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Essays on Development Policy and thePolitical Economy of Conflict
Miri Stryjan
ii
© Miri Stryjan, Stockholm, 2016
ISBN 978-91-7649-451-6ISSN 0346-6892
Cover Picture: Rain coming in over Moroto town© Miri Stryjan, 2015.Portrait photo taken by Anna Sandberg, 2016.
Printed in Sweden by Holmbergs, Malmö 2016
Distributor: Institute for International Economic Studies
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Doctoral DissertationDepartment of EconomicsStockholm University
AbstractElectoral Rules and Leader Selection: Experimental Evidence from Ugan-dan Community Groups. This paper studies leadership selection in communitygroups. Despite a large body of work documenting how electoral systems affect pol-icy outcomes, less is known about their impact on leader selection. We compare twotypes of participatory decision making in Ugandan community savings groups: voteby secret ballot and open discussion with consensus. Random assignment of electoralrules allows us to estimate the causal impact of the rules on leader types and socialservice delivery. We find that vote groups elect leaders more similar to the averagemember while discussion group leaders are positively selected on socio-economic char-acteristics. Further, the dropout rates are significantly higher in discussion groups,particularly for poorer members. After 3.5 years, vote groups are larger in size andtheir members save less and get smaller loans. We conclude that the secret ballot votecreates more inclusive groups while open discussion groups are more exclusive andfavor the economically successful. The appropriate method for leader selection thusultimately depends on the objective and target group of the program. Our findings offerimportant contributions to the literature on leader selection and to the understandingof public service delivery in developing countries.
Preparing for Genocide: Community Meetings in Rwanda. How do po-litical elites prepare the civilian population for participation in violent conflict? Weempirically investigate this question using sector-level data from the Rwandan Geno-cide in 1994. Every Saturday before 1994, Rwandan villagers had to meet to workon community infrastructure, a practice called Umuganda. The practice was highlypoliticized and, according to anecdotal evidence, regularly used by the local politicalelites for spreading propaganda in the years before the genocide. This paper presentsthe first quantitative evidence of this abuse of the Umuganda community meetings. Toestablish causality, we exploit cross-sectional variation in meeting intensity induced byexogenous weather fluctuations. We find that an additional rainy Saturday resulted ina five percent lower civilian participation rate in genocide violence. We find no resultsfor other weekdays. Moreover, this result is entirely driven by places under the controlof the pro-genocide Hutu parties. In the few places with the pro-Tutsi minority in
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power the effects are reversed. The results pass a number of indirect tests regardingthe exclusion restriction as well as other robustness checks and placebo tests.
Selection into Borrowing: Survey Evidence from Uganda. In this paper,I study how modifications to the standard credit contract affect loan demand andselection into borrowing, using a representative sample of micro enterprises in urbanUganda. Despite widespread enthusiasm about microfinance as a tool in alleviatingpoverty, recent evaluations of microfinance-initiatives have found the long run impacton firm growth and the welfare of borrower-households to be limited. Existing studiesfocus on present or previous borrowers, and can therefore provide only limited insightinto how contractual changes would affect credit demand and investment behaviorthrough changes in the composition of the borrower pool. I study loan attitudes in arepresentative sample of 925 entrepreneurs, most with no experience of borrowing, incore sectors within both retail and manufacturing. Hypothetical loan demand ques-tions are used to test whether firm owners respond to changes in loans’ contractualterms and whether take-up varies by firms’ risk type and firm owner characteristics.The results indicate that contracts with lower interest rates and less stringent col-lateral requirements are likely to attract less risky borrowers. This is especially trueamong manufacturing sectors. The findings are robust across different ways of definingriskiness and suggest that there is scope for improvement of standard financial contractterms.
Credit Contract Structure and Firm Growth: Evidence from a Random-ized Control Trial.We study the effects of credit contract structure on firm outcomesamong small- and medium- sized firms. A randomized control trial was carried out todistinguish between some of the key constraints to efficient credit use connected tofirms’ business environment and production function, namely (i) backloaded returns;(ii) uncertain returns; and (iii) indivisible fixed costs. Each firm was followed for theone-year loan cycle. We describe the experiment and present preliminary results fromthe first 754 out of 2,340 firms to have completed their loan cycle. Firms offered agrace period on their repayments early in the loan cycle have higher profits and higherhousehold income than firms receiving a grace period later on as well as the controlgroup. They also increased the number of paid employees and reduced the number ofunpaid employees, an effect also found among firms that received a cash subsidy atthe beginning of the loan cycle. We discuss potential mechanisms behind these effects.
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AcknowledgmentsHow come it takes so long to do a PhD? I mean, how hard can it be? Countless
times in the past years I have been asked various flavors of that question. I have oftenfelt intimidated by it despite understanding that the person asking usually has no wayof knowing the struggles involved in completing a dissertation. To make matters worse,I have been asking myself the same question often enough. So why, indeed, does it takeso long to complete a PhD? In my case, the coursework and rigidity of the first yearof the doctoral program killed most of my inspiration and self-esteem. Finding themagain, and learning to ask relevant questions and sticking to ideas and projects hasbeen a long process. My PhD years have also involved living, working and studyingon three different continents - something which required much learning and digestionalso of issues not directly related to my research. I am very grateful to everyone whohas accompanied and helped me on this journey.
First and foremost, I want to thank my main advisor Jakob Svensson, who intro-duced me to the field of development economics and without whom the PhD wouldprobably have taken a very different turn. Jakob’s support and input during the workwith the chapters of the dissertation has been invaluable, especially during the lastyear of the PhD including the job market process. It is also thanks to Jakob that Igot the opportunity to work in Uganda and get my first experiences of practical fieldwork. Secondly I want to thank my co-advisor Andreas Madestam who invited me onboard on another project in Uganda and thereby gave me the opportunity to spendmore time in the field, which gave rise to additional projects. Living and working for12 months in Uganda and East Africa has shaped all the projects that are included inthis dissertation. Andreas has both been my advisor and my coauthor in the past fewyears and has taught me a lot about research, paper writing and how to stay optimisticin a very stressful environment.
Two people had a decisive impact on my choice to pursue a research career, andthose are Jan Pettersson and Lena Nekby, who supervised my bachelor and mastertheses, respectively. Thank you Janne for all your support and encouragement duringthe thesis writing and the first steps of the PhD. To Lena, who very sadly passed awaytwo years ago, I am also deeply thankful for believing in me and my ideas, and forsupporting me as a mentor during the first half of my PhD.
Over the past four years I have had the privilege to work with a number ofco-authors and I want to thank Evelina Bonnier, Erika Deserranno, Selim Gulesci,Francesco Loiacono, Andreas Madestam, Jonas Poulsen, Munshi Sulaiman and Thorsten
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Rogall for fruitful collaborations. A special thanks to Selim, who has taught me a lotover the past three years of working together. I have also been fortunate to work withthe NGO BRAC in Uganda, where I have had my office on and off for almost one yearand learned a lot about practical development work in the process, and I am thankfulto everyone who made this possible. I am also grateful to Ted Miguel who invited meto visit one year at the economics department of UC Berkeley.
For most of the PhD I have been based at Stockholm University where a numberof people have had an extra impact on my life and research. I am immensely thankfulto Anna Tompsett for her supportiveness and many concrete suggestions around myjob market process and paper. At the IIES I am also extra grateful to Ingvild Almås,Konrad Burchardi, Tom Cunningham and Masayuki Kudamatsu for many helpful andinteresting discussions and to Jon de Quidt and Kurt Mitman for encouragement andhelp during my last year at the IIES and during the job market, to Anna Sandbergfor great feedback on two of my papers and to Torsten Persson for his useful input,advice and support on the job market. I want to send a big thank you to the admin-istrative staff of the IIES: Annika Andreasson, Viktoria Garvare, Christina Lönnblad,Åsa Storm and Hanna Weitz, and of the Department of Economics: Ingela Arvids-son, Anne Jensen, Anita Karlsson, and Audrone Mozuraitiene. A special thanks toAnita for providing a compass in the arbitrariness surrounding the rights of a PhDstudent, Christina for language work on the thesis, Annika for her assistance and en-couragement during the job application process and Viktoria for help with the thesislayout.
The PhD is definitely a lonely experience, but nevertheless my fellow PhD col-leagues during these years have meant a lot. Sara Fogelberg and Manja Gärtner whostarted the doctoral program together with me have been especially important. Sara,thank you for your endless support, loyalty and great sense of humor - and for all ourgreat times at the staff gym. Manja, the discussions with you about economics andlife, and your wonderful sarcasms have lifted me up countless times. During the firstyear, the work in the "coal mine" was made more bearable by the great company ofSara and Manja as well as of PO Robling, Laurence Malafry, Erik Prawitz, ShuheiKitamura, Yangzhou Yang and Daniel Hedblom, and during the last year, the jobmarket period was made easier and even fun thanks to the company of Audi Bal-trunaite, Mounir Karadja, Shuhei, Niels-Jakob Harbo Hansen and Andrea Guariso.Other colleagues in Stockholm and Uppsala have also helped put a silver lining onthe PhD years: Lotta Boström, Linnea Wickström Östervall, Emma von Essen, Maria
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Cheung, Alex Schmitt, Abdulaziz Behiru Shifa, Hanna Mühlrad, Erik Lundin, EvelinaBonnier, Anna Aevarsdottir, Mathias Iwanowsky and Karl Harmenberg, to name onlya few. Jonas Poulsen, our interesting discussions about development and academiahave cheered me up many times. During my long stays in Uganda I was especiallyhappy for the company of Benedetta Lerva who showed me the real Kampala, andtaught me to bargain with the boda drivers, Vittorio Bassi with whom I had manyrewarding conversations about our parallel research projects, and Mozammel Huq whoinvited me to share his Kampala home and made me feel less like an outsider.
My years as a doctoral student have also been shaped by the people who haveshared my everyday life outside of work and university. Some of the friends from beforethe PhD journey started are surprisingly still around. I am grateful to Elsa, Palmina,Jocke and Itay for being there throughout, despite my sometimes very distant mood.I am so fortunate to have you as my friends. I also want to thank all my lovelyhousemates from Stavsund on Ekerö where I have lived for the past four years, withwhom I have cried and laughed and who have provided their perspectives from outsideof economics and academia. Especially to Virlani, Daniel, Gabriella, Sofie, Mario, Lisaand Clara. Yotam, who has accompanied me and made me happy during the last crazyperiod of the PhD also deserves a very special thank you.
Finally, I want to thank my family. To my dear sister Noa, thank you for alwaysbeing there and for your wise words that have helped me so many times when the PhDjourney seemed endless. And to my parents, Signe and Yohanan Stryjan - thank youfor encouraging me to be curious and open to impressions and knowledge, for teachingme languages to understand the world and independence to dare to take it on. I knowthat without you I would not have been where I am today.
Stockholm, July 2016Miri Stryjan
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Contents
1 Introduction 1References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2 Electoral Rules and Leader Selection: Experimental Evidence fromUgandan Community Groups 92.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92.2 Background: Karamoja and BRAC Uganda . . . . . . . . . . . . . . . . 162.3 Setup of saving groups and leader selection methods . . . . . . . . . . . 182.4 Conceptual framework . . . . . . . . . . . . . . . . . . . . . . . . . . . 202.5 Data and Descriptives . . . . . . . . . . . . . . . . . . . . . . . . . . . 232.6 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 262.7 Welfare effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 382.8 Discussion and Concluding remarks . . . . . . . . . . . . . . . . . . . . 41References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43Figures and Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47Appendix 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59Appendix 2: Variable construction details . . . . . . . . . . . . . . . . . . . . 67
3 Preparing for Genocide: Community Meetings in Rwanda 693.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 693.2 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 743.3 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 773.4 Empirical Strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 813.5 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 853.6 Channels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 893.7 Discussion and Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . 91References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93
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x CONTENTS
Figures and Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109
4 Selection into Borrowing: Survey Evidence from Uganda 1154.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1154.2 Institutional Background . . . . . . . . . . . . . . . . . . . . . . . . . . 1204.3 Hypotheses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1224.4 Survey methodology and Data . . . . . . . . . . . . . . . . . . . . . . . 1244.5 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1354.6 Validation checks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1434.7 Discussion and Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . 146References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153Appendix 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159Appendix 2: Loan contract variations . . . . . . . . . . . . . . . . . . . . . . 168
5 Credit Contract Structure and Firm Growth: Evidence from a Ran-domized Control Trial 1695.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1695.2 Conceptual framework . . . . . . . . . . . . . . . . . . . . . . . . . . . 1735.3 Experimental design . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1755.4 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1805.5 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1835.6 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1875.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 190References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 192Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 195Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 201
Sammanfattning 209Referenser . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 216
Chapter 1
Introduction
This thesis consists of four self contained chapters that all revolve around the themes
of development policy and political economy aspects of the implementation of develop-
ment programs. Chapters 2, 4 and 5 analyze development initiatives related to financial
inclusion of the poor while chapter 3 deals with a state-led development project with
political undertones.
In developing countries, NGOs and other external agents often assume responsi-
bility for the provision of critical services that the state fails to provide, and for the
delivery of financial services inadequately supplied by banks and other formal institu-
tions (Baland et al., 2011; Casey et al., 2012; Grossman; 2014). In the past decades,
leading NGOs have increasingly favored development projects that involve the local
community in decision making (Mansuri and Rao, 2012). This is believed to increase
the legitimacy and long-term sustainability of projects. Evaluations of such projects
point to several advantages of direct local participation as compared to central de-
cision making. However, so far, we know very little about the relative effectiveness
of different types of direct participation. Moreover, with the exception of Grossman
(2014), previous studies are silent on the subject of leaders, despite that the mode
of governance in community-led development projects can be crucial for their service
delivery and outreach.
Chapter 2, Electoral Rules and Leader Selection: Experimental Evidence
from Ugandan Community Groups, studies self governance and leadership in
1
2 INTRODUCTION
NGO-initiated local savings groups for young women in an impoverished area of
Uganda. More specifically, it studies how the design of electoral rules determines lead-
ership selection in and performance of the savings groups. Despite a large body of work
documenting how electoral systems affect policy, less is know about their impact on
leader selection. Moreover, exogenous variation in electoral rules is notoriously hard to
find, in particular for real world contexts. We randomly assigned Ugandan community
saving groups to use one of two distinct methods when selecting leaders for the first
time: vote by secret ballot or open discussion with decision making by consensus. Ran-
dom assignment allows us to estimate the causal impact of the rules on leader types
and on measurable outcomes resulting from the leaders’ implemented policy: member
retention, savings and loans. We find that vote groups elect leaders more similar to the
average member while discussion group leaders are richer and more educated than the
average member. Further, dropout rates are significantly higher in discussion groups,
particularly for the initially poorer members. After 3.5 years, vote groups are larger in
size and their members save less and get smaller loans than discussion group members.
We thus find that the leader election rule affects leader types and community group
outcomes, with secret ballot vote creating more inclusive groups while open discussion
leads to lower financial inclusion of society’s poorest members. Our findings are con-
sistent with elite capture being higher in discussion groups, leading to outcomes less
representative of the preferences of the average member. This is in line with studies
of the introduction of the secret ballot (Baland and Robinson (2008) and Hinnerich
and Pettersson-Lidbom (2014)). Given the crucial role played by community groups
in delivery of many public and financial services in low-income contexts, our study has
policy implications for public service delivery in developing countries.
Community meetings and civic organizations are widely believed to foster social
capital by providing arenas for people to meet, exchange ideas, solve free-rider prob-
lems, and create public goods (Grootaert and van Bastelaer, 2002; Guiso et al., 2008;
Knack and Keefer, 1997; Putnam, 2000). This view partly motivates the increasing
focus of development agencies on "community driven" development projects, in which
3
deliberative forums and grass root participation play a central role (see Mansuri and
Rao (2012) for a recent overview). A recent literature shows that social forums and
civic organizations can also serve to enforce ties within social groups, and increase
tensions between them, rather than providing forums for bridging between members
from different social groups, thereby highlighting a more destructive potential of such
forums (Satyanath et al., 2015).
Chapter 3, Preparing for Genocide: Community Meetings in Rwanda, re-
lates to this work by studying a very different kind of development program, that due
to its political nature had devastating consequences. The practice of mandatory com-
munity work has been present in Rwanda since pre-colonial times and similar practices
existed during the early post-colonial period also in other East and Central African
countries (Guichaoua, 1991). During the period of 1973-1994, the mandatory com-
munity work became a state policy. Every Saturday, Rwandan villagers had to meet
to work on community infrastructure, a practice called Umuganda. The practice was
motivated by development arguments, but was also highly politicized and, according
to qualitative evidence from scholars such as Straus (2006) and Verwimp (2013), reg-
ularly used by the local political elites for spreading propaganda in the years before
the genocide. This paper presents the first quantitative evidence of this (ab)use of
the Umuganda community meetings. Identifying the causal effect of these meetings
on participation in genocide is difficult for two reasons. First, we lack data on the
number of people participating in Umuganda or the number of meetings taking place
in a given locality. Second, even if that data existed our estimates would likely suf-
fer from omitted variable bias. To establish the causal link between meeting intensity
and participation in genocidal violence we therefore exploit cross-sectional variation
in meeting intensity induced by exogenous weather fluctuations. The assumption that
we make is that when it rains heavily, the community meeting is either cancelled or
less intensive. Using daily rainfall data from the period 1984-1998 and sector-level
prosecution data from the Rwandan Genocide in 1994, we find that an additional
rainy Saturday resulted in a five percent lower civilian participation rate in genocide
4 INTRODUCTION
violence. We find no results of rainfall on other weekdays on genocide participation.
Moreover, this result is entirely driven by localities that were governed by the pro-
genocide Hutu parties. In the few places with the pro-Tutsi minority in power, the
effects are reversed. These results indicate that Umuganda meetings were indeed used
as an arena to mobilize and prepare civilians for the genocide. Despite the specific
geographical focus of this paper, we argue that examining the possibly negative effect
of these community meetings is of more general importance. While the attitude to
Umuganda and similar initiatives is generally positive, we show evidence of a "dark
side" to these community meetings where social capital does not bridge the societal,
ethnic divides but rather enforces bonding within the different ethnic groups. Under-
standing this process is even more important since, despite its history, Umuganda was
formally reintroduced in Rwanda in 2008, and similar practices have been installed in
Burundi and are have recently been proposed in Kenya.1
One of the most widely praised forms of development aid in the past decades
is microfinance. Microcredit and the broader concept of microfinance became well
known to the general public as Grameen bank and its founder Mohammad Yunus were
awarded the Nobel Peace Price in 2006. The idea behind microfinance is that small
loans can help poor people improve their livelihood through small-scale commercial
activity. As Amendariz de Aghion and Morduch (2005) write in their book about
microfinance: "Microfinance presents itself as a new market-based strategy for poverty
reduction, free of the heavy subsidies that brought down large statebanks. In a world
in search of easy answers, this win-win combination has been a true winner itself".
Despite widespread enthusiasm about microfinance as a tool for poverty-alleviation,
recent evaluations of microfinance initiatives have, however, found its long run impact
on firm growth and the welfare of borrower-households to be limited (Banerjee et al.,
2015). Chapters 4 and 5 of this thesis examine possible ways in which changes to the
standard microfinance contract could lead microfinance to better fulfill its promised
1For details about the Kenyan case, see Daily Nation (March 2016).
5
objective of business growth. Both studies focus on micro, small and medium sized
enterprises and concern individual loans.
Chapter 4, Selection into Borrowing: Survey Evidence from Uganda, re-
ports the results of a survey that elicits loan demand among a representative sample
of firm owners in urban Uganda. A literature in credit contract theory shows that
raising the price of credit (the interest rate) can lead to either advantageous selection
effects (Stiglitz and Weiss, 1981) or adverse selection effects (De Meza and Webb,
1987), in terms of the likelihood of project success, while increasing the collateral size
will lead to advantageous selection (Stiglitz and Weiss, 1981; Wette, 1983). Examining
the selection into microfinance is particularly relevant, as this market is characterized
by credit rationing partly due to asymmetric information. Existing studies of micro-
finance focus on individuals or firms that are already borrowers, and can therefore
provide only limited insight into how contractual changes would affect credit demand
and investment behavior through changes in the composition of the borrower pool. I
study loan attitudes among a representative sample of entrepreneurs, most with no
experience of borrowing, in core sectors within both retail and manufacturing. Hy-
pothetical loan demand questions are used to test whether firm owners respond to
changes in loans’ contractual terms and whether take-up varies by firms’ risk type and
firm owner characteristics. The results indicate that contracts with lower interest rates
or with less stringent collateral requirements are likely to attract less risky borrowers,
in terms of both stated risk behavior and the riskiness of their business environment.
This is true also when controlling for wealth. These results are more pronounced among
manufacturing firm owners, something which is likely to be explained by differences
in available investment options. Less wealthy firm owners are more likely to borrow
if collateral rates are lowered. The results are robust across different ways of defining
riskiness and suggest that there is scope for improvement of standard financial contract
terms. Chapter 5, Credit contract structure and firm growth: Evidence from
a randomized control trial, studies the effects of credit contract structure on firm
outcomes among small- and medium- sized firms in Uganda. We build on recent work
6 INTRODUCTION
which suggests that take-up and effectiveness of microfinance may improve if contrac-
tual terms are changed (Field et al., 2013; Karlan and Zinman, 2008). A randomized
control trial was carried out to distinguish between some of the key constraints to
efficient credit use connected to firms’ business environment and production function,
namely (i) backloaded returns; (ii) uncertain returns; and (iii) indivisible fixed costs.
Firms that participated in the experiment had been approved for borrowing from our
partnering NGO and, as part of our experiment, received rebates that subsidized the
equivalence of two out of 12 monthly repayments during their one year loan cycle. The
findings presented in Chapter 5 are preliminary results from the first 754 out of 2,340
firms to have completed their loan cycle. We find that firms that were given a 2-month
grace period at the beginning of the loan cycle increased their profits and household
income relative to firms that received a rebate later in the loan cycle, and to the control
group. They also increased the number of paid employees, while decreasing the number
of unpaid ones, but wage expenditures did not increase in accordance. Further, the
households of firm-owners in the early grace-period group started significantly more
new household-owned firms than the households of firm-owners that received a rebate
later in the loan cycle, and the control group. Firms that were offered a flexible grace
period scheme, in which they were free to skip repayments in any 2 months of their
choice, predominantly chose to use these rebates in the first months of the loan cycle.
These findings provide some support for backloadedness of returns being a more im-
portant constraint than the uncertainty of returns. Firms that received a cash subsidy
at the start of the loan cycle increased their number of employees relative to the control
group, and they also increased their wage costs. To the extent that this implies that
they hired higher quality workers, which can be seen as an indivisible investment, this
finding provides suggestive evidence for the importance of indivisible costs hampering
investments.
REFERENCES 7
References
Amendariz de Aghion, B. and Morduch, J. 2005. The Economics of microfinance:
Cambridge, MA: MIT Press.
Baland, J. M. and J. A. Robinson. 2008 Land and Power: Theory and Evidence
from Chile. The American Economic Review, 1737-1765.
Baland, J.M., R. Somanathan and L. Vandewalle. 2011 Socially disadvantaged
groups and Microfinance in India. University of Namur, Department of Economics
Working Paper no. 1117.
Banerjee, A., Karlan, D. and Zinman, J. 2015. Six randomized evaluations of
microcredit: introduction and further steps. American Economic Journal: Applied Eco-
nomics, 7(1), pp.1-21.
Casey, K., R. Glennerster and E. Miguel. 2012. Reshaping Institutions: Evidence
on aid impacts using a preanalysis plan. The Quarterly Journal of Economics, 1755,
1812.
Daily Nation (retrieved on March 5, 2016). Emuhaya MP Seeks to Introduce Manda-
tory Community Cleaning Day, http://allafrica.com/stories/201603030209.html.
DeMeza, D. andWebb, D.C. 1987. Too much investment: a problem of asymmetric
information. The quarterly journal of economics, pp.281-292.
Field, E., Pande, R., Papp, J. and Rigol, N. (2013. Does the classic micro-
finance model discourage entrepreneurship among the poor? Experimental evidence
from India. The American Economic Review, 103(6), pp.2196-2226.
Grossman, G. 2014. Do Selection Rules Affect Leader Responsiveness? Evidence
from Rural Uganda. Quarterly Journal of Political Science 9.1: 1-44.
Grootaert, C. and T. van Bastelaer. 2002. Understanding and Measuring Social
Capital: A Multi-Disciplinary Tool for Practitioners, Washington, World Bank.
Guichaoua, A. 1991. Les Travaux Communautaires en Afrique Centrale, Revue Tiers
Monde, t.XXXII, n. 127, July-September, pp. 551-573.
8 INTRODUCTION
Guiso, L., Sapienza P. and L. Zingales. 2008. Alfred Marshall Lecture: Social
Capital as Good Culture, Journal of the European Economic Association, 6(2-3), pp.
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Chapter 2
Electoral Rules and Leader Selection:
Experimental Evidence from Ugandan
Community Groups∗
2.1 Introduction
Leader characteristics and representativeness is widely believed to be important for
the way political entities and organizations perform (Besley, 2005). Numerous studies
put forward links between electoral rules and policy outcomes (see Cox, 1997; Huber et
al., 1993; Myerson 1993; Persson and Tabellini, 2000; 2005; Hinnerich and Pettersson-
Lidbom, 2014). A less explored subject is the role of electoral rules for selecting leaders
with certain characteristics, and how these characteristics impact on the quality of
policy outcomes. As noted by Beath et al. (2014), exogenous variation in electoral
∗This paper is co-authored with Erika Deserranno and Munshi Sulaiman. We would like to ex-tend our gratitude to the staff of the BRAC Uganda Karamoja initiative, in particular to AlbertSsimbwa and the BRAC Uganda Research and Evaluation Unit. We also thank Emanuele Brancatifor excellent research assistance. This paper has benefited from discussions and helpful commentsfrom Ingvild Almås, Tessa Bold, Jonathan de Quidt, Selim Gulesci, Andreas Madestam, TorstenPersson, Johanna Rickne, Anna Sandberg, David Strömberg, Jakob Svensson and Anna Tompsett,as well as from seminar participants at the IIES, the OXDEV Workshop 2015, Hebrew University,Ben Gurion University, CERGE-EI, Universidad de los Andes and Warwick. Miri Stryjan is gratefulfor funding from Handelsbanken’s Research Foundation, the Mannerfelt Foundation and the NordicAfrica Institute.
9
10 ELECTORAL RULES AND LEADER SELECTION
rules is notoriously hard to find, in particular for real world contexts.
This paper provides causal evidence of how leader electoral rules affect the types of
leaders selected and, in turn, the quality of policy outcomes. In an experimental setting,
we are able to isolate the impact of these rules on the provision of a specific service: a
system for personal savings and loans. Leader selection procedures were randomized
across community groups whose objective it is to make savings and loans possible for
vulnerable members of the community. Over a 3.5 year period, the performance of
the groups was monitored both in terms of continued membership and the amount of
savings accumulated by, and loans given to, members.
Our experiment contributes to the understanding of service delivery in developing
countries. In these contexts, NGOs and community groups often assume responsibility
for the provision of critical service and public goods that the state fails to provide,
and the delivery of financial services inadequately supplied by banks and other formal
institutions.1 The governance of such groups is one factor that affects their service
delivery.
We study how electoral rules affect leader selection using 2 different rules for elect-
ing leaders: (a) secret ballot plurality voting and (b) open discussion with consensus
decision. These procedures are central to the practice of direct democracy, and may
strongly affect the selection of leaders. Leaders can vary in terms of their skill level
and their representativeness. The previous literature has highlighted a trade-off be-
tween these two aspects in terms of service delivery (see e.g. Beath et al., 2014), since
measures of skills, such as education, tend to be correlated with higher socio-economic
positions while a leader closer to the median is representative of a larger fraction of
the electorate. When decisions are made in an open discussion format, less influen-
tial members may feel intimidated and refrain from contributing to decisions. This
1For more information about the role of NGOs and community groups in public service delivery:see Bernard et al. (2008), Casey et al. (2012) and Grossman (2014). For financial services: 97 millionIndian households were covered by the self-help program NABARD in 2011 (Baland et al., 2011) andaccording to data collected by VSL Associates, a consultancy, Village Savings and Loan Associations,i.e. groups with a model similar to that of the savings and loans groups studied here, reach close to12 million people worldwide, 10 million of these are in Sub-Saharan Africa (Vsla.net, 2015).
2.1. INTRODUCTION 11
tilts selection decisions towards leaders with more visible skill indicators that are less
representative for low-income group members. The representativeness of leaders can,
in turn, have an impact on public goods delivery. If leaders are closer to the average
member, public goods delivery can be expected to shift toward lower income members
(Besley and Coate 1997; Osborne and Slivinsky 1996; Phillips 1995; Pitkin 1967).
The specific context of the study is a saving program in the most impoverished
region of Uganda: Karamoja. Members are organized into savings groups, each group
jointly keeping a savings fund from which members can take loans. These groups
were founded by the NGO BRAC Uganda and are a version of self-help groups that
have become popular in Sub-Saharan Africa in the last decade (see e.g. Ksoll et al.,
2013; Greaney et al., 2016). The groups aim at empowering and improving livelihood
options for young women and, as a step towards empowerment and sustainability, they
are self-governed through a managing committee, consisting of group members. Before
the committee-formation, 46 groups were randomly assigned to elect their committees
through secret ballot, plurality rule voting (vote groups). In the remaining 46 groups,
committee members were nominated and agreed upon in an open discussion with
consensus (discussion groups). Both procedures took place at a meeting attended by
all group members, and in both cases the election was preceded by a discussion. The
difference lies in the openneness of decision making: one procedure (vote) imposed a
secret vote at the end of the discussion while the other (discussion) did not.
We find that selection rules affect both the type of leader chosen and the subse-
quent performance of the savings group. First, vote groups elected leaders who were
more representative of the average group member in terms of economic status, while
groups that selected leaders via open discussion chose less representative leaders who
were richer and more educated than the average member. For example, leaders in dis-
cussion groups were 46 percent more likely to have employment connections and 25
percent more likely to have some education as compared to regular members. They
also had larger asset holdings and scored significantly higher on a wealth index. Mean-
while, none of the differences between leaders and regular members were statistically
12 ELECTORAL RULES AND LEADER SELECTION
or economically significant in the vote treatment. These findings are consistent with
theories of representation and elite capture where open participatory selection proce-
dures give power to those who are already powerful.2 However, they are also consistent
with open discussion improving coordination and generating leaders with higher skill
levels as compared to the secret vote.3
Second, service delivery on the extensive margin was lower in the discussion groups:
while dropout was high in both types of groups, it was significantly higher in discus-
sion groups, where the poorest members were more likely to drop out. Specifically,
in the first year, dropout was 32 percent higher (15 percentage points) in discussion
groups than in vote groups, and members in the lowest quartile of the group’s wealth
distribution were 12.3 percent more likely to drop out in the first year compared to
wealthier members, while members with no market income at baseline were 18.2 per-
cent more likely to leave the discussion groups than those with market income4 and
members who kept no savings at baseline were 33.4 percent more likely to drop out
from discussion groups than to those with initial savings. These findings are in line
with committee members elected under the vote treatment having policy preferences
closer to those of the average member. Third, 3.5 years after committee formation,
service delivery on the intensive margin was lower in vote groups: members in vote
groups were 15 percent less likely to be saving at endline and also reported getting
smaller and fewer loans than their counterparts in discussion groups. The dropout has
implications for welfare: members who dropped out of groups were significantly less
likely than stayers to have access to saving or loans at the time of the endline sur-
vey. Moreover, members who were initially poor are less likely to have access to such
services (from any service provider) at endline if randomly assigned to a discussion
group.
Taken together, the results suggest that the discussion treatment can lead to more
2see e.g. Baland and Robinson (2008) and Hinnerich and Pettersson-Lidbom (2014).3That open discussion, in particular if it takes the form of deliberation, may lead to better quality
outcomes is suggested by Humphreys et al., 2006 among others.4Members with no market income were either subsistence farmers/animal rearers or dependents.
2.1. INTRODUCTION 13
efficient savings groups through selective dropout, but at the expense of more inequal-
ity in access to savings and credit. In terms of mechanism, we provide supportive
evidence that elite capture is taking place in the discussion group while we find no
support for discussion groups being more economically efficient than vote groups. The
effects thus seem to work through leader representativeness rather than leader skill
level. Hence, the appropriate method for leader selection ultimately depends on the
objective and target group of a program.
This paper adds to several strands of literature. First, we contribute to the lit-
erature on electoral systems and their role for policy outcomes, especially to new
knowledge about the selection of leaders. As pointed out by Besley (2005), political
selection is an overlooked area in the political science and economics literature. Just
like the literature on electoral rules, the literature on leader selection is primarily con-
cerned with understanding the selection of representatives at the national or county
level. As a consequence, the electoral rules studied to date are models of represen-
tative democracy, where variation in district magnitude or the number of political
parties can affect policy. A recent example is Beath et al. (2014) who compare at-large
voting to voting by districts in an experiment in Afghanistan. Closer in spirit to our
study, Grossman (2014) studies leaders of Ugandan farmer group councils and finds
direct vote by farmer group members to be superior to appointment by council rep-
resentatives. Our study unpacks this finding by comparing two different participatory
methods involving a comparable set of agents, and also has the advantage of exogenous
variation in the leader selection rule.
Our findings also add to the literature on participatory democracy and the role of
openness in decision making. The decision-making models we study are not feasible
for national politics but are typical within the field of direct democracy.5 Consensual
5"Direct democracy" typically refers to systems in which community members directly decideon policy outcomes and thus, where there is no intermediate step of "representation". However,although this paper deals with the selection of leaders, the methods employed are the most centralones in the direct democracy literature and practice. For example, Matsusaka (2005) writes that"Direct democracy is an umbrella term that covers a variety of political processes, all of which allowordinary citizens to vote directly on laws rather than candidates for office. The town meeting, in whichcitizens assemble at a particular place and time to make public decisions, is the earliest form of direct
14 ELECTORAL RULES AND LEADER SELECTION
discourse has been found to be a way for leaders to maintain and reaffirm a social order
(Humphreys et al., 2006 and Murphy, 1990). Moreover, under open decision making,
less powerful members can be coerced or intimidated into supporting certain proposals
(Hinnerich and Pettersson-Lidbom, 2014) while the opportunity to cast a secret vote
has been shown to increase the representation of economically disadvantaged groups
(Baland and Robinson, 2008).
We recognize that one must be careful in generalizing our findings to the national
level. However, in many developing countries, service delivery is primarily provided by
agents such as NGOs and community based groups. Studying the design of electoral
systems at the macro level is not sufficient to understand efficient service delivery in
these contexts. Our results offer insights into public good delivery that are complemen-
tary to those of studies of national and district elections. This also relates our study
to the growing literature on Community Driven Development (CDD) - development
projects involving the local community in public service delivery provided by exter-
nal agents (see Mansuri and Rao (2012) for a recent review). Previous studies that
evaluate such projects indicate that direct local participation has several advantages
as compared to central decision making by an NGO. However, so far, we know very
little about the relative effectiveness of different types of direct participation. More-
over, previous studies focus on systems where decisions are made directly about policy
outcomes, which requires an infrastructure provided by external agents for their func-
tioning. Our study addresses both these shortcomings. To our knowledge, this is the
first study to compare two participatory decision-making systems in a development
program setting. Moreover, holding fixed who had access to the decision-making forum
gives us an advantage over previous studies where typically discussions with consensus
decision making only take place among a limited group of people, such as a local elite
(Beath et al., 2012) or an elected body of representatives (Grossman, 2014). While
pure direct democracy setups such as those brought forward in previous evaluations
of CDD projects (Olken, 2010; Madajewicz et al., 2014; Beath et al., 2012) can be
democracy /.../ The most prominent form of direct democracy today is an election in which citizensvote yes or no on specific laws listed on the ballot...".
2.1. INTRODUCTION 15
suitable for case-by-case decision making, they are less appropriate for services contin-
uously provided by community organizations. In such cases, the role of leaders becomes
important. Our findings can thus offer policy advice to external actors that want to
put in place sustainable and equitable/inclusive mechanisms for service provision.
Finally, this paper also increases the knowledge in the specific policy area of how
to increase saving among the poor. Using statistics from IMF, Aggarwal et al. (2012)
estimate that less than 19% of the population of Sub Saharan Africa had a bank
account in 2011.6 A number of recent studies investigate the take up of such formal
saving technologies, for example Dupas and Robinson (2013a, 2013b). For people living
below the poverty line, informal financial institutions for saving, such as self-help
groups or Village Savings and Loan Associations are more accessible than banks and
are increasing in importance in Sub-Saharan Africa and Asia.7 A handful of recent
papers study these groups (Burlando and Canidio, 2015; Greaney et al., 2016; Ksoll
et al.,2013). These studies measure the economic performance and vary the purely
economic incentives of administrators or members, but abstract from the organization
of group members. We complement this previous work with our focus on the governance
of groups.
In the next section, we present the context in which the study took place and
the partnering NGO Program. Section 3 explains the setup of the program and the
experiment. In section 4, we lay out the conceptual framework and section 5 presents
the data. Section 6 presents the results for leader types and group policy outcomes.
Section 7 discusses the welfare effects of these findings. Section 8 concludes the paper.
6This excludes South Africa, where a significantly higher share of the population is banked thanin the rest of the region.
7See Ksoll et al. (2013) and Greaney et al. (2016).
16 ELECTORAL RULES AND LEADER SELECTION
2.2 Background: Karamoja and BRAC Uganda
2.2.1 Karamoja region
This study took place in Karamoja which is located in the North-Eastern corner of
Uganda, bordering Kenya and South Sudan.8 With its dry climate, it is the poorest
region in Uganda. According to the 2014 Uganda Poverty Status Report, 74% of its
population lived below the local poverty line (1 USD per day) compared to 19.7% in
the country as a whole.9 10 The inhabitants of the region traditionally relied on agro-
pastoralism and pastoralism for their livelihood, but these livelihood options have
become compromised in the last few decades, due to conflict and insecurity combined
with harsher climate conditions. This has resulted in Karamoja having the largest
number of food insecure people in Uganda. Other development indicators also lag
behind those of the rest of the country. Figures taken from an 2004 Uganda Bureau
of Statistics survey by Irish Aid show that the literacy rates in the region were 21%
as compared to a national average of 68%, and that 60.3% of the 6 - 25 year olds had
never been to school as compared to only 13.8% nationally.
2.2.2 BRAC Uganda and the Karamoja Initiative
The authors collaborated with the NGO BRAC Uganda. BRAC is a large non-profit
organization founded in Bangladesh in 1974, currently active in 12 developing countries
in Asia, Sub-Saharan Africa and the Caribbean. BRAC was launched in Uganda in
2006 and had by 2013, it had become one of the largest development organizations
and micro finance institutions in Uganda. Its core activity is microfinance, and other
programs include a system of health workers, agricultural extension, and self-help8For more information on the socioeconomic characteristics of Karamoja and BRAC’s activities
in the region, see Czuba; 2011, 2012a, 2012b.9The National poverty line of Uganda is 1 USD per day, but the country employs 8 additional
local poverty lines, that are adjusted to the consumption baskets in urban and rural areas in each ofthe four main regions, for more details, see Appleton, 2003.
10According to the UNDP Millennium Development Goals Report, the fraction of people living onless than 1.25 USD per day in 2015 is 14% in the World’s Developing regions and 41% in Sub-SaharanAfrica.
2.2. BACKGROUND: KARAMOJA AND BRAC UGANDA 17
groups for young women.
Our experiment takes place within BRAC Uganda’s "Karamoja Initiative" which
started in 2011 in five of the seven districts of the Karamoja region. As traditional
livelihoods have become more difficult and solely relying on agriculture is not a viable
option in Karamoja, small-scale market activities have gained increased importance
in recent years. This provides the motivation for BRAC’s activities for adolescents
in the region. Partly modeled on BRAC’s self-help groups for young women,11, the
Karamoja Initiative targets children and young women with the objective of improv-
ing education take-up among children and, in parallel, to provide ways of promoting
income generating activities (IGA) for young women. BRAC’s activity in the 114 lo-
cal Youth Development Centers (YDC’s) in Karamoja is structured around 9 branch
offices, each with a defined catchment area and employing 2-4 members of staff who
monitor the activities of their centers.12 These centers are typically located in small
houses or huts rented by BRAC in the targeted villages and communities. Each center
employs two women: One caretaker and one mentor, usually recruited from within the
community. The centers are open every weekday. In the morning hours, the caretaker
receives pre-school aged children and in the afternoons the mentor keeps the center
open for adolescent girls and young women. During its opening hours, members of the
center can engage in leisure activities such as board games combined with "life skills"
training sessions led by the mentor, on topics such as reproductive health, relationships
and water and sanitation. The center members can also join the Income Generating
Fund (IGF) savings group which is our focus in this paper. This system for savings was
introduced to the YDC members in mid-2011. Because monetization of the economy is
relatively recent in Karamoja, most YDC members had little capital at their disposal
and limited experience in managing financial flows. The objective of the IGF was to
encourage savings and facilitate the starting of business activities. Group members’
11BRAC’s self-help groups in Uganda are called Empowerment and Livelihood of Adolescents (ELA)clubs. They are present in 80 districts of Uganda (excluding Karamoja) and have been studied byBandiera et al. (2014).
12Three in the Napak district, two in the Nakapiripirit district, two in the Moroto district and onein the Kotido and the Amudat districts, respectively.
18 ELECTORAL RULES AND LEADER SELECTION
savings are collected at weekly meetings and placed in a box, initially safeguarded by
BRAC staff but eventually to be handed over, along with the responsibility for the
other IGF activities, to the saving members. After an initial 12 months of saving, the
groups started providing loans to their saving members. Occasionally, more structured
courses in income generating activities are offered to members of the center. These are
courses in agriculture, poultry rearing, hairdressing and baking with the aim of pro-
viding skills for starting small scale business activity, but by 2015, the saving meetings
are the only structured activity for adolescents hosted regularly at the Youth centers.
2.3 Setup of saving groups and leader selection meth-
ods
In mid-2011, BRAC started the YDC in Karamoja. After a few months, the IGF
groups were started as one component of the activities of the centers. Young women
in the age range of 13-21 were invited to join the groups and start saving. In January
2012, local BRAC staff members instructed the groups to form committees. This was
an important step in handing over ownership and governance of the groups to the
members themselves.
Before committees were chosen, the committee selection method was randomly
assigned to the groups. The randomization was carried out by the BRAC Research
Unit under the supervision of one of the authors. Randomization was stratified at the
branch level to ensure variation in the appointment method between the groups within
each branch, and the smallest and largest groups were excluded before randomization,
leaving 92 groups in our study.13 In all groups, the mentor and a staff member from
the corresponding branch office informed the members of the IGF savings group that
the group was to select a committee. Information was given about the role of the
committee and about each committee position. The members were told that they13Groups with less than 10 or more than 30 members were excluded to leave a set of more com-
parable groups in the experiment. The groups that were not included in the experiment selectedcommittees according to the discussion setup.
2.3. SETUP OF SAVING GROUPS AND LEADER SELECTION METHODS 19
would meet again approximately one week later to select the committee and were also
told that desirable characteristics of a committee member were that she should be
trustworthy with money and accepted in the community. At the second meeting, the
members were reminded by the local BRAC staff about which committee positions
were to be chosen, and the specific tasks associated with each position. The positions
were: chairperson, treasurer, secretary, keyholder 1 and keyholder 2. The role of the
two latter members is to store keys to the saving box, which was to be kept at the
house of the treasurer. The mentor was usually suggested to become the secretary or
the treasurer. The selection of committees then happened in two distinct ways.
Open Discussion: The group members openly discussed each position and anyone
who was a saving member could nominate candidates for the position. Other members
could then second or oppose the nomination openly until the group agreed on a name.
Then, they proceeded to discuss and fill the next position.
Secret Ballot (Vote): The group members openly nominated candidates for each
position. For each position, at least two candidates were required. Members would
then vote by writing the name of the person they preferred for each candidate and
drop it into one of 4 boxes or baskets, one for each voteable position.14. BRAC staff
assisted with writing (for those unable to write themselves) and with compiling the
votes.
In both appointment systems used, all members were invited to attend the meeting,
and potential committee members were discussed. In the discussion treatment, this
open discussion was the means to make the final selection for each committee position.
In the vote treatment, the discussion only served to nominate at least two people for
every position, after which the final decision was made by secret ballot. The role of the
committee involves tasks that can be divided into four main areas of responsibility: The
first is to encourage members to attend weekly saving meetings and to save regularly.
The second is concerned with the safeguarding of the money in a saving box, and
the third is allocating loans to members and ensuring that these are being repaid.14The mentor was automatically given a position either as treasurer or as secretary so only four
positions remained to be voted for.
20 ELECTORAL RULES AND LEADER SELECTION
Finally, the committee is responsible for keeping books at the group level in order to
keep track of the savings, loans and interest rate amounts. Newly elected committee
members received a few days of training by BRAC, organized at the branch level.15
The committees officially had a term limit of one year. After this, every group was free
to decide how to appoint their next committee. In the summer of 2012, approximately
6 months after the committee appointment, most groups started giving out loans
from their pooled savings. The decision about when to start lending was taken at
the branch level. As the saving groups became free to adjust their bylaws, most of
the groups started following a model used by Village Savings and Loans Associations
(VSLAs) that became increasingly popular in the Karamoja region. This is a model
with saving cycles, typically one year in length, at the end of which a "share out" takes
place. In this meeting, all funds in the saving box including the interest rate generated
by lending, is shared among the saving members according to their level of savings.
After this, a new saving group is formed, and a new committee is appointed.16
Figure 2.1 shows the timeline of the program implementation and data collection
activities from 2011 until 2015.
2.4 Conceptual framework
In this section, we discuss the characteristics potentially relevant for leaders that have
been highlighted in the previous literature, and relate them directly to the role and
tasks of leaders in our context. Then, we outline the hypotheses about how the electoral
15The training covered basic concepts of financial literacy. Committees from all centers within onebranch attended the training together. This ensures that committee members in both treatmentsattended identical training sessions, independent of method used when electing them. Spillover ef-fects between treatments from this joint training are not likely since the mechanisms through whichtreatment would affect the groups played out either before the training took place, in the meetingitself, through the appointment methods used producing different types of committee members, orthrough the feeling of legitimacy of regular members who did not attend this training.
16In practice, local BRAC staff from the branch office associated with a given youth center, whovisit each group at least one time per month, still function as a type of mentors for group membersand can be influential in their governance. Since we are not able to observe exactly how this affectsthe group performance, we include branch fixed effects in the analysis to account for this unobservablecharacteristics that are common for the groups within one branch and fixed over time.
2.4. CONCEPTUAL FRAMEWORK 21
rule can be expected to affect these characteristics and how this, in turn, may affect
the policy outcomes we study: member retention, savings and loans.
2.4.1 Leader representativeness and skill level
The literature on political selection highlights a tradeoff between leader representa-
tiveness and leader quality in terms of skills.
Representativeness of leaders would imply leaders with similar preferences to
those of the median group member, in terms of rules for savings and loans and the
ensuing stringency. It can be measured in terms of similarity in observed socio eco-
nomic characteristics. Grossman (2014) shows that personal ties substitute for rule
enforcement in Ugandan farmer groups. In a similar way, friendship or kinship ties
with leaders can proxy for trust and convey information about commonly held prefer-
ences. Being a member of the same tribe can work in a similar way (Alesina and La
Ferrara, 2002).
Representativeness can affect outright favoritism, for example by leaders offering
loans to members of their own group, or being more lenient with the repayment from
such members. It may also lead to policy more targeted to the preferences of the own
group: by setting the level of minimal savings high, a rich committee member can push
out poorer members or, conversely, by choosing a low interest rate and offering few
loans, which makes the aggregate money grow at a slower pace, a committee member
can push economically successful members out of the group, as they may have more
profitable outside options.
Skills are features that affect every group member in the same direction, what
Besley et al. (2005) refer to as a valence issue. A leader with higher skills makes
everyone in the group better off. Examples would be better accounting skills, general
honesty and reliability with money, and the ability to make members who took loans
repay them to the group. Since the objective of the groups we study is to encourage
savings and give loans for profitable market activities, skill indicators in our setting are
variables such as education, economic performance at the baseline, market experience
22 ELECTORAL RULES AND LEADER SELECTION
and labor market connections.
2.4.2 Treatments’ relative effects on representativeness and skills
of leaders
Recent work predicts that elite capture and intimidation may lead discussion treat-
ment groups to select less representative leaders (Hinnerich and Pettersson-Lidbom,
2014). Introducing a secret poll has been shown to generate leaders that are more rep-
resentative of the preferences of the electorate (Baland and Robinson, 2008). Based on
these findings, we would expect the leaders emerging in the vote treatment to be closer
in characteristics and preferences to the average group member than leaders selected
in discussion. In addition, the Discussion framework may provide better conditions
for coordination which could lead to more informed decisions regarding leader skills
(Humphreys et al., 2006).
If the coordination channel is at work, it would lead to positively selected committee
members, in terms of economic characteristics, in particular education and wealth. If
we observe that committees in discussion groups are richer and more educated than
the non-committee members of their group, this can thus be a result of either the
discussion treatment facilitating coordination and leading to more skilled leaders, or
of elite capture taking place in the discussion treatment. To disentangle the two,
we need to follow the group performance over time and construct measures of group
economic effectiveness.17
17One additional mechanism that can differ between open discussion and secret vote is legitimacy.The literature shows that people like to participate in decision making and a more inclusive appoint-ment procedure may lead to greater feeling of legitimacy among a higher share of members. Legitimacyeffects have been observed in both Olken (2010) and Beath et al. (2012). As vote treatment is themore inclusive process in our setting, it is also expected to entail greater feeling of legitimacy here.Dal Bó et al. (2010) use a lab experiment to disentangle satisfaction with the decision-making pro-cess from satisfaction with the outcome, and show that being "heard" increased subjects’ feeling oflegitimacy, regardless of whether the rule they had voted for was chosen.
