Mobile Phones and Development in Africa: Does the Evidence Meet the Hype? (Palgrave Studies in Agricultural Economics and Food Policy) 3031418840, 9783031418846

Table of contents :
Foreword
Acknowledgments
Praise for Mobile Phones and Development in Africa
Contents
List of Figures
List of Tables
List of Boxes
1 Introduction
References
2 Where There Was No Phone
2.1 Leapfrogging the Landline?
2.2 Digital “Divide” or “Provide”?
2.2.1 The Coverage Gap
2.2.2 The Usage Gap
2.3 Keeping up with the Joneses
2.4 A Tale of Two Countries: Digital Infrastructure and Adoption in Niger and Ghana
2.5 What Does this Mean for “Digital Development”?
References
3 The Economics of the Phone
3.1 Where There Is No Power
3.1.1 The State of Public Services in Africa
3.1.2 Market Failures in Public Service Provision
3.1.3 Market Failures, More Broadly
3.2 Jack of All Trades, Master of All? The Multitude of Uses of Digital Technology
3.2.1 Digital Technologies Can Make It Cheaper to Develop and Transport Content
3.2.2 Digital Technologies Can Reduce the Cost of Collecting, Processing, and Storing Information
3.2.3 Digital Technologies Can Reduce the Costs of Accessing Public and Private Transfers
3.3 A Framework for Analysis
3.3.1 Not All Information Is Created Equal
3.3.2 The Missing Market
3.3.3 If You Build It, Will They Come?
3.3.4 Technology for All, Benefits for…Some?
References
4 Digitizing Development?
4.1 IF You Build It, Will They Come?
4.2 Health at Your Fingertips
4.2.1 What Are the Challenges?
4.2.2 The Promise of Digital Health
4.3 Hello, Tractor! Digital Agriculture
4.3.1 The Agricultural Challenge
4.3.2 The Promise of Digital Agriculture?
4.4 Call Me Educated
4.4.1 The Learning Challenge
4.4.2 Digital learning
4.5 A Bank in Your Pocket? Digital Financial Services
4.5.1 The “Financial Exclusion” Conundrum
4.5.2 The Digital Solution?
4.6 To Infinity and Beyond? Big Data and AI
4.6.1 Using Digital Data to Measure Poverty and Target Interventions
4.7 Conclusion
References
5 (Don’t) Believe the Hype?
5.1 Texting for Health
5.1.1 Providing Health Information
5.1.2 Health Worker Training and Monitoring
5.1.3 Supply Chain Management
5.1.4 Data Collection and Monitoring
5.2 Dial “a” for Agriculture? Digital Technology and Agricultural Development
5.2.1 Agricultural Information Provision
5.2.1.1 Private Networks
5.2.1.2 Public Information Provision
5.2.2 Extension Agent and Farmer Training
5.2.3 Extension Agent Monitoring
5.2.4 Buyer-Seller Coordination and Supply Chain Management
5.2.5 Data Collection and Monitoring
5.3 ABC, 123? Digital Education
5.3.1 Teacher and Student Training
5.3.2 Monitoring Teacher Performance
5.3.3 Parent-Teacher Coordination and Information Provision
5.3.4 Data Collection and Monitoring
5.4 Zap It to Me: The Impact of Digital Financial Services
5.4.1 First-Generation DFS: The Impact of Mobile Money
5.4.1.1 Government to Person Transfers (G2P)
5.4.1.2 Other Transactions: B2P, P2B, and Taxation
5.4.2 Second-Generation DFS: Credit, Savings, and Insurance
5.5 Big Data and AI
5.6 Conclusion
References
6 Rethinking ICT4D
6.1 What Have We Learned?
6.1.1 The “Myth” of More Information
6.1.2 The Importance of Complementary Markets
6.1.3 Using a Digital Technology (Effectively) Is Not Always Simple
6.1.4 It Isn’t Always About the “Average” Effect
6.1.5 Understanding What, When, Why, and How
6.1.6 Digital May Not Always Be Sustainable…Is that Okay?
6.2 A Framework for “Digital Development”
6.3 Conclusion
References
Index

Citation preview

PALGRAVE STUDIES IN AGRICULTURAL ECONOMICS AND FOOD POLICY

Mobile Phones and Development in Africa Does the Evidence Meet the Hype?

Jenny C. Aker · Joël Cariolle

Palgrave Studies in Agricultural Economics and Food Policy

Series Editor Christopher B. Barrett, Cornell University, Ithaca, NY, USA

Agricultural and food policy lies at the heart of many pressing societal issues today and economic analysis occupies a privileged place in contemporary policy debates. The global food price crises of 2008 and 2010 underscored the mounting challenge of meeting rapidly increasing food demand in the face of increasingly scarce land and water resources. The twin scourges of poverty and hunger quickly resurfaced as highlevel policy concerns, partly because of food price riots and mounting insurgencies fomented by contestation over rural resources. Meanwhile, agriculture’s heavy footprint on natural resources motivates heated environmental debates about climate change, water and land use, biodiversity conservation and chemical pollution. Agricultural technological change, especially associated with the introduction of genetically modified organisms, also introduces unprecedented questions surrounding intellectual property rights and consumer preferences regarding credence (i.e., unobservable by consumers) characteristics. Similar new agricultural commodity consumer behavior issues have emerged around issues such as local foods, organic agriculture and fair trade, even motivating broader social movements. Public health issues related to obesity, food safety, and zoonotic diseases such as avian or swine flu also have roots deep in agricultural and food policy. And agriculture has become inextricably linked to energy policy through biofuels production. Meanwhile, the agricultural and food economy is changing rapidly throughout the world, marked by continued consolidation at both farm production and retail distribution levels, elongating value chains, expanding international trade, and growing reliance on immigrant labor and information and communications technologies. In summary, a vast range of topics of widespread popular and scholarly interest revolve around agricultural and food policy and economics. The extensive list of prospective authors, titles and topics offers a partial, illustrative listing. Thus a series of topical volumes, featuring cutting-edge economic analysis by leading scholars has considerable prospect for both attracting attention and garnering sales. This series will feature leading global experts writing accessible summaries of the best current economics and related research on topics of widespread interest to both scholarly and lay audiences.

Jenny C. Aker · Joël Cariolle

Mobile Phones and Development in Africa Does the Evidence Meet the Hype?

Jenny C. Aker Medford, MA, USA

Joël Cariolle Clermont-Ferrand, France

ISSN 2662-3889 ISSN 2662-3897 (electronic) Palgrave Studies in Agricultural Economics and Food Policy ISBN 978-3-031-41884-6 ISBN 978-3-031-41885-3 (eBook) https://doi.org/10.1007/978-3-031-41885-3 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 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 Palgrave Macmillan imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland Paper in this product is recyclable.

Foreword

Information and communications technology (ICT) has advanced incredibly rapidly around the world. Just thirty years ago, there were countries where virtually no one had any mobile phone or internet connectivity. Even middle-class households in high-income nations often lacked a mobile phone and many rural ones had no internet connection at home. Today, even in remote parts of low-income countries one can typically find a cell phone signal. The ICT revolution is partly a consequence of rapid socioeconomic change. As standards of living improve, people typically seek more convenience, which is a central feature of ICT. As socioeconomic mobility and a range of disasters have sparked a surge in migration within and among countries, the desire to maintain connectivity to loved ones left behind, even while on the move, has helped spur mobile phone uptake. As authoritarian regimes began to fall across much of the low- and middle-income world, mobile communications and social media have become central means of sociopolitical mobilization. And, as incomes rise and rural peoples increasingly integrate into commercial markets, mobile phones become ever more valuable as a financial services platform. These changes have been most evident and rapid in rural areas where ICT, in particular mobile phones, enables technology leapfrogging. Communities, indeed countries, can largely skip the prior step of installing extensive physical infrastructure to connect everyone to a landline-based communications grid, which is especially expensive in sparsely populated areas. Only a v

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relatively sparse and affordable network of cell phone towers remains essential, dramatically accelerating diffusion of mobile phone service. The harder question to answer is the extent to which the ICT revolution, in particular the rapid diffusion of mobile phones, has been a cause of rapid socioeconomic change, especially in low-income rural areas. By reducing information, search, and transaction costs that have historically impeded market participation by smallholder farmers and the reach of commercial enterprises and government services into rural areas, intuition suggests that there must be significant effects. In this volume, Jenny C. Aker and Joël Cariolle brilliantly summarize the data and evidence on the spread and impacts of mobile phones on people’s lives and livelihoods, with a particular emphasis on rural areas of low- and middle-income countries (LMICs), especially those in sub-Saharan Africa. A range of studies, several especially prominent ones by one or both of the book’s authors, have offered glimpses into the impacts of mobile phones on specific outcomes or activities in particular places. But this volume offers the first comprehensive, integrative overview on the impacts of mobile phones in the developing world. This is a major scholarly contribution. They pull together a vast literature across computer science, economics, geography, political science, public health, and sociology—much of it in “grey” outlets less easy to find or access. They describe a wide range of private and public initiatives to try to stimulate the spread of ICT and to ensure it reaches and serves the poor, not just better-off subpopulations. And they flag some of the unintended consequences of ICT rollout across Africa. Ultimately, they convincingly demonstrate both that ICT has had measurable, positive impacts across a range of areas—agricultural, education, financial services, health care, and social protection programming—but that ICT has often been overhyped, delivering somewhat less than its most fervent champions promise. Arguably the most valuable chapter of the volume is their closing one, in which Aker and Cariolle offer a framework for development practitioners, policymakers, and scholars to use in thinking about how ICT can (and cannot) be effectively deployed to advance socioeconomic development objectives. By developing a clear, thoughtful progression of questions to guide decision-makers as they consider investments to boost ICT diffusion, Aker and Cariolle offer more than merely a comprehensive and timely review of a scattered literature, they synthesize the diffuse body of research on ICT for development into a useful tool to guide further advances, both on the ground and in the research community. Bravo!

FOREWORD

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It is a pleasure to include Jenny C. Aker and Joël Cariolle’s excellent new volume in the Palgrave Studies in Agricultural Economics and Food Policy series. This insightful, beautifully written contribution to the literature is both timely and important. Christopher B. Barrett Cornell University Ithaca, USA

Acknowledgments

We have many people to thank in the preparation of this manuscript, which has been the result of many years of work with a variety of partners across several continents. Most recently, this book draws on and extends research under the “Digital Trust” chair with FERDI at the University of Clermont-Ferrand, financed by FERDI and Be-YS, the partners of the Chair. We also benefitted from the financial support of the French Agence Nationale de la Recherche under the grant “Investissement d’Avenir ANR10-LBX-14-01”. Second, the authors are grateful to the extensive research assistance of Alex David Carroll at Tufts University, as well as editorial support of Neil Ardiff, who effortlessly combined our two writing styles. Third, and most importantly, we would like to acknowledge all of the research conducted by various authors in various fields—without whom this book would not have been possible. We would also like to acknowledge the work of those who have contributed to this field, but who may not be visible (or cited) in this book. While we have attempted to conduct a thorough and rigorous review of research in this field, and narrow it down based upon “objective” criteria, unfortunately, there is no such thing as perfectly “objective” social science. We fully recognize, therefore, that the references that we cite in this book have historically been built on a small set of privileged voices. As a result, there may be both overt and covert biases in the material due to the lens with which it was written. We are, therefore, open to integrating more diverse references and perspectives for future editions of this book. ix

Praise for Mobile Phones and Development in Africa

“A must read for any development policy maker or practitioner especially if they are thinking about how and where digital technologies can add value and improve the livelihoods of the poor…Most important of all…(they) provide a framework to think about when and how digital is the solution.” —Tavneet Suri, Louis E. Seley, Professor of Applied Economics, Massachusetts Institute of Technology “ I strongly recommend this book to all researchers and students interested in this area.” —Yaw Nyarko, Professor of Economics and Director of NYU Africa House, Center for Technology and Economic Development, New York University “This is a very important book about a technology that has transformed all of our lives along multiple dimensions—mobile phones. This book cuts through the hype and hyperbole, and it provides a meaningful and theoryinformed treatment of how information technology is shaping economic development in low-income countries—as a communication device and a financial service device.” —Erwin Bulte, Professor of Development Economics, Wageningen University

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Contents

1 7

1

Introduction References

2

Where There Was No Phone 2.1 Leapfrogging the Landline? 2.2 Digital “Divide” or “Provide”? 2.2.1 The Coverage Gap 2.2.2 The Usage Gap 2.3 Keeping up with the Joneses 2.4 A Tale of Two Countries: Digital Infrastructure and Adoption in Niger and Ghana 2.5 What Does this Mean for “Digital Development”? References

9 9 14 14 18 19

The Economics of the Phone 3.1 Where There Is No Power 3.1.1 The State of Public Services in Africa 3.1.2 Market Failures in Public Service Provision 3.1.3 Market Failures, More Broadly 3.2 Jack of All Trades, Master of All? The Multitude of Uses of Digital Technology 3.2.1 Digital Technologies Can Make It Cheaper to Develop and Transport Content

29 29 29 32 34

3

21 23 24

35 38

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CONTENTS

3.2.2

Digital Technologies Can Reduce the Cost of Collecting, Processing, and Storing Information 3.2.3 Digital Technologies Can Reduce the Costs of Accessing Public and Private Transfers 3.3 A Framework for Analysis 3.3.1 Not All Information Is Created Equal 3.3.2 The Missing Market 3.3.3 If You Build It, Will They Come? 3.3.4 Technology for All, Benefits for…Some? References 4

5

Digitizing Development? 4.1 IF You Build It, Will They Come? 4.2 Health at Your Fingertips 4.2.1 What Are the Challenges? 4.2.2 The Promise of Digital Health 4.3 Hello, Tractor! Digital Agriculture 4.3.1 The Agricultural Challenge 4.3.2 The Promise of Digital Agriculture? 4.4 Call Me Educated 4.4.1 The Learning Challenge 4.4.2 Digital learning 4.5 A Bank in Your Pocket? Digital Financial Services 4.5.1 The “Financial Exclusion” Conundrum 4.5.2 The Digital Solution? 4.6 To Infinity and Beyond? Big Data and AI 4.6.1 Using Digital Data to Measure Poverty and Target Interventions 4.7 Conclusion References (Don’t) Believe the Hype? 5.1 Texting for Health 5.1.1 Providing Health Information 5.1.2 Health Worker Training and Monitoring 5.1.3 Supply Chain Management 5.1.4 Data Collection and Monitoring 5.2 Dial “a” for Agriculture? Digital Technology and Agricultural Development

39 40 42 42 44 45 46 47 57 58 60 60 61 65 65 66 70 70 71 74 74 74 79 80 81 82 91 93 100 101 102 103 104

CONTENTS

5.2.1 5.2.2 5.2.3 5.2.4

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Agricultural Information Provision Extension Agent and Farmer Training Extension Agent Monitoring Buyer-Seller Coordination and Supply Chain Management 5.2.5 Data Collection and Monitoring 5.3 ABC, 123? Digital Education 5.3.1 Teacher and Student Training 5.3.2 Monitoring Teacher Performance 5.3.3 Parent-Teacher Coordination and Information Provision 5.3.4 Data Collection and Monitoring 5.4 Zap It to Me: The Impact of Digital Financial Services 5.4.1 First-Generation DFS: The Impact of Mobile Money 5.4.2 Second-Generation DFS: Credit, Savings, and Insurance 5.5 Big Data and AI 5.6 Conclusion References

104 111 112

Rethinking ICT4D 6.1 What Have We Learned? 6.1.1 The “Myth” of More Information 6.1.2 The Importance of Complementary Markets 6.1.3 Using a Digital Technology (Effectively) Is Not Always Simple 6.1.4 It Isn’t Always About the “Average” Effect 6.1.5 Understanding What, When, Why, and How 6.1.6 Digital May Not Always Be Sustainable…Is that Okay? 6.2 A Framework for “Digital Development” 6.3 Conclusion References

155 155 155 157

Index

113 113 114 115 121 122 122 123 134 135 137 137 138

158 160 161 162 162 166 167 171

List of Figures

Fig. 2.1

Fig. 2.2

Fig. 2.3

Fig. 2.4

Fig. 2.5 Fig. 2.6

Fig. 2.7

Fig. 3.1 Fig. 3.2

Mobile phone growth in sub-saharan Africa (Source Authors. Data from ITU [2019] dataset. Plotted average values are weighted by population size) Number of mobile phone subscriptions (per 100 people) and HDI (Source Authors. Data from ITU/World Telecommunications [2020] and UNDP [2020]) Percentage of the population covered by mobile (Over time) (Source Authors. Data from ITU/World Telecommunications [2020] and UNDP [2020]) ICT market liberalization in sub-saharan Africa (Source ITU World Telecommunications/ICT Indicators Database, the World Bank) Economics of the last mile problem (Franco et al. [2019]) Mobile phone subscriptions per 100 people, by country, in sub-saharan Africa (Source ITU World Telecommunications/ICT Indicators Database, the World Bank) a. Mobile internet infrastructure rollout in Niger; b. Mobile internet infrastructure rollout in Ghana (Source GSMA-Collins Bartholomew Coverage Maps) Percentage of the population with access to electricity (Source World Bank Global Electrification Database) Percentage of the population with access to basic drinking services (Source WHO/UNICEF Joint Monitoring Program for Water Supply, Sanitation and Hygiene)

11

13

15

16 17

19

22 31

31

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LIST OF FIGURES

Fig. 3.3 Fig. 3.4 Fig. 4.1

Fig. 4.2

Fig. 4.3 Fig. 4.4 Fig. 4.5 Fig. 4.6 Fig. 4.7 Fig. 4.8 Fig. 5.1

Fig. 6.1 Fig. 6.2

Percentage of the population with access to universal health coverage (Source WHO) Number of mobile money deployments worldwide (Source GSMA Deployment tracker) Mobile innovations in 2021, by region (Source Authors. Data from GSMA Mobile Deployment Trackers, encompassing 2249 mobile innovations [live, planned, or merged] worldwide) mHealth innovations by country (Source Authors. Data-GSMA mobile deplyment trackers, 480 mHealth innovations in Africa) mHealth by technology device and technology channel Information in the agricultural production process (Ghandi et al. [2009]) How digital agricultural services are provided Mobile money innovations, by region and over time Mobile money initiatives by country (Source Authors. Data-GSMA Mobile Money Deployment tracker) Changes in number of new mobile money deployments over time Number of rigorous empirical studies on ICT4D by country and sector (Source Data from literature review by authors) Electricity and internet access in sub-saharan Africa, 2014–2018”. ITU and WDI Ten questions for digital development

32 41

59

63 64 67 68 76 78 78

92 159 164

List of Tables

Table 2.1 Table 4.1 Table Table Table Table

5.1 5.2 5.3 5.4

Internet usage in Ghana and Niger Key uses of digital technology for development across sectors Health Agriculture Education Mobile money

23 64 94 105 116 124

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List of Boxes

Box Box Box Box

2.1 3.1 3.2 3.3

Box Box Box Box

4.1 4.2 4.3 4.4

Box Box Box Box Box Box Box Box Box

4.5 4.6 4.7 4.8 4.9 4.10 5.1 5.2 5.3

Box Box Box Box

5.4 6.1 6.2 6.3

Key terms Some (Economics) Terms Defined Search Costs for Farmers in Niger Digitizing Content: Phone Call-Based Pedagogy in Botswana Some Key “Digital for Development” Terms Categorization of Digital Health Interventions Is it a bird? Is it a plane? It’s…Zipline? Types of Digital Agriculture Interventions Information provision (e.g., prices, weather) Need a Tractor? Never Fear! Hello Tractor is Here… Paying Teachers’ Salaries (Digitally) in Liberia Learning by texting? Key Terms in DFS Pay for Water by Phone? Key Terms for Big Data Key Takeaways from Research on M-Health Key Takeaways from Research on Digital Agriculture Key Takeaways from Research on Digital Education (Edutech) Key Takeaways from Research on DFS Principles of Digital for Development (Waugaman, 2016) Key Takeaways from Research in Digital Development A Case Study on Digital Development: Mobile Money for School Fees in Benin

10 32 35 38 58 62 65 68 69 71 72 75 76 79 103 114 123 136 162 163 165

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CHAPTER 1

Introduction

Over the course of the past 20 years, the mobile phone has become a lifeline to the increasingly nebulous concept of “the outside world.” By providing access to such ubiquitous services as WhatsApp, email, and social media such as Twitter, Instagram, and TikTok, the mobile phone has become something that people leave at home at their own peril. Whereas we once used landlines to talk with friends and traveled to the store to pay for our groceries with cash, we now have information, cash, and access to goods—and friends—from across the world, all at the tap of a screen. These technological advances have been particularly dramatic in remote rural areas in low-income countries, where mobile phones have often represented the first modern telecommunications infrastructure. Take the example of Maradi, the second-largest town in Niger, one of the poorest countries in the world. At the turn of the century, Maradi had few paved roads, sporadic access to water and electricity, and less than one landline for every 1,000 people. In 2001, a mobile phone tower was built, and life changed forever. As one agricultural trader explained, “I can (now) communicate with my clients and suppliers without moving.” Before the tower was built, he had to travel by truck to buy grain, learn prices, or meet customers, and he never knew what he might find when he

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. C. Aker and J. Cariolle, Mobile Phones and Development in Africa, Palgrave Studies in Agricultural Economics and Food Policy, https://doi.org/10.1007/978-3-031-41885-3_1

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reached his destination. Now he uses his mobile phone to find prices, communicate with buyers, and place orders (Aker, 2010). Since 2001, the population of Maradi has grown to over 350,000, and yet, despite its status as the commercial hub of Niger, it still experiences power outages, water shortages, and poor roads. Nevertheless, Maradi is now filled with hundreds of kiosks that enable customers to purchase SIM cards and airtime, charge handsets, and send and receive money. Despite stagnant economic growth, increasing climate volatility, and overwhelming poverty, Niger is now more connected than ever. Approximately 60% of the population has mobile phone coverage, and 50% has a mobile phone subscription, ranging from 40 to 90% in some regions (Aker et al., 2020). This telecommunications boom is being repeated all over the world, where people are using mobile phones at rates that far exceeded the early expectations. In 1999, Safaricom, a mobile network operator (MNO) based in Kenya, projected that it would reach three million subscribers by 2020. Safaricom currently has over 35 million subscribers, representing 2 out of 3 households. Worldwide, there are now over 4.9 billion mobile phone subscribers, including 1.7 billion in Asia, 460 million in Latin America, and approximately 495 million in sub-Saharan Africa (GSMA, various years). While initial adopters were male, wealthy, educated, and urban, current adopters span the spectrum. Adoption has occurred in different political environments, in countries with multiple languages, with different mobile phone service providers and, in many cases, without substantial investment from the public sector. In fact, some of the poorer populations in the poorest countries in the world are adopting mobile phones—all despite the fact that mobile phone handsets are still relatively expensive as compared with per capita income (World Bank, 2016). These global rates of adoption and usage suggest that people really like mobile phones. The question is, why? Quite simply, the mobile phone (simple or otherwise) is a communication device (Aker & Blumenstock, 2014; Aker & Mbiti, 2010; Aker et al., 2016). Across urban-rural and rich-poor divides, mobile phones connect individuals to individuals, information, markets, and services, at significantly lower costs than the traditional alternatives. In Mali, residents of Timbuktu are able to call relatives living in the capital city of Bamako—or relatives in France. In Tanzania, farmers in Arusha are able to send a text message to learn corn and sunflower prices in the capital, a ten-hour bus trip. In Nigeria, day

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INTRODUCTION

3

laborers are able to call contacts in Benin to find out about job opportunities without making the US$40 trip. In Botswana, students affected by COVID-19 can receive text messages daily, teaching them math while they cannot attend school (Angrist et al., 2022). Citizens in countries as diverse as Kenya, Niger, and Mozambique are able to receive text messages or report violent confrontations to a centralized server that is viewable, in real time, by the entire world (Aker et al., 2017; Marx et al., 2021). Furthermore, with the introduction of M-Pesa in 2008, and mobile money platforms more broadly, mobile phones have also become a financial service device, allowing households to store money, receive transfers from their friends, family, or the government, develop a digital credit history and smooth income (Jack & Suri, 2014; Suri et al., 2021). These reductions in information and financial transaction costs are especially relevant for small-scale farmers in rural areas, where the previously-unmet demand for information and financial services is high. Farmers are often faced with considerable uncertainty—environmental, climatic, and health—and hence have a need for timely information to allow them to make decisions about planting, harvesting, and sales. In addition, agro-food systems are based upon spatial and temporal arbitrage, thereby requiring coordination between buyers and sellers over long distances—and sometimes short time frames, especially for perishable goods. Finally, the lag between planting, production, and sales means that income is highly volatile, requiring access to financial tools to smooth consumption. Nevertheless, limited access to power, roads, and landlines in rural areas means that the costs of searching for information or accessing financial institutions can become prohibitively high (Aker, 2017; Batley et al., 2012; McKinsey & Co, 2015). Mobile phones, by contrast, can reduce information search costs by as much as 50% (Aker, 2010). A growing body of research suggests that mobile phones are doing exactly what they had promised—reducing search costs, increasing access to information and financial services, and making markets more efficient. In Kerala, India, the introduction of mobile phones across fishing markets reduced the costs of search for the best price—thereby increasing trade across markets, moving supply from high to low areas, and increasing prices—all while reducing waste (Jensen, 2007). In Niger, mobile phone technology reduced price dispersion across grain and cowpea markets, primarily by improving the flow of information and spatial arbitrage (Aker, 2010). In Kenya, the introduction of mobile money allowed households

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affected by shocks to get access to remittances when and how they needed it—thereby smoothing consumption (Jack & Suri, 2014). Yet despite this potential, the evidence on these “successes” is limited to particular sectors, countries, and products. The broader evidence on their impact on people’s lives and livelihoods is often unstudied. Perhaps more importantly, there are hundreds—if not thousands—of mobile phone-based “applications for development” for which the evidence of their impact is mixed, or for which there is simply no evidence at all. This, therefore, raises the question: What is the role of information technology in development? This book argues that while there is great potential for information technology to address key barriers to development—information, financial transactions, and access to public services—existing public and private sector initiatives have not lived up to the hype, especially in rural areas. Why is this? Firstly, many “ICT4D” initiatives fail to understand what market failures (other than information and liquidity constraints) are at the root of development problems, and how—or whether—information technology can address them. Secondly, there is great heterogeneity in who has access to information technology and how it is used—which can mask impacts for the average user. Thirdly, these interventions often fail to account for design features that are important in adopting a new technology—such as literacy, networks, and income—especially at scale. As a result, many mobile innovations, despite their relevance, are kept to the local level and hardly find the path to scaling up and sustained impacts on citizens’ welfare. This book primarily focuses on the impact of information technology on the lives and livelihoods of rural households in sub-Saharan Africa, where simple mobile phones have leapfrogged traditional communication and financial technologies, and thus, arguably, offer some of the greatest potential for development.1 ,2 Drawing on primary and secondary data 1 This book focuses primarily on sub-Saharan Africa for several reasons. Firstly, among the 30 lowest-ranked countries on the UN’s Human Development Index (HDI), 28 of these are from sub-Saharan Africa. Secondly, the growth of mobile phone adoption and usage in SSA has increased substantially in sub-Saharan Africa in contexts where other infrastructure have often been limited. Thirdly, mobile phone-related innovations are particularly striking in SSA as compared with other regions. 2 Digital technologies encompass different types of infrastructure, technologies, and platforms, each of which has unique features. This book primarily focuses on one type of digital technology—simple mobile phones—rather than smart phones, the internet, or tablets. This is because simple mobile phones are the most ubiquitous digital technology

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5

from the economics, political science, computer science, and sociology fields, we examine the evolution of mobile phone coverage and adoption in sub-Saharan Africa over the past two decades, before exploring the main channels through which mobile phones can affect development. We then review private and public sector initiatives on “digitizing development” before evaluating empirical evidence on their impact. We also outline some of the pitfalls associated with ICT4D, before proposing some of the necessary conditions for mobile phones to promote broader development in Africa. Our book is divided into five main chapters and a conclusion. We offer a brief guide to the contents in the following paragraphs. Chapter 2: Where There Is No Phone This chapter provides a short review of the growth of information technology over the past 30 years in sub-Saharan Africa. It first defines the different types of information technology and then documents on the growth of one technology—the simple mobile phone. It then reviews the trends in mobile phone coverage and adoption throughout sub-Saharan Africa, as well as highlighting some of the factors that drive coverage and adoption. It also discusses the introduction of internet usage and some recent mobile phone-based applications, such as digital financial services. The chapter concludes by considering the “last mile” problem in mobile phone coverage in remote rural areas and among the poorest populations and what this means for development. Chapter 3: The Economics of the Phone This chapter gives an overview of the multiple functions of information technology and the mechanisms through which information technology can affect development. It first discusses the different purposes and applications of digital technologies across different markets and industries, as well as the ways in which they can address market failures and affect development outcomes. It then discusses the cost–benefit analyses that individuals and households must consider before deciding to adopt a mobile phone (and its various functionalities). As mobile phones are network goods whose utility depends upon the size of the user network, in terms of coverage and adoption, especially in rural areas of sub-Saharan Africa, and thus offer the greatest potential in terms of development.

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we also discuss the interaction between the user network and adoption. Finally, we present a framework for analyzing the utility of information technology, in order to illustrate the interdependence between different market failures. For example, a monopolistic market in an agricultural value chain may result in lower producer prices for farmers, which in turn means that price information provided via a digital market information system may have little to no impact. We present a typology of different market failures, the ways in which information technology can address them, and the potential for improving development outcomes. In particular, this third chapter addresses the potential pitfall of searching for a “silver bullet” in development, namely, the factors that may constrain adoption, usage, and impact.3 Chapter 4: Digitizing Development? This chapter reviews the ways in which information technology has “digitized development,” discussing the rollout of information technology by the private sector, as well as development-driven interventions that use ICT. It provides a systematic review of different digital “development” interventions—broadly known as ICT4D—in five key areas: health, agriculture, education, financial services, and social protection programs.4 As of 2020, for example, there were more than 2,000 digital initiatives worldwide, in a variety of contexts, digital forms, and sectors. Chapter 5: (Don’t) Believe the Hype Based upon the potential of digital for development outlined in the prior chapter, this chapter reviews existing academic literature on the impact of these digital initiatives on development outcomes, focusing on the key thematic areas outlined above. The literature spans the fields of economics, computer science, and sociology, with some additional references from the public health field. We then seek to explain the null results 3 These factors include, yet are not limited to, the market failures in a given context, which information technology may be able to address; the source and quality of the service provided via information technology, whether information, financial, or otherwise; and the enabling environment for local information technology access and usage, which is in part related to literacy, gender, and income. 4 This book will exclude key digital services such as tax design, collection, and enforcement.

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in some studies, addressing the issues of complementary markets, heterogeneous effects, sample size, imperfect compliance (in particular for SMS interventions), and technological interfaces. Chapter 6: Rethinking Digital Development The final chapter proposes a slightly modified framework for policymakers and academics when using information technology as a means of addressing development problems, introducing a series of questions that should lead policymakers to decide if, how, and when information technology interventions are appropriate in a given context, and, if so, how to design, test, and evaluate them. It points out some of the “pitfalls” related to using digital for improving development outcomes in sub-Saharan Africa, related to some of the issues raised in Chapter 3.

References Aker, J. C. (2010). Information from markets near and far: Mobile phones and agricultural markets in Niger. American Economic Journal. Applied Economics, 2(3), 46–59. https://doi.org/10.1257/app.2.3.46/ Aker, J. C. (2017). Using digital technology for public service provision in developing countries. In S. Gupta, M. Keen, A. Shah, & G. Verdier (Eds.), Digital revolutions in public finance (pp. 201–225). IMF. Aker, J. C., & Blumenstock, J. E. (2014). The economic impacts of new technologies in Africa. In The Oxford handbook of africa and economics (pp. 354– 371). Oxford University Press. https://doi.org/10.1093/oxfordhb/978019 9687107.013.021 Aker, J. C., Collier, P., & Vicente, P. (2017). Is information power? Using mobile phones and free newspapers during an election in Mozambique. The Review of Economics and Statistics, 99(2), 185–200. Aker, J. C., Ghosh, I., & Burrell, J. (2016). The promise (and pitfalls) of ICT for agriculture initiatives. Agricultural Economics, 47 (S1), 35–48. https:// doi.org/10.1111/agec.12301 Aker, J. C., & Mbiti, I. M. (2010). Mobile phones and economic development in Africa. Journal of Economic Perspectives, 24(3), 207–232. https://doi.org/ 10.1257/jep.24.3.207 Aker, J. C., Prina, S., & Welch, C. J. (2020, May). Migration, money transfers, and mobile money: Evidence from Niger. In AEA Papers and Proceedings (Vol. 110, pp. 589–593).

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Angrist, N., Bergman, P., & Matsheng, M. (2022). Experimental evidence on learning using low-tech when school is out. Nature Human Behavior, 6, 941– 950. Batley, R., McCourt, W., & Mcloughlin, C. (2012). The politics and governance of public services in developing countries. Public Management Review, 14(2), 131–145. Jack, W., & Suri, T. (2014). Risk sharing and transactions costs: Evidence from Kenya’s mobile money revolution. American Economic Review, 104(1), 183– 223. https://doi.org/10.1257/aer.104.1.183 Jensen, R. (2007). The digital provide: Information (technology), market performance, and welfare in the South Indian fisheries sector. The Quarterly Journal of Economics, 122(3), 879–924. Marx, B., Suri, T., & Pons, V. (2021). Voter mobilisation and trust in electoral institutions: Evidence from Kenya. The Economic Journal, 131(638), 2585– 2612. McKinsey & Company. (2015, February). Brighter Africa: The growth potential of the sub-Saharan electricity sector. McKinsey & Company. Suri, T., Bharadwaj, P., & Jack, W. (2021). Fintech and household resilience to shocks: Evidence from digital loans in Kenya. Journal of Development Economics, 153, 102697. https://doi.org/10.1016/j.jdeveco.2021.102697 World Bank Group. (2016). World development report 2016: Digital dividends. World Bank Publications. https://www.worldbank.org/en/publication/wdr 2016

CHAPTER 2

Where There Was No Phone

2.1

Leapfrogging the Landline?

As we walk down the street of any major city—Amsterdam, Boston, Shanghai, Caracas, or Nairobi—we are surrounded by technology. Technological innovations continue to shape our society. Developments in information and communications technologies (ICTs), defined as the different types of platforms through which information flows and communications are made, as well as the types of content transmitted through them, have had a huge impact globally. ICTs broadly encompass a number of technologies (mobile phones, computers, tablets, radios, and pagers/ beepers), via a variety of content (calls, text messages, emails, websites, videos, and radio broadcasts). When we consider ICTs, we often think of digital technologies: tools that can be used to collect, store, analyze, and share information digitally, i.e., in “bits” rather than “atoms,” thereby significantly reducing the cost of storing, computing, and transmitting of data (Goldfarb & Tucker, 2019; World Bank Group, 2016). Over the past three decades, the world has experienced an ICT boom. Mobile phone coverage has expanded rapidly worldwide, from largely non-existent networks at the turn of the century to a point where over 94% of the world’s population is covered by mobile broadband networks, up from 70% in 2014 (Aker & Blumenstock, 2014; GSMA, 2021). Remarkably, more households in low-income countries own a mobile

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. C. Aker and J. Cariolle, Mobile Phones and Development in Africa, Palgrave Studies in Agricultural Economics and Food Policy, https://doi.org/10.1007/978-3-031-41885-3_2

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phone than have access to electricity or clean water, and it is estimated that 83% of people in developing countries own a mobile phone (Klapper, 2019). The number of (mobile) internet users has more than tripled— from 1 billion in 2005 to over 60% of the world’s population, or 4.9 billion people in 2021 (ITU, 2021a). Sub-Saharan Africa, where ICTs have often represented the first modern infrastructure of any kind (Aker, 2011; Aker & Mbiti, 2010), has arguably witnessed the greatest increase in both coverage and usage. Despite limited investments in basic infrastructure—power, roads, and landlines, for example—ICT coverage has increased substantially, from 50% of the population in 2000 to over 80% in 2020 (Fig. 2.1). This increase in coverage has translated into adoption and usage: Where there were 16 million mobile phone subscriptions in 2000, this has risen to 495 million in 2020, representing 46% of the region’s population.1 While impressive, but the real picture is a bit murky: These figures potentially overestimate the actual number of mobile phone users because many individuals own several handsets or have multiple Subscriber Identity Module (SIM) cards. At the same time, these figures could also underestimate the number of mobile phone users, as sharing mobile phones is a common practice.2 Box 2.1 Key terms 2G: Second-generation network technology, using a digital network instead of the analog radio signals that were used in 1G technology. 3G: Third-generation technology, using the Universal Mobile Telecommunications System (UMTS) technology, with a faster minimum speed of 200Kbps. Networks are compliant with International Mobile Telecommunications-2000 (IMT-2000) standards.

1 Other sources estimate that only one out of two people in Africa have a mobile phone (The Economist, 2017), and 7 out of 10 people have an active mobile phone subscription (Fig. 2.1). 2 When talking about ICT in this context, there are two separate aspects—coverage, defined as having the necessary infrastructure in place (such as towers, satellites, and cables) to provide a digital connection; and adoption, defined as using the technology—by purchasing a handset, a SIM card, a tablet, or some type of subscription.

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Fig. 2.1 Mobile phone growth in sub-saharan Africa (Source Authors. Data from ITU [2019] dataset. Plotted average values are weighted by population size)

4G: Fourth-generation technology, which differs markedly from 3G in its use of Multiple Input Multiple Output (MIMO) and Orthogonal Frequency Division Multiplexing (OFDM) technologies. Long-Term Evolution (LTE) is a 4G standard consisting of upgrades to existing UMTS technology. Broadband: A series of high-capacity transmission technologies enabling large amounts of data, voice, and video to be transmitted across long distances at high speeds. Transmission occurs through media such as coaxial cables, fiber optic cables, and radio waves. Coverage gap: An area or population that does not have mobile or internet infrastructure, and hence no access to mobile and/or internet connections. Usage gaps: An area or population that has access to the infrastructure but does not use the technology. For example, an individual may not own a handset or another mobile device, may not have an (active) subscription, or may not be actively using the service.

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Last mile problem: The final leg of a communication network being delivered to the final end user, usually the last 20%, is typically the most expensive. Interoperability: The ability for two or more networks, systems, devices, applications or components to communicate. In the context of mobile phone services, this could imply that two or more operators share the same mobile phone tower, or the ability to send money between different mobile phone providers. Mobile money will be discussed in more detail in Chapter 4.

Regardless of the exact numbers, the increase in mobile phone subscriptions is all the more surprising considering the prevalence of poverty in sub-Saharan Africa, coupled with the price of mobile phone handsets and services. Approximately 420 million sub-Saharan Africans are classified as poor (living on less than US$1.90 per day, defined in 2011 purchasing power parity), with 28 million classified as “ultra-poor” (living on less than US$0.50 per day) (World Bank, 2020). While the price of a handset has dropped substantially over the past decade, the simplest internet-enabled handset still costs more than 26% of the monthly income per capita in the region (GSMA, 2021). Despite these constraints, households in some of the world’s poorest countries are still using mobile phones in some way, shape, or form. Figure 2.2 shows the number of mobile phone subscribers per 100 people among countries in sub-Saharan Africa. (The countries are sorted in ascending order by their ranking on the UN’s Human Development Index (HDI), which is an index combining information on a country’s combined income, health, and education indicators.) The pattern is clear: Even in those countries with an HDI ranking of lower than 160, there is an average of 73 subscribers for every 100 people (Aker & Mbiti, 2010; UNDP, 2020). In fact, only 14% of people in sub-Saharan Africa reported never having used a mobile phone, with 72% using it on a daily basis (Afrobarometer surveys, various years).3

3 The Afrobarometer is a public attitude survey on democracy, governance, corruption, and related issues in African countries. A randomly selected sample of 1,200 or 2,400 people is collected in each country through face-to-face interviews. Our sample consists of 70,913 observations across 26 African countries.

