capture prior experience as traders before each entrepreneur in our sample. With 15 missing observations, the minimum and maximum years of experience is zero and forty-six, respectively, with a mean of 13.26 years and a relatively large standard deviation of 10.79 years. Business Outlook (Strengths and Challenges) We asked the entrepreneurs to identify the top two factors that were the strengths and challenges for the sub-sector that they were functioning in. We asked this question in terms of overall macro variables which impact all units, and not their own business operations alone. This variable helps us capture the shared visions and goals at the

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sub-sectoral level among entrepreneurs. Interestingly, there was no uniformity in business outlook in terms of challenges, even within our small sample. Among the important problems are: corruption and law and order, availability of land, inadequate infrastructure, inadequate information and policy thrust and finance. However, in terms of sub-sectoral strengths, there was an uniformity in responses: abundant raw material resources, large local demand (low competition other than for rice mills) and no labour trouble. Once again, we find a corroboration of our claim about a lack of cohesiveness in the industrial layer through this exercise. As capturing the entrepreneurial mindset is the primary objective of this exercise, we first define how we measure rCFT and risk perception.

7.4.3 Empirical Measurement of Risk Perception and rCFT As mentioned in Saha (2019), we measure risk perception and rCFT as follows: 1. Risk Perception: we measure this in two ways. For one, we use a direct scale measure of risk perception and second, as a categorical variable with two categories (low risk and high risk). The first is a scale measure of perception of total risk for an entrepreneur. It is measured on a scale of zero to five, with zero reflecting very high-risk perception and 5 reflecting minimal risk perception. The categories 1 and 2 reflect high-risk perception (value less than or equal to the average of 2.5) and low-risk perception (value greater than 2.5) respectively. The cut-off of 2.5 is based on the mean value from N = 72 valid responses to this question. We refer to this categorized measure as the ‘risk code’. 2. rCFT: is captured as a Bihar-specific issue that would not be present, had the entrepreneur not been from Bihar. We asked the entrepreneur whether he/she would do business in Bihar had he/she not originated from Bihar and was an outsider. This second measure is intended to understand how much of a pure input advantage in food processing that an entrepreneur perceives, to the extent that he/she would find it profitable to enter Bihar to set up operations even if his origins were outside Bihar. This measure (we term it counterfactual) is categorical, the categories being ‘yes’ (entrepreneur would do business in Bihar even if from outside Bihar), ‘no’ (entrepreneur would not do business in Bihar if a native of the state) and ‘uncertain’ (entrepreneur is uncertain about his/her choice). This measure captures the rCFT, present in entrepreneurial identity arising from a location-specific factor.

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7.4.4 Empirically Testable Hypotheses Our theory leads to two empirically testable hypotheses.16 First is regarding the nature of relationship between risk perception and rCFT. In order to link the theory of rCFT to behaviour of entrepreneurs post-entry, one needs to show that these two concepts have a negative relationship, i.e. negative rCFT is associated with high-risk perception and vice versa. The second testable hypothesis has to do with the importance of rCFT itself. Suppose we do find the correct association between risk perception and rCFT. That alone does not help the cause of the latter concept. We could have worked out remaining results of behaviour using risk perception alone, on which there is already a large body of the literature (see Sitkin and Weingart (1995) and Sitkin and Pablo (1992)). rCFT is needed because it provides a theory relevant for trajectories like Bihar, which a concept like risk perception cannot capture standalone. Perception of risk is present in any business in any region, whereas rCFT is peculiar to entrepreneurs in a particular region. As we mentioned before, it highlights the difference in perception between ‘doing business’ as opposed to ‘doing business in Bihar’. We expect rCFT to provide a theory about entrepreneurial behaviour given the policy framework and business environment in Bihar. Informal conversations with stakeholders indicated the validity of our model. However, a strong support for our theory should be a regression analysis that would establish causation. This type of analysis would predict policy outcomes, after controlling for sub-sector-specific factors, such as different physical costs in grain milling as opposed to dairy as well as rCFT. Note here that this type of causal analysis is ruled out given our sampling technique. As mentioned earlier, one of the drawbacks of non-randomized sampling is the difficulty in drawing out causal inferences from the data. Empirical analysis with this kind of sampling is limited to studying the nature of associations between variables. Given the history of the state and the lack of entrepreneurial awareness and trust, the only way to collect a decent sample size was through snowballing. While this data limitation constrains the empirical analysis, we can draw some idea about the relationship between important parameters in the model from the sample. This serves the purpose of an indirect support for our theory. How do we ascertain that the categorical answers (yes, no or uncertain) that we asked in framing our rCFT question does indeed capture this effect? Is there a direct way to measure this? Consider our theory of rCFT. This variable is itself the difference between net outside options (Δ1 ) and net inexperience costs (Δ2 ) between two regions for any entrepreneur. The source of rCFT comes from the regional difference in outside options relative to these costs in the mentalization of the entrepreneur. However, it is impossible to measure this directly for any individual. What we can do instead is to focus on some measures of inexperience costs which 16 Note that we present a richer framework here, with a theoretical understanding of non-physical costs and rCFT than in Saha (2019). However, this framework constrains how we develop the empirically testable hypotheses. The latter are different in Saha (2019), as it presents the empirical results without the constraints of the theoretical framework proposed here.

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feature in rCFT through Δ2 . Therefore, this is not a direct test of rCFT. Rather, we test for the empirical nature of the association between these inexperience costs and contrast that against what the theory would predict for this linkage. What are these theoretical predictions? Among the non-physical costs, we can measure the parameter of experience (which potentially is a measure of t or local knowledge). We expect to find a mitigating effect of experience on negative rCFT. The reason is as follows: for a given value of Δ1 , if Δ2 is lower due to higher experience (and local knowledge), rCFT will be less negative. Therefore, experience should reduce negative rCFT. Note again, it is prior experience and not the functional status of the unit that matters for the type of rCFT the entrepreneur has. We do not expect to see any sub-sector-specific correlations between rCFT and subsectoral attributes, as rCFT is specific to regions and not product networks in food processing. Now, efficiency of operations and outlook of business risk are both likely to be a function of whether entry into business is due to self-interest or due to existing family concerns as well as the role of government subsidy. Regarding family influence, continuous engagement of family members in various aspects of enterprise operations has been mentioned by almost 59 entrepreneurs. None of the units are listed on stock exchanges and a few began as Limited Liability Partnerships (LLPs), with equity participation from various family members. Majority of these operations involve family members in managerial positions and the distinction between profits earned from the business and personal income is blurred. In order to measure family influence, we check for father’s occupation. Let us see the theoretical prediction about the relationship of rCFT with government subsidy, access to finance and father’s occupation. Government subsidies are associated with physical costs in operations and not non-physical costs that is in the domain of rCFT. Like subsidy, size of landholding is unlikely to affect non-physical costs. Additionally, access to formal finance and other family attributes like father’s profession influences ψ j , which are costs due to imperfect financial access. This parameter drops off our rCFT definition as it is constant across two regions. All variables, like access to finance or family background, which are constant across the two regions k = N , N B drop out of the rCFT. These correlations are an indirect test of our theory: if the nature of correlation is in line with what theory predicts, then it validates our theory. This will give us confidence that rCFT indeed captures the entrepreneurial mindset for the Bihar trajectory. On the other hand, if the associations are signed differently from what the theory predicts, we will have to refine our model of entrepreneurial mindset. In a nutshell, our testable hypotheses are the following: I. rCFT is correlated with an entrepreneur’s risk perception such that a high-risk perception is associated with negative rCFT and vice versa. II. Business-Specific Linkage: rCFT has no association with the specific sub-sector in which the entrepreneur operates. III. Resource-Specific Linkage: rCFT is not associated with enterprise size, landholding and access to finance.

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IV. Policy Linkage: rCFT is not associated with government subsidy. V. Attribute-Specific Linkage: rCFT is negatively associated with entrepreneurial experience and not associated with father being in the same occupation.

7.4.5 Results In our sample, three entrepreneurs are multi-plant and multi-sector (one of them has three separate units), which we count as individual responses for our analysis. Otherwise, there would be a duplication of observations for the risk perception variables, as we find no variation in these responses. We also have some non-responses for the counterfactual variable, so that the valid number of observations for our study is 71. We have remarkably consistent answers, showing that our two measures are indicative of risk perceptions. We discuss the results on individual empirical claims below. [I.] rCFT is correlated with an entrepreneur’s risk perception such that a high-risk perception is associated with negative rCFT and vice versa. In order to test this hypothesis, we check whether responses with a high-risk perception also answer in the negative (‘no’) for the region-based counterfactual. Table 7.2 shows the cross-tabulations of the categorized risk code against the counterfactual. We find 10 responses with zero or uncertain rCFT. An overwhelming 40 of the total 71 responses are with negative rCFT, which are likely from entrepreneurs with low t. The remaining 21 are positive rCFT responses. We find a clear correlation between rCFT and risk perception. A majority of negative rCFT responses (23 out of 40) have a high perception of risk, whereas this is the opposite for those with positive rCFT. A majority of 16 out of 21 positive rCFT responses have a low-risk perception. If rCFT is uncertain, then the pattern between high- and low-risk perceptions is indistinguishable. [II.] Business-Specific Linkage: rCFT has no association with the specific subsector in which the entrepreneur operates. We find no direct evidence of any subsectoral pattern with risk perception. It is not the case that rice millers display a significantly rCFT than cold storage or other units. Hence, for the purpose of the risk assessment, we club all units together and present