2.5. DATA AND DESCRIPTIVES 23
2.4.3 Hypotheses
Group performance in public service delivery is affected by leader characteristics. A
member’s utility from the group is affected by the skill level of the leader and by the
closeness of the leader’s group affiliation to the member’s own group. A higher skill of
a committee member has a positive effect on each member, regardless of her own group
affiliation. The skill level is thus expected to increase the overall level of savings and
loans in a group. The distance between a member’s group affiliation and the leader’s
group affiliation implies a cost for the member. The leader’s group affiliation thus
affect the types of members asymmetrically and the larger is the difference, the higher
is the cost imposed on the member. If the distance between a member’s own type and
the leader type is too large, the member will be better off by leaving the group.
2.5 Data and Descriptives
2.5.1 Baseline census and survey data
A baseline survey was administered to the saving members before the treatment, in
September-November 2011. The baseline survey was preceded by a census of the groups
in August 2011, listing the name and age of all members. 85% of the listed members
were interviewed at the baseline. Reassuringly, there is no difference between treat-
ments in the share of members interviewed. Baseline summary statistics and random-
ization checks of the baseline variables are presented in Table 2.1.18
Table 2.1 shows the means in the population and by treatment of a number of
variables that measure group characteristics (such as group size), the economic per-
formance of members at baseline, or the socio-economic characteristic of the members
and their household. The last two columns of table 2.1 report treatment differences
and p-values from weighted least squares regressions of the following form:
18Additional explanations for construction of baseline variables are provided in Appendix 2.
24 ELECTORAL RULES AND LEADER SELECTION
Ygb = α +βVoteg +θb + εgb, (2.1)
where Ygb is the mean of the variable in group g, branch b, Voteg is an indicator
variable equal to 1 if the group received the vote treatment, θb is a branch indicator,
controlling for unobserved characteristics at the branch level such as the local BRAC
staff serving the centers within a branch, or local market or agriculture conditions.
The regressions are weighted by the number of individuals interviewed in the group at
baseline. The sample is balanced across treatments: Table 2.1 shows that groups in the
vote treatment and the discussion treatment are, on average, comparable on baseline
characteristics. The only variable that is significantly different at the 1% level is the
share of members that had received a training in income generating activities (IGA)
offered by BRAC. These are practical trainings focusing on skills such as baking or
animal rearing, occasionally offered at the branch level to a few members from each
group within the branch. To ensure that this characteristic does not drive observed
differences between treatments, we control for the baseline share of members with IGA
training in our regressions that focus on baseline variables.
2.5.2 Committee member data
The next set of data that we use is the complete list of the original committee mem-
bers, i.e. the committee members that were selected during the treatment which took
place in January 2012. Merging this list with the baseline data enables us to analyze
differences in predetermined characteristics between the committee members elected
in vote (secret ballot) treatment compared to those selected in discussion treatment.
Out of the 462 people listed as original committee members of the groups, we have
baseline data for 323. Out of the remaining 139 original committee members, 37 were
listed as members in the census made before the baseline, but were not interviewed at
the baseline due to non-response, and the other 102 were not listed as members in the
census made before the baseline; these are hence members who joined the groups in
2.5. DATA AND DESCRIPTIVES 25
the time period between the baseline (September-December 2011) and the committee
selection (January 2012). Importantly, there is no difference between the treatments in
the number or the share of committee members interviewed in the baseline survey.19
2.5.3 Follow-up in 2013
One year after the treatment, we collected individual level data for all baseline members
on their current membership status and group level data on savings and loans in the
group during the first year of self governance. We use this data to examine dropout
patterns from the groups between 2011 and 2013.
2.5.4 Census data collected in 2015
A census in March 2015 collected information on all current members in the groups, and
followed up on all baseline members. This census provides individual-level information
on the membership status of each of the baseline members who we classified into
stayers or dropouts. In addition, we have information on all new members, i.e. those
who joined the group after the treatment. We use the census data to examine dropout
patterns from the groups between 2011 and 2015, and it also provides the sampling
frame for the endline survey.
2.5.5 Endline survey
An endline survey was collected in May-July 2015. This survey was conducted within
a random sample of each of the three sub-groups defined in the 2015 census: Stayers,
Dropouts and New Members. This survey collected socio-economic data similar to the
baseline data, and also data on network variables for the original leaders and person-
ality features and satisfaction measures of group members. All members in the census
19The committee size deviated from 5 in four cases: One group had 4 committee members, 2groups had 6 committee members and one group had 7 committee members. These differences arenot significantly correlated with group treatment status.
26 ELECTORAL RULES AND LEADER SELECTION
could not be interviewed for cost and time reasons. Since our main focus is the treat-
ment effect on members who were present when the leaders were selected, we sampled
all stayers and only a random sample of new members (42 %). We also sampled 41%
of the dropouts. The over-sampling of stayers in relation to dropouts was motivated
by cost reasons as stayers were easier to locate and interview than dropouts. Table A.7
shows sampling and attrition rates. All regressions using endline data include sample
probability weights to account for oversampling of stayers in relation to dropouts and
new members. In the endline survey, we also collected group-level policy variables in
an additional module. This module collected information from one committee member
of each group about the interest rate imposed on loans, whether the group employs a
VSLA model with annual share-outs, questions about the saving cycle, and how the
committee deals with loan defaults and with members who are not saving on a regular
basis. Wealth score, assets and income variables are constructed in a way similar to the
one described for the baseline survey.20 Social network variables were also collected in
the endline survey. Here, the focus was on the original members and the committee
members. To all respondents, old and new, we posed a series of questions about the
five original committee members. This information serves to construct basic network
measures and to investigate how members evaluated the performance of original com-
mittee members. To all old respondents (stayers and dropouts) we also asked, for each
of the original group members, if they knew who the person is and if they were a close
friend or relative known before joining the savings group.
2.6 Results
In this section, we present the results of the electoral system on committee member
selection, member retention, and savings and loan services at the intensive margin. The
estimating equations are presented along with the corresponding results. Throughout
20For assets, we only use the respondent’s self reported value for the assets houses and land holdings.For the other assets included in the roster, electronics and various livestock and poultry, we imputetypical asset values for the region collected by BRAC Research and Evaluation Unit.
2.6. RESULTS 27
the results section, the base category will be the discussion treatment, while we regress
outcomes on an indicator for the group being exposed to the vote treatment. All
individual level regressions standard errors are clustered at the group level (the level
of randomization).
2.6.1 Leader selection
The first question we address is the effect of the appointment method on the type of
leaders selected. First, we restrict our sample to committee members, and estimate
the following linear model:
Yigb = α +βVoteg +θb + εigb, (2.2)
where Yigb: an economic or social characteristic of committee member i in group
g, branch b, θb is a branch indicator, controlling for unobserved characteristics at the
branch level such as the local BRAC staff serving the centers within a branch, or local
market or agriculture conditions. β is the coefficient of interest, measuring the effect
of the vote treatment on the likelihood that the committee members appointed have
the predetermined characteristic Y .
Columns 1-4 of Table 2.2 show the result of estimating equation 4.1 using proxies for
the economic performance as the dependent variable. On average, leaders (committee
members) in the vote treatment are poorer than leaders in the discussion treatment,
as measured by a lower value on the Wealth index21. The effect of vote treatment on
the likelihood that leaders have received employment advice from anyone outside the
household is negative, but just below conventional confidence levels. For robustness, we
also estimate equation 4.1 using other measures of wealth such as income, assets or the
raw wealth score, rather than a member’s position in her group’s wealth score distribu-
21The Wealth index is composed using the Uganda Progress out of Poverty index, compiled by theGrameen foundation (2011). This index combines information on poverty indicators such as materialof roof, walls and floor of a household’s main house, its ownership of shoes and clothes, access towater, power sources and sanitation, and education level in the household.
28 ELECTORAL RULES AND LEADER SELECTION
tion, as a measure of relative wealth (see Table A.1 in the Appendix).22 Columns 5-7
of Table 2.2 show the result of estimating equation 4.1 using socio economic character-
istics as dependent variables. Leaders in vote treatment score lower on socioeconomic
proxies: they are 27 percent more likely to have children and were, on average. 0.9 years
younger at the time of birth of their first child.23 They are also 34 percent less likely to
have migration experience than leaders in the discussion treatment. Migration among
young women in Karamoja is normally short-term migration for cultivation or casual
work in farming on more fertile land in neighboring districts, or sometimes migration
to urban areas for studies or for unskilled work. Migration experience is positively
correlated with wealth in our sample. The last three columns of Table 2.2 show the
results for social characteristics, such as tribe and the number of connections to other
group members. Leaders in vote groups are more likely to belong to the majority tribe
of their group and also somewhat more likely to have reported fellow leaders to be
among their four best friends at baseline - these differences are not statistically sig-
nificant at conventional confidence levels, however. Overall, the variables measuring
wealth or economic situation of the leaders are consistently higher in the discussion
treatment, albeit the difference between treatments is not always statistically signif-
icant. This suggests a positive selection of leaders on economic characteristics in the
discussion treatment.
The next question that we analyze is whether leaders in vote treatment groups
select leaders that are more similar to the average group member measured in socio-
economic variables, or more likely to be their friends, compared to the leaders of
discussion treatment groups. The upper panel of figure 2.2 plots the position in the
group’s wealth score distribution for leaders (committee members) and non leaders
separately, by treatment. The lower panel of the figure does the same for the initial
assets held at the time of baseline. In discussion groups, the distribution for leaders
22For log income and for the raw wealth score, the results confirm the finding that leaders invote groups are poorer. For asset score, the point estimate is negative but no longer statisticallysignificant when controlling for the baseline share of group members that had received training inincome generating activities (see Table A.1 in the Appendix).
23This variable is only available for members with children, which constitute 67% of the sample.
2.6. RESULTS 29
has more mass at higher levels of the wealth score and asset distribution in discussion
than that for non leaders. This indicates that leaders in discussion treatment groups
are positively selected on wealth and assets, while the distribution among leaders in
vote groups more closely follows that of non leaders.24 To examine differences between
leaders and non leaders in a more systematic way, we estimate the following OLS
regression:
Yigb = α +βVoteg + γLeaderi +σ [Voteg×Leaderi]+ηg + εigb, (2.3)
Yigb is a characteristic that proxies either for economic performance or the social
connectedness of a group member, CM is an indicator for whether an individual was
selected to become a leader (committee member), Voteg indicates the vote treatment
and ηg is a group indicator. γ [γ+σ ] estimates the correlation between the variable
Y and being a leader in the discussion [vote] treatment. The coefficient of interest is
σ , indicating the difference in the correlation between characteristic Y and becoming
a leader between the two treatments. The sum of γ and σ , shows how leaders differ
from non leaders if the treatment is vote.
Table 2.3 shows the result from estimating equation 4.2 with baseline characteristics
proxying for economic performance (columns 1-4), socio economic variables (columns
5-7) or social proxies (columns 8-10) of members as the dependent variable. For three
out of the four economic outcome variables, it applies that for the discussion treatment
groups, the mean difference between leaders and other group members is statistically
significant at least at the 95 percent confidence level. For example, leaders in discussion
groups are 46% (12.9 ppt) more likely to have employment connections and 25% (12.2
ppt) more likely to have some education than regular members. The differences for a
wealth index and for asset holdings point in the same direction. For the vote treatment,
none of the differences in economic variables between leaders and regular members are
statistically or economically significant. The interaction terms with the vote treatment24Wealth score indicates the position (decile) of an individual in the wealth score distribution of her
group. The score is constructed using the Uganda PPI index compiled by the Grameen foundation(2011). Higher value=less poor. The wealth score does not include the value of household assets.
30 ELECTORAL RULES AND LEADER SELECTION
attenuates the difference between leaders and non leaders, meaning that in vote groups,
leaders resemble non leaders more than in discussion groups. Looking at the sum of σ
and γ , we cannot reject the hypothesis that the difference between leaders and regular
members in vote groups equals zero for any of the four variables.25
Thus, in general, on economic performance variables, the vote treatment leaders are
more similar to the average of regular members in their group than in the discussion
treatment where leaders are positively selected in terms of wealth, education and
market connections.
Columns 5-7 of Table 2.3 show the result from estimating of 4.2, using baseline
socio-economic proxies as dependent variables, and columns 8-10 of Table 2.3 show the
result from estimating of equation 4.2 with social connectedness-measures of members
as the dependent variables. For the socio-economic variables children, age at first birth,
and migration experience, the difference between leaders and regular members in vote
groups is also significant at least at the 95 percent confidence level and indicates lower
socio economic scores among leaders in vote groups. The hypothesis that the value of
this characteristic is the same for leaders as for regular members within a given group
if the treatment is vote can be rejected at the 99 percent confidence level for all social
connectedness variables. In vote groups, leaders are more likely than other members
to have strong links both to other group members and to other leaders (committee
members), as measured in the baseline survey. Leaders in vote are more likely to come
from the majority tribe of the group than non leaders. Overall, the results for socio-
economic characteristics indicate a negative selection for leaders in vote treatment.
Taken together, the results in this section suggest that in the vote treatment,
members do, to a higher extent, elect people who are similar to themselves, and for
individuals more socially connected within the group: they are more likely to belong to
the majority tribe and more likely to be well connected to other members who ended
up becoming leaders. In the discussion treatment, leaders are positively selected in
25The results are robust to adding the individual baseline wealth score or assets as a control,to ensure that the results are not driven by any correlation with leader wealth (Table A.2 in theAppendix shows results controlling for the wealth score).
2.6. RESULTS 31
terms of wealth, education and market connections.
2.6.2 Results on member retention and dropout
2.6.2.1 Dropout rates in each treatment
From January 2012, the time of the committee appointment, until March 2013, 45%
of the original members left the vote treatment groups and 60% left the discussion
treatment groups. The dropout continued between 2013 and the endline but at a
slower rate, and the difference between treatments became less stark over time. In
March 2015, 33.5% of the original members remained in the vote groups compared to
27.3% in the discussion groups
Figure 2.3 shows the share of original members that was still saving in the group
in March 2013 and in March 2015, respectively. Regressing dropout on treatment
shows that this large difference between treatments after one year is highly statistically
significant. Table 2.4 shows the result of this regression, which is an estimation of the
model:
Dropouttigb = α +βVoteg +θb + εigb, (2.4)
t ∈ {2013,2015}, Dropout is a binary outcome variable for individual i, group g
branch b, equal to 1 if the individual had dropped out of the group at time t, which is
either in 2013 or 2015, and 0 otherwise. θb is a branch indicator.
As can be seen from both Figure 2.3 and Table 2.4, the difference between treat-
ments in dropout by 2015 is weaker, when clustering standard errors at the level of
randomization and controlling for branch indicators, the p-value of the difference be-
tween the treatment is 0.11. The sizable dropout from both types of treatment groups
means that composition effects need to be taken into account in order to understand
differences between the groups at endline. In the next subsection, we examine who
drops out in more detail.
32 ELECTORAL RULES AND LEADER SELECTION
2.6.2.2 Results for selective dropout
To understand how dropout affects the efficiency of the group and to understand the
welfare implications of the dropout, we need to know what characteristics determine
dropout and if the dropout pattern differs across treatments in terms of what type of
members leave the group. To examine this, we estimate the following OLS regression:
Dropouttigb = α +βXigb +σ [Voteg×Xi]+ δVoteg +θb + εigb, (2.5)
where t ∈ {2013,2015} and X is an economic characteristic, proxying for poverty,
of member i measured at baseline, such as a dummy for being in the bottom 25%
of the wealth or asset distribution of her group or a dummy for having no market
income. θb is a branch indicator. β and σ are the coefficient of interest. β tells us the
predictive power of characteristic X on dropout if Vote=0, that is, if the treatment is
discussion. σ is the treatment difference in the effect of characteristic X on dropout
for a member in the vote treatment. The sum of the coefficients β and σ tell us to
what extent characteristic X affects dropout if the treatment is vote.
Table 2.5 shows the result from the regression of dropout by 2013 on five eco-
nomic characteristics (poverty proxies) measured at baseline. For all five variables, the
coefficient measuring the predictive value of the variable on dropout is positive and
significantly so for all variables except assets. This tells us that dropout in discussion
treatment is negatively correlated with wealth and with having savings, loan experi-
ence and market income at baseline. Specifically, members in the lowest quartile of
the group’s wealth distribution were 12.3 percent (7.1 percentage points) more likely
to drop out in the first year compared to wealthier members, while members with no
market income at baseline were 18.2 percent (10.3 ppt) more likely to leave the discus-
sion groups than those with market income. Members who kept no savings at baseline
were 33.4 percent (19.4 percentage points) more likely to drop out from discussion
groups than those with initial savings, and members with no loan experience were
53.5 percent (24.5 ppt) more likely to leave discussion groups than those with loans at
2.6. RESULTS 33
baseline. The interaction term between each of the five economic variables and the vote
treatment goes in the opposite direction, implying a much smaller effect of economic
background variables on dropout patterns in vote groups. Tests of whether dropout
rates are the same for poor people across the two treatments are rejected at the 95%
confidence level for all five economic variables, while tests of whether dropouts and
stayers in the vote treatment are similar can not be rejected for any of the variables
(p-values reported below table).
Table A.3 in the Appendix shows the same regressions but now the dependent
variable is dropout by the year 2015. The results point in the same direction but are
not as conclusive and significance levels are lower. This is likely to be explained by the
high initial dropout rates implying that the groups are, by 2015, less affected by the
original treatment.
To sum up, members who had lower economic indicators at baseline leave the dis-
cussion groups at a significantly higher rate than richer members while vote groups
retain a member group which is more diverse in terms of their initial economic char-
acteristics and thus more inclusive of initially poor members.
2.6.2.3 Results for group size in 2015
To understand if the dropout leads to smaller groups over time, or if it is compensated
by new members joining the groups, Figure 2.4 shows the distribution of group sizes
in 2015, separately for vote treatment groups and for discussion treatment groups.26
Data on group size comes from the 2015 census and is the number of old stayers plus
the new members. As shown in the figure, discussion groups are smaller than vote
groups. Regressing group size on the treatment also yields a positive point estimate,
but the relation is not statistically significant. However, the number of observations
are fewer: only 84 out of the 92 initial groups are active in 2015, and some of those
were very small at the time of the 2015 census. Table A.8 in the Appendix presents the
26For 2013, we only have dropout data but do not have access to data for new members and cantherefore not perform the same exercise.
34 ELECTORAL RULES AND LEADER SELECTION
regression of group size in 2015 on the treatment. For the full sample of groups, the
difference between treatments is not significant. When restricting the sample to the
84 groups that are active at the time of the endline survey (column 3), we approach
conventional significance levels and when restricting the sample to groups with above
5 members at endline (column 4), the p-value is 0.11. To sum up, vote groups are
slightly larger at endline but the overall group level activity is low in terms of number
of active members.
2.6.3 Group performance in savings and loans delivery
Next, we turn to the question of how the appointment method affects the perfor-
mance of the groups in terms of savings and loans activity measured at the endline.
To examine the effect of treatment on savings in 2015, the following OLS model is
estimated:
Y 2015igb = α +βVoteg +θb + εigb, (2.6)
where Y is a dummy for having savings in the group, a dummy for having savings
anywhere, or log savings (intensive margin) and θb is a branch indicator. Regressions
include sample weights to account for the over-sampling of stayers as compared to
dropouts in the endline survey.
Table 2.6 shows the result from estimating equation 2.6 on all old members, as well
as on old members broken down by whether they are stayers or dropouts in 2015. The
dependent variable in columns 1, 3 and 7 of Table 2.6 is a dummy for having savings
in 2015 (regardless of where they are kept), and in column 5 the dependent variable
is a dummy taking the value of one if the individual is saving in the BRAC group
(available for stayers only).27
For the union of stayers and dropouts, the point estimate for the effect of vote
treatment on savings in 2015 is negative across specifications, but not statistically27Here, it is important to note that while stayers can have savings both in the BRAC group and
elsewhere, dropouts, by definition, have no saving in the BRAC group.
2.6. RESULTS 35
significant. Looking separately at stayers and dropouts; stayers in vote groups are 11
% (9 ppt) less likely than stayers in discussion groups to be saving at endline. This
result is statistically significant at the 90 percent confidence level. Focusing on saving
in the BRAC group as the dependent variable, the results point in the same direction:
stayers in vote groups are 15 % less likely than stayers in discussion groups to be saving
in the group at endline. We see no difference in the saving behavior of dropouts across
the two treatments. Columns 2, 4 and 8 of Table 2.6 show the result from estimating
equation 2.6 with log savings in 2015 as the dependent variable. Although the point
estimates are not statistically significant, the results point in the same direction: Vote
groups members save less also at the intensive margin.
To examine the effect of treatment on loans in 2015, we estimate the following OLS
regression:
Yigb = α +βVoteg + δXigb +θb + εigb, (2.7)
where Y is a dummy for having ever taken a loan (extensive margin), the number
of loans taken, or log loans (intensive margin), and X is a control for assets at baseline.
Assets at baseline are chosen as a control because it is an important predictor of who
is given a loan. As before, θb is a branch indicator. Regressions include sample weights
to account for the over-sampling of stayers as compared to dropouts in the endline
survey.
To measure the loan activity within BRAC savings groups, we use 3 different
variables: (i) extensive margin of loans, (ii) intensive margin of loans for those with
nonzero loan values and (iii) number of loans ever given by the group (values of zero
are also included). In order to understand the welfare impact of any effects, we also
use loans taken anywhere (where we have information for the extensive margin only)
as outcome variables .
Table 2.7 shows the result for loans within the group, for all old members as well
as on old members broken down by whether they are stayers or dropouts in 2015.
There are no statistically significant effects on the likelihood of having taken a loan
36 ELECTORAL RULES AND LEADER SELECTION
(extensive margin), although when reducing the sample to stayers, the coefficients are
negative for the vote treatment and closer to conventional confidence levels than in
the whole sample or in the sample of dropouts. On the intensive margin, as shown
in columns 2, 5 and 8, of Table 2.7, stayers who took loans in vote treatment groups
took smaller loan amounts: the coefficient of the vote treatment is negative across
ways of measuring loan size, but the results are not significant at conventional levels.
Any difference between treatments in the number of loans and loan size appears to
be driven by stayers; the coefficient for dropouts is less negative than for stayers and
for the intensive margin, dropouts from vote groups appear to borrow larger amounts
than dropouts in discussion groups. Finally, columns 3, 6 and 9 of Table 2.7 show the
results for the number of loans taken in the group. Results are similar.28
Finally, we estimate the effect of treatment on new members, i.e. members that
joined the groups after the committee selection in January 2012, and are only in-
terviewed at the endline. Since we do not have any baseline data for this group, we
estimate the following equation:
Yigb = α +βVoteg + δXg +θb + εigb, (2.8)
where Y is a dummy for ever having taken a loan (extensive margin), the number of
loans taken, or log loans (intensive margin), and X is a group level control. As before, θb
is a branch indicator. Columns 1-4 of Table 2.8 show the result of estimating equation
2.8 using savings at the endline as the outcome variable. At the extensive margin, we
see that also among new members, the likelihood of being actively saving is smaller in
vote groups. This difference appears to be driven by savings kept outside of BRAC.
On the intensive margin, point estimates for the vote treatment are positive but not
statistically significant. Columns 5-8 of Table 2.8 show results from the estimation of
equation 2.8 using loans at endline as the outcome variable. Among new members,
the number of loans accessed through BRAC is significantly lower for members of the28Table A.4 in the Appendix shows results for ever having taken loans anywhere. As for the like-
lihood of having taken a loans within the group, the coefficient is negative for vote groups but theestimate is not significant at conventional levels.
2.6. RESULTS 37
vote treatment group. There appears to be no difference between treatments in having
received a loan, nor in the loan size conditional on having borrowed - the coefficients
are negative for the vote treatment but far from significant.
To sum up, a lower fraction of members are actively saving in vote treatment
groups, than in discussion groups in 2015. The number of loans taken in vote groups
is also lower, both for old and new members. The amount of loans taken is smaller
in vote groups. The results are statistically weak, and the result for loans are only
significant in the smaller subsamples of respondents for whom we have baseline data
on assets. However, the point estimates are negative for vote groups across the different
models and robust to alternative ways of measuring savings and loans.
2.6.4 Potential mechanisms
The results found for leader types and subsequent dropout patterns indicate that elite
capture may be taking place in the discussion groups, with powerful members electing
leaders of their own type, favoring members that are similar to themselves. The results
may however also be explained by coordination taking place in the open discussion,
resulting in more skilled leaders. In this subsection we discuss and present suggestive
evidence that can help us distinguish between these two mechanisms.
Elite capture: To obtain a measure of whether leaders were favoring a specific type of
members, we examine loan allocation data. The committee decides which loan appli-
cations to approve and thereby have the opportunity to favor certain members. Since
dropout was already occurring during the time when groups started giving out loans,
it is difficult to isolate the causal impact of loan allocation on dropout. For this reason,
we focus on stayers since for these members the problem of selection out of groups is
less severe. Table 2.9 shows the loan allocation for members by their baseline poverty
status. Members who were poor at baseline or who had no loan access at baseline are
significantly less likely to be granted a loan (columns 1 and 3) and get fewer loans
(columns 4 and 6) in discussion groups than non poor members. The interaction be-
38 ELECTORAL RULES AND LEADER SELECTION
tween poverty indicators and the vote treatment cancels out this negative effect of
poverty on loan access within the groups. The results for aggregated loan amount
point in the same direction but are less statistically significant.29
Efficiency: To measure whether discussion groups are more efficient than vote groups,
and if wealth is correlated with efficient loan behavior of members, we examine de-
fault data. Table 2.10 shows the propensity to default on a loan as a function of the
electoral rule and poverty, proxied as before by the member’s position inverse wealth
distribution. There are no significant differences between vote and discussion group
in the likelihood to default on a loan, and being poor does not appear to affect the
likelihood of default, conditional on having been offered a loan.30
Our findings suggest that, in our setting, the open discussion rule entails elite
capture rather than efficiency. Leaders in discussion groups favor richer members when
approving loans, while such members are not better borrowers, as measured by default
rates.31
2.7 Welfare effects
We have showed that the electoral rule for appointing leaders leads to different pol-
icy outcomes. Groups that elected their leaders through open discussion have higher29Table A.5 shows the loan allocation for the sample of all old members (stayers and leavers).
Results are stronger than for the subsample of stayers only due to the selection out of groups, withpoor people in discussion groups more likely to be among the leavers and thus without access to thegroup’s loans.
30The measure for default is taken from the endline survey and is self-reported. The findings areconsistent with default information from group-level administrative data from BRAC available uponrequest.
31A third potential mechanism behind why more members, and poorer members, stay in the votegroups than in the discussion groups is legitimacy. If members of vote groups perceived the leaderelection mechanism as more legitimate this may explain their staying in the group. Although difficultto disentangle from elite capture, we attempted to measure the perception of fairness and voice byquestions asked in the endline survey. Among original members (stayers and leavers), we find nodifference between the treatments in the perceived fairness of the way the initial meeting was beingconducted. Nor do we find that members perceive themselves to have more "voice" in any of thetreatments. Results are available upon request.
2.7. WELFARE EFFECTS 39
dropout, in particular by economically weak members, as compared to groups who
elected leaders by secret vote. In this section, we discuss the interpretation of our find-
ings in terms of welfare effects on the entire target population of the savings and loans
groups, both for those who left the groups and those who stayed.
The objective of the groups studied here is to make saving and borrowing possi-
ble for the most vulnerable members of society; often those not allowed to join other
savings groups. In order to understand the long-run welfare implications of the differ-
ences in governance induced by our treatment, we do not only care about the mem-
bers remaining in the groups but also how the dropouts fare. The results presented in
the previous section suggest that for these members who dropped out of the groups,
there are no significant differences in savings and loans across treatments. To examine
whether stayers differ from dropouts in their access to savings and loans, we estimate
the following equations:
Yigb = α +βDropouti +θb + εigb (2.9)
Yigb = α +βDropouti +σ [Voteg×Dropouti]+θb + εigb, (2.10)
Yigb is a dummy for having savings (anywhere), log savings kept (anywhere) or a
dummy for having ever taken a loan from any lender. The coefficient of interest is β
which indicates if dropouts have a different value of the outcome variable than staying
members. The sample is restricted to old members.
The first two columns of Table 2.11 show the results from the estimation of equa-
tions 2.9 and 2.10 with loan dummy as the dependent variable, while the next two
columns use the saving dummy as the dependent variable. Dropouts are less likely to
save or to have taken a loan than stayers. This finding is highly significant and the
difference is large: Dropouts are about 56% (or 45 percentage points) less likely to be
saving at endline than staying members and are 31% (or 20 percentage points) less
likely to have taken a loan. The last two columns of Table 2.11 are estimated only for
those who save at endline and use log savings as the dependent variable. The dropouts
40 ELECTORAL RULES AND LEADER SELECTION
who are saving save larger amounts than the stayers who are saving, but this is clearly
a highly selected group given the low savings rate among dropouts. Thus, we can rule
out that members drop out to join other savings groups. Instead, dropouts display
substantially lower levels of savings and loans at endline. Columns 2, 4 and 6 in Table
2.11 show results from the estimation of 2.10, for each of the three dependent variables,
and tell us whether the correlation between dropout and access to savings and loans
differs between the two types of treatments. The interaction term suggests that this
is not the case.32
Finally, we examine if initially poor members are less likely to have access to saving
or loans in general, not only from the BRAC group. Table A.6 in the Appendix shows
that members who were initially poor are less likely to have access to such services at
endline if they were randomly assigned to a discussion treatment group. This holds
true both if poverty is proxied by market income, position in the wealth distribution,
or baseline access to loans.
Since a larger fraction of members left the groups in the discussion treatment, the
findings in this subsection imply that more members from the discussion treatment
groups end up without access to savings or loans. Moreover, these are disproportion-
ately the members who were initially poor or economically vulnerable. Given that
access to such financial services is instrumental for poverty reduction and that the ob-
jective of the groups was to offer access to such services, the overall welfare of original
discussion group members is lower at endline than that of vote group members.
32We asked all respondents at endline if they were currently saving (financially) somewhere elsethan in the BRAC savings and loans group. 29 % of members report saving elsewhere, compared to33% of non-members. Out of the non-members who save, 84% keep savings with another institution,while the remaining 16% report that they only keep saving with someone they know or at home.In comparison, among stayers, over 95% of those who save elsewhere do so with another institutionand only 4.65% keep their savings at home. We also asked all respondents about loans from othersources than the BRAC group. The share of respondent that have had access to such loans is similaracross dropouts and stayers, with a slightly higher share among dropouts (26% compared to 22%among stayers). The majority of those loans have been provided by other village savings and loansassociations. However, compared to dropouts, stayers clearly have had higher access to loans fromthe BRAC group.
2.8. DISCUSSION AND CONCLUDING REMARKS 41
2.8 Discussion and Concluding remarks
In this paper, we estimate the causal effect of the electoral rule used for leader selection
on the types of leaders and the performance of community savings and loans groups.
We specifically examine leader characteristics and group service delivery in terms of
continued membership of the vulnerable, and the savings and loans of individual mem-
bers over a period of 3.5 years.
We find that groups that chose their leaders in a secret ballot vote selected leaders
that were more representative of the average group member, as compared to leaders
appointed in an open discussion where leaders were positively selected in terms of
socio-economic characteristics. The dropout was high in both types of treatments but
substantially higher in discussion groups, which lost 60% of their original members
in their first year after committee selection, as compared to a 45% dropout rate in
vote groups. Moreover, in discussion groups, the dropout was substantially higher
for members with lower values on economic performance indicators at the baseline,
if measured in terms of wealth, income, loans or savings. Finally, at the endline, 3.5
years after selecting their first committee, vote groups are larger in size than discussion
groups. Also, their members are less likely to be active savers, while those who borrow
take smaller and fewer loans. We conclude that the secret poll voting creates more
inclusive groups while open discussion generates groups that are more exclusive and
selective in favor of more economically successful members.
This paper contributes to the understanding of public goods delivery in developing
countries, where community groups are assuming the responsibility for a large fraction
of the delivery of social and financial services to the poor. In particular, it is of relevance
for the growing literature on Community Driven Development (CDD). Knowledge of
how to set up inclusive mechanisms for local governance in such programs is important
for their long-term sustainability.
When interpreting the results in terms of external validity, it is important to no-
tice that we study savings groups where all members are female, young, have a low
42 ELECTORAL RULES AND LEADER SELECTION
education and are relatively poor, and where members of a group are recruited from
within the same community. Compared to other contexts in which decision making
and different democratic systems have been studied, ours is a very homogenous set-
ting. The results we find are likely to be a lower bound on the potential differences
that can arise from open discussion as compared to secret voting. An interesting exten-
sion would be to analyze similar treatments in more diverse groups where mechanisms
such as intimidation can be more pronounced and treatment effects are likely to be
stronger.
This study offers complementary explanations to the literature on efficiency and
inclusion in community groups in Sub-Saharan Africa. Our explicit focus on governance
and the role of group leaders enables us to reveal one potential mechanism behind the
exclusion of less economically able members and reconcile findings of previous studies.
Our findings suggest that the discussion treatment can lead to more efficient savings
groups through selection, but that this happens at the expense of more inequality in
access to savings and credit. In terms of mechanism, we provide supportive evidence
for the effect working through leader representativeness rather than leader skill level.
The appropriate method for leader selection ultimately depends on the objective and
target group of a program.
REFERENCES 43
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46 ELECTORAL RULES AND LEADER SELECTION
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FIGURES AND TABLES 47
Figures and Tables
Figure 2.1: Timeline
November 2012 November 2014 May 2015
Data collection
Census collected
March 2015
Endline survey collected May-‐
July 2015
Program implementation
BRAC Karamoja saving groups: Timeline for program implementation and data collection activities
Follow up data collected March 2013
May 2013
Groups start lending June-‐Aug 2012
BRAC Saving groups started in Karamoja May-‐June 2011
Baseline survey collected Oct-‐Dec
2011
May 2012May 2011
Randomization + committee
choice Jan 2012
November 2011
48 ELECTORAL RULES AND LEADER SELECTION
Figure 2.2: Position in group wealth and asset distribution, leaders vs non leaders.0
4.0
5.0
6.0
7.0
8.0
9.1
.11
Kern
el D
ensi
ty
0 2 4 6 8 10Discussion groups: Wealth score
Non CMs CMs (Leaders).0
4.0
5.0
6.0
7.0
8.0
9.1
.11
Kern
el D
ensi
ty
0 2 4 6 8 10Vote groups: Wealth score
Non CMs CMs (Leaders)
.04
.06
.08
.1.1
2K
erne
l Den
sity
0 2 4 6 8 10Discussion groups: Log asset distr.
Non CMs CMs (Leaders)
.04
.06
.08
.1.1
2K
erne
l Den
sity
0 2 4 6 8 10Vote groups: Log asset distr.
Non CMs CMs (Leaders)
Note: The top panel shows the wealth score distribution at baseline separately for regular members (Non CMs) andcommittee members (CMs (Leaders)), by treatment. The wealth score is constructed using the Uganda PPI indexcompiled by the Grameen foundation (2011), and the value distribution indicates the position (decile) of anindividual in the wealth score distribution of her savings group. Higher value=less poor. Kernel density plot;Epanechikov Kernel, bandwidth: 2. A Kolmogorov-Smirnov test rejects equality of distributions in discussion (p-value0.01) and cannot reject equality of distributions in vote (p-value 0.8). The bottom panel shows the position of amember in the log asset distribution of her group at baseline separately for regular members (Non CMs) andcommittee members (CMs (Leaders)), by treatment. Kernel density plot; Epanechikov Kernel, bandwidth: 1.7. AKolmogorov-Smirnov test rejects equality of distributions in discussion (p-value 0.015) and cannot reject equality ofdistributions in vote (p-value 0.9).
FIGURES AND TABLES 49
Figure 2.3: Share of initial members staying in group
0.2
.4.6
Mem
bers
hare
sta
ying
2013 2015
VoteDiscussion
Note: Figure 2.3 above shows the share of original members that were stayers in March 2013 and in March 2015,respectively, by treatment. The bars indicate a 95% confidence interval around the mean. In 2013, the differencebetween the staying rate in vote and discussion groups is significant at the 99 percent confidence level (p-value 0.006,regression estimates displayed in Table 2.4). For 2015, the difference between the staying rate in vote and discussiongroups is just below the conventional confidence level with a p-value of 0.11 (Table 2.4).
Note: Figure 2.4 below shows the distribution of group size in 2015 by treatment. Kernel density plot;Epanechikov Kernel, bandwidth 3.5.
Figure 2.4: Group size 2015
0.0
2.0
4.0
6Ke
rnel
Den
sity
0 10 20 30Groupsize 2015
Vote Discussion
50 ELECTORAL RULES AND LEADER SELECTIONTable
2.1:Baseline
variables,treatment
balancecheck
Full
sample
mean
st.dev
Discussion
treatmVote
treatmAdjusted
p-valuemean
(N=46)
mean
(N=46)
Difference
Grou
pC
haracteristics
Average
initialIGFgroup
size(2011)
205.581
20.0219.98
0.0650.954
Initialcommittee
size5.033
0.2755.07
5.000.056
0.375Share
ofCMs2011
thatare
inBaseline
data0.705
0.2260.69
0.72-0.049
0.267Share
ofall2011
mem
bersin
Baseline
data0.843
0.1350.85
0.840.001
0.954G
roup
perform
ance
Characteristics
Shareof
mem
berswho
keptsavings
atbaseline
0.930.164
0.940.92
0.0160.617
Mean
baselinesavings
keptby
groupmem
bers,1000sUGX
12.7517.278
12.7712.73
0.0800.868
Shareof
mem
bersthat
hasany
debt(not
with
BRAC)at
baseline0.101
0.1610.11
0.090.022
0.545Mean
amount
ofloans
outstandingat
baseline,1000sUGX
0.8291.554
1.010.64
1.0280.269
Mem
ber
hou
sehold
characteristics
Mean
householdsize
5.9841.115
5.876.10
-0.2670.259
Mean
wealth
scoreof
household19.865
9.17321.03
18.702.154
0.121Mean
totalvalueof
assetsheld
atbaseline,1000s
UGX
1831.0121194.318
1908.951753.07
500.3030.438
Shareof
householdswho
ownland
0.7230.239
0.720.72
0.0100.832
Shareof
householdswho
ownhouse
0.9020.141
0.890.91
-0.0170.355
Shareof
householdmem
bersyounger
than18
0.5330.083
0.530.54
-0.010.509
Shareof
householdmem
bers(above
9y)who
areworking
0.360.23
0.340.38
-0.0260.317
Shareof
householdmem
bersaged
6-18with
someschool
0.6420.216
0.620.67
-0.0490.170
Shareof
householdmem
bersaged
6-14with
primary
school0.038
0.0450.03
0.04-0.007
0.436M
ember
characteristics
Mean
mem
berage
201118.534
1.63618.63
18.440.056
0.826Share
ofmem
berswho
arethe
main
earnerin
herhousehold
0.2710.203
0.260.28
-0.0250.468
Sharewith
noschooling
0.4260.257
0.460.39
0.0940.049
Sharewith
primary
school0.171
0.1410.16
0.18-0.028
0.332Share
ofmem
berswho
evergot
trainingin
IGA
0.2270.248
0.280.18
0.1030.007
Mean
totalearningsduring
last12
months,1000s
UGX
448.369330.126
477.49419.24
62.7390.320
Shareof
mem
bersever
married
orhad
partner0.836
0.2120.82
0.85-0.03
0.479Mean
number
ofchildren
1.8191.014
1.701.94
-0.3050.090
Mean
ageat
birthof
firstchild
18.2291.448
18.4018.06
0.4560.102
Sharefriends
ingroup
0.1650.058
0.170.16
0.0060.523
Shareof
mem
berswith
migration
experience0.303
0.2330.29
0.32-0.023
0.592
Notes:
Com
mittee
sizedeviated
from5in
fourcases:
One
grouphad
4com
mittee
mem
bers,2groups
had6com
mittee
mem
bersand
onegroup
had7com
mittee
mem
bers.Not
alloriginalgroupmem
bersor
originalcommittee
mem
bersare
representedin
thebaseline
data.Asthe
firstsection
ofthetable
shows,reassuringly,the
sharesinterview
edare
comparable
acrosstreatm
ents.Wealth
scoreis
thescore
obtainedin
anindex
constructedby
theGram
eenFoundation
(2011)measuring
povertyindicators
inUganda.
Higher
value=
lesspoor.
Sharefriends
ingroup:
anorm
alizeddegree
centralitymeasure
basedon
howmany
othermem
bersnam
edher
among
their2best
friendsin
thegroup
atbaseline,
normalized
bythe
number
ofother
mem
bersin
thegroup
atbaseline.C
olumns1-2
reportthe
mean
andstandard
deviationfor
thefull
baselinesam
ple.Colu
mns3-4
reportthe
means
indiscussion
treatment
andvote
treatment,respectively.D
ifferen
ceand
P-valu
esare
fromW
LSregressions
ofeach
characteristic’sgroup
mean
ontreatm
ent,controllingfor
branchfixed
effects,andweighting
bythe
fractionof
initialmem
bersin
thegroup
interviewed
atbaseline.*
p<0.1,**
p<0.05,***
p<0.01.
FIGURES AND TABLES 51
Tab
le2.2:
Differencesbe
tweenlead
ers(com
mitteemem
bers)across
treatm
ents,S
ocio-econo
mic
characteristics(sam
plerestricted
tolead
erson
ly)
Econo
mic
variab
les
Socioecon
omic
Social
conn
ection
prox
ies
Wealthscore
Logassets
Has
some
Employm
Migration
Has
Age
atMajority
Sharefriend
s#
Link
sto
distribu
tion
2011
educ
ation
netw
ork
expe
rience
child
ren
1stbirth
tribe
ingrou
potherCMs
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
(10)
Votetreatm
-0.684∗∗
-0.218
-0.017
-0.063
-0.122∗
0.180∗∗∗
-0.878∗∗∗
0.065
-0.004
0.115
[0.334]
[0.239]
[0.065]
[0.052]
[0.063]
[0.062]
[0.330]
[0.040]
[0.018]
[0.132]
Discussionmean
5.766
13.656
0.660
0.333
0.357
0.671
18.710
0.884
0.200
0.553
Fixed
Effe
cts
bran
chbran
chbran
chbran
chbran
chbran
chbran
chbran
chbran
chbran
chIG
ACon
trol
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
Observation
s312
292
304
323
307
310
225
184
279
323
Adjusted
R2
0.034
0.170
0.087
0.169
0.088
0.109
0.125
0.126
0.094
0.094
Note:
Wealthscoredistribution
indicatesthepo
sition
(decile
)of
anindividu
alin
thewealthscoredistribu
tion
ofhergrou
p.The
scoreis
constructedusingtheUgand
aPPI
indexcompiledby
theGrameenfoun
dation
(2011).Highervalue=
less
poor.Lo
gAssets20
11:The
logged
valueof
assets
atba
selin
e,cond
itiona
lon
having
nonzeroassets.Has
noeducationdu
mmy=
1iftheindividu
alha
sno
scho
oling.
Employmentnetwork:
dummy=
1iftheindividu
alha
din
thepa
styear
received
help
from
anyone
outsidetheclose
family
formatters
ofem
ployment.
Thisis
aproxyforha
ving
labo
rmarketconn
ection
s.Migration
experience:du
mmy=
1iftheindividu
alha
sever
lived
outsidethevilla
ge.
Migration
isdo
neforshorttimeworkor
stud
iesan
dis
positively
correlated
withassets
andwealth.
Has
child
ren:du
mmy=
1iftherespon
dent
hasan
ychild
ren.
Age
at1st
birth:Indicatestheindividu
al’s
agewhenshefirst
gave
birth.
Low
erageindicatesaworse
socio-econ
omic
situation.
Onlyavailableforthosewho
have
child
renMajoritytribe:
dummy=
1iftheindividu
albe
long
sto
themostcommon
tribein
hersaving
sgrou
pSh
arefriend
sin
grou
p:ano
rmalized
degree
centralitymeasure
basedon
how
man
yother
mem
bers
named
heram
ongtheir2be
stfriend
sin
thegrou
pat
baselin
e,no
rmalized
bythenu
mbe
rof
othermem
bers
inthegrou
pat
baselin
e#
Links
toCMs:
thenu
mbe
rof
individu
alsna
med
amon
gher4be
stfriend
sat
baselin
ewho
laterbe
camecommitteemem
bers
(equ
ivalentto
thenu
mbe
rof
CMsin
this
sampleon
lyrestricted
toCMs)6=
therespon
dent.D
iscussionmean:The
meanvalueof
each
outcom
evariab
lein
thediscussion
treatm
ent.IG
ACon
trol:thesharethat
hadreceived
incomegenerating
activity
training
from
BRAC,is
includ
edas
acontrolin
allregression
sto
accoun
tforim
balancein
this
variab
leacross
treatm
ents
atba
selin
e.Rob
uststan
dard
errors
inbrackets,
clusteredat
thegrou
plevel(level
ofrand
omization).Regressions
areweigh
tedforthefraction
ofinitialcommitteemem
bers
interviewed
atba
selin
e,an
dinclud
ebran
chfix
edeff
ects.*p<
0.1,
**p<
0.05,***p<
0.01.
52 ELECTORAL RULES AND LEADER SELECTION
Table
2.3:Baseline
characteristicsof
leaders(com
mittee
mem
bers)com
paredto
othergroup
mem
bers(sam
ple:alloldmem
bers)
Econom
icvariables
Socioeconom
icSocialconnection
proxies
Wealth
LogAssets
Has
some
Empl
Migration
Has
Age
atMajority
Sharestrong
ShareCMs
score2011
educationNetw
orkexperience
children1st
birthtribe
linkslinked
to(1)
(2)(3)
(4)(5)
(6)(7)
(8)(9)
(10)
Leader3.940 ∗∗∗
0.1580.122 ∗∗∗
0.129 ∗∗∗0.057 ∗
0.132 ∗∗∗-0.012
-0.0350.044 ∗∗∗
0.030[1.244]
[0.141][0.040]
[0.031][0.034]
[0.043][0.408]
[0.024][0.015]
[0.026]Vote*Leader
-2.946 ∗-0.195
-0.080-0.123 ∗∗∗
-0.179 ∗∗∗0.063
-0.5480.097 ∗∗
-0.0040.066
[1.579][0.192]
[0.064][0.045]
[0.052][0.060]
[0.482][0.038]
[0.018][0.040]
Leader+Vote*Leader
0.994-0.037
0.0430.005
-0.122 ∗∗∗0.195 ∗∗∗
-0.561 ∗∗0.062 ∗
0.040 ∗∗∗0.096 ∗∗∗
[0.973][0.131]
[0.049][0.032]
[0.039][0.042]
[0.256][0.030]
[0.010][0.031]
Discussion
mean
19.50913.485
0.4870.279
0.2830.531
18.5570.542
0.1350.109
Fixed
effectsgroup
groupgroup
groupgroup
groupgroup
groupgroup
groupObservations
14491351
13941483
13701408
8401483
11071483
Adjusted
R2
0.3630.269
0.2100.330
0.2220.225
0.0780.690
0.1950.211
Note:
Wealth
scoreindicates
theposition
(decile)of
anindividualin
thewealth
scoredistribution
ofher
group.The
scoreisconstructed
usingthe
Uganda
PPIindex
compiled
bythe
Gram
eenfoundation
(2011).Higher
value=less
poor.Log
Assets
2011The
loggedvalue
ofassets
atbaseline,
conditionalon
havingnonzero
assets.Has
noeduc.:
dummy=
1ifthe
individualhas
noschooling.
Employm
entnetw
ork:dum
my=
1ifthe
individualhad
inthe
pastyear
receivedhelp
fromanyone
outsidethe
closefam
ilyfor
matters
ofem
ployment.
This
isaproxy
forhaving
labormarket
connections.Migration
experience:dum
my=
1ifthe
individualhas
everlived
outsidethe
village.Migration
isdone
forshort
timework
orstudies
andispositively
correlatedwith
assetsand
wealth.
Has
children:dum
my=
1ifthe
respondenthas
anychildren.
Age
at1st
birth:Indicates
individual’sage
when
shefirst
gavebirth.
Alow
erage
indicatesaworse
socio-economic
situation.Only
availablefor
thosewho
havechildren.
Majority
tribe:dum
my=
1if
theindividual
belongsto
themost
common
tribein
hersavings
group,Share
friendsin
group:anorm
alizeddegree
centralitymeasure
basedon
howmany
othermem
bersnam
edher
among
their2best
friendsin
thegroup
atbaseline,
normalized
bynum
berof
othermem
bersin
groupat
baselineShare
CMslinked
to:the
number
ofindividuals
named
among
her4best
friendsat
baselinewho
laterbecam
ecom
mittee
mem
bers,divided
bythe
number
ofcom
mittee
mem
bers6=
therespondent.
Discussion
mean
:Mean
valueam
ongnon-leaders
inthe
discussiontreatm
ent.Robust
standarderrors
inbrackets,
clusteredat
thegroup
level(level
ofrandom
ization).Regressions
areweighted
forthe
fractionof
initialcom
mittee
mem
bersinterview
edin
baseline,and
includebranch
fixedeffects.