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Fig. 2.2 Number of mobile phone subscriptions (per 100 people) and HDI (Source Authors. Data from ITU/World Telecommunications [2020] and UNDP [2020])

The picture for broadband internet connection, while still positive, is less optimistic. Since 2009, investment in telecommunications submarine cables along the coast of Africa has connected most African countries to the internet and substantially increased the connection speed (AUC/ OECD, 2021; Cariolle, 2021). Yet despite this progress, only 20% of the population has access to broadband internet. In fact, it is estimated that nearly 300 million people on the continent live over 50 km away from a fiber or cable broadband connection (GSMA, 2021). This is mirrored in usage patterns: 65% of people reported never having used the internet (Afrobarometer surveys, various years). This rapid diffusion of adoption of mobile phones, and, to a lesser extent, broadband, has generated a great deal of speculation and optimism regarding its effect on economic development in Africa. Policymakers, newspapers, and mobile phone companies have all touted the povertyeradicating potential of mobile phones (Corbett, 2008). Paul Kagame,

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President of Rwanda, said: “In 10 short years, what was once an object of luxury and privilege, the mobile phone, has become a basic necessity in Africa” (Kagame, 2007). An article in The Economist (2017) similarly reported: “A cluster of new technologies promise to have a huge impact on Africa, not least because they can help solve some of Africa’s biggest and longest-standing problems.” The expanding digitalization of African societies has raised the prospects of industrialization, employment, and poverty reduction on the continent (Aker, 2017; Aker & Mbiti, 2010; Cariolle & Le Goff, 2023; Hjort & Poulsen, 2019; World Bank, 2016).4 Yet that same diffusion of digital technologies in daily socioeconomic interactions also carries the risk of widening the divide between those with and without access (Cariolle & Le Goff, 2023; Wu et al., 2022). Perhaps Kwabena Frimpong Boateng, Ghana’s Minister for Science and Technology, put it best when he stated that “the poverty gap is a technology gap” (The Economist, 2017).

2.2

Digital “Divide” or “Provide”? 2.2.1

The Coverage Gap

Just as digital technology has advanced more slowly in Africa by comparison to higher-income countries, the rollout of digital technologies across countries within Africa has been similarly uneven, with important differences in access to digital technologies among and across populations. This “digital divide—defined as unequal access to digital technology—is multidimensional, divided according to geography, economic status, gender, age, and education levels.5 Understanding where and why there is more limited digital access is necessary for realizing the potential dividends from digitalization—in other words, the digital “provide” (Jensen, 2007). We focus on two aspects of the digital divide—the geographic divide, along 4 Digitalization is defined as “The increasing resort to digital technologies in socioeconomic interactions,” while digitization refers to the “conversion of a signal (voice, text, image, video, etc.) into digital format, i.e., a binary signal readable by computers” (Goldfarb & Tucker, 2019). 5 The digital divide is a term that refers to the gap between demographics and regions that have access to modern information and communications technology (ICT), and those that have restricted or no access. This technology can include the telephone, television, personal computers, and internet connectivity.

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country-specific and rural-urban dimensions; and the socioeconomic divide, along income and education dimensions. While over 81% of the sub-Saharan African population has access to mobile phone coverage as of 2020 (GSMA, 2021), there have been huge disparities in its geographic rollout. In 1999, few countries in Africa had mobile phone coverage (Fig. 2.3). Since then, coverage across the continent has followed a distinct pattern, starting in Northern and Southern Africa before expanding to the coastal countries and finally reaching the landlocked countries. Country-level coverage ranges from 48% in South Sudan to 100% in Mali, Guinea-Bissau, South Africa, and Namibia (ITU, 2022). The disparity is more drastic when examining coverage with (at least) a 3G mobile network, covering 65% of the population by 2020, but ranging from just 15% in South Sudan to 99.8% in South Africa (ITU, 2022). What explains the geographic divide across countries? The decisions about when, where, and how to provide mobile phone coverage have been primarily driven by economic considerations. Service providers

Fig. 2.3 Percentage of the population covered by mobile (Over time) (Source Authors. Data from ITU/World Telecommunications [2020] and UNDP [2020])

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constructed mobile phone towers in countries with higher demand (e.g., those with higher population densities and wealthier populations), lower costs (flat topography and access to paved roads), and an enabling policy and regulatory environment (Aker & Mbiti, 2010; BaliamouneLutz, 2003; Buys et al., 2009; Kamssu, 2005; Kshetri & Cheung, 2002; Minges, 1999; Rouvinen, 2006; Yan & Thong, 2003). This is, in part, what Fig. 2.3 suggests: The “first adopting” countries were those with the highest population densities, relatively higher incomes, and better infrastructure. This rollout has been further sustained—or constrained—by the telecommunications market structure within a given country: Whereas all telecommunications sectors were monopolies in 1995, many countries had been at least partially liberalized by 2020, with sub-Saharan Africa having one of the highest percentages of liberalized telecommunications markets in the world (Fig. 2.4). As of 2020, most countries had fully or partially liberalized mobile phone and broadband networks, with international gateway liberalization falling slightly behind. Market liberalization is highly correlated with the number of service providers and coverage: fully liberalized markets have more service providers and greater coverage as compared with partially liberalized or monopoly markets (Elliot et al., 2021; Wallsten, 2001). This is unsurprising, as economic theory would predict that greater competition would increase the quantity of digital offerings (including coverage) and lower prices. Despite huge improvements in access over the past 20 years, 19% of the continent was still not covered by mobile phone networks as of 2020

Fig. 2.4 ICT market liberalization in sub-saharan Africa (Source ITU World Telecommunications/ICT Indicators Database, the World Bank)

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(GSMA, 2021), and 18% of the population was not covered by mobile broadband internet (3G or higher) (ITU, 2021a). This is known as the “last mile” problem: The final leg of a communication network delivered to the final end-user is typically the most expensive. Figure 2.5 shows this premise: The marginal cost (the additional resources required to reach the last percentage of the population) can be multiple times the average cost (AC) (Franco et al., 2019), which implies that reaching the poorest and most vulnerable communities can become increasingly and exponentially expensive (Björkegren, 2019). Combined with potentially lower revenues in these areas, this makes it unprofitable for firms.6

Fig. 2.5 Economics of the last mile problem (Franco et al. [2019])

6 Fabregas et al. (2022) argue that, while initial mobile phone coverage involves fixed costs, the marginal cost of phone communication in rural areas is close to zero, since cell phone towers typically operate below capacity.

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2.2.2

The Usage Gap

While the “coverage gap” across sub-Saharan Africa is only 20%, the “usage gap” is much higher (Fig. 2.6).7 While average mobile phone usage is relatively high (well over 60%), this figure hides marked disparities within and across countries. As of 2020, the highest rates of mobile phone subscriptions were in South Africa, Botswana, and Ivory Coast; the lowest were in South Sudan, Ethiopia, Liberia, and the Central African Republic. While coverage has expanded into more remote areas, mobile phone subscriptions are positively (but weakly) correlated with living in urban areas, higher levels of education, and greater access to electricity. For example, almost 30% of surveyed citizens without access to electricity reported never having used a mobile phone, as compared with 10% of those with access (Afrobarometer surveys, various years). When concentrating on internet usage, almost 80% of those located in rural areas reported never having used the internet, compared to less than 50% in urban areas.8 A key challenge in identifying the usage gap, however, is measuring it. Firstly, how is digital “usage” defined? Does it mean owning a handset? Purchasing a SIM card? Actively making calls, sending SMS, and using the internet? Secondly, once usage is defined, how is it measured? These issues sit at the crux of the problem. For example, while some data suggest that fewer than half of those in sub-Saharan Africa own mobile phones (e.g., The Economist, 2017), others suggest that at least 50% of the population has an active mobile phone subscription (Fig. 2.1 and 2.6). Without primary data on mobile phone ownership and usage, it is difficult to know which dataset paints the most accurate picture. Some individuals have multiple SIM cards in order to take advantage of different pricing schemes; other individuals own more than one SIM card but rarely use them. As a result, accurate measures of digital adoption remain unclear 7 The coverage gap is defined as an area or population that does not have mobile or internet infrastructure; the usage gap is defined as an area or population that has access to the infrastructure but does not use the technology—either owning a handset or having an (active) subscription or actively using the service (GSMA, 2019). 8 Mothobi and Gillwald (2021) argue that this digital divide was further compounded during COVID-19, where low levels of smart phone and internet penetration left certain populations excluded during lockdowns, when many work activities went online.

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Fig. 2.6 Mobile phone subscriptions per 100 people, by country, in sub-saharan Africa (Source ITU World Telecommunications/ICT Indicators Database, the World Bank)

and difficult to pin down. This may not necessarily be a first-order concern when assessing trends, but it can be a thorny issue for academics and others when attempting to develop policies and interventions that rely upon mobile phones.

2.3

Keeping up with the Joneses

The above figures show a technological boom over the past 20 years across the world. But who adopts this technology? And what explains its rapid adoption? As the numbers have grown, the demographics have also changed. Between 2008 and 2018, for example, the percentage of the Kenyan population living in areas with mobile phone coverage remained largely constant, but the number of subscriptions doubled, from 41 (per 100 people) to 96 (ITU/ICT Indicators Database). The first

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adopters were primarily male, educated, young, wealthy, and urban. But over time—with cheaper handsets, cheaper services, and more offerings— usage has extended to a much broader population (Aker & Mbiti, 2010). While there is a rich body of literature on the individual, household, and national-level factors that are correlated with mobile phone adoption (Donner, 2008), there has been little economic literature on the subject, especially on the determinants of adoption at the individual level (Berrou et al., 2020)9 ,10 This could be due to a number of factors, such as data limitations and the difficulty in assessing the impact of peers and social networks on adoption (known as the “reflection problem” [Manski, 1993]). A simple, stylized way to model digital technology adoption would be to compare the net present value of benefits received by the user and the costs of adoption (Hall & Khan, 2002). In general, these benefits are simply the difference in profits (or utility) when a firm or household shifts from an older technology to a newer one. These benefits can also include “non-economic” factors, such as the enjoyment of being the first person in the neighborhood with a new good. Yet mobile phone technology is unique in several ways, which complicates this simple model somewhat. First, while more advanced mobile phone applications might require some learning—especially for illiterate users—the technology is fairly straightforward and easy-to-use, especially for basic operations, so there is less of a need to learn about the “optimal” use of the technology (as in traditional learning-by-doing models). Second, the shared nature of the mobile phone allows others to benefit from the technology without purchasing it themselves (free-riding), and potentially share the costs of the technology (Aker & Mbiti, 2010). Third, unlike many new technologies, mobile phones have multiple uses (voice, SMS, and internet) and multiple purposes, which can, therefore, translate 9 The economic literature on adoption has often focused on ICT diffusion in developing countries rather than individual adoption (Baliamoune-Lutz, 2003; Kshetri & Cheung, 2002). For example, in a study conducted by Howard and Mazaheri (2009), they found that population size and the quality of the landline infrastructure are positively associated with mobile phone adoption. 10 While there is a longstanding economics literature on the impacts of extension services, network effects, and learning-by-doing on agricultural technology adoption (e.g., Bandiera & Rasul, 2006; Conley & Udry, 2010; Foster & Rosenzweig, 1995, 2010; Genius et al., 2014; Munshi, 2004), few researchers explicitly model how ICTs alter these effects (Nakasone et al., 2014).

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into diverse economic and social benefits—such as talking with friends and family members, obtaining price or labor market information, or asking colleagues for financial help. Fourth, many of these benefits are tangible and immediate, thereby allowing people to decide fairly quickly what those benefits are—rather than making an investment and waiting for the returns, which may be important for individuals, households, and firms with high discount rates. Finally, mobile phones are a network good, whose demand (and returns) depends partly upon other users.

2.4 A Tale of Two Countries: Digital Infrastructure and Adoption in Niger and Ghana We turn to a case study of two countries—Niger and Ghana—to illustrate the rollout of digital coverage, adoption, and usage. Niger is a landlocked country in West Africa with a total land area of 1.2 million km2 , with over 80% of its land area located within the Sahara Desert. With a population of 24 million people (2020), a majority of the population lives in remote rural areas. The road network is relatively poor, with less than 20% of roads paved.11 Ghana, by contrast, is a coastal West African country with total land area of 238,000 km2 and a population of 31 million people, of which only 44% live in rural areas (World Bank, 2020). Whereas 45% of the population lives on less than $1.90 in Niger, this figure is only 14% in Ghana (World Bank, 2020). What, then, would be the predictions about mobile phone coverage, access, and usage in these two countries? Consistent with our previous predictions, mobile phone coverage arrived in Ghana earlier and spread more quickly. Where there were no mobile phone service providers at all in Niger in 2000, 18% of the population had mobile phone coverage in Ghana, one of the 15 countries with service at the time. Twenty years later, the situation has drastically changed: 92% of the Nigerien population has access to the mobile network (Fig. 2.7A), compared to 98% of the population in Ghana (Fig. 2.7B). In Niger, the coverage gap persists in remote and isolated areas, i.e., those areas with low population density (the Sahara Desert) and far away from major cities (e.g., the extreme East). This gap is much smaller in Ghana. The largest difference between the two countries, 11 In 2014, the road density was very low, equal to 0.69 km of roads per 1,000 km2 , and in 2016, only 20.4% of the road network was paved. Data drawn from the Sustainable Competitiveness Observatory (FERDI): https://competitivite.ferdi.fr/en.

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Fig. 2.7 a. Mobile internet infrastructure rollout in Niger; b. Mobile internet infrastructure rollout in Ghana (Source GSMA-Collins Bartholomew Coverage Maps)

however, is quality: Where 80% of the population of Ghana has access to at least a 3G network, only 24% of the Nigerien population is covered by 3G, thereby limiting their access to internet services—even on the mobile phone.12 This is, in turn, correlated with the number of service providers: While Ghana has nine telecoms operators (four of which have appreciable market shares), Niger has four (GSMA, 2021). Despite these stark differences in quality, adoption and usage in each country have followed broadly similar patterns. Access to the 3G network does not immediately translate into more usage (Table 2.1). For example, despite the fact that 80% of the population has access to 3G in Ghana, only 41% report having access to the internet on their phone, with substantially lower usage in rural areas. The access gap is smaller in Niger, perhaps because 3G coverage is still concentrated in urban areas and internet usage among those populations is relatively high.

12 Mobile internet penetration in Ghana is the second highest in West Africa (at 45%) and compares to an SSA average of 28% (GSMA, 2021).

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Table 2.1 Internet usage in Ghana and Niger Ghana

Internet usage No internet usage Number of obs

Niger

Total

% of Urban

% of Rural

Total

% of Urban

% of Rural

41%

53%

24%

20%

38%

14%

59%

47%

76%

81%

63%

86%

2,066

1,230

836

728

165

563

Source Afrobarometer surveys, 2022

2.5 What Does this Mean for “Digital Development”? While access to, and use of, digital technology has increased substantially across sub-Saharan Africa in the past 20 years, there is still a digital divide. Populations and individuals most affected by this digital divide are, arguably, those who could benefit the most, namely residents of remote rural areas; those who are relatively poor; and those who have lower levels of education. Any initiatives, whether public or private, must, therefore, address the multidimensional aspects of the digital divide in order to have a significant impact. This requirement is even more pronounced for internet-based applications, given the fragmented nature of internet coverage in many countries of the region.13 Despite success in bringing the 3G network in most parts of Africa, the connectivity infrastructure does not yet offer the conditions for effective and efficient delivery of services through 4G technologies. At an international level, connectivity in sub-Saharan Africa is often undermined by telecommunications submarine cable outages and limited redundancy,

13 A key focus for policymakers and international organizations has often been on interoperability of mobile phone and mobile money services as a way to increase outreach and financial inclusion. Recent research in this area calls into question some of these assumptions (Bianchi et al., 2022; Brunnermeier et al., 2023).

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provoking recurrent internet shutdowns.14 At a national level, key terrestrial connectivity infrastructures—such as data centers, Internet eXchange Points (IXPs), and energy infrastructure—are missing. This is particularly problematic since most African websites are hosted in foreign data centers, inducing higher costs, and further highlighting the dependency of these networks on international infrastructure. This is further exacerbated by poor energy infrastructure coverage and frequent power outages. Worldwide, COVID-19 has revealed socioeconomic inequality in many areas. The pandemic has offered new challenges and opportunities in the use of digital in our daily lives—from work to education to health and banking. In this case, the digital divide is felt more strongly, as unconnected individuals are unable to get access to basic services, nor do they have access to the digital technology that could serve as a substitute. Reaching the last mile—in terms of coverage and access—is one key step.

References Afrobarometer Data, Various Countries, Rounds 6 & 7 [2016 & 2019]. http:// www.afrobarometer.org. Aker, J. C. (2011). Dial “A” for agriculture: A review of information and communication technologies for agricultural extension in developing countries. Agricultural Economics, 42(6), 631–647. Aker, J. C. (2017). Using digital technology for public service provision in developing countries. In S. Gupta, M. Keen, A. Shah, & G. Verdier (Eds.), Digital revolutions in public finance (pp. 201–225). IMF. Aker, J. C., & Blumenstock, J. E. (2014). The economic impacts of new technologies in Africa. In The Oxford handbook of Africa and economics (pp. 354– 371). Oxford University Press. https://doi.org/10.1093/oxfordhb/978019 9687107.013.021 Aker, J. C., & Mbiti, I. M. (2010). Mobile phones and economic development in Africa. Journal of Economic Perspectives, 24(3), 207–232. AUC/OECD. (2021). Africa’s development dynamics 2021: Digital transformation for quality jobs. https://doi.org/10.1787/0a5c9314-en Baliamoune-Lutz, M. (2003). An analysis of the determinants and effects of ICT diffusion in developing countries. Information Technology for Development, 10(3), 151–169.

14 As in Togo, Benin, Mauritania, Somalia, Cameroon, Mauritius, Comoros, and other countries over the last decade (Cariolle, 2018; Cariolle & Le Goff, 2023).

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Bandiera, O., & Rasul, I. (2006). Social networks and technology adoption in northern Mozambique. The Economic Journal, 116(514), 869–902. Berrou, J. P., Combarnous, F., Eekhout, T., & Mellet, K. (2020). Mon mobile, mon marché. Reseaux, 219(1), 105–142. Bianchi, M., Bouvard, M., Gomes, R., Rhodes, A., & Shreeti, V. (2022). Mobile payments and interoperability: Insights from the academic literature. (Working Paper, hal-03629513f). Björkegren, D. (2019). The adoption of network goods: Evidence from the spread of mobile phones in Rwanda. The Review of Economic Studies, 86(3), 1033–1060. Brunnermeier, M. K., Limodio, N., & Spadavecchia, L. (2023). Mobile money, interoperability and financial inclusion. Centre for Economic Policy Research. Buys, P., Dasgupta, S., Thomas, T. S., & Wheeler, D. (2009). Determinants of a digital divide in Sub-Saharan Africa: A spatial econometric analysis of cell phone coverage. World Development, 37 (9), 1494–1505. Cariolle, J. (2018). Telecommunication submarine-cable deployment and the digital divide in sub-saharan Africa (CESifo Working Paper Series 7415). CESifo Group Munich. Cariolle, J. (2021). International connectivity and the digital divide in SubSaharan Africa. Information Economics and Policy, 55, 100901. Cariolle, J., & Le Goff, M. (2023). Spatial internet spillovers in manufacturing. The Journal of Development Studies, 59(8), 1163–1186. https://doi.org/10. 1080/00220388.2023.2204177 Conley, T. G., & Udry, C. R. (2010). Learning about a new technology: Pineapple in Ghana. American Economic Review, 100(1), 35–69. Corbett, S. (2008, April 13). Can the cellphone help end global poverty? The New York Times. https://www.nytimes.com/2008/04/13/magazine/ 13anthropology-t.html Coulibaly, S. S. (2021). A study of the factors affecting mobile money penetration rates in the West African Economic and Monetary Union (WAEMU) compared with East Africa. Financial Innovation, 7 (1), 1–26. https://doi. org/10.1186/s40854-021-00238-0 Donner, J. (2008). Research approaches to mobile use in the developing world: A review of the literature. The Information Society, 24(3), 140–159. Elliott, J., Houngbonon, G. V., Ivaldi, M., & Scott, P. (2021, May). Market structure, investment and technical efficiencies in mobile telecommunications (TSE Working Paper, n. 21–1207). Revised November 2021. Fabregas, R., Kremer, M., Lowes, M., On, R., & Zane, G. (2022). Digital information provision and behavior change: Lessons from six experiments in East Africa (Working Paper). Forthcoming at AEJ:AE.

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Foster, A. D., & Rosenzweig, M. R. (1995). Learning by doing and learning from others: Human capital and technical change in agriculture. Journal of Political Economy, 103(6), 1176–1209. Foster, A. D., & Rosenzweig, M. R. (2010). Microeconomics of technology adoption. Annual Review of Economics, 2(1), 395–424. Franco, A., Wong, J., Schmidt-Fellner, A., & Rau, A. (2019, September 29). Solving the last mile problem for global development. The Center for Inclusive Growth: Mastercard Foundation. Genius, M., Koundouri, P., Nauges, C., & Tzouvelekas, V. (2014). Information transmission in irrigation technology adoption and diffusion: Social learning, extension services, and spatial effects. American Journal of Agricultural Economics, 96(1), 328–344. Goldfarb, A., & Tucker, C. (2019). Digital economics. Journal of Economic Literature, 57 (1), 3–43. GSMA. (2019). State of the industry report on mobile money. GSMA, 36. https://www.gsma.com/mobilefordevelopment/resources/2018-state-ofthe-industry-report-on-mobile-money/ GSMA. (2021). The state of mobile connectivity in 2021. https://www.gsma. com/r/wp-content/uploads/2021/09/The-State-of-Mobile-Internet-Con nectivity-Report-2021.pdf Hall, B. H., & Khan, B. (2002). Adoption of new technology. New economy handbook (pp. 1–38). Berkeley University, Berkeley. https://doi.org/10.3386/ w9730 Hjort, J., & Poulsen, J. (2019). The arrival of fast internet and employment in Africa. American Economic Review, 109(3), 1032–1079. Howard, P. N., & Mazaheri, N. (2009). Telecommunications reform, Internet use and mobile phone adoption in the developing world. World Development, 37 (7), 1159–1169. ITU. (2021a). Measuring digital development—Facts and figures 2021. https:// www.itu.int/en/ITU-D/Statistics/Documents/facts/FactsFigures2021.pdf ITU. (2021b). Most of the world population is covered by a mobile-broadband signal, but blind spots remain. https://www.itu.int/itu-d/reports/statistics/ 2021/11/15/mobile-network-coverage/ ITU. (2022). Digital development dashboard. ITU Statistics. https://www.itu. int/en/ITU-D/Statistics/Dashboards/Pages/Digital-Development.aspx Jensen, R. (2007). The digital provide: Information (technology), market performance, and welfare in the South Indian fisheries sector. The Quarterly Journal of Economics, 122(3), 879–924. http://www.jstor.org/stable/25098864 Kagame, P. (2007, October 29). Speech given at the connect Africa Summit, Kigali, Rwanda.

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Kamssu, A. J. (2005). Global connectivity through wireless network technology: A possible solution for poor countries. International Journal of Mobile Communications, 3(3), 249–262. Klapper, L. (2019). Mobile phones are key to economic development: Are women missing out? Brookings Institute. https://www.brookings.edu/blog/fut ure-development/2019/04/10/mobile-phones-are-key-to-economic-develo pment-are-women-missing-out/ Kshetri, N., & Cheung, M. K. (2002). What factors are driving China’s mobile diffusion? Electronic Markets, 12(1), 22–26. Manski, C. F. (1993). Identification of endogenous social effects: The reflection problem. The Review of Economic Studies, 60(3), 531–542. Minges, M. (1999). Mobile cellular communications in the southern African region. Telecommunications Policy, 23(7–8), 585–593. Mothobi, O., & Gillwald, A. (2021, April). COVID-19 compounds historical disparities and extends the digital divide. Policy Brief. Cape Town, South Africa, Research ICT Africa. Munshi, K. (2004). Social learning in a heterogeneous population: Technology diffusion in the Indian Green Revolution. Journal of Development Economics, 73(1), 185–213. Nakasone, E., Torero, M., & Minten, B. (2014). The power of information: The ICT revolution in agricultural development. Annual Review of Resource Economics, 6(1), 533–550. Rouvinen, P. (2006). Diffusion of digital mobile telephony: Are developing countries different? Telecommunications Policy, 30(1), 46–63. The Economist. (2017, November 8). In much of sub-Saharan Africa, mobile phones are more common than access to electricity. The Economist. https:// www.economist.com/graphic-detail/2017/11/08/in-much-of-sub-saharanafrica-mobile-phones-are-more-common-than-access-to-electricity United Nations Development Programme (UNDP). (2020). Human development report 2020. The next frontier: Human development and the anthropocene. New York. Wallsten, S. J. (2001). An econometric analysis of telecom competition, privatization, and regulation in Africa and Latin America. The Journal of Industrial Economics, 49(1), 1–19. World Bank. (2020). Poverty and inequality platform. https://pip.worldbank. org/home World Bank Group. (2016). World development report 2016: Digital dividends. World Bank Publications. https://documents.worldbank.org/curated/ en/896971468194972881/pdf/102725-PUB-Replacement-PUBLIC.pdf Wu, S., Wang, P., & Sun, B. (2022). Can the Internet narrow regional economic disparities? Regional Studies, 56(2), 324–337. Yan, X., & Thong, J. Y. (2003). Tariff-quality equilibrium and its implications for telecommunications regulation. Telecommunications Policy, 27 (3–4), 301– 315.

CHAPTER 3

The Economics of the Phone

The previous chapter outlined the introduction, growth, and adoption of digital technology in sub-Saharan Africa over the course of the past two decades and the consequent digital divide. This chapter turns its attention specifically to the digital provide—in other words, the challenges that poor (often rural) households face, the potential of digital technology to address these challenges, and the mechanisms of impact. We then develop a framework to assist in assessing how and whether these technologies will reach their potential.

3.1 3.1.1

Where There Is No Power The State of Public Services in Africa

Economists and policymakers alike generally recognize the role of public services —broadly defined as the provision of goods and services to promote economic, social, and environmental sustainability—in improving consumer and producer welfare, and hence a country’s productivity, growth, and development (Bartik, 1991; Fisher, 1997; Fox & Murray, 1991; Munnell, 1992; Wasylenko, 1991). Public services often include (among others), electricity, education, environmental protection, financial services, health care, public security, transport, social welfare, and access to water. Typically, such services are either provided directly and

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. C. Aker and J. Cariolle, Mobile Phones and Development in Africa, Palgrave Studies in Agricultural Economics and Food Policy, https://doi.org/10.1007/978-3-031-41885-3_3

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financed by the public sector, and outsourced to other service providers, or provided via a public-private partnership (Aker, 2017). Despite the importance of such services for development, there is widespread variation in the quantity and quality of public services worldwide (Aker, 2017; Batley and others 2012). Road networks represent a good example: Average road density in sub-Saharan Africa is 0.14 km/ km2 , compared to an average of 1.41/km2 in Southeast Asia, with the greatest density in Southern Africa (International Road Federation, 2019; Malah Kuete & Asongu, 2022). Nevertheless, only about 29% of subSaharan Africa’s over two million kilometers of roads are paved.1 Similarly, while sub-Saharan Africa has 17% of the world’s population, it accounts for only 3% of electricity consumption, with over 600 million people having no access to electricity (International Energy Agency, 2019; Malah Kuete & Asongu, 2022). Approximately 50% of the population do have access to electricity, ranging from 10% in countries such as the Central African Republic, Niger, the Democratic Republic of Congo, Chad, and Somalia and over 95% in South Africa (Fig. 3.1). Finally, while 50% of the population in sub-Saharan Africa has access to basic drinking water, there is a wide disparity across countries—notably between urban and rural areas (Fig. 3.2). The statistics are similar in education and health. One in every five children of primary school age were not in school (UNESCO, 2018), due to a number of supply and demand-side factors. While pupil-teacher ratios—a common indicator of human resource capacity in this sector— have improved since 2000, they remain at 56:1, compared with an average of less than 17:1 in Europe (UNESCO, 2021). However, even in those areas where teachers are present, teacher absenteeism remains a problem, ranging from 11 to 30% in some countries (Transparency International, 2013). In health care, approximately 48% of the population—some 615 million people—have access to the health care they need, with great variation by country (Fig. 3.3). Of course, there are a multitude of constraints to development that do not neatly fall into the “public services” bucket, and yet have direct—and

1 The World Bank, “Sustainable Energy for All” database is from the Sustainable Energy for All Global Tracking Framework led jointly by the World Bank, International Energy Agency, and the Energy Sector Management Assistance Program. While 85% of the world’s population has access to electricity, electricity access ranges from 20 to 80% across countries (World Bank Sustainable Energy for All database).

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Fig. 3.1 Percentage of the population with access to electricity (Source World Bank Global Electrification Database)

Fig. 3.2 Percentage of the population with access to basic drinking services (Source WHO/UNICEF Joint Monitoring Program for Water Supply, Sanitation and Hygiene)

crucial—consequences for development. We will address these in greater detail below.

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Fig. 3.3 Percentage of the population with access to universal health coverage (Source WHO)

3.1.2

Market Failures in Public Service Provision

What explains the poor and varied provision of public services in subSaharan Africa? Poor governance, weak institutions, information asymmetries, and high transaction costs are often cited as potential explanations of the under-provision and low quality of public services in low-income countries (Aker, 2017; Batley et al., 2012). While this is certainly part of the story, the provision of public services is also plagued by a number of market failures. Firstly, some public services are pure public goods and therefore will not be provided at optimal levels. This is partly due to the existence of free riders and the difficulty of governments—especially those with weak institutions—to identify citizens’ preferences and willingness to pay for such goods, as their value cannot be reflected by market prices. As a result, it is difficult both to finance such goods and determine their most efficient allocation. Box 3.1 Some (Economics) Terms Defined Public good: A good that is non-rival and non-excludable. Externality. An externality is present whenever the well-being of a consumer or the production possibilities of a firm are directly affected (not

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through prices) by the actions of another agent in the economy. They arise whenever the actions of one party make another party worse off or better off, yet the first party neither bears the costs nor receives the benefits of doing so. E.g., neighbor listening to loud music at night, second-hand smoke, pollution of a lake by a firm. Network good: A good whose socio-economic impact increases with the number of users (Björkegren, 2019; Katz & Shapiro, 1985, 1994).

Secondly, even if a public service is not a pure public good, it can generate positive externalities and network effects . For example, digital technologies and transport have important externalities (Cariolle & le Goff, 2023)—in general, the network becomes more useful as more people use it, or there are positive spillovers on non-users2 —but the product or service itself (e.g., a mobile phone, or a train) is not a public good (Besley & Ghatak, 2006). For public services of this nature, they will similarly be underprovided or underconsumed (Besley & Ghatak, 2006).3 Thirdly, public services are often plagued by issues of imperfect and asymmetric information: In contexts with poor infrastructure, high transaction costs, and limited budgets, a government may be unable to monitor teachers, doctors, or public service contractors. This can undermine the administration and delivery of public goods, which can lead to corruption, absenteeism, or shirking (Acemoglu & Verdier, 2000). Imperfect information can also affect citizens’ knowledge about public services, such as how to access and use them or how to provide feedback on their allocation and quality.4

2 We also consider digital technologies to be network goods, namely, goods whose socio-economic impact increases with the number of users (Björkgeren, 2019; Katz & Shapiro, 1985, 1994). 3 While we may think of digital technology or infrastructure as generating positive spillovers, we can also cite examples of negative spillovers. For example, investment in the road network reduces transaction costs, improves access to basic services, and—in theory— should increase market efficiency, but could simultaneously stimulate higher demand for cars and increase pollution. 4 “Imperfect information can also affect a government’s ability to finance the provision of public services. If governments are unable to identify consumers’ preferences and willingness to pay for such services, it can be difficult to determine their optimal provision. This, in turn, makes it more challenging to design tax schemes to fund public goods.

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Finally, given the nature of the public service contracts, some public services may be offered by only a few providers, either due to the fact that they are provided by the public sector or because of economies of scale (Aker, 2017). Uncompetitive markets will typically lead to higher prices, lower quantity, and lower quality of public services. 3.1.3

Market Failures, More Broadly

While we have discussed these market failures in the context of the provision of public services, they are not unique to public service provision—nor to sub-Saharan Africa. In fact, the key assumptions of economic theory—perfect information, no externalities, and perfect competition— are far from reality in both low- and high-income countries, and constrain individuals, households, firms, and institutions on their pathway to development. Some examples may be helpful to illustrate this point. A poor, illiterate farmer living in Rawayou, Niger, must travel a long distance on foot on very poor roads to obtain information about the price of the millet that he would like to sell. If the price is too low, he can either decide to sell his millet that day, or wait another day to sell in another market, where he may or may not receive a higher price. This results in lower profits for him, less income for his family, and broader inefficiencies in the agricultural system (Aker, 2010; Aker & Fafchamps, 2015). Similarly, in the United States, a poor, low-literate worker living in East Oakland must travel one hour (via bus) to reach the nearest supermarket. She is unable to use the internet to search for better prices elsewhere, and hence buys the food items that she has memorized by heart—which are often 15–20% more expensive. This results in higher costs for her, less income for her family, and broader inefficiencies in the food system (Viswanathan et al., 2005). The contexts are quite different, but the issue of imperfect information is the same. A similar type of thought experiment can be extended to other markets—such as labor and credit—and other types of market failures. These types of analyses are outside of the scope of this book,

Even if consumers’ preferences could be revealed, an additional question is whether tax schemes could be effectively enforced, thus further reducing the financing mechanisms available to finance public goods” (Aker, 2017).

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but they are useful to keep in mind as we discuss the capabilities and limitations of digital technology.

3.2 Jack of All Trades, Master of All? The Multitude of Uses of Digital Technology Given the above constraints, there are good reasons to believe that mobile phones could be the gateway to better lives and livelihoods, especially for the poor in sub-Saharan Africa. There are two main potential mechanisms through which mobile phones can affect consumers’ and producers’ welfare in low-income countries. The first is as a communication device, as digital technology can increase access to—and use of—information in a variety of sectors, thereby modifying individuals’ behavior, improving market efficiency, and increasing access to public services. The second is as a financial service device, as digital technology—especially with the advent of mobile money—can improve individuals’ access to public and private transfers, enable firms to receive more timely payments, as well as save and borrow. Given the rate of the adoption of internet and 4G across the continent, we primarily focus on simpler devices—such as mobile phones, laptops, or computers. Box 3.2 Search Costs for Farmers in Niger Farmers require information on a variety of topics at each stage of the agricultural production process, including inputs and production techniques, post-harvest storage, and input and output prices. In many low-income countries, such information has traditionally been provided via personal exchanges, radio and—to a lesser extent—newspapers. Compared with these mechanisms, mobile phones have significantly reduced the costs of obtaining agricultural information. Prior to the introduction of mobile phones in Niger, the cost of a personal visit to the closest market to obtain price information would have been $0.80. The equivalent cost of a mobile phone call would have been $0.20, about 25% of the price. While other mechanisms—such as radio and landlines—could have been cheaper, landlines are not readily available in most regions of the country, and radio only provides price information for specific products and markets on a weekly basis. These cost reductions were greater for traders, who typically had to travel longer distances across more markets to search for price information.

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In many low-income countries—and especially those in sub-Saharan Africa—producers, traders, and consumers have relied primarily upon personal travel, radio, and—to a lesser extent—newspapers, to obtain information on prices, employment opportunities, and wages, health topics, weather, and politics (Aker, 2011; Aker & Blumenstock, 2014; Aker & Mbiti, 2010).5 Travel costs can be relatively high in countries with long distances and poor transportation infrastructure (Robinson et al., 2022). Broadly speaking, digital technologies reduce the costs of storing, processing, and transmitting information from a wide variety of sources, whether personal or professional networks, information “clearinghouses” (like the internet) or governments (Goldfarb & Tucker, 2019). Digital information transmission is, therefore, on-demand, immediate, and bidirectional, in contrast to radio broadcasts, newspapers, or letters (Aker & Mbiti, 2010). As a result, the digitization of information and communication flows considerably reduces the monetary and opportunity costs of information searching, especially over long distances, as compared with the per-search equivalent of other technologies.6 In Niger, for example, the introduction of mobile phones significantly reduced the costs of searching for agricultural price information as compared with personal travel (Box 3.2). This reduction in information search costs should enlarge the scope, quality, and timeliness of searching for information on a variety of topics. This can happen via two potential pathways. The first is via a reduction in imperfect information: In theory, reduced search costs should allow individuals and firms to increase their knowledge and engage in more optimal decision-making, thereby improving welfare (Fabregas et al., 2019; Fu & Akter, 2016; LaRochelle et al., 2019). This can happen in a variety of contexts—agricultural and health inputs and prices, health and financial 5 This reduction in search costs does not necessarily require an outside intermediary— such as a webpage, or information clearinghouse—to facilitate information-sharing. Rather, it can rely upon individuals’ existing social and commercial networks. 6 For example, while radios can be used across all segments of the population, they generally provide a limited range of information. In addition, newspapers are primarily concentrated in urban areas, are expensive, and are inaccessible to illiterate populations. Approximately 1 in 5 individuals in sub-Saharan Africa read a newspaper at least once per week, with a much smaller share in rural areas (Aker & Mbiti, 2010). Estimates of ownership of a television set range considerably, from 30 to 70%, primarily driven by ownership in North and South Africa.

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services, labor markets and different types of shocks. Increased information should, under certain conditions, improve market efficiency (in terms of price dispersion and the allocation of goods across markets), and hence increase net welfare gains (Aker, 2010; Aker & Fafchamps, 2015; Aker & Ksoll, 2016; Aron, 2018; Björkegren & Grissen, 2018; Dammert et al., 2014; Jensen, 2007; Suri, 2017).7 Yet how these gains are distributed among different actors is ambiguous. The second potential pathway is via a reduction in asymmetric information, i.e., information in transactions between two parties, such as employers and employees, buyers and sellers, lenders and borrowers, among others. The reduction in search costs can improve one actor’s ability to obtain information about another agent’s actions in a variety of domains. For example, the reduction in search costs could better allow the Ministry to monitor a teacher’s presence in the classroom (rather than making a visit), or a buyer’s ability to obtain necessary price information prior to a contractual negotiation (Adida et al., 2018; Aker & Ksoll, 2020; Cilliers et al., 2018; McNabb et al., 2015; Zant, 2022). In a public sector context, this improved monitoring could reduce absenteeism (of doctors, nurses, teachers, extension agents) and improve agents’ performance, thereby leading to improved quality of services provided and downstream outcomes (such as learning). In a contractual context, the reduction in search costs could improve one party’s bargaining position via the other in a contractual negotiation. For example, if farmers are able to access price information more quickly, in theory, they could increase their bargaining power vis-à-vis traders (Courtois & Subervie, 2014; Jensen, 2010). Yet if traders also benefit from improved price information, or if markets are uncompetitive, then the reduction in search costs could further strengthen their bargaining power vis-à-vis farmers (Zant, 2022). None of the above pathways require an external information source— or information clearinghouse—for these impacts to occur. In fact, the

7 The concept of “information overload” suggests that too much information makes

decision-making difficult and causes stress. Behavioral scientists argue that bounded rationality—the idea that we make rational decisions within the constraints of time, available information, and brain power—helps us to find shortcuts to information overload and make decisions that satisfy us. This can lead to second-best decisions, rather than optimal decisions.

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sources of information often occur via social networks, personal, or otherwise. This can then have important applications for households’ ability to cope with risk. Sub-Saharan Africa is an inherently risky environment—whether natural disasters, conflicts, and epidemics—and kinship ties have important social and economic roles, specifically in creating informal insurance networks, increasing access to credit and savings, and reducing risk (de Weerdt & Dercon, 2006; Grimard, 1997). By improving communications among members of a social network, this can increase the speed of information flows within the network, thereby allowing them to respond better to shocks and affect social learning. 3.2.1

Digital Technologies Can Make It Cheaper to Develop and Transport Content

Beyond the costs of searching for information, digital technologies drastically reduce the cost of transporting content, under the assumption that there is no network congestion (Goldfarb & Tucker, 2019). As outlined above, communication through mobile phones can replace expensive, time-consuming personal travel that was once necessary for a variety of transactions, including doctor follow-up with patients, agricultural extension agent field visits, borrower monitoring in the financial sector, etc. Box 3.3 Digitizing Content: Phone Call-Based Pedagogy in Botswana During the COVID-19 pandemic, school closures forced 1.6 billion students out of the classrooms. The same was true in Botswana, which faced multiple school closures and then “double shift” schools in order to limit the number of students in the classroom. To stem this, a program worked with the Ministry to develop simple math lessons via SMS and phone calls, which were provided to parents (along with the participation of students) (Angrist et al., 2022).