Table 7.2 Risk code * location-specific counterfactual (rCFT) cross-tabulation rCFT Uncertain Negative Positive (ρ > 0) Total (ρ = 0) (ρ < 0) Risk code 1 (high risk) Risk code 2 (low risk) Total

6 4 10

23 17 40

5 16 21

Source Author’s calculations based on primary survey in Bihar, 2016–2017

34 37 71

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our central results for the rest of the analysis. This is not surprising in the light of the theory we have presented here. [III.] Resource-Specific Linkage: rCFT is not associated with enterprise size, landholding and access to finance. [IV.] Policy Linkage: rCFT is not associated with government subsidy. For testing these two claims, we use our measure of direct perception of risk (on the scale from zero to five). Neither loan size (access to formal finance) nor government subsidy show a statistically significant correlation with the risk perception (scale measure). Though size of landholding (and hence size of the enterprise) is positively correlated with risk perception (scale measure), the Pearson correlation coefficient of 0.013 is not statistically significant. [V. ] Attribute-Specific Linkage: rCFT is negatively associated with entrepreneurial experience and not associated with father being in the same occupation. Experience is positively correlated with the scale measure of risk perception with a statistically significant correlation coefficient value of 0.27. This reflects the perception of risk in doing business in Bihar falls with experience and is along the lines of existing literature, such as Podoynitsyna et al. (2012) and Munshi (2010) as well as our theory. Hence, we find evidence that perception of risk goes down with experience, but has no association with enterprise size, landholding, access to finance as well as government subsidy. Note that this is with respect to the perception of risk and not in relation to actual objective measures of risk. In our exploration of physical costs, the variable of size (fixed costs of plant and machinery) was a significant explanator of firm performance. It is likely that some of the variables uncorrelated with perceived risk would be correlated with measures of risk based on physical costs. That does not challenge our theory about entrepreneurial mindsets in Bihar. In fact, all the results are along the lines of what we should expect from our theory of non-physical costs and rCFT.

7.4.6 Limits to Theory or Limits to Empirical Testing of Theory? There is one confounding result though: there is a positive correlation in risk perception and membership in business associations, as Table 7.3 shows. The higher the number of memberships, lower is the perception of risk in Table 7.3, where percentages are mentioned in parentheses. Clearly, higher number of memberships in associations (which comes with associated membership fees) is exhibited by entrepreneurs who are established in business and who benefit from information exchange, networking with other members and lobby the government. There is every reason to believe that association membership is correlated negatively with risk perception. However, we cannot build a theoretical connection of this parameter with the manner in which we model rCFT. Note that in our model of non-physical costs, asso-

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Table 7.3 Risk code* business association membership cross-tabulation Risk code 1 (%) Risk code 2 (%) Total (%) Zero memberships One membership More than one membership

21 (0.55) 11 (0.41) 2 (0.33)

17 (0.45) 16 (0.59) 4 (0.67)

38 (100) 27 (100) 6 (100)

Source Author’s calculations based on primary survey in Bihar, 2016–2017

ciation membership is a parameter for costs due to financial market imperfections, ψ j . This is not a part of our definition of rCFT, which only includes inexperience costs relative to outside options in two different regions. It is reasonable to argue that while association membership is correlated with risk perception, it is not reflected in rCFT. Empirically, we have merely shown the nature of association between rCFT and risk perception. In either case, we have to take a call as to whether this points to a limitation of the theory itself or a problem with the manner of empirical testing of our theory. Note that the empirical tests are regarding associations between variables and are not causal. Given data limitations, we are unable to provide causal evidence for our theory regarding entrepreneurial mindset and behaviour.

7.5 Going Forward: What Should Industrial Policy Focus On? For Bihar, the primary policy focus should be on increasing the density of middlesized firms. Our discussion on physical costs in the previous Chap. 6 supports this. For dairy, grain milling and rice mills, we find that size improves profitability and therefore, firm survival. Our exploration of non-physical costs and entrepreneurial mindsets in the Bihar trajectory yield two clear policy suggestions. First, as a contract between the entrepreneur and the government, IP lacks strong screening properties. Hence, targeting of entrepreneurs with the right mindset would be difficult in the first place. Note that our reasoning is different from the asymmetric information logic, which is concerned with the provision of adequate incentives to solve the problems of adverse selection and moral hazard. We have a full information framework in our theory. However, these observations are in common with the critiques of IP which come from an incomplete information framework. Problems of targeting the correct mindset exist. Overall, this would be an unduly pessimistic conclusion for policy. Incentives are not high enough in Bihar to attract middle-sized firms. At the same time, new entrants would be uneager to expand the size of operations due to high negative rCFT. This is also not due to cut-off-based policies which discourage firm expansion. These are inherent limitations of the policy itself. However, we have shown that in the absence of the kind of incentives that Bihar experimented with

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between 2008 and 2016, possibly no investments would be forthcoming to the state. This becomes a double bind: without the policy, there is no entry and with the policy, there is incorrect targeting and inefficiency in outcomes. This is not all, though. The second policy lesson comes from the link between experience and risk perception (and rCFT). As an entrepreneur gains experience, ceteris paribus other factors, his/her perception of risk is lower in doing business in Bihar. Therefore, there is a role for the horizontal arm of the policy in providing entrepreneurial skill development programmes, which can provide similar human capital as experience. This observation is the same as our conclusions in Chap. 5: for a state like Bihar, investments in horizontal aspects of IP should complement vertical elements, such as subsidies to entrepreneurs. Providing the latter also matter, irrespective of entrepreneurial experience. It is an interesting observation for us to find that some existing entrepreneurs (particularly in rice milling) are considering Assam as an alternative state for expansion of business interests and not Bihar. This is yet another instance of inter-state rivalry to attract investments, as discussed in Chap. 5. If Bihar removes these subsidies, it will lose out to other states which provide these incentives. We provide in the text box below a discussion on a more general set of ideas regarding policy: Interpreting the Empirical Results for Policy Implications Entrepreneurial expectations are not formed in a vacuum. It takes into account the local conditions and projects forward. Given the history of Bihar, the policy-based incentives since 2008 in food processing saw a sudden rise in entrepreneurship in Bihar (between 2008 and 2016), with a large fraction of entrepreneurs with low t and a negative rCFT, particularly in rice milling. As we show that negative rCFT is associated with high-risk perception, we contend that the typical entrant with negative rCFT would not be interested in future expansion of business, rather immediate survival would be the main concern. This would imply that the subsidies would not be leveraged to finance firm expansion due to negative business expectations of entrepreneurs. By the time we conducted the survey in 2016, a sense of how the incentive policy was being implemented had set in. Majority of the qualitative responses, including government officials, indicated a large amount of bureaucratic delay in accessing the subsidies promised by the government. Additionally, access to institutional credit had run into the problem of lack of adequate working capital. This comes out most clearly from an observation by one of the PMAs, which were helping these entrepreneurs design their projects to access subsidies: The banks would typically reduce the size of the project submitted for approval in most new applications for subsidy (possibly to cover the risk of losses). The entrepreneur, to reduce costs of operations, despite objections from the PMA, would start out on the reduced capital base (small project cost) once it received the approval of subsidy from the government. With this limited scale of operations, funding working capital and

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7 Food Processing in Bihar: Entrepreneurial Perceptions accessing institutional loans for working capital (collateralized on size of operations) would become a challenge for the entrepreneur...

So, a hurried start of operations would run into financial constraints (delays in getting subsidy reimbursements and working capital loans), triggering cognitive dissonance in their thinking in order to cope with the undesirable performance in business. Managing these multiplicity of constraints, in a business environment that is nascent and underdeveloped, would add to this distressed response: rather than identifying individually the cause (working capital access or delay in subsidy individually), regret of having to do business in Bihar is likely to dominate their perceptions, unless countered with adequate experience and information access through business associations, holding constant the institutional mechanisms of delivery of working capital and access to subsidy. If anything, rCFT is likely to improve with better support from institutional finance for Bihar, and should be an important limb of industrial policy. The development of strong business networks and entrepreneurial coaching and exposure to the supply chain are clear policy lessons from the theory and empirics we provide here. Policy in the industrial space for Bihar is not only to foster firm entry but also to aid firm survival and growth. Note here that our analysis shows that an entrepreneur with small scale of operations is not necessarily a bad policy bet, particularly if she/he can leverage specific regional information. The local entrepreneur is not only entrenched in Bihar, she/he can identify opportunities in food processing due to specific regional information. This is the primary reason why a high t results in a positive rCFT. Therefore, policy investments in the small local entrepreneur is an appropriate direction for IP to consider in Bihar, as long as she or he has sufficient local knowledge/experience. Our contention is that it will be difficult for a general IP to target these entrepreneurial mindsets through incentives. Policy has to go beyond IP-based interventions in the economy. If small entrants cannot expand operations within a five-year period, one has to rethink the design and targeting of policy. It is not only our survey that finds a difficulty of firm expansion and lack of mobility in firm sizes for Bihar. We use data on Bihar’s food processing units from the World Bank Enterprise Survey (WBES, 2014) to demonstrate this in the next section.