*p<0.1,
**p<0.05,
***p<0.01.
FIGURES AND TABLES 53
Table 2.4: Dropout over time, all old members
Dep. var Dropout 2013 Dropout 2015(1) (2) (3) (4)
Vote treatm -0.149∗∗∗ -0.143∗∗∗ -0.062∗∗∗ -0.064[0.025] [0.051] [0.022] [0.040]
Discussion mean 0.607 0.607 0.727 0.727Fixed effects none branch none branchObservations 1624 1624 1811 1811Adjusted R2 0.022 0.232 0.004 0.190
Note: The dependent variable is an indicator of having dropped out by2013 (columns 1-2) or 2015 (columns 3-4). Columns (1) and (3): OLS re-gression with robust standard errors. Columns (2) and (4): OLS regressionwith robust standard errors clustered at group level, and controlling forbranch fixed effects. p-value for dropout in 2015 is 0.11. The fewer ob-servations in 2013 compared to 2015 are due to the followup data beingcollected using an incomplete list of original members. 2013. Results arerobust to imputing 2015-dropout-status for the 187 members missing in2013. * p<0.1, ** p<0.05, *** p<0.01
54 ELECTORAL RULES AND LEADER SELECTION
Table
2.5:Dropout
by2013
relatedto
economic
variablesat
baseline,alloldmem
bers,extensivemargin
Poverty
proxy:Poor
(bottom25%
Poor
(bottom25%
Has
nomarket
Has
nosavings
Has
noloans
ofwealth
distr.)of
assetdistr.)
incomein
2011in
2011in
2011(1)
(2)(3)
(4)(5)
Characteristic
0.071 ∗∗0.046
0.103 ∗∗0.194 ∗∗
0.245 ∗∗[0.034]
[0.028][0.047]
[0.054][0.086]
Vote*characteristic
-0.065-0.069 ∗
-0.153 ∗∗-0.187 ∗
-0.133[0.047]
[0.040][0.071]
[0.098][0.123]
Vote
-0.103 ∗-0.113 ∗∗
-0.093-0.115 ∗
-0.019[0.059]
[0.056][0.061]
[0.059][0.129]
Discussion
mean
0.5770.582
0.5650.581
0.458P-value
Disc.=
Vote
forChar=
10.011
0.0040.002
0.0020.008
P-value
Char0
=Char1
inVote
0.8630.437
0.3420.940
0.170Fixed
Effects
branchbranch
branchbranch
branchIG
AControl
yesyes
yesyes
yesObservations
1,4061,312
1,3671,356
1,273Adjusted
R2
0.2220.247
0.2330.233
0.237
Note:
The
dependentvariable
inall
regressionsis
anindicator
ofhaving
droppedout
by2013.
The
independentvariables
aredum
mies
proxyingfor
povertyin
differentways.
The
2first
columns
indicatebeing
inthe
bottom25%
ofthe
wealth
distributionor
assetdistribution,
respectively,of
one’sgroup.
Has
nomarket
incomeindicates
beingeither
asubsistence
farmer
oradependent
atbaseline.
Has
nosavings
indicatesnot
savingat
thetim
eof
thebaseline
surveyand
Has
noloans
indicatesnot
havingtaken
loansfrom
otherlenders
thanBRAC
(atthe
timeof
baseline,theBRAC
groupshad
notstarted
givingout
loans).The
resultsare
robustto
reducingthe
sample
onlyto
individualswith
non-missing
valuesfor
theasset,
wealth
score,incom
e,savings
andloans
indicatorvariables.D
iscussionmean
:Shareofdropouts
inthe
discussiontreatm
entfor
mem
berswith
independentvariable
=0.p-value
Discussion
=Vote
forChar=
1:pvalue
fromatest
ofwhether
dropoutrate
isthe
sameacross
treatments
forindividuals
with
povertycharacteristic=
1.p-value
Char0
=Char1
inVote
isfrom
atest
ofwhether
dropoutsare
similar
(interm
sofeach
povertycharacteristic)
tostayers
inthe
votetreatm
ent.IGA
Control:the
sharethat
hadreceived
incomegenerating
activitytraining
fromBRAC,is
includedas
acontrolin
allregressionsto
accountfor
imbalance
inthis
variableacross
treatments
atbaseline.
Robust
standarderrors
inbrackets,
clusteredat
thegroup
level(level
ofrandom
ization).Regressions
includebaseline
sample
weights,
andbranch
fixedeffects.
*p<0.1,
**p<0.05,
***p<0.01.
FIGURES AND TABLES 55
Table 2.6: Savings in 2015, extensive and intensive margin, all old members
Sample: All Stayers Dropouts
Dep.var.: Extensive Intensive Extensive Intensive Extensive Intensive Extensive IntensiveAnywhere Anywhere In BRAC group Anywhere
(1) (2) (3) (4) (5) (6) (7) (8)
Vote treatm -0.035 -0.127 -0.090∗ -0.206 -0.110∗ -0.114 -0.012 -0.088[0.050] [0.153] [0.050] [0.202] [0.061] [0.188] [0.059] [0.244]
Discussion mean 0.593 10.962 0.831 10.794 0.757 10.398 0.360 11.357Fixed effects branch branch branch branch branch branch branch branchObservations 878 421 436 302 435 267 442 119Adjusted R2 0.034 0.106 0.129 0.134 0.173 0.144 0.020 0.072
Note: The dependent variable in columns 1 and 3 and 7 is an indicator for having Savings, anywhere, in 2015. The depen-dent variable in column 5 is an indicator for having Savings in the BRAC group in 2015, and is only available for stayers. Thedependent variable in columns 2 and 4 and 8 is log Savings in 2015 kept anywhere, conditional on having nonzero savings. Thedependent variable in column 6 is log Savings in 2015 in the BRAC group, conditional on having nonzero savings, and is onlyavailable for stayers. The mean value of the dependent variable for the discussion treatment. is displayed below the table.Standard errors in brackets are clustered at the group level (level of randomization). Regressions include endline sample weights,and branch fixed effects. * p<0.1, ** p<0.05, *** p<0.01.
Table 2.7: Loans in BRAC group by 2015, All old members; stayers and dropouts
Sample: All Stayers Dropouts
Dep.var.: Extensive Intensive # Loans Extensive Intensive # Loans Extensive Intensive # Loans(1) (2) (3) (4) (5) (6) (7) (8) (9)
Vote group -0.047 -0.035 -0.066 -0.083 -0.210 -0.202 -0.039 0.175 -0.021[0.048] [0.138] [0.098] [0.062] [0.182] [0.161] [0.052] [0.180] [0.095]
Discuss. mean 0.407 11.346 0.717 0.553 11.425 1.014 0.272 11.224 0.438Fixed Effects branch branch branch branch branch branch branch branch branchObservations 896 274 895 437 175 436 459 99 459Adjusted R2 0.094 0.125 0.112 0.134 0.163 0.146 0.062 0.138 0.078
Note: Loans Extensive margin: a dummy=1 if the individual has ever taken a loan from the BRAC saving group by 2015).# Loans: The number of loans taken with BRAC group by 2015, including zero loans. Loans Intensive margin: log value ofloans taken with BRAC group by 2015 conditional on having taken a loan. The mean value of the dependent variable for thediscussion treatment. is displayed below the table. Standard errors in brackets are clustered at the group level (level of rand-omization). Regressions include endline sample weights, and all regressions include branch fixed effects.* p<0.1, ** p<0.05, *** p<0.01.
56 ELECTORAL RULES AND LEADER SELECTION
Table
2.8:Savingsand
loansin
2015;New
mem
bersonly
Dep.var.:
SavingsLoans
Extensive
margin
Intensivemargin
Extensive
margin
#Loans
Intensivemargin
Anyw
hereBRAC
Anyw
hereBRAC
Anyw
hereBRAC
BRAC
BRAC
(1)(2)
(3)(4)
(5)(6)
(7)(8)
Vote
treatm-0.060 ∗∗
-0.0140.309 ∗
0.309-0.017
-0.018-0.273 ∗
-0.051[0.028]
[0.044][0.177]
[0.210][0.045]
[0.057][0.152]
[0.133]
Discuss.m
ean0.945
0.86910.896
10.4340705
0.6231.285
11.663Fixed
Effects
branchbranch
branchbranch
branchbranch
branchbranch
Observations
383383
240240
401401
401181
Adjusted
R2
0.1230.156
0.2120.269
0.1770.216
0.1260.160
Note:
SavingExtensive
margin
:adum
my=
1ifthe
individualhas
savingsin
2015,anyw
here(colum
n1)
orwith
BRAC
(column2).
SavingsIntensive
margin
:log
valueof
savingsin
2015,anyw
here(colum
n3)
orwith
BRAC
(column4),
conditionalon
havingnonzero
savings.Loans
Extensive
margin
:adum
my=
1ifthe
individualhas
evertaken
aloan
by2015,
anywhere
(column5)
orwith
BRAC
(column6).#
LoansThe
number
ofloans
takenwith
BRAC
by2015,
includingzero
loans.Loans
Intensivemargin
:log
valueof
loanstaken
with
BRAC
by2015
conditionalon
havingtaken
aloan.
Wedo
nothave
surveyinform
ationabout
theam
ountof
loansnor
ofthe
number
ofloans
takentaken
outsideof
theBRAC
group.The
mean
valueof
thedependent
variablefor
thediscussion
treatment.
isdisplayed
belowthe
table.Standard
errorsin
bracketsare
clusteredat
thegroup
level(level
ofrandom
ization).Regressions
includeendline
sample
weights,
andall
regressionsinclude
branchfixed
effects.*p<0.1,
**p<0.05,
***p<0.01.
FIGURES AND TABLES 57
Tab
le2.9:
Loan
allocation
amon
gstayers,
measuredin
2015
Dep.v
ar.:
Loan
dummy
#Lo
ansgiven
Total
logloan
amou
ntPoverty
proxy:
Poo
rNoincome
Noloan
Poo
rNoincome
Noloan
Poo
rNoincome
Noloan
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
Cha
racteristic*Vote
0.207∗
0.060
0.296∗
0.411∗
0.013
0.550
1.915
1.423
3.792∗
[0.116]
[0.123]
[0.151]
[0.216]
[0.303]
[0.416]
[1.373]
[1.526]
[1.967]
Cha
racteristic
-0.093
-0.040
-0.272∗∗
-0.351∗∗
-0.038
-0.639∗∗
-1.018
-0.796
-3.003∗
[0.091]
[0.086]
[0.121]
[0.176]
[0.263]
[0.308]
[1.098]
[1.149]
[1.630]
Vote
-0.133∗
-0.111
-0.343∗∗
-0.306∗
-0.297∗
-0.729∗
-1.372∗
-1.449
-4.353∗∗
[0.067]
[0.074]
[0.143]
[0.166]
[0.169]
[0.395]
[0.809]
[0.913]
[1.893]
Discussionmean
0.564
0.576
0.650
1.053
1.104
1.350
5.630
5.731
6.827
Fixed
Effe
cts
bran
chbran
chbran
chbran
chbran
chbran
chbran
chbran
chbran
chObservation
s359
347
311
358
346
310
310
297
267
AdjustedR
20.131
0.132
0.189
0.136
0.142
0.191
0.129
0.131
0.178
Note:
Loan
dummy=
1ifthemem
berha
sever
takenaloan
intheBRAC
IGF
grou
p#
Loan
sgiven:Num
berof
loan
sever
takenfrom
theIG
Fgrou
pTotal
loan
amou
nt:T
helogam
ount
oftheloan
staken,
totalm
argin.
Discussionmean:M
eanvalueof
thedepe
ndentvariab
leam
ongmem
bers
inthediscussion
grou
pswithpo
vertyprox
y=0.
Stan
dard
errors
inbrackets
areclusteredat
grou
plevel.*p<
0.1,
**p<
0.05,***p<
0.01
58 ELECTORAL RULES AND LEADER SELECTION
Table 2.10: Default on loans, reported in 2015
Dep. var.: Dummy=1 if ever defaulted on IGF loan
Sample: All old members Stayers only
(1) (2) (3) (4)
Vote 0.0140 0.014 0.004 0.024[0.0408] [0.056] [0.051] [0.071]
Poor -0.069 -0.059[0.069] [0.121]
Poor*Vote 0.048 0.041[0.094] [0.149]
Discussion mean 0.102 0.102 0.111 0.111Fixed Effects branch branch branch branchObservations 354 278 249 204Adjusted R2 0.002 0.017 0.048 0.0739
Note: Note: Default loan: A dummy =1 if the member has ever defaultedon a loan taken in the group. The sample is restricted to members whohave ever taken a loan from the group. Discussion mean: Mean value ofthe dependent variable among non-poor members in the discussion groups.Standard errors in brackets are clustered at the group level (level of ran-domization). Regressions include endline sample weights, and all regres-sions include branch fixed effects. * p<0.1, ** p<0.05, *** p<0.01.
Table 2.11: Savings and loans in 2015 for dropouts compared to staying members
Dep var: Loan extensive margin Saving extensive margin Saving intensive margin
(1) (2) (3) (4) (5) (6)
Dropout -0.189∗∗∗ -0.207∗∗∗ -0.429∗∗∗ -0.466∗∗∗ 0.395∗∗ 0.338[0.035] [0.051] [0.045] [0.066] [0.186] [0.212]
Vote -0.055 -0.079 -0.160[0.054] [0.055] [0.202]
Dropout*Vote 0.034 0.073 0.106[0.069] [0.088] [0.339]
Discussion mean 0.648 0.659 0.808 0.831 10.773 10.794Fixed Effects branch branch branch branch branch branchObservations 896 896 878 878 421 421Adjusted R2 0.136 0.135 0.209 0.210 0.121 0.119
Note: Dropout is a dummy=1 if the respondent has dropped out, 0 if the respondent is still a member of theBRAC saving group Extensive margin: a dummy=1 if the individual has savings (has taken loan) anywhere in2015. Intensive margin: log value of savings in 2015, conditional on having nonzero savings. We do not have surveyinformation about the amount of loans nor of the number of loans taken taken outside of the BRAC group. The meanvalue of the dependent variable for stayers is displayed below the table for columns 1, 3 and 5. Discussion mean:The mean value of the dependent variable for stayers in the discussion treatment is displayed below the table forcolumns 2, 4 and 6. Standard errors in brackets are clustered at the group level (level of randomization). Regressionsinclude endline sample weights, and all regressions include branch fixed effects. * p<0.1, ** p<0.05, *** p<0.01.
APPENDIX 1 59
Appendix 1
60 ELECTORAL RULES AND LEADER SELECTION
Table A.1: Differences between leaders (committee members) across treatments, alternative economicvariables
Wealth score raw Asset score Income score Log income 2011(1) (2) (3) (4)
Vote -3.856∗ -0.438 -0.526 -0.385∗[2.118] [0.331] [0.402] [0.227]
IGA Control 5.418 1.724∗ -1.331 0.265[4.214] [0.903] [1.085] [0.539]
Fixed Effects branch branch branch branchObservations 312 302 304 232Adjusted R2 0.240 0.016 0.009 0.228
Note:Wealth score raw is compiled at the household level using the Uganda Progress out of povertyindex constructed by the Grameen foundation (2011). Contains variables measuring the household’s(inverse) poverty status. Higher value=less poor. Asset (income) score indicates the position (decile)of an individual in the asset(income) distribution of her group at baseline. Log income 2011 is the logvalue of income at baseline conditional on having nonzero income. Robust standard errors in brackets,clustered at the group level (level of randomization). IGA Control : Control for the share that hadreceived income generating activity training from BRAC, included as control in all regressions toaccount for imbalance in this variable across treatments at baseline. and include sample weights forthe fraction of initial committee members interviewed in baseline, and branch fixed effects. * p<0.1,** p<0.05, *** p<0.01.
APPENDIX 1 61
Tab
leA.2:B
aselinelead
ercharacteristicscompa
redto
regu
larmem
bers,c
ontrollin
gforindividu
alba
selin
ewealthscore(sam
ple:
allo
ldmem
bers)
Econo
mic
variab
les
Socioecon
omic
Social
conn
ection
proxies
LogAssets
Has
some
Empl
Migration
Has
Age
atMajority
Sharestrong
ShareCMs
2011
education
Network
expe
rien
cechild
ren
1stbirth
tribe
links
linkedto
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
Lead
er0.113
0.077∗
0.123∗∗∗
0.036
0.136∗∗∗
-0.127
-0.024
0.044∗∗∗
0.032
[0.141]
[0.044]
[0.032]
[0.032]
[0.043]
[0.428]
[0.026]
[0.017]
[0.026]
Vote*Le
ader
-0.157
-0.046
-0.120∗∗
-0.152∗∗∗
0.069
-0.451
0.099∗∗
0.002
0.065
[0.196]
[0.065]
[0.047]
[0.050]
[0.058]
[0.489]
[0.039]
[0.019]
[0.040]
Wealthscore2011
0.008∗∗
0.010∗∗∗
0.001
0.005∗∗∗
-0.002
0.028∗∗
-0.002∗∗
0.001∗
-0.000
[0.004]
[0.001]
[0.001]
[0.001]
[0.001]
[0.011]
[0.001]
[0.000]
[0.001]
Lead
er+Vote*Le
ader
-0.044
0.031
0.002
-0.116∗∗∗
0.206∗∗∗
0.579∗∗
0.075∗
0.047∗∗∗
0.097∗∗∗
[0.134]
[0.049]
[0.032]
[0.039]
[0.038]
[0.239]
[0.029]
[0.010]
[0.031]
Discussionmean
13.485
0.487
0.279
0.283
0.531
18.557
0.542
0.135
0.109
Fixed
Effe
cts
grou
pgrou
pgrou
pgrou
pgrou
pgrou
pgrou
pgrou
pgrou
pObservation
s1323
1365
1449
1338
1379
824
1449
1008
1449
Adjusted
R2
0.268
0.250
0.330
0.232
0.230
0.086
0.697
0.184
0.213
Note:
Wealthscoreindicatesthepo
sition
(decile
)of
anindividu
alin
thewealthscoredistribu
tion
ofhergrou
p.The
scoreis
constructedusingtheUgand
aPPI
indexcompiledby
theGrameenfoun
dation
(2011).H
ighervalue=
less
poor.L
ogAssets20
11The
logged
valueof
assets
atba
selin
e,cond
itiona
lonha
ving
nonzero
assets.H
asno
educ.:du
mmy=
1ifindividu
alha
sno
scho
oling.
Employmentnetwork:
dummy=
1ifindividu
alha
din
thepa
styear
gotten
help
from
anyo
neou
tside
closefamily
formatters
ofem
ployment.Thisisaproxyforha
ving
labo
rmarketconn
ection
s.Migration
experience:du
mmy=
1ifindividu
alha
sever
lived
outside
thevilla
ge.Migration
isdo
neforshorttimeworkor
stud
iesan
dis
positively
correlated
withassets
andwealth.
Has
child
ren:du
mmy=
1iftherespon
dent
has
anychild
ren.
Age
at1stbirth:Indicatesindividu
al’s
agewhe
nshefirst
gave
birth.
Low
erageindicatesworse
socio-econ
omic
situation.
Onlyavailableforthose
who
have
child
renMajoritytribe:
dummy=
1iftheindividu
albe
long
sto
themostcommon
tribein
hersaving
grou
p,Sh
arefriend
sin
grou
p:ano
rmalized
degree
centralitymeasure
basedon
how
man
yothermem
bers
named
heram
ongtheir2be
stfriend
sin
thegrou
pat
baselin
e,no
rmalized
bynu
mbe
rof
othermem
bers
ingrou
pat
baselin
eSh
areCMslin
kedto:thenu
mbe
rof
individu
alsna
med
amon
gher4be
stfriend
sat
baselin
ewho
laterbe
camecommitteemem
bers,divided
bythenu
mbe
rof
committeemem
bers6=
therespon
dent.Discussionmean:Meanvalueam
ongno
n-lead
ersin
thediscussion
treatm
ent.
IGA
Con
trol:Con
trol
forthesharethat
hadreceived
incomegenerating
activity
training
from
BRAC,includ
edas
controlin
allregression
sto
accoun
tforim
balancein
this
variab
leacross
treatm
ents
atba
selin
e.Rob
uststan
dard
errors
inbrackets,clusteredat
thegrou
plevel(level
ofrand
omization).Regressions
areweigh
tedforthefraction
ofinitialcommitteemem
bers
interviewed
inba
selin
e,an
dinclud
ebran
chfix
edeff
ects.*p<
0.1,
**p<
0.05,***p<
0.01.
62 ELECTORAL RULES AND LEADER SELECTION
Table
A.3:D
ropoutby
2015related
toeconom
icvariables
atbaseline,allold
mem
bers,extensivemargin
Poor
(bottom25%
Poor
(bottom25%
Has
nomarket
Has
nosavings
Has
noloans
ofwealth
distr.)of
assetdistr.)
incomein
2011in
2011in
2011(1)
(2)(3)
(4)(5)
Characteristic
0.0120.064
0.082 ∗∗0.102
0.057[0.033]
[0.040][0.041]
[0.115][0.058]
Vote*characteristic
-0.076-0.082 ∗
-0.0230.005
-0.040[0.048]
[0.049][0.059]
[0.127][0.083]
Vote
-0.040-0.036
-0.049-0.071
-0.052[0.046]
[0.046][0.051]
[0.044][0.092]
Discussion
mean
0.7180.705
0.6860.714
0.663p-value
Discussion
=Vote
forChar=
10.045
0.0390.192
0.6000.044
p-valueChar0
=Char1
inVote
0.0740.492
0.1490.037
0.769Fixed
Effects
branchbranch
branchbranch
branchIG
AControl
yesyes
yesyes
yesObservations
1,4491,351
1,4101,396
1,310Adjusted
R2
0.2310.231
0.2300.238
0.236
Note:
The
dependentvariable
inallregressions
isan
indicatorof
havingdropped
outby
2015.The
independentvariables
aredum
mies
proxyingfor
povertyin
differentways.T
he2first
columns
indicatebeing
inthe
bottom25%
ofthe
wealth
distributionor
assetdistribution,respectively,of
one’sgroup.H
asno
market
incomeindicates
beingeither
asubsistence
farmer
oradependent
atbaseline.
Has
nosavings
indicatesnot
savingat
thetim
eof
thebaseline
surveyand
Has
noloans
indicatesnot
havingtaken
loansfrom
otherlenders
thanBRAC
(atthe
timeof
baseline,the
BRAC
groupshad
notstarted
givingout
loans).The
resultsare
robustto
reducingthe
sample
onlyto
individualswith
non-missing
valuesfor
theasset,
wealth
score,incom
e,savings
andloans
indicatorvariables.
Discussion
mean
:Share
ofdropouts
inthe
discussiontreatm
entfor
mem
berswith
independentvariable=
0.p-value
Discussion
=Vote
forChar=
1is
fromatest
ofwhether
dropoutrate
isthe
sameacross
treatments
forindividuals
with
povertycharacteristic=
1.p-value
Char0
=Char1
inVote
isfrom
atest
ofwhether
dropoutsare
similar
(interm
sof
eachpoverty
characteristic)to
stayersin
thevote
treatment.
IGA
Control:
theshare
thathad
receivedincom
egenerating
activitytraining
fromBRAC,is
includedas
acontrolin
allregressionsto
accountfor
imbalance
inthis
variableacross
treatments
atbaseline.R
obuststandard
errorsin
brackets,clusteredat
thegroup
level(levelofrandom
ization).Regressions
includebaseline
sample
weights,and
branchfixed
effects.*p<0.1,
**p<0.05,
***p<0.01.
APPENDIX 1 63
Table A.4: Loans anywhere by 2015, extensive margin, all old members
All Stayers Dropouts(1) (2) (3)
Vote -0.027 -0.062 -0.018[0.047] [0.061] [0.051]
log assets2011 0.001 -0.009 0.011[0.007] [0.007] [0.009]
Fixed Effects branch branch branchObservations 691 337 354Adjusted R2 0.118 0.127 0.098
Note: Dependent variable is a dummy for having taken aloan anywhere by the year 2015. Standard errors in brack-ets are clustered at group level. * p<0.1, ** p<0.05, ***p<0.01
64 ELECTORAL RULES AND LEADER SELECTION
Table
A.5:Loan
allocationam
ongallold
mem
bers,measured
in2015
Dep.var.:
Loandum
my
#Loans
givenTotallog
loanam
ountPoverty
proxy:Poor
Noincom
eNoloan
Poor
Noincom
eNoloan
Poor
Noincom
eNoloan
(1)(2)
(3)(4)
(5)(6)
(7)(8)
(9)
Characteristic*V
ote0.175 ∗∗
0.0330.180 ∗∗
0.347 ∗∗0.119
0.481 ∗∗1.667 ∗∗
0.4302.352 ∗∗
[0.069][0.083]
[0.087][0.147]
[0.204][0.204]
[0.694][0.984]
[1.067]Characteristic
-0.097 ∗0.016
-0.205 ∗∗∗-0.223 ∗∗
-0.008-0.507 ∗∗∗
-1.097 ∗∗0.148
-2.424 ∗∗[0.051]
[0.058][0.070]
[0.092][0.149]
[0.164][0.497]
[0.722][0.925]
Vote
-0.094 ∗-0.055
-0.198 ∗∗-0.167 ∗
-0.118-0.497 ∗∗
-0.915-0.572
-2.555 ∗∗[0.051]
[0.057][0.098]
[0.100][0.104]
[0.209][0.559]
[0.616][1.208]
Discussion
mean
0.4230.406
0.5220.766
0.7591.087
4.1783.887
5.434Fixed
effectsbranch
branchbranch
branchbranch
branchbranch
branchbranch
Observations
714701
639713
700638
650636
582Adjusted
R2
0.1190.121
0.1350.125
0.1320.145
0.1090.110
0.122
Note:
Loandum
my=
1ifthe
mem
berhas
evertaken
aloan
inthe
BRAC
IGFgroup
#Loans
given:N
umber
ofloans
evertaken
fromthe
IGFgroup
Total
loanam
ount:The
logam
ountof
loanstaken,
totalmargin.
Discussion
mean
:Mean
valueof
thedependent
variableam
ongmem
bersin
thediscussion
groupswith
povertyproxy=
0.Standard
errorsin
bracketsare
clusteredat
grouplevel.
*p<0.1,
**p<0.05,
***p<0.01
APPENDIX 1 65
Tab
leA.6:W
elfare
effects
Dep.v
ar.:
Has
loan
s2015
Has
saving
s2015
Savedam
t.2015
Poverty
proxy:
Poo
rNoincome
Noloan
sPoo
rNoincome
Noloan
sPoo
rNoincome
Noloan
s(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
Cha
racteristic*Vote
0.153∗∗
0.070
0.137
0.008
-0.018
-0.002
-0.444
0.693∗∗
0.688∗
[0.074]
[0.085]
[0.098]
[0.082]
[0.110]
[0.089]
[0.284]
[0.318]
[0.384]
Cha
racteristic
-0.107∗∗
-0.032
-0.158∗∗
-0.078
-0.052
0.015
0.014
-0.370
-0.674∗∗∗
[0.053]
[0.056]
[0.067]
[0.061]
[0.070]
[0.071]
[0.213]
[0.241]
[0.253]
Vote
-0.077∗
-0.046
-0.150
-0.038
-0.027
0.001
0.037
-0.222
-0.692∗
[0.045]
[0.053]
[0.091]
[0.059]
[0.063]
[0.075]
[0.200]
[0.203]
[0.358]
Cha
racteristic+
[Cha
r.*V
ote]
0.046
0.039
-0.021
-0.069
-0.071
0.013
-0.431
0.322
0.014
[0.053]
[0.065]
[0.077]
[0.055]
[0.087]
[0.075]
[0.190]
[0.233]
[0.355]
Discussionmean
0.557
0.544
0.652
0.588
0.576
0.556
10.91
10.92
11.17
Fixed
Effe
cts
bran
chbran
chbran
chbran
chbran
chbran
chbran
chbran
chbran
chObservation
s714
701
639
699
686
627
327
319
286
Adjusted
R2
0.112
0.105
0.130
0.043
0.042
0.049
0.123
0.122
0.130
Note:
The
depe
ndentvariab
lein
columns
1-3is
anindicatorof
having
loan
s,withBRAC
oran
yotherlend
er,in
2015,while
incolumns
4-6thedepe
ndent
variab
leis
anindicatorof
having
saving
s,withBRAC
oran
ywhere
else,in
2015
andthedepe
ndentvariab
lein
columns
7-9is
thelogtotalUGX
valueof
all
saving
sheld
(any
whe
re)in
2015.The
indepe
ndentvariab
lesaredu
mmiesprox
ying
forpo
vertyin
diffe
rent
way
s.Poor(C
olum
ns1,
4an
d7)
indicatesbe
ingin
thebo
ttom
25%
ofthewealthdistribu
tion
ofon
e’sgrou
p.Noincome(colum
ns2,
5an
d8)
indicatesha
ving
nomarketincome,
i.e.be
ingeither
asubsistence
farm
eror
adepe
ndentat
baselin
e.Noloan
s(colum
ns3,
6an
d9)
indicatesno
tha
ving
takenloan
sfrom
otherlend
ersthan
BRAC
atba
selin
e(atthetimeof
baselin
e,theBRAC
grou
psha
dno
tstartedgiving
outloan
s).Discussionmean:Meanvalueof
therelevant
outcom
evariab
lein
thediscussion
treatm
entfor
mem
bers
withindepe
ndentvariab
le=0.
Regressions
includ
eendlinesampleweigh
ts,an
dbran
chfix
edeff
ects.*p<
0.1,
**p<
0.05,***p<
0.01.
66 ELECTORAL RULES AND LEADER SELECTION
Table A.7: Sampling and attrition rates, endline survey 2015
# in census 2015 # sampled Share sampled # responded response rate(Share sampled)
Stayers discussion 247 246 1.00 206 0.84Stayers vote 304 304 1.00 237 0.78Leavers discussion 657 270 0.41 225 0.83Leavers vote 603 248 0.41 228 0,92New discussion 518 221 0.43 208 0.94New vote 441 187 0.42 194 1.04
Total Observ. 2770 1476 0.53 1298 0.88Note: Data was collected using lists of sampled individuals, divided by their member type (stayer, dropout or new member).We sampled all stayers but only about 40% of dropouts and new members respectively. If a dropout (a new member) couldnot be interviewed, the data collectors were instructed to interview the first person in the same branch in a correspondingreplacement list for dropouts (new members). The replacement respondent was often a dropout (new member) in a nearbysaving group but not necessarily in the same group, and was not separated by treatment. This explains why share of newmembers in vote groups exceeds 1, new members in discussion groups that were not located have been replaced by newmembers in vote groups.
Table A.8: Group size 2015: stayers and new members active at census
All groups All groups Active >5 members(1) (2) (3) (4)
Vote treatm 0.994 0.994 1.184 2.550[1.104] [1.102] [1.185] [1.567]
Group size 2011 0.141 0.109 -0.0278[0.124] [0.134] [0.188]
Fixed Effects branch branch branch branchObservations 92 92 84 56Adjusted R2 0.511 0.512 0.452 0.260
Note: Dependent variable in all regressions is Group size at census 2015. Groupsize is computed as the sum of stayers and new members. Robust standard errorsin brackets. * p<0.1, ** p<0.05, *** p<0.01
APPENDIX 2: VARIABLE CONSTRUCTION DETAILS 67
Appendix 2: Variable construction details
Baseline variables
A few baseline variables central in the analysis require some additional explanation.
The wealth score is constructed using the Uganda PPI (Progress out of poverty) score
table (Grameen foundation, 2011, 2015). This is an index combining variables that that
measure the (inverse) poverty level of a household. These variables include measures of
the building materials of the main house of the household, the main source of lighting
used by the household, the education level of the young household members and of
its female household head and basic asset holdings (shoes and clothes of all household
members). The baseline survey did not contain questions about the full set of variables
required to compute the PPI score and we have therefore not converted the score into
likelihood of being below a certain poverty line, which is the ultimate purpose of the
PPI. Instead the score is used in its raw format to compare individual members’ wealth
status. We also construct the wealth score distribution in the group by decile, and use
the member’s position in this distribution as an outcome variable in some regressions.
Assets holdings of the household is constructed using answers to a roster listing a
number of household assets For each listed asset the member reported the quantity
of such assets owned by her household, and assessed their value. Asset value can in
principle be zero if the household owns none of the listed assets, but this is rarely
observed (4.32% of the members at baseline report a zero asset value) . Income of the
member is defined as income from market activities during the past 12 months and is
constructed using answers to a roster listing a number of possible income generating
activities. If a respondent reports having worked in an activity during the past 12
months, she is asked follow up questions including how much she earned in this activity
during the past 12 months. Income can be zero if a person only has non market or in
kind income (i.e. if she is a subsistence farmer/cattle keeper) or if she is not active
on the labor market. 23.33% of the members at baseline report a zero income. To
construct social network variables we make use of a question in the baseline survey
68 ELECTORAL RULES AND LEADER SELECTION
where the member is asked to list her four best friends in the group ("if she had to
choose 4". Using this information we construct two types of network variables for each
member: (i) variables measuring the relative degree centrality of the member, i.e. how
many other members mentioned her among their two or four best friends, normalized
by the number of members in the group interviewed at baseline and (ii) a variable
measuring how many of those who later became committee members that were listed
among a member’s four best friends.
Chapter 3
Preparing for Genocide: Community
Meetings in Rwanda∗
3.1 Introduction
In many civil wars and conflicts, ordinary seemingly unorganized civilians participate
in violence. For example, during the Rwandan Genocide in 1994, around 430,000 Hutu
civilians joined the army and militiamen in killing an estimated 800,000 Tutsis and
"moderate" Hutus in only 100 days.1 Civilian participation in violence often magni-
fies and escalates a given conflict with disastrous effects on the social fabric and the
economy, let alone the human suffering. Thus, it is crucial to understand the causes
of civilian participation in violence. Anecdotal evidence for the Rwandan case sug-
gests that in the years before the genocide, weekly-held community meetings called
Umuganda were used to sensitize and mobilize the civilian Hutu population. While
∗This paper is co-authored with Evelina Bonnier, Jonas Poulsen and Thorsten Rogall. We thankEli Berman, Tom Cunningham, Meilssa Dell, Jonas Hjort, Juanna Joensen, Magnus Johannesson, ErikLindqvist, Andreas Madestam, Eva Mörk, Suresh Naidu, Nathan Nunn, Torsten Persson, CristianPop-Eleches, Marit Rehavi, David Strömberg, Jakob Svensson, Erik Verhoogen and Miguel Urquiola,as well as seminar participants at Harvard, UCSD, IIES, SSE, NEUDC, the ASWEDE Conferenceon Development Economics, the Annual Bank Conference on Africa and Columbia DevelopmentColloquium for many helpful comments. Miri Stryjan is grateful for funding from Handelsbanken’sResearch Foundation
1In 1990, Rwanda had 7.1 million inhabitants out of which 6 million were Hutus.
69
70 PREPARING FOR GENOCIDE: COMMUNITY MEETINGS IN RWANDA
Umuganda was originally designed as mandatory work meetings to improve the village
infrastructure, earlier accounts of the genocide suggest that in the beginning of the
1990s, these meetings were abused by the political elites to spread anti-Tutsi senti-
ments and prepare the population for genocide (Cook, 2004; Straus, 2006; Thomson,
2009; Verwimp 2013).
This paper provides the first empirical analysis of how important local, elite led
community meetings might have been in inducing the civilian population to partici-
pate in violence. Despite the specific focus of this paper, we argue that examining the
possibly negative effect of these community meetings is of more general importance.
There is a widely held belief that community meetings foster social capital, by pro-
viding arenas for people to meet, exchange ideas, solve free-rider problems and create
public goods (Grootaert and van Bastelaer, 2002; Guiso, Sapienza and Zingalez, 2008;
Knack and Keefer, 1997; Putnam, 2000). Consistently, many important development
agencies today increasingly focus on ‘community driven’ development projects in which
deliberative forums and grass root participation play a central role (see Mansuri and
Rao, (2012) for a recent overview). We investigate whether there is a ‘dark side’ to
these community meetings where social capital does not bridge the societal, ethnic
divides but rather enforces bonding within the different ethnic groups, i.e. the Hutu
population in the Rwandan case. Understanding this process is even more important
since Umuganda was formally reintroduced in Rwanda in 2008, and similar practices
have been installed in Burundi and are discussed in the Democratic Republic of Congo
(DRC) and recently in Kenya.2 Identifying the causal effect of these meetings on par-
ticipation in genocide is difficult for two reasons. First, we lack data on the number
of people participating in Umuganda or the number of meetings taking place in a
given locality. Second, even if that data existed, our estimates would likely suffer from
omitted variable bias. On the one hand, village-specific unobservable characteristics
that affect both genocide violence and Umuganda intensity, for instance local leader
quality, could produce a spurious positive correlation between the two, thus biasing
2For details about the Kenyan case, see Daily Nation (March 2016).
3.1. INTRODUCTION 71
the estimate upwards. On the other hand, if Umuganda meetings were strategically
used in areas where genocide participation would have been unobservably low, the
estimate would be downward biased.
To overcome these data and endogeneity issues, we use exogenous rainfall variation
to estimate the effect of Umuganda meetings on participation in civil conflict. The idea
is simple: we expect the meetings to be less enjoyable when it rains and furthermore
to be cancelled altogether under heavy rains. The fact that the community work only
took place on Saturdays makes it possible to isolate the Umuganda effect from general
rainfall effects (e.g. rainfall affecting income through agriculture) by only using the
variation in Saturday rainfall while controlling for average daily rainfall. We use the
number of Saturdays with heavy rainfall during the 3.5 year pre-genocide period (from
October 1990, the outbreak of the civil war, to March 1994, the eve of the genocide)
as our variable of interest. After the start of the civil war in October 1990, tensions
between Hutu and Tutsi intensified and the Hutu-dominated government became more
aggressive towards the Tutsi minority, finally culminating in the genocide. To control
for local characteristics, we include 142 commune fixed effects. Furthermore, we can
provide a first placebo check by controlling for heavy rainfall on all other six week-
days. Thus, we ensure that identification only stems from local variation in rainfall on
Saturdays, which is arguably exogenous and should only affect genocide participation
through its effect on Umuganda meeting intensity.
However, there is one major concern regarding the exclusion restriction. In partic-
ular, the effect we estimate might not be due to the political element of Umuganda
per se, but merely a consequence of people getting together in general. We will argue
in great detail why this concern is unwarranted. In particular, we will show that nei-
ther rainfall on Sundays, a church day where people traditionally meet, nor rainfall on
public holidays, affect participation. Moreover, the estimates are robust to excluding
Kigali, the Rwandan capital, where one might expect major outdoor events to take
place on weekends.
We proxy for genocide violence by the number of people prosecuted in the Gacaca
72 PREPARING FOR GENOCIDE: COMMUNITY MEETINGS IN RWANDA
courts, normalized by sector Hutu population.3 About 10,000 local Gacaca courts were
set up all over the country to prosecute the crimes committed during the genocide.
Importantly, these courts distinguished between civilian perpetrators and perpetrators
belonging to an organized group such as militia gangs, the national army or local
police. Using prosecution instead of actual participation rates might introduce some
bias. However, the Gacaca data is strongly correlated with other measures of violence
from other various sources and we also present a number of additional tests to rule
our that systematic errors are biasing our results.
Our reduced-form results indicate a negative relationship between Umuganda inten-
sity and civilian participation in genocide: one additional rainy Saturday is associated
with a 5 percent decrease in the civilian participation rate. Interestingly, this negative
relationship is entirely driven by sectors that are ruled by the pro-genocide Hutu par-
ties. In places with pro-Tutsi parties in power the effects are reversed, suggesting that
meetings may have been used to create bonds between the two ethnicities.
All effects are similar although statistically weaker for organized participation. This
is not surprising since militia and army men would often not have been affected by
pre-genocide rainfall in the village where they were prosecuted (they moved around
during the genocide and did not necessarily commit their crimes in their hometowns).
Our results have important policy implications and are also relevant for other coun-
tries. In 2008, the Rwandan government reintroduced Umuganda. Our results show
that these meetings can easily be abused and that caution is warranted, in particu-
lar since tension between the Tutsi and the Hutu still exist in Rwanda. Furthermore,
similar practices have been implemented in Burundi and are being discussed in the
Democratic Republic of Congo (DRC) and Kenya. These countries all have histories of
violent conflict along ethnic lines, which once more calls for caution when establishing
an institution such as mandatory community meetings.
Our paper contributes to the literature in several ways. First of all, it adds to the
vast conflict literature. Blattman and Miguel (2010) give an excellent review of this3A sector corresponds to the Rwandan administrative unit of a sector with an average size of 14
square kilometers and 4,900 inhabitants.
3.1. INTRODUCTION 73
literature, vehemently calling for well-identified studies on the roots of individual par-
ticipation in violent conflict. This paper adds to the conflict literature by providing
novel evidence on the strong effects of local community meetings, controlled by the
political elite, on civilian participation in violence. Recent studies on the determinants
of conflict and participation in violence consider institutions, government policy, in-
come and foreign aid (Besley and Persson, 2011; Dell, 2012; Dube and Vargas, 2013;
Mitra and Ray, 2014; Nunn and Qian, 2014, respectively). Furthermore, our paper
complements the literature on the Rwandan Genocide (Rogall, 2014; Straus, 2004;
Verpoorten, 2012a-c; Verwimp, 2003, 2005, 2006; Yanagizawa-Drott, 2014) by provid-
ing novel evidence on its careful preparation.
On the methodology side, our findings speak to the recent discussion on the effects
of rainfall on conflict other than through the income channel (Iyer and Topalova, 2014;
Rogall, 2014; Sarsons, 2011). Prominent studies that use rainfall as an instrument for
income in Africa include Brückner and Ciccone (2010), Chaney (2013) and Miguel,
Satyanath and Sergenti (2004). Our results suggest that rainfall might have negative
direct effects on conflict.
Finally, our results are in line with Satyanath, Voigtlaender and Voth (2015) who
speak for a "dark side" of social capital, in contrast to several contributions high-
lighting its positive effects (Grootaert and van Bastelaer, 2002; Guiso, Sapienza and
Zingalez, 2008; Knack and Keefer, 1997).
The remainder of the paper is organized as follows. Section 2 provides some back-
ground information on the Rwandan genocide. Section 3 presents the data used for
the analysis and Section 4 lays out our empirical strategy. Section 5 presents the main
results and assesses their robustness and Section 6 discusses mechanisms and channels.
Section 7 concludes with possible policy implications.
74 PREPARING FOR GENOCIDE: COMMUNITY MEETINGS IN RWANDA
3.2 Background
A History of Conflict A long chain of events lead up to the 1994 Rwandan geno-
cide.4 German and Belgian colonizers promoted the Tutsi minority and gave them
supremacy over the Hutu majority.5 This division created a strong tension between
the two groups and culminated in the Rwandan revolution, or the Social revolution of
1959, where the Tutsi monarchy was dissolved in favor of a republic led by the Hutus.
Many Tutsi civilians were killed; others fled Rwanda for neighboring countries such as
Burundi, Tanzania and, in particular, Uganda. During the following decade, the coun-
try faced several attacks from exiled Tutsi rebel groups with following Hutu retaliation.
In 1974 - paramount to the introduction of a modern version of Umuganda - Juvénal
Habyarimana took power in Rwanda through a coup d’état. His subsequent rule was
based on a pro-Hutu ideology ("Hutu power"), further discussed in the next section. In
October 1990, the RPF - a rebel army mostly composed of Tutsi exiles eager to replace
the Hutu-led government - invaded Rwanda from Uganda, starting the Rwandan civil
war. Fighting between the Hutu-led government and the Tutsi rebels continued until
the Arusha Accords were signed in August 1993.6 While a multi-party system had
been installed in the early phase of the peace talks, this had little (or no) effect on
reducing societal tension and conflict. On April 6 1994, the airplane carrying president
Habyarimana was shot down. Responsibility for the attack is still disputed today, but
within only a few hours of the attack, extremists within the Hutu-dominated parties
managed to take over key positions of government and initiated a 100-day period of
ethnic cleansing throughout Rwanda. Estimates suggest that around 800,000 people,
mostly Tutsi and "moderate" Hutus (i.e. Hutus believed to side with the Tutsi), were
killed. The mass killings ended in mid-July, when the RPF rebels, who in the meantime
4For insightful accounts of this period, see for example Prunier (1995), Gouveritch (1998), DesForges (1999), Dallaire (2003), Hatzfeld (2005, 2006) and Straus (2006).
5In the 1991 census data used in this paper, the average reported share of Hutus per commune is87%.
6The essence of this treaty was a power-sharing government, including representatives from bothsides of the conflict.
3.2. BACKGROUND 75
renewed the civil war, defeated the Rwandan Hutu army and the various militia groups.
A large number of Hutu civilians participated in the genocide violence, directed
by the interim government (Dallaire, 2003). In our sample, there are approximately
416,000 civilian perpetrators.7
Umuganda The practice of Umuganda dates back to pre-colonial times. During a
day of community service, villagers would get together to build houses for those not
able to do this themselves, or help each other out on the fields in times of economic
hardship (Mukarubuga, 2006). Rather than being mandatory, Umuganda was initially
considered a social obligation (Melvern, 2000). This changed during the the colonial
period, when the Belgian colonizers used Umuganda for organizing compulsory work.
Consistently, the local term for Umuganda was then uburetwa, or forced labor (IRDP,
2003). All men had to provide communal work 60 days per year. Most of the manual
labor was hereby carried out by members of the ethnic Hutu majority under the
supervision of Tutsi chiefs (Pottier, 2006): a first sign of Umuganda’s potential to
create a division between the two ethnic groups.
During the post-colonial era from 1974 onwards, the meaning of Umuganda changed
again when the newly elected Hutu president Habyarimana turned it into a political
doctrine (Mamdani, 2001). Verwimp (2000, p. 344) cites Habyarimana:
"The doctrine of our movement [Movement for Development, MRND] is
that Rwanda will only be developed by the sum of the efforts of its people.
That is why it has judged the collective work for development a necessary
obligation for all inhabitants of the country."
The program combined a practical motivation - achieving development objectives
with weak state finances - with a strong ideological element. Participation was again
compulsory through government coercion and failure to participate usually involved
paying a fine.8 The local leaders of the neighborhood who presided over a group of7For more information, see for example Dallaire (2003), Des Forges (1999), Gouveritch (1998),
Hatzfeld (2005, 2006), Prunier (1995) and Straus (2006).8In today’s Rwanda, the fine for not participating in Umuganda is slightly less than $10.
76 PREPARING FOR GENOCIDE: COMMUNITY MEETINGS IN RWANDA
ten households were responsible for the weekly Umugandas and had the power to
decide who were to participate and to demand fines from those failing to do so (Ver-
wimp, 2000). The state chose the projects on which at least one adult male per family
had to work every Saturday morning (Uvin, 1998). According to a report from 1986:
56 percent of the work performed during Umuganda included various types of anti-
erosion measures, such as terracing and digging ditches; 15 percent were construction
of communal buildings; 21 percent consisted of maintenance work of communal roads;
3 percent were related to construction of water supply systems, and another 3 per-
cent were related to agriculture. In this period, Umuganda substantially contributed
to Rwanda’s GDP (Guichaoua, 1991).
Habyarimana’s ideology stressed the importance of the cultivator as the true Rwan-
dan (Straus, 2006). This view clearly embraced the Hutu population with their history
as cultivators, as opposed to the Tutsi who were said to be pastoralists. During the
period leading up to the genocide, Umuganda was used to strengthen group cohe-
sion within the "indigenous" ba-Hutu and marginalize the "non-indigenous" ba-Tutsi
(Lawrence and Uwimbabazi, 2013). The patriotic focus of Umuganda became par-
ticularly salient in the early 1990’s when "government propaganda gave no choice to
Rwandans other than to attend Umuganda for political mobilization" (Lawrence and
Uwimbabazi 2013, p. 253). Furthermore, " (...) those who could not attend were re-
garded as enemies of the country who ran the risk of being brutalised and killed."
(ibid.).
Although little is known about the link between participation in Umuganda before
the genocide and participation in violence during the genocide - a link which we hope
to shed some new light on in this paper - anecdotal evidence speaks to the importance
of Umuganda as an instrument for local party and state officials to mobilize the peasant
population. The fact that all Rwandans of working age, be it farmers or intellectuals,
were required to participate in Umuganda (Guichaoua, 1991) made it a potential
arena for reaching the entire population. Straus (2006) shows that 88 percent of the
perpetrators he interviewed regularly participated in Umuganda before the genocide
3.3. DATA 77
broke out.9 Verwimp (2013, p. 40) notes:
"Umuganda gave the local party and state officials knowledge and experi-
ence in the mobilization and control of the labor of the peasant population.
A skill that [would] prove deadly during the genocide."
Umuganda was also used during the genocide itself, with the new name gukorn
akazi, or "do the work", which meant the killing of Tutsis (Verwimp, 2013). Other
slogans related to Umuganda used before the genocide such as "clearing bushes and
removing bad weeds" now had a completely altered connotation (Lawrence and Uwim-
babazi, 2013). By equating the participation in genocide violence with participation
in Umuganda, the Hutu elite could signal that participation in genocide violence, just
like participation in Umuganda, was a social obligation for all ’true’ Rwandans.
