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Integrations of microfinance and business development servicesVu, Nhung

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Integrations of Microfinance and

Business Development Services Empirical Evidence on Microfinance Institutions and Clients

Vu Thi Hong Nhung

Copyright 2014 © Vu Thi Hong Nhung

All rights reserved. No part of this publication may be reproduced, stored in a retrieval system of any nature, or transmitted in any form or by any means, electronic, mechanical, now known or hereafter invented, including photocopying or recording, without prior written permission of the author.

Publisher: University of Groningen Groningen, The Netherlands Printer: Ipskamp Drukkers B. V. Enschede, The Netherlands ISBN: 978-90-367-7400-0 (book)

978-90-367-7399-7(e-book)

Integrations of Microfinance and Business Development Services

Empirical Evidence on Microfinance Institutions and Clients

PhD thesis

to obtain the degree of PhD at the University of Groningen on the authority of the

Rector Magnificus Prof. E. Sterken and in accordance with

the decision by the College of Deans.

This thesis will be defended in public on

Monday 17 November 2014 at 16.15 hours

by

Vu Thi Hong Nhung

born on 5 May 1980 in Cantho City, Vietnam

Supervisors

Prof. B.W. Lensink

Prof. E.H. Bulte

Assessment committee

Prof. R.J.M. Alessie

Prof. A. Bedi

Prof. P. Mosley

To my parents, my husband, my son and my daughter

Con kính tặng Ba Mẹ, em tặng chồng yêu và các con

Acknowledgements

The journey of studying MSc Finance, then Research Master in International Economics and

Business at the University of Groningen, and later writing this PhD dissertation, is a special time

in my life. It has become more challenging because I combined my PhD career and motherhood

at the same time. My little son was born in 2010 and my little daughter was just born last

summer 2014. They caused my PhD student life to become busier, but filled with great

happiness. This is one of the most wonderful periods that I have had so far in my life. I would

like to express my deepest appreciation to all people who provided me the opportunity to

complete this book.

I would like to express my deep gratitude to Prof. Robert Lensink for his patient

guidance, enthusiastic encouragement and useful critiques on this research work. He inspires me

to love microfinance. Without his enthusiasm and efforts, we would have never received funds

for a 3ie’s project on business training for poor female microfinance clients in Vietnam from

which this thesis greatly benefits. Prof. Lensink is always great in encouraging me to acquire

new knowledge. At the time I started as a PhD student, I was a newcomer in the field of

microfinance, randomization control trial method and experimental economics. During several

years of writing this book, I have gained a lot of knowledge and expertise from him. These are

valuable assets for my academic career.

My grateful thanks are extended to Prof. Erwin Bulte. His useful and constructive advice

and contributions to chapters three, four and five have helped to improve the quality of this book

substantially. Next, I also would like to express my gratitude to my co-author Prof. Roy

Mersland for his permission to use his data for the second chapter.

Special thanks should be also given to the International Initiative for Impact Evaluation

(3ie) and the Global Development Network (GDN) for providing funds for the research project

of business training in Vietnam.

Moreover, I would like to thank the assessment committee Prof. Rob Alessie, Prof. Ajrun

Bedi and Prof. Paul Mosley for spending time and effort on evaluating the thesis and giving

valuable recommendations.

I also would like to offer my great appreciation to Prof. Paul Gertler and Adam Ross for

their valuable impact evaluation training course in Amsterdam in 2010 and Paul’s permission to

read his preliminary book “Impact evaluation in practice”; to Prof. Arthur Schram and Jeroen

Van de Ven for their useful experimental economics summer course in 2011 at the University of

Amsterdam and their valuable comments on designing the behavioral games in this project. In

addition, I acknowledge with much appreciation my friends in the Netherlands and in Vietnam,

especially Yên Hảo, Kim Hiệu, Minh Ánh, Ngọc, Ngọc Gia, Quốc Khánh, ThanhTâm, Hoàng

Anh, Cẩm Tú, Thanh Tuyết, Đức Nhã, Thu Hằng, Xuân Anh, Hồng Yến and Thu Trang for their

patience and valuable input when joining the tests of my experiments.

Special thanks go to Dương Thị Ngọc Linh, Dương Thị Hải Yến, Nguyễn Duy Trường,

Nguyễn Thị Hà and other staff at the headquarters of TYM fund for their invested effort in

supporting the business training project in Vietnam; to chị Hương, chị Hội, chị Vân Anh, chị

Nga, anh Ngọ, anh Sang, chị Hoa, chị Hợi, anh Nguyễn, chị Thúy, chị Thủy (CN7), chị Tuyến,

chị Yến, chị Châu, chị Hải, chị Hạnh, chị Liên, chị Vân, chị Vỵ, chị Phương, chị Bình, anh Linh,

chị Thủy (CN15) at Vinh Phuc and Me Linh branches for carrying out all interventions on which

the project is based and for helping me to conduct the surveys. I also would like to thank them

for their personal support when I was Ha Noi and Vinh Phuc. Besides, I would like to express

sincere gratitude to all of the microfinance clients at TYM fund and their husbands who joined in

the business training project. I thank Karen van Zaal, a student at Wageningen University, for her

help with collecting a subsample data and Lê Thị Huyền for her support on organizing focus

groups discussions in Vinh Phuc.

I would also like to extend my thanks to Rob Alessie, Tom Wansbeek, Aljar Meesters

and Jacob Bosma for their assistance in econometrics questions; to Steffen Eriksen for his

spending time on merging data in the research project; to Nguyễn Tuấn Anh and Lê Văn Hà,

who helped me with technical questions and computer problems; and to Nguyễn Phương Hồng,

Nguyễn Lan Hương, Dương Thị Hải Yến, Ms.Trang and Hoàng Thục Nhi for their help in

translating questionnaires and other documents in the experiments into Vietnamese.

Many thanks go to the friendly people from the SOM office, especially Ellen Nienhuis,

Arthur de Boer, Rina Koning, Linda Toolsema-Veldman (former PhD Coordinator), Martin Land

(former PhD Coordinator), Bart Los (former Research Master Coordinator); and to secretaries at

the 8th floor Grietje Pol, Ellie Jelsema, other secretaries at the 7th floor and colleagues at the

Department of Economics, Econometrics and Finance for their great help with organizing

everything and offering useful advice and warm support whenever I needed help. I would like to

thank deeply my paranymphs, Pim and Aljar for their great support during my defense. I am very

thankful to Henk von Eije for his help in writing my Dutch summary; to Hải Yến, Ngọc Trân,

Huỳnh Mai for their help in checking my Vietnamese summary; to Lauren and Kristina for their

help in proofreading this acknowledgement.

I had several chances to present the research papers in this book at department seminars,

SOM PhD conferences and international conferences. I would like to thank everyone I met there

for their interest and comments on my research and for discussions we had.

I would like to express my great appreciation to the School of Economics and Business at

Can Tho University for all assistance during the time I studied abroad. I wish to thank Anita

Veltmaat, Wiebe Zijlstra, Gonny Lakerveld and Ger Lanjouw for their support during the time I

followed the Master programs in Groningen and for their friendship.

Lunch time and sport training in Aclo energized me and helped me get out of working

stress. I would like to thank Zubeda, Yi-Chun, Pim, Karina, Scott, Yanping, Vo Van Dut and

other colleagues on the 7th and 8th floors for sharing this wonderful time with me. Many thanks

go to my Vietnamese friends and other friends in Groningen: family anh Cường- chị Hương-Huy

Anh-Minh Anh, cô Nguyệt, cô Gái, chị Hà nhỏ, chị Hà lớn, chị Nguyệt Surinam, family Tuấn

Anh-Tính-Ben-Bun, family Tâm-Thuận-Khôi, family Hiệu-Hảo-Kẹo, family anh Thế Anh-chị

Maria, family em Hà, family Minh Ánh-Cường-Tom, family Tuấn Anh-Hoa, chị Hương-anh

Ngân, chị Hồng, chị Thảo, chị Trà, thầy Thông, anh Khôi, chị Uyên, em Gia, em Dương, em

Ngọc, chị Mai, em Xuân Anh, chị Thanh Hà, anh Việt Thành, em Thái, em Hằng, Kristina, Jans

Kiers, my neighbor Peter, Thu Trang and your food blog (savourydays.com), em Thịnh, Lauren,

Netty, anh Tú, anh Dứt, Anton, Verena and other friends whose names are not mentioned here.

Thank you very much for your friendship, your warm support that made my family’s life in

Groningen easier, enjoyable and more fun.

My family has a special place in these acknowledgments. I deeply thank my parents Vũ

Viết Châu and Vũ Thị Minh Thi, my parents-in-law Chu Công Ngạn and Chu Thị Chò, my

aunt’s family cậu Hùng – dì Nguyên – em Minh, my younger sister Vũ Thị Hồng Yến and my

younger sisters- and brothers-in-law for your love, support and encouragement throughout my

study abroad.

Most of all, my deepest thanks are given to my dear husband Chu Công Đạt, my dear son

Chu Vũ Tùng Dương (Ben) and my dear daughter Chu Vũ Quỳnh Anh (Bella). Thank you for

always being at my side through life. Your love is the most precious thing I need in my life. This

book is especially dedicated to you.

Groningen, September 2014

Vu Thi Hong Nhung

i

CONTENTS CHAPTER 1 1

INTRODUCTION 1

1.1 MOTIVATION 1

1.2 RESEARCH OBJECTIVES, DESIGN, INNOVATIVE CONTRIBUTIONS AND MAIN FINDINGS 2

1.3 OVERALL CONCLUSION, LIMITATIONS AND FURTHER RESEARCH 10

CHAPTER 2 15

DO MICROFINANCE INSTITUTIONS BENEFIT FROM INTEGRATING FINANCIAL

AND NONFINANCIAL SERVICES? 15

2.1 INTRODUCTION 15

2.2 CONCEPTUAL FRAMEWORK, RESEARCH QUESTIONS AND HYPOTHESES 16

2.2.1. What Is Microfinance Plus? 16

2.2.2. Different Ways to Integrate Plus Services 17

2.2.3. Conceptual Framework for the Effects of Microfinance Plus 17

2.2.4. Research Questions and Hypotheses 21

2.3 DATA AND ESTIMATION METHODOLOGY 22

2.3.1. Data 22

2.3.2. Estimation Methodology 23

2.3.3. Dependent Variables 23

2.3.4. Control Variables 26

2.3.5. Estimation Approach 27

2.3.6. Descriptive Statistics 29

2.4 EMPIRICAL RESULTS 31

2.4.1. The Effects of Microfinance Plus on Financial Performance 31

2.4.2. The Effects of Microfinance Plus on Social Performance 36

2.5 CONCLUSIONS 38

APPENDICES 39

Appendix 2.1: Hausman-Taylor estimator 39

Appendix 2.2: List of countries studied 40

ii

CHAPTER 3 41

THE SHORT-TERM IMPACT OF GENDER AND BUSINESS TRAINING ON

BUSINESS OUTCOMES AMONG FEMALE MICROFINANCE CLIENTS IN

VIETNAM 41

3.1 INTRODUCTION 41

3.2 RELEVANT LITERATURE 43

3.3 CONTEXT AND INTERVENTION 50

3.3.1. Context 50

3.3.2. Intervention 51

3.4 THEORY OF CHANGE 52

3.5 EXPERIMENTAL DESIGN 55

3.6 DATA AND ATTRITION ANALYSIS 57

3.6.1. Data 57

3.6.2. Overall Attrition Rate 61

3.6.3. Nonrandom Attrition 61

3.7 TRAINING QUALITY ASSESSMENT 62

3.7.1. Descriptive Statistics of Female Clients’ Participation 63

3.7.2. Results of Training Quality Assessment 63

3.7.3. Qualitative Assessment of Husbands’ Presence by Female Clients 68

3.8 PARTICIPATION OF HUSBANDS ANALYSIS 70

3.8.1. Descriptive Statistics of Invited Husbands 70

3.8.2. Determinants of Husbands’ Participation 71

3.8.3. Husbands’ Reasons to Attend or Not Attend the Training and Training Evaluation 73

3.8.4. Compensation Elasticity and Husbands’ Participation 75

3.8.5. Risk Analyses Summary 76

3.9 ESTIMATION METHODS 77

3.10 ESTIMATED RESULTS OF G&B TRAINING EFFECTS 80

3.10.1. Effects of G&B Training on Business Knowledge 80

3.10.2. Effects of G&B Training on Business Practices 82

3.10.3. Effects of G&B Training on Business and/ or Farming Outcomes 85

3.10.4. Effects of G&B Training on Business and Farming Startups and Their Survival 89

iii

3.11 CONCLUSION, DISCUSSION AND POLICY RECOMMENDATIONS 90

APPENDICES 92

Appendix 3.1: Map of TYM’s operating areas 92

Appendix 3.2: Descriptions of outcome variables 93

Appendix 3.2: Descriptions of outcome variables (cont.) 94

Appendix 3.3: Principal Component Analysis of Business Practices 94

Appendix 3.4: IV estimates 98

Appendix 3.5: Questions on Measuring Business Knowledge 100

Appendix 3.6: Questions on Measuring Business Practices 106

Appendix 3.7: Post treatment estimates without covariates 108

Appendix 3.8: CACE estimates 110

CHAPTER 4 113

THE SHORT-TERM IMPACTS OF GENDER AND BUSINESS TRAINING ON

GENDER OUTCOMES AMONG FEMALE MICROFINANCE CLIENTS IN VIETNAM

113

4.1 INTRODUCTION 113

4.2 A BRIEF SURVEY OF THE RELEVANT LITERATURE 116

4.3 THEORY OF CHANGE 119

4.4 ESTIMATION METHODS 126

4.5 ESTIMATED RESULTS 128

4.5.1. Effects of G&B Training on Gender Knowledge 128

4.5.2. Effects of G&B Training on Non-Cognitive, Business-related Skills 129

4.5.3. Effects of G&B Training on Female Empowerment 136

4.5.4. Effects of G&B Training on Household Domestic Violence 137

4.6 LIST EXPERIMENT ANALYSIS 141

4.6.1. List Experiment Design 141

4.6.2. Estimated Results 142

4.7 CONCLUSION AND DISCUSSION 146

APPENDICES 149

Appendix 4.1: Descriptions of outcome variables 149

iv

Appendix 4.2: IV estimates 151

Appendix 4.3: Principal component analysis of household bargaining power 154

Appendix 4.4: Questions measuring gender knowledge 154

Appendix 4.5: Questions measuring non-cognitive skills 155

Appendix 4.6: Questions measuring female empowerment 158

Appendix 4.7: Questions measuring household domestic violence 159

Appendix 4.8: Post-treatment estimates without covariates 160

Appendix 4.9: CACE estimates 162

CHAPTER 5 167

BUSINESS TRAINING AND INTERTEMPORAL CONSUMPTION: EXPERIMENTAL

EVIDENCE FROM VIETNAM 167

5.1 INTRODUCTION 167

5.2 A BRIEF SURVEY OF THE RELEVANT LITERATURE 169

5.3 THE THEORETICAL MODEL 171

5.4 EXPERIMENTAL CONTEXT, DESIGN, DATA, AND IDENTIFICATION 173

5.4.1. The RCT and the Business Training 174

5.4.2. The Behavioral Game 175

5.4.3. Data 177

5.4.4. Identification 181

5.5 RESULTS 182

5.6 CONCLUSIONS 196

APPENDICES 198

Appendix 5.1: First-stage regression of IV estimates - CRRA 198

Appendix 5.2: First-stage regression of IV estimates - CARA 200

REFERENCES 203

SAMENVATTING (SUMMARY IN DUTCH) 217

TÓM TẮT (SUMMARY IN VIETNAMESE) 221

v

List of Tables Table 2.1: Effects of microfinance plus 21

Table 2.2: Dependent variables description 26

Table 2.3: Independent variables description 27

Table 2.4: Descriptive statistics 30

Table 2.5: Descriptive statistics for specialists and plus providers 31

Table 2.6: Effects of microfinance plus on financial sustainability 33

Table 2.7: Effects of microfinance plus on efficiency 34

Table 2.8: Effects of microfinance plus on portfolio quality 35

Table 2.9: Effects of microfinance plus on social performance (Outreach) 37

Table 3.1: Review of the impact of business training 46

Table 3.2: Descriptive statistics and balancing test 59

Table 3.3: Overall attrition rate 61

Table 3.4: Nonrandom Attrition (Logit regression) 62

Table 3.5: Descriptive statistics of female clients’ participation 63

Table 3.6: Descriptive statistics of training quality 64

Table 3.7: Descriptive statistics training module ranking 66

Table 3.8: The importance ranking of business practices 67

Table 3.9: Qualitative training assessment of husband attendance by treated women in groups T1

69

Table 3.10: Descriptive statistics of husbands’ participation 70

Table 3.11: Descriptive statistics of invited husbands 71

Table 3.12: Determinants of husbands’ participation 73

Table 3.13: Reasons to attend or not attend G&B training and training self-evaluation by men 75

Table 3.14: Financial compensation elasticity on husbands’ participation 76

Table 3.15: Impact of G&B training on business knowledge 82

Table 3.16: Impact of G&B training on business practices 84

Table 3.17: Impact of G&B training on business outcomes 87

Table 3.18: Impact of G&B training on farming outcomes 88

Table 3.19: Impact of G&B training on business, and farming startup and survival 89

vi

Table 4.1: Impact of G&B training on gender knowledge 129

Table 4.2: Impact of G&B training on locus of control and self-esteem 131

Table 4.3: Impact of G&B training on trustƱ 134

Table 4.4: Impact of G&B training on married women’s bargaining power 139

Table 4.5: Impact of G&B training on domestic violence for married women 140

Table 4.6: Observed data from the list experiments 143

Table 4.7: Results of list experiment and direct report on household physical domestic violence

145

Table 4.8: Proportion comparisons of household physical domestic violence 146

Table 5.1: Choice sets of experiment 176

Table 5.2: Descriptive statistics 178

Table 5.3: Allocations to later over time and rate of return, in VND 180

Table 5.4 : OLS estimates – CRRA ( Dependent variable: lnct -lnct+k) 186

Table 5.5: OLS estimates – CARA (Dependent variable: ct –ct+k) 189

Table 5.6: IV estimates – CRRA ( Dependent variable: lnct -lnct+k ) 192

Table 5.7: IV estimates – CARA (Dependent variable: ct –ct+k) 194

vii

List of abbreviations

ANCOVA Analysis of covariance

BDS Business development services

CARA Constant Absolute Risk Aversion

COOP Cooperative

CRRA Constant Relative Risk Aversion

CTB Convex Time Budget

DD Double difference

FE Fixed effects

G&B Gender and business

GDP Gross Domestic Product

GSS General Social Survey

HDI Human Development Index

ILO International Labor Organization

ITT Intention to treat

IV Instrumental variable

MFI Microfinance institutions

NGO Non-governmental organization

OLS Ordinary least squares

RCT Randomized control trials

RE Random effects

SS Social services

TOT Treatment on treated

CACE Complier-average causal effect

1

Chapter 1

Introduction 1.1 Motivation

During the past decade, microfinance has generated two contradicting opinions. On the one hand,

it has been recognized as an effective means to achieve the Millennium Development Goals,

especially the first (i.e. reducing poverty) and third (gender equality and female empowerment)

goals. Enthusiasm for microfinance was emphasized when Muhammad Yunus and the Grameen

Bank were jointly awarded the Nobel Peace Prize in 2006. On the other hand, researchers have

begun to view microfinance with doubt and mistrust due to the worldwide repayment crises in

2008/2010. Moreover, many impact evaluations regarding microfinance provide mixed results:

some studies show modest positive impacts of microfinance on income, expenditure, and related

social well-being variables, but others indicate that the positive impacts disappear when selection

biases are addressed (van Rooyen et al., 2012). Recent studies using randomized controlled trials

(RCTs) to address the problem of selection bias provide new evidence on the impact of

microcredit services. These studies suggest that only increasing access to credit is not sufficient

to raise the poor out of poverty. Although microcredit seems to have a modest impact on

business investment and outcomes, the impact on broad measures of poverty, female

empowerment, and social well-being for the poor seem small (Banerjee et al., 2010, Karlan and

Zinman, 2010, Crépon et al., 2011, Karlan and Zinman, 2011).

These findings imply that microcredit alone may not be a panacea to lift the poor out of

poverty. Poor households benefit from a combination of services, rather than a simple provision

of credit (Armendáriz and Morduch, 2010). The State of the Microcredit Summit Campaign

2011 emphasizes that “microcredit is a tool for unlocking human dreams. But microcredit, by

itself, is usually not enough” (Reed, 2011). Because poverty is multidimensional, poor people

need to have access to a coordinated combination of microfinance and other developmental

services to overcome their poverty (Khandker, 2005). Some of these developmental services aim

to provide borrowers with some useful skills, such as health education, which can help them

2

avoid or reduce the impacts of unexpected events on income or savings. Others target business or

financial management skills training, which can help people effectively use financial services

and facilitate better access to jobs or income-generating opportunities. Although many

microfinance institutions (MFIs) focus only on providing financial services to maintain their

sustainability, the State of the Microcredit Summit Campaign 2012 indicates that adding

nonfinancial services and products not only improves value for beneficiaries but also can

increase advantages to service providers (Maes and Reed, 2012). Figures from 2011 indicate that

approximately 54 percent of MFIs offer nonfinancial services, such as business/financial literary,

technical assistance, and health education, along with financial services (Microfinance

Barometer, 20131). Many studies suggest that integrating nonfinancial services and microfinance

services may be important. However, rigorous evidence on the impact of combining both types

of services is still lacking. To address this research gap, this thesis provides new evidence on the

relevance of MFIs combining financial and nonfinancial services.

1.2 Research Objectives, Design, Innovative Contributions and

Main Findings

The main objective of this thesis is to evaluate the impact of integrating nonfinancial services,

especially business development services, and microfinance services on the performance of

microfinance institutions and their clients. To achieve this goal, we use three approaches:

- A quasi-experimental approach,

- An RCT, and

- A lab in the field behavioral game.

The quasi experimental approach

The research begins by using an existing global panel data set of MFIs to investigate the

potential benefits of combining financial and nonfinancial services. The approach can be

qualified as quasi-experimental, because we distinguish different types of MFIs and examine

them at several different times. The focus is on benefits to MFIs. Using a global sample of 1 http://www.citi.com/citi/microfinance/data/2013a_barometer.pdf

3

various types of MFIs ensures that the external validity of the analysis is reasonably good. That

is, the results from this analysis can be used to make predictions about the entire population of

MFIs, in contrast to most studies that address the impact of nonfinancial services, which use case

studies of specific MFIs. These studies offer relatively little external validity because their results

may only apply in a particular context of the evaluation (e.g., phenomena observed in Tanzania

may or may not apply in Vietnam).

However, the drawback of using a global panel data set of MFIs, and a quasi-

experimental approach, is that the internal validity of the analysis may be inferior to randomized

experiments. That is, it is difficult, if not impossible, to control for all types of endogeneity

biases, so that attribution (i.e., causality) questions are not easily addressed. For example, the

analysis may suffer from selection biases: clients may self-select (self-selection bias), and/or

MFIs may deliberately go to a certain area (program placement bias), which could imply that the

results are not due to a combination of financial and nonfinancial services but are caused by self-

selection and/or program placement biases.

To reduce endogeneity biases, we apply panel techniques. More specifically, we analyze

the global data set of MFIs using a Hausman-Taylor estimation method. The global data set does

not contain reliable external instruments; thus, standard instrumental variable techniques cannot

be used. Moreover, the variables of interest do not change over time; therefore, a standard fixed

effects regression cannot identify the pertinent parameter. The Hausman-Taylor regression

technique enables researchers to control for endogeneity biases due to unobserved variables that

do not change over time and allows for identifying the parameters of interest.

The RCT

Although the Hausman-Taylor estimator controls for some endogeneity biases, it is likely that

the internal validity of the estimates will remain low due to, for example, unobserved

heterogeneity that does change over time. A randomized experiment can more effectively

address this type of problem. Therefore, the main part of this thesis uses an RCT to address the

research question.

This research involves an RCT to assess the impact of nonfinancial services for an MFI’s

female microfinance clients in Vietnam. To this end, the first study uses a broad worldwide data

4

set and focuses on MFIs, and the second study investigates one particular MFI and considers the

impact on microfinance end-users. The first study has relatively high external validity but may

suffer from internal validity; the second study has high internal validity at the expense of a lower

external validity. Chapters 3 and 4 report the results of the RCT.

The RCT technique dominates clinical research in medicine and has been increasingly

applied in development economics research. This approach works as follows. A target population

(e.g., microfinance borrowers) is assigned randomly to two groups: a treatment group and a

control group. Every person or unit in the treatment group receives an intervention (in our case, a

business training program), while those in the control group remain as before (i.e., they have

access to microfinance services only). The RCT is a fair allocation rule in that it ensures that

each person or unit has the same chance of receiving the program. If we randomly assign units to

the treatment and control groups and the sample size is sufficiently large, two statistically

identical groups will result. Therefore, the difference between the average outcome of the

treatment group and the average outcome of the control group constitutes a true average impact

of the program. That is, RCTs are particularly useful for addressing attribution questions because

the internal validity is high. The best internal validity can be obtained if control and treatment

groups come from the same country, region, and village. A possible drawback may then be that

results are difficult to generalize; that is, the external validity is low.

A substantial part of this thesis reports on an RCT at TYM fund, the largest microfinance

organization in North Vietnam, in operation since 1992. The main goal is to evaluate the impact

of providing gender and business training for female microfinance clients. In addition, we

examine whether inviting husbands to join the training with their wives results in any additional

impact on women’s outcomes. In recent years, both practitioners and researchers have paid more

attention to the impact of business development services. Previous experiments in Sri Lanka (De

Mel et al., 2008, De Mel et al., 2009 ) and Ghana (Fafchamps et al., 2011), for example, suggest

that physical capital alone does not help microentrepreneurs raise income above a subsistence

level, especially in cases of female-owned enterprises. These studies conclude that managerial

and business skills are a crucial determinant in increasing productivity and growth of micro and

small businesses (Bloom et al., 2010, Bruhn et al., 2010 ). Thus, business training programs have

been developed to improve business outcomes. However, little rigorous evidence on the impact

5

of these trainings exists, even though several evaluations of business training have been

conducted recently (McKenzie and Woodruff, 2014).

Although the information from household surveys appears accurate in general, some

variables are difficult to measure, especially time and risk preferences. Therefore, we applied a

third approach, which is particularly useful in measuring impact on such difficult-to-measure

variables as preferences: the lab in the field behavioral game.

The lab in the field behavioral game

The current literature distinguishes several types of experiments. A conventional lab experiment

usually uses a standard subject pool (e.g., students) and imposed rules. An artifactual field

experiment is similar to the conventional lab experiment but employs a nonstandard subject pool.

A framed field experiment resembles the artifactual field experiment but has a field context such

as commodity, task, and information that participants can use. Finally, a natural field experiment

is similar to the framed field experiment, but participants undertake their tasks in a natural

environment and do not know they are in an experiment (Harrison and List, 2004).

We conducted some artifactual field experiments to estimate the impact of business

training on time preferences and thus saving behavior. For this analysis, we use a subgroup of

TYM fund clients and their spouses. Basically, we combine an RCT (the random assignment of

business training) with a lab in the field behavioral game.

Specifically, we conducted post-treatment experiments with subsamples of

approximately 600 husbands and wives, explicitly focusing on the impact of the training on time

and risk preferences.

Some additional methodological remarks

Although the RCT and the artifactual field experiments can produce reliable estimates of the

causal effects of the training program, they provide limited insights regarding program

implementation. Using only quantitative analysis may not shed light on why certain results are or

are not achieved. Therefore, we add qualitative analysis by conducting focus group discussions

with women in the treatment and control groups. Mixing quantitative and qualitative analyses

6

can explain observed results and get inside the “black box” of what happened in the program

(Bamberger et al., 2010).

Innovations

This section highlights some of this research’s innovative contributions to the current literature.

First, we examine a broad view of the impact of offering nonfinancial services on MFIs’

performance outcomes by using a large global panel data set. To our knowledge, this is the only

study available that investigates the potential benefits to MFIs of integrating financial and

nonfinancial services using a global panel data. Second, this study is among the few that use an

RCT with a large sample size to evaluate the impact of business training. Our review of related

literature indicates that many studies suffer from low statistical power due to small sample sizes

(McKenzie and Woodruff, 2014). Third, this research is among the first to investigate the

relevance of inviting men to join business training with their wives. Practitioners and researchers

recommend that to improve the status of women and promote gender equality, more attention

should be paid to increasing male involvement when addressing gender issues (Council of

European Union, 2006, World Bank, 2011). Excluding husbands may trigger frustration and

invite intrahousehold conflicts (Allen et al., 2010), possibly eroding the positive effects of the

training. Fourth, we combine behavioral experiment games and the RCT to study the impact of

the training: the behavioral experiment games data provide reliable estimates of underlying

preferences, and the RCT design facilitates exploration of whether the business training has an

effect on the underlying preferences.

The RCT experimental design

This section summarizes the RCT experimental design and the training intervention used in

chapters 3, 4, and 5. We began by randomly assigning preexisting credit centers, each with an

average of 30 female clients, to two treatment groups and a control group. We randomized the

training at the credit center level, which reduces the threat of spillover effects, and used a cluster

sampling approach. In the first treatment group, we invited both female clients and their spouses

to join the training as part of the mandatory monthly meeting. In the other treatment group, we

invited only female clients to join the training. Control groups remained the same: female clients

participated only in TYM fund’s credit and saving activities.

7

We used training materials developed and adapted from the Gender and Entrepreneurship

Together (GET) Ahead for Women in Enterprise Training Package and Resource Kit of the

International Labor Organization. We conducted a baseline survey before the intervention with a

sample of approximately 4,000 female clients and two post-treatment follow-up surveys to trace

the trajectories of the impacts by capturing both short- and long-term effects of the training. This

thesis addresses only the baseline and midline surveys, because analysis of the endline survey

falls outside the time frame of the project.

In addition to interviewing female clients, we conducted a small post-treatment survey of

approximately 600 invited husbands. The data from this subsample sheds light on the relevance

of inviting husbands to the trainings.

Research Structure and Research Questions

In addition to this introductory chapter, this thesis contains four main chapters. This section

presents the structure of the entire thesis and the main research questions.

Chapter 2 focuses on the effects of integrating financial and non-financial services on

MFIs’ performance and addresses the following questions:

i. Do MFIs that combine financial and nonfinancial services achieve better financial

performance, in terms of financial sustainability and efficiency and portfolio quality, than

MFIs that specialise in financial services?

ii. Do microfinance plus providers attain better social performance, in terms of outreach,

than their specialist peers?

iii. When we differentiate nonfinancial services as business development or social services,

which combination of nonfinancial and financial services is most effective for improving

the financial and social performance of MFIs?

The rest three chapters evaluate the impact of gender and business training on poor

female microfinance clients’ outcomes. More specifically, Chapter 3 focuses on the following

questions:

iv. What is the impact of the gender and business training on business outcomes of female

microfinance clients?

8

v. What is the additional impact of inviting husbands to the training on business outcomes

of female microfinance clients?

Chapter 4 answers the research questions:

vi. What is the impact of the gender and business training on gender outcomes of female

microfinance clients?

vii. What is the additional impact of inviting husbands to the trainings on gender outcomes of

female microfinance clients?

The last research questions, answered in Chapter 5, are:

viii. What is the impact of the gender and business training on intertemporal consumption

smoothing behaviour of female microfinance clients?

ix. To what extent do actual intertemporal consumption choices depart from optimal

consumption smoothing?

x. Is the impact of training conditional on the presence of husbands during the training?

Main Findings

This section provides short answers to the research questions posed above.

Chapter 2 investigates the impact of combining financial and nonfinancial services on

providers’ performance. In particular, we determine whether MFIs that specialize in financial

services attain better financial and/or social performance than those that provide both financial

and nonfinancial services. Regarding the nonfinancial services, we differentiate business

development services, such as business training, from social services. We use secondary data

from 290 rated MFIs from 61 countries. The data covers the period 1998–2007, though most data

are from 2001–2005. The chapter suggests that MFIs that provide social services achieve better

social performance, albeit at the expense of their financial results. With regard to business

development service providers, their performance is similar to that of MFIs that specialize in

financial services.

Chapter 3 employs an RCT to analyze the impact of business development services

training on business outcomes of female microfinance clients. This study also examines the

additional impact of inviting husbands to the training sessions on women’s business outcomes.

Although the midline survey took place only six months after the completion of the entire

9

training, we do find some promising short-term impacts of the training on women’s business

outcomes. The training leads to significant improvements in business knowledge and business

practices. Furthermore, we also find that the gender and business training has a positive impact

on business performance of female-run businesses, providing some initial evidence that offering

gender and business training leads to improvements in business profits and profit margins among

surviving businesses. However, we do not find any evidence that the training improves farming

outcomes, which may not be surprising considering the training did not focus on farming

practices. In addition, we do not find strong statistically significant positive short-term effects of

the training on female outcomes if husbands are also invited to attend the training sessions. A

possible reason for this finding is the low participation rate of husbands, in combination with

small size effects (e.g., due to the short time period under consideration).

Chapter 4 uses the same RCT as chapter 3, except that the outcome variables differ in

that we test the extent to which business training for TYM female microfinance borrowers helps

foster gender equality by improving gender outcomes for women. We find strong evidence that

the training leads to significant improvements in gender knowledge. The training also exhibits

some limited positive impacts on women’s noncognitive, business-related skills. In addition, we

provide some evidence that the training improves women’s household bargaining power on

major expenditure decisions and reduces the levels of physical domestic violence in families for

married women. Similar to Chapter 3, we do not find a significantly additional impact of inviting

husbands to join the training on female gender outcomes. We note that partner physical violence

against women is a sensitive issue, so women are more likely to underreport this information,

leading to possibly biased estimates. Thus, we use a qualitative survey technique, the so-called

list experiment, to re-estimate the impact of the training on physical domestic violence. In

contrast to the direct questioning results, the list experiment suggests that women who followed

the training were more often confronted with physical violence than women in the control group.

Chapter 5 combines data of the RCT with data from the artifactual field experiment. We

conducted a convex time budget experiment (Andreoni and Sprenger, 2012) to elicit the impact

of business training intervention on time preferences and consumption smoothing of female

microfinance clients. We find that, on average, financial choices are not fully rational.