The idea is simple but powerful: Rather than providing information in-person, information service providers—whether governments, universities, Khan Academy, or the private sector—can develop and transport such information digitally. This is based upon the idea of the zero-marginal cost (and non-rivalry) associated with the production of digital “public goods” (Goldfarb & Tucker, 2019); in other words, once the product

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has been produced (a fixed cost), the cost of every additional unit is essentially zero.8 This can have important consequences for the provision of public services in the agriculture, health, and education sectors, especially information-based services. For example, providing technical agricultural information via SMS and a phone hotline is, on average, cheaper than an extension visit, and the equivalent of providing the same information via radio (Aker, 2011). This could reduce the costs of disseminating technical information by the public sector, thereby increasing the extension system’s geographic scope and scale (Aker, 2011; Cole & Fernando, 2021) and improve farmers’ access to information at more crucial moments (Björkman & Svennson, 2009; Patel et al., 2010; Veeraraghavan et al., 2007). In the education sector, pedagogical materials could be easily developed—whether phone calls, SMS, or digitized pedagogical materials—and shared, thereby allowing students, teachers, and other stakeholders to gain access to content that was previously unavailable (Box 3.3). A similar process could occur in the health sector, as health workers can easily and remotely send medication reminders and advice through text messages, requiring simple communication technologies such as mobile phones (Yé et al., 2018). 3.2.2

Digital Technologies Can Reduce the Cost of Collecting, Processing, and Storing Information

The digitization of information can also reduce the costs of collecting, processing, and storing information on a variety of topics (Aker, 2017). Digital data collection can take several forms, from phone calls, or SMSbased surveys to mobile phone transaction data or “digital footprints left behind in the transaction logs of mobile phones” (Blumenstock, 2016). Putting aside the important privacy issues stemming from the exploitation of personal information made available by digital technology usage, information digitization can be beneficial to the provision of public goods in a number of ways. Firstly, in theory, digital data can allow governments to better obtain information on citizen’s preferences and hence allow them to better allocate public goods (Blumenstock, 2016; McNabb et al., 8 “A key distinction between goods made of atoms and goods made of bits is that bits are non-rival, meaning that they can be consumed by one person without reducing the amount or quality available to others” (Goldfarb & Tucker, 2019).

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2015) or enable citizens to better monitor governments and increase accountability (Aker, 2017; Aker & Blumenstock, 2014; Blumenstock, 2016). 3.2.3

Digital Technologies Can Reduce the Costs of Accessing Public and Private Transfers

So far, we have focused on the basic functions of the mobile phone as a communications device over voice and Short Message Service (SMS) protocols, and, more recently, internet access. More recent innovations, however, have utilized the mobile phone network as a platform for other services that facilitate innovation in several sectors. Arguably, the most prominent of these are digital financial services , the primary of which is “mobile money” (m-money, or “MoMo”). First introduced in 2005, basic m-money applications allow clients to store value in an account accessible by a handset, convert cash in and out of the stored value account, and transfer value between individual users, as well as between individuals and firms, public utilities, educational institutions, or other entities (Aker & Mbiti, 2010; Lashitew et al., 2019). A “pseudo account” can be established by purchasing “electronic money” from an agent, usually a third party (such as a shop owner, trader, or other business) or someone who works for the mobile phone operator or bank. The user can then send e-money to another recipient with a phone, who then withdraws the e-money from their local transfer agent.9 Fees are generally charged for each transaction. Different institutional and business models provide these services: Some are offered entirely by banks; others entirely by telecommunications providers and still others via a partnership between a financial institution and a mobile phone service provider (Lashitew et al., 2019; Porteous, 2006). Since its inception, m-money applications have emerged in Asia, Latin America, and Africa, with the highest penetration in sub-Saharan Africa. With over 145 m-money deployments in Africa as of 2020, the continent contains almost half of the m-money systems implemented worldwide (Fig. 3.4). While 60% of the adult population in sub-Saharan Africa 9 Users can also use the balances of their m-money accounts to pay for public utilities such as electric and municipal water services (Lashitew et al., 2019), as well as to pay employee salaries, pensions, and social protection program benefits (Aker et al., 2016; Akinyemi & Mushunje, 2020;).

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Fig. 3.4 Number of mobile money deployments worldwide (Source GSMA Deployment tracker)

has a mobile money account (almost 100% in Kenya), there is large country-level heterogeneity in adoption and usage (GSMA, 2019).10 The introduction of m-money services has significantly reduced the cost of transferring money as compared with other means, including friends, public transport, MoneyGram, and banks. This can have mechanisms of impact in different ways. This reduction in transaction costs can, in turn, for payments between a number of different agents—whether person-to-person (P2P), person to businesses (P2B), governments to persons (G2P), or businesses to people (B2P). P2P payments allow individuals to transfer money when and where they need it to members of their social network (Aker et al., 2016; Blumenstock et al., 2015; Jack & Suri, 2014) over larger geographic areas and at times when needed. This, in turn, can potentially increase the frequency and number of transfers received and allow households to smooth consumption in the face of idiosyncratic or covariate shocks (Batista et al., 2018; Blumenstock et al., 2016; Economides & Jeziorski, 2017; Jack & Suri, 2014; Riley, 2018). G2P and B2P payments could reduce the costs associated with implementing public transfer programs, paying employees’ salaries,

10 Among these 13 countries, 4 were West African countries: Benin, Burkina Faso, Côte d’Ivoire, and Ghana.

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providing input vouchers, or making insurance payments (Blumenstock et al., 2013). Beyond the payment features, m-money offers other potential mechanisms for improving well-being. First, m-money could be used to create a secure place to save—usually at zero interest—whereby individuals can deposit smaller savings amounts for more immediate needs (Aker & Wilson, 2013; Mas, 2011). As the “account” is password-protected, mmoney might offer greater security while increasing access. In addition, m-money could encourage individuals to save for particular objectives, thereby serving as a form of mental accounting (Aker & Blumenstock, 2014; Suri et al., 2021). Finally, mobile money user data could potentially be used to predict loan amounts and loan repayments (Björkegren & Grissen, 2020; Blumenstock et al., 2015), potentially overcoming a crucial information constraint in credit markets.

3.3

A Framework for Analysis

Given the potential impacts of digital technology on well-being, an obvious question is: Does digital technology live up to the hype? To help us to assess this, a framework for analyzing the potential impact of digital technologies is needed. Overall, and unsurprisingly, the potential impacts of digital technology are mediated by a number of factors. These include, among others, the quality of information provided, trust in the source of that information and the size (and influence) of one’s social network; the presence of other (non-information) market failures that could constrain, or support, the impact of the technology; and whether the technology (or service) will be adopted, and hence used. 3.3.1

Not All Information Is Created Equal

A key assumption of using digital for development is that these digital innovations are addressing a key market failure: namely, imperfect or asymmetric information or high transaction costs. While this is a relevant assumption in many contexts and markets, digital will only be successful in increasing knowledge, changing behavior, and improving welfare if a number of necessary conditions exist. The first is the development

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problem itself. In general, one should ask the question: Is information a constraint in a given market, for a given problem, for the specific target population? Wealthier farmers and traders in Niger, for example, may be less information-constrained than poorer farmers, who may (or may not) be the target of the digital for agriculture service. Similarly, more complicated concepts—such as intercropping or diagnosing specific diseases—cannot always be easily conveyed via SMS or even audio, and hence might need some type of digital imagery or training to convey the concepts. Secondly, even if information is a constraint, it is not certain that digital can provide that information in a format that is useful, timely, and “easy to share” via the relevant digital tool—whether via SMS, a hotline, or via the web. An implicit assumption in most digital content is that the information provided is of high quality, meaning that it meets the agent’s specific information need, in a timely manner, and is provided via a reliable (and trustworthy) source. For example, if a market price information system provides consumer prices for crops in markets that are irrelevant for local farmers, then the utility of that information will be limited. Similarly, if pedagogical information is provided to students in a language that they do not easily understand, this diminishes the utility of the information. Thirdly, even if the information is of high quality, the source of that information and whether it can be trusted must be considered.11 Overall, research across different disciplines emphasizes that trust in the information source affects the way information is interpreted, accepted, and acted upon. Thus, a key issue for many individuals is whether one’s social network—compared to agricultural extension agents, doctors, nurses, or teachers—is the most reliable sources of information. If such information is traditionally exchanged in person rather than digitally, disentangling the information from the information provider via a nameless, faceless digital program can affect participants’ willingness to use such systems or trust the information, whether in agriculture or health (Banerjee et al., 2021; Oreglia et al., 2011; Srinivasan, 2007). 11 While there is a substantial economics literature on trust and information provision in general (Markiewicz & Adamus, 2013; Shapiro et al., 1999; Tu & Bulte, 2010), there is relatively less literature on how trust affects the way in which agricultural information provision is received and interpreted. Adegbola and Gardebroek (2007) and Moser and Barrett (2006) study how farmers learn about agricultural technologies from agricultural extension agents and community farmers, and find differences in adoption depending upon the information source. Sociological and anthropological research yields similar findings.

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Finally, even if the information is of high quality and from a trusted source, a number of studies find that individuals only adopt once they are connected to a threshold number of adopters, a matter of “critical mass” (Acemoglu et al., 2011; Beaman et al., 2016; Centola & Macy, 2007; Kondylis et al., 2017).12 All of the above, however, presumes that the information provided comes from an external source (such as an information clearinghouse) that is available to all. But what if the information is shared only (or primarily) through one’s social network? Dillon et al. (2020) note that the potential private returns associated with a reduction in communication costs may not necessarily be realized if one’s pre-existing social network is limited. Given the importance of face-to-face interaction in building trust, digital technology may not be able to entirely replace these interactions (Duncombe & Heeks, 2002; Jagun et al., 2008; Molony, 2008; Slater & Kwami, 2005). Sociological research demonstrates that the value of the mobile phone is primarily in consolidating social networks (Burrell, 2014), thereby building trust within one’s existing social network (Overå, 2006). 3.3.2

The Missing Market

Even if the information provided via digital means is necessary, relevant, timely, of high quality and from a trusted source, individuals, households, and firms still need access to other markets—such as financial services—and public goods (roads, property rights, schools, and health centers) in order to translate information into action. For example, if farmers have limited bargaining power with traders, primarily because they have monopsony power, then price information will not necessarily change farmers’ bargaining capacity and hence improve their welfare outcomes (Aker, 2008; Goyal, 2010; Mitra et al., 2018; Nakasone et al., 2014; Zant, 2022). Similarly, if there are interdependencies between

12 A related issue is the type of information provided. For example, in agriculture, while price information is arguably the most frequently offered type of information service, other types of information may be under-recognized. For example, Burrell and Oreglia (2015) find that fisherman needed timely updates about travel delays between fishermen and traders, as well as the need for ice and fuel. Fabregas et al. (2021) find that farmers demand detailed information about their plots, soil quality, etc. in order to make the most optimal choices.

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information provision, trade, and related markets, often termed “interlinked transactions”—in other words, a transaction whereby the output price is jointly determined with credit (e.g., Bardhan, 1980; Bell, 1988; Braverman & Stiglitz, 1982; Deb & Suri, 2013; Grosh, 1994), then any benefits from improved digital information transmission may not pass through to farmers (Burrell & Oreglia, 2015; Casaburi & Reed, 2014; Srinivasan & Burrell, 2013). Other “missing markets” can also impinge on an individual’s ability to act on digitally provided information. Given the high rates of financial exclusion in sub-Saharan Africa, the unavailability of timely and affordable credit can realistically limit farmers’ capacity to meaningfully use any information provided, as they may not have the necessary cash at the time they need to purchase inputs or sell (Casaburi & Reed, 2014; Srinivasan & Burrell, 2013). Similarly, if an individual knows where to find a given medication, but they arrive at the pharmacy (or hospital) and there are stock shortages, or no doctors or nurses, then this does not allow them to get the medical attention that they need. 3.3.3

If You Build It, Will They Come?

A key question, of course, is whether the target population owns the digital device that is needed—whether a smart or “dumb” phone, tablet, computer, or radio. As outlined in Chapter 1, 20% of the population of sub-Saharan Africa does not have mobile phone coverage, and approximately 40% do not have a subscription. This suggests that a substantial number of people—approximately 200 million, or the entire population of Nigeria—do not have access to digital technology. These populations are some of the poorest and most remote in Africa. Beyond access, there are also significant cost constraints that shape how rural users interact with their devices (Wyche & Steinfeld, 2016). Users may switch off their phones to “preserve the charge” or only maintain small airtime balances on their phones, which constrains their ability to make calls or send texts. Similarly, phones (or SIM cards) are often used interchangeably or switched out, with high churn rates, which can make targeting the intended beneficiary difficult. And finally, wearand-tear on phones can result in broken screens or numerical keypads rubbed blank, which further confound users’ ability to use the technology (Dodson et al., 2013; Wyche & Steinfield, 2016). All of these

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practical features limit agricultural agents’ access to and sustained use of the service, regardless of its potential utility. Beyond access to the technology itself, other factors may constrain individuals’ use of digitally-provided service. Due to the widespread availability and adoption of basic “feature” phones in rural areas in most developing countries, many digital services are primarily provided for such phones, using SMS or USSD technology. Yet these “simple” phones are used in very limited ways by low-literate and non-literate users, primarily voice and audio channels (Aker et al., 2012; Dodson et al., 2013; Wyche & Steinfield, 2016). Many digital initiatives have attempted to address these barriers by designing interfaces specifically for low-literate users (Medhi et al., 2010, 2011) by incorporating interactive voice response (IVR), training or relying on intermediaries; however, these complementary “add ons” can be significantly more costly to develop, implement, and sustain (Parikh & Ghosh, 2006; Sambasivan et al., 2010).13 The language of digital information provision is also a potential challenge, as indigenous languages may not be written or the correct alphabet may not be available on users’ phones (Dodson et al., 2013; Wyche & Steinfeld, 2016). In the area of m-money, a specific constraint is access to mobile money agents, which are key for the functioning of the mobile money system. The success of m-money systems is certainly underpinned by the rapid deployment and growth of the agent network, i.e., the end distributors of the service. This growth and reliability are associated with a network that is trustworthy, efficient, liquid, and profitable for the agents. While the m-money agent network has grown significantly in sub-Saharan Africa— for example, it is at least triple the number of bank branches in Kenya, Uganda, and Tanzania—this is not universally the case (Demirgüç-Kunt et al., 2018). Without access to an agent network, mobile money users have to travel long distances, thus reducing the efficacy of the mobile money technology. 3.3.4

Technology for All, Benefits for…Some?

While economic theory predicts that mobile phones and digitally-based services can help make markets more efficient, the distribution of these 13 Intermediated use of digital is the practice of relying upon the skills of another person to operate an ICT as a way to overcome a lack of requisite skill.

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gains across different actors is unclear, and striking disparities exist between mobile phone owners and non-owners (Blumenstock & Eagle, 2012). Growing disparities as benefits accrue to those able to access or effectively use digital technologies while those without access are left behind. In addition, significant heterogeneity exists within the population of mobile phone owners along gender, ethnicity, income, and caste dimensions. Blumenstock et al. (2015) showed that the wealthiest mobile phone users were the most likely to receive interpersonal m-money transfers after idiosyncratic negative shocks. Gender-focused sociological and anthropological research has shown that mobile phone ownership, sharing, and usage have tended to be skewed in favor of male heads of household (Burrell, 2010). A key dimension worth consideration (although there are many others) is that of gender. In sub-Saharan Africa, as is the case in other parts of the world, women play an important role in the agricultural sector (World Bank, 2008). While women could potentially benefit the most from digitally-based agricultural services, as was seen in Chapter 2, there is a wide “digital divide” in terms of women’s access to and use of digital technology, with women 14% less likely to own a mobile phone than men, in part due to costs, education, and gendered social norms. However, even if women have access to a mobile phone within the household, they may have limited privacy or independence while using it (Burrell, 2010; Dodson et al., 2013) or be further constrained due to low levels of language and technical literacy (Geldof, 2011; Stromquist, 1992; Wyche & Steinfeld, 2016). These differences in access and usage not only affect women’s potential usage of digital technology, but also their ability to translate these services into action.

References Acemoglu, D., Ozdaglar, A., & Yildiz, E. (2011, December). Diffusion of innovations in social networks. In 2011 50th IEEE conference on decision and control and European control conference (pp. 2329–2334). IEEE. Acemoglu, D., & Verdier, T. (2000). The choice between market failures and corruption. American Economic Review, 90(1), 194–211. https://doi.org/ 10.1257/aer.90.1.194 Adegbola, P., & Gardebroek, C. (2007). The effect of information sources on technology adoption and modification decisions. Agricultural Economics, 37 (1), 55–65. https://doi.org/10.1111/j.1574-0862.2007.00222.x

48

J. C. AKER AND J. CARIOLLE

Adida, C. L., Chabi Bouko, A., Verink, A., Chockalingam, G., & Burney, J. (2018). Pilot of a mobile money school fee payment system in rural Benin. PLOS One, 13(6), e0198240. https://doi.org/10.1371/journal.pone. 0198240 Aker, J. C. (2008). Does digital divide or provide? The impact of cell phones on grain markets in Niger (Center for Global Development working paper, 154). Aker, J. C. (2010). Information from markets near and far: Mobile phones and agricultural markets in Niger. American Economic Journal: Applied Economics, 2(3), 46–59. https://doi.org/10.1257/app.2.3.46 Aker, J. C. (2011). Dial “A” for agriculture: A review of information and communication technologies for agricultural extension in developing countries. Agricultural Economics, 42(6), 631–647. Aker, J. C. (2017, November). Using digital technology for public service provision in developing countries potential and pitfalls. In S. Gupta, M. Keem, A. Shah, & G. Verdier (Eds.), Digital revolutions in public finance. IMF. Aker, J. C., & Blumenstock, J. E. (2014). The economic impacts of new technologies in Africa. In The Oxford handbook of Africa and economics (pp. 354– 371). Oxford University Press. https://doi.org/10.1093/oxfordhb/978019 9687107.013.021 Aker, J., Blumenstock, J. E., & Dillon, B. (2020, March). How important is the yellow pages? Experimental evidence from Tanzania (CEPR Discussion Paper No. DP14489). https://ssrn.com/abstract=3560296 Aker, J. C., Boumnijel, R., McClelland, A., & Tierney, N. (2016). Payment mechanisms and antipoverty programs: Evidence from a mobile money cash transfer experiment in Niger. Economic Development and Cultural Change, 65(1), 1–37. https://doi.org/10.1086/687578 Aker, J. C., & Fafchamps, M. (2015). Mobile phone coverage and producer markets: Evidence from West Africa. The World Bank Economic Review, 29(2), 262–292. https://doi.org/10.1093/wber/lhu006 Aker, J. C., & Ksoll, C. (2016). Can mobile phones improve agricultural outcomes? Evidence from a randomized experiment in Niger. Food Policy, 60, 44–51. Aker, J. C., & Ksoll, C. (2020). Can ABC lead to sustained 123? The mediumterm effects of a technology-enhanced adult education program. Economic Development and Cultural Change, 68(3), 1081–1102. Aker, J. C., Ksoll, C., & Lybbert, T. J. (2012). Can mobile phones improve learning? Evidence from a field experiment in Niger. American Economic Journal: Applied Economics, 4(4), 94–120. Aker, J. C., & Mbiti, I. M. (2010). Mobile phones and economic development in Africa. Journal of Economic Perspectives, 24(3), 207–232.

3

THE ECONOMICS OF THE PHONE

49

Aker, J. C., & Wilson, K. (2013). Can mobile money be used to promote savings? Evidence from Northern Ghana (SWIFT Institute Working Paper No. 2012003). https://doi.org/10.2139/ssrn.2217554 Akinyemi, B. E., & Mushunje, A. (2020). Determinants of mobile money technology adoption in rural areas of Africa. Cogent Social Sciences, 6(1), 1815963. https://doi.org/10.1080/23311886.2020.1815963 Angrist, N., Bergman, P., & Matsheng, M. (2022). Experimental evidence on learning using low-tech when school is out. Nature Human Behaviour, 6(7), 941–950. https://doi.org/10.1038/s41562-022-01381-z Aron, J. (2018). Mobile money and the economy: A review of the evidence. The World Bank Research Observer, 33(2), 135–188. Banerjee, A., Chandrasekhar, A. G., Dalpath, S., Duflo, E., Floretta, J., Jackson, M. O., ... & Shrestha, M. (2021). Selecting the most effective nudge: Evidence from a large-scale experiment on immunization (No. w28726). National Bureau of Economic Research. Bardhan, P. K. (1980). Interlocking factor markets and agrarian development: A review of issues. Oxford Economic Papers, 32(1), 82–98. Bartik, T. J. (1991). Boon or boondoggle? The debate over state and local economic development policies. W.E. Upjohn Institute for Employment Research. Batley, R., McCourt, W., & Mcloughlin, C. (2012). Editorial: The politics and governance of public services in developing countries. Public Management Review, 14(2), 131–144. https://doi.org/10.1080/14719037.2012.657840 Batista, C., Fafchamps, M., & Vicente, P. C. (2018). Keep it simple: A field experiment on information sharing in social networks (No. w24908). National Bureau of Economic Research. Beaman, L., BenYishay, A., Fatch, P., Magruder, J., & Mobarak, A. M. (2016). Making networks work for policy: Evidence from agricultural technology adoption in Malawi (3ie Impact Evaluation Report 43). International Initiative for Impact Evaluation (3ie). Beaman, L., BenYishay, A., Magruder, J., & Mobarak, A. M. (2021). Can network theory-based targeting increase technology adoption? The American Economic Review, 111(6), 1918–1943. https://doi.org/10.1257/AER.202 00295 Bell, C. (1988). Credit markets and interlinked transactions. Handbook of Development Economics, 1, 763–830. Besley, T., & Ghatak, M. (2006). Public goods and economic development. In A. V. Banerjee, R. Benabou, & D. Mookherjee (Eds.), Understanding poverty (pp. 285–302). Oxford University Press. Björkegren, D. (2019). The adoption of network goods: Evidence from the spread of mobile phones in Rwanda. The Review of Economic Studies, 86(3), 1033–1060.

50

J. C. AKER AND J. CARIOLLE

Björkegren, D., & Grissen, D. (2018, May). The potential of digital credit to bank the poor. In AEA Papers and Proceedings (Vol. 108, pp. 68–71). Björkegren, D., & Grissen, D. (2020). Behavior revealed in mobile phone usage predicts credit repayment. The World Bank Economic Review, 34(3), 618–634. Björkman, M., & Svensson, J. (2009). Power to the people: Evidence from a randomized field experiment on community-based monitoring in Uganda. The Quarterly Journal of Economics, 124(2), 735–769. Blumenstock, J. E. (2016). Fighting poverty with data. Science (American Association for the Advancement of Science), 353(6301), 753–754. https://doi. org/10.1126/science.aah5217 Blumenstock, J. E., Callen, M., & Ghani, T. (2013). Mobile salary payments in Afghanistan: Policy implications and lessons learned (PEDL Research Paper No. 9). Blumenstock, J. E., Callen, M., Ghani, T., & Koepke, L. (2015, May). Promises and pitfalls of mobile money in Afghanistan: Evidence from a randomized control trial. In Proceedings of the seventh international conference on information and communication technologies and development (pp. 1–10). Blumenstock, J. E., & Eagle, N. (2012). Divided we call disparities in access and use of mobile phones in Rwanda. Information Technologies & International Development, 8(2), 1. Blumenstock, J. E., Eagle, N., & Fafchamps, M. (2016). Airtime transfers and mobile communications: Evidence in the aftermath of natural disasters. Journal of Development Economics, 120, 157–181. https://doi.org/10.1016/ j.jdeveco.2016.01.003 Braverman, A., & Stiglitz, J. E. (1982). Sharecropping and the interlinking of agrarian markets. The American Economic Review, 72(4), 695–715. Burrell, J. (2010). Evaluating shared access: Social equality and the circulation of mobile phones in rural Uganda. Journal of Computer-Mediated Communication, 15(2), 230–250. Burrell, J. (2014). Modernity in material form? Mobile phones in the careers of Ghanaian market women. Review of African Political Economy, 41(142), 579–593. https://doi.org/10.1080/03056244.2014.928611 Burrell, J., & Oreglia, E. (2015). The myth of market price information: Mobile phones and the application of economic knowledge in ICTD. Economy and Society, 44(2), 271–292. https://doi.org/10.1080/03085147.2015.101 3742 Cariolle, J., & le Goff, M. (2023). Spatial internet spillovers in manufacturing, The Journal of Development Studies, 59(8), 1163–1186, https://doi.org/10. 1080/00220388.2023.2204177 Casaburi, L., & Reed, T. (2014). Interlinked transactions and pass-through: Experimental evidence from Sierra Leone. Stanford Institute for Economic Policy Research, Stanford University.

3

THE ECONOMICS OF THE PHONE

51

Centola, D., & Macy, M. (2007). Complex contagions and the weakness of long ties. American Journal of Sociology, 113(3), 702–734. https://doi.org/10. 1086/521848 Cilliers, L., Viljoen, K. L. A., & Chinyamurindi, W. T. (2018). A study on students’ acceptance of mobile phone use to seek health information in South Africa. Health Information Management Journal, 47 (2), 59–69. https://doi. org/10.1177/1833358317706185 Cole, S. A., & Fernando, A. N. (2021, January). ‘Mobile’izing Agricultural Advice Technology Adoption Diffusion and Sustainability. The Economic Journal, 131(633), 192–219. https://doi.org/10.1093/ej/ueaa084 Conley, T. G., & Udry, C. R. (2010). Learning about a new technology: Pineapple in Ghana. American Economic Review, 100(1), 35–69. https:// doi.org/10.1257/aer.100.1.35 Courtois, P., & Subervie, J. (2014). Farmer bargaining power and market information services. American Journal of Agricultural Economics, 97 (3), 953–977. Courtois, P., & Subervie, J. (2015). Farmer bargaining power and market information services. American Journal of Agricultural Economics, 97 (3), 953–977. https://doi.org/10.1093/ajae/aau051 Deb, R., & Suri, T. (2013). Endogenous emergence of credit markets: Contracting in response to a new technology in Ghana. Journal of Development Economics, 101, 268–283. https://doi.org/10.1016/j.jdeveco.2012. 11.006 Dammert, A. C., Galdo, J. C., & Galdo, V. (2014). Preventing dengue through mobile phones: Evidence from a field experiment in Peru. Journal of Health Economics, 35, 147–161. Demirgüç-Kunt, A., Klapper, L. Singer, D., Ansar, S. & Hess. J. (2018). The global findex database 2017: Measuring financial inclusion and the fintech revolution. World Bank, Washington, DC. https://doi.org/10.1596/978-14648-1259-0 De Weerdt, J., & Dercon, S. (2006). Risk-sharing networks and insurance against illness. Journal of Development Economics, 81(2), 337–356. https://doi.org/ 10.1016/j.jdeveco.2005.06.009 Dillon, B., Aker, J. C., & Blumenstock, J. E. (2020). How important is the yellow pages? experimental evidence from Tanzania (CEGA Working Paper Series No. WPS-21). Center for Effective Global Action. University of California, Berkley. https://doi.org/10.26085/C3988V Dodson, L. L., Sterling, S. R., & Bennett, J. K. (2013, December). Minding the gaps: Cultural, technical and gender-based barriers to mobile use in oral-language Berber communities in Morocco. In Proceedings of the Sixth International Conference on Information and Communication Technologies and Development: Full Papers-Volume 1 (pp. 79–88).

52

J. C. AKER AND J. CARIOLLE

Duncombe, R., & Heeks, R. (2002). Enterprise across the digital divide: Information systems and rural microenterprise in Botswana. Journal of International Development: The Journal of the Development Studies Association, 14(1), 61–74. https://doi.org/10.1002/jid.869 Economides, N., & Jeziorski, P. (2017). Mobile money in Tanzania. Marketing Science, 36(6), 815–837. https://doi.org/10.1287/mksc.2017.1027 Fabregas, R., Kremer, M., & Schilbach, F. (2019). Realizing the potential of digital development: The case of agricultural advice. Science, 366(6471), 1328. https://doi.org/10.1126/science.aay3038 Fisher, R. C. (1997). The effects of state and local public services on economic development. New England Economic Review, 2, 53–66. Fox, W. F., & Murray, M. N. (1991). The effects of local government public policies on the location of business activity. Industry location and public policy, 97119. Fu, X. L., & Akter, S. (2016). The impact of mobile phone technology on agricultural extension services delivery: Evidence from India. The Journal of Development Studies, 52(11), 1561–1576. https://doi.org/10.1080/002 20388.2016.1146700 Geldof, M. (2011). Earphones are not for women: Gendered ICT use among youths in Ethiopia and Malawi. Information Technologies & International Development, 7 (4), 69. Goldfarb, A., & Tucker, C. (2019). Digital economics. Journal of Economic Literature, 57 (1), 3–43. Goyal, A. (2010). Information, direct access to farmers, and rural market performance in central India. American Economic Journal: Applied Economics, 2(3), 22–45. Grimard, F. (1997). Household consumption smoothing through ethnic ties: Evidence from Cote d’Ivoire. Journal of Development Economics, 53(2), 391– 422. https://doi.org/10.1016/S0304-3878(97)00016-3 Grosh, B. (1994). Contract farming in Africa: An application of the new institutional economics. Journal of African Economies, 3(2), 231–261. GSMA. (2019). State of the industry report on mobile money 2019. https:// www.gsma.com/sotir/wp-content/uploads/2020/03/GSMA-State-of-theIndustry-Report-on-Mobile-Money-2019-Full-Report.pdf International Energy Agency (IEA). (2019). Africa Energy Outlook 2019. IEA, Paris. https://www.iea.org/reports/africa-energy-outlook-2019, License: CC BY 4.0. International Road Federation. (2019). IRF world road statistics 2019: Assessing the global road network, South Asian countries report highest road network density, whilst North Africa scores lowest as a region. IRF News. https://irf net.ch/2019/09/30/irf-world-road-statistics-2019-data-north-af/

3

THE ECONOMICS OF THE PHONE

53

Jack, W., & Suri, T. (2014). Risk sharing and transactions costs: Evidence from Kenya’s mobile money revolution. American Economic Review, 104(1), 183– 223. https://doi.org/10.1257/aer.104.1.183 Jagun, A., Heeks, R., & Whalley, J. (2008). The impact of mobile telephony on developing country micro-enterprise: A Nigerian case study. Information Technologies & International Development, 4(4), 47. Jensen, R. (2007). The digital provide: Information (technology), market performance, and welfare in the South Indian fisheries sector. The Quarterly Journal of Economics, 122(3), 879–924. Jensen, R. T. (2010). Information, efficiency, and welfare in agricultural markets. Agricultural Economics, 41, 203–216. https://doi.org/10.1111/j. 1574-0862.2010.00501.x Katz, M. L., & Shapiro, C. (1985). Network externalities, competition, and compatibility. The American Economic Review, 75(3), 424–440. Katz, M. L., & Shapiro, C. (1994). Systems competition and network effects. Journal of Economic Perspectives, 8(2), 93–115. https://doi.org/10.1257/ jep.8.2.93 Kondylis, F., Mueller, V., & Zhu, J. (2017). Seeing is believing? Evidence from an extension network experiment. Journal of Development Economics, 125, 1–20. Kondylis, F., Loeser, J. A., Mobarak, M., Jones, M. R., & Stein, D. (2023). Learning from self and learning from others: Experimental evidence from Bangladesh. (World Bank Policy Research Working Paper No. 10545). World Bank Group. Larochelle, C., Alwang, J., Travis, E., Barrera, V. H., & Dominguez Andrade, J. M. (2019). Did you really get the message? Using text reminders to stimulate adoption of agricultural technologies. The Journal of Development Studies, 55(4), 548–564. https://doi.org/10.1080/00220388.2017.1393522 Lashitew, A. A., van Tulder, R., & Liasse, Y. (2019). Mobile phones for financial inclusion: What explains the diffusion of mobile money innovations? Research Policy, 48(5), 1201–1215. https://doi.org/10.1016/j.respol.2018.12.010 Malah Kuete, Y. F., & Asongu, S. A. (2022). Infrastructure development as a prerequisite for structural change in Africa. Journal of the Knowledge Economy, 1–27. https://doi.org/10.1007/s13132-022-00989-w Markiewicz, P., & Adamus, M. (2013). Trust and implicit information asymmetry in repeated games. In Active citizenship by knowledge management & innovation: Proceedings of the Management, Knowledge and Learning International Conference 2013 (pp. 739–746). ToKnowPress. Mas, I. (2011, September 2). Savings as forward payments: Innovations on mobile money platforms. Chapter 10 in E. Y. Mohammed & Z. B. Uraguchi (Eds.), Financial inclusion for poverty alleviation: Banking on the unbanked. Routledge. Available at SSRN: https://doi.org/10.2139/ssrn.1825122

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McNabb, M., Chukwu, E., Ojo, O., Shekhar, N., Gill, C. J., Salami, H., & Jega, F. (2015). Assessment of the quality of antenatal care services provided by health workers using a mobile phone decision support application in northern Nigeria: A pre/post-intervention study. PLOS One, 10(5), e0123940. Medhi, I., Cutrell, E., & Toyama, K. (2010). It’s not just illiteracy. India HCI 2010/Interaction Design & International Development 2010 (pp. 1–10). British Computer Society. Medhi, I., Patnaik, S., Brunskill, E., Gautama, S. N., Thies, W., & Toyama, K. (2011). Designing mobile interfaces for novice and low-literacy users. ACM Transactions on Computer-Human Interaction (TOCHI), 18(1), 1–28. https://doi.org/10.1145/1959022.1959024 Mitra, S., Mookherjee, D., Torero, M., & Visaria, S. (2018). Asymmetric information and middleman margins: An experiment with Indian potato farmers. The Review of Economics and Statistics, 100(1), 1–13. Molony, T. (2008). Running out of credit: The limitations of mobile telephony in a Tanzanian agricultural marketing system. The Journal of Modern African Studies, 46(4), 637–658. https://doi.org/10.1017/S0022278X08003510 Moser, C. M., & Barrett, C. B. (2006). The complex dynamics of smallholder technology adoption: The case of SRI in Madagascar. Agricultural Economics, 35(3), 373–388. https://doi.org/10.1111/j.1574-0862.2006.00169.x Munnell, A. H. (1992). Policy watch: Infrastructure investment and economic growth. Journal of Economic Perspectives, 6(4), 189–198. https://doi.org/ 10.1257/jep.6.4.189 Nakasone, E., Torero, M., & Minten, B. (2014). The power of information: The ICT revolution in agricultural development. Annual Review of Resource Economics, 6(1), 533–550. https://doi.org/10.1146/annurev-resource-100 913-012714 Oreglia, E., Liu, Y., & Zhao, W. (2011, May). Designing for emerging rural users: Experiences from China. In Proceedings of the SIGCHI conference on human factors in computing systems (pp. 1433–1436). Overå, R. (2006). Networks, distance, and trust: Telecommunications development and changing trading practices in Ghana. World Development, 34(7), 1301–1315. Parikh, J. S., & Ghosh, K. (2006). Understanding and designing for intermediated information tasks in India. IEEE Pervasive Computing, 5(2), 32–39. https://doi.org/10.1109/MPRV.2006.41 Patel, N., Chittamuru, D., Jain, A., Dave, P., & Parikh, T. S. (2010, April). Avaaj otalo: A field study of an interactive voice forum for small farmers in rural India. In Proceedings of the SIGCHI conference on human factors in computing systems (pp. 733–742).

3

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Porteous, D. (2006). The enabling environment for mobile banking in Africa. United Kingdom Department for International Development (UK DFID) (pp. 3–18). Riley, E. (2018). Mobile money and risk sharing against village shocks. Journal of Development Economics, 135, 43–58. Robinson, J., Park, D. S., & Blumenstock, J. E. (2022). The impact of digital credit in developing economies: A review of recent evidence. Sambasivan, N., Cutrell, E., Toyama, K., & Nardi, B. (2010, April). Intermediated technology use in developing communities. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 2583–2592). Shapiro, C., Varian, H. R., & Carl, S. (1999). Information rules: A strategic guide to the network economy. Harvard Business Press. Slater, D., & Kwami, J. (2005). Embeddedness and escape: Internet and mobile use as poverty reduction strategies in Ghana (Information Society Research Group (ISRG) report). Srinivasan, J. (2007). The role of trustworthiness in information service usage: The case of Parry information kiosks, Tamil Nadu, India. 2007 International Conference on Information and Communication Technologies and Development (pp. 1–8). https://doi.org/10.1109/ICTD.2007.4937422 Srinivasan, J., & Burrell, J. (2013, December). Revisiting the fishers of Kerala, India. In Proceedings of the Sixth International Conference on Information and Communication Technologies and Development: Full Papers-Volume 1 (pp. 56– 66). Stromquist, N. P. (1992). Women and literacy: Promises and constraints. The Annals of the American Academy of Political and Social Science, 520(1), 54– 65. https://doi.org/10.1177/0002716292520001008 Suri, T. (2017). Mobile money. Annual Review of Economics, 9(1), 497–520. Suri, T., Bharadwaj, P., & Jack, W. (2021). Fintech and household resilience to shocks: Evidence from digital loans in Kenya. Journal of Development Economics, 153, 102697. https://doi.org/10.1016/j.jdeveco.2021.102697 Transparency International. (2013). Global corruption report: Education. Transparency International, EarthScan. Routledge. Tu, Q., & Bulte, E. (2010). Trust, market participation and economic outcomes: Evidence from rural China. World Development, 38(8), 1179–1190. https:// doi.org/10.1016/j.worlddev.2009.12.014 UNESCO Institute for Statistics (UIS). (2018). One in five children, adolescents and youth is out of school (UIS fact sheet No. 48). UIS. https://uis.unesco. org/sites/default/files/documents/fs48-one-five-children-adolescents-youthout-school-2018-en.pdf UNESCO. (2021). World Teachers’ Day 2021 fact sheet. International Task Force on Teachers for Education 2030, UNESCO Institute for Statistics, ED/PLS/ TED/2021/04.

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Veeraraghavan, R., Yasodhar, N., & Toyama, K. (2007, December). Warana unwired: Replacing PCs with mobile phones in a rural sugarcane cooperative. In 2007 International Conference on Information and Communication Technologies and Development (pp. 1–10). https://doi.org/10.1109/ICTD. 2007.4937395 Viswanathan, M., Rosa, J. A., & Harris, J. E. (2005). Decision making and coping of functionally illiterate consumers and some implications for marketing management. Journal of Marketing, 69(1), 15–31. https://doi. org/10.1509/jmkg.69.1.15.55507 Wasylenko, M. (1991). Empirical evidence on interregional business location decisions and the role of fiscal incentives in economic development. Industry location and public policy (pp. 13–30). University of Tennessee Press. Wyche, S., & Steinfield, C. (2016). Why don’t farmers use cell phones to access market prices? Technology affordances and barriers to market information services adoption in rural Kenya. Information Technology for Development, 22(2), 320–333. https://doi.org/10.1080/02681102.2015.1048184 Yé, M., Kagoné, M., Sié, A., Bagagnan, C., Sanou, H., Millogo, O., Duclos, A., Tinto, I., & Bibeau, G. (2018). Promoting access equity and improving health care for women, children and people living with HIV/AIDS in Burkina Faso through mHealth. Journal of Public Health (Oxford, England), 40(2), ii42–ii51. https://doi.org/10.1093/pubmed/fdy196 Zant, W. (2022). Measuring trade cost reductions through a new bridge in Mozambique: Who benefits from transport infrastructure? Journal of African Economies, 31(4), 384–408. https://doi.org/10.1093/jae/ejab018

CHAPTER 4

Digitizing Development?

Given the high rates of adoption of digital technology in sub-Saharan Africa, coupled with early studies on its potential impact, enthusiasm about digital technology’s potential as a force for economic development is high. Mobile phone-related coverage in Africa today is full of ambitious claims about impact. “A device that was a yuppie toy not so long ago has now become a potent force for economic development in the world’s poorest countries” (The Economist, 2008). Mobile phones are a “transformative technology” that increase GDP and, quite simply, revolutionize people’s lives (Ghose, 2017). “By expanding their use of digital health tools, African countries could realize up to 15 percent efficiency gains by 2030” (Mc Kinsey & Co, 2023). Equally prevalent are the aspiration advertising slogans of mobile phone companies, promising better days for those who use their products: “Together We Can Do More,” “A Wonderful Life,” “Making Life Better,” and “Tudo bom” (“All is good”). Do these images, slogans, and sentiments truly reflect what mobile phones can do? Can mobile phones actually have a transformational effect on the lives of the world’s poor? By contributing to the emergence and dissemination of innovations in various sectors, the expansion of digitalization has raised the prospects of growth and poverty reduction (Aker, 2017; Aker & Mbiti, 2010; Cariolle & Le Goff, 2023; Hjort & Poulsen,

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2019; World Bank, 2016). At the same time, the diffusion of digital technologies in daily interactions also carries the risk of widening the digital and socioeconomic divides (Cariolle & Le Goff, 2023; Wu et al., 2022). This chapter reviews the ways in which information technology has “digitized development,” discussing the rollout of information technology by the private sector, as well as development-driven public interventions. The chapter begins by summarizing the state of these innovations across a variety of sectors and platforms, focusing on health, agriculture, education, and digital financial services, before providing some case studies on the key digital solutions in each of these areas.1 We end the chapter by talking about the next frontier of digital in development: big data and artificial intelligence (AI).

4.1

IF You Build It, Will They Come?

As of today, thousands of mobile initiatives are deployed worldwide in a variety of contexts, digital forms and sectors. It is estimated that donors have allocated over 1.8 trillion US dollars to financing and supporting “digital development” initiatives (Carosella et al., 2021). About a quarter of the world’s mobile-based innovations originate from (or are implemented in) sub-Saharan Africa, making the region the second-largest provider of digital development solutions worldwide (Fig. 4.1).2 Box 4.1 Some Key “Digital for Development” Terms M-health: The use of the mobile phone and other wireless devices in health care service provision (Betjeman et al., 2013).