In 2008, the Tutsi-led government re-introduced Umuganda in Rwanda with the
general aim to promote development and reduce poverty in the aftermath of the geno-
cide (Uwimbabazi, 2012). Participation is again mandatory for all able-bodied individ-
uals between 18 and 65 years of age, and typical tasks include cleaning streets, cutting
grass and trimming bushes along roads, repairing public facilities or building houses
for vulnerable individuals on the last Saturday of every month.
3.3 Data
We combine several datasets from different sources to construct our final dataset, which
comprises 1433 Rwandan sectors. Sectors are the second smallest administrative level,
and the level for which the outcome data on the perpetrators is available. Table 3.1
reports the summary statistics for our variables.
Participation Rates Our two key measures are participation in civilian violence
and organized violence. Ideally, we would like to have a direct measure of participation
in the genocide. Since no such data exists, we follow the literature and use prosecution9These findings are, however, based on correlation studies and it is not possible to claim causality.
78 PREPARING FOR GENOCIDE: COMMUNITY MEETINGS IN RWANDA
rates for crimes committed during the genocide as a proxy (Friedman, 2013; Heldring,
2014; Rogall, 2014; Yanagizawa-Drott, 2014). This data are taken from a nation-wide
sector-level dataset, provided by the government agency "National Service of Gacaca
Jurisdiction", which records the outcome of the almost 10,000 Gacaca courts set up
all over the country. The released sector level data classifies the accused into one of
two categories, depending on the role played by the accused and the severity of the
crime.
The first category which we refer to as "organized participants" concerns: (i) plan-
ners, organizers, instigators, supervisors of the genocide; (ii) leaders at the national,
provincial or district level, within political parties, army, religious denominations or
militia; (iii) the well-known murderer who distinguished himself because of the zeal
which characterized him in the killings or the excessive wickedness with which killings
were carried out; (iv) people who committed rape or acts of sexual torture. These per-
petrators mostly belonged to the army and militia or were local leaders. Approximately
77,000 people were prosecuted in this category.10
The second category which we refer to as "civilian participants" concerns: (i) au-
thors, co-authors, accomplices of deliberate homicides, or of serious attacks that caused
someone’s death; (ii) the person who - with the intention of killing - caused injuries or
committed other serious violence, but without actually causing death; (iii) the person
who committed criminal acts or became the accomplice of serious attacks, without
the intention of causing death. People accused in this category are not members of
any of the organized groups mentioned for the first category and are thus considered
to be civilians. Approximately 430,000 people were prosecuted in this category. As
mentioned, the second category is our main outcome variable since civilian participa-
tion in the killings is more likely to have been affected by Umuganda than organized
participation.
The reliability of the prosecution data is a key issue for the analysis. One concern
when using prosecution data instead of actual participation is the presence of survival10Since we lose some observations for category 1 and category 2 in the matching process, our sample
consists of 415,935 category 2 perpetrators and 74,168 category 1 perpetrators.
3.3. DATA 79
bias: in those sectors with high participation rates, the violence might have been
so widespread that no witnesses were left, or the few remaining were too scared to
identify and accuse the perpetrators, thus resulting in low prosecution rates. However,
this concern is unlikely to be warranted: Friedman (2013) shows that the Gacaca data
is positively correlated with several other measures of violence from three different
sources.11 Furthermore, Friedman (2013, pp. 19-20) notes that "the Gacaca courts
have been very thorough in investigating, and reports of those afraid to speak are rare,
so this data is likely to be a good proxy for the number of participants in each area."
Nevertheless, to be cautious, in the following analysis we will show that our results are
robust to dropping those sectors with mass graves (an indication of high death rates)
and also to an alternative specification using the presence of a mass grave directly as
a dependent variable proxying for violence.
Another concern is that some of those people prosecuted in the Gacaca courts might
have committed their crimes not during the genocide, but rather during the period
of civil war preceding the genocide (October 1990 until August 1993). In particular,
we cannot rule out that (a) some perpetrators may, in fact, have been accused of
participation in massacres and other violence during the civil war (and not during
the genocide) and (b) that individuals who had previously participated in violence
during the civil war were more likely to have been recognized and trialed for genocide
crimes than individuals who "only" participated in the genocide. In order to mitigate
this concern, we exclude communes with violence against the Tutsi during the period
from October 1990 to March 1994 (Viret, 2010). Importantly, violence directed against
Hutus was not trialed in the Gacaca courts (Human Rights Watch, 2011; Longman,
2009).
Rainfall Data We use the National Oceanic and Atmospheric Administration (NOAA)
database of daily rainfall estimates, which stretches back to 1983, as a source of ex-
11These sources are a 1996 report from the Ministry of Higher Education, Scientific Research, andCulture (Kapiteni, 1996); the PRIO/Uppsala data on violent conflicts (Gleditsch et al, 2002); and adatabase of timing and lethality of conflict from Davenport and Stam (2009).
80 PREPARING FOR GENOCIDE: COMMUNITY MEETINGS IN RWANDA
ogenous weather variation. The NOAA data relies on a combination of actual weather
station gauge measures and satellite information on the density of cloud cover to de-
rive rainfall estimates at 0.1-degree (∼ 11 kilometers at the equator) latitude-longitude
intervals. Considering the small size of Rwanda, this high spatial resolution data, to
our knowledge the only such data available, is crucial for obtaining reasonable rain-
fall variation. Furthermore, the high temporal resolution, i.e. daily estimates, allows
us to confine variation in rainfall to the exact days of Umuganda. Since Rwanda is
a very hilly country, there is considerable local variation in rainfall. Moreover, these
sectors criss cross the various rainfall grids and each sector polygon is likely to overlap
with more than one rainfall grid. The overall rainfall in each sector is thus obtained
through a weighted average of the grids, where the weights are given by the relative
areas covered by each grid.
Village Boundary, Road and City Data The Center for Geographic Information
Systems and Remote Sensing of the National University of Rwanda (CGIS-NUR)
in Butare provides a sector boundary map, importantly with additional information
on both recent and old administrative groupings. Since Rwandan sectors have been
regrouped under different higher administrative units a number of times after the
genocide, this information allows us to match sectors across different datasets (e.g. the
1991 census and the Gacaca records).
Africover provides maps with the location of major roads and cities derived from
satellite imagery. We use these maps to calculate the sector area, as well as to calculate
various distance measures, such as the distance of the sector centroid to the closest
main road, to the closest city, to the borders of the country and to Kigali and Nyanza,
the recent capital and the old Tutsi Kingdom capital, respectively.
Additional Data The remaining data is drawn from Genodynamics and the IPUMS
International census data base. This data includes population, ethnicity and radio
3.4. EMPIRICAL STRATEGY 81
ownership from 1991.12 Except for population, all these variables are only available
at the commune level. Ethnicity is defined as the fraction of people that are Hutu
or Tutsi, respectively. About 10 percent of the population are Tutsi. Importantly, the
Tutsi minority is spread out across the entire country. We calculate the Tutsi minority
share used in the analysis as the fraction of Tutsi normalized by the fraction of Hutu.
Verpoorten (2012c) provides data on the location of mass graves which she con-
structs using satellite maps from the Yale Genocide Studies Program. Guichaoua
(1991) provides information on the party affiliation of the commune leaders (called
burgomasters) at the eve of the genocide.
Matching of data and summary statistics The different data sets are matched
by sector names within communes. A commune is an administrative unit above the
sector. There were 142 communes in total, which were, in turn, grouped into 11
provinces. Unfortunately, the matching is imperfect, since some sectors either have
different names in different data sources, or use alternate spelling. It is not uncommon
for two or more sectors within a commune to have identical names, and this prevents
successful matching. However, overall, only about 5 percent of the sectors do not have a
clear match across all sources. Furthermore, as these issues are idiosyncratic, the main
implication for our analysis is lower precision in the estimates than would otherwise
have been the case.
3.4 Empirical Strategy
To identify the effect of Umuganda meetings on participation in genocide violence,
we use local variation in rainfall as a proxy. Since we lack data on the number of
people participating in Umuganda, we focus on the reduced form effect. Our identifi-
cation strategy thus rests on two assumptions. First, sectors with heavier rainfall on
12This data is only available for 1991. However, mobility was extremely limited because of gov-ernmental restrictions and land markets were also strongly controlled (Andre and Platteau, 1998;Prunier, 1995).
82 PREPARING FOR GENOCIDE: COMMUNITY MEETINGS IN RWANDA
Saturdays experienced fewer or less intensive Umuganda meetings (first stage). Sec-
ond, conditional on our control variables, rainfall on Saturdays does not have a direct
effect on genocide violence other than through the Umuganda meetings (exclusion
restriction).
First Stage Ideally, we would like to directly test the first-stage relationship using
data on the number of people participating in Umuganda before the genocide. Since
such data does not exist, we will instead provide indirect evidence for expecting a
strong first stage.
Several other studies have documented and exploited negative relationships be-
tween rainfall and participation in open-air events. One of the first examples is Collins
and Margo (2007) who use rainfall in April 1968 as an instrument for participation
in the US riots after the death of Martin Luther King. More recent examples include
Madestam et al. (2013) and Madestam and Yanagizawa-Drott (2011). Similarly, sev-
eral other studies use rainfall and other weather phenomena for exogenous variation
in voter turnout on election days (Eisinga et al., 2012; Fraga and Hersh, 2011; Gomez
et al., 2012; Hansford and Gomez, 2010; Horiuchi and Saito, 2009).
However, in all these cases, rain has an effect both on the direct cost of attending
the open-air event and the opportunity cost of attending. For example, Lind (2014)
finds that voter turnout in Norway increases when it rains on election day because bad
weather reduces the opportunity cost of going to the polling station. Since Umuganda
was mandatory, the opportunity cost mechanism is however unlikely to play any role
in our case, however. Instead, rainfall likely made the meetings and the work less
productive, or even lead to cancellations. Still, the true functional form between rainfall
and participation in mandatory community work is unknown. To make progress, we
reasonably assume that the typical Umuganda tasks, exclusively outdoor work, become
difficult or impossible to perform once a certain rainfall threshold has been reached.13
Following Harari and La Ferrara (2013) who define an extreme weather shock as
13The typical Umuganda tasks took place outside and, as mentioned above, included landscaping,road maintenance, construction, and agriculture (Guichaoua, 1991).
3.4. EMPIRICAL STRATEGY 83
two standard deviations from the long-term average, we choose this threshold to be
10mm.14 Thus, we will use the number of Saturdays from October 1990 to March
1994, that each sector received more than 10 mm of rainfall as a our main explanatory
variable.15 Furthermore, in the appendix, Table A.1, we show that our results are also
robust to using average daily rainfall on Saturdays and all other weekdays as our main
explanatory variables. Figure 3.3 shows the distribution of Saturday rainfall across
Rwanda in our period of interest.
Furthermore, to better understand whether rainfall affected the extensive or the
intensive margin of Umuganda meetings, we can vary these thresholds. More specif-
ically, we will also use thresholds of 6 mm, 8 mm, 9 mm and 12 mm, respectively.16
If we see effects already at low thresholds, it speaks to less enjoyable meetings or an
effect at the intensive margin. If the effects begin only at higher levels, cancellations
are more likely to be driving the results, i.e. affecting the extensive margin. Average
daily rainfall in Rwanda is low, however (see Table 3.1), which means that for very
high thresholds, the variation will be too small to detect any effects.
Exclusion Restriction Once more, our empirical strategy relies on the counterfac-
tual assumption that, absent the Umuganda meetings, rainfall on Saturdays had no
effect on genocide violence. This is unlikely to be true without further precautions.
Rainfall on Saturdays, like all other weekdays, is likely to affect rain-fed production
and is therefore correlated with income. Income, in return, potentially affects genocide
participation as the reasons for participating were often driven by material incentives
14The long-term average daily rainfall in Rwanda from 1984 to 1994 was 2.6 mm with a standarddeviation of 3.8 mm. We calculate this number taking the average across all sectors and all days from1984 to 1994. Two standard deviations from the long-term average corresponds to 10.24 mm.
15Madestam et al. (2013) use a threshold of 0.1 inches (2.5 millimeter) of rainfall, a light drizzle,to predict participation in the Tea Party Tax Day rally in the US. While a 2.5 mm threshold maybe appropriate to capture participation in a voluntary rally in the US, we believe that our case, i.e.mandatory meetings, requires a higher threshold. Madestam et al. (2013) also use 0.35 inches (≈ 9mm) as a robustness check for a higher threshold of rainfall. In Table 3.3, we show that our resultsare robust also to using this threshold.
16The 8 mm and 12 mm thresholds correspond to the average of the 95th and the 99th percentileof daily rainfall in Rwanda over the period from 1984 to 1994. Here, we follow Dyson (2009) who,in order to understand the characteristics of rainfall in South Africa, defines heavy and very heavyrainfall as the average of the 95th and 99th percentile of daily rainfall, respectively.
84 PREPARING FOR GENOCIDE: COMMUNITY MEETINGS IN RWANDA
and genocide perpetrators were given the opportunity to loot the property of the vic-
tims, or could bribe themselves out of participating (Hatzfeld, 2005). Besides affecting
agricultural outcomes, heavy rainfall might destroy infrastructure such as roads or
housing, which is also likely to affect households’ economic well-being and therefore
participation in conflict.
To address this problem, and to solely isolate the Saturday rainfall effect, we control
for average daily rainfall from January 1984 to September 1990 and our period of
interest from October 1990 to March 1994. Furthermore, we control for rainfall on all
other six weekdays. The absence of systematic, significant effects for days other than
Saturdays serves as a first placebo test. To account for local characteristics, we also
add 142 commune fixed effects.
At this point, we still need to argue that no other events potentially happening
parallel to Umuganda on Saturdays could be driving our results. In particular, one
might be concerned that people meeting and interacting in general might affect the
participation in genocide violence. Although we cannot directly test for this, we will
provide several indirect tests alleviating this concern.
Specifications We run the following reduced-form regression to estimate the effect
of Umuganda meetings on participation in genocide violence
Gic
Hic= α +β #Saturdays(Rain f all > t mm)+Xicπ + γc + εic (3.1)
where Gic is the number of Hutu prosecuted in either category 1 or category 2, i.e.
our proxy for genocide violence and Hic is the Hutu population in sector i in commune
c. #Saturdays(Rain f all > t mm) is our explanatory variable of interest: the number
of Saturdays from October 1990 to March 1994 with rainfall above t mm. Our main
specification uses 10 mm as a measure of heavy rainfall, but our results are robust
to using other rainfall thresholds. Xic is a vector of sector-specific controls, including
average daily rainfall from January 1984 to September 1990, average daily rainfall
from October 1990 to March 1994 and the number of all other weekdays with rainfall
3.5. RESULTS 85
above t mm during our period of interest, October 1990 to March 1994. Finally, γc are
commune fixed effects, and εic is the error term. We allow error terms to be correlated
across sectors within the same commune by clustering the standard errors at the
commune level. For the sake of robustness, we also allow error terms to be correlated
across sectors within a 25, 50 and 75 km radius (Conley, 1999).17 Moreover, since
the prosecution rates are heavily skewed to the right, we weight our observations by
total sector population size, but our results do not rely on this weighting scheme. The
coefficient of interest β captures the percentage point change in genocide participation
following an additional Saturday with rainfall above t mm.
3.5 Results
Main Effects The reduced-form relationship between the number of civilian perpe-
trators per Hutu and the number of Saturdays with rainfall above 10 mm is strongly
negative and statistically significant at the 99 percent confidence level (column 1 in Ta-
ble 3.2) and this relationship holds up when adding 142 commune fixed effects (column
2) and the number of other weekdays with rainfall above 10 mm (column 3). Regarding
magnitude, the point estimate of 0.409 (column 3 with all controls) suggests that one
additional rainy Saturday reduces the civilian participation rate by 0.409 percentage
points (note that the civilian participation rate is measured in percent). If we assume
a one-to-one relationship between the number of rainy Saturdays and the number of
cancelled Umuganda meetings, an additional cancelled meeting reduces the average
civilian participation rate by 5.4 percent (interpreted at the mean of civilian perpe-
trators per Hutu, which is 7.7 percent). Reassuringly, none of the other weekdays is
systematically and significantly related to civilian violence (we cannot reject the null
that all coefficients are equal to zero, p-value 0.937).
The results for organized perpetrators are statistically weaker, they are significant
at the 90 percent confidence level (columns 4 to 6). This is not surprising: since the
17The results are reported in Table A.2 in the appendix.
86 PREPARING FOR GENOCIDE: COMMUNITY MEETINGS IN RWANDA
category of organized perpetrators mostly consists of members of the militia, it is
unclear whether the sector where they committed their genocide crimes (and were
subsequently prosecuted) is the same as the one where they lived before the genocide
(October 1990 to March 1994). Thus, they will not have been exposed to the same
number of Umugandas as the inhabitants of that sector. If this is the case, our data
is likely to suffer from measurement error increasing standard errors. Since the main
focus in this paper is to examine if Umuganda can explain civilian violence, we ex-
clude organized violence from our main analysis. However, we report the corresponding
results for organized perpetrators in the appendix (Tables A.3 to A.6).
To understand whether rainfall led to cancellations, or rather made the Umuganda
meetings less enjoyable, we vary the threshold in increments of 2 mm: from 6 mm to 12
mm.18 Table 3.3 reports the results. Heavy rainfall on Saturdays is negatively related
to civilian participation for all thresholds and significant at least at the 90 percent
confidence level for all thresholds above 6 mm. Importantly, we find the strongest
effects for thresholds above 9mm, suggesting that it was rather cancellations that led to
a decrease in violence. Once more, we find no significant effects for other weekdays and,
consistently, we cannot reject the null hypothesis that the non-Saturday coefficients
are jointly equal to zero (the p-values range from 0.34 to 0.97).
Robustness Checks Next, we perform a number of robustness checks and placebo
tests, reported in Table 3.4. The potential survival bias in the prosecution data is un-
likely to matter: the reduced form point estimates are virtually identical to the baseline
results and similarly significant at the 99 percent confidence level when dropping sec-
tors with at least one mass grave (indicating high death rates, column 1). Furthermore,
we can also use the presence of a mass grave directly as a dependent variable. Consis-
tently, columns 7 and 8 show that sectors with many rainy Saturdays are less likely to
have a mass grave site altogether. The point estimate of -0.013 (standard error 0.004,
column 8), significant again at the 99 percent level, suggests that a sector is 26 percent
18To be consistent with Madestam et al. (2013), we also use 0.35 inches (which corresponds to 9mm) as a threshold.
3.5. RESULTS 87
less likely to have a mass grave site, given an additional rainy Saturday.
One might also be concerned that the UN troops which were stationed in Kigali,
although few, affected the Umuganda meetings, thus biasing our estimates. But once
more, the results are robust to dropping sectors in Kigali city (column 2). Furthermore,
the results are robust to dropping all main cities and sectors close to them (column
3).
The results are also unaffected by adding a number of additional controls that
potentially affect civilian participation in violence (column 4). These include distance
to the border, distance to major cities, distance to Kigali and distance to Nyanza
as well as population density. To illustrate this, being close to the border potentially
made it easier for the Tutsi or for those Hutu unwilling to participate in the killings
to leave the country. Distance to cities, in particular the capital Kigali, is likely to be
correlated with urbanization and public goods provision (economic activity). Nyanza
was the old Tutsi Kingdom capital and sectors further away from it do still, on average,
exhibit lower Tutsi shares. Population density eventually captures social pressure as
well as food pressure, both said to be important reasons for the genocide (Boudreaux
2009; Diamond, 2005; Verpoorten, 2012b).19
Finally, as a placebo check, we re-estimate the reduced-form regressions, instead
using the number of Saturdays (and other weekdays) with high rainfall during the
period October 1994 to March 1998 instead (from here on denoted post-genocide
period). To account for possible seasonality in the rainfall data, we chose the same
calendar period as our period of interest, i.e. October 1, 1994 to March 31, 1998.
Reassuringly, the coefficient for high rainfall on Saturdays in the post-genocide period
is small and insignificant (-0.012, standard error 0.106, column 5) and the same is true
for the coefficients on all other weekdays of the post-genocide period (except Monday).
These small and insignificant point estimates furthermore remain unchanged when
adding rainfall by weekday during our period of interest, October 1990 to March 1994
19The food pressure argument essentially assumes a Malthusian type of model: a fixed amount ofland to grow crops feeds a growing population (fertilizers were seldom used in Rwanda (Percival andHomer-Dixon, 2001)).
88 PREPARING FOR GENOCIDE: COMMUNITY MEETINGS IN RWANDA
(column 6).
As another placebo check, we rerun the main specification for both militia and civil-
ian violence using Saturday rainfall during the 3.5 year pre-genocide period (October
1, YEAR to March 31, YEAR+4) where YEAR ∈ {1983,2013}. To illustrate this, we
begin with the period from October 1, 1984 until March 31, 1988 and end with the
period from October 1, 2009 until March 31, 2013. As expected, the two distributions
of the resulting 20 coefficients are both somewhat centered around 0 and, reassuringly,
the coefficient on Saturday rainfall from 1990 to 1994, the actual pre-genocide period,
is an extreme outlier to the left in both cases: None of the other point estimates is
larger in absolute value (the results are shown in Figures 3.1 and 3.2).
Exclusion Restriction After demonstrating a strong and robust effect of high Sat-
urday rainfall on civilian participation in genocide, we still have to argue that this
effect results from people participating in Umuganda together.
Most importantly, since major outdoor events, such as music festivals or soccer
games, usually take place on weekends, potentially affected by rainfall, one might be
concerned that people meeting and interacting in general could affect the participa-
tion in genocide violence. However, recalling our main result in Table 3.2, we find
no significant effect for Sunday rainfall. Since people traditionally attend church on
Sundays, this is the first piece of evidence speaking against the effects being driven by
people meeting in general. Besides, as seen above, our results are robust to dropping
the capital Kigali and other major cities in the sample; places where one might expect
these major outdoor events to predominantly take place.
In a similar vein, heavy rainfall on public holidays, another occasion for people to
meet, does not seem to matter: the point estimate on the number of public holidays
with rainfall above 10mm is statistically insignificant and small, when expressed in
standard deviations (column 1 in Table 3.5).20 The same is true when adding religious
20Note that we exclude holidays that fall on a Saturday since these might still have been subjectto Umuganda.
3.6. CHANNELS 89
and non-religious holidays separately to the regression (column 2).21
Throughout our period of interest from 1990 to 1994, violent acts against Tutsi
and "moderate" Hutu were already taking place. If these pre-genocide perpetrators
are included in the Gacaca data, and there is a relationship between rainfall before
the genocide and targeted violence during that period, for instance through transport
costs, our estimates might be biased. To rule out this possibility, we drop communes
where violence against the Tutsi took place before the genocide (Viret, 2010). Reas-
suringly, our results for civilian participation are robust (column 3).
To provide further evidence that the effects we measure above result from the
political elites abusing Umuganda meetings, we split the sample of sectors into those
located in communes with local pro-genocide Hutu party leaders and those located in
communes with pro-Tutsi party leaders22. Interestingly, the negative relationship from
above seems to be entirely driven by the pro-genocide Hutu-governed sectors. The
point estimate on Saturday rainfall is -0.466 (standard error 0.123, column 5), slightly
larger than our main effect and again highly significant at the 99 percent confidence
level. The opposite is true in pro-Tutsi sectors: the point estimate on Saturday rainfall
is large and positive, albeit given the small sample of only 161 sectors, it is insignificant
(0.706, standard error 0.896, in column 6 and 0.399, standard error 0.796, in column
7 with all other weekday controls). The numbers suggest that in these sectors, the
meetings were used to create bonds between the two ethnicities.
3.6 Channels
In the following section, we try to better understand the channels and mechanisms
through which Umuganda worked. Since the mechanisms in Hutu-governed sectors
and pro-Tutsi-governed sectors are likely to differ, we always analyze the two sub-
samples separately. All results are reported in Table 3.6.
21Religious holidays are, for instance, Easter and Christmas, non-religious holidays in Rwanda are,for instance, Independence Day and Labor Day.
22This data is available from the introduction of a multiparty system in January 1992.
90 PREPARING FOR GENOCIDE: COMMUNITY MEETINGS IN RWANDA
Interaction Effects Starting with the Hutu-run sectors, a natural first question is
whether the political Hutu elites mostly spread propaganda and informed civilians
about the views of the government - something a radio broadcast might have done
just as well - or whether the local elites rather brought civilians together, practicing
mobilization, something that certainly would have required physical presence in the
locality. Importantly, there were two radio stations in Rwanda (Radio Rwanda and
Radio RTLM, the former having national coverage), which relentlessly informed lis-
teners about the pro-genocide view of the government. This hints at a way of testing
the initial question: if the Umuganda meetings mostly worked through information,
then the effect of the Umuganda meetings, i.e. Saturday rainfall, should be smaller
(in absolute values) in sectors that were already informed, i.e. exhibited high levels of
radio ownership. Thus, we should observe a positive interaction effect of Saturday rain-
fall with radio ownership among the Hutu population in the data. The point estimate
on the interaction term is positive (0.659, column 1); however with a standard error
of 0.786, it is clearly insignificant. Furthermore, when we replace the radio ownership
variable by a dummy taking on the value of 1 if radio ownership lies above the median,
the interaction effect is essentially zero (the result is not shown). Thus, it seems to be
the case, that Umuganda worked beyond information and propaganda.
Rather, consistent with the local elites using Umuganda to bring people together,
the interaction effect of Saturday rainfall with population density is positive and highly
significant at the 99 percent confidence level. The point estimate of 0.134 (standard
error 0.023, column 2) suggests that a one standard-deviation increase in population
density reduces the effects of Umuganda by about 28 percent. Thus, Umuganda was
particularly effective in less densely populated areas - bringing people together.
The effectiveness of Umuganda might also depend on the size of the Tutsi minor-
ity. Large Tutsi minorities might boycott or hinder the meetings. However, the data
suggests that this is not the case. The point estimate on the interaction between Sat-
urday rainfall and the Tutsi population size is insignificant and, if anything, negative
(column 3). This is once more unsurprising: since the Tutsi were the clear minority
3.7. DISCUSSION AND CONCLUSION 91
in Rwanda, never holding the majority in any sector, the Hutu elites were not very
concerned about their presence. In fact, taken at face value, the negative point esti-
mate of -1.090 (standard error 1.526) suggests that the meetings were more successful
in sectors with larger Tutsi minorities. The perceived Tutsi threat might have been
more salient in these sectors and the enemy easier to point out. All results are robust
to adding all three heterogeneous effects at once (column 4). The opposite is true in
sectors run by Tutsi party elites. In these sectors, it seems that the local elites had
to use the Umuganda meetings to compensate for the anti-Tutsi propaganda spread
on the radio. The interaction effect of Saturday rainfall with radio ownership among
the Hutu is negative and significant at the 90 percent confidence level. The point esti-
mate of -12.925 (standard error 6.542) suggests that the positive effect of Umuganda
is about 26 percent lower in places with a radio ownership level of one standard devi-
ation, as compared to places with no radio ownership at all (column 5). Furthermore,
the local pro-Tutsi elites seemed to have been more effective in sectors with fewer
Hutu inhabitants. The interaction effect of Saturday rainfall with the size of the Tutsi
minority is positive and almost statistically significant (p-value 0.124) in column 7.
This is consistent with the Tutsi elites having to overcome a potential pro-genocide
bias in the Hutu population. However, population density did not seem to matter in
these sectors. The interaction effect of Saturday rainfall with population density in
column 6 is insignificant and, if anything, positive (0.355, standard error 0.494). Thus,
contrary to the Hutu-run sectors, Umuganda in pro-Tutsi sectors was more successful
in highly populated areas. The above results are once more robust to controlling for
all three heterogeneous effects at once (column 8).
3.7 Discussion and Conclusion
Our results show that the local Hutu elites used mandatory community meetings
to mobilize the civilian population for genocide. Using exogenous variation in heavy
rainfall on the day of the mandatory community-work meetings Umuganda, we find
92 PREPARING FOR GENOCIDE: COMMUNITY MEETINGS IN RWANDA
that one additional rainy community day decreased the share of civilian perpetrators in
the Rwandan genocide by around 5 percent. Interestingly, this negative effect becomes
positive in sectors run by anti-genocide pro-Tutsi parties. Thus, in these sectors, the
meetings may have been used to compensate for the various forms of Hutu propaganda
on the radio and bridge the differences between the two ethnic groups. Our findings
are important for several reasons.
First, a large number of civilians participated in the killings during the Rwandan
genocide. While it is a common understanding that the genocide was centrally planned
and organized, little is known about the link between the planning and the wide accep-
tance of the genocide among the civilian population. Our paper suggests that weekly
held community meetings played a major role in this preparation and mobilization pro-
cess. Second, people getting together during community meetings is commonly said
to foster a sense of belonging and create social capital, generally viewed as positive
for development and community building (see, for example, Knack and Keefer, 1997;
Grootaert and van Bastelaer, 2002; Guiso, Sapienza and Zingalez, 2008). As empha-
sized by Putnam (2000), social capital can bridge the divides in a society. However,
we show that there is a ‘dark side’ to these community meetings. Thus, although the
institution of Umuganda may have the potential to act as a community building force,
our results show that when placed in the wrong hands, the effects can become dis-
astrous. However, somewhat comfortingly, our results also suggest that in Tutsi-led
sectors, Umuganda was used to work against propaganda and overcome hatred.
The more optimistic view of this type of institution might explain why the current
Rwandan government reinstalled Umuganda in 2008. Indeed, official statements about
Umuganda today emphasize values such as "solidarity" and "reconciliation" and the
practice is said to foster a sense of community. These mandatory work days are now
held monthly, on the last Saturday of every month. A similar practice is also present
in Burundi and is being discussed in DR Congo and Kenya. Our analysis clearly shows
that these meetings are powerful instruments and caution is warranted, especially in
countries with histories of ethnic tension.
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FIGURES AND TABLES 99
Figures and Tables
100 PREPARING FOR GENOCIDE: COMMUNITY MEETINGS IN RWANDA
Figure 3.1: Placebo Check: Civilian Partcipation
Figure 3.2: Placebo Check: Organized Participation
Notes: The figures shows the distribution of coefficients on Saturday Rainfall for civilian violence (Figure 3.1) andorganized violence (Figure 3.2) when using Saturday rainfall during the 3.5 years of the pre-genocide period (October1, YEAR to March 31, YEAR+4) from the years 1984 to 2013.
FIGURES AND TABLES 101
Figure 3.3: Rainfall map
Note: Map of Rwanda showing the number of rainy Saturdays in 1990-1994 by sector. Communeborders are indicated on the map as grey lines.
102 PREPARING FOR GENOCIDE: COMMUNITY MEETINGS IN RWANDA
Figure 3.4: Prosecuted civilians
Note: Map of Rwanda showing the number of prosecuted civilians by sector.
FIGURES AND TABLES 103
Table 3.1: Summary Statistics
Mean Std.dev. Obs.
A. Violence & Population
# Civilian Perpetrators 290.25 286.43 1433# Organized Perpetrators 51.76 70.51 1433% Civilian Perpetrators per Hutu (p.H.) 7.66 7.93 1433% Organized Perpetrators per Hutu (p.H.) 1.40 2.09 1433Pre-Genocide Violence against Tutsi, dummy 0.15 0.36 1433Mass Grave found in Sector, dummy 0.05 0.21 1432Population in Sector, ’000 4.88 2.48 1433Hutu Population in Sector, ’000 4.26 2.17 1433Population Density, ’000 0.50 0.85 1433
B. Rainfall
# Sat(Rain>10mm) 18.25 4.24 1433# Sun(Rain>10mm) 15.14 5.19 1433# Mon(Rain>10mm) 15.13 4.22 1433# Tue(Rain>10mm) 18.10 3.52 1433# Wed(Rain>10mm) 20.51 4.76 1433# Thu(Rain>10mm) 21.53 3.97 1433# Fri(Rain>10mm) 17.02 4.75 1433Average Daily Rainfall, 1980s 2.58 0.48 1433Average Daily Rainfall, 1990s 2.44 0.55 1433# Pub. Holidays(Rain>10mm) 0.85 0.20 1433# Non-Rel. Holidays(Rain>10mm) 1.56 0.21 1433# Rel. Holidays(Rain>10mm) 1.00 0.11 1433
C. Other Variables
Fraction of Hutu with Radio 0.33 0.09 1433Tutsi Minority Share 0.10 0.13 1433Distance to Kigali (km) 3.99 0.64 1433Distance to Main City (km) 2.91 0.71 1433Distance to Nyanza (km) 4.00 0.66 1433Distance to the Main Road (km) 1.41 1.23 1433Distance to the Border (km) 2.82 0.91 1433
Note: The # prosecuted militiamen is crime category 1: prosecutions against organizers, leaders, armyand militia; # prosecuted civilians is crime category 2: prosecutions against civilians. The per Hutu(p.H.) variables are expressed in percent. Pre-Genocide Violence against Tutsi is a dummy takingthe value of 1 if the sector experienced violence against Tutsi in the pre-genocide period. The twoaverage daily rainfall variables are measured in millimeters. The distance variables are measured inkilometers. Population is the population number in the sector and Population Density is populationper square kilometers, from the 1991 census. Radio ownership and ethnicity data are taken from the1991 census, available only at the commune level. There are 142 communes in the sample. The TutsiMinority Share is defined as the fraction of Tutsi normalized by the fraction of Hutu.
104 PREPARING FOR GENOCIDE: COMMUNITY MEETINGS IN RWANDA
Table 3.2: Main Effects
Dependent Variable: % Civilian Perpetrators, p.H. % Militiamen, p.H.
(1) (2) (3) (4) (5) (6)
# Sat(Rainfall>10mm) -0.580∗∗∗ -0.425∗∗∗ -0.409∗∗∗ -0.115∗∗∗ -0.065∗ -0.057∗[0.118] [0.125] [0.128] [0.033] [0.033] [0.030]
# Sun(Rainfall>10mm) 0.041 -0.037[0.102] [0.031]
# Mon(Rainfall>10mm) 0.080 0.100∗∗∗[0.112] [0.031]
# Tue(Rainfall>10mm) 0.023 -0.046[0.084] [0.030]
# Wed(Rainfall>10mm) 0.031 0.007[0.111] [0.028]
# Thu(Rainfall>10mm) -0.007 -0.064[0.134] [0.041]
# Fri(Rainfall>10mm) -0.057 0.006[0.099] [0.027]
Standard Controls yes yes yes yes yes yesCommune Effects no yes yes no yes yesR2 0.15 0.52 0.52 0.07 0.36 0.37N 1433 1433 1433 1433 1433 1433
Note: # of Sat(Rainfall>10mm) is the number of Saturdays with rainfall above 10mm during the periodOctober 1990 to March 1994 (and similarly for all other weekdays). % Civilian Perpetrators per Hutu (p.H)and % Militiamen per Hutu are measured in percent. Standard Controls include average daily rainfall forJanuary 1984 to September 1990 and average daily rainfall for October 1990 to March 1994. All regressions arerun using weighted least squares (WLS) estimation with population size as weights. There are 142 communesin the sample. Standard errors are clustered at the commune level. *significant at 10 percent, **significant at5 percent, ***significant at 1 percent.
FIGURES AND TABLES 105
Table 3.3: Different Rainfall Thresholds
Dependent variable: % Civilian Perpetrators, p.H.
Rainfall Threshold x: 6 mm 8 mm 9 mm 10 mm 12 mm
(1) (2) (3) (4) (5)
# Sat(Rainfall > x mm) −0.142 −0.241∗ −0.250∗ −0.409∗∗∗ −0.385∗∗∗
[0.115] [0.140] [0.141] [0.128] [0.132]# Sun(Rainfall > x mm) 0.080 0.068 0.073 0.041 −0.043
[0.084] [0.124] [0.117] [0.102] [0.137]# Mon(Rainfall > x mm) 0.069 0.009 0.079 0.080 −0.053
[0.088] [0.118] [0.117] [0.112] [0.120]# Tue(Rainfall > x mm) −0.020 0.000 0.069 0.023 0.135
[0.128] [0.123] [0.099] [0.084] [0.123]# Wed(Rainfall > x mm) 0.003 0.043 −0.065 0.031 −0.058
[0.093] [0.111] [0.106] [0.111] [0.118]# Thu(Rainfall > x mm) 0.129 0.004 0.140 −0.007 −0.233∗∗
[0.096] [0.107] [0.123] [0.134] [0.107]# Fri(Rainfall > x mm) −0.048 0.106 −0.079 −0.057 −0.216
[0.086] [0.094] [0.086] [0.099] [0.137]Standard Controls yes yes yes yes yesCommune Effects yes yes yes yes yesR2 0.51 0.51 0.51 0.52 0.52N 1433 1433 1433 1433 1433
Note: # of Sat(Rainfall>x mm) is the number of Saturdays with rainfall above x mm during the period October 1990 toMarch 1994 (and similarly for all other weekdays). The value of x is given in the column header. % Civilian Perpetratorsper Hutu (p.H) is measured in percent. Standard Controls include average daily rainfall for January 1984 to September1990 and average daily rainfall for October 1990 to March 1994. All regressions are run using weighted least squares(WLS) estimation with population size as weights. There are 142 communes in the sample. Standard errors are clusteredat the commune level. *significant at 10 percent, **significant at 5 percent, ***significant at 1 percent.
106 PREPARING FOR GENOCIDE: COMMUNITY MEETINGS IN RWANDATable
3.4:Robustness
andPlacebo
Tests
Dependent
variable:%
Civilian
Perpetrators,
p.H.
Massgrave
inVillage
Without
Without
Without
Additional
FutureAlternative
Mass
Graves
Kigali
Major
Cities
Controls
Rainfall
Dep.
Var.
(1)(2)
(3)(4)
(5)(6)
(7)(8)
#Sat(R
ainfall>10m
m)
−0.403 ∗∗∗
−0.426 ∗∗∗
−0.425 ∗∗∗
−0.368 ∗∗∗
−0.452 ∗∗∗
−0.015 ∗∗∗
−0.013 ∗∗∗
[0.129][0.131]
[0.133][0.125]
[0.126][0.004]
[0.004]
#Sun(R
ainfall>10m
m)
0.0540.048
0.0650.033
0.0510.002
[0.103][0.106]
[0.108][0.107]
[0.110][0.004]
#Mon(R
ainfall>10m
m)
0.0560.076
0.0630.113
0.108−
0.003[0.109]
[0.113][0.115]
[0.114][0.101]
[0.004]#
Tue(R
ainfall>10m
m)
0.0740.028
0.0490.021
0.0620.008
[0.086][0.089]
[0.089][0.080]
[0.086][0.004] ∗
#Wed(R
ainfall>10m
m)
0.0290.015
0.0350.033
0.0550.006
[0.107][0.121]
[0.127][0.105]
[0.133][0.004]
#Thu(R
ainfall>10m
m)
−0.011
0.0210.018
0.0190.059
−0.003
[0.128][0.136]
[0.140][0.126]
[0.144][0.004]
#Fri(R
ainfall>10m
m)
−0.014
−0.054
−0.034
−0.006
−0.034
−0.009 ∗∗
[0.097][0.099]
[0.104][0.098]
[0.103][0.003]
#Sat(R
ainfall>10m
m),94-98
−0.012
0.008[0.106]
[0.111]#
Sun(Rainfall>
10mm),94-98
0.1300.111
[0.114][0.112]
#Mon(R
ainfall>10m
m),94-98
−0.279 ∗∗
−0.323 ∗∗
[0.118][0.139]
#Tue(R
ainfall>10m
m),94-98
−0.153
−0.099
[0.109][0.105]
#Wed(R
ainfall>10m
m),94-98
−0.168
−0.231
[0.152][0.155]
#Thu(R
ainfall>10m
m),94-98
−0.123
−0.113
[0.128][0.131]
#Fri(R
ainfall>10m
m),94-98
0.1240.200 ∗
[0.110][0.103]
StandardControls
yesyes
yesyes
yesyes
yesyes
Additional
Controls
nono
noyes
nono
nono
Com
mune
Effects
yesyes
yesyes
yesyes
yesyes
R2
0.510.51
0.510.52
0.510.52
0.160.16
N1367
14221358
14331433
14331432
1432
Note:#
ofSat(R
ainfall>10m
m)isthe
number
ofSaturdayswith
rainfallabove10m
mduring
theperiod
October
1990to
March
1994(and
similarly
forall
otherweekdays).
%Civilian
Perpetrators
perHutu
(p.H)ismeasured
inpercent.
Incolum
n1wedrop
sectorswith
mass
graves(indicating
highdeath
rates).In
column2wedrop
sectorsin
thecapital
Kigali
andin
column3wedrop
allsectors
inand
closeto
themain
cities.In
column
4weadd
additionalcontrols.These
arepopulation
density,distanceto
Kigali,N
yanza,theborder,the
closestmain
roadand
theclosest
main
city.In
columns
5and
6,wealso
controlfor
futurerainfall.
Incolum
ns7and
8,weuse
adum
myindicating
whether
amass
gravewas
foundin
thesector
asan
alternativedependent
variable.StandardControls
includeaverage
dailyrainfallfor
January1984
toSeptem
ber1990
andaverage
dailyrainfall
forOctober
1990to
March
1994.Allregressions
arerun
usingweighted
leastsquares
(WLS)
estimation
with
populationsize
asweights.
There
are142
communes
inthe
sample.
Standarderrors
areclustered
atthe
commune
level.*significantat
10percent,**significant
at5percent,
***significantat
1percent.
FIGURES AND TABLES 107
Tab
le3.5:
Exclusion
Restriction
Dep
endent
variab
le:
%Civilian
Perpe
trators,
p.H.
Excl.Pre-
LocalH
utu
LocalT
utsi
Pub
licHoliday
sViolence
Lead
ers
Lead
ers
(1)
(2)
(3)
(4)
(5)
(6)
(7)
#Sa
t(Rainfall>
10mm)
−0.
407∗∗∗
−0.
393∗∗∗
−0.
483∗∗∗
−0.
481∗∗∗
−0.
466∗∗∗
0.70
60.
399
[0.1
24]
[0.1
22]
[0.1
49]
[0.1
20]
[0.1
23]
[0.8
96]
[0.7
96]
#Su
n(Rainfall>
10mm)
0.04
50.
049
0.05
30.
043
0.58
3[0
.103]
[0.1
06]
[0.1
21]
[0.0
98]
[0.8
96]
#Mon
(Rainfall>
10mm)
0.08
20.
074
0.11
60.
045
0.08
7[0
.110]
[0.1
11]
[0.1
31]
[0.1
10]
[0.3
87]
#Tue(R
ainfall>
10mm)
0.03
10.
040
0.00
20.
037
0.00
2[0
.095]
[0.0
94]
[0.0
95]
[0.0
82]
[0.4
24]
#Wed(R
ainfall>
10mm)
0.03
00.
028
−0.
025
−0.
002
0.67
3∗
[0.1
13]
[0.1
10]
[0.1
16]
[0.1
16]
[0.3
49]
#Thu
(Rainfall>
10mm)
−0.
006
−0.
000
0.12
6−
0.07
20.
814
[0.1
34]
[0.1
35]
[0.1
51]
[0.1
27]
[0.6
81]
#Fri(Rainfall>
10mm)
−0.
050
0.00
5−
0.01
60.
010
−0.
193
[0.1
17]
[0.1
32]
[0.1
18]
[0.0
92]
[0.4
00]
#Pub
.Holidays(Rainfall>
10mm)
−0.
597
[2.3
69]
#Non
-Rel.H
olidays(Rainfall>
10mm)
−1.
439
[1.7
59]
#Rel.H
olidays(Rainfall>
10mm)
−5.
424
[3.8
71]
Stan
dard
Con
trols
yes
yes
yes
yes
yes
yes
yes
Com
mun
eEffe
cts
yes
yes
yes
yes
yes
yes
yes
R2
0.52
0.52
0.49
0.54
0.55
0.32
0.35
N1433
1433
1213
1272
1272
161
161
Note:
#of
Sat(Rainfall>10
mm)is
thenu
mbe
rof
Saturdayswithrainfallab
ove10mm
during
thepe
riod
Octob
er1990
toMarch
1994
(and
simila
rlyfor
allo
ther
weekd
ays).%
Civilian
PerpetratorsperHutu(p.H
)ismeasuredin
percent.In
columns
1an
d2wealso
controlfor
thenu
mbe
rof
public
holid
ays
(separated
into
relig
ious
andno
n-relig
ious
holid
aysin
column2)
withrainfallab
ove10mm.In
column3wedrop
sectorswhere
violence
againstTutsi
took
placebe
fore
thegeno
cide.In
columns
4an
d5thesampleis
restricted
tosectorswithpro-geno
cide
partiesrulin
gthecommun
e.In
columns
6an
d7,
thesampleis
restricted
tosectorswithan
ti-genocidepa
rtiesrulin
gthecommun
e.Stan
dard
Con
trolsinclud
eaverageda
ilyrainfallforJa
nuary1984
toSeptem
ber1990
andaverageda
ilyrainfallforOctob
er1990
toMarch
1994.Allregression
sarerunusingweigh
tedleastsqua
res(W
LS)
estimation
withpo
pulation
size
asweigh
ts.There
are14
2commun
esin
thesample.
Stan
dard
errors
areclusteredat
thecommun
elevel.*significan
tat
10pe
rcent,
**sign
ificant
at5pe
rcent,***significan
tat
1pe
rcent.
108 PREPARING FOR GENOCIDE: COMMUNITY MEETINGS IN RWANDA
Table
3.6:Channels
-Interaction
Effects
Dependent
variable:%
Civilian
Perpetrators,p.H
.
LocalHutu
LeadersLocalT
utsiLeaders
(1)(2)
(3)(4)
(5)(6)
(7)(8)
#Sat(R
ainfall>10m
m)
−0.695 ∗∗
−0.533 ∗∗∗
−0.378 ∗∗∗
−0.541 ∗
4.292 ∗0.295
−1.463
1.820[0.294]
[0.124][0.140]
[0.309][2.396]
[0.801][0.870]
[1.470]#
Sat(Rainfall>
10mm)x
Radio
Own-
ership0.659
0.414−
12.925 ∗−
11.722 ∗
[0.786][0.895]
[6.543][6.572]
#Sat(R
ainfall>10m
m)xPopulation
Density
0.134 ∗∗∗0.125 ∗∗∗
0.3550.279
[0.023][0.032]
[0.494][0.540]
#Sat(R
ainfall>10m
m)x
Tutsi
Mi-
norityShare
−1.090
−1.505
10.01610.834 ∗
[1.526][1.533]
[6.208][6.133]
StandardControls
yesyes
yesyes
yesyes
yesyes
Other
Weekday
Controls
yesyes
yesyes
yesyes
yesyes
Com
mune
Effects
yesyes
yesyes
yesyes
yesyes
R2
0.550.55
0.550.55
0.360.36
0.360.38
N1272
12721272
1272161
161161
161
Note:
#of
Sat(Rainfall>
10mm)is
thenum
berof
Saturdayswith
rainfallabove
10mm
duringthe
periodOctober
1990to
March
1994.%
Civilian
Perpetrators
perHutu
(p.H)is
measured
inpercent.
Radio
Ownership
isthe
fractionof
Hutu
owning
aradio.
Tutsi
Minority
Shareis
thefraction
ofTutsi
dividedby
thefraction
ofHutu.
Incolum
ns1to
4the
sample
isrestricted
tovillages
with
pro-genocideHutu
partiesruling
thecom
mune.
Incolum
ns5to
8the
sample
isrestricted
tovillages
with
anti-genocidepro-T
utsiparties
rulingthe
commune.
StandardControls
includeaverage
dailyrainfall
forJanuary
1984to
September
1990and
averagedaily
rainfallfor
October
1990to
March
1994.Other
Weekday
Controls
includethe
number
ofSun/M
on/Tue/W
ed/Thu/Fri
with
rainfallabove
10mm
duringthe
periodOctober
1990to
March
1994.Allregressions
arerun
usingweighted
leastsquares
(WLS)
estimation
with
populationsize
asweights.
There
are142
communes
inthe
sample.
Standarderrors
areclustered
atthe
commune
level.*significant
at10
percent,**significant
at5percent,
***significantat
1percent.
APPENDIX 109
Appendix
Table A.1: Main Effects - Linear Specification
Dependent Variable: % Civilian Perpetrators, p.H. % Militiamen, p.H.
(1) (2)
Average Rainfall Sat −4.093∗∗ −0.569[1.635] [0.355]
Average Rainfall Sun 0.541 −0.175[1.783] [0.480]
Average Rainfall Mon 0.306 0.507[1.543] [0.432]
Average Rainfall Tue 1.378 0.287[1.351] [0.422]
Average Rainfall Wed 1.703 −0.107[1.454] [0.303]
Average Rainfall Thu −0.082 −0.057[1.113] [0.298]
Average Rainfall Fri 0.262 −0.001[0.983] [0.235]
Standard Controls yes yesCommune Effects yes yesR2 0.51 0.36N 1433 1433
Note: Average Rainfall Sat is the average daily Saturday rainfall during the period from October1990 to March 1994 (and similarly for all other weekdays). % Civilian Perpetrators per Hutu (p.H) ismeasured in percent. Standard Controls include average daily rainfall for January 1984 to September1990. All regressions are run using weighted least squares (WLS) estimation with population sizeas weights. There are 142 communes in the sample. Standard errors are clustered at the communelevel. *significant at 10 percent, **significant at 5 percent, ***significant at 1 percent.
110 PREPARING FOR GENOCIDE: COMMUNITY MEETINGS IN RWANDA
Table A.2: Conley Standard Errors
Dependent Variable: % Civilian Perpetrators, p.H. % Militiamen, p.H.
