Climate Change, Livelihood Diversification and Well-Being: The Case of Rural Odisha (SpringerBriefs in Economics) 981167048X, 9789811670480

Table of contents :
Acknowledgements
Contents
About the Authors
1 Climate Change Impact on Livelihood and Well-Being of Rural Poor
1.1 Introduction
1.2 Background and the Theoretical Framework for the Research
1.2.1 Sustainable Livelihood Framework
1.3 Livelihood Approach
1.3.1 Sources of Income of Rural Households
1.3.2 Linkages Between the Farm and Non-farm Sector
1.4 Determinants of Livelihood Diversification
1.4.1 Seasonality
1.4.2 Risk Strategies and Coping Behaviour
1.4.3 Returns to Labour Markets
1.4.4 Asset Strategies
1.4.5 Credit Market Failure
1.4.6 Changes in Climatic Variables
1.4.7 Other Determinants
1.5 Possible Policy Implications
1.5.1 Market Liberalization
1.5.2 Targeting the Most Vulnerable
1.5.3 Market Failure
1.5.4 Credit Support
1.5.5 Infrastructure
1.5.6 Education, Extension Education and Skill Development
1.6 Scope of the Present Study
References
2 Primary and Secondary Information
2.1 Primary Data
2.2 Sampling
2.3 Indicators from the Primary Survey
2.4 Profile of the State
2.5 Disasters and Livelihood
References
3 Perceptions of Climate Change and Adaptation Strategies
3.1 Climate Change Impacts
3.2 People’s Perception of Climate Change
3.2.1 Temperature
3.2.2 Rainfall and Rainfall Variability
3.2.3 Determinants of Perception of Climate Change
3.3 Adaptation to Climate Change
3.3.1 Adaptation Strategies in the Farming Sector
3.3.2 Adaptation Strategies in the Livestock Sector
3.3.3 Adaptation Strategies in Forestry Occupation
3.3.4 Constraints Impending Climate Change Adaptation
3.4 Summing Up
References
4 Livelihood Diversification in Odisha
4.1 Number of Activities
4.2 Individual-Level Diversification
4.3 Factors Affecting Diversification
5 Climate Change, Diversification Strategy, and Its Effectiveness: Assessing Well-being from Inter-Temporal Changes in Consumption Outcomes
5.1 Introduction
5.2 Job Profile and Occupational Diversification
5.3 Occupational Diversification Index
5.4 Consumption: Past and Present
5.5 Determinants of Change in Consumption
5.6 Concluding Remarks
References
6 Policy Recommendations
Appendix A Snapshot of Pilot Visit to Selected Villages in Odisha (April and June 2017)
Appendix B Sample at a Glance
Socio-economic Profile
Sources of Irrigation
Housing Structure and Sources of Drinking Water
Income Pattern
Present Annual Income in INR
Various Government Schemes
Uncited References

Citation preview

SPRINGER BRIEFS IN ECONOMICS

Arup Mitra · Saudamini Das · Amarnath Tripathi · Tapas Kumar Sarangi · Thiagu Ranganathan

Climate Change, Livelihood Diversification and Well-Being The Case of Rural Odisha 123

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Arup Mitra · Saudamini Das · Amarnath Tripathi · Tapas Kumar Sarangi · Thiagu Ranganathan

Climate Change, Livelihood Diversification and Well-Being The Case of Rural Odisha

Arup Mitra Institute of Economic Growth New Delhi, India

Saudamini Das Institute of Economic Growth New Delhi, India

Amarnath Tripathi School of Business Studies Sharda University Greater Noida, India

Tapas Kumar Sarangi National Institute of Labour Economics Research and Development New Delhi, India

Thiagu Ranganathan Centre for Development Studies Thiruvananthapuram, India

ISSN 2191-5504 ISSN 2191-5512 (electronic) SpringerBriefs in Economics ISBN 978-981-16-7048-0 ISBN 978-981-16-7049-7 (eBook) https://doi.org/10.1007/978-981-16-7049-7 © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 This work is subject to copyright. All rights are solely and exclusively licensed 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

Acknowledgements

The authors are grateful to the Indian Council of Social Science Research (ICSSR) for sponsoring the project (file number G-1/2016-17/ICSSR/RP). The comments from Prof. J. V. Meenakshi, Prof. Keshab Das, Prof. Manoj Panda, the evaluation committee appointed by ICSSR, and the anonymous referees of the publishing house helped us enormously in preparing the revised version. Deepankar Panda and Rajnish Kumar offered excellent assistance. Finally, thanks are due to the publishing house.

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Contents

1 Climate Change Impact on Livelihood and Well-Being of Rural Poor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 Background and the Theoretical Framework for the Research . . . . . 1.2.1 Sustainable Livelihood Framework . . . . . . . . . . . . . . . . . . . . . 1.3 Livelihood Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3.1 Sources of Income of Rural Households . . . . . . . . . . . . . . . . . 1.3.2 Linkages Between the Farm and Non-farm Sector . . . . . . . . 1.4 Determinants of Livelihood Diversification . . . . . . . . . . . . . . . . . . . . . 1.4.1 Seasonality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.4.2 Risk Strategies and Coping Behaviour . . . . . . . . . . . . . . . . . . 1.4.3 Returns to Labour Markets . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.4.4 Asset Strategies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.4.5 Credit Market Failure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.4.6 Changes in Climatic Variables . . . . . . . . . . . . . . . . . . . . . . . . . 1.4.7 Other Determinants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.5 Possible Policy Implications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.5.1 Market Liberalization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.5.2 Targeting the Most Vulnerable . . . . . . . . . . . . . . . . . . . . . . . . . 1.5.3 Market Failure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.5.4 Credit Support . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.5.5 Infrastructure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.5.6 Education, Extension Education and Skill Development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.6 Scope of the Present Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

19 20 21

2 Primary and Secondary Information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1 Primary Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Sampling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3 Indicators from the Primary Survey . . . . . . . . . . . . . . . . . . . . . . . . . . .

27 27 27 29

1 1 3 4 5 8 9 10 12 12 13 13 14 14 15 15 16 16 17 18 19

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2.4 Profile of the State . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.5 Disasters and Livelihood . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

31 33 37

3 Perceptions of Climate Change and Adaptation Strategies . . . . . . . . . 3.1 Climate Change Impacts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 People’s Perception of Climate Change . . . . . . . . . . . . . . . . . . . . . . . . 3.2.1 Temperature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.2 Rainfall and Rainfall Variability . . . . . . . . . . . . . . . . . . . . . . . . 3.2.3 Determinants of Perception of Climate Change . . . . . . . . . . . 3.3 Adaptation to Climate Change . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3.1 Adaptation Strategies in the Farming Sector . . . . . . . . . . . . . . 3.3.2 Adaptation Strategies in the Livestock Sector . . . . . . . . . . . . 3.3.3 Adaptation Strategies in Forestry Occupation . . . . . . . . . . . . 3.3.4 Constraints Impending Climate Change Adaptation . . . . . . . 3.4 Summing Up . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

39 40 41 41 42 44 48 48 49 50 50 50 51

4 Livelihood Diversification in Odisha . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1 Number of Activities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Individual-Level Diversification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3 Factors Affecting Diversification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

53 53 56 63

5 Climate Change, Diversification Strategy, and Its Effectiveness: Assessing Well-being from Inter-Temporal Changes in Consumption Outcomes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2 Job Profile and Occupational Diversification . . . . . . . . . . . . . . . . . . . . 5.3 Occupational Diversification Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.4 Consumption: Past and Present . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.5 Determinants of Change in Consumption . . . . . . . . . . . . . . . . . . . . . . 5.6 Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

69 69 72 73 74 79 82 83

6 Policy Recommendations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

85

Appendix A: Snapshot of Pilot Visit to Selected Villages in Odisha (April and June 2017) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

89

Appendix B: Sample at a Glance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Uncited References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107

About the Authors

Arup Mitra is Professor of Economics at the Institute of Economic Growth, Delhi. He earlier served as Director General of the National Institute of Labour Economics Research and Development (NILERD) and as Dean, Faculty of Economics, South Asian University. He also worked as Senior Researcher at the International Labour Organization (Geneva), was offered a visiting fellowship at the Institute of Developing Economies (Tokyo), held the Indian Economy Chair at Sciences Po (Paris) and was Visiting Professor at the Graduate School of International Development, Nagoya University (Japan). The Indian Econometric Society awarded him the Mahalanobis Memorial Gold Medal for his outstanding contributions in the field of quantitative economics and his book on Inclusive Growth (Springer, 2013) received the S. R. Sen best book award in 2019. Saudamini Das is NABARD Chair Professor at the institute and is Fellow of South Asian Network for Development and Environmental Economics (SANDEE), Kathmandu, and worked as Mälar scholar at the Beijer Institute of Ecological Economics, Royal Swedish Academy of Sciences, Stockholm, during 2011–2012. Her research areas are climate change adaptation, assessment of loss and damage to livelihood due to climate change, valuation of ecosystem services, coastal vulnerability analysis and evaluation of public policy. Amarnath Tripathi is Associate Professor of Economics at the Department of Economics and International Business, School of Business Studies, Sharda University, India. Previously, he was with the Symbiosis School of Economics, Pune; Institute of Economic Growth, Delhi and Indira Gandhi Institute of Development Research (IGIDR), Mumbai. He completed his Ph.D. in Economics from Banaras Hindu University, Varanasi, and post doctorate from Institute of Economic Growth, New Delhi, under Think Tank Initiative of International Development Research Centre, Ottawa, Canada. Tapas Kumar Sarangi is Assistant Director, NILERD, New Delhi and before that he worked as Think Tank Initiative Fellow at Institute of Economic Growth, Delhi; as ix

x

About the Authors

Senior Researcher and as Visiting Fellow at Centre for Economic and Social Studies (CESS), Hyderabad; Senior Research Officer (DGM Rank) with the NABARD— Bankers Institute of Rural Development (BIRD), Lucknow, and as Consultant at Xavier Institute of Management (XIM), Bhubaneswar. Thiagu Ranganathan is Associate Professor, Centre for Development Studies, Trivandrum, India. Prior to that he was Assistant Professor at IIM Nagpur, Institute of Economic Growth, Delhi. He obtained his Ph.D. from IIT Bombay, and his areas of specialization are agriculture and plantation crops, employment and social security.

Chapter 1

Climate Change Impact on Livelihood and Well-Being of Rural Poor

1.1 Introduction Human-induced global warming reached 1.0 °C above pre-industrial levels (IPCC 2018). It is further likely to reach 1.5 °C sometime between 2030 and 2052 if global warming continues to increase at the current rate, causing an increase in the frequency and intensity of extreme climate events such as heatwaves, droughts, floods. (IPCC 2018). Now, people started realizing year after year rising incidence of extreme events and how these events are impacting on their lives and livelihoods (Badjeck et al. 2010; Gentle and Maraseni 2012; Mendelsohn 2014; Tanner et al. 2015; Ghosh and Ghosal 2020; Das et al. 2020). More distressing is that the poor citizens are found to be more susceptible to climate change than others because their adaptive capacity is extremely low (Tol et al. 2004). Even, their vulnerability is likely to rise in the future as climate change weakens their capacity to build sustainable livelihoods (Tanner et al. 2015). Likewise, a region with high exposure to change in climatic factors and low adaptive capacity is more vulnerable than the other regions (Mendelsohn et al. 2006; Tripathi 2017). Livelihood diversification is widely acknowledged as coping strategy to minimize adverse impacts of climate change. However, its success depends on people’s access to the five livelihood capitals (physical, human, financial, natural, and social) and their ability to put these into productive use (Ellis 1998; Scoones 1998; Bebington 1999; Ellis 2000). In view of the above situation, this book examines empirically climate changeinduced livelihood diversification and their impacts on the well-being of the households in terms of income security and food consumption, focusing on the rural households. To accomplish this, Odisha state of India is chosen. It is a typical example of a resource-rich poor economy. The diverse natural resource base of Odisha state has provided the scope for a variety of livelihood diversification patterns over time. It has also been through climatic stress, and climate change has increased the intensity and frequency of heatwaves, droughts, floods, cyclones, and saline intrusion in various parts of the state. This, along with the structural transformation happening in

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 A. Mitra et al., Climate Change, Livelihood Diversification and Well-Being, SpringerBriefs in Economics, https://doi.org/10.1007/978-981-16-7049-7_1

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1 Climate Change Impact on Livelihood and Well-Being of Rural Poor

the country has paved the way for changes in the livelihood diversification patterns among the rural households in Odisha. This book is divided into six chapters. Chapter 1 sets the perspective and presents the analytical frame following the livelihood approach and reviews two sets of literature: one on sustainable livelihood diversification and another highlighting the phenomenon of climate change and its adverse effects on livelihood. This chapter considers livelihood diversification as a strategy adopted by people in the face of climate change and shrinking employment opportunities. Livelihood diversification can be viewed both as an outcome of and as a response to climate change. Whether diversification has helped them to reduce employment and income risk, or it has added to their vulnerability is an important question. The review of literature also focuses on seasonality, risk strategies and coping behaviour, returns to labour market, asset strategies, and credit market behaviour and also brings out the farm–non-farm interlinkages. Chapter 2 deals with data and methodology. This chapter first briefs the data collection methodology for the primary survey. Then, it details the secondary sources of data that have been used for the analysis. It also describes macroeconomic conditions, regional structural and geographical factors, and economic performance of Odisha state. Chapter 3 makes an attempt to assess if people in the rural areas do perceive a change in climatic variables and if so how they react to these changes in order to minimize the adverse effect of climate change. Further, the role of education and exposure to change in physiological variables like temperature, precipitation, etc., in the forming right perception of climate change is evaluated. The chapter also examines the constraints that people face in adapting to climate change. Chapter 4 analyses the livelihood diversification in the study area. There are four aspects of livelihood diversification which are as follows: the number of livelihood activities participated by the households, the individual level diversification, the seasonal allocation of labour, and the diversification across different months. In this respect, we looked at the issues across the dimensions of geography (districts), caste, education, and also the primary occupation of the household. This chapter also analyses the factors affecting diversification. One important aspect of well-being is consumption; thus, by focusing on consumption changes over time and relating it to livelihood diversification, we may comprehend its nature. Diversification adopted in the face of compulsion and a situation of stagnancy without much prospects may result in a range of residual or low productivity activities, whereas diversification as an attempt to explore newer pathways in a vibrant situation to reduce income risks and smooth consumption can be highly beneficial. Keeping these in view, Chap. 5 focuses on the job profile and occupational diversification of the sample households and the extent of instability in occupations by drawing a monthly profile round the year. This chapter further examines the distribution of households in terms of consumption pattern expressed as calorie intake and the inter-temporal changes in it and makes an attempt to understand the determinants of change in calorie intake.

1.1 Introduction

3

Chapter 6 summarizes the major findings and proposes strong and multiple interventions from the government and multilateral agencies to equip the poor people of Odisha to face climate challenges.

1.2 Background and the Theoretical Framework for the Research Rural households typically earn their incomes by diversifying into a variety of livelihood activities. Diversification is defined as households branching out in different activities as a means of survival. It is desirable to have a diverse basket of activities because it spreads the risks of households and guards them from adverse shocks. Some reasons that are identified for diversification are seasonality of some professions, risk strategies, coping behaviour and adaptation to changes, inter-temporal savings and investments strategies, gender stereotypes, etc. (Ellis 1998). Even within agriculture and allied activities, households engage in multiple livelihood activities like livestock, cultivation, fisheries, forest product collection, and trading/marketing of agricultural products. While some of the activities are conducted as the main activity, some others are done as a secondary work, mainly to enhance livelihood resilience. Diversification within a livelihood source and diversification between livelihood sources has been identified as a strategy associated with more resilient livelihood trajectories (Jha and Tripathi 2010; Sallu et al. 2010), and the benefits are seen both in terms of cash and in terms of non-cash income. In areas where still some common lands are present, households (particularly the low-income ones) utilize them as secondary resources to augment their incomes (Cousins 1999). Apart from augmenting incomes, livelihood diversification is an important informal risk management strategy for the rural poor (Ellis 2000; Babatunde and Qaim 2010; Bezu et al. 2012; Vetter 2013). Though income generation and income diversification are key aspects of livelihoods and livelihood diversification, they could be defined in a broader context as well. Over the years, the issues related to defining livelihoods and framework for defining it caught the attention of academicians and policymakers. Ellis (1998) defines a livelihood as something that encompasses income, both cash and in-kind and the social institutions (kin, family, compound, village, and so on), gender relations, and property rights required to support and to sustain a given standard of living. Livelihood diversification in this context is defined as the process by which a household constructs a diverse portfolio of activities and social support capabilities in their struggle for survival and in order to improve their standards of living. Such dependency on diverse activities helps in risk management (Ellis 2000). Livelihood patterns depend on economic, social, and geographical factors. As the economy grows, several new opportunities emerge which leads to livelihood diversification. Together with that some social and economic changes arise that pose challenges to conventional or prevailing livelihoods. Well-being or welfare of a household directly

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links with its livelihood pattern, and hence, any small change in its livelihood pattern can bring a significant change in household well-being or welfare. Well-being or welfare is a multidimensional concept, and therefore, multiple indicators such as income, asset, and consumption are used to represent this concept. These indicators are called as livelihood outcomes in the related development literature.

1.2.1 Sustainable Livelihood Framework A useful framework that can be used to understand livelihood diversification is the sustainable livelihood framework suggested by DFID (1999). It might be worthy to elaborate a bit more on this framework. This framework is built around five livelihood assets—human capital, natural capital, physical capital, financial capital, and social capital (Fig. 1.1). The framework suggests that these various assets are interconnected and the livelihood depends on the combination of these assets, as is depicted from Fig. 1.1. Here, it is important to understand people’s access to different types of assets (physical, human, financial, natural, and social) and their ability to put these into productive use. Also, it is interesting to know that how organizations, policies, institutions, and cultural and social norms affect people’s access to various assets and their ability to productive use of assets. The framework offers a way of assessing how organizations, policies, institutions, cultural norms shape livelihoods, both by determining who gains access to which type of asset and by defining what range of livelihood strategies are open and attractive to people (Carney 1998). People’s access to different types of asset, and their capability of utilization helps to achieve different livelihood outcomes—more income, increased well-being,

Fig. 1.1 Sustainable livelihood framework. Source DFID. Sustainable Livelihoods Guidance Sheets. DFID, London, 1999. http://www.eldis.org/vfile/upload/1/document/0901/section2.pdf

1.2 Background and the Theoretical Framework for the Research

5

reduced vulnerability, improved food and nutrition security, more sustainable use of natural resources, and many more. These outcomes also affect livelihood assets. Besides livelihood outcomes, these are exogenous factors such as climate stress and growth in the economy which affect livelihood assets. The sustainable livelihood framework brings these factors into analysis under ‘vulnerability context’, which is further divided into shocks, trend, and seasonality based on the nature of these external factors. The framework, however, misses out on some aspects that might be critical to the understanding of the livelihoods in the rural areas. De Haan (2012) shows how ‘power’ is an important element that is missed in the framework. Also, he argues for the need for a meta-analytical approach which could provide us with a robust theory of livelihoods than numerous local studies on livelihoods. The livelihood framework is an actor-oriented approach which makes it an important framework. But sometimes, it is seen as a household/individual strategizing livelihood diversification to deal with a situation. Such an approach to optimization by the rural households might not be applicable always and, therefore, needs to be revisited.

1.3 Livelihood Approach The livelihoods approach focused mainly on how people organized their life based on opportunities, agencies, and social institutions and the services provided by the state to support those institutes. This approach can be considered to have stemmed as an effort to reduce rural poverty and focus on poor people’s life by using participatory methods rather than direct support and intervention by the state. Studies on livelihood have been inspired most initially by the work of Sen (1981) on entitlements and of Robert Chambers (Chambers et al. 1989; Chambers 1994a, b, c). Among other definitions of livelihood, there was one given by Carney which was built from Chambers and Conway which stated: ‘A livelihood system comprises the capabilities, assets (including both material and social resources) and activities required for a means of living. A livelihood is sustainable when it can cope with and recover from stresses and shocks and maintain or enhance its capabilities and assets both now and in the future, while not undermining the natural resource base’ (Carney 1998, 2). Moreover, it is important to note that ‘livelihoods rarely refer to a single activity. It includes complex, contextual, diverse, and dynamic strategies developed by households to meet their needs’ (Gaillard et al. 2009, 12). Frank Ellis defines livelihood as ‘the process by which rural families construct a diverse portfolio of activities and social support capabilities in order to survive and improve their standard of living’ (Ellis 1998). The livelihood approach saw how the poor organized their livelihood strategies in accordance with the structure and institutions governed by the government, thereby lending it a wider context by highlighting the opportunities and constraints in these structures that would either enable or prevent the rural households from diversifying into a particular livelihood. It also provided scope for improving these institutions

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and better policy intervention which would result in the reduced vulnerability of the rural poor and a better standard of living. Hence, diversification was expected to reduce the risk of income failure from a single income source by compensating it through some other source of income and improve the standard of living of the rural poor by providing diversified income opportunities. The livelihood approach did not only focus on the structures and constraints to different livelihood strategies but also highlighted the potential of livelihood diversification in bringing about a change in these structures. Once significant diversification takes place towards a particular occupation, it brings with it changes in the government policies and structural transformation usually supporting diversification. As is clear from the above discussion, livelihood diversification is guided by two views: a traditional transient view which observes diversification as a necessity and a modern view which observes diversification as an opportunity for increased income and a better standard of living. The transient view claims that survival is the main motive of diversification. When a rural household is faced with the risk of income failure, income variability owing to inter-year or intra-year seasonality or actual contingency in case of crop failure, then they will look for alternate means of livelihood for survival. Hence, this view suggests that diversification will take place either as a risk mitigation strategy or for coping and adapting to the income loss due to unforeseen contingencies. The modern view, however, treats diversification and a choice for better income opportunities. This approach associates it with success at achieving livelihood security by improving economic conditions and standard of living. For better conceptual understanding, a distinction has to be made between income and livelihood. Livelihood is certainly more than just income (Lipton and Maxwell 1992). Income comprises of cash earnings of households plus all the in-kind earnings that can be valued at market prices. Examples of income can be cash earnings from the sale of crop or livestock, wages of labour and remittances, etc. It would also include in-kind earnings like crop share agreements or transfer and exchanges of consumption items among households. Livelihood, on the other hand, consists of income, both cash and in-kind. In addition to this, it also includes social institutions like family, village, kinship, etc., gender relations, and property rights that sustain and support a particular standard of living. It also encompasses the benefits derived from all the social and public services which are provided by the state such as education, health, infrastructure, and others. It is argued that social institutions play a critical role in determining the structures and constraints of livelihood diversification. For example, the gender relations in a particular village represent the courses of action that are available to women to access various livelihood options and the property rights and rights of access to land are also key determinants of distinct livelihood strategies. Hence, livelihood diversification is not synonymous with income diversification and is much broader as compared to the latter; however, while studying diversification, the focus of an economist is usually on different income sources of the rural poor and how does it affect other income sources and income distribution. Income diversity refers to the various sources of the composition of household incomes at a point

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in time. Income diversification, on the other hand, is a participatory social process whereby individuals look for alternative portfolios to supplement their current income over time. As mentioned earlier, diversification can be driven by necessity or by choice. This shows that multiple motives prompt rural households to diversity in other sources of income. These motives broadly are classified as ‘pull factors’ and ‘push factors. The pull factors dominate diversification strategies when a household wishes to diversify on account of the presence of strategic complementarities between various occupations, the incentives of higher income offered by various occupations, and the individuals wish to have a better standard of living. On the contrary, the push factors dominate diversification when the household views diversification as a necessity. When income from a particular source becomes scarce or risky, then in order to supplement the scarce income and to avert the risk, the household will look up for more income opportunities. This will also be the case when a rural household is faced with any unforeseen contingency in the current occupation; they will look up for more opportunities as a part of their copping the adaptation behaviour. In case of push factors, it can also be observed that once the individuals start diversifying owing to scarce income because of floods, droughts, income risk, or any other necessity: they experience higher incomes and, in some cases higher than what is required to fulfil their necessities. This higher income leads to a change in their consumption pattern. For example, a household who earlier could not afford education for his kids can now send them to school. Now, if the household who diversified due to seasonal risk stops diversifying, he would face a fall in income levels. The fall in the income levels would not be accompanied by an equal fall in consumption because of the operation of Duse berry’s ‘ratchet effect’. Conventionally, if income falls, consumption should fall proportionally with marginal propensity to consume but Dusenberry opposed this view. He said that once consumption habits are acquired, it is hard to get rid of them. Thus, income shocks should have slightly different effects on consumption. Certain consumption habits are formed at high-income levels which are not completely abandoned when income falls. In the case of diversification, in order to sustain the higher levels of consumption once achieved, the household will continue to diversify to add on to the current income. Hence, diversification which started as a necessity would become diversification by choice; push motive would be converted to pull motive. This lends one possible explanation for the growth of diversification over time. When policy discussion is initiated in areas like poverty reduction, intra-household relations, non-farm activity, rural–urban migration, household risk strategies, coping, and adaptation strategies, and rural growth linkages, all these arenas essentially encompass livelihood diversification. Livelihood diversification provides an answer to all the above overlapping arenas of policy discussion.