7.6 Benchmarking Results: World Bank Enterprise Survey The World Bank Enterprise Survey (WBES) for the year 2014 presents data on food processing enterprises at the subnational level for Bihar. Now, our classification of firm size is not comparable with that used by the World Bank, which is based purely on the size of employees. Capital subsidies given in many subnational states for India, not only Bihar, is not employment-based but based on project cost. Even the current MSME, GoI definition of enterprise size is not employment-based in India. That caveat apart, this data for Bihar uses the following employment size classes:

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Table 7.4 Mobility matrix of enterprise size in food processing in Bihar, 2014 Now small Now medium Now large Total Started small Started medium Started large Total

16 0 0 16

1 8 1 10

0 1 2 3

17 9 3 29

Source Author’s calculations based on WBES, 2014

5–19 employees for small enterprises, 21–99 employees for middle size and greater than 100 employees for large enterprises. We find 29 enterprises from the data which are in food processing as per ISIC Rev 3.1 schedule. As of 2014, we find a plenitude of grain milling enterprises (as is the case with our sample): 17 enterprises out of the total of 29. Regarding the distribution of size of enterprises, the employmentbased classification generates a missing middle and large rather than only a missing middle size for Bihar. However, it clearly shows the lack of mobility/growth out of a particular size class into the next higher size class, as summarized in Table 7.4. Most of the firms had started operations around 2009. By 2014, 16 of the 17 enterprises which started out as small operations had stayed within the small category. Only one enterprise migrated to the middle size. Of the 9 enterprises that started out as middlesized, 8 remained in the middle with only 1 growing out to become large. And of the minuscule 3 large operations, shockingly one had reduced in size to middle, with 2 remaining as large. These transitions (or the lack thereof) underscores the difficulty in transitioning from small to large size classes in the Bihar trajectory. Our discussion highlights the importance of horizontal policies that help nurture entrepreneurial skill, such that the probability of attracting the right mindset becomes higher. Acknowledgements I thank IGC, London for letting me use parts of our final report we submitted to them on the IGC-sponsored primary survey among entrepreneurs in food processing in Bihar in 2016–2017. This report was finalized by Barna Ganguli and the author of this book, who was the Principal Investigator for that project. I would like to thank the participants of the 13th International Annual Symposium on Economic Theory and Policy organized by ATINER, Greece in July 2018 for their inputs on my work on entrepreneurial perceptions. A part of this chapter is based on my article in the Athens Journal of Business and Economics and another part has been selected for presentation at the 30th Annual Game Theory Society Conference, Stony Brook (New York) in July 2019.

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Chapter 8

Comparing Lessons Across Trajectories

8.1 Bihar and Food Processing: Central Features The Bihar food processing trajectory has three core elements: history-dependence, centrality of the government as an actor (directly through policy as well as an operator in food processing) and an abundance of agricultural raw material relative to other resources within the state. These elements interact with each other to generate outcomes in the manufacturing space of processed food by influencing entrepreneurial expectations and other elements of the industrial ecosystem. History has played a big role in determining outcomes in Bihar, as our timeline of developments in the state showed in Chap. 4. We can create interesting counterfactuals, ‘had the state not been bifurcated…’, ‘had there been no political misrule in the state prior to 2006…’ and it would be obvious that the outcomes at present are driven by the path of development that the state has witnessed. Nonetheless, its recent spectacular growth performance, relative to other states in India, shows the promise that this region holds, given appropriate governance. This leads us to the role of the government. For an industrially backward region, with a skewed distribution in firm size, private initiative is unlikely to be forthcoming to engender industrial density and development. This is partly because such actions require a thriving ecosystem and complementary elements of infrastructure, finance, access to markets, etc. Left to itself, private investments would reach out to better-developed regions, which will provide infrastructural support. In this sense, horizontal investments in the industrial layer leading to better infrastructure and ease of doing business is a public good which the private sector will under-invest in. A clear example of this is the problem of establishing Effluent Treatment Plants (ETPs) in industrial zones, which we discussed in Chap. 5. Land acquisition for developing manufacturing industries requires government involvement as well. The cost of acquiring land for setting up industries requires a minimum scale, which typically involves multi-party bargaining and contract implementation. It is at this point that the government comes in with a plethora of institutions, as mentioned in our discussion on policy networks in the previous chapter, such as the industrial land authority © Springer Nature Singapore Pte Ltd. 2020 D. Saha, Economics of the Food Processing Industry, Themes in Economics, https://doi.org/10.1007/978-981-13-8554-4_8

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and financial institutions. However, the role of the government is not limited only to the provision of these public goods. It has a proscriptive as well as a nurturing role to play. Through its policies on taxation, regulation, licensing policies, determination of a negative list of industries that provide goods not aligned with the objectives of the government, the latter can control the denouement of industrial outcomes. Engagement with private initiatives through its IP, encouraging legal cartels (like the MITI in Japan1 ) to generate optimal investment size and proactive engagement in manufacturing through state-owned enterprises are the more enhanced versions of a government’s involvement. The extent to which a government has to drive the process of industrialization depends on the industrial sector that we consider. This links us to the third angle in food processing in Bihar: its resource abundance in agriculture. Bihar produces a large basket of agricultural, horticultural as well as animal husbandry items that can be processed in the state. Some products, such as shahi litchi, tea in Kishanganj,2 maize (with the dominance of Purnea controlling the maize market for the eastern part of India) and varieties of mangoes and other fruits and vegetables are unique to the state. It has a sizeable population of milch animals and goats. However, is Bihar competitive with respect to other states in terms of its agricultural yield? Other than maize, dairy and to some extent poultry, the total production of Bihar is smaller than many other states in India. Though much is made of exotic tropical fruits from Bihar, such as shahi litchi, an entrepreneur in Bihar informed us that due to lack of research on increasing the thickness of the pulp, litchi plants in Bihar do not produce commercially viable quantities of the juice. The same applies to the supply of carcasses for meat processing, particularly goats and its related product network. Without adequate supply, an industrial abattoir will not be able to break even in costs. Though rice grows in plenty in Bihar, its neighbour West Bengal (with its Bardhaman district) is the leader in rice production. They also produce many of the secondary processed items from rice, such as rice flakes and flattened rice, so that regional competition in rice milling is intense. Bihar’s initial advantage in sugarcane and the related industry in processed sugar has declined, the dominance having shifted to states like Uttar Pradesh. The policy change of 2016, as mentioned before, presumably reflects some of the frustration of the government in its attempts to develop the state industrially, in the face of these constraints from the input side. Let us consider the policy counterfactual, then, in terms of what would have happened if the government did not provide the subsidies from 2008 to 2016. The fact is that without these subsidies, even the small steps towards a processing industry that have emerged in Bihar would probably also not have been present (see our discussion in Chap. 5). If the stand-alone relative input advantage for Bihar was that high, then private industry should have stepped into 1 The

MITI (Ministry of International Trade and Industry) has been credited for the miraculous industrial density of hi-tech industries in Japan in the 1950s through its control of industrial, trade and credit policies (see Johnson (1982)). 2 Kishanganj has emerged as a major tea estate in recent times. This was covered by the India Today State of the State (SOTS) survey (November 2018), where Bihar was the focus state.

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the space of food processing, which by its very nature, requires nearness to inputs. This argument, however, is problematic. Again, we have to refer to the history of the state with its troubled past in terms of law and order. If the threats to property rights are large enough or they are not implemented effectively in a court of law,3 then private industry, despite seeing an input advantage, would not step into a region. Here comes the core rationale for government intervention in food processing: as a signal of commitment to a particular strategy with a focus sector in the lead. This is the vertical intervention of policy with a deliberate choice of a sector in preference to others. Note that this assurance itself has generated the development of units in food processing, discussed earlier. While its performance could have been better, the 2008 policy did deliver some industrial outcomes. The dominance of small units and large informality in processing was not the desired policy goal; however, the attraction of doing business in Bihar, and therefore, policy leverage was low (in part due to historical outcomes). It is this perception of the industry that should be targeted as a core focus of IP in Bihar. While focusing on horizontal policies (improving roads, electricity, communication facilities, law and order, institutional finance and other infrastructure variables) should be the first priority, some other ‘nudges’ can be integrated into policy, by learning from successful initiatives in food processing from around the world.

8.2 Successful and Not-So-Successful Regional Trajectories in Food Processing When it comes to comparing regional trajectories in food processing, the problem is how to find the common ground which should be the basis for comparison. We use the geographical feature of being a land-locked region as this common factor, along with some other similarities in terms of the industrial ecosystem. Bihar, as we mentioned in Chap. 4, is a land-locked state. This feature limits access to ports and makes the export market inherently more costly to access. This is a disadvantage for Bihar (as opposed to its neighbour West Bengal, which has access to sea-ports and has consistently attracted higher investments than Bihar). We decided on landlocked regions of Africa, with somewhat comparable histories of bad governance and missing markets and infrastructure, to compare against Bihar. One possibility is to consider Ethiopia. Though it is a country, and not a subnational region, it has some similarities with Bihar and comes under the category of a land-locked developing country. Lack of access to a large water body raises costs of transport and is seen to be correlated with a low Human Development Index (HDI). We picked up a case study of processed cereal (enjera: the local Ethiopian pancake traditionally made from the cereal teff grown in the region) and how a successful export market developed using a product as a core strategy. An exception to our selection criteria is an interesting 3 Not only the executive part of the government, but the judiciary also has a role to play in attracting

private investments into a region.

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warehouse receipts program from Tanzania. We keep this, purely because it offers a possibility of understanding how to improve the farmer-processor linkages in Bihar. Next, we highlight some examples from within Bihar, including its publicly-owned dairy co-operative COMFED. We end this eclectic collection with a mention of a technology-based initiative in food processing, which we believe is a possible strategy for Bihar’s entrepreneurs to consider.