25 km 50 km 75 km 25 km 50 km 75 km
(1) (2) (3) (4) (5) (6)
# Sat(Rainfall>10mm) −0.391∗∗∗ −0.391∗∗∗ −0.391∗∗∗ −0.053 −0.053∗ −0.053∗∗∗
[0.129] [0.134] [0.132] [0.035] [0.029] [0.026]∗∗
Number Sun>10 0.054 0.054 0.054 −0.031 −0.031 −0.031[0.097] [0.088] [0.078] [0.032] [0.034] [0.032]
Number Mon>10 0.129 0.129 0.129 0.121∗∗∗ 0.121∗∗∗ 0.121∗∗∗
[0.096] [0.108] [0.108] [0.036] [0.038] [0.041]Number Tue>10 0.062 0.062 0.062 −0.049∗ −0.049∗ −0.049∗
[0.104] [0.119] [0.116] [0.029] [0.027] [0.026]Number Wed>10 0.071 0.071 0.071 0.010 0.010 0.010
[0.105] [0.09] [0.087] [0.028] [0.024] [0.021]Number Thu>10 0.025 0.025 0.025 −0.046 −0.046 −0.046
[0.128] [0.14] [0.155] [0.036] [0.036] [0.035]Number Fri>10 −0.057 −0.057 −0.057 0.003 0.003 0.003
[0.11] [0.103] [0.09] [0.027] [0.03] [0.03]Standard Controls yes yes yes yes yes yesCommune Effects yes yes yes yes yes yesR2 0.48 0.48 0.48 0.37 0.37 0.37N 1433 1433 1433 1433 1433 1433
Note: # of Sat(Rainfall>10mm) is the number of Saturdays with rainfall above 10mm during the period October 1990to March 1994 (and similarly for all other weekdays). % Civilian Perpetrators per Hutu (p.H) and % Militiamen perHutu are measured in percent. Standard Controls include average daily rainfall for January 1984 to September 1990and average daily rainfall for October 1990 to March 1994. There are 142 communes in the sample. Standard errorscorrecting for spatial correlation within a radius of 25km, 50km and 75km are in square brackets, Conley (1999). Theradius used in each regression is given in the column header. *significant at 10 percent, **significant at 5 percent,***significant at 1 percent.
APPENDIX 111
Table A.3: Different Rainfall Thresholds
Dependent variable: % Militiamen, p.H.
Rainfall Threshold x: 6 mm 8 mm 9 mm 10 mm 12 mm
(1) (2) (3) (4) (5)
# Sat(Rainfall > x mm) 0.020 −0.037 −0.041 −0.057∗ −0.072∗∗
[0.030] [0.032] [0.030] [0.030] [0.034]# Sun(Rainfall > x mm) −0.022 0.005 −0.009 −0.037 −0.014
[0.027] [0.034] [0.033] [0.031] [0.038]# Mon(Rainfall > x mm) 0.021 0.024 0.05∗ 0.100∗∗∗ 0.060∗
[0.039] [0.034] [0.027] [0.031] [0.031]# Tue(Rainfall > x mm) 0.020 0.022 0.011 −0.046 0.022
[0.029] [0.031] [0.026] [0.030] [0.037]# Wed(Rainfall > x mm) −0.001 −0.027 −0.021 0.007 −0.016
[0.027] [0.037] [0.035] [0.028] [0.036]# Thu(Rainfall > x mm) −0.002 −0.015 −0.014 −0.064 −0.045
[0.024] [0.036] [0.039] [0.041] [0.042]# Fri(Rainfall > x mm) 0.023 0.049 −0.008 0.006 −0.026
[0.031] [0.035] [0.030] [0.027] [0.035]Standard Controls yes yes yes yes yesCommune Effects yes yes yes yes yesR2 0.36 0.36 0.36 0.37 0.36N 1433 1433 1433 1433 1433
Note: # of Sat(Rainfall>x mm) is the number of Saturdays with rainfall above x mm during the period October 1990to March 1994 (and similarly for all other weekdays). The value of x is given in the column header. % Militiamen perHutu (p.H) is measured in percent. Standard Controls include average daily rainfall for January 1984 to September 1990and average daily rainfall for October 1990 to March 1994. All regressions are run using weighted least squares (WLS)estimation with population size as weights. There are 142 communes in the sample. Standard errors are clustered atthe commune level. *significant at 10 percent, **significant at 5 percent, ***significant at 1 percent.
112 PREPARING FOR GENOCIDE: COMMUNITY MEETINGS IN RWANDATable
A.4:R
obustnessand
Placebo
Tests
Dependent
variable:%
Militiam
en,p.H
.Massgrave
inVillage
Without
Without
Without
Additional
FutureAlternative
Mass
Graves
Kigali
Major
Cities
Controls
Rainfall
Dep.
Var.
(1)(2)
(3)(4)
(5)(6)
(7)(8)
#Sat(R
ainfall>10m
m)
−0.044
−0.061 ∗∗
−0.069 ∗∗
−0.052 ∗
−0.069 ∗∗
−0.015 ∗∗∗
−0.013 ∗∗∗
[0.028][0.031]
[0.031][0.030]
[0.030][0.004]
[0.004]
#Sun(R
ainfall>10m
m)
−0.026
−0.037
−0.030
−0.036
−0.034
0.002[0.031]
[0.032][0.031]
[0.032][0.030]
[0.004]#
Mon(R
ainfall>10m
m)
0.089 ∗∗∗0.098 ∗∗∗
0.097 ∗∗∗0.112 ∗∗∗
0.104 ∗∗∗−
0.003[0.029]
[0.031][0.031]
[0.031][0.032]
[0.004]#
Tue(R
ainfall>10m
m)
−0.035
−0.048
−0.041
−0.057 ∗
−0.038
0.008 ∗
[0.030][0.031]
[0.031][0.029]
[0.029][0.004]
#Wed(R
ainfall>10m
m)
0.0120.001
0.0120.002
0.0090.006
[0.027][0.030]
[0.031][0.028]
[0.027][0.004]
#Thu(R
ainfall>10m
m)
−0.062
−0.062
−0.073 ∗
−0.052
−0.054
−0.003
[0.041][0.042]
[0.043][0.036]
[0.041][0.004]
#Fri(R
ainfall>10m
m)
0.0160.004
0.0040.015
0.017−
0.009 ∗∗
[0.026][0.027]
[0.029][0.025]
[0.026][0.003]
#Sat(R
ainfall>10m
m),94-98
0.0020.014
[0.028][0.028]
#Sun(R
ainfall>10m
m),94-98
0.078 ∗∗0.075 ∗∗
[0.033][0.031]
#Mon(R
ainfall>10m
m),94-98
−0.063 ∗
−0.060 ∗∗
[0.032][0.030]
#Tue(R
ainfall>10m
m),94-98
0.0390.043
[0.039][0.036]
#Wed(R
ainfall>10m
m),94-98
−0.022
−0.033
[0.037][0.033]
#Thu(R
ainfall>10m
m),94-98
−0.021
−0.025
[0.035][0.035]
#Fri(R
ainfall>10m
m),94-98
0.0390.050 ∗∗
[0.027][0.025]
StandardControls
yesyes
yesyes
yesyes
yesyes
Additional
Controls
nono
noyes
nono
nono
Com
mune
Effects
yesyes
yesyes
yesyes
yesyes
R2
0.370.37
0.380.38
0.360.38
0.160.16
N1367
14221358
14331433
14331432
1432
Note:#
ofSat(R
ainfall>10m
m)isthe
number
ofSaturdayswith
rainfallabove10m
mduring
theperiod
October
1990to
March
1994(and
similarly
forall
otherweekdays).
%Militiam
enper
Hutu
(p.H)is
measured
inpercent.
Incolum
n1wedrop
sectorswith
mass
graves(indicating
highdeath
rates).In
column2wedrop
sectorsin
thecapital
Kigali
andin
column3wedrop
allsectors
inand
closeto
themain
cities.In
column4
weadd
additionalcontrols.
These
arepopulation
density,distance
toKigali,
Nyanza,
theborder,
theclosest
main
roadand
theclosest
main
city.In
columns
5and
6wealso
controlfor
futurerainfall.
Incolum
ns7and
8weuse
adum
myindicating
whether
amass
gravewas
foundin
thesector
asan
alternativedependent
variable.StandardControls
includeaverage
dailyrainfallfor
January1984
toSeptem
ber1990
andaverage
dailyrainfall
forOctober
1990to
March
1994.Allregressions
arerun
usingweighted
leastsquares
(WLS)
estimation
with
populationsize
asweights.
There
are142
communes
inthe
sample.
Standarderrors
areclustered
atthe
commune
level.*significantat
10percent,**significant
at5percent,
***significantat
1percent.
APPENDIX 113
Tab
leA.5:E
xclusion
Restriction
Dep
endent
variab
le:
%Militiam
en,p
.H.
Excl.Pre-
LocalH
utu
LocalT
utsi
Pub
licHoliday
sViolence
Lead
ers
Lead
ers
(1)
(2)
(3)
(4)
(5)
(6)
(7)
#Sa
t[Rainfall>
10mm)
−0.
052∗
−0.
054∗
−0.
048
−0.
092∗∗∗
−0.
083∗∗∗
0.38
60.
214
[0.0
29]
[0.0
29]
[0.0
32]
[0.0
31]
[0.0
28]
[0.2
23]
[0.1
86]
#Su
n(Rainfall>
10mm)
−0.
030
−0.
033
−0.
019
−0.
034
−0.
017
[0.0
30]
[0.0
30]
[0.0
38]
[0.0
31]
[0.1
80]
#Mon
(Rainfall>
10mm)
0.10
4∗∗∗
0.10
4∗∗∗
0.10
7∗∗∗
0.06
6∗∗
0.41
9∗∗∗
[0.0
31]
[0.0
31]
[0.0
33]
[0.0
28]
[0.1
40]
#Tue(R
ainfall>
10mm)
−0.
032
−0.
033
−0.
057∗
−0.
035
−0.
214∗
[0.0
29]
[0.0
31]
[0.0
30]
[0.0
31]
[0.1
20]
#Wed(R
ainfall>
10mm)
0.00
40.
005
0.00
50.
007
−0.
101
[0.0
30]
[0.0
29]
[0.0
27]
[0.0
29]
[0.1
66]
#Thu
(Rainfall>
10mm)
−0.
061
−0.
060
−0.
030
−0.
080∗
0.04
5[0
.042]
[0.0
41]
[0.0
40]
[0.0
41]
[0.1
73]
#Fri(Rainfall>
10mm)
0.01
80.
026
0.03
30.
010
0.06
4[0
.028]
[0.0
30]
[0.0
27]
[0.0
28]
[0.1
53]
#Pub
.Holidays(Rainfall>
10mm)
−1.
055∗
[0.5
57]
#Non
-Rel.H
olidays(Rainfall>
10mm)
−0.
777∗∗
[0.3
59]
#Rel.H
olidays(Rainfall>
10mm)
−0.
837
[0.8
66]
Stan
dard
Con
trols
yes
yes
yes
yes
yes
yes
yes
Com
mun
eEffe
cts
yes
yes
yes
yes
yes
yes
yes
R2
0.37
0.37
0.36
0.36
0.37
0.33
0.37
N1433
1433
1213
1272
1272
161
161
Note:#
ofSa
t(Rainfall>10
mm)isthenu
mbe
rof
Saturdayswithrainfallab
ove10mm
during
thepe
riod
Octob
er1990
toMarch
1994
(and
simila
rlyforall
otherweekd
ays).%
Militiam
enperHutu(p.H
)is
measuredin
percent.
Incolumns
1an
d2,
wealso
controlforthenu
mbe
rof
public
holid
ays(separated
into
relig
ious
andno
n-relig
ious
holid
aysin
column2)
withrainfallab
ove10mm.Incolumn3,
wedrop
sectorswereviolence
againstTutsitook
placebe
fore
thegeno
cide.In
columns
4an
d5,
thesampleis
restricted
tosectorswithpro-geno
cide
partiesrulin
gthecommun
e.In
columns
6an
d7,
thesampleis
restricted
tosectorswithan
ti-genocidepa
rtiesrulin
gthecommun
e.Stan
dard
Con
trolsinclud
eaverageda
ilyrainfallforJa
nuary1984
toSeptem
ber1990
andaverageda
ilyrainfallforOctob
er1990
toMarch
1994.Allregression
sarerunusingweigh
tedleastsqua
res(W
LS)
estimationwithpo
pulation
size
asweigh
ts.T
here
are14
2commun
esin
thesample.Stan
dard
errors
areclusteredat
thecommun
elevel.*significan
tat
10pe
rcent,**sign
ificant
at5pe
rcent,
***significan
tat
1pe
rcent.
114 PREPARING FOR GENOCIDE: COMMUNITY MEETINGS IN RWANDA
Table
A.6:C
hannels-Interaction
Effects
Dependent
variable:%
Militiam
en,p.H.
LocalHutu
LeadersLocalT
utsiLeaders
(1)(2)
(3)(4)
(5)(6)
(7)(8)
#Sat(R
ainfall>10m
m)
−0.185 ∗∗
−0.105 ∗∗∗
−0.045
−0.089
−0.281
0.0760.011
−0.958
[0.079][0.029]
[0.038][0.083]
[1.055][0.179]
[0.406][1.479]
#Sat(R
ainfall>10m
m)x
Radio
Own-
ership0.292
0.1071.646
3.081
[0.216][0.265]
[3.661][4.640]
#Sat(R
ainfall>10m
m)xPopulation
Density
0.034 ∗∗∗0.040 ∗∗∗
0.326 ∗∗0.307 ∗∗
[0.006][0.008]
[0.114][0.123]
#Sat(R
ainfall>10m
m)x
Tutsi
Mi-
norityShare
−0.447
−0.647
1.0930.664
[0.535][0.606]
[1.870][1.605]
StandardControls
yesyes
yesyes
yesyes
yesyes
Other
Weekday
Controls
yesyes
yesyes
yesyes
yesyes
Com
mune
Effects
yesyes
yesyes
yesyes
yesyes
R2
0.370.37
0.370.37
0.370.39
0.370.40
N1272
12721272
1272161
161161
161
Note:
#of
Sat(Rainfall>
10mm)isthe
number
ofSaturdays
with
rainfallabove
10mm
duringthe
periodOctober
1990to
March
1994.%
Militiam
enper
Hutu
(p.H)
ismeasured
inpercent.
Radio
Ownership
isthe
fractionof
Hutu
owning
aradio.
Tutsi
Minority
Shareis
thefraction
ofTutsi
dividedby
thefraction
ofHutu.
Incolum
ns1to
4,the
sample
isrestricted
tovillages
with
pro-genocideHutu
partiesruling
thecom
mune.
Incolum
ns5to
8,the
sample
isrestricted
tovillages
with
anti-genocidepro-T
utsiparties
rulingthe
commune.
StandardControls
includeaverage
dailyrainfall
forJanuary
1984to
September
1990and
averagedaily
rainfallfor
October
1990to
March
1994.Other
Weekday
Controls
includethe
number
ofSun/M
on/Tue/W
ed/Thu/Fri
with
rainfallabove
10mm
duringthe
periodOctober
1990to
March
1994.Allregressions
arerun
usingweighted
leastsquares
(WLS)
estimation
with
populationsize
asweights.
There
are142
communes
inthe
sample.
Standarderrors
areclustered
atthe
commune
level.*significant
at10
percent,**significant
at5percent,
***significantat
1percent.
Chapter 4
Selection into Borrowing: Survey
Evidence from Uganda∗
4.1 Introduction
Business growth is tightly linked to credit access. Credit rationing caused by infor-
mation asymmetries, weak legal institutions and high transaction costs are, however,
believed to restrain developing credit markets (Amendariz de Aghion and Morduch,
2005). Microfinance emerged in Asia in the 1970’s as a solution to this challenge, and
has attracted much attention and praise during the past decades.1 Much of the opti-
mism around Microfinance stems from the fact that repayment rates were surprisingly
high - lending to the poor provided to be a profitable activity and this held the promise
that credit rationing would decrease with economic growth as a result. Recent studies
have however found the impact of microfinance initiatives to be limited. First of all,
∗I would like to thank the staff at BRAC Uganda Research and Evaluation Unit, in particularMunshi Sulaiman and Paul Sparks, for their collaboration and practical help during field work, andthe Uganda Small Scale Industries Association for helpful input during the survey design process.The paper has benefited from helpful comments from Selim Gulesci, Andreas Madestam, Anna Sand-berg and Jakob Svensson as well as from seminar participants at the IIES and the Stockholm andUppsala Development Group (DSG). Financial support from Handelsbanken’s Research Foundationis gratefully acknowledged.
1Among the pioneers of Microfinance are Grameen Bank of Bangladesh, founded in 1976, that to-gether with its founder Muhammad Yunus received the Nobel Peace price in 2006; and the BangladeshiNGO BRAC that started Microfinance activities in 1974 (BRAC, 2016).
115
116 SELECTION INTO BORROWING
despite large efforts to reach target clients, take-up rates remain low (Banerjee 2013;
Banerjee et al. 2015a). Moreover there seems to be little impact on the likelihood
of business startup and the income and welfare of borrower-households (Banerjee et
al. 2015a). Some experimental studies suggest that take-up and effectiveness of mi-
crofinance may improve if contractual terms are changed (Field et al., 2013; Karlan
and Zinman, 2008). However, the experimental work to date concerns existing clients;
i.e. clients who select into borrowing under prevailing contract terms, and therefore
can not shed light on how contractual changes would affect credit demand through
composition of the borrower pool among micro enterprises.2 Taking selection effects
into account may also lead to different conclusions regarding investment behavior and
success. Lenders may be reluctant to change loan terms out of fear of attracting riskier
clients that have a lower probability of repaying the loan. Therefore, understanding
how selection into borrowing is affected by contract terms is a first order concern when
seeking to understand how loans can be made more effective.
This study offers a first insight into selection effects due to changes in the standard
loan contract structure. I examine loan demand in a representative sample of urban
micro enterprises in Uganda - most without credit experience. Using hypothetical loan
demand questions, I explore how firm owners’ stated interest in taking a loan changes
when contract terms are altered from those of a standard loan contract. Specifically, I
vary the interest rate and the collateral requirement; two aspects of loan contracts that
play a crucial role for selection into borrowing in theoretical models of credit contract
structure and credit rationing (Stiglitz and Weiss, 1981; Wette, 1983; De Meza and
Webb, 1987). With my representative sample of firms, I am able to draw conclusions
about firm owners who normally would not borrow.
The sample, 925 micro and small firms, was drawn from a firm census conducted
specifically for the study in the Kampala metropolitan area in Uganda, and contains
both manufacturing and retail firms.3 The survey collected comprehensive information2In particular, Field et al. (2013) randomly offer a grace period to clients once loan groups are
formed and loans already approved; while Karlan and Zinman (2008) induce random variation in theloan maturity for previous borrowers.
3The census firms are representative for some of the most prevalent business sectors in urban and
4.1. INTRODUCTION 117
about the business activities along with measures of the firm owners risk-attitudes
and measures of the riskiness of the business environment in which the firm operates.
Moreover, respondents were presented a number of loan contracts that varied in core
features such as the interest rate, the collateral level and the flexibility of repayment. I
elicit loan demand based on the responses to these hypothetical questions. Specifically,
I focus on the interaction of loan demand with individual risk aversion, since the risk
aversion/riskiness of the borrower is a central aspect of loan contract theory due to its
relation to the likelihood of making successful investments.
I find that around 14 percent of the surveyed firms in my sample express an interest
in a "standard" loan similar to the business loans currently offered by Microfinance
institutions and NGOs in Uganda. This figure is close to actual take up rates in other
studies of microfinance: Crépon et al. (2015) observe a take-up rate of 16% in North
Africa while Banerjee et al. (2015b) find a rate of 19% in India. It is also similar to
actual borrowing experience of my respondents.4 Interestingly I find hypothetical loan
take up to be highly sensitive to changes in the contract. Moreover, the propensity to
select into borrowing as contract terms are changed from the standard ones depends
on individual risk aversion of the firm owner and the volatility in demand ("riskiness")
of her business environment. Specifically, while firm owners that are not risk averse
become 8-10 percentage points more interested in a loan if the interest rate is lowered
from 25% to 20% annually, firm owners with a risk aversion score above the median
are 16-18 percentage points more likely to start borrowing following this change, i.e.
about twice the effect size. Owners that face a less risky business environment in terms
of unpredictability and fluctuation in sales display a similar pattern: they are 15 per-
centage point more likely to start borrowing following a lower interest rate compared
to an 8 percentage point increase in demand among those with a more risky business
semi-urban areas of Uganda. According to the 2010/2011 Business Registry, welding and carpentryare two of the 3 single largest groups among manufacture in the country, together accounting for 30%of the manufacturing sector. According to the same report motor repair and the retail sectors I focuson are also among the most prevalent in the country, with wholesale of food and beverages being thesingle largest retail sector (Uganda Bureau of Statistics 2011).
4In my sample, 20.6% of respondents had experience of borrowing from semi-formal or formalsources while 9.7% had borrowed in the 2 years preceding the survey.
118 SELECTION INTO BORROWING
environment. If the collateral requirement is reduced from 100% to 50% of the loan
value, less risky and less wealthy firm owners start to borrow. Analyzing the results for
manufacturing firms and retail firms separately, I find that fluctuations in the business
environment affects take-up of both contracts substantially for manufacturing firms
while for retail sectors I do not observe differentially higher loan demand among firms
facing more fluctuations than among those with less volatile business environment.
Risk attitudes also affects take-up of the low interest rate-contract more for manufac-
turing firms than for retail firms. The results are robust to using alternative measures
of risk-aversion and risky environment, such as financial vulnerability and absence of
precautionary savings, also when controlling for wealth level of the firm owner, which
differs between sectors. Taken together, these findings suggest that repayment behav-
ior may improve as a result of changing these contractual details, given that loans are
also extended to new prospective clients. To address the concern that hypothetical
questions may lead to overestimation in the willingness to accept a contract, I con-
trol for firm owner specific effects to reduce possible measurement error. I also run
a series of validation tests to make sure that the hypothetical questions are properly
understood by my respondents.
This paper contributes to several strands of the literature. Firstly, it provides a
test of some of the central theoretical results in credit contract theory that seek to
explain the prevalence of credit rationing. (Stiglitz and Weiss, 1981; Wette, 1983; De
Meza and Webb, 1987). Empirical tests of these models are complicated by the fact
that one normally only observes borrowing patterns among those who have selected
in to the credit market. With my representative random sample of firms I am able to
observe firm owners who normally would not borrow and therefore I can test whether
credit markets are characterized by adverse or advantageous selection in my setting.
The paper also adds to the growing literature on credit access and use in developing
country contexts, including evaluations of Microfinance and other forms of semi-formal
credit. A handful of recently published studies provide the first larger scale randomized
evaluations of microfinance initiatives (Attanasio et al., 2015; Angelucci et al., 2015;
4.1. INTRODUCTION 119
Augsburg et al., 2015; Banerjee et al. 2015b; Crépon et al., 2015; Tarozzi et al., 2015).
Taken together, these studies do not find evidence of transformative effects of microfi-
nance on the lives of the poor, nor do they find positive effects on the extensive margin
of business ownership (startups). They do however find modestly positive effects on
business outcomes for already existing micro-businesses (Banerjee et al., 2015a). At
the same time, none of the studies find significant increases in household income or
consumption following this business growth. This is explained by microfinance merely
offering more freedom in decision making about occupational choice within the house-
hold, leading to a reshuffling of resources and labor towards the micro-enterprises
at the expense of other activities (ibid.). However, none of these studies is able to
deal with selection effects: they study households or businesses that have selected into
borrowing.
Within this literature, I more specifically contribute to the limited work on the role
of loan contract structure for the profitability and use of loans. In developing country
contexts I am aware of two recent empirical studies of how changes in the contract
terms affect loan demand and loan use (Field et al., 2013; Karlan and Zinman, 2008).
Just as the abovementioned work, these two studies are also restricted to exploring
intensive margin demand and loan use among existing or former borrowers. Mine is not
an experimental study and the results are therefore more suggestive in nature. Because
we know little about selection into borrowing and the loan attitudes of non-borrowers,
my results are nevertheless interesting.
By focusing on businesses rather than households, this study also contributes to
the literature on small business growth. In developing countries both in Africa and
Asia there is a large group of small businesses (both formal and informal) while very
few businesses grow beyond medium size. Recent studies have tried to understand the
determinants of and obstacles to business growth in developing countries by offering
cash grants (de Mel et al., 2008; Fafchamps et al., 2014), business training (Karlan
and Valdivia, 2011) and combinations of the two (Bandiera et al., 2016; Berge et al.,
2014; Fiala, 2013). The comprehensive survey data that I collect on firm characteristics
120 SELECTION INTO BORROWING
related to the firms’ production function, and the fact that I also have detailed data
on owner households, allows me to study the interaction of the production function
and the loan contract, and also to capture heterogenous effects, which have been
shown to matter in related work: Fiala (2013) finds small positive effects of cash
grants on business growth in northern Uganda, but only among high ability males.
de Mel et al. (2009) find that the propensity to innovate is strongly linked to firm
owners’ individual characteristics. Compared to previous studies my sample consists
to a larger share of manufacturing firms as opposed to retailers. Manufacturing firms
have different investment possibilities than retailers and my sample therefore allows for
investigating new aspects of investment in labor and capital compared to the previous
related literature on small business growth.
The remainder of the paper is structured as follows. In the next section, I provide
some institutional background regarding the geographic context and the credit prod-
ucts relevant in my studied setting. In section 3, I outline my hypotheses. Section 4
describes the sampling and survey methodology and the survey data I collected. Sec-
tion 5 presents and discusses the results along with the empirical specifications and
section 6 presents validation checks. Section 7 concludes the paper.
4.2 Institutional Background
In this section, I briefly describe the background of the study in terms of the market
in which the small firms are active and the type of credit available in this setting. This
lays the foundation for my hypotheses about selection connected to amendments to
the interest rate or collateral requirements of a credit contract.
4.2.1 Labor market and small businesses in Sub Saharan Africa
Unemployment and lack of formal sector jobs are significant problems in Sub-Saharan
Africa. Setting up a small business is a common livelihood strategy in urban and semi
urban regions. In 2013, it was estimated that 90% of Uganda’s private sector consisted
4.2. INSTITUTIONAL BACKGROUND 121
of micro, small or medium sized businesses (MSMEs)5 and around 28% of the labor
force was active in such businesses (World Bank, 2013).6
4.2.2 Standard loan contract
Previous studies have shown that the market for lending to the poor suffers from mar-
ket imperfections and is characterized by credit rationing (See e.g. Banerjee (2001)).
The type of credit available to businesses in my setting is most commonly offered
by Microfinance institutions including both NGOs and commercial credit institutions.
Contracts offered by this type of lenders typically display the following features:
1. Constant repayments starting early. This is a typical feature of microfinance
contracts, believed to decrease transaction and monitoring costs of the lender
while fostering a habit to make repayments on time.
2. Limited loan size Due to asymmetric information issues, the loan size is limited
both by the collateral held by the borrower and by the relation between borrower
and lender. Often repeat loans (with the same lender) are allowed to be larger.
3. High interest rates Studies in other developing country contexts report typical
rates around 20-25% APR. In the two studies most closely related to the current
one, interest rates (APR) are 22% (Field et al., 2013) and 200% (Karlan and
Zinman, 2008). In Uganda at the time of this study, annual interest rates offered
by large MFIs were also in the 20-25% interval.7
4. Collateral requirements, often in the form of land titles.8
5Uganda Investment Authority (2013).6According to the World bank’s Uganda Economic Update, 14% pf Uganda’s workforce were
employed in the non agriculture informal enterprise sector in 2013, while another 14% of the workforcewas employed in formal business, most of which was in the private sector. Both of these categorieswould correspond to micro or small enterprises.
7For example, one of the biggest MFIs in Uganda at the time of the study; PRIDE Microfinancein Uganda offered loans with 26% APR and demanding a 100% collateral (Fiala (2013). A "mysteryshopper" investigation carried out by the research team at the time of my data collection revealedsimilar conditions among other prominent MFIs in the Kampala area.
8While not a typical feature of the classical microcredit contract, that instead sometimes relies onthe "social collateral", business loans in Uganda, typically do require collateral. Such loans are larger
122 SELECTION INTO BORROWING
Prevailing contract terms offered by Micro finance institutions are standardized in
order to simplify operations for both loan officers and borrowers, and make it possible
for lenders to extend borrowing activities to a large number of clients. While designed
to minimize risk and lower the monitoring cost of the lenders (Banerjee, 2001) these
contract terms may however also tilt investment towards investments that are smaller
and involve less learning than optimal. When designing business loans, as opposed to
individual micro loans, it may therefore be of particular interest to test the optimality
of the prevailing loan terms.
4.3 Hypotheses
In this section, I list the hypotheses tested in this paper. The first set of hypotheses
are about firm owner responses to lowering the interest rate on business loans. I in-
vestigate whether a lower interest rate leads to adverse or advantageous selection into
the borrower pool. In the theoretical literature, Stiglitz and Weiss (1981) showed that
credit rationing can prevail in equilibrium since increasing the interest rate is associ-
ated with a borrower pool more dominated by risky borrowers/projects (with a high
risk of failure). In their model, when the interest rate is raised beyond a certain level,
extending credit to riskier borrowers by further increasing the interest rate is associ-
ated with a decrease in profits for the lender. This results in an equilibrium with excess
demand of credit: risky borrowers are willing to pay higher interest rates but lenders
are not willing to extend such loans. Conversely, De Meza and Webb, (1987) show that
under different assumptions about the distribution of project returns, lower interest
is associated with excess investment in risky projects. In their model, increasing the
interest rate can be a way to curb the over investment that would occur under a lower
interest rate. I do not have a direct measure of the project that borrowers invest in
and I therefore focus on the riskiness of borrowers, proxied by their self reported risk
and often have a longer time horizon than loans that can be accessed through microcredit groups.
4.3. HYPOTHESES 123
aversion and the volatility of their business environment.9
H01: Firm owners that are more risk averse or operate in a less risky environment
are more likely to select into borrowing when the interest rate is lowered (Stiglitz and
Weiss, 1981).
HA1: Firm owners that are more risk averse or operate in a less risky environment are
less likely to select into borrowing when the interest rate is lowered (De Meza and
Webb, 1987).
The next hypotheses are about firm owner responses to lowering the required collat-
eral. Stiglitz and Weiss (1981) show that just as increases in the interest rate attracts
riskier projects, so does increases in the collateral, keeping interest rates constant, if
borrowers are risk averse. Wette (1983) shows that the latter result of Stiglitz and
Weiss holds also for risk neutral borrowers. Keeping the interest fixed and instead
raising the collateral can lead to similar adverse selection into borrowing, lowering the
expected return of the lender. Boucher et al. (2008) more directly discuss how credit
rationing through the collateral channel is affected by borrower wealth. They distin-
guish between two types of credit rationing that collateral requirements may entail in
developing country credit markets: quantity rationing where the inability to provide
collateral excludes poorer borrowers from credit markets, and risk rationing, where
borrowers with collateral refrain from borrowing due to the risk of losing the collat-
eral. Contrary to quantity rationing - which unambiguously excludes poorer potential
borrowers from credit, risk rationing may prevent wealthier people from borrowing if
the wealth is in liquid assets, while results are ambiguous if wealth is in the form of
land.
9As in Stiglitz and Weiss (1981) and De Meza and Webb (1987) I vary the interest rate whilekeeping the collateral fixed. An equilibrium in which no credit rationing prevails is characterized byBester (1985) whose model allows both interest and collateral to change at the same time. Such amodel is less relevant for my microfinance setting where credit rationing is a stylized fact and therigidity of credit contracts prohibit lenders from tailoring loan agreements to specific borrowers.
124 SELECTION INTO BORROWING
H02: Firm owners that are more risk averse or operate in a less risky environment are
more likely to select into borrowing when the collateral requirement is lowered (Stiglitz
and Weiss, 1981; Wette, 1983).
HA2: Firm owners that are more risk averse or operate in a less risky environment are
less likely to select into borrowing when the collateral requirement is lowered.
In addition to testing these hypotheses, I also examine if and how the responses to
changes in the loan contract affect firm owners in different types of sectors differently.
In particular, I estimate results separately for retain sectors and for manufacturing
sectors. The success probability of a project is tied to the type of investment implied
by the project. Risk attitudes and riskiness of the business environment is likely to
be of less importance for certain types of investments (e.g. in already familiar tech-
nologies) than for others. Since investment options are very different between retail
and manufacturing firms, this sectoral distinction is likely to affect loan demand and
borrower behavior.
4.4 Survey methodology and Data
The survey data was collected in the Kampala metropolitan area in March and April
2013. Fieldwork was carried out in collaboration with the Research and Evaluation
Unit of the NGO BRAC Uganda, managed and supervised by the author together
with local research officers and carried out by a team of enumerators (interviewers)
recruited specifically for the project. The businesses surveyed are a random sample
drawn from a larger pool of businesses whose contact details were collected in a census
preceding the survey. Details about the sampling and data collection are described
below.
4.4. SURVEY METHODOLOGY AND DATA 125
4.4.1 Census and Sample selection
The census was conducted in January and February 2013. Firms were chosen on the
basis of their business sector and geographic location. Business sectors were selected
as to represent the main sectors in urban and semi-urban Uganda within both man-
ufacture and retail, and to ensure that both female and male business owners would
be represented. The specific sectors included are broadly grouped into retail sectors
and manufacturing sectors, with the former category including supermarkets, smaller
food retail shops, food and beverages wholesale and hardware shops, and the latter
category including carpentry, welding/metal works and motor repair workshops (both
of cars and of motorcycles). The enumerators were instructed to approach all firms
in selected business sectors, with some restrictions on the size and type of business
structure. The lower bound set on firm size depended on the sector: to be included
in the census; manufacturing firms (including motor repair) were required to have at
least 1 employee (formal or informal) in addition to the owner, while firms in retail
were required to have a permanent business location and a well-stocked shop. The
upper bound was set at 15 employees (formal or informal) regardless of the business
sector. The sample thus consists of larger and more established firms than the micro
businesses and households that are the focus of most related studies, and has a heav-
ier emphasis on manufacturing sectors. According to the Ugandan Business registry
2010-11, 98% of all businesses in the country had less than 10 employees and thus
were classified as micro, small or medium sized businesses, and 87% of the workers in
the private sector were working in a business with less than 50 employees (Uganda
Bureau of Statistics 2011).10 I study loan attitudes among owners of micro- and small
10The official definition of Micro, small, medium and large businesses in Uganda is the following:Micro businesses were those with an annual turnover of less than 5 million shillings irrespective ofthe number of employees, while small businesses were those with an annual turnover of between 5and 10 million shillings irrespective of the number of employees. Medium businesses on the otherhand were those with an annual turnover of more than 10 million shillings but employing less than50 persons while the large businesses were those with an annual turnover of more than 10 millionshillings and employing at least 50 persons (Uganda Bureau of Statistics 2011). While definitionsdiffer substantially between countries, an international standard definition has been created by theInternational Labor Organization. This definition states that a micro business is an enterprise withup to ten employees, while small enterprises are those that have 10-100 employees, and medium-sized
126 SELECTION INTO BORROWING
businesses in sectors that make up the bulk of the urban private sector in Uganda.11
1,353 businesses were interviewed for the census. Importantly, most of them had
no previous loan experience. The enumerators approached the businesses with a script
saying that they were part of a research project by researchers at Stockholm University
(in Europe), about business growth in "enterprises like yours" and "learning about the
difficulties and opportunities for growth of firms in your sector", and that the data
would be treated with anonymity. Since BRAC is well known as a Microfinance insti-
tution, in order not to prompt respondents to think about loans, potentially deterring
loan averse individuals from taking part in the survey the name of "BRAC" was not
mentioned to respondents.
Based on this census listing a random sample was drawn, stratified by business sec-
tor, of 985 businesses to be interviewed in the main survey .12 Female owned businesses
were over-sampled. The response rate was 94% resulting in 925 businesses participating
in the survey.
4.4.2 Survey data
The survey provided detailed information on firms’ input and investment choices and
their demand for credit under different hypothetical loan contracts. By examining the
choices made by firm owners, the survey allows me to explore how possible take up of
credit may be affected by changes in the cost of lending and collateral levels.
Specifically; the survey was designed so that the hypothetical contracts presented
to the respondents reveal the effect of credit constraints caused by interest rate and
enterprises have 100 to 250 employees (International Labor Organization 2015).11According to the 2010/2011 Business Registry published by the Uganda Bureau of statistics
(UBOS), welding and carpentry are among the 3 single largest groups among manufacture in thecountry and together account for 30% of the manufacturing sector. According to the same reportmotor repair and the retail sectors I focus on are also among the most prevalent in the country, withwholesale of food and beverages being the single largest retail sector (Uganda Bureau of Statistics2011).
12Based on statistics from the census it was decided not to stratify according to geography orborrowing experience since the distribution of sectors was similar across geographical locations andsince loan experience was very limited in all sectors.
4.4. SURVEY METHODOLOGY AND DATA 127
collateral requirements.13 In addition, the survey includes modules on firms’ employ-
ees, assets, costs and revenues, seasonality of sales, vulnerability to shocks, credit
history, types of interactions with other businesses, the business-owners’ background,
education, financial literacy, risk attitudes and his/her household’s demographics. A
few sections of the survey require additional explanation, as they are central for my
analysis. These are described in more detail below.
4.4.2.1 Measures of loan demand
To learn more about selection into borrowing, and to investigate which firm charac-
teristics are particularly relevant for loan demand, I included a module eliciting hy-
pothetical loan demand. This section began by offering firms a generic contract with
terms and amounts similar to those of the contract offered by BRAC Small Enterprises
Program and other MFIs in urban Uganda, and then went on to present additional
contracts that amended the contractual aspects to measure the relevance of various
constraints. The benefit of this approach is twofold. First, by exploring within-subject
responses, I partially circumvent the critique that respondents tend to over- or under-
state hypothetical demand compared to the real willingness to accept a contract (See
e.g. Neill et al. (1994)). Also, by presenting each contract unconditionally of the stan-
dard one, the survey overcomes the problem of firms self-selecting on the generic loan,
which could potentially bias the responses to the remaining contracts. In my case, all
925 firms were offered the option of the generic and the perturbed contracts. Loan
officers at BRAC provided input about the phrasing of the contract description for
the hypothetical contracts, to make sure that the loan contracts would be adequately
explained to respondents with varying degrees of loan experience and financial literacy.
The benchmark, "standard" contract is described as follows:
"Imagine you were offered the opportunity to take a loan. If you decide to take this loan,
you can borrow up to 8 million Shillings. You would need to repay this amount plus a 25%
13The survey also included contract amendments designed to study constraints such as uncertainand back-loaded return paths as well as the constraints large fixed costs may pose.
128 SELECTION INTO BORROWING
interest within one year. The repayments have to be done in equal monthly repayment install-
ments over the year. [Here, the enumerator was urged to show an example to the respondent].
The lender requests security (collateral) in the form of land. That is, in order to borrow a
certain amount, for example, 3 million14, you need to have formal property rights to land
valued to 3 million and in case you fail to repay, the lender will claim the 3 million in terms
of your land."
The respondent is then asked if they would take such an offer, how much they
would borrow and for what main use. Thereafter, other contracts are described to the
respondent. Here my focus is on the following contract variations15:
• Low interest rate contract: the APR is lowered from 25% to 20%.
• Low collateral contract: The collateral requirement is lowered from amounting
to 100% of the loan size value to only 50% of the value. The collateral is still in
the form of land.
The difference between the standard contract and each amended contract was made
salient and an example was used to show the repayment structure and the size of each
installment with the low interest rate contract, and to show the size of the collateral
with the low collateral contract. Thereafter the respondent is asked if they would take
a loan under those contract terms. The exact wording of the contract variations can
be seen in the Appendix.
4.4.2.2 Measures of risk attitudes and riskiness
The literature has emphasized risk attitudes of the borrower as an important factor
affecting credit demand and investment behavior. Moreover, the riskiness of the project
that the loan is used to finance is also a central component in the decision of the
lender of whether or not to approve a loan, and in the determination of interest rate14Using the 2013World Bank PPP adjusted exchange rate for Uganda (1,014 UGX/USD), 3,000,000
corresponds to 2960 USD. Using the nominal exchange rate of April 1, 2013 (2585 UGX/USD),3,000,000 UGX corresponds to 1161 USD.
15In the validation checks section where I examine the quality of the hypothetical loan contracts Ialso discuss an additional contract amendment in which the collateral type was changed.
4.4. SURVEY METHODOLOGY AND DATA 129
and collateral levels. I collect data on risk attitudes. Since I do not observe actual
borrowers, I do not have a perfect proxy for the riskiness of projects. Instead I construct
measures of the risk aversion of the owner and the riskiness of the firm owner’s business
environment. These variables are explained in the next paragraphs.
Risk attitudes: as my measure of risk aversion, I use a survey question where the
respondent was asked to make a judgement of their own willingness to take risks. More
specifically, I ask them to place themselves on a 0−10 scale between "Not at all willing
to take risks" and "Very willing to take risks". I define "risk aversion" as a dummy
variable taking the value 1 if the respondent is below median on this self-reported risk
taking scale, and 0 otherwise. This measure is taken from the German Socio-Economic
Panel and validated by Dohmen et al. (2011) to be predictive of financial risk. Unlike
other commonly used methods of eliciting risk preferences it involves no computations
and should therefore be appropriate for my setting of less educated respondents.16
Riskiness: In addition to a measure of risk attitudes I am also interested in a measure
of the riskiness of the enterprises’ business environment and activities. To capture this,
I construct two indices based on the responses to a list of statements about possible
reasons why loan repayment may be hard, where the respondent indicates to what
extent they agree with each statement. The measures obtained here are thus directly
related to the business practices and environment of the enterprise. The first index
that I construct is the riskiness index which is higher if the respondent agrees that
fluctuations and uncertainty are important constraints for repaying a loan.17 To ensure
16The survey also included a section with a lottery-type elicitation of risk attitudes. Here therespondents answered five question in which they choose between (a) a sure amount and (b) a coinflip between 0 and a gradually increasing amount. The seminal work for measuring risk attitudes usinga list of choices the Binswanger (1981) protocol where the probabilities of the lottery are varied. Ichose not to use it because the concept of probabilities would be too difficult to explain to my poolof less educated respondents. Our questions are instead an adaptation of the protocol used in Holtand Laury (2002), with simplified probabilities, as in Abdellaoui et al. (2011) and Bosch-Domènechand Silvestre (2013). Related studies in developing country contexts have also used similar methodto measure risk preferences (see e.g. de Mel et al. (2008)). Despite these simplification of the list ofchoices, responses turned out to vary with the interviewer. For this reason the 11 point scale (forwhich answers were not correlated with interviewer) provides the basis of the risk averseness measureused in my final analysis.
17More specifically, the respondent was asked to respond on a 4-point scale between strongly agreeand strongly disagree to the following statements: (1) It is difficult to make loan repayments on time
130 SELECTION INTO BORROWING
that I do not just capture a general unwillingness to borrow, and that my obtained
measure is not correlated with the confidence of the respondent (some respondents may
be more inclined to saying that they agree with all the statements because they find
all aspects of repayment equally challenging) I also construct a placebo index based on
responses to three other statements about difficulties with repaying loans; constraints
that are not associated with fluctuation and uncertainty.18 I will also examine three
alternative measures of a risky business environment or behavior, explained in the
next subsection.
4.4.2.3 Alternative ways of measuring risk behavior
In addition to the measures of risk aversion and riskiness described in the previous
subsection, in my robustness tests, I use three alternative ways of proxying for risk.
These are (i) financial vulnerability, (ii) having precautionary savings, and (iii) the
habit of selling on credit. Financial vulnerability is assessed using survey questions
about the respondent’s ability to access a certain amount of money for an emergency. I
asked the questions for 2 amounts: 500,000 Ugandan shillings (193 USD) and 2 Million
Ugandan shillings (775 USD).19 For each value, I create a dummy that takes the value
of one if the respondent says that she would be able to obtain the amount interest
free (i.e. in other ways that borrowing the amount from an MFI or a moneylender).
High financial vulnerability may be correlated with a higher risk of not being able to
repay a loan. Precautionary savings is a dummy that equals one if the respondents
answers yes to a survey question asking whether the respondent is currently saving
anywhere. Having savings is used as a proxy for being a less risky borrower. Finally,
due to sales fluctuations (2) It is difficult to make loan repayments on time because it is hard topredict when sales will be good or bad. The index is the average score for these two questions.
18Specifically, the statements are: (1) It is difficult to get a loan because it is hard to know whereto get the best terms (2) It is difficult to get large enough loans to make good business investments(3) It is difficult to make loan repayments on time because it takes a while to know how to generateprofits from an investment.
19In real terms, 500,000 UGX corresponds to 493 USD (using the 2013 World Bank PPP adjustedexchange rate for Uganda) while 2,000,000 corresponds to 1973 USD. Using the nominal exchangerate of April 1, 2013, 500,000 UGX corresponds to 193 USD while 2,000,000 UGX corresponds to 774USD.
4.4. SURVEY METHODOLOGY AND DATA 131
selling on credit is a dummy that equals one if the respondents reports to regularly
sell to customers on credit. This is used as a proxy for risky behavior.
4.4.3 Using hypothetical questions
I measure loan demand using a series of hypothetical questions. The choice of using hy-
pothetical questions is motivated by two factors. First, to understand selection effects
in relation to changing credit contract terms, interviewing a representative sample of
businesses - both borrowers and non borrowers - is of crucial importance. I interview a
representative sample of businesses and cover sectors that are not currently the main
target of MFIs and other semi-formal loan-providers.20 While extending credit to these
businesses is a goal of any MFI, doing so requires learning more about their loan de-
mand and loan use. Hypothetical questions is a first step in building this knowledge.
Second, given the wide-spread reluctance to loan take-up and to MFIs among many
business owners in my setting21, the hypothetical question setup provides a way to
approach business owners who might otherwise refuse to participate. Since this study
aims to address the low efficiency of loans, some of which may be explained by the
low take-up rate of businesses with certain preferences or technologies; I did not want
to deter businesses that were reluctant to think about loans from participating in the
study. Presenting the loan attitude questions as purely hypothetical alleviated the risk
of this happening. For similar reasons, I presented the study as a research project from
Stockholm university rather than from BRAC, which was my partner in carrying out
the fieldwork, since BRAC may be known for its micro finance activities and, again,
prevent those business owners reluctant to loans from participating.
Hypothetical questions are, however, often associated with concerns about misre-
porting and bias (See e.g. Neill et al., 1994). For example, certain respondents may
overestimate their demand for goods while others give estimates that are lower than20These are sectors in manufacturing and services: Carpenters, welders and motor repair workshops
for cars and motorcycles.21Several business owners told me during the piloting that they distrust MFIs since these will trick
people and steal their land, and 71% of the respondents report that they distrust NGO’s/developmentorganizations.
132 SELECTION INTO BORROWING
their actual demand. Replies could also be affected by the timing and circumstances
of the interview or by the interaction between the respondent and the interviewer. In
other words, these concerns mainly regard individual- and interview occasion-specific
unobservables that complicate the interpretation of the valuations and would be less
serious if focusing on within subject variation, since the level of misreporting is corre-
lated across responses from the same individual (List and Shogren, 2002). Thus, my
main specification is a within-subject specification where I include firm fixed effects.22
In an alternative specification without firm fixed effects, I include controls for the
interview occasion and interviewer.
4.4.3.1 Sector specific firm performance data
The relation between loan take-up and riskiness is likely to be affected by important
factors within the firms’ production function. Investment choices as well as the types
of risk that affect the success of an investment depends on the sector in which the firm
is active. I collect data that allows me to describe retail and manufacturing sectors
and examine differences between them. I include modules on firms’ employees, assets,
costs and revenues. To obtain a reliable measure of business assets and output, survey
construction required detailed knowledge about the equipment and production meth-
ods commonly used by manufacturing firms. To gain such insight I relied on official
manuals for vocational training in Uganda (used in the BTVET or Business, Tech-
nical, Vocational Education and Training schools). In addition, short surveys about
tools and machines were carried out among the manufacturing businesses during the
piloting phase.23 I also collect information on whether demand for the products sold
in the sector is sensitive to business cycles or seasonal variation, or to various shocks,22Since only one interview was done with each firm owner this fixed effect also captures the interview
occasion and interviewer.23Research assistants visited 20 firms from each of the sectors interviewed and asked them to list
all the machines and tools that they owned or used in their business. This data was then aggregatedand allowed me to identify the most important tools and machines in each sector. I also held twomeetings with experts in a local trade association for carpenters, welders, and motor repair shopsin Uganda (Uganda Small Scale Industries Association, USSIA) to confirm the list of machines andtools and to gain further insight into the manner in which firms of different size have access to suchtechnology.
4.4. SURVEY METHODOLOGY AND DATA 133
and about the access to employees and the mode of employing them. Moreover, I col-
lected data on vulnerability to shocks, credit history, business networks and types of
interactions with other businesses.
4.4.4 Summary Statistics
Columns 1-3 of Table 4.1 shows the summary statistics on a number of important
variables for the entire sample of firms. The average firm in my sample is 6.7 years
old and 31% of firms are in manufacturing sectors while the remaining are in retail
sectors. The average firm size is 2.7 workers, including the owner, and the average re-
ported asset value corresponds to about 850 USD24 while average reported stock-value
corresponds to around 5000 USD. Many of the firms can thus be classified as micro
or small enterprises.25 The average level of education is 11.5 years which corresponds
to finished secondary school (O-level). Around 20% of owners have ever taken a loan,
and only 11% have taken a loan in the past 2 years. This is similar to shares found
for loan take-up in related studies of the effectiveness of microfinance, where take-up
rates are observed after microfinance was introduced in a geographical area. About
44% of the firm owners in the sample are landowners, which means they have access
to collateral for a loan.