Specifically, we find evidence of over-saving. Furthermore, our results indicate that while

10

business training does not change preferences, it does tend to improve the optimality of

intertemporal consumption choices by stimulating current consumption at the expense of future

consumption. For this subgroup of borrowers, we also find some evidence that the impact of

business training on women is conditional on the presence of husbands: their contribution

accentuates the impact of the formal training.

1.3 Overall Conclusion, Limitations and Further Research

In this section, we discuss how our research findings of the impact of integrating microfinance

and non-financial services relate to the literature on other developing countries. Then we address

various limitations of the analyses presented in this thesis and provide some suggestions for

further research.

The State of the Microcredit Summit Campaign 2012 indicates that adding nonfinancial

services and products not only improves value for beneficiaries but also can increase advantages

to service providers (Maes and Reed, 2012). However, different forms of credit plus may

generate different effects on MFIs and their clients. Regarding the impact of credit plus on MFIs’

performance, in line with previous studies, this thesis shows that credit plus services especially

social services benefit clients but increase costs for MFIs (Vor der Bruegge et al., 1999). In

particular, we find that MFIs that provide social services in addition to financial services perform

worse financially but better in terms of reaching out to the poor.

Regarding the effects of credit plus especially offering business training on MFI’s

clients, our findings suggest that the business training leads to significant improvements in

business knowledge and has improved business practices for microfinance female clients in

Vietnam. The results are in line with previous studies which show that business training has

positive effects on business knowledge and business practices (Berge et al., 2011, Giné and

Mansuri, 2011, Karlan and Valdivia, 2011, Bruhn and Zia, 2013, Valdivia, 2013, De Mel et al.,

2014, Drexler et al., 2014). Most of these studies, except Bruhn and Zia (2013), provide further

evidence that the increased business knowledge and adoption of better business practices did not

lead to an improvement of business performance in terms of profits or sales for female

entrepreneurs. In contrast to the existing literature, the thesis provides evidence that the training

has a positive impact on business performance of female-run businesses. In particular, offering

11

the business training leads to improvements in business profits and profit margins among

surviving female-owned businesses.

In addition, most recent RCTs report that providing business training does not lead to

improvements in female empowerment (Giné and Mansuri, 2011, Karlan and Valdivia, 2011) or

attitude changes toward domestic violence and gender relations. In contrast to existing literature,

this research provides some new evidence that the training improves women’s household

bargaining power on major expenditure decisions and reduces the levels of physical domestic

violence within families for married women. To some extent, these results are in line with Kim et

al. (2007), who show that integrating microfinance services with gender and health training

significantly improves female empowerment and reduces intimate partner violence. However,

this dissertation does not find evidence of additional impact of the training on female outcomes if

husbands are also invited to attend the training. These results are somewhat in line with Allen et

al. (2010), who also do not find evidence that the including husbands in microfinance solidarity

groups improved women’s bargaining power.

Moreover, in contrast to “conventional wisdom” in the literature on underdevelopment

about under-saving in developing countries, this thesis provides evidence that microfinance

women in Vietnam tend to save too much at the expense of short-term consumption relative to

their own preferences. The study shows that attending business training helps to reduce such

inefficiencies. Trained women behave more “rational” than untrained ones, and the research

presents tentative evidence that this is (partly) due to the transfer of knowledge.

Research Limitations

There are some limitations in this thesis. First, the results in Chapter 2 may suffer from

endogeneity bias. Although we use a Hausman-Taylor estimation method to address potential

endogeneity problems, it is well known that this method is sensitive to the choice of exogenous

and endogenous time-variant and time-invariant variables. Without reliable external instruments,

little can be done to address this issue.

Second, the RCT approach used in chapters 3 and 4 helps control for endogeneity

problems mentioned in Chapter 2. However, the RCT refers to training provided to microfinance

12

borrowers of one MFI, in North Vietnam. Thus, the results are context specific. It is not clear

whether they will also hold for other MFIs and in other settings.

Third, another drawback of chapters 3 and 4 is that husbands’ participation rates are

relatively low, even though we incentivized them by providing financial compensation. Thus, the

study may suffer from low power, implying that small effect sizes cannot be identified. It is thus

possible that we incorrectly reject a positive impact of husbands’ attendance to the training.

Fourth, a limitation of Chapter 5 is that the sample of couples invited to take part in the

behavioral experimental games is not random. To simplify the organization of the games, loan

officers invited a random sample of couples who actually followed the training described in

chapters 3 and 4 (instead of a random sample of couples invited to the trainings) to join the

experiments. In doing so, the loan officers may have skewed the sample toward couples who

were the “most interested” in the training.

Finally, in our study the business training is provided to microfinance clients as an add-

on service along with microfinance services (i.e., microcredit, micro-saving, and micro-

insurance). Thus, we cannot disentangle the impact of providing only business training and the

effects of offering only credit access.

Suggestions for Further Research

There are several fruitful possibilities for further research. First, our study only considers

average effects of the training. However, impacts may differ depending on characteristics of the

borrowers. An extension of this research could consider heterogeneous effects. Second, our study

focuses on the impact of training for female members of a microfinance organization. Thus, we

have considered the additional impact of the training for a group of women who already have

access to credit. It would be useful to investigate whether the impact of the training differs for

women with and without access to credit.

Third, our study points to the relevance of inviting husbands to attend the training

sessions. However, participation rates were low due to the high opportunity costs. Further

research could implement alternative experimentation methods to improve the attendance rates

of husbands. Fourth, we distinguish business and gender outcomes when examining the impact

13

of the training. It would be worthwhile for further research to investigate the extent to which

gender outcomes influence female-owned business outcomes. Finally, the RCT and the

behavioral games in chapters 3, 4, and 5 only consider short-term effects of business training. It

seems worthwhile to examine the extent to which results change over time.

15

Chapter 2

Do Microfinance Institutions Benefit from

Integrating Financial and Nonfinancial Services?

2.1 Introduction

Microfinance emerged in the late 1970s when Dr Muhammad Yunus began offering small

loans to ‘unbankable’ poor people in Bangladesh. Initially, the microfinance movement

promoted “specialization”. Yunus, in Banker to the Poor (1998) for instance stated:

“Rather than waste our time teaching them new skills, we try to make maximum use

of their existing skills. Giving the poor access to credit allows them to immediately put into

practice the skills they already know.” (Yunus, 1998)

This idea was endorsed by many researchers and also international organisations: it is

like capitalism at the best. The poor don’t need anything else than credit. If you give them

credit, everything will be fine. However, several recent studies have suggested that the impact

of microcredit has been considerably overstated (Roodman, 2012). Moreover, there is no

rigorous evidence that microcredit positively affects wealth indicators like income and/or

consumption (Armendáriz and Morduch, 2010, Banerjee et al., 2010, Karlan and Zinman,

2011, Angelucci et al., 2014). These findings seem to imply that single microcredit solutions

may be an inadequate way to confront the prevalence of poverty. Poor households benefit

from a combination of services, rather than the simple provision of credit (Armendáriz and

Morduch, 2010). Because poverty is multidimensional, poor people need to access to a

coordinated combination of microfinance and other development services, like business

training or financial literacy training to overcome their poverty (Khandker, 2005). Such

developmental services are crucial for making credit more productive. However, many

microfinance institutions (MFIs) prefer a minimalist approach, focused on providing financial

This chapter is co-authored with Robert Lensink and Roy Mersland

16

services, to maintain their sustainability. The aim of this article is to examine whether

combining financial and non-financial services may be beneficial for MFIs.

There is an inherent trade-off between serving the poor and attaining financial

sustainability (Cull et al., 2007, Hermes et al., 2011). Many microfinance providers are

interested in supporting social goals but also need to maintain sustainability and growth.

Controversies thus persist, related to whether suppliers should follow a minimalist approach

or provide microfinance alongside other important social services, that is, “microfinance

plus” (Morduch, 2000, Bhatt and Tang, 2001).

Several studies attempt to evaluate the impact of microfinance plus, mostly using case

studies of specific MFIs, which offer relatively little external validity. In addition, these

studies focus on the impact of microfinance plus on recipients, without considering the

outcomes for providers (Copestake et al., 2001, Dunford, 2002, McKernan, 2002, Halder,

2003, Noponen and Kantor, 2004, Karlan and Valdivia, 2011, Smith, 2002). In contrast, in

this article we use a global data set to investigate the potential benefits to providers of

combining financial and nonfinancial services. In addition, we adopt an advanced Hausman-

Taylor estimation method to address potential endogeneity.

In the next section, we discuss the concept of microfinance plus, then provide our

conceptual framework of the effects of such services. From our empirical literature review,

we derive some hypotheses; we then describe our data and methodology for testing these

hypotheses. Finally, we present estimates regarding financial and social performance and

conclude with a discussion of our findings.

2.2 Conceptual Framework, Research Questions and

Hypotheses

2.2.1. What Is Microfinance Plus?

Microfinance plus refers to the provision of developmental services to customers, such as

training or health services, alongside financial services. An overall understanding of the

concept is relatively straightforward, but a more detailed assessment also is possible. For

example, an MFI that provides savings, insurance, or money transfers together with loans is

not involved in microfinance plus, because all its services are financial in nature. An MFI that

provides informational sessions to potential customers or trains existing customers in the use

of credit or the importance of repayment is not practicing microfinance plus, nor is a MFI that

17

partners with another organization that provides customers with plus services. Rather, a plus

service refers specifically to a nonfinancial service provided by the MFI itself.

Various MFIs offer a wide variety of plus services, ranging from access to markets

and business development services (BDS) to health provision and literacy training (Maes and

Foose, 2006). Generally though, plus services involve either BDS or social services. The

former aims to boost competitiveness by improving productivity, product design, service

delivery or market access (Sievers and Vandenberg, 2007). These services comprise a broad

range of nonfinancial offerings, including management or vocational skills training;

marketing and technical assistance; technology access; productivity and product design;

accounting and legal services; and access to various information about standards, regulations

or ideas in an enterprise field. In contrast, social services (SS) integrate credit with health,

education or other programs intended to raise health consciousness, practices and formal

uses.

2.2.2. Different Ways to Integrate Plus Services

An MFI can offer plus services in least three forms: unified, parallel or linked/partner

(Dunford, 2002, Sievers and Vandenberg, 2007). In the unified form, both financial and

nonfinancial services are offered by the staff of one institution, such as credit officers. Thus,

additional costs are minimised (Vor der Bruegge et al., 1999), and the services can be funded

mainly by the customers through loan interest payments. In the parallel form, one institution,

with two different departments for people versus accounting management, delivers financial

and nonfinancial services, respectively. Parallel services tend to be funded by special

donations or customer service charges. Finally, in the linked (partner) form, the two types of

services are offered by separate institutions that may operate in the same area and connect by

sharing clients’ network or use joint marketing strategies.

2.2.3. Conceptual Framework for the Effects of Microfinance Plus

The traditional banking literature documents advantages and disadvantages of specialisation

versus diversification. Traditionally, studies have indicated that banks should diversify as

much as possible, because doing so reduces the possibility of financial distress and helps

banks achieve economies of scope. If they develop long-term, contractual relationships with

their customers, banks can use customer information in their focal business area, as well as in

other, unrelated areas (Elsas et al., 2010). However, specialising in a single line of business

18

might help financial institutions take advantage of managerial expertise and reduce agency

problems.

Abundant empirical evidence thus details the costs and benefits of diversification on

firm and bank performance, as summarised in extensive surveys of this literature. Yet neither

field offers unambiguous evidence of the effects of these two strategies on financial

performance. Moreover, recent studies points to problems in prior work, such as

measurement concerns (Whited, 2001), data issues (Villalonga, 2004), sample selection

biases (Graham et al., 2002) and a failure to account for endogeneity in the diversification

decision (Campa and Kedia, 2002). Such problems may create the ambiguous evidence of the

impact of diversification on performance. We account for all these issues in our study and

thereby provide new evidence about the relevance of these conflicting strategies.

The results from the banking literature may not apply to MFIs though, because MFIs

differ from traditional institutions, and comparing financial results achieved by microfinance

plus providers against those of specialised MFIs is not the same as comparing specialised and

diversified banks. Thus, we must turn to microfinance literature to find a more accurate

analysis of the financial performance of plus versus specialised MFIs. Many policy makers

argue that the only way for MFIs to become self-sufficient, obtain sustainability and reach

optimal scale is to concentrate on financial services (Otero, 1994, Dunford, 2002). However,

nonfinancial services also can make substantial, positive contributions to profits for not only

microcredit users but also BDS providers and MFIs in general. Such outcomes may relate to

the quality and type of BDS, which can be improved by tending to the specifics and focusing

on vocational skills training and market access instead of traditional management training

(Sievers and Vandenberg, 2007).

Although no clear-cut, unambiguous theory about the influence of microfinance plus

activities on financial and social performance is available, we can use different theories from

extant literature to derive a framework that reveals the influence of microfinance plus, as we

illustrate in Table 2.1. Specifically, we classify impacts according to the relationship with

positive or negative effects and causation types (i.e. direct or indirect).

First, microfinance plus can result in direct positive effects (Quadrant I) by

stimulating client loyalty, especially among good clients whose businesses are growing and

who might therefore tend to switch to other credit institutions (Sievers and Vandenberg,

2007). If plus activities improve customer satisfaction, they should help increase retention

rates. A recent study assesses the impact of incorporating BDS, in the form of entrepreneurial

19

training, into a microfinance program using a randomised control trial (Karlan and Valdivia,

2011). This study clearly shows that the BDS program increases client retention rates.

Another example, from Financiera Solucion, also shows that the institution benefits from

including management training because it can better retain clients (Sievers and Vandenberg,

2007). Second, MFIs might earn a comparative advantage in terms of attracting new clients if

they provide a range of plus services (Mosley and Hulme, 1998, Khandker, 2005).

Competition among MFIs has been increasing in countries such as Bangladesh, Uganda,

Kenya, Guatemala, El Salvador and Nicaragua (McIntosh and Wydick, 2005); plus services

might help MFIs differentiate their products and attract new customers in such competitive

settings. Third, microfinance plus can help reduce the risk of default. Training should reduce

credit risks that arise when borrowers use loans to support consumption rather than

production activities (Marconi and Mosley, 2006), which then should increase repayment

rates. Fourth, plus activities, especially training, can improve MFIs’ financial performance by

enhancing customers’ human capital. In specialised MFIs, borrowers tend to be traders who

participate in simple production and service provision (Dawson, 1997). Even if they plan to

use loans efficiently, their attempts may be limited by their lack of or narrow knowledge.

Many borrowers never go beyond traditional food processing, handicrafts or petty trade

(Dawson, 1997). Without sufficient skills, micro-entrepreneurs even might suffer negative

returns on capital (De Mel et al., 2008). We argue that improved human capital enables

microfinance clients to service bigger loans, which then enhances the financial performance

of MFIs though economies of scale. A study of Sarvodaya’s Rural Enterprise Development

Service in Sri Lanka confirms this. It reveals that adding technical assistance to a standard

microcredit program can help increase both loan disbursement and repayment rates (Dawson,

1997). Fifth, plus services may help MFIs achieve self-sustainability. When demand-driven

plus services are managed suitably, their providers can cover the costs of credit and services

with client fees (Sievers and Vandenberg, 2007). Sixth, plus services may help to improve the

social outreach of MFIs. Although MFIs aim to reach poor people, most of them access the

‘upper poor’ much better than the ‘very poor’. Thus microfinance offers an effective means

of reducing poverty but may not influence extreme poverty (Mosley, 2001). In addition,

pressure from government and donors to ensure financial sustainability leads many MFIs to

ignore social protection objectives and target only less risky, easier markets. That is, the

poorest segments are not the primary clients of MFIs (Remenyi, 2011). A major argument in

support of microfinance plus is that it enables MFIs to reach poorer and more vulnerable

20

customers (Halder, 2003, Maes and Foose, 2006). Other antipoverty modalities, including

primary health, primary education and agricultural extensions, are needed to reach the poorest

sectors (Mosley, 2001). Microfinance production loans that are combined with necessary

training and appropriate technology transfers have significant unrealised potential to

sustainable, hunger-free livelihoods (Remenyi, 2011).

However, plus services may create direct negative effects (Quadrant II), including

higher operational and administrative costs. A study of four Freedom from Hunger affiliates

reveals that the direct cost of including learning sessions, related to family, health, nutrition,

business development and self-confidence, accounted for between 4.7 and 10 per cent of each

MFI’s operational costs (Vor der Bruegge et al., 1999). Dunford (2002) provides evidence of

the combined effects of financial and education services, with a particular focus on health and

nutrition training in Credit with Education programs in Ecuador, Honduras, Burkina Faso,

Thailand and other locations. Credit with Education programs offer benefits for borrowers

However, education increases the costs of village banking in these studies, such that over

three years, the average added costs ranged from 5.9 per cent in Bolivia to 9.6 per cent in

Burkina Faso. Additionally, integrated services also increase administrative burdens, because

providing training and technical assistance likely distracts MFIs from their credit

administration, which could decrease repayment rates (Berger, 1989). Furthermore, plus

services may require extra commitments by management in staff recruitment, training and

supervision (Dunford, 2002). Many MFIs, already struggling with self-sustainability, thus are

unwilling to incorporate nonfinancial services that demand more investment. At the same

time, microfinance borrowers do not consider training useful and do not retain or apply their

acquired knowledge, such that time spent in training appears to be an opportunity cost for

credit (Goldmark, 2006). This perception could damage the reputation and client base of plus

providers and perhaps contribute to their abandonment in competitive microfinance markets

(Sievers and Vandenberg, 2007).

Microfinance plus also can create indirect positive effects for providers (Quadrant

III), such as assessing the creditworthiness of existing borrowers and, moreover, potential

clients when they share client information with their nonfinancial service provider partners

(Sievers and Vandenberg, 2007).

Finally, some indirect negative effects (Quadrant IV) may include the difficulty of

evaluating performance; plus-providing MFIs need a clear and concise measure of

performance. But performance by MFIs that provide plus services is more difficult to

21

measure and requires more time to verify (Tendler, 1989). Some MFIs offer plus services

simply to distract attention from their inefficient microfinance services (Berenbach and

Guzman, 1994).

Table 2.1: Effects of microfinance plus

Positive effect Negative effect Direct

- Stimulate client loyalty increase retention rates

- Create comparative advantage attract new clients

- Reduce default risk increase repayment rate

- Improve financial performance through economies of scale

- Achieve sustainability - Improve social outreach

- Higher operational and administrative costs

- Administrative burden - Extra commitment for

management - Poor quality or irrelevant plus

services damage reputation and client base

Indirect

- Assess clients’ creditworthiness

- Assess credit risk of potential clients

- Difficulty to measure good performance

- Need time to verify the impact of plus services

- Serve as a veil to hide inefficient performance

2.2.4. Research Questions and Hypotheses

In our empirical assessment of the effects of combining financial and nonfinancial services,

we attempt to answer the following questions:

1. Do MFIs that combine financial and nonfinancial services achieve better financial

performance, in terms of financial sustainability and efficiency and portfolio quality,

than MFIs that specialise in financial services?

2. Do microfinance plus providers attain better social performance, in terms of outreach,

than their specialist peers?

3. When we differentiate nonfinancial services as BDS or SS, which combination of

nonfinancial and financial services is most effective for improving the financial and

social performance of MFIs?

Our theoretical framework made clear that the impact of providing plus services on

financial performance is ambiguous. On the one hand, many studies suggest that training and

other plus provisions increase costs. Therefore, we hypothesize that plus providers –both

BDS and SS- will experience higher costs ratios than specialists. On the other hand, there is

22

ample evidence that training - especially in the form of BDS - may improve the

creditworthiness of borrowers. The impact on financial performance is a trade-off between

the costs and creditworthiness effects. Since the positive effects probably only hold for BDS

providers, and not for SS plus providers, we hypothesize that BDS plus providers is more

effective in improving financial performance than SS plus providers. We do not expect BDS

plus providers to differ from specialists in terms of financial performance.

The social performance of different types of MFIs seems more straightforward. The

theoretical framework suggests that social outreach of plus providers, and especially the SS

plus providers, is better than for specialists. Therefore, we hypothesize that the social

performance of SS plus providers is better than for BDS plus providers. Moreover, we

hypothesize that BDS plus providers perform better socially than specialists.

2.3 Data and Estimation Methodology

2.3.1. Data

The dataset is hand-collected from risk assessment reports (i.e., rating reports) from the five

leading rating agencies in the microfinance industry; i.e. Microrate, Microfinanza, Planet

Rating, Crisil and M-CRIL. Assessment reports are narrative reports consisting of contextual

and MFI specific information including accounting details and benchmarks. The rating

reports have been downloaded from www.ratingfund2.org and www.ratinginitiative.org

which were programs co-funding the costs involved of being rated. The programs, which

were closed after 10 years of operations in 2012, were set up by international donors like the

Consultative Group to Assist the Poor (CGAP) and the Interamerican Development Bank

(IDB) with the aim to increase the transparency in the microfinance industry ((Beisland et al.,

2014). The rating reports are not fully standardized and therefore differ in their emphasis and

in the amount of information available. The result is that not all reports have information on

all variables. When necessary, all numbers in the dataset have been annualized and dollarized

using the official exchange rates from the given time. For a further description of the dataset

please see (Strøm et al., 2014). We used observations of 290 rated MFIs from 61 countries.

Our data cover 1998–2007. Most data are from 2001–2005.

No data set can be perfectly representative of the microfinance field; ours contains

relatively fewer mega-sized MFIs and does not cover the virtually endless number of small

savings and credit cooperatives. The former are rated by agencies such as Moody’s and

23

Standard & Poor’s; the latter are not rated. However, our use of rating reports should be

relevant for studying the effects of microfinance plus, because MFIs that are rated have a

common interest in accessing funding and increasing their sustainability. The data set

includes specialists and providers of plus services, so it enables meaningful comparisons.

To compare data across 61 countries, we converted the monetary variables into U.S.

dollar (USD) amounts at the going exchange rate. We also assumed that the rating agency

made the necessary corrections to financial reports to enable a reasonable comparison of

MFIs. This assumption is in line with the benchmarking objective outlined by the Rating

Fund, which funds the rating reports that constitute our data set.

2.3.2. Estimation Methodology

Our main model is specified as follows:

(1)

where the dependent variable yijt is a measure of financial and social performance of the ith

MFI located in country j at time t.

We distinguish three types of MFI services: (1) specialised financial services only, (2)

financial services and BDS and (3) financial services and social services. We include BDS

and SS dummies, as well as a constant. In addition, BDSij equals 1 if the ith MFI is a plus

provider that integrates BDS and 0 otherwise; SSij equals 1 if the ith MFI is a plus provider of

social services and 0 otherwise. Furthermore, Mjt is a vector of control variables describing

the macroeconomic environment in country j at time t; MFijt is a vector of control variables

describing the features of the ith MFI in county j at time t; is the MFI’s individual

unobserved effects; and εijt is an IIDN(0, σ2) error term. All independent variables are

assumed to be strictly exogenous.

2.3.3. Dependent Variables

We focus on financial sustainability, efficiency and portfolio quality as measures of financial

performance and outreach as a measure of the social performance of MFIs.

For financial sustainability, we consider the operational self-sufficiency ratio as a

main indicator of financial performance. This ratio demonstrates the ability of MFIs to be

fully sustainable in the long run, in the sense that they can cover all their operating costs and

maintain the value of their capital. The operational self-sufficiency ratio is a better measure

24

of financial performance than standard financial ratios, such as return on assets or equity,

because it entails a more complete list of inputs and outputs. As a robustness check, we

include financial self-sufficiency and return on assets measures. Operational self-sufficiency,

financial self-sufficiency and return on assets have been used widely to measure the financial

sustainability of MFIs (Cull et al., 2007, Mersland and Strom, 2009). Better gains of financial

sustainability are associated with higher operational self-sufficiency, financial self-

sufficiency and returns on assets.

We also use several indicators for efficiency. The operating expense ratio measures

the MFI’s operating expenses compared with an average loan portfolio. A decrease in this

ratio implies an increase in efficiency, because the portfolio grows. Although the operating

expense ratio is one of the most widely used indicators of efficiency in the microfinance field,

it suffers several substantial drawbacks. For example, it makes an MFI offering small loans

look worse than one offering large loans, even if both are managed efficiently (Rosenberg,

2009). Therefore, we use an alternative ratio, cost per client, which measures the operating

expenses that the MFI requires to serve a single active client, and increasing efficiency is

associated with decreasing cost per client. Next, we employ the ratio of credit clients per loan

officer to evaluate how efficiently the staff serves the clients. A higher ratio per officer means

more clients will be served, so greater efficiency will be gained. A similar ratio to evaluate

efficiency is credit clients per staff member, but this ratio does not differentiate between

credit staff and administrative staff. Again, a greater number of credit clients per employee

ratio are desirable, but adding plus services may lower this ratio, which implicitly reduces

administrative efficiency.

Next, we examine two indicators of portfolio quality. First, the portfolio at risk

beyond 30 days reveals the potential for future losses based on the current performance of the

portfolio. It is the most widely accepted standard measure of portfolio quality in banking and

microfinance. Second, the write-off ratio or default rate measures the actual amount of loans

that have been written off as unrecoverable during a given period of time, in relation to the

loan outstanding. Better financial performance relates to a smaller portfolio at risk and write-

off ratio.

To evaluate social performance, we use several indicators of outreach. First, the

number of clients served as a proxy for the breadth of outreach; this indicator is widely

accepted as the best measure of the breadth of outreach (Schreiner, 2002, Rosenberg, 2009).

A drawback of this indicator is that it only measures the number of people using

25

microfinance services during a certain period, but does not say anything about the social

status of the borrowers. Regarding “social status” of the borrowers we need indicators for the

depth of outreach. Considering our available data set, we chose very rough proxies for

clients’ poverty levels, including average loan size and share of female borrowers, which also

have been used in prior literature (Schreiner, 2002, Olivares-Polanco, 2005, Cull et al., 2007,

Cull et al., 2009, Mersland and Strom, 2009, Ahlin et al., 2011, Hermes et al., 2011).

Average loan size is a rough proxy for the poverty of borrowers, in that smaller loans

imply greater outreach depth, because less impoverished clients may not be interested in

smaller loans (Elsas et al., 2010). This indicator generally involves a percentage of gross

domestic product per capita, which enables a deep comparison of how MFIs from different

countries reach their own national income distribution. The relationship between average loan

size and poverty level is not as close, because small average loan sizes do not always imply

poor clients, and larger average loan sizes do not necessarily mean MFIs are undergoing

‘mission drift’ (Rosenberg, 2009). For more rigorous measures of client poverty and outreach

of MFIs, it would be necessary to screen the income level of served clients, though such

indicators are expensive to implement. Moreover, a higher percentage of female borrowers

are a rough indicator of greater outreach depth, because lending to women generally relates to

lending to the poor. We expect that with better outreach, the number of clients served and

percentage of women measures will be significantly positive; the average loan size

coefficient should be negative. Table 2.2 provides detail of all dependent variables.

26

Table 2.2: Dependent variables description

2.3.4. Control Variables

To control for the macroeconomic environment, we include gross domestic product (GDP, in

constant 2000 USD), which offers a proxy for the overall level of development. By

controlling for GDP, we capture institutional differences (Claessens et al., 2001, Lensink and

Hermes, 2004). In addition, we include inflation to control for macroeconomic conditions,

because all programs tolerate inflation costs. Microfinance programs have a better chance of

achieving self-sufficiency if they operate in countries where inflation is controlled and

moderate (Rhyne and Otero, 1992). In addition, we add the countries scores on the human

development index. The human development index (HDI) is a composite index that combines

three dimensions of human development: knowledge, standard of living and life expectancy.

In alternative regressions, we included different separate indicators for education as well as

region dummies. However, since variables were never significant, did not affect the results,

and were often highly collinear with the other control variables, we did not include them in

the finals of estimates.

We control for MFI characteristics with several variables: number of credit officers,

assets, age and whether the MFI is a member of an international network, was initiated by a

2 Adjusted numbers are obtained from rating reports

Dependent variables Description Operational self-sufficiencyijt Operating revenue / ( Financial expense + loan loss

provision expense + operating expense)

Financial self-sufficiencyijt Adjusted2 operating revenue / (Adjusted financial expense + adjusted loan loss provision expense + adjusted operating expense)

Return on Assetsijt Net operating income / average total assets Operating expense ratioijt Operating expenses/ annual average loan portfolio

Cost per clientijt Operating expenses/ average number of active borrowers

Credit clients per loan officerijt Number of credit clients/ number of loan officers Credit clients per staff memberijt Number of credit clients / number of employees Portfolio at riskijt Portfolio at risk (30 days)

Write-off ratioijt Loans written off and counted as losses/ loan outstanding

Number of clientsijt Total number of credit clients active at the end of the year.

Average loan size/GDP per capitaijt Average loan size/ GDP per capita Percentage of womenij Percentage of female borrowers

27

religious organization or offers group lending. We also add the organizational form of the

MFI (non-governmental organization - NGO, bank, cooperative, State bank and non-bank

financial institution). Table 2.3 provides detail of all explanatory variables.

Table 2.3: Independent variables description

Note: Time-Variant (TV), Invariant (TI), Exogenous (Ex), Endogenous (En)

2.3.5. Estimation Approach

We have only one observation per MFI for the percentage of women dependent variable. For

this variable, we rely on ordinary least squares (OLS).

For the other dependent variables we have panel data and conduct several steps. First,

we check whether panel techniques are more appropriate than pooled OLS by applying the

Independent variables Description TV/TI Ex/En

BDSij 1 if MFI provides business development services, 0 otherwise

TI En

SSij 1 if MFI provides social services, 0 otherwise

TI En

Group lendingij 1 if MFI uses village banking or solidarity group lending system, 0 otherwise

TI En

Ageijt The years since an MFI started microfinance operations

TV Ex

Number of credit officersijt Number of credit officers active with the MFI at the end of the year

TV En

Assetsijt Total assets of the MFI TV Ex Bankij 1 if a MFI is registered as a bank, 0

otherwise TI Ex

Nonbankijt 1 if a MFI is registered as a non-financial institution, 0 otherwise

TV Ex

Ngoijt 1 if a MFI is registered as non-governmental organization, 0 otherwise

TV Ex

Coopij 1 if a MFI is registered as a cooperative, 0 otherwise

TI Ex

International networkij 1 if the MFI is member of an international network, 0 otherwise

TI Ex

Religious organizationij 1 if the MFI was initiated by an organization with a religious agenda, 0 otherwise

TI Ex

HDIijt Human Development Index TV Ex

Inflationijt Inflation rate TV Ex

GDPijt GDP (constant 2000 US dollars) TV Ex

28

Breusch-Pagan test (Greene, 2003). If the test rejects the null hypothesis, the random effects

(RE) model is preferable. Second, we test the assumed correlation between MFI-specific

effects and regressors using Hausman’s specification test in the random effects model. The

rejection of the null hypothesis in Hausman’s specification test shows that MFI-specific

effects correlate with regressors, such that a fixed effects (FE) model is preferable. However,

since our main variables of interest, the combination of financial and nonfinancial services,

are time invariant, an FE model is impossible. Therefore, if the results of the Hausman test

show that the estimation of fixed effects is consistent; we perform a Hausman-Taylor

estimator. The goal of the Hausman Taylor estimator is to distinguish between regressors that

are uncorrelated with FEs and those potentially correlated with them. Hausman and Taylor

(1981) suggest using an economics intuition to determine which variables should be treated

as potentially correlated with the FE. The model also distinguishes time-varying from time-

invariant regressors. The model is

+ (2)

where the dependent variable yijt is a measure of the financial and social performance

of the ith MFI located in country j at time t; X denotes time-varying regressors: Inflation,

GDP, Assets, Age, Credit officers, the human development index (HDI), non-governmental

organisations (Ngo), non-bank financial institutions (nonbank); and W denote time-invariant

regressors: international network, religious organization, BDS, SS, group lending,

cooperatives (coop), banks (bank) and are MFI-specific unobserved effects; and εijt is

idiosyncratic errors. Regressors with subscripts 1 are uncorrelated with , whereas those

with subscripts 2 are specified as correlated with . All regressors are assumed uncorrelated

with εijt 3. For more detail of the Hausman Taylor estimator, see Appendix 2.1.

The MFI’s choice to integrate financial and plus services depends substantially on its

specific characteristics. Therefore, we treat our time-invariant dummies for MFIs that

combine financial and nonfinancial services (BDS and SS) as endogenous. We similarly

assume that group lending is endogenous and must be instrumented. The same holds for the

number of credit officers. Group lending offers an excellent platform for the delivery of plus

services, alongside microfinance (MkNelly et al., 1996). The decision to provide individual

3The Hausman and Taylor (1981) estimator assumes that the exogenous variables serve as their own instruments; is instrumented by its deviation from individual means; and is instrumented by .

29

or group lending also depends on the presence of some MFI-specific characteristics. Control

variables related to the macroeconomic environment, such as GDP (in constant 2000 USD),

HDI, and inflation, and MFI characteristics such as the number of credit officers, assets, age,

whether the MFI is member of an international network (International network) or was

initiated by an organization with a religious agenda (Religious organization), and

organizational forms such as bank, nonbank, coop and Ngo are treated as exogenous

variables. Table 2.3 provides detail of all independent variables.

2.3.6. Descriptive Statistics

We provide the general descriptive statistics in Table 2.4. In addition to descriptive statistics

for the dependent variables and the explanatory variables, we offer a list of countries in

Appendix 2.2.

Then in Table 2.5, we provide general descriptive statistics regarding the relations of

different types of plus providers and specialists, as well as financial and social performance

aspects, such as financial sustainability, efficiency, portfolio quality and outreach.

Microfinance plus providers that combine BDS with financial services perform better

financially than their peers that integrate social with financial services; they even perform

more effectively than specialised MFIs. In particular, the mean values for operational self-

sufficiency, financial self-sufficiency and return on assets are higher for plus providers of

BDS than for plus providers of social services or specialists.

In terms of efficiency, plus providers of social services have better efficiency than

plus peers of BDS and specialists. Plus providers of social services have the smallest cost per

client ratio compared with plus peers of BDS and specialists. Furthermore, the plus providers

of social services gain greater efficiency, as indicated by the amount of clients served loan

officer. However, when comparing the credit clients per staff member, specialists score better

than do plus providers. For portfolio quality, specialists have the lowest portfolio at risk ratio

but the highest write off ratio.