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1.3.1 Sources of Income of Rural Households Though income can be classified as a subset of livelihood possibilities, we are usually concerned with the various sources of income of a rural household in the context of economic policy discussion. These sources of income of a rural household are majorly classified as farm, off-farm, and non-farm income. The value of output from a farm activity is classified as farm income. It would include agriculture income in terms of output sold and in-kind consumption of agricultural produce and would comprise livestock too. Off-farm income, on the other hand, refers to that portion of the farm household income which is earned off the farm. For example, wages, salaries, and pensions, etc., received by a farm household would fall into this category. Non-farm income refers to income from non-agricultural sources. The nonfarm sector thus includes income from all the economic activities other than the production of primary agricultural commodities. Thus, income of the rural households could be enhanced by providing a boost to both farm and non-farm sectors. Farm income is directly associated with farm output. Farm output can be increased by increasing farm production or by increasing technical efficiency. Since land is a fixed factor of production, we cannot go on increasing production indefinitely owing to the explanation provided by the law of fixed proportions. Hence, the most feasible policy option that we are left with is to increase the technical efficiency. Some of the ways of increasing the technical efficiency are the use of better inputs such as high-yield variety seeds, better fertilizers, and manures and use of better machinery and equipment and putting in more investment in research and development of these inputs. The other way of enhancing income of rural citizens is improving participation of the rural households in the non-farm activities. The most commonly accepted categories of non-farm income are: (i) non-farm rural wage employment, (ii) nonfarm rural self-employment, (iii) property income which includes rent, etc., (iv) national urban to rural remittances and (v) international remittances from the crossborder and overseas migration (Ellis 1998). While all the other sources of income are clearly understood and are standardized, the remittances and transfers incomes are defined ambiguously and are less standardized. Generally, the income earned from members of the family who are travelling or are migrating temporarily or seasonally but form a part of the rural household is included as non-farm income, whereas the transfers made by the government, family members, or relatives are usually classified as transfer income (Haggblade et al. 2010). Contribution of the non-farm sector in providing livelihood in India is increasing. As per the NSS surveys, at all India levels, the percentage of the workforce in the non-agricultural sector increased from 21.6% in 1993–94 to 27.3% in 2004–05, i.e., around 5.7% points in 11 years. In this book, we focus on the important role of rural non-farm employment as a strategy of livelihood diversification in bringing about an increase in the income of rural farmers. We will begin by discussing various categories into which various researchers classify the rural non-farm income, followed by the determinants of rural non-farm income, linkages between farm and non-farm income, analysis of the studies conducted by

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various economists on the non-farm economy of the different Indian states and finally concluding with the various policy options that can be exercised to give a boost to the non-farm economy.

1.3.2 Linkages Between the Farm and Non-farm Sector The rural non-farm economy has close linkages with the agriculture sector. These two sectors have strong interdependence among each other, and they coexist; in fact, one cannot exist without the other. When productivity in the agricultural sector rises, the labour in the non-farm sector gets attracted towards the agricultural sector, which lowers the wages in the agricultural sector and increases in the non-farm sector, but the employment in the non-farm sector falls. On the other hand, when backward linkages operate, higher demand for the products and services of the non-farm sector would lead to increases in wages in the non-farm sector and hence attract the agricultural labour and increases employment in the non-farm sector. It is difficult to assess if an increase in the productivity of the agriculture sector would provide a boost to the non-farm sector or otherwise. Ranis (1990) suggested that the direction of causation cannot be made clear. However, various economists have diverse views on the nature of causality: the kind of linkages that exist between the farm and non-farm economy has been considered in various studies. Vaidhyanathan (1983) used regression analysis based on several state-level datasets, and he estimated the importance of non-farm employment in the context of total employment and considered the determinants of non-agricultural employment. His study concluded that there was strongly significant and positive correlation between unemployment and non-farm employment. He laid strong emphasis on the importance of consumption linkages by showing that as agricultural productivity increases faster than the agricultural population it brings about commercialization in agriculture spilling over to the non-farm sector. He argued that this commercialization has caused integration of the rural markets for manufacturing and caused reorganization and reallocation of traditional rural industry leading to technological change. As a result of these structural changes, a parallel economy known as the nonfarm economy starts operating. Vaidyanathan hence emphasized strongly on the role of forward linkages. The increased productivity of the agricultural sector provides a boost to the building of human and physical capital required for the non-farm sector and hence drives growth in that sector. His study also showed that in areas where the agricultural sector was not able to provide much employment, the non-farm sector compensated for the same. His study highlighted the interdependence among both the sectors. Hazel and Hagggblade (1990) conducted an econometric analysis of district and state-level cross-sectional data. They fitted a semi input–output model to a national input–output table from 1979 to 1980. They analysed the relationship that exists between agricultural growth and the rural non-farm economy. They found out strong linkages among both the sectors because of which the growth in the rural non-farm

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economy was faster as compared to agriculture. They concluded that even with a rapid increase in the production of agricultural output, the agriculture sector cannot absorb the increased labour force growth. This gap in employment could be bridged by increasing the role of the rural non-farm economy, hence demonstrating strong linkages. It is seen that agriculture can affect the non-farm economy in three different ways. They classified them as production, consumption, and labour market linkages. While production linkages refer to how the agricultural sector is dependent on the non-farm sector for its growth as the latter provides it with inputs (fertilizers, seeds, machinery, etc.) for increased production. Consumption linkages as explained earlier refer to the growth of the non-farm economy owing to the increased income of the farm labour which boosts the demand for non-farm products and services. The role of production and consumption linkages has been highlighted by various economists like Ellis (1998), (Mellor and Lele 1972) and (Johnson and Kllby 1975). However, the role of labour market linkages is comparatively new. Hossain (1988), Ahmed and Hossain (1990) have contributed significantly to this type of linkages. They noted that the rising wages in the agricultural sector due to increased agricultural productivity raised the opportunity cost of the rural labour in the non-farm sector. Due to this increase, the composition of the rural non-farm activities has shifted from low-income, lowreturn activities to high-income, high-return, and highly skilled activities. This makes a strong case in favour of the linkages that exist in rural farm and non-farm sector and how an increase in agricultural productivity causes high returns to the rural non-farm sector as well. Few conclusions can be drawn from the close linkages that exist in the rural farm and non-farm economy; (i) increase in the agricultural productivity will lead to increase in agricultural output which is high value and high return like livestock. This might cause the production of labour-intensive products to rise, and as a result, it will be pro-poor; (ii) agriculture income is also positively related to the development indicators such as rural infrastructure and services in the rural sector like banking, etc., and hence, it needs strong policy measures to influence this multiplier; (iii) considering the relevance of these linkages, government policies need to be framed in such a way to magnify these linkages. Focus on financial inclusion, agricultural technology, rural infrastructure, and other physical and human capital is essential.

1.4 Determinants of Livelihood Diversification The determinants of livelihood diversification are guided by pull and push factors as mentioned earlier. These factors determine whether an individual would look for alternate means of livelihood guided by survival in mind or an improved standard of living. We specified earlier that livelihood diversification is guided by two views: a traditional or transient view which classifies diversification as a survival strategy and modern view which looks into the various other impacts of diversification whereby it is viewed as an additional source of income for the rural households giving them an

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opportunity to a better standard of living. Through this way, diversification has positive attributes for livelihood security rather than being just a mere survival strategy. The reasons to diversify are often similar to the reasons to migrate. Davies (1996) classifies the pull and push factors as reasons to migrate as being determined by survival and choice, whereas (Hart 1994) uses the terms ‘survival’ and ‘accumulation’ for the determinants of migration. We take survival and livelihood security to be two broad determinants of migration. Survival is analogous to the necessity which is a conservative reason to diversify in order to avoid downturns faced in a particular occupation. It is an involuntary act and is usually distress driven. An important negative implication of diversifying in due to necessity is that it limits the choices available to the households and forces them to take up any occupation that is available which sometimes has poor growth prospects and fails to uplift their standard of living. Many studies have also proved that usually low-income households tend to diversify into low growth jobs as compared to highincome households. As specified by (Ghosh and Bhardwaj 1992), diversification due to necessity makes diversification as ‘the last resort rather than an attractive livelihood alternative’. Due to this, sometimes the households are also prone to taking up a job which is more vulnerable than the jobs they previously had. An example of this entire situation could be when a farmer suffers considerable losses on land because of either environmental deterioration or crop failures due to seasonality. This situation forces a farmer, especially a poor farmer, to take up any opportunity that is available to them, example maybe working in industries generating hazardous wastes; this has serious health implications and sometimes increases the vulnerability of the entire family. On the other hand, when diversification is led by the motive of improving the prospects of livelihood security, in such cases, diversification is led by choice which is exercised voluntarily and proactively by the members of the households. As diversification in such cases is a choice-led decision, it becomes an attractive alternative source of liveihood. Here, the implications are reversed, and an individual can make a rational choice among the best possible alternatives and would end up usually in a high-return occupation, the returns of which could be further invested for a better standard of living. An example of such diversification decision could be saving the income earned in agriculture and investing it in non-farm business like handicrafts and providing self-employment to the women in the house. Another dimension of looking at the determinants of diversification could be through various characteristics of human capital. Examples of such type of determinants could be the age of the household head, dependency ratio, education, family size, land–man ratio, skill training, geographical characteristics like distance to the nearest town, irrigation facilities, investment in rural non-farm activities, etc. Taking into consideration these broad classifications of the determinants of diversification, we can go into the details of their secondary classification. The secondary classification of the survival guided determinants is as follows.

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1.4.1 Seasonality Seasonality is one of the most important inherent features of the farm livelihoods. In places where agriculture is essentially rain-fed like India, the production cycles of crops and livestock are determined by the onset of the rains, their length of duration versus the required length of duration, temperature variations across the growing season, and the length of the growing season. The seasonal variations apply to landowners as well as landless farmers and have a very significant impact on the trading activity. In case of some crops, the sowing and harvesting take places just once in the year where the trading cycle becomes relatively short, and hence, the cultivators are faced higher periods of seasonal unemployment. There are two major components to crop seasonality which are (i) harvest lows and (ii) post-harvest rally. The first one is driven by the on–off nature of the crop harvest. As mentioned earlier, the crops which have a single harvest season become available in the market in a very short period and the markets get flooded with the supply of the commodity. This component is called the harvest lows. Following the harvest, lows is a period when the supply of the commodity is drastically reduced because of the domestic consumption and export demand. This is where the forwards market steps in. In order to ensure that some of the crops are available throughout the year, forward bids are initiated, the price of which is higher than the harvest price, and hence, the prices start following an upward track known as a post-harvest rally. Seasonality thus brings about fluctuations in the farmers’ income and becomes a major reason for diversification. The economic interpretation of seasonality defines it as the returns of labour time, which means income earned by labour per hour, per day, per week, or per month which varies from time to time. While labour income varies due to seasonality, household consumption cannot vary, and hence, it makes a stronger case in favour of diversification. It is also clearly understood that seasonality as a determinant of diversification is not just driven by necessity but also by choice. Sometimes, household exercises diversify due to seasonality as a choice to increase income and avoid the periods of seasonal unemployment.

1.4.2 Risk Strategies and Coping Behaviour Before undertaking the discussion of this variable, it is important to understand the difference between the two. Risk strategies refer to the behaviour of the households in order to avoid the causes of the downturn due to fluctuations in income when engaged in a risky venture. In terms of agriculture, in the Indian scenario, a lot of dependence on monsoon has been seen so the households prefer to diversify in various activities to build a portfolio to compensate for any unforeseen losses. Coping strategies, on the other hand, is the behaviour of the household when exposed to an income failure. It is the adaptation mechanism followed by a household in order to compensate for his losses. One of the coping and adaptation strategies is to diversify in the rural

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non-farm sector to make up for the losses incurred and to supplement the livelihood. However, the difference between risk strategy and coping and adaptation strategies is that the former one is an ex-ante strategy, i.e. taking measures to mitigate any unforeseen risk, whereas the latter is an ex-post strategy which views diversification as a resort to cope up with the damage caused due to income losses.

1.4.3 Returns to Labour Markets One of the most important determinants of diversification is the returns to labour market. While seasonality talked about the returns to labour time, returns to the labour market talk about the wages earned by the labour in the farm sector which is compared with the wages in the non-farm sector and become one of the most important factors determining whether a rural household would diversity or not. The theory of returns to labour market is applied analogously in rural non-farm diversification as dual sector model explaining labour migration built by Lewis. Lewis built the dual sector model of development economics which showed the basis of growth of a developing country as a result of labour transition from the subsistence to the capital sector. It argued that the susbsistence sector employs surplus labour and offers a subsistene wage which is equal to the marginal product of labour. The industrial sector, on the other hand, offers higher wages to the labour as producivity levels are higher. When the industrial sector expands, the surplus labour from the subsistence sector moves towards the industrial sector which increases the urban employment followed by capital accumulation and increased growth of the modern sector. (Lewis). This concept applies to diversification as well. Rural households diversify when the marginal returns to labour are higher in some other occupation, hence guaranteeing them higher wages. This offers an incentive of increased wages to the rural households and lures them to the opportunities of better livelihood security and an improved standard of living.

1.4.4 Asset Strategies Investments undertaken to enhance future livelihood prospects are known as asset strategies. Rural households try to take advantage of the current income-earning opportunities, and this constitutes an additional motive of livelihood diversification. Assets here are classified into five major categories: (i) natural capital which consists of water, land and other biological resources which are required by an individual for survival purposes, (ii) physical capital which consists of resources required for the economic production process such as canals, buildings, machinery, and roads, (iii) human capital which refers to the skills knowledge and experience possessed by an individual or a household and increases by investment in skills and education of an individual, (iv) financial capital and substitutes refer to the monetary resources

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available to a household. These constitute savings and access to credit through loans, etc., and (v) social capital though has various dimensions but on the whole, and it refers to the relationships among people who live and work in a particular society (Frank Ellis). The motive to build these assets is also one of the major reasons why households diversify in non-farm activities.

1.4.5 Credit Market Failure Agriculture markets are quite commonly faced with credit market failures. The inadequacy of funds makes the households unable to purchase machines, inputs, and equipments which are required to carry out the production of the agricultural commodities. Credit needs vary from short term to long term as credit is required for a variety of purposes such as buying seeds, fertilizers, pesticides, carrying on improvements on the land, buying new land and machinery for sowing and harvesting. Due to the lack of availability of funds, a rural household has to look for other ways of survival and hence diversifies into the rural non-farm sector. However, diversification due to credit market failure is not exercised by choice, here diversification becomes a necessity and hence has the same negative implications as mentioned earlier.

1.4.6 Changes in Climatic Variables Climate change affects the livelihoods of rural households by impacting both occupation and livelihood assets. For instance, about 70–80% of the population in the rural sector depends on agriculture for their livelihood, and on the other hand, agriculture is severely affected by changes in climatic variables. Climate change impacts agriculture production in both ways—directly and indirectly. Changes in climatic factors (e.g. temperature and rainfall) affect agricultural productivity through physiological changes in crops (Chakraborty et al. 2000). In addition, climate change also affects other factors of production agriculture, such as water availability, soil fertility, and pests. Several studies (i.e. Dinar et al. 1998; Mall et al. 2006; Mendelsohn 2008; Jha and Tripathi 2011; Auffhammer et al. 2012; Lobell et al. 2012; Gupta et al. 2014; Rao et al. 2014; Mishra et al. 2016; Jha and Tripathi 2017; Srivastava et al. 2021) have been found in both India and abroad reporting adverse impact of climate change on agriculture. It has also been confirmed that not only the crop sector while all subsectors of agriculture (Livestocks, Fisheries, and Forestry) have badly been hit by climate change (Kirilenko and Sedjo 2007; Thornton et al. 2009; Allison et al. 2009; Brander 2010). By reducing agriculture production, climate change poses a great threat to household income and food security. The problem, even, aggravates in the absence of alternative sources of income or the implementation of adaptation strategies (Black et al. 2011). To smooth household income and manage risks, households follow both

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on-farm diversification and off-farm diversification strategies. On-farm diversification strategies aim at enhancing agriculture production and reducing the risk of crop failure (Di Falco and Chavas 2009). On the other hand, non-farm diversification strategies are targeted at supplying labour to the non-farm sector (Asravor 2018).

1.4.7 Other Determinants Household characteristics such as age and gender of household head and education also play an important role in determining livelihood diversification, as is reported in a number of studies conducted in both India and abroad. For example, Dev (2009), using NSS household-level data from 1999–00 to 2004–05, observed that status of land rights, social category, and education are important determinants of nonfarm employment growth. This has also been confirmed by other studies (e.g. Vatta et al. 2008; Rahut and Scharf 2012, etc.). Vatta et al. (2008) used the logit model to estimate the determinants of non-farm employment growth based on primary data of 315 households from 17 districts of Punjab. They found that (i) male were found to be less likely employed in RNF activity than their female counterpart, (ii) the probability of getting employed in RNF varied directly with age till the age of 46 and then varied inversely, (iii) lower caste workers and households with larger household size were more likely to be employed in RNF sector, and also the closer a household to the urban periphery, the better chances of being employed in RNF sector. Rahut and Scharf (2012) used survey data from the Himalayas and found that poor are usually employed in the agricultural sector and diversify in low-return non-farm sector while the rich diversify in high-return non-farm sector. They also found a significant role played by education in accessing more remunerative nonfarm employment and that farm size was not a constraint to diversification in the rural non-farm sector. The important role of geographical location in guiding the diversification behaviour of the households was highlighted.

1.5 Possible Policy Implications In the previous sections, we discussed the two major reasons for diversification. Diversification decisions are either guided by necessity or choice. Due to these two poled reasons, diversification has two important implications which are poverty reduction and increased income inequality. When the diversification decisions are guided by necessity, it allows the rural households to be able to meet their basic needs and supplement their income by diversifying in the rural non-farm sector. It then becomes a risk mitigation strategy and hence leads to a reduction of poverty. However, when diversification is a choice-based decision, then it is found that high-income household tends to diversify in high-income and high-return activities, whereas the low-income households diversify in the low-income, low-return activity.

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This leads to widening of the income inequality among households. The low-return activities sometimes rather than supplementing the income lead to more vulnerability to the risk of income failure and losses. In these cases, the households do not get to choose from a variety of options available to diversify, and they usually pick up any option to survive, and because of lack of evaluation of the diversification options available, these households are more prone to the risk of income failure. Because of the wide gaps associated with returns to diversification, this phenomenon is often faced with increased income inequalities. Hence, poverty reduction and increased income inequality are like the two sides of the coin of diversification. Diversification in the non-farm sector cannot be left to be guided by the market forces of supply and demand. The entire economic literature and evidences point out in the direction that the farm sector cannot sustain the entire population as land is subjected to diminishing returns. Not only this, but the close linkages in the farm and non-farm sector also show how the two are dependent on each other, and by exploring the potential of the non-farm sector, widespread changes could take place for the rural households. The policy implications talk about what lessons are to be learnt from the existing situation of the non-farm sector and what role can the government play in diversification towards the non-farm sector. This section discusses the scope of government intervention in the non-farm economy.

1.5.1 Market Liberalization Government intervention in livelihood diversification occurs in order to remove the constraints and promote the opportunities for livelihood security. Market liberalization is seen to play a positive role in the context of removing the constraints to diversification (Jazairy et al. 1992). Booth et al. (1993) conducted a study in the Tanzanian village in during the nineties and found that the villagers benefitted significantly by an increase in the number of options available for the rural non-farm sector. It proved to be one of the most important economic policy for them.

1.5.2 Targeting the Most Vulnerable The government assistance in the development of small-scale and rural non-farm enterprises has taken the form of assistance directed towards the target groups. Lipton and Ravallion (1995) in their paper have explained that the purpose of targeting is to help the most vulnerable social groups in the events and shocks that lead to income failure and thereby providing safety net support and livelihood security. Targeting works at various levels is of different types. The income-sensitive targeting identifies the households according to their income and economic status. This is a sort of indicator targeting which works by identifying various social groups which need assistance, and thereafter, policies are defined keeping these vulnerable sections

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in mind. Gender-specific targeting leads to the classification of social groups as per their gender, and by accessing the role played by each gender, the policies are defined which abide by the social and physical constraints to both genders. Similarly, geographical targeting aims at identifying the signature geographical demands of the households and framing policies which are region-specific. Self-targeting is a practice where wages or food is provided in exchange for work. These wages and food are not sufficient for a better living standard but are sufficient for survival. The government intervention in diversification calls for the most effective targeting which involves successful efforts in collecting adequate information about the incidences of food insecurity and various social institutions that exist in the rural economy, these institutions and the constraints they pose for livelihood diversification. Targeting is hence one of the most important government interventions to promote the rural non-farm economy by identifying the various sections and framing policies oriented towards these sections.

1.5.3 Market Failure Another important role of the government is to reduce the risk of market failure. Market failure is one situation where government intervention is the most appropriate. The reasons for market failure are usually related to high costs of the transaction, asymmetric information, lack of proper infrastructure and political and social instability. The state agencies can here play a very active role by improving the constraints in the labour markets by improving the flow of information and by providing better infrastructure facilities. In India, a policy of import substitution was followed after independence which emphasized heavy industries. There emerged a policy of protection for the traditional small-scale industries. Production of some goods was reserved for the small-scale firms, and the capacity of larger firms was limited. This led to the rural non-farm economy gaining importance. However, there are various constraints to this. The small-scale industries face a significant amount of competition from the already established large-scale firms, and hence, these industries are vulnerable to losses. In the light of this argument, the protection to small and medium enterprises was proposed under the various Industrial Policy Resolutions. India has successfully implemented the practice of reserving the production of some goods to the small-scale and traditional enterprises in order to prevent these industries from competition and promote their growth. For example, during the decade of 1950s, India put a limit on the expanding capacity of the textile mills, and also to boost the handloom industry, it restricted the production of synthetic cloth to the small-scale power loom and handloom production and even though the production would have been more profitable on the large-scale power loom sector. This was done to provide a boost to the handloom sector as through handloom promotion a large portion of the population will find employment. If the cost–benefit analysis of this approach is carried out, the reservation policy is not beneficial. It no doubt provides increased employment

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opportunities, and it lowers the production, thereby destroying the export potential. However, in cases like India, the social benefit from increased employment opportunities offsets the profit loss due to lower production (Lanjouw 2000).

1.5.4 Credit Support Employing the small-scale sector would not be sufficient. Another major constraint to the potential diversification is the lack of availability of credit. One of the very important aspects of policy related to boosting the rural non-farm sector microcredit. The definition of micro-credit as provided by the Grameen bank is all small loans extended to the poor for undertaking self-employment projects for generating income and enabling them to provide for themselves and their families. The microcredit institution solves is beneficial in boosting the rural non-farm employment in several ways. It is based on a variety of innovative financial practices and deals with the problem of targeting by directly specifying the eligibility criteria based on the asset ownership or indirectly by making the loan amounts small, and also at times, it applies various other conditional ties. It discourages the non-poor from borrowing and thereby helps to reduce the inequalities in income. Most of the loans that are offered under the micro-credit schemes are for activities which are very safe from the risk of turning to a bad investment (Hulme and Mosley 1996a). These loans are usually provided for activities such as poultry raising, paddy husking, cattle fattening, rice and other seasonal crop trading, handloom weaving and grocery shopkeeping. Moreover, these are sort of group loans which make them even less risky and free from the process of screening. They are provided to the members of a certain social group who are faced with similar economic conditions and constraints. This is promoted through the idea of self-help groups. The Grameen Bank has important measures for enforcement and monitoring of loan usage individually as well as through groups which further reduces the risk of payments defaulter. Following the example of India, various other countries have started with the practice of micro-credit and self-help groups. Micro-credit institutions work through a very vast network of rural banking which commenced in India in the post-nationalization period. This network has been utilized very efficiently in recent years and helped in finding new and better lending opportunities which helped in the development of small-scale industries and boosting self-employment in rural areas. Puhazhendi (1995) and Karmakar and Puhazhendhi and Satyasai (2000) in their studies have shown that lending to groups and through the self-help groups saves the costs of the public banks in operations of lending and supervision. Also, the interest rates that the micro-credit institutions apply are such that they give enough margins to the banks as well as the groups. The interest rates are not subject to the ceiling imposed by the Reserve Bank of India. Evidence has shown that these micro-credit programmes and institutions have been more successful in targeting and reaching the target population more effectively than any other initiatives.