8.2.1 Enjera in Ethiopia Minten et al. (2016) describes the development of the enjera market in Ethiopia. This processed food product is a local pancake, traditionally made from teff (a local coarse cereal) and home-cooked and eaten fresh. It is a part of the local cuisine. Though culturally, enjera was not sold as a processed pre-packaged product, this market has emerged in recent times in Ethiopia. Some of the processed enjera is now being exported out of the country. Commercial viability has led to some input substitution: from teff (the traditional cereal for making enjera), commercially made enjera uses a mixture of teff with rice flour. In terms of our product network definition, processed enjera would come under secondary processing in cereal-based products. The very fact that a food product that was historically not marketed in its processed form has emerged as a successful value-added item shows the nature of changing tastes and preferences. This creates a space for new products such as enjera, which is convenience-and-regional-diet based. Comparison with litti chokha From Bihar The possibility of marketing enjera in this manner opens up possibilities for Bihar’s ‘litti chokha’. This is a part of the traditional diet in Bihar and Jharkhand. Litti consists of stuffed round whole wheat dough balls and chokha is a vegetable mash served on the side (made from roasted eggplant, potatoes and tomatoes.) The central ingredient of litti is the stuffing of spiced ‘sattu’, which is a flour made from roasted gram. The latter grows extensively in Bihar and sattu is a ubiquitous ingredient in many a Bihari dish. With its high protein content, sattu is now a part of the health-conscious vegetarian diet. Many urban hubs, such as Delhi, have upmarket restaurants specializing in a ‘Bihari thali’ or platter from Bihar which feature sattu and litti chokha prominently. In terms of our product network, sattu would be part of the cereal-based value-added secondary product. Sattu, in the processed and packed form, is sold alongside the unpackaged loose variety, which creates a competitive constraint on the milled and packaged variety. Additionally, in Bihar, litti chokha is consumed cooked fresh either at home or in small unorganized sector outlets. The markups from these operations are very low. Taking a cue from the development of the enjera market and our later discussion of JustMyRoots regional-cuisine based venture, one can work out a value-added product offered to the diaspora from Bihar that have migrated out of the state and would still like to retain connections with their roots through food.

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8.2.2 Warehouse Receipts Program in Tanzania Though Tanzania does not compare easily with the Bihar trajectory, we discuss a warehouse receipts program (WRS) from this country to demonstrate the possibilities of similar agriculture-processing linkages in Bihar. Note that we have shown that cold-storage density has not kept pace with other units in food processing in Bihar in Chap. 5. It is imperative that the state government considers more options for cold storages using modern technology. This example of the WRS demonstrates how it is possible to increase liquidity in the hands of farmers while developing cold storage facilities in the state. Warehouse Receipts Scheme, Tanzania This is an example of a successful Warehouse Receipts Scheme (WRS) in Tanzania, Sub-Saharan Africa. With the involvement of the Ifakara Poverty Alleviation Savings, Credit and Cooperative Society (IPASSACOS) & AKIGIRO Kilombero Rice Growers Company Ltd., this scheme was successful in the operationalization of a warehouse for paddy (Charles 2018). The mechanism involved the delivery of packed bags of paddy by the farmer to the warehouse. The latter would issue a Goods Receipt Note (GRN) upon inspection of the raw produce. The intermediary bank would then extend a loan up to 50% of the value of the GRN, which now acts as a collateral. The National Microfinance Bank (NMB) would redeem the cash advance of the farmers. The warehouse would also link the seller with potential buyers, leading to higher credit access for the farmer, better storage as well as price realization.

Application of a WRS in Bihar For a state with a strong preference for unprocessed foods, storage of highly processed edibles and frozen foods is unlikely to find a large market domestically. However, extending the shelf life of raw agri-produce (in Bihar, there are mostly potato cold storages) would lead to less post-harvest loss and potentially lower input costs for processing. A problem mentioned by a cold storage owner in Nalanda, Bihar is the reluctance of farmers to store their output in cold storages. What puzzled him most was the reluctance to take advantage of free training that he was providing to farmers to store their raw product in his cold storage. A WRS-type scheme can act as an incentive for a cash-strapped farmer to intensively use a cold storage. Free training in itself might not provide adequate incentives to farmers.

8.2.3 Dairy Initiatives Bihar’s production of milk increased significantly in the post-2006 era. The dairy sector in Bihar is dominated by the large state-owned co-operative, the Bihar State

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Milk Co-operative Federation Ltd. or COMFED for short. Alongside is another very large private initiative, the ITC’s Munger dairy unit, which we referred to earlier, has recently started operations. On the basis of our interactions with COMFED (the State Government dairy cooperative), ITC Pvt. Ltd. and some ice-cream units (one of the units has been in continued operation since 1948), we find that this sector has the largest employment generation potential. A typical mechanized factory in any other sub-sector in food processing generates direct and indirect employment, which is often seasonal in nature. On the other hand, dairy production generates livelihoods (in the form of animal husbandry) and not just direct and indirect employment. In rural Bihar, even a small landless family owns a milch cow. With proper training and access to information, they will be able to generate incomes through the sale of milk to any of the private or government processing units in the state. The ITC unit uses solar power for its BMC (bulk milk coolers/ cold chain) in the fields, has a current milk collection capacity of 1 lakh litres per day and 2 lakh litres per day milk processing capacity, and generates incomes for 4000 members from nearby villages. The company makes payments within seven days to 250 MPGs (milk producer groups) from surrounding villages. Currently, this six-month-old unit is producing ‘ghee’ or clarified butter for sale in Southern states at the B2B and B2C segments and milk for sale within and outside the state. In contrast, the government-owned COMFED was established in 1983. By 2016, COMFED had put up processing plants in 19 districts of Bihar with a processing capacity of 31 lakh litres per day and 8 functional milk unions covering 33 districts. It also has three plants in Jharkhand, three farmer training centres in Bihar and a diversified product basket ranging from milk, milk products (flavoured and unflavoured yoghurt, ice-creams, sweets (balushahi, peda, gulab jamun, kalakand, rosogolla) ghee, lassi, paneer). It has a very large distribution network (111 whole-day milk parlours in Patna alone) both within and outside the state. The total employment (direct and indirect) as well as livelihoods generated by COMFED runs into lakhs and is a flagship company promoted by the State government. Individual ice-cream units that we have interviewed purchase milk from COMFED (the ITC factory was also selling its milk to COMFED for a few months initially) and are generating employment for around 500 individuals per unit (through employment in the factory and ice-cream carts for distribution). COMFED, Bihar The Making of Brand Sudha Animal husbandry in Bihar is primarily targeted to meet the household need for milk. It is the commendable success of COMFED that milk derivatives marketed under its brand name ‘Sudha’ have reached outside the state. It serves a number of towns and cities of Jharkhand, such as Ranchi, East and West Singhbhoom, Hazaribagh, Gumla, Khunti, Palamu, Lohardaga, Bokaro, Dhanbad, Giridih, etc. Sudha Milk and some products are now available in Delhi/NCR region and Uttarakhand also apart from a

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number of towns and cities of U.P. and West Bengal. Production facilities have extended to three dairies at Jamshedpur, Ranchi and Bokaro in Jharkhand. The Farmers Training Centre at Patna, Barauni and Begusarai provide training to the milk producers and society functionaries in various aspects of dairy, clean milk production, society operation, artificial insemination, etc. Additionally, peripheral activities which enhance yields such as cattle breed improvement programme through artificial insemination, awareness creation about animal health maintenance, prophylactic vaccinations to prevent the occurrence of certain diseases, feed and fodder, training of manpower at various levels of the organization are now integrated into the functioning of COMFED. Its state of the art fully automated plant at Nalanda, with a project cost of 150 crore INR (the largest project size in our data) has ventured into modern milk derivatives such as dried milk powder, which require separate plant and machinery from the standard chilling plants for milk processing. At present, the three-tier structure of COMFED is 1. Village-level Dairy Co-operative Societies (DCS): The DCS in villages collect the surplus milk from farmer-members. 2. District-level Milk Producing Unions: The milk unions collect milk from entire village DCS of its district, process and market it. 3. State-level Cooperative Milk Producing Union (State-level Federation): is responsible for overall policy-making and governance. Note, however, that the impressive COMFED growth trajectory also has to be seen against some indications of potential abuse of dominant position. New dairy units are in a less advantaged position in comparison to COMFED and government regulation should ensure a fair playing field for all businesses in dairy. Co-operative governance structures, such as that of COMFED, are likely to succeed in businesses closely linked with dairy and animal husbandry. Instead of a policybias favouring big businesses which will put further pressure on scarce industrial land, the government should consider investments to improve the existing Farmer Producer Organizations (FPOs), such that small-scale co-operative style units develop with strong backward linkages with agriculture. The Bihar State Vegetable Co-operative Society, which we discuss below, is a successful case study showcasing this. Another very large private initiative in Bihar is the Ganga Dairy in Begusarai, which has established IQF facilities. Among the only three food processing initiatives from Bihar which find mention at the national level data from MoFPI (for having been able to access funds from the Central government), Ganga Dairy is one. We interviewed the entrepreneur during our survey of Bihar in 2016, which revealed the untapped dairy potential for Bihar. Some of the new innovations will ensure higher participation by milk farmers in the processing of raw milk. For instance, using technology for financial inclusion, such as the recent UPI (Unified Payments Interface) in India, quick repayments to dairy farmers can be made. This will make collection and aggregation of milk volumes easier for processing.