1 This book does not attempt to provide an exhaustive list of all digital services— such as national identification schemes or elections—nor a comprehensive review of all initiatives, interventions, and projects in each field. The field is in a constant state of flux, and so a comprehensive overview risks being out of date quickly. Rather, we try to provide a broad overview of interventions in this area and their purpose. For example, Gelb and Metz (2018) provide an overview of digital identification innovations and their contribution to development. 2 In the agriculture sector alone, 700 digital services were rolled out in sub-Saharan Africa, Asia, and Latin America, 62% of which were located in sub-Saharan Africa. While m-Education (or “EdTech”) of m-Health initiatives are also common, there are no upto-date available data on mobile initiatives in these sectors.

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Fig. 4.1 Mobile innovations in 2021, by region (Source Authors. Data from GSMA Mobile Deployment Trackers, encompassing 2249 mobile innovations [live, planned, or merged] worldwide)

M-Agri (or AgriTech): The integration of digital technologies into crop and livestock management, as well as other processes in agriculture. Digital financial services (DFS): A broad range of financial services accessed and delivered through digital channels (such as mobile phones), including payments, credit, savings, remittances and insurance. Can also be used interchangeably with FinTech. First-generation DFS: Digital services, such as mobile money, which allow customers to store money, top up airtime, send money to other users via SMS and withdraw already lodged money for a small price per transfer or withdrawal (CGAP, 2016). Second-generation DFS: DFS which offer other types of financial services—such as savings, credit, and insurance—through other digital channels. M-Education (or EdTech): When students use portable devices to access learning materials, information and systems, create and share documents, pictures, videos and audio files, and interact with other students,

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teachers, experts, learning systems, apps, and the world around them (GSMA, 2020). This can include the enhancement of teaching, assessment, and educational administration and management in addition to learning, all via mobile technologies. Interactive Voice Response (IVR ): An automated phone system technology that allows incoming callers to access information via a voice response system of pre-recorded messages without having to speak to an agent, as well as to utilize menu options via touch tone keypad selection or speech recognition to have their call routed to specific departments or specialists.

How are these digital initiatives provided? Services can be broken down into the modality of the service (e.g., simple or smartphone, computer or laptop, TV, or radio) and the means of communication (e.g., SMS, voice, video, email, or internet). Voice-based services range from simple services (e.g., call-in centers or hotlines) to more complicated technology and computing applications, such as Interactive Voice Response (IVR) (Box 4.1). Given the lag in internet growth (Cariolle, 2021), a majority of digital initiatives are based on SMS—whether mass messages or SMSbased questionnaires—which do not rely on internet access nor require a sophisticated communication device (Aker & Blumenstock, 2014; FARA, 2009).3 We look below at “digital developments” by sector.

4.2

Health at Your Fingertips 4.2.1

What Are the Challenges?

While sub-Saharan Africa represents 13% of the world’s population, the region bears 24% of the global disease burden (WEF, 2021). Approximately 1.6 million people in sub-Saharan Africa die per year of tuberculosis (TB), malaria, or HIV-related illnesses, with severe disruptions to treatment due to the COVID-19 pandemic in 2020–2022.4 Despite 3 Internet-based interventions include telecenters, internet kiosks, smart phones, or other digital devices that allow individuals to connect to the internet (or other pre-recorded content) on a given topic. 4 One particularly vital supply chain affected by poor infrastructure is healthcare logistics, including distribution of blood and medications which are often vital to save the lives of patients experiencing medical emergencies or chronic health conditions (Chi et al.,

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the dire need these issues present, it is estimated that fewer than 50% of the population can access modern health facilities, with 80% relying on public health facilities (Clausen, 2015). As outlined in the previous chapters, the supply of adequate health services in sub-Saharan Africa faces several serious challenges. The first is access: On average, there is 1 hospital bed per 1,000 people, with huge disparities within and across countries (Paintsil, 2020). The second is staffing: Sub-Saharan Africa has fewer medical workers per capita than any other region of the world (an estimated 0.2 doctors per 1,000 people), exacerbated by an absenteeism problem that ranges from 13 to 53% (Sheffel et al., 2023).5 The third is capacity, with recent research suggesting that health workers in certain countries lack basic clinical competencies. The final serious challenge is stockouts, either of life-saving medical tests or medications. One report estimated that stockouts of Anti-Retrovirals (ARVs) ranged between from between 20% (South Africa) to 77% (Democratic Republic of the Congo) over a three-month period (MSF, 2022), and most countries in subSaharan Africa will not have widespread access to COVID-19 vaccines before 2023. These supply-side bottlenecks should also be viewed in the context of a number of demand-side market failures. These include, but are not limited to, imperfect information about diseases, vaccines, or curative care; asymmetric information about provider quality or availability; uncertainty about the frequency of health shocks, especially pandemics such as Ebola, COVID-19, and meningitis; and positive and negative externalities associated with vaccines and infectious diseases. 4.2.2

The Promise of Digital Health

In such contexts, it is easy to see why—and how—digital technologies could be used to address many of the weaknesses in the healthcare system in sub-Saharan Africa. While fewer than 15% of health workers have

2015; Conway et al., 2020). This is exacerbated by poorly-developed procurement systems for imported drugs, lack of storage facilities, weak manufacturing capacity, and unreliable energy supply and cold chain facilities (Pheage, 2017; Pothering, 2019). As a result, a majority of countries in sub-Saharan Africa import up to 70–90 % of drugs consumed, with high prices for imported drugs (Conway et al., 2020). 5 Sheffel et al. (2023) define absenteeism as the proportion of health workers not at work who are expected to be present.

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reported using “formal” m-Health interventions, they use digital technology informally to address many of the market failures described above (Hampshire et al., 2021). This is often done in innovative ways, such as making appointments, notifying other health workers of costly medication stockouts, crowdsourcing feedback on how to diagnose (and treat) an illness, sharing confidential patient information, and avoiding long and costly travel (Hampshire et al., 2021). This information-sharing is often done with simple mobile phones, as well as with smartphones and via messaging platforms such as WhatsApp. Box 4.2 Categorization of Digital Health Interventions Health worker training and capacity-building Health worker monitoring Supply chain management Patient-provider coordination Information provision Data collection and monitoring

Digital health initiatives have attempted to formalize these informal use cases, in the hope of sharing information and reducing market failures at greater scale, lower cost, and with a more homogeneous quality. As of 2021, conservative estimates suggested that there were approximately 500 digital health initiatives in sub-Saharan Africa (Fig. 4.2), primarily located in Kenya, Uganda, Ethiopia, Tanzania, and Nigeria. In fact, it is estimated that the digital health space in Africa saw over US$55 million in investment and a doubling of the number of start-ups between 2019 and 2021 (Disrupt Africa, 2021). Broadly speaking, these initiatives fall into six broad categories (Box 4.2 and Table 4.1): health-worker capacitybuilding and monitoring, drug supply chain management, patient sensitization, and data collection and reporting (Agarwal et al., 2015; Aker, 2017; Aranda-Jan et al., 2014; Banerjee et al., 2021, 2022; Hampshire et al., 2017; Head et al., 2013).6 Prior to COVID-19, the majority of these initiatives were concentrated on HIV/AIDS, malaria, TB, and antenatal care. Over 65% of these interventions use simple (feature) phones,

6 These include, among others, Totohealth in Kenya, The Audrey Pack in Nigeria, Wazazi Nipendeni in Tanzania, Human Networks International in Malawi and Zambia, and Living Goods in Uganda.

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with the remainder using smartphones (35%), or laptops/tablets (5%). As a result, the predominant channels are SMS (50%) and voice, followed by internet or web-based applications (30%) (Fig. 4.3). Some concrete examples might help to fix ideas. In the area of patient monitoring, SMS reminders have been sent for timely immunizations as well as medication, particularly for ARVs, which follow rigorous guidelines (Hoffman et al., 2010; Lester et al., 2010; Pop-Eleches et al., 2011). In the area of capacity-building, text messages have been used to bridge the gap between community health workers (CHWs) and hospitals in rural Malawi, whereas smartphones with drug information, telemedicine appointments, and other features have been provided to resident physicians in Botswana. Similar types of applications have been used for drug supply chain management: In Kenya and Tanzania, SMS messages have been used to report stockouts of anti-malarial medications and drones have been used to provide such medications in Rwanda and Ghana

Fig. 4.2 mHealth innovations by country (Source Authors. Data-GSMA mobile deplyment trackers, 480 mHealth innovations in Africa)

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Table 4.1 Key uses of digital technology for development across sectors Digital health

Digital Ag

Digital Ed

Health worker training and capacity building Health worker monitoring

Extension agent and farmer training Extension agent monitoring

Teacher and student training/learning Teacher/director monitoring

Supply chain management (drugs, tests) Patient-service provider coordination Information provision (for patients, communities) Data collection and monitoring (e.g. pandemics)

Supply chain management (inputs, etc.) Buyer-seller coordination Information provision (on prices, weather, etc.) Data collection and monitoring (e.g., prices, weather) Digital financial services (salaries, transfers, credit, savings, etc.)

School-parent coordination Information provision and behavioral information Data collection and monitoring

Fig. 4.3 mHealth by technology device and technology channel

(Box 4.3). In terms of patient sensitization, mass SMS have been sent to thousands of patients on a variety of topics, such as family planning, nutrition, HIV/AIDS, and COVID-19. In fact, during the COVID19 pandemic, there were over 71 m-Health interventions rolled out in Africa alone, in the hopes of providing information on the disease and encouraging behavioral change (WHO, 2022).7 Finally, in the area of data collection and reporting, m-Health interventions have been used 7 http://stories.digitalhealthatlas.org.

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to monitor and report disease surveillance. During the Ebola crisis of 2014–2016 in West Africa, a variety of health partners used digital technologies—often via CommCare8 —to collect, store and manage data in real time, without relying on internet connectivity. Box 4.3 Is it a bird? Is it a plane? It’s…Zipline? Zipline is a US-based company that uses drones to deliver blood, medications, and other supplies to medical centers throughout Rwanda and Ghana. The company uses a fixed-wing style drone with a top speed of 128 km/h, a round trip range of 160 km, and the capacity to carry up to 1.75 kg of cargo. Drone delivery of medical products occurs as the drone drops off a parachuted package from a height of approximately 80 meters which then lands in a designated area—often a small quadrangle in the courtyard or campus of a medical center. In one instance, yellow fever vaccines were delivered to the New Tafo Government Hospital in the Eastern Region of Ghana in 21 minutes, a process that would have normally taken at least two hours if delivered by road (Asiedu, 2019). Nevertheless, Zipline has faced obstacles, as many question remain about whether its drone-based model is the optimal solution to existing healthcare logistics challenges. Critics question whether it would in fact be more effective to invest in more basic infrastructure such as medical supplies and ambulances (Pothering, 2019).

4.3

Hello, Tractor! Digital Agriculture 4.3.1

The Agricultural Challenge

Since the 1960s, agricultural yields have more than doubled around the world. The sole exception to this trend is sub-Saharan Africa, where yield growth has stagnated, and some regions are predicted to meet only 13% of their food needs by 2050 (Montpellier Panel, 2013). Approximately 60% of sub-Saharan Africa is dependent on small-scale agriculture, with agricultural systems characterized by lack of modern inputs and techniques, extensive farming practices, marginal and degraded soils,

8 One of the most prominent digital applications in use in the healthcare sector is CommCare, an open-source software that allows for a variety of flexible ways to support health workers.

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reliance on rainfed agriculture, and increasingly drastic climatic fluctuations (Fonta et al., 2018; Kurukulasuriva & Mendelsohn, 2008). These obstacles are further compounded by a number of constraints in agricultural value chains, including missing (or thin) input and output markets, high transport costs, costly stockouts for seeds, fertilizers, and other inputs, and limited competition in certain markets.9 Given the complexity of the agricultural production process (Fig. 4.4), imperfect and asymmetric information plays a particularly important role in farmers’ decision-making processes, as well as the efficiency of the agricultural market system (Aker, 2011; de Silva & Ratnadiwakara, 2008; Mittal et al., 2010). Prior to the expansion of mobile networks, information collection generally relied on personal travels to markets, listening to radio or reading newspapers, or passing phone calls to colleagues, friends, or relatives, for the few that benefitted from a fixed landline phone connection (Aker, 2011; Aker & Mbiti, 2010; Anderson & Feder, 2007; Conley & Udry, 2010; Fabregas et al., 2019; Feder, 1982; Feder et al., 1999; Spielman et al., 2021). 4.3.2

The Promise of Digital Agriculture?

In such contexts, it is easy to see why—and how—digital technologies could be used to address many of the weaknesses in the agricultural sector. Similar to health, there is ample evidence that agents in the agricultural value chain—from farmers to consumers, wholesalers, and retailers—have been informally using mobile technology to find price and transport cost information, search for inputs, and coordinate between buyers and sellers—all without an outside intermediary (Aker et al., 2016). Early research in Uganda, India, and Niger suggested that improved access to price information via digital technologies (radio and mobile phones) improved market agents’ access to information, improved bargaining power, increased market efficiency—and, in some cases—welfare (Aker, 2010; Jensen, 2007; Svensson & Yanagizawa, 2009). The early observed “success” of these informal uses cases promoted the growth of digital agricultural initiatives on a larger scale, in the

9 These constraints are particularly pronounced in the agricultural “last mile,” which is the web of relationships and transactions between buyers of crops, such as agribusinesses, cooperatives, and middlemen, and the farmers who produce and sell them (GSMA, 2022).

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Fig. 4.4 Information in the agricultural production process (Ghandi et al. [2009])

hopes of providing these services at lower cost and with more homogeneous quality. As of 2021, there were hundreds of digital initiatives in sub-Saharan Africa, although the exact number is difficult to pinpoint (Fabregas et al., 2019).10 These initiatives have primarily fallen into six key categories (Box 4.4): sharing data on “simple” information, such as input and output prices, weather shocks, pests, and transport costs (Aker, 10 The GSMA E-Agri tracker estimated that there were 437 m-Agri initiatives as of 2020, with 95 in Kenya, 47 in Nigeria, and 45 in Ghana (GSMA, 2020).

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2011; Aker et al., 2016); providing technical advice and advisory services on agricultural practices; the management of supply chains, whether ecommerce or digital procurement; monitoring public or private sector agent attendance and performance (Aker et al., 2016); and data collection and analysis. A separate category is related to that of facilitating access to financial services, either agricultural subsidies, credit, or insurance (Aker et al., 2016; Fabregas et al., 2019).11 Over 50% of recorded interventions use simple (feature) phones (via SMS), with the remainder using smartphones (35%), computers, or radios (5%). As a result, the predominant channels are SMS (50%) and voice, followed by internet or web-based applications (30%) (Fig. 4.5). Box 4.4 Types of Digital Agriculture Interventions Information provision (e.g., prices, weather) Agricultural extension advice (for farmers and agents). Supply chain management. Buyer–seller coordination. Agent performance monitoring. Data collection and reporting Accessing digital financial services*

Fig. 4.5 How digital agricultural services are provided

11 GSMA categorizes these a bit differently, but with some overlap. These include digital advisory, digital financial services, Agri e-commerce, digital procurement, and “smart” farming (GSMA, 2020).

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Box 4.5 Need a Tractor? Never Fear! Hello Tractor is Here… With nearly 900 million hectares considered suitable for agricultural activities, Africa is home to 60% of the world’s arable land. In sub-Saharan Africa, 60% of crops are plowed by hand, which in turn impacts the productivity of their farms and the eventual crop yield. Founded in 2014, Hello Tractor! was founded to connect farmers to nearby tractor owners. Early versions connected farmers via SMS—which later developed into an app, as farmers were unwilling to commit to large sums of money via SMS. The app is now used by thousands of farmers in Africa, including those in Mozambique, Senegal, and Tanzania.

In the area of information-sharing, digital technology—such as mobile phones—has been used for market and weather information systems in Ghana, Uganda, and Niger. In the area of supply chain management, a hotline was established in Kenya that enabled farmers to call the company to report problems related to late fertilizer delivery (Casaburi et al., 2019a), whereas an “Uber” for tractors has been rolled out in Nigeria (Box 4.5). The use of digital technology for agricultural extension advice has ranged from simple to complex: In Kenya and Rwanda, farmers received agricultural extension advice via SMS (Casaburi et al., 2019b; Fabregas et al., 2019), whereas in Madagascar and Ethiopia, farmers can listen to pre-recorded information on agricultural information and ask questions. In Sierra Leone, a digital Farmer Field School (including video lessons) was developed during the Ebola crisis, in an effort to maintain farmer training programs for cocoa production certification, providing an innovative and stimulating learning environment (Witteveen & Lie, 2012; Witteveen et al., 2017). A similar type of videobased agricultural extension initiative (Digital Green) has been deployed in India, Ghana, and Ethiopia (Harwin & Gandhi, 2014). In the area of monitoring, a mobile phone hotline was used to provide feedback to agricultural extension workers in Rwanda. And finally, with the use of drones, satellites, and GPS, data collection can be used to track rainfall, pest infestations, and provide feedback to farmers on the areas of their field affected by nutrient deficiencies, drought stress, crop diseases, or pest and weather damage (Commodity Port, 2019) or aerial mapping of farmer plots (Karim, 2020). Overall, digital agriculture is providing low-cost but effective solutions to farmers facing structural obstacles and constraints. Thanks to the

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dramatic deployment of the mobile network infrastructure in the remotest areas of many low-income countries over the last decades, there have been numerous deployments providing information on prices, weather, crop monitoring, and production techniques through mobile phones.

4.4

Call Me Educated

4.4.1

The Learning Challenge

Despite the importance of education for development, educational achievements are still remarkably low in some of the world’s poorest countries. The UN’s “Education for All” report estimated that there are over 880 million adults worldwide who are unable to read and write in any language. This is of particular concern in sub-Saharan Africa, which has some of the lowest education indicators in the world, and which is the only region along with South Asia that has experienced a decline in education quality12 over the last half-century (Le Nestour et al., 2022). In fact, in countries such as Niger, classified as the lowest of the UN’s Human Development Index, adult literacy rates are below 30%, and 90 and 72% of children in grade 2 are below the accepted thresholds in literacy and math, respectively (PASEC, 2015). Why educational outcomes have been so poor has been the subject of long debate. While policymakers and practitioners alike know a reasonable amount about how to increase school participation, there is still considerable debate about how to improve learning in a cost-effective way. There are a variety of factors that affect educational outcomes and learning. The first is access: While the number of primary schools has increased considerably over the past few decades, distance and poverty remain important barriers, limiting access for vulnerable populations. The second is staffing: As outlined in Chapter 3, sub-Saharan Africa has some of the highest pupil-teacher ratios in the world, exacerbated by absenteeism ranging from 15 to 45%, depending upon the region (Karamperidou et al., 2020). The third is capacity: Recent research suggests that only 65% of primary school teachers in sub-Saharan Africa have the necessary qualifications,

12 Defined as literacy conditional on completing five years of schooling (Le Nestour et al., 2022).

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compared to 95% in 2000 (Bold et al., 2017; UNESCO, 2021).13 The final factor is complementary inputs, including desks, chairs, chalkboards, chalk, books, and others, that are provided to teachers, students, and schools when and where they are needed. All of these constraints are exacerbated by market failures in education. A key one is imperfect and asymmetric information: Either parents and students do not necessarily know about the returns to education, or they focus on the present (rather than the future) while weighing investment decisions; or governments, policymakers, and other decision-makers cannot observe the performance of employees, namely, school directors and teachers. This is also related to market failures in the credit market, which can thereby make it difficult for students (and parents) to save and invest in education in the medium and longer terms. Box 4.6 Paying Teachers’ Salaries (Digitally) in Liberia Civil servants, such as teachers, have traditionally had to incur significant time, travel, and financial costs to withdraw cash from the bank or to cash in a paper check after being paid. In Liberia, teachers spend 15% of their salaries in cashing their checks, incurring costs for travel, lodging, food, bank fees, and bribes, missing at least two days per month collecting each paycheck. In 2016, the Mobile Solutions Technical Assistance and Research (mSTAR) project partnered with the Government of Liberia to develop a pilot mobile money-based public salary payment system, resulting in significant monetary and time savings. By 2018, the mSTAR program had reached 3,187 teachers across 14 counties, and was also extended to the health sector, reaching 803 health workers.

4.4.2

Digital learning

Mobile (or digital) education is, of course, nothing new. It has been around for decades, with the introduction of the mobile phone and the personal computer. Examples of digital education initiatives in the United 13 Using survey data from seven African countries covering 16,543 teachers from 2,001 schools, Bold et al. (2017) find that on average, 44% of teachers were absent from class, that less than a half of the scheduled teaching time was actually spent teaching, that only half were able to understand a factual text, and that a small minority were mastering basic pedagogical tools.

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States and Europe abound: The global “EdTech” sector is worth approximately USD 161 billion (Escueta et al., 2020; GSMA, 2020; Morrison, 2017). Most of this is concentrated in the United States and Europe, with a relatively high density in Latin America and lower density in sub-Saharan Africa, perhaps due to more limited mobile and internet coverage. This could, however, simply be due to a “missing data” problem: While organizations such as GSMA have digital agriculture, health, and financial service trackers, data on m-education are largely absent. This, therefore, makes it difficult to truly comprehend the size and scale of digital education in sub-Saharan Africa. While the categorization of digital education initiatives varies, digital technology has primarily been used for six purposes: • As an educational input in the classroom, either as a complement or substitute to teachers, to improve children’s and adults’ learning. • To provide capacity-building for teachers and other educational workers. • To monitor public or private sector teacher and school director attendance and performance. • To disseminate information to parents and teachers. • To provide behavioral nudges, primarily for parents. • To collect data on key educational outcomes.

Box 4.7 Learning by texting? While most schools in the US are trying to keep phones out of school, simple mobile phones offer new opportunities for children to learn both in and out of school. Nowhere was this more apparent than during the COVID-19 pandemic, which resulted in thousands of school closures across sub-Saharan Africa. In an effort to keep children learning, two initiatives—Eneza Education and Young Love—used phone calls and SMS to provide lessons to children at home, without requiring internet connections. The Eneza system—created in 2014 and with 2.2 million subscribers as of 2021—breaks out the local curriculum into bite-sized lessons with quizzes and an “Ask a Teacher” feature that allows students to send text messages to a pool of teachers. Young Love, which primarily operated in Botswana and coordinated with the Ministry of Education, offered SMS

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lessons and short phone calls to parents and children during the pandemic, in an effort to minimize learning losses (Angrist et al., 2022).

Similar to agriculture and health, a cross-cutting category is related to that of digital financial services , either to pay salaries or to provide cash transfers (Box 4.6). While a breakdown of the type of technology— computers, tablets, laptops, smartphones, or simple mobile phones—is not available, in the United States and Europe, these technologies have primarily been focused on computer-assisted learning (CAL). In the area of educational inputs, simple mobile phones have been used as an educational input in an adult education program in Niger, and SMS and phone calls have been used as pedagogical devices in Botswana, Kenya, Ghana, Ivory Coast, Rwanda, and Zambia (Box 4.7). In the area of capacity-building for education service providers, the Initiative Francophone pour la Formation à Distance des Maîtres (IFADEM) has provided distance learning support and training—primarily via computers and laptops, but also via mobile phones—to over 400,000 teachers, training teachers in French language, grammar, and instruction. In Niger, pedagogical support (via WhatsApp) in the subjects of French and math has been provided to primary school teachers, with the aim of complementing in-person training. In the area of performance, simple and smart mobile phones have been used to monitor primary school and adult education teacher attendance in Niger and Uganda, as well as to provide personalized information to parents in Kenya. There are few, if any, studies on the use of digital in parent-school coordination in Africa, or on the use of digital to inform the availability of inputs for the classroom. Beyond simple mobile phones, the provision of other technological inputs for education—such as computers—has also been popular. In 2005, the “One Laptop Per Child” (OLPC) initiative was launched, with the aim of providing schools in sub-Saharan Africa with low-cost energy-saving computers, including access to pedagogical platforms. Since that time, laptops have been distributed to approximately 10 schools in sub-Saharan Africa. Overall, digital education has been spurred by the spread of simple mobile phone devices and related applications among the poorest segments of the population. By sending text messages or using phones to conduct simple mathematical calculations, receiving notification alerts

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about school administration, or allowing school fees to be paid through mobile money systems, mobile telephony has become the last mile education infrastructure in more remote places, filling in infrastructure gaps (Aker & Mbiti, 2010).

4.5 A Bank in Your Pocket? Digital Financial Services 4.5.1

The “Financial Exclusion” Conundrum

Financial services are crucial for development (Demirgüç-Kunt et al., 2018). They allow households to transact, smooth consumption in the face of shocks, invest in their education and businesses, and save money. Throughout the world, many of the most resource-strained households lack access to the full range of financial services that would enable them to conduct the above-mentioned functions; instead, these households must rely upon cash or informal financial services to get by. This is frequently insufficient for households to thrive. Yet a combination of formal and informal financial services, including (but not limited to), credit, savings, remittances, and insurance, and more recently digital financial services, are needed. Despite the importance of financial services for productivity, growth, and development, a significant portion of the world’s population remains unbanked, for a number of reasons. The first is due to transaction costs : Financial institutions are often far away, in urban centers, with relatively high opening costs. The second is related to imperfect information: Adverse selection and moral hazard make it difficult for financial institutions to identify clients, thereby increasing interest rates, documentation requirements, and collateral needs. This is exacerbated by imperfect competition between financial services providers, due to regulatory challenges and a lack of sufficient interoperability. The final constraint is related to poverty itself: Low incomes and poor-quality financial products stifle demand for financial services across the region. 4.5.2

The Digital Solution?

Despite the millions who remain unbanked, these number have improved significantly over the past decade, in part due to digital financial services (DFS). DFS—a broad range of financial services accessed and delivered

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through digital channels (such as mobile phones), including payments, credit, savings, remittances, and insurance—can be divided into two types: (1) first-generation services, such as mobile money, which allow customers to store money, top up airtime, send money to other users via SMS, and withdraw already stored money for a small price per transfer or withdrawal (CGAP, 2016); and (2) second-generation services, which primarily offer more sophisticated financial services—such as savings, credit, and insurance—through a variety of digital channels (e.g., simple phones, smartphones, computers) (Box 4.8). First-generation DFS are particularly well-suited to combat the various market failures which limit financial inclusion in low-income countries, especially in rural areas. Box 4.8 Key Terms in DFS First generation DFS: Digital services that primarily allow customers to store money, top up airtime, send money to other users via SMS, and withdraw already stored money for a small price per transfer or withdrawal. Second generation DFS: More sophisticated financial services—such as savings, credit, and insurance—offered through a variety of digital channels, including the mobile phone. Mobile Money: “A pay-as-you-go digital medium of exchange and store of value using mobile money accounts, facilitated by a network of mobile money agents. It is a financial service offered to its clients by a mobile network operator or another entity that partners with mobile network operators, independent of the traditional banking network” (IMF). Mobile Banking: The “use of an application on a mobile device to access and execute banking services, such as check deposits, balance inquiry, and payment transfers” (IMF). Digital credit : Loans accessed through a digital channel, either online, through a mobile device or through a third-party agent (Owens, 2018).

Since the introduction of the first mobile money platform—M-Pesa in Kenya—mobile money has grown substantially, operating with over 300 deployments worldwide (Fig. 4.6). Sub-Saharan Africa accounts for almost half of all mobile money deployments. As of 2021, there were 548 million registered mobile money account users in SSA, of which 20% were considered to be “active.” Perhaps unsurprisingly, mobile money adoption increased significantly during the COVID-19 pandemic, partly spurred by the necessity of governments and other international organizations to use digital financial services as a means to provide social

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protection payments. Such was the case in Togo in 2020, where mobile money was used to transfer financial compensation to households that incurred income losses due to the pandemic (Aiken et al., 2022) Box 4.9 Pay for Water by Phone? Many urban and rural residents are not connected to water, energy, or other utilities at all, or become disconnected after being unable to pay for unexpectedly large bills over a period of several months (Gridley, 2020). Outstanding unpaid bills and large numbers of disconnected subscribers affect the ability of many SSA utilities to provide reliable services to communities. Additionally, sending personnel to issue and write off bills, collect payments, and disconnect subscribers adds to transaction costs (Gridley, 2020). The growing number of fintech ventures cropping up across SSA has seen the technology being increasingly proposed as an innovative means to leverage mobile and digital technologies to digitize municipal tax collection and thereby public utilities provision (Aker, 2017;

Fig. 4.6 Mobile money innovations, by region and over time

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Maino et al., 2019). One example is CityTaps, a start-up that harnesses digital technologies through its CTSuite platform. This technology consists of a prepaid smart water meter installed at subscriber households and an integrated cloud-based software (CTCloud) that provides financial and operational data to utilities companies and consumers (Gridley, 2020; Kore, 2020). The smart water meters, called CTMeters, allow households to use a pay-as-you-go (PAYG) model to monitor and pay for water usage.

Yet there is a stark heterogeneity in deployments, adoption, and usage across the continent (Fig. 4.7). For example, East Africa, the “founder” of mobile money, accounts for about one third of all deployments and over half of all accounts (GSMA, 2020). This is also evident in the adoption data: While 97% of households in Kenya had a mobile money account as of 2014 (Suri & Jack, 2016), this is significantly lower in other countries and regions (Demirgüç-Kunt et al., 2018). This could, in part, be due to the agent infrastructure across countries: While mobile money is digital, most users still require cash in order to make daily transactions, thus requiring an agent and cash in, cash out (CICO) services. The rollout of the agent infrastructure has not necessarily kept pace with adoption estimates (Aker et al., 2020).14 Despite the growth of second-generation services, first-generation services, such as person-to-person (P2P) transfers (including remittances), person-to-business (P2B) transfers, business to business (B2B) transfers, airtime top-up and bill payments, still dominate most platforms. While government-to-person (G2P) payments have existed in a number of countries, especially Kenya, the onset of COVID-19 saw an important increase in the use of government-to-person (G2P) payments (Fig. 4.8). More recently, second-generation services have launched to varying degrees—from digital credit in Kenya, Uganda, Ghana, and Tanzania to digital savings in Tanzania to digital insurance and tax payments in Senegal (person to government, or P2G). In the area of bill payments, this

14 A key issue with digital financial services is that of interoperability, which can occur at

the network, platform, agent, and data levels. While interoperability has often been soughtafter as a policy goal in many contexts, in the hopes of increasing the ease of money transfers across time and space, recent evidence suggests that platform interoperability lowers mobile money fees and reduces network coverage and mobile towers, especially in rural and poor districts (Bianchi et al., 2022; Brunnermeier et al., 2023).

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Fig. 4.7 Mobile money initiatives by country (Source Authors. Data-GSMA Mobile Money Deployment tracker)

Fig. 4.8 Changes in number of new mobile money deployments over time

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has also evolved into innovative payment meters and systems for utilities, such as water (Box 4.9) and energy (such as off-grid solar).15

4.6

To Infinity and Beyond? Big Data and AI

Box 4.10 Key Terms for Big Data Artificial intelligence: Algorithms that mimic the intelligence of humans, able to resolve problems in ways we consider “smart.” From the simple to more complex algorithms, such as machine learning or deep learning. Machine learning: Algorithms that parse data, learn from it, and then apply what they’ve learned to make informed decisions. They use human extracted features from data and improve with experience. Deep learning: Neural Network algorithms that learn the important features in data by themselves. Able to adapt themselves through repetitive training to uncover hidden patterns and insights. Call Detail Records (CDRs): Records that capture information on calls made on telephone systems, including who made the call (name and number), who was called (name if available, and number), the date and time the call was made, the duration of the call, and other usage and diagnostic information elements. Source IBM, https://www.ibm.com/blogs/systems/ai-machine-lea rning-and-deep-learning-whats-the-difference/

With the rising diffusion of mobile phones in low-income countries, a large share of the world population generates a mobile footprint from both physical (phone mobility data and phone call records) and digital interactions (digital financial transactions, social network activity, web searches, or web browsing footprints). This digital footprint is currently being considered as an input for financial institutions to provide digital credit, but also for researchers and policy-makers, who increasingly resort

15 Beyond the sectors mentioned in this chapter, there are a number of other initiatives using digital technology in sub-Saharan Africa, covering the areas of employment, elections, supply chains, and energy. Several digital job platforms in SSA have adapted traditional job platform models to meet the needs of both informal and formal sector workers (Lakemann & Jay, 2019). Simple mobile phones have been used to share information on candidates and voter rights in Mozambique and Kenya, as well as to report electoral violence in both countries (Aker et al., 2017). Finally, off-grid solar solutions have been introduced and integrated with mobile money platforms in places as diverse as Togo, Kenya, and Nigeria (Roach & Cohen, 2016).

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to machine-learning algorithms to target the poor and design development policy accordingly (Aiken et al., 2023; Blumenstock, 2016, 2018; Blumenstock et al., 2015). High-frequency and granular mobile phone data has the potential for applications in development policy, including humanitarian assistance, digital credit, or poverty targeting and cash transfer delivery (Blumenstock et al., 2015). Applications are equally large for the private sector, since mobile data enter in the firm’s production process, hence fueling their economic expansion and the industrialization process at a larger scale. 4.6.1

Using Digital Data to Measure Poverty and Target Interventions

In higher-income countries, the usage of mobile internet is widespread and encompasses a range of geo-referenced applications, which enable the large-scale collection and use of high-frequency and highly-disaggregated mobility data (known as Call Detail Records, or CDRs) or mobile money usage data to predict spatial economic activity and approximate local development level (Matsumura et al., 2021; Putra & Arini, 2020). In lower-income countries, poverty mapping is often done in-person, via surveys and other measures, and can be costly, both in terms of budgetary and administrative needs. As a result, poverty maps are often out of date or at lower levels of granularity. This was particularly relevant during the COVID-19 pandemic, when border closures and lockdowns made in-person data collection difficult, if not possible. In such contexts, digital data—whether mobile-phone mobility (CDRs) data, mobile money transactions, or satellite or remote sensing data—have the potential to provide lower-cost and higher frequency measures of the spatial distribution of wealth or other measures (Aiken et al., 2023; Blumenstock, 2016; Blumenstock et al., 2015; Jean et al., 2016). CDR data, for example, provide detailed information on proxies of an individual’s socioeconomic status, such as the frequency, timing, and duration of communications; the size and structure of their social network; their mobility (e.g., daily travels, location choices, and vacations); and their consumption and expenditure records, especially if individuals use a digital wallet (Björkegren & Grissen, 2018, 2020; Blumenstock et al., 2015; Eagle et al., 2008). Remote sensing data, on the other hand, can provide data on physical properties, such as rainfall, temperature, and vegetation, as well as distance to roads, cities, and other

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public services. The analysis of these datasets, using a variety of techniques (Box 4.10), can be used to provide microestimates of wealth and poverty. To date, these techniques have been used in specific countries (such as Rwanda, Ivory Coast, Bangladesh, and Turkey), as well as across 100 lower and middle-income countries (Bertoli et al., 2021; Blumenstock, 2016; Blumenstock et al., 2015; Chi et al., 2022; Steele et al., 2017) to build out poverty and wealth maps. A key issue with such outputs, of course, is how they are used and whether such approaches outperform the traditional approaches. Recent advances in this field have focused on the use of big data and machine learning for two purposes: (1) as a targeting mechanism for development programs, particularly social protection (cash transfer) programs; and (2) for identifying clients in the private sector, in particularly in the area of digital financial services (Aiken et al., 2023). Recent examples of digital targeting for anti-poverty program abound. In Togo, deep-learning algorithms trained to process satellite imagery, as well as CDR data, were used to target poor households for the country’s COVID-19 aid response (Novissi). In Uganda, mobile phone data were used to help direct government assistance to subscribers who lived in vulnerable communities (Aiken et al., 2022; Blumenstock, 2020). Beyond targeting for anti-poverty programs, mobile data have also been used to generate credit scores for potential borrowers, including those who are unbanked. In fact, most digital credit products rely upon some type of mobile phone usage data to develop a digital credit score, which is then used to determine if an individual can become a borrower (Björkegren & Grissen, 2018; Suri et al., 2021).

4.7

Conclusion

Despite the numerous and persistent challenges described above, recent years have seen a proliferation of start-ups leveraging digital technologies to leapfrog missing infrastructures, reduce information and transaction costs, and transform SSA’s ecosystems. Significant increases in mobile penetration throughout the continent have led to a rapid expansion in the number of companies offering a wide range of digital innovations. These range from digital marketplaces—where carriers and shippers can interact—to mobile-piloted drone-based technologies, to mobile platforms for teacher training and mobile-based weather insurance services.

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As “smart” as these technologies and platforms may be, the capacity for these innovations to spur technological shift and generate a long-term development process depends on three interrelated factors. First, the positive impacts of these technologies must be proven, based upon scientific evaluation approaches. Second, the technologies need to be scaled up to reach a critical mass of users. And third, the general equilibrium effects of these technologies need to be measured. The next chapter tries to document the first characteristic and to answer the following question: Does the evidence meet the hype?

References Agarwal, S., Perry, H. B., Long, L., & Labrique, A. B. (2015). Evidence on feasibility and effective use of mHealth strategies by frontline health workers in developing countries: Systematic review. Tropical Medicine & International Health, 20(8), 1003–1014. https://doi.org/10.1111/tmi.12525 Aiken, B. S., Karlan, D., Udry, C., & Blumenstock, J. E. (2022). Machine learning and phone data can improve targeting of humanitarian aid. Nature, 603(7903), 864–870. https://doi.org/10.1038/s41586-022-04484-9 Aiken, E. L., Bedoya, G., Blumenstock, J. E., & Coville, A. (2023). Program targeting with machine learning and mobile phone data: Evidence from an anti-poverty intervention in Afghanistan. Journal of Development Economics, 161, 103016. https://doi.org/10.1016/j.jdeveco.2022.103016 Aker, J. C. (2010). Information from markets near and far: Mobile phones and agricultural markets in Niger. American Economic Journal: Applied Economics, 2(3), 46–59. https://doi.org/10.1257/app.2.3.46 Aker, J. C. (2011). Dial “A” for agriculture: A review of information and communication technologies for agricultural extension in developing countries. Agricultural Economics, 42(6), 631–647. https://doi.org/10.1111/j. 1574-0862.2011.00545.x Aker, J. C. (2017). Using digital technology for public service provision in developing countries. In S. Gupta, M. Keen, A. Shah, & G. Verdier (Eds.), Digital revolutions in public finance (pp. 201–225). IMF. Aker, J. C., & Blumenstock, J. E. (2014). The economic impacts of new technologies in Africa. In The Oxford handbook of Africa and economics (pp. 354– 371). Oxford University Press. https://doi.org/10.1093/oxfordhb/978019 9687107.013.021 Aker, J. C., Collier, P., & Vicente, P. C. (2017). Is information power? Using mobile phones and free newspapers during an election in Mozambique. The Review of Economics and Statistics, 99(2), 185–200. https://doi.org/10. 1162/REST_a_00611

4

DIGITIZING DEVELOPMENT?

83

Aker, J. C., Ghosh, I., & Burrell, J. (2016). The promise (and pitfalls) of ICT for agriculture initiatives. Agricultural Economics, 47 (S1), 35–48. https:// doi.org/10.1111/agec.12301 Aker, J. C., & Mbiti, I. M. (2010). Mobile phones and economic development in Africa. Journal of Economic Perspectives, 24(3), 207–232. https://doi.org/ 10.1257/jep.24.3.207 Aker, J. C., Prina, S., & Welch, C. J. (2020). Migration, money transfers, and mobile money: Evidence from Niger. In AEA Papers and Proceedings, 110, 589–593. Anderson, J. R., & Feder, G. (2007). Agricultural extension. Handbook of Agricultural Economics, 3, 2343–2378. https://doi.org/10.1016/S15740072(06)03044-1 Angrist, N., Bergman, P., & Matsheng, M. (2022). |Experimental evidence on learning using low-tech when school is out. Nature Human Behaviour, 6, 941–950. Aranda-Jan, C. B., Mohutsiwa-Dibe, N., & Loukanova, S. (2014). Systematic review on what works, what does not work and why of implementation of mobile health (mHealth) projects in Africa. BMC Public Health, 14(1), 188– 188. https://doi.org/10.1186/1471-2458-14-188 Asiedu, K. G. (2019). An ambitious drone delivery health service in Ghana is tackling key logistics challenges. Quartz Africa. https://qz.com/africa/1604374/ Ziplines-drone-deliverylaunches-in-ghana-with-vaccines/. Accessed 3 August 2020. Banerjee, A., Alsan, M., Breza, E., Chandrasekhar, A. G., Duflo, E., GoldsmithPinkham, P., & Olken, B. A. (2022). Mobile phone messaging to promote preventive behaviors. Science, 375(6585), 1109–1110. Banerjee, A., Chandrasekhar, A. G., Dalpath, S., Duflo, E., Floretta, J., Jackson, M. O., Kannan, H., Loza, F. N., Sankar, A., Schrimpf, A., & Shrestha, M. (2021). Selecting the Most effective nudge: Evidence from a large-scale experiment on immunization (NBER Working Paper Series). https://doi.org/10. 3386/w28726 Bertoli, S., Ozden, C., & Packard, M. (2021). Segregation and internal mobility of Syrian refugees in Turkey: Evidence from mobile phone data. Journal of Development Economics, 152, 102704. https://doi.org/10.1016/j.jdeveco. 2021.102704 Betjeman, T. J., Soghoian, S. E., & Foran, M. P. (2013). mHealth in sub-Saharan Africa. International Journal of Telemedicine and Applications, 482324–482327. https://doi.org/10.1155/2013/482324 Bianchi, M., Bouvard, M., Gomes, R., Rhodes, A., & Shreeti, V. (2022). Mobile payments and interoperability: Insights from the academic literature.