1.5 Possible Policy Implications

19

1.5.5 Infrastructure Almost all the literature points out to the fact that education and infrastructure are the most important variables determining the growth of the rural non-farm economy. The central theme of the entire literature review revolving around the promotion of livelihood diversification is based on enabling the infrastructure facilities and how education is one of the greatest facilitators of livelihood diversification. Hence, the government policy needs to focus on these two major determinants. The development of infrastructure that facilitates the growth of the rural non-farm economy is important. Better roads are required to provide better connectivity of rural and urban centres, and better transport facilities are required so as the households can explore better avenues. Investments in the agriculture sector are also of the utmost importance; constructing canals, tube wells, and better irrigation facilities so that the farm production rises, and the time taken on every crop is also saved which gives more time for diversification. The power supply is one of the major constraints in the villages which should be dealt with.

1.5.6 Education, Extension Education and Skill Development The government needs to set up centres in rural areas where rural households are informed about various diverse livelihood options that are available to them and how can these be made accessible. Occupations based on the gender roles which exist in particular villages should be recognized and promoted. Education and vocational should be promoted. Poverty has a strong correlation with low-level education and lack of skills, and hence, education is the most important factor in determining the ability of the individuals to exercise the option of livelihood diversification. The government hence need to focus strongly on targeting of education and skill training in order to promote livelihood security. The entire discussion above shows the scope of government intervention to promote the non-farm sector which is tailored to specific local conditions. Mechanisms for proper information flows need to be in a place where the bottlenecks are relieved and specific avenues are exploited. The policies like creating large-scale infrastructure and promoting education which generate the non-farm employment need highly decentralized implementation. Another important aspect of government policy intervention should be that it does not distort the distribution goals while promoting employment in the non-farm economy. More extensive and productive non-farm activities need to be promoted. In order to control the income inequalities, it is important to monitor the distributional impact of the government policies and their beneficial impact needs to be enhanced.

20

1 Climate Change Impact on Livelihood and Well-Being of Rural Poor

1.6 Scope of the Present Study Our research programme has used the framework and tries to extend it along some similar dimensions. These are the key points in the way we have used and expanded this framework: • First, we emphasize the heterogeneity of shocks in determining the livelihood diversification strategies. For this, we consider regions where the shocks in the vulnerability context are different. We have considered regions characterized by different types of shocks—droughts, salinity/seawater intrusion, heatwaves. With this, we try to bring the centrality of shocks in the region in defining the livelihoods of the households in the region. • Second, we have built the framework to incorporate on some of the power relations in defining the livelihood diversification strategy of the households in the region. Two power relations could play a significant role in both the livelihood diversification and well-being of the households—caste power relations in the region and gender relations in the households in the region. Caste could severely act as a hindrance to livelihood opportunities of the households, and there is evidence of it playing a role in occupational, educational, and income mobility of the individual/households (Reddy 2015; Ranganathan et al. 2016). Gender relations also could play a significant role in labour market participation and also in the intra-household well-being (Agarwal 1997; Narayanan 2005; Doss et al. 2011). • Third, we have attempted to draw lessons from heterogeneous case studies as prescribed by De Haan (2012). Rather than just the understanding of the framework in a local context, a meta-analysis of studies involving variations in geography and livelihoods would provide us with a common understanding of the themes. With this as the background, we have addressed the following issues in our research. In the Indian context, decreasing land sizes and declining profitability in agriculture meant that the households’ dependence on non-farm sources has been increasing over time (Binswanger-Mkhize 2013; Himanshu et al. 2013). At the macrolevel, the increasing dependence on non-farm livelihood activities has been accompanied by increasing feminization of agriculture even though female labour force participation has reduced. This could also have implications on intrahousehold gender relations and possibly on technology adoption in agriculture itself. The proportion of cultivators is declining, particularly among Scheduled Castes (SCs) and Scheduled Tribes, and this has meant that their dependence on non-farm sources has increased over the years. Given this background, there is a need for systematic knowledge of livelihood strategies and income sources of rural households. Over 70% to the total population in India still resides in rural areas and depends directly on climate-sensitive sectors (i.e. agriculture, forests, and fisheries) for their subsistence and livelihood. Further, the adaptive capacity of farmers, forest dwellers, fisherfolk, and nomadic shepherds to climate stress is very low because of limited

1.6 Scope of the Present Study

21

resources, lack of information and poor extension services, and limited access to climate-resilient technology and practices. Climate change is likely to impact allnatural ecosystems as well as socio-economic systems. Climate change has also triggered a set of uncertainties and negative impacts on the agricultural sector, especially in developing countries including India, and studies document the significant loss in agricultural yield due to climate change (Bruinsma 2003; Cline 2007; Ciscar et al. 2012; Auffhammer et al. 2006, 2011; Jha and Tripathi 2011). India may be one of the worst sufferers due to the high level of dependency on agriculture for livelihood which means high exposure to climate risk in India (Schneider and Sarukhan 2001). With such a background, there is high uncertainty in poverty eradication in the country. Ending poverty in all its forms is the first Sustainable Development Goal (SDG) that every country including India has promised to fulfil by 2030, and role of rural livelihood diversification in overcoming poverty and climate change challenges needs to be studied carefully to help design policies to attain the SDGs. Indicators of climatic stress like water scarcity impact not just farming but also non-farm enterprises and casual labour, as a bad monsoon can affect the local demand which will have spiralling effects on local labour supply and profitability of non-farm enterprises. Similarly, heatwaves not only have an impact on mortality but also the productivity of crops and labour (Das 2012). Loss and damage from climate-induced natural disasters like cyclones, heatwaves, droughts, etc., on livelihoods of the rural population are very high (Adelphi et al. forthcoming) calling for risk transfer and livelihood diversification as adaptation strategies. Both the state economy of Odisha and its agricultural sector are seen to be getting shrunken significantly in years when there is a severe drought, which is a proof of the strong impact of climate in this state. Climatic stress in this context could then force households into and out of livelihood activities. The role of climatic stress on livelihood diversification is an under-researched topic, and this study focuses on this aspect. The impact of livelihood diversification could be positive in terms of coping up with seasonality and risks, reducing poverty and vulnerability, and augmenting income while it could have negative effects on income distribution, farm output, and gender inequality (Ellis 2000). The exact nature and magnitude of impacts are to be understood empirically, and one of our chapters looks into this aspect. It is important to understand the effect of livelihood diversification on different variables in a specific context.

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Chapter 2

Primary and Secondary Information

The research programme used a mix of both qualitative and quantitative methods to address the research questions. The analysis was conducted using information collected from both primary and secondary sources. Given the dynamic nature of livelihoods in rural areas, only a panel data could provide a proper insight into the changes happening in households over time. In this regard, we have taken data on current and earlier situation of the household. The analysis is conducted using appropriate econometric techniques to address the desired research questions. This section will first brief the data collection methodology for the primary survey. Then, it will detail the secondary sources of data used for the analysis.

2.1 Primary Data The primary data has been collected through extensive rural household surveys, the intra-household survey of women and men to understand the gender relations, surveys of small non-farm enterprises in the study region, collection of village information like wages and experience with climate change and disasters, and surveys of migrants in the nearby urban areas of the study regions. The surveys have been conducted in two rounds: a qualitative survey followed preceded by a rigorous quantitative survey. The survey collects information related to various aspects of livelihoods, climate change, dynamics and power relations, gender aspect, and demographic information.

2.2 Sampling The research studies have been conducted in four districts of Odisha, namely Dhenkanal, Angul, Sambalpur, and Bargarh. Odisha is broadly divided into four geographical regions—northern plateau, central river basins, eastern hills, and coastal © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 A. Mitra et al., Climate Change, Livelihood Diversification and Well-Being, SpringerBriefs in Economics, https://doi.org/10.1007/978-981-16-7049-7_2

27

28

2 Primary and Secondary Information

plains. It has a 480 km coastline, and its population is 41,947,358 as per the 2011 Census. Administratively, the state is divided into 30 districts, 58 subdivisions, 314 blocks (administrative units in descending order of geographical area and population), and 103 urban local bodies. Out of 30 districts, the above four districts are selected following the criterion of geographical location and bio-physiological condition. Such features of the chosen districts are: Angul (central river basin, midcentral tableland); Sambalpur (western central tableland; red soil; black soil); Bargarh (western central tableland; black soil; mixed red and black soil; mixed red and yellow soil), and Dhenkanal (South Eastern Ghats; red soil; laterite soil). The choice of these four districts has also accounted for heterogeneity in livelihoods and climatic stress. These districts are from two zones of Odisha—the southwest zone (Sambalpur and Bargarh), which is drought-prone and characterized by persistent crop failures, and from the relatively prosperous mid-central zone (Dhenkanal and Angul). Of the two districts in the south-west zone, one district with a high dependency on agriculture for livelihood and a district with a high dependency on forest-based livelihoods are selected for the study. In the central region, similar criteria also have been adopted to choose the study districts. From these four districts, we selected blocks based on differences in intensity and heterogeneity of the disaster experienced by the block. For instance, in a droughtprone district, a block which has experienced highest intensity/frequency of droughts in the last 20 years and the one which has experienced the lowest intensity/frequency of droughts were selected. In each of these blocks, villages were selected based on population and heterogeneity in the distance between the nearest towns. This is one key characteristic that defines the livelihood options of the rural households and hence is considered for sampling procedures. In all, around 1200 rural households are selected for the study in the four districts. Multi-stage sampling procedure has been followed to select a sample of 1200 rural households. Around 300 rural households are surveyed from each of the sample districts. Further, sample households are distributed between the chosen blocks on the basis of the share of each block’s population in the total population of the selected blocks in the district. From each sample district, two blocks are chosen. The criterion of selection of blocks varies from district to district. The share of irrigated areas in the total cropped area (irrigation ratio) is considered as a criterion of selection of blocks in Sambalpur and Bargarh. One block with the highest irrigation ratio and another with the lowest irrigation ratio are chosen from both the districts. In Dhenkanal and Angul, the distance from the forest area is the criterion of selection of block: one block close to the forest area and another away from the forest area are chosen for the field survey. From each selected block, four panchayats are selected on the basis of their distance from block head quarter (closest, farthest, and in between). To select the sample villages from each chosen panchayat, all villages are first distributed into three groups (small, medium, and large) on the basis of population, and then, one village is chosen randomly from each group for the study. Weighted sampling is used to select number of households from a village, and finally, rural households from each sample village are chosen randomly.

2.2 Sampling

29

Table 2.1 District-wise sample selected S. no.

Name of the district

Sample HHs

Total number of HHs as per 2011 Census

1

Dhenkanal

303

279,364

2

Angul

300

297,050

3

Sambalpur

299

249,597

4

Bargarh

300

370,308

Source Census of India, 2011

The following weighted formula is used to select the number of households for a village.  HHi jk =

Bi HH

2

i=1

Bi HH



P j HH

4

j=1



P j HH

= B(i) ∗ P( j) ∗ V (k) ∗ 300

Vk HH

3

k=1

Vk HH

 ∗ 300 (2.1)

where HHikj is the number of households chosen for the kth village of jth Gram Panchayat of the ith block. In Eq. 2.1, B represents a block (i = 1 and 2), P represents a panchayat (j = 1 to 4), and V represents a village (k = 1 to 3). Bi HH is total number of households in the ith block, Pj HH is total number of household in the jth panchayat, and V k HH is total number of households in the kth village. B(i) is the weightage for the ith block population, P(j) is the weightage for the jth panchayat population, and V (k) is the weightage for the kth village population. B(i)*P(j)*V (k)*300 is the sample size for the kth village (of jth panchayat and ith block). After selecting the sample size, random sampling is done to select and survey the households (Tables 2.1 and 2.2).

2.3 Indicators from the Primary Survey The information has been collected from the households on various aspects related to livelihoods, climatic stress, and well-being. This has included data related to socioeconomic characteristics of the households like caste, religion, household composition, education of the household members, asset holding of the households, savings of the households, consumption and its composition, different income-generating activities, time for which the household/individual has been involved in these activities, income from each of these activities, preference to be involved in the livelihood activity, constraints involved in it, aspirations of the households, perceived well-being of the household, climatic stress, perceptions related to climate stress, perceptions related to climate changes, experiences with disaster, etc. We have

30

2 Primary and Secondary Information

Table 2.2 Block-wise and caste-wise sample S. no. 1

District Dhenkanal

Blocks

Caste category SC

ST

OBC

Others

Total

Hindol

73

21

87

08

189

Kamakhyanagar

44

03

65

01

113

11

155

00

00

166

2

Angul

Pallahara Athamallik

04

130

00

00

134

3

Bargarh

Paikmal

11

106

44

00

161

Gaisilet

35

56

35

13

139

Kuchinda

19

90

32

02

143

Jujumora

59

49

47

00

155

256

610

310

24

1200

4 5

Sambalpur

Total (4 districts and 8 blocks)

Source Field survey

also asked female members of the household in the age group of 15–49 years (if present) on the gender relations and gender dimensions of livelihood and livelihood changes related to climatic stress. For instance, climate change-induced migration could have a significant impact on the women members of the household if only the male member migrates. Women might have to take decisions of the households, and there could be some positive impact on gender relations. Our survey has included questions addressing such issues. In terms of income-generating activities, we have identified a wide array of activities. Some of the surveys just identify the following income-generating activities— cultivation, agricultural labour, allied sector, non-farm labour, and migration. But we listed down the various livelihood activities (according to the NCO and NIC codes) and collect information on the amount of time spent on each activity by the different individuals and the incomes generated by them. This information has been useful in defining the livelihood profiles of the individual and the household. It must be kept in mind that in some cases, the individuals might be specializing in only one activity, but the households have a highly diversified livelihood portfolio. Similarly, in some cases, an individual might be working on diverse activities, but the household is not so involved in diverse livelihood activities. Also, diversification with respect to time and diversification with respect to incomes could be heterogeneous. For instance, households could be spending almost 80% of their times in farming but earning only 20% of their household incomes from it while spending the remaining 20% of the time in urban seasonal migration and earning 80% of their incomes. By analysing the variety of livelihoods, we have tried to first understand the livelihood profiles of the individuals and households. Also, we have described the gender-based patterns involved in this livelihood diversification. This is the first part of our analysis. We have also collected information related to climatic stress and indicators that have to identify the differential vulnerability of households to these climatic stresses. For instance, seawater intrusion will affect each of the households differently based on

2.3 Indicators from the Primary Survey

31

their location. The amount of agricultural land affected is different. Also, some of the households have affected more by it, while some of the households in the same village or other villages have affected less by it. We aimed to first identify the appropriate climate shock indicator and an appropriate level at which this indicator could be used (household level, village level, or block level). Once we have identified a variety of such indicators, we have found out the association between these indicators and the livelihood profiles identified in the first part. We then measure various indicators of the well-being of the household—income, food security, risk and vulnerability indicators for the households, consumption, composition of consumption, gender roles, an indicator of social status, etc. The third part of our analysis has been related to the patterns of livelihood diversification identified in the first part with the different well-being indicators. A key methodological consideration in addressing this question is that households with a particular level of well-being could select themselves into a particular livelihood profile. In this sense, it could well be that the households’ well-being and livelihood diversification are simultaneously determined; i.e. livelihood profile is endogenous to households’ well-being. This endogeneity must be addressed using appropriate econometric strategy. We also understand that the livelihood profiles could have a differential impact on each of these indicators, and if such is the case, we will be able to highlight such trade-offs from this analysis.

2.4 Profile of the State Odisha economy is predominantly agriculture-based. The agriculture sector in the state accounts for 22% in gross state domestic products and generates employment for 62% of the state’s total working population. The agriculture sector is characterized by low production with wide fluctuation in crop output. This situation is mainly due to uncertain weather conditions, frequent occurrence of climate change extreme events, and a lack of assured irrigation facilities in two-thirds arable areas. Though crops are grown in both kharif and rabi seasons in Odisha, kharif is the main cropping season as cropping during rabi season is primarily confined to irrigated areas. Rice is the major crop in the state, and it is cultivated in both the seasons. It accounts for 66% of cropped area in kharif season and 11% in rabi season. Other crops than rice cultivated in the state are pulses (Arhar, Mung, Biri, Kulthi), oilseeds (groundnut, til, mustard, and niger), fibres (jute, mesta, cotton), sugarcane, vegetables, and spices. Mango, banana, coconut, and cashew nut are the main horticultural crops of the state (Fig. 2.1). Odisha witnessed many types of ‘climate-induced natural disasters (CIND)’ throughout the year and is known as the ‘disaster capital of India’ because of the overwhelming impacts (see Table 2.3). Impacts get amplified due to high population density and widespread poverty in disaster-prone areas. Natural disasters impose strong setbacks on the state economy, especially on livelihood sectors like agricultural and animal husbandry sector, and years having a major natural disaster drags

32

2 Primary and Secondary Information

Fig. 2.1 District-wise agricultural map of Odisha. Source MapsofIndia.com

the state domestic product down (Das 2016a). The state witnesses different types of natural disasters, the major ones being cyclone, flood, drought, and heatwaves. All these are climatic disasters and are intensified in recent years due to climate change. Sea-level rise due to climate change and the consequent displacement and livelihood loss has been added to the list of disasters now. The below table distributes the 30 districts of the state into the 10 agro-climatic zones and lists out the frequent disasters that hit these areas frequently. Of the 30 districts, 27 (90%) get battered by floods, 17 (57%) by cyclones, 14 (47%) by droughts, and 27 (90%) by heatwaves, and 10 (33%) face moderate risk from earthquakes. The entire state is disaster-prone, and all districts, irrespective of their location, suffer from more than one disaster. All the districts of a coastal plain called the food basket of the state have high population density, more than 450 per km2 area compared to the state average of 269. Thus, the three frequent disasters, flood, cyclone, and heatwaves, occurring there take a heavy toll on the lives and livelihoods of the state. Between 1900 and 2011, the state experienced high floods in 59 years; severe cyclones in 24 years; droughts in

2.4 Profile of the State

33

Table 2.3 Districts in different agro-climatic zones and high-frequency disasters of the areas Agro-climatic zones

Districts falling into the zones

Type of disasters affecting the areas with high frequency

North-eastern coastal plain

Balasore, Bhadrakh, and Jajpur

Flood, cyclone, heatwaves, sea-level rise (in Balasore and Bhadrakh)

East and south-eastern coastal plain

Kendrapada, Jagatsinghpur, Cuttuck, Khurdha, Puri, Nayagarh, and Southern Ganjam

Flood, cyclone, and heatwaves (sea-level rise in Kendrapada and Jagatsinghpur)

North-western plateau

Sundergarh and Deogarh

Drought, heatwaves, a moderate earthquake

North central plateau

Mayurbhanj and Keonjhar

Flood, cyclone, moderate earthquake

North Eastern Ghat

Gajapati, Northern Ganjam, Phulbani, and Rayagada

Flood, cyclone, heatwaves

Eastern Ghat highland

Koraput and Nawarangpur

Drought, flood, and heatwaves

South Eastern Ghat

Malkangiri and a part of central Koraut

Drought and heatwaves

Western undulating zone

Nuapada and Kalahandi

Drought and flood

West central tableland

Bolangir, Bargarh, Jharsuguda, Sambalpur, Sonepur, and Boudh

Drought, flood, heatwaves, and earthquake zone II (moderate risk)

Mid-central tableland

Angul and Dhenkanal

Flood, heatwaves, and cyclone

Source HDR (2004) Orissa Human Development Report, Annual Reports on Natural Calamities, Special Relief Commissioner 2003–04, and onwards

42 years; severe heatwaves in 14 years; and tornadoes in 7 years; however, the annual frequency of disasters has increased after 1965 (Das 2016a).

2.5 Disasters and Livelihood A range of climatic hazards coupled with a high dependency on agriculture and roughly one-third of the population living below the poverty line is making the state very vulnerable to climate change, including slow onsets as well as extreme events. This study has investigated the livelihood impacts and coping strategies of affected people due to different climate stresses such as cyclone, heatwaves, and flood. All these disasters have affected and displaced people, and they have to adapt to different occupations and new lifestyles altogether. There is little secondary information available on this, and research is urgently needed to find out the impact of sea-level rise on these communities. Concerning cyclones, the state of Odisha is ranked to be one of the most vulnerable states in India (Patwardhan et al. 2003). During 1970–2014, the

34

2 Primary and Secondary Information

state suffered 14 highly damaging cyclones of which the two most damaging ones occurred in 1971 and 1999. The observed wind speed of the super cyclone in 1999 amounted to 256 km/h (Das and Vincent 2009). However, cyclonic systems might be more intense in future which could imply greater loss per event. Though heatwaves were a less frequent and less threatening calamity previously, there has been a regime shift in heatwave occurrences since 1998 and the state of Odisha is witnessing intense heatwaves with severe consequences like high mortality almost regularly during summer (Das and Smith 2012). The only relevant adaptation activity implemented in the state has been an awareness campaign on ‘do’s and don’ts’ during heatwaves which has reduced the mortality to some extent (Das 2016b; Das and Smith 2012), but the livelihood sector has been left untouched so far and the state is likely to face more intensive heatwaves in future (Murari et al. 2014). Focus group discussions (FGDs) by one of the researchers found the possible impacts of these disasters on livelihood to be too serious. Table 2.4 lists out some of the negative impacts on livelihood as per the community. These need to be studied and assessed, and coping strategies need to be formulated. A brief background of the two zones for the study is provided in the following paragraphs. The livelihoods in both the study zones are different given the profiles of shocks and variations in institutions in these regions. The KBK region in southwest Odisha and the eight districts in that region are characterized by a range of hills covered with forests (http://www.kbk.nic.in/pdf/KBKProfile.pdf). These districts are also rich in mineral resources. Given the hilly nature, the level of infrastructure growth in these regions has been poor. Along with this, the tribal population in the region has not been able to accumulate required human capital because of lack of spending and efforts from the state and central governments. Figure 2.2 shows the literacy rates of different districts in Odisha along with percentage of ST population in these districts (Fig. 2.3). We find that the literacy rates in the KBK districts are abysmally low. In 7 of the 8 KBK districts, the literacy rate was less than 53%. This leads to a reduction of available job opportunities among the households in the region. Agriculture is backwards with practices like shifting cultivation practised in the region. Adding to the issues is the frequent droughts and erratic rainfall in the region which has left households with no other option but to diversify livelihoods. Migration has been a key component of livelihood diversification in the regions. In such a scenario, migration is also a key component of the livelihoods of people in the regions. This option increases when there are climatic shocks in the region. For instance, Wandschneider and Mishra (2003) estimate that nearly 60,000 people migrated from one district (Bolangir) of the region during the 2001 drought. Research conducted under the Western Orissa Rural Livelihood Programme (WORLP) identifies that most of the migrants in the two blocks of the KBK region (Turekela in Bolangir district and Khariar in Nuapada district) went away for work in construction sites, brick kilns, rickshaw pulling, and agricultural labour. Sixty-one percentage of migrants were male, and 28% of the migrants were children under 14 years (Deshingkar 2010). This is also a region that has some forests, and in part of the region, forests provide some livelihood opportunities for the households in that region. In some of the

2.5 Disasters and Livelihood

35

Table 2.4 Disasters and impacts on livelihood sectors Livelihood category Cyclones

Heatwaves

Agriculture

• Flooding of village and agricultural land • Loss of trees: cashew nuts, casuarinas, coconut trees, Keora, and other plantations • Reduction in the production of salt due to entry of freshwater into salt pans • Crop loss due to brackish and barren soil • Loss of vegetables • Loss of rabi crops as most damaging storms come during October: green gram, chilli, and other pulses

• Drying up of sources of irrigation • Reduction in summer crop production and rabi crops • Stopped cultivation of pulses (like green gram and black gram) and oilseeds (like til and groundnut) • Crops affected by diseases: Patra Poda and HaladiaPoka • Delay in harvesting work and loss due to late entry in the market • Loss of natural fertilizer and bio-fertilizer • Scarcity of crop residue and cattle feed • Destruction of 30–40% of crops due to wild boars and monkeys as forest area is getting degraded, trees are drying up, and wild animals get nothing to eat there

Fishing

• Loss of fishing material: nets and boats • Effect on freshwater fish: due to brackish water entering the pond • Much lower catch/availability for nearly 3/4 months after the cyclone • Reduced fishing due to constant fear among fishermen in cyclone season as cyclones have become so frequent

• Drying up of ponds and tanks • Loss of fish due to water getting warm

Livestock

• Loss of livestock and domestic birds: cock, hen, duck, etc. • Different types of illness to livestock due to eating grasses covered with mud from flooding (the most serious issue) • Arising up of sanitation and health problem as animals and humans have to stay together in same room/shelter for days 0 after storms

• Reduction in the rearing of domestic animals: cattle, goats, sheep, poultry birds due to (a)Lack of grass and forest coverage, (b)Lack of agricultural husk and fodder • Diseases to poultry birds—Ghuma Roga

districts in the 1990s, tribal households generated significant incomes from the forestbased products. In a study conducted by the Indian Institute of Forest Management (IIFM) (MoEF 1998) in 1996, 301 tribal families were surveyed from six districts of Odisha (Boudh, Pholbani, Keonjhar, Mayurbhanj, Sundergarh, and Gajapati) and they found that 50% of incomes of tribal households (Kondhs, Mundas, and Saoras) were from forest-based products while only 18% were from crop cultivation and

36

Fig. 2.2 Odisha districts

Fig. 2.3 Education and tribal population in Odisha

2 Primary and Secondary Information

2.5 Disasters and Livelihood

37

animal rearing. There are diverse non-timber forest products (NTFPs) and a lot of livelihoods dependent on it in some of the districts of Odisha. Saxena (2003) provides details of non-timber forest products (NTFPs) in Odisha. The climatic stress could have a lesser impact on forest-based livelihoods as climate change might not hugely impact forest resources. But, the value generated from forest-based products is also declining over the years. So, the exact impact on these likelihoods is also important to study. The coastal regions of Odisha also have diversified livelihoods. Cyclones, floods, and seawater intrusion have differential and significant impacts on the livelihoods of rural households. In particular, fishing is key to livelihoods in these regions (Salagrama (2006) provides details of poverty and livelihoods in the coastal region of Odisha). They change not just the livelihoods after the shocks but also in preparation for its ex-ante. The impact of these on livelihoods and the impact of such diversification on well-being are significant to study.