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Bihar boasts of one of the oldest Indian ice-cream brands (Golden Dairy)4 which sells own-brand ice-creams since 1948. It has a well-connected distribution facility in the state and has evolved over time to be able to compete against major brands such as Kwality Walls. However, for a new entrant to achieve the scale of operations that this unit has will require significant time and investments. Land prices in urban centres have gone up sharply in recent times, creating unsustainable rents and financial difficulties for new entrants. For every successful Golden Dairy ice-cream brand in Bihar, there are multiple instances of failures in business. Technology in food processing is an issue that we have waived off, classified its manufacturing processes as low-tech. However, the convergence in digital technologies, particularly communication, can be leveraged to design financially buoyant business strategies in food processing, leveraging unique region-centric food items and cuisines.

8.3 Technology-Based Food Start-Ups Patna is witnessing a new trend in food-based start-ups, which specialize in the food retail of eclectic products. A very recent example is https://onlinecakebhejo.com/. With its main office at BIA’s (Bihar Industries Association) Venture Park, Patna, OBC Cakes Pvt. Ltd. had its e-commerce venture incubated in December 2018. This service provides home delivery of cakes and other gift items, such as flowers. This high-value addition to bakery processing, which links products in the network generated from cereals as well as dairy. The Bihar Entrepreneurs Association (BEA) is one of the largest platforms for young entrepreneurs in the eastern part of India,5 and incubates many young entrepreneurs through the start-up model of business. Young entrepreneurs are considering initiatives, such as BEA member Mr. Shashank Kumar’s De Haat, which link agriculture with higher end value-added processes. Brick-and-mortars manufacturing, which requires large capital expenditure upfront, is giving way to small-scale start-ups that require much lower seed capital and is more service-oriented than the traditional manufacturing units. They create the linkage between manufacturing and retail, that is missing in Bihar at present. However, caution is necessary in the design of these initiatives. We had interviewed the owner of the start-up Ganga Fresh.6 This interesting venture, which was being incubated by Venture Park at the BIA, tried to create backward linkages with farmers, by procuring fresh fruits and vegetables, cooking them in prepared menus in mobile vans and selling them outside urban hotspots in the capital city of Patna. This project 4 https://www.goldenicecream.in/index.html. 5 BEA

has a membership of more than 18,000 entrepreneurs from Bihar and neighbouring states, engaged in health, agri-business and clean technology projects. Further details are at http://beabihar. com/. 6 The interview of the entrepreneur is available on the IGC project page at https://www.theigc.org/ project/study-of-the-food-processing-sector-in-bihar/.

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has not survived, showing the large uncertainty with innovations in food products, as we mentioned in Chap. 1. A possible way of integrating technology is to cater to regional tastes of the diaspora using this rigidity in preferences for particular cuisines, as our last example shows. An example similar to the Ganga Fresh venture, which is not e-commerce based, is the nascent Bihar State Vegetable Co-operative Society which started operations in 2018. It tries to link the farm gate to the retail-end through a four-pronged strategy: • a line of fresh fruits and vegetables packaged and sold in the retail markets (different fruits and vegetables, such as litchi, sponge gourd, green chillies, bottle-gourd, cauliflower and cabbages, brinjal, potato and tomatoes). This is along the lines of the Safal initiative in Delhi and the 1965 Horticultural Producers’ Cooperative Marketing and Processing Society, popularly known by its acronym HOPCOMS in Karnataka; • another line of the same agri-produce in the pulp form with no further processing: such as pulped tomatoes; • a third line of processed agri-produce; • a fourth line of fresh produce for further sale. With an integrated set-up using modern technologies, such as IQFs, the project has a core focus in districts like Samastipur, Muzaffarpur, Vaishali and Nalanda. It had started out with an initial project cost of 810 crore INR in 2018 and has now attained a scale of 1500 crore INR. The organizational set-up uses the co-operative structure, which stitches together individual pieces of capital to attain a minimum scale of investment and size of operations. These initiatives, particularly in fresh green perishables with a high wastage, is promising for Bihar whose quantum of such produce, though not the highest in the country, is sizeable. These structures, however, lack the entrepreneurial skill and ability to find retail markets. Poor business management often lead to lack of growth in these institutions, including FPOs. Hand-holding by central agencies, like a government or private skill development agency, as is the case with Israel’s IEI (Israel Export Institute7 ) is necessary for these initiatives to succeed. JustMyRoots Smartphone App Location-specific cuisine delivered using smartphones As India’s first interstate home delivery service, the USP of JustMyRoots rests on stylized fact S3. discussed in Chap. 1. Strong regional food preferences, starting with localized appetites for fresh as well as cooked produce, means that inter-state migration in India results in loss of consumer utility: one has to adjust to the locally available varieties of food items that one is not used to. These preference rigidities, coupled with fast-paced lifestyles in Indian urban centres and ease of use (as an Android or iOS-based app service with a functional e-commerce

7 This government-owned institution provides information services as well as technical expertise to

entrepreneurs in Israel.

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website at http://justmyroots.com), ensures a viable business model for the smartphone app of JustMyRoots. In its profile introduction, the company states Even in a new state, don’t lose the feeling of belongingness…with JustMyRoots by your side.

JustMyRoots, at present, provides location-specific services to the National Capital Region, Bengaluru, Jaipur, Chennai, Hyderabad, Kolkata, Mumbai, Sonipat, Pune and Mumbai serving items under the following categories: organic items, sweets, fruits and vegetables, cooked food, foodgrains, spices, bakery items, beverages, street food, accompaniments and assorted raw and fresh items. The offer package varies by location. In economic terms, JustMyRoots engages in arbitrage across regions (inter-state) for many food items that are distinct to particular regions. Community-specific cuisines available through this service include Bengali, Rajasthani and Kashmiri cuisine. Additionally, Punjabi, Mughlai, continental cuisines are also available alongside specialty Indian desserts. The logistics of delivery involve an application of S6.: this service is linked with major regional food service providers (such as Kamdhenu, 36 Ballygunge Place, Royal Indian Restaurant or Aminia (all in Kolkata) for delivering Bengal-specific cuisine, such as ‘chingri-maach-diyemoong-dal’ which translates to lentils (of the moong variety) with prawn. A large Bengali diaspora outside West Bengal use this service and user comments on the homepage of the website (Fig. 8.1 in the Appendix) shows the extent of customer satisfaction.

8.4 Going Forward: Viable Strategy in Processed Food for Bihar We feel that strengthening access to finance, particularly working capital, for processed food projects that work across the product networks, from basic to high-valueadded items using modern communication technology and which leverage the taste and preferences of the Bihari diaspora in the country as well as abroad is possibly one of the most productive directions for all stakeholders to consider. The role of the government is crucial: through its investments in horizontal elements of IP, creating a ‘Brand Bihar’ through local cuisine and food products for the Bihari diaspora through investments in certification and labelling and playing a coordinating role among financial, land and other institutions and the entrepreneur. This kind of brand creation provides an umbrella under which small firms can operate with reduced marketing costs, as we demonstrate in the case of ‘Khadi’. Reduction in marketing expenses is at the heart of our discussion on the management of physical costs. This kind of public investment also reduces negative rCFT through non-physical costs through two channels. First, the notion on outside option in Bihar as opposed to being outside Bihar can be reversed if a successful ‘Brand Bihar’ emerges. Second,

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as firms expand in size, inexperience costs of entrants are likely to go down due to the higher density of middle-sized firms. Therefore, both the channels of physical and non-physical costs will work towards reducing overall costs for small new businesses in Bihar. This will enable novice entrepreneurs in food processing enter higher value-added items in the product network at a lower cost. This is not only true for Bihar: all land-locked regions with troubled histories and agri-resources can, potentially, leverage consumer preferences, research the product network and develop low-cost business strategies, like JustMyRoots, with horizontal support policies of the government. This would tap the entire value-chain, from farm gate to retail and not restrict the narrative only to manufacturing. Note here that we withhold judgement about the vertical aspects of the IP, such as sectoral targeting. Vertical interventions are expensive and is unlikely to succeed without the necessary support from horizontal investments in law and order and infrastructure in the industrial ecosystem by the government (see Yülek (2018)). While a fiscally unconstrained government, with some degree of tolerance for failure in targeting of vertical IPs (as Rodrik (2008) mentions) can experiment with both, a constrained government should first develop the horizontal aspects of the ecosystem, before rushing in with sectoral targeting, as stressed by Yülek (2018). We have discussed food processing with the blinker of manufacturing alone, keeping in mind the nature of policy incentives that were directed at manufacturing units in Bihar. Even within this narrow domain of manufacturing, pecuniary incentives can be padded with behavioural ‘nudges’ to entrepreneurs, such as appreciation, recognition and awards for successful entrepreneurship. This is motivated by our discussion on entrepreneurial mindsets and counterfactual thinking. Encouraging appropriate risk-taking to find profitable business propositions using ‘nudges’ (see Thaler and Sunstein (2008)) is also a promising part of behavioural policy towards industries. These ‘nudges’ might be considered for fostering the notion of sustainable industrial growth leading to Green Industrial Policies. As we said in the beginning, we live in the age of the Anthropocene, and have made unalterable adverse changes to the planet and its resources. Going forward, sustainable industrial policy should be of utmost importance for regional trajectories in food processing. Acknowledgements I take this opportunity to thank Ms. Kathleen Charles, for sharing her paper and presentation on the WRS scheme in Tanzania. I owe my gratitude to Chef Pallabi at the Trident, Jaipur for alerting me to JustMyRoots. Additionally, Mr. Abhishek Singh (Chief AdvisorEntrepreneurs Association of India; Secretary General- Bihar Entrepreneurs Association & IVLP Fellow) helped me with his inputs on entrepreneurs in Bihar, particularly those associated with the Bihar Entrepreneur’s Association (BEA). I would also like to acknowledge inputs from Mr. Abhishek Kumar (Bihar Industries Association’s Venture Park) for helping me interview some entrepreneurs being incubated there.