84

J. C. AKER AND J. CARIOLLE

Björkegren, D., & Grissen, D. (2018). The potential of digital credit to bank the poor. AEA Papers and Proceedings, 108, 68–71. https://www.aeaweb. org/doi/10.1257/pandp.20181032. Accessed 22 February 2023. Björkegren, D., & Grissen, D. (2020). Behavior revealed in mobile phone usage predicts credit repayment. The World Bank Economic Review, 34(3), 618–634. https://doi.org/10.1093/wber/lhz006 Blumenstock, J. E. (2016). Fighting poverty with data. Science, 353(6301), 753– 754. https://doi.org/10.1126/science.aah5217 Blumenstock, J. E. (2018). Don’t forget people in the use of big data for development. Nature, 561(7722), 170–172. https://doi.org/10.1038/d41586018-06215-5 Blumenstock, J. E. (2020). Machine learning can help get COVID-19 aid to those who need it most. Nature. https://doi.org/10.1038/d41586-020-013 93-7 Blumenstock, J., Gabriel, C., & On, R. (2015). Predicting poverty and wealth from mobile phone metadata. Science, 350(6264), 1073–1076. https://doi. org/10.1126/science.aac4420 Bold, T., Filmer, D., Martin, G., Molina, E., Stacy, B., Rockmore, C., ... & Wane, W. (2017). Enrollment without learning: Teacher effort, knowledge, and skill in primary schools in Africa. Journal of Economic Perspectives, 31(4), 185–204. Brunnermeier, M. K., Limodio, N., & Spadavecchia, L. (2023). Mobile Money, Interoperability and Financial Inclusion. Centre for Economic Policy Research. Cariolle, J. (2021). International connectivity and the digital divide in SubSaharan Africa. Information Economics and Policy, 55, 100901. Cariolle, J., & Le Goff, M. (2023). Spatial internet spillovers in manufacturing. Journal of Development Studies, 59(8), 1163–1186. https://doi.org/ 10.1080/00220388.2023.2204177 Carosella, G., Findling, S., Fitzgerald, S., Jimenez, D.-Z., Little, G., O’Brien, A., Parker, R., Pucciarellia, J. C., Yorifuji, Y. (2021). IDC FutureScape: Worldwide digital transformation 2022 predictions. IDC FutureScape - Doc # US47115521. Available https://www.idc.com/getdoc.jsp?containerId=US4 7115521 Casaburi, L., Kremer, M., & Mullainathan, S. (2019a). Harnessing ICT to increase agricultural production: Evidence from Kenya. Harvard University. Available https://www.poverty-action.org/sites/default/files/publications/ Harnessing-ICT-to-Increase-Agric-Production_Cassaburi-et-al._Sept2019. pdf. Accessed 19 February 2023. Casaburi, L., Kremer, M., & Ramrattan, R. (2019b). Crony capitalism, collective action, and ICT: Evidence from Kenyan contract farming. PEDL Research

4

DIGITIZING DEVELOPMENT?

85

Paper. Available https://www.econ.uzh.ch/dam/jcr:e2ffc4e5-ab32-4405bfa4-70b0e962aa81/hotline_paper_20191015_MERGED.pdf. Accessed 19 February 2023. Chi, G., Fang, S., & Blumenstock, J. E. (2022). Microestimates of wealth and poverty for all low- and middle-income countries. Proceedings of the National Academy of Sciences, 119(3), 1–11. Chi, P. C., Bulage, P., Urdal, H., & Sundby, J. (2015). Barriers in the delivery of emergency obstetric and neonatal care in post-conflict Africa: Qualitative case studies of Burundi and Northern Uganda. PLoSOne, 10(9), E0139120. https://doi.org/10.1371/journal.pone.0139120 CGAP. (2016). CGAP annual report 2016: Advancing financial inclusion to improve the lives of the poor. Consultative Group to Assist the Poor. https:// www.cgap.org/about/key-documents/cgap-annual-report-2016 Clausen, L. B. (2015). Taking on the challenges of health care in Africa. Insights by Stanford Business. https://www.gsb.stanford.edu/insights/taking-challe nges-health-care-africa Conley, T. G., & Udry, C. R. (2010). Learning about a new technology: Pineapple in Ghana. American Economic Review, 100(1), 35–69. https:// doi.org/10.1257/aer.100.1.35 Commodity Port. (2019). Côte d’Ivoire: How drones are helping agriculture. https://www.commodity-port.com/cote-divoire-how-drones-are-hel ping-agriculture/. Accessed 3 August 2020. Conway, M., Holt, T., Sabow, A., & Sun, I. Y. (2020). Should sub-Saharan Africa make its own drugs? A comprehensive analysis of the business, economic, and public-health impact finds the potential for local production of pharmaceuticals to be a mixed bag. McKinsey & Company. https://www.mckinsey. com/industries/public-and-social-sector/our-insights/should-sub-saharan-afr ica-make-its-own-drugs. Accessed 19 February 2023. Demirgüç-Kunt, A., Klapper, L., Singer, D., & Ansar, S. (2018). The global findex database 2017: Measuring financial inclusion and the fintech revolution. World Bank Publications. https://openknowledge.worldbank.org/han dle/10986/29510. Accessed 22 February 2023. De Silva, H., & Ratnadiwakara, D. (2008). Using ICT to reduce transaction costs in agriculture through better communication: A case-study from Sri Lanka. LIRNEasia, Colombo, Sri Lanka. http://www.kiwanja.net/database/doc ument/document_ICT_transaction_costs.pdf. Accessed 19 February 2023. Disrupt Africa. (2021). The African tech startups funding report. Available at https://disrupt-africa.com/2022/02/04/african-tech-startup-funding-in2021-more-and-more-for-the-big-four/ Eagle, N., Pentland, A., & Lazer, D. (2008). Mobile phone data for inferring social network structure. In Social computing, behavioral modeling, and prediction (pp. 79–88). Springer US.

86

J. C. AKER AND J. CARIOLLE

Escueta, M., Nickow, A. J., Oreopoulos, P., & Quan, V. (2020). Upgrading education with technology: Insights from experimental research. Journal of Economic Literature, 58(4), 897–996. https://doi.org/10.1257/jel.201 91507 Fabregas, R., Kremer, M., & Schilbach, F. (2019). Realizing the potential of digital development: The case of agricultural advice. Science, 366(6471), eaay3038. https://doi.org/10.1126/science.aay3038 Feder, G. (1982). Adoption of interrelated agricultural innovations: Complementarity and the impacts of risk, scale, and credit. American Journal of Agricultural Economics 64(1), 94–101. Feder, G., Willett, A., & Zijp, W. (1999). Agricultural extension: Generic challenges and some ingredients for solutions. Policy Resources Working Paper, World Bank, Washington, DC. https://elibrary.worldbank.org/doi/pdf/10. 1596/1813-9450-2129 Fonta, W. M., Sanfo, S., Kedir, A. M., & Thiam, D. R. (2018). Estimating farmers’ willingness to pay for weather index-based crop insurance uptake in West Africa: Insight from a pilot initiative in Southwestern Burkina Faso. Agricultural and Food Economics, 6(1), 1–20. https://doi.org/10.1186/s40 100-018-0104-6 Gakuru, M., Winters, K., & Stepman, F. (2009). Innovative farmer advisory services using ICT. Forum for Agricultural Research in Africa (FARA): W3C Workshop “Africa perspective on the role of mobile technologies in fostering social development”, Maputo, Mozambique. https://www.w3.org/ 2008/10/MW4D_WS/papers/fara_slides.pdf Gelb, A., & Metz, A. D. (2018). Identification revolution: Can digital ID be harnessed for development? Brookings Institution Press. Ghose, A. (2017). Mobile phones: A truly transformative technology. In Tap: Unlocking the mobile economy (pp. 15–24). MIT Press. Gridley, J. (2020). CityTaps raises funding to increase clean water access. ImpactAlpha. https://impactalpha.com/CityTaps-raises-funding-to-increaseclean-water-access/. Accessed 3 August 2020. GSMA. (2020). 2020 mobile industry impact report: Sustainable development goals. https://www.gsma.com/betterfuture/2020sdgimpactreport GSMA. (2022). State of the industry report on mobile money. GSM Association. Hampshire, K., Mwase-Vuma, T., Alemu, K., Abane, A., Munthali, A., Awoke, T., Mariwah, S., Chamdimba, E., Owusu, S. A., Robson, E., Castelli, M., Shkedy, Z., Shawa, N., Abel, J., & Kasim, A. (2021). Informal mHealth at scale in Africa: Opportunities and challenges. World Development, 140, 105257–105257. https://doi.org/10.1016/j.worlddev.2020.105257 Hampshire, K., Porter, G., Mariwah, S., Munthali, A., Robson, E., Owusu, S. A., Abane, A., & Milner, J. (2017). Who bears the cost of “informal

4

DIGITIZING DEVELOPMENT?

87

mhealth”? Health-workers’ mobile phone practices and associated politicalmoral economies of care in Ghana and Malawi. Health Policy and Planning, 32(1), 34–42. https://doi.org/10.1093/heapol/czw095 Harwin, K., & Gandhi, R. (2014). Digital green: A rural video-based social network for farmer training (Innovations case narrative: Digital green). Innovations Technology Governance, Globalization, 9(3–4), 53–61. https://doi. org/10.1162/inov_a_00216 Head, K. J., Noar, S. M., Iannarino, N. T., & Grant Harrington, N. (2013). Efficacy of text messaging-based interventions for health promotion: A metaanalysis. Social Science & Medicine, 97 , 41–48. https://doi.org/10.1016/j. socscimed.2013.08.003 Hjort, J., & Poulsen, J. (2019). The arrival of fast internet and employment in Africa. The American Economic Review, 109(3), 1032–1079. https://doi. org/10.1257/aer.20161385 Hoffman, J. A., Cunningham, J. R., Suleh, A. J., Sundsmo, A., Dekker, D., Vago, F., Munly, K., Igonya, E. K., & Hunt-Glassman, J. (2010). Mobile direct observation treatment for tuberculosis patients. A technical feasibility pilot using mobile phones in Nairobi, Kenya. American Journal of Preventive Medicine, 39(1), 78–80. https://doi.org/10.1016/j.amepre.2010.02.018 Jean, N., Burke, M., Xie, M., Davis, W. M., Lobell, D. B., & Ermon, S. (2016). Combining satellite imagery and machine learning to predict poverty. Science, 353(6301), 790–794. https://doi.org/10.1126/science.aaf7894 Jensen, R. (2007). The digital provide: Information (technology), market performance, and welfare in the South Indian fisheries sector. The Quarterly Journal of Economics, 122(3), 879–924. Karamperidou, D., Brossard, M., Peirolo, S., & Richardson, D. (2020). Time to teach: Teacher attendance and time on task in Eastern and Southern Africa (IDEAS Working Paper Series from RePEc). Karim, A. (2020). Drones and the Cocoa cooperatives of Biankouma, Côte d’Ivoire. We Robotics. https://blog.werobotics.org/2020/04/27/dronesand-the-cocoa-cooperatives-of-biankouma-cote-divoire/. Accessed 3 August 2020. Kore, L. (2020). CityTaps: Delivering safe water to the urban poor through prepaid smart metering and mobile money. GSMA Mobile for Development Utilities. https://www.gsma.com/mobilefordevelopment/programme/m4d utilities/CityTaps-delivering-safewater-to-the-urban-poor-through-prepaidsmart-metering-and-mobile-money/. Accessed 3 August 2020. Kurukulasuriya, P., & Mendelsohn, R. (2008). Crop switching as a strategy for adapting to climate change. African Journal of Agricultural and Resource Economics, 2(1), 105–126. https://doi.org/10.22004/ag.econ.56970 Lakemann, T., & Lay, J. (2019). Digital platforms in Africa: The “Uberisation” of informal work.

88

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Le Nestour, A., Moscoviz, L., & Sandefur, J. (2022). The long-run decline of education quality in the developing world (Working Paper 608). Center for Global Development. https://www.cgdev.org/sites/default/files/long-rundecline-education-quality-developing-world.pdf. Accessed 21 February 2023. Lester, R. T., Ritvo, P., Mills, E. J., Kariri, A., Karanja, S., Chung, M. H., Jack, W., Habyarimana, J., Sadatsafavi, M., Najafzadeh, M., Marra, C. A., Estambale, B., Ngugi, E., Ball, T. B., Thabane, L., Gelmon, L. J., Kimani, J., Ackers, M., & Plummer, F. A. (2010). Effects of a mobile phone short message service on antiretroviral treatment adherence in Kenya (WelTel Kenya1): A randomised trial. The Lancet (British Edition), 376(9755), 1838–1845. https://doi.org/10.1016/S0140-6736(10)61997-6 Maino, M. R., Massara, M. A., Saiz, H. P., Sharma, P., & Sy, M. A. N. (2019). FinTech in sub-Saharan African countries: A game changer? International Monetary Fund. Matsumura, K., Oh, Y., Sugo, T., & Takahashi, K. (2021, June 1). Nowcasting economic activity with mobility data. ABFER 8th Annual Conference. http://abfer.org/media/abfer-events-2021/annual-conference/papers-realestate/AC21P6023_Nowcasting-Economic-Activity-with-Mobility-Data.pdf. Accessed 22 February 2023. Mc Kinsey & Co. (2023, March 10). How digital tools could boost efficiency in African health systems. Available at https://www.mckinsey.com/industries/ healthcare/our-insights/how-digital-tools-could-boost-efficiency-in-africanhealth-systems#/ Mittal, S., Gandhi, S., & Tripathi, G. (2010). Socio-economic impact of mobile phones on Indian agriculture (IDEAS Working Paper Series from RePEc). https://www.econstor.eu/handle/10419/176264. Accessed 19 February 2023. Morrison, N. (2017, May 9). Google leapfrogs rivals to be classroom king. Forbes. https://www.forbes.com/sites/nickmorrison/2017/05/09/googleleapfrogs-rivals-to-be-classroomking/#32966ae927a6 . Accessed 21 February 2023. MSF. (2022). Empty shelves come back tomorrow: ARV stockouts undermine efforts to fight HIV. Medecins Sans Frontières (MSF). https://www.msf.org/ sites/default/files/empty_shelves_come_back_tomorrow.pdf Owens, J. (2018). Responsible digital credit. Center for Financial Inclusion. https://www.rfilc.org/wp-content/uploads/2020/08/Respon London. sible_Digital_Credit_FINAL_2018.07.18.pdf. Accessed 21 February 2023. Paintsil, E. (2020). COVID-19 threatens health systems in sub-Saharan Africa: The eye of the crocodile. Journal of Clinical Investigation, 130(6), 2741– 2744. https://doi.org/10.1172/jci138493 PASEC. (2015). PASEC2014—Performances des systèmes éducatifs en Afrique subsaharienne francophone: Compétences et facteurs de réussite au primaire.

4

DIGITIZING DEVELOPMENT?

89

PASEC, CONFEMEN, Dakar. http://www.pasec.confemen.org/public ation/pasec2014-rapport/ Pheage, T. (2017). Dying from lack of medicines—Encouraging local production, right policies the way out. Africa Renewal—Health and Well-Being. https://www.un.org/africarenewal/magazine/december-2016march-2017/dying-lack-medicines. Accessed 5 August 2020. Pop-Eleches, C., Thirumurthy, H., Habyarimana, J. P., Zivin, J. G., Goldstein, M. P., De Walque, D., MacKeen, L., Haberer, J., Kimaiyo, S., Sidle, J., Ngare, D., & Bangsberg, D. R. (2011). Mobile phone technologies improve adherence to antiretroviral treatment in a resource-limited setting: A randomized controlled trial of text message reminders. AIDS (London), 25(6), 825–834. https://doi.org/10.1097/QAD.0b013e32834380c1 Pothering, J. (2019). How Zipline raised $190 million to build a global drone logistics network, starting in Rwanda and Ghana. Impact Alpha. https://impactalpha.coMcm/how-Ziplineraised-190-million-to-builda-global-drone-logistics-network-starting-in-rwanda-and-ghana/. Accessed 5 August 2020. Putra, R. A. A., & Arini, S. (2020). Measuring the economics of a pandemic: How people mobility depict economics? An evidence of people’s mobility data towards economic activities. In 8th IMF Statistical Forum: Measuring the Economics of the Pandemic (Washington). https://www.imf.org/-/media/Files/Conferences/2020/8th-statsforum/paper-rendra-putra-and-silvia-arini.ashx. Accessed 22 February 2023. Roach, M., & Cohen, I. (2016). Mobile for development utilities—Lumos: Pay-asyou-go solar in Nigeria with MTN . GSMA: The Innovation Fund. https:// www.gsma.com/mobilefordevelopment/wp-content/uploads/2016/11/ Case-Study-Lumos-Payas-you-go-solar-in-Nigeria-with-MTN.pdf. Accessed 7 August 2020. Sheffel, A., Andrews, K. G., Conner, R., Di Giorgio, L., Evans, D. K., Gatti, R., Lindelow, M., Sharma, J., Svensson, J., Wane, W., & Welander, A. (2023). Rethinking the human resource crisis in Africa’s health systems: Evidence across ten countries. medRxiv. https://doi.org/10.1101/2022.06. 17.22276571 Spielman, D., Lecoutere, E., Makhija, S., & Van Campenhout, B. (2021). Information and communications technology (ICT) and agricultural extension in developing countries. Annual Review of Resource Economics, 13, 177–201. Steele, J. E., Sundsøy, P. R., Pezzulo, C., Alegana, V. A., Bird, T. J., Blumenstock, J., Bjelland, J., Engø-Monsen, K., de Montjoye, Y.-A., Iqbal, A. M., Hadiuzzaman, K. N., Lu, X., Wetter, E., Tatem, A. J., & Bengtsson, L. (2017). Mapping poverty using mobile phone and satellite data. Journal of the Royal Society Interface, 14(127), 20160690. https://doi.org/10.1098/ rsif.2016.0690

90

J. C. AKER AND J. CARIOLLE

Suri, T., Bharadwaj, P., & Jack, W. (2021). Fintech and household resilience to shocks: Evidence from digital loans in Kenya. Journal of Development Economics, 153, 102697. https://doi.org/10.1016/j.jdeveco.2021.102697 Suri, T., & Jack, W. (2016). The long-run poverty and gender impacts of mobile money. Science, 354(6317), 1288–1292. https://doi.org/10.1126/science. aah5309 Svensson, J., & Yanagizawa, D. (2009). Getting prices right: The impact of the market information service in Uganda. Journal of the European Economic Association, 7 (2–3), 435–445. https://doi.org/10.1162/JEEA.2009.7.2-3.435 The Economist. (2008). Halfway there: How to promote the spread of mobile phones among the world’s poorest. The Economist. https://www.economist. com/business/2008/05/29/halfway-there The Montpellier Panel. (2013). Sustainable intensification: A new paradigm for African agriculture, London. https://ag4impact.org/publications/mon tpellier-panel-report2013/. Accessed 12 September 2022. UNESCO. (2021). World Teachers’ Day 2021 fact sheet. https://unesdoc.une sco.org/ark:/48223/pf0000379187. Accessed 21 February 2023. WEF. Bastos de Morais, J.-C. (2021). Digital technologies can deliver better healthcare to sub-Saharan Africa. Here’s how. https://www.weforum.org/ agenda/2017/10/digital-paths-for-better-healthcare-in-sub-saharan-africa/. Accessed 29 September 2022. WHO. (2022). Digital health atlas. World Health Organization. https://stories. digitalhealthatlas.org/space/dha/tag/blog. Accessed 19 February 2023. Witteveen, L. M., & Lie, R. (2012). Learning about “wicked” problems in the Global South. Creating a film-based learning environment with “visual problem appraisal”. MedieKultur: Journal of Media and Communication Research, 28(52), 81–99. Witteveen, L., Lie, R., Goris, M., & Ingram, V. (2017). Design and development of a digital farmer field school. Experiences with a digital learning environment for cocoa production and certification in Sierra Leone. Telematics and Informatics, 34(8), 1673–1684. https://doi.org/10.1016/j.tele.2017. 07.013 World Bank Group. (2016). World development report 2016: Digital dividends. World Bank Publications. https://www.worldbank.org/en/publication/wdr 2016 Wu, S., Wang, P., & Sun, B. (2022). Can the Internet narrow regional economic disparities? Regional Studies, 56(2), 324–337. https://doi.org/10.1080/003 43404.2021.1942444

CHAPTER 5

(Don’t) Believe the Hype?

Chapter 4 outlined the proliferation of digital initiatives across a range of sectors—health, agriculture, education, and financial services—and the hype surrounding their prospects. How do these initiatives impact the lives and livelihoods of poor households in sub-Saharan Africa? Are digital technologies living up to the hype? In order to answer these questions satisfactorily, we must examine the evidence. This represents a significant challenge for a number of reasons. Firstly, among the thousands of digital initiatives implemented, only a small subset is being studied (Spielman et al., 2021). Secondly, among those initiatives that are being studied, a smaller number still are being evaluated in what could reasonably be considered a “rigorous” manner, meaning that there are limitations related to sample size (allowing for generalizability) and selection bias (allowing us to draw a causal relationship between the intervention and the outcomes of interest). Beyond these factors, there is significant heterogeneity across contexts, interventions, targeted populations, and impact indicators. For example, the number of studies is heavily skewed toward East and Southern Africa, with relatively fewer studies in Central, West, and North Africa (Fig. 5.1), with important implications for their generalizability to other contexts. Finally, among those studied interventions, few have succeeded to scale, generally

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. C. Aker and J. Cariolle, Mobile Phones and Development in Africa, Palgrave Studies in Agricultural Economics and Food Policy, https://doi.org/10.1007/978-3-031-41885-3_5

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Fig. 5.1 Number of rigorous empirical studies on ICT4D by country and sector (Source Data from literature review by authors)

facing unsustainable business and governance models, missing infrastructure and insufficient financial support (Abate et al., 2023; Anderson & Feder, 2007). This chapter reviews evidence-based studies on the impact of information technology on a variety of development outcomes, based upon the framework introduced in Chapter 4. We take stock of rigorous quantitative studies of technology-based interventions, with the goals of identifying insights for academics, practitioners, policymakers, and students, as well as highlighting key areas for future inquiry.1 In particular, 1 Scientific rigor depends upon the academic discipline and the type of data used— qualitative or quantitative. Both of these approaches yield important insights into the potential for technology and its positive and negative impacts. Throughout this chapter, we primarily focus on quantitative research, concentrating on studies that have a sufficient

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we focus on studies that employ specific types of empirical methodologies, namely randomized controlled trials (RCTs), regression discontinuity designs (RDDs) or differences-in-differences (DDs). This approach has been adopted for several reasons. Focusing on these designs allows us to have a clear set of inclusion criteria, as well as to contribute to broader and more methodologically diverse policy research dialogues. It also enables us to draw some conclusions about the cost-effectiveness of these approaches, not only as compared with traditional means of providing public services (such as health, education, and agriculture), but also across countries. Our focus on certain types of methodological approaches comes with its own set of weaknesses. Firstly, there is no such thing as perfectly “objective” social science. The social science fields have historically been built on a small set of privileged voices and hence may have both overt and covert biases due to the lens through which they were observed, even if the material is primarily of a technical nature. Integrating a diverse set of experiences is important for a more comprehensive understanding of policy analysis tools and their application to real-world problems; this focus may ignore voices from different fields—such as sociology, computer science, and political science—as well as represent a particular world view. Secondly, given the sheer growth of interventions and studies in this space, it is impossible for us to provide a systematic—and hence comprehensive—review of studies in this field, or this book risks quickly becoming obsolete. The key studies in each area are summarized in Tables 5.1 to 5.4.

5.1

Texting for Health

Chapter 4 outlined the different ways in which digital technologies could potentially affect health outcomes (information provision, health worker training and capacity-building, performance monitoring, supply chain management, and data collection and reporting). Since 2009, no fewer

sample size and use techniques that can estimate the causal relationship between digital technology and outcomes of interest. We also reference qualitative studies by sociologists, anthropologists, computer scientists, and those in the health and education fields, when appropriate. This chapter is not intended to be a systematic review of all research in each area, but rather to highlight key findings to date in this field and draw general conclusions about the impact of digital for development.

Study

Rotheram-Borus et al. (2012)

Chang et al. (2011)

Talisuna et al. (2017)

Zurovac et al. (2011)

Data collection and monitoring (e.g. pandemics)

Information provision (for patients, communities)

Improvision provision (for patients, communities)

Health worker capacity-building

Health

Mechanism

Table 5.1

Kenya

Kenya, Uganda

Uganda

South Africa

Country

Health worker adherence to malaria treatment guidelines

Pediatric malarial treatment adherence

Patient virologic failure (AIDS)

Behavioral change among diabetes patients

Key indicator(s)

Both positive and negative results observed—women responded to 29% of texted questions and attended at least 9 of 12 intervention sessions. Increased sleep but 3.3 pt increase in blood glucose No significant differences between patients served by health workers with phones to follow-up with higher level providers and those without phones; qualitative results showed improved patient care and logistics RCT of SMS messages to parents; SMS reminders increased post-treatment return to the health facility, but had no effect on AL adherence which was high in both control and intervention groups, but had effects in Uganda Improvement in malaria treatment administered by health workers receiving case management-related SMS—24.5 percentage point increase in correct treatment immediately after intervention, 24.5 percentage points 6 months later

Impact

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Ghana

Rokicki et al. (2017) and Rokicki and Fink (2017) Unger et al. (2018)

Kenya

Kenya

Harrington et al. Kenya (2019)

Ivanova (2019)

Tanzania

L’Engle et al. (2013)

Information provision (for patients, communities) Information provision (for patients, communities) Information provision (for patients, communities) Information provision (for patients, communities) Information provision (for patients, communities)

Kenya

Jones et al. (2012)

Health worker capacity-building

Country

Study

Mechanism

Contraceptive use, exclusive breastfeeding, time to first initiation of method, etc

Sexual and reproductive health knowledge, adherence intentions, feasibility/acceptability

Sexual and reproductive health knowledge; self-reported pregnancy, sexual activity, contraceptive use Facility delivery, exclusive breastfeeding and contraceptive use, maternal/infant mortality

Contraceptive feasibility

Perceptions of health workers regarding SMS messages about malaria case management

Key indicator(s)

(continued)

Sending weekly SMS on sexual and reproductive health resulted in higher rate of family planning in the intervention group (0.74 vs. 0.65) but was not statistically significant

SMS campaign was successful in creating an enabling environment and prompting implementation of previously identified case management practices Engagement with Interactive and menu-based SMS system resulted in higher contraceptive use (2.3) than those who did not engage with the system (1.4) Interactive weekly SMS on sexual and reproductive health (OR = 3.47), increased use of birth control pill (OR = 13.23) and decreased use of emergency contraception Weekly motivational SMS on maternal health cause significantly higher contraceptive use at 16 weeks postpartum, but no statistiacal effect at 6 months Interactive web-based intervention improved sexual and reproductive health by 0.3 points

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ART monitoring and patient adherence

South Africa

De Tolly et al. (2012)

ART monitoring and patient adherence

ART monitoring and patient adherence

Kenya

Pop-Eleches et al. (2011)

ART monitoring and patient adherence

ART monitoring and patient adherence

Key indicator(s)

Mbuagbaw et al. Cameroon (2012)

Kenya

Lester et al. (2010)

Information provision (for patients, communities) Information provision (for patients, communities)

Uganda

Chang et al. (2008)

Information provision (for patients, communities) Information provision (for patients, communities) Information provision (for patients, communities)

Country

Study

(continued)

Mechanism

Table 5.1

Receiving 10 motivational SMS on HIV counselling and testing resulted in increased odds of testing (OR = 1.7); Receiving 3 motivational SMS had no effect, nor did receiving 3 or 10 informational SMS, respectively

Sending daily reminder SMS associated with 13 percentage point increase in patients with 90% ART adherence. Weekly SMS associated with 9 percentage point drop in treatment interruptions exceeding 48 hrs Receiving standardized motivational text messages did not significantly improve ART treatment adherence

Reception of SMS support greatly enhanced ART adherence

Primary health worker and mobile phone intervention significantly increased overall patient health and ART adherence

Impact

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South Africa

Uganda

Lund et al. (2012)

Van-Velthoven et al. (2013)

Linnemayr et al. (2017)

Patient-service provider coordination

Information provision (for patients, communities) Information provision (for patients, communities) Information provision (for patients, communities) Health worker training and capacity building ART patient adherence, feasibility, acceptability

Mobile tablets for CHWs on pneumonia in Uganda

O’donovan et al. Uganda (2018a)

ART patient adherence

ART monitoring and patient adherence

Skilled delivery attendance

Factors associated with indicating a problem in response to ART follow-up messages

Key indicator(s)

MacCarthy et al. Uganda (2020)

Tanzania

Kenya

Van der Kop et al. (2012)

Information provision (for patients, communities)

Country

Study

Mechanism

(continued)

No impact on knowledge of CHWs

Provision of one’s own adherence to ART as well as adherence of peers

Those with higher education were more likely to respond to messages (incidence rate ratio of 0.81); 62% of participants stated there were no issues with the system—cell phone problems were most common issue. Rural residence (IRR = 1.96) and age (IRR = 0.63 per increase in age group) associated with indicating a problem via SMS SMS and mobile voucher intervention associated with 13 percentage point increase in skilled delivery attendace (47% vs. 60%). OR = 5.73 for urban women; no effect on rural women Adherence to HIV prevention medicine among sexual assault victimes was not different between those receiving reminder calls and those in the control group Receiving bidirectional weekly SMS on ART had no significant impact on ART adherence in relation to the control group

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Study

Zakus et al. (2019)

Toda et al. (2016)

Kabadi et al. (2013)

Health worker training and capacity building

Data collection and monitoring (e.g. pandemics)

Data collection and monitoring (e.g. pandemics)

(continued)

Mechanism

Table 5.1

Tanzania

Kenya

Niger

Country Increased knowledge, more questions, improved referrals

Impact

Routine COVID-19 cases surveillance is complemented by a SMS-based disease outbreak alert system, composed of formatted SMS communication from health workers to health managers, consisting in sending patient-level information on suspected cases that require immediate notification. The results showed that timely notifications were 16.6% higher in mSOS intervention, which despite large improvements remained suboptimal with only one-fifth of detected cases notified Birth and death events reported in Routine vital registration process is the district civil registry complemented by a SMS-based disease outbreak alert system, consisting in community officers sending birth and death event-detailed SMS to a central database linked to the district civil registry. The results showed that registered birth events increased from 448 to 938 (+109%) and registered death events increased from 28 to 154 (+450%) once the SMS intervention has been implemented

CHWs trained in iCCM, with the treatment group provided additional training through a mobile application CH managers receiving timely notifications on suspected by CHWs

Key indicator(s)

98 J. C. AKER AND J. CARIOLLE

Study

Gibson et al. (2017)

Bangure et al. (2015)

Mechanism

Health worker capacity-building

Information provision (for patients, communities)

Zimbabwe

Kenya

Country

Vaccination rates, patient adherence

Vaccination/immunization rates

Key indicator(s) This study assesses whether randomly-assigned SMS reminders about vaccination schedule to new-borns caregivers in villages (clusters), combined (or not) with monetary incentives, can improve immunisation uptake in Kenya. The study shows that SMS reminders coupled with incentives improved immunisation coverage and timeliness, despite high baseline immunisation coverage levels The assesses whether randomly assigned SMS appointment reminders to mothers can improve immunization in Kadoma City in Zimbabwe. The study shows that SMS reminders increased immunization coverage

Impact

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than 10 review papers have been written on the impact of digital health (Agarwal et al., 2015; Aranda-Jan et al., 2014; Hampshire et al., 2017, 2021; Mahmood et al., 2020; Onukwugha et al., 2022). As of 2022, there were over 9,000 peer-reviewed and/or “gray literature” articles; a majority of which have been implemented on a small-scale nor based upon rigorous quantitative techniques (Aranda-Jan et al., 2014; Hampshire et al., 2017, 2021; Onukwugha et al., 2022).2 This changed, in part, during the COVID-19 crisis, when rigorous research on the provision of health services via digital means increased substantially (Chitungo et al., 2021a, 2021b, 2021c). How has digital health affected patients’ and health-care providers’ access to information, knowledge and behavior? Have these approaches improved health outcomes, and if so, how? Is the digital approach more cost-effective than traditional approaches? We summarize existing research, take stock of the findings and highlight key impacts across the five domains of digital health interventions (Table 5.1 and Box 5.1). 5.1.1

Providing Health Information

Studies that assess the impact of digital on information provision often fall into two categories: (1) sharing information with patients and broader communities about health inputs, as well as the causes and consequences of particular diseases; and (2) reminding patients and other caregivers about adhering to medication schedules in a timely manner. The majority of these studies use SMS, which, as outlined above, has wide outreach at relatively low cost. Studies in the area of digital information-sharing have primarily focused on four key areas: maternal and child health, sexual health, appointment attendance, vaccinations and, more recently, COVID-19 (Harrington et al., 2019; Laar et al., 2019; Linnemayr et al., 2017; MacCarthy et al., 2020; Rokicki et al., 2017; Unger et al., 2018). Overall, the bulk of these studies have found that, while providing information via SMS improves general knowledge about a given health issue, and patients are more likely to attend appointments, impacts upon behaviors are mixed (Harrington et al., 2019; Linde et al., 2019; Orr & King, 2015; Rokicki & Fink, 2 In a systematic review of digital health interventions in the area of sexual and reproductive health in sub-Saharan Africa, only 11% were included in the review, based upon strict inclusion and exclusion criteria (Hampshire et al., 2021; Onukwugha et al., 2022).

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2017; Rokicki et al., 2017; Unger et al., 2018). The one area that seems to have been relatively more successful is in the use of SMS for vaccination campaigns: RCTs in Kenya, Zimbabwe and India have found that SMS, with or without incentives, increased demand for childhood vaccinations (Banerjee et al., 2020; Bangure et al., 2015; Gibson et al., 2017; Linde et al., 2019). With the onset of COVID-19 in 2020, there was an explosion of studies that sought to assess the impact of digital health on compliance with COVID-19 preventative behaviors. Many of these originated in India, suggesting that SMS campaigns had positive impacts upon communities’ adherence to COVID-19 preventative behaviors, such as social distancing, hand-washing and mask-wearing (Banerjee et al., 2021; Banerjee et al., 2022), with one study finding null results (Bahety et al., 2021). The impact of this approach in sub-Saharan Africa, however, has been more limited; one study in South Africa provided “nudges” to university staff during the pandemic, finding that such nudges modified staff’s behavior during the pandemic (LuSava et al., 2022).3 Evidence is mixed regarding adherence to medications (Onukwugha et al., 2022). Earlier studies on the impact of SMS reminders on anti-retroviral (ARV) adherence found a positive relationship in Kenya and Uganda (Chang et al., 2008; Lester et al., 2010; Pop-Eleches et al., 2011). However, more recent studies in Uganda, Cameroon and Kenya have found no effects (Linnemayr et al., 2017; MacCarthy et al., 2020; Mbuagbaw et al., 2012).4 5.1.2

Health Worker Training and Monitoring

Beyond services that provide health-related information to patients and communities, a number of studies have focused on the use of digital technologies to build health workers’ capacity.5 A systematic review in this

3 While SMS-based health-care delivery interventions were implemented in Rwanda, South Africa and other countries during the time of COVID-19, most of these studies were qualitative in nature and with relatively small sample sizes (e.g., Babili et al., 2022). 4 Beyond ARV adherence, studies in South Africa suggest that SMS can be effective in improving adherence to diabetes behaviors and TB medications. 5 Mahmood et al. (2020)’s review included 23 articles, 6 of which were RCTs, 6 quasi-experimental designs, 6 qualitative studies, 1 cohort study, 1 case study, and 1 cost evaluation study. Most were from sub-Saharan Africa, mainly East Africa.

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area highlighted three key takeaways (Mahmood et al., 2020). Firstly, low levels of digital literacy were common among health-care workers, thus limiting the impact of digital technical support. These barriers were mediated via in-person mentorship and/or the provision of equipment, such as phones, solar chargers, etc. Secondly, while the impacts of digital technology upon health worker knowledge and performance were either higher or the same (as compared with traditional means), the digital approach was less costly for health workers and patients alike (Chang et al., 2011; Jones et al., 2012; Kateera et al., 2022; O’Donovan et al., 2018b; Zakus et al., 2019)—primarily due to reduced travel costs and wait times. For example, Kenya and Niger, digital health support of CHWs significantly improved their management and referrals for malaria at lower cost (Zakus et al., 2019; Zurovac et al., 2011). Thirdly, measuring impacts upon downstream outcomes—such as health workers’ capacity or population-based health outcomes—was rare in most studies. In cases where these outcomes were measured, the results were mixed (Bergmo, 2015). Compared with the large number of initiatives and studies related to the provision of health information and CHW capacity-building, there seem to be few—if any—studies on the use digital as a monitoring tool for CHWs in sub-Saharan Africa (Aranda-Jan et al., 2014). In India, however, the use of biometric tracking devices placed in TB centers increased health workers’ attendance and reduced fraud (Bossuroy et al., 2019). 5.1.3

Supply Chain Management

While there is significant potential for digital technologies to improve supply chains for medications or vaccines, as well as report inefficiencies or fraud (Aranda-Jan et al., 2014), similar to health worker monitoring, the rigorous research in this area is negligible. In general, small pilots have found that the use of SMS to provide real-time updates on drug supplies in health facilities reduced stockouts and improved drug stock management in Kenya and Tanzania (Barrington et al., 2010; Githinji et al., 2013). Yet both of these are small pilots, and must be interpreted with caution.

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Data Collection and Monitoring

One of the final potential areas for the use of digital is in data collection and monitoring, either in preparation for large-scale immunization campaigns or in the context of disease surveillance (Osei & MashambaThompson, 2021). For example, studies in India, Ghana and Zambia measured the impact of digital health tools on disease surveillance during the COVID-19 pandemic (Jalabneh et al., 2021), whereas others have documented the use of mobile applications for measles-rubella campaigns in Kenya or pregnancy data in Liberia. While much has been written on the potential of using digital tools for data collection in Africa, there are few studies on the subject; those that do exist are often qualitative in nature (McKinsey and Co, 2023). Two noteworthy studies occurred in Kenya and Tanzania; using RCTs, the studies found that SMS interventions significantly increased vital registration coverage (e.g., births and deaths), as well as the timely notification of communicable diseases (such as measles) as compared with the traditional means of reporting (Kabadi et al., 2013; Toda et al., 2016). Yet these improvements fell short of the actual data in the communities and did not necessarily lead to timely responses, thereby questioning the effectiveness of the data provided. Box 5.1 Key Takeaways from Research on M-Health • Digital initiatives (mainly SMS) have improved health knowledge and attendance at health appointments, yet have mixed impacts on behavioral changes, with the exception of vaccinations. • While digital initiatives designed to train and monitor health workers have had limited impacts upon knowledge and performance, they are less costly than in-person means. The one exception has been management and referrals for malaria. • There are few rigorous studies on the impact of digital technologies on supply chain management, suggesting an area of promising future research. • The use of digital for data collection and disease monitoring has had positive impacts upon registrations and disease notification as compared with paper trails, yet still suffer from measurement error.