References Das S (2016a) Television is more effective in bringing behavioral change: evidence from heat-wave awareness campaign in India. World Dev 88:107–121. 0305-750X/https://doi.org/10.1016/j.wor lddev.2016.07.009 Das S (2016b) The economics of natural disaster in Odisha. In: The economy of Odisha: a profile. In: Nayak PB, Santosh CP, Pattanaik PK (eds). Oxford University Press, New Delhi, pp 266–301. ISBN 13 978-0-19-946478-4 Das S, Vincent JR (2009) Mangroves protected villages and reduced death toll during Indian Super Cyclone. Proc Natl Acad Sci USA 106(18):7357–7360 Das S, Smith SC (2012) Awareness as an adaptation strategy for reducing mortality from heat waves: evidence from a disaster risk management programme in India. Clim Change Econ 3(2):29 Deshingkar P (2010) Migration, remote rural areas and chronic poverty in India. Overseas Development Institute (ODI) Working Paper 323 Murari KK, Ghosh S, Patwardhan A, Daly E, Savi E (2014) Intensification of future severe heat waves in India and their effect on heat stress and mortality. Reg Environ Change. https://doi.org/ 10.1007/s10113-014-0660-6 MoEF (Ministry of Environment and Forests of India) (1998) Report of the expert committee on conferring ownership rights of NTFPs on Panchayats. Government of India (unpublished), New Delhi Patwardhan A, Narayan K, Parthasarathy D, Shama U (2003) Impacts of climate change on coastal zones. In: Sukla PR, Sharma SK, Ravindranath NH, Garg A, Bhattachanrga S (eds) Climate change and India: vulnerability assessment and adaptation. University Press, Hyderabad, pp 326–359 Saxena NC (2003) Livelihood diversification and non-timber forest products in Orissa: wider lessons on the scope for policy change? Overseas Development Institute, London Salagrama V (2006) Trends in poverty and livelihoods in coastal fishing communities of Orissa State, India (No. 490). Food & Agriculture Org, Rome Wandschneider T, Mishra P (2003) The role of small rural towns in Bolangir District, India: a village-level perspective. Report 2750, DFID–World Bank Collaborative Research Project on the Rural Non-farm Economy and Livelihood Enhancement. NRI, Chatham

Chapter 3

Perceptions of Climate Change and Adaptation Strategies

Extreme weather event is a common phenomenon in the Odisha state of India. People in this state are frequently realizing several extreme events like cyclones, floods, droughts, heatwaves, etc. According to the Odisha State Disaster Management Authority (OSDMA), for 95 of the last 105 years, Odisha has been affected by disasters brought on by heatwaves, cyclones, droughts, and floods. Since 1965, these calamities have become more frequent and widespread (Government of Odisha, 2018). These climatic phenomena affect people’s livelihood in the state and its economy seriously (Das 2012a; b; Das and Smith 2012; Bahinipati 2014; Mishra et al. 2016; Panda 2016). The most of citizens in Odisha—about 70% of its total population—depend on agriculture for their livelihood, which is itself highly vulnerable to climate change. Mishra et al. (2016) revealed that climate has a significant influence on agriculture production in Odisha. The frequent occurrence of natural calamities badly affects the production of kharif rice. Even, there has been noticed a decline in area cultivated under rice in the state due to abiotic stress such as water stress and submergence (Gumma et al. 2015). In drought years, there is a considerable loss in production of pulses and oilseeds both during kharif and rabi (Government of Odisha 2018). Apart from high dependency on the agriculture sector, Odisha stands second among the 14 states in the country with the highest incidence of poverty after Bihar. While Bihar topped the list registering the incidence at 33.34%, Odisha followed it at 32.59% in 2011–12. The incidence of poverty was 54.40 and 57.20% in Bihar and Odisha, respectively, in 2004–05. Yet poverty level at aggregate state level fell from 57% in 2004–05 to around 33% in 2011–12, it showed an increasing trend in its southern and coastal regions. More interesting, these regions also emerged as highly exposed to extreme climate events in the state. Odisha’s entire coastline of 480 kms is exposed to frequent floods and waterlogging. Similarly, its southern region is extremely exposed to heatwaves and frequent droughts.

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 A. Mitra et al., Climate Change, Livelihood Diversification and Well-Being, SpringerBriefs in Economics, https://doi.org/10.1007/978-981-16-7049-7_3

39

40

3 Perceptions of Climate Change and Adaptation Strategies

3.1 Climate Change Impacts Climate change impacts are no more distant future occasions in Odisha. Society has now started realizing these instances in the state. The rise in sea level, crop loss, livelihood insecurity, migration, and food insecurity are common impacts that people face in the state (Das, 2012a, b; Bahinipati 2014; Mishra et al. 2016). Government of Odisha calculated that the state suffered |1.05 billion economic loss due to the extreme events (i.e. cyclone, flood, and drought) during the 1970s, which increased to |6.82 billion, |70.81 billion, and |105.04 billion during the 1980s, 1990s, and 2000s, respectively (Government of Odisha 2004, 2011). Another estimate by Odisha State Disaster Management Authority revealed that natural disasters have killed over 50,000 people over 38 years (1970–2007), and the average financial loss per annum was to the tune of |12.42 billion (Sulagna and Poyyamoli 2010). Mishra et al. (2016) highlighted that climate has a significant influence on the agricultural production of Odisha. In the study, the possible future climate scenarios are found to have a negative impact on the net revenue from agricultural production of Odisha towards the end of the twenty-first century. The severity of climate change impacts mentioned above underlines the need for policy actions to curb these impacts. Climate policy literature recommends two policy alternatives to address problems emerged due to climate change. These alternatives are mitigation and adaptation. Initially, mitigation has received higher importance in the policy area. However, significant progress has not been made due to slow mitigation response (Füssel 2007). These days, adaptation has secured worldwide interest as policy response to climate change because it responds quickly to climate change (Tripathi and Mishra 2017b). Adaptation to climate change means alterations in the system to minimize the negative impact and optimize the positive impacts of climate change (Tripathi and Mishra 2017b). More important, it could be practised at different levels—regional, national, subnational, and local levels. Adaptation at the local level is the most critical issue as local actors are the ones that realize the severity of climate change (UNFCCC 2009). Adaptation to climate change is a two-step procedure—first, climate change and associated risks are perceived; then, steps are taken to minimize the adverse effects and optimize the positive effects of climate change. More crucial, perception should be accurate. If not, measures taken on basis of the incorrect perception could have an adverse effect. Correct perception depends on the knowledge and access to information (Tripathi and Mishra 2017b). On the other hand, knowledge itself depends on the educational level and experience of the person. Even though perceiving the incident accurately, at times people are not able to respond to the effects of climate change due to certain constraints such as lack of capacity, lack of resources, and lack of information. Apart from these limitations, people do not react to perceived climate change due to their orientation or beliefs. For example, farmers in India are aware of the hydrological consequence of eucalyptus tree; in spite of this, they continue with plating eucalyptus tree—their focus is on income security rather than environmental sustainability. Therefore, it is imperative to comprehend

3.1 Climate Change Impacts

41

the level of people’s perception of climate change, its correctness, and how the perception encourages adaptation. Against the above backdrop, this section aims to find if people in rural areas perceive a change in climatic variables and if so, how they react to these changes in order to minimize the adverse effect of climate change. Further, the role of education and exposure to change in physiological variables like temperature, precipitation, etc., in the farming right perception of climate change is evaluated. The study also examines the constraints that people face in adaptation to climate change.

3.2 People’s Perception of Climate Change People’s perception of climate change plays a crucial role in both environmental problems and possible solutions (Weber 2010). Perception is a cognitive process that involves receiving sensory information and interpreting it. The accuracy of perception is a necessary condition for a meaningful response, which eventually depends on knowledge and experience (Tripathi and Mishra 2017b). In the present study, an attempt has been made to explore people’ perception of climate change and associated risks to see whether they perceive climate change and associated risks correctly or not. The study also aims to identify factors that help to shape the perception of climate change. Respondents were asked about their perception of change in temperature, rainfall, and rainfall variability. Also, experiences about extreme weather shocks like drought, flood, and heatwave were examined in the study reason.

3.2.1 Temperature Out of 1200 rural households, 452 (37.7%) households recognized an increase in average surface temperature in the last 20 years (rise in a number of hot days), while 529 (44%) respondents perceived a decrease in average surface temperature in the last 20 years (Fig. 3.1). Out of total sampled households, 61 (5%) households realized no temporal change in temperature, whereas 158 (13%) households said they do not know if there is any change in average surface temperature. Observed temperature data collected from India Meteorology Department suggests that there is an increasing trend in mean annual surface temperature in each study district. It is projected to increase by more than 2 °C in each district of Odisha by 2100 (Government of Odisha 2018). Hence, one could conclude that the largest chunk of the rural population is still not able to perceive a change in temperature correctly. But it would not be fair as there is strong variation in the perception of increased temperature in the state, as is reflected from Table 3.1. Even perception was found inconsistent at micro (block)-level. Surprisingly, Sambalpur district behaved differently—about 51% respondent perceived a decrease in temperature in the last 20 years, and about 35% respondent claimed that they do not know. Also, less intra-district variation in

42

3 Perceptions of Climate Change and Adaptation Strategies 66.4

ProporƟon of Respondents (In %)

70 60 50 40 30

17.8

20 10

9.8 5.9

0 Increased

Decreased

Same

DnK

Fig. 3.1 People’s perception of change in rainfall at aggregate state level. Source Authors’ calculation based on primary data collected in this study

the perception of temperature is observed in Sambalpur in comparison with other sample districts. A higher number of respondents claiming an increased average of surface temperature were reported in Dhenkanal (60%) and Bargarh (57%). Within Dhenkanal district, they are mostly concentrated in its Hindol block (85%). However, they are almost equally distributed in both study blocks of Bargarh—50% in Paikmal block and 65% in Gaisilet block.

3.2.2 Rainfall and Rainfall Variability People’s perception of change in average annual rainfall and rainfall variability appears to be more similar across sample districts than the perception of change in temperature. About 66% respondents claimed that there has been a decline in average annual rainfall/number of rainy days over the last 20 years (Fig. 3.2). About 10% of respondents said that there has been an increase in average annual rainfall over the last 20 years. About 18% of rural households were found unable to ascertain the precipitation trend in their location. Majority of people perceived decreased in rainfall in each sample block (except for Jujomura block of Sambalpur district), suggesting less spatial variation in the perception (Table 3.2). The perception of change in average rainfall in most cases was observed consistent with observed participation trends in the study region. According to the rainfall data provided by the Indian Meteorological Department (IMD), rainfall has been more erratic in the state since the 1960s, with below-normal rainfall across all districts being recorded for most years.

3.2 People’s Perception of Climate Change

43

Table 3.1 Percentage of households reporting decrease in average annual rainfall/number of rainy days in the last 20 years District

Percentage Block of households

Dhenkanal 59

Hindol

Percentage Village of households

Percentage of households

68

87.5

Kamakhyanagar 45

Angul

71

Pallahara

80

Nuapada Beruanpal

7.1

Giridharprasad

95

Siarimalia

31.6

Jogidhia

96.8

Bhairpur

23.8

Ranjana

37.9

Tiribi

93.2

Saharagurujang 87.1 Athmallik

Bargarh

85

Paikmal

Gaisilet

Sambalpur 51

Kuchinda

Jujomura

59

98

70

76

28

Bantol

33.3

Kantapada

90.2

Thakurgarh

49.3

Kechhodadar

90

Purena

100

Bartunda

98.8

Lobide

100

Dangbahal

24.1

Jamutpali

98.4

Kunkum

84.6

Banke

35.6

Satakama

98.6

Hero

34.4

Padibahal

0

Jhankarpali

46.4

Total

66.4

Source Authors’ calculation based on primary data collected in this study

Here, another interesting finding is that respondents belonging to blocks having better access to irrigation, better forest cover, and proximity to the river were found relatively less inclined to agree with the decrease in rainfall in the last 20 years. In Dhenkanal and Bargarh districts, the largest chunk of respondents claimed both decline in the amount of rainfall and an increase in rainfall deviation (anomalous behaviour of seasonal rainfall), as is reflected from Tables 3.2 and 3.3. Further, drought was found as major weather shocks experienced by rural households in the sample districts (Fig. 3.3). About 78% respondents stated that they experienced severe drought in the last 5 years. These respondents were found largely concentrated

44

3 Perceptions of Climate Change and Adaptation Strategies 80

ProporƟon of Respondents (In %)

70 60 50 40 30 20 10 0 Drought

Flood

Pest

Heatwaves

Fig. 3.2 Different shocks reported by the rural households. Source: Authors’ calculation based on primary data collected in this study

in Dhenkanal district. Only 10% respondents claimed that they faced floods in the last 5 years. They were concentrated in Gaisilet block of Bargarh district. In order to understand the anomalous behaviour of seasonal rainfall, change in monthly rainfall pattern was assessed using the peoples’ perception. The results are presented in Fig. 3.3. The majority of survey respondents told that most of the ‘heavy rainfall’ incidents are concentrated in summer months (April to September). 20 years ago, there was a similar patter, as is reflected from replies of survey respondents. However, it has been highlighted that the number of ‘heavy rainfall’ incidents have now decreased during the above months (Fig. 3.3).

3.2.3 Determinants of Perception of Climate Change In this section, an attempt is made to ascertain factors affecting people’s perception of climate change. Here, the focus is not on whether people can perceive changes in climatic parameters, while our emphasis is on whether people perceive changes in climatic factors correctly and if so, what are factors that play a crucial role in forming the right perception. In a sample of 4553 people, 87% people perceived temperature change. This figure was observed about 84% in the case of perception of change in annual rainfall. On the other hand, only 37% of people have perceived the trend in mean temperature correctly, whereas 53% of people have understood the rainfall trend properly. Accurateness of the perception of climate change was assessed using the observed climatic data. District-wise long-term trends in both annual rainfall and mean temperature estimated from the observed climate data are provided in Figs. 3.4 and 3.5. Twelve districts in Odisha showed an increasing trend in annual rainfall,

3.2 People’s Perception of Climate Change

45

Table 3.2 Percentage of households reporting increase in rainfall variability (deviation in seasonal rainfall) in the last 20 years District

Percentage of Block households

Dhenkanal 32

Hindol

Percentage of Village households

Percentage of households

35

Nuapada

34.4

Beruanpal

0

Kamakhyanagar 27

Angul

08

Pallahara

07

Giridharprasad

54.5

Siarimalia

52.6

Jogidhia

3.2

Bhairpur

31.7

Ranjana

20.7

Tiribi

0

Saharagurujang 6.5 Athmallik

Bargarh

39

Paikmal

Gaisilet

Sambalpur 05

Kuchinda

Jujomura

08

38

40

03

06

Bantol

25

Kantapada

0

Thakurgarh

7.2

Kechhodadar

46.7

Purena

95.9

Bartunda

0

Lobide

58.3

Dangbahal

29.6

Jamutpali

41

Kunkum

0

Banke

2.2

Satakama

5.6

Hero

3.1

Padibahal

1.9

Jhankarpali

11.6

Total

20.9

Source Authors’ calculation based on primary data collected in this study

whereas other than these 12 districts noticed a decrease in annual rainfall (Fig. 3.4). Odisha districts showed a mixed trend in rainfall observed since 1901. However, an increasing trend in mean temperature has been observed in all districts except for Deogarh where the mean surface temperature has shown a decreasing trend (Fig. 3.5). To examine factors affecting precisions in people’s perception of climate change, a logistic regression framework was used in this study because here the response variable is binary. A set of socio-economic and demography variables were chosen as explanatory variables for the above regression analysis on basis of the review of the existing studies (Elum et al. 2017; Tripathi and Mishra 2017a; Tripathi and

46

3 Perceptions of Climate Change and Adaptation Strategies

Table 3.3 Factors affecting perception of climate change (from logit model) Perception of climate change (‘0’ for incorrection)

Odds ratio Std. err

z

P > |z|

Social group (base: general category) Scheduled Tribes (ST)

1.03

0.4725888

0.07 0.946

Scheduled Caste (SC)

1.623

0.7519158

1.04 0.300

Other Backward Class (OBC)

1.115

0.5094429

0.22 0.829

0.4729148

7.94 0.000

Weather information received from extension agents 3.21 and other related agencies Educational level (base: illiterate) Primary

1.78

0.2803205

3.66 0.000

Middle

1.41

0.3839341

1.27 0.203

Secondary

1.33

0.3591502

1.07 0.286

Senior secondary

1.97

0.5090269

2.61 0.009

Higher education

1.19

0.502062

0.41 0.681

Gender

1.073

0.2508824

0.30 0.764 0.26 0.795

Whether engaged in cultivation activity

1.03

0.1339203

Age (in years)

1.01

0.0054067

Intercept

0.15

0.090848

1.79 0.074 −3.13 0.002

Model summary 0.0695

Log likelihood

720.87

Number of observation

1168

ProporƟon of Respondents (in %)

Pseudo-R2

250 200 150 100 1.6

0.3

0.2

7.4

7.5

0.3

0.3

4.6

8

51.3

50 0 1.6

35.9

79.3

58.3

72.2

53.9

54

40.8

1.3

0.3

0.5

1.1

0.5

0.5

Now Heavy

Now Avg

Now Less

Now No

Earlier (20 Years ago) Heavy

Earlier (20 Years ago) Avg

Earlier (20 Years ago) Less

Earlier (20 Years ago) No

Fig. 3.3 People’s perception of intensity of rainfall (month-wise). Source Authors’ calculation based on primary data collected in this study

3.2 People’s Perception of Climate Change

47

Fig. 3.4 District-wise rainfall trend (1901–2015). Source Climate Information Division, India Meteorological Department, Pune

Fig. 3.5 District-wise temperature trend (1901–2015). Source Climate Information Division, India Meteorological Department, Pune

48

3 Perceptions of Climate Change and Adaptation Strategies

Mishra 2017b; Chingala et al. 2017; Haq and Ahmed 2017, and many more). Estimated results presented in Table 3.3 highlight the importance of education, access to extension services, presence of civil societies, age, and gender in forming the correct perception of climate change. Successful adaptation to climate change depends on right perception of changes in climatic variables. Results of this study suggest that an emphasis on education and climate information services will play a critical role in both forming the right perception and enhancing adaptation to climate change.

3.3 Adaptation to Climate Change The majority of respondents informed their adaptation strategies that are categorized into three categories—farming, animal husbandry, and forestry. This categorization is based on the occupations of rural households. The above economic activities were found the major activities, wherein the largest chunk of respondents was engaged. Rural households’ adaptation behaviour noticed during the survey suggests that rural households unintentionally (passively) adapt to climate change by undertaking several changes mentioned below. Passive adaptation to climate change has also been reported by many other researchers (see, e.g., Tripathi and Mishra 2017b; Mertz et al. 2009; Apata et al. 2009).

3.3.1 Adaptation Strategies in the Farming Sector About 19% respondents informed that they adopted any strategies in order to adapt to climate change. Most of these respondents were found from Bargarh district. Further, there have been several adaptation strategies observed from the remaining respondents. These include a change in crop variety, crop replacement (shifting from one crop to other crops), change in planting and harvesting dates, change in a field location, increased use of irrigation, increased consumption of fertilizer, and keeping land fallow. Table 3.4 shows the frequency with which the different strategies were adopted, revealing that both the least and the most frequently employed options vary district to district. The most frequently employed options were ‘change in planting dates’ in Sambalpur, ‘keeping land fallow’ in Angul, ‘change in crop variety’ in Dhenkanal, and ‘shifting from one crop to other’ in Bargarh. Similarly, the least frequently employed options were ‘increased fertilizer use’ in Sambalpur and Angul, ‘keeping fallow land’ in Bargarh, and ‘change in location’ in Dhenkanal.

3.3 Adaptation to Climate Change

49

Table 3.4 Frequency of adaptation strategies/options queried in the farming sector (In %) Strategies/options

Dhenkanal

Angul

Bargarh

Sambalpur

Odisha

Change in crop variety

22

10

54

13

25

Shift from one crop to another crop

19

14

56

8

24

Change in planting dates

11

16

54

22

26

Change in field location

9

10

22

12

13

Fallow land

11

17

7

2

9

Increased number of irrigations

17

5

24

4

12

Increased use of fertilizer

17

3

48

1

17

Source Authors’ calculation based on primary data collected in this study

3.3.2 Adaptation Strategies in the Livestock Sector Major adaptation strategies under livestock sectors noticed in the study region include increased number of animal populations (buying new animals), decreased number of populations (selling animals), change in animal feed, more use of veterinary services, change in portfolio of animal species, and change in animal location (Table 3.5). Different strategies were found accepted by people differently. Among these strategies, change in feed, decreased number of animal populations, change in the animal portfolio, and change in animal location were found relatively more common than other strategies. The most frequently employed options were ‘buying new animals’ in Dhenkanal, ‘change in animal feed’ in Angul, and ‘decreased the number of animals’ in Bargarh and Sambalpur. The least preferred options are ‘decreased the number of animals’ in Dhenkanal, ‘change in portfolio of animal species’ in Angul and Sambalpur, and ‘increased use of veterinary services’ in Bargarh. Table 3.5 Frequency of adaptation strategies/options queried in the livestock sector (In %) Strategies/options

Dhenkanal Angul Bargarh Sambalpur Odisha

Increase the number of animals (buying new animals)

19

12

2

20

13

Decrease the number of animals (selling animals)

8

14

55

5

20

Change in livestock feed

19

27

55

27

32

Improved veterinary services

15

11

2

8

9

Change in the portfolio of animal species 12 (buying different species of animals)

8

50

1

18

Moved animal in another site

5

51

0

18

16

Source Authors’ calculation based on primary data collected in this study

50

3 Perceptions of Climate Change and Adaptation Strategies

Table 3.6 Frequency of adaptation strategies/options queried in the forestry sector (In %) Strategies/options

Dhenkanal Angul Bargarh Sambalpur Odisha

Change in timing of collection of forestry 17 products

36

47

1

25

Shift in occupation, from one to another

20

32

54

1

27

Engage on more than one occupation

18.2

5.7

50.0

0.0

18.5

Source Authors’ calculation based on primary data collected in this study

3.3.3 Adaptation Strategies in Forestry Occupation Members of several sampled households were engaged in collecting forestry products like kendu leaves, sal seeds, etc. These households were found to be confined in two districts—Angul and Bargarh. There have been three major changes observed in the above occupation in the villages considered in our study. These changes include a change in timing of collection of forestry products, shifting from the collection of forestry products to another occupation, and engaging other occupation along with the collection of forestry products. Table 3.6 shows the frequency with which the different options were employed, revealing that change in timing of collection of forestry products and moving away from the occupation based on a collection of forestry products were the most frequently employed.

3.3.4 Constraints Impending Climate Change Adaptation Figure 3.6 presents the main constraints considered as impediments to the adaptation to climate change in the study area. Lack of money (finance) was perceived as the most serious constraint affecting rural households’ efforts to reduce climate change impacts. Shortage of labour was ranked second in the order of adaption constraints by rural households in the study area. Though a strong spatial variation was observed in the perception of and adaptation to climate change in the study area, minimal spatial variability in identifying constraints impending climate change adaptation by rural households was observed.