30

Table 2.4: Descriptive statistics

Mean S.D. Min Max Operational self-sufficiencyijt 1.12 0.383 0.08 2.95 Financial self-sufficiencyijt 0.93 0.312 0.06 1.94 Return on assetsijt 0.015 0.125 -0.9 0.79 Number of clientsijt 12588.97 26638.59 74 3.94E+05 Average loan sizeijt 4.46 10.798 0.007 193 Percentage of womenij 0.729 0.251 0.09 1 Portfolio at riskijt 0.07 0.103 0 0.98 Write-off ratioijt 0.022 0.046 0 0.74 Operating expense ratioijt 0.322 0.295 0.03 4.26 Cost per clientijt 138.259 132.777 0.309 1079.75 Credit clients per staff memberijt 123.391 78.575 7 720 Credit clients per loan officerijt 273.775 180.784 14 2073 GDPijt 1.37E+11 2.31E+11 2.84E+08 8.13E+11 Inflationijt 0.066 0.11 -0.08 1.7 Group lendingij 0.446 0.497 0 1 Assetsijt 7.30E+06 1.63E+07 19073 2.50E+08 Ageijt 9.914 8.404 1 84 Number of credit officersijt 43.534 86 1 1169 International networkij 0.329 0.47 0 1 Religious organizationij 0.175 0.38 0 1 BDSij 0.068 0.252 0 1 SSij 0.113 0.317 0 1 Bankij 0.048188 0.214205 0 1 Nonbankijt 0.218967 0.413626 0 1 Ngoijt 0.584811 0.49285 0 1 Coopij 0.116423 0.320793 0 1 HDIijt 0.648396 0.127181 0.3 0.898

Note: Extreme outliers (observations with age < 0) are ignored.

With regard to social performance, plus providers seem to achieve better depth of

outreach: the average loan size is lower, and the percentage of women is higher. The breadth

of outreach is also higher: the indicator for the breadth of outreach (number of clients) is

higher for the plus providers not significant. Therefore, plus providers, regardless of the type

of services, seem to focus more on poor borrowers, whose average loan sizes are lower than

those of wealthier borrowers. Plus providers that include BDS reach the very poorest best by

providing smaller loans than their plus peers with social services. In addition, plus providers,

whether they offer BDS or social services, perform better on the reaching female borrower

variable. Whereas specialists attract 70 percent female borrowers, the rates are 76 percent for

31

BDS and 91 percent for social services plus providers.

Table 2.5: Descriptive statistics for specialists and plus providers

Plus providers of

BDS Plus providers of

SS Specialists

Financial sustainability Operational self-sufficiencyijt 1.168 0.922 1.140 (0.258) (0.432) (0.381) Financial self-sufficiencyijt 1.026 0.823 0.934 (0.229) (0.419) (0.301) Return on assetsijt 0.035 -0.003 0.016 (0.080) (0.141) (0.126)

Efficiency and Productivity Operating expense ratioijt 0.323 0.325 0.322 (0.245) (0.245) (0.305) Cost per clientijt 110.748 107.814 144.297 (103.488) (145.336) (132.814) Credit clients per loan officerijt 117.594 136.500 122.308 (65.785) (110.985) (74.758) Credit clients per staff memberijt 244.444 252.420 278.779 (115.860) (139.859) (189.115)

Loan repayment (Portfolio quality) Portfolio at riskijt 0.094 0.072 0.068 (0.122) (0.120) (0.099) Write-off ratioijt 0.019 0.015 0.023 (0.029) (0.021) (0.049)

Outreach Number of clientsijt 14539.290 13267.300 12337.930 (22361.180) (17798.260) (27873.540) Average loan sizeijt 2.697 3.371 4.726 (4.944) (8.952) (11.307) Percentage of womenij 0.759 0.913 0.695 (0.268) (0.169) (0.249)

Notes: Standard errors are in parentheses. Extreme outliers (observations with age < 0) are ignored.

2.4 Empirical Results

2.4.1. The Effects of Microfinance Plus on Financial Performance

We distinguish the three types of MFI services and include both the BDS and SS dummies, as

well as a constant. In this specification, the constant measures the impact of MFIs that

specialise; the impact of MFIs that also provide social services equals the sum of the constant

and SS; and the impact of MFIs that also provide BDS equals the sum of the constant and

32

BDS. Significant values of BDS or SS imply that the impact of plus providers differs from

that of MFIs that specialise in financial services.

The results in Table 2.6 show that Hausman-Taylor estimators are appropriate for all

three specifications. Although BDS have a positive effect on MFIs’ financial sustainability

and profitability, the positive coefficient is not significant, so the financial sustainability and

profitability of plus providers do not differ from those of MFIs that specialise. The negative,

significant coefficient of SS confirms that MFIs that provide social services, such as health

services, literacy and nutrition training perform less profitably than specialised MFIs. Social

services may impose additional costs; our results confirm that they significantly decrease the

self-sustainability and profits of MFIs. Even though the provision of social services may have

a sizable social impact, in terms of self-sustainability and profits, MFIs that use Credit with

Education programs may hinder their drive toward sustainability (Dunford, 2002).

When we consider efficiency (Table 2.7), it turns out that the amount of credit clients

per employee is lower for SS providers. This suggests that efficiency is lower for plus

providers focusing on social services than for specialists. However, it should be noted that for

the other efficiency indicators we cannot confirm a difference between the three types of

MFIs. Hence, in general, there seems to be no differences between the three types of MFIs in

terms of efficiency.

Integrating plus services may reduce the risk of default by reducing the credit risks of

borrowers, which should help increase repayment rates. However, the empirical results in

Table 2.8 do not confirm this. The coefficients of BDS are not significant; therefore, the loan

portfolio quality of plus providers focusing on BDS does not differ statistically at the usual

significance levels from that of specialists. Yet regarding plus providers focusing on social

services, the results suggest that these MFIs are faced with higher credit risk as it is indicated

by the positive and significant coefficient for SS in the “portfolio at risk” estimate.

33

Table 2.6: Effects of microfinance plus on financial sustainability

(1) (2) (3) VARIABLES Operational self-

sufficiencya Financial self-

sufficiencya Return on

assetsa HDIjt 0.08312 0.12887 0.08325 (0.695) (0.416) (0.205) GDPjt -2.85e-13 4.62e-13 3.00e-13 (0.539) (0.393) (0.246) Inflationjt 0.01410 -0.07430 -0.03461 (0.893) (0.323) (0.305) Assetsijt 3.33e-09 -6.93e-10 -2.06e-09* (0.489) (0.868) (0.093) Ageijt 0.02537** 0.05295*** 0.02303*** (0.027) (0.000) (0.000) Nonbankijt 0.22776 0.80070 -0.00245 (0.759) (0.405) (0.996) Ngoijt 0.60014 0.47761 -0.01297 (0.511) (0.669) (0.978) Number of credit officersijt 0.00053 -0.00013 -0.00004 (0.357) (0.766) (0.771) International networkij -0.03212 -0.22841 -0.22013 (0.909) (0.515) (0.262) Religious organizationij -0.19647 -0.00534 0.03709 (0.526) (0.990) (0.852) Bankij 0.05664 0.67282 -0.13627 (0.952) (0.638) (0.813) Coopij 0.23541 0.23815 -0.02619 (0.760) (0.809) (0.960) BDSij -0.64513 3.48156 1.57570 (0.819) (0.393) (0.330) SSij -2.35655* -3.82377** -2.99938* (0.060) (0.028) (0.059) Group lendingij -0.53066 -0.07909 0.49755 (0.613) (0.957) (0.328) Constant 0.91358 -0.05753 -0.23269 (0.273) (0.958) (0.609) Observations 454 438 724 Number of identifiers 137 139 212 P-value Breusch-Pagan test 0.0000 0.0000 0.0000 P-value Hausman Test (FE vs RE) 0.0608 0.0000 P-value Sargan-Hansen 0.4890 0.3722 0.8915 Note: p-values are in parentheses. Extreme outliers (observations with age < 0) are ignored. *** p < .01. ** p < .05. * p < .1. aHausman-Taylor model

34

Table 2.7: Effects of microfinance plus on efficiency

(4) (5) (6) (7) VARIABLES Operating

expense ratioa Cost per clientsb Credit clients

per employeeb Credit clients per

loan officerb HDIjt 0.06644 87.85906** -16.18300 -132.21169** (0.534) (0.020) (0.490) (0.043) GDPjt 2.33e-13*** 4.80e-11 3.62e-11 1.07e-10 (0.002) (0.362) (0.483) (0.452) Inflationjt 0.10854 -39.35823** 6.44737 11.76299 (0.149) (0.046) (0.595) (0.729) Group lendingij 0.15123*** -173.49575** 66.96419 75.07125 (0.000) (0.026) (0.389) (0.727) Assetsijt -7.06e-09*** 3.61e-07 5.04e-07 -1.97e-06 (0.000) (0.605) (0.250) (0.107) Ageijt -0.00563** -2.92031 4.09562*** 8.38027*** (0.016) (0.107) (0.000) (0.009) Number of credit officersijt

-0.00003 -0.10254 -0.01280 -0.14103

(0.867) (0.237) (0.816) (0.355) International networkij

0.04287 -0.72608 1.36950 3.78550

(0.165) (0.980) (0.962) (0.962) Religious organizationij

-0.06947* -21.73435 1.56734 11.13892

(0.079) (0.473) (0.960) (0.898) BDSij -0.00002 -79.68740 133.71267 570.39531 (1.000) (0.804) (0.654) (0.489) SSij -0.02325 -6.83638 -472.64753* -782.74527 (0.662) (0.983) (0.091) (0.312) Bankij 0.28980** -158.92602** 64.78237 267.10000 (0.013) (0.032) (0.431) (0.238) Nonbankijt 0.04625 -84.56465 48.13500 57.56685 (0.600) (0.172) (0.476) (0.755) Ngoijt 0.01360 -91.20154 90.41853 103.32520 (0.874) (0.234) (0.234) (0.618) Coopij -0.04317 -89.83986 44.70499 148.62110 (0.665) (0.216) (0.565) (0.486) Constant 0.25555** 278.50376*** 15.10270 179.06020 (0.024) (0.000) (0.823) (0.336) Observations 700 725 726 730 Number of id 213 213 213 214 P-value Breusch-Pagan test

0.0000 0.0000 0.0000 0.0000

P-value Hausman Test (FE vs RE)

0.4905 0.0025 0.0241 0.0059

P-value Sargan-Hansen

0.3709 0.8451 0.1261

Note: p-values are in parentheses. Extreme outliers (observations with age < 0) are ignored. *** p < .01. ** p < .05. * p < .1. aRandom effects model. bHausman-Taylor model.

35

Table 2.8: Effects of microfinance plus on portfolio quality

(8) (9) VARIABLES Portfolio at riska Write-off ratiob HDIjt -0.05528 -0.02405 (0.326) (0.251) GDPjt -3.65e-15 2.52e-14 ** (0.981) (0.043) Inflationjt 0.00164 0.01688 (0.956) (0.308) Assetsijt 6.66e-11 -2.89e-10 (0.954) (0.428) Ageijt -0.00963*** 0.00034 (0.001) (0.380) Nonbankijt 0.04671 0.01217 (0.845) (0.409) Ngoijt 0.04195 0.01998 (0.867) (0.163) Number of credit officersijt 0.00010 -0.00005 (0.443) (0.232) International networkij 0.10555 -0.00414 (0.293) (0.405) Religious organizationij 0.00979 0.00054 (0.923) (0.933) Bankij 0.08124 0.01881 (0.777) (0.324) Coopij 0.06554 0.00155 (0.798) (0.926) BDSij -0.66414 -0.00236 (0.489) (0.792) SSij 1.86429* -0.01040 (0.061) (0.232) Group lendingij -0.45562 0.00600 (0.143) (0.243) Constant 0.16987 0.01975 (0.462) (0.340) Observations 706 681 Number of identifiers 211 202

P-value Breusch-Pagan test 0.0000 0.0000 P-value Hausman Test (FE vs RE) 0.0002 0.1369 P-value Sargan-Hansen 0.6576

Note: p-values are in parentheses. Extreme outliers (observations with age < 0) are ignored. *** p < .01. ** p < .05. * p < .1. aHausman-Taylor model. bRandom effects model

36

2.4.2. The Effects of Microfinance Plus on Social Performance

We expect that microfinance plus helps stimulate client loyalty, which may improve

customer satisfaction, increase retention rates and enable the MFI to serve more clients.

Moreover, plus providers should have a comparative advantage for attracting new clients.

The results in Model (10), explaining the number of clients in Table 2.9, confirm the positive

effects of plus services for reaching clients, according to the positive coefficients of BDS and

SS. However, these coefficients are not significant; the breadth of outreach of plus providers

does not differ from that of specialists.

More importantly, we hypothesised that lower values for the average loan size and a

higher percentage of female borrowers would be associated with lending to poorer people and

a greater depth of outreach. Our estimates confirm this to be the case for SS MFIs. However,

one should note that with only one observation per MFI for the percentage of female

borrowers, we again cannot use panel estimators; instead, we use simple OLS (see Model

(12), Table 2.9). The results reveal that only plus providers of social services focus more on

the poor; the SS coefficient is significantly negative in Model (11), and positive in Model

(12). It should be noted, though, that the latter result may be biased since we could not

control for unobserved heterogeneity by using a Hausman-Taylor estimator. Therefore, it

may be the case that SS providers focus more on women to begin with, and that the positive

effect on the percentage of women served is not due to the SS service in itself.

37

Table 2.9: Effects of microfinance plus on social performance (Outreach)

(10) (11) (12) VARIABLES Number of

clientsa Average loan

sizea Percentage of

womenc HDIjt -1,562.70005 -103.59314*** -0.68149** (0.724) (0.000) (0.018) GDPjt -8.46e-09 4.61e-11 ** 2.11e-13* (0.383) (0.019) (0.083) Inflationjt 1,589.24276 3.95692 -1.41447** (0.487) (0.431) (0.028) Assetsijt 0.00090*** 1.06e-07 -5.44e-09 (0.000) (0.529) (0.407) Ageijt -18.34408 1.07835** 0.00318 (0.933) (0.015) (0.561) Nonbankijt 10,675.50781 -4.47717 0.03056 (0.400) (0.850) (0.830) Ngoijt 18,947.05717 0.95914 0.06325 (0.183) (0.969) (0.625) Number of credit officersijt 184.78596*** -0.02978 0.00009 (0.000) (0.150) (0.878) International networkij 3,829.43406 -3.73288 0.07891 (0.477) (0.687) (0.143) Religious organizationij -6,283.03502 7.95235 0.02987 (0.294) (0.483) (0.631) Bankij 6,545.72124 -27.07506 (0.674) (0.364) Coopij 17,016.30420 -11.08564 0.08277 (0.244) (0.682) (0.582) BDSij -75119.70069 106.23574 -0.00119 (0.173) (0.358) (0.988) SSij 4,017.46251 -169.35790* 0.20291*** (0.938) (0.107) (0.006) Group lendingij -825.66393 -28.60047 0.19432*** (0.954) (0.235) (0.000) Constant -10765.58695 79.40317*** 0.98239*** (0.396) (0.003) (0.000) Observations 727 664 73 Number of identifiers 213 214 P-value Breusch-Pagan test 0.0000 0.0105 P-value Hausman Test (FE vs RE)

0.0002 0.0000

P-value Sargan-Hansen 0.6005 0.3135 Note: p-values are in parentheses. Extreme outliers (observations with age < 0) are ignored. *** p < .01. ** p < .05. * p < .1. aHausman-Taylor model. bRandom effects model. cOrdinary least squares.

38

2.5 Conclusions

We investigate whether MFIs that specialise in financial services perform differently

financially or socially than MFIs that also provide BDS and/or social services. Using a large

global data set, we find that MFIs that provide social services in addition to financial services

perform worse financially but better in terms of reaching out to the poor. In terms of

efficiency and portfolio quality, we do not find significant differences in the performance of

specialist MFIs and plus service providers in general. These results contrast with the

conventional wisdom that implies nonfinancial services hinder sustainability. Providing plus

services, especially BDS services does not harm financial self-sustainability, efficiency or

portfolio quality. Although social service provision imposes costs, it offers a clear gain in

terms of better outreach to the poor, and it does not harm MFI efficiency.

While we use a Hausman-Taylor estimation method to address potential endogeneity

problems, it is well-known that the Hausman-Taylor method is quite sensitive to the choice of

exogenous and endogenous time variant and time invariant variables. Without the availability

of reliable “external” instruments there is not much that can be done about this. Therefore,

control for endogeneity problems, the randomised control trials (RCT) approach will be used

in the next chapters.

This study represents a first attempt to understand the synergy effects of different

types of microfinance services on financial and social performance. The importance of

including nonfinancial services highlights the need for continued research efforts. Of

particular interest would be an investigation of how “smart” subsidies might account for the

additional costs of providing plus services, as well as how coordinated nonfinancial services

provided by non-MFIs, in cooperation with MFIs, might influence MFIs’ performance.

Rigorous studies also should determine if different plus services actually enhance customer

impacts.

39

Appendices

Appendix 2.1: Hausman-Taylor estimator The Hausman-Taylor (1981) estimator distinguishes regressors that are uncorrelated with

fixed effects and those potentially correlated with the fixed effect. It also distinguishes time-

varying from time-invariant regressors. The model is specified as

+ (2)

where the dependent variable yijt is a measure of financial and social performance of

the ith MFI located in country j at time t; β0 is a constant term; X denotes time-varying

regressors and W denotes time-invariant regressors; is MFI-specific unobserved effects;

and εijt is idiosyncratic errors. Regressors with subscripts 1 are assumed to conditionally mean

independent with , and regressors with subscripts 2 are correlated with . All regressors are

assumed to be uncorrelated with εijt

Hausman and Taylor (1981) suggest estimating Equation (2) using the following

instrumental variables: , . That is, the exogenous variables serve

as their own instruments; is instrumented by its deviation from individual means, which

is similar to the fixed effects model; and is instrumented by . When the number of

time-varying exogenous regressors is equal or greater than the number of time-invariant

endogenous regressors , the parameter i can be identified. The strong advantage of

the Hausman-Taylor approach is that we do not need to use external instruments, because the

instruments can be derived within the model. For a regular instrumental variables estimator to

be consistent, the instruments must be uncorrelated with the error term, as is similar for

Hausman-Taylor estimator. For it to be consistent, all regressors must be uncorrelated with

the error term, and a subset of regressors must be uncorrelated with individual-specific effects

(Cameron and Trivedi, 2005 ). We conduct Sargan tests of overidentifying restrictions to

check the validity of instrumental variables. The hypothesis being tested in this case is the

prediction that the instrumental variables are uncorrelated with some set of residuals. The

Hausman-Taylor estimator applies first to re-estimate the effect of schooling in wage

equation; it performs better than traditional instrumental variables methods that rely on

external exogenous variables (Hausman and Taylor, 1981). This approach also has been used

widely in economic research (Egger and Pfaffermayr, 2004, Serlenga and Shin, 2007,

McPhersona and Trumbull, 2008, Dixit and Pal, 2010)

ic

ic ic

40

Appendix 2.2: List of countries studied Albania Chad Honduras Morocco Sri Lanka Argentina Chile India Mozambique Tajikistan

Armenia Colombia Indonesia Nepal Tanzania, U. Rep. Of

Azerbaijan Croatia Jordan Nicaragua Timor-Leste

Bangladesh Dominican Republic Kazakhstan Nigeria Togo

Benin Ecuador Kenya Pakistan Trinidad and Tobago

Bolivia Egypt Kyrgyzstan Paraguay Tunisia Bosnia and Herzegovina El Salvador Madagascar Peru Uganda Brazil Ethiopia Mali Philippines Vietnam Bulgaria Georgia Mexico Romania

Burkina Faso Guatemala Moldova, Rep. Of

Russian Federation

Cambodia Guinea Mongolia Senegal Cameroon Haiti Montenegro South Africa

41

Chapter 3

The Short-Term Impact of Gender and Business

Training on Business Outcomes among Female

Microfinance Clients in Vietnam

3.1 Introduction

Microfinance has expanded rapidly since it began in the late 1970s. For example,

CGAP, a branch of World Bank, notes, “There is mounting evidence to show that the

availability of financial services for poor households - microfinance - can help

achieve the Millennium Development Goals.” However, many researchers caution

that microfinance alone is not enough to increase economic opportunities for the poor

(Banerjee et al., 2010, Karlan and Zinman, 2010). Previous experiments in Sri Lanka

(De Mel et al., 2008, De Mel et al., 2009 ) and Ghana (Fafchamps et al., 2011), for

example, suggest that physical capital alone cannot help micro-entrepreneurs raise

income above a subsistence level, especially for women-owned enterprises. Many

researchers argue that managerial and business skills are crucial to increase

productivity and growth of micro and small businesses (Bloom et al., 2010, Bruhn et

al., 2010 ). Consequently, business training programs have begun to focus on

improving business outcomes. As Chapter 2 highlights, providing business training

alongside financial services does not harm the financial and social performance of

microfinance plus providers. However, using a global data set of microfinance

institutions (MFIs) does not allow us to examine how business training programs

influence microfinance clients’ outcomes. Little rigorous evidence is available

regarding the impact of business training on business outcomes. Several recent

evaluations of business training study the impact on beneficiaries by focusing on a

specific institution case. McKenzie and Woodruff (2014) provide an overview of

these evaluations. They point out that many evaluations suffer from low statistical

power due to small sample sizes, in combination with a high variability of the

42

outcome variables analyzed (e.g., profits).

To focus on recipients’ outcomes, in this chapter we evaluate the impact of

gender and business (G&B) training on business outcomes among female clients of an

MFI, namely, the TYM fund, which is the largest MFI in the North Vietnam. To

address endogeneity problems, as discussed in Chapter 2, we employ a randomized

control trial (RCT) by assigning a randomly preselected sample of microfinance

clients to treatment and control groups. The current study is among the few that uses

an RCT with a large sample size to evaluate the impact of business training.

The contribution of this project is twofold. First, in contrast with other recent

RCT evaluations of business training, we combine modules focusing on gender issues

and business knowledge in one gender and business training. We use the training

materials developed and adapted from the GET Ahead for Women in Enterprise

Training Package and Resource Kit of International Labor Organization (ILO).4 This

training material differs from conventional business training materials in that it

highlights business skills from a gender perspective. Second, we pioneer to

investigate the relevance of inviting men to join business training with their spouses.

Practitioners and researchers recommend that to improve the status of women and

promote gender equality, more attention should be paid to increasing the involvement

of men and boys when addressing gender issues (Council of European Union, 2006,

World Bank, 2011). Excluding husbands may trigger frustration and invite intra-

household conflicts (Allen et al., 2010), possibly eroding the positive effects of the

training.

We conducted a baseline survey before the intervention and two post-

treatment follow-up surveys to trace the trajectories of the impacts by capturing both

short- and long-term effects of the training. Due to the limited time frame of the PhD

project, we have not analyzed the endline data yet. We present only the short-term

impacts of the training, which are based on the baseline and midline results, here. We

discuss the results of the impact of G&B training on business outcomes in this

chapter. Chapter 4 reports the results of the effects of the training on gender

outcomes. In chapter 5, we analyze the impact of the training on intertemporal

consumption.

4 Paruzzolo and Mckenzie (2013) are currently conducting an ongoing evaluation of a similar ILO training package in Uganda.

43

Our main findings in this chapter, based on the intention-to-treat (ITT) and

instrumental variables (IV) estimates, show that G&B training leads to increased

business knowledge, better business practice, and increased business profits and profit

margins. We find no evidence that G&B training improves household farming

outcomes, which may not be surprising because the training did not focus on farming

practices.

Although we incentivized husbands to attend the training by offering them

some financial compensation if they followed the training, the participation rates of

husbands remained rather low. Using a survey of a subsample of husbands, we find

few determinants which have significant effects on whether husbands attended the

trainings or not. For example, age is positively related to husbands’ decisions to join

the training. In addition, husbands who own farming activities were more likely to

attend the training while those involved in salaried employment were less likely to

attend the training. These results suggest that many husbands did not attend the

training due to time constraints. However, the majority of invited husbands who

joined the training were positive about the contents of the training. The probably low

power due to the low attendance rates of husbands and the short time period under

consideration may be why we do not find statistically significant short-term effects of

giving husbands the opportunity to attend the training.

The remainder of this chapter is structured as follows. Section 2 discusses the

relevant literature. Section 3 explains the context and the intervention in detail.

Section 4 discusses our theory of change and addresses potential risks of the

intervention. Section 5 presents the experimental design. Section 6 describes the data

and reports the attrition analysis. Section 7 presents the training quality assessment

results. Section 8 focuses on the husbands’ participation analysis. The latter two

sections (7 and 8) test the potential risks of the intervention. Section 9 describes the

estimation methods. Section 10 reports the estimated results, and Section 11

concludes with a discussion of the findings.

3.2 Relevant Literature

The literature on the impact of business training on microfinance clients has produced

ambiguous results. A few recent RCTs provide evidence that business training helps

44

improve business knowledge and business practices, and sometimes business

outcomes. Table 3.1 summarizes the results of the impact of business training in the

current literature. For example, Bjorvatn and Tungodden (2010) show that business

training has a positive effect on business knowledge for small entrepreneurs in

Tanzania, and Drexler et al. (2014) indicate positive effects on management practices

of small businesses in the Dominican Republic. However, Karlan and Valdivia (2011)

do not find strong general effects of business training, but their study suggests that

business training may have small positive effects on female microfinance borrowers.

Berge et al. (2011), Giné and Mansuri (2011) and Bruhn and Zia (2013) produce

similar results. For a recent survey of the various impacts of business training, see Xu

and Zia (2012) and McKenzie and Woodruff (2014). The general picture signaled by

the existing evaluations is that business training has positive effects on business

knowledge but only minor effects on business outcomes. Yet it must be mentioned

that McKenzie and Woodruff (2014) note that we cannot learn much from the existing

studies, because most are underpowered.

Several recent rigorous impact evaluations of business training focus on both

gender effects and effects on business performance. For example, offering business

training improved business knowledge for both male and female microfinance clients,

but only male entrepreneurs experienced better business practices and an increase in

business sales and profits (Berge et al., 2011, Giné and Mansuri, 2011). Bruhn and

Zia (2013) in a study of microcredit clients in Bosnia and Herzegovina, provide

evidence for larger effects of business training on women-run businesses than on

men-run businesses. Yet, this study is an exception: almost all other studies suggest

larger impacts on men-run businesses.

When we examined the business training content in the current literature, we

found that most of the existing training programs center on business literacy or

combined business and financial literacy and do not address whether the training were

provided for men, women, or both (see Table 3.1). A core set of topics focuses on

business records, separation of household and business finances, marketing, pricing

and costing, inventory management, customer service, and financial planning. Only

few training programs were aimed to change entrepreneurial attitudes or aspirations

(Field et al., 2010, Berge et al., 2011, Klinger and Schündeln, 2011, Valdivia, 2013)

and those that did address these issues devoted little time to them (McKenzie and

45

Woodruff, 2014). These findings suggest that it may be worthwhile to combine

gender equality and enterprise elements to promote development of business

outcomes among female entrepreneurs.

Furthermore, these existing studies advocate that targeting women is not

enough. It is crucial to include men rather than ignore them, and gender equality must

be added to intervention programs (Johnson, 2005). Eliminating men may generate

frustration and increase intra-household conflicts (Armendariz and Roome, 2008,

Allen et al., 2010), possibly counteracting the impact of the training. In addition, we

expect that the presence of men, who bring their own expertise and experience to the

event, changes the nature and depth of the discussions during the training.

46

Tab

le 3

.1: R

evie

w o

f the

impa

ct o

f bus

ines

s tra

inin

g

No

Stud

y C

ount

ry

Met

hod

Sam

ple

Inte

rven

tion

Mai

n re

sults

1.

B

erge

et

al. (

2011

) Ta

nzan

ia

RC

T 64

4 m

ale

and

fem

ale

mic

rofin

ance

cl

ient

s

- 21

sess

ions

, eac

h la

stin

g 45

m

inut

es o

n en

trepr

eneu

rshi

p,

cust

omer

serv

ices

, m

anag

emen

t and

mar

ketin

g -

Bus

ines

s gra

nt in

cas

h

- N

o ef

fect

s on

bus

ines

s pe

rfor

man

ce o

f tra

inin

g fo

r fe

mal

e en

trepr

eneu

rs;

how

ever

, mal

e en

trepr

eneu

rs e

xper

ienc

ed

an

incr

ease

in

sa

les

and

prof

its

of

appr

oxim

atel

y 20

–30

perc

ent.

- N

o ef

fect

s of

the

busi

ness

gra

nt n

oted

for

ei

ther

men

or w

omen

. -

Trai

ning

ha

s im

prov

ed

the

busi

ness

kn

owle

dge

of

both

fe

mal

e an

d m

ale

entre

pren

eurs

and

cha

nged

thei

r min

d-se

t. -

Evid

ence

from

lab

expe

rimen

ts sh

ows t

hat

wom

en a

re l

ess

will

ing

to c

ompe

te t

han

men

. 2.

B

erge

et a

l. (2

012)

Ta

nzan

ia

RC

T 56

5 m

icro

finan

ce

clie

nts i

n ex

tern

al tr

aini

ng

and

114

mic

rofin

ance

cl

ient

s in

inte

rnal

tra

inin

g.

21 se

ssio

ns, e

ach

last

ing

45

min

utes

on

reco

rd k

eepi

ng,

mar

ketin

g pr

actic

es, c

usto

mer

ca

re, a

nd e

mpl

oyee

man

agem

ent.

Com

paris

on b

etw

een:

-

One

gro

up tr

aine

d by

pr

ofes

sion

al tr

aine

rs (e

xter

nal

train

ing)

-

One

gro

up tr

aine

d by

inte

rnal

cr

edit

offic

ers

- B

oth

the

atte

ndan

ce

and

subj

ectiv

e ev

alua

tion

of th

e co

urse

wer

e si

gnifi

cant

ly

low

er in

the

inte

rnal

ly tr

aine

d gr

oup

than

in

the

exte

rnal

ly tr

aine

d gr

oup.

-

Entre

pren

eurs

in

th

e ex

tern

ally

tra

ined

gr

oup

had

mor

e bu

sine

ss k

now

ledg

e an

d w

ere

mor

e sa

tisfie

d w

ith

thei

r ov

eral

l si

tuat

ion

than

en

trepr

eneu

rs

in

the

inte

rnal

ly tr

aine

d gr

oup.

3.

Bjo

rvat

n an

d Tu

ngod

den

(201

0)

Tanz

ania

R

CT

300

smal

l en

trepr

eneu

rs

(mic

rofin

ance

cl

ient

s)

21 se

ssio

ns o

f bus

ines

s tra

inin

g,

each

last

ing

45 m

inut

es

- Th

e re

sults

sh

ow

posi

tive

aver

age

treat

men

t eff

ects

on

busi

ness

kno

wle

dge.

-

Trai

ning

ha

d a

stro

nger

eff

ect

on

the

entre

pren

eurs

with

les

s fo

rmal

edu

catio

n bu

t with

stro

ng c

ogni

tive

skill

s.

47

Tab

le 3

.1: R

evie

w o

f the

impa

ct o

f bus

ines

s tra

inin

g (c

ont.)

No

Stud

y C

ount

ry

Met

hod

Sam

ple

Inte

rven

tion

Mai

n re

sults

4.

B

ruhn

and

Zi

a (2

013)

Bos

nia

and

Her

zego

vina

R

CT

445

mic

rocr

edit

clie

nts

Six

com

preh

ensi

ve m

odul

es a

nd la

sted

9

hour

s in

tota

l on

gene

ral c

once

pts o

f en

trepr

eneu

rshi

p, b

usin

ess p

lann

ing,

m

arke

ting

and

sale

s stra

tegy

, fin

anci

al

man

agem

ent,

busi

ness

gro

wth

, and

fin

anci

al li

tera

cy.

- Tr

aini

ng

led

to

sign

ifica

nt

impr

ovem

ents

in

ba

sic

finan

cial

kn

owle

dge

for t

hose

with

low

leve

ls

of fi

nanc

ial l

itera

cy a

t bas

elin

e.

- Tr

aini

ng

sign

ifica

ntly

im

prov

ed

busi

ness

pra

ctic

es,

inve

stm

ents

and

lo

an te

rms f

or su

rviv

ing

busi

ness

es.

- N

o si

gnifi

cant

tre

atm

ent

effe

cts

on

busi

ness

star

t-up

and

surv

ival

. -

The

prog

ram

sho

wed

lar

ge p

ositi

ve

effe

cts

on t

he p

rofit

s of

fem

ale-

run

firm

s bu

t no

t on

tho

se o

f m

ale-

run

firm

s. 5.

C

alde

ron

et

al. (

2013

) M

exic

o R

CT

928

fem

ale

mic

ro

entre

pren

eurs

Six

wee

ks tr

aini

ng w

ith tw

o fo

ur-h

our

mee

tings

per

wee

k on

und

erst

andi

ng

cost

s, se

t pric

es, b

asic

lega

l rig

hts a

nd

oblig

atio

ns o

f sm

all b

usin

ess o

wne

rs,

busi

ness

org

aniz

atio

n, c

hoic

e of

pr

oduc

ts to

pro

duce

or s

ell,

mar

ketin

g,

and

to b

e an

eff

ectiv

e sa

lesp

erso

n.

- Se

ven

mon

ths

afte

r th

e in

terv

entio

n,

train

ing

has

posi

tive

effe

ct

on

prof

its,

reve

nues

, nu

mbe

r of

clie

nts

and

the

use

of f

orm

al a

ccou

ntin

g pr

actic

es

6.

De

Mel

et

al. (

2014

) Sr

i Lan

ka

RC

T 1,

256

curr

ent

and

pote

ntia

l fe

mal

e bu

sine

ss

owne

rs

Nin

e-da

y IL

O tr

aini

ng o

n ge

nera

ting,

st

artin

g, a

nd im

prov

ing

busi

ness

C

ompa

rison

bet

wee

n:

- Tr

aini

ng o

nly

- Tr

aini

ng p

lus a

cas

h gr

ant

cond

ition

al o

n fin

ishi

ng th

e tra

inin

g

- Tr

aini

ng

prod

uced

si

gnifi

cant

ch

ange

s in

bus

ines

s pr

actic

es b

ut

had

no i

mpa

ct o

n bu

sine

ss p

rofit

s, sa

les,

or c

apita

l sto

ck.