Compared to the related literature, a few differences are particularly noteworthy.
First, the businesses in my sample are larger on average than those in most related
studies, where the focus has been on household enterprises with no employees. More-
over, there is a higher share of manufacturing businesses in my sample. This may
affect borrowing capacity and demand differently, something I discuss in more detail
in the next paragraph. In addition, the business owners in my sample are much less
used to borrowing than those observed in related studies of small business growth and
24Average reported asset value is 2.2 Million UGX. In real terms, this corresponds to 2170 USD(using the World bank PPP adjusted exchange rate for 2013) and in nominal terms to 845 USD (April1, 2013).
25According to a definition used by Uganda Bureau of Statistics, a micro-enterprise is one in whichthe annual turnover does not exceed 10 Million UGX and number of workers is up to 5, while a smallbusiness employs between 5 and 49 people and total assets between UGX: 10 million-100 Million.
134 SELECTION INTO BORROWING
microfinance, mainly due to the fact that they are not sampled from among a set of
previous borrowers or in collaboration with a Microfinance institution as in e.g. Field
et al. (2013), Karlan and Zinman (2008), Fiala (2013), Berge et al. (2014), Karlan and
Valdivia (2011) and Valdivia (2013). Although the differences in borrowing experience
between these and my study could to some extent be explained by differences between
geographical regions; even compared to the studies carried out in East Africa (Fiala,
2013; Berge et al., 2014), the share with loan experience in my sample is considerably
lower.
Columns 4 and 5 of Table 4.1 breaks down the sample by whether a firm is classified
as retail or manufacturing. Given the differences in production function between these
broad categories they are likely to have different investment needs which may affect
their credit demand. From the last 2 columns of the table, it is clear that manufactur-
ing and retail businesses differ on many characteristics. Manufacturing businesses in
my sample are on average 1.4 year older than retail businesses and have more employ-
ees: the average number of workers is 4.12 in manufacturing firms, compared to just
2.14 in retail firms, and 93% of the manufacturing firms have at least one employee
compared to only 46% of retail firms. Both reported profits, asset value and stock value
are significantly lower in manufacture firms than in retail firms. Due to the choice of
sectors in manufacture, the share of owners who are female is much lower than in
retail.26 Owners in manufacturing sectors also have on average 1 year less education
than owners in retail. Retailers are more likely to be banked and to keep books than
owners of manufacturing firms, while the latter - somewhat surprisingly - score higher
on financial literacy questions.27 Retailers are more likely to sell to customers on credit
while manufacture firm owners score higher on the "risk index" that is constructed
out of questions that measure riskiness of the business environment described above.
26Manufacturing sectors with a high share of female workers include tailoring and some types offood processing.
27To measure financial literacy I used the 3 simplest questions from the financial literacy sectionof the American Life Panel survey: Numeracy, Inflation and Money illusion, with amounts adaptedto my context. The questions are designed to measure the firm owner’s numeracy and understandingof the concepts of interest rate and inflation.
4.5. RESULTS 135
There is, however, no difference in the self assessed riskiness score. It is noteworthy
that no differences between manufacture and retail owners appear for factors related
directly to loan experience: the share with loan experience is almost identical, as is the
share who have ever delayed on loan repayment or ever been denied the requested loan
size when applying for a loan. Moreover, the share of owners who own land, the most
common type of collateral for loans, is similar between manufacturing and retail sec-
tors. Finally, the fact that manufacturing firms employ more labor is also reflected in
their reported investment demand, where manufacturing firms are considerably more
likely to report that they would want to hire more labor if they could. The manufac-
turing firms also appear more capital constrained: 76% answer in the affirmative to a
question about wanting more capital, compared to only 41% in the retail categories.28
Taken together, firm owners in retail and manufacturing are similar on loan experi-
ence characteristics, as well as on access to collateral, one prerequisite for borrowing
that has been highlighted by the literature. However, manufacturing firms have lower
levels of capital and turnover than retailers and their owners have lower education -
factors that may make them less suited for borrowing. Meanwhile, their demand for
investments, in both labor and capital, is higher than among owners of retail firms.
4.5 Results
4.5.1 Main Results
Table 4.2 shows an overview of the share of respondents expressing interest in the
standard loan contract as well as in each of the contract variations. The contracts
are presented in the order in which they were asked to respondents in the survey
instrument. 14.14% of the respondents reported that they would take a loan if offered
the standard contract. The take up rate of each of the amended contracts is significantly
higher, with 24.67% saying yes to the low interest contract and 27.84% saying yes to28The precise question asked for demand of labor is "If you could find suitable workers, would you
like to employ more workers than you are currently employing?" and for demand of capital: "Is thereanything you would like to buy for this business (e.g. a machine) but cannot do so?".
136 SELECTION INTO BORROWING
the low collateral contract.
4.5.1.1 Hypothetical loan demand - standard contract
To examine demand for the standard loan contract, I perform a simple comparison
of a number of key characteristics, between respondents who say no to the standard
contract and those who express interest in the contract. The results are presented
in Table 4.3. Firm owners who want a loan under the standard contract terms own
businesses that are on average one year older, have about 0.6 years less education,
and score higher on the financial literacy score. Not surprisingly they are also more
likely to have borrowing experience and fulfill formal requirements for borrowing: they
are more likely to own land and to have borrowed from formal or semi formal sources
previously. Conditional on loan experience, they are not more or less likely to have
been denied the loan size they applied for in the past. They face a slightly more
risky environment, in terms of unpredictability in sales (the riskiness index) and are
more likely to report wanting to employ more labor in the firm. There is no difference
between manufacturing and retail sectors in the expressed demand for the standard
loan contract.
4.5.1.2 Hypothetical loan demand - contractual changes
Next I analyze how individuals’ loan demand is affected by changes to the standard
credit contract. In particular I focus on amendments to the interest rate and to the
collateral requirements associated with a loan. To estimate how the demand changes
when amendments are made to the standard loan contract, I estimate two models.
The unit of observation is the contract*individual. First I estimate:
Demandic = α + γContractc +βYi +σ [Contractc×Yi]+Xiθ + εic, (4.1)
I also estimate the following within-subject model:
4.5. RESULTS 137
Demandic = α + γContractc +βYi +σ [Contractc×Yi]+ηi + εic, (4.2)
Here, the outcome variable Demandic is either a dummy=1 if individual i states
that she would take the loan, or the log of the loan amount the respondent reports
wanting to apply for. In each regression the standard contract is the baseline category,
and is compared to one other contract: Contractc ∈ {Low interest rate contract, Low
collateral contract}. Y is a characteristic measuring risk aversion or riskiness in business
environment of respondent i, or the wealth quartile of individual i. X in equation 4.1
is a vector of interviewer and interview time-fixed effects, used in the between subject
specification while ηi in equation 4.2 is an individual fixed effect (in the within-subject
specification, the β coefficient will be absorbed by the individual fixed effect). The
coefficients of interest are γ indicating the difference in take up between the amended
contract c and the standard contract for individual with characteristic Y=0, and σ ,
indicating the additional difference in take-up between the standard contract and the
amended contract if characteristic Y=1. Standard errors are clustered at the firm level
for all specifications.
Panel A of Table 4.4 shows the extensive margin estimation for take-up of the
Low interest rate contract compared to the standard loan contract. Columns 1 and 4
show results from the between-subject specification 4.1 while the remaining columns
show the results for the within-subject specification, without and with controls for
individual wealth. Starting with column 1 and 2, the coefficient on the indicator for the
Low interest rate contract (top row) shows that individuals with a high risk business
environment are 9.8 percentage points more likely to take the low interest contract
compared to the standard contract (for which the mean demand in this group is
14.5%). By examining the interaction term, we see that the corresponding difference
in take up for individuals in a low-risk environment (with a low score on the risk
index) is 18.3 percentage points (9.8+8.5). Among individuals who say "no" to the
standard contract, the effect size is thus almost twice as high among low risk firms
than among high risk firms. When individual fixed effects are added in column 2, the
138 SELECTION INTO BORROWING
point estimates change only marginally and remain statistically significant at the 90
percent confidence level.29 Columns 4-6 show that introducing a Low interest rate
contract increases demand for borrowing by 6.5-8 percentage points compared to the
standard contract (i.e. from 13.2% to 20-22%) among non risk-averse respondents,
while firm owners categorized as risk averse are an additional 7.7-8.1 percentage points
more likely to start borrowing when offered the low interest contract. There is no
significant correlation between wealth and take-up of the low interest rate contract, all
point estimates for the wealth controls in columns 3 and 6 are small and statistically
insignificant.30
Panel B of Table 4.4 shows the extensive margin demand for the Low collateral
contract. Again, columns 1 and 4 show results from the between-subject specification
while the remaining columns show the results for the within-subject specification, with-
out and with controls for individual wealth, which is likely to be positively correlated
with the ability to put up collateral for a loan. Also for the low collateral contract,
take-up is higher among firm owners in a low risk business environment (with a low
score on the risk index). The top row shows that individuals with a high risk index are
between 14.6-15.7 percentage points more likely to desire the low collateral contract
than the standard contract. Firm owners that have a low risk index are an additional
8.4-11.2 percentage points more likely to demand the low collateral contract. The point
estimate on the interaction term is significant at the 95 percent confidence level in all
specifications and robust to controlling for wealth.31 The results for self reported risk
aversion are weaker: the point estimate on the interaction term between risk aversion
and take up suggests that risk averse people are 5 percentage points more likely to
start borrowing under the low collateral contract as compared to self reported risk
29As expected, the score on the placebo index has no effect on an individual’s take-up of the lowinterest rate contract, results shown in columns 1-3 of Table A.1 in the Appendix.
30Table A.2 shows the intensive (total) margin estimation for take-up of the Low interest rateloan, margin. Results are similar as for the extensive margin, with take-up being differentially higheramong business owners in a low risk environment, and among those reporting to be risk averse.
31The placebo index is not significantly correlated with take-up of the low collateral contract, pointestimates are low and not statistically significant, results shown in columns 4-6 of Table A.1 in theAppendix.
4.5. RESULTS 139
takers. The point estimate is, however, significant at conventional levels only in the
between subject specification - when adding firm fixed effects in column 8 the p-value
for the interaction term increases to 0.153.32
I now turn to the point estimates for the wealth controls, where the omitted cate-
gory is the highest wealth quartile. When adding wealth controls in columns 3 and 6
we see that respondents with lower wealth are more likely to crowd into borrowing as
the collateral requirement is lowered. The point estimate for the lowest and the 3rd
wealth quartiles are positive and statistically significant while the point estimate for
the 2nd wealth quartile is also positive but not significant. Thus, lowering the collat-
eral affects take-up more for the poorer 75% of potential borrowers than for the richest
quartile. The point estimates on the risk-aversion and low risk index terms are not
affected by controlling for wealth. Using the terminology of Boucher et al. (2008) this
finding is in line with poorer borrowers being quantity rationed (not having access to
sufficient collateral).
Taken together, the main results support the adverse selection results of Stiglitz
and Weiss (1981) and Wette (1983): both lowering the interest rate and lowering
the collateral disproportionately attracts less risky borrowers (and correspondingly
adverse selection would occur if the interest rate/collateral was increased). Wealth has
no differential impact on the hypothetical demand for a lower interest rate contract,
but compared to the most wealthy firm owners in my sample, less wealthy ones are
more likely to increase their loan demand if the collateral is lowered.
4.5.2 Heterogenous effects
As significant differences in characteristics were observed between retail and manu-
facturing firms in Table 4.1, and those differences appear to be related to investment
choices, estimating loan demand separately for manufacturing businesses and retail
businesses can shed more light on the determinants of loan demand. In Table 4.5, I
32I did not collect data on loan size for the low interest rate contract, ans can therefore not estimatethe intensive or total margin loan demand for this contract amendment.
140 SELECTION INTO BORROWING
present the result from running regressions 4.1 and 4.2 on these two categories sep-
arately for the low interest rate contract. In Panel A we see that the higher take-up
among respondents who are self reportedly risk averse holds in both groups, with
point estimates ranging between 0.072 and 0.090, although the smaller sample within
manufacturing means point estimates are just below conventional significance level
in the within-subject specifications (columns 5 and 6). Turning to Panel B of Table
4.5, we see that, conversely, the higher demand result for the low interest contract
among owners with low risk index (less risky business environment) is entirely driven
by manufacturing firms. While manufacturing firms who face a risky environment are
10-11 percentage points more likely to start borrowing if the interest rate is lowered,
those with a low risk index i.e. that do not face a risky environment are an additional
23 percentage points more likely to do so. For retail firms, the point estimate on the
interaction of low risk index with take up of the low interest contract is much smaller
and statistically insignificant.33 Table 4.6 shows the result from running regression 4.1
and 4.2 on these two categories separately for the low collateral contract. In Panel A
of Table 4.6, I examine whether self stated risk aversion affects a respondent’s take-up
of the low collateral contract when separating between retail (columns 1-3) and man-
ufacturing firms (columns 4-6). The differentially higher take-up of the low collateral
contract among risk averse firms is driven by manufacturing firms. While risk attitude
does not seem to matter for the take up of retail firms, risk averse manufacturing firm
owners are twice as likely as non risk averse manufacturing firm owners to start bor-
rowing when offered such a contract (the coefficient on the interaction term is 10.4-11.3
percentage points). This difference is significant at the 95 % confidence level in the
between subject specification (column 4) and stays significant at the 90 % confidence
level in the within subject specification (columns 5 and 6). Turning to the risk index,
Panel B of Table 4.6 shows the corresponding results. Just as observed for the low
interest rate contract, while among retail firms there is no difference between indi-
33Results for the placebo index are presented in Panel A of Table A.4 in the Appendix. Reassuringlyalso when analyzing manufacturing and retail separately there is no correlation between scores onthe placebo index and expressed take-up of the low interest rate contract.
4.5. RESULTS 141
viduals with a low risk index and those with riskier environments in their likelihood
to "crowd in" when offered a low collateral loan, among manufacturing firms, those
with a low risk index are 21-23 percentage points more likely than firms facing riskier
environments to select into borrowing when the collateral is lowered. This result holds
also when controlling for wealth of the business owner (column 6).34
The results in this subsection nuance the main findings. While more risk averse
owners among both manufacture firms and retail firms express higher demand for the
lower interest rate contract, the differential impact of riskiness on loan demand when
the collateral is lowered is driven by manufacturing firms. Moreover, the risk index,
i.e. the volatility in demand and sales appears much more important for loan demand
among manufacturing firms.
4.5.3 Robustness checks
In this subsection, I show that the main results are robust to excluding current bor-
rowers and to alternative ways of measuring risky behavior and environment of the
firm owner.
For non borrowers, answers to the hypothetical loan questions can be interpreted
as extensive margin loan demand. However, about 10% of my respondents are already
currently borrowing. One might be concerned that these respondents interpret the
hypothetical questions differently than non borrowers do. Since my main focus is on
estimating loan demand and behavior for the non borrowers, this is not a concern
in itself. It is, however, important to rule out that the current borrowers are driving
my results. For this reason, I estimate the main model excluding respondents that
are current borrowers. Table A.3 reassuringly shows that the results for both the low
interest rate contract and the low collateral contract are robust to excluding current
borrowers.
Since I do not have a direct measure of risky loan or investment behavior, I proxied
34Corresponding regression results for the placebo index are presented in the Appendix, Panel Bof Table A.4.
142 SELECTION INTO BORROWING
risk with survey measures of risk aversion and of fluctuations in demand. In this section,
estimate equations 4.1 and 4.2, for three alternative measures that can inform us about
a respondent’s risk profile: financial vulnerability, having precautionary savings, and
the habit of selling on credit. Table A.5 shows extensive margin take-up of the low
interest rate contract and focuses on how it differs depending on a firm owners financial
vulnerability. Columns 1-3 show results for the full sample. No clear pattern emerges
between being able to obtain 500,000 UGX and the interest expressed in the low
interest contract. However, when separately analyzing the results for the subgroups
of respondents that do not have access to the highest amount (2 Million UGX) in
columns 4-6, and respondents who are not among the most vulnerable (i.e. who do
have access to at least 500,000 UGX) in columns 7-9, the results suggest that it is
not the most vulnerable group that crowds in when interest rates are lowered. Table
A.6 shows the same for the low collateral contract. Among respondents who can not
obtain 2 million UGX, those who can obtain 500,000 UGX are around 22 ppt more
likely to take up a loan when the collateral value is lowered compared to those who
can not obtain such an amount. Among the non-vulnerable sample: i.e. those who do
have access to at least 500,000 in the case of am emergency, the richer subset, i.e. those
who have access also to 2 million UGX are less likely to crowd into borrowing when
collateral is lowered. This is not surprising given that these "rich" respondents seem
to have access to capital amounting roughly to the value of a loan.
In panel A of Table A.7, I study extensive margin demand for the contract de-
pending on whether a firm owner has saved any money in the past year. Access to
precautionary savings can be seen as a proxy for financial prudence. Columns 1-3
show the results for the low interest rate contract. Having saved last year does not
appear to matter for the hypothetical take-up of the low interest contract. Column 4-6
present results for the low collateral contract. The point estimate on the interaction
term between savings and saying "yes" to the low collateral contract is positive and
significant in all 3 specifications. Respondents who have savings are around 8 ppt more
likely to crowd in to borrowing when offered a low collateral contract than those who
4.6. VALIDATION CHECKS 143
did not have savings. Moreover in column 6 where I control for wealth, respondents in
the lower wealth quartiles are significantly more likely to crowd in when collateral is
lowered as compared to those in the richest quartile. Finally, panel B of Table A.7 I
use a dummy for "Sells to customers on credit" as an alternative measure of risky firm
owner behavior.35 Firm owners that sell on credit are no different in their demand of
the low interest contract from those who do not sell on credit. Such more "risky" own-
ers do however appear to be less interested in the low collateral contract than "safer"
owners (i.e. those who do not sell on credit). The point estimates on the interaction
term between selling on credit and take up of the low collateral loan are negative in all
three specifications and significant in the between subject specification (column 4 of
Panel B in Table A.7) but below conventional significance levels in the within-subject
specifications of columns 5 and 6.
4.6 Validation checks
In this paper, I study loan demand using hypothetical questions rather than actual
choices, something which may lead to concerns regarding to what extent the findings
can predict actual behavior. In this section, I perform a number of validation checks to
confirm that the answers to the hypothetical questions indeed are informative about
the preferences of a respondent.
First, I address the concern that respondents may not understand that my loan
offer questions are hypothetical. If this were the case, I would most likely observe a
negative correlation between being a previous borrower and take-up of any of the hy-
pothetical loan contracts, since those with borrowing experience would be less likely to
say "yes" to the hypothetical loans as they are already involved with other lenders. To
investigate whether this pattern appears, I exploit information from the survey section
on previous loan experience, which comes before the section of hypothetical demand in
35Note that selling on credit is a proxy for risky behavior, unlike having precautionary savings,which is a proxy for safe behavior. One would thus expect any results to work in the oppositedirection here.
144 SELECTION INTO BORROWING
the questionnaire: A dummy for whether the respondent is currently borrowing, and
the answers to the following two questions: "Are you planning to take a loan in the
next 2 years to use (mainly) in your business?" and "would you be able to take another
loan from the same loan provider if you wanted to?" (the latter question was asked
only to those who had borrowing experience). Respondents who are current borrowers
or plan to take a loan in the next two years and respondents who say that they would
be able to borrow with their previous lender again are not less likely to say "yes" to
the hypothetical offers. Instead, they are significantly more likely to express interest
in at least one of the hypothetical contracts (Table A.8 in the Appendix). Hence this
concern appears to be unwarranted.
Next, I study the internal consistency between answers to different survey question
about loans. Here, I focus on the respondents who say no to the question "Are you
planning to take a loan in the next 2 years to use (mainly) in your business?" (which is
asked in the section about loan experience, before the hypothetical question section).
The respondents who state that they are not planning to take a loan are asked to spec-
ify the reason for not planning to borrow. An overview of the most common stated
reasons is presented in Table A.9. I focus here on the stated reasons for not planning a
loan that are most closely related to the mechanisms that my contract variations tar-
get, and examine the correlation between, on one hand, stated reasons such as (a) high
cost (interest) of the loan (b) lack of collateral (c) fear of losing the collateral and (d)
the repayment structure, and, on the other hand, the respondents’ expressed interest
in hypothetical contracts that address these specific types of borrowing constraints. I
would expect those who say the interest rate is too high to be more convinced by the
low interest contract, those who have no collateral or who fear losing their collateral to
be more affected by changed collateral contracts, etc. As the upper panel of Table A.10
shows, this is precisely what we see. Looking at the correlation between the stated rea-
sons of not wanting a loan and a dummy for saying no to the standard loan but saying
yes to contract i where i ∈ {low interest, low collateral, any-asset collateral} I find
that respondents who say the interest rate is too high are significantly more likely to
4.6. VALIDATION CHECKS 145
switch to (hypothetical) borrowing when offered the low-interest contract while those
who have no collateral are not affected by a lower interest rate but are more likely to
switch to (hypothetical) borrowing if collateral is lowered or if any asset can be used as
collateral. Those who fear losing their collateral are however not convinced by any of
the contract amendments. Reassuringly, those reporting to be constrained mainly by
high interest rates are not systematically more likely to take up the contracts where
the collateral is changed, nor are those constrained by collateral likely to crowd in
when interest rate is amended.
Within-subject designs can be sensitive to carry over bias and range effects (Char-
ness et al., 2012). Although I did not vary the order in which loan contracts were
presented in the survey instrument, and can thus not present a perfect test for this,
it is still possible to examine the data for simple patterns. A pattern in which stated
demand steadily increases as we move through the list of hypothetical questions would
be a symptom of such bias. This is however not the case - demand is not monotonically
increasing between one contract amendment and the next.
An additional possible concern is that respondents without borrowing experience
may be less informed than previous borrowers about their real preferences regarding
loans. To address this, I restrict the sample to previous borrowers and compare, in this
subsample, the characteristics between those who do and those who do not "take up"
the various hypothetical contracts. While this approach suffers from similar selection
issues that the hypothetical setup is designed to avoid; under the assumption that
the directions of correlations are similar although level effects may be different, this
exercise can still tell us something about whether the effects I find are sensible. I ran
the most basic specifications of the same regressions in this subsample, and I also
examine simple pairwise correlations, shown in the lower panel of Table A.10 in the
Appendix. The patterns are consistent with the regression results of the within-subject
analysis of hypothetical take-up.
Taken together, people’s reasons for not planning a loan, stated in earlier sections
of the questionnaire, are consistent with how they actually respond to the hypothetical
146 SELECTION INTO BORROWING
contracts that are described to them later in the questionnaire. I also find evidence
that borrowers’ previous loan experience shapes their hypothetical demand. Results for
the subgroup of respondents who have loan experience, and can therefore be thought
more informed about their real loan preferences go in the same direction as overall
results, and it appears as if the respondents understand the hypothetical nature of the
questions.
4.7 Discussion and Conclusion
In this paper I study how changes in the standard credit contract available to small
entrepreneurs in Uganda affects the demand for the contract. In addition to studying
demand at the intensive margin, my representative sample of business owners allows
me to capture the changes in demand also at the extensive margin, i.e. to shed light
on the selection into borrowing.
In a sample of respondents where 20% have previous loan experience, I find that
around 14% express interest in a standard loan contract of the type offered by NGOs
and Micro Finance Institutions in the area. While there is no difference across business
sectors in the demand for the standard loan contract, firms that demand the standard
contract are more likely to have previous loan experience and to own land than those
who are not interested in the contract. They are also operating in a riskier business
environment and are less educated.
Furthermore, my results indicate that demand for loans is affected by contractual
changes. Lowering the annual interest rate from 25% to 20% attracts clients that are
more risk averse, and that operate in a business environment with less fluctuations
and less demand uncertainties. The same thing is true for loans with lower collateral
requirement, although here the result for the risk aversion variable is significant only in
the subsample of manufacturing firm owners. Importantly, the results for the low col-
lateral contract are unaffected by controlling for wealth of the firm owner. I show that
the results are robust to using alternative measures of firm (owner) riskiness, such as
4.7. DISCUSSION AND CONCLUSION 147
financial vulnerability and the absence of precautionary savings. Since sectoral differ-
ences affect the type of investment options available to a firm, and since fluctuations in
sales and demand appear more important for manufacturing businesses, I also analyze
the results separately for firms in manufacture and retail sectors and find that there
are noteworthy differences. In particular, I find that among manufacturing firms, de-
mand for both contracts is more affected by a risky business environment than among
retail firms. When it comes to the low collateral contract, the higher demand observed
among risk averse owners is driven by manufacturing firms. Given the differences in
production functions between these two categories of firms there are several possible
explanations for this differential effect of risk exposure and riskiness on loan demand
between the sectors. Manufacturing firms in my sample employ more labor and may
therefore be slower to adapt their expenditures to lower demand. Unlike food retailers,
who make up the bulk of retail firms in our sample, the type of inputs held by man-
ufacturing firms are not consumable for the firm owner, making manufacturers more
vulnerable in the case of unexpected drops in demand. The literature on small firm
business growth has placed a heavy emphasis on retail firms and on micro-enterprises
with no employees apart from the owner, and has therefore not been able to shed light
on such sectoral differences.
Taken together, the results for both the low interest rate contract and the low
collateral contract indicate that these contracts attract safer borrowers in terms of
their current business activity and situation, and their stated risk preferences. Since
I elicit hypothetical loan demand and do not observe any actual investments, I am
however not able to analyze the effect on the riskiness of the project.
Due to the hypothetical nature of the loan contract changes, these results should be
interpreted with caution and further studies of actual loan contract changes are needed
to confirm the selection into borrowing. The fact that previous studies of small firms
and credit has studied only clients that have self-selected into borrowing nevertheless
means that this study fills a gap in the literature both on credit markets in general
and on the effectiveness of microfinance. An ideal follow up to this project would be
148 SELECTION INTO BORROWING
to examine actual selection in an experiment where the price of credit or the collateral
requirements are randomly lowered. This would also make it possible to study the
actual investments that loans are used for and follow borrowers over time, thereby
getting a more direct measure of the riskiness of a project.
As a first step, a randomized control trial that builds on this study (Gulesci et al.
(2016)) examines the effect of actual contractual changes on loan use and effectiveness
in a sample of firms that have been approved for borrowing under the standard contract
prior to the experiment. Combining the knowledge from these two studies will allow for
better projections of the intensive and extensive margin demand effects from changes
to the standard microfinance contract.
REFERENCES 149
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TABLES 153
Tables
154 SELECTION INTO BORROWING
Table
4.1:Summary
statisticsfor
entiresam
ple,andseparately
forManufacture
vs.Retailsecors
Fullsam
ple
mean
St.dev.
NRetailm
eanManufacture
mean
Difference
p-value(N
=636)
(N=289)
difference
Firm
characteristics
Sectorin
manufacturing
0.3120.464
925-
--
-Firm
age6.671
5.277891
6.247.61
-1.37***0.000
Totalno.of
workers
2.7621.771
9252.14
4.12-1.98***
0.000Share
firmswith
employees
0.7340.442
9250.64
0.93-0.29***
0.000Typicalm
onthlyprofit
lastyear
(1000UGX)
997.1541325.759
8821055.506
868.356187.149*
0.052Aggregate
assetvalue
(1000UGX)
2238.3675217.137
9252293.303
2117.471175.832
0.635Value
ofcurrent
stock/inventories(1000
UGX)
12883.21516439.932
81513789.455
10787.0733002.382**
0.017Owner
characteristics
Owner
isfem
ale0.282
0.45925
0.380.06
0.32***0.000
Owner
yearsof
education11.458
3.011889
11.7510.80
0.95***0.000
Owner
owns
land0.442
0.497902
0.450.43
0.020.589
Cred
itexp
erience
andfinan
cialinclu
sionEver
borrowed
fromform
al/semi-form
al0.206
0.405922
0.200.21
-0.010.668
Borrow
edin
last2years
0.0970.296
9210.10
0.100.00
0.986Has
bankaccount
0.7470.435
8940.79
0.650.14***
0.000Keeps
books0.732
0.443899
0.810.57
0.24***0.000
Financialliteracy
score0.565
0.316920
0.540.62
-0.08***0.000
Risk
attitudes
andriskin
essSelf
reportedriskiness
4.4372.704
9104.40
4.51-0.11
0.574Risk
index2.226
0.644919
2.202.28
-0.09*0.063
Placebo
index1.831
0.65921
1.851.80
0.050.314
Savedlast
year2.465
12.46917
2.582.21
0.380.159
Can
obtain500K
UGX
0.7660.424
9130.80
0.690.11***
0.000Can
obtain2M
UGX
0.510.5
8880.56
0.410.15***
0.000Sells
tocustom
erson
credit0.733
0.443925
0.790.62
0.17***0.000
Investment
dem
and
Wants
more
labor0.186
0.389925
0.140.30
-0.16***0.000
Wants
more
capital0.518
0.5925
0.410.76
-0.35***0.000
Notes:
Total
no.of
workers
isthe
totalnum
berof
workers
inafirm
,including
theow
nerand
bothpaid
andunpaid
employees.
Monetary
variablesare
reportedin
1000’sUgandan
Shillings(U
GX).Risk
indexis
compiled
fromquestions
measuring
whether
therespondent
facesabusiness
environment
with
fluctuationsor
unpredictability.The
Placebo
indexis
compiled
fromquestions
aboutthe
businessenvironm
entthat
arenot
relatedto
thesetypes
ofrisks.
Selfreported
riskiness:score
when
therespondent
isasked
torank
herselfon
a0-10
scaleaccording
tohow
much
sheis
willing
totake
risks.*p<0.1,
**p<0.05,
***p<0.01
TABLES 155
Table 4.2: Hypothetical take-up of loan contracts, overview
Full sample Retail sectors Manufacturing sectors
% Yes % Yes # responded %Yes # responded
Yes to standard contract 14.14 13.68 614 15.14 284Yes to low interest contract 24.67 22.54 621 29.27 287Yes to grace period contract 28.98 26.92 624 33.45 287Yes to flexible repayment contract 33.26 32.26 623 35.42 288Yes to low collateral contract 27.84 26.8 582 30.0 280
No to all the contracts 54.27 54.95 586 52.86 280
Note: Wants none of the contracts: Dummy =1 if respondent said no to all of the listed contract variations. In case ofmissing response to one or more of the contracts and the respondent said no to the remaining contracts, the variable iscoded as missing. The contracts are listed in the order that they were presented to the respondents.
Table 4.3: Who says yes to the standard loan contract?
No to Yes to Difference p-valuestandard contract standard contract
Manufacturing sector 0.31 0.34 -0.03 0.560Age of business 6.53 7.53 -0.99* 0.051Owner is female 0.27 0.31 -0.04 0.401Years of education 11.55 10.94 0.61** 0.041Total # of workers 2.73 2.99 -0.26 0.130Has employees apart from owner 0.73 0.77 -0.04 0.299Owns land anywhere 0.43 0.51 -0.08* 0.076Borrowing experience 0.18 0.37 -0.19*** 0.000Denied loan size 0.34 0.45 -0.11 0.309Ever delayed on loan repayment 0.13 0.07 0.06 0.373Sells to customers on credit 0.74 0.69 0.06 0.180Self reported riskiness 4.43 4.51 -0.09 0.742Risk index 2.21 2.32 -0.11* 0.070Placebo index 1.82 1.83 -0.01 0.862Has a bank account 0.74 0.77 -0.03 0.481Currently keeps books for business 0.73 0.74 -0.01 0.895Financial literacy score 0.56 0.61 -0.05* 0.070Monthly profit past year (1000’s UGX) 992.47 970.67 21.81 0.866Aggregate assets (1000’s UGX) 2160.91 2525.09 -364.18 0.448Stock/inventories value (1000’s UGX) 12703.46 11785.09 918.37 0.575Wants more labor 0.17 0.28 -0.11*** 0.002Wants more capital 0.52 0.50 0.02 0.675
Notes: Self reported riskiness: score when asked to rank herself on a 0-10 scale according to how much she is willing to takerisks. Risk index is compiled from questions measuring whether the respondent faces a business environment with fluctuationsor unpredictability. The Placebo index is compiled from questions about the business environment that are not related tothese types of risks. Denied loan size and Ever delayed on loan repayment are dummies and shares are reported conditionalon respondent having taken a loan in the past 2 years. * p<0.1, ** p<0.05, *** p<0.01
156 SELECTION INTO BORROWING
Table 4.4: Extensive margin demand, full sample
Panel A: Demand for Low interest rate contract(1) (2) (3) (4) (5) (6)
Low interest 0.098∗∗∗ 0.099∗∗∗ 0.079∗∗∗ 0.080∗∗∗ 0.080∗∗∗ 0.065∗∗[0.012] [0.016] [0.030] [0.013] [0.018] [0.030]
Low interest * risk index low 0.085∗∗∗ 0.087∗ 0.085∗[0.033] [0.046] [0.046]
Low interest * risk averse 0.077∗∗∗ 0.081∗∗ 0.079∗∗[0.023] [0.032] [0.032]
Low interest * bottom wealth q 0.014 0.007[0.041] [0.041]
Low interest * 2nd wealth q 0.035 0.027[0.043] [0.043]
Low interest * 3rd wealth q 0.034 0.030[0.043] [0.044]
Mean demand standard contr. 0.145 0.145 0.132 0.132 0.132 0.134Interviewer& time controls yes no no yes no noFirm FE no yes yes no yes yesObservations 1792 1792 1792 1765 1765 1765Adjusted R2 0.199 0.672 0.627 0.153 0.673 0.672
Panel B: Demand for Low collateral contract(1) (2) (3) (4) (5) (6)
Low collateral 0.157∗∗∗ 0.146∗∗∗ 0.089∗∗∗ 0.150∗∗∗ 0.143∗∗∗ 0.088∗∗∗[0.014] [0.020] [0.033] [0.017] [0.024] [0.034]
Low collateral * risk index low 0.084∗∗ 0.112∗∗ 0.108∗∗[0.039] [0.055] [0.053]
Low collateral * riskaverse 0.052∗ 0.055 0.052[0.027] [0.039] [0.039]
Low collateral * bottom wealth q 0.090∗ 0.087∗[0.051] [0.051]
Low collateral * 2nd wealth q 0.023 0.020[0.047] [0.047]
Low collateral * 3rd wealth q 0.116∗∗ 0.120∗∗[0.052] [0.054]
Mean demand standard contr. 0.145 0.145 0.132 0.132 0.132 0.134Interviewer& time controls yes no no yes no noFirm FE no yes yes no yes yesObservations 1712 1712 1712 1688 1688 1688Adjusted R2 0.198 0.57 0.576 0.196 0.566 0.572
Notes: Low interest (Low collateral) is a dummy=1 if the contract offered is the low interest (low collateral) contract.Risk index is compiled from questions measuring whether the respondent faces a business environment with fluctuationsor unpredictability. The Placebo index is compiled from questions about the business environment that are not relatedto these types of risks. Risk averse: a dummy variable =1 if the respondents gives herself an above median score whenasked to rank herself on a 0-10 scale according to how much she is willing to take risks. Mean demand standard contr.displayed below the table indicates the mean hypothetical takeup of the standard contract in the base category, i.e.respondents with risk index low=0 in columns 1-3 and with risk aversion=0 in columns 4-6, and, in addition, in wealthquartile=4 in columns 3 and 6. Standard errors in brackets are clustered at the firm level,* p<0.1, ** p<0.05, *** p<0.01
TABLES 157
Table 4.5: Extensive margin demand for Low interest contract, retail vs. manufacturing
Panel A: Self stated risk aversionRetail Manufacturing
(1) (2) (3) (4) (5) (6)
Low interest 0.064∗∗∗ 0.066∗∗∗ 0.054∗ 0.113∗∗∗ 0.112∗∗∗ 0.090[0.015] [0.020] [0.032] [0.029] [0.038] [0.068]
Low interest * riskaverse 0.072∗∗∗ 0.076∗∗ 0.075∗∗ 0.084∗ 0.085 0.090[0.027] [0.037] [0.037] [0.047] [0.062] [0.061]
Low interest * bottom wealth q 0.027 -0.038[0.048] [0.079]
Low interest * 2nd wealth q 0.034 0.010[0.049] [0.088]
Low interest * 3rd wealth q -0.014 0.092[0.046] [0.091]
Mean demand standard contr. 0.127 0.127 0.119 0.145 0.145 0.182Interviewer& time controls yes no no yes no noFirm FE no yes yes no yes yesObservations 1210 1210 1210 555 555 555Adjusted R2 0.148 0.698 0.698 0.212 0.628 0.631
Panel B: Risk indexRetail Manufacturing
(1) (2) (3) (4) (5) (6)
Low interest 0.090∗∗∗ 0.094∗∗∗ 0.073∗∗ 0.112∗∗∗ 0.111∗∗∗ 0.096[0.014] [0.019] [0.034] [0.022] [0.029] [0.059]
Low interest * risk index low 0.031 0.032 0.034 0.228∗∗∗ 0.229∗∗ 0.227∗∗[0.035] [0.048] [0.047] [0.077] [0.103] [0.104]
Low interest * bottom wealth q 0.033 -0.041[0.048] [0.074]
Low interest * 2nd wealth q 0.044 0.016[0.049] [0.088]
Low interest * 3rd wealth q 0.000 0.075[0.046] [0.087]
Mean demand standard contr. 0.143 0.143 0.140 0.148 0.148 0.113Interviewer& time controls yes no no yes no noFirm FE no yes yes no yes yesObservations 1225 1225 1225 567 567 567Adjusted R2 0.142 0.693 0.693 0.236 0.650 0.651
Notes: Low interest is a dummy=1 if the contract offered is the low interest contract.Risk averse: a dummyvariable =1 if the respondents gives herself an above median score when asked to rank herself on a 0-10 scaleaccording to how much she is willing to take risks. Risk index is compiled from questions measuring whetherthe respondent faces a business environment with fluctuations or unpredictability. Mean demand standard contr.displayed below the table indicates the mean hypothetical takeup of the standard contract in the relevant basecategory for each column. Standard errors in brackets are clustered at the firm level,* p<0.1, ** p<0.05, *** p<0.01
158 SELECTION INTO BORROWING
Table 4.6: Extensive margin demand for Low collateral contract, retail vs. manufacturing
Panel A: Self stated risk aversionRetail Manufacturing
(1) (2) (3) (4) (5) (6)
Low collateral 0.157∗∗∗ 0.156∗∗∗ 0.103∗∗ 0.129∗∗∗ 0.116∗∗∗ 0.051[0.022] [0.030] [0.042] [0.030] [0.039] [0.057]
Low collateral * riskaverse 0.031 0.026 0.018 0.104∗∗ 0.114∗ 0.113∗[0.034] [0.047] [0.048] [0.050] [0.067] [0.066]
Low collat.* bottom wealth q 0.063 0.138[0.061] [0.095]
Low collateral * 2nd wealth q 0.049 -0.039[0.059] [0.074]
Low collateral * 3rd wealth q 0.121∗ 0.132[0.069] [0.088]
Mean demand standard contr. 0.127 0.127 0.119 0.145 0.145 0.182Interviewer& time controls yes no no yes no noFirm FE no yes yes no yes yesObservations 1148 1148 1148 540 540 540Adjusted R2 0.192 0.553 0.556 0.266 0.593 0.607
Panel B: Risk indexRetail Manufacturing
(1) (2) (3) (4) (5) (6)
Low collateral 0.163∗∗∗ 0.153∗∗∗ 0.099∗∗ 0.143∗∗∗ 0.133∗∗∗ 0.072[0.018] [0.025] [0.040] [0.024] [0.032] [0.059]
Low collateral * risk index low 0.030 0.063 0.056 0.215∗∗∗ 0.231∗∗ 0.222∗∗[0.045] [0.062] [0.060] [0.081] [0.109] [0.106]
Low collateral * bottom wealth q 0.065 0.133[0.061] [0.094]
Low collateral 2nd wealth q 0.049 -0.038[0.059] [0.073]
Low collateral 3rd wealth q 0.115∗ 0.122[0.067] [0.086]
Mean demand standard contr. 0.143 0.143 0.140 0.148 0.148 0.113Interviewer& time controls yes no no yes no noFirm FE no yes yes no yes yesObservations 1160 1160 1160 552 552 552Adjusted R2 0.192 0.554 0.557 0.278 0.606 0.618
Notes: Low collateral is a dummy=1 if the contract offered is the low collateral contract. Risk averse: a dummyvariable =1 if the respondents gives herself an above median score when asked to rank herself on a 0-10 scale accordingto how much she is willing to take risks. Risk index is compiled from questions measuring whether the respondentfaces a business environment with fluctuations or unpredictability. Mean demand standard contr. displayed belowthe table indicates the mean hypothetical takeup of the standard contract in the relevant base category for eachcolumn. Standard errors in brackets are clustered at the firm level, * p<0.1, ** p<0.05, *** p<0.01
APPENDIX 1 159
Appendix 1
160 SELECTION INTO BORROWING
Table A.1: Extensive margin demand for Low interest and low collateral contract, Placebo index
(1) (2) (3) (4) (5) (6)
Low interest 0.116∗∗∗ 0.118∗∗∗ 0.097∗∗∗[0.014] [0.019] [0.030]
Placebo low -0.023 -0.001[0.030] [0.030]
Low interest * placebo low -0.008 -0.007 -0.011[0.023] [0.032] [0.033]
Low interest * bottom wealth q 0.011[0.041]
Low interest* 2nd wealth q 0.035[0.043]
Low interest * 3rd wealth q 0.040[0.044]
Low collateral 0.179∗∗∗ 0.171∗∗∗ 0.112∗∗∗[0.017] [0.024] [0.035]
Low collateral *placebo low -0.022 -0.014 -0.017[0.028] [0.039] [0.039]
Low collateral * bottom wealth q 0.088∗[0.051]
Low collateral * 2nd wealth q 0.026[0.047]
Low collateral * 3rd wealth q 0.127∗∗[0.053]
Interviewer& time controls yes no no yes no noFirm FE no yes yes no yes yesObservations 1786 1786 1786 1706 1706 1706Adjusted R2 0.153 0.671 0.671 0.197 0.566 0.572
Notes: Low interest (Low collateral) is a dummy=1 if the contract offered is the low interest (low collateral) contract.The Placebo index is compiled from answers to questions about the difficulty of repaying loans that are unrelated tosales and demand fluctuations. Standard errors in brackets are clustered at the firm level,* p<0.1, ** p<0.05, *** p<0.01
APPENDIX 1 161
Tab
leA.2:Intensive
(Total)margindeman
dforLo
winterest
contract
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
Low
interest
1.476∗∗∗
1.500∗∗∗
1.192∗∗∗
1.798∗∗∗
1.766∗∗∗
1.462∗∗∗
1.240∗∗∗
1.237∗∗∗
1.005∗∗
[0.177]
[0.241]
[0.445]
[0.213]
[0.290]
[0.448]
[0.206]
[0.272]
[0.447]
Low
interest
*risk
indexlow
1.427∗∗∗
1.254∗
1.224∗
[0.496]
[0.692]
[0.694]
Low
interest*p
lacebo
indexlow
-0.195
-0.129
-0.178
[0.347]
[0.480]
[0.487]
Low
interest
*risk
averse
1.135∗∗∗
1.115∗∗
1.087∗∗
[0.348]
[0.478]
[0.480]
Low
interest*b
ottom
wealthq
0.204
0.167
0.113
[0.610]
[0.610]
[0.616]
Low
interest
*2n
dwealthq
0.524
0.518
0.412
[0.653]
[0.657]
[0.653]
Low
interest
*3rdwealthq
0.520
0.599
0.452
[0.655]
[0.664]
[0.667]
Interviewer&
timecontrols
yes
nono
yes
nono
yes
nono
Firm
FE
noyes
yes
noyes
yes
noyes
yes
Observation
s1782
1782
1782
1776
1776
1776
1755
1755
1755
Adjusted
R2
0.166
0.676
0.676
0.165
0.675
0.675
0.165
0.676
0.675
Notes:Lo
winterest
isadu
mmy=
1ifthecontract
offered
isthelow
interest
contract.Stan
dard
errors
inbrackets
areclusteredat
thefirm
level,
*p<
0.1,
**p<
0.05,***p<
0.01
162 SELECTION INTO BORROWING
Table A.3: Extensive margin demand, sample: non borrowers
Panel A: Demand for low interest loan, sample: non-borrowers(1) (2) (3) (4) (5) (6)
Low interest 0.073*** 0.079*** 0.065** 0.091*** 0.098*** 0.079***[0.014] [0.018] [0.030] [0.012] [0.016] [0.030]
Low interest*risk index low 0.072* 0.084* 0.083*[0.037] [0.049] [0.050]
Low interest*risk averse 0.071*** 0.078** 0.076**[0.024] [0.033] [0.033]
Low interest*bottom wealth q 0.009 0.017[0.043] [0.042]
Low interest*2nd wealth q 0.025 0.033[0.044] [0.044]
Low interest*3rdwealth q 0.023 0.026[0.045] [0.044]
Interviewer& Time controls yes no no yes no noFirm FE no yes yes no yes yesObservations 1724 1724 1724 1750 1750 1750Adjusted R2 0.148 0.671 0.670 0.149 0.673 0.672
Panel B: Demand for low collateral loan, sample: non-borrowers(1) (2) (3) (4) (5) (6)
Low collateral 0.150*** 0.145*** 0.091*** 0.143*** 0.139*** 0.090**[0.015] [0.020] [0.034] [0.018] [0.025] [0.035]
Low collateral*risk index low 0.082* 0.116* 0.115**[0.042] [0.059] [0.058]
Low collateral*risk averse 0.051* 0.060 0.056[0.028] [0.040] [0.040]
Low collateral*bottom wealth q 0.084 0.078[0.053] [0.053]
Low collateral*2nd wealth q 0.022 0.018[0.049] [0.049]
Low collateral*3rd wealth q 0.110** 0.108*[0.054] [0.056]
Interviewer& Time controls yes no no yes no noFirm FE no yes yes no yes yesObservations 1675 1675 1675 1652 1652 1652Adjusted R2 0.190 0.566 0.571 0.188 0.562 0.567
Notes: Low interest (Low collateral) is a dummy=1 if the contract offered is the low interest (low collateral) contract.Risk index is compiled from questions measuring whether the respondent faces a business environment with fluctuationsor unpredictability. The Placebo index is compiled from questions about the business environment that are not related tothese types of risks. Risk averse: a dummy variable =1 if the respondents gives herself an above median score when askedto rank herself on a 0-10 scale according to how much she is willing to take risks. Standard errors in brackets are clusteredat the firm level, * p<0.1, ** p<0.05, *** p<0.01
APPENDIX 1 163
Table A.4: Extensive margin demand by placebo index, retail vs manufacture
Panel A: Low interest rate contractRetail Manufacturing
(1) (2) (3) (4) (5) (6)
Low interest 0.100∗∗∗ 0.104∗∗∗ 0.084∗∗ 0.150∗∗∗ 0.149∗∗∗ 0.128∗∗[0.016] [0.022] [0.034] [0.029] [0.038] [0.061]
Low interest * placebo low -0.014 -0.016 -0.014 -0.000 0.002 0.004[0.027] [0.036] [0.037] [0.047] [0.063] [0.064]
Low interest * bottom wealth q 0.029 -0.034[0.048] [0.080]
Low interest * 2nd wealth q 0.042 0.016[0.049] [0.090]
Low interest * 3rd wealth q 0.004 0.085[0.047] [0.090]
Interviewer& time controls yes no no yes no noFirm FE no yes yes no yes yesObservations 1219 1219 1219 567 567 567Adjusted R2 0.144 0.696 0.695 0.218 0.628 0.630
Panel B: Low collateral contractRetail Manufacturing
(1) (2) (3) (4) (5) (6)
Low collateral 0.174∗∗∗ 0.168∗∗∗ 0.114∗∗∗ 0.188∗∗∗ 0.179∗∗∗ 0.109∗[0.020] [0.028] [0.042] [0.031] [0.042] [0.061]
Low collateral*placebo low -0.018 -0.010 -0.016 -0.027 -0.022 -0.027[0.035] [0.049] [0.049] [0.050] [0.067] [0.066]
Low collateral*bottom wealth q 0.061 0.145[0.061] [0.094]
Low collateral*2nd wealth q 0.050 -0.031[0.059] [0.073]
Low collateral*3rd wealth q 0.123∗ 0.136[0.068] [0.089]
Interviewer& time controls yes no no yes no noFirm FE no yes yes no yes yesObservations 1154 1154 1154 552 552 552Adjusted R2 0.192 0.554 0.558 0.268 0.585 0.598
Notes: Low interest (Low collateral) is a dummy=1 if the contract offered is the low interest (low collateral)contract. The Placebo index is compiled from answers to questions about the difficulty of repaying loans that areunrelated to sales and demand fluctuations. Standard errors in brackets are clustered at the firm level,* p<0.1, ** p<0.05, *** p<0.01
164 SELECTION INTO BORROWING
Table
A.5:E
xtensivemargin
demand
forLow
interestcontract,by
financialvulnerability
Fullsample
Respondents
who
cannotobtain
2M
Respondents
who
canobtain
500K
(1)(2)
(3)(4)
(5)(6)
(7)(8)
(9)
Lowinterest
0.092 ∗∗∗0.096 ∗∗∗
0.066 ∗0.087 ∗∗∗
0.091 ∗∗∗0.062
0.163 ∗∗∗0.157 ∗∗∗
0.144 ∗∗∗[0.021]
[0.029][0.039]
[0.022][0.030]
[0.054][0.025]
[0.034][0.048]
Lowinterest
*Can
obtain500K
0.0260.023
0.0280.074 ∗∗
0.0660.068
[0.025][0.034]
[0.034][0.033]
[0.045][0.045]
Lowinterest
*Can
obtain2M
-0.063 ∗∗-0.054
-0.054[0.029]
[0.040][0.042]
Lowinterest
*bottom
wealth
q0.016
0.011-0.001
[0.041][0.064]
[0.050]Low
interest*2nd
wealth
q0.047
0.0380.027
[0.043][0.068]
[0.054]Low
interest*3rd
wealth
q0.042
0.0650.030
[0.043][0.076]
[0.051]Can
obtain500K
0.022-0.057
[0.034][0.042]
Can
obtain2M
0.128 ∗∗∗[0.031]
Interviewer&
timecontrols
yesno
noyes
nono
yesno
noFirm
FE
noyes
yesno
yesyes
noyes
yesObservations
17741774
1774850
850850
13081308
1308Adjusted
R2
0.1520.672
0.6720.160
0.6220.621
0.1680.648
0.647
Notes:
Lowinterest
isadum
my=
1ifthe
contractoffered
isthe
lowinterest
contract.Can
obtain500K
/2M
:Dum
my=1ifrespondent
saysthat
shecould
obtainsaid
amount
within
amonth
ifsheneeded
itfor
anem
ergency.Colum
ns1-3
displayresults
forthe
fullsample.In
columns
4-6Iexclude
therichest
respondents,i.e.respondentswho
saythat
theycannot
obtain2Million
UGX.In
columns
7-9the
sample
isrestricted
tonon
vulnerablerespondents,
i.e.respondents
who
saythat
theycan
obtain500,000
UGX.Standard
errorsin
bracketsare
clusteredat
thefirm
level,*p<0.1,
**p<0.05,
***p<0.01.
APPENDIX 1 165
Tab
leA.6:E
xtensive
margindeman
dforLo
wcolla
teralc
ontract,by
finan
cial
vulnerab
ility
Fullsample
Respo
ndents
who
cann
otob
tain
2M
Respo
ndents
who
canob
tain
500K
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
Low
colla
teral
0.114∗∗∗
0.118∗∗∗
0.040
0.088∗∗∗
0.093∗∗∗
0.040
0.329∗∗∗
0.320∗∗∗
0.267∗∗∗
[0.024]
[0.033]
[0.046]
[0.023]
[0.031]
[0.068]
[0.033]
[0.046]
[0.058]
Low
colla
teral*Can
obtain
500K
0.078∗∗∗
0.066∗
0.073∗
0.242∗∗∗
0.227∗∗∗
0.221∗∗∗
[0.029]
[0.040]
[0.041]
[0.040]
[0.055]
[0.055]
Low
colla
teral*
Can
obtain
2M
-0.214∗∗∗
-0.211∗∗∗
-0.204∗∗∗
[0.037]
[0.052]
[0.052]
Low
colla
teral*
bottom
wealthq
0.106∗∗
0.083
0.074
[0.052]
[0.083]
[0.062]
Low
colla
teral*
2ndwealthq
0.046
-0.007
0.003
[0.047]
[0.081]
[0.059]
Low
colla
teral*
3rdwealthq
0.138∗∗
0.141
0.116∗
[0.054]
[0.095]
[0.061]
Can
obtain
500K
-0.016
-0.122∗∗∗
[0.034]
[0.040]
Can
obtain
2M0.124∗∗∗
[0.031]
Interviewer&
timecontrols
yes
nono
yes
nono
yes
nono
Firm
FE
noyes
yes
noyes
yes
noyes
yes
Observation
s1696
1696
1696
830
830
830
1239
1239
1239
Adjusted
R2
0.200
0.566
0.573
0.241
0.533
0.540
0.233
0.560
0.565
Notes:Lo
wcolla
teralisadu
mmy=
1ifthecontract
offered
isthelow
colla
teralcontract.Can
obtain
500K
/2M
:Dum
my=1ifrespon
dent
says
that
shecouldob
tain
said
amou
ntwithinamon
thifshene
eded
itforan
emergency.