3.4 Summing Up The results indicate a mix perception of climate change. However, about 40% respondents claimed that there has been an increase in average annual temperature in the last 20 years, showing consistency with change observed in temperature suggested by observed data collected from India Meteorology Department. The perception of precipitation trend was found stronger than that of temperature trend as about 66%

3.4 Summing Up

51

Odisha Sambalpur Bargarh Angul Dhenkanal 0%

20%

40%

60%

80%

100%

Proportion of Responses (In %) Lack of access to information

Shortage of labour

Lack of access to land

Lack of access to credit

Lack of money

Fig. 3.6 Constraints impending climate change adaptation. Source Authors’ calculation based on primary data collected in this study

respondents said that there has been a decline in average annual rainfall in the last 20 years and it became more erratic than it was earlier. Though few rural households informed that they have adopted some strategies like change in planting and harvesting dates, change in crop varieties, crop shifting, and change in occupation to curb the adverse effect of climate change, passive adaptation to climate change was observed in the study area. Lack of money and lack of information were observed as the major constraints impeding climate change adaptation in the study region.

References Apata TG, Samuel KD, Adeola AO (2009) Analysis of climate change perception and adaptation among arable food crop farmers in South Western Nigeria (No. 1005–2016–79140) Bahinipati CS (2014) Assessment of vulnerability to cyclones and floods in Odisha, India: a districtlevel analysis. Current Science, 1997–2007 Chingala G, Mapiye C, Raffrenato E, Hoffman L, Dzama K (2017) Determinants of smallholder farmers’ perceptions of impact of climate change on beef production in Malawi. Clim Change 142(1–2):129–141 Das S (2012) The role of natural ecosystems and socio-economic factors in the vulnerability of coastal villages to cyclone and storm surge. Nat Hazards 64(1):531–546 Das S (2012) The role of natural ecosystem and socio-economic factors in the vulnerability of coastal villages to cyclones and stormsurge. Nat Hazards 65:531–546 Das S, Smith SC (2012) Awareness as an adaptation strategy for reducing mortality from heat waves: evidence from a disaster risk management programm in India. Climate Change Econ 3(2):29 Elum ZA, Modise DM, Marr A (2017) Farmer’s perception of climate change and responsive strategies in three selected provinces of South Africa. Clim Risk Manag 16:246–257

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3 Perceptions of Climate Change and Adaptation Strategies

Füssel H (2007) Adaptation planning for climate change: Concepts, assessment approaches, and key lessons. Sustain Sci 2 (2):265–275. https://doi.org/10.1007/s11625-007-0032-y. Government of Odisha (2018) Odisha Climate Change Action Plan: For the Period 2018–2023. Forest and Environment Department. Government of Odisha. Gumma MK, Mohanty S, Nelson A, Arnel R, Mohammed IA, Das SR (2015) Remote sensing based change analysis of rice environments in Odisha, India. J Environ Manage 148:31–41 Haq SMA, Ahmed KJ (2017) Does the perception of climate change vary with the sociodemographic dimensions? A study on vulnerable populations in Bangladesh. Nat Hazards 85(3):1759–1785 Mertz O, Halsnæs K, Olesen JE, Rasmussen K (2009) Adaptation to climate change in developing countries. Environment Management 43(5):743–752 Mishra D, Sahu NC, Sahoo D (2016) Impact of climate change on agricultural production of Odisha (India): a Ricardian analysis. Reg Environ Change 16(2):575–584 Panda A (2016) Vulnerability to climate variability and drought among small and marginal farmers: a case study in Odisha. India. Climate and Development 9(7):605–617. https://doi.org/10.1080/ 17565529.2016.1184606 Sulagna S, Poyyamoli G (2010) A study of natural disasters in Orissa coast for last 100 years with reference to cyclonic disaster risk reduction. World Appl Sci J 10(7):748–755 Tripathi A, Mishra AK (2017) Knowledge and passive adaptation to climate change: An example from Indian farmers. Clim Risk Manag 16:195–207 Tripathi A, Mishra A (2017) Farmers need more help to adapt to climate change. Econ Pol Wkly 52(24):53–59 UNFCCC (2009) Report of the Conference of the Parties on its fifteenth session, held in copenhagen from 7 to 19 December 2009; part two: decisions adopted by the conference of the parties. Weber EU (2010) What shapes perceptions of climate change? Wiley Interdisciplinary Reviews: Climate Change 1(3):332–342

Chapter 4

Livelihood Diversification in Odisha

This chapter analyses the livelihood diversification in the study sample. There are four aspects of livelihood diversification we look at in this chapter. The four aspects are the number of livelihood activities participated by the households, the individuallevel diversification, the seasonal allocation of labour, and the diversification across different months. In these aspects, we look at the issues across the dimensions of districts, castes, education, and based on the primary occupation of the household.

4.1 Number of Activities This subsection explores the number of activities that a household engages in the year. The households have been involved in many activities—cultivation, livestock, other agricultural activities, non-agricultural enterprise, wage or salaried employment, pension, remittances, and rural non-farm wages, and other activities. We found that the households were involved in up to five livelihood activities. The distribution of the number of households in the study area based on the total number of activities is provided in Table 4.1. Table 4.1 presents the number of activities done and the number of households which pursue these activities. We find that 501 households or 41.75% of the households are involved in only 1 livelihood activity. Then, around 479 households or 39.92% of the households were involved in 2 activities. Two hundred and two households or 16.83% were involved in livelihood activities. Only 18 households or 1.5% were involved in the 4 or 5 livelihood activities. We now look at the household-level diversification across districts, castes, education of the household head, and the primary occupation of the household. We first analyse the number of activities across the four study districts. Table 4.2 provides the number of activities across the different districts. We find that in Dhenkanal 79% of the sample households are involved in only one livelihood activity. In Angul, 53% of the households were involved in only one livelihood © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 A. Mitra et al., Climate Change, Livelihood Diversification and Well-Being, SpringerBriefs in Economics, https://doi.org/10.1007/978-981-16-7049-7_4

53

54

4 Livelihood Diversification in Odisha

Table 4.1 Number of activities performed by the households Number of activities

Frequency

Percentage

1

501

41.75

2

479

39.92

3

202

16.83

4

16

1.33

5

2

0.17

Total sample

1200

100

Table 4.2 Number of activities performed by the households across districts Number of activities

Dhenkanal

Angul

Bargarh

Sambalpur

Total

1

237 (78.5%)

158 (52.7%)

25 (8.3%)

81 (27.2%)

501 (41.8%)

2

56 (18.5%)

78 (26.0%)

200 (66.7%)

145 (48.7%)

479 (39.9%)

3

9 (3.0%)

61 (20.3%)

64 (21.3%)

68 (22.8%)

202 (16.8%)

4

0 (0.0%)

2 (0.7%)

10 (3.3%)

4 (1.3%)

16 (1.3%)

5

0 (0.0%)

1 (0.3%)

1 (0.3%)

0 (0.0%)

2 (0.2%)

Total sample

302 (100.0%)

300 (100.0%)

300 (100.0%)

298 (100.0%)

1200 (100.0%)

activity. In Sambalpur and Bargarh, only 27 and 8% households were involved in only one livelihood activity. So, we find that there is more diversification in Bargarh and Sambalpur as compared to Dhenkanal and Angul. Bargarh is a district that is more dependent on agriculture than other districts, and we find that many households are involved in 2 (67%) or 3 (21%) livelihood activities. In Sambalpur, 72% of the households participated in 2 or 3 livelihood activities. Most of the households in Dhenkanal were involved in labour activities. Many households were involved in agricultural of non-farm rural wage labour activities. We also looked at the differences in the household diversification across households belonging to different social groups—scheduled castes (SCs), scheduled tribes (STs), other backward castes (OBCs), and others. From Table 4.3, we find that the level of diversification and backwardness of the castes are related. The households belonging to scheduled castes, scheduled tribe, and other backward castes were diversifying in a greater number of livelihood activities compared to others. Around 37% of the SC households, 44% of the ST households, 49% of the OBC households, and 54% of the other households were involved in only one livelihood activity. Around 62% of the scheduled tribes’ households were

4.1 Number of Activities

55

Table 4.3 Number of activities performed by the households across castes Number of activities

ST

SC

OBC

Others

Total

1

223 (36.6%)

113 (44.1%)

152 (49.0%)

13 (54.2%)

501 (41.8%)

2

248 (40.7%)

102 (39.8%)

123 (39.7%)

6 (25.0%)

479 (39.9%)

3

130 (21.3%)

36 (14.1%)

31 (10.0%)

5 (20.8%)

202 (16.8%)

4

8 (1.3%)

5 (2.0%)

3 (1.0%)

0 (0.0%)

16 (1.3%)

5

1 (0.2%)

0 (0.0%)

1 (0.3%)

0 (0.0%)

2 (0.20%)

Total sample

610

256

310

24

1200

involved in two or three activities, 54% of the scheduled caste households and 50% of the other backward classes’ households were involved in two or three livelihood activities. Forty-six percentage of the other households were involved in two or three livelihood activities. We also analyse the number of activities across the education of the household head. We categorized the education of the households’ head into illiterate, primary education, secondary education, and higher secondary education. The level of diversification in households across these education categories is provided below. Table 4.4 shows that there is no linear relation between the education category and household-level diversification. Among the households with illiterate household heads, 45% households participated in only one activity, 36% participated in two activities, 15% participated in three activities and 3% participated in more than three livelihood activities. Among the households with household heads who had attained primary education, 49% were involved in only one activity, 34% were involved in two activities, 16% were involved in three activities, and only 1% were involved in four activities. Among households with the heads having secondary and higher secondary Table 4.4 Number of activities performed by the households across education categories Number of activities

Illiterate

Primary

Secondary

Higher Secondary

Degree

1

134 (45%)

189 (49%)

157 (41%)

14 (42%)

17 (68%)

2

108 (36%)

129 (34%)

152 (40%)

16 (48%)

4 (16%)

3

46 (15%)

60 (16%)

68 (18%)

3 (9%)

4 (16%)

4

7 (2%)

4 (1%)

4 (1%)

0 (0%)

0 (0%)

5

2 (1%)

0 (0%)

0 (0%)

0 (0%)

0 (0%)

Total sample

297

382

381

33

25

56

4 Livelihood Diversification in Odisha

Table 4.5 Number of activities performed by the households across primary occupation Number of activities

Cultivation (%)

Livestock (%)

Non-agri enterprises(%)

Wage salaried(%)

1

24

3

55

18

2

48

57

22

45

3

24

34

21

32

4

2

6

2

5

5

0

1

1

0

Total sample

537

177

130

208

education, 41 and 42% of households were involved in only one livelihood activity, while 58 and 57% were households were involved in 2 and 3 activities, respectively. Among those with household heads who had a degree in education, we find that 68% of the households participated in only one livelihood activity while 32% were involved in 2 or 3 livelihood activities. Households with different primary occupations could have different levels of diversification. We explored the variations in the household-level diversification based on their principal occupation. We analysed the household-level diversification with different primary occupations—cultivation, livestock, non-agricultural enterprises, and wage or salaried employment (Table 4.5). Households with cultivation, livestock, and wage or salaried employment as the primary occupation diversify more than the households with non-farm enterprises as the primary occupation. For households with cultivation as a primary occupation, 72% of households were involved in two or three activities, while 24% were involved in only one activity. Among those with livestock as the primary occupation, only 3% were involved in only one livelihood activity and 91% were involved in 2 or 3 livelihood activity. Among households with wage employment as the primary occupation, 18% participated in only one activity while 77% participated in 2 or 3 activities. The households with non-agricultural enterprises as the primary occupation were less diversified with 55% households participating in only one activity and 43% involved in 2 or 3 activities.

4.2 Individual-Level Diversification This section analyses the diversification at the individual level. We analysed the different occupations of the households and also the differences across districts, caste, education, and primary occupation. We consider the primary occupation of the individuals for this analysis. The primary occupation could be cultivation, dairy, fishery, agricultural labour, collection of forest products, other agricultural activities, nonfarm rural wage-based activities, non-farm rural self-employment, migration (rural

4.2 Individual-Level Diversification Table 4.6 Primary occupation of individuals in the sample

57

Primary occupation

Number

Proportion

Cultivation

875

19.23

Agricultural labour

439

9.65

Non-farm labour and self-employment

526

11.56

Others

141

3.1

Unemployed

798

17.53

Dependent

1772

38.94

Total

4551

100

to urban, interstate, or international), salaried job or employment in MGNREGA. First, we present the individual-level diversification for the whole sample. Table 4.6 shows that around 4551 individuals had some or other primary occupation. As expected, a very high proportion had cultivation as the primary occupation. 19.2% of the individuals had cultivation as the primary occupation. The next frequent occupation was in non-farm rural wage employment. 11.6% of the individuals in employment were involved in non-farm rural wage employment. 9.7% were involved in agricultural labour and 3% in other activities. Interestingly, non-farm rural wage employment is higher than the agricultural labour among the sample. It is pertinent that we analyse where this change is driven in the sample. To understand this, we analyse these variations across the district, caste, education of the individual, and the primary occupation of the household. We also find that 39% were dependents who were not in the workforce and not looking for employment. 18% of the individuals were unemployed. This comes to 29% of those who are looking for a job. This is a high percentage of unemployment, and we analyse the differences in these and the occupational patterns across various factors. First, we analyse the differences in the principal occupation of the individuals across the districts. Table 4.7 indicates that there are significant differences across the districts in the study. We find that not all regions have cultivation as the principal occupation as Table 4.7 Primary occupation of individuals in the sample in different districts Principal activity

Dhenkanal (%)

Angul(%)

Bargarh(%)

Sambalpur(%)

Total(%)

Cultivation

14.7

6.5

37.2

17.3

19.2

Agricultural labour

19.5

7.1

4.4

7.6

9.6

Non-farm labour and self-employment

7.9

14.1

4.6

20.7

11.6

Others

3.7

2.9

3.2

2.6

3.1

Unemployed

20.7

26.8

10.5

12.4

17.5

Dependent

33.5

42.6

40.2

39.5

38.9

Total

1160

1114

1205

1072

4551

58

4 Livelihood Diversification in Odisha

Table 4.8 Primary occupation of individuals in the sample in different castes Occupation

ST (%)

SC(%)

OBC(%)

Others(%)

Total(%)

Cultivation

18.5

12.9

25.9

22.8

19.2

Agricultural labour

7.6

14.0

9.6

14.1

9.6

Non-farm labour and self-employment

13.0

13.9

7.2

4.3

11.6

Others

2.7

4.0

3.2

1.1

3.1

Unemployed

19.1

13.9

17.3

20.7

17.5

Dependent

39.0

41.4

36.8

37.0

38.9

Total

2314

995

1150

92

4551

the dominant livelihood. In Angul and Sambalpur, more individuals were involved in non-farm rural wage employment than in cultivation. In Dhenkanal, agricultural labour is the most dominant livelihood activity. It is only in Bargarh that cultivation is the dominant livelihood activity. We also find that unemployment is highest in Angul and lowest in Bargarh. The proportion of unemployed to those looking for jobs (excluding dependents from the sample) is 47% in Angul. This is the lowest in Bargarh where only 18% of the people looking for jobs are unemployed. We also analyse the individual-level livelihood diversification across the different castes (Table 4.8). There are also differences in livelihood across castes. Among STs, 19% had cultivation as the principal occupation, 13% had non-farm employment, and 8% had agricultural labour as the primary occupation. Among SCs, 13% had cultivation as the principal occupation, 10% had agricultural labour, and 7% had rural nonfarm employment as a primary occupation. Among OBCs, 22% had cultivation as a primary occupation, 14% had agricultural labour as a primary occupation, and 4% had non-farm rural wage employment as the primary occupation. Among other castes, 19% had cultivation as the principal occupation and 10% had agricultural labour as the principal occupation. Unemployment was highest in Others and STs, and lower among OBCs and SCs. Table 4.9 provides employment of individuals across different activities. Among the illiterates cultivation is the dominant livelihood activity. Non-farm rural wage employment and agricultural labour are the next dominant activities. This order is the same for people who were educated at the primary, secondary, and higher secondary level. Among individuals with degree education, cultivation and nonfarm employment are equally dominant occupations. Unemployment was also high among individuals with less education. Apart from the unit of analysis (household or individual), the time at which the livelihood activity is measured will also make a difference in the estimation of livelihood diversification. We estimated the days per week the household head spent on agricultural and rural wage labour to understand the diversification of the household head in kharif, rabi, and summer. Table 4.10 shows the distribution of wage labour across different seasons. We find that in kharif, 1067 household heads were involved in agricultural labour and they

0.42

33.94

1202

Dependents

Total

16.39

Non-farm labour and self-employment

8.99

14.81

Agricultural labour

Unemployed

25.46

Cultivation

Other

Illiterate or informally literate

Activity

648

39.7

13.7

0.7

12.7

10.5

22.8

Below primary

595

41

14.3

1

8.2

9.1

26.4

Primary

406

38.9

17.5

1.5

14.5

8.1

19.5

Middle

609

40.2

20.9

0.5

11.5

7.9

19

Secondary

Table 4.9 Primary occupation of individuals in the sample in different education categories

566

28.4

33.2

0.7

15.6

7.2

14.9

Higher secondary

171

20.5

55

1.2

5.2

6.4

11.7

Diploma

100

21

33

4

13

5

24

Graduate

11

0

27.3

0

18.2

9.1

45.5

Postgraduate

4308

35.5

18.5

0.8

13.2

10.2

21.8

Total

4.2 Individual-Level Diversification 59

60 Table 4.10 Days per week of labour

4 Livelihood Diversification in Odisha Days per week labour

Kharif

Rabi

Summer

Agriculture labour

4.02 (1067)

3.29 (918)

2.24 (860)

Wage labour

4.61 (62)

4.44 (62)

1.56 (62)

worked for around 4 days a week on the same. On rabi, 918 household heads were involved in agricultural labour, and they worked for 3.29 days per week on the same. In summer, 860 household heads were involved in agricultural labour and worked for 2.24 days per week on the same. Across all three seasons, 62 household heads were involved in wage labour, but the number of days they worked per week was different across seasons. In kharif, they worked for 4.61 days per week, while in rabi, they worked for 4.44 days per week, and in summer, they worked only for 1.56 days per week. Table 4.11 presents the occupational diversification across the districts. In Dhenkanal, 269 household heads were involved in agricultural labour over all three seasons. We find that the household heads are not involved much in wage labour. Also, the number of days spent per week in agricultural labour is quite consistent across seasons. In Angul, the number of household heads involved in agricultural and wage labour was consistent across the three seasons. The number of days in agricultural labour was 4.17 days in kharif and rabi and 2.51 days in summer. The wage labour was 4.42 days in kharif, 4.9 days in rabi, and 1.52 days in summer. In Bargarh, agricultural labour was the main occupation for household heads. In Sambalpur, 418 household heads were involved in agricultural labour for 4.1 days Table 4.11 Days per week of labour across districts

Days per week labour

Kharif

Rabi

Summer

Agriculture labour

4.04

3.80

3.64

N

269

269

269

Agriculture labour

4.17

4.17

2.51

N

202

202

201

Wage labour

4.42

4.90

1.52

N

50

50

50

Agriculture labour

4.17

4.08

4.15

N

63

53

47

4.10

2.75

1.02

Dhenkanal

Angul

Bargarh

Sambalpur Agriculture labour N

418

279

228

Wage labour

5.42

2.50

1.75

N

12

12

12

4.2 Individual-Level Diversification Table 4.12 Days per week of labour across castes

Days per week labour

61 Kharif

Rabi

Summer

4.05

3.78

2.28

ST Agriculture labour N

458

362

313

Wage labour

4.40

4.90

1.58

N

48

48

48

Agriculture labour

4.19

3.18

2.55

N

237

222

219

Wage labour

5.40

4.00

2.00

N

5

5

5

Agriculture labour

4.16

3.54

3.00

N

241

208

202

Wage labour

5.33

2.22

1.22

N

9

9

9

Agriculture labour

3.38

3.27

3.00

N

16

11

11

SC

OBC

Others

in kharif, 279 household heads were involved in agricultural labour for 2.75 days in rabi, and 228 household heads were involved in agricultural labour for 1.02 days in summer. In kharif, rabi, and summer, 12 household heads were involved in wage labour for 5.42, 2.50, and 1.75 days. From Table 4.12, we find that different castes have different levels of livelihood diversifications. Among STs, household heads worked in agricultural labour for 458, 362, and 313 days. In agricultural labour, the heads worked on average for 4.05, 3.78, and 2.28 days in kharif, rabi, and summer, respectively. We find that the number of heads involved in agricultural labour is more stable among SC households in all three seasons. From Table 4.13, we find that the days per week for agricultural labour vary with season but does not vary across different education categories. From Table 4.14, we find that those with rural non-farm enterprises as the primary occupation have stable dependence on agricultural and wage labour. The number of days is 3.64 in kharif, 3.58 in rabi, and 2.14 days in summer. We now estimate the variations in livelihood dependency across different months. We do this analysis for the household heads only. Among the different household heads, we now look at different proportion involved in different occupations across different months of the year.

62 Table 4.13 Days per week of labour across household head education categories

4 Livelihood Diversification in Odisha Days per week labour

Kharif

Rabi

Summer

4.17

3.49

2.58

Illiterate Agriculture labour N

250

230

226

Wage labour

4.00

4.88

1.32

N

25

25

25

Agriculture labour

4.04

3.61

2.58

N

346

293

274

Wage labour

5.06

2.94

1.19

N

16

16

16

Agriculture labour

4.09

3.41

2.50

N

308

240

210

Wage labour

5.18

5.18

2.65

N

17

17

17

4.31

4.29

2.35

Primary

Secondary

Higher secondary Agriculture labour N

29

24

20

Wage labour

4.00

4.00

0.00

N

1

1

1

Agriculture labour

4.32

4.06

3.27

N

19

16

15

Wage labour

4.33

4.67

0.00

N

3

3

3

Degree

Table 4.15 indicates that the dependence on cultivation increases from January through October and reduces until December. At its maximum, 37.2% of the household heads were involved in cultivation during the month of July and only 8.8% were involved in cultivation in the month of January. The dependence in agricultural and rural non-farm wage labour follows a pattern that is opposite to that of cultivation. 39.8% of the household heads were involved in rural non-farm wage during the month of February, while 31.2% were involved in the same during the month of July. Also, agricultural labour shows a similar pattern where the maximum participation happens in January and minimum in July. Figure 6.1 presents a description of the monthly diversification (Figure 4.1). We also look at how households belonging to different land classes diversify. In this regard, Table 4.16 presents the data on a number of activities by households belonging to different land classes.

4.2 Individual-Level Diversification

63

Table 4.14 Days per week of labour across the household’s primary occupation Days per week labour

Kharif

Rabi

Summer

Agriculture labour

4.05

3.60

2.84

N

420

362

323

Wage labour

4.70

4.10

1.80

N

30

30

30

4.20

3.81

3.65

Cultivation

Livestock Agriculture labour N

74

58

55

Wage labour

5.50

6.00

4.50

N

2

2

2

Agriculture labour

3.64

3.58

2.14

N

113

113

112

Wage labour

2.80

3.40

3.40

N

5

5

5

Agriculture labour

4.15

2.37

1.28

N

235

147

128

Wage labour

3.67

3.67

0.00

N

3

3

3

Rural NFE

Wage labour

We find that landless farmers do not diversify much in terms of a number of activities. We find that 64% of the marginal farmers were involved in 2 or 3 activities and 59% of small farmers are involved in 2 or 3 activities. We also find that 74% of semi-medium farmers, 63% of medium and farmers, and 70% of large farmers are involved in 2 or 3 activities. We also look at individual-level diversification across different landholding classes. Table 6.17 presents the occupations across different land classes We find that households with more land are more involved with cultivation as the primary occupation. Landless households were involved most in non-farm labour and selfemployment. There are also 18% and 19% unemployed in landless, small, and marginal land classes (Table 4.17).