- Tr

aini

ng

and

gran

t co

mbi

natio

n in

crea

sed

busi

ness

pr

ofita

bilit

y in

th

e fir

st e

ight

mon

ths,

but t

his

effe

ct

vani

shed

in th

e se

cond

yea

r.

48

Tab

le 3

.1: R

evie

w o

f the

impa

ct o

f bus

ines

s tra

inin

g (c

ont.)

No

Stud

y C

ount

ry

Met

hod

Sam

ple

Inte

rven

tion

Mai

n re

sults

7.

G

iné

and

Man

suri

(201

1)

Paki

stan

R

CT

4,16

2 m

ale

and

fem

ale

mic

rofin

ance

cl

ient

s

- Ei

ght f

ull d

ays o

f bus

ines

s tra

inin

g -

Opp

ortu

nity

to p

artic

ipat

e in

a

lotte

ry to

acc

ess b

usin

ess l

oans

- O

ffer

ing

busi

ness

tra

inin

g le

ads

to

incr

ease

d bu

sine

ss k

now

ledg

e, b

ette

r bu

sine

ss

prac

tices

, an

d im

prov

emen

ts i

n se

vera

l ho

useh

old

and

mem

ber

outc

omes

fo

r m

ale

clie

nts.

- A

cces

s to

a l

arge

r lo

an a

mou

nt h

as

little

eff

ect o

n bu

sine

ss p

erfo

rman

ce.

- W

omen

sp

end

mor

e tim

e in

ho

useh

old

chor

es th

an m

en.

8.

K

arla

n an

d V

aldi

via

(201

1)

Peru

R

CT

4,59

1 m

icro

finan

ce

clie

nts

Trai

ning

was

inco

rpor

ated

into

wee

kly

or b

iwee

kly

mee

tings

and

focu

sed

on

gene

ral b

usin

ess s

kills

and

stra

tegy

tra

inin

g, n

ot c

lient

-spe

cific

pro

blem

so

lvin

g.

- Tr

aini

ng

impr

oved

bu

sine

ss

know

ledg

e an

d bu

sine

ss

prac

tices

an

d in

crea

sed

clie

nt re

tent

ion

rate

s. -

No

evid

ence

of

chan

ges

in b

usin

ess

reve

nue,

pro

fits,

or e

mpl

oym

ent.

9.

V

aldi

via

(201

3)

Peru

R

CT

1,97

9 fe

mal

e m

icro

en

trepr

eneu

rs

Gen

eral

bus

ines

s tra

inin

g de

liver

ed

over

a th

ree-

mon

th p

erio

d w

ith th

ree

thre

e-ho

ur se

ssio

ns a

wee

k on

per

sona

l de

velo

pmen

t, bu

sine

ss d

evel

opm

ent

and

man

agem

ent,

and

prod

uctiv

ity

impr

ovem

ents

. C

ompa

rison

bet

wee

n -

Trea

tmen

t gro

ups r

ecei

ving

onl

y bu

sine

ss tr

aini

ng

- Tr

eatm

ent g

roup

s rec

eivi

ng

busi

ness

trai

ning

and

tech

nica

l as

sist

ance

- Tr

aini

ng

sign

ifica

ntly

im

prov

ed

busi

ness

pra

ctic

es a

nd s

ales

but

onl

y fo

r cl

ient

s w

ho

rece

ived

bo

th

gene

ral

busi

ness

tra

inin

g an

d te

chni

cal a

ssis

tanc

e.

49

Tab

le 3

.1: R

evie

w o

f the

impa

ct o

f bus

ines

s tra

inin

g (c

ont.)

No

Stud

y C

ount

ry

Met

hod

Sam

ple

Inte

rven

tion

Mai

n re

sults

10

. M

ano

et a

l. (2

012)

Gha

na

RC

T 16

7 m

etal

wor

k en

trepr

eneu

rs

Thre

e-w

eek

elem

enta

ry m

anag

emen

t tra

inin

g pr

ogra

m o

n en

trepr

eneu

rshi

p,

busi

ness

pla

nnin

g, p

rodu

ctio

n an

d qu

ality

man

agem

ent,

reco

rd k

eepi

ng,

and

cost

ing.

Bas

ic-le

vel

man

agem

ent

train

ing

impr

oves

bu

sine

ss

prac

tices

an

d pe

rfor

man

ce.

11.

Klin

ger a

nd

Schü

ndel

n (2

011)

El S

alva

dor,

Gua

tem

ala,

an

d N

icar

agua

Qua

si-

expe

rimen

tal

desi

gn

(a

regr

essi

on

disc

ontin

uity

de

sign

)

655

acce

pted

an

d re

ject

ed

appl

ican

ts to

en

trepr

eneu

rial

trai

ning

w

orks

hops

Thre

e ro

unds

: -

The

acce

pted

app

lican

ts fo

llow

ed

entre

pren

euria

l tra

inin

g on

te

chni

cal b

usin

ess s

kills

. The

n pa

rtici

pant

s sub

mitt

ed b

usin

ess

plan

s. -

Parti

cipa

nts f

urth

er re

fined

thei

r bu

sine

ss p

lans

and

rece

ived

mor

e on

e-on

-one

ass

ista

nce

with

men

tors

an

d co

nsul

tant

s. -

Top

refin

ed b

usin

ess p

lans

rece

ived

a

mon

etar

y re

war

d be

twee

n U

S$6,

000

and

US$

15,0

00.

- Tr

aini

ng

has

a st

atis

tical

ly

sign

ifica

nt im

pact

on

the

crea

tion

of

new

bus

ines

s or

exp

ansi

on o

f an

ex

istin

g bu

sine

ss.

- Fi

rst

roun

d of

se

min

ar-b

ased

tra

inin

g ha

s a

larg

er i

mpa

ct o

n th

e ex

pans

ion

of b

usin

esse

s th

an o

n th

e la

unch

ing

of n

ew b

usin

esse

s -

Seco

nd

roun

d of

su

ppor

t of

co

nsul

tanc

y ha

s m

ore

sign

ifica

nt

effe

cts

on

the

crea

tion

of

new

bu

sine

ss

than

th

e ex

pans

ion

of

busi

ness

es.

- Th

e la

st

roun

d of

pr

ize

mon

ey

rece

ipt

has

mor

e si

gnifi

cant

im

pact

on

the

cre

atio

n of

new

bus

ines

ses

than

on

the

expa

nsio

n of

exi

stin

g bu

sine

sses

. -

Wom

en

are

mor

e fin

anci

ally

co

nstra

ined

than

men

from

obt

aini

ng

fund

ing

for

busi

ness

st

art-u

p or

ex

pans

ion.

50

3.3 Context and Intervention

3.3.1. Context We collaborate with TYM fund to evaluate the impact of gender and business training and the

conditional impact of inviting spouses to accompany poor microfinance female clients. The

TYM fund is the largest microfinance organization, operating since 1992, in northern

Vietnam. Its main mission is to improve the quality of life and status of poor women and their

families by providing financial and nonfinancial services to female entrepreneurs. The fund

started as a microfinance project of the Vietnam Woman Union in 1989. TYM is partner of

RIMANSI, a network of micro-insurance mutual benefit associations that provides quality

microfinance products to poor people in Asia. In addition to this partnership, RIMANSI has

12 partner organizations in the Philippines and 2 in Cambodia.

The TYM fund operates mainly in areas with high ratios of poor households. As of

September 2011, the organization ran operations in 10 areas in northern Vietnam through 43

branches (for their locations, see Appendix 3.1). It has also established 1,450 credit centers,

each serving 30–40 female clients, for a total of approximately 48,000 female clients. These

clients receive financial and nonfinancial services; in return, they must become members of a

credit center. All the services are provided at weekly or monthly center meetings, in which

loan officers assess loan application forms and collect repayments and savings. The center

meetings also allow TYM members to exchange experiences and information about

production and business; in addition, TYM staff and external experts disseminate knowledge

on family, gender, and other issues. Finally, the centers host social activities.

The TYM fund offers three main financial products: loans, savings, and mutual

assistance funds. First, loans are designated to be disbursed without collateral; instead, they

follow a cycle with increasing loan amounts (minimum loan amount is VND 1 million). The

cycles range from 10 to 100 weeks. Principal and interest adjust weekly. Most of these loans

are used for income-generating activities and housing repairs. In addition, the TYM offers

multipurpose (emergency) loans of smaller amounts and with shorter terms, which can be

used for consumption and other purposes. Second, the TYM fund requires all clients to

deposit compulsory savings of VND3,000 ($0.14) every week. Clients earn interest from

these compulsory savings and can withdraw the funds when they reach a certain minimum

amount. The organization also encourages clients to deposit additional voluntary savings,

starting with a small amount of VND5,000 ($0.23) every week. In the near future, it aims to

51

introduce more comprehensive voluntary savings products to not only TYM clients but also

poor people in the general public. Third, in 1996, the TYM fund introduced its mutual

assistance fund package to clients, in response to demand and in an attempt to strengthen the

mutual links and assistance among clients. This package includes two products: life mutuality

and loan mutuality. This offer has been appealing to clients and has attracted significant

participation.

3.3.2. Intervention We investigate training sessions held in Vinh Phuc and Ha Noi. These areas are relatively

close to the TYM headquarters in Ha Noi, which kept our survey costs low. Moreover, by

focusing on only two provinces, we minimized program placement biases. Because the Vinh

Phuc and Ha Noi microfinance centers are a representative sample of all TYM centers, the

external validity of the project is high. In terms of economic and geographical conditions,

Vinh Phuc and Ha Noi are comparable to the main provinces in Vietnam. These regions

contain a mixture of plain, midland, and mountainous regions. As a result of the

industrialization strategy of the Vietnamese government, the importance of industrial and

services sectors has increased substantially in Vinh Phuc and Ha Noi. A similar trend marks

most other provinces in Vietnam. In addition, even though they have experienced strong

economic growth, Vinh Phuc and Ha Noi, similar to other provinces in Vietnam, face many

social problems, including high poverty rates for women.

The training provided by the TYM fund is based on the Gender and Entrepreneurship

Together (GET) Ahead for Women in Enterprise Training Package and Resource Kit,

designed by the ILO and modified to fit the Vietnamese context. The GET Ahead training

package has been used since 2004 in more than dozen countries, centering on promoting

gender equality, basic enterprise management, developing women’s confidence, and taking

opportunities in the business environment. The program is split into nine modules: (1) basics

on gender and entrepreneurship, promotion of equality between men and women, and the life

cycle of people and enterprises; (2) the businesswoman and her self-confidence; (3) the

businesswoman’s environment and self-development and business mapping; (4) business

projects, including business ideas, opportunities, and challenges; (5) marketing and how to

sell with success; (6) calculating interest rates; (7) managing cash; (8) recording accounts

receivable and accounts payable; and (9) calculating costs of production and cost of goods

sold. Before the training started, all loan officers in treatment groups attended “training of

trainers” courses taught by the TYM’s headquarters staff.

52

The training took place during nine monthly center meetings. Each module took 45–

60 minutes. Because the trainees typically lacked strong educational backgrounds, TYM’s

trainers used support tools such as role play, color cards, and pictures to help trainees

understand and remember the content. In addition to the monthly training module, the trainers

organized discussions and consultations on client-specific problem solving for the trainees

every week, for about 15–30 minutes, concurrent with TYM clients coming in to pay their

debts. Some of staff members at TYM’s headquarters were trained by ILO about the GET

training package. The training was free of charge and voluntary; clients could leave after they

made their loan payment and before the training began.

3.4 Theory of Change

The main goals of G&B training are to improve business outcomes for poor microfinance

clients. The purpose of inviting spouses to participate in this training is to resolve issues that

can arise in women-only training groups. More specifically, our goal in inviting spouses was

to improve information dissemination of the training, which in turn should help improve

business outcomes. Figure 3.1 presents a summary of the theory of change underlying our

experiment.

In terms of the general impact of G&B training, we expect that the training will

improve business knowledge for female clients. This improved business knowledge should

change their business practices. In other words, improvements of business knowledge will

help women implement some of the ideas and business knowledge they have learned to their

business practices. The subsequent changed behaviors should result in improved business

outcomes.

Moreover, we expect that knowledge dissemination will be improved when spouses

are present. The presence of men during the training can change the nature and depth of the

discussions during the training sessions because men bring their own expertise and experience

to the event. In addition, if spouses attend the training sessions, women are more likely to

discuss the contents of the training at home, which we expect to improve knowledge

dissemination.

Appendix 3.2 summarizes our main outcomes and expected signs of intended

outcomes. It should be noted that the training content primarily focuses on the gender issues

and business literacy. Although in theory, the training may have some effects on farming

outcomes, we expect that the training has only effects on business outcomes. Therefore, we

53

distinguish business outcomes and farming outcomes to focus our test on our theory of

change. We expect an increase in business outcomes and not in farming outcomes. It is even

possible that the training could lead to decreased farming outcomes if it encourages a shift to

more business activities.

Potential risks

For several reasons, these predictions may be overly optimistic. We recognize the

following potential risks of the intervention. First, the training may be given by unqualified

trainers. In this case, even if the training material is good, the impact of the training may be

mitigated. To address this concern, we added a separate block of questions related to the

quality of the training (for the results, see Section 3.7).

Second, the training might not be relevant. For example, participants may believe that

the training does not apply to their business practices or is not specific enough. Moreover, the

training also could be perceived as too theoretical. To determine whether this concern was

valid, we added separate question blocks to the questionnaire to obtain more information

about participants’ perceptions of the training material (for the results, see Section 3.7).

Third, we recognize that in Vietnamese culture, most men are the primary

breadwinners and have more experience doing business; thus, they may generate elite group

discussions among one another. Therefore, if trainers do not organize the training discussions

appropriately, women may be ignored in these elite discussions. Consequently, women with

limited knowledge level may not gain anything from the training. To address this potential

shortcoming, we added separate questions to the questionnaire asking women whether they

appreciated husbands’ attendance (for the results, see Section 3.7).

Fourth, husbands might not be willing to join the training because of opportunity

costs. For this reason, we incentivized invited husbands to attend the training by providing

financial compensation. However, we recognize that the financial compensation might not be

high enough or that husbands attended only to obtain money without actually being interested

in the training. For a more detailed discussion of this potential risk, see Section 3.8.

54

Fi

gure

3.1

: The

ory

of c

hang

e of

the

impa

ct o

f the

gen

der

and

busin

ess t

rain

ing

on b

usin

ess o

utco

mes

INPU

TS

AC

TIV

ITIE

S O

UT

PUT

S O

UT

CO

ME

S L

ON

GE

R-T

ER

M

IMPA

CT

S

-Gen

der

and

busi

ness

tra

inin

g m

ater

ials

-Tra

ined

st

aff

-Pro

vide

train

ing

and

faci

litat

e

disc

ussi

on

on

gend

er

and

busi

ness

issu

es.

-Inv

ite

husb

ands

to c

ome

to

the

train

ing

Trai

ned

fem

ale

clie

nts

Trai

ned

clie

nts’

hu

sban

ds

Bus

ines

s kn

owle

dge

- Red

uced

po

verty

- Wom

en

bene

fit fr

om

econ

omic

gr

owth

in

rura

l are

as

Bus

ines

s pr

actic

es

Bus

ines

s and

fa

rmin

g ou

tcom

es

Bus

ines

s and

fa

rmin

g en

try a

nd

surv

ival

- Bus

ines

s kn

owle

dge

inde

x 1

- Bus

ines

s kn

owle

dge

inde

x 2

Out

com

es v

aria

bles

- Gen

eral

bu

sine

ss

prac

tices

- I

nnov

atio

n - M

arke

ting

skill

s - R

ecor

d an

d pl

anni

ng

- Mon

thly

bus

ines

s/fa

rmin

g pr

ofits

- M

onth

ly b

usin

ess/

farm

ing

sale

s - M

onth

ly b

usin

ess/

farm

ing

prof

it m

argi

n - M

onth

ly fa

rmin

g to

tal

prod

uctio

n - B

usin

ess/

farm

ing

star

tup

- Bus

ines

s/fa

rmin

g su

rviv

al

55

3.5 Experimental Design

We evaluate the impact of providing G&B training for female microfinance clients by using

an RCT. We randomly assigned the preexisting credit centers, each with an average of 30

female clients, to the treatment and control conditions. We randomized G&B training at the

credit center level, which reduces the threat of spillover effects, and used a cluster sampling

approach. Because we randomize at the center level, we need a reasonably large sample to

ensure enough power. Moreover, we acknowledge that not all women and men invited to

attend the training would actually participate.

In the three selected branches in Vinh Phuc and the one selected branch in Ha Noi,

there are a total of 187 credit centers. We stratified the randomization by lending branch,

taking the same proportion of treatment and control groups in each branch. Initially, we

planned to select 50 centers per treatment group and 87 centers for the control groups.

However, because of concerns about the expected participation rates among husbands and the

potential low power calculation, we decided to oversample the treatment groups to which

husbands were invited. In doing so, we expected to obtain enough power to analyze the

impact of intra-household relations and mixed-group training. Our ultimate approach resulted

in 70 credit centers in which male partners were invited to join the G&B training with female

clients (i.e., T1 contains 70 centers) and 31 credit centers for which only female clients were

invited to join the training (i.e., T2 consists of 31 credit centers). The control groups (C)

include 86 credit centers.

To select a sample for the baseline survey, we excluded female clients who are

employees. They had received permission from the TYM fund to not attend the monthly

compulsory center meetings that took place during working hours. Because these clients

lacked time to participate in the G&B training, they had not received any benefits from it. We

randomly selected 23 members per center for the interviews, and hence did not interview all

members per center. We followed this approach for “power” considerations. The sample size

at the highest level is the main limiting characteristic (Snijders, 2005). A few centers had

fewer than 23 clients; in these cases, we interviewed all borrowers.

Our list of interviewees in the baseline condition included 4,041 borrowers. The

baseline survey was conducted in October and November 2011 before we determined which

centers would receive G&B training. The TYM fund provided training to its clients from

February 2012 onward, until the end of October 2012. We monitored the attendance and the

content of the monthly training sessions and weekly discussions, by asking the loan officers to

56

write brief training diaries for each training and discussion session. Loan officers also kept

attendance lists.

During February 2013, we conducted six focus group discussions with six women per

group (two groups of the treatment T1, two groups of the treatment T2, and two groups of the

control groups C). The data from focus group discussions enters our qualitative analyses and

helps us to test our theory of change.

In addition to interviewing female clients, we conducted a small post treatment survey

of approximately 600 invited husbands. These data help us better understand the relevance of

inviting husbands.

The midline survey was conducted in the period March–May 2013, approximately six

months after the end of G&B training. We expected female clients to make changes to their

businesses as a result of the business training quite soon after the training was completed. ()

McKenzie and Woodruff (2014) suggest that firms typically start to apply some business

practices immediately after the training, but stop using them later. Thus, collecting data a long

time after the training may fail to provide some relevant short-term impacts of the

intervention. We experienced some dropouts after the baseline survey. Our midline sample

contains 3,513 women. To increase our sample size for T2 treatment groups, we decided to

interview all members per center (30 instead of 23) in this group during the midline

interviews. The training was given at center meetings, so these “additional” women had been

also treated.

Because we expected that the attendance of husbands would be especially important

for implementing changes in intra-household decision making, we encouraged husbands to

participate in the gender and gender equality training sessions by paying a compensation fee

of VND100,000 (approximately US$5). However, because our study does not focus on the

impact of training on business practices and outcomes for men, their attendance at the other

training sessions is less important. Therefore, we gradually reduced the compensation for

husbands for the other training sessions and did not pay any compensation to husbands from

the seventh training module onward.

For each survey round, the process of interviewing took place over 2–2.5 months with

a team of 23 experienced surveyors. We used double data entries to minimize mistakes. The

questionnaire included questions about members and their households. In addition to the usual

set of demographic variables such as age, education, and marital status, we collected

individual characteristics such as measures of business knowledge, business practices,

cognitive and noncognitive skills, time preferences, decision-making autonomy across various

57

household decisions, outstanding loans, physical and psychological household domestic

violence, health, social network, and social trust. Household characteristics included

information on wealth, expenditure, past and current savings, and insurance held by

household members. Business and farming activity characteristics included age, location, and

types of business activities; hired workers; and monthly sales, costs, and profits. The survey

also contained information on credit center cohesion, such as the number of center members

living nearly, borrowing and lending among members in a center, and helping among

members in a center. We also included one section on how participants evaluate the quality of

the G&B training. Figure 3.2 details the timeline of the entire project.

Figure 3.2: Timeline of the whole experiment of G&B training

Oct

2011

Nov

2011

Feb

2012

Oct

2012

Nov 2012 March

2013

April

2013

Oct

2013

Nov

2013

Baseline

survey

G&B training Experimental

games

Midline

survey

Endline

survey

3.6 Data and Attrition Analysis

3.6.1. Data In this section, we describe the data and balancing test performed between treatment and

control groups to test the reliability of the randomization. Table 3.2 reports descriptive

statistics and results of the balancing tests. In general, women in treatment groups T1 and T2

are comparable with the control groups. The average age among women at the baseline was

43 years. In addition, 94 percent of the women were of the Kinh (Vietnamese) ethnic group,

and approximately 81 percent are married. They had received on average 6.7 years of

education. Households contained on average 4.7 members, had farming landholdings of 1,400

square meters, and an average monthly income of 6,000,000VND (approximately US$292).

Our baseline survey measured business and financial knowledge with 14 questions, from

which we constructed an overall business and financial literacy index by counting correct

answers. The average score was 8.9 correct answers. At the baseline, 77 percent of female

58

clients indicated they would be interested in the training if they were invited. Approximately

78 percent of the clients in the sample managed at least one farming activity, and 30 percent

ran at least one business. In the questionnaire, we asked respondents to report the information

of three main farming activities and three main business activities. However, most households

had a maximum of two farming activities and were involved only in one business activity.

The most popular farming activity was rice or flower cultivation (approximately 67 percent).

Business activity was mainly concentrated on retail trade (approximately 17 percent); services

and manufacturing (approximately 6 percent and 8 percent, respectively); wholesale trade and

vendor trade also had small percentages.

Columns (7) to (11) in Table 3.2 present balancing tests between the treatment groups

T1 and T2 and the control groups. We achieved these results by performing ordinary least

squares (OLS) estimates using cluster standard errors at the center level. Particularly, we

regress each variable on the treatment dummies T1 and T2. The results show that, in general,

the sample is balanced. Only some of the variables seem to differ for the groups such as

manufacturing dummy or monthly business profits5. Therefore, we conclude that the

randomization worked satisfactorily.

5 The difference at baseline between T1 and C for monthly business profits may indeed suggest that the randomization did not lead to entirely similar groups. Yet, it should be noted that about 5 percent of the variables that we have tested may turn out to be significantly different simply due to chance. Therefore, the fact that we found that for this variable which has no balance at baseline does not imply that the randomization did not work correctly. Moreover, in order to control for remaining differences, we have presented impact results using a double difference model including controls.

59

Tab

le 3

.2: D

escr

iptiv

e st

atis

tics a

nd b

alan

cing

test

(1

) (2

) (3

) (4

) (5

) (6

) (7

) # (8

) # (9

) # (1

0) #

(11)

#

Obs

.T1

Mea

n T1

O

bs.T

2 M

ean

T2

Obs

. C

Mea

n C

T1

p-

valu

e T2

p-

valu

e O

bs

Dem

ogra

phic

cha

ract

eris

tics

A

ge

1504

43

.476

66

8 43

.879

18

46

44.0

21

-0.5

45

(0.3

88)

-0.1

42

(0.8

23)

4,01

8 H

ouse

hold

size

14

42

4.73

5 64

5 4.

71

1783

4.

772

-0.0

366

(0.6

77)

-0.0

617

(0.5

69)

3,87

0 M

arrie

d (1

= y

es)

1509

0.

81

673

0.83

1 18

59

0.82

1 -0

.011

6 (0

.521

) 0.

0092

0 (0

.651

) 4,

041

Ethn

ic (1

= K

inh-

Vie

tnam

ese)

15

09

0.93

8 67

3 0.

942

1859

0.

944

-0.0

0569

(0

.611

) -0

.002

01

(0.8

92)

4,04

1 A

vera

ge m

onth

ly h

ouse

hold

in

com

e (in

VN

D10

00s)

15

09

6,02

1.94

67

3 6,

425.

71

1185

5 5,

968.

09

53.8

5 (0

.805

) 45

7.6

(0.2

12)

4,03

7

Yea

rs o

f sch

oolin

g 15

07

6.71

5 67

3 6.

848

1850

6.

899

-0.1

84

(0.2

68)

-0.0

505

(0.8

49)

4,03

0 Tr

aini

ng in

tere

st

1509

0.

775

673

0.73

7 18

55

0.75

8 0.

0174

(0

.624

) -0

.021

0 (0

.618

) 4,

037

Tota

l far

min

g la

nd (s

quar

e m

eter

s)

1509

1,

472.

89

673

1,37

3.08

18

59

1,43

6.30

36

.59

(0.7

08)

-63.

22

(0.5

87)

4,04

1 N

umbe

r of l

oans

at T

YM

15

09

1.16

4 67

3 1.

156

1854

1.

196

-0.0

321

(0.4

27)

-0.0

398

(0.4

82)

4,03

6 B

usin

ess k

now

ledg

e in

dex

1 15

09

8.90

8 67

3 8.

856

1859

8.

989

-0.0

808

(0.6

33)

-0.1

33

(0.5

19)

4,04

1 F

arm

ing

and

busi

ness

ch

arac

teri

stics

Farm

ing

(1 =

yes

) 15

09

0.78

1 67

3 0.

762

1854

0.

786

-0.0

0456

(0

.909

) -0

.023

6 (0

.643

) 4,

036

Num

ber o

f far

min

g ac

tiviti

es

1507

1.

156

673

1.10

5 18

51

1.14

6 0.

0095

3 (0

.901

) -0

.040

9 (0

.656

) 4,

031

Bus

ines

s (1

= ye

s)

1508

0.

306

672

0.34

1 18

55

0.34

1 -0

.034

9 (0

.327

) -0

.000

466

(0.9

93)

4,03

5 N

umbe

r of b

usin

ess a

ctiv

ities

15

04

0.30

9 67

2 0.

338

1850

0.

334

-0.0

255

(0.4

91)

0.00

374

(0.9

42)

4,02

6 N

otes

: Rob

ust c

lust

er p

-val

ues a

re in

par

enth

eses

; Sta

ndar

d er

rors

are

clu

ster

ed a

t cen

ter l

evel

s (18

7 ce

nter

s); *

** p

< .0

1, *

* p

< .0

5, *

p <

.1. # S

igni

fies r

esul

ts o

f OLS

re

gres

sion

of e

ach

varia

ble

on T

1, T

2 du

mm

ies.

60

Tab

le 3

.2: D

escr

iptiv

e st

atis

tics a

nd b

alan

cing

test

(con

t.)

(1

) (2

) (3

) (4

) (5

) (6

) (7

) #

(8) #

(9

) #

(10)

#

(11)

#

O

bs.T

1 M

ean

T1

Obs

.T2

Mea

n T2

O

bs. C

M

ean

C

T1

p-va

lue

T2

p-va

lue

Obs

. F

arm

ing

and

busi

ness

c

hara

cter

istic

s (co

nt.)

Gro

win

g ric

e/flo

wer

s (1

= Y

es)

1509

0.

677

673

0.65

8 18

59

0.67

6 0.

0027

1 (0

.981

) -0

.047

8 (0

.728

) 4,

041

Bre

edin

g pi

gs a

nd

poul

try (1

= Y

es)

1509

0.

031

673

0.01

9 18

59

0.02

1 0.

170

(0.2

21)

-0.0

341

(0.8

41)

4,04

1

Man

ufac

turin

g (1

= Y

es)

1328

0.

052

565

0.05

7 16

17

0.07

9 -0

.215

* (0

.075

5)

-0.1

73

(0.2

13)

3,51

0 V

endo

r tra

de (1

= Y

es)

1328

0.

008

565

0.00

5 16

17

0.00

6 0.

105

(0.4

91)

-0.0

535

(0.8

07)

3,51

0 R

etai

l tra

de (1

= Y

es)

1328

0.

175

565

0.16

8 16

17

0.18

2 -0

.024

4 (0

.820

) -0

.053

1 (0

.709

) 3,

510

Who

lesa

le tr

ade

(1 =

Yes

) 13

28

0.01

6 56

5 0.

032

1617

0.

019

-0.0

779

(0.5

95)

0.21

7 (0

.209

) 3,

510

Serv

ices

(1 =

Yes

) 13

28

0.06

3253

56

5 0.

0761

16

17

0.05

1 0.

104

(0.3

93)

0.20

0 (0

.156

) 3,

510

Num

ber o

f em

ploy

ees

at b

usin

ess

1505

0.

147

673

0.14

4 18

47

0.18

9 -0

.042

1 (0

.308

) -0

.044

8 (0

.346

) 4,

025

Mon

thly

farm

ing

prof

it 61

6 90

6.55

0 25

7 12

71.5

72

613

921.

272

.111

1 (0

.734

)

60

3 M

onth

ly fa

rmin

g sa

le

616

4542

.553

25

7 55

71.6

61

613

4050

.288

-.3

367

(0.1

20)

-.063

0 (0

.838

) 72

8 M

onth

ly b

usin

ess p

rofit

31

6 5,

586.

05

154

6,64

5.15

41

4 7,

954.

76

-2,3

69**

(0

.035

8)

-1,3

10

(0.3

26)

884

Mon

thly

bus

ines

s sal

e 31

6 49

,479

.44

154

56,1

21.7

0 41

4 47

,691

.20

1,78

8 (0

.840

) 8,

430

(0.5

85)

884

Not

es: R

obus

t clu

ster

p-v

alue

s are

in p

aren

thes

es; S

tand

ard

erro

rs a

re c

lust

ered

at c

ente

r lev

els (

187

cent

ers)

; ***

p <

.01,

**

p <

.05,

* p

< .1

. # Sig

nifie

s res

ults

of O

LS

regr

essi

on o

f eac

h va

riabl

e on

T1,

T2

dum

mie

s.

61

3.6.2. Overall Attrition Rate The midline survey results indicate that the overall attrition rate is approximately 13 percent.

Table 3.3 shows that the attrition rates (in terms of female attrition) were 11.99 percent, 16.05

percent and 12.91 percent for T1 (the treatment groups to which husbands were invited), T2

(the treatment groups were husbands were not invited) and C (the control group),

respectively. Compared with other studies, the attrition rates in our study are relatively low

(e.g., 24 percent for Karlan and Valdivia [2011)]; 26 percent in Calderon et al. [2013] and 28

percent in Klinger and Schündeln [2011]).

Table 3.3: Overall attrition rate

C T1 T2 Total Total households at the baseline 1.859 1,509 673 4,041 Total households at the midline 1.619 1,328 565 3,512 Attrition 240 181 108 529 Attrition rate 12.91% 11.99% 16.05% 13.09% Response rate 87.09% 88.01% 83.95% 86.91%

3.6.3. Nonrandom Attrition Next, we examine whether dropouts differ in terms of baseline observable characteristics. To

do so, we use a logistical regression to determine whether dropouts are nonrandom. The

dependent variable is a dummy with a value of 1 for a dropout and 0 otherwise. Table 3.4

reports the results, which indicate that most of the included variables are non-significant,

suggesting that our study is not biased due to nonrandom attrition.

62

Table 3.4: Nonrandom Attrition (Logit regression)

VARIABLES Treatment with husbands: T1 – dummy (1=yes) 0.190 (0.227) Treatment with husbands: T2 – dummy (1=yes) -0.262 (0.214) Women’s age 0.00884 (0.183) Household size 0.0444 (0.254) Marital status dummy (1 = yes) 0.292* (0.0688) Ethnic dummy (1 = Kinh – Vietnamese) 0.0300 (0.939) Years of schooling -0.0127 (0.559) Monthly household income 9.46e-06 (0.582) Farming land size -4.00e-05 (0.528) Business and financial literacy scores -0.00544 (0.876) Farming dummy (1 = yes) -0.219 (0.272) Number of farming activities 0.140 (0.192) Business dummy (1 = yes) 0.0312 (0.905) Number of business activities) -0.211 (0.382) City dummy (1 = Hanoi) 0.411* (0.0836) Constant 1.299** (0.0286) Observations Pseudo R2

3,832 0.0136

Notes: Robust cluster p-values are in parentheses; Standard errors are clustered at center levels (187 centers); *** p < .01, ** p < .05, * p < .1.

3.7 Training Quality Assessment

Before we discuss the impact of the training on our main outcomes of interest, we report the

results of analyses testing the previously mentioned potential shortcomings of the

intervention. If the intervention exhibits a high probability that these shortcomings exist, we

might not expect the training program to have significant impact on clients’ outcomes. In this

section, we present the assessment of the training quality evaluated by female clients. We use

63

two main data sources: attendance lists obtained from loan officers and midline surveys.

3.7.1. Descriptive Statistics of Female Clients’ Participation Table 3.5 presents the participation rates of women according to data from attendance lists.

Because we integrated the G&B training with credit center meetings, the participation rates of

female clients are quite high - approximately 82 percent - even though the training is

voluntary, meaning that a client could leave a center meeting after she fulfilled her loan

repayment. The attendance rates of female clients are stable and high for all of nine training

modules.

Table 3.5: Descriptive statistics of female clients’ participation

Variables Obs Mean Std. Dev. Fraction of total number of modules that female clients joined 2171 0.820871 0.219692 Fraction of invited women that followed:

- Module 1 2171 0.823584 0.381262 - Module 2 2171 0.788577 0.408412 - Module 3 2171 0.833257 0.372833 - Module 4 2171 0.834178 0.372007 - Module 5 2171 0.808844 0.393302 - Module 6 2171 0.839245 0.36739 - Module 7 2171 0.833257 0.372833 - Module 8 2171 0.82865 0.376901 - Module 9 2171 0.79825 0.401399

3.7.2. Results of Training Quality Assessment Table 3.6 provides descriptive statistics of training quality using the data from midline

surveys. On average, most women found the quality of the monthly training and the weekly

discussions to be high. Most treated women appreciated to a great extent the quality of the

trainers, training content, training methods and training time (total average score of more than

8 out of 10). Moreover, most women indicated that they preferred to combine the training and

the credit center meetings. More than 90 percent of the invited women agreed that they have

changed the way they do their businesses as a result of the training. Although the treated

women appreciated the training quality, only approximately 16 percent indicated they would

be willing to pay for similar training.