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5.2 Dial “a” for Agriculture? Digital Technology and Agricultural Development Chapter 4 outlined the different ways in which digital technologies could affect agricultural outcomes: via information provision (e.g., prices, weather), agricultural extension advice, supply chain management, buyerseller coordination, agent performance monitoring, data collection and reporting, and via digital financial services. Several review papers have been written on the impact of digital agriculture over the past decade, assessing the pathways and impact of digital agriculture for development (Abate et al., 2023; Aker, 2010; Aker et al., 2017; Fabregas et al., 2019, 2022; Spielman et al., 2021). While it is outside of the purview of this book to provide a systematic review of these studies, we summarize some of them here, highlighting the key takeaways across different areas (Table 5.2 and Box 5.2). 5.2.1

Agricultural Information Provision

There are a number of studies assessing the impact of digital information provision, which often fall into two categories: (1) the impact of digital on private information provision, namely, the digital information-sharing within one’s personal social and commercial networks; and (2) the impact of digital on public information provision, namely, via the sharing of agricultural information through an information clearinghouse, often (but not always) provided by a public sector provider. 5.2.1.1 Private Networks Existing evidence suggests that improved access to mobile phone coverage and usage—and without an externally provided information platform— can affect farmers’ and traders’ access to information, behavior, and market performance. Several studies have found that increased access to mobile phone coverage is associated with greater efficiency of agricultural markets, as defined as a reduction in spatial price dispersion (Aker, 2010; Jensen, 2007; Aker & Fafchamps, 2015; Mittal et al., 2010; Tack & Aker, 2014). Jensen (2007) found that mobile phones are associated with a significant reduction in fish price dispersion in India, whereas Aker (2010), Aker and Fafchamps (2015) and Zant (2018) found that mobile phones are associated with a reduction in consumer and producer price dispersion for agricultural crops in Niger and Mozambique, respectively.

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Table 5.2 Agriculture Mechanism

Study

Country

Key indicator(s)

Results

Agent performance monitoring

Jones and Kondylis (2018)

Rwanda

Feedback on extension providers

Agent performance monitoring

Kondylis et al. (2017)

Rwanda

Adoption of good agricultural practices

Agent performance monitoring Agricultural extension advice

Amadu and McNamara (2019) Tata and McNamara (2016)

Uganda

Extension worker performance Number of farmer groups

Agricultural extension advice

Maredia et al. Burkina (2018) Faso

Farmer learning, adoption of practices

Agricultural extension advice

Van Campenhout et al. (2021)

Yields

Using a hotline to provide feedback on extension services was as effective but significantly cheaper than providing feedback in person Contact farmers monitored by extension agents adopt 0.73 additional techniques than control contact farmers—no effect on non-contact farmers ICT-based payment incentive system improves extension worker performance Extension workers who receive ICT-based skills package work with significantly higher numbers of farmer groups Mobile phone-based animated videos on postharvest technologies for cowpea improved farmer learning and adoption of good practices Videos shown to farmers on increasing productivity and profitability increased maize yields by 10.5%. Little evidence of an incremental effect of complemenytary IVR service or SMS reminders

Uganda

Uganda

(continued)

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Table 5.2 (continued) Mechanism

Study

Country

Key indicator(s)

Results

Agricultural extension advice

Van Campenhout (2021)

Uganda

Yields, input use, and adoption of technologies

Agricultural extension advice

Tjernström et al. (2021)

Kenya

Farmer beliefs and behaviors

Agricultural extension advice

Arouna et al. (2021)

Nigeria

Yield and profit

Agricultural extension advice

Fabregas et al. (2019)

Kenya

Yields and adoption of technologies

Two types of intervention, based on showing a video on agricultural production technologies, aimed at (i) reinforcing technological absorption capacity and (ii) filling the knowledge gap about the returns of adopting the technology. Both interventions lead to increase in technology adoption or in production yield, once modelling spillvoer effects among farmers Farmers update their beliefs and behaviors in purchasing agricultural lime after interacting with virtual learning app RCT designed to assess the impact of providing personalized advice on rice nutrient management through a mobile application. Treated households increased their yield by 7% and increased their profit by 10%. Overall, receiving personalized advice through mobile increased yields holding the quantity of fertilizer constant Yields increased by 4% and adoption of agricultural lime increased by 22% among farmers who received advice via ICT

(continued)

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Table 5.2 (continued) Mechanism

Study

Country

Key indicator(s)

Results

Agricultural extension advice

Fabregas et al. (2022)

Kenya, Rwanda

Input use

Agricultural extension advice Agricultural extension advice

Casaburi and Kremer (2016) Harigaya et al. (2018)

Kenya

Yields

Rwanda

Input use/ Information sharing

Data collection

Dillon (2012) Tanzania

Collection of production data

Information provision (prices)

Futch and McIntosh (2009)

Rwanda

Arranging own transportation to markets

Information provision (prices)

Svensson and Yanagizawa (2009)

Uganda

Information provision (prices)

Muto and Yamano (2009)

Uganda

Price dispersion between markets Farmer market participation

Text messaging campaign produced odds ratio of 1.20 for following recommendations on input use; however, input purchase diminished after program conclusion, but rebounded after follow-up messages were sent Yields did not increase among farmers who received agricultural advice via ICT SMS treatments increased likelihood of ordering agricultural lime by 20%. Diverse messages increased adoption of lime among farmers who did not receive the messages by 9–14% Use of mobile phones to collect agricultural production data was cost-effective, timely, and confidential Introuction of village mobile phone increases proportion of farmers arranging own transportation to markets by 18.3 percentage points 15% increase in maize sales prices for farmers

Information provision (prices)

Aker (2010)

Niger

Information provision (prices)

Nakasone (2013)

Peru

Price dispersion between markets Price dispersion between markets

Expanded mobile coverage results in higher market participation for banana, with larger gains in areas farther from district centers Reduction of 10–15% for millet consumer prices

Average sales prices incresaed by 11–13%, primary due to increases in perishable crops

(continued)

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Table 5.2 (continued) Mechanism

Study

Country

Key indicator(s)

Results

Information provision (prices)

Courtois and Subervie (2015)

Ghana

Sales prices at farmgate increased 10% for maize and 7% for groundnuts

Information provision (prices)

Hildebrandt et al. (2020)

Ghana

Price dispersion between markets Price dispersion between markets

Information provision (prices)

Aker and Fafchamps (2015)

Niger

Information provision (prices)

Tack and Aker (2014)

Niger

Information provision (prices)

Zanello et al. (2014)

Ghana

Decision to sell at farmgate or on market

Supply chain management (inputs, etc.)

Casaburi et al. (2019)

Kenya

Yields

Supply chain management and Buyer–seller coordination

Overå (2006) Ghana

Price dispersion between markets Information search costs

Harvest purchase timing and delivery financing

Sales prices of yam increased by 7% during first year of study only—no change for maize, cassava, and groundnut Reduction of 6% for cowpea, nothing for millet or sorghum Information search costs drop by 50% with mobile tecnology, with a 5–6% yearly increase in # of markets investigated With access to reliable information from multiple ICTs (ex. radio and mobile phone), farmers are more likely to sell on the market Yields increased by 11.5% relative to farmers who did not receive agricultural advice via ICT. Late fertilizer delivery was reduced by 21.6% and non-delivery by 54% Traders with access to mobile phones can more effectively time purchase of harvests and change the terms of delivery financing

The mechanisms behind such reductions appear to be due to changes in spatial arbitrage, especially for traders: Jensen (2007) finds that fishermen were more likely to sell their stock in low supply markets, whereas Tack and Aker (2014) find that agricultural traders in mobile phone markets change their search behavior, searching in more markets and increasing the number of market contacts.

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While these reductions in price dispersion suggest that markets are more efficient, the distribution of welfare gains among consumers, producers, and traders is ambiguous across these studies. For example, Jensen (2007) finds an increase in producers’ prices and profits, whereas others have found little to no effect on farm-gate prices (Aker & Fafchamps, 2015; Futch & McIntosh, 2009; Mitra et al., 2018).6 Muto and Yamano (2009), for example, found that mobile phone coverage increases the likelihood that Ugandan farmers sell their commodity, primarily for a perishable crop, and Futch and McIntosh (2009) find that the introduction of the village mobile phone in Rwanda increased the proportion of farmers arranging their own transport to markets. 5.2.1.2 Public Information Provision The presence of information spillovers and network externalities in agricultural markets is one of the key justifications for the implementation of digital market information systems (MIS), aimed at transmitting agricultural market information on price and to a large number of farmers and extension agents at low cost. Other reasons relate to the public and non-rival nature of information, which is associated with high fixedcosts but low marginal costs of diffusion (Fabregas et al., 2019; Van Campenhout, 2021). While there is potential for centralized agricultural extension provision via digital means (Aker et al., 2016a), the benefits of such systems rely on the size of the users’ network, i.e., the number of mobile phone owners and MIS subscribers. Moreover, centralization of information diffusion may come at the cost of a greater standardization and lower granularity of transmitted information, as well as information manipulation by undue interests (Fabregas et al., 2019). There have been a number of studies on the impact of externallydeveloped digital information systems for agriculture, often categorized into two types: those that provide “simple,” standardized information, 6 Beyond the narrow focus of the impact of mobile phone coverage on price dispersion, prices, and agricultural behavior, there are several studies that measure the impact of mobile phone coverage and adoption on household expenditures and consumption. For example, Beuermann et al. (2012) show that villages with mobile phone coverage in rural areas of Peru experienced an 11% increase in total household expenditures, whereas Labonne and Chase (2009) find that mobile phone ownership increases households’ growth rate of per capita consumption by between 11 and 17% among farmers in the Philippines. Finally, in Tanzania, Roessler et al. (2021) found that women’s smartphone ownership increased households’ annual per capita consumption by 20%.

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such as weather, prices, pests, or reminders; or those that provide agricultural extension, either customized or general. One of the original studies of the digital (yet not mobile phone)based market information system (MIS) was that conducted by Svensson and Yanagizawa (2009) in Uganda, who studied the impact of radio shows broadcasting agricultural market prices, finding that farmers who received the price information received farm-gate prices that were 15% higher and sold more maize as compared to farmers without access to such services (Svensson & Yanagizawa, 2009). Since that time, there have been a number of evaluations of digital-based MIS, primarily using mobile phone technology. These range from the provision of market price information to weather and pests. Using a non-experimental evaluation of market information platform in Ghana, Courtois and Subervie (2015) found that farmers who subscribed to the platform received higher maize and groundnut prices than those who did not. In an RCT of the same platform, Hildebrandt et al. (2020) found moderate increases in prices received by farmers for yams, but not for other crops, suggesting heterogeneous impacts of the technology.7 Going beyond the simple provision of price information, Camacho and Conover (2011) and Fafchamps and Minten (2012) assessed the impacts of agricultural information services more broadly. In an RCT in Colombia, Camacho and Conover (2011) found that a SMS-based price and weather information system delivered via mobile phones improved farmers’ knowledge about prevailing market conditions, but did not result in better sales prices, agricultural revenues, or household expenditures. Similarly, Fafchamps and Minten (2012) investigated the impact of Reuters Market Light (RML) in India, a mobile phone-based agricultural information system. Using an RCT that provided a free yearly RML

7 Outside of sub-Saharan Africa, Nakasone (2013) implemented a RCT among smallholders in Peru, restricting mobile phone usage to information provision. He found that farmers’ average prices increased by between 11 and 13%, primarily due to price increases for perishable crops, as well as changes in farmers’ bargaining power vis-à-vis traders. Mitra et al. (2018) conducted an RCT in India, providing potato price information via public boards versus phone calls. While the authors found that the provision of price information increased farmers’ knowledge, it did not have an impact on their prices received. Beyond mobile phones, Goyal (2010) found that the introduction of e-choupals in India— internet kiosks that provided price information and quality-testing—had a positive effect on soybean prices and yielded a 19% increase in soy production.

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subscription, the authors found no differences in average prices between farmers with RML subscriptions and those without. Outside of the digital provision of information, Dillon et al. (2020) used a two-sided (farmer and trader) RCT to assess the impact of a complementary mobile phone input—namely, a phone directory with the names and phone numbers of those in the agricultural sector—on farmers’ and traders’ welfare. Overall, the authors found that enterprises listed in the directory saw increases in customer contact, sales, and employment, with positive spillovers within villages. For farmers, households who received directories were more likely to rent land and hire labor, had lower rates of crop failure, and sold crops for weakly higher prices, suggesting that the phone directory reduced information frictions in economies recently transformed by mobile phones. The above differences in the impacts of the digital provision of “simple” information are hypothesized to result from a combination of factors, including differences in target populations, crop varieties, the importance of informational constraints, message design, and barriers to the effective use of information and communications technology (Fabregas et al., 2019). 5.2.2

Extension Agent and Farmer Training8

In addition to services that primarily provide price, weather and quality information, a growing number of studies have focused on the impact of providing agricultural extension advice via digital technologies. One of the largest studies has been a meta-analysis of six RCTs in Ghana, Kenya, Rwanda, Uganda, and Nigeria, implemented by three types of organizations (a public agency, a social enterprise, and a research-oriented nonprofit), and covering 156,000 individuals. While details of each program differed, their common objective was to provide agricultural extension via SMS, in an effort to increase lime and chemical fertilizer usage, and to apply these inputs correctly (Fabregas et al., 2022). Overall, the meta-study found that farmers were more likely to follow

8 A number of studies in the field of human–computer interaction (HCI) have piloted digital-based agricultural extension services, focusing primarily on farmers’ and extension workers’ adoption and use. For example, Gandhi et al. (2007) developed Digital Green in India, a product that disseminates targeted agricultural information to farmers via digital video.

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the text-based recommendations for lime and fertilizer, yet these effects diminished over time. Other studies of digitally-based agricultural extension yield mixed results on adoption, yields, production, and profits, suggesting that the type of information provided, the type of technology and accounting for spillovers can be important. For example, blanket text messages sent by an agribusiness to sugar cane farmers on input usage in Kenya had positive yield impacts in one trial, but not in another (Casaburi et al., 2019). Yet a more tailored approach on fertilizer usage in Nigeria found that farmers’ yields and profits increased by 7–10%, respectively, although longer-term effects were not studied (Arouna et al., 2021). An experiment in Burkina Faso studied the impact of a video-based agricultural extension program, finding that the video method was equally effective as traditional extension in facilitating learning and understanding about new agricultural technologies, but had differential effects on adoption, depending upon whether the agricultural technology was familiar or unfamiliar to farmers (Maredia et al., 2018). Van Campenhout (2021) and Van Campenhout et al. (2021) investigated the impact of video-based provision of agricultural extension in Uganda and found that the video intervention increased farmers’ knowledge, input use, and maize and rice production, without significant additional effects of voice, SMS reminders, and IVR technologies. Moreover, Van Campenhout (2021) stressed that interventions were subject to important within-village spillover effects due to information exchange between farmers. Several statistical approaches indicate that digitally-delivered advice to farmers increased yields, with impacts similar to in-person interventions (Fabregas et al., 2019). On average, the value of increased output greatly exceeds the marginal cost of delivery via mobile phones, although accounting for the type of advice, the type of technology, and spillovers is important. 5.2.3

Extension Agent Monitoring

Compared with the large number of initiatives and studies related to the provision of agricultural information and extension services, there are relatively few studies on the application of digitally-enabled extension to improve extension agent motivation and performance, or farmers’ demand for extension services (Spielman et al., 2021). One of the earliest studies of the subject was by Jones and Kondylis (2018), who

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used an RCT in Rwanda to test different feedback mechanisms (a mobile phone hotline or in-person) on agricultural extension providers. Overall, the authors found that both types of feedback mechanisms were equally effective in increasing demand for agricultural extension, but that hotline services were substantially cheaper. Other studies in Uganda and Kenya attempted to improve extensions agents’ performance by providing digitally-based payment incentives (Amadu & McNamara, 2019), as well as a digitally-based extension skills package (Tata & McNamara, 2016). 5.2.4

Buyer-Seller Coordination and Supply Chain Management

An additional mechanism is the potential for digital technologies to improve supply chains by helping farmers shop for adequate inputs or report inefficiencies or fraud. An earlier study by Overå (2006) documented traders’ use of mobile phones in Ghana to coordinate with buyers and sellers and increase the volume of transactions, in part based upon pre-existing trade networks. Casaburi et al. (2019) expanded this concept by looking at contracts between farmers and an enterprise, whereby the company provided inputs to farmers and provided a hotline to report problems about input delivery. As a result, late delivery of inputs was reduced by 23%, and non-delivery was reduced by 54%. 5.2.5

Data Collection and Monitoring

Similar to disease surveillance and monitoring in health, the use of digital technology (e.g., tablets, smartphones, computers, mobile phones, etc.) in agricultural data collection is widespread, yet rigorous studies of its impact (as compared to traditional data collection) are limited. Dillon (2012) highlights the use of simple mobile phones as a means of collecting remote and more frequent farmer and agent-level data, rather than waiting for annual agricultural surveys, when recall data on costs and production are often subject to measurement error (Dillon, 2012). Yet while the study highlights lessons learned, it does not compare the digital means with the traditional (paper-based) survey. Outside of “touch” tablets and phones, a potentially promising approach for obtaining agricultural data—such as soil moisture and yields—is GPS location information for farmers’ plots, and then using satellite imagery to assess yields. Recent studies in Kenya and Mali have demonstrated a strong correlation between satellite yield measurements,

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crop cut data, and full-plot harvests (Burke & Lobell, 2017; Lambert et al., 2018), suggesting that digital data collection may be a good proxy for in-person efforts.9 Yet research on this in sub-Saharan Africa is more limited and still worth exploring. Box 5.2 Key Takeaways from Research on Digital Agriculture • The introduction of simple mobile phones has made agricultural markets more efficient, primarily due to increased arbitrage. The distribution of these gains among different market players—traders and farmers—depends on the local context. • Public market information systems (MIS) have been successful in increasing farmers’ knowledge (about prices, weather, etc.), yet impacts upon prices, revenues and profits is mixed. • The use of digital to provide agricultural extension information suggests that simple agricultural advice can improve the correct use of inputs, as well as increase yields and profits. Yet results are mixed across countries, and do not necessarily persist into the longer term. • Evidence on the use of digital for extension agent monitoring, buyerseller coordination and data collection and monitoring are scarcer. While the impacts as compared to in-person approaches are similar, the digital approach is less costly.

5.3

ABC, 123? Digital Education

Chapter 4 outlined the different ways in which digital technologies could affect educational outcomes: via teacher and student training, teacher monitoring, parent-teacher coordination, or data collection. While traditional communication technologies, such as radio or television, have been mobilized to support national educational programs in sub-Saharan Africa (Menascé & Clément, 2017), research on the use of new digital technologies—computers, laptops, and mobile phones—is more limited, especially

9 A study in India suggests that satellite yield measurements can reduce standard errors of treatment effects—in other words, assessing the difference in means between a treatment and control group—by over 50% when compared to farmer-reported data (Cole et al., 2020).

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when compared to other continents.10 We summarize some of these here, highlighting on the key takeaways across different areas (Table 5.3 and Box 5.3). 5.3.1

Teacher and Student Training

The primary means of using technology to support learning has been twofold: (1) As a pedagogical tool for students (and parents), both inside and outside of the classroom; (2) As a means of providing capacitybuilding support to teachers. One of the earliest studies of the subject was by Aker et al. (2012), who studied the impact of using simple mobile phones as a pedagogical tool in an adult education program in Niger. Using an RCT, villages were randomly assigned either to a standard adult education program or a program that taught adults how to conduct simple operations on a mobile phone (ABC). Overall, the authors found that learning how to use the mobile phone increased students’ writing and math skills by 0.18–0.25 standard deviations, with stronger effects for math. These results persisted after the program, mainly because learning how to use the technology allowed adults to continue practicing outside of class, as well as integrate their literacy skills into their daily lives (Aker et al., 2012). Aker and Ksoll (2020) returned to the same households several years after the program and found that the ABC intervention not only led to sustained learning, but also improved other outcomes—such as savings, assets, and food security. Angrist et al. (2022) evaluated two technology-based interventions during COVID-19 in Botswana. The intervention consisted of a series of SMS messages and phone calls to parents while schools were closed, in an effort to provide continued learning to students while they were at home. The study found that both the SMS and phone calls significantly increased students’ learning, with no effect from the SMS intervention alone. More recent evidence from RCTs across five countries—including Kenya and Uganda—provides consistent evidence of the impacts of phone 10 There are numerous studies on the use of technology, primarily computers and laptops, in the US, Europe, Latin America, and India. While a majority of these studies find that computers have positive effects on student learning (Banerjee et al., 2007; Lai et al., 2013, 2015; Linden, 2008; Yang et al., 2013), some find no effects (BarreraOsorio & Linden, 2009; Beuermann et al., 2012) or even negative effects (Linden, 2008; Malamud & Pop-Eleches, 2011). Few of these studies measure the impacts upon learning outcomes in the longer term, except Banerjee et al.

Gambari et al. (2016)

Teacher and student training/ learning Teacher and student training/ learning

Teacher and student training/ learning Teacher and student training/ learning

Aker et al. (2012)

Teacher and student training/ learning Nigeria

Niger

Country

Tanzania

Angola

Borzekowski (2018)

Cardim et al. (2019)

Johnston and Ghana Ksoll (2022)

Study

Education

Topic

Table 5.3

Student learning

Student learning

Student learning

Student performance in geometry

Student learning

Key indicator(s)

Math scores

Tablets for students and computer for teacher increased familiarity with technology but did not result in significant increase in performance

Mobile phone technology 0.25 improved adult education outcomes by 0.19–0.26 standard deviations over standard methods Video instruction platform to teach difficult mathematical concepts leads to positive effects on all treatment arms Live instruction broadcast via 0.23–0.26 satellite to primary school students resulted in significant gains in literacy and numeracy skills (no impacts on attendance and classroom time-on-task) Showing of educational videos 0.22 improved several learning outcomes for preschoolers

Impacts

0.15

0.07–0.08 (2-year only)

0.2

Reading scores

116 J. C. AKER AND J. CARIOLLE

Zambia

Zambia

De Hoop et al. (2020)

Jere-Folotiya et al. (2014)

Teacher and student training/ learning Teacher and student training/ learning

The Gambia

Malawi

Pitchford et al. (2019)

Blimpo et al. (2020)

Senegal

Lehrer et al. (2017)

Teacher and student training/ learning

Rwanda

Borzekowski et al. (2019)

Teacher and student training/ learning Teacher and student training/ learning Teacher and student training/ learning

Country

Study

Topic

Student learning

Student learning

Student learning

Student learning

Student learning

Student learning

Key indicator(s)

Computer-based literacy game improved spelling scores

Video-based instruction for preschool to 2nd grade primary students increased math and language scores significantly In-classroom provision of interactive, tech-based whiteboards improved student math scores Learning activities delivered on tablet-based apps removed gendered differences in math and reading learning outcomes observed when using standard pedagogical methods In-classroom computers, smartboards, and smart responders improved outcomes for final year secondary school students Integration of technology into eduction improved math and reading scores

Impacts

0.14

0.54

0.186

Not enough information to convert to SDs

Math scores

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(continued)

Not enough information to convert to SDs

0.33

0.2

Not enough information to convert to SDs

Reading scores

5

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Côte d’Ivoire Student use of phone-based literacy technology Botswana Student learning

Derksen et al. (2022)

Chatterjee et al. (2020)

Angrist et al. (2022)

Sudan

Brown et al. (2023)

Teacher and student training/ learning

Kenya

Lysenko et al. (2019)

Student learning

Student learning

Student learning

Student learning

Kenya

Abrami et al. (2016)

Key indicator(s)

Teacher and student training/ learning Teacher and student training/ learning Teacher and student training/ learning Teacher and student training/ learning Teacher and student training/ learning

Country

Study

(continued)

Topic

Table 5.3

Not enough information to convert to SDs

Math scores

Machine learning time-series classification models used to predict gaps in voluntary usage of system by children (not an impact eval.) SMS messages and phone calls during COVID-19 pandemic improved learning by 0.12 SD

0.12 for SMS + phone call intervention (no effects for SMS intervention alone)

Digital game-based learning Not enough program produced positive information to learning effects in math, Arabic, convert to SDs and psychological well-being Access to Wikipedia, when provided to students, improves English learning outcomes

Interactive multimedia literacy software improved reading comprehension and listening skills Computer-based literacy game improved spelling scores

Impacts

Not enough information to convert to SDs Not enough information to convert to SDs Not enough information to convert to SDs 0.103

Reading scores

118 J. C. AKER AND J. CARIOLLE

Study

Angrist et al. (2023)

Aker and Ksoll (2020)

Cilliers et al. (2018)

Topic

Teacher and student training/ learning

Teacher monitoring

Teacher monitoring

Uganda

Niger

Uganda and Kenya (with Philippines, Nepal)

Country

Teacher absenteeism, student learning Teacher absenteeism, student enrollment

Student learning

Key indicator(s)

8 percentage point increase wth SMS reporting and bonus payments; 14 percentage point increase with SMS reporting and bonus payments

Large and robust effect of phone call and SMS tutorials on numeracy outcomes during COVID-19 pandemic. In Uganda, phone call and SMS tutorials combined produced improvements of 0.891 standard deviation, while SMS alone led to improvement of 0.207 SD in child test scores. Combined phone calls and SMS lead to a smaller 0.085 SD increase in test scores in Kenya Teacher monitoring by mobile phone improved student performance by 0.15–0.30 SD

Impacts 0.207 for SMS alone in Uganda, 0.891 for SMS + phone call intervention in Uganda, 0.085 for SMS + phone call intervention in Kenya

Math scores

(continued)

Reading scores

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Study

Wolf et al. (2019)

Ome and Menendez (2022)

Lichand and Wolf (2021)

School-parent coordination

Teacher and student training/ learning

Information provision

(continued)

Topic

Table 5.3

Ivory Coast

Zambia

Ghana

Country

Student learning, teacher absenteeism

Student learning

Student learning

Key indicator(s)

Math scores

Video-based parental discussion 0.09 groups coupled with teacher trainng improved student learning outcomes (0.14 SD in overall school readiness) Short stories delivered by SMS to primary students increased reading skills by 19 to 28% of a standard deviation. Cost effectiveness—$20–22 per child to expand nationally in Zambia SMS to parents alone increases students’ test scores, but sending it to both parents and teachers negatively affected teacher effort and learning

Impacts

0.19–0.28

0.08

Reading scores

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call tutorials on student learning (Angrist et al., 2023). In a similar vein, sending short stories to primary school students via SMS also increased learning (Ome & Menendez, 2022). Impact analyses of technological support using tablets or computers show mixed results. While a number of evaluations of the OLPC program exist in Latin America, with null or mixed results, to date, there are few, if any, studies of the program in Africa, despite widespread laptop distribution. In general, the simple distribution of laptops has yielded null results (Cardim et al., 2019). Those interventions that have been effective in increasing test scores have often been classroom-based, interactive games, or apps via a tablet or computer (Blimpo et al., 2020; Borzekowski, 2018; Johnston & Ksoll, 2022; Lehrer et al., 2017). 5.3.2

Monitoring Teacher Performance

Beyond interventions that provide pedagogical support to students and teachers, a number of studies have focused on the use of digital technologies to monitor teachers’ performance.11 In Niger, Aker and Ksoll (2020) studied the impact of an intervention that made weekly phone calls to several stakeholders (including teachers, the village chief and students) in the context of an adult education program and found that the intervention significantly increased students’ test scores. This was primarily due to increased teacher and student engagement. In Uganda, Cilliers et al. (2018) assessed the impact of a digital monitoring intervention that encouraged head teachers to use mobile technology to report teacher attendance to the community.12 Digital reporting increased teacher attendance, reduced student dropout, and improved students’ performance, but only when it was combined to an incentive scheme (i.e., bonuses); the digital monitoring intervention alone was ineffective, suggesting that complementary inputs are needed. Finally, in Ivory Coast, Lichand and Wolff (2021) studied the impacts of a SMS intervention that nudged 11 Mahmood et al. (2020)’s review included 23 articles, 6 of which were RCTs, 6 quasi-experimental designs, 6 qualitative studies, one cohort study, one case study, and one cost evaluation study. Most were from sub-Saharan Africa, mainly East Africa. 12 The study also had a bonus component, whereby the teachers’ bonuses were tied to attendance.

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both teachers and parents, in an effort to monitor teachers and mobilize parents. While the SMS to parents increased students’ test scores and attendance, sending messages to both parties essentially crowded out teachers’ intrinsic motivation, and thereby negatively affects their performance.13 5.3.3

Parent-Teacher Coordination and Information Provision

Unlike the other themes in education, there are relatively fewer examples of the use of digital for parent-teacher coordination and information provision. Two notable exceptions are those of Lichand and Wolf (2021) and Wolf et al. (2019). Lichand and Wolf’s study in Ivory Coast not only assessed the impact of digital on teacher monitoring, but also provided SMS and audio nudges to parents, in the hopes of increasing parental engagement. They used a similar, yet slightly different, approach in Ghana, providing video-based information to parents. Both of these studies found somewhat surprising effects: While individual interventions alone (such as SMS and video to parents or teacher trainings) were effective in improving educational outcomes, there are either no or null or negative combined effects. This suggests that there can be limits to the impacts of digital educational interventions; whereas such digital interventions are relatively easy and cost-effective, too many of them may have unintended (negative) impacts. 5.3.4

Data Collection and Monitoring

Given the potential of digital for data collection, there are a number of initiatives using digital for data collection and monitoring in subSaharan Africa, from monitoring learning in Botswana, Rwanda, Malawi, and Rwanda to monitoring teacher absenteeism in DRC, Nigeria, and Sierra Leone (Koomar et al., 2020). Despite the proliferation of these types of interventions, rigorous evidence of their impact is limited. The one exception is that of Angrist et al. (2022), who used phone-based 13 Aker and Awonon (2022) evaluate the impact of a digital (SMS and phone call) intervention designed to monitor and motivate teachers in Niger, with the monitoring component involving the entire community.

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surveys to assess student learning. Yet while the RCT used phone surveys as a data measurement tool for learning, they did not compare it with paper tests. As a result, it is difficult to assess how digital versus paper (or in-person) assessments of learning vary. Box 5.3 Key Takeaways from Research on Digital Education (Edutech) • Studies on the use of simple mobile phones as a pedagogical tool show positive impacts on learning. However, Edutech programs using tablets or computers show mixed (and sometimes negative) results. • Digitally-based means of monitoring teachers has had mixed results; some studies suggest that phone calls alone can improve teacher attendance and student performance; others suggest that it should be combined with other interventions in order to be effective. • There are relatively fewer examples of the use of digital technologies for other purposes, such as parent-teacher coordination, information provision and data collection and monitoring. Those that focus primarily on parents suggest that it can increase parental engagement, but there is limited evidence of downstream effects.

5.4 Zap It to Me: The Impact of Digital Financial Services The rapid emergence and diffusion of mobile money (m-money) in lowincome countries, especially sub-Saharan Africa, has occurred in a context of high transaction costs and market failures in various areas of public service provision (Aron, 2018; Suri, 2017). As outlined above, digital financial services have the potential to impact development outcomes in a number of ways, via agriculture, health, and educational service provision. Chapter 4 discussed the difference between first-generation and secondgeneration DFS, as well as the different use cases for simple mobile money, namely, P2P, B2P, B2B, G2P, and P2G. There is a growing body of research on the impact of mobile money on a variety of these outcomes (Suri et al., 2023, Table 5.4 and Box 5.4). Yet most of these are concentrated in a few countries (e.g., Kenya, Uganda and Mozambique), where mobile money has had the longest history and the agent networks are fairly robust.

Study Morawczynski (2009)

Jack and Suri (2011)

Batista and Vicente (2013)

Jack et al. (2013)

Munyegera and Matsumoto (2016)

P2P transfers

P2P transfers

P2P transfers

P2P transfers

P2P transfers

Mobile money

Topic

Table 5.4

Uganda

Kenya

Mozambique

Kenya

Kenya

Country

HH per capita consumption

Sending and receiving transfers

Willingness to remit

Sending and receiving transfers

MM usage and vulnerability to consumption shocks

Key indicator(s)

MM usage increased during post-election violence in 2007 and resulted in reduced vulnerability to consumption shocks Cost of sending transfer of average distance (200 km) reduced from $5 by bus to $0.35 by MM (average fee) Marginal willingness to remit increased by MM (76% of targeted individuals made at least one transaction) Households with at least one MM user 37.4/34.3 percentage points more likely to receive/send transfers MM associated with 69% increase in HH per capita consumption; MM users 20 percentage points more likely to receive remittances; 33% higher value of remittances than non-MM HHs

Results

124 J. C. AKER AND J. CARIOLLE

Study Jack and Suri (2014)

Economides and Jeziorski (2015)

Riley (2018)

Topic

P2P transfers

P2P transfers

P2P transfers

Tanzania

Tanzania

Kenya

Country

Results

(continued)

HHs with MM more likely to receive a remittance (and greater overall amount) when experiencing shocks. Shocks reduced consumption by 7% for HHs that didn’t receive remittances Demand for mobile money Demand is more inelastic to fee increases for long-distance transfers. Consumers willing to pay 1% of transaction amount for each extra kilometer and 1.1% for each extra day Risk sharing MM improves risk-sharing for HHs that use it—increased likelihood of receiving remittances and higher remittance value after rainfall-related shock; however, no spillovers to non-MM users in the village

Household consumption

Key indicator(s)

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Study Blumenstock et al. (2016)

Abiona and Koppensteiner (2018)

Egami and Matsumoto (2020)

Ahmed and Cowan (2021)

P2P transfers

P2P transfers

P2P transfers

P2P transfers

(continued)

Topic

Table 5.4

Kenya

Uganda

Tanzania

Rwanda

Country

Healthcare spending

Antenatal care take-up

HH expenditure and human capital investments

Airtime transfers and calls made

Key indicator(s)

$84 more in airtime sent to individuals affected by Lake Kivu earthquake and $16,959 increase in value of calls made HHs that adopt MM are better able to smooth consumption during idiosyncratic shocks due to access to remittances Mobile phone adoption significantly increased antenatal care take-up and reduced liquidity constraints Mobile money helps households increase their healthcare expenditures through increased access and informal loans

Results

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Study Batista and Vicente (2023)

Aker et al. (2016a)

Aggarwal et al. (2020)

Dalton et al. (2022)

Topic

P2P transfers

G2P transfers and social protection

G2P transfers and social protection

P2B transfers

Kenya

Malawi

Niger

Mozambique

Country

Credit access, sales volatility, revenues, profits

Opening of accounts, transfers

Dietary diveristy and consumption

Consumption smoothing through remittances

Key indicator(s)

(continued)

HH’s in areas with MM access were more likely to send out-migrants and to receive remittances, which helped them smooth consumption during shocks MM transfer results in a 9 to 16% improvement in dietary diversity over in-person transfer and more purchases of protein-energy-rich foods 53% of those offered an account made 5 deposits (use for savings). Half used to send and receive transfers Business owners who use Lipa na M-PESA e-payment service to accept payment from clients for goods and services have 50% higher access to digital credit and lower volatility in sales, especially for small firms. No impacts on revenues or profits

Results

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Study Beck et al. (2018)

Wieser et al. (2019)

Batista et al. (2020)

B2B transfers

P2P transfers

P2P transfers

(continued)

Topic

Table 5.4

Mozambique

Uganda

Kenya

Country

Average monthly profit

Non-farm self-employment rate, food insecurity

Value of trade credit

Key indicator(s)

Mobile money reduced theft and output losses, made transactions between entrepreneurs and suppliers faster, and increased the value of trade credit Among HHs adopting MM, the non-farm self-employment rate increased from 3.4% to 6.4% and households with severe food insecurity lowered from 62.9% to 47.2% MM and financial management training improve profits by $120/ month (35% increase over control) for female entrepreneurs; no effect for males

Results

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Study Riley (2022)

Carlson (2018)

Bharadwaj and Suri (2020)

Suri et al. (2021)

Topic

P2B transfers

Digital credit

Digital credit

Digital credit

Kenya

Kenya

Kenya

Uganda

Country

Average loan size, indebtedness, spending cuts due to shocks

Borrowing rates

Loan repayment

Business capital and profits

Key indicator(s)

(DON’T) BELIEVE THE HYPE?

(continued)

Disbursement of MF loans through MM increases business capital by 11% and business profits by 15% Larger initial loan associated with higher risk of default, but repeat borrowers receiving repeated larger loans are less likely to default in the long term Interest rate caps increase borrowing for lower risk borrowers, while higher-risk borrowers took out more loans, increased savings, and grew credit limits on M-Shwari products not subject to interest rate caps Average loan size of those bove the credit score cutoff was twice that of those just below the cutoff; no over-indebtedness due to high interest loan repayments; those above the cutoff reported less spending cuts in response to shocks, especially health shocks or family losses

Results

5

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Study Brailovskaya et al. (2021)

Björkegren et al. (2022)

Morawczynski and Pickens (2009)

Digital credit

Digital credit

Digital savings

(continued)

Topic

Table 5.4

Kenya

Nigeria

Malawi

Country

Savings and transfers

Subjective measure of well-being

Knowledge, loan repayment, loan demand

Key indicator(s)

RCT-phone-based financial literacy marginally improved knowledge and loan repayment, but also increased loan demand, leading to higher default risk Receiving a loan increases subjective measures of well-being. Among those already approved for a loan, a larger loan amount does not have additional impacts on well-being MM increases savings for both banked and unbanked individuals, increases women’s empowerment, and facilitates transfers during adverse events

Results

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Habyarimana and Jack (2016)

Gurbuz Cuneo (2019)

Bastian et al. (2018)

Bharadwaj et al. (2019) Kenya

Digital savings

Digital savings

Digital savings

Digital savings

Tanzania

Kenya

Kenya

Kenya

Suri and Jack (2016)

Digital savings

Country

Study

Topic

Loan takeup, credit access, resilience to shocks

Savings and micro-loans

Likelihood of saving, average HH savings

Educational savings and secondary school enrollment

Poverty rates

Key indicator(s)

(DON’T) BELIEVE THE HYPE?

(continued)

Access to long-term savings via MM reduced poverty rates by 2 percentage points Increased savings and secondary school enrollment among HH’s that receive digital bank accounts with targeted messages encouraging educational savings MM use leads to a 16 to 22% increase in probability of saving in HH; average household savings increased by between 15 and 21% Women provided with digital bank accounts saved more there, less in other forms of saving, and obtained more microloans through the mobile account Availability of short-term loans from M-Shwari increases loan takeup, does not crowd out other forms of credit access, and improves HH resilience to shocks

Results

5

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Study Dizon et al. (2020)

Batista and Vicente (2020)

Riley and Shonchoy (2022)

Digital savings

Digital savings

Digital savings

(continued)

Topic

Table 5.4

Ghana

Mozambique

Kenya

Country

No. of mobile bank transactions vs. visits to bank branch

Savings, use of fertilizer and other ag. Inputs

Savings and risk-sharing

Key indicator(s)

MM savings reduced risk-sharing among women, but this was more than compensated for by increased savings Farmers with MM accounts that accrued interest between harvest and planting had higher savings and higher use of fertilizer and other inputs than farmers with non-interest bearing MM accounts IVR messages sent to mobile bank clients encouraging them to use MM to access their accounts results in threefold increase in bank account transactions conducted through MM, with an 11% reduction in visits to a brick and mortar branch location

Results

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Study Kipchumba and Sulaiman (2021)

Topic

Gender Equality

Kenya

Country Financial autonomy

Key indicator(s) MM increases financial autonomy for both men and women, but with a larger effect for women. No impact on household decision-making processes

Results

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5.4.1

First-Generation DFS: The Impact of Mobile Money

One of the earliest studies on the impact of first-generation mobile money services was carried out by Jack and Suri (2014), who assessed the impact of access to mobile money (P2P) in Kenya.14 The authors found that households with access to mobile money were better able to smooth risks in the face of shocks, mainly via increased remittances from friends and family members. Apeti (2023) found similar impacts of mobile money on consumption volatility for a large sample of lower-income countries, as did other studies in Mozambique, Uganda, and Tanzania (Batista & Vicente, 2023; Riley, 2018). The primary channels for these results appear to be due to lower transaction costs for interpersonal transfers (Jack et al., 2013), increases in the timing and amount of P2P transfers (remittances) and improved access to complementary financial accounts (Apeti, 2023; Munyegera & Matsumoto, 2016). Access to mobile money has also led to reductions in poverty and food insecurity in Kenya, Uganda, and Mozambique, partly due to changes in occupation (Batista & Vicente, 2023; Suri & Jack, 2016; Wieser et al., 2019). Some of these studies also suggest that the success of mobile money depends on the size of the agent network (Abiona & Koppensteiner, 2022; Riley, 2018; Suri, 2017).15 Beyond the impact on households, there are a variety of studies that assess the impact of mobile money on entrepreneurs. In a study encouraging the use of microentrepreneurs in Malawi, Aggarwal et al. (2020) found that businesses did use mobile money, primarily for savings. A similar study in Mozambique found that access to mobile money combined with financial training increased female-owned entrepreneurs’ profits (Batista et al., 2020). Finally, in Uganda, providing microfinance loans via mobile money (rather than cash) increased business capital and profits, primarily because entrepreneurs were able to use mobile money as a savings device (Riley, 2022).

14 Blumenstock et al. (2016) studied how individuals use airtime (a precursor to mobile money) to cope with an earthquake in Rwanda. Consistent with the work on mobile money, they found that individuals sent $84 in airtime to those affected by the shock. 15 Moreover, evidence also stresses that user network size is also a critical determinant of mobile money adoption (Murendo, 2018), supporting the idea that mobile money is a network good whose social and private benefits increase with the size of other users’ networks (Bjorkegren, 2019).