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Appendix See Fig. 8.1

Fig. 8.1 Homepage for JustMyRoots available at https://justmyroots.com/

References Charles K (2018) Business “Unusual”: the use of blended finance mechanisms in agricultural infrastructure in Tanzania, Paper presented at the Blended Finance and industrial policy conference in Geneva at IHEID, 2018 Johnson CA (1982) MITI and the Japanese miracle: the growth of industrial policy, 1925–1975. Stanford University Press, California Minten B, Assefa T, Abebe G, Engida E, Tamru S (2016) Food processing, transformation, and job creation: The case of Ethiopia’s enjera markets. ESSP Working Paper 96 Rodrik D (2008) Normalizing industrial policy. Copy at http://j.mp/2o6K6Ye Thaler RH, Sunstein CR (2008) Nudge: improving decisions about health, wealth, and happiness. Yale University Press, New Haven Yülek MA (2018) How nations succeed: manufacturing, trade, industrial policy and economic development. Palgrave McMillan, London

Chapter 9

Conclusion: Lessons From Bihar’s Food Processing

9.1 Lessons From Bihar Bihar has been the focus region for discussing the food processing industry in this book. The state presents an interesting mixture of opportunities and challenges in sustainable industrialization. The interesting interplay here is that the same opportunities, as presented by its raw material advantage (Chaps. 4 and 6), policy framework (Chap. 5) and model success stories (Chap. 8), also can be read as challenges. Let us start with a raw material advantage. While we show in Chap. 4 that the state has an abundance of agri-resources, particularly in a number of agricultural crops such as paddy, maize, vegetables and fruits as well as dairy, Chap. 6 points out that this notion of plenty can be challenged when we compare against the produce from other regions. States like Karnataka and Uttarakhand have a larger basket of fruits and vegetables, West Bengal and Assam have comparative strengths in paddy and states in the southern part of India, including Karnataka, grow maize plentifully. Rajasthan and Uttar Pradesh have larger cattle reserves than Bihar. When we bring in an interregional focus to discuss input advantage, Bihar would probably stand out only in a few crops such as maize and makhana. The latter crop is found most extensively in Bihar among all other neighbouring regions. An arbitrage-based input advantage reason for setting up processing industries near the location of inputs, when seen in a trans-trajectory framework, poses challenges in equal amounts as opportunities. Thankfully, input advantage alone does not drive successful initiatives in food processing. Government policy can influence outcomes, by providing incentives or imposing taxes and regulations. Bihar has seen a special policy focus from 2008– 16 for its food processing industries. The policy treatment of the sector was one of the most generous among other states in the neighbouring region. It came with the promise of Bihar being the land of opportunities for food processing. Once again, these opportunities masked the challenges for developing manufacturing industries related to food in Bihar. Numerous issues in policy implementation, as discussed in Chap. 5, have shown up as hiccups for entrants. Inadequate infrastructure in the state has also meant that it has not been able to absorb the central government schemes © Springer Nature Singapore Pte Ltd. 2020 D. Saha, Economics of the Food Processing Industry, Themes in Economics, https://doi.org/10.1007/978-981-13-8554-4_9

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set aside by the MoFPI. Other states have raced ahead with subsidies and incentives being leveraged for developing food parks. Again, the government is not the sole stakeholder in an industrial ecosystem. Private initiatives drive investments in food processing, as we see for the India trajectory (Chap. 3). Successful private (such as Ganga Dairy in Begusarai, Golden Dairy and the makhana unit Shakti Sudha Industries in Patna) as well as government enterprises (such as the dairy initiative COMFED) are models from Bihar, as discussed in Chap. 8. However, for every such interesting story, there are numerous instances of firms facing challenges in expansion and growth. Instead of a broad base of manufacturing units across the product network in processed food, what we find is a concentration of rice milling units (Chap. 6) in the state at present. Most of these units are struggling financially and are not in a position to expand business. Interesting new ventures, in the form of food start-ups such as Ganga Fresh, have not succeeded (Chap. 8). Failure, in itself, is not a problem. International experience with firm exit shows that regions where new entry takes place, exit also takes place simultaneously. The process of industrialization is a dynamic one of continuous discovery, with exit going hand in hand with entry (see, for instance, Dunne et al. (1988) and Eriksson (1984), though Rosenbaum and Lamort (1992) reports ambiguity regarding simultaneity in entry and exit). Large majors have struggled to establish themselves in developed countries.1 As long as the entrepreneur freely experiments and finds out new ventures and fresh risks to undertake, the industrial environment will stay buoyant. Developments in the manufacturing sector in food processing come with promises of increased farm incomes as well as employment creation (Chap. 2). The importance of this in a densely populated and poor state like Bihar cannot be overestimated. For this buoyancy in the manufacturing sector, there are some important inputs that should be available without constraints. Foremost among these is finance, in particular, working capital for food processing industries (see Chap. 2). We have characterized food processing as an industry that intensely uses this form of finance due to the nature of inputs. Credit constraints due to inadequate sources of industrial finance can become one of the biggest stumbling blocks for an entrepreneur (Chap. 7). The industrial ecosystem of many developing countries, particularly India and especially Bihar, have this problem of inadequate supply of institutional credit. A difficult situation arises: the cost of entry into business is subsidized through government policy, making entry an attractive proposition for an entrepreneur. Post-entry, the entrepreneur runs into problems of financing working capital. The difficulty is compounded if entry is small scale. Working capital, if collateralized against the stock of output, will work against the small entrepreneur. Difficulties in post-entry firm expansion also faces an externality from government policy. Often the latter, particularly tax and labour laws, are cut-off based: they matter only beyond a certain firm size. More often than not, this kind of threshold-based policies also discourages firm expansion. Firms try to avoid the added costs imposed by government policy. 1 We

have mentioned the case of Unilever’s struggles in entering the ice-cream market in France earlier. It developed a presence in the market through re-entry after an initial exit from the market.

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Another critical element for developing industries is functioning property rights and conducive law and order conditions. Bihar, yet again, presents one of the most interesting case studies of a turnaround on these counts (Chap. 4). The history of the state shows a drastic change in 2006, from an earlier situation of political misrule to much improved conditions of governance. Infrastructure, such as roads and electricity, have improved sharply and the state has climbed to the top position in terms of rate of growth of GSDP. While this has been hailed as the ‘Bihar miracle’, overturning the baggage of years of misgovernance is no small task. Particularly in the industrial space, which shows much history-dependence. Capital flight from the state prior to 2006 has resulted in a ‘missing middle’ size of firms with a plenitude of many small and a few large units. Almost all chapters in this book use this phenomenon as a cornerstone to explain outcomes in food processing in Bihar. What are these outcomes we refer to? In the trade-off between opportunities and challenges, we find that Industrial Policy (IP) in Bihar has been only marginally successful in leveraging the opportunities since 2006. The challenges have overwhelmed many of the policy-based incentives to develop manufacturing in food processing. At present, Bihar has a very low industrial base and as we mentioned, a very narrow basket of processed items manufactured in the state (Chap. 4). Firm entry has taken place in food processing. Despite a policy bias favouring large-scale entry, the actual entry has been small scale (mostly informal) and possibly by displacing investments in other areas of agri-business which did not get a similar subsidy treatment. The leverage of policy in Bihar seems to be low relative to other regions, which have managed to attract larger investments with similar policies. Second, entrants have not expanded their size of operations to transition from small to large units. These are the outcomes, particularly the lack of expansion plans of entrants, that we refer to. A central quest in this book is the reason for these modest outcomes from policy incentives. We propose that an explanation of these outcomes does not emanate from individual actions of each of the stakeholders. Rather, it is the historically determined ‘missing middle’ size that we place centrally as a driver of these outcomes. Our explanation uses this phenomenon in two ways. First, we use the channel of physical costs of operations. We contend that the mid-sized firms use co-processing services from small entrants. Industrial sales to mid-sized firms is a less costly route for the survival of small units than retailing own-brand products. The latter requires additional marketing costs. In the absence of these mid-sized units, small entrants have to opt for the more risky own-brand retail. This makes their existence fragile and explains the lack of expansion. Our empirical investigation into grain milling, dairy and more particularly, rice mills validate this theory (Chap. 6). The overall efficiency in operations of these units is low due to marketing inefficiency and not technical inefficiency. The second route through which the missing middle matters is through nonphysical costs and regret-based decision-making by entrepreneurs (Chap. 7). The two components of non-physical costs we discuss are: (i) due to the inexperience of entrepreneurs and (ii) due to financial market imperfections. These are distinct from the physical costs that we have described in Chap. 6. The inexperience costs,

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according to our theory, is due to the ‘missing middle’ phenomenon. Borrowing from our earlier exploration of physical costs, we claim that the mid-size firms provide the necessary experience to small entrants through co-processing services. In the absence of these firms, inexperience costs are large. The presence of non-physical costs now implies that policy will fail to target entrepreneurs with the appropriate mindset to take risks for business expansion. This provides an explanation for the failure of policy targeting. This we have already discussed in terms of firm size: despite a policy bias for large firms, it failed to attract these firms. We show a lack of policy targeting now in terms of entrepreneurial mindsets, which is a reformulation of the firm in terms of entrepreneurial skills. Using a modified regret-based decisionmaking, where we relate the heuristic of counterfactual thinking to the region as an rCFT (region-based counterfactual thinking), we show that unless an indigenous entrepreneur has significant local knowledge/experience, rCFT will be negative in his/her mindset. They are more likely to opine that they would not do business in Bihar had they not been native to Bihar. This negative rCFT has the potential to translate into negative expectations of outcomes from firm expansion. Once again, rCFT, being driven by inexperience costs, is a function of the ‘missing middle’. Larger is this phenomenon, higher is rCFT. If there are a plethora of new indigenous entrants with low local knowledge t, their negative rCFT can translate into a high perception of doing risk in the entire sample of entrepreneurs.