4.3 Factors Affecting Diversification We analyse the factors affecting diversification. For this, we regress the number of activities performed by households with various independent variables. We consider

Cultivation

Non-farm Rural wage

Agricultural labour

Fishery

Other agri Activities

Non-farm rural self-employment

Dairy

Salaried job

Rural to urban Migration

Collection of Forest Products

MGNREGA

Interstate migration

International migration

1

2

3

4

5

6

7

8

9

10

11

12

13

N

Occupation

Sr.No

1079

0.0%

0.0%

0.4%

2.3%

0.3%

0.9%

0.6%

2.3%

2.8%

0.5%

42.5%

38.6%

8.8%

Jan

Table 4.15 Monthly livelihood diversification

1043

0.0%

0.1%

0.4%

2.4%

0.8%

1.0%

0.6%

2.4%

2.9%

0.5%

39.0%

39.8%

10.3%

Feb

1084

0.0%

0.2%

3.3%

2.4%

1.1%

0.8%

0.5%

2.3%

2.7%

0.4%

37.2%

37.7%

11.4%

Mar

April

1063

0.0%

0.2%

3.5%

2.4%

1.4%

0.9%

0.5%

2.2%

2.9%

0.4%

36.1%

37.9%

11.7%

May

1032

0.0%

0.3%

0.9%

2.2%

1.0%

1.0%

0.5%

2.2%

3.0%

0.5%

33.3%

38.9%

16.3%

June

1048

0.0%

0.2%

0.4%

0.3%

0.3%

1.0%

1.0%

2.1%

2.3%

7.3%

18.6%

34.5%

32.0%

July

1130

0.0%

0.0%

0.2%

0.2%

0.3%

1.0%

1.0%

1.9%

1.9%

7.4%

17.9%

31.2%

37.2%

Aug

1140

0.0%

0.0%

0.3%

0.2%

0.3%

1.0%

1.0%

1.8%

1.7%

7.6%

19.3%

32.5%

34.5%

Sept

1114

0.1%

0.0%

1.0%

0.2%

0.3%

0.0%

1.0%

1.9%

1.6%

6.8%

21.1%

33.6%

32.5%

Oct

1110

0.0%

0.0%

0.0%

0.2%

0.3%

1.0%

1.3%

1.9%

1.4%

0.1%

23.6%

33.4%

36.8%

Nov

1115

0.0%

0.0%

0.3%

0.1%

0.2%

1.0%

1.3%

2.2%

1.7%

0.1%

29.3%

38.2%

25.7%

Dec

1114

0.0%

0.0%

0.3%

0.1%

0.3%

1.0%

1.3%

2.2%

1.7%

0.1%

33.2%

38.7%

21.3%

64 4 Livelihood Diversification in Odisha

4.3 Factors Affecting Diversification 45.0%

40.0%

65

42.5% 38.6%

39.8% 39.0%

37.7% 37.2%

37.9% 36.1%

38.9%

38.2%

37.2%

32.0%

38.7%

36.8%

34.5%

33.3%

35.0%

34.5%

33.6% 32.5%

32.5%

31.2%

33.4%

33.2%

30.0%

Nonfarm Rural Wage

Other Agri Activities 25.7%

Fishery

23.6%

25.0%

21.3%

21.1% 18.6%

20.0%

Salaried Job Rural to Urban Migration

15.0% 10.3%

11.4%

Nonnfarm Rural Self Employment Dairy

19.3% 17.9%

16.3%

10.0%

Cultivation

Agricultural Labour

29.3%

Collection of Forest Products

11.7%

MGNREGA

8.8% 7.3%

7.6%

7.4%

Interstate migration

6.8%

International migration 5.0%

2.8% 2.3% 0.9% 0.6% 0.5% 0.4% 0.3% 0.0%

2.9% 2.4% 1.0% 0.8% 0.6% 0.5% 0.4% 0.1% 0.0%

3.3% 2.7% 2.4% 2.3% 1.1% 0.8% 0.5% 0.4% 0.2% 0.0%

3.5% 2.9% 2.4% 2.2% 1.4% 0.9% 0.5% 0.4% 0.2% 0.0%

3.0% 2.2% 1.0% 0.9% 0.5% 0.3% 0.0%

2.3% 2.1% 1.0% 0.4% 0.3% 0.2% 0.0%

1.9% 1.0% 0.3% 0.2% 0.0%

1.8% 1.7% 1.0% 0.3% 0.2% 0.0%

1.9% 1.6% 1.0% 0.3% 0.2% 0.1% 0.0%

Jan

Feb

Mar

April

May

June

July

Aug

Sept

1.9% 1.4% 1.3% 1.0% 0.3% 0.2% 0.1% 0.0%

2.2% 1.7% 1.3% 1.0% 0.3% 0.2% 0.1% 0.0%

2.2% 1.7% 1.3% 1.0% 0.3% 0.1% 0.0%

Oct

Nov

Dec

0.0%

Fig. 4.1 Monthly diversification Table 4.16: Landholding and the number of activities Number of activities

Land class Landless (%)

Marginal (%)

Small(%)

Semi-medium (%)

Medium (%)

Large(%)

Total(%)

1

56

34

39

23

34

30

501

2

34

42

42

50

34

20

479

3

10

22

17

24

29

50

202

4

0

1

2

3

2

0

16

5

0

1

0

0

0

0

2

Total

427

223

339

160

41

10

1200

Table 4.17 Landholding and occupational activities Activity

Landless (%)

Marginal (%)

Small (%)

Semi-medium (%)

Medium (%)

Large (%)

Total

Cultivation

4

22

26

33

36

33

875

Agricultural labour

9

12

11

6

6

14

439

Non-farm casual labour and self-employment

21

8

7

4

5

3

526

Others

3

2

4

3

2

0

141

Unemployed

18

18

19

15

11

17

798

Dependent

44

39

33

39

40

33

1772

Total

1549

851

1283

658

174

36

4551

66

4 Livelihood Diversification in Odisha

the age of the household head, gender of the household head, the education level of the household head, caste to which the household belongs, the land owned by the household, and percentage of area irrigated for the household. Table 4.18 presents the results of the multiple regression analysis. From Table 4.18, we find that the age of the household head has a non-linear effect on the number of activities a household performs. With increasing age, the number of activities increases first and then decreases as indicated by the negative sign in the age squared variable. There is no relation between the gender of the household head and the diversification level of the household. There is a slight decrease in the number of activities as education increases from illiterate to informal literacy, and then to primary and middle school, but there is no relation with higher education and the number of activities performed by the household. Compared to ST households, SC, OBC, Other caste households are involved in a smaller number of activities. Table 4.18 Regression results Independent variables

Dependent variable = number of activities

Age of the household head

0.021** (0.009)

Age of the household head squared

−0.0001* (0.00008)

Gender of the household head (=1 if female)

−0.002 (0.075)

Education level (Base = Illiterate) Literate (Elementary)

−0.274*** (0.086)

Literate (TLC)

−0.264** (0.127)

Literate (Others)

−0.211* (0.115)

Below primary

−0.199*** (0.067)

Primary

0.077 (0.073)

Middle

−0.147* (0.088)

Secondary

0.131 (0.083)

Higher secondary

0.009 (0.096)

Diploma

−0.172 (0.152)

Graduate

−0.093 (0.169)

Postgraduate

0.279 (0.806)

Caste (Base=ST) SC

−0.144*** (0.055)

OBC

−0.251*** (0.053)

Others

−0.314** (0.156)

Land owned (in acres)

0.010 (0.011)

Percentage of land irrigated

0.006*** (0.0006)

R-squared

0.128

N

1169

4.3 Factors Affecting Diversification

67

The area owned by the household and the number of activities is not related. But, the percentage of land irrigated is positively related to the number of activities performed by the household.

Chapter 5

Climate Change, Diversification Strategy, and Its Effectiveness: Assessing Well-being from Inter-Temporal Changes in Consumption Outcomes

5.1 Introduction Occupational diversification can be viewed as a strategy to mitigate the risks associated with the earnings of an individual. With the occurrence of climatic changes, the dependence on one source of income, especially agriculture-related, throughout the year may result in the inadequacy of resources required to sustain the levels of consumption. Even at a given point in time, households or an individual member of the household may have to depend on several sources to reduce the consumption risks. For example, an agriculture-dependent household during the slack season, in the face of climatic stress, may have to undertake a few petty activities in the non-agriculture sector for earning a livelihood. Morduch (1995) urged that diversification always played a role in the context of ‘consumption smoothing’. While the risk-averse households protect consumption levels by borrowing and using insurance mechanisms, another common practice is to diversify economic activities and make conservative production and employment choices. Households which are risk averse get only limited exposure to shocks that can be handled with available credit and insurance. Both types of mechanisms have been studied independently, but much more can be learned by analysing them together. For example, we may obtain a more complete picture of risks, costs, and insurance possibilities and at the same time realize the biases in analysing the impact of credit and insurance. Usually, the workers from landless and submarginal households show a greater tendency to become ‘multi-active’ (Bhaumik 2007). Based on the primary data collected from Hooghly and Cooch Behar districts, it was noted that the majority of the rural workers were working in the farm sector and nearly 33 and 23% of the workers from those two districts were engaged in the non-farm sector. Apart from this, about one-eighth of the workers were found ‘multi-active’, as they participated in both the farm and non-farm sectors. The extent of ‘multi-activity’ was greater among the male workers as compared to the female workers. The most predominant mode of employment in the non-farm sector appeared to be wage employment, which

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 A. Mitra et al., Climate Change, Livelihood Diversification and Well-Being, SpringerBriefs in Economics, https://doi.org/10.1007/978-981-16-7049-7_5

69

70

5 Climate Change, Diversification Strategy and Its Effectiveness …

was followed by self-employment and regular wage employment. While male farmworkers were engaged more in self-employment, the female farmworkers pursued wage employment. The caste dimensions in the context of diversification are important. In India, where caste plays a pertinent role in the labour market and for the choice of activities, the climate change effects are also expected to generate unequal outcomes across social categories. In general, while certain positive changes could be discerned for the scheduled castes in terms of occupation diversification, similar changes were missing for the scheduled tribes (Gang et al. 2013). With the five rounds of all-India employment data from the NSS, starting from 1983 to 2004, Gang et al. (2013) assessed whether political and social changes weakened the relationship between the low caste status and the occupational segregation. While the occupational structure of the SC households was seen to be converging with that of the non-scheduled households, no such convergence was noticeable for the scheduled tribe households. Scheduled caste population was able to move out of the occupation which had the highest incidence of poverty, and much of the movement away from agricultural labour was to self-employment in non-agriculture and more diversified income portfolios. These asymmetrical outcomes of SCs and STs on occupational convergence with the non-scheduled households may be related to locational differences between the SCs and STs. Also, the political economy factors which could influence the political mobilization of the SCs versus the STs need to be considered. In our sample, since we had a greater representation of the scheduled population in certain blocks, the occupational diversification strategies and the outcomes are expected to be quite different in these blocks compared to the others. Besides, climate change effects are expected to be quite diverse. The relationship between rural diversification and poverty through NSS data for 1987/8, 1993/4, and 1999/0 has been analysed by Kijima and Lanjouw (2005). They noted that agricultural wage employment, comprising uneducated and low caste population, increased over time. Non-farm employment is generally associated with higher educational attainments and social status which are rare among the poor. Given this situation, in response to the climate change phenomenon, the non-farm sector is less likely to provide opportunities to low-income households for reducing the income risks. However, the expansion in non-farm employment influences poverty indirectly, via an impact on agricultural wages. The study observed that the incidence of agricultural labour is strongly correlated with poverty and low consumption levels. Hence, with less possibility to shift to the non-farm sector, the low-income households in the agriculture sector are unlikely to mitigate the adverse climate change effect. Further, rural women need to diversify their occupations if they are engaged in agriculture, that is rain-fed and, therefore, seasonal (Ajani and Igbokwe 2013). This is to enable them to acquire additional income to take care of economic responsibilities during the off-season periods. Some of the existing studies on occupational diversification reveal that the forces of development and distress both impact on rural occupational structure simultaneously, though the extent of their influence is difficult to judge from the available evidence (Rani and Shylendra 2002). Distress diversification has clear nexus with

5.1 Introduction

71

the degradation of forests; improving the local resource base and its management can potentially reverse this trend. Generation of agricultural surplus and a changing pattern of consumption demand may result in an increase in demand for labour in the non-agricultural sector through the spill-over effects (Unni 1994) ; Dev 1994). Hence, if the non-farm sector itself is not demand induced, the shift strategy adopted at the household level may not deliver a better outcome. Bhalla (1989) identified two kinds of distress diversification in which non-agricultural rural activities become an absorber of the residual labour force: the first is the case of supplementary workers who have no main occupation but engage themselves in some subsidiary work to supplement household income; the second refers to those who with main occupations engage in the non-farm activities. Basant (1993), in a micro-level study noted that for households not operating any land, income from non-farm activities (72%) was the major source of income compared to those who operated land. Diversification by households involved the participation of members in different activities, and it also involved the participation of a single worker in multiple economic activities. Factors such as access to land, family size, and nearness of the village to a town influenced diversification in terms of different activities. Jayaraj (1996) observed that households with access to land and individuals with educational attainments had relatively better access to non-agricultural employment though women and low caste population were at a loss. On the whole, the occupational diversification has been observed as a vital tactic in order to cope with crisis and seasonal stress in both farm and non-farm activities by those who are dependent on livelihood sources impacted by seasonal factors and are, thus, vulnerable to climate change effects (Ajani and Igbokwe (2013).1 Whether similar patterns can be retrieved from our sample data from Odisha needs to be explored. Particularly, from inter-temporal data, the upward mobility and factors influencing mobility can be studied in order to reflect on the climate change effect and the effectiveness of the strategy to cope with the phenomenon. One important aspect of well-being is consumption; thus, by focusing on consumption changes over time and relating it to livelihood diversification, we may comprehend its nature.2 Diversification adopted in the face of compulsion and in a situation of stagnancy without many prospects may result in a bunch of residual or low productivity activities, whereas diversification as an attempt to explore newer pathways in a vibrant situation to reduce income risks and smooth consumption can be highly beneficial. The rest of the chapter is organized as follows: Sect. 5.2 focuses on the job profile and occupational diversification of the sample households. It also focuses on the extent of instability in occupations by drawing a monthly profile round the year. Section 5.3 examines the distribution of households in terms of consumption pattern expressed as calorie intake and the inter-temporal changes in it. Section 5.4 pursues the econometric analysis to understand the determinants of change in calorie intake. Finally, Sect. 5.5 summarizes the major findings.

1 2

Portions of this chapter are re-used from the IEG working paper Das and Mitra (2021). See Das and Mitra (2021).

72

5 Climate Change, Diversification Strategy and Its Effectiveness …

5.2 Job Profile and Occupational Diversification Some of the empirical evidence on income diversification and its impact on wellbeing in the rural areas may be retrieved from the existing literature. Sultana et al. (2015) noted that the extent of income diversification had a positive and significant effect on households’ well-being. Besides, the demographic and household characteristics played a significant role in determining income diversification. While examining the effects of income diversification on poverty Minot et al. (2006), Barett and Reardon (2000), Escobal (2001) and Adugna (2006) found a beneficial effect of income diversification on household well-being. Further, income diversification through non-farm activities is seen to reduce poverty by providing higher incomes and increased food consumption. The study by Kinsley et al. (1998) indicates that income diversification helps wealth formation and it increases the ability to cope with shocks: in other words, diversification reduces livelihood vulnerability as it helps rural households insure themselves against the occurrence of shocks. Joshi et al. (2003) diagnosed the status of agricultural diversification in South Asian countries, and detailed investigations were carried out for India to decipher the determinants of diversification. The study noted gradual diversification with certain inter-country variations, in favour of high-value commodities. Price policy, infrastructure development, urbanization, and technological improvements contributed to agricultural diversification, and occupational diversification positively impacted on the well-being levels of the farmers helping them shift from the inferior to high-value crops. Besides, agricultural diversification contributed to employment creation in agriculture and expansion in exports. Turning to occupational dimension, it may be noted from Table 5.1 that the number of households (heads) engaged in an activity tends to vary considerably across the months. Some of the activities with an exceptionally high coefficient of variation, of course, comprise a very small proportion of workers even if we consider the maximum magnitude. For example, the workers in MGNREGA scheme corresponds to a very high coefficient of variation of 150, though the highest number of workers as seen in the months of March/April has been only 36/37. Among the other categories like cultivation, agricultural labour and non-farm labour each of which comprises a sizeable number the agriculture sector unravels much larger instability compared to the non-farm sector. Since agricultural activities involve seasonality, the fluctuations are evident; on the other hand, the non-farm sector is seen to offer stable sources of livelihood. Hence, it may be inferred that larger is the dependence on agriculture, higher is the requirement for diversification particularly during the off-seasons. Also, dependence on agriculture tends to raise the climate change effect as a shortage of water affects the cropping intensity and yield.

5.3 Occupational Diversification Index

73

Table 5.1 Occupational pattern Occupation types Jan

Feb Mar April May June Jul

Cultivation

107 124

95

124

168

335

Aug Sept Oct Nov Dec

419 392

361

408 286

237 14

Dairy

6

6

5

5

5

10

11

11

11

14

14

Fisheries

5

5

4

4

5

77

84

87

76

1

1

Agricultural labour

458 407 403

384

344

195

202 220

235

262 327

369

Collection from forest

25

25

26

25

23

3

2

2

2

2

1

1

Other agricultural 30 activities

30

29

31

31

24

21

19

18

16

19

19

Non-farm labour

417 415 409

1

403

401

362

353 370

374

371 426

431

Self-employed in 25 non-farm sector

25

25

23

23

22

21

21

21

21

24

24

Rural–urban migration (state)

3

8

12

15

10

3

3

3

3

3

2

3

Interstate migration

0

1

2

2

3

2

0

0

1

0

0

0 11

Salaried

10

10

9

10

10

11

11

11

11

11

11

MGNERGA

4

4

36

37

9

4

2

3

0

0

3

3

Other

122 157 116

137

168

152

71

61

87

91

86

87

5.3 Occupational Diversification Index Information on the month-wise occupation of the household head is used to measure the number of different types of occupations he is doing throughout the year (Table 5.1).3 Diversification index is simply taken as the sum of different occupations he has taken up in different months of the year. We defined the occupational diversification index in the following way: Diversification index = D I =

12 

oi ⇔ oi = o j

(5.1)

i=1

where o is occupation type, i and j are months. The occupation categories are added only if the occupation in ith month is different from the occupation in jth month. The number of occupations pursued and household income do not seem to be correlated. The coefficient of correlation between diversification index and HH head’s annual income is seen to be insignificant (r = −0.011, P = 0.70), low and negative. However, Table 5.2 brings out a very interesting pattern between the number of 3

See IEG working paper Das and Mitra (2021).

74

5 Climate Change, Diversification Strategy and Its Effectiveness …

Table 5.2 Number of occupations pursued in a year and average annual income Number of occupations in a year

Number of HHs

Percentage of HHs

Average annual income of the HH head

Range of annual income

1

469

39.08

40,248.83

0–800,000

2

479

39.92

45,208.14

3000–800,000

3

144

12

44,919.31

3600–90,000

4

61

5.08

42,445.9

5000–100,000

5

36

3

28,555.56

3000–50,000

6

11

0.92

31,818.18

10,000–70,000

activities and the average annual income of the household. The latter increases with a rise in the number of activities from 1 to 2 or 3, and subsequently, it tappers off substantially as the number of activities goes up to 4, 5, and 6. Also, the maximum income with 2 activities is more than that the corresponding figure for more number of activities. The maximum income corresponding to 5 or 6 activities is substantially lower than that for 2. It is quite distinct that beyond a certain point the increase in the number of activities reflects a bunching of petty/residual activities through a combination of two or three activities is beneficial in comparison with a single activity. However, Table 5.2 is indicative of a higher figure of a minimum income for household heads with 4 or 6 activities. This tends to suggest that the households at the bottom can raise their income by taking up several activities but households at the relatively higher end do not benefit much by doing so. Thus, a greater number of activities may not be always a mitigating factor as far as income/consumption risks are concerned. It could be a compulsion for some of the households to meet the basic consumption requirements. On the other hand, household heads with a fewer number of activities in comparison with their single activity counterparts can meet the livelihood challenges better.

5.4 Consumption: Past and Present From the survey data, the consumption pattern of the households is considered. In addition to the current consumption break up (at the time of the survey), the past consumption pattern in terms of quantity was also collected. Keeping in view that the recall problem could be serious in the case of expenditure, the quantity information was recorded. We used information on food items consumed both by purchasing and from other sources like fair price shop, free receipts, etc. Further, the quantity information has been converted into calorie intake given the household size and the equivalent consumer units. Thus, calorie consumption per consumer unit has been worked out. We follow NSS guidelines and nutritional charts of dieticians to convert food consumed to their equivalent calorie content. The calorie conversion ratios

5.4 Consumption: Past and Present Table 5.3 Calorie equivalence of food items

75

Food items

Calorie content per 10 gm ( cal)

Rice, wheat, and pulses

33.33

Butter/ghee/oil

68

Milk/curd/other dairy products

5.5

Vegetables (mixed)

6

Fruits (mix of orange, grapes, papaya, and banana)

5.1

Non-vegetarian items

11.67

Sugar

38.7

Spices

26

Source (October 2017): Report No. 560(68/1.0/3), Nutritional Intake in India, NSS 68th Round (July 2011–June 2012), National Sample Survey Office, National Statistical Organisation, Ministry of Statistics and Programme Implementation, Government of India

used for different types of food are shown in the following table (Table 5.3). We also followed NSS consumer unit equivalence weights to convert the family size of a household into their total consumer units. Table 5.4 shows these weights used in the study. We used the age and gender of each of the household members to convert them to equivalent consumer units and then added the units for each household. The following formula is used: Table 5.4 Number of consumer units assigned to a person

Age in completed years

Males

Females

Less than 1

0.43

0.43

1–3

0.54

0.54

4–6

0.72

0.72

7–9

0.87

0.87

10–12

1.03

0.93

13–15

0.97

0.80

16–19

1.02

0.75

20–39

1.00

1.71

40–49

0.95

0.68

50–59

0.90

0.64

60–69

0.80

0.51

70 +

0.70

0.50

Source (October 2017): Same as Table 5.1

76

5 Climate Change, Diversification Strategy and Its Effectiveness …

cu =

n 

jk

wi

(5.2)

i=1

where cu is the total consumer unit of a family, i is the ith member of jth age group, and kth gender category in the family. Tables 5.5 and 5.6 show the average per capita calorie consumption in the current period and in previous times. As it can be checked from Tables 5.5 and 5.6, a very large percentage of households seem to be lying below the minimum threshold level of 24,00 kilocalorie both in the present and the past. It may be further noted from Table 5.7 which distributes the households in terms of past and present calorie consumption that a very large percentage remained by and large in the same calorie size class. Less than one-tenth of the households registered a downward shift, while nearly 20% experienced upward mobility. However, remaining in the same calorie size class does not mean that households at the individual level did not register any change, which we consider later in the text. In terms of quantity per capita, however, there has been an improvement in the weekly consumption of rice. Even in the case of wheat, pulses, and vegetables, there has been a marginal increase (Table 5.8). Further, the distribution of households by income and calorie classes shows that the two attributes are not strongly associated (Table 5.9). In other words, higher levels of income do not necessarily translate into higher levels of food consumption that raises the calorie intake. At the level of low-income households, increase in income is expected to raise the food consumption as many of them are below the minimum threshold limit. But its absence can be because of the alcohol consumption of the adult males (Table 5.10). Table 5.5 Daily calorie consumption per consumer unit at present

Table 5.6 Daily calorie consumption per consumer unit earlier

Calorie range

Number of HH

500–1500

97

Percentage of HH 8.08

1500–2400

446

37.17

2400–3500

422

35.17

3500–4500

121

10.08

4500–5500

57

4.75

> 5500

57

4.75

Calorie range

Number of HH

500–1500

117

9.75

1500–2400

479

39.92

2400–3500

379

31.58

3500–4500

110

9.17

4500–5500

59

4.92

> 5500

56

4.67

Percentage of HH

Daily Calorie consumption per consumer unit at earlier period 80 17 0 0 0 0 97

500–1500

1500–2400

2400–3500

3500–4500

4500–5500

> 5500

Total households

500–1500

Daily calorie consumption range

446

0

1

1

39

368

37

1500–2400

Daily Calorie consumption per consumer unit at present

Table 5.7 Upward and downward mobility in the calorie intake of households

422

0

6

29

294

93

0

2400–3500

121

3

12

60

45

1

0

3500–4500

57

10

27

19

1

0

0

4500–5500

57

43

13

1

0

0

0

> 5500

1200

56

59

110

379

479

117

Total

5.4 Consumption: Past and Present 77

78

5 Climate Change, Diversification Strategy and Its Effectiveness …

Table 5.8 Weekly average consumption of food items by sample households

Items consumed

Consumption now (kg)

Consumption before (kg)

Rice

9.669 (5.09)

8.865 (4.77)

Wheat

1.072 (1.61)

0.945 (1.36)

Cereals

0.124 (0.52)

0.099 (0.41)

Pulses

1.306 (0.94)

1.176 (0.73)

Vegetables

2.694 (1.66)

2.448 (1.40)

Fruits

0.248 (0.84)

0.209 (0.68)

Non-veg

1.195 (0.96)

1.124 (0.96)

Dairy

0.284 (0.67)

0.271 (0.65)

Oil

0.751 (0.38)

0.741 (0.39)

Spices

0.183 (0.12)

0.170 (0.12)

Sugar

0.762 (0.33)

0.745 (0.34)

Table 5.9 Daily per capita (per consumer unit) calorie consumption in different income group Income groups (INR)

Daily calorie consumption per consumer unit at present (kcal)

500–1500

1500–2400

2400–3500

3500–4500

4500–5500

> 5500

Total

< 20,000

6

58

32

15

9

17

137

20,000 – 50,000

66

264

250

61

25

31

697

50,000 – 100,000

22

118

134

43

23

9

349

100,000 – 200,000

2

3

3

1

0

0

9

> 200,000

1

3

3

1

0

0

8

Total

97

446

422

121

57

57

1200

Note Income and calorie consumption are not related. There are many households with zero income Table 5.10 Alcohol consumption by income group Income group (INR)

Number of households

Per capita alcohol consumption now

Per capita alcohol consumption before

< 20,000

137

0.0124

0.0124

20,000–50,000

697

0.0783

0.0637

50,000–100,000

349

0.1196

0.0479

100,000–200,000

9

0

0

> 200,000

8

0

0

All HH

1200

0.0816

0.0524

5.4 Consumption: Past and Present

79

Fig. 5.1 Kernel density estimate

Table 5.9 shows a rising tendency of per capita alcohol consumption along with the income classes. Also, these households need to incur expenditure on various non-food essential items. Hence, with an increase in income, there may not be a proportionate increase in food consumption; rather, there is a greater motivation to divert the income towards the non-food items which were not consumed before. For example, if investment in housing is pursued, it would not necessarily result in an increase in food consumption in spite of a rise in income. The kernel density function shows that the modal value relating to calorie consumption corresponds to a very low level of income. In other words, at a higher level of income, there are few households with higher levels of calorie intake (Fig. 5.1).