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5.4.1.1 Government to Person Transfers (G2P) Beyond P2P transfers, mobile money has been extensively studied and used for governmental or NGO transfers to private individuals, primarily for cash transfer programs (G2P). One of the earliest studies of the subject was in Niger, where Aker et al. (2016b) used a randomized experiment to disentangle the effect of an unconditional cash transfer program implemented through various delivery mechanisms—cash or mobile money—on a variety of outcomes. Overall, receiving a cash transfer via mobile money improved household food security, primarily due to the reduction in travel costs associated with obtaining the transfer and increased intra-household bargaining. Since that time, there have been a number of other studies that assess the impact of mobile money-based cash transfers in Kenya, Malawi, and Liberia (e.g., Aggarwal et al., 2022; Egger et al., 2022; Haushofer & Shapiro, 2014). Nevertheless, none of these studies compare mobile money-enabled cash transfers with a pure control; as a result, we can say very little about the impact of mobile money (as a delivery mechanism) compared to cash. 5.4.1.2 Other Transactions: B2P, P2B, and Taxation A third potential use of traditional mobile money services is from businesses to individuals in the form of salary payments. While there is substantial evidence of this effect in Afghanistan and Bangladesh, there are few in sub-Saharan Africa. The results are mixed: In Afghanistan, mobile money significantly decreased the costs of obtaining salaries, but had no effect on employees’ usage of the platform (Blumenstock et al., 2015). By contrast, in Bangladesh, salary payments made via mobile money significantly increased employees’ usage of mobile money, but it had no effects on other outcomes. While there is no rigorous evidence of B2P transfers in sub-Saharan Africa, research on P2B and P2G suggests that mobile money can increase tax revenues, as well as increase firm’s sales (Apeti & Edoh, 2023; Dalton et al., 2022). 5.4.2

Second-Generation DFS: Credit, Savings, and Insurance

As outlined in Chapter 4, first-generation DFS laid the groundwork for the diffusion of more sophisticated financial products, such as digital saving or credit (Aker & Carroll, 2022; Bharadwaj & Suri, 2020; Björkegren & Grissen, 2018, 2020; Björkegren et al., 2022; Brailovskaya

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et al., 2021; Robinson et al., 2022; Suri et al., 2021). Mobile money diffusion has increased financial transparency by providing a recorded financial history of every deposit, withdrawal, transfer, or payment made by mobile money users, whether they be individuals, businesses, or governments. This greater financial transparency—together with the reduction in fixed banking costs—spearheaded a range of mobile-based financial innovations supporting the formal financial inclusion of the unbanked. In late 2011, the Kenyan telecom operator Safaricom introduced a savings and micro-credit platform, M-Shwari, based on M-Pesa technology, which allowed users to open bank accounts, deposit money, and request micro-loans. Since that time, similar products have been introduced in Tanzania, Uganda, Rwanda, Ivory Coast, and Nigeria. Box 5.4 Key Takeaways from Research on DFS • Access to mobile money has consistently helped households to smooth consumption in the face of shocks, primarily due to increased access to remittances. In some cases, this has also led to longer-term improvements in welfare. • One of the few studies on mobile (versus cash) transfers shows that mobile money significantly reduced the costs of distributing the transfers, with improved impacts upon welfare. Other studies have not tested the “digital versus cash” hypothesis. • Research on the uses of mobile money for other purposes—such as salary payments, purchases and taxation—are growing but limited in nature in sub-Saharan Africa. • Access to second-generation DFS (such as digital credit and savings) has improved users’ access to credit, with positive effects on wellbeing in Kenya and Nigeria, and negative effects on perceived wellbeing in Malawi.

In the past few years, a number of studies have assessed the impact of digital credit on users’ outcomes in Kenya, Malawi, Ghana, and Nigeria. For example, in Kenya, digital credit “crowded in” new credit for users, allowed them to smooth consumption in the face of shocks, as well as invest in education (Suri et al., 2021). Nevertheless, the results on well-being are mixed. While digital credit had no impact on income, consumption in Kenya, it appeared to increase subjective well-being in Nigeria and Malawi (Brailovskaya et al., 2023, Björkegren et al., 2022).

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Yet these studies have also noted that the widespread phenomenon of paying late fees on such loans may lead to potential harm (Brailovskaya et al., 2023). There appear to be few (if any) studies on digital savings alone.

5.5

Big Data and AI

As outlined in Chapter 4, big data have primarily been used for targeting purposes, to identify both potential beneficiaries of social protection programs and potential clients for the private sector. How have these approaches fared in comparison with traditional poverty mapping and targeting approaches? While the use of big data for targeting has been used in a growing number of countries and contexts, there are few studies that assess its performance vis-à-vis the traditional means. The one exception is research conducted in Togo, where mobile phone data were used to target recipients of a cash transfer program in response to the COVID-19 crisis there (Aiken et al., 2023). Compared to geographic targeting, the big data approached reduced targeting errors (broadly defined) by 4–21%; yet, compared to a social registry, it increased exclusion errors by 9–35%. As a result, the takeaway is mixed: While such approaches can be useful, especially in humanitarian settings, they call for careful data collection prior to rollout and may significantly increase exclusion, especially for those who do not own mobile phones.

5.6

Conclusion

Despite the thousands of digital “development” interventions in subSaharan Africa, only a subset of these are studied in a rigorous manner. As this chapter has shown, many of these studies are more heavily focused on certain geographic areas, primarily East Africa, as well as Anglophone countries. This provides some limits to the external validity of these studies to some of the most remote, landlocked, and vulnerable countries in sub-Saharan Africa, where access to digital infrastructure and public services, as well as digital technological adoption, is already more limited. This chapter opened with the question, “Does the evidence meet the hype”? The answer is, “Maybe.” What is clear is that digital is relatively cheaper in comparison with many in-person (and traditional) alternatives, such as training, information-sharing, data collection, or remittances. Yet

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whether it is also more effective depends upon a variety of factors, as we will discuss in more detail in Chapter 6. In some cases, digital approaches are a clear substitute for the traditional means, especially in the area of mobile money (for remittances) and data collection (for health, agriculture, etc.). In other cases, the digital approach serves as a complement to the traditional way of doing things, such as monitoring, training, or information provision. What is clear is that, while most of these studies show that digital approaches can improve access to information and change behaviors, their impact on welfare, especially in the long-term, is more mixed. Unsurprisingly, then, it is not the “silver bullet” for development.

References Abate, G. T., Abay, K. A., Chamberlin, J., Kassim, Y., Spielman, D. J., & TabeOjong, M. P. J. (2023). Digital tools and agricultural market transformation in Africa: Why are they not at scale yet, and what will it take to get there? Food Policy, 116, 102439. Abiona, O., & Koppensteiner, M. F. (2018). The impact of household shocks on domestic violence: Evidence from Tanzania (Working Paper). Available at: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3301756 Abiona, O., & Koppensteiner, M. F. (2022). Financial inclusion, shocks, and poverty evidence from the expansion of mobile money in Tanzania. Journal of Human Resources, 57 (2), 435–464. Abrami, P. C., Wade, C. A., Lysenko, L., Marsh, J., & Gioko, A. (2016). Using educational technology to develop early literacy skills in Sub-Saharan Africa. Education and Information Technologies, 21, 945–964. Aggarwal, S., Aker, J., Jeong, D., Kumar, N., Park, D. S., Robinson, J., & Spearot, A. (2022). The dynamic effects of cash transfers: Evidence from rural Liberia and Malawi. Aggarwal, S., Brailovskaya, V., & Robinson, J. (2020, May). Cashing in (and out): Experimental evidence on the effects of mobile money in Malawi. In AEA Papers and Proceedings (Vol. 110, pp. 599–604). Agarwal, S., Perry, H. B., Long, L. A., & Labrique, A. B. (2015). Evidence on feasibility and effective use of mHealth strategies by frontline health workers in developing countries: systematic review. Tropical Medicine & International Health, 20(8), 1003–1014. Ahmed, H., & Cowan, B. (2021). Mobile money and healthcare use: Evidence from East Africa. World Development, 141, 105392. Aiken, E. L., Bedoya, G., Blumenstock, J. E., & Coville, A. (2023). Program targeting with machine learning and mobile phone data: Evidence from an

5

(DON’T) BELIEVE THE HYPE?

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anti-poverty intervention in Afghanistan. Journal of Development Economics, 161, 103016. Aker, J. C. (2010). Information from markets near and far: Mobile phones and agricultural markets in Niger. American Economic Journal of Applied Economics, 2(3), 46–59. https://doi.org/10.1257/app.2.3.46 Aker, J. C., & Awonon, J. (2022). Call me, maybe? Technology and teacher support in Niger. Aker, J. C., Boumnijel, R., McClelland, A., & Tierney, N. (2016a). Payment mechanisms and antipoverty programs: Evidence from a mobile money cash transfer experiment in Niger. Economic Development and Cultural Change, 65(1), 1–37. Aker, J. C., Collier, P., & Vicente, P. C. (2017). Is information power? Using mobile phones and free newspapers during an election in Mozambique. The Review of Economics and Statistics, 99(2), 185–200. https://doi.org/10. 1162/REST_a_00611 Aker, J. C., & Fafchamps, M. (2015). Mobile phone coverage and producer markets: Evidence from West Africa. The World Bank Economic Review, 29(2), 262–292. Aker, J. C., Ghosh, I., & Burrell, J. (2016b). The promise (and pitfalls) of ICT for agriculture initiatives. Agricultural Economics, 47 (S1), 35–48. Aker, J. C., & Ksoll, C. (2020). Can ABC lead to sustained 123? The mediumterm effects of a technology-enhanced adult education program. Economic Development and Cultural Change, 68(3), 1081–1102. Aker, J. C., Ksoll, C., & Lybbert, T. J. (2012). Can mobile phones improve learning? Evidence from a field experiment in Niger. American Economic Journal: Applied Economics, 4(4), 94–120. Amadu, F. O., & McNamara, P. E. (2019). Performance incentives and information communication technologies in Ugandan agricultural extension service delivery. African Journal of Food, Agriculture, Nutrition and Development, 19(1), 14113–14136. Anderson, J. R., & Feder, G. (2007). Agricultural extension. Handbook of Agricultural Economics, 3, 2343–2378. Angrist, N., Ainomugisha, M., Bathena, S. P., Bergman, P., Crossley, C., Cullen, C., Letsomo, T., Matsheng, M., Panti, R. M., Sabarwal, S., & Sullivan, T. (2023). Building resilient education systems: Evidence from large-scale randomized trials in five countries (No. w31208). National Bureau of Economic Research. Angrist, N., Bergman, P., & Matsheng, M. (2022). Experimental evidence on learning using low-tech when school is out. Nature Human Behaviour, 6(7), 941–950.

140

J. C. AKER AND J. CARIOLLE

Apeti, A. E. (2023). Household welfare in the digital age: Assessing the effect of mobile money on household consumption volatility in developing countries. World Development, 161, 106110. Apeti, A. E., & Edoh, E. D. (2023). Tax revenue and mobile money in developing countries. Journal of Development Economics, 161, 103014. Aranda-Jan, C. B., Mohutsiwa-Dibe, N., & Loukanova, S. (2014). Systematic review on what works, what does not work and why of implementation of mobile health (mHealth) projects in Africa. BMC Public Health, 14(1), 188– 188. https://doi.org/10.1186/1471-2458-14-188 Aron, J. (2018). Mobile money and the economy: A review of the evidence. The World Bank Research Observer, 33(2), 135–188. Arouna, A., Michler, J. D., Yergo, W. G., & Saito, K. (2021). One size fits all? Experimental evidence on the digital delivery of personalized extension advice in Nigeria. American Journal of Agricultural Economics, 103(2), 596–619. Babili, A., Nsanzimana, S., Rwagasore, E., & Lester, R. T. (2022). SMS-based digital health intervention in Rwanda’s home-based care program for remote management of COVID-19 cases and contacts: A qualitative study of sustainability and scalability. Frontiers in Digital Health, 4, 1071790. https://doi. org/10.3389/fdgth.2022.1071790 Bahety, G., Bauhoff, S., Patel, D., & Potter, J. (2021). Texts don’t nudge: An adaptive trial to prevent the spread of COVID-19 in India. Journal of Development Economics, 153, 102747. https://doi.org/10.1016/j.jdeveco.2021. 102747 Banerjee, A., Alsan, M., Breza, E., Chandrasekhar, A. G., Chowdhury, A., Duflo, E., Goldsmith-Pinkham, P., & Olken, B. A. (2020). Messages on COVID-19 prevention in India increased symptoms reporting and adherence to preventive behaviors among 25 million recipients with similar effects on non-recipient members of their communities (No. w27496). National Bureau of Economic Research. Banerjee, A., Alsan, M., Breza, E., Chandrasekhar, A. G., Duflo, E., GoldsmithPinkham, P., & Olken, B. A. (2022). Mobile phone messaging to promote preventive behaviors. Science, 375(6585), 1109–1110. Banerjee, A., Chandrasekhar, A. G., Dalpath, S., Duflo, E., Floretta, J., Jackson, M. O., Kannan, H., Loza, F. N., Sankar, A., Schrimpf, A., & Shrestha, M. (2021). Selecting the most effective nudge: Evidence from a large-scale experiment on immunization (No. w28726). National Bureau of Economic Research. Banerjee, A., Cole, S., Duflo, E., & Linden, L. (2007). Remedying education: Evidence from two randomized experiments in India. The Quarterly Journal of Economics, 122(3), 1235–1264. Bangure, D., Chirundu, D., Gombe, N., Marufu, T., Mandozana, G., Tshimanga, M., & Takundwa, L. (2015). Effectiveness of short message services

5

(DON’T) BELIEVE THE HYPE?

141

reminder on childhood immunization programme in Kadoma, Zimbabwe-a randomized controlled trial. BMC Public Health, 15(1), 1–8. Barrera-Osorio, F., & Linden, L. (2009). The use and misuse of computers in education: Evidence from a randomized controlled trial of a language arts program. http://www.leighlinden.com/Barrera-Linden%20C omputadores_2009-03-25.pdf Barrington, J., Wereko-Brobby, O., Ward, P., Mwafongo, W., & Kungulwe, S. (2010). SMS for life: A pilot project to improve anti-malarial drug supply management in rural Tanzania using standard technology. Malaria Journal, 9, 1–9. Bastian, G., Bianchi, I., Buvinic, M., Goldstein, M., Jaluka, T., Knowles, J., ... & Witoelar, F. (2018). Are mobile savings the silver bullet to help women grow their businesses?. Gender Innovation Lab. Available at: https://documents1.worldbank.org/curated/en/512121538488834 978/pdf/130417-BRI-Aremobilesavingsthesilverbullet.pdf Batista, C., Sequeira, S., & Vicente, P. C. (2020). Closing the gender gap in financial management and performance: Evidence from an experiment on training and mobile savings. Nova SBE and LSE. Batista, C., & Vicente, P. C. (2013). Introducing mobile money in Rural Mozambique: Initial evidence from a field experiment (Working Paper No. 1301). Nova Africa Center for Business and Economic Development. Batista, C., & Vicente, P. C. (2020). Improving access to savings through mobile money: Experimental evidence from African smallholder farmers. World Development, 129, 104905. Batista, C., & Vicente, P. C. (2023). Is mobile money changing rural Africa? Evidence from a field experiment. Review of Economics and Statistics, 1–29. https://doi.org/10.1162/rest_a_01333 Beck, T., Pamuk, H., Ramrattan, R., & Uras, B. R. (2018). Payment instruments, finance and development. Journal of Development Economics, 133, 162–186. Bergmo, T. S. (2015). How to measure costs and benefits of eHealth interventions: An overview of methods and frameworks. Journal of Medical Internet Research, 17 (11), e254. https://doi.org/10.2196/jmir.4521 Beuermann, D. W., McKelvey, C., & Vakis, R. (2012). Mobile phones and economic development in rural Peru. The Journal of Development Studies, 48(11), 1617–1628. Bharadwaj, P., & Suri, T. (2020). Improving financial inclusion through digital savings and credit. In AEA Papers and Proceedings, 110, 584–588. American Economic Association. Bharadwaj, P., Jack, W., & Suri, T. (2019). Fintech and household resilience to shocks: Evidence from digital loans in Kenya (No. w25604). National Bureau of Economic Research.

142

J. C. AKER AND J. CARIOLLE

Björkegren, D. (2019). The adoption of network goods: Evidence from the spread of mobile phones in Rwanda. The Review of Economic Studies, 86(3), 1033–1060. Björkegren, D., Blumenstock, J., Folajimi-Senjobi, O., Mauro, J., & Nair, S. R. (2022). Instant loans can lift subjective well-being: A randomized evaluation of digital credit in Nigeria. arXiv preprint arXiv:2202.13540. Björkegren, D., & Grissen, D. (2018). The potential of digital credit to bank the poor. AEA Papers and Proceedings, 108, 68–71. https://doi.org/10.1257/ pandp.20181032. Accessed 22 February 2023. Björkegren, D., & Grissen, D. (2020). Behavior revealed in mobile phone usage predicts credit repayment. The World Bank Economic Review, 34(3), 618–634. Blimpo, M. P., Gajigo, O., Owusu, S., Tomita, R., & Xu, Y. (2020). Technology in the classroom and learning in secondary schools. The World Bank. Blumenstock, J. E., Callen, M., Ghani, T., & Koepke, L. (2015). Promises and pitfalls of mobile money in Afghanistan: Evidence from a randomized control trial. In Proceedings of the Seventh International Conference on Information and Communication Technologies and Development (pp. 1–10). Blumenstock, J. E., Eagle, N., & Fafchamps, M. (2016). Airtime transfers and mobile communications: Evidence in the aftermath of natural disasters. Journal of Development Economics, 120, 157–181. Borzekowski, D. L. (2018). A quasi-experiment examining the impact of educational cartoons on Tanzanian children. Journal of Applied Developmental Psychology, 54, 53–59. Borzekowski, D. L., Lando, A. L., Olsen, S. H., & Giffen, L. (2019). The impact of an educational media intervention to support children’s early learning in Rwanda. International Journal of Early Childhood, 51, 109–126. Bossuroy, T., Delavallade, C., & Pons, V. (2019). Biometric tracking, healthcare provision, and data quality: Experimental evidence from tuberculosis control (No. w26388). National Bureau of Economic Research. Brailovskaya, V., Dupas, P., & Robinson, J. (2023). Is digital credit filling a hole or digging a hole? Evidence from Malawi. Forthcoming. The Economic Journal. Brown, F. L., Farag, A. I., Hussein Abd Alla, F., Radford, K., Miller, L., Neijenhuijs, K., ... & Jordans, M. J. (2023). Can’t Wait to Learn: A quasiexperimental mixed-methods evaluation of a digital game-based learning programme for out-of-school children in Sudan. Journal of Development Effectiveness, 15(3), 320–341. Burke, M., & Lobell, D. B. (2017). Satellite-based assessment of yield variation and its determinants in smallholder African systems. Proceedings of the National Academy of Sciences, 114(9), 2189–2194. Camacho, A., & Conover, E. (2011). Manipulation of social program eligibility. American Economic Journal: Economic Policy, 3(2), 41–65.

5

(DON’T) BELIEVE THE HYPE?

143

Cardim, J., Molina Millán, T., & Vicente, P. (2019). Technology-aided instruction in primary education: Experimental evidence from Angola (Working Paper). Carlson, S. S. L. (2018). Essays in financial innovation and development (Doctoral dissertation). Massachusetts Institute of Technology. Casaburi, L., & Kremer, M. (2016). Management information systems and firm performance: Experimental evidence from a large agribusiness company in Kenya. PEDL Research Note. Casaburi, L., Kremer, M., & Ramrattan, R. (2019). Crony capitalism, collective action, and ICT: Evidence from Kenyan contract farming (PEDL Research Paper). Chang, L. W., Kagaayi, J., Arem, H., Nakigozi, G., Ssempijja, V., Serwadda, D., Quinn, T. C., Gray, R. H., Bollinger, R. C., & Reynolds, S. J. (2011). Impact of a mHealth intervention for peer health workers on AIDS care in rural Uganda: A mixed methods evaluation of a cluster-randomized trial. AIDS and Behavior, 15, 1776–1784. Chang, L. W., Kagaayi, J., Nakigozi, G., Packer, A. H., Serwadda, D., Quinn, T. C., Gray, R. H., Bollinger, R., & Reynolds, S. J. (2008). Responding to the human resource crisis: peer health workers, mobile phones, and HIV care in Rakai, Uganda. AIDS patient care and STDs, 22(3), 173. https://doi.org/ 10.1089/apc.2007.0234 Chatterjee, R., Madaio, M., & Ogan, A. (2020, June). Predicting gaps in usage in a phone-based literacy intervention system. In International Conference on Artificial Intelligence in Education (pp. 92–105). Springer International Publishing. Chitungo, I., Mhango, M., Dzobo, M., Denhere, K., Chimene, M., Musuka, G., & Dzinamarira, T. (2021a). Towards virtual doctor consultations: A call for the scale-up of telemedicine in sub-Saharan Africa during COVID-19 lockdowns and beyond. Smart Health, 21, 100207. Chitungo, I., Mhango, M., Mbunge, E., Dzobo, M., & Dzinamarira, T. (2021b). Digital technologies and COVID-19: Reconsidering lockdown exit strategies for Africa. Pan African Medical Journal, 39(1). https://doi.org/ 10.11604/pamj.2021.39.93.29773 Chitungo, I., Mhango, M., Mbunge, E., Dzobo, M., Musuka, G., & Dzinamarira, T. (2021c). Utility of telemedicine in sub-Saharan Africa during the COVID-19 pandemic. A rapid review. Human Behavior and Emerging Technologies, 3(5), 843–853. https://doi.org/10.1002/hbe2.297 Cilliers, J., Kasirye, I., Leaver, C., Serneels, P., & Zeitlin, A. (2018). Pay for locally monitored performance? A welfare analysis for teacher attendance in Ugandan primary schools. Journal of Public Economics, 167 , 69–90. Cole, S., Harigaya, T., Killeen, G., & Krishna, A. (2020). Using satellites and phones to evaluate and promote agricultural technology adoption: Evidence from smallholder farms in India.

144

J. C. AKER AND J. CARIOLLE

Courtois, P., & Subervie, J. (2015). Farmer bargaining power and market information services. American Journal of Agricultural Economics, 97 (3), 953–977. Dalton, P. S., Pamuk, H., Ramrattan, R., Uras, B., & van Soest, D. P. (2022). E-payment technology and business finance: A randomized controlled trial with mobile money (CentER Discussion Paper). Tilburg University. De Hoop, T., Ring, H., Siwach, G., Dias, P., Tembo, G., Rothbard, V., & Toungui, A. (2020, March). Impact of e-learning technology and activitybased learning on learning outcomes: Experimental evidence from community schools in rural Zambia. In 2020 APPAM Fall Research Conference. APPAM. Derksen, L., Michaud-Leclerc, C., & Souza, P. C. (2022). Restricted access: How the internet can be used to promote reading and learning. Journal of Development Economics, 155, 102810. Dillon, B. (2012). Using mobile phones to collect panel data in developing countries. Journal of International Development, 24(4), 518–527. Dillon, B., Aker, J. C., & Blumenstock, J. E. (2020). How important is the yellow pages? experimental evidence from Tanzania (CEGA Working Paper Series No. WPS-21). Center for Effective Global Action. University of California, Berkley. https://doi.org/10.26085/C3988V Dizon, F., Gong, E., & Jones, K. (2020). The effect of promoting savings on informal risk sharing: Experimental evidence from vulnerable women in Kenya. Journal of Human Resources, 55(3), 963–998. Economides, N., & Jeziorski, P. (2017). Mobile money in Tanzania. Marketing Science, 36(6), 815–837. Egami, H., & Matsumoto, T. (2020). Mobile money use and healthcare utilization: Evidence from rural Uganda. Sustainability, 12(9), 3741. Egger, D., Haushofer, J., Miguel, E., Niehaus, P., & Walker, M. (2022). General equilibrium effects of cash transfers: Experimental evidence from Kenya. Econometrica, 90(6), 2603–2643. Fabregas, R., Kremer, M., Lowes, M., On, R., & Zane, G. (2022). Digital information provision and behavior change: Lessons from six experiments in East Africa (Working Paper). Forthcoming at AEJ: AE. Fabregas, R., Kremer, M., & Schilbach, F. (2019). Realizing the potential of digital development: The case of agricultural advice. Science, 366(6471), eaay3038. Fafchamps, M., & Minten, B. (2012). Impact of SMS-based agricultural information on Indian farmers. The World Bank Economic Review, 26(3), 383–414. Futch, M. D., & McIntosh, C. T. (2009). Tracking the introduction of the village phone product in Rwanda. Information Technologies & International Development, 5(3), 54. Gambari, A. I., Shittu, A. T., Daramola, F. O., & Jimoh, M. A. (2016). Effects of video instructional packages on achievement of senior secondary school students

5

(DON’T) BELIEVE THE HYPE?

145

in mathematics in Minna, Nigeria. http://repository.futminna.edu.ng:8080/ jspui/handle/123456789/14776 Gandhi, R., Veeraraghavan, R., Toyama, K., & Ramprasad, V. (2007). Digital green: Participatory video for agricultural extension. In 2007 International conference on information and communication technologies and development (pp. 1–10). IEEE. Gibson, D. G., Ochieng, B., Kagucia, E. W., Were, J., Hayford, K., Moulton, L. H., Levine, O. S., Odhiambo, F., O’Brien, K. L., & Feikin, D. R. (2017). Mobile phone-delivered reminders and incentives to improve childhood immunisation coverage and timeliness in Kenya (M-SIMU): A cluster randomised controlled trial. The Lancet Global Health, 5(4), e428–e438. https://doi.org/10.1016/S2214-109X(17)30072-4 Githinji, S., Kigen, S., Memusi, D., Nyandigisi, A., Mbithi, A. M., Wamari, A., Muturi, A. N., Jagoe, G., Barrington, J., Snow, R. W., & Zurovac, D. (2013). Reducing stock-outs of life saving malaria commodities using mobile phone text-messaging: SMS for life study in Kenya. PLOS OnE, 8(1), e54066. Goyal, A. (2010). Information, direct access to farmers, and rural market performance in central India. American Economic Journal: Applied Economics, 2(3), 22–45. GSMA. (2019). Agritech deployment tracker. https://www.gsma.com/mobilefor development/m4d-tracker/magri-deployment-tracker/ Gurbuz Cuneo, A. (2019). The economics of rural populations in Sub-Saharan Africa: Financial inclusion and agriculture (Doctoral dissertation). Georgetown University. Habyarimana, J., & Jack, W. (2016). State versus consumer regulation: An evaluation of two road safety interventions in Kenya. In African successes, Volume I: government and institutions (pp. 307–330). NBER, University of Chicago Press. Hampshire, K., Mwase-Vuma, T., Alemu, K., Abane, A., Munthali, A., Awoke, T., Mariwah, S., Chamdimba, E., Owusu, S. A., Robson, E., Castelli, M., Shkedy, Z., Shawa, N., Abel, J., & Kasim, A. (2021). Informal mhealth at scale in Africa: Opportunities and challenges. World Development, 140, 105257–105257. https://doi.org/10.1016/j.worlddev.2020.105257 Hampshire, K., Porter, G., Mariwah, S., Munthali, A., Robson, E., Owusu, S. A., Abane, A., & Milner, J. (2017). Who bears the cost of “informal mhealth”? Health-workers’ mobile phone practices and associated politicalmoral economies of care in Ghana and Malawi. Health Policy and Planning, 32(1), 34–42. https://doi.org/10.1093/heapol/czw095 Harigaya, T., Kremer, M., Lowes, M., On, R., & Zane, G. (2018). Behavioral spillovers via sms: Experimental evidence from Rwanda (Working Paper). Harrington, R. A., Scarborough, P., Hodgkins, C., Raats, M. M., Cowburn, G., Dean, M., Doherty, A., Foster, C., Juszczak, E., Mhurchu, C. N., & Rayner,

146

J. C. AKER AND J. CARIOLLE

M. (2019). A pilot randomized controlled trial of a digital intervention aimed at improving food purchasing behavior: The front-of-pack food labels impact on consumer choice study. JMIR Formative Research, 3(2), e9910. Haushofer, J., & Shapiro, J. (2014). The effect of changes in absolute and relative Wealth due to unconditional cash transfers on psychological wellbeing: Pre-analysis plan. Hildebrandt, N., Nyarko, Y., Romagnoli, G., & Soldani, E. (2020). Price iInformation, inter-village networks, and ‘bargaining spillovers’: Experimental evidence from Ghana. Forthcoming: NYU Stern School of Business. Ivanova, O., Wambua, S., Mwaisaka, J., Bossier, T., Thiongo, M., Michielsen, K., & Gichangi, P. (2019). Evaluation of the elimika pilot project: Improving art adherence among HIV positive youth using an ehealth intervention in Mombasa, Kenya. African Journal of Reproductive Health, 23(1), 100–110. Jalabneh, R., Syed, H. Z., Pillai, S., Apu, E. H., Hussein, M. R., Kabir, R., Arafat, S. Y., Majumder, M. A. A., & Saxena, S. K. (2021). Use of mobile phone apps for contact tracing to control the COVID-19 pandemic: A literature review. Applications of Artificial Intelligence in COVID-19, 389–404. Springer. Jack, W., & Suri, T. (2011). Mobile money: The economics of M-PESA (No. w16721). National Bureau of Economic Research. Jack, W., & Suri, T. (2014). Risk sharing and transactions costs: Evidence from Kenya’s mobile money revolution. American Economic Review, 104(1), 183– 223. Jack, W., Ray, A., & Suri, T. (2013). Transaction networks: Evidence from mobile money in Kenya. American Economic Review, 103(3), 356–361. Jensen, R. (2007). The digital provide: Information (technology), market performance, and welfare in the South Indian fisheries sector. The Quarterly Journal of Economics, 122(3), 879–924. Jere-Folotiya, J., Chansa-Kabali, T., Munachaka, J. C., Sampa, F., Yalukanda, C., Westerholm, J., ... & Lyytinen, H. (2014). The effect of using a mobile literacy game to improve literacy levels of grade one students in Zambian schools. Educational Technology Research and Development, 62, 417–436. Johnston, J., & Ksoll, C. (2022). Effectiveness of interactive satellite-transmitted instruction: Experimental evidence from Ghanaian primary schools. Economics of Education Review, 91, 102315. Jones, C. O., Wasunna, B., Sudoi, R., Githinji, S., Snow, R. W., & Zurovac, D. (2012). “Even if you know everything you can forget”: Health worker perceptions of mobile phone text-messaging to improve malaria case-management in Kenya. PLoS ONE, 7 (6), e38636. https://doi.org/10.1371/journal.pone. 0038636 Jones, M., & Kondylis, F. (2018). Does feedback matter? Evidence from agricultural services. Journal of Development Economics, 131, 28–41.

5

(DON’T) BELIEVE THE HYPE?

147

Kabadi, G. S., Mwanyika, H., & de Savigny, D. (2013). Innovations in monitoring vital events: A scalable intervention using mobile phone messaging (SMS) support for vital registration coverage. The Lancet, 381, S69. Kateera, F., Riviello, R., Goodman, A., Nkurunziza, T., Cherian, T., Bikorimana, L., Nkurunziza, J., Nahimana, E., Ntakiyiruta, G., Matousek, A., & HedtGauthier, B. (2022). The effect and feasibility of mhealth-supported surgical site infection diagnosis by community health workers after cesarean section in rural rwanda: Randomized controlled trial. JMIR mHealth and uHealth, 10(6), e35155. https://doi.org/10.2196/35155 Kipchumba, E., & Sulaiman, M. (2021). Digital finance and intra-household decision-making: Evidence from mobile money use in Kenya (Working Paper). Koomar, S., Moss Coflan, C., & Kaye, T. (2020). Using EdTech in settings of fragility, conflict and violence: A curated resource list (No. 11). EdTech Hub. Labonne, J., & Chase, R. S. (2009). The power of information: the impact of mobile phones on farmers’ welfare in the Philippines (World Bank Policy Research Working Paper, 4996). Lai, F., Luo, R., Zhang, L., Huang, X., & Rozelle, S. (2015). Does computerassisted learning improve learning outcomes? Evidence from a randomized experiment in migrant schools in Beijing. Economics of Education Review, 47 , 34–48. Lai, F., Zhang, L., Hu, X., Qu, Q., Shi, Y., Qiao, Y., Boswell, M., & Rozelle, S. (2013). Computer assisted learning as extracurricular tutor? Evidence from a randomised experiment in rural boarding schools in Shaanxi. Journal of Development Effectiveness, 5(2), 208–231. Lambert, M. J., Traoré, P. C. S., Blaes, X., Baret, P., & Defourny, P. (2018). Estimating smallholder crops production at village level from Sentinel-2 time series in Mali’s cotton belt. Remote Sensing of Environment, 216, 647–657. Lehrer, K., Mawoyo, M., & Mbaye, S. (2017). The impacts of interactive smartboards on learning achievement in Senegalese primary schools (Working Paper). Available at: https://www.3ieimpact.org/sites/default/files/2019-01/GFRPW2.14-NEPAD-Senegal-e-school.pdf L’Engle, K. L., Vahdat, H. L., Ndakidemi, E., Lasway, C., & Zan, T. (2013). Evaluating feasibility, reach and potential impact of a text message family planning information service in Tanzania. Contraception, 87 (2), 251–256. Lester, R. T., Ritvo, P., Mills, E. J., Kariri, A., Karanja, S., Chung, M. H., Jack, W., Habyarimana, J., Sadatsafavi, M., Najafzadeh, M., Marra, C. A., Estambale, B., Ngugi, E., Ball, T. B., Thabane, L., Gelmon, L. J., Kimani, J., Ackers, M., & Plummer, F. A. (2010). Effects of a mobile phone short message service on antiretroviral treatment adherence in Kenya (WelTel Kenya1): A randomised trial. The Lancet (british Edition), 376(9755), 1838–1845. https://doi.org/10.1016/S0140-6736(10)61997-6

148

J. C. AKER AND J. CARIOLLE

Lichand, G., & Wolf, S. (2021). Arm-wrestling in the classroom: the nonmonotonic effects of monitoring teachers. University of Zurich, Department of Economics, Working Paper, 357). Linde, D. S., Korsholm, M., Katanga, J., Rasch, V., Lundh, A., & Andersen, M. S. (2019). One-way SMS and healthcare outcomes in Africa: Systematic review of randomised trials with meta-analysis. PLoS ONE, 14(6), e0217485. https://doi.org/10.1371/journal.pone.0217485 Linden, L. L. (2008). Complement or substitute? The effect of technology on student achievement in India (Working Paper, p. 47). Columbia University: InfoDev. Linnemayr, S., Huang, H., Luoto, J., Kambugu, A., Thirumurthy, H., Haberer, J. E., Wagner, G., & Mukasa, B. (2017). Text messaging for improving antiretroviral therapy adherence: No effects after 1 year in a randomized controlled trial among adolescents and young adults. American Journal of Public Health, 107 (12), 1944–1950. https://doi.org/10.2105/AJPH.2017. 304089 Lund, S., Hemed, M., Nielsen, B. B., Said, A., Said, K., Makungu, M. H., & Rasch, V. (2012). Mobile phones as a health communication tool to improve skilled attendance at delivery in Zanzibar: A cluster-randomised controlled trial. BJOG: An International Journal of Obstetrics & Gynaecology, 119(10), 1256–1264. LuSava, R., Haijing Huang, C., Brockman, B., Mkandawire, Y., & Roy Mathur, M. (2022, October 13). How behavioral ‘nudges’ can improve COVID-19 selfreporting. Blog, Ideas and Insights. Available at: https://www.idinsight.org/ article/how-behavioral-nudges-can-improve-covid-19-self-reporting/ Lysenko, L., Abrami, P. C., Wade, C. A., Marsh, J. P., WaGioko, M., & Kiforo, E. (2019). Promoting young Kenyans’ growth in literacy with educational technology: A tale of two years of implementation. International Journal of Educational Research, 95, 176–189. MacCarthy, S., Izenberg, M., Barreras, J. L., Brooks, R. A., Gonzalez, A., & Linnemayr, S. (2020). Rapid mixed-methods assessment of COVID-19 impact on Latinx sexual minority men and Latinx transgender women. PLoS ONE, 15(12), e0244421. https://doi.org/10.1371/journal.pone.0244421 Malamud, O., & Pop-Eleches, C. (2011). Home computer use and the development of human capital. The Quarterly Journal of Economics, 126(2), 987–1027. Mahmood, S., Hasan, K., Carras, M. C., & Labrique, A. (2020). Global preparedness against COVID-19: We must leverage the power of digital health. JMIR Public Health and Surveillance, 6(2), e18980. https://doi.org/ 10.2196/18980 Maredia, M. K., Reyes, B., Ba, M. N., Dabire, C. L., Pittendrigh, B., & BelloBravo, J. (2018). Can mobile phone-based animated videos induce learning

5

(DON’T) BELIEVE THE HYPE?

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and technology adoption among low-literate farmers? A field experiment in Burkina Faso. Information Technology for Development, 24(3), 429–460. Mbuagbaw, L., Thabane, L., Ongolo-Zogo, P., Lester, R. T., Mills, E. J., Smieja, M., Dolovich, L., & Kouanfack, C. (2012). The Cameroon Mobile Phone SMS (CAMPS) trial: A randomized trial of text messaging versus usual care for adherence to antiretroviral therapy. PLOS One, 7 (12), e46909. https:// doi.org/10.1371/journal.pone.0046909 McKinsey & Company. (2023, March 10). How digital tools could boost efficiency in African health systems. Available at: https://www.mckinsey.com/indust ries/healthcare/our-insights/how-digital-tools-could-boost-efficiency-in-afr ican-health-systems#/ Menascé, D., & Clément, F. (2017). Le numérique au service de l’éducation en Afrique. À propos de « Le numérique au service de l’éducation en Afrique », série Savoirs communs, n 17, AFD, AUF, Orange. Mitra, S., Mookherjee, D., Torero, M., & Visaria, S. (2018). Asymmetric information and middleman margins: An experiment with Indian potato farmers. Review of Economics and Statistics, 100(1), 1–13. Mittal, S., Gandhi, S., & Tripathi, G. (2010). Socio-economic impact of mobile phones on Indian agriculture (Working paper, No. 246). Morawczynski, O. (2009). Exploring the usage and impact of “transformational” mobile financial services: the case of M-PESA in Kenya. Journal of Eastern African Studies, 3(3), 509–525. Morawczynski, O., & Pickens, M. (2009, August). Poor people using mobile financial services: Observations on customer usage and impact from M-PESA. Brief, CGAP. Available at: https://www.cgap.org/sites/default/files/CGAPBrief-Poor-People-Using-Mobile-Financial-Services-Observations-on-Cus tomer-Usage-and-Impact-from-M-PESA-Aug-2009.pdf Munyegera, G. K., & Matsumoto, T. (2016). Mobile money, remittances, and household welfare: Panel evidence from rural Uganda. World Development, 79, 127–137. Muto, M., & Yamano, T. (2009). The impact of mobile phone coverage expansion on market participation: Panel data evidence from Uganda. World Development, 37 (12), 1887–1896. Nakasone, E. (2013, August 4–6). The role of price information in agricultural markets: Experimental evidence from rural Peru. Agricultural and Applied Economics Association (AAEA) 2013 Annual Meeting, Washington, D.C. O’Donovan, J., Kabali, K., Taylor, C., Chukhina, M., Kading, J. C., Fuld, J., & O’Neil, E. (2018a). The use of low-cost Android tablets to train community health workers in Mukono, Uganda, in the recognition, treatment and prevention of pneumonia in children under five: A pilot randomised controlled trial. Human Resources for Health, 16, 1–9.