64

Table 3.6: Descriptive statistics of training quality

Variables Obs Mean Std. Dev. Overall training quality was good (1 = Yes) 2029 0.998029 0.044368 Overall discussion quality was good (1 = Yes) 2022 0.998516 0.0385 The training content was designed appropriately (1 = Yes) 2001 0.997002 0.05469 Teaching method of trainers was good (1 = Yes) 2000 0.995 0.070551 Teaching tools like pictures, games, etc. were good (1 = Yes) 2010 0.993035 0.083187 The course covered the material I expected (1 = Yes) 1962 0.992864 0.084192 The training time was designed appropriate (1 = Yes) 1994 0.987462 0.111295 Combine training with center meetings (1 = Yes) 2013 0.970691 0.168715 Changed the way to do business due to the training (1 = Yes) 2061 .9044153 .2940922 Benefited from the course (1 = Not at all, to 10 = A lot) 2175 8.446437 2.531086 Willing to pay for the training (1 = Yes) 2162 .1655874 .3717959

We also asked respondents to rank each training module—considering only the

modules they completed—from most to least important. Table 3.7 presents descriptive

statistics related to the ranking. For each module, we calculated the percentage of women who

ranked it the best, second best, and so on. The table shows that almost 42 percent of the

women who completed module 1 scored it best. Next, we assigned points to each rank, with

the highest rank receiving the highest number of points (9 points), the lowest rank receiving

the lowest number of points (1 point). Then, we multiplied points at each rank with the

percentage of total women defined previously and added them to determine the importance of

each module (Column 12 of Table 3.7). In general, modules 1, 7 and 8, which focused on

gender issues, managing cash, and managing records of account receivables and accounts

payables, respectively, were considered most important. Module 6, which deals with

compound interest rate calculations, was ranked lowest. These results were confirmed in our

focus group discussions: most women appreciated the gender training modules but found the

module on how to calculate interest rates too difficult. They furthermore noted that although

the overall training content was good, some of modules were too theoretical.

In addition, we asked whether the training influenced their business practices and, if

so, which activity was affected most. Table 3.8 presents the results. We asked respondents to

rank the three most important business practices that they have changed in their businesses.

Next, we calculated the percentage of women that evaluated each item as the first, second and

third ranks. We assigned points to each rank, with the highest rank receiving the highest

number of points (3 points), the lowest rank receiving the lowest number of points (1 point).

Then, we multiply points at each rank with the percentage of total women defined previously.

We next determined the importance of each item by the aggregated points. Overall, the treated

65

women found that “keep written business/farming records,” “re-invest profits for growth or

continuity of their business,” and “actively discuss all business/ faming activities with their

spouses and family members” are the most important practices they have changed in their

businesses as a result of the training.

66

Tab

le 3

.7: D

escr

iptiv

e st

atis

tics t

rain

ing

mod

ule

rank

ing

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(1

1)

(12)

(1

3)

Mod

ule

Con

tent

O

bs.

Per.1

st Pe

r.2nd

Pe

r.3rd

Pe

r.4th

Pe

r.5th

Pe

r.6th

Pe

r.7th

Pe

r.8th

Pe

r.9th

To

tal

Tota

l po

ints

R

ank

1 G

ende

r and

Gen

der

Equa

lity

1,91

9 41

.74

12.6

6 11

.36

11.8

8 7.

14

4.53

3.

39

2.76

4.

53

100

17

,271

.00

1

7 M

anag

ing

Cas

h 1,

888

17.5

3 19

.28

14.3

5 11

.02

9.32

10

.65

12.7

6 3.

13

1.96

10

0

614.

28

2

8 H

ow to

Rec

ord

Acc

ount

s Rec

eiva

ble

and

Acc

ount

s Pay

able

1,

887

11.6

1 15

.69

14.2

12

.08

11.7

6 10

.44

8.96

12

.82

2.44

10

0

557.

41

3

4 Th

e B

usin

ess P

roje

ct:

Bus

ines

s Ide

as

1,82

8 10

.34

11.1

1 14

.39

18.6

5 8.

97

8.92

13

.46

8.15

6.

02

100

53

7.80

4

2 Th

e B

usin

ess W

oman

an

d H

er S

elf-

Con

fiden

ce

1,81

1 6.

57

21.5

9 8.

34

13.5

8 13

.64

9.44

9.

44

11.2

6 6.

13

100

53

4.64

5

5 M

arke

ting

and

How

to

Sel

l with

Suc

cess

1,

803

5.32

8.

37

12.7

6 11

.2

20.4

7 11

.87

10.8

7 11

.87

7.27

10

0

484.

81

6

3 Th

e B

usin

ess W

oman

an

d H

er E

nviro

nmen

t 1,

748

7.32

10

.41

16.2

5 8.

58

8.58

13

.5

13.3

3 11

.04

10.9

8 10

0

484.

34

7

9 H

ow to

Cal

cula

te

Cos

t of P

rodu

ctio

n an

d C

ost o

f Goo

ds

1,78

5 6.

67

7 10

.59

12.4

4 10

.81

12.3

8 9.

41

11.0

4 19

.66

100

43

8.34

8

6 C

alcu

latio

ns a

nd H

ow

to C

alcu

late

Inte

rest

R

ate

1,72

2 4.

47

5.75

8.

65

11.2

7 14

.34

18.6

4 10

.92

10.8

6 15

.1

100

43

0.24

9

Not

es: C

olum

ns (2

) to

(10)

repo

rt th

e pe

rcen

tage

of w

omen

in th

e tre

ated

gro

ups w

ho e

valu

ated

a sp

ecifi

c tra

inin

g m

odul

e as

firs

t thr

ough

nin

th ra

nks.

We

assi

gned

poi

nts t

o th

e ra

nk o

f eac

h ite

m, w

ith th

e hi

ghes

t-ran

king

item

rece

ivin

g th

e hi

ghes

t num

ber o

f poi

nts (

9), t

he lo

wes

t ran

king

item

rece

ivin

g th

e lo

wes

t num

ber o

f poi

nts (

1). P

oint

s of

each

item

at e

ach

rank

= a

ssig

ned

poin

ts ×

per

cent

age

defin

ed a

bove

. Col

umn

(12)

con

tain

s the

agg

rega

ted

poin

ts o

f eac

h m

odul

e. C

olum

n (1

3) p

rese

nts t

he ra

nk o

f eac

h m

odul

e ba

sed

on th

e to

tal p

oint

s.

67

Tab

le 3

.8: T

he im

port

ance

ran

king

of b

usin

ess p

ract

ices

(1)

(2)

(3)

(4)

(5)

No

Var

iabl

e Pe

rcen

tage

1st

ran

k Pe

rcen

tage

2nd

ran

k Pe

rcen

tage

3rd

ran

k T

otal

po

ints

R

ank

1 K

eep

writ

ten

busi

ness

/ far

min

g re

cord

s 0.

550

0.09

8 0.

058

1.90

1

2 R

e-in

vest

pro

fits f

or g

row

th o

r con

tinui

ty o

f you

r bu

sine

ss

0.20

5 0.

284

0.16

6 1.

35

2

3 A

ctiv

ely

disc

uss a

ll bu

sine

ss/ f

arm

ing

activ

ities

w

ith y

our h

usba

nds a

nd fa

mily

mem

bers

0.

064

0.19

8 0.

275

0.86

3

4 Se

t a ta

rget

set f

or sa

les a

nd p

rofit

s 0.

045

0.11

2 0.

123

0.48

4

5 V

isite

d at

leas

t one

of i

ts c

ompe

titor

’s b

usin

esse

s 0.

059

0.12

7 0.

034

0.46

5

6 A

dver

tised

in a

ny fo

rm (p

ast s

ix m

onth

s)

0.04

8 0.

079

0.05

4 0.

35

6

7 R

evie

w th

e fin

anci

al p

erfo

rman

ce o

f you

r bus

ines

s an

d an

alyz

e w

here

ther

e ar

e ar

eas f

or im

prov

emen

t 0.

012

0.06

0 0.

160

0.32

7

8

Dec

orat

e yo

ur p

lace

, pro

duct

or s

ervi

ce to

ent

ice

a cu

stom

er to

vis

it yo

ur st

and,

shop

or o

ther

pr

emis

es

0.01

3 0.

032

0.06

3 0.

17

8

9 H

ave

any

activ

ities

to st

reng

then

bus

ines

s net

wor

k w

ith su

pplie

rs, c

usto

mer

s 0.

004

0.01

0 0.

029

0.06

9

10

Oth

er

0.00

0 0.

000

0.03

8 0.

04

10

To

tal

100

100

100

Not

es: C

olum

ns (1

), (2

), an

d (3

) den

ote

the

perc

enta

ge o

f wom

en in

the

treat

ed g

roup

s who

eva

luat

ed th

e sp

ecifi

c bu

sine

ss p

ract

ice

as th

e fir

st, s

econ

d, o

r thi

rd ra

nk,

resp

ectiv

ely.

We

assi

gned

poi

nts t

o th

e ra

nk o

f eac

h ite

m, w

ith th

e hi

ghes

t-ran

king

item

rece

ivin

g th

e hi

ghes

t num

ber o

f poi

nts (

3 po

ints

) and

the

low

est r

anki

ng it

em

rece

ivin

g th

e lo

wes

t num

ber o

f poi

nts (

1 po

int).

Poi

nts

of e

ach

item

at e

ach

rank

= a

ssig

ned

poin

ts ×

per

cent

age

defin

ed p

revi

ousl

y. C

olum

n (4

) ind

icat

es th

e ag

greg

ated

po

ints

for e

ach

item

. The

rank

show

n in

Col

umn

(5) i

s bas

ed o

n th

e to

tal p

oint

s.

68

3.7.3. Qualitative Assessment of Husbands’ Presence by Female Clients This section examines how women in the T1 treatment evaluated their husbands’ presence

after the training was finished using several data sources. First, according to information from

focus group discussions, we found that most women in the T1 treatment appreciated their

husbands’ attendance at the G&B training, especially the gender training module. Some

women in this group mentioned that their husbands have changed their behaviors in positive

ways toward their spouses. However, these women also indicated that attending the training

had high opportunity costs for their husbands. They suggested that to reduce men’s

opportunity costs, men should only join the gender training module. Moreover, this training

module is considered as the most valuable for men.

Second, we added some qualitative questions on the midline questionnaires to evaluate

the relevance of inviting husbands. Group T1 women reported their evaluations of husband

attendance using a five-point Likert scale from “strongly disagree” to “strongly agree.” Table

3.9 shows the results of these assessments. Overall, more than 96 percent of the women in

group T1 appreciated that husbands were encouraged to participate in the G&B training.

Moreover, approximately 97 percent of these women agreed that as a result of their husbands’

training attendance, the discussion during the training became more interesting. In addition,

94 percent of these women mentioned that as a result of their husbands’ attendance at the

training, women’s intra-household bargaining position has improved. Overall, these results

suggest that most women highly valued husband’s attendance.

69

Tab

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70

3.8 Participation of Husbands Analysis

3.8.1. Descriptive Statistics of Invited Husbands In this section, we discuss statistics regarding husbands’ participation. We use two main

sources of data: training participation lists from the loan officers and small post-treatment

surveys of a random sample of approximately 600 invited husbands.

Table 3.10 presents the participation rates of husbands using the data from the

participation lists. We incentivized husbands who were invited to attend the training with

financial compensation. However, the participation rates of husbands remained low.

Approximately 40 percent of the invited husbands did not join any of the training modules.

Approximately 23 percent participated in more than 50 percent of total training modules, and

only 1.7 percent joined all nine training modules. On average, invited husbands participated in

24 percent of the training modules. Although approximately 40 percent of the invited

husbands followed the first training module, which focused on gender issues, participation

rates of other training modules are lower.

Table 3.10: Descriptive statistics of husbands’ participation

Variable Obs Mean Std. Dev. Fraction of total number of modules that husbands joined 1,055 0.253291 0.286312 Fraction of invited husbands that attended

- Module 1 1,055 0.405687 0.491257 - Module 2 1,055 0.372512 0.483703 - Module 3 1,055 0.337441 0.473061 - Module 4 1,055 0.317536 0.465739 - Module 5 1,055 0.276777 0.447618 - Module 6 1,055 0.272038 0.44522 - Module 7 1,055 0.12891 0.335259 - Module 8 1,055 0.091943 0.289083 - Module 9 1,055 0.076777 0.266364

To find out more details about the invited husbands, we conducted a small post

treatment survey among 600 invited husbands in November 2012. The sample included 390

husbands who joined at least one training module and 219 husbands who did not join any

training modules.

71

Table 3.11 reports descriptive statistics of both groups using the data of subgroup

husbands survey. Men who joined at least one training were more likely to be self-employed

(83 percent vs. 69 percent), conduct more own farming activities (87 percent vs. 73 percent),

have lower monthly income (VND3.57 million vs. VND4.1 million), and be less involved in

business (20 percent vs. 25 percent) and salaried employment (12 percent vs. 27 percent). The

average age in both groups is approximately 45 years. Approximately 70 percent of the

invited men completed secondary school. The majority of the invited men (98 percent) are

Kinh (Vietnamese), and the rest are ethnic minorities. The majority of the invited men are

Buddhist (approximately 85 percent). Manufacturing is the main business types, followed by

services, retail trade and wholesale trade. Regarding farming activities, growing rice is the

main activity, followed by raising pigs/cows and poultry.

Table 3.11: Descriptive statistics of invited husbands

Did not follow any training modules

Followed at least one training module

Variable Obs Mean Std. Dev. Obs Mean Std. Dev.

Self-employment (1 = yes) 218 0.693 0.462 390 0.826 0.380 Salary employment (1 = yes) 218 0.266 0.443 390 0.118 0.323 Own business (1 = yes) 218 0.243 0.430 388 0.198 0.399 Total number of own business 218 0.252 0.466 387 0.204 0.416 Own farming activity (1 = yes) 219 0.731 0.445 389 0.874 0.332 Total of own farming activities 219 1.370 1.082 389 1.725 0.946 Average monthly income (in millions of VND) 214 4.151 1.659 381 3.574 1.476

Percentage of income contribution to household 205 61.902 20.184 370 61.197 20.528

Primary school (1 = yes) 218 0.234 0.424 389 0.254 0.436 Secondary school (1 = yes) 218 0.500 0.501 389 0.566 0.496 High school (1 = yes) 218 0.229 0.421 389 0.159 0.367 College/university/vocational training (1 = yes) 218 0.037 0.188 389 0.021 0.142

Religion_Christian (1 = yes) 216 0.111 0.315 380 0.071 0.257 Religion_Buddhist (1 = yes) 216 0.861 0.347 380 0.855 0.352 Ethnic_Kinh (1 = yes) 218 0.995 0.068 390 0.985 0.123 Age of husband 219 45.352 10.104 390 46.282 10.080

3.8.2. Determinants of Husbands’ Participation Table 3.12 presents the determinants of husbands’ participation. We use two specifications to

examine these determinants. For the first specification, we use data from a survey among a

random subsample of husbands invited to the training. To do so, we use a logit model. The

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dependent variable is a dummy with a value of 1 indicating that an invited husband follows at

least one training session, and a value of 0 indicating that the invited husbands did not follow

any training modules. For the second specification, we combine data from the husbands’

survey with the data collected during the training by the loan officers. This second

specification uses an OLS estimate. The dependent variable in this specification measures the

percentage of total training modules that a husband has joined (information obtained by loan

officers). Note that for a few cases, loan managers forgot to document whether a husband was

present; therefore, the amount of observations for the two specifications differs somewhat.

We cluster all standard errors within credit centers. Columns (1) and (2) depict the results of

the first and second models, respectively.

The table shows that the probability to follow the training is significantly lower if the

husband is involved in salary employment. This indicates that these husbands were facing

time constraints which made it difficult for them to attend the training. Apparently, for

husbands engaged in farming it is easier to attend the training. The table also suggests that

older husbands are more willing to follow the training than young husbands.

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Table 3.12: Determinants of husbands’ participation

(1) (2) VARIABLES Husband followed at least one

training module (1 = yes)a Logit estimates

Percentage of total modules that a husband joinedb

OLS estimates Age of husband 0.00214 0.00337** (0.838) (0.0397) Self-employment (1 = yes)

-0.439 -0.0588

(0.474) (0.506) Salary employment (1 = yes)

-1.347** -0.157

(0.0477) (0.102) Primary school (1 = yes) 0.103 -0.0298 (0.701) (0.397) Own business (1 = yes) -0.163 -0.0302 (0.678) (0.454) Own farming activity (1 = yes)

0.945*** 0.134***

(0.00628) (0.00226) Ethic (1 = Kinh_Vietnamese)

-1.113*** 0.0514

(0.000434) (0.359) Religion_Christian (1 = yes)

-0.574 0.0244

(0.233) (0.756) Household size at baseline

0.0213 -0.00229

(0.769) (0.833) City dummy (1 = Hanoi) -0.378 -0.0796 (0.323) (0.162) Constant 1.455 0.0786 (0.125) (0.603) Observations 585 572 Pseudo R2 / R2 0.0577 0.075 Notes: Robust cluster p-values are in parentheses; Standard errors are clustered at center levels (187 centers); *** p < .01, ** p < .05, * p < .1. aUsing data from subsample post treatment husbands’ survey. bUsing data from the loan officers’ attendance list.

3.8.3. Husbands’ Reasons to Attend or Not Attend the Training and

Training Evaluation Using the data from the survey among the random sample of husbands that who invited (both

attendees and non-attendees), we continue considering the main reasons that husbands

attended or did not attend the training sessions. Table 3.13 presents the main results. The table

suggests that 64 percent of the invited men who followed at least one training module

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mentioned that they still would have participated in the training without any financial

compensation. Yet, as we discuss subsequently, this answer does not seem to be in line with

reality, because financial compensation played a major role in incentivizing husbands to

attend the training.

We asked husbands to report the main reasons for attending the training.

Approximately 36 percent reported that they mainly attended the training to learn how to

improve their businesses. Approximately 34 percent responded that they attended the training

because their spouses asked them. Approximately 25 percent responded that they attended

because of the financial compensation.

The interviewed husbands appeared to value the training highly. Between 87 percent

and 97 percent reported that they learned something new from the training, and they applied

what they learned to their businesses. Moreover, they were willing to recommend the training

to others and regarded the training as useful for their spouses. Possibly most important, they

reported that the training helped them change their opinions about female rights.

Approximately 91 percent of the men who were invited but decided not to attend any

training modules responded that time constraints made it impossible for them to do so. This is

in line with what we learned from the training diaries of the loan officers. They reported that

many men did not join (more) training modules because of time constraints.

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Table 3.13: Reasons to attend or not attend G&B training and training self-evaluation by men

Variables Obs Mean Std. Dev. Still would have joined the training without financial compensation (1 = yes) 366 0.642 0.480

Main reason to attend training for those who attended at least one module:

It is paid (1 = yes) 389 0.249 0.433 Their spouses asked them to attend (1 = yes) 389 0.344 0.476

The training may help improve business (1 = yes) 389 0.362 0.481

Some friends were also attending (1 = yes) 389 0.039 0.193 Self- reported evaluation about the training for those who attended at least one module:

Learned something new (1 = yes) 376 0.963 0.190 Use knowledge gained (1 = yes) 354 0.873 0.334 The training is useful for their spouses (1 = yes) 382 0.963 0.188

Recommend to others (1 = yes) 353 0.898 0.303 Changed opinion of female rights (1 = yes) 372 0.954 0.209 Main reason not to attend any training modules:

At the time the training took place, they had other activities to do (1 = yes) 215 0.912 0.284

The compensation was too low (1 = yes) 215 0.023 0.151 They lived too far away from the center where training took place (1 = yes) 215 0.009 0.096

Attended business training before (1 = yes) 215 0.019 0.135 Not interested in G&B training (1 = yes) 215 0.005 0.068 Their wives did not want them to come (1 = yes) 215 0.014 0.118

Somebody else advised them not to go (1 = yes) 215 0.019 0.135

3.8.4. Compensation Elasticity and Husbands’ Participation Because we varied the financial compensation per training module, we were able to examine

the extent to which financial compensation affects husbands’ participation rates. We use data

on husband’s attendance, which was reported by the loan officers during the training modules.

Thus, the data set contains attendance rates for husbands who attended at least once. The

resulting panel data set contains information on attendance for each husband for each training

module.

We paid the highest financial compensation (VND100,000, or approximately US$5) to

husbands who attended the first training module. The first module dealt with the gender

76

issues, and our main goal was for husbands to attend this module. The financial compensation

was gradually reduced by VND10,000 (US$.50) for the next modules. Thus, if a man attended

training module 6, he would receive 50,000 VND (2.5$USD). We did not pay any

compensation for the last three training modules (modules 7, 8, and 9).

We employ a simple fixed effects regression to estimate the “compensation” elasticity

of husbands’ participation. The dependent variable takes a value equal to 1 if a husband

joined a specific training module and 0 otherwise. We also add training module dummies to

control for “time” fixed effects. We find that the joint test in which the dummies of all

training modules are equal to 0 cannot be rejected. In addition, we cluster all standard errors

within credit centers.

Table 3.14 reports the compensation elasticity for husbands’ participation, which we

find to be significantly positive. In particular, if the compensation increases by VND10,000

(US$.50), the participation rate increases 2.7 percent. These results indicate how important it

is to financially compensate husbands to attend the training.

Table 3.14: Financial compensation elasticity on husbands’ participation

VARIABLES Fixed effect

estimates Financial compensation 2.69e-06*** (0) Constant 0.121*** (0) Observations 10,188 Number of id 1,132 R2 0.122 Training module dummies Yes

Notes: Robust cluster p-values are in parentheses; Standard errors are clustered at center levels (187 centers); *** p < .01, ** p < .05, * p < .1.

3.8.5. Risk Analyses Summary This subsection briefly summarizes the results of our risk analyses. Most women

found the training very useful, which partly explains why the attendance rates were so high.

They also mentioned that they use what they have learned from the training in their current

businesses. The trained women also mentioned that they were satisfied with the quality of the

teachers and the training content, in general; there were only a few complaints regarding the

difficulty of module 6. Moreover, most women greatly appreciated the presence of their

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husbands during the training sessions. Regarding husbands presence, many husbands attended

only a few modules or did not attend at all, except when financial compensation was

relatively high. Nevertheless, husbands who attended at the least one module seem to have

appreciated the training.

3.9 Estimation Methods

We use different types of estimates to analyze the impact of the G&B training on business

outcomes for the baseline and midline data. In principle, all of them should provide unbiased

estimates the impact of G&B training on our outcome variables and including additional

control variables is theoretically unnecessary. We use randomization, which implies that the

G&B training practices should not be correlated with household characteristics, to produce

equivalent treatment and control groups. Therefore, after the G&B training was launched, any

differences in outcomes between the treatment and control groups can be explained only by

the introduction of the intervention because these groups are identical at the baseline and are

exposed to the same external environmental elements. However, we include control variables

in some models to increase the precision of our estimates.

First, using the OLS regression technique, we estimate post-treatment specifications.

The specification is as follows:

, (1)

where denotes the outcome variable for client i at the center j at the midline survey (t=1).

We summarize all of outcome variables of interest in Appendix 3.2. Again, note that even

though husbands are invited to the training sessions, we only consider the impact of the

training on women’s outcomes. is a dummy variable that takes a value of 1 if a female

client and her husband were invited to the training, and 0 otherwise. is a dummy

variable that takes a value of 1 if only the female client was invited to the training and 0

otherwise. are covariates measured at the baseline. We add the following controls: age,

household size, marital status dummy (1= married, 0 = otherwise), years of schooling, and a

city dummy (1= Hanoi, 0= Vinhphuc). is an error term. We assume regressors are

orthogonal to the error terms for all observations. Our coefficients of interest are β and . β

measures the training impact on female outcome variables for the group of (invited) female

clients whose husbands were also invited. measures the training impact of (being invited to)

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the group in which husbands were not invited. The next section discusses in detail the various

outcome variables.

We also estimate single difference specifications (also called analysis of covariance -

ANCOVA). We regress the outcome variables on and , lagged outcome

variables (t=0), and a set of control variables. We use the single difference estimates

because they have higher power than traditional fixed effects estimates (McKenzie, 2012).

(2)

Similar to post treatment estimates, β and are the coefficients of interest.

In addition, using the OLS regression technique, we estimate a double difference (DD)

model:

(3)

where is a dummy with a value of 0 for the baseline observations and 1 for the midline

observations. Similar to both specifications above, β and are the coefficients of interest. We

estimate the DD specification on a balanced panel, which implies that the results are similar to

a household fixed effects specification with time dummies. In line with a fixed effects

specification, our DD controls for possible biases due to unobserved variables that do not

change over time. We cluster all standard errors at the credit center level, because the

randomization took place at the center level. When we had only midline data, we estimated

only post treatment models.

Because clients cannot be forced to attend the training, our dummy variables indicate

whether individuals are invited to the training and not whether they actually participated. This

factor implies that our estimates are intention-to treat (ITT) estimates. An ITT estimate is

relevant for policy makers; in real-life, social programs can only be offered to and not forced

on participants. However, policy makers may be also interested in the impact of the program

on those who are offered and actually participate in the program (Gertler et al., 2011). This is

the treatment on the treated (TOT) or complier-average causal effect (CACE) estimates. If

there is 100 percent compliance, the ITT and CACE estimates will be the same. In this study,

we present CACE estimates by employing instrumental variable (IV) regressions. For the

CACE estimates, we regress in the first stage the “percentage of total training modules that is

79

attended by a female client in the groups T1 and T2, respectively” ( ) on the

training dummies (T1 and T2). In the second stage, we regress outcome variables on the

predicted values of variables and the control variables. We estimated the models

with a 2SLS procedure. However, we do not expect substantial differences between the ITT

and CACE estimates because most women attended all training modules (more than 80%)6.

We use the following specifications for the post treatment and single difference specifications

in the second stage of the regression:

(4)

(5)

where ( ) refers to the percentage of total training modules that a woman in the

groups T1 (T2) has followed (t=1). are covariates measured at the baseline. We include

controls for age, household size, marital status, years of schooling, and a city dummy in all

specifications. is an IIDN(0, σ2) error term. Our coefficients of interest are β and . β

measures the impact of the training on business outcomes for women in the groups T1 who

were invited and actually joined the training. captures the impact of the training on business

outcomes for women in the groups T2 who were invited and actually joined the training. The

results of CACE estimates are reported in Appendix 3.8.

Because most women followed most training modules, our impact estimates are not

affected by low compliance of women. However, our ITT results, especially regarding the

impact of husbands’ attendance on women’s outcomes (T1) may be seriously affected by low

husband compliance. Therefore, we also employ instrumental variable (IV) regressions, by

which we aim to control for the low compliance of husbands. We use the following

specifications for the post treatment and single difference specifications, in the second stage

of the regression:

(6)

6 It should be noted that indeed the coefficients of the CACE estimates are somewhat higher than those

in the ITT estimates, but this also holds for the standard errors. In other words, while the IV approach provides a

consistent estimator for CACE estimates, the precision of the estimator deteriorates as the rate of noncompliance

increases. In terms of “statistical significance” there is therefore no difference between the ITT and CACE

estimates.

80

(7)

where refers to the percentage of total training modules that a husband has

followed (t=1). is a dummy variable that takes a value of 1 if a (female) client is

invited to the training, and 0 if not, irrespective of being in group T1 or T2. are covariates

measured at the baseline. We include controls for age, household size, marital status, years of

schooling, and a city dummy in all specifications. is an IIDN(0, σ2) error term. in

specifications (6) and (7) captures the additional impact of inviting husbands to join the

training on women’s outcomes.

To obtain consistent estimates, we instrument in the first stage with the

randomly determined variable T1. We estimated the models with a 2SLS procedure. By using

this approach, we control for the low compliance of some husbands. To control for low

husband compliance, we have also used two alternative approaches. First, as an alternative for

, we constructed a dummy variable indicating whether the husband participated

in the gender module. Next, we followed the approach explained previously. Second, as an

alternative for , we calculated the total amount of women and men that

followed the nine modules, for each credit center for which husbands were invited to attend

the G&B training. If a participant attended more than one module, he or she counts for the

amount of modules attended. For example, if a participant attended two modules, he/she

counts for two. Next, we calculated the percentage of men (in the entire group of participants)

that participated in the nine modules. Next, we followed the approach described previously.

Using this approach enables us to test whether men being part of the group influences the

results irrespective of them being married to a particular woman. For reasons of space we

only present the results of the first specification, using , in the Appendix 3.4.

The two alternative specifications provide the same results, and can be obtained on request.

3.10 Estimated Results of G&B Training Effects

3.10.1. Effects of G&B Training on Business Knowledge We construct two indicators to measure the impact of the training on knowledge. We

constructed the knowledge indices by counting correct answers. Business knowledge index 1

is based on the sum of correct answers about general business knowledge (10 questions) and

financial literacy (6 questions), with underlying data available in both the baseline and the

midline. Business knowledge index 2 is based on the number of correct answers related to

81

additional financial literacy questions (7 questions that differ from the 6 questions in the other

index), marketing (5 questions), accounting (8 questions), and production (5 questions). The

underlying data for this index is only available in the midline survey (see detail questions on

measuring business knowledge and descriptive statistics in Appendix 3.5).

Before describing the results, we briefly explain the approach used for all estimates

regarding the impact of the training on business outcomes. We present the ITT estimates with

covariates in the main tables. Appendix 3.7 reports the results of the post-treatment estimates

without covariates with the same table number with adding suffixes “B”. In general, the

estimated results of with and without covariates are not different. We report the results of

CACE estimates in Appendix 3.8 with same table number with adding suffixes “C”.

Appendix 3.4 presents the results of the IV estimates for the additional impact of inviting

husbands with the same table number with adding suffixes “A”. The DD estimates presented

in this document are based on a “balanced panel.” By using a balanced panel, the DD model

results are equal to the results of a fixed effects estimate, with different fixed effects per

woman. We estimate the post treatment and single difference specifications using all available

data in the midline and control variables in the baseline.

Table 3.15 shows that the G&B training significantly affects the two knowledge

indices for women in both treatment groups T1 and T2 ( the coefficients for T1 and T2 [T1 ×

time and T2 × time] are both significantly different from 0). Trained women have

significantly higher approximately 2 and 3 corrected answers in business knowledge index 1

and business knowledge index 2, respectively than those in the control groups. Because we

conduct the RCT, the estimated results of three methods including post-treatment, single

difference and double difference are similar. The results in Table 3.15 in the main text and in

Table 3.15B in Appendix 3.7 show that the findings in the post-treatment estimates are stable

with and without covariates. The impacts of the training on business knowledge are

significantly stronger in the CACE estimates for women who actually participated the

training. In particular, the coefficients of the variables P1 and P2 are statistically significant

for all specifications of the post-treatment and single difference estimates (see Table 3.15C in

Appendix 3.8). We conclude that the training improves business knowledge. This result

supports our expectation in the theory of change. In addition, the effects of the training on the

knowledge are greater for women in the groups to which husbands were invited. We

conducted F-tests to compare the equality of coefficients for T1 and T2 (P1 and P2). The

results in the lower part of Table 3.15 and Table 3.15C (in Appendix 3.8) show that there is

82

no significant difference in the impact of the training on the business knowledge of women

between groups T1 and T2. The IV estimates of Table 3.15A in Appendix 3.4 also show that

percentage variables are not statistically significant for all specifications. Thus, we conclude

that the additional impact of inviting husbands to the training is not statistically significant.

Table 3.15: Impact of G&B training on business knowledge

(1) (2) (3) (4) Post-treatment Single

difference Double

difference VARIABLES Business

knowledge index 1

Business knowledge

index 2

Business knowledge

index 1

Business knowledge

index 1 T1 2.229*** 2.723*** 2.239*** -0.113 (0) (4.45e-10) (0) (0.526) T2 2.050*** 2.700*** 2.066*** -0.186 (0) (1.27e-06) (0) (0.373) T1 × time 2.358*** (0) T2 × time 2.237*** (1.90e-10) Constant 10.14*** 17.97*** 9.405*** 8.474***

(0) (0) (0) (0) F-test# 0.53 0.00 0.49 0.15 Prob > F 0.4686 0.9670 0.4837 0.6975 Observations 3,496 3,496 3,496 6,992 R2 0.232 0.116 0.236 0.372

Notes: Robust cluster p-values are in parentheses; Standard errors are clustered at center levels (187 centers); *** p < .01, ** p < .05, * p < .1. Covariates: age, household size, marital status, years of schooling, and city dummies. # F-test – = 0.

3.10.2. Effects of G&B Training on Business Practices To test the impact of the training on business practices, we asked participants whether they had

implemented various types of business practices (20 in total). In the baseline survey, only 7 of

these practices were included, but the full set of 20 activities was included in the midline (see

detail questions on measuring business practices and descriptive statistics in Appendix 3.6).

We apply principal component analysis to create a “business practices index”7 (Lattin et al.,

2003, Hair et al., 2006). We conducted the principal component analysis separately for the 7

7 We apply principal component analysis to construct indices because many variables refer to the same underlying construct (latent variable). By testing each variable individually the probability that we incorrectly reject a true null for at least one outcome variable would be high. In order to avoid this, we follow common practice in many RCTs to construct an index (Duflo et al., 2007; Schultz and Strauss, 2008; Karlan and Valdivia, 2011).

83

business practices (the first set of business practices) as well as the remaining 13 business

practices (the second set of business practices). It should be noted that, for the first set of

business practices, we used weights of each components by using the baseline data; these

weights are used to predict values of the used factors in both the baseline and midline.

We label the first factor “general business practices”, because it has high factor

loadings for the indicators related to recording, business discussions, basic marketing, and

business plans. The second factor is labeled “innovation,” because it has high factor loadings

on the indicators of innovation, such as implementing new ideas or performing any activities

to increase the number of customers. For the second set of business practices, marketing

strategies have high factor loadings on the first factor. Therefore, we label this factor

“marketing.” Keeping records and business planning are statistically significant for the second

factor; therefore we label this factor “record and planning” (see Appendix 3.3 for more

detail).

Table 3.16 reports the results for the four business practices indices. The results

indicate that the G&B training has a significantly positive impact on business practices of

women in both treatment groups T1 and T2: the four indices are significantly improved by the

training. These results hold for the various estimation techniques used and with and without

covariates (see Table 3.16B in Appendix 3.7). Moreover, the effects of the training on the

business practices are significantly stronger for invited women who actually joined the

training in both groups T1 and T2 in CACE estimates (see Table 3.16C in Appendix 3.8).