Colum
ns1-3displayresultsforthefullsample.
Incolumns
4-6Iexclud
etherichestrespon
dents,i.e
.respo
ndents
who
saythat
they
cann
otob
tain
2MillionUGX.In
columns
7-9thesampleis
restricted
tono
nvu
lnerab
lerespon
dents,
i.e.respon
dentswho
saythat
they
canob
tain
500,000UGX.Stan
dard
errors
inbrackets
areclusteredat
thefirm
level,*p<
0.1,
**p<
0.05,***p<
0.01.
166 SELECTION INTO BORROWING
Table A.7: Extensive margin demand, related to savings or selling on credit
Panel A: Demand related to having precautionary savings(1) (2) (3) (4) (5) (6)
Low interest 0.079∗∗∗ 0.092∗∗ 0.065[0.029] [0.038] [0.050]
Saved last year -0.036 -0.048[0.039] [0.038]
Low interest * saved last year 0.044 0.030 0.033[0.031] [0.042] [0.043]
Low interest * bottom wealth q 0.016[0.042]
Low interest * 2nd wealth q 0.044[0.045]
Low interest * 3rd wealth q 0.042[0.044]
Low collateral 0.097∗∗∗ 0.100∗∗ 0.026[0.031] [0.041] [0.054]
Low collateral *saved last year 0.087∗∗ 0.078∗ 0.088∗[0.034] [0.046] [0.048]
Low collateral * bottom wealth q 0.101∗[0.052]
Low collateral * 2nd wealth q 0.035[0.048]
Low collateral * 3rd wealth q 0.123∗∗[0.054]
Interviewer& time controls yes no no yes no noFirm FE no yes yes no yes yesObservations 1754 1754 1754 1675 1675 1675Adjusted R2 0.153 0.663 0.662 0.201 0.563 0.569
Panel B: Demand related to selling on credit(1) (2) (3) (4) (5) (6)
Low interest 0.095∗∗∗ 0.099∗∗∗ 0.080∗∗[0.020] [0.028] [0.035]
Sells on credit -0.056∗∗ -0.030[0.028] [0.028]
Low interest rate* sells on credit 0.026 0.023 0.021[0.024] [0.033] [0.034]
Low interest rate * bottom wealth q 0.010[0.041]
Low interest rate * 2nd wealth q 0.034[0.043]
Low interest rate * 3rd wealth q 0.038[0.044]
Low collateral 0.211∗∗∗ 0.202∗∗∗ 0.139∗∗∗[0.028] [0.039] [0.044]
Low collateral* sells on credit -0.053∗ -0.047 -0.048[0.032] [0.045] [0.044]
Low collateral*bottom wealth q 0.092∗[0.051]
Low collateral* 2nd wealth q 0.034[0.047]
Low collateral*3rd wealth q 0.127∗∗[0.054]
Interviewer& time controls yes no no yes no noFirm FE no yes yes no yes yesObservations 1792 1792 1792 1712 1712 1712Adjusted R2 0.154 0.669 0.669 0.200 0.566 0.572
Notes: Columns 1-3 display results for contract 2 (low interest) while columns 4-6 display results for the low collateralcontract. Standard errors in brackets are clustered at the firm level, * p<0.1, ** p<0.05, *** p<0.01.
APPENDIX 1 167
Table A.8: Correlation between having access to borrowing and the stated interest in hypotheticalcontracts
Interested in at least one contract
Correlation coeff. N
Is currently borrowing 0.094∗∗∗ 860Plans loan next 2 years 0.055(*) 842Can borrow from same lender 0.255** 71
Notes: Simple pairwise correlations. The variable Can borrow from same lenderis only asked to respondents who have borrowed in the past 2 years.(*) p<0.15, * p<0.1, ** p<0.05, *** p<0.01.
Table A.9: Stated reasons for not planning to borrow in the next 2 years
Full sample Retail sectors Manufacturing sectors
# % # % # %
Do not need capital 168 26.97 136 31.70 32 16.49Interest rate too high 165 26.48 108 25.17 57 29.38Fear losing collateral 158 25.36 104 24.24 54 27.83Do not have access to collateral 64 10.27 43 10.02 21 10.82Installment too often 24 3.85 12 2.80 12 6.19Other reason 44 7.06 26 6.06 18 9.28
Notes: The table shows the main stated reasons for not planning a loan for the respondents who said thatthey were not planning a loan in the next 2 years.
Table A.10: Correlation between hypothetical demand and stated reasons for not planning to borrow
Crowds in to (contract): Low interest Low collateral Any collateral N
Do not need capital -0.039 -0.022 0.011 684Interest rate too high 0.126*** 0.877 -0.016 684Fear losing collateral -0.078 -0.102*** -0.049 684Do not have access to collateral 0.015 0.172*** 0.136*** 684Installment too often -0.008 0.944 -0.043 684
Risk averse 0.159** 0.058 0.017 183Risk index low 0.229*** 0.130* 0.028 190
Notes: Simple pairwise correlation. In the first 5 rows, the sample is restricted to respondents stating thatthey do not plan to borrow in the next 2 years. In the 2 last rows, the sample is restricted to respondentswith borrowing experience. Any collateral contract is not included in the main analysis, but I include ithere since it provides additional support for the validity of the responses to the hypothetical questions.* p<0.1, ** p<0.05, *** p<0.01.
168 SELECTION INTO BORROWING
Appendix 2: Loan contract variations
1. Standard contract. "Imagine you were offered the opportunity to take a loan. If
you decide to take this loan, you can borrow up to 3 million Shillings. You would
need to repay this amount plus a 25% interest within one year. The repayments
have to be done in equal monthly repayment installments over the year. SHOW
EXAMPLE. The lender requests security (collateral) in the form of land. That
is, in order to borrow a certain amount, for example, 3 million, you need to have
formal property rights to land valued to 3 million and in case you fail to repay,
the lender will claim the 3 million in terms of your land." If you were offered
such a loan, would you choose to borrow? If yes, how much would you like to
borrow?
2. Low interest rate contract. "Now think about the loan contract we had above
(remind the respondent about the terms equal monthly repayments starting one
month after the loan is taken, and collateral in the form of land). Suppose all
the terms stay the same except the interest rate on the loan is 20% instead of
25%. SHOW EXAMPLE." Do you think this is a better offer compared to the
previous loan contract you were offered? If you were offered such a loan, would
you choose to borrow? If yes, how much would you like to borrow under this
contract?
3. Low collateral contract. If the collateral/security was land for 50% (=half)
of the value of the loan, would that be better than the very first contract? If you
were offered such a loan, would you choose to borrow?
Chapter 5
Credit Contract Structure and Firm
Growth: Evidence from a Randomized
Control Trial∗
5.1 Introduction
Credit markets in many developing countries are characterized by credit rationing,
which excludes certain prospective borrowers from credit access, thereby hindering
business growth. Another, less recognized, constraint is that key aspects of the most
common and accessible form of financing: debt, may inhibit firm expansion. Expanding
a business requires learning, or training employees, in how to use new inputs or market
new products efficiently and how to build a reputation. To the extent that learning
is mechanical, returns to an investment start low but increase gradually. When in-
troducing new products, there is also often uncertainty about demand, implying that
the timing of returns is uncertain. In addition, starting or expanding a business may
∗This paper is co-authored with Selim Gulesci, Francesco Loiacono and Andreas Madestam. Theauthors would like to thank BRAC Uganda, in particular the SEP program staff and the staff atBRAC Uganda Research and Evaluation Unit, for their collaboration and practical help in the im-plementation of the experiment. We also thank Emanuele Bracanti, Chiara Dall’Aglio, FrancescaVisinoni and Matteo Voltan for excellent research assistance. Financial support from ESRC, theSwedish Research Council and Handelsbanken’s Research Foundation is gratefully acknowledged.
169
170 CREDIT CONTRACT STRUCTURE AND FIRM GROWTH
entail sizable indivisible fixed costs in the form of bulky investments such as machines
or buildings. Meanwhile, most debt contracts available to micro-entrepreneurs are de-
signed to reduce lender risk: they stipulate constant repayments and the loan size is
subject to concerns of asymmetric information. The implication is that investment is
distorted toward technologies that involve less learning and are subject to less aggre-
gate risk than otherwise optimal, and avoid indivisible large costs; hampering firm
growth.
Although recent evaluations have found the effect of microfinance on business
growth to be limited, very few studies have studied how debt structure of micro loans
affects business investments and growth by exploring possible changes to the standard
contract. We implemented a randomized control trial designed to measure to what ex-
tent contractual features of the most common contract inhibit the expansion of firms,
and examine contracts amendments that possibly better support firm growth. Together
with the NGO BRAC Uganda’s Small Enterprise Lending Program (SEP), we study
the effect of credit contract terms on small and medium sized firms’ profits, labor and
capital. Using a randomized-control trial methodology we measure whether standard
contractual terms, such as constant and monthly repayments and small initial loan
amounts, are particularly restrictive for firms with (i) backloaded project returns; (ii)
uncertain project returns and; (iii) large, indivisible fixed costs.
The standard contract offered in BRAC’s SEP stipulates monthly repayments dur-
ing the 12 month loan cycle, starting one month after loan disbursement. To investigate
whether the standard contractual terms are restrictive for firms that face indivisible
costs and/or are characterized by backloaded or uncertain project returns, we im-
plemented the following types of interventions for firms approved for BRAC’s SEP
funding: (i) a grace period treatment;1 (ii) a flexible grace period treatment; and (iii)
a cash subsidy treatment. Treatments (i) and (ii) are intended to distinguish the effects
of backloaded returns from those of uncertain project returns while (iii) is expected
to ease the purchase of indivisible goods. These treatments were implemented in the1We define grace period as an additional period, beyond the one stipulated in the standard loan
contract, between the date of disbursement of the loan and the due date of the first loan repayment.
5.1. INTRODUCTION 171
form of repayment rebates covering the equivalent of 2 out of the 12 repayments in a
standard BRAC SEP loan contract (the control group). In addition, we implemented
a late repayment rebate and an alternative control group that received flat rebates,
equivalent of the same share of repayments as the rebates in treatments (i)-(iii), to
account for the income effect caused by granting rebates.
The full sample size in our study will be 2,340 borrowers (clients). In the current
draft, we present findings from the first 754 firms to have completed the loan cycle.
Therefore, we stress that the results presented in this draft are preliminary and can
only provide suggestive evidence about the effects of our treatments.
We find that firms that were given a 2-month grace period increased their prof-
its and household income relative to firms that received a rebate later in the loan
cycle, and to the control groups. They also increased the number of paid employees,
while decreasing the number of unpaid ones, but wage expenditures did not increase
in accordance. Further, the owner households of Early treatment firms started signifi-
cantly more new household-owned firms than the Late rebate and the control groups.
Firms that were offered a Flexible grace period scheme, in which they were free to
skip repayments in any 2 months of their choice, predominantly chose to use these
rebates in the first months of the loan cycle. These findings provide some support for
backloadedness of returns being a more important constraint than the uncertainty of
returns. Firms that received a cash subsidy at the start of the loan cycle increased their
number of employees relative to the control groups, and they also increased their wage
costs. To the extent that this implies that they hired higher quality workers, which
can be seen as an indivisible investment, this finding provides suggestive evidence for
the importance of indivisible costs hampering investments.
This paper contributes to several strands of literature. First it adds to the grow-
ing literature on credit access and use in developing country contexts. A handful of
recently published studies provide the first larger scale randomized evaluations of mi-
crofinance initiatives (Attanasio et al., 2015; Angelucci et al., 2015; Augsburg et al.,
2015; Banerjee et al. 2015b; Crépon et al., 2015; Tarozzi et al., 2015). Taken together,
172 CREDIT CONTRACT STRUCTURE AND FIRM GROWTH
these studies find no evidence of transformative effects of microfinance on the lives of
the poor, nor do they find positive effects on the extensive margin of business owner-
ship (startups). They do however find modestly positive effects on business outcomes
for already existing micro-businesses (Banerjee et al., 2015a). At the same time, none
of the studies find significant increases in household income or consumption following
the observed positive effects on business activity.
Within this literature, we more specifically contribute to a limited number of studies
of the role of loan contract structure for the profitability and use of loans. We are aware
of only two empirical studies of how changes in the contract terms affect loan use and
efficiency in developing country contexts (Field et al., 2013; Karlan and Zinman, 2008).
Field et al. (2013) randomly offered Indian microfinance borrowers an initial two-month
grace period on their repayments while Karlan and Zinman (2008) introduce a lower
interest rate among previous borrowers of a micro finance institution. Their results
highlight the importance of debt structure but leave a number of questions unanswered.
While the evidence suggests that the grace period allowed for larger initial investments,
it is difficult to disentangle the importance of start-up cost from other factors, such as
the project return path. Our experiment is designed to distinguish between the main
alternative explanations for the findings of these studies, and rather than focusing on
households we focus on existing businesses.2 The study also offers an empirical test of
how some of the central theoretical results about loan contract structure may interact
with the firm’s production function. We discuss these mechanisms in section 2.
We further contribute to the literature on small business growth and return to
capital in SMEs in low income countries. In developing countries both in Africa and
Asia, a large share of the workforce is employed in (both formal and informal) micro
and small sized businesses (Ayyagari et al., 2011). Meanwhile, very few businesses grow.
Recent studies have strived to understand the determinants of and obstacles to business
growth in developing countries by offering cash grants (de Mel et al., 2008; Fafchamps
et al., 2014), business training (Karlan and Valdivia, 2011) and combinations of the two2Although Field et al. (2013) do study firms, their focus on micro sized household businesses.
Karlan and Zinman (2008) sample households, not businesses.
5.2. CONCEPTUAL FRAMEWORK 173
(Bandiera et al., 2016; Fiala, 2013). We add to this literature by our explicit focus on
credit. Returns to capital in the form of giving cash grants may differ from the returns
when capital is given in the form of a loan, precisely because of the implications of the
loan repayment structure. Moreover, the comprehensive survey data that we collect on
firm characteristics related to the firms’ production function, and the fact that we also
have detailed data on owner households, allows us to analyze the interaction of the
production function and the loan contract. Our study also contributes to understanding
the importance of growth constraints as well as drivers of business growth among a
group of small and medium-sized firms who demand loans of greater size than standard
microfinance loans, yet are not large enough to participate in the formal financial
sector.3
The rest of the paper is structured as follows. Section 2 presents the conceptual
framework. Section 3 outlines the experimental design and implementation and section
4 presents the data we collect. In section 5 we present the results, which are further
discussed in section 6. Section 7 concludes the paper.
5.2 Conceptual framework
In this section, we outline the main theoretical mechanisms that our experiment is de-
signed to test for. In particular, we focus on three main features of the firm’s production
function that are likely to be relevant in the contractual design of loan contracts:
1. Backloaded returns to investments: When a firm invests in a new input or tech-
nique, often returns will take time to be realized. It takes time to learn how to
use new inputs or to train new workers, to market new products and to build
a reputation. To the extent that such learning is mechanical and simply time-
consuming, returns to an investment will start low but increase gradually;
2. Uncertain returns to investments: Introducing new products entails a risk if3see Ayyagari et al., (2011) for an overview of the characteristics of small and medium-sized firms
in low-income countries
174 CREDIT CONTRACT STRUCTURE AND FIRM GROWTH
there is uncertainty about demand. In the presence of market shocks or under
experimentation with new techniques, the timing of revenues from investments
are likely to be uncertain;
3. Indivisible investment costs: Starting or expanding a business may entail sizable
costs in the form of bulky investments such as machines or buildings.
The standard loan contract in microfinance is characterized by regular repayments
that start early in the loan cycle.4 Introducing a grace period, or in other ways making
the repayment scheme more flexible, affects both firms facing backloaded returns to in-
vestment and firms that suffer from uncertain returns to investment. For a borrower,
a more flexible repayment plan may induce higher-return investments either because
such investments have back-loaded returns that take a while to accumulate (and a
flexible plan gives the firm more time to realize the returns before repayments start)
or because there is uncertainty due to idiosyncratic shocks that affect the firm’s re-
turns, and a more flexible repayment plan enables the business owner to smooth such
risks. For example, a grace period allows more time for returns to the investment to
materialize. This is especially relevant if investments are made in a new technology
that requires learning or training employees in using the technology or in new types of
products that need to be advertised and marketed. At the same time, a grace period
also reduces the risk associated with idiosyncratic shocks to profits (e.g. due to mar-
ket conditions). Previous literature has found that the introduction of a grace period
in loan contracts in microfinance leads to higher investments and profits, and higher
default rates (Field et al., 2013). This evidence is consistent both with backloaded
and uncertain returns.5 Our experimental design, explained in detail in the following
section, is designed to shed light on which of these two mechanism may be at work.
On the question of indivisible investment costs, an influential literature shows that
4In related work by Field et al. (2013), the standard period before repayments started was twoweeks. The business loans of our implementing partner BRAC Uganda has a 1 month period betweendisbursement and the first repayment for business loans, whereafter repayments are made monthly.
5In line with the latter mechanism, the authors find a larger effect of the grace period treatmentamong risk averse borrowers.
5.3. EXPERIMENTAL DESIGN 175
the interaction between financial constraints and indivisible investments may give rise
to poverty traps as fixed costs bar the poor from productive activities (Banerjee and
Newman, 1993; Galor and Zeira, 1993; Aghion and Bolton, 1997). On the other hand,
due to the presence of asymmetric information problems in the credit market, loan
size is often constrained and most lenders, including the microfinance institution that
we partner with, enforce borrowing limits on their clients. Whether these limits indeed
imply binding investment constrains is an empirical question, and the empirical evi-
dence on this issue is not conclusive. McKenzie and Woodruff (2006) find that start-up
costs for Mexican micro-enterprises are low in many industries, leading them to reject
the idea that sizable costs hamper investment. However, their sample consists mainly
of small enterprises and they note that indivisibilities may be more relevant for larger
firms. Also, they find evidence of non-convex production technologies in some sectors.
Evaluating a policy experiment that increased credit access in Thailand, Kaboski and
Townsend (2011) document that the program primarily allowed households to under-
take lumpy investments. While this suggests that fixed costs can be important, it is
not clear whether the results apply to firms. In order to examine the importance of
indivisible costs, we will implement a treatment arm in which the size of the loan paid
out to the client will be supplemented with a cash subsidy. This will enable us to test
if access to a larger amount of capital (beyond the amount provided by the lender)
leads to greater investment and business growth.
5.3 Experimental design
The experiment was carried out in collaboration with the NGO BRAC Uganda. BRAC
is a large non-profit organization founded in Bangladesh in 1974, currently active in
12 developing countries in Asia, Sub-Saharan Africa and the Caribbean. BRAC was
launched in Uganda in 2006 and is currently one of the largest development organiza-
tions and micro finance institutions in the country.6 Its core activity is microfinance6BRAC is the largest MFI in Uganda in number of borrowers and the 4th largest in terms of total
loan volume, the only larger providers being the banks Centenary Bank and Equity Uganda and the
176 CREDIT CONTRACT STRUCTURE AND FIRM GROWTH
which encompasses both microcredit groups and the Small Enterprise Lending (SEP)
program that targets existing business owners.
The loans offered through BRAC Uganda’s SEP program are individual liability
loans offered to business owners. Loans are larger than those given through typical
microcredit arrangements, with a minimum loan size of 2.5 Million UGX, or approxi-
mately 900 USD, and maximum loan sizes following a ladder where first time borrowers
maximum loan size is 8 Million UGX (2,900 USD) while repeat borrowers are allowed
larger loans. The median loan size at the start of our experiment was 3 Million UGX.7
Prospective borrowers are evaluated by BRAC credit officers at the local office closest
to the borrower’s business location. A borrower needs to be the sole business owner,
registered with the tax authorities, a permanent resident of the branch office area
(s)he is applying in, and is not allowed to have outstanding loans with BRAC or other
lenders. In addition, a borrower needs to provide collateral (land) amounting to the
value of the loan and two guarantors who will be responsible for the repayments in
case the borrower fails to repay the loan. A loan cycle lasts for 1 year during which
the loan is paid off in 12 equal installments at an annual interest rate of 25 percent.
We collaborated with the SEP program in 76 local branch offices in Central, West-
ern, and Eastern Uganda. From November 2014, clients that were approved for a loan
in any of these 76 offices, and belonging to one of the business sectors we had pre-
selected, were enrolled into the experiment. Upon enrollment, local research officers
administered a baseline survey to the client, collecting information about business, in-
dividual and household characteristics. Data was collected electronically using tablets,
and after the baseline interview was submitted and received by our central research
team in Kampala, the client was assigned to one out of the five treatment groups, or
to the control group. The local research officer was then informed of the treatment
and - in case the client was not assigned to the control group - met again with the
credit union/cooperative TBS. (Mixmarket, 2016)7Using the nominal exchange rate at the launch of the experiment (November 1, 2014) the mini-
mum SEP loan size corresponded to 912 USD, the median to 1,100 USD and the maximum for firsttime borrowers corresponded to 2,900 USD. Corresponding real values, using the World Bank PPPadjusted exchange rate for 2014, are 2450 USD, 2,940 USD and 7,840 USD, respectively.
5.3. EXPERIMENTAL DESIGN 177
client to explain the altered contract terms.
The borrowers were randomly allocated into one of the following 6 groups:
T1. "Early repayment voucher": Firms in treatment group 1 were allowed to skip
the first 2 repayments (months 1-2 in the 1-year loan cycle);
T2. "Late repayment voucher" firms in treatment group 2 were allowed to skip the
last 2 repayments (months 11-12 in the 1-year loan cycle);
T3. "Flexible repayment voucher" firms in treatment group 3 were allowed to skip
any 2 repayments of their own choosing (any 2 months in the 1-year loan cycle);
T4. "Flat repayment voucher" firms in treatment group 4 received rebates on all
repayments such that the total loan repayment over the 1-year loan cycle is
equivalent to the total repayment in each of treatments T1-T3;
T5. "Subsidy voucher" firms in treatment group 5 received a cash grant equivalent
in value to 2 repayments (one sixth of the principal plus the interest payment).
This grant was paid to the firms on the same day as their loan disbursement (i.e.
at the beginning of their loan cycle);
C. "Control" firms in the control group received the standard BRAC loan contract
as described above.
The experiment was designed to introduce exogenous variation in contractual terms
that would enable us to test for the relative importance of the theoretical mechanisms
discussed in Section 2. In particular, treatment arms 1-3 introduce repayment rebates
at different points in time to disentangle the effect of backloaded returns due to deter-
ministic learning, from that of uncertain returns. Under the assumption of independent
and identically distributed negative sales shocks occurring during the 1-year loan cy-
cle, firms will benefit from rebates at any time if uncertain returns to investment is a
178 CREDIT CONTRACT STRUCTURE AND FIRM GROWTH
constraint. Meanwhile, firms suffering from problems of backloaded returns to invest-
ments will benefit relatively more from rebates early in the loan cycle. An early rebate
will ease the repayment burden in the beginning, when the firm has not yet started
earning the full returns on the investment. This implies that the firm will be able to
take on more backloaded investments if they are, through the early treatment, offered
a grace period. If we observe greater returns to T1-firms (Early rebates) relative to T2
(Late rebates), this supports the backloaded returns channel.
The firms in T1 start repaying 3 months after the loan is disbursed to them, and
finish repaying in month 12, while firms in T2 start repaying 1 month after disburse-
ment and finish in month 10. This setups allows for a straightforward comparison of
our findings to those of Field et al., (2013), who introduced a 2 month grace period to
microfinance clients, in which all repayments were shifted 2 months ahead in time. Our
discounting adjusted treatments (explained in more detail below) and our additional
treatment arms T3 (Flexible rebate scheme) and T5 (Subsidy), will allow us to better
disentangle the mechanisms behind their findings.
If we observe all firms in T3 (Flexible rebate scheme) opting to use their rebates
for the first two repayments, this would also be in line with a backloaded returns
mechanism since it shows that firms prefer to have the rebates early in the loan cycle.
On the other hand, if we observe T3 firms using the rebates at different points in their
loan cycle (instead of the first two repayments), this would be in line with uncertainty
playing a more important role.8
One challenge with the above argument is that firms in treatment groups T1, T2,
and T3 have an overall lower repayment burden than the control group, due to the
dynamic rebates. The rebates are subsidizing the total amount that the client owes
BRAC by the equivalent of 2 repayments. In order to account for this change in
repayment burden, we introduce a Flat repayment rebate over the full loan cycle in
8Even if we observe firms in T3 using their rebates early on in their loan cycle, this in itself is notsufficient for us to claim that backloaded returns are more important than uncertain returns, sincethis may also be due to the business owners having self-control problems. To further investigate thiswe will, once the full data is available, make use of survey modules on time preferences and householdconsumption.
5.3. EXPERIMENTAL DESIGN 179
treatment arm 4. The value of the Flat repayment rebate is equivalent, in its share of
the total loan size, to the rebates in T1-T3. The difference is that in T4, the value of
the rebate is to be subtracted equally from each of the 12 repayments such that the
only difference between T4 and the control group is a constant (level) difference each
month. T4 thus provides an alternative control group that accounts for the income
effects caused by the other treatments.9
The Subsidy treatment, T5, was designed to measure the importance of indivisible
or bulky investment costs. If we observe that firms in T5 make types of investments
that are more bulky in nature than firms in the control group or in T4 (the Flat rebate
scheme), this provides support that indivisible fixed costs is an important constraint.
In addition, by comparing T5 to T1 (the Early rebate scheme) we can separate between
backloaded returns and indivisible investment costs channels.
Every firm in the sample is a BRAC borrower that has been approved by BRAC
credit officers and is eligible for a loan. Upon being eligible, firms are then randomized
into one of the treatment (and control) arms. Randomization was implemented at
the individual client level across 76 branches throughout Uganda. The randomization
was stratified (Bruhn and McKenzie, 2009) by region10, sector (manufacturing vs.
retail), and previous experience with BRAC SEP loans (new vs. repeat borrower).
Furthermore, firms that entered into the sample consecutively within a stratum were
assigned blocks and the randomization was conducted within each block. In this way,
we effectively stratify the sample by the order in which firms enter into our sample
9To test for the effect of discounting, we implemented a cross-cutting treatment involving cashtransfers to cover inflation costs, which we will refer to as the "Discounting adjusted treatment".Within each treatment group, 50% of the firms were randomly selected to receive monthly cashtransfers, making the present discounted value of their rebates equivalent to the Subsidy treatment(T5) where no discounting was necessary (since the cash grant was paid at the beginning of the loancycle) assuming 10% annual discount rate. The clients assigned to receive this additional discountingadjustment were transferred a monthly payment via mobile money, every month, the size of whichdepended on the timing of their rebates. For example, firms in T1 were transferred a cash transfer inmonths 1 and 2, making the present discounted value of the total subsidy they received the same ashaving received the cash grant at the beginning of their loan cycle (as in T5).
10Each local BRAC office ("branch") is served and supervised by a BRAC Area Office and some ofthe lending activities of the local offices in a given Area offices are overseen by the same Area officestaff. The 76 branches in our study are grouped into 15 such Areas.
180 CREDIT CONTRACT STRUCTURE AND FIRM GROWTH
within an area, sector, and previous loan experience. The sample size (390 firms per
treatment group and 390 for the control group) is based on calculations to detect a
0.20 S.D. shift in key outcomes with 80 percent power under a 95 percent confidence
level. This implies that our current preliminary analysis with only 754 borrowers is
under-powered, making our results suggestive in nature.
The treatments were designed such that the value of the repayment voucher pro-
vided each firm with a subsidy, or rebate equivalent to two monthly installments. In
other words, without discounting, the value of the subsidy provided in T1-T5 is one
sixth of the total amount (principal plus interest) that the firm owed BRAC.
The rebates were implemented by giving the clients vouchers to be used instead of
repayment for certain installments. Client specific vouchers were prepared by the cen-
tral team in Kampala and sent to the local branch office where they were distributed
to the client by our locally based research officer, the week after the baseline interview
had been conducted with a given client. The validity date of the vouchers differed
depending on the client’s disbursement date and the treatment group that the client
was assigned to and their value was set according to each client’s loan size and corre-
sponding installment size. The Subsidy treatment clients (T5) received one voucher,
the Flat treatment clients (T4) received 12 vouchers while treatment groups T1-T3
received two vouchers each.
5.4 Data
5.4.1 Baseline and endline survey
Before assigning them to a treatment, we collected baseline data on each client. The
baseline survey instrument includes detailed information about the firm’s history and
present firm operations, as well as about individual and household characteristics of
the business owner/client. The business operation data includes information about
sales and profits volumes, expenditures, the level and type of business assets held at
baseline, and the number and type of workers employed in the business. Individual
5.4. DATA 181
characteristics of the business owner include gender and education as well as measures
of financial literacy, time and risk preferences. We also collect comprehensive data
on the business owner’s household, including household size, income, data on other
household members and their occupations, and other business activities run by the
household. Upon completion of the client’s loan cycle, 12 months after the baseline
survey, an endline survey was conducted, collecting the same type of information that
was included in the baseline.
5.4.2 Business diaries
In addition to the baseline and endline surveys, bi-monthly business diaries were con-
ducted with all the clients in our study. Apart from providing us with regular data on
sales and profits, the diaries focused on changes made in the business, as well as in
the owner’s household, between each visit of the interviewer. This high-frequency data
collection allows us to observe any adjustments made following the disbursement and
use of the loan, as well as in connection to the use of vouchers. In the current version
of the paper we only focus on the baseline and endline survey data, but once the full
data is available the business diary data will provide additional detail to the analysis.
5.4.3 Summary statistics and randomization balance check
Table 5.1 displays summary statistics and balance checks for the entire sample for
some of the central outcome variables of interest, measured at the baseline, with the
Flat treatment as the reference category. The first column shows the mean and stan-
dard deviation for a variable among the clients in the Flat treatment group, and the
following 5 columns show results from a regression of the variable on indicators for the
other treatment groups, and the control group. All monetary values were measured
in Ugandan Shillings (UGX) and then deflated to their October 2014 value and con-
verted to PPP adjusted US dollars.11 The average firm is around 8 years old and has
11The values were deflated to the October 2014 value using the CPI values from Bank of Uganda.October 2014 was chosen since this was the month when the data collection was started, and the
182 CREDIT CONTRACT STRUCTURE AND FIRM GROWTH
1.32 employees, and the average asset value lies around 16,000 in PPP adjusted US
dollars. 44% of firm owners are female, and the average education of a firm owner is
10 years which corresponds to the lower of two secondary school degrees in Uganda
(O-level). The businesses in this study are thus larger on average than those in most
related studies that have focused on household enterprises with no employees. Over-
all, the sample looks balanced. The two last rows of Table 5.1 reports the F-statistic
and P-value of a joint test of orthogonality of the full set of variables to each of the
treatment dummies. Reassuringly, none of these tests are significant.
A majority of the firms whose baseline summary statistics are shown in Table 5.1
have entered the sample more recently than the subsample we use for the preliminary
analysis, and we therefore do not have endline data on them yet. In the present version
of the analysis, we will include all business owners who had completed their loan
cycle by May 25, 2016.12 Table A.1 in the Appendix shows summary statistics and
balance checks for this sub-sample. There are 754 such businesses. This preliminary
sub-sample is slightly less balanced across treatments. This is to be expected given
the smaller sample. In particular, there are some significant differences in the types
of employees, with Early treatment firms having, on average, fewer paid employees at
baseline and the Flexible treatment firms having more paid employees than firms in
the Flat treatment category. The Flexible and the Subsidy groups have fewer unpaid
employees than the Flat comparison group. To account for these differences, we control
for the baseline value of the outcome variables in all the regressions. However, as above,
in the joint tests, orthogonality of the full set of variables to each of the treatments
cannot be rejected.
deflation makes baseline surveys carried out in different months and years more comparable. Forconversion to USD we use the 2014 average rate of the World Bank PPP adjusted exchange rate forUganda.
12By completing the loan cycle we mean that 1 year has passed since the disbursement date,implying that the endline interview as well as the final repayment installments should have beencompleted.
5.5. RESULTS 183
5.4.4 Attrition
Table A.5 reports attrition shares by treatment, with attrition defined as not having
been interviewed for the endline, despite being past the due date of performing the
endline survey on May 25, 2016 which is the cutoff date for the current sample. In total,
731 out of the 754 firms completed the endline survey. Table A.6 presents the results
from a regression of the treatments on a dummy variable for attrition. Attrition results
of all treatments are shown relative to the Flat treatment group. None of the treatment
groups, nor the control group have significantly different attrition rates from the Flat
treatment group, with the exception of the Late treatment group where the attrition
rate is significantly lower than for the Flat treatment. The p-values at the lower panel
show that the Late treatment also has significantly lower attrition compared to the
Subsidy treatment and the control groups (p-value of 0.011 and 0.031 respectively).
Results focusing on the Late treatment group should therefore be interpreted with
some caution.
5.5 Results
In this section, we present preliminary results for the first 731 firms that finished their
loan cycle within the experiment. Since the Flat treatment group, contrary to the con-
trol group, adjusts for the income effect introduced by any treatment, our specification
uses the Flat treatment as the reference group. In each result table we display the p-
values of pairwise test of equality between the other groups to facilitate comparison
between any pairs of treatments, including comparisons between each treatment group
and the pure control group clients.
5.5.1 Empirical specification
We estimate the following linear regression model, where the Flat treatment group
(T4) is the reference category:
184 CREDIT CONTRACT STRUCTURE AND FIRM GROWTH
Yi1 = α +β1T 1+β2T 2+β3T 3+β4T 5+β5C+σYi0+ηs + εi1, (5.1)
The outcome variable Yi1 is a characteristic of the business or of the owner’s house-
hold, such as profit value, asset value or household income, measured at the endline.
Following McKenzie (2012), we control for the baseline level of the outcome, Yi0, in
order to improve the efficiency of the estimated treatment effect. T1, T2, T3 and T5
are the Early, Late, Flexible and Subsidy treatments, respectively, and C is the pure
control group. The omitted treatment category is the Flat treatment (T4). ηs is a
stratum fixed effect. The coefficients of interest are β1 to β4 (and β5), indicating the
effect of treatment T on the outcome variable relative to the Flat treatment group.
All regressions are estimated using robust standard errors.
5.5.2 Key business and household outcomes
In this section, we examine the effect of the treatments on the level of business oper-
ations, as well as on economic status measures of the firm owners’ household.
Tables 5.2 and 5.3 show results for key economic variables of the business or the
business owner’s household. In addition to estimating effects on the deflated and PPP
adjusted monetary values, we also report results for three dummy variables for high
profits, sales and household income. A value=1 indicates that the value of the corre-
sponding monetary variable lies above the highest threshold of intervals that we used
in the questionnaire to measure the profits/sales/household income for respondents
who were not able to recall the precise value. Table 5.2 displays the findings for prof-
its, sales, and expenditures of the business. Controlling for baseline differences, the
profits at endline are significantly higher for businesses in the Early treatment group
than for those in the Flat treatment, the Late treatment and the control group. Com-
pared to the mean profit level among Flat treatment firms of 10,273 PPP adjusted US
dollars, profits in the Early treatment firms are on average 50% higher, while they are
on average 53% higher than in the Late treatment firms. However, the fact that we
5.5. RESULTS 185
find no significant result for the high profit dummy suggests that the result for profits
may be driven by a small number of successful firms in the Early treatment group.13
Point estimates for the other variables in Table 5.2 are imprecise and with the current
sample, it is hard to draw conclusions about effects on sales and costs.
Table 5.3 shows the results for types of employees and asset value in the business
that the loan was officially intended for, as well as for household asset value and income.
Column 1 of Table 5.3 indicates that, controlling for the baseline number of employees,
the number of paid employees is higher at endline for all treatment groups relative to
the Flat treatment group or to the pure control group. This difference is significant
at least at the 95 percent confidence level for the Early, Late and Subsidy treatments,
and approaching conventional levels of significance for the Flexible treatment group
(p-value 0.108). In terms of magnitudes, the number of paid employees are 36.6%
higher in the early than in the Flat group, it is 33.7% higher in the Late than in
the flat, and 27.7% higher in the Subsidy treatment than among Flat treatment group
clients. None of the differences between the treatments (excluding Flat) are significant.
Meanwhile, the endline number of unpaid employees in the Early treatment group is
36% lower than in the Flat treatment group, and a similar pattern is seen for the
three other treatment groups: Late, Flexible, and Subsidy. Controlling for the baseline
values, firm owner household income is higher at endline in the Early treatment group
than in the Flat, Late, and Subsidy treatment groups. These findings are significant
at least at the 95 percent confidence level, both when measured as a monetary value
(column 5), or as the likelihood to be above the "high income" threshold (column 6).14
To account for the fact that the real value of a repayment voucher depends on the
timing of its use due to inflation, we randomly assigned 50% of the firms within each
treatment group to receive monthly cash transfers, making the present discounted value
13Indeed, in Table A.2 in the Appendix, we show the regression results for the monetary variables,with the sample trimmed for outliers that lie above the 99th percentile of the distribution of thevariable. In this trimmed sample we do not observe significantly higher profits for our Early rebatetreatment firms.
14The finding for household income is robust: also in the trimmed sample in Table A.2 we observesignificantly higher household income in the Early rebate treatment group than in the Flat, Late,and Subsidy treatment groups, and the control group.
186 CREDIT CONTRACT STRUCTURE AND FIRM GROWTH
of their rebates equivalent to the Subsidy treatment (T5) where no discounting was
necessary (since the cash grant was paid at the beginning of the loan cycle), assuming
a 10% annual discount rate. Tables A.3 and A.4 in the Appendix show the results for
the same outcomes as in Tables 5.2 and 5.3, broken down by whether or not a client
was assigned to this cross-cutting discounting adjustment treatment. For three out of
the four variables where we found significant differences between the treatment groups
in the pooled sample, point estimates are similar across the two sub-groups. For the
number of unpaid employees we observe a difference, with point estimates being more
negative in the non-discounting adjusted group. This can be due to the small sample
size in our preliminary sample.
5.5.3 Additional outcomes
In the previous subsection, we found effects on the types of employment in the firm
and on household income in the absence of any clear effects on profits or sales of the
business. In this subsection, we take a closer look at expenditures connected to em-
ployment and at other income-generating activities of the business owner’s household.
In small businesses such as the ones we study, the distinction between the finances
of the business and those of the business owner’s household is often vague. Previous
studies of microfinance have found direct effects of business loans on spending in the
business owner’s/borrower’s household. One reason for why treated businesses reduce
their unpaid labor and increase the paid labor may be that they switch from employ-
ing unpaid household members to hiring externally employed workers. To investigate
this, in Table 5.4 we show results from regressions using equation 5.1 on three addi-
tional outcomes that can help rationalize our results for employment and household
income. Specifically, we examine the effect of the treatments on wage expenditures
(in the borrowing firm) and additional businesses owned by members of the owner’s
household. Subsidy treatment firms, for whom we saw a significant increase in paid
labor relative to the Flat treatment, also have significantly higher wage costs than all
the other treatment groups, and the control group. Compared to the average wage
5.6. DISCUSSION 187
costs among Flat treatment firms of 818 PPP adjusted US dollars, wage costs in the
Subsidy treatment firms almost doubled, or increased by 781 PPP adjusted US dol-
lars, an effect significant at the 95 percent confidence level. Early treatment firms, for
which we saw the most pronounced reduction in unpaid labor and increase in paid la-
bor, have a positive point estimate for wage costs, but it is not significant. Turning to
the effect on other businesses owned by the firm owners’ households, Early treatment
firms, for whom we saw large positive effects on household income relative to the Flat
reference group, also have significantly more household owned businesses, controlling
for the number of businesses owned at baseline (column 1) and has started more new
household businesses during the loan cycle than owner households in the Flat treat-
ment group (column 3).
To sum up this section, we observe an increase in profits in the Early rebate treat-
ment group, but it appears to be driven by a small number of firms. Even in the
trimmed sample, we observe increases in paid employment at the expense of unpaid
employment, and we also find increases in the household income, along with increases
in wage costs for the Subsidy treatment group and an increase in the number of
household-owned firms, especially among firm owners in the Early treatment group.
In the next section, we discuss potential mechanisms behind these results.
5.6 Discussion
In this section, we discuss the results in relation to the constraints to business growth
that the experiment is designed to test. More specifically, we discuss what the results
presented in the previous section reveal about the relevance of backloaded returns and
indivisible costs, respectively.
188 CREDIT CONTRACT STRUCTURE AND FIRM GROWTH
5.6.1 Backloadedness of returns vs. uncertain returns
Early (T1) vs. Flexible (T3) rebate treatment. The Flexible repayment voucher
treatment was introduced as a comparison to the Early repayment voucher. Examining
the timing of voucher use for the clients in the Flexible treatment helps us understand
whether skipping payments in a certain part of the loan cycle is particularly desirable to
clients, and comparing the outcomes between treatments we can understand whether
skipping repayments early in the loan cycle is more beneficial. Table 5.5 shows the
timing of voucher use for the Flexible clients. A majority of Flexible treatment clients
chose to use their vouchers within the first 2 months: 71 percent of clients used the first
voucher at the time of the first installment, and 63 percent used the second voucher at
the second installment occasion. It thus appears as if the borrowers preferred skipping
repayments early in the loan cycle, which supports the importance of backloaded
returns. Moreover, while the Flexible treatment does not yield significantly different
profits compared to the Early rebate treatment, it does yield slightly lower profits
and yield significantly lower household income. This also suggests that backloaded
returns may be more important than uncertainty. The fact that firm owners prefer to
use vouchers early could, however, also be a sign of self-control problems. To further
investigate this we will, once the full data is available, make use of survey modules on
time preferences and household consumption.
Early (T1) vs. Late (T2) rebate treatment. As discussed in section 3, treat-
ments T1 and T2 both stipulate repayments for ten consecutive months, but for the
Early rebate treatment (T1) these are shifted two months ahead in time so that repay-
ment starts after 3 months, while for T2, repayments begin after 1 month. This allows
for a comparison with Field et al. (2013). If we observe greater returns to T1-firms
(Early rebates) relative to T2 (Late rebates), this would support the importance of
the backloaded returns channel. We do find that endline household income is higher
for Early treatment firms than for the Late treatment group. Furthermore, when con-
trolling for the baseline number of household owned businesses, the households of firm
owners in the Early treatment group have a higher number of household owned busi-
5.6. DISCUSSION 189
nesses at endline than the Late treatment group. Thus, there is supportive evidence
that backloaded returns matters more than uncertain returns. These results are in line
with those of Field et al. (2013), who also found greater profits and a higher likelihood
of business formation among their grace period clients. Note, however, that since attri-
tion rates are significantly lower in the Late treatment than in both the Flat reference
group and the Early treatment in this preliminary dataset, any results that include
this treatment group should be interpreted with extra caution.
5.6.2 Indivisibility of costs
Subsidy (T5) vs. Flat (T4) treatment. The Subsidy treatment (T5) was designed
to measure the importance of indivisible or bulky investment costs. To properly inves-
tigate the impact of the subsidies, we would like to examine the timing and types of
investments made in the firms over the entire loan cycle which requires access to the
business diary information unavailable at the time of the current draft. However, the
existing data offers some evidence that the subsidies may have enabled the firms to
undertake indivisible investments otherwise out of reach.
More precisely, the Subsidy firms doubled the wage-related expenditures while
slightly increasing the size of their paid workforce, thus substantially increasing the
wages of their paid employees. Interestingly, the wage costs only rise significantly
in the Subsidy group as compared to all the other treatments (an effect statistically
significant at least at the 90 percent confidence level across treatments). This indicates
that the Subsidy firms may have hired more skilled (and expensive) workers on the
margin.15 To the extent that contracting better-qualified employees requires firms to
make more binding and long-term commitments, this scaling up is a significant fixed-
cost constraint that the Subsidy can help alleviate. Once the business diary data
becomes available, we will further investigate if the costlier workers are matched by
investments in bulkier technology that perhaps require a higher skill level.15The smaller increase in household income offers further indirect evidence that the Subsidy treat-
ment induced increased activity within the respondent’s firm rather than an expansion into otheractivities raising household income.
190 CREDIT CONTRACT STRUCTURE AND FIRM GROWTH
5.7 Conclusion
This paper presents the preliminary results of a randomized control trial designed to
evaluate the effect of loan contract structure on investment outcomes for borrowing
micro, small and medium sized enterprises. After the one year loan cycle we find no
conclusive effects on the overall profits or revenues of treated businesses compared to
the control groups. We do, however, observe significant effects on the allocation of
labor. Moreover, we find effects on the household income from some of the treatments.
In terms of mechanisms we find suggestive evidence that returns seem to be backloaded
rather than uncertain, while we also find support for the indivisible costs channel.
In particular, firms in the Early treatment group increased their profits and house-
hold income relative to firms in the Late and the Flat treatment and the control
group. Relative to the Flat treatment, the Early treatment firms also changed their
allocation of hired labor between paid and unpaid employees: their number of paid
employees at endline is 36.6% higher than in the Flat rebate group, while the endline
number of unpaid employees in the Early treatment group is 36% lower than that
in the Flat treatment group. Wage expenditures for the Early treatment firms did,
however, not change in accordance. Further, the owner households of Early treatment
firms started significantly more new household-owned firms than the Late and Flat
treatment, as well as the control group. Firms in the Flexible treatment predominantly
chose to use their two rebates in the first months of the loan cycle. All these findings
provide support for the backloadedness of returns being a more important constraint
than the uncertainty of returns. Turning to the indivisible fixed cost channel, firms in
the Subsidy treatment increased their number of employees relative to the Flat treat-
ment group and the control group: the number of paid employees is on average 27.7%
higher in the Subsidy treatment than among Flat treatment group clients, and the
Subsidy firms also significantly increased their wage expenditures. To the extent that
this implies that they hired higher quality workers, which can be seen as an indivisible
investment, this finding provides suggestive evidence for the importance of indivisible
5.7. CONCLUSION 191
costs.