5.5 Determinants of Change in Consumption Climate change impacts are likely to affect agriculture which further endangers food security (Pandve and Patil 2009). However, climate changes remain often unnoticed by individual farmers, and people see drought and dry conditions as natural events (Thomas et al. 2007). Therefore, long-term strategies specifically for climate change may be difficult to identify from a sample survey. However, given the repeated occurrence of a phenomenon (rains starting late for example) which may affect production

80

5 Climate Change, Diversification Strategy and Its Effectiveness …

and consumption, certain initiatives might have been adopted by the rural population to work out earning possibilities and even out the consumption fluctuations. Hence, when we consider the inter-temporal changes in consumption, the effect of such initiatives is expected to have been included. The study by Raghuvanshi et al. (2017) in Tehri Garhwal district of Uttarakhand state, however, noted that 100 per cent farmers were of the climate change through the level of awareness varied from person to person. Most of the farmers reported crop failures, migration to other places, and flooding’ as three major consequences of climate change in the study area. Hence, it may be erroneous to overlook the efforts made by the individual farmers to smooth their production and consumption over time. Further in the context of the non-farm sector, Ito and Kurosaki (2009) investigated the effects of climate hazards by focusing on the non-farm labour supply of agricultural households. The study reflects that the share of the non-farm labour supply increases with weather risk, the increase is much larger in the case of non-agricultural work than in the case of agricultural wage work, and the increase is much larger in the case of agricultural wages paid in kind than in the cash, suggesting farmers’ considerations of food security. On the whole, consumption smoothing is an important objective of the rural households, and hence, it becomes meaningful to model intertemporal changes in consumption and identify factors which contribute to the rise in consumption of the households, particularly in the areas affected by the climate changes. How regional diversity may still be present depending on the nature of climate change and other factors also need to be explored. Below we explain the difference between the present and past consumption in terms of calorie intake by estimating Eq. 5.3. Ci = β X i + δ Z i + γ Ai + φ + εi

(5.3)

In Eq. 5.3, the dependent variable is change in calorie consumption for the ith household and X, Z, A and are the vectors of social (religion, social class), economic (land holding, diversification, income, family size, etc.), and asset (owning tv, radio, etc.) variables for the ith household, respectively. The block-level fixed effects are represented by φ, and ε is the error term. Thud, in addition to the livelihood diversification, the other control variables in the model include household size, caste and religion dummies, family income, owning of land and a certain type of assets, household structure, and the block characteristics captured through dummies. The results are presented in Table 5.11. Household size is a significant determinant of consumption increase. Muslims are better off in comparison with the Hindus and Christians. Caste as such does not seem to be significant as the dummies are not statistically significant relative to the comparison group (scheduled tribes). On the other hand, the regional diversity captured through dummies turns out to be significantly different from the comparison group at least in the case of four blocks. The blocks with negative intercept dummies (Kuchinda and Pallhara) are not statistically significant. Households with better housing seem to have experienced a greater positive change in consumption over time. The accessibility to land or other asset does not seem to be exerting any

5.5 Determinants of Change in Consumption

81

Table 5.11 Estimated OLS coefficients of variables explaining a change in per capita calorie consumption by consumer unit (Dependant variable = change in calorie consumption per consumer unit) Explanatory variable

Estimated OLS coefficient T values

Family size

65.74***

6.14

Islam

162.93**

2.22

Christianity

100.14

0.65

SC

4.10

0.09

OBC

−79.87*

−1.87

Others

1.94

0.02

Semi-pucca

87.25**

2.21

Pucca

106.04**

2.40

Land owned

−6.12*

−1.63

Occupation diversificationa

−14.38

−0.90

Family income now in lakhs (head + spouse) 246.91***

4.10

Income squared (in lakhs)

−31.46***

−3.40

Own TV

−46.44

−1.44

Own mobile

29.98

0.84

Gaisilet

233.31***

3.18

Hindol

395.34***

5.34

Jujomora

135.75*

1.73

Kamakhaya_Nagar

218.92***

2.75

Kuchinda

−10.82

−0.14

Pallahara

−39.32

−0.64

Paikmal

201.21***

2.91

Constant

−394.22***

−4.90

Adj R-squared = 0.156

Root MSE = 439.92

Number of observations = 988 F( 21, 966) = 9.71 (P = 0.00)

positive impact on consumption increase. The diversification of livelihood measured as the number of activities of the household head in a year does not turn out to be significant. But its impact must have been captured through the income variable which is highly significant. At the lower echelons with an increase in income, consumption is expected to rise. However, there is no linear relationship between income and consumption change. On the other hand, beyond a certain level, the consumption increase tends to decline, as the need for other non-food requirements may increase significantly. Overall, as seen from Table 5.11, consumption changes perceived as a prerequisite of well-being change seem to be responsive to the individual efforts pursued through income augmenting/smoothing strategies which might have been perceived through livelihood diversification. Instead of remaining idle in the slack seasons, the

82

5 Climate Change, Diversification Strategy and Its Effectiveness …

households seem to have explored other avenues of earning. Though the livelihood diversification does not turn out to be significant as such, its effect is captured through the income variable. The responses tend to vary across religious and caste categories. Better-off households perceived through housing conditions seem to respond better compared to households with poor living conditions. Large households are more likely to adopt consumption augmenting strategies as for them the compulsions could be alarming and adoption might have been easier: as some members could be deputed to the marginal activities, the head can be more selective in picking up the gainful opportunities. Land ownership does not turn out to be significant possibly because many of the sample households do not actually own land to any sizeable extent.

5.6 Concluding Remarks One important aspect of well-being is consumption; thus, by focusing on consumption changes over time and relating it to livelihood diversification, we may comprehend its nature. Diversification adopted in the face of compulsion and a situation of stagnancy without much prospects may result in a range of residual or low productivity activities whereas diversification as an attempt to explore newer pathways in a vibrant situation to reduce income risks and smooth consumption can be highly beneficial. A very large percentage of households seem to be lying below the minimum threshold level of 24,00 kilocalorie both in the present and in the past. It may be further noted from the past and present calorie consumption that a very large percentage remained by and large in the same calorie size class. Less than one-tenth of the households registered a downward shift, while nearly 20 per cent experienced upward mobility. However, remaining in the same calorie size class does not mean that households at the individual level did not register any change. In terms of quantity per capita, however, there has been an improvement in the weekly consumption of rice. Even in the case of wheat, pulses and vegetables, there has been a marginal increase. Consumption changes perceived as a prerequisite of well-being change seem to be responsive to the individual efforts pursued through income augmenting/smoothing strategies which might have been perceived through livelihood diversification. Instead of remaining idle in the slack seasons the households seem to have explored other avenues of earning. Though the livelihood diversification does not turn out to be significant as such, its effect is captured through the income variable. The responses tend to vary across religious and caste categories. Better-off households perceived through housing conditions seem to respond better compared to households with poor living conditions. Large households are more likely to adopt consumption augmenting strategies as for them the compulsions could be alarming and adoption might have been easier: as some members could be deputed to the marginal activities, the head can be more selective in picking up the gainful opportunities. Land

5.6 Concluding Remarks

83

ownership does not turn out to be significant possibly because many of the sample households do not actually own land to any sizeable extent.

References Adugana L (2006) The dynamics of income diversification in Ethiopia. www.ideas.repec.org/mab/ wpapers/3.html Ajani EN, Igbokwe EM (2013) Occupational diversification among rural women in sub-saharan Africa: a review. African J Food Agrocil Nutrition Develop 13(5). ISSN: 16845374 Barret CB, Reardon T (2000) Asset, activity and income diversification among African agriculturalists: some practical issues. www.Ieswise.edu/itc/live/basglooo3a Basant R (1993). Diversification of economic activities in rural Gujarat: key results of a field survey. Working Paper No. 53, Ahmedabad, Gujarat Institute of Development Research Bhalla S (1989) Employment in indian agriculture: retrospect and prospect. Soc Scient 17(5–6):3–21 Bhaumik SK (2007) Occupational diversification among rural workers: results from fieldsurveys in West Bengal. Indian J Labour Econ 50(4):2007 Das S, Mitra A (2021) Does climate change perception make livelihood diversification more effective? evidence from the consumption mobility study of rural households. Delhi, Institute of Economic Growth Dev M (1994) Some aspects of non-agricultural employment in rural India: evidence ata disaggregate level. In: Visaria P, Rakesh B (eds) Non-agricultural employment in India: trends and prospects, New Delhi, Sage Publications, pp 258–88 Escobal J (2001) The determinants of non-farm incomediversification in rural peru. World Dev 29(30):497–508 Gang I, Sen K, Yun M-S (2013) Is caste destiny? Occupational diversification among Dalits in rural India. University of Manchester, BWPI Working Paper 162 Ito T, Kurosaki T (2009) Weather risk, wages in kind, and the off-farm labor supply of agricultural households in a developing Country. American J Agricul Econ 91(3):697–710 Jayaraj D (1996) Structural transformation of the rural workforce in Tamilnadu: an analysis of the impact of sociological factors. Mimeo, Madras, Madras Institute of Development Studies Joshi PK, Gulati AA, Birthal PS, Twari L (2003) Agriculture diversification in South Asia: pattern, determinants and policy implications. Discussion paper no. 57.Market structure studies division. International Food Policy Research Institute. Washington D.C. Kijima Y, Lanjouw P (2005) Economic diversification and poverty in rural India. Indian J Labour Econ 48(2) Kinsley B, Burger K, Gunning JW (1998) Coping withdrought in zimbabwe: survey evidence on responses of rural households to risks. World development 26(1):89–110 Minot N, Epprecht M, Anh TTT, Trung LQ (2006) Income diversification in the northern uplands of Vietnam: Research Report No.145. International Food Policy Research Institute, Washington D.C. Morduch J (1995) Income smoothing and consumption smoothing. J Econ Perspect 9(3):103–114 Pandve HT, Patil DY (2009) India’s national action plan on climate change. Indian J Occupational Environ Med 13(1) Raghuvanshi R, Ansari MA (2017) A study of farmers’ awareness about climate change and adaptation practices in India. Young (Less than 45) 45:40–90 Rani U, Shylendra HS (2002) Occupational diversification and rural-urban migration in India: a review of evidence and some issues for research. J Social Econ Develop Sultana N, Ghosh ME, Islam MK (2015). Income diversification and household well-being: a case study in rural areas of Bangladesh. Int J Business Econ Res 4(3):172–179. https://doi.org/10. 11648/j.ijber.20150403.20

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Thomas DSG, Twyman C, Osbahr H, Hewitson B (2007) Adaptation to climate change and variability: farmer responses to intra-seasonal precipitation trends in South Africa. Clim Change 2007(83):301–322 Unni J (1994) Inter-regional variations in non-agricultural employment in rural India: an exploratory analysis. In: Visaria P, Rakesh B (eds) Non-agricultural employment in india: trends and prospects, New Delhi, Sage Publications, pp 289–329

Chapter 6

Policy Recommendations

This study, based on a sample survey from Odisha, focused on the following questions: What are the different sources of livelihood and what kind of activities that the rural households are engaged in? What is the level of dependence on different activities of these households? What are the levels of diversification within a livelihood source and between livelihood sources, what factors are accountable for it, and since when such diversification was initiated? What are the impacts of climatic stress (water scarcity, droughts, heatwaves, etc.) on the levels of livelihood diversification by rural households? What is the impact of livelihood diversification on the well-being of the household in terms of levels of income, seasonality of income and food consumption, reduction of risk, vulnerability to climate change-induced poverty, and intra-household gender inequality? What are the impacts of interventions addressed towards the adaptation to climate change on the livelihood diversification and well-being of the households? After setting the perspective with a detailed review of the literature, a description of the Odisha economy is presented. This is followed by the sample description and a brief profile of the households covered under the survey. The differences in the geographic patterns and the behavioural pattern tend to suggest certain climate change effects, though it is only a cross-sectional study. The findings summarized in each chapter are only suggestive. Considering the most vulnerable space/region as the future image of an area which is climatically affected but relatively better off at present, the conclusions may be read to visualize the deterioration process in the absence of interventions. The interventions can be viewed at two different levels: individual or household and government. At the micro-level (individual or household), diversification of livelihood is a consecrated effort though the limitations can be enormously large which in turn call for major interventions by the government in order to develop mitigating strategies. The results indicate that people have mix perception of climate change. However, around 40% of the respondents claimed that there has been an increase in the average annual temperature in the last 20 years, which is consistent with the change in the temperature as suggested by India Meteorology Department. The perception © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 A. Mitra et al., Climate Change, Livelihood Diversification and Well-Being, SpringerBriefs in Economics, https://doi.org/10.1007/978-981-16-7049-7_6

85

86

6 Policy Recommendations

of precipitation trend was found stronger than that of temperature trend as about 66% respondents said that there has been a decline in average annual rainfall in the last 20 years and it became more erratic than it was earlier. Though few rural households informed that they have adopted some strategies like change in planting and harvesting dates, change in crop varieties, crop shifting and change in occupation to curb the adverse effect of climate change, passive adaptation to climate change was observed in the study area. Lack of money and lack of information were observed as the major constraints impeding climate change adaptation in the study region. Diversification in terms of a greater number of activities is seen to be widely practised strategy. It is noted that the age of the household head has a nonlinear effect on the number of activities a household performs. With increasing age, the number of activities increases first and then decreases as indicated by the negative sign in the age squared variable. There is no relation between the gender of the household head and the diversification level of the household. There is a slight decrease in a number of activities as education increases from illiterate to informal literacy, and then to primary and middle school, but there is no relation with higher education and number of activities performed by the household. Compared to ST households, SC, OBC, and other caste households are involved in a smaller number of activities. The area owned by the household and the number of activities are not related. But, the percentage of land irrigated is positively related to a number of activities performed by the household. One important aspect of well-being is consumption; thus by focusing on consumption changes over time and relating it to livelihood diversification, we may comprehend its nature. Diversification adopted in the face of compulsion and in a situation of stagnancy without many prospects may result in a range of residual or low productivity activities, whereas diversification as an attempt to explore newer pathways in a vibrant situation to reduce income risks and smooth consumption can be highly beneficial. A very large percentage of households seem to be lying below the minimum rural threshold level of 2400 kcal both in the present and in the past. It may be further noted from the past and present calorie consumption that a very large percentage remained by and large in the same calorie size class. Less than one-tenth of the households registered a downward shift, while nearly 20% experienced upward mobility. However, remaining in the same calorie size class does not mean that households at the individual level did not register any change. In terms of quantity per capita, however, there has been an improvement in the weekly consumption of rice. Even in the case of wheat, pulses, and vegetables, there has been a marginal increase. Consumption changes perceived as a prerequisite of well-being change seem to be responsive to the individual efforts pursued through income augmenting/smoothing strategies which might have been perceived through livelihood diversification. Instead of remaining idle in the slack seasons, the households seem to have explored other avenues of earning. Though the livelihood diversification does not turn out to be significant as such, its effect is captured through the income variable. The responses tend to vary across religious and caste categories. Better-off households perceived through housing conditions seem to respond better compared to households

6 Policy Recommendations

87

with poor living conditions. Large households are more likely to adopt consumption augmenting strategies, as for them the compulsions could be alarming and adoption might have been easier: as some members could be deputed to the marginal activities, the head can be more selective in picking up the gainful opportunities. Land ownership does not turn out to be significant possibly because many of the sample households do not own land to any sizeable extent. Based on the above findings, the study proposes strong and multiple interventions from the government and multilateral agencies to equip the poor people of Odisha to face climate challenges. The following policy suggestions are advised. • Odisha, popularly known as disaster capital of India, is strongly affected by climate change, but most of the rural poor seem to be unaware of it. Information on the variability of climatic variables and their possible impacts need to be provided to people. Awareness helps, and knowing the changes in weather pattern well can induce people to undertake self-initiated private adaptations. Proper information may turn the passive adaptors to active adaptors and minimize the adverse impacts of climate change on livelihood. • Like awareness, financial constraints are stopping people to take up adaptation activities. Provision of easy institutional finance for rural livelihood adaptation activities should be promoted by government and public sector financial institutions. There being a demand for such help, tying financial provision to certain water-saving activities can promote water conservation and induce climate-friendly behaviour. • Livelihood diversification of the sample households shows a picture of distress diversification as too many activities have not resulted in increased income or increased consumption. Without any gainful employment opportunities, people are doing too many things to supplement their income. Providing opportunities like MGNREGA for a longer duration can be helpful. Penetration of MGNREGA work among the sample households is very marginal implying the government interventions to be too inadequate. • More than 50% of the sample households seem to consume less than 2400 kcal per unit per day, the minimum requirement as defined by the poverty line. This falsifies the claim of only 33% being under the poverty line in the state. Income supplementing and consumption augmenting schemes need to be initiated by the government departments. • Irrigation facility comes out as a strong facilitating factor for livelihood diversification. Providing such facilities and encouraging water use efficiency can help people cope with climate stress. • For the non-farm sector to provide gainful opportunities, rural industrialization has to take place in a significant manner. Agro-based industries may provide productive channels for employment creation. • Rural construction and irrigation programmes can also be beneficial for livelihood creation.

Appendix A

Snapshot of Pilot Visit to Selected Villages in Odisha (April and June 2017)

S. No.

Village

Block

District

HH size

Dominant cast

Major occupation

Major issues in livelihood diversification and climate change

1

Bhalumunda

Kamakhya Nagar

Dhenkanal

90–100

ST, SC

Agriculture, NTFPs collection, wage labour

The current livelihood opportunities (farming and forestry) were not self-sufficient. Climate change and lack of access to the appropriate market were having an impact in terms of reduced levels of productivity. NTFPs collection substitutes their agriculture-based livelihood. However due to deforestation, the per-head collection has also come down since last few years, which has further led to a vulnerable condition for many of the poor HHs in the village

2

Malapura

Kamakhya Nagar

Dhenkanal

200–300

SC, OBC

Agriculture, NTFPs collection, wage labour, petty trade

Though migration was not considered prestigious/enjoyable livelihood activity, households felt that if they do get any opportunity to work outside the village, they will migrate outside the village. Alcohol consumption is a major issue. Village women’s organization is effective and has managed to reduce alcohol consumption. More women should participate and turn up for palli sabha meetings. If the women’s organization can be made more effective in creating livelihood opportunities (such as preparation of food products and tailoring), it would contribute substantially to well-being of the population. This can be treated as a climate-change-livelihood-threat-mitigating strategy

3

Guriyapada

Dhamanagar

Bhadrak

100

OBC, SC

Agriculture, wage labour, petty trade

Sharecropping continues for four months in a year, and during the rest of the eight months, they look for labour work which is available on an average for 8 days a month. Floods and adequate road facility are endemic problems. Due to climate change, paddy production over time has declined sizably over time. Pulse (like mung and biri) cultivation has almost stopped for shortage of water. Many young people have migrated out to cities outside Odisha. Even the educated youngsters are engaged in labour-type work after the migration (continued)

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 A. Mitra et al., Climate Change, Livelihood Diversification and Well-Being, SpringerBriefs in Economics, https://doi.org/10.1007/978-981-16-7049-7

89

90

Appendix A: Snapshot of Pilot Visit to Selected Villages in Odisha (April and June 2017)

(continued) S. No.

Village

Block

District

HH size

Dominant cast

Major occupation

Major issues in livelihood diversification and climate change

4

Piteipur

Dhamanagar

Bhadrak

500

OBC, general

Agriculture, wage labour, service/salary

The main source of livelihood is cultivation (sharecropping). Women were engaged earlier in the field, but presently most of them are out except a few engaged in sharecropping. The land fertility has declined significantly. Labour demand has decelerated because of mechanization though it has benefitted people otherwise. Hardly any household uses plough any more. Growing vegetable is a nominal activity except for self-consumption only. Shortage of water is the main problem, which has aggravated after the ‘pani panchayat’ was created. Even those who grow paddy do not take the trouble of making rice. They sell the paddy and buy rice in return. Livestock growing is not popular as there is a shortage of grazing field. Due to rise in temperature and heat, cattle death is prevalent

5

Koknara

Paikmal

Bargarh

200

ST, SC

Petty agriculture, wage labours, NTFPs collection

Agriculture is the main occupation for wage labour (the most pursued alternative). Both men and women go for labour work to nearby Paikmal town during the non-agriculture season. The village has not faced any drought though the nearby villages have faced no rain for many years. The village gets rain, there is a natural canal from the hills, and they definitely get one crop every year though some years have less rain. They follow the same agricultural pattern as their father/grandfathers, but now they use HYV seeds and pesticides, fertilizers, etc., and get high levels of production. Livestock holding has gone down, and people sell them as pasture area was not available. Selling has stopped after the ban. Temperature is rising every year, and they consider local factors like road construction, deforestation being responsible. Corrupt forest official promotes monoculture planting which is destroying the quality of the forest. They do not even allow the villagers for dry wood collection from forests (continued)

Appendix A: Snapshot of Pilot Visit to Selected Villages in Odisha (April and June 2017)

91

(continued) S. No.

Village

Block

District

HH size

Dominant cast

Major occupation

Major issues in livelihood diversification and climate change

6

Brahmandihi

Padampur

Bargarh

280

OBC, SC

Petty agriculture, wage labour (seasonal migration), NTFPs collection, petty trade, livestock rearing

Main occupation is bamboo work, and agriculture is marginal. They buy bamboo from nearby forest. Most SC and STs do bamboo work, and some Meher community (OBC) do cloth/saree making. Bamboo is getting scarce due to deforestation, and people have to travel far in the forest to get bamboo: the price of bamboo is increasing. Maximum people migrate to areas outside Odisha for labour work, mostly in brick kilns. Most of them do manual labour like carrying bricks as they lack the skill of brick making. Contractor gives them advance money and takes them on contract, many of them are tortured, one person died as he did not get timely care, and many such heart touching stories can be heard from villagers. All have MGNREGA job card but are never given timely payment to their a/c. Many feel the earlier middlemen payment system was good as they were getting money in time

7

Damapala

Khariar

Nuapada

200

OBC

Agriculture, wage labour, service/job

Most farmers do labour work in Khariar town during non-agriculture period. No outside migration. Farmers feel difficulty due to climate change, especially delayed monsoon. Since the draught of 1965, the volume of rain is going down continuously. Agriculture is being delayed and that is lowering productivity. No return without the use of fertilizers and pesticides. Indigenous variety of paddy is riskier as they take more time to mature, whereas HYV varieties mature quickly. The area is an onion belt, so having cold storage will help farmers a lot. Just having an irrigation facility can increase farm income quickly. Used to grow sugar cane, millets before, not now. Groundwater has high fluoride content, but can be used for agriculture. Now life is better due to facilities like hospital, PDS, etc., but alcoholism is increasing. Nearly 70–80% of men including young children drink. They seem to be getting paid for MGNREGA work timely, no complaint against officers

8

Tanwat

Nuapada

Nuapada

350

ST

Agriculture, wage labour, NTFPs collection

Single crop agriculture during kharif season and then labour work for rest of the season like construction or mason work in Nuapada town. Many also go for brick kiln work in UP, MP, etc. Contractors come and recruit people. People used to grow millets, chana (grams), many pulses earlier, but now only paddy. They prefer doing labour work rather than growing these crops. Many have diverted from agriculture to construction work. Women SHGs do fish farming and other income-generating activities. Reduced rain is causing pest infection in paddy, and increased temperature is causing low growth and death of fish. No adaptation so far

Appendix B

Sample at a Glance

Here, we try to provide a brief outline of the surveyed households. The four districts we have covered are Dhenkanal, Angul, Bargarh, and Sambalpur with two blocks from each of the districts.