These results are in line with our expectation in the theory of change. The results in focus

group discussions also confirm that trained women in both groups T1 and T2 mentioned that

they have applied what they learnt from the training in their business practices. In addition,

the effects of the training on business practices are greater for women in the groups without

men in most of specifications in ITT and CACE estimates. However, F-tests suggest that the

training impacts on business practices for women in the treatment groups T1 and T2 do not

differ significantly from each other (see Table 3.16 and Table 3.16C in the Appendix 3.8).

These results are confirmed by the IV estimates in Table 3.16A in Appendix 3.4.

84

Tab

le 3

.16:

Impa

ct o

f G&

B tr

aini

ng o

n bu

sine

ss p

ract

ices

(1

) (2

) (3

) (4

) (5

) (6

) (7

) (8

)

Post

-tre

atm

ent

Sing

le d

iffer

ence

D

oubl

e di

ffer

ence

V

AR

IAB

LES

Gen

eral

bus

ines

s pr

actic

es In

nova

tion

Mar

ketin

g sk

ills

Rec

ord

&

plan

ning

G

ener

al b

usin

ess

prac

tices

Inno

vatio

n G

ener

al b

usin

ess

prac

tices

Inno

vatio

n

T1

1.24

6***

2.

988*

**

1.69

5***

1.

941*

**

1.26

1***

2.

977*

**

-0.0

622

0.06

31

(0

) (0

) (0

) (0

) (0

) (0

) (0

.605

) (0

.303

) T2

1.

260*

**

3.17

2***

2.

002*

**

2.04

0***

1.

322*

**

3.13

5***

-0

.261

0.

169*

(0)

(1.1

6e-0

7)

(0)

(0)

(0)

(1.3

9e-0

7)

(0.1

50)

(0.0

995)

T1

× ti

me

1.31

1***

2.

915*

**

(0

) (5

.07e

-11)

T2

× ti

me

1.51

8***

3.

001*

**

(0

) (8

.20e

-07)

C

onst

ant

0.34

8 0.

0481

-1

.317

***

-1.2

84**

* 0.

418*

0.

0628

-0

.186

-0

.877

**

(0

.147

) (0

.942

) (9

.82e

-06)

(3

.46e

-06)

(0

.071

2)

(0.9

24)

(0.3

29)

(0.0

122)

F

test

# 0.

01

0.08

1.

59

0.17

0.

23

0.06

1.

39

0.02

Pr

ob >

F

0.91

90

0.78

35

0.20

94

0.68

42

0.63

03

0.81

13

0.23

96

0.89

99

Obs

erva

tions

3,

485

3,48

5 3,

480

3,48

0 3,

484

3,48

4 6,

968

6,96

8 R

-squ

ared

0.

203

0.13

5 0.

230

0.29

4 0.

249

0.13

8 0.

223

0.25

9 N

otes

: Rob

ust c

lust

er p

-val

ues a

re in

par

enth

eses

; Sta

ndar

d er

rors

are

clu

ster

ed a

t cen

ter l

evel

s (18

7 ce

nter

s); *

** p

< .0

1, *

* p

< .0

5, *

p <

.1. C

ovar

iate

s: a

ge, h

ouse

hold

size

, m

arita

l sta

tus,

year

s of s

choo

ling,

and

city

dum

mie

s. #

F-te

st

–

= 0

85

3.10.3. Effects of G&B Training on Business and/ or Farming Outcomes We provided the G&B training in rural areas; therefore, approximately 80 percent of women

in our sample conduct at least one farming activity and approximately 30 percent carry out at

least one business activity. To differentiate the effects of the training on business and farming

outcomes, we evaluate the impact of the program on business outcomes (non-farming) and

farming outcomes separately. In the baseline and midline surveys, we asked respondents to

report outcomes of three main business activities and three main farming activities. However,

most respondents indicated that they have one major business activity and/or one major

farming activity. Therefore, we focus the discussion on the results in terms of improving

outcomes related to the main business activity and/or the main farming activity.

Table 3.17 reports the impact of the training on business performance. The sample

only contains observations for those women who conducted this business at both the baseline

and the midline. Subsequently, we address the impact of the training on start-ups and/or

dropouts. We focus on profits (defined as sales minus costs), sales, and profit margins

(defined as profits divided by sales). The training seems to have induced an increase in profits

and profit margins for existing business for women in both groups T1 and T2 (especially in

the DD specifications). The estimated results are stables with and without covariates (see

Table 3.17B in Appendix 3.7). In particular, trained women have approximately 10 percent of

profit margins and approximately 3 million VND business profits higher than those in the

control groups. The impact of training on business outcomes is stronger in the CACE than

those in ITT estimates, but the statistical significance of coefficients remains the same (see

Table 3.17C in Appendix 3.8). The results match our expectation discussed in the theory of

change. Sales, however, were not affected significantly, suggesting that the training reduced

costs of the existing businesses. We realize that the estimated coefficients in the DD

specifications are different from those in the post-treatment and single difference approaches.

One possible explanation is that there may be non-random attrition in the groups of women

who owned business. Again, the effects of the training on the business profits and profit

margins are higher for women in the group with invited men, but these differences are not

statistically significant in any specifications (see F tests in Table 3.17 and Table 3.17C in

Appendix 3.8). These results were confirmed by the IV estimates in Table 3.17A in Appendix

3.4, which also indicates no additional impact of the invited husbands on business outcomes.

Table 3.18 summarizes the impact of the training on agricultural activity. Again, the

sample only contains women who engaged in this activity both at the baseline and midline.

86

Because of unique characteristics of farming activities, many households use farming outputs

for their own consumption. In addition, the farming outputs can be only obtained at the end of

production cycle. Thus, we asked respondents to report the estimated costs related to the main

farming activity, the estimated value of the farming product their households consumed, the

estimated value of farming products they sold, and the estimated value of farming products

left after the whole production cycle. Then we calculated monthly farming outcomes. We

focus our analysis in this section on monthly farming profits (defined as the estimated value

of products sold outside minus the estimated costs), monthly farming sales (equal to the

estimated value of the products sold outside) and monthly profit margins (defined as profits

divided by sales) for the main farming activity. The Table 3.18 suggests that the training did

not have a significant impact on the main existing farming activity. These results also hold in

the specifications without covariates and in the CACE estimates (see Table 3.18B in

Appendix 3.7 and Table 3.18C in Appendix 3.8). In addition, we do not find any additional

impact of the invited husbands on farming outcomes (see Appendix 3.18A in Appendix 3.4).

87

Tab

le 3

.17:

Impa

ct o

f G&

B tr

aini

ng o

n bu

sine

ss o

utco

mes

(1

) (2

) (3

) (4

) (5

) (6

) (7

) (8

) (9

)

Post

trea

tmen

t Si

ngle

diff

eren

ce

Dou

ble

diff

eren

ce

VA

RIA

BLE

S M

onth

ly

bus

ines

s pro

fits

Mon

thly

bu

sine

ss

sale

s

Mon

thly

bu

sine

ss

prof

it m

argi

n

Mon

thly

b

usin

ess

prof

its

Mon

thly

bu

sine

ss

sale

s

Mon

thly

bu

sine

ss p

rofit

m

argi

n

Mon

thly

b

usin

ess

prof

its

Mon

thly

bu

sine

ss

sale

s

Mon

thly

bu

sine

ss

prof

it m

argi

n

T1

2,95

8*

1,21

7 0.

0638

2,

872

1,04

1 0.

0658

-2

,320

**

1,83

4 -0

.103

**

(0

.088

0)

(0.8

21)

(0.1

69)

(0.1

02)

(0.8

48)

(0.1

56)

(0.0

373)

(0

.831

) (0

.041

3)

T2

2,46

1 -7

,059

0.

0993

* 2,

397

-7,5

97*

0.10

00*

-1,3

21

8,16

3 -0

.023

6

(0.1

38)

(0.1

21)

(0.0

513)

(0

.150

) (0

.096

8)

(0.0

508)

(0

.332

) (0

.592

) (0

.528

) T1

× ti

me

5,15

6**

-1,2

25

0.16

6***

(0.0

134)

(0

.909

) (0

.007

61)

T2 ×

tim

e

3,

611*

-1

7,25

1 0.

126*

(0.0

923)

(0

.271

) (0

.062

3)

Con

stan

t 5,

829

27,1

57*

0.31

2***

5,

982

24,4

87*

0.31

0***

7,

143*

* 37

,151

***

0.27

7***

(0.1

48)

(0.0

699)

(0

.001

88)

(0.1

38)

(0.0

984)

(0

.001

99)

(0.0

294)

(0

.002

45)

(0.0

0129

) F-

test

# 0.

07

2.23

0.

89

0.06

2.

44

0.81

0.

52

1.07

0.

38

Prob

> F

0.

7916

0.

1369

0.

3473

0.

8011

0.

1205

0.

3702

0.

4699

0.

3022

0.

5363

O

bser

vatio

ns

879

879

879

877

877

877

1,75

4 1,

754

1,75

4 R

2 0.

012

0.01

1 0.

011

0.01

5 0.

022

0.01

1 0.

013

0.02

4 0.

017

Not

es: R

obus

t clu

ster

p-v

alue

s are

in p

aren

thes

es; S

tand

ard

erro

rs a

re c

lust

ered

at c

ente

r lev

els (

187

cent

ers)

; ***

p <

.01,

**

p <

.05,

* p

< .1

. Cov

aria

tes:

age

, hou

seho

ld si

ze,

mar

ital s

tatu

s, ye

ars o

f sch

oolin

g, a

nd c

ity d

umm

ies.

# F-

test

–

=

0.

88

Tab

le 3

.18:

Impa

ct o

f G&

B tr

aini

ng o

n fa

rmin

g ou

tcom

es

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

Po

st tr

eatm

ent

Sing

le d

iffer

ence

D

oubl

e di

ffer

ence

V

AR

IAB

LES

Mon

thly

fa

rmin

g pr

ofits

Mon

thly

fa

rmin

g sa

les

Mon

thly

fa

rmin

g pr

ofit

mar

gin

Mon

thly

fa

rmin

g pr

ofits

Mon

thly

fa

rmin

g sa

les

Mon

thly

fa

rmin

g pr

ofit

mar

gin

Mon

thly

fa

rmin

g pr

ofits

Mon

thly

fa

rmin

g sa

les

Mon

thly

fa

rmin

g pr

ofit

mar

gin

T1

-1

4.63

-3

7.93

-0

.003

63

-14.

36

0.69

6 0.

0832

-7

5.76

-2

01.8

-0

.030

5

(0.8

13)

(0.7

60)

(0.9

72)

(0.8

17)

(0.9

95)

(0.3

09)

(0.6

06)

(0.2

83)

(0.8

14)

T2

-94.

91

-141

.1

-0.1

58

-96.

60

-133

.5

0.00

966

-30.

01

-21.

68

0.00

298

(0

.168

) (0

.184

) (0

.279

) (0

.169

) (0

.139

) (0

.930

) (0

.852

) (0

.951

) (0

.989

) T1

× ti

me

60.9

3 17

6.9

0.10

6

(0.6

82)

(0.3

37)

(0.3

88)

T2 ×

tim

e

-5

9.35

-1

07.6

0.

0044

6

(0.6

83)

(0.7

38)

(0.9

79)

Con

stan

t -1

49.7

38

2.7

-0.0

522

-127

.0

281.

5 0.

0228

15

.13

817.

5*

0.09

62

(0

.221

) (0

.206

) (0

.839

) (0

.300

) (0

.288

) (0

.935

) (0

.946

) (0

.065

0)

(0.7

73)

F-te

st#

1.13

0.

67

1.13

1.

13

1.34

0.

44

1.06

1.

00

0.37

Pr

ob >

F

0.28

92

0.41

44

0.28

96

0.28

98

0.24

86

0.50

66

0.30

56

0.31

97

0.54

38

Obs

erva

tions

2,

559

2,55

9 1,

434

2,51

2 2,

512

570

5,02

4 5,

024

1,14

0 R

2 0.

004

0.00

6 0.

007

0.00

5 0.

126

0.03

5 0.

008

0.00

2 0.

011

Not

es: R

obus

t clu

ster

p-v

alue

s are

in p

aren

thes

es; S

tand

ard

erro

rs a

re c

lust

ered

at c

ente

r lev

els (

187

cent

ers)

; ***

p <

.01,

**

p <

.05,

* p

< .1

. Cov

aria

tes:

age

, hou

seho

ld si

ze,

mar

ital s

tatu

s, ye

ars o

f sch

oolin

g, a

nd c

ity d

umm

ies.

# F-

test

–

=

0.

89

3.10.4. Effects of G&B Training on Business and Farming Startups and

Their Survival We also considered the impact of the training on business/ farming startups and their survival.

We constructed two zero-one dummy variables indicating whether a woman started a new

business/ farming activity and/or is still holding the same business/ farming in the baseline

and midline surveys. We estimated simple linear probability models. Table 3.19 presents the

results, which indicate that the training encourages women to form new business startups

when they were in groups with invited men. In particular, invited women in the groups T1

have probability of establishing a new business approximately 2 percent significantly higher

than those in the control groups. The results are stronger in the CACE estimates (see Table

3.19C in Appendix 3.8). However, F-test results show that these effects of the training on the

business entry are not statistically significant difference for the women in both groups with

men and without men. Surprisingly, when we estimate the post-treatment models without

covariates, we find that the impact of the training on business entry is significantly stronger

for trained women in both groups T1 and T2 (see Table 3.19B in Appendix 3.7).

The IV estimates in the Table 3.19A in the Appendix 3.4 do not exhibit any additional

significant impact of the invited husbands on business startups. In addition, we do not find

any significant evidence that the training encourages new farming entry. Regarding business

and farming survival, there is no difference between the treatment and the control groups.

Table 3.19: Impact of G&B training on business, and farming startup and survival

(1) (2) (3) (4) VARIABLES Business startup Business survival Farming startup Farming survival T1 0.0159* 0.000807 0.0100 0.00905 (0.0939) (0.981) (0.674) (0.710) T2 0.0161 0.0533 -0.0111 0.0305 (0.265) (0.158) (0.521) (0.292) Constant 0.0988*** 0.838*** 0.206*** 0.877*** (0.000228) (0) (1.28e-05) (0) F-test# 0.00 1.97 0.95 0.61 Prob > F 0.9929 0.1623 0.3306 0.4352 Observations 3,496 1,167 3,496 2,881 R2 0.010 0.007 0.035 0.005

Notes: Robust cluster p-values are in parentheses; Standard errors are clustered at center levels (187 centers); *** p < .01, ** p < .05, * p < .1. Covariates: age, household size, marital status, years of schooling, and city dummies. # F-test – = 0.

90

3.11 Conclusion, Discussion and Policy Recommendations

In this chapter, we test the impact of providing G&B training on business outcomes of female

microfinance clients in Vietnam and whether the impact of the training is conditional on the

presence of husbands. Our findings suggest that the training leads to significant improvements

in business knowledge and has improved business practices. Our results are in line with

previous studies that show that business training has positive effects on business knowledge

and business practices (Berge et al., 2011, Giné and Mansuri, 2011, Karlan and Valdivia,

2011, Bruhn and Zia, 2013, Valdivia, 2013, De Mel et al., 2014, Drexler et al., 2014). Most of

these studies, except Bruhn and Zia (2013), provide further evidence that the increased

business knowledge and adoption of better business practices did not lead to an improvement

of business performance in terms of profits or sales for female entrepreneurs. In contrast to

the existing literature, we find that G&B training has a positive impact on business

performance of female-run businesses. We provide some evidence that offering G&B training

leads to improvements in business profits and profit margins among surviving businesses.

However, we do not find any evidence that the training improves farming outcomes - an

unsurprising result considering that the training primarily focuses on business activities with

no explicit attention to farming activities. Obviously, parts of the training may be relevant for

“farmers” as well, but apparently these affects are too small to be detected.

Our study does not indicate strong evidence for a positive impact of the training on

female outcomes if husbands were also invited to attend the training. For some outcome

variables - in particular, business knowledge, business profits, profit margins, and business

entry - the average impact of the training is greater when husbands were also invited.

However, the additional impact due to inviting husbands does not appear to be statistically

significant. A possible reason for this result is the relatively low attendance of husbands, and

the implied negative impacts on the power of the estimates. It is possible that the additional

impact of the training when husbands were invited is too small to be picked up by the

relatively small sample of husbands who actually attended the training modules.

Although the midline analyses do not provide strong quantitative evidence in support

of inviting husbands, this result does not necessarily imply that inviting husbands is not

important, because these results are based on midline data. It may take some additional time

before husbands’ attendance affects women’s outcome variables more significantly. In

addition, the qualitative analyses suggest that most women in our study appreciated the

91

involvement of husbands in the training. Moreover, men who participated in the training

found it useful. These results indicate that, at minimum, there may be some relevance in

inviting husbands to attend the training as well. Yet, the low participation rates of husbands

suggest that the opportunity costs for husbands to follow the training outweigh the

compensation we provided to them.

A possible caveat must be noted; most of the business practices are self-reported

changes. Thus, it is possible that these self-reported changes of business practices are biased

in favor of women in the treatment groups because they are more inclined than the control

group to report that they changed their business practices. To reduce these biases, we included

some “checking” questions in our surveys. For example, if a respondent answered that she has

kept records of her withdrawals, a surveyor asked to see these records. In addition, most

interviews took place at a woman’s house or her business place, so a surveyor had a chance to

observe whether her business practices were in line with her answers. Yet, we acknowledge

that some changes may be overestimated due to self-reporting.

On the basis of the midline analyses, we propose the following policy

recommendations. First, integrating the training with credit center meetings seems worthwhile

because it reduces opportunity costs for female entrepreneurs when attending training. The

high participation rates and the results of focus group discussions suggest that the integration

of the G&B training with the center meetings is greatly appreciated.

Second, a specific module on farming activities should be integrated into the training.

In principle, the training could improve farming outcomes because improved knowledge on

issues such as accounting, marketing, and bookkeeping could improve farming outcomes. Yet

we do not find much evidence for a positive impact of the training on farming outcomes,

which signals the need to integrate modules that specifically pay attention to farming

activities. This recommendation is especially important if the training is provided to a group

of women who primarily focus on farming activities.

Third, our study points to some relevance of inviting husbands to attend the training.

Yet participation rates were low due to the high opportunity costs, such that a sizable financial

compensation is needed to incentivize husbands to attend the training. Women in the treated

group recommended that men should attend only the gender training module to scale up

subsequent interventions, to reduce the opportunity costs for men, and because they

considered this training module the most valuable.

92

This study suggests several avenues for further research. Our study only considered

average effects. However, impacts may differ depending on characteristics of the borrowers.

A fruitful extension of this research could consider so-called heterogeneous effects. In

addition, our study focuses on the impact of training for female members of a microfinance

organization. Thus, we consider the additional impact of training a group of women who

already have access to credit. It would be useful to investigate whether the impact of the

training differs for women with and without access to credit. We leave this topic to further

research.

Appendices

Appendix 3.1: Map of TYM’s operating areas

Note: TYM’s operating areas are marked by flag symbols

93

Appendix 3.2: Descriptions of outcome variables

Variable Expected sign

Description Time of measurement

Business knowledge Business knowledge index 1 (BKI1)

+ Sum of correct answers of general and business knowledge (10 questions) and financial literacy (6 questions)

baseline and midline

Business knowledge index 2 (BKI2)

+ Sum of correct answers of financial literacy (8 questions that differ from 6 questions in BKI1), marketing, accounting, and production knowledge

midline

Business practices General business practices + 1st component of principal

component analysis (consisting of 7 business practices)

baseline and midline

Innovation + 2nd component of principal component analysis (consisting of 7 business practices)

baseline and midline

Marketing skills + 1st component of principal component analysis (consisting of 13 business practices)

midline

Record and planning + 2nd component of principal component analysis (consisting of 13 business practices)

midline

Business outcomes Monthly business profits + Difference between business sales

and business costs of a main business activity at normal months

baseline and midline

Monthly business sales + Business sales of a main business activity at normal months

baseline and midline

Monthly business profit margin

+ Business profits divided business sales of a main business activity at normal months

baseline and midline

Farming outcomes Monthly farming profits + Difference between estimated

value of the products sold outside and farming costs of a main farming activity at normal months

baseline and midline

Monthly farming sales + Estimated value of the products sold outside of a main farming activity at normal months

baseline and midline

Monthly farming profit margin

+ Farming profits divided farming sales of a main farming activity at normal months

baseline and midline

94

Appendix 3.2: Descriptions of outcome variables (cont.)

Variable Expected sign

Description Time of measurement

Business Startups Their Survival Business startup + Dummy variable: 1 if a woman

started a new business activity, 0 otherwise

midline

Business survival + Dummy variable: 1 if a woman is still holding the same business in the baseline and midline surveys.

baseline and midline

Farming Startups Their Survival Farming startup + Dummy variable: 1 if a woman

started a new farming activity, 0 otherwise

midline

Farming survival + Dummy variable: 1 if a woman is still holding the same farming in the baseline and midline surveys.

baseline and midline

Appendix 3.3: Principal Component Analysis of Business Practices

Factor analysis

We asked respondents whether they had implemented 20 business practices. We apply factor

analysis to achieve data reduction by creating an entirely new set of business practices

variables, which is much smaller, to replace the original set of business practices variables

with minimum loss of information(Lattin et al., 2003, Hair et al., 2006) . To facilitate our

other subsequent analyses, we conducted factor analysis twice, once for the first set of 7

business practices available in both the baseline and midline and once for the 13 business

practices available only in the midline data.

There are several types of factor analysis, including R-factor analysis, analyzing

relationship between variables, Q-factor analysis, and analyzing relationships between cases

(Lattin et al., 2003, Hair et al., 2006). In this research, our objective in the first step was to

summarize business practices; thus, we applied R-factor analysis.

Moreover, factor analysis techniques can be implemented from either an exploratory

or a confirmatory perspective (Lattin et al., 2003, Hair et al., 2006). The main aim in this step

is to reduce data of business practices into small number of dimensions; therefore we apply

exploratory factor analysis in this step.

95

Furthermore, the exploratory factor analysis distinguishes common factor analysis and

component analysis (Lattin et al., 2003, Hair et al., 2006). Because the main objective in this

step is to summarize most of original information (variance) of business practice variables in

a minimum number of factors for prediction purposes in the second step, we decide to use

principal component analysis.

To implement principal component analysis, we take the following steps:

Stage 1: Check data including sample size and number of observations before

performing factor analysis to determine whether data is suitable for factor analysis.

Stage 2: Determine whether it is appropriate to use principal component analysis. We

will base on variable correlation matrix, Kaiser-Meyer-Olkin measure of sampling adequacy

Stage 3: Derive factors and assess overall fit. In this stage, we discuss which methods

to apply to select numbers of factors.

Stage 4: Interpret the factors. In this stage, we focus on examining factor loading

matrix, choosing factor rotation methods, identifying the significant loadings for each

variable, and then labeling the factors.

After implementing these steps with component analysis, we obtain factor scores.

Factor scores are the best method for completing data reduction because they represent all

variables loading on the factor (Hair et al., 2006). We use these factor scores for subsequent

analyses. Note that for the first set of business practices, we conduct factor analysis using

only the baseline data, and then using the results of weights to predict factor scores for both

the baseline and midline.

Principal Component Analysis Result

Assessing the Appropriateness of Factor Analysis

To determine whether principal component analysis is suitable, we implement some tests.

First, checking the data, we have 20 business practices variables and approximately 4,000

observations. Following Hair et al.’s (2006) rules, these data are sufficient to implement

factor analysis. In addition, based on the correlation matrix of the first and the second set of

business practices, we find that most of business practices in these two set business practices

are substantially and highly significantly correlated.

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Moreover, the Kaiser-Meyer-Olkin measure of sampling adequacy equals .69 for the

first set and .90 for the second set of business practices. The results indicate that the degree of

inter-correlations among the business practice variables is good enough to continue the

principal component analysis (Hair et al., 2006).

Deriving Factors and Assessing Overall Fit

Following Hair et al. (2006) and Lattin et al. (2003), we apply several criteria for extracting

the number of factors:

Latent Root Criteria

For latent root criteria, we use the eigenvalue. The rationale for the latent root criterion

is that any individual factor should account for the variance of at least a single variable if it is

to be retained for interpretation. With the component analysis, each variable contributes a

value of 1 to the total eigenvalue. Thus, only the factors having latent roots or eigenvalues

greater than 1 are considered significant (Hair et al., 2006). Based on eigenvalue, we decide to

extract two factors from the first set and two factors from the second set.

Parallel Analysis

To make a robust decision on the number of factors, we also use other criteria, parallel

analysis. With the component factor analysis, the later factors extracted contain both common

and unique variance. Although all factors contain at least some unique variance, the

proportion of unique variance is substantially higher in later factors. We use the parallel

analysis to identify the optimum number of factors that can be extracted before the amount of

unique variance begins to dominate the common variance structure. From the shape of

resulting curve in parallel analysis, we conclude that there are two factors that can be

extracted for the first set variables and two factors for the second set.

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In general, the results in eigenvalue rule and parallel analysis support our final

decision to extract two factors for the first data set and two other factors for the second data

set of business practices.

Interpreting factors

Although initial un-rotated factors obtain the objective of data reduction, these results are

difficult to interpret. Therefore, we must employ a rotational method to achieve simpler and

theoretically more meaningful factor solutions. The ultimate effect of rotating the factor

matrix is to redistribute the variance from earlier factors to later ones to obtain relatively

fewer high loadings per factor (Hair et al., 2006). There are two types of factor rotation

methods: orthogonal and oblique factor rotation. Tabachnick and Fidell (2007, p. 656) note:

“Perhaps the best way to decide between orthogonal and oblique rotation is to

request oblique rotation with the desired number of factors and look at the

correlations among factors.… If factor correlations are not driven by the data, the

solution remains nearly orthogonal. Look at the factor correlation matrix for

correlations around .32 and above. If correlations exceed .32, then there is 10% (or

more) overlap in variance among factors, enough variance to warrant oblique

rotation unless there are compelling reasons for orthogonal rotation.” (Tabachnick

and Fidell, 2007a)

Using this rule, we apply orthogonal rotation methods for the first set of practices

because the factor correlation is approximately .19. There are three major orthogonal

approaches: Quartimax, Varimax, and Equimax (for a detailed discussion, see Hair et al.,

.51

1.5

2

0 2 4 6 8Component

PCA Parallel Analysis

Parallel Analysis

02

46

0 5 10 15Component

PCA Parallel Analysis

Parallel Analysis

Figure a: Parallel Analysis of the first set of business practices

Figure b: Parallel Analysis of the second set of business practices

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2006, p. 150). Among these approaches, the Varimax method provides a clearer separation of

the factors. Moreover, it has proved successful as an analytic method to obtain an orthogonal

rotation of factors. Therefore, we decide to use Varimax approach in orthogonal rotation

method. We apply the oblique promax rotation method for the second set of business

practices because the factors’ correlation is approximately .68.

The results of factor loadings for the first set of business practices indicate that the

first factor has high factor loadings for the indicators related to recording, business discussion,

marketing, and business plan. Therefore, we label this factor “general business practices.” The

second factor has high factor loadings on indicators such as innovation, new ideas, and

activities to increase number of buyers associated with “innovation”; thus we use this phrase

to label this factor.

For the second set of business practices, marketing strategies have high factor loadings

on the first factor. Therefore, we label this factor “marketing.” Recording and business

planning are statistically significant for the second factor; therefore we label this factor

“record and planning.”

Appendix 3.4: IV estimates

Table 3.15A: Impact of G&B training on business knowledge (IV estimates) (1) (2) (3) Post-treatment Single

difference VARIABLES Business

knowledge index 1

Business knowledge

index 2

Business knowledge

index 1 Percentage# 0.920 0.127 0.898 (0.378) (0.955) (0.386) Training 2.049*** 2.700*** 2.065*** (0) (5.01e-07) (0) Observations 3,300 3,300 3,300 R-squared 0.232 0.117 0.235

Notes: Robust cluster p-values are in parentheses; Standard errors are clustered at center levels (187 centers); *** p < .01, ** p < .05, * p < .1. Covariates: age, household size, marital status, years of schooling, and city dummy. #Percentage of total training modules in which an invited husband participated.

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Table 3.16A: Impact of G&B training on business practices (IV estimates) (1) (2) (3) (4) (5) (6)

Post-treatment Single difference VARIABLES General

business practices

Innovation Marketing skills

Record &

planning

General business practices

Innovation

Percentage# 0.0797 -0.293 -1.005 -0.319 -0.117 -0.192 (0.893) (0.917) (0.326) (0.756) (0.825) (0.945) Training 1.260*** 3.172*** 2.003*** 2.040*** 1.321*** 3.135*** (0) (3.59e-08) (0) (0) (0) (4.29e-08) Observations 3,290 3,290 3,284 3,284 3,289 3,289 R-squared 0.213 0.142 0.233 0.300 0.255 0.145

Notes: Robust cluster p-values are in parentheses; Standard errors are clustered at center levels (187 centers); *** p < .01, ** p < .05, * p < .1. Covariates: age, household size, marital status, years of schooling, and city dummy. #Percentage of total training modules in which an invited husband participated.

Table 3.17A: Impact of G&B training on business outcomes (IV estimates) (1) (2) (3) (4) (5) (6)

Post-treatment Single difference VARIABLES Monthly

business

profits

Monthly business

sales

Monthly business

profit margin

Monthly

business profits

Monthly business

sales

Monthly business

profit margin

Percentage# 2,903 38,926 -0.146 3,172 40,487 -0.138 (0.754) (0.163) (0.410) (0.725) (0.146) (0.444) Training 2,469 -7,197 0.100** 2,371 -7,714* 0.102** (0.134) (0.112) (0.0466) (0.152) (0.0888) (0.0440) Observations 848 848 848 846 846 846 R-squared 0.009 0.012 0.012 0.014 0.021 0.012

Notes: Robust cluster p-values are in parentheses; *** p < .01, ** p < .05, * p < .1; Standard errors are clustered at center levels (187 centers); Covariates: age, household size, marital status, years of schooling, and city dummy. #Percentage of total training modules in which an invited husband participated. Table 3.18A: Impact of G&B training on farming outcomes (IV estimates)

(1) (2) (3) (5) (6) (7) Post-treatment Single difference

VARIABLES Monthly farming

profits

Monthly farming

sales

Monthly farming

profit margin

Monthly farming

profits

Monthly farming

sales

Monthly farming

profit margin

Percentage# 303.1 351.7 0.542 304.9 445.3 0.148 (0.291) (0.476) (0.292) (0.300) (0.329) (0.665) Training -95.85 -143.0 -0.159 -97.63 -135.6 0.0134 (0.162) (0.173) (0.275) (0.162) (0.129) (0.901) Observations 2,439 2,439 1,358 2,394 2,394 537 R-squared 0.001 0.005 0.001 0.003 0.124 0.039

Notes: Robust cluster p-values are in parentheses *** p < .01, ** p < .05, * p < .1. Standard errors are clustered at center levels (187 centers); Covariates: age, household size, marital status, years of schooling, and city dummy. #Percentage of total training modules in which an invited husband participated.

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Table 3.19A: Impact of G&B training on business, farming entry and survival (IV estimates)

(1) (2) (3) (4) VARIABLES Business entry Business survival Farming entry Farming survival Percentage# 0.0235 -0.238 0.102 -0.0574 (0.726) (0.176) (0.237) (0.578) Training 0.0160 0.0525 -0.0110 0.0305 (0.263) (0.161) (0.519) (0.291) Observations 3,300 1,126 3,300 2,738 R2 0.009 0.028 0.001

Note: Robust cluster p-values are in parentheses *** p < .01, ** p < .05, * p < .1. Standard errors are clustered at center levels (187 centers); Covariates: age, household size, marital status, years of schooling, and city dummy. #Percentage of total training modules in which an invited husband participated.

Appendix 3.5: Questions on Measuring Business Knowledge8

Please select True/False in the following sentences 1 = True, 0 = False, 88. Refused to answer, 99. Don’t know

General business knowledge

Fraction of answers 0 1 88 99

1 Sales do not remain the same over long periods of time, so you must think of other ways to improve or expand your business

Baseline 8.22 91.78

Midline 6.2 93.44 0.37

2 Only price determines whether customers will buy from you or your competitors

Baseline 18.9 81.1

Midline 29.15 70.59 0.05 0.21

3 Sales records help evaluate which products sell and which do not

Baseline 16.32 83.68

Midline 14.33 85.44 0.24

4 The surest path to success is to sell what you are already good at producing, rather than what your customers want

Baseline 54 46

Midline 62.2 37.51 0.18 0.1

5 Your sister sells high quality cloth. A new seller offers a lower quality cloth at lower price. Your sister should reduce her price too

Baseline 63.44 36.56

Midline 60 39.74 0.16 0.1

8 Correct answer is bold; Fraction of each answer in baseline and midline surveys is given next to the questions

101

Fraction of answers 0 1 88 99

6 If you charge more than another seller, customers will not buy from you

Baseline 26.18 73.82

Midline 37.7 62.14 0.16

7 Villagers with small businesses do not need to advertise their products

Baseline 31.81 68.19

Midline 64.09 35.78 0.13

8 Word-of-mouth does not affect the sales of business

Baseline 52.69 47.31

Midline 72.35 27.57 0.08

9 Many businesses lose part of their products because of poor storage facilities

Baseline 15.83 84.17

Midline 18.96 80.96 0.08

10 It is not necessary to separate money used for business and money used for household

Baseline 36.04 63.96

Midline 68.69 31.18 0.13

Financial literacy

Fraction correct answers Baseline Midline

11 What is 400 plus 300? 95.49 95.67

12 What is one tenth of 100? 91.42 90.28

13

In a sale, a shop is selling all items at half price. Before the sale a TV costs 4,000,000VND. How much will it cost in the sale?

Fraction of answers Baseline Midline

1. 4,000,000VND 0.24 2. 3,000,000VND 5.54 3. 2,000,000VND 94.56 94.22

99. Don’t know 88. Refused to answer

14

If you sold two items for 8,000 VND each and your customer gave you 20,000 VND, how much balance do you owe the customer?

Fraction of answers Baseline Midline

1. 12,000VND 0.21 2. 4,000VND 93.49 97.8 3. 8,000VND 1.74

99. Don’t know 0.03 88. Refused to answer 0.21

102

15

Imagine that five brothers are given a gift of 1,000,000VND. If the brothers have to share the money equally how much does each one get?