To better understand the channels behind the observed effects on employees and
profits, it is crucial to examine data on specific investments carried out by the firms in
our experiment, and the degree of irreversibility of those investments. The bi-monthly
business diaries will enable us to do this. Examining the impact on the borrower’s
household in more detail is also important as benefits of the loan may accrue to
the household. Once the full data is available to us we plan to address these points.
The preliminary findings on the types of labor employed by treated firms, and the
connection to increases in the number of household owned businesses gives a first
indication that some of the effect of the loan may show up in other parts of the
household income and activities, rather than in the business that the loan was officially
intended to benefit.
We want to stress that results presented here are preliminary and should be inter-
preted with caution. Future analysis using the complete experimental data will enable
us to draw more precise conclusions regarding the effects of our treatments.
Given the scarcity of empirical work investigating the interaction of firms’ financial
structure and their production technology, this paper and project provides unique
evidence on the constraints governing firm behavior, complementing the micro finance
literature’s previous emphasis on access to finance. The findings are relevant both
to academia and policymakers. A better understanding of the economic impact of
debt contract design will provide insights to entrepreneurial behavior in developing
as well as developed markets. Also, shedding light on contractual mechanisms that
affect the size and profitability of business investment is important to guide credit
policies aimed at boosting firm growth, especially given the large sums spent by the
development community on credit programs.
192 CREDIT CONTRACT STRUCTURE AND FIRM GROWTH
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TABLES 195
Tables
196 CREDIT CONTRACT STRUCTURE AND FIRM GROWTH
Table 5.1: Summary statistics/balance check, full sample
Differences between flat and other treatmentsFlat Early Late Flexible Subs. Contr. All
Mean Coeff. Coeff. Coeff. Coeff. Coeff. N(s.d.) (s.d.) [st.err.] [st.err.] [st.err.] [st.err.]
(1) (2) (3) (4) (5) (6) (7)
Female owner 0.436 -0.025 -0.013 -0.003 -0.012 -0.035 2339(0.497) [0.035] [0.035] [0.034] [0.034] [0.035]
Years educ. owner 10.038 -0.178 0.048 -0.214 -0.188 0.094 2330(4.042) [0.283] [0.283] [0.281] [0.281] [0.284]
Profit past year 10550.227 -667.209 1025.266 566.522 826.768 973.935 2339(15941.594) [1254.431] [1253.788] [1247.646] [1246.067] [1256.517]
High profits=1 0.196 0.005 0.053∗ 0.027 0.031 0.005 2339(0.398) [0.029] [0.029] [0.029] [0.029] [0.029]
Sales past year 54432.556 6110.512 9979.132 8053.571 1966.017 3332.859 2339(85546.21) [6942.854] [6939.296] [6905.302] [6896.562] [6954.397]
High sales=1 0.173 0.025 0.015 0.013 0.012 0.017 2339(0.379) [0.027] [0.027] [0.027] [0.027] [0.027]
Costs past year 37305.61 15986.543∗∗∗ 4917.341 9320.744 4756.741 5837.885 2339(58449.136) [5853.241] [5850.242] [5821.583] [5814.215] [5862.973]
Wage costs 1094.88 -30.328 187.19 350.965 328.56 595.083∗∗ 2332(3473.682) [291.107] [290.951] [289.518] [289.164] [291.803]
Business capital 15762.831 -622.906 -1585.965 1595.017 1757.667 -1823.75 2327(32901.8) [2359.525] [2362.812] [2346.646] [2345.178] [2368.215]
Age of business (years) 8.429 -0.671 -0.303 -0.556 -0.474 -0.621 2053(7.81) [0.533] [0.533] [0.530] [0.529] [0.534]
#Employees 1.319 -0.142 -0.02 0.06 -0.108 -0.09 2339(1.279) [0.095] [0.095] [0.094] [0.094] [0.095]
# paid empl. 1.14 -0.165 0.023 0.16 -0.072 -0.074 2339(1.449) [0.102] [0.102] [0.102] [0.102] [0.103]
# unpaid empl. 0.309 -0.083∗∗ -0.028 -0.078∗∗ -0.076∗ -0.017 2339(0.666) [0.040] [0.040] [0.040] [0.040] [0.040]
HH asset value 95677.437 1994.468 4301.542 10211.520∗∗ 1878.728 -574.975 2339(124946.48) [4898.733] [4896.223] [4872.237] [4866.070] [4906.877]
HH inc. past year 15721.238 -407.954 -329.863 -2054.335 -1037.384 -911.889 2327(27156.714) [1577.922] [1578.108] [1571.405] [1567.426] [1582.792]
High HH income=1 0.3 -0.02 0.015 -0.021 0.003 -0.031 2327(0.459) [0.030] [0.030] [0.030] [0.030] [0.031]
# HH businesses 0.482 0.062 0.065 -0.026 -0.053 0 2339(0.701) [0.048] [0.048] [0.047] [0.047] [0.048]
F stat joint test 0.97 1.17 0.85 0.88 0.64 0.8P-value joint test 0.510 0.254 0.672 0.642 0.915 0.75
Note: Column 1 shows the descriptive statistics for the flat treatment group (the reference group). The remaining columns show the difference betweeneach additional treatment group and the flat treatment group. The last column shows the number of observations for the full sample (all 5 treatments andthe control group). Coefficients and standard errors are from OLS regressions of each variable on the Early, Late, Flexible and Subsidy treatment, and thecontrol group, controlling for stratification. The two last lines report the F-statistic and p-value from a joint test of the significance of the set of variablesin explaining each treatment dummy. Business capital Value of business assets, including inventory and excluding the value of land and buildings, in PPPadjusted USD. HH asset value Value of household assets, excluding land, in PPP adjusted USD. #HH businesses the number of other businesses ownedby members of the borrower’s household. Robust standard errors are reported in square brackets. All regressions control for stratum fixed effects. * p<0.1,** p<0.05, *** p<0.01.
TABLES 197
Tab
le5.2:
Businessou
tcom
esI
Profitspa
styear
Highprofi
ts=1
Salespa
styear
Highsales=1
Costs
past
year
(1)
(2)
(3)
(4)
(5)
Early
5097
.287
*0.01
5-163
2.810
0.01
018
94.892
[281
5.19
5][0.048
][110
39.531]
[0.044
][938
4.91
9]La
te-609
.306
-0.036
-276
9.57
3-0.015
1159
8.74
1[277
5.76
4][0.048
][108
80.850]
[0.043
][922
8.07
7]Flexible
991.327
-0.025
-104
39.586
-0.033
-168
8.66
1[276
2.80
8][0.047
][108
32.717]
[0.043
][918
7.89
5]Su
bsidy
2132
.773
-0.012
-646
4.64
9-0.018
-588
7.54
2[269
3.05
0][0.046
][105
60.095]
[0.042
][896
7.10
4]Con
trol
-637.817
-0.063
-107
16.653
-0.046
-127
49.150
[286
6.50
8][0.049
][112
38.272]
[0.045
][953
4.44
3]
pearly=
flex
0.147
0.40
90.42
70.33
00.70
4pearly=
late
0.04
40.28
90.91
90.57
20.30
4pearly=
subs.
0.28
50.55
90.65
70.52
00.39
9pearly=
control
0.05
00.11
90.42
80.22
20.13
2pflex=
late
0.56
60.81
20.48
40.67
90.15
3pflex=
subs.
0.67
40.79
00.70
80.71
90.64
2pflex=
control
0.57
20.44
30.98
00.77
90.24
9psubs.=
late
0.31
80.61
30.73
10.94
80.056
psubs.=
control
0.32
80.30
00.70
20.52
90.466
plate
=control
0.99
20.593
0.48
50.49
80.01
2
Meanfla
t10
273.46
0.24
264
529.24
0.18
039
005.08
Stratum
FE
Yes
Yes
Yes
Yes
Yes
AdjustedR
20.13
10.20
70.28
10.22
30.15
1Observation
s73
173
173
173
173
1Note:
Profits/Sa
les/Costs
past
year
areexpressed
intheirOctob
er2014
value,
asPPP-adjusted
US
dolla
rs.Highprofi
ts=1(H
ighsales=1):
Dum
mies=
1ifthevalueof
repo
rted
profi
ts(sales)lie
abovethehigh
estthresholdof
theintervalsthat
weused
tomeasure
theprofi
ts(sales)for
respon
dentswho
wereno
tab
leto
recalltheprecisevalue.
Meanflat
isthemeanvalueof
thevariab
lein
thefla
ttreatm
entgrou
p.Rob
uststan
dard
errors
arerepo
rted
insqua
rebrackets.Allregression
scontrolforstratum
fixed
effects.*p<
0.1,
**p<
0.05,***p<
0.01.
198 CREDIT CONTRACT STRUCTURE AND FIRM GROWTHTable
5.3:Business
outcomes
IIand
householdeconom
icstatus
#paid
empl.
#unpaid
empl.
Business
Household
HH
income
High
HH
capitalasset
valuepast
yearincom
e=1
(1)(2)
(3)(4)
(5)(6)
Early
0.369 ∗∗∗-0.118 ∗
-1231.53011393.741
7045.756 ∗∗∗0.194 ∗∗∗
[0.136][0.071]
[2591.722][9547.490]
[2387.284][0.055]
Late0.339 ∗∗
-0.072-889.771
2313.1161015.147
-0.009[0.133]
[0.070][2572.283]
[9416.095][2354.726]
[0.054]Flexible
0.213-0.113
452.17410168.408
3840.4850.056
[0.133][0.069]
[2544.747][9424.883]
[2350.025][0.054]
Subsidy0.277 ∗∗
-0.08915.109
13063.5031617.632
0.059[0.130]
[0.068][2478.928]
[9144.405][2290.295]
[0.053]Control
0.0850.072
236.0694500.433
4718.861 ∗0.008
[0.138][0.072]
[2656.963][9722.069]
[2430.015][0.056]
pearly
=flex
0.2560.936
0.5180.899
0.1820.013
pearly
=late
0.8240.512
0.8960.346
0.0120.000
pearly
=subs.
0.4920.669
0.6250.859
0.0210.013
pearly
=control
0.0430.009
0.5880.487
0.3470.001
pflex
=late
0.3510.559
0.6030.408
0.2330.235
pflex
=subs.
0.6250.723
0.8610.753
0.3350.958
pflex
=control
0.3530.011
0.9360.564
0.7200.393
psubs.=
late0.644
0.8060.722
0.2480.796
0.207psubs.=
control0.157
0.0230.933
0.3730.197
0.357plate
=control
0.0690.048
0.6770.824
0.1320.768
Mean
flat1.007
0.32813357.69
19034.0712206.79
0.266Stratum
FE
Yes
Yes
Yes
Yes
Yes
Yes
Adjusted
R2
0.5910.154
0.2060.081
0.1840.177
Observations
731731
719731
728728
Note:#
paidem
pl.and
#paid
empl.
aretaken
fromthe
employee
roster.The
majority
ofunpaid
employees
areunpaid
family
workers
ofthe
owner.
Business
capital:Aggregation
ofthe
valueof
listedbusiness
assets(such
astools,m
achinesequipm
entand
furniture)and
thereported
totalvalueof
inventory.Excludes
thevalue
ofland
orbuildings.
Household
assetvalue:
The
valueof
alist
ofhousehold
assets.Excludes
thevalue
ofland
orbuildings.
Business
capital,Household
assetvalue
andHH
incomepast
yearare
expressedin
theirOctober
2014value,
asPPP-adjusted
US
dollars.High
HH
income=1:
Adum
my=
1ifthe
valueof
reportedhousehold
assetslie
abovethe
highestthreshold
ofintervals
thatweused
tomeasure
thehousehold
incomefor
respondentswho
were
notable
torecallthe
precisevalue.M
eanflat
isthe
mean
valueof
thevariable
inthe
flattreatm
entgroup.
Robust
standarderrors
arereported
insquare
brackets.Allregressions
controlfor
stratumfixed
effects.*p<0.1,
**p<0.05,
***p<0.01.
TABLES 199
Table 5.4: Employment details and other household businesses
Wage costs # Other # Newpast year HH businesses HH businesses
(1) (2) (3)
Early 169.697 0.147∗ 0.249∗∗∗[318.661] [0.083] [0.081]
Late -37.485 -0.014 0.131∗[313.995] [0.082] [0.079]
Flexible 270.486 -0.009 0.047[312.843] [0.081] [0.079]
Subsidy 781.552∗∗ 0.101 0.095[305.436] [0.079] [0.077]
Control 29.321 -0.059 0.002[324.692] [0.084] [0.082]
p early = flex 0.753 0.061 0.013p early =late 0.518 0.053 0.147p early =subs. 0.052 0.568 0.052p early =control 0.672 0.016 0.003p flex =late 0.329 0.949 0.291p flex =subs. 0.096 0.17 0.536p flex =control 0.459 0.55 0.584p subs. = late 0.008 0.156 0.642p subs. = control 0.019 0.055 0.251p late = control 0.839 0.595 0.119
Mean flat 818.23 0.422 0.195Stratum FE Yes Yes YesAdjusted R2 0.202 0.255 0.035Observations 729 731 731
Note: Wage costs past year : Wage expenditures in past 12 months in the borrowing firm.#Other HH businesses: Number of other businesses owned by owner’s/borrower’s HH.#NewHH businesses: Number of new businesses started by the household in the past 12 months,measured only at the endline.Mean flat is the mean value of the variable in the flat treatmentgroup. Robust standard errors are reported in square brackets. All regressions control forstratum fixed effects.* p<0.1, ** p<0.05, *** p<0.01.
200 CREDIT CONTRACT STRUCTURE AND FIRM GROWTH
Table 5.5: Voucher use timing among flexible treatment clients
Month in # Voucher 1 Fraction #Voucher 2 Fractionloan cycle used used used used
1 87 0.71 0 0.002 19 0.15 76 0.633 11 0.09 17 0.144 4 0.03 11 0.095 0 0.00 5 0.046 1 0.01 5 0.047 0 0.00 3 0.038 1 0.01 0 0.009 0 0.00 2 0.0210 0 0.00 0 0.0011 0 0.00 1 0.0112 0 0.00 0 0.00
Total 123 1.00 120 1.00Note: Column 1 shows the month in the loan cycle (1-12). Column 1 reports the number of "voucher1" used in that month, and colum 3 reports the share of "voucher 1" used in the respective month.Columns 4 and 5 does the corresponding thing for "voucher 2". Note that three out of the 123clients in our partial sample that were assigned to the flexible treatment only used one out of theirtwo vouchers.
APPENDIX 201
Appendix
202 CREDIT CONTRACT STRUCTURE AND FIRM GROWTH
Table A.1: Summary statistics/balance check, partial sample
Differences between flat and other treatmentsFlat Early Late Flexible Subs. Contr. All
Mean Coeff. Coeff. Coeff. Coeff. Coeff. N(s.d.) (s.d.) [st.err.] [st.err.] [st.err.] [st.err.]
(1) (2) (3) (4) (5) (6) (7)
Female owner 0.485 -0.066 -0.124∗∗ -0.077 -0.128∗∗ -0.092 754(0.502) [0.061] [0.060] [0.059] [0.058] [0.061]
Years educ. owner 10.504 -0.338 -0.761 -0.26 -0.517 -0.269 751(3.997) [0.508] [0.505] [0.496] [0.491] [0.514]
Profit past year 11475.637 -1049.791 -1414.305 -560.479 -354.681 -876.877 754(21155.554) [2023.083] [2010.830] [1974.013] [1944.972] [2047.205]
High profits =1 0.194 -0.001 -0.008 0.065 -0.004 -0.019 754(0.397) [0.050] [0.050] [0.049] [0.048] [0.051]
Sales past year 57920.443 -7157.674 -4882.702 750.596 -2633.959 -4349.727 754(110050.533) [11612.968] [11542.632] [11331.297] [11164.592] [11751.433]
High sales =1 0.142 0.034 0.013 0.045 0.029 0.025 754(0.35) [0.046] [0.046] [0.045] [0.044] [0.047]
Costs past year 36528.647 20010.830∗∗ 3856.71 5952.664 13062.724 7888.914 754(57361.967) [9980.630] [9920.180] [9738.551] [9595.279] [10099.632]
Wage costs year 1405.183 -570.182 228.016 585.596 -68.74 814.982 752(3555.742) [521.198] [518.277] [508.846] [502.404] [527.493]
Business capital 18960.35 -1394.209 -2448.075 -209.893 1685.439 567.108 749(46789.357) [4674.038] [4666.171] [4560.525] [4500.906] [4745.595]
Age of business (years) 8.463 -1.335 -0.388 -0.446 -0.029 -0.894 674(8.016) [0.932] [0.915] [0.901] [0.896] [0.930]
#Employees 1.455 -0.264 0.022 0.167 -0.216 -0.259 754(1.412) [0.192] [0.191] [0.187] [0.184] [0.194]
# paid empl. 1.269 -0.363∗ 0.196 0.375∗ -0.218 -0.112 754(1.537) [0.214] [0.212] [0.208] [0.205] [0.216]
# unpaid empl. 0.351 -0.028 -0.087 -0.151∗∗ -0.141∗ -0.13 754(0.816) [0.079] [0.078] [0.077] [0.076] [0.080]
HH asset value 12741.119 -1298.235 5619.316 21273.015∗∗∗ 8035.263 2270.85 754(29661.88) [7769.903] [7722.843] [7581.445] [7469.908] [7862.546]
HH inc. past year 13487.642 1855.154 1808.075 2048.307 1186.561 427.041 752(17026.426) [2517.816] [2503.706] [2458.145] [2427.026] [2548.224]
High HH income=1 0.263 0.019 0.007 0.072 0.02 -0.027 752(0.442) [0.054] [0.053] [0.052] [0.052] [0.054]
# HH businesses 0.567 0.069 0.131 0.055 -0.057 -0.01 754(0.74) [0.093] [0.093] [0.091] [0.090] [0.094]
F stat joint test 1.14 1.24 0.69 0.93 0.84 0.68P-value joint test 0.30 0.22 0.84 0.55 0.67 0.85
Note: Column 1 shows the descriptive statistics for the flat treatment group (the reference group). The remaining columns show the difference between eachadditional treatment group (or the control) and the flat treatment group. The last column shows the number of observations for all 6 treatments in the partialsample (the clients due until May 25, 2016). Coefficients and standard errors are from OLS regressions of each variable on the Early, Late, Flexible, Subsidy andControl groups, controlling for stratification. Last lines report the F-statistic and p-value from a joint test of the significance of the set of variables in explainingeach treatment dummy. Business capital Value of business assets, including inventory and excluding the value of land and buildings, in PPP adjusted USD.HH asset value Value of household assets, excluding land, in PPP adjusted USD. #HH businesses the number of other businesses owned by members of theborrower’s household. Robust standard errors are reported in square brackets. All regressions control for stratum fixed effects. * p<0.1, ** p<0.05, *** p<0.01.
APPENDIX 203
Tab
leA.2:K
eymon
etaryou
tcom
esrelative
tofla
ttreatm
ent,
trim
med
forou
tliers
Profit
year
Salesyear
Costs
year
Businesscapital
HH
assets
HH
incyear
(1)
(2)
(3)
(4)
(5)
(6)
Early
708.08
0-105
9.41
7-495
8.64
744
1.44
537
64.459
4538
.139∗∗∗
[109
9.52
2][647
5.37
2][682
9.86
2][159
6.765]
[501
9.06
5][160
3.25
9]La
te-909
.790
-587
6.18
612
57.607
249.72
037
22.667
1384
.472
[108
1.14
3][639
2.68
8][670
6.91
3][158
2.645]
[493
0.52
4][157
4.90
1]Flexible
68.935
-886
2.40
5-458
5.47
912
27.821
951.24
028
22.281∗
[107
1.97
0][634
1.04
4][667
9.69
5][156
6.973]
[497
0.73
4][158
1.56
9]Su
bsidy
754.91
0-392
5.17
5-345
5.56
1-376
.452
-499
1.28
310
91.525
[105
1.55
4][619
1.77
8][651
5.21
4][152
9.998]
[482
1.67
5][153
6.21
9]Con
trol
-595
.512
-613
4.68
2-9882.35
910
09.562
1117
.460
1084
.100
[111
5.16
5][657
7.75
2][691
8.65
9][163
9.685]
[509
7.04
4][164
1.42
5]
pearly=
flex
0.56
10.22
80.95
70.62
30.58
20.29
1pearly=
late
0.14
30.46
00.36
80.90
50.99
30.05
1pearly=
subs.
0.96
60.65
20.82
40.60
30.080
0.03
0pearly=
control
0.25
20.44
80.48
80.73
40.61
20.04
0pflex=
late
0.36
50.64
10.38
90.53
70.58
20.36
8pflex=
subs.
0.51
50.42
60.86
40.29
70.22
70.26
6pflex=
control
0.55
20.67
90.44
90.89
40.97
40.29
6psubs.=
late
0.12
00.75
70.47
90.68
90.07
70.85
1psubs.=
control
0.22
10.73
30.34
90.39
30.23
00.99
6plate=control
0.78
10.96
90.114
0.64
80.61
50.85
7
Stratum
FE
Yes
Yes
Yes
Yes
Yes
Yes
AdjustedR
20.24
90.37
00.20
20.373
0.06
80.21
3Observation
s72
171
872
1707
717
716
Note:Profit
year
:Profitsin
thepa
st12
mon
ths.Sa
lesyear
:Sales
inthepa
st12
mon
ths.Businesscapital:Aggregation
ofthevalueof
listed
business
assets
(suchas
tools,
machinesequipm
entan
dfurniture)
andtherepo
rted
totalvalueof
inventory.
Excludesthevalueof
land
orbu
ildings.Hou
seho
ldassets:The
valueof
alistof
householdassets.Excludesthevalueof
land
orbu
ildings.HH
incomeyear
:Hou
seho
ldincomein
thepa
st12
mon
ths.Allmon
etaryvariab
lesareexpressedin
theirOctob
er2014
value,as
PPP-adjustedUSdo
llars.R
obuststan
dard
errors
arerepo
rted
insqua
rebrackets.Allregression
scontrolforstratum
fixed
effects.*p<
0.1,
**p<
0.05,***p<
0.01.
204 CREDIT CONTRACT STRUCTURE AND FIRM GROWTH
Table
A.3:B
usinessoutcom
esI,separate
fordiscounting
adjustedtreatm
entclients
Discounting
adjustedtreatm
entNon-D
iscountingadjusted
treatment
profitshigh
saleshigh
costsprofits
highsales
highcosts
yearprofit
yearsales
yearyear
profityear
salesyear
(1)(2)
(3)(4)
(5)(6)
(7)(8)
(9)(10)
Early
480.3000.025
-21792.922-0.046
-15498.2217772.794 ∗
0.00616143.931
0.05412521.394
[2338.820][0.067]
[13642.305][0.063]
[12594.974][4670.535]
[0.068][15590.470]
[0.061][12099.558]
Late-531.845
-0.001-8221.225
-0.02420930.552 ∗
424.675-0.064
7002.1620.016
4447.773[2325.747]
[0.067][13566.278]
[0.062][12450.319]
[4557.927][0.066]
[15162.812][0.059]
[11765.946]Flexible
-660.094-0.047
-20333.559-0.077
-11063.5621713.044
-0.003557.122
0.0235884.033
[2282.571][0.066]
[13315.467][0.061]
[12218.327][4613.906]
[0.067][15389.571]
[0.060][11968.877]
Subsidy1685.277
0.003-18298.955
-0.078-12551.719
1736.383-0.032
5839.0990.044
-1124.610[1954.688]
[0.056][11409.629]
[0.052][10471.840]
[3871.206][0.056]
[12925.125][0.050]
[10039.359]Control
-66.683-0.048
-21406.514 ∗-0.105 ∗
-17290.261-1657.889
-0.0731594.468
0.018-9856.310
[2047.169][0.059]
[11948.404][0.055]
[10963.491][4026.782]
[0.058][13445.306]
[0.052][10432.516]
pearly=
flex0.624
0.2840.914
0.6170.723
0.2040.897
0.3280.620
0.591pearly=
late0.669
0.7010.326
0.7240.004
0.1180.302
0.5600.530
0.507pearly=
sub0.549
0.6990.766
0.5520.785
0.1380.523
0.4480.846
0.196pearly=
control0.792
0.2230.975
0.2960.873
0.0250.196
0.2990.511
0.040pflex=
late0.956
0.4970.372
0.3900.010
0.7830.364
0.6790.900
0.905pflex=
sub0.229
0.3800.858
0.9870.887
0.9950.620
0.6920.693
0.498pflex=
control0.771
0.9850.928
0.6210.569
0.4150.245
0.9400.924
0.142psub=
late0.271
0.9470.391
0.3130.002
0.7400.569
0.9300.584
0.587psub=
control0.296
0.2950.751
0.5600.597
0.3060.393
0.7010.552
0.310plate=
control0.823
0.4360.279
0.1500.001
0.6130.887
0.6930.963
0.180
Mean
flat10196.09
0.23471352.48
0.23446053.19
10350.820.25
577060.125
31956.96Stratum
FE
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Adjusted
R2
0.2700.219
0.3680.242
0.1610.178
0.2110.357
0.2270.165
Observations
490490
490490
490482
482482
482482
Note:
In[T
1]-[T4]
arandom
50%of
clientsare
assignedto
thediscounting
adjustedtreatm
entversions,
while
for[T
5](the
subsidytreatm
ent)and
theControl
group,there
isno
discountingadjusted
group.Therefore,the
sample
incolum
ns1-5
includesthe
discountingadjusted
treatment-subgroups
oftreatm
entarm
s1-4,plus
allclientsin
T5and
Control.
The
sample
incolum
ns6-10
includesthe
non-discountingadjusted
treatment-subgroups
oftreatm
entarm
s1-4,
plus,again,
allclients
inT5and
Control.
Profits/Sales/C
ostspast
yearare
expressedin
theirOctober
2014value,
asPPP-adjusted
USdollars.
High
profits=1(H
ighsales
=1):
Dum
mies=
1ifthe
valueof
reportedprofits
(sales)lie
abovethe
highestthreshold
ofthe
intervalsthat
weused
tomeasure
theprofits
(sales)for
respondentswho
were
notable
torecall
theprecise
value.Robust
standarderrors
arereported
insquare
brackets.All
regressionscontrol
forstratum
fixedeffects.
*p<0.1,
**p<0.05,
***p<0.01.
APPENDIX 205
Tab
leA.4:B
usinessou
tcom
esII
andho
useholdecon
omic
status,s
eparatefordiscou
ntingad
justed
treatm
entclients
discou
ntingad
justed
treatm
ent
Non
-discoun
ting
adjusted
treatm
ent
#pa
id#
unpa
idBus.
HH
HH
inc
High
#pa
id#
unpa
idBus.
HH
HH
inc
High
empl
empl
capital
assets
year
HH
inc
empl
empl
capital
assets
year
HH
inc
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
(10)
(11)
(12)
Early
0.36
0*0.03
5-206
6.11
415
654.57
010
000.56
7∗∗∗
0.21
3∗∗∗
0.37
1∗-0.255∗∗
609.75
228
9.32
345
28.162
0.19
8∗∗
[0.193
][0.104
][337
4.68
1][140
01.452
][3757.65
0][0.079
][0.210
][0.109
][425
1.55
4][142
39.926
][319
2.32
0][0.078
]La
te0.35
7∗-0.059
1496
.081
6626
.925
-249
.053
-0.038
0.30
6-0.082
-274
5.97
0-436
9.64
630
10.850
0.01
8[0.192
][0.103
][335
5.76
1][139
49.803
][3742.24
1][0.078
][0.204
][0.106
][418
1.12
1][138
58.583
][310
5.90
7][0.075
]Flexible
0.27
80.00
910
71.844
3026.957
5169
.818
0.10
10.13
8-0.188∗
-33.52
014
129.84
624
34.023
0.01
9[0.188
][0.102
][330
7.04
8][137
38.897
][3670.75
7][0.077
][0.208
][0.108
][417
5.33
5][141
27.641
][317
1.43
3][0.077
]Su
bsidy
0.29
8∗0.00
587
2.00
510
492.05
910
02.138
0.04
90.29
3∗-0.162∗
-114
5.25
810
247.00
926
91.818
0.07
7[0.161
][0.087
][282
4.78
0][117
09.037
][3151.76
1][0.066
][0.174
][0.090
][350
9.49
2][118
18.217
][265
0.97
4][0.064
]Con
trol
0.09
20.16
2∗13
52.423
974.57
944
62.740
0.00
50.10
6-0.004
-815
.646
4906
.107
5482
.694∗∗
0.02
9[0.169
][0.091
][297
3.47
7][122
57.436
][3292.37
1][0.069
][0.181
][0.094
][367
0.28
0][122
75.097
][275
2.09
5][0.067
]
pearly=
flex
0.66
60.79
50.35
20.36
80.19
50.15
20.28
00.54
70.88
20.34
30.52
30.02
5pearly=
late
0.98
80.36
60.29
80.52
60.00
70.00
20.757
0.11
50.43
60.74
50.63
70.02
2pearly=
subs.
0.70
60.73
10.31
10.66
80.00
50.01
50.66
80.32
90.63
50.42
30.51
00.07
5pearly=
control
0.11
90.16
80.25
70.23
80.09
60.00
30.161
0.01
10.71
00.71
90.74
00.01
6pflex=
late
0.67
80.50
90.90
00.79
60.14
60.07
60.42
40.33
20.52
40.19
70.85
70.99
4pflex=
subs.
0.899
0.96
50.94
40.52
40.18
30.42
90.39
10.78
10.75
80.75
00.925
0.38
7pflex=
control
0.27
10.08
90.92
50.86
70.82
90.16
00.86
40.05
80.83
40.46
70.28
50.88
4psubs.=
late
0.72
30.47
00.830
0.74
80.69
80.19
90.94
20.38
60.66
10.22
70.90
60.37
4psubs.=
control
0.13
70.03
40.844
0.34
30.19
80.42
90.21
00.04
20.91
30.59
80.22
00.38
8plate=control
0.12
50.017
0.96
20.65
10.15
90.54
60.27
90.41
80.61
20.46
00.37
90.87
6
Meanfla
t1.00
0.30
1159
6.65
1717
9.17
1283
1.56
0.28
11.01
60.35
915
146.69
2088
8.97
1158
2.03
0.25
Stratum
FE
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
AdjustedR
20.55
70.14
50.29
80.07
50.20
10.17
60.55
30.11
70.19
70.11
70.16
10.17
2Observation
s49
049
048
449
048
848
848
248
247
248
248
048
0Note:
In[T
1]-[T4]
arand
om50%
ofclientsareassign
edto
thediscou
ntingad
justed
treatm
entversions,while
for[T
5](the
subsidytreatm
ent)
andtheCon
trol
grou
p,thereisno
discou
ntingad
justed
grou
p.Therefore,the
samplein
columns
1-6includ
esthediscou
ntingad
justed
treatm
ent-subg
roup
sof
treatm
entarms1-4,
plus
allclie
ntsin
T5an
dCon
trol.T
hesamplein
columns
7-12
includ
estheno
n-discou
ntingad
justed
treatm
ent-subg
roup
sof
treatm
entarms1-4,
plus,again,
allclientsin
T5an
dCon
trol.#paid
empl.an
d#paid
empl.aretakenfrom
theem
ployee
roster.The
majorityof
unpa
idem
ployeesareun
paid
family
workers
oftheow
ner.
Business
capital:Aggregation
ofthevalueof
listedbu
siness
assets
(suchas
tools,
machine
sequipm
entan
dfurniture)
andtherepo
rted
totalvalueof
inventory.
Excludesthevalueof
land
orbu
ildings.Hou
seho
ldassetvalue:
The
valueof
alistof
householdassets.Exclude
sthevalueof
land
orbu
ildings.Businesscapital,Hou
seho
ldassetvaluean
dHH
incomepast
year
areexpressedin
theirOctob
er2014
value,
asPPP-adjustedUSdo
llars.High
HH
income=1:
Adu
mmy=
1ifthevalueof
repo
rted
householdassets
lieab
ovethehigh
estthresholdof
intervalsthat
weused
tomeasure
theho
useholdincomeforrespon
dentswho
wereno
tab
leto
recalltheprecise
value.
Rob
uststan
dard
errors
arerepo
rted
insqua
rebrackets.Allregression
scontrolforstratum
fixed
effects.*p<
0.1,
**p<
0.05,***p<
0.01.
206 CREDIT CONTRACT STRUCTURE AND FIRM GROWTH
Table A.5: Attrition rates by treatment
N N N Sharedue clients interviewed attrited attrited
Early 119 116 3 0.03Late 123 123 0 0.00Flexible 129 123 6 0.05Flat 134 128 6 0.04Subsidy 136 134 2 0.01Control 113 107 6 0.05
Total 754 731 23 0.03Note: Due client: A dummy variable =1 if a client’s due date for the endline interviewis before May 25, 2016. Attrited: a dummy=1 if the client’s is Due (before May 25) andshe has not been reached for the endline interview by June 25.
APPENDIX 207
Table A.6: Attrition by treatment
Early −0.016[0.022]
Late −0.048∗∗
[0.022]Flexible 0.000
[0.022]Subsidy −0.032
[0.021]Control 0.011
[0.022]
p early = flex 0.461p early = late 0.166p early = subs. 0.479p early = control 0.233p flex = late 0.031p flex = subs. 0.136p flex = control 0.625p subs. = late 0.473p subs. = control 0.055p late = control 0.011
Stratum FE YesAdjusted R2 -0.005Observations 754
Note: The table shows results from a regression of a dummy for attrition on alltreatments relative to the flat treatment. Robust standard errors are reportedin brackets. Controls for stratum included in regression. * p<0.1, ** p<0.05,*** p<0.01.
208 CREDIT CONTRACT STRUCTURE AND FIRM GROWTH
Sammanfattning
Denna avhandling består av fyra fristående kapitel som alla kretsar kring teman som
utvecklingspolitik och politisk-ekonomiska aspekter av genomförandet av utvecklings-
program. Kapitel 2, 4 och 5 analyserar utvecklingsinitiativ som är kopplade till eko-
nomisk integration av fattiga medan kapitel 3 behandlar ett utvecklingsprogram med
politiska undertoner.
I utvecklingsländer står icke-statliga organisationer och andra externa aktörer ofta
för en stor del av tillhandahållandet både av offentliga tjänster som ej tillhandahålls av
staten, och av finansiella tjänster som endast i otillräcklig utsträckning erbjuds av ban-
ker och andra formella institutioner (Baland et al., 2011;. Casey et al., 2012; Grossman,
2014). Under de senaste decennierna har ledande icke-statliga organisationer priorite-
rat utvecklingsprojekt som, trots att de införts av externa initiativtagare, involverar
lokalsamhället i beslutsfattandet kring programmen (Mansuri och Rao, 2012). Detta
anses öka projektens legitimitet och långsiktiga hållbarhet. Tidigare utvärderingar av
sådana projekt framhåller flera fördelar med direkt lokalt deltagande i beslut jämfört
med mer centralt beslutsfattande. Än så länge vet vi dock mycket lite om den relativa
effektiviteten hos olika typer av direkt deltagande. Med undantag av Grossman (2014)
har dessa studier inte heller behandlat lokalt ledarskap, trots att en viktig faktor för
effektiviteten i dessa projekt är hur deras lokala ledning är organiserad.
I Kapitel 2, Electoral Rules and Leader Selection: Experimental Evidence
from Ugandan Community Groups (Valregler och val av ledare: experimentell
evidens från ugandiska sparandegrupper), studeras hur utformningen av valregler av-
gör vilka typer av ledare som väljs i sparande-grupper för unga kvinnor i en fattig
209
210 SAMMANFATTNING
del av Uganda. Trots en omfattande teoretisk litteratur om hur valsystem påverkar
policy, vet vi mindre om deras inverkan på ledare. Dessutom är det mycket svårt att
finna sammanhang där exogen variation i valregler förekommer. Vi instruerade ugan-
diska sparande-grupper att använda en av följande två metoder när de valde ledare
för första gången: val genom sluten omröstning eller val i en öppen diskussion med
beslut fattat genom konsensus. Instruktionerna randomiserades mellan grupper, vilket
gör det möjligt för oss att estimera den kausala effekten av valreglerna både på ledares
egenskaper och på mätbara grupputfall till följd av ledarnas genomförda politik. Mer
specifikt undersöker vi här andelen medlemmar som blir kvar i grupperna över tid
samt nivåer och allokering av sparande och lån inom gruppen. Vi finner att grupper
som valt ledare genom sluten omröstning i högre utsträckning väljer ledare som liknar
den genomsnittliga gruppmedlemmen, medan grupper som valt sina ledare i en öppen
diskussion väljer rikare ledare med högre utbildning än den genomsnittliga medlem-
men. Vidare finner vi att avhopp från gruppen är betydligt vanligare i grupper som
använt öppen diskussion, i synnerhet bland de medlemmar som i början var fattigast i
sin sparande-grupp. Efter 3,5 år är grupper som valt ledare genom sluten omröstning
större och deras medlemmar sparar mindre belopp och beviljas mindre lån än medlem-
mar som valt ledare genom öppen diskussion. Våra resultat tyder på att inflytelserika
medlemmar, med mer makt i gruppen initialt, i högre utsträckning påverkar utfallet
i diskussionsförfarandet vilket leder till utfall som är mindre representativa för den
genomsnittliga medlemmens preferenser än vid sluten omröstning. Detta ligger i linje
med resultat från tidigare studier av införandet av slutna val (Baland och Robinson
(2008) och Hinnerich och Pettersson-Lidbom (2014)). Vi finner alltså att valmetoden
som används påverkar både ledartyper och grupputfall, där ett system med sluten
omröstning skapar mer inkluderande grupper medan ett system med öppen diskussion
leder till sämre ekonomisk integration av samhällets fattigaste medlemmar. Med tan-
ke på den avgörande roll som denna typ av grupper spelar för tillhandahållandet av
många offentliga och finansiella tjänster i låginkomstländer har vår studie konsekven-
ser för offentlig service i dessa länder.
211
En utbredd uppfattning är att stormöten samt idéella organisationer och klubbar
främjar socialt kapital genom att utgöra arenor där människor kan mötas, utbyta idéer,
lösa snålskjuts-problem och skapa kollektiva nyttigheter (Grootaert och van Bastelaer,
2002; Guiso, Sapienza och Zingalez, 2008; Knack och Keefer, 1997; Putnam, 2000).
Denna syn förklarar delvis det ökade fokus hos centrala utvecklingsorganisationer på
projekt där byrådsmöten och gräsrotsdeltagande spelar en central roll, som diskuteras
ovan (se Mansuri och Rao (2012) för en översikt). Nyare forskning har dock visat att
föreningsliv och olika sociala forum även kan ha negativa effekter på socialt kapital:
istället för att överbrygga samhälleliga, sociala och etniska klyftor, kan denna typ av
forum istället förstärka dem (Satyanath et al. (2015).
Kapitel 3, Preparing for Genocide: Community Meetings in Rwanda (För-
beredelser för folkmord: bymöten i Rwanda), anknyter till denna litteratur genom att
studera en helt annan typ av utvecklingsprogram, som på grund av sin politiska ka-
raktär fick förödande konsekvenser. Olika former av obligatorisk samhällstjänst på
lokal nivå har använts i Rwanda sedan innan kolonialtiden och liknande institutioner
förekom under den tidiga postkoloniala perioden även i andra Öst- och Centralafri-
kanska länder (Guichaoua, 1991). Under perioden 1973-1994, blev den obligatoriska
samhällstjänsten statlig politik i Rwanda. Varje lördag möttes Rwandiska bybor för
att arbeta med gemensamma projekt som vägar och annan infrastruktur, en praxis
som kallas Umuganda. Detta fenomen motiverades genom utvecklingsargument, men
var också mycket politiserat och enligt kvalitativ forskning av bl.a. Straus (2006) och
Verwimp (2013), användes mötena i anslutning till dessa arbetsdagar regelbundet av
den lokala politiska eliten för att sprida propaganda under åren före folkmordet. Detta
kapitel presenterar de första kvantitativa bevisen för detta (miss)bruk av Umuganda.
Att identifiera den kausala effekten av mötena på senare deltagande i folkmordet är
svårt, av två skäl. För det första har vi inte data över antalet personer som deltog
i Umuganda under perioden innan 1994, eller över antalet möten som ägde rum på
212 SAMMANFATTNING
en viss plats. Även om sådan data fanns tillgänglig skulle våra estimat dessutom ka-
raktäriseras av s.k. omitted variable bias. För att fastställa ett orsakssamband mellan
mötenas intensitet och deltagande i våld under folkmordet utnyttjar vi därför exo-
gena väderförhållanden. Antagandet vi gör är att när det regnar kraftigt blir mötet
antingen inställt eller mindre intensivt. Vi använder daglig regnfallsdata från perioden
1984-1998 samt data från lokala domstolar, särskilt inrättade efter folkmordet, över
mål mot civila för deltagande i folkmordet. Vi finner att en ytterligare regnig lördag i
en viss by under åren före folkmordet resulterade i fem procent lägre civilt deltagande
i folkmordsvåld i samma by. Vi finner inga resultat av regn under andra veckodagar på
deltagande i folkmordet. Dessutom drivs vårt resultat helt av de orter som innan 1994
styrdes av hutu-ledda partier (som stödde folkmordet). På de få platser som styrdes
av pro-tutsiminoriteten är effekterna omvända. Trots vårt specifika geografiska fokus
i denna studie, menar vi att det är av mer allmänt intresse att undersöka eventuellt
negativa effekten av olika typer av stormöten. Medan dessa möten allmänt ses som
något positivt, framhåller vi stöd för att det finns en mörkare sida av dessa möten där
det sociala kapitalet som skapas inte överbryggar samhälleliga, etniska klyftor, utan
istället förstärker dem. Att förstå denna process är ännu viktigare eftersom Umugan-
da, trots sin tidigare användning, formellt återinfördes i Rwanda år 2008, och liknande
institutioner har införts i Burundi och nyligen varit uppe på förslag i Kenya.1
En av de former för utvecklingsstöd som uppmärksammats mest under de senaste
årtiondena är mikrofinansiering. Mikrokrediter och det vidare begreppet mikrofinan-
siering blev känt för allmänheten då Grameen bank och dess grundare Mohammad
Yunus tilldelades Nobels fredspris 2006. Tanken bakom mikrofinansiering är att små
lån kan hjälpa fattiga människor att förbättra sin försörjning genom småskalig kom-
mersiell verksamhet. Som Amendariz de Aghion och Morduch (2005) skriver i sin bok
om mikrofinansiering: Mikrofinans presenterar sig som en ny marknadsbaserad stra-
tegi för fattigdomsbekämpning, fri från de tunga subventioner som fällt stora statliga
1För mer information om den kenyanska fallet, se Daily Nation, (2016).
213
banker. I en värld på jakt efter enkla svar har denna win-win kombination blivit en
riktig vinnare.2 Trots stor entusiasm kring mikrofinansiering som ett verktyg för att
minska fattigdomen har de större utvärderingar av mikrofinansiering som genomförts
de senaste åren funnit att de långsiktiga effekterna på tillväxt och låntagares välfärd är
begränsade (Banerjee et al., 2015). Kapitel 4 och 5 i avhandlingen undersöker möjliga
sätt på vilka mikrofinansiering, genom ändringar i lånekontraktets utformning, bättre
skulle kunna uppfylla löftena om tillväxt. Båda kapitel fokuserar på mikroföretag samt
små och medelstora företag och gäller individuella lån.
Kapitel 4, Selection into Borrowing: Survey Evidence from Uganda (Se-
lektion på kreditmarknaden: evidens från Uganda), rapporterar resultaten från en
undersökning som estimerar efterfrågan på lån hos ett representativt urval av småfö-
retagare i urbana Uganda. Forskning inom kontraktsteori visar att en höjning av priset
på krediter (räntan) kan leda till antingen fördelaktiga (Stiglitz och Weiss, 1981) eller
negativa selektionseffekter (De Meza och Webb, 1987), med avseende på sannolikhe-
ten för projektets framgång, samt att ökningar av storleken på den säkerhet3 som
långivaren kräver leder till fördelaktiga selektionseffekter (Stiglitz och Weiss, 1981;
Wette, 1983). Att förstå selektionseffekter inom mikrofinansiering är av särskilt in-
tresse eftersom denna marknad kännetecknas av kreditransonering, delvis på grund
av asymmetrisk information. Befintliga studier av mikrofinansiering fokuserar på in-
divider eller företag som redan tar lån, och kan därför endast ge begränsad insikt i
hur kontrakts-förändringar skulle påverka kreditefterfrågan och investeringsbeteende
genom förändringar i sammansättningen av låntagare. Jag studerar låneattityder i ett
representativt urval av småföretagare, de flesta utan erfarenhet av att ta lån, som är
aktiva i centrala branscher inom både detaljhandel och tillverkning. Hypotetiska frågor
om efterfrågan på olika typer av lån används för att testa om småföretagare reagerar
2Det ursprungliga citatet lyder: Microfinance presents itself as a new market-based strategy forpoverty reduction, free of the heavy subsidies that brought down large statebanks. In a world in searchof easy answers, this win-win combination has been a true winner itself.
3Med säkerhet menas här en pant som långivaren kräver av låntagaren, vars värde långivaren kangöra anspråk på om återbetalning av lånet uteblir.
214 SAMMANFATTNING
på förändringar i lånevillkoren och huruvida efterfrågan varierar beroende på företa-
gens och entreprenörernas riskattityder och egenskaper. Resultaten visar att kontrakt
med lägre räntor eller lä gre säkerhets-belopp än standard-kontraktet leder till hög-
re efterfrågan blan alla typer av (potentiella) låntagare men effekten är särskilt stor
bland mindre risktagandeföretagare i jämförelse med riskbenägna företagare. Detta
gäller oavsett om risktagandedefinieras utifrån företagets uttalade riskbeteende eller
utifrån riskfaktorer i deras företagsklimat. Resultaten kvarstår även då vi kontrollerar
för förmögenhet. Dessa resultat är starkare bland tillverkningsföretag än bland detalj-
handlare, vilket kan förklaras av skillnader i tillgängliga investeringsalternativ mellan
sektorer. Mindre förmögna företagare blir mer benägna att låna då nivån på säkerheten
sänks. Resultaten tyder på att det finns utrymme för förbättringar av standardiserade
lånevillkor.
Kapitel 5, Credit Contract Structure and Firm Growth: Evidence from a
Randomized Control Trial (Lånekontraktets utformning och företagstillväxt: evi-
dens från en randomiserad kontrollstudie), presenterar de första resultaten från en
pågående randomiserad kontrollstudie bland faktiska låntagare i Uganda. Vi bygger
vidare på tidigare studier som funnit att förändringar av avtalsvillkoren kan förbättra
effektiviteten hos mikrolån (Field et al., 2013;. Karlan och Zinman, 2008), genom ett
experiment utformat att skilja mellan några av de viktigaste hinder för framgångsrikt
investerande som små företag ställs r inför. Vi varierar med vilken frekvens samt i
vilken del (när) i lånecykeln som återbetalning av lånet utkrävs för att kunna sär-
skilja mellan utmaningar såsom (i) fördröjd avkastning på investeringar, (ii) osäker
avkastning på investeringar och (iii) förekomsten av höga fasta kostnader. Företagen
i experimentet fick rabatter, som kunde användas för att täcka 2 av 12 månatliga
avbetalningar i den ettåriga lånecykeln. Resultaten som presenteras i kapitel 5 är pre-
liminära resultat från de första 754 av 2340 företag som avslutat sin lånecykel. Vi finner
att företag/företagare som fick en 2-månaders frist i början av lånecykeln ökade sina
vinster och hushållsinkomster i förhållande till företag/företagare som fick en rabatt
215
senare i lånecykeln, samt till kontrollgruppen. Antalet betalda anställda ökade även i
dessa företag, samtidigt som antalet obetalda sådana inskade, men löneutgifterna öka-
de inte i enlighet med detta. Vidare finner vi att företagsägares hushåll i gruppen som
beviljades en 2-månaders frist i början av lånecykeln startade fler nya verksamheter i
jämförelse med hushållen i gruppen som beviljades en rabatt senare i lånecykeln, samt
kontrollgruppen.
Bland företag som erbjöds en mer flexibel frist, där de kunde hoppa över återbetal-
ningen i två valfria månader, valde de flesta att använda sina rabatter under de första
månaderna av lånecykeln. Dessa resultat ger ett visst stöd för att fördröjd avkastning
utgör ett viktigare hinder än osäker avkastning för företagen i vår studie. Företag som
mottog ett kontant bidrag i början av lånecykeln ökade antalet anställda i förhållande
till kontrollgruppen, och de ökade också sina lönekostnader. Om detta innebär att de i
högre utsträckning anlitade mer välkvalificerad arbetskraft, vilket kan ses som en odel-
bar investering, ger detta resultat stöd för att även odelbara kostnader utgör ett hinder
för framgångsrika investeringar inom ramarna för det standardmässiga lånekontraktet.
216 SAMMANFATTNING
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Banerjee, A., Karlan, D. and Zinman, J. 2015. Six randomized evaluations of
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