9.2 Alternative Theories In this book, we provide a template to understand industrial outcomes in lessindustrialized regions. We start with reasons for the non-existence of industries in a particular industrial sector and investigate its history as well as current developments. In order to understand them, particularly in relation to actions of government policy, we proceed along two parallel tracks. In one, we investigate the issue of firm profitability using the lens of physical costs. The other track investigates the behaviour of the entrepreneur using the notion of non-physical costs and regret-based decisionmaking linked to the region. This framework provides some insights into what has worked or not worked in terms of the region’s industrial growth. Note that this is not the only way to understand these outcomes. With the same overall reading of policy outcomes, we can think of a number of alternative explanations to the ones that we have provided. First, to get at a simple theoretical framework that works across the dimensions of physical and non-physical costs, we have had to gloss over some empirical details. The government policy provides for separate incentives for business setup as opposed to expansion. Given that an entrant is attracted to the policy by first considering the setup subsidies, we have not separately modelled the subsidy for expansion. It is possible that different incentives for establishing a unit as opposed to expanding it explain non-expansion of small entrants. We implicitly assume that the entrepreneur has full information about the policy framework: that she/he would know that a different subsidy applies

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for firm expansion as opposed to establishment of a new unit. Our explanation of physical costs for new entrants shows that these firms will become very fragile post-entry, particularly in the face of financial market imperfections. Thinking of expansion-related subsidies does not arise in such a situation. Second, problems in firm expansion are also due to infrastructure constraints and not only due to a lack of experience with supply-chains through co-processing. Increased land cost is a prime candidate. As we mention in Chap. 5, increased rental cost due to inflated land price is discouraging expansion of some units in Bihar. These costs are particularly high near urban centres, such as the Patliputra Industrial Area. However, there remains the issue of why firms would like to locate near urban centres and not further away, where land prices are lower. An easy answer comes from marketing challenges: it is easier to distribute the processed product from urban locations. If that is the case, difficulties in marketing and distribution would again become the forefront. While it is not necessary that these problems work out in precisely the manner in which we model the context, marketing difficulties for entrants still remain as an important explanator for outcomes of policy. Third, our model of non-physical costs and rCFT is in a complete information framework with uncertainty in production to explain modest outcomes of policy. An alternative model with asymmetric information between the government and agents also generates similar implications for policy ineffectiveness. However, the regional dimension is clear in our analysis, whereas the asymmetric information argument is agnostic to region.

9.3 Data Issues A big problem that we encountered in taking our theory regarding policy outcomes to the data was data availability itself. There is no centralized database of information on firms functioning in food processing in Bihar. The Department of Industries gave us some initial numbers which we followed up with Udyog Mitra. Information on large registered manufacturing is easy to find, as they have an online presence. However, it is the information on smaller units, particularly unregistered manufacturing, that is hardest to collect information on, though the bulk of the processing activity is carried on by these units in Bihar. This difficulty is reflected in our usage of multiple data sources, which we feel is an inferior option. The problem is definitions. For instance, what the ASI defines as capital is different from what other sources, particularly existing primary surveys and reports have used. We have used the ASI data at the unit level at the 3-digit level of aggregation, along with CMIE data on projects, IL&FS data on firms, our own primary survey data on food processing units in Bihar along with the World Bank Enterprise survey data for Bihar’s units in food processing in 2014 in this book. Despite our efforts to achieve consistency in definitions, some discrepancies might remain.

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Lack of a centralized database in manufacturing at the state-level is not unique to Bihar, though it is probably one of the most challenging state to work with when it comes to empirical industrial organization. First of all, large firms are very few and the plethora of small units are very difficult to track. Some states in India, such as Haryana, have recently initiated a state-sponsored survey of industrial units.2 As part of government initiatives to enhance research on industries, it is our humble request to the government of Bihar to engage in such an activity and make the data publicly available for research purposes.

9.4 So What?.. It is understood that a system functions well if all its subcomponents carry out their individual duties as well as perform combined activities in perfect coordination. Both individual actions and combined performance matters. We model food processing in Bihar as an outcome of a particular ecosystem. Just like any standard system, it has components: the individual entrepreneur, the government, financial institutions and miscellaneous infrastructure items (roads, electricity, water, etc.). To achieve desired targets through this ecosystem requires that each individual component functions appropriately as well as in collaboration with each other. The latter function requires significant coordination among the different elements in the ecosystem. While free-market incentives drive individual entrepreneurs, ecological sustainability and provision of a level playing field for all aspirants has to be a task for the government. Delivering policy outcomes relating to these criteria require not only planning (using the platform of Industrial Policies (IP) or Green IP), but also its communication and execution through the cooperation of individual entrepreneurs. The latter task of communication requires trust and confidence between the government and the entrepreneur, as is required of a cohesive policy network (as discussed in Chap. 5). Cohesiveness is also necessary between different entrepreneurs in the product network, who are connected with each other through supply-chain linkages. For Bihar, we find that the ecosystem fails to deliver outcomes due to the lack of cohesiveness on both fronts: between the government and the entrepreneur and that between entrepreneurs. The disturbed history of law and order in the state explains a lot about the existing mistrust between the government and the entrepreneur. Regarding the latter, we find deep segmentation between large and small firms. The difference between these two firm types is much larger than in other trajectories. There are no links between these two types of firms in the ecosystem. The large units operate mostly through vertically integrated supply-chains with diversification outside the state. There are minimal co-processing linkages between the large and the very small. A middle layer of firms, which can act as a potential bridge between these two types of firms, is absent. This, according to us is the best explanation for outcomes in food processing in Bihar. 2 https://hsiidc.org.in/node/326.

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As a lesson, the central takeaway is the creation of institutions and policy investments that can enhance cohesiveness. The state government’s initiative of ‘Make in Bihar’ is a step in the right direction. Requiring large firms who enter Bihar to source locally and create the co-processing linkages is one way to increase cohesiveness. Though there are problems with preferential purchase, this is an alternative for the government to create the necessary entrepreneurial skill by minimizing marketing costs for small entrants. We have suggested, in the previous chapter, horizontal investments by the state government to create a ‘Brand Bihar’ in food processing. This raises awareness among consumers regarding the cuisine of Bihar, its health benefits and significantly reduces marketing costs for entrants in specialty items like makhana or litchi juice. Providing information and training regarding certification and labelling should be part of this policy, so that consumer confidence about product quality is enhanced. Among other possibilities is the contest mode for financing new projects, as is common for start-ups, so that innovative ideas from potential entrepreneurs get easy access to finance. Alternative and less costly entry routes into the industry, such as cooperatives and start-ups, should be considered in the manufacturing policy of food processing. While Bihar has a separate Start-Up Policy 2017, we argue in favour of an integrated framework. An ideal institution is the Udyog Mitra, which should coach the potential entrant about the alternative routes to enter manufacturing and provide dissemination of information about these policies through a single ‘Awareness Package for Entrants’. All of these policy measures point to the importance of horizontal policy investments. Vertical elements of policy is subservient to these horizontal investments to get all the agents in the ecosystem to be able to coordinate their functions efficiently. In terms of our theory, these recommendations for strengthening outcomes in food processing in Bihar are possible ways to compensate for the missing middle size of firms. Note here the limitations or ‘what not to do’ when discussing the nature of trajectories other than Bihar. The analysis in this book identifies the central role that the government has played in fostering industrial growth, showing the limitations of arbitrage-based advantages in developing processing industries in food. The historical outcomes and the background of the region inform the manner in which we developed the twin theories around physical costs and non-physical costs to explain policy outcomes. The central objective of policy was, according to us, a reduction of the ‘missing middle’ problem in firm size distribution and the core focus of the theories was to show how this problem generates hurdles for small entrants in doing business. We believe that one has to do a detailed survey of a region to work out theories appropriate for that place, at least in the industry of food processing. We do not recommend a direct application of our theories in other contexts without modifications which accommodate peculiarities of those regions. That said, the ‘missing middle’ problem is present in various degrees in different industries and different regions. What we see in Bihar is possibly one of its most stark representations. Regions with less severe skewness in firm size distribution can prioritize their policy objectives accordingly. What is obvious is that one size will not

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fit all trajectories. Contexts such as Bihar require many more horizontal investments in the ecosystem than others that we have discussed in this book. In sum, the regional context matters.