Fraction of answers Baseline Midline

1. 1,000,000VND 0.35 2. 500,000VND 2.69 3. 200,000VND 96.37 4. 100,000VND 0.53

99. Don’t know 0.03 88. Refused to answer 0.03

16

Now imagine that you get a gift of 1,000,000VND, and you put it in the drawer at home for 12 months. After one year you can buy with this

Fraction of answers Baseline Midline

1. More than today 6.56 2. The same amount as today 19.93 3. Less than today 33.18 4. It depends on inflation 39.18 99. Don’t know 0.24 88. Refused to answer 0.91

17

You lend 1,000,000VND to a friend one evening and he gives you exact 1,000,000VND back the next day. How much interest has he paid on this loan?

Fraction of answers Baseline Midline

1. more than 0% 2.05 2. 0% 96.69 3. less than 0 % 0.86 99. Don’t know 0.05 88. Refused to answer 0.35

18

Suppose you had 1,000,000VND in a savings account and the interest rate was 2% per year. You don’t make any further payments into this account and you don’t withdraw any money. How much would be in the account at the end of the first year, once the interest payment is made?

Fraction of answers Baseline Midline

1. More than 1,020,000VND 35.05 2. Exactly 1,020,000VND 50.03 3. Less than 1,020,000VND 13.33 99. Don’t know 0.11 88. Refused to answer 1.49

103

19

Use the same information in the previous question: Suppose you had 1,000,000VND in a savings account and the interest rate was 2% per year. After 5 years, how much do you think you would have in the account if you left the money to grow?

Fraction of answers Baseline Midline

1. More than 1,100,000VND 49.93 46.18

2. Exactly 1,100,000 VND 38.76 33.07

3. Less than 1,100,000 VND 11.31 19.05

99. Don’t know 0.24 88. Refused to answer 1.47

20

Imagine that the interest rate on your savings account was 1% per year and inflation was 2% per year. After 1 year, how much would you be able to buy with the money in this account?

Fraction of answers Baseline Midline

1. More than today 20.99 6.18

2. Exactly the same 27.37 19.62

3. Less than today 51.63 72.17

99. Don’t know 0.37 88. Refused to answer 1.65

21

‘An investment with a high return is likely to be high risk.’ Is this true or false?

Fraction of t answers Baseline Midline

1. True 94.66 2. False 4.36 99. Don’t know 0.43 88. Refused to answer 0.56

22

‘High inflation means that the cost of living is increasing rapidly?’

Fraction of answers Baseline Midline

1. True 95.58 2. False 3.24 99. Don’t know 0.56 88. Refused to answer 0.62

104

23

‘It is less likely that you will lose all of your money if you invest it in more than one project.’ Is this true or false?

Fraction of answers Baseline Midline

1. True 85.86 2. False 10.52 99. Don’t know 1.57 88. Refused to answer 2.06

Accounting skills9 (Interviewer: Read the following to the client)

Ms. Hoa sells pork meat at the open market. She has a small kiosk there. To calculate her profit from this business, she should subtract expenses from the sales. Which of the following should she treat as expenses for this purpose? (1= Yes, 0= No; 99. Don’t know; 88. Refused to answer)

Fraction of answers 0 1 88 99

24 Cost of pork meat 12.47 87.5 0.03

25 Money taken to pay school fees for Ms. Hoa’s son 77.25 22.67 0.03 0.05

26 Payments for hiring an assistant to transport pork meat from suppliers to the market 14.25 85.7 0.03 0.03

27 Money taken to buy food for her family 75.31 24.66 0.03

28 Payment for hiring the kiosk in the market 15.27 84.68 0.05

29 A loan given to her friend to assist her wedding party 78.45 21.5 0.05

30 Telephone calls to friends to check on their health 75.62 24.33 0.05

31 Salary to assistant cleaning the kiosk at the end of the day 17.77 82.2 0.03

Marketing skills10 A marketing strategy consists of 4 elements which are known as 4Ps’ marketing: Product, Price, Place and Promotion. Please let us know the following statements belong to which “P” marketing element:

9 These questions are only in the midline surveys 10 These questions are only in the midline surveys

105

(1= Product; 2= Price; 3= Place; 4= Promotion; 99. Don’t know; 88. Refused to answer)

Fraction of answers 1 2 3 4 88 99

32 Here are some good methods to attract more customers: posters, home visits, loudspeakers, radio, handbills, clear signs, and interesting ‘look’ of your place of business. 15.02 2 5.84 73.8 0.13 3.2

33 It is important to review the price of your product or service on a regular basis. 25.6 68.19 3.39 1.55 0.13 1.15

34 Your product or service must meet customers’ needs. 83.73 10.83 2.32 1.84 0.19 1.09

35 Things to think about when you set your price: your costs, your production level, your competition, and your customers. 24.9 64.01 4.88 4.64 0.24 1.33

36 Your place of sales should be near your customers. 2.35 2.64 92.74 1.34 0.08 0.85

In order to set price of your new product, which information below are relevant:

(1= Yes, 0= No; 99. Don’t know; 88. Refused to answer)

Fraction of answers 0 1 88 99

37 Total costs per product 2.69 97.05 0.05 0.21

38 Percentage of profit you expect 8.42 91.37 0.21

39 Education fee for your children 86.66 13.16 0.18

40 Competitor’s price of similar products 17.67 82.01 0.03 0.29

41 Price client is willing to pay 13.57 86.14 0.29

106

Appendix 3.6: Questions on Measuring Business Practices

(1= Yes, 0= No; 99. Don’t know; 88. Refused to answer)

Records score

Fraction of answers 0 1 88 99

1 Do you record sales/ withdrawals/ record payment for workers in a registry or notebook/?

Baseline 72.5 27.5

Midline 33.09 66.91

2 If yes, can you show us any of these records? Midline 43.27 55.61 0.71 0.42

3 Use records to see how much cash the business has on hand at any point in time

Midline 50.51 49.36 0.13

4 Use records to see how much debt has to pay and to whom

Midline 32.97 67.03

5 Use record to know which goods you make the most profit per item selling

Midline 49 51.29

Marketing/ Sales score

Fraction of answers 0 1 88 99

6 During last 6 months, have you ever tried to diversify and improve quality of products or services which you produce or sell?

Baseline 76.67 23.33

Midline 40.99 58.95 0.03 0.03

7 Visited at least one of its competitor’s businesses to see what products, prices its competitors are charging

Midline

58.14 41.86

8 Asked existing customers whether there are any other products the customers would like the business to sell or produce

Midline

57 42.85

9 Talked with at least one former customer to find out why former customers have stopped buying from this business

Midline

60.78 39 0.03

10 Advertised in any form (last 6 months) Midline 70.89 29 0.03

11 Do you make sale on credit? Baseline 63.18 36.82

Midline 49.8 50 0.03

12 Does your farming/ business face a specific problem last 6 months?

Baseline 78.62 21.38

Midline 84.08 16 0.03

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13 Do you have an idea for a change or innovation to improve your business or to solve the problems faced?

Baseline 98.37 1.63

Midline 92.96 6.96 0.03 0.05

14 Do you implement any activities to increase number of buyers or sell more products during last 6 months?

Baseline 99.33 0.67

Midline 70.14 30 0.03

15 Cooperation with other business people to sell or distribute together

Midline 64.03 36 0.05

16 Decorate your place, product or service to entice a customer to visit your stand, shop or other premises

Midline

67 33.31 0.03

17 Actively discuss all business/ farming activities with your husbands and family members

Midline 30.78 69.22

Business and financial planning score

Fraction of answers 0 1 88 99

18 Do you re-invest profits for growth or continuity of your business?

Baseline 29.33 70.67

Midline 15 85 0

19 Set the business target for sales over the next year Midline 50 50 0

20 Set the business budget of the likely costs your business will have to face over the next year

Midline 63.11 36.81 0.03 0.05

21 Review the financial performance of your business and analyze where there are areas for improvement

Midline

59 40.61 0.03 0

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Appendix 3.7: Post treatment estimates without covariates

Table 3.15B: Impact of G&B training on business knowledge (Post-treatment estimates without covariates)

VARIABLES Business knowledge index 1

Business knowledge index 2

T1 2.231*** 2.707*** (0) (6.64e-10) T2 1.987*** 2.493*** (0) (3.66e-06) Constant 10.20*** 18.56*** (0) (0) Observations 3,826 3,826 R-squared 0.199 0.099

Notes: Robust cluster p-values are in parentheses; Standard errors are clustered at center levels (187 centers); *** p < .01, ** p < .05, * p < .1

Table 3.16B: Impact of G&B training on business practices (Post-treatment estimates without covariates)

(1) (2) (3) (4) VARIABLES General business

practices Innovation Marketing

skills Record & planning

T1 1.254*** 2.959*** 1.690*** 1.934*** (0) (6.04e-11) (0) (0) T2 1.104*** 3.231*** 1.870*** 1.860*** (3.10e-10) (1.15e-07) (0) (0) Constant 0.223** 1.200*** -1.014*** -1.096*** (0.0450) (0) (0) (0) Observations 3,813 3,813 3,805 3,805 R-squared 0.157 0.111 0.193 0.255 Notes: Robust cluster p-values are in parentheses; Standard errors are clustered at center levels (187 centers); *** p < .01, ** p < .05, * p < .1

109

Table 3.17B: Impact of G&B training on business outcomes (Post-treatment estimates without covariates)

(1) (2) (3) VARIABLES Monthly

business profits Monthly

business sales Monthly business

profit margin T1 2,940* 1,254 0.0631 (0.0889) (0.816) (0.168) T2 2,407 -8,070* 0.0980** (0.134) (0.0719) (0.0436) Constant 2,809*** 40,609*** 0.120*** (0.00909) (0) (0.00285) Observations 881 881 881 R-squared 0.008 0.003 0.007

Notes: Robust cluster p-values are in parentheses; Standard errors are clustered at center levels (187 centers); *** p < .01, ** p < .05, * p < .1

Table 3.18B: Impact of G&B training on farming outcomes (Post-treatment estimates without covariates)

(1) (2) (3) VARIABLES Monthly

farming profits Monthly farming

sales Monthly farming

profit margin T1 -17.76 -39.88 -0.00323 (0.777) (0.754) (0.976) T2 -96.70 -147.3 -0.153 (0.161) (0.160) (0.301) Constant -119.8*** 676.8*** -0.224*** (0.00196) (0) (0.00370) Observations 2,565 2,565 1,436 R-squared 0.002 0.001 0.003

Notes: Robust cluster p-values are in parentheses; Standard errors are clustered at center levels (187 centers); *** p < .01, ** p < .05, * p < .1

110

Table 3.19B: Impact of G&B training on business, and farming startup and survival (Post-treatment estimates without covariates)

(1) (2) (3) (4) VARIABLES Business startup Business survival Farming startup Farming survival T1 0.0163* 0.00186 0.0125 0.00883 (0.0913) (0.957) (0.616) (0.713) T2 0.0669*** 0.0511 0.166*** 0.0310 (0.00120) (0.183) (2.71e-05) (0.282) Constant 0.0297*** 0.754*** 0.0606*** 0.899*** (6.74e-08) (0) (3.82e-05) (0) F-test# 5.70 13.92 Prob>F 0.0180 0.0003 Observations 3,826 1,170 3,826 2,887 R-squared 0.014 0.002 0.049 0.001

Notes: Robust cluster p-values are in parentheses; Standard errors are clustered at center levels (187 centers); *** p < .01, ** p < .05, * p < .1. # F-test – = 0.

Appendix 3.8: CACE estimates

Table 3.15C: Impact of G&B training on business knowledge (CACE estimates) (1) (2) (3) Post-treatment Single

difference VARIABLES Business

knowledge index 1

Business knowledge

index 2

Business knowledge

index 1 P1& 2.682*** 3.313*** 2.695*** (0) (0) (0) P2+ 2.463*** 3.250*** 2.486*** (0) (1.72e-07) (0) Constant 10.64*** 18.63*** 9.797***

(0) (0) (0) F-test# 0.52 0.01 0.49 Prob > F 0.4705 0.9196 0.4852 Observations 3,459 3,459 3,459 R2 0.213 0.122 0.218

Notes: Robust cluster p-values are in parentheses; Standard errors are clustered at center levels (187 centers); *** p < .01, ** p < .05, * p < .1. Covariates: age, household size, marital status, years of schooling, and city dummies. # F-test – = 0 &Percentage of total training modules in which invited women in the group T1 participated. +Percentage of total training modules in which invited women in the group T2 participated.

111

Table 3.16C: Impact of G&B training on business practices (CACE estimates)

(1) (2) (3) (4) (5) (6) Post-treatment Single difference

VARIABLES General business practices

Innovation Marketing skills

Record & planning

General business practices

Innovation

P1& 1.494*** 3.599*** 2.053*** 2.331*** 1.512*** 3.584*** (0) (0) (0) (0) (0) (0) P2+ 1.513*** 3.809*** 2.403*** 2.455*** 1.590*** 3.767*** (0) (3.90e-08) (0) (0) (0) (4.75e-08) Constant 0.642*** 0.640 -0.946*** -0.873*** 0.719*** 0.650 (0.00397) (0.320) (0.000755) (0.00120) (0.000871) (0.312) F test# 0.01 0.07 1.41 0.18 0.26 0.05 Prob > F 0.9113 0.7939 0.2356 0.6697 0.6075 0.8189 Observations 3,448 3,448 3,443 3,443 3,447 3,447 R-squared 0.190 0.129 0.221 0.271 0.237 0.132

Notes: Robust cluster p-values are in parentheses; Standard errors are clustered at center levels (187 centers); *** p < .01, ** p < .05, * p < .1. Covariates: age, household size, marital status, years of schooling, and city dummy. # F-test – = 0. &Percentage of total training modules in which invited women in the group T1 participated. +Percentage of total training modules in which invited women in the group T2 participated.

Table 3.17C: Impact of G&B training on business outcomes (CACE estimates) (1) (2) (3) (4) (5) (6)

Post treatment Single difference VARIABLES Monthly

business

profits

Monthly business

sales

Monthly business

profit margin

Monthly

business profits

Monthly business

sales

Monthly business

profit margin

P1& 3,607* 1,520 0.0775 3,504* 1,351 0.0796 (0.0850) (0.817) (0.165) (0.0986) (0.838) (0.152) P2+ 2,988 -8,856 0.122** 2,909 -9,416* 0.123** (0.134) (0.108) (0.0430) (0.146) (0.0872) (0.0424) Constant 5,936 26,132* 0.319*** 6,074 23,693 0.317*** (0.139) (0.0773) (0.00132) (0.130) (0.105) (0.00137) F-test# 0.07 2.31 1.00 0.07 2.50 0.93 Prob > F 0.7860 0.1285 0.3161 0.7945 0.1141 0.3353 Observations 866 866 866 864 864 864 R2 0.012 0.012 0.011 0.016 0.021 0.011

Notes: Robust cluster p-values are in parentheses; *** p < .01, ** p < .05, * p < .1; Standard errors are clustered at center levels (187 centers); Covariates: age, household size, marital status, years of schooling, and city dummy. # F-test – = 0. &Percentage of total training modules in which invited women in the group T1 participated. +Percentage of total training modules in which invited women in the group T2 participated.

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Table 3.18C: Impact of G&B training on farming outcomes (CACE estimates)

(1) (2) (3) (4) (5) (6) Post treatment Single difference VARIABLES Monthly

farming profits

Monthly farming

sales

Monthly farming profit margin

Monthly farming

profits

Monthly farming

sales

Monthly farming profit

margin P1& -9.617 -35.74 0.000929 -8.625 8.624 0.0940 (0.895) (0.808) (0.994) (0.906) (0.950) (0.311) P2+ -110.1 -163.5 -0.185 -112.2 -155.4 0.0112 (0.181) (0.196) (0.271) (0.181) (0.147) (0.929) Constant -158.6 383.5 -0.0376 -138.3 282.0 0.0314 (0.196) (0.208) (0.883) (0.260) (0.292) (0.908) F-test# 1.24 0.71 1.23 1.25 1.40 0.43 Prob > F 0.2661 0.3981 0.2670 0.2635 0.2359 0.5138 Observations 2,533 2,533 1,425 2,487 2,487 569 R2 0.004 0.006 0.007 0.005 0.126 0.032

Notes: Robust cluster p-values are in parentheses *** p < .01, ** p < .05, * p < .1. Standard errors are clustered at center levels (187 centers); Covariates: age, household size, marital status, years of schooling, and city dummy. # F-test – = 0. &Percentage of total training modules in which invited women in the group T1 participated. +Percentage of total training modules in which invited women in the group T2 participated.

Table 3.19C: Impact of G&B training on business, farming entry and survival (CACE estimates)

(1) (2) (3) (4) VARIABLES Business startup Business survival Farming startup Farming survival P1& 0.0204* -0.00488 0.0140 0.0122 (0.0745) (0.908) (0.628) (0.668) P2+ 0.0196 0.0712 -0.0129 0.0356 (0.256) (0.130) (0.532) (0.298) Constant 0.104*** 0.848*** 0.211*** 0.887*** (8.90e-05) (0) (2.96e-06) (0) F-test# 0.00 2.65 1.04 0.52 Prob > F 0.9680 0.1037 0.3073 0.4701 Observations 3,459 1,152 3,459 2,851 R2 0.010 0.009 0.035 0.006

Note: Robust cluster p-values are in parentheses *** p < .01, ** p < .05, * p < .1. Standard errors are clustered at center levels (187 centers); Covariates: age, household size, marital status, years of schooling, and city dummy. &Percentage of total training modules in which invited women in the group T1 participated. +Percentage of total training modules in which invited women in the group T2 participated. # F-test – = 0.

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Chapter 4

The Short-Term Impacts of Gender and Business

Training on Gender Outcomes among Female

Microfinance Clients in Vietnam

4.1 Introduction

The Millennium Development Goals highlight the need to promote gender equality and

empower women as a critical foundation of human capital.11 Over the past 30 years,

advocates of microcredit have considered it a “silver bullet” in promoting gender equality and

strengthening women’s empowerment (Littlefield et al., 2003, Khandker, 2005, Pitt et al.,

2006, Armendáriz and Morduch, 2010). However, many reviews of microcredit and its impact

on women’s empowerment provide mixed results, engendering doubts about methodological

quality due to selection bias (Hulme and Mosley, 1996, Goldberg, 2005, Odell, 2010, Stewart

et al., 2012, Duvendack et al., 2014). Moreover, a few recent randomized control trials

(RCTs) show that there is no discernible impact of microcredit on female empowerment,

health, education, or borrower well-being with regard to work satisfaction, job stress,

socioeconomic status, and so on. In addition, these studies show that microcredit has modest

or no impact on business and farming activities, especially for female entrepreneurs (Banerjee

et al., 2010, Karlan and Zinman, 2010, Crépon et al., 2011, Karlan and Zinman, 2011).

These findings seem to suggest that merely transferring income or expanding access to

credit does not improve women’s status in the household. Nonfinancial services such as adult

literacy and business training programs with credit services can facilitate women’s access to

better jobs or income-generating opportunities and are perhaps the most effective means of

promoting gender equality (Mayoux, 2007). However, recent studies document that women

have obtained limited benefits from business training programs due to both internal and

external barriers (Berge et al., 2011, Giné and Mansuri, 2011). For example, most 11 http://www.un.org/millenniumgoals/gender.shtml

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businesswomen faced time constraints because they bore primary responsibility for

housework. Giné and Mansuri (2011) show that Pakistani businesswomen spent 6.4 hours per

day doing housework whereas businessmen spent only 2 hours. Similarly, Tanzanian female

entrepreneurs spent approximately 10 hours less on their businesses than male entrepreneurs

(Berge et al., 2011). In addition to limiting their time, household chores limit female

entrepreneurs’ flexibility. Therefore, most of these female entrepreneurs are primarily

engaged in operating businesses close to their home.

Field et al. (2010) note that in addition to gender disparity, social barriers reduce the

relative benefits of business training. They show evidence that Muslim women who face the

greatest social constraints do not benefit from business training. In contrast, business training

increased borrowing and business income for upper-caste Hindu women, who faced fewer

social constraints than Muslim women. In addition, women also face internal constraints such

as aversion to competition (Berge et al., 2011), which can create barriers for female

entrepreneurs when implementing important business decisions. Previous studies also point

out that business training had no impact on reducing external constraints for women (Berge et

al., 2011).

Berge et al. (2011) argue that promoting business growth for female entrepreneurs is

more challenging than for male entrepreneurs. Therefore, these findings seem to suggest that

mainstream gender equality and women’s empowerment should be embedded throughout all

“credit plus” activities to promote women’s rights and gender advocacy (Mayoux, 2007).

In contrast to Chapter 3, this chapter focuses on several specific objectives. First, we

evaluate the impact of gender and business (G&B) training on gender outcomes among

female microfinance clients in Vietnam. Specifically, we test the extent to which the

integration of gender perspectives and business skills training helps foster gender equality by

improving gender outcomes for women. To our knowledge, most recent RCTs that evaluate

the impact of business training program have not focused on gender equality outcomes.

Second, we test whether inviting men to join the G&B training helps improve female gender

outcomes. Previous studies suggest that targeting women is not enough when addressing

gender issues. Thus, we posit that it could be crucial to include men rather than ignore them,

and gender equality must be added to intervention programs (Johnson, 2005).

Similar to Chapter 3, we conduct our experiment by employing an RCT to evaluate

the impact of G&B training on female gender outcomes. Chapter 3 describes in detail the

setup of intervention, experimental design, and the data.

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Because we conducted our training intervention in a rural area in Vietnam, examining

its impact on gender outcomes is important not only for researchers but also for policy

makers. Vietnam has made remarkable progress in adopting a legal framework based on

gender equality and creating institutions and programs to support women, but inegalitarian

gender norms still persist (Schuler et al., 2006, World Bank, 2011). Previous literature

discusses two overlapping gender constructions in Vietnam: the Confucian and socialist

models (Schuler et al., 2006, Duvvury et al., 2012). In Confucian tradition, a virtuous woman

should obey the lead of the men in her family, especially her father before marriage, her

husband after marriage, and her eldest son when widowed. After 1946, Vietnamese socialism

had significant impact on “women’s liberation” and encouraged women to participate in

social and political life, even though patriarchal norms were still deeply entrenched.

This country also has passed laws and policies on gender equality and domestic

violence prevention and control, but their implementation is far from satisfactory. Gender

inequality persists in all areas and sectors, especially in rural, mountainous areas dominated

by ethnic minorities. Moreover, high health-care costs and lost productivity due to gender-

based violence are a significant issue for Vietnamese governing bodies. A study conducted by

UN Women shows that Vietnam’s productivity loss due to domestic violence was nearly 1.78

percent of gross domestic product in 2010. The results also indicated that women who

experienced domestic violence earned 35 percent less than those who did not (Duvvury et al.,

2012). The World Economic Forum 2013 Global Gender Gap Index, which ranks countries

according to their gender gaps in economic participation and opportunity, educational

attainment, political empowerment, and health and survival, rated Vietnam 73th out of 136

major and emerging economies. Moreover, compared with other Southeast Asian countries

such as Thailand and China, which have shown improvements in political empowerment

(Thailand) and/or an absolute increase in overall score (China), Vietnam’s score has dropped

7 places from 66 in 2012, mainly due to significant wage inequalities.12.

Our findings, based on intention-to-treat (ITT) and instrumental variables (IV)

estimates, show that G&B training leads to increased gender knowledge and some modest

improvements on noncognitive, business-related skills such as self-esteem and trust behavior.

The training also improves female bargaining power on major expenditure decisions and

reduces physical domestic violence for married women. Moreover, our results show that

inviting husbands has additional impact on women’s behavior changes toward trust but not on

12See for details, The 2013 Global Gender Gap, http://www3.weforum.org/docs/WEF_GenderGap_Report_2013.pdf.

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the other gender outcomes. This limited impact could be due to husbands’ low attendance

rates and the short time frame under consideration. Furthermore, we note that because partner

physical violence against women is a sensitive issue, women are more likely to underreport its

incidence. To counteract this limitation, we use an alternative technique, the so-called list

experiment, to examine the impact of G&B training on physical domestic violence. The list

experiment estimates result in contradictory effects obtained from ITT estimates: invited

women reported partner physical violence more often than those in the control groups. In

addition, women in the groups with invited men were more likely to experience physical

domestic violence than those in the treated groups without men. Moreover, the list experiment

results show that the proportions of women who reported domestic violence are higher than

those who did so under direct questioning. These differential effects in the list experiment

estimates are statistically significant.

The remainder of this chapter is structured as follows: Section 2 discusses the relevant

literature. Section 3 discusses our theory of change of the intervention and addresses potential

risks of the intervention. We use a similar intervention, experimental design, and data as that

described in detail in Chapter 3. To avoid replication, we do not discuss these aspects again in

this chapter. Section 4 briefly discusses the estimation methods. Section 5 reports the

estimated results. Section 6 focuses on list experiment analysis, and Section 7 concludes with

a discussion and suggestions for further research.

4.2 A Brief Survey of the Relevant Literature

Before we examine how microfinance and business training plus services influence women’s

bargaining power, it is necessary to understand a theoretical framework of how decisions are

made within a household. The standard approach of modeling household behavior assumes

that a household acts as a single unit. In particular, the so-called unitary model of the

household is based on Becker’s seminal work (Becker, 1965, Becker, 1974). The unitary

approach assumes that the existence of a household utility function aggregates the preferences

of all members. Households maximize their joint utility function subject to time, technology,

and resource constraints. This approach of household decision making leaves no room for

analyzing conflicts between men and women because common preferences are only one way

in which the household is hypothesized to act as one.

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However, subsequent economists have posited that the unitary approach of modeling

household behaviors is not realistic. The assumption that a household utility function reflects

the preferences of all members is problematic, because each person in the household may

have difference preferences. Thus, economists have proposed alternative models of household

behavior, such as collective models (Chiappori, 1988, Chiappori, 1992). This model assumes

that each person in the household has his or her own preferences. In the existing literature,

there are two broad types of collective models: non-cooperative and cooperative. In the non-

cooperative approach, individual people’s actions are assumed to be conditional on other

actions. Household members may choose not to cooperate with one another (Lundberg and

Pollak, 1993 ). In the cooperative approach, people have the choice to remain single or form a

household. They will choose the latter option when the utility of being married is greater than

the utility associated with being single. With collective models, household decisions can be

representing as outcomes of some bargaining process. The bargaining approach involves

intra-household decision making containing elements of both cooperation and conflict. In

essence, household members will choose to cooperate if cooperative arrangements result in

each of them being better off than noncooperation. However, many cooperative outcomes are

possible, some more favorable to one party than to others. Thus, which outcomes emerge

depend on the relative bargaining power of the household members.

A household member’s bargaining power is defined by his or her relative fallback

positions or “threat points” in the bargaining process. The fallback positions or threat points

refer to a person’s ability to survive and succeed outside the household if he or she does not

cooperate. Improving a person’s fallback position leads to increased power in that person’s

household.

The preceding discussion of household decision-making theories leads us to define

women’s empowerment or bargaining power as their ability to threaten to leave the household

or their husbands. Those threats may depend on factors such as divorce, employment

legislation, support from social networks, and rights and access to communal resources.

Microfinance and business development training services can be among factors influencing

women’s fallback positions.

We now discuss existing microfinance literature that pertains to how integrations of

microfinance and business development services improve female empowerment and reduce

household domestic violence. By December 31, 2011, microfinance institutions reported

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reaching 195 million clients; approximately 75 percent of them are women (2013 Microcredit

Summit Campaign13). The majority of these women live in traditional societies, are married,

and form a household with their husbands. Although advocates have argued that microfinance

is an effective means to promote gender empowerment, microfinance research on women’s

rights documents mixed results. On the one hand, microfinance has improved women’s

empowerment and reduced men’s violence against women (Schuler and Hashemi, 1994 ,

Hashemi et al., 1996, Schuler et al., 1996, Littlefield et al., 2003, Khandker, 2005,

Armendáriz and Morduch, 2010). On the other hand, it has provoked tension and frustration

among household members and increased violent behaviors of men against women because

men felt their authority over their wives was being undermined (Schuler et al., 1998, Rahman,

1999).

These findings suggest that credit alone may not effectively improve women’s intra-

household decision-making power. Unskilled women have few working opportunities outside

the households. Even when they can access microcredit, their loans may be taken over by

their spouses (Armendáriz and Morduch, 2010). Ngo and Wahhaj (2012) argue that a woman

with few skills to implement a productive activity will be unlikely to experience an increase

in bargaining power within the household, if she can access to only credit. Thus, researchers

view offering “credit plus” services such as business development training to female

microfinance clients as a way to help them effectively plan their use of financial services,

protect their interests, and promote an image of women as respected and equal actors in

households and communities (Mayoux, 2007).

Recent RCTs of the impact of training on gender outcomes show mixed results. Kim

et al. (2007) document that combining microfinance with training on HIV infection, gender

norms, domestic violence, and sexuality leads to significant improvements in female

empowerment and reductions in both physical and sexual violence by an intimate partner in

South Africa. Their results also indicate that economics and social empowerment of women

can reduce intimate partner violence. However, the majority of recent RCTs of the impact of

business training show no significant changes in female empowerment or that the effects are

small (Giné and Mansuri, 2011, Karlan and Valdivia, 2011). These studies also report that the

business training does not change attitudes toward domestic violence and gender relations.

13 http://stateofthecampaign.org/data/2011-data/

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These findings suggest that the content of the training plays an important role in the extent to

which the training influences female gender outcomes. Although some training programs aim

to change entrepreneurial attitudes, aspirations, and personal development, the time devoted

to these issues is relatively low (McKenzie and Woodruff, 2014), which could explain why

most recent business training interventions have limited effects on gender outcomes.

As we discuss in Chapter 3, we expect that including men in training that targets

gender issues will enhance gender equality outcomes for women. Excluding husbands could

even negate the impact of the training in that it could generate frustration and intra-household

conflicts (Armendariz and Roome, 2008, Allen et al., 2010). Existing studies show promising

significant results of reducing intimate partner violence and improving female empowerment

in developing countries when men and boys are engaged in the interventions (Kim et al.,

2007, Greubel, 2012).

4.3 Theory of Change

Using the arguments from household decision making theories, we propose a theory of

change to shed new light on how integrating G&B training with microfinance services affects

female economic empowerment and household domestic violence. “Female economic

empowerment” refers to improvements in a woman’s bargaining power in a household

through increased influence on household and business decisions. Figure 4.1 presents a

summary of the theory of change underlying our experiment. Note that we expect that

offering G&B training will influence both business (channel A) and gender (channel B)

outcomes. Although Figure 4.1 depicts a complete summary theory of change, in this section

we discuss only the channel B: how providing G&B training influences gender outcomes. For

a discussion of how G&B training affects business outcomes (channel A), see Chapter 3.

First, we expect that the training will improve knowledge on gender issues for female

clients. Second, the improved gender knowledge will raise the awareness of gender equality

and build women’s gender competence. This in turn will lead to improvements of non-

cognitive, business-related skills such as locus of control, self-esteem, and trust. Many studies

show that non-cognitive abilities can affect social and economic outcomes (Heckman et al.,

2006). We consider these non-cognitive skills signs of personal empowerment, defined as a

person’s improved ability to make strategic life choices.

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Third, we expect greater gender knowledge combined with improved non-cognitive

skills to improve female empowerment. If women believe their fallback positions are better,

they will have better household bargaining power. This belief comes from several sources.

Access to microcredit gives women more opportunities to generate income independently

from their husbands. Together with better gender knowledge and improvements in non-

cognitive abilities, women will be more confident to threaten their husbands that they can

leave the households and still survive and succeed. Those threats can benefit women to gain

more power on household decision making process.

Using some arguments and evidence from existing literature, we discuss the extent to

which non-cognitive abilities determine women’s fallback positions in intra-household

bargaining. First, we define “locus of control” as personal perceptions of the extent to which a

person can influence life events (Begley and Boyd, 1988). People with internal locus of

control believe that they can influence events in life, whereas those with external locus of

control believe that their decisions and lives are controlled by events beyond their influence.

We expect that improved gender knowledge will help to improve women’s belief that they

can control their own lives, an important belief if women are to survive outside the family.

Therefore, greater internal locus of control can help increase intra-household bargaining

power for women due to strengthened fallback positions.

Next, “self-esteem” refers to a person’s overall evaluation of his or her own worth,

value, or importance (Blascovich and Tomaka, 1991). Self-esteem is defined as the sum of

self-confidence (i.e., personal capacity evaluation) and self-respect (i.e., personal worth

evaluation). This belief has important implications for women’s success if they move out of

the households. Thus, we expect improved gender knowledge to enhance women’ self-

esteem. This in turn should increase women’s empowerment because they are more confident

to credibly threaten their husbands that they are still able to survive and succeed outside the

households.

To avoid confusion, we clarify our definition of “trust”: in our context, trust reflects

the willingness to accept uncertainty in a situation on the basis of positive expectations of

other parties’ actions or behaviors (Rousseau et al., 1998, Schoorman et al., 2007, Fulmer and

Gelfand, 2012). It plays important role in the formation of communities in social networks. It

also influences quality and credibility assessments of information and determines how

information flows through a social network (Adali et al., 2010). Strong social networks are

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formed by entities that trust one another. We expect that the improved awareness of gender

quality and gender competence through gender knowledge will strengthen trust behavior for

women. Improving women’s trust can help them build better social networks. This in turn will

enhance their bargaining power in intra-household decision making. The reason is that social

network support from friendship or any other social groups benefits women’s ability to

survive well outside of the households. As a result, women can strengthen their fallback

positions by relying on support from social networks.

Fourth, we expect improved gender knowledge, better non-cognitive skills, and

enhanced female empowerment to reduce household domestic violence. Violence against

women is an explicit manifestation of gender inequality and has become increasingly

recognized as an important risk factor for poor health and economic development outcomes

(Kim et al., 2007). Physical violence is the most obvious form of domestic violence,

involving the intentional use of physical force to harm, injure, disable, or kill another using a

weapon, restraints, or physical size or strength to harm the person. However, physical

violence is not the only form of domestic violence. Acts, threats of acts, or coercive tactics to

cause someone emotional trauma are considered psychological, emotional, or mental

violence. We argue that household domestic violence is a gender obstacle of female economic

empowerment and business development. Previous work also shows that economic and social

female empowerment contributes significantly to reducing intimate partner violence (Kim et

al., 2007).