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O’Donovan, J., O’Donovan, C., Kuhn, I., Sachs, S. E., & Winters, N. (2018b). Ongoing training of community health workers in low-income and middleincome countries: A systematic scoping review of the literature. British Medical Journal Open, 8(4), e021467. Ome, A., & Menendez, A. (2022). Using SMS and parental outreach to improve early reading skills in Zambia. Education Economics, 30(4), 384–398. Onukwugha, F. I., Smith, L., Kaseje, D., Wafula, C., Kaseje, M., Orton, B., Hayter, M., & Magadi, M. (2022). The effectiveness and characteristics of mHealth interventions to increase adolescent’s use of sexual and reproductive health services in sub-Saharan Africa: A systematic review. PLOS One, 17 (1), e0261973. https://doi.org/10.1371/journal.pone.0261973 Orr, J. A., & King, R. J. (2015). Mobile phone SMS messages can enhance healthy behaviour: A meta-analysis of randomised controlled trials. Health Psychology Review, 9(4), 397–416. Osei, E., & Mashamba-Thompson, T. P. (2021). Mobile health applications for disease screening and treatment support in low-and middle-income countries: A narrative review. Heliyon, 7 (3), e06639. Overå, R. (2006). Networks, distance, and trust: Telecommunications development and changing trading practices in Ghana. World Development, 34(7), 1301–1315. Pitchford, N. J., Chigeda, A., & Hubber, P. J. (2019). Interactive apps prevent gender discrepancies in early-grade mathematics in a low-income country in sub-Sahara Africa. Developmental Science, 22(5), e12864. Pop-Eleches, C., Thirumurthy, H., Habyarimana, J. P., Zivin, J. G., Goldstein, M. P., De Walque, D., MacKeen, L., Haberer, J., Kimaiyo, S., Sidle, J., Ngare, D., & Bangsberg, D. R. (2011). Mobile phone technologies improve adherence to antiretroviral treatment in a resource-limited setting: A randomized controlled trial of text message reminders. AIDS (london), 25(6), 825–834. https://doi.org/10.1097/QAD.0b013e32834380c1 Riley, E. (2018). Mobile money and risk sharing against village shocks. Journal of Development Economics, 135, 43–58. Riley, E. (2022). Resisting social pressure in the household using mobile money: Experimental evidence on microenterprise investment in Uganda (Vol. 04, Issue 04). University of Oxford. Riley, E., & Shonchoy, A. (2022). A national information campaign encouraging financial technology use in Ghana (Vol. 2206). Department of Economics, Florida International University. Robinson, J., Park, D. S., & Blumenstock, J. E. (2022). The impact of digital credit in developing economies: A review of recent evidence. Roessler, P., Carroll, P., Myamba, F., Jahari, C., Kilama, B., & Nielson, D. (2021). The economic impact of mobile phone ownership: Results from a randomized controlled trial in Tanzania.

5

(DON’T) BELIEVE THE HYPE?

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Rokicki, S., Cohen, J., Salomon, J. A., & Fink, G. (2017). Impact of a textmessaging program on adolescent reproductive health: A cluster–randomized trial in Ghana. American Journal of Public Health, 107 (2), 298–305. https:// doi.org/10.2105/AJPH.2016.303562 Rokicki, S., & Fink, G. (2017). Assessing the reach and effectiveness of mHealth: Evidence from a reproductive health program for adolescent girls in Ghana. BMC Public Health, 17 (1), 1–14. Rotheram-Borus, M. J., Tomlinson, M., Gwegwe, M., Comulada, W. S., Kaufman, N., & Keim, M. (2012). Diabetes buddies: Peer support through a mobile phone buddy system. The Diabetes Educator, 38(3), 357–365. Spielman, D., Lecoutere, E., Makhija, S., & Van Campenhout, B. (2021). Information and communications technology (ICT) and agricultural extension in developing countries. Annual Review of Resource Economics, 13, 177–201. Suri, T. (2017). Mobile money. Annual Review of Economics, 9(1), 497–520. Suri, T., Aker, J., Batista, C., Callen, M., Ghani, T., Jack, W., Klapper, L., Riley, E., Schaner, S., & Sukhtankar, S. (2023). Mobile money. VoxDevLit, 2(2), 3. Suri, T., Bharadwaj, P., & Jack, W. (2021). Fintech and household resilience to shocks: Evidence from digital loans in Kenya. Journal of Development Economics, 153, 102697. Suri, T., & Jack, W. (2016). The long-run poverty and gender impacts of mobile money. Science, 354(6317), 1288–1292. Svensson, J., & Yanagizawa, D. (2009). Getting prices right: The impact of the market information service in Uganda. Journal of the European Economic Association, 7 (2–3), 435–445. Tack, J., & Aker, J. C. (2014). Information, mobile telephony, and traders’ search behavior in Niger. American Journal of Agricultural Economics, 96(5), 1439– 1454. Talisuna, A. O., Oburu, A., Githinji, S., Malinga, J., Amboko, B., Bejon, P., ... & Zurovac, D. (2017). Efficacy of text-message reminders on paediatric malaria treatment adherence and their post-treatment return to health facilities in Kenya: a randomized controlled trial. Malaria Journal, 16(1), 1–11. Tata, J. S., & McNamara, P. E. (2016). Social factors that influence use of ICT in agricultural extension in Southern Africa. Agriculture, 6(2), 15. Tjernström, E., Lybbert, T. J., Hernández, R. F., & Correa, J. S. (2021). Learning by (virtually) doing: Experimentation and belief updating in smallholder agriculture. Journal of Economic Behavior & Organization, 189, 28–50. Toda, M., Njeru, I., Zurovac, D., Tipo, S. O., Kareko, D., Mwau, M., & Morita, K. (2016). Effectiveness of a mobile short-message-service–based disease outbreak alert system in Kenya. Emerging Infectious Diseases, 22(4), 711.

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de Tolly, K., Skinner, D., Nembaware, V., & Benjamin, P. (2012). Investigation into the use of short message services to expand uptake of human immunodeficiency virus testing, and whether content and dosage have impact. Telemedicine and e-Health, 18(1), 18–23. Unger, J. A., Ronen, K., Perrier, T., DeRenzi, B., Slyker, J., Drake, A. L., Mogaka, D., Kinuthia, J., & John-Stewart, G. (2018). Short message service communication improves exclusive breastfeeding and early postpartum contraception in a low-to middle-income country setting: a randomised trial. BJOG: An International Journal of Obstetrics & Gynaecology, 125(12), 1620–1629. https://doi.org/10.1111/1471-0528.15337 Van Campenhout, B. (2021). The role of information in agricultural technology adoption: Experimental evidence from rice farmers in Uganda. Economic Development and Cultural Change, 69(3), 1239–1272. Van Campenhout, B., Spielman, D. J., & Lecoutere, E. (2021). Information and communication technologies to provide agricultural advice to smallholder farmers: Experimental evidence from Uganda. American Journal of Agricultural Economics, 103(1), 317–337. Van der Kop, M. L., Karanja, S., Thabane, L., Marra, C., Chung, M. H., Gelmon, L., ... & Lester, R. T. (2012). In-depth analysis of patient-clinician cell phone communication during the WelTel Kenya1 antiretroviral adherence trial. van Laar, E., van Deursen, A. J., van Dijk, J. A., & de Haan, J. (2019). Determinants of 21st-century digital skills: A large-scale survey among working professionals. Computers in Human Behavior, 100, 93–104. https://doi.org/ 10.1016/j.chb.2019.06.017 van-Velthoven, M. H., Car, L. T., Gentry, S., & Car, J. (2013). Telephone delivered interventions for preventing HIV infection in HIV-negative persons. Cochrane Database of Systematic Reviews, (5). Wieser, C, Bruhn, M., Kinzinger, J., Ruckteschler C., & Heitmann, S. (2019). The impact of mobile money on poor rural households: Experimental evidence from Uganda (Working Paper 8913). World Bank Policy Research. Wolf, S., Aber, J. L., Behrman, J. R., & Tsinigo, E. (2019). Experimental impacts of the “quality preschool for Ghana” interventions on teacher professional well-being, classroom quality, and children’s school readiness. Journal of Research on Educational Effectiveness, 12(1), 10–37. Wolf, S., & Lichand, G. (2022). Nudging parents and teachers to improve learning and reduce child labor in Cote d’Ivoire. Mimeo. Yang, T. C., Hwang, G. J., & Yang, S. J. H. (2013). Development of an adaptive learning system with multiple perspectives based on students’ learning styles

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and cognitive styles. Journal of Educational Technology & Society, 16(4), 185– 200. Zakus, D., Moussa, M., Ezechiel, M., Yimbesalu, J. P., Orkar, P., Damecour, C., ... & Nganga, G. (2019). Clinical evaluation of the use of an mhealth intervention on quality of care provided by Community Health Workers in southwest Niger. Journal of Global Health, 9(1). Zanello, G., Srinivasan, C. S., & Shankar, B. (2014). Transaction costs, information technologies, and the choice of marketplace among farmers in Northern Ghana. The Journal of Development Studies, 50(9), 1226–1239. Zant, W. (2018). Impact of mobile phones on staple food markets in Mozambique: Improved arbitrage or increased rent extraction?. (Working Paper). https:// papers.ssrn.com/sol3/papers.cfm?abstract_id=2914944 Zurovac, D., Sudoi, R. K., Akhwale, W. S., Ndiritu, M., Hamer, D. H., Rowe, A. K., & Snow, R. W. (2011). The effect of mobile phone textmessage reminders on Kenyan health workers’ adherence to malaria treatment guidelines: A cluster randomised trial. The Lancet, 378(9793), 795–803.

CHAPTER 6

Rethinking ICT4D

The only viable path is one in which we use our confidence as faith in our eventual ability to reach a solution, while we apply our humility to temper every step along the way. We have to believe a solution exists, while doubting – and therefore intensively seeking confirmation for – each element of any proposed solutions. (Madon & Toyama, 2023)

Despite the numerous digital deployments worldwide and growing research in a number of areas, there are several shortcomings in relation to the design of digital development interventions—as well as research surrounding their impact—which can partly explain the potential (and in some cases muted) results. We will summarize the key findings, propose a renewed framework, and suggest a potential way forward.

6.1

What Have We Learned?

6.1.1

The “Myth” of More Information

A key assumption of using digital technology for development is that these technologies can address a key market failure for poor rural populations: imperfect information. While this is a relevant assumption in most

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. C. Aker and J. Cariolle, Mobile Phones and Development in Africa, Palgrave Studies in Agricultural Economics and Food Policy, https://doi.org/10.1007/978-3-031-41885-3_6

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contexts, digital technology will only be successful in increasing knowledge, changing behavior, and improving welfare if a number of necessary conditions exist. Firstly, the information must be a constraint in a given context— not only in general, but also for the specific information provided (i.e., weather, prices, absenteeism, and diseases) and for the target population. Wealthier farmers and traders in Niger, for example, may be less information-constrained than their poorer counterparts who may or may not be the target of the ICT for agriculture service. Similarly, more complicated concepts—fertilizer dosage, or intercropping or planting, for example—cannot be easily conveyed via SMS or audio and hence might need some type of digital imagery or training to convey the concepts. Secondly, even if information is a constraint, it is not certain that the information can be acquired in a format that is both useful and timely, as well as “easy to share” via the relevant digital technology—whether via SMS, a hotline or the internet. For example, if a market price information system provides consumer prices for crops in markets that are of no relevance to local farmers, the usefulness of that information will be limited. Similarly, if data on disease prevalence is not updated on a weekly (or, in the case of COVID19, a daily) basis, this diminishes the usefulness of the information. Thirdly, even if the information is of high quality, it does not necessarily follow that it can be “trusted” from the recipient’s point of view. Because the trust in digitally-diffused information is critical, the source of that information must be considered. For many farmers or traders, a key issue is whether one’s social network, as compared with agricultural extension agents, is the most reliable source of agricultural information. For instance, Patel et al. (2012) find that the source of the information—the social network or extension agents—affected farmers’ use of digitalbased agricultural extension services.1 If such information is traditionally exchanged in person rather than long distance, then separating the information from the information provider via a nameless, faceless digital program can affect farmers’ willingness to use such systems or indeed to trust the information (Oreglia et al., 2011; Srinivasan, 2007). Zanello 1 As Beaman et al. (2016, 2021) point out, these models can yield different implications on whether entry points matter for diffusion, and which entry points would be most effective. In the context of ICTs, this is related to a discussion as to who is targeted for the information service.

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et al. (2014) also pinpointed that diversifying the sources of information—combining digitized information from market information systems with information gathered from private social network, for example,— reduces the uncertainty in farmers’ decision-making and improves their bargaining power. Finally, even if the information transmitted through digital technology is of high quality and from a trusted source, a number of diffusion models and studies find that individuals only adopt once they are connected to a threshold number of adopters, a matter of “critical mass” (Acemoglu et al., 2011; Beaman et al., 2016, 2021; Centola & Macy, 2007; Kondylis et al., 2017). While “simple” information is arguably the most frequently offered type of information service (prices in agriculture, disease surveillance in health, for example), other types of information may be under-recognized for their contribution to market efficiency gains. For example, Burrell and Oreglia (2015) find that problems of waste in the case of perishable goods (fish) were avoided by timely updates about travel delays between fishermen and traders, and by communicating the need for ice and fuel. 6.1.2

The Importance of Complementary Markets

Even if the information provided via digital is appropriate, relevant, timely, of high quality and from a trusted source, farmers, traders, households, and public service agents still need access to competitive complementary markets and public goods (roads, property rights) in order to translate that information into action. For example, several economic research papers have analyzed farmers’ limited bargaining power vis-àvis traders, thereby limiting the usefulness of addressing the information constraint (Nakasone et al., 2014). While some studies have noted that access to digital-based market information services actually improves farmers’ bargaining power, if traders have monopsony power, this price information will not necessarily change their bargaining capacity (Aker, 2008; Goyal, 2010; Mitra et al., 2018). At the same time, other factors could possibly impinge on an individual’s ability to act on price or other agriculture-related information. For example, unavailability of timely and affordable credit remains a major constraint that can realistically limit farmers’ capacity to use meaningfully any information provided, either because of limited access to financial

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markets or because of interlinked transactions (Casaburi & Reed, 2014; Srinivasan & Burrell, 2013). 6.1.3

Using a Digital Technology (Effectively) Is Not Always Simple

A majority of economic studies of the impact of digital technology focus on specific outcomes, such as increased access to information, improved knowledge, changes in marketing, planting, or health behavior and wellbeing. While some of these studies also address issues surrounding individuals’ initial adoption and usage of the technology, they focus primarily on the cost of the service and, less frequently, the type of information provided. Few of these studies address issues related to the “usability” of the technology platform or the handset and its relationship to the initial or sustained adoption of such services, an area that is primarily addressed by the human computer interaction (HCI) field. Beyond the cost and quality issues mentioned above, limited adoption of such services can be attributed to several other factors. Due to the widespread availability and adoption of basic “feature” phones in rural areas in most developing countries, many digital services are primarily provided for such phones, using SMS or USSD technology. Yet these “simple” phones are used in limited ways by low-literate and non-literate users, primarily using voice and audio channels (Dodson et al., 2013; Wyche & Steinfield, 2015). Several digital interventions have attempted to address these barriers by designing interfaces specifically for low-literate users (Medhi et al., 2010, 2011), deploying technologies such as interactive voice response (IVR), or by incorporating training or relying on intermediaries, but this can be significantly more costly to implement and sustain (Parikh & Ghosh, 2006; Sambasivan et al., 2010).2 The language of digital information provision is also a potential challenge, as indigenous languages may not be written or the correct alphabet may not be available on users’ phones (Dodson et al., 2013; Wyche & Steinfeld, 2015). Beyond literacy issues, there are also significant cost constraints that shape rural users’ basic phone use, a result of the multi-dimensional digital divide addressed in Chapter 2. A primary constraint is access to reliable energy sources, without which access to both mobile phones and the internet is more limited (Fig. 6.1). It is hence common that potential 2 Intermediated use of ICTs the practice of relying upon the skills of another person to operate an ICT as a way to overcome a lack of requisite skill.

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users switch off their phones to “preserve the charge,” which hampers their frequency and intensity of mobile usage. Another constraint is poverty, which forces households to maintain small airtime balances on their phones, thereby limiting their ability to make frequent calls or receive and send texts. For the same reason, phones (or SIM cards) are often used interchangeably or switched out, in order to benefit from tariff reductions across operators, which can make targeting the intended beneficiary difficult. Finally, wear-and-tear on phones can result in broken screens or numerical keypads rubbed blank, which further confound users’ ability to use the technology (Dodson et al., 2013; Wyche & Steinfield, 2015). All of these practical features limit rural households’ and agents’ access to and sustained use of the service, regardless of its potential utility.

Fig. 6.1 Electricity and internet access in sub-saharan Africa, 2014–2018”. ITU and WDI

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6.1.4

It Isn’t Always About the “Average” Effect

Gender, religion, caste, and family ties may serve as a commonality that catalyzes or bounds information circulation (Conley & Udry, 2010). Yet much of the potential benefits of digital technology depends upon access to those technologies, which can, in turn, affect their use of such programs and their impacts. While some economic studies focus on the effects of these technologies on different populations—as differentiated by gender, wealth, ethnicity, geography, and age—a majority of studies still focus primarily on the average effects. This can fail to measure the unintended consequences of these technologies, potentially exacerbating existing inequalities (Fabregas et al., 2022). While economic theory predicts that mobile phones and digital-based services can help make markets more efficient, the distribution of these gains across different actors is unclear, and striking disparities exist between mobile phone owners and non-owners (Blumenstock & Eagle, 2012). Growing disparities as benefits accrue to those able to access or effectively use ICTs while those without access are left behind is often referred to as the “digital divide” (Keniston & Kumar, 2003). In addition, significant heterogeneity exists within the population of mobile phone owners along gender, ethnicity, income, and caste dimensions. Within economics, Blumenstock et al. (2015) showed that the wealthiest mobile phone users were the most likely to receive interpersonal m-money transfers after idiosyncratic negative shocks. Gender-focused sociological and anthropological research has shown that mobile phone ownership, sharing, and usage have tended to be skewed in favor of male heads of household (Burrell, 2010). For example, in many parts of the world, women play an important role in the agricultural sector, both in production and marketing. While women could arguably potentially benefit the most from digital agriculture services, there is a wide “digital divide” in terms of women’s access to and use of ICTs, with women 14% less likely to own a mobile phone than men (GSMA, 2015), in part due to costs and gendered social norms.3 However, even if women do have access to a mobile phone within the household, they may have limited privacy or independence while using

3 For example, whether public computing facilities are accessible to women depends upon whether they are located where women are expected to be present and whether they are granted the time and freedom of mobility to travel (Kuriyan & Kitner, 2007).

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it (Burrell, 2010; Dodson et al., 2013; GSMA, 2015) or be further constrained due to low levels of language and technical literacy (Geldof, 2011; Stromquist, 1992; Wyche & Steinfeld, 2015). These differences in access and usage affect not only women’s potential access to and use of digital services, but their ability to translate these services into action. As a result, accounting for gender in both the design and research of these services would provide greater insights into the differential impacts of services both within and across agricultural households. 6.1.5

Understanding What, When, Why, and How

With the widespread growth of mobile phone coverage and adoption over the past decade, there has been widespread enthusiasm over the use of the digital in agriculture, health, education, and financial services. As was evident in Chapters 4 and 5, thousands of digital deployments exist worldwide, yet economic research on these initiatives remains limited. A priori, it is not clear that such programs will improve the welfare of targeted populations, and substantial public resources have been invested in these initiatives. As a result, more economics research into these initiatives is needed, using a combination of experimental and non-experimental techniques, such as difference-in-differences and regression continuity designs (Aker, 2011). As part of these designs, it will also be important to consider the cost effectiveness of such interventions by comparing the digital intervention with the standard approach, as has been done for e-learning interventions for instance (Banerjee et al., 2007). This is particularly important for low-income users; although the service may be provided more cheaply via the information technology, it may also result in additional expenses for low-income users. While empirical approaches could potentially identify a causal relationship between a digital development program and the outcomes of interest, several other considerations are warranted. Firstly, while economics often considers the question of adoption and usage of new technologies, this is often for an “existing” or “as is” technology. In this vein, the computer science literature—through the design and evaluation of alternate devices and interfaces—can provide important insights into how potential users interact with and understand a given technology. This can be complemented with quantitative economic and qualitative sociological approaches, which can provide additional insights into how and why a given technology or service can be used.

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6.1.6

Digital May Not Always Be Sustainable…Is that Okay?

An oft-missing component in the design and evaluation of digital services is an assessment of agents’ willingness to pay for such services. To date, many of these services have been heavily subsidized at the outset (e.g., Fafchamps & Minten, 2012), with adoption and usage dropping off once such subsidies are removed. A key question is whether such services can and should be subsidized, and if so, how. This suggests that there is great potential to assess agricultural agents’ initial willingness to pay for such services at the outset, as well as continued adoption or usage.

6.2 Box • • • • • • • • •

A Framework for “Digital Development”

6.1 Principles of Digital for Development (Waugaman, 2016) Design with the User Understand the Ecosystem Design for Scale Build for Sustainability Be Data Driven Use Open Data, Open Source Reuse and Improve Address Privacy and Security Be Collaborative

As we have seen in previous chapters, the digital approach is not always (more) successful achieving the intended development outcomes. Why? And what can be done? In their chapter in Introduction to Developing Engineering (2023), Madon and Toyama ask, “Why is it that, despite our consciously positive intentions, development engineering often fails to meet its objectives? And what can we do to increase our chances of success?” Insert “digital for development” for “development engineering” in this phrase, and this book is, in essence, asking the same questions. The authors answer the first question with four potential explanations: (1) counterproductive unconscious intentions; (2) a flawed approach; (3) “bad luck,” and (4) a problem without a solution—and conclude that “Taken together, these possibilities should instill in us a deep humility: Either we are at fault (possibilities 1 and 2), or the circumstances are beyond us (3 and 4).” “Their suggestion, as a way forward, is to combine confidence – in data, analysis and problem-solving – with humility, to

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temper each step.” In short, they state that “We have to believe a solution exists, while doubting – and therefore intensively seeking confirmation for – each element of any proposed solutions” (Madon & Toyama, 2023). Box 6.2 Key Takeaways from Research in Digital Development Contextual fit: Ensuring that the digital technology is appropriate for the specific context and population, considering factors such as existing information needs and access to complementary markets and services. Quality of information: Ensuring that the information provided is relevant, timely and from a trusted source. Usability and access: addressing issues of affordability, accessibility and usability of the technology platform, considering the specific needs of the target population. Interplay with broader development context: Considering the interplay between the technology and the broader development context, including existing power relations and existing social networks. Sustainability: Considering how the technology will be sustained over the long-term, considering factors such as financial sustainability, technical sustainability and social sustainability.

To date, there have been a number of frameworks and guidelines proposed to help policymakers, practitioners, and donors to balance that confidence with humility. Waugaman (2016), for example, proposed six key principles (Box 6.1), ranging from “designing with the user” to “being data driven and collaborative.” Due to the complex nature of the problems that digital technology tries to address, a holistic approach is needed to balance “confidence” with “humility” while designing, implementing, and researching “digital for development” interventions in an effective and ethical way. This not only includes considering the social, economic, and political context, including beneficiaries’ perspectives and aspirations, but also as the technical feasibility of the solution (Box 6.2). A narrow, technocentric approach to digital development is unlikely to yield sustainable results. A more integrated approach that considers the interplay between the technology and the broader context, including the role of information, existing power relations, social networks, and dynamics, is needed. In an effort to bring together these different frameworks and principles, we propose the following ten questions as a potential “guide” for stakeholders interested in using digital technology as a potential means to

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address a variety of socioeconomic problems. These appear below, as well as in Fig. 6.2.

Fig. 6.2 Ten questions for digital development

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Ten Questions for Digital Development 1. What is the key development problem, and why does it exist? What are the key market failures at play? 2. Is part of the development problem due to imperfect information or high transaction costs? 3. If information is the constraint, can it be easily (and credibly) provided via digital means? (E.g., SMS, Audio, Video) 4. What is the “low-tech” solution to this problem? How is it working? Why isn’t it working? 5. Would beneficiaries require access to a complementary market or services (e.g., financial services, mobile money agents, roads, health centers, etc.) for the intervention to be effective? If so, do these markets or services already exist, or can they be easily provided? 6. Does the target population already own and use digital technologies? If so, what types (simple, smartphones, etc.)? And if not, can they easily get access to them? 7. What evidence (if any) exists of the impact of digitally-based interventions in similar or other contexts? Do the benefits rely upon a minimum level of adoption? Are there differential effects for different sub-populations? 8. Is there evidence of positive or negative spillovers of the technology? If so, for whom? 9. Can and should the intervention be piloted first before going to scale ? 10. Can the intervention be sustained without outside assistance? If not, what would be the optimal policy intervention to sustain it?

Box 6.3 A Case Study on Digital Development: Mobile Money for School Fees in Benin In low-income countries, rural households often lack the necessary financial resources to send their children to school. As a result, households often seek interpersonal transfers (P2P) from relatives located in distant locations, implying long travel times (if the money is delivered inperson) or potential fraud. In such contexts, a mobile money-based school fee payment system could leverage existing interpersonal transfers and subsequently increase school attendance.

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A pilot program tested this hypothesis in Benin, developing a mobile money platform for school fee payments for rural households (Adida et al., 2018). The results were not as expected: Only 3% of students had their school fees covered, and for those who were covered, it was primarily for students who reported having no difficulties paying school fees. This was, in part, due to low adoption of mobile money by “donors” of school fees in urban areas, long distances to mobile money agents, and legal and technical challenges allowing schools to process mobile money “donations” into their bank accounts (a regulatory issue).

Box 6.3 presents a case study on the use of mobile money as a means to pay students’ school fees in Benin. In applying the ten questions to the case study, we can see that the first four questions suggest that a digital “solution” (in this case, mobile money) is promising. Yet the fifth and sixth questions provide important insights as to where such an initiative could go wrong, if not addressed: low mobile money adoption by a key stakeholder in the process, a “missing” mobile money infrastructure (agents), and regulatory constraints that prevent the technology from being used as intended. As a result, piloting the intervention on a smaller-scale provided useful insights, which they could then address before deciding whether the intervention could or should be attempted at a larger scale. These ten questions could be applied for past and future interventions that have been less successful than originally intended.

6.3

Conclusion

Digital technologies have been undeniably instrumental in addressing market failures worldwide, with important impacts in key countries and contexts. Yet their potential for impact and scale is, at times, hampered by the large digital divide in SSA, characterized by the low penetration and absorption of digital technologies in remote areas and among more vulnerable populations. The potential will be fully unleashed if policymakers and other stakeholders are able to address persistent obstacles to digital access and usage in sub-Saharan Africa: affordable energy access; a mobile network and internet backbone infrastructure; regulatory

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frameworks and institutions; and low levels of (digital) literacy.4 If these constraints are not addressed, the digital divide may preclude a scalingup of more sophisticated deployment of digital technologies, particularly those based on internet, AI, cloud-computing, or big data.5 Is digital technology the silver bullet for development? No. But if designed, implemented and researched with confidence and humility— as well as the ten-question framework as a guide—perhaps it can be a silver lining.

References Acemoglu, D. (2021). Harms of AI (No. w29247). National Bureau of Economic Research. Acemoglu, D., Dahleh, M. A., Lobel, I., & Ozdaglar, A. (2011). Bayesian learning in social networks. The Review of Economic Studies, 78(4), 1201– 1236. Adida, C. L., Chabi Bouko, A., Verink, A., Chockalingam, G., & Burney, J. (2018). Pilot of a mobile money school fee payment system in rural Benin. PLOS One, 13(6), e0198240. https://doi.org/10.1371/journal.pone. 0198240 Aker, J. C. (2008). Does digital divide or provide? The impact of cell phones on grain markets in Niger. Center for Global Development working paper, (154). Aker, J. C. (2011). Dial “A” for agriculture: A review of information and communication technologies for agricultural extension in developing countries. Agricultural Economics, 42(6), 631–647. Banerjee, A. V., Cole, S., Duflo, E., & Linden, L. (2007). Remedying education: Evidence from two randomized experiments in India. The Quarterly Journal of Economics, 122(3), 1235–1264.

4 While there are a variety of definitions of digital literacy, UNESCO defines it “an individual’s ability to access information, understand, evaluate, integrate, manage, communicate and reliably and appropriately create it” (UNESCO, 2018). 5 A number of authors have warned about the risks of using big data and AI for

development policy (Acemoglu, 2021; Blumenstock, 2018), with the World Bank calling for a new social contract around the creation, storage, use, reuse and sharing of data (World Bank, 2021). At the same time, given that the digital footprint of the poorest is often softer, big data algorithms used to design and guide development policies may favor the wealthier individuals who have access.

168

J. C. AKER AND J. CARIOLLE

Beaman, L., BenYishay, A., Fatch, P., Magruder, J., & Mobarak, A. M. (2016). Making networks work for policy: Evidence from agricultural technology adoption in Malawi (3ie Impact Evaluation Report 43). International Initiative for Impact Evaluation (3ie). Beaman, L., BenYishay, A., Magruder, J., & Mobarak, A. M. (2021). Can network theory-based targeting increase technology adoption? American Economic Review, 111(6), 1918–1943. Blumenstock, J. (2018). Don’t forget people in the use of big data for development. Nature, 561(7722), 170–172. Blumenstock, J. E., Callen, M., Ghani, T., & Koepke, L. (2015, May). Promises and pitfalls of mobile money in Afghanistan: Evidence from a randomized control trial. In Proceedings of the seventh international conference on information and communication technologies and development (pp. 1–10). Blumenstock, J. E., & Eagle, N. (2012). Divided we call: Disparities in access and use of mobile phones in Rwanda. Information Technologies & International Development, 8(2), 1. Burrell, J. (2010). Evaluating shared access: Social equality and the circulation of mobile phones in rural Uganda. Journal of Computer-Mediated Communication, 15(2), 230–250. Burrell, J., & Oreglia, E. (2015). The myth of market price information: Mobile phones and the application of economic knowledge in ICTD. Economy and Society, 44(2), 271–292. Casaburi, L., & Reed, T. (2014). Interlinked transactions and pass-through: Experimental evidence from Sierra Leone. Stanford Institute for Economic Policy Research, Stanford University. Centola, D., & Macy, M. (2007). Complex contagions and the weakness of long ties. American Journal of Sociology, 113(3), 702–734. Conley, T. G., & Udry, C. R. (2010). Learning about a new technology: Pineapple in Ghana. American Economic Review, 100(1), 35–69. Dodson, L. L., Sterling, S. R., & Bennett, J. K. (2013). Minding the gaps: Cultural, technical and gender-based barriers to mobile use in oral-language Berber communities in Morocco. In Proceedings of the Sixth International Conference on Information and Communication Technologies and Development: Full Papers-Volume 1 (pp. 79–88). Fabregas, R., Kremer, M., Lowes, M., On, R., & Zane, G. (2022). Digital information provision and behavior change: Lessons from six experiments in East Africa (Working Paper). Forthcoming at AEJ:AE. Fafchamps, M., & Minten, B. (2012). Impact of SMS-based agricultural information on Indian farmers. The World Bank Economic Review, 26(3), 383–414. Geldof, M. (2011). Earphones are not for women: Gendered ICT use among youths in Ethiopia and Malawi. Information Technologies & International Development, 7 (4), 69.

6

RETHINKING ICT4D

169

GSMA. (2015). Bridging the gender gap: Mobile access and usage in low and middle-income countries. GSMA. https://www.gsma.com/mobilefordevelo pment/wp-content/uploads/2016/02/Connected-Women-Gender-Gap.pdf Goyal, A. (2010). Information, direct access to farmers, and rural market performance in central India. American Economic Journal: Applied Economics, 2(3), 22–45. Keniston, K., & Kumar, D. (2003). The four digital divides. Online eri¸sim, 21, 2010. Kondylis, F., Mueller, V., & Zhu, J. (2017). Seeing is believing? Evidence from an extension network experiment. Journal of Development Economics, 125, 1–20. Kuriyan, R., & Kitner, K. R. (2007). Constructing class boundaries: Gender and shared computing. Proceedings of the international conference on information and communication technologies and development (pp. 1–10). IEEE. Madon, T., & Toyama, K. (2023). Chapter 4: Asking the “right” questions. In T. Madon et al. (Eds.), Introduction to development engineering. Springer. Medhi, I., Menon, S. R., Cutrell, E., & Toyama, K. (2010). Beyond strict illiteracy: Abstracted learning among low-literate users. In Proceedings of the 4th ACM/IEEE International Conference on Information and Communication Technologies and Development (pp. 1–9). Medhi, I., Patnaik, S., Brunskill, E., Gautama, S. N., Thies, W., & Toyama, K. (2011). Designing mobile interfaces for novice and low-literacy users. ACM Transactions on Computer-Human Interaction (TOCHI), 18(1), 1–28. Mitra, S., Mookherjee, D., Torero, M., & Visaria, S. (2018). Asymmetric information and middleman margins: An experiment with Indian potato farmers. Review of Economics and Statistics, 100(1), 1–13. Nakasone, E., Torero, M., & Minten, B. (2014). The power of information: The ICT revolution in agricultural development. Annu. Rev. Resour. Econ., 6(1), 533–550. Oreglia, E., Liu, Y., & Zhao, W. (2011, May). Designing for emerging rural users: Experiences from China. In Proceedings of the SIGCHI conference on human factors in computing systems (pp. 1433–1436). Parikh, J. S., & Ghosh, K. (2006). Understanding and designing for intermediated information tasks in India. IEEE Pervasive Computing, 5(2), 32–39. Patel, N., Shah, K., Savani, K., Klemmer, S. R., Dave, P., & Parikh, T. S. (2012, March). Power to the peers: Authority of source effects for a voicebased agricultural information service in rural India. In Proceedings of the fifth international conference on information and communication technologies and development (pp. 169–178).

170

J. C. AKER AND J. CARIOLLE

Sambasivan, N., Cutrell, E., Toyama, K., & Nardi, B. (2010, April). Intermediated technology use in developing communities. In Proceedings of the SIGCHI conference on human factors in computing systems (pp. 2583–2592). Srinivasan, J. (2007, December). The role of trustworthiness in information service usage: The case of Parry information kiosks, Tamil Nadu, India. In 2007 International Conference on Information and Communication Technologies and Development (pp. 1–8). IEEE. Srinivasan, J., & Burrell, J. (2013, December). Revisiting the fishers of Kerala, India. In Proceedings of the Sixth International Conference on Information and Communication Technologies and Development: Full Papers-Volume 1 (pp. 56– 66). Stromquist, N. P. (1992). Women and literacy: Promises and constraints. The Annals of the American Academy of Political and Social Science, 520(1), 54– 65. UNESCO. (2018). A global framework of reference on digital literacy skills for indicator 4.4.2. Information Paper No. 51, June 2018. Available at https://uis.unesco.org/sites/default/files/documents/ip51-global-fra mework-reference-digital-literacy-skills-2018-en.pdf Waugaman, A. (2016). From principle to practice: Implementing the principles for digital development. The Principles for Digital Development Working Group. World Bank. (2021). World development report 2021: Data for better lives. The World Bank. Wyche, S., & Steinfield, C. (2015, May). Why don’t farmers use cell phones to access market prices. Technology Affordances and Barriers to Market Information Services Adoption in Rural Kenya. Zanello, G., Srinivasan, C. S., & Shankar, B. (2014). Transaction costs, information technologies, and the choice of marketplace among farmers in Northern Ghana. The Journal of Development Studies, 50(9), 1226–1239.

Index

A Access gap, 22 Adverse selection, 74 Afrobarometer, 12, 13, 18 Agent network, 46, 123, 134 Agent performance monitoring, 104 Agricultural extension, 38, 43, 69, 104, 109–113, 156 Agricultural markets, 66, 104, 109 Agricultural price, 36 Agricultural technology, 20, 112 Agricultural traders, 108 Agricultural value chain, 6, 66 Agriculture, 6, 39, 43, 44, 58, 65, 66, 69, 72, 73, 91, 93, 104, 109, 123, 138, 156, 157, 160, 161 Airtime, 2, 45, 75, 77, 134, 159 Artificial intelligence (AI), 58 Asymmetric information, 33, 37, 42, 61, 66 B Banking, 24, 136 Big data, 58, 81, 137, 167

Broadband, 9, 13, 16, 17 Buyer-seller coordination, 104

C Call detail record (CDR), 80, 81 Computer, 5, 6, 9, 14, 35, 45, 60, 68, 71, 73, 75, 93, 113–115, 121, 158, 161 Computer-assisted learning (CAL), 73 Connectivity, 14, 23, 24, 65 Consumption smoothing/smooth consumption, 3, 4, 41, 74, 136 Coverage internet, 5, 10, 18, 22–24, 72 mobile phone, 2, 4, 5, 9, 10, 15–19, 21, 45, 57, 71, 104, 109, 161 network, 9, 16, 21–24 COVID-19, 3, 18, 24, 60–62, 64, 75, 77, 80, 81, 100, 101, 103, 115, 137 Credit score/scoring, 81

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. C. Aker and J. Cariolle, Mobile Phones and Development in Africa, Palgrave Studies in Agricultural Economics and Food Policy, https://doi.org/10.1007/978-3-031-41885-3

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172

INDEX

D Data collection, 39, 62, 64, 68, 69, 80, 93, 103, 104, 113, 114, 122, 137, 138 Digital agriculture, 69, 72, 104, 160 Digital credit, 3, 77, 79–81, 136 Digital data, 39, 80, 114 Digital divide, 14, 18, 23, 24, 29, 47, 158, 160, 166, 167 Digital footprint, 39, 79, 167 Digitalization, 14, 57 Digital literacy, 102, 167 Digital marketplace, 81 Digital provide, 29 Digitization, 14, 36, 39

E Education, 6, 12, 14, 15, 18, 23, 24, 29, 30, 39, 47, 58, 70–74, 91, 93, 115, 121, 122, 136, 161 E-learning, 161 Extension agent, 37, 109, 112, 156 Extension service, 20, 112

F Financial inclusion, 23, 75, 136 Financial service, 3, 6, 29, 35, 37, 44, 72, 74, 91, 161, 165 digital financial service (DFS), 5, 40, 58, 68, 73–75, 77, 81, 104, 123, 135 Financial transparency, 136

G Gap coverage, 18, 21 gender, 14 infrastructure, 74 usage, 18 Geographic targeting, 137

H Hazard, 74 Health, 3, 6, 12, 24, 30, 36, 39, 43, 44, 57, 58, 60–62, 65, 66, 72, 73, 91, 93, 100–103, 123, 138, 157, 158, 161, 165 Healthcare, 29, 30, 60, 61, 65, 100, 102 Health worker, 62, 93, 102 I Information provider, 43, 156 Information search cost, 3, 36 Information technology, 4–7, 58, 92, 161 Infrastructure energy, 24 road, 10 telecommunications, 1 transportation, 36 Insurance, 38, 42, 68, 74, 75, 77, 81 Interactive voice response (IVR), 46, 60, 112, 158 Interlinked transactions, 45, 158 Internet, 4, 5, 10, 12–14, 17, 18, 20, 22–24, 34–36, 40, 60, 63, 65, 68, 72, 80, 110, 156, 158, 166, 167 Interoperability, 23, 74, 77 L Laptop, 60, 121 Leapfrog, 81 Liberalization, 16 M Machine learning (ML), 81 Market failures, 4–6, 32, 34, 42, 61, 62, 71, 75, 123, 155, 165, 166 Market information system (MIS), 6, 109, 110, 157

INDEX

Mobile broadband, 9, 17 Mobile education/m-education, 58, 72 Mobile health/m-health, 58, 62, 64 Mobile innovations, 4 Mobile money/m-money, 3, 23, 35, 40–42, 46, 47, 74–77, 79, 80, 123, 134–136, 138, 160, 165, 166 Mobile network, 15, 21, 66, 70, 166 Moral hazard, 74 M-Pesa, 3, 75, 136

N Network effect, 33 Network good, 5, 21, 33, 134 Network technology Fourth generation (4G), 23, 35 Second generation (2G), 75 Third generation (3G), 15, 17, 22, 23

O One Laptop Per Child (OLPC), 73, 121

P Pandemic, 24, 60, 64, 75, 76, 80, 101, 103 Pedagogical platform, 73 Poverty mapping, 80, 137 Price dispersion, 3, 37, 104, 109 Price information, 6, 37, 43, 44, 66, 110, 156, 157 Privacy, 39, 47, 160 Public good, 32, 33, 38, 39, 44, 157 Public services, 4, 29, 30, 32–35, 39, 81, 93, 137

173

R Radio, 9, 36, 39, 45, 60, 66, 68, 110, 114 Randomized controlled trial (RCT), 93, 101, 103, 110, 111, 113, 115, 121, 123 Regression discontinuity design (RDD), 93 S Satellite imagery, 81, 113 Service providers, 2, 15, 16, 21, 22, 30, 38, 73 Shocks idiosyncratic, 41, 47, 160 negative, 47, 160 Social network, 20, 38, 41–44, 79, 80, 156, 157, 163 Social protection, 6, 40, 76, 81, 137 Spillovers, 33, 109, 111, 112, 165 Submarine cable, 13, 23 Subscriber Identity Module (SIM), 2, 10, 18, 45, 159 Supply chain management, 62, 63, 69, 93, 104 T Telecommunications, 1, 2, 13, 16, 23, 40 Television (TV), 14, 36, 114 Text message/short message service (SMS), 2, 3, 9, 39, 40, 63, 73, 112 Transaction costs, 3, 32, 33, 41, 42, 74, 81, 123, 134, 165 Transfer businesses to people (B2P), 41, 135 cash, 73, 80, 81, 135, 137 governments to persons (G2P), 41, 135 money, 41, 77, 135

174

INDEX

person to businesses (P2B), 41, 77, 135 person-to-person (P2P), 41, 77, 134, 135

U USSD, 46, 158