Socio-economic Profile Most of the sample population in the Dhenkanal district belongs to SC or OBC category, whereas the majority of the sample population surveyed in Angul district is found in the ST category. In the Bargarh district, while the sample population in the Paikamal block predominantly belongs to ST category, the sample population in the Gaisilet block has a diverse representation from SC, ST, and OBC. Lastly, in Sambalpur district, the sample population in the Kuchinda block has primary representation from ST category and in the Jujomura block, just like in Gaisilet block, there is a diverse representation from SC, ST, and OBC in the surveyed population. On the whole, most of the sample from Angul comes from the tribal population, while in Bargarh and Sambalpur both SC and ST comprise a large majority of the sample. Though to begin with the general category does not have any significant presence in either of the districts, OBC is the other social category which figures out in all the three districts except Angul. In fact, in the two blocks in Dhenkanal around 50 and 40% are from SC cum ST categories. The average household size is by and large the same across all the blocks except in Paikmal where it is slightly above 4 and Kuchinda with a figure slightly lower than 3.5. There are single-member households, and the maximum household size is as large as 9 in 4 blocks. The sample sex ratio tends to vary considerably across the blocks. Kuchinda registered the highest ratio, the number of females exceeding the males. On the other hand, the sample from Gaisilet has the least sex ratio among all the eight blocks. It may be noted that Gaisilet belongs to a relatively worse district that is Bargarh, © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 A. Mitra et al., Climate Change, Livelihood Diversification and Well-Being, SpringerBriefs in Economics, https://doi.org/10.1007/978-981-16-7049-7

93

94

Appendix B: Sample at a Glance

Table 1 Distribution of sample households across different categories District

Block

Dhenkanal Hindol

No of observation SC

OBC Others Total ST

22

73

86

8

189

11.6 38.6 45.5

4.2

100

44

66

1

113

1.8 38.9 58.4

0.9

100 100

Kamakhya 2 Nagar Angul Bargarh

% to total

ST

SC

OBC Others Total

Pallahara

155 11

0

0

166

93.4

6.6

0.0

0.0

Athmallik

130 4

0

0

134

97.0

3.0

0.0

0.0

100

Paikmal

106 11

44

0

161

65.8

6.8 27.3

0.0

100

56

35

35

13

139

40.3 25.2 25.2

9.4

100

Sambalpur Kuchinda

Gaisilet

90

19

32

2

143

62.9 13.3 22.4

1.4

100

Jujomura

49

59

47

0

155

31.6 38.1 30.3

0.0

100

Total

610 256 310

24

1200 50.8 21.3 25.8

2.0

100

Source Field Survey, 2018

while Kuchinda is located in Sambalpur a relatively better district in the droughtprone area in the western region. Similarly, the other block from Sambalpur that is Jujomura shows a higher sex ratio than that in Paikmal from Bargarh district. It is quite possible that within the drought-prone area the better district registers a higher rate of male outmigration compared to the worse-off district. But as we will see later, the outmigration is almost negligible in the sample. In the flood-prone area, Dhenkanal and Angul do not show such a unique difference. Kamakhya Nagar in Dhenkanal district is relatively better than the other block, Hindol as the former is closer to the River Brahmani, and incidentally, Hindol shows a lower sex ratio (794). On the whole, distressed led migration does not seem to have been a prevalent phenomenon. On the other hand, in relatively worse-off places a lower sex ratio may be attributed to a higher female mortality rate. A higher sex ratio in the age bracket 30–44 with a greater number of females than males is usually indicative of male outmigration. But given that outmigration is almost absent, this comes as a big surprise. Whether it reflects on alcoholism and higher mortality among the males or it is related to dislocation of adult males to nearby areas in search of jobs but could not be seen by the respondents as out-migration, are some of the questions that remain unresolved. The age structure across blocks does not unfold any major variation except in Athmallik (Angul district) where the percentages of sample population below 5 years and above 60 years are very low (around 1 and 4%, respectively, contrasting 5 and 9% as the sample average) and the percentage of the population in the age cohort 30–44 is much higher (31% in comparison with 24% as sample average). The incidence of illiteracy shows considerable variations across blocks. Kamakhya Nagar from Dhenkanal, Paikmal from Bargarh, and Jujomura from Sambalpur are on the higher side. Similarly, the category of literate but below primary and primary shows significant variation (Tables 1, 2, 3, 4, 5, 6, and 7),

Appendix B: Sample at a Glance

95

Table 2 Distribution of population across different social categories District

No. of observation Block

Dhenkanal Hindol

Angul Bargarh

ST

SC

% to total

OBC Others Total ST

SC

OBC Others Total

94

277 330

31

732

12.8 37.8 45.1

4.2

100

Kamakhya 13 Nagar

168 243

4

428

3.0 39.3 56.8

0.9

100

Pallahara

573

47

0

0

620

92.4

7.6

0.0

0.0

100

Athmallik

482

12

0

0

494

97.6

2.4

0.0

0.0

100

Paikmal

468

51

150

0

669

70.0

7.6 22.4

0.0

100

219

136 134

47

536

40.9 25.4 25.0

8.8

100

Sambalpur Kuchinda

Gaisilet

299

61

10

485

61.6 12.6 23.7

2.1

100

Jujomura

166

243 178

0

587

28.3 41.4 30.3

0.0

100

Total

2314 995 1150 92

4551 50.8 21.9 25.3

2.0

100

115

Source Field Survey, 2018

Table 3 Average household size in different sample blocks District

HH size

N

Mean

Std. Dev

Min

Max

Dhenkanal

Hindol

189

3.87

1.29

1

9

Kamakhya Nagar

113

3.79

1.37

1

8

Angul

Pallahara

166

3.73

1.15

1

7

Athmallik

134

3.69

1.14

1

7

Paikmal

161

4.16

1.60

1

9

Gaisilet

139

3.86

1.33

1

9

Kuchinda

143

3.40

1.33

1

7

Jujomura

155

3.79

1.42

1

9

Total

1200

3.79

1.35

1

9

Bargarh Sambalpur

Source Field Survey, 2018

The percentage of the population in the workforce tends to be sizeably different between the two districts in the flood-prone areas. Both the blocks in Dhenkanal reported a much higher work participation rate compared to Angul, lying much below the average figure. On the other hand, two blocks from two districts in the drought-affected area reported an estimate of much higher than the sample average. Cultivators and agricultural labour account for much of the workforce though in Angul district both the blocks show a considerably smaller number in absolute terms, explaining lower participation rates. The category of non-farm labour which is next in the line is on the low side in Kamakhya Nagar in Dhenkanal and Paikmal and Gaisilet in Bargarh, while both the districts are relatively worse off. Incidentally, outmigration is almost negligible in the entire sample.

96

Appendix B: Sample at a Glance

Table 4 Sex ratio among the sample population District

Block

Male

Female

Total

Sex ratio

% Male

% Female

% Total

Dhenkanal

Hindol

408

324

732

794

55.74

44.26

100

Kamakhya Nagar

225

203

428

902

52.57

47.43

100

Angul

Pallahara

339

281

620

829

54.68

45.32

100

Athmallik

273

221

494

810

55.26

44.74

100

Bargarh

Paikmal

365

304

669

833

54.56

45.44

100

Gaisilet

304

232

536

763

56.72

43.28

100

Kuchinda

237

248

485

1046

48.87

51.13

100

Jujomura

297

290

587

976

50.60

49.40

100

Total

2448

2103

4551

859

53.79

46.21

100

Sambalpur

Source Field Survey, 2018 Table 5 Age-group-wise sex ratio age_cat

Male

Female

Total

Sex ratio

Male

Female

Total

74

31

24

55

774

56.36

43.64

100

Total

2448

2103

4551

859

53.79

46.21

100

Source Field Survey, 2018 Table 6 Demography of study blocks District

Block

74

Total

Dhenkanal

Hindol

4.8

12.6

29.6

22.5

19.7

9.7

1.1

100

Kamakhya Nagar

6.8

13.3

29.9

24.5

16.4

7.9

1.2

100

Angul

Pallahara

5.0

13.7

29.2

25.2

20.2

6.6

0.2

100

Athmallik

1.0

10.9

34.8

31.2

18.4

3.6

0.0

100

Paikmal

6.7

14.8

30.8

22.4

15.8

8.2

1.2

100

Gaisilet

7.6

14.2

29.7

21.5

17.7

8.8

0.6

100

Kuchinda

5.8

15.9

26.6

22.3

15.9

9.9

3.7

100

Jujomura

4.9

12.6

32.2

23.5

16.7

8.0

2.0

100

Total

5.3

13.5

30.3

24.0

17.7

7.9

1.2

100

Bargarh Sambalpur

Source Field Survey, 2017

4.3

3.7

3.7

12.8

17.1

9.9

14.6

15.2

2.3

1.6

1.0

100

Lit_EGS

Lit_TLC

Lit_oth

Bel_pri

Primary

Middle

Secondary

Hr_secondary

Diploma

Graduate

Postgraduate

Total

Source Field Survey, 2018

13.8

Dhenkanal

Hindol

N_lit

Level

100

0.0

0.0

1.0

10.0

13.8

6.5

12.5

17.5

3.8

1.8

6.3

26.8

Kamakhya

Table 7 Education status in study blocks

100

0.0

0.3

13.1

14.9

4.4

9.3

7.0

16.8

1.5

1.2

17.5

13.9

Angul

Pallahara

100

0.2

0.0

10.2

18.4

3.9

13.9

4.7

25.2

1.0

0.2

9.4

12.9

Athmallik

100

0.0

0.4

2.8

9.3

18.6

11.3

14.7

17.8

4.4

3.2

2.4

14.9

Bargarh

Gaisilet

100

0.0

4.5

0.3

8.7

16.5

8.7

29.3

2.9

1.0

0.8

1.4

26.0

Paikmal

100

0.7

6.1

1.1

16.6

19.0

11.2

8.8

17.7

3.7

2.2

6.1

6.8

Sambalpur

Kuchinda

100

0.0

5.2

0.5

11.8

22.4

4.8

11.8

14.3

2.2

1.6

0.9

24.4

Jujomura

100

0.3

2.3

4.0

13.1

14.1

9.4

13.8

15.0

2.6

1.9

6.0

17.4

Total

Appendix B: Sample at a Glance 97

98

Appendix B: Sample at a Glance

The relatively smaller number of workers in both the blocks of Angul can be explained in terms of poor land possession. Even in Sambalpur, less than 50% of the households seem to have land possession. While analysing the livelihood sources in the last 365 days of the surveyed population, it is observed that in both the surveyed blocks of Dhenkanal district, Hindol and Kamakhya Nagar, cultivation is the primary activity. The surveyed population in Pallahara block in Angul district majorly depends upon livestock as a source of livelihood. On the other hand, the surveyed population in Athmallik block in Angul district is engaged in non-agricultural enterprises. In both Paikmal and Gaisilet blocks in Bargarh, the majority of the surveyed population is involved in either cultivation or livestock. On the contrary, the surveyed blocks of Kuchinda and Jujomura in Sambalpur district show a different trend: most of the population in these two blocks are engaged in wage/salaried employment. However, the gap between the population that is involved in cultivation or livestock and the population engaged in wage employment is not so prominent as it is observed in other surveyed blocks (Tables 8, 9, and 10).

Sources of Irrigation Tube well is observed to be the primary source of irrigation in all the surveyed blocks apart from Jujomura in Sambalpur district where people depend on canal, pond, or river for water accessibility (Table 11).

Housing Structure and Sources of Drinking Water Most of the houses in the sample in all the four surveyed districts were found to be ‘katcha’. This not only highlights their vulnerability but also emphasizes the lack of accessibility to government schemes such as PMAY-G. Under some of these schemes, the rural households are entitled to receive support from the government in order to invest in housing. However, there are certain trade-offs in the sense that after investing in housing the same households may not remain entitled to BPL benefits. The fact that a large majority of the sample households live in katcha houses tends to suggest that our sample comprises households from the lower rungs. This seems to be appropriate for an intensive study on climate change effect because those at the lower echelons can unfold the adversity of the highest order. As regards drinking water, tap and tube well are seen to be the major sources. Though in the flood-affected and drought-prone areas the relatively better districts (Angul and Sambalpur) show a somewhat higher percentage of households with access to tap water compared to the other two districts in the respective regions (Dhenkanal and Bargarh), the worse district in the drought-prone area reported a much higher percentage of households with tap water accessibility than the relatively

732

47.13

Total

% Work

Source Field Survey, 2018

387

Other = non-work

0

RUMigration

0

0

Nonfarm_SE

0

70

Nonfarm_lab

MGNERGA

25

Oth_agri

Salaried

147

3

Collection

0

Dairy

Agri_lab

100

Dhenkanal

Hindol

Cultivation

Now

43.46

428

242

0

5

0

2

22

5

1

79

2

70

Kamakhya

Table 8 Occupation pattern in study blocks

30.00

620

434

1

2

2

5

72

2

2

59

1

40

Angul

Pallahara

31.17

494

340

1

4

0

11

85

0

1

20

0

32

Athmallik

54.41

669

305

0

1

0

0

10

14

0

33

1

305

Bargarh

Paikmal

42.91

536

306

0

1

2

2

45

14

3

20

0

143

Gaisilet

49.90

485

243

1

1

0

4

82

0

1

55

0

98

Sambalpur

Kuchinda

46.68

587

313

0

1

0

19

140

0

1

26

0

87

Jujomura

43.53

4551

2570

3

15

4

43

526

60

12

439

4

875

Total

Appendix B: Sample at a Glance 99

100

Appendix B: Sample at a Glance

Table 9 Land possession scenario in study blocks Block

Yes

No

Total

% Yes

Dhenkanal

Hindol

186

3

189

98.4

Kamakhya Nagar

102

11

113

90.3

Angul

Pallahara

60

106

166

36.1

Athmallik

43

91

134

32.1

Paikmal

160

1

161

99.4

Gaisilet

124

15

139

89.2

Kuchinda

68

75

143

47.6

Jujomura

69

86

155

44.5

Total

812

388

1200

67.7

Bargarh Sambalpur

Source Field Survey, 2018

better district in the flood-affected region. Since groundwater is more difficult to access in the drought-prone areas and digging tube wells can be highly expensive, a larger dependence on tap water is an obvious choice (Tables 12 and 13).

Income Pattern The table above shows the distribution of principal income sources in the percentage of the total sample population in the respective block. In Dhenkanal district, in both the surveyed blocks Hindol and Kamakhya Nagar, the principal source of income comes from cultivation. It is also observed that almost one-fourth of the sample population in Hindol depends upon pension as their primary source of income. In contrast to Dhenkanal, Angul district shows a diverse distribution of income sources, primarily in the baskets of cultivation and non-agricultural enterprises. Among all surveyed blocks, Athmallik block in Angul district shows the highest number of people engaged in non-agricultural enterprises for their principal source of income. In Bargarh district, sample population in both the surveyed blocks Paikmal and Gaisilet is observed to be occupied in cultivation and livestock for their principal income. Furthermore, in Sambalpur district, wage employment, followed by cultivation, is the most frequent principal source of income. Livestock as a source of income has been reported by a significant percentage of households in both the blocks of Bargarh. If we presume that the drought-prone areas are more precarious than the flood-affected areas, then Bargarh can be treated as the most vulnerable district among the four. Since agriculture in this district is unlikely to provide a stable source of livelihood and non-agricultural activities may not have expanded in a significant manner, it is quite likely that the rural population had to take recourse to livestock as a major source of income (Table 14).

62.04

Kamakhya Nagar

21.19

20.77

Kuchinda

Jujomura

45.85

Gaisilet

20.77

43.02

Athmallik

Paikmal

22.51

Pallahara

Source Field Survey, 2018

Sambalpur

Bargarh

Angul

72.38

Hindol

Dhenkanal

Cultivation

23.94

28.81

33.55

39.39

18.84

40.22

24.82

9.52

Livestock

0.70

0.00

9.30

1.40

1.45

0.74

2.19

0.00

Other agriculture activities

19.37

10.93

0.33

1.12

38.65

11.81

0.00

0.00

Non-agriculture enterprises

Livelihood activities performed in the last 365 days (in percentage)

Blocks

District

Table 10 Livelihood pattern during last one year in study blocks

29.93

33.44

8.31

10.61

0.97

2.58

8.76

0.00

Wage/salaried employment

4.23

5.63

2.66

3.91

0.48

0.74

2.19

18.10

Pension

0.00

0.00

0.00

0.28

0.48

0.00

0.00

0.00

Remittances

1.06

0.00

0.00

0.28

18.36

21.40

0.00

0.00

Others

Appendix B: Sample at a Glance 101

102

Appendix B: Sample at a Glance

Table 11 Sources of irrigation District

Blocks

Source of irrigation Canal/river/pond

Tube well

Others

Hindol

0

14

0

Kamakhya Nagar

0

7

0

Angul

Pallahara

1

53

0

Athmallik

8

29

0

Bargarh

Paikmal

1

0

0

Gaisilet

13

50

1

Kuchinda

0

2

0

Jujomura

38

0

0

Dhenkanal

Sambalpur

Rainfed

Source Field Survey, 2018

Table 12 Housing structure in study blocks District

Block

No. of observations

%

Katcha Semi-pucca Pucca TOTAL Katcha Semi-pucca Pucca Total Dhenkanal Hindol

147

Kamakhya 94 Nagar Angul Bargarh

35

7

189

77.8

18.5

3.7

100

9

10

113

83.2

8.0

8.8

100

Pallahara

84

37

45

166

50.6

22.3

27.1

100

Athmallik

48

72

14

134

35.8

53.7

10.4

100

Paikmal

135

25

1

161

83.9

15.5

0.6

100

Gaisilet

86

15

38

139

61.9

10.8

27.3

100

Sambalpur Kuchinda

118

9

16

143

82.5

6.3

11.2

100

Jujomura

77

17

61

155

49.7

11.0

39.4

100

Total

789

219

192

1200

65.8

18.3

16.0

100

Source Field Survey, 2018

Present Annual Income in INR In terms of present annual income, the Jujomura block of Sambalpur and Kamakhya Nagar of Dhenkanal are seen to comprise a sizeable percentage of households (around 10%) at the lowest size class. Similarly, in one of the top size classes 48,000–960,000 the share of households in these two blocks—in addition to Kuchinda the other block from Sambalpur—is on the low side. Hence, though the district indicators suggest that Bargarh is worse-off compared to Sambalpur in the drought-prone area, the distribution of sample households across income classes tends to unfold a different picture. Hindol from Dhenkanal and Paikmal from Angul reported almost similar percentage shares in the lowest and one of the top size classes. The difference in the distribution over time suggests that there has been an improvement: most of

Appendix B: Sample at a Glance

103

Table 13 Sources of drinking water in study blocks District

Block

Dhenkanal Hindol

Bargarh

Tube Well_prot Spring_un Rainwater SurfaceWa Total well

8.5 89.9

1.6

0.0

0.0

0.0

100

2.7 97.3

0.0

0.0

0.0

0.0

100

Pallahara

13.9 78.9

0.6

6.6

0.0

0.0

100

Athmallik

32.1 67.2

0.0

0.7

0.0

0.0

100

Paikmal

51.6 48.4

0.0

0.0

0.0

0.0

100

Gaisilet

48.9 47.5

1.4

0.0

2.2

0.0

100

Kamakhya Nagar Angul

Tap

Sambalpur Kuchinda

100.0

0.0

0.0

0.0

0.0

0.0

100

Jujomura

93.5

1.3

1.3

0.0

0.6

3.2

100

Total

43.7 53.9

0.7

1.0

0.3

0.4

100

Source Field Survey, 2018

the entries in the lowest size class are negative and in the top size classes positive (Tables 15 and 16).

Various Government Schemes The table below shows the enrolment of the sample population into various government welfare schemes. Apart from Gaisilet block in Bargarh and Kamakhya Nagar block in Dhenkanal, every other surveyed block has more than 100 sampled households that possess an MGNREGA job card, helping them find employment. The same trend can be observed regarding the possession of ration card and Aadhar card. Most of the ration cardholders in the surveyed population have a ration card meant for BPL category. Angul district also has a large section of the sampled population that possess a Soil Health card. These indicators tend to suggest that our selection of the sample seems appropriate: we are able to include the low-income households on which the climate change impact can be severe given their vulnerability in terms of lack of resources to adopt mitigating strategies. Possibly because of these interventions, the worse districts are able to record better income distribution as mentioned above (Table 17).

85.71

Kamakhya Nagar

41.38

35.26

Kuchinda

Jujomura

52.92

Gaisilet

28.36

44.02

Athmallik

Paikmal

48.48

Pallahara

Source Field Survey, 2018

Sambalpur

Bargarh

Angul

64.79

Hindol

Dhenkanal

Cultivation

1.92

2.76

24.90

41.11

8.21

12.12

8.16

9.86

Livestock

1.28

0.00

10.89

1.46

2.24

1.01

1.02

0.00

Other agriculture activities

Principal income source (in percentage)

Blocks

District

Table 14 Average income from different sources

11.54

0.00

0.00

1.17

58.21

31.31

0.00

0.00

Non-agriculture enterprises

46.79

51.72

8.95

8.45

1.49

7.07

2.04

0.00

Wage/salaried employment

3.21

4.14

2.33

3.50

0.75

0.00

3.06

25.35

Pension

0.00

0.00

0.00

0.29

0.75

0.00

0.00

0.00

Remittances

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

Others

104 Appendix B: Sample at a Glance

Appendix B: Sample at a Glance

105

Table 15 Distribution of sample households (present income) Block

96,001 Total

Dhenkanal Hindol

3.2

13.9

38.1

44.2

0.6

100

Kamakhya

9.3

34.1

44.5

11.5

0.5

100

Paikmal

3.8

26.3

28.3

41.6

0.0

100

Athmallik

1.3

2.0

58.9

35.1

2.6

100

Pallahara

0.6

3.4

63.7

27.9

4.5

100

Gaisilet

1.8

3.2

46.8

47.7

0.5

100

Sambalpur Kuchinda

5.7

43.7

43.7

6.1

0.8

100

Jujomura

10.6

23.2

53.2

12.7

0.4

100

4.7

20.0

44.9

29.4

1.0

100

Angul Bargarh

Total Source Field Survey, 2017

Table 16 Distribution of sample households (past income) Block Dhenkanal Hindol

96,001 Total 3.2

21.2

47.2

28.0

0.3

100

Kamakhya 9.9

36.3

44.0

9.3

0.5

100

Angul

Paikmal

31.7

40.8

22.8

0.0

100

Athmallik

3.0

3.0

74.4

18.8

0.8

100

Bargarh

Pallahara

1.1

7.3

65.9

22.3

3.4

100

Gaisilet

4.7

2.3

13.5

53.2

28.8

2.3

100

Sambalpur Kuchinda

7.3

55.9

32.8

3.2

0.8

100

Jujomura

8.1

32.7

51.8

7.0

0.4

100

Total

5.0

27.2

48.9

18.0

0.9

100

Source Field Survey, 2018

Source Field Survey, 2018

101

Jujomura

77 141

119

32

112

Gaisilet

141

132

153

124

132

Athmallik

Paikmal

143

Pallahara

98

Sambalpur Kuchinda

Bargarh

Angul

180

145

No. of No. of households that households that have MGNREGA have a ration card job card

Kamakhya Nagar 59

Blocks

Dhenkanal Hindol

District

Table 17 Access to different beneficiary cards

0

45

2

38

0

11

13

25

No. of households that have Kisan Credit Card

1

54

8

3

104

101

2

4

No. of households that have Soil Health card

149

143

74

132

134

164

71

160

8

0

3

10

11

58

5

35

133

119

68

129

121

95

65

137

0

0

6

2

0

0

28

8

No. of Type of ration card households where Antyodaya BPL Others each member of households has Aadhar card

106 Appendix B: Sample at a Glance

Uncited References

Hulme D, Mosley P (1996) Finance for the poor: impacts on poverty, vulnerability and deprivation. Finan Against Poverty 1:105–137 Narayan-Parker D (ed) (2005) Measuring empowerment: cross-disciplinary perspectives. World Bank Publications Arthur W (1954) Economic development with unlimited supplies of labor. Manch Sch 22(2):139– 191

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 A. Mitra et al., Climate Change, Livelihood Diversification and Well-Being, SpringerBriefs in Economics, https://doi.org/10.1007/978-981-16-7049-7

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