References Dunne T, Roberts MJ, Samuelson L (1988) Patterns of Firm Entry and Exit in U.S. Manufacturing Industries. RAND J Econ 19(4): 495–515 Eriksson G (1984) Growth, entry and exit of firms. Scand J Econ 52–67 Rosenbaum DI, Lamort F (1992) Entry, barriers, exit, and sunk costs: an analysis. Appl Econ 24(3):297–304

Glossary

Atta Powdered processed wheat. Anarsa A local pastry-like snack common not only in Bihar, but in Maharashtra as well in other parts India. Its ingredients include jaggery (unrefined cane sugar), rice, poppy seed and ghee (clarified butter). Arwa rice Local variety of processed rice in Bihar. Badi/Bari Dried paste of crushed lentils made into small balls. Added as a seasoning to curries. Belgrami Sweet dessert made from milk solid (a form of cheese), sugar and ghee. This is commonly found in Bihar. Choora Flattened rice, a by-product from rice milling and is consumed in large parts of Bihar and West Bengal, with a side helping of curd (mostly in Bihar) or some other dish. Crore An Indian unit of account, with value 107 . Most commonly used for measuring money amounts, e.g. INR 1 crore = INR 1,00,00,000. Dahi Curd, as a by-product of dairy processing. Flavoured curds (yoghurts) are also part of the same category with some secondary processing. Dhenki Traditional mechanical rice-milling equipment common in the eastern part of India and Bangladesh. It has a wooden plank strategically placed over a pit with raw unhusked rice. A second wooden handle is operated in a manner that lifts the plank up and down on the raw rice in the pit, which dehusks the rice with repeated pounding. This style of dehusking retains the endosperm and is considered more nutritious than polished rice. However, it is labour intensive, operated mostly by rural women mostly for domestic consumption. These are not scale-intensive operations. Dosa Fried thin slice of rice batter, round in shape. It is common to many states in South India and is available in many varieties. Enjera Traditional Ethiopian rice pancakes, made with the local cereal teff. Modern processed enjera uses rice flour along with teff and is sold in a pre-cooked packaged form.

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Glossary

Gaja A flaked sweet made with maida and is deep-fried in oil and dipped in sugar syrup. This is made in Bihar as well as West Bengal and Odisha. Ghee Clarified butter, commonly used in many recipes in India for flavouring as well as a cooking medium (substitute for edible oil) as well as an essential component in formulations required for Ayurvedic cure. Idli Small steamed rice cakes commonly from South India. Available in many varieties. Jaggery/Gur Traditional concentrated cane/palm/coconut sugar consumed mostly in Asia and also in South America (referred to as ‘gud.a’ in Sanskrit language as opposed to ‘rapadura’ in Brazil or ‘piloncillo’ in Mexico. It contains up to 50% sucrose and up to 20% invert sugars along with wood ash, proteins and bagasse fibres. Khadi/Khaddar Rough homespun cloth popularized by Mahatma Gandhi prior to India’s independence from colonial rule in 1947. It has now diversified as a brand with a variety of processed food items like honey, pickles, papad, bari, etc. Khaja Crispy layered dessert made from white flour (maida), sugar and mawa, deep fried in oil. This is a specialty sweet of Bihar along with some other states in the eastern part of India. Kheer Pudding made by cooking milk with sugar and any cereal, such as rice, broken wheat, tapioca, vermicelli or sweet corn. Traditionally originated in India and is made pan-India, with regional variations. Khoya Milk product made by drying whole milk. Used as a filling for many Indian sweets. Found in most parts of the Indian subcontinent. Lai This sweet, commonly available in Bihar, is traditionally made from ‘ram dana’ seeds, which are processed and mixed with ‘mawa/khoya’ and sugar to give rise to a round-disk shaped sweet dessert. This is a specialty of Bihar. Lakh An Indian unit of account, with value 105 . Most commonly used for measuring money amounts, e.g. INR 1 lakh = INR 1,00,000. Note that 100 lakh = 1 crore. Litchi A tropical juicy and sweet fruit, with white pulp surrounding a glossy dark brown oblong-shaped seed; a special variety ‘shahi litchi’ is a specialty of some districts of Bihar. This variety has very thin seeds and are considered exotic delicacies. Litti chokha Litti is a ball of dough filled with sattu and other condiments, usually baked or roasted over fire. Chokha is a vegetable mash, typically with eggplants, which is served on the side. This food item is found very commonly in Bihar. Maida Refined white flour, made out of processing of grains. Commonly used all over India. Makhana An aquaponic crop, makhana is also referred to as lotus seeds, fox nuts, Euryale ferox, gorgon nuts and phool makhana. Commonly grown in watersubmerged areas, these white coloured nuts are generally white in colour after processing. These are grown in plenitude in Bihar and are considered a part of a healthy vegetarian diet due to its high iron content. Muri Puffed rice, one of the side products that can be processed in a rice mill during paddy processing.

Glossary

259

Mawa/Khoya A dairy product, with origin in India and wide usage in Nepal, Bangladesh and Pakistan, is made of either dried whole milk or milk thickened by heating in an open iron pan. Namkeen Salty snack items, available in wholesale unbranded as well as branded varieties. Paneer Fresh cheese. Papad Thin, crisp, disc-shaped snack food that is eaten either as a fried or baked product. Typically, it is made from a paste of pulses/lentils. Peda Sweet made from milk solids and is traditionally made in Bihar. It is also made in other parts of eastern India. Sattu Processed grounded gram flour, a regular part of the diet from Bihar and Jharkhand. An important ingredient in litti chokha. Tandoor Clay oven. Tilkut Sweets (made at home or sold after processing in factories) made from sesame seeds and sugar/jaggery. This is a specialty of Bihar. Thekua A deep-fried delicacy of wheat flour and jaggery and is a specialty dessert from Bihar. Vada Savoury fried snacks, mostly made from a paste of lentils and are common to many states in South India.

Index

A Abattoir, 29 Adverse selection, 200 Agricultural Produce Marketing Committee (APMC), 208 Agro-based, 19 Animal husbandry, 22 Anthropocene, 13 Arwa rice, 126

B Basmati rice, 69, 126 Boning, 29 Boundedly rational, 199, 202

C Certification, 32 Clusters, 27 Cognitive, 199 Cold chain systems, 54 Commercial layer farming, 138 Commercial layer poultry, 22 Co-operatives, 55, 150 Corporatization, 55 Counterfactual, 225 Counterfactual Thinking (CFT), 203

D Dairy, 20 Data Envelopment Analysis (DEA), 173 Decision-making unit, 174 De-husking, 20 Difference-in-difference, 119, 131

Difference-in-Difference-in-Difference estimation (DDD), 135 Difference-in-Difference-in-Differences (DDD), 120 Difference-in-differences estimation, 119 Dressing, 29

E Entrepreneurial mindset, 201 Entrepreneurs, 203

F Farinaceous products, 21 Fishmeal, 31 Food-away-from-home, 58 Food parks, 27, 73 Food processing, 22 Food Value Chain (FVC), 25 Fox nut, 36 Fruit and vegetable processing (F&V), 20

G Gibrat’s Law, 71 Gorgon nut, 36 Grading, 20 Grain milling, 20 Greenhouse Gas, 14 Green Industrial Policies, 15, 145, 247 Gross Fixed Capital Formation (GFCF), 90 Gross Value Added (GVA), 83

H Harmonized concepts, 19

© Springer Nature Singapore Pte Ltd. 2020 D. Saha, Economics of the Food Processing Industry, Themes in Economics, https://doi.org/10.1007/978-981-13-8554-4

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262 Hazard Analysis and Critical Control Points (HACCP), 18 I Incentive compatibility constraint, 155, 156 Individual Quick Freezing, 17, 27 Industrial layer, 115 Industrial Policy, 72, 116 Industrial sales, 151 Integrated cold chain, 37 J Jarque-Bera, 170 L Land conversion fees, 142 M Makhana, 37 Manufacturing processes, 22 Market capitalization, 69 Meat and fish processing, 20 Minimum efficient scale (m.e.s.), 63, 72, 150 Missing middle, 124, 201, 202 Monopolistic competition, 27 N Net Value Added (NVA), 83 Non-parametric, 173 Non-physical costs, 201 Non-retail, 7 Nudges, 239

Index Parboiled rice, 126 Partnership, 126, 151 Pasteurization, 20 Perfect competition, 27 Perpetual Inventory Method, 97 Physical costs, 201 Poultry feed, 22 Probiotics, 34 Proprietorships, 38, 126, 151

R Ready-to-Eat (RTE), 2 Ready-to-Eat (RTE) cereals, 35 Refrigerated transportation systems, 54 Region-based counterfactual thinking (rCFT), 203 RTE breakfast cereals, 61, 150

S Screening, 205 Semi-parametric, 173 Shrinkage, 29 Single window, 137 Skim milk, 34 Slaughter, 29 Snowball sampling, 219, 220 Start-ups, 255 State Food Corporation, 180 Stochastic Frontier Analysis (SFA), 173

T Tandoori chicken, 30 Total Factor Productivity (TFP), 92 Trajectory, 3

O Ordinary Least Squares, 170 Outside option, 202, 216 Own Account Enterprise (OAE), 83

V Value-added, 22 Value-addition, 20

P Parametric, 173

W Warehousing, 54