Fifth, inviting husbands to participate in the G&B training aims to circumvent

potential issues with women-only training groups. We expect that the training will improve

not only women’s but also their spouses’ gender knowledge. In turn, this knowledge should

change husbands’ behavior toward their spouses. For example, they may be more willing to

discuss issues and cooperate with their wives in household decision making. Under collective

models of intra-household decision making, although men and women have different utility

functions, if the assumption that their cooperation through bargaining process still holds, their

collective decisions are Pareto efficient (Manser and Brown, 1980, McElroy and Horney,

1981). In this case, we expect that if women and their spouses are on equal footing in the

household decision process, women will have equal bargaining power with men. Although we

do not have data from husbands on household making decisions and therefore cannot test

whether men and women have equal bargaining power when both of them have opportunities

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to access the training, we expect that changes in husbands’ behaviour due to more willingness

to discuss and cooperate will increase bargaining power for women.

In addition, we expect husbands’ behavior change from the training to reduce intra-

household conflicts by reducing asymmetric information between men and women. If men

know more about the training and microfinance activities, they may be less likely to complain

about the time diverted from household chores and may be willing to share these chores with

their spouses. Thus, women will have more time to attend the training and conduct their

business activities.

Sixth, existing literature on entrepreneurship suggests that non-cognitive, business-

related skills are also associated with entrepreneurial success. In addition, researchers

consider these aspects as determinants of gender gaps in entrepreneurship outcomes and keys

for success in female-owned businesses (Kabeer, 2001, Rauch and Frese, 2007). These

arguments suggest that improvement in non-cognitive abilities also leads to more favorable

business outcomes for female entrepreneurs. For example, many researchers argue that people

with higher internal locus of control are more likely to be involved in entrepreneurship

activities, exploit opportunities, undertake innovative strategies, and be more effective leaders

(Diaz, 2003, Wijbenga and van Witteloostuijn, 2007, Van Praag et al., 2009). In addition,

many studies have indicated that self-confidence (part of self-esteem) affects entrepreneurs’

decisions and actions in their ongoing businesses, thereby acting as a means to achieve

business success and better business outcomes (McClelland, 1987, McCarthy et al., 1993).

Entrepreneurship research has studied trust extensively as well. Researchers consider trust a

form of social capital or an economic lubricant that helps reduce transaction costs between

parties, maintain business-to-business relations, influence successful establishments of new

business venture, form and develop entrepreneurs’ networks, and affect ongoing business

performance (Siu-Lun, 1996, Sanner, 1997, Ali and Birley, 1998, Friman et al., 2002, Smith

and Lohrke, 2008). In addition, trust has been argued to involve in taking risks when it

involves how much confidence a person has in others (Capra et al., 2007). Risk and making

use of trust are essential in entrepreneurial activities (Ali and Birley, 1998). These non-

cognitive abilities play important roles in an entrepreneur’s success; however, many studies

report that women have a lower internal locus of control, lower self-confidence, and higher

risk aversion than men (Croson and Gneezy, 2009, Kirkwood, 2009, Bengtsson et al., 2012).

Therefore, we expect adding gender equality in business training to boost women’s non-

cognitive skills in addition to their entrepreneurship skills. This in turn will help women

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improve their business outcomes. The increases of business outcomes will enhance female

empowerment in that women can generate more independent income from men. Conversely,

female empowerment is also considered a predictor of entrepreneurship outcomes (Kantor,

2005). Existing literature shows that in developing economies, men are often in charge of

most important household and business decisions (Berge et al., 2011, Giné and Mansuri,

2011). The increase in female autonomy may increase the possibilities for women to change

their businesses practices in line with the training they have received. Then better business

practices will increase business outcomes. We describe the impact of the training on business

practices and business outcomes in Chapter 3 (Channel A in our theory of change).

Finally, focusing on female empowerment and household domestic violence is also

important for the longer-term impact of the training on household living standard and poverty

reduction. An increasing body of literature shows that exogenous increases in female share of

income have a greater impact on household welfare such as education, housing, and nutrition

for children (Thomas, 1990, Thomas, 1994, Lundberg et al., 1997, Duflo, 2003). Bargaining

power models of households with non-cooperation as a threat point may lead to the same

predictions if they assume that women have stronger preferences than their spouses for

household-related goods (Lundberg and Pollak, 1993 , Lundberg and Pollak, 1994 ,

Bergstrom, 1996 ); however, we do not test the longer-term impact of the training because it

is beyond the scope of this study. Appendix 4.1 summarizes our main outcomes of interest

and expected signs of intended outcomes.

Potential risks

For several reasons, the preceding predictions may be overly optimistic. We recognize

the following limitations. First, the gender training module may not be relevant or the

participants might not like the module. However, the results from training quality assessment

in Chapter 3 highlight the self-reported importance of the gender training module. Both

invited women and their husbands reported that they appreciated this training module. In

addition, the results from focus group discussions show that the invited women valued the

gender module highly.

Second, the training may lead to increased intra-household conflicts if husbands do not

approve of their spouses attending the training. They may feel frustrated because they believe

the training diverts women’s time from household chores. Moreover, the training may

124

generate more household conflicts in that women may be likely to argue more often with their

husbands in household decision process and be less passive.

Third, even if the training decreases intra-household conflicts, it could also lead to a

loss of female autonomy. Husbands might notice that their spouses do not acquiesce to their

decisions easily. Because women more often threaten their spouses by the possibilities of

moving out the households, men may feel that their authority over their wives is undermined.

Thus, men might decide to increase their bargaining power regarding household decision

making.

Fourth, the presence of other men at the training could cause husbands to become

jealous and/or consider the training “unsafe” for their spouses. This may provoke tension and

frustration between men and their spouses, which may lead to increased violence.

Fifth, inviting men to join the training may lead to no improvements in female

empowerment or even worsen female autonomy. If both men and women attend the training,

they have both learned the same knowledge. Thus, the women have no “superior” knowledge

and may not easily argue or “threaten” their husbands in household decision processes. As a

result, their bargaining power may not be improved or even worsened. We explicitly test for

these risks on our main outcomes of interest.

125

Figu

re 4

.1: T

heor

y of

cha

nge

of th

e im

pact

of G

&B

trai

ning

INPU

TS

AC

TIV

ITIE

S O

UT

PUT

S O

UT

CO

ME

S L

ON

GE

R-T

ER

M

IMPA

CT

S

-G&

B

train

ing

mat

eria

ls

-Tra

ined

st

aff

-Pro

vide

train

ing

and

faci

litat

e

disc

ussi

on

on

G&

B

issu

es.

-Inv

ite

husb

ands

to c

ome

to

the

train

ing

Trai

ned

fem

ale

clie

nts

Trai

ned

clie

nts’

hu

sban

ds

Bus

ines

s kn

owle

dge

- Red

uced

po

verty

- Wom

en

bene

fit fr

om

econ

omic

gr

owth

in

rura

l are

as

Bus

ines

s pr

actic

es

- Bus

ines

s & fa

rmin

g ou

tcom

es

- Bus

ines

s & fa

rmin

g en

try a

nd su

rviv

al

Gen

der

know

ledg

e

Non

-cog

nitiv

e sk

ills:

- L

ocus

of c

ontro

l - S

elf-

este

em

- Tru

st

- Wom

en e

mpo

wer

men

t -

Hou

seho

ld d

omes

tic

viol

ence

B

A

Out

com

es v

aria

bles

Gen

der

know

ledg

e

- Loc

us o

f con

trol

- Sel

f-es

teem

- T

rust

frie

nds

- Gen

eral

Soc

ial S

urve

y (G

SS) T

rust

- G

SS F

airn

ess

- GSS

Hel

pful

ness

- T

rust

stra

nger

s

- Maj

or e

xpen

ditu

re

deci

sion

s - D

aily

nee

ds

deci

sion

s - B

usin

ess d

ecis

ion

- Far

min

g de

cisi

on

- Phy

sica

l vio

lenc

e - P

sych

olog

ical

vi

olen

ce

126

4.4 Estimation Methods

We briefly summarize the estimation methods in this section. We use similar estimation

methods in Chapter 3, where they are discussed in detail in Section 3.9.

We use three estimation techniques: (1) a simple ordinary least squares regression

using midline data only (post-treatment); (2) a single difference method (also called analysis

of covariance - ANCOVA), in which baseline values for the outcome variables are added to

the set of control variables; and (3) a double difference (DD) model using both baseline and

midline data. These estimation methods are specified as follows.

First, we estimate post treatment specifications:

(1)

where denotes the outcome variable for client i at the center j at the midline survey (t=1).

We summarize all of outcome variables of interest in the Appendix 4.1. is a dummy

variable that takes a value of 1 if a female client and her husband were invited to the training,

and 0 otherwise. is a dummy variable that takes a value of 1 if only the female client

was invited to the training and 0 otherwise. are covariates measured at the baseline (t=0).

We add the following controls: age, household size, marital status dummy (1= married, 0 =

otherwise), years of schooling, and a city dummy (1= Hanoi, 0= Vinhphuc). is an error

term. We assume regressors are orthogonal to the error term for all observations. Our

coefficients of interest are β and . β measures the training impact on female outcome

variables for the group of (invited) female clients whose husbands were also invited.

measures the training impact of (being invited to) the group in which husbands were not

invited.

Second, we estimate single difference specifications. We regress the outcome

variables on and , lagged outcome variables and a set of control variables

(t=0). Similar to post treatment estimates, β and are the coefficients of interest:

(2)

Third, we estimate a DD model:

127

(3)

where is a dummy with a value of 0 for the baseline observations and 1 for the midline

observations. Similar to both specifications above, β and are the coefficients of interest.

We estimate the DD specification on a balanced panel, which implies that the results

are similar to a household fixed effects specification with time dummies. We cluster all

standard errors at the credit center level. In case in which we have only midline data, we

estimate only post treatment models.

In addition, we employ instrumental variable (IV) regressions to estimate complier-

average causal effect (CACE) of the impact the training. We regress in the first stage the

“percentage of total training modules that is attended by a female client in the groups T1 and

T2, respectively” ( ) on the training dummies (T1 and T2). In the second stage, we

regress outcome variables on the predicted values of variables and the control

variables. We estimated the models with a 2SLS procedure. We use the following

specifications for the post treatment and single difference specifications in the second stage of

the regression:

(4)

(5)

where ( ) refers to the percentage of total training modules that a woman in the

groups T1 (T2) has followed (t=1). are covariates measured at the baseline. We include

controls for age, household size, marital status, years of schooling, and a city dummy in all

specifications. is an IIDN(0, σ2) error term. Our coefficients of interest are β and . β

measures the impact of the training on gender outcomes for women in the groups T1 who

were invited and actually joined the training. captures the impact of the training on gender

outcomes for women in the groups T2 who were invited and actually joined the training. The

results of CACE estimates are reported in Appendix 4.9.

Similar to Chapter 3, we also employ instrumental variable (IV) regressions to

estimate the additional impact of inviting husbands on gender outcomes. We use the

following specifications for the post-treatment and single difference specifications in the

second stage of the regression:

128

(6)

(7)

where refers to the percentage of total training modules that a husband has

followed (t=1). is a dummy variable that takes a value of 1 if a (female) client is

invited to the training, and 0 if not, irrespective of being in group T1 or T2. are covariates

measured at the baseline. We include controls for age, household size, marital status, years of

schooling, and a city dummy in all specifications. is an IIDN(0, σ2) error term. in

specifications (6) and (7) captures the additional impact of inviting husbands to join the

training on women’s outcomes.

To obtain consistent estimates, we instrument in the first stage with the

randomly determined variable T1. We estimated the models with a 2SLS procedure. To

control for low rates of husband compliance, we also used two alternative approaches,

discussed in detail in Section 3.9 in Chapter 3. For reasons of space we present only the

results of the first specification, using , in the Appendix 4.2. The two

alternative specifications provide similar results and can be obtained on request.

4.5 Estimated Results

We estimate post-treatment, single difference and DD specifications with and without

covariates. In general, the estimated results in specifications with and without covariates are

not different. We present the intention to treat (ITT) estimates with covariates in the main

tables. The estimated results of the post-treatment without covariates are reported in

Appendix 4.8 with corresponding table number and suffix “B”. We report the results of

CACE estimates in Appendix 4.9 with the corresponding table number and suffix “C”. The

results of the IV estimates of the additional impact of inviting husbands are reported in

Appendix 4.2 with the corresponding table number and suffix “A”.

4.5.1. Effects of G&B Training on Gender Knowledge We construct a gender knowledge index by counting correct answers on gender questions (see

Appendix 4.4 for detail questions on measuring gender knowledge and descriptive statistics).

This knowledge index is based on the sum of correct answers to four questions on gender

issues. The underlying data for this index is only available in the midline survey. Table 4.1

presents the results of offering G&B training on the gender knowledge. It shows that after the

129

training, women in both treatment groups T1 and T2 significantly improved their gender

knowledge. Specifically, trained women in the groups T1 and T2 have approximately one

score significantly higher than those in the control groups. The impact of the training is

significantly stronger in the CACE estimates for women who actually participated the training

(see Table 4.1C in Appendix 4.9). These results are in line with our expectation in the theory

of change. The estimated results are similar in the specifications with and without covariates

(see Table 4.1B in Appendix 4.8). The effects of the training on gender knowledge are

stronger for women in the groups to which men are invited (T1). However, the results of the

F-test show that the difference of G&B training impact on the gender knowledge is not

statistically significant for women between groups with men (T1) and without men (T2). The

IV estimates of Table 4.1A in Appendix 4.2 also confirm that the additional impact of inviting

husbands on the gender knowledge is not statistically significant.

Table 4.1: Impact of G&B training on gender knowledge

VARIABLES Gender knowledge T1 1.085*** (0) T2 0.978*** (0) Constant 2.270*** (0) F test# 0.90 Prob > F 0.3437 Observations 3,496 R2 0.246

Note: Robust cluster p-values are in parentheses; Standard errors are clustered at center levels (187 centers); *** p < .01, ** p < .05, * p < .1. Covariates: age, household size, marital status, years of schooling, and city dummies. # F-test – = 0

4.5.2. Effects of G&B Training on Non-Cognitive, Business-related Skills

4.5.2.1 Effects of G&B Training on Locus of Control

To measure locus of control, we use a four-item abbreviated version of the Rotter Internal–

External Locus of Control Scale (Rotter, 1966) (see Appendix 4.5 for detail questions on

measuring locus of control and descriptive statistics). The scale is designed to measure the

extent to which women believe that they can control their lives (internal control) or whether

their lives are controlled by the environment (e.g., by chance, fate, luck) (external control). A

higher score implies more external control. A respondent viewed a set of paired statements

130

and selected the statement most in line with her own opinion. To calculate the Rotter scores,

we generated a two-point scale of each paired question. Next, we added the scores of each

pair to calculate the total scores. Our Rotter scores have a minimum value of 4 (high internal

control), and maximum value of 8 (high external control). The average value of the Rotter

score in our sample is approximately 5.5 (SD = .89).

Columns (1), (3), and (5) of Table 4.2 describe the impact of the training on locus of

control. The results show that women in both treatment groups believe they have more control

over their lives. However, the results are not significant. These results also hold in the post –

treatment specifications without covariates and in CACE estimates (see Table 4.2B in

Appendix 4.8 and Tables 4.2C in Appendix 4.9). If we accept a significance level at 11%

(especially for DD specifications), we observe significant improvements on internal locus of

control for the women in the treatment groups without men (T2). Again, inviting husbands

does not add any significant impact on locus of control for women. This result is confirmed

by the results of F-tests and the IV estimates in Tables 4.2A in Appendix 4.2.

4.5.2.1 Effects of G&B Training on Self-esteem

To measure self-esteem, we use the Rosenberg (1965) self-esteem scale, which includes 10

items using five-point Likert-type scales (“strongly disagree,” to “strongly agree”) (see

Appendix 4.5 for detail questions on measuring self-esteem and descriptive statistics).

Approximately half these items were positively worded, and half the other were negatively

worded. Therefore, we reverse coded items c, e, h, i, and j before calculating the total scores.

Higher scores are associated with the higher self-esteem. In our sample, a minimum score on

the Rosenberg self-esteem index is 16, a maximum value is about 48, and an average score is

around 35 (SD= 3.6).

Columns (2), (4), and (6) of Table 4.2 report the results of the training impact on self-

esteem. We find that the training improves women’s self-esteem. However, these

improvements on self-esteem are only statistically significant on the DD specifications for

women in the treatment groups without men (T2). If we assume a significance level of 12%,

we also find significant improvements of self-esteem at post treatment and single difference

specifications for invited women in both treatment groups. These results do not change much

in the post-treatment without covariates (see Table 4.2B in Appendix 4.8). However, the

results in the CACE estimates show that the training improves significantly self-esteem for

invited women in the groups T1 who actually participated the training (see Table 4.2C in

131

Appendix 4.9). The results of F-tests show that the training impact on self-esteem is not

significantly different between treated women in the groups with men (T1) and without men

(T2). These results are confirmed by the IV estimates in Table 4.2A in Appendix 4.2, which

exhibit no additional significant impact of inviting husbands on women’s self-esteem.

Table 4.2: Impact of G&B training on locus of control and self-esteem

(1) (2) (3) (4) (5) (6) Post-treatment Single difference DD

VARIABLES Locus of control

Self-esteem

Locus of control

Self-esteem

Locus of control

Self-esteem

T1 -0.0236 0.638 -0.0290 0.632 0.0841 0.257 (0.781) (0.101) (0.744) (0.103) (0.249) (0.538) T2 -0.150 0.829 -0.202 0.823 0.0871 -0.398 (0.328) (0.119) (0.204) (0.118) (0.372) (0.470) T1 × time -0.108 0.369 (0.343) (0.512) T2 × time -0.285 1.211* (0.101) (0.0927) Constant 5.782*** 34.37*** 5.568*** 32.53*** 5.837*** 34.88*** (0) (0) (0) (0) (0) (0) F test# 0.67 0.13 1.16 0.13 0.99 1.20 Prob > F 0.4148 0.7184 0.2839 0.7139 0.3201 0.2739 Observations 3,396 3,427 2,803 3,347 5,606 6,694 R2 0.013 0.018 0.016 0.021 0.037 0.012

Note: Robust cluster p-values are in parentheses; Standard errors are clustered at center levels (187 centers); *** p < .01, ** p < .05, * p < .1. Covariates: age, household size, marital status, years of schooling, and city dummies. # F-test – = 0.

4.5.2.2 Effects of G&B Training on Trust

We use various indicators to measure trust. First, we employed a trust question widely used in

the General Social Survey (GSS), initially introduced by (Almond and Verba, 1963) in a

study of civil society in postwar Europe. The content of this question is as follows:

“Generally speaking, would you say that most people can be trusted, or that you can’t be too

careful in dealing with people? (GSS Trust) This trust question is broadly used in not only the

GSS but also the World Values Survey and Australian Community Survey, though it has been

criticized as vague, abstract, and difficult to interpret. For example, (Glaeser et al., 2000)

examine whether behavior in trust game is correlated with the result of the trust question and

do not find significant correlations. Their results are also confirmed by many subsequent

studies (Gächter et al., 2004, Johansson-Stenman et al., 2005, Haile et al., 2008, Holm and

Nystedt, 2008, Ermisch et al., 2009). Therefore, an alternative trust question has been

proposed “You can’t count on strangers anymore” (Trust stranger) (Glaeser et al., 2000).

132

Next, we use two other questions often asked in the GSS: “Do you think most people

would try to take advantage of you if they got the chance, or would they try to be fair?” (GSS

Fairness), and “Would you say that most of the time people try to be helpful, or that they are

mostly just looking out for themselves?” (GSS Helpfulness). Our last question to measure trust

is as follows: “Do you trust someone who is not a relative (any close friend)?” Rather than

measure a general trusting attitude, this trust question gauges trust toward friends. When

comparing the results from behavioral experimental games and survey questions on trust,

many studies show that trust toward friends and trust toward strangers are significantly related

to the amount sent by senders in the trust game (Glaeser et al., 2000, Fehr et al., 2003, Naef

and Schupp, 2009). The trust toward strangers and two items from the GSS including GSS

fairness and GSS helpfulness are also significantly correlated with cooperation results in a

one-shot public goods game (Gächter et al., 2004). These findings suggest that our

measurements of trust including trust toward friends, trust toward strangers, GSS fairness,

GSS helpfulness and GSS trust are reliable. These indicators capture a person’s confidence in

others (see descriptive statistics Appendix 4.5). We construct these variables as dummy

variables, with 1 indicating positive trust features and 0 indicating negative trust features (see

detail in Appendix 4.1). Table 4.3 reports the training impact on trust behavior. The results

show that the training leads to significant improvements in women’s trust toward strangers in

the treatment groups with invited men (T1). These results still holds in the post-treatment

estimates without covariates (see Table 4.3B Appendix 4.8). The results of F-tests also

confirm that the effects of the training on trust toward strangers are significantly different for

women between the treatment groups with men (T1) and without men (T2) (especially in the

DD specifications). The IV estimates in Table 4.3A in Appendix 4.2 do not indicate any

additional significant impact of the training on women’s trust in the treatment groups in which

men were invited and participated the training. Beside stronger effects of the training on trust

toward strangers, the CACE estimates also show that the training has significant impact on

trust toward close friends and belief of fairness for invited women in the groups T1 who

actually joined the training (see Table 4.3C in Appendix 4.9). These results match our

expectations discussed in the theory of change. Therefore, we conclude that the training has

positively significant impact on trust behavior for women.

In short, six months after the training was complete, we find some limited

improvements of non-cognitive skills such as self-esteem and trust behavior for women in

treated groups. These results also hold in the post-treatment specifications without covariates

133

(see Table 4.2B, 4.3B in Appendix 4.8). These limited positive changes in these non-cognitive

skills are factors that could strengthen the fallback positions for women in intra-household

decision making processes.

134

Tab

le 4

.3: I

mpa

ct o

f G&

B tr

aini

ng o

n tr

ustƱ

(1

) (2

) (3

) (4

) (5

) (6

) (7

) (8

) (9

) (1

0)

Po

st tr

eatm

ent

Sing

le d

iffer

ence

V

AR

IAB

LES

Trus

t frie

nd

GSS

Tr

ust

GSS

Fa

irnes

s G

SS

Hel

pful

ness

Trus

t Stra

nger

Tru

st fr

iend

G

SS

Trus

t G

SS

Fairn

ess

GSS

H

elpf

ulne

ss Tr

ust S

trang

er

T1

0.03

96

-0.0

193

0.05

87

0.00

0369

0.

119*

0.

0397

-0

.027

1 0.

0608

-0

.003

17

0.12

8**

(0

.112

) (0

.750

) (0

.151

) (0

.995

) (0

.053

7)

(0.1

11)

(0.6

46)

(0.1

40)

(0.9

54)

(0.0

387)

T2

0.

0271

-0

.044

1 -0

.021

7 0.

0232

0.

0473

0.

0269

-0

.047

4 -0

.043

9 0.

0166

0.

0010

7

(0.5

18)

(0.5

88)

(0.7

23)

(0.7

56)

(0.5

64)

(0.5

22)

(0.5

57)

(0.4

94)

(0.8

33)

(0.9

89)

Con

stan

t 0.

813*

**

0.28

1***

0.6

48**

* 0.

284*

**

0.04

77

0.80

7***

0.

249*

* 0.

493*

**

0.21

8**

0.00

524

(0

) (0

.003

33)

(0)

(0.0

0171

) (0

.581

) (0

) (0

.012

6) (

3.27

e-05

) (0

.038

0)

(0.9

56)

F-te

st#

0.10

0.

09

1.76

0.

09

0.70

0.

10

0.06

2.

80*

0.06

2.

64

Prob

> F

0.

7539

0.

7675

0.

1868

0.

7641

0.

4033

0.

7489

0.

8066

0.

0963

0.

8068

0.

1057

O

bser

vatio

ns

3,48

7 3,

347

2,98

2 2,

946

2,71

0 3,

487

3,01

3 1,

989

2,19

1 2,

330

R2

0.03

8 0.

008

0.01

5 0.

045

0.04

2 0.

038

0.03

6 0.

047

0.06

2 0.

064

Not

e: R

obus

t clu

ster

p-v

alue

s ar

e in

par

enth

eses

; Sta

ndar

d er

rors

are

clu

ster

ed a

t cen

ter l

evel

s (18

7 ce

nter

s); *

** p

< .0

1, *

* p

< .0

5, *

p <

.1. C

ovar

iate

s: a

ge, h

ouse

hold

size

, m

arita

l sta

tus,

year

s of s

choo

ling,

and

city

dum

mie

s. #

F-te

st

–

= 0

. Ʊ

We

also

con

duct

pro

bit e

stim

ates

, and

the

resu

lts a

re si

mila

r (av

aila

ble

on re

ques

t).

135

Tab

le 4

.3: I

mpa

ct o

f G&

B tr

aini

ng o

n tr

ustƱ

(con

t.)

(1

1)

(12)

(1

3)

(14)

(1

5)

D

oubl

e di

ffer

ence

V

AR

IAB

LES

Trus

t frie

nd

GSS

Tr

ust

GSS

Fa

irnes

s G

SS

Hel

pful

ness

Trus

t Stra

nger

T1

-0.0

124

-0.0

0204

-0

.005

99

-0.0

319

-0.0

500

(0

.704

) (0

.970

) (0

.879

) (0

.551

) (0

.366

) T2

0.

0187

-0

.051

9 -0

.004

54

-0.0

396

0.03

32

(0

.653

) (0

.425

) (0

.928

) (0

.577

) (0

.644

) T1

× ti

me

0.05

42

-0.0

246

0.06

64

0.02

02

0.16

9**

(0

.189

) (0

.708

) (0

.135

) (0

.762

) (0

.028

1)

T2 ×

tim

e 0.

0085

1 -0

.003

69

-0.0

395

0.05

02

-0.0

288

(0

.890

) (0

.970

) (0

.549

) (0

.563

) (0

.721

) C

onst

ant

0.65

4***

0.

158*

* 0.

670*

**

0.33

8***

0.

120

(0

) (0

.030

4)

(0)

(0.0

0010

5)

(0.1

42)

F-te

st#

0.52

0.

04

2.80

* 0.

11

6.89

***

Prob

> F

0.

4731

0.

8371

0.

0962

0.

7350

0.

0094

O

bser

vatio

ns

6,97

4 6,

026

3,97

8 4,

382

4,66

0 R

2 0.

035

0.03

1 0.

013

0.02

9 0.

030

Not

e: R

obus

t clu

ster

p-v

alue

s ar

e in

par

enth

eses

; Sta

ndar

d er

rors

are

clu

ster

ed a

t cen

ter l

evel

s (18

7 ce

nter

s); *

** p

< .0

1, *

* p

< .0

5, *

p <

.1. C

ovar

iate

s: a

ge, h

ouse

hold

size

, m

arita

l sta

tus,

year

s of s

choo

ling,

and

city

dum

mie

s. #

F-te

st

–

= 0

. Ʊ

We

also

con

duct

pro

bit e

stim

ates

, and

the

resu

lts a

re si

mila

r (av

aila

ble

on re

ques

t).

136

4.5.3. Effects of G&B Training on Female Empowerment In this section, we examine whether G&B training improves women’s decision-making power

in both household and business decisions. In both the baseline and midline surveys, we asked

10 questions related to household decisions and 2 questions related to business decisions

regarding primary business and farming activities (see Appendix 4.6 for detail questions on

measuring female empowerment and descriptive statistics).

First, the questions involving household decisions relate to different issues, such as

decision power on asking for a loan, food and clothing item purchases, educational

expenditures, expenditures related to durable items, health expenditures, saving for

households, housing purchases, improvement or repair, where to invest surplus money, and

how to assist family members in case of financial problems. For each decision category, we

record whether the principal decision maker is the woman (1), her spouse or other (0), or both

of them (.5). Then we employ principal component analysis to extract two factors: one related

to daily decisions such as food, clothing, and the tuition fee and the other related to major

expenditure decisions (See Appendix 4.3 for more detail of principal component analysis).

Second, regarding business decisions, we asked respondents who made the most

business decisions on how to manage the primary business and farming activities. These two

questions were only asked if a household is conducting business and/or farming activities.

Then we record who is a principal decision maker in each question and assign the scores as

described above.

Because decision power can only be affected by training if a woman is married, we

focus on a sample of married women (approximately 80 percent of the entire sample). Table

4.4 shows that the training improves household bargaining power, particularly on major

expenditure decisions for married women in both treatment groups (especially in DD

specifications). These results are in line with our expectations mentioned in the theory of

change. In the post-treatment and single difference specifications, the impact of training on

improving household bargaining power of major expenditure decisions is only significant for

women in the groups T1. These results hold in the specifications without covariates and in the

CACE estimates (see Table 4.4B in Appendix 4.8 and Table 4.4C in Appendix 4.9). However,

the F-test results show that the training impact on household bargaining power of major

expenditure decisions is not significantly different between the treatment groups with and

without men. In addition, we do not find evidence for a significant impact of the training on

137

household bargaining position related to daily decisions. The results are understandable

considering the focus group discussions: we noted that most of women had strong household

decision-making power on small purchases such as food or clothing before the training for

both treatment and control groups. The results of Table 4.4 also report that the training did not

improve bargaining power on business and farming decision for married women. However,

we puzzle over the results of the CACE estimates of the impact of training on farming

decision for married women in the groups T1 (see Table 4.4C in Appendix 4.9). It seems after

the training, women in the groups T1 who actually participated the training are less likely to

involve in farming decisions. On the one hand, these findings may support our expectations

discussed in the theory of change. Trained women who actually participated on the training

may spend their time more on business activities. Consequently, they involve less in farming

decisions. On the other hand, the results of CACE estimates may be in line with potential

risks of the training mentioned in the theory of change. Inviting men to the training may lead

to a loss of female autonomy. Because most households in the sample are doing at least one

farming activity, farming decisions are considered as one of important decisions in a

household. If men feel their authority over their wives is undermined, they may decide to

increase their bargaining power regarding household decision making. It may explain the

reasons for the negative impact of the training on the farming decisions. However, the F-test

results show that the training impact on female empowerment in all specifications is not

significantly different between the treatment groups with and without men. These results are

confirmed by our finding of no additional impact of inviting husbands on female

empowerment in both household and business decisions in the IV estimates in Table 4.4A in

Appendix 4.2.

4.5.4. Effects of G&B Training on Household Domestic Violence To measure household domestic violence, we asked respondents how often their spouses had

engaged in physical or psychological violence toward them within the past six months on a

five-point scale (0 = “never to 4 = “very often”). We adapted the questions of physical

violence from (Straus, 1979). We drew the psychological violence questions from the

Domestic Violence against Women study conducted by the international World Health

Organization (Garcia-Moreno et al., 2006) (see Appendix 4.7 for detail questions on

measuring household domestic violence and descriptive statistics). We employ principal

component analysis to construct physical and psychological violence indices. The physical

violence and psychological violence indices are the first factors of individual responses across

138

three categories of physical violence and four categories of psychological violence,

respectively.

Table 4.5 reports the effects of G&B training on household domestic violence for

married women. We find that being invited to the training helps to reduce physical violence

significantly for women in both treatment groups. These results are significant stronger for

those who were invited and actually joined the training in the CACE estimates (see Table

4.5C in Appendix 4.9). These findings are in line with what we expected in the theory of

change. However, these findings only hold in the post treatment and single difference

specifications. These results also hold in the post-treatment specifications without covariates

(see Table 4.5B in Appendix 4.8). In addition, the training effects on reductions of physical

violence are stronger for women in the treatment groups without men (at least in the post

treatment and single difference specifications). Moreover, we find slightly significant

evidence that the training also helps to reduce psychological violence for trained women in

the groups without men. However, the results only hold in DD specifications. The F-test

results suggest that the impacts of training on physical and psychological domestic violence

for women in groups T1 and T2 do not statistically differ from each other. These results are

confirmed by the IV estimates in Table 4.5A in Appendix 4.2.

139

Tab

le 4

.4: I

mpa

ct o

f G&

B tr

aini

ng o

n m

arri

ed w

omen

’s b

arga

inin

g po

wer

(1

) (2

) (3

) (4

) (5

) (6

) (7

) (8

) (9

) (1

0)

(11)

(1

2)

Po

st tr

eatm

ent

Sing

le d

iffer

ence

D

oubl

e di

ffer

ence

V

AR

IAB

LES

Maj

or

expe

nditu

re

deci

sion

Dai

ly

need

s de

cisi

on

Bus

ines

s de

cisi

on§

Farm

ing

deci

sion

§ M

ajor

ex

pend

iture

de

cisi

on

Dai

ly

need

s de

cisi

on

Bus

ines

s de

cisi

on§

Farm

ing

deci

sion

§ M

ajor

ex

pend

iture

de

cisi

on

Dai

ly

need

s de

cisi

on

Bus

ines

s de

cisi

on§

Farm

ing

deci

sion

§

T1

0.23

0**

0.20

4 0.

0817

-0

.077

0 0.

238*

* 0.

200

0.04

63

-0.0

786

-0.1

25

-0.0

108

0.05

05

0.00

114

(0

.020

9)

(0.1

79)

(0.2

97)

(0.1

43)

(0.0

143)

(0

.171

) (0

.526

) (0

.124

) (0

.240

) (0

.943

) (0

.102

) (0

.953

) T2

0.

0688

0.

0278

-0

.070

1 -0

.089

4 0.

106

0.05

82

-0.0

843

-0.0

805

-0.3

37**

-0

.185

0.

0223

-0

.024

5

(0.6

18)

(0.8

99)

(0.5

09)

(0.1

53)

(0.4

56)

(0.7

81)

(0.3

67)

(0.1

84)

(0.0

357)

(0

.429

) (0

.557

) (0

.415

) T1

× ti

me

0.35

0***

0.

228

-0.0

158

-0.0

656

(0

.007

45)

(0.2

05)

(0.8

18)

(0.1

61)

T2 ×

tim

e

0.

374*

0.

209

-0.0

937

-0.0

362

(0

.080

5)

(0.3

91)

(0.1

92)

(0.5

33)

Con

stan

t -0

.779

***

0.41

5 0.

560*

** 0

.771

***

-0.5

89**

* 0.

553*

* 0.

384*

** 0

.653

***

-0.4

27**

0.

642*

**

0.45

6***

0.6

77**

*

(0.0

0058

0)

(0.1

25)

(6.2

1e-0

8)

(0)

(0.0

0768

) (0

.033

3)

(8.0

8e-0

5)

(0)

(0.0

209)

(0.

0024

5) (

6.20

e-09

) (0

) F-

test

# 1.

73

0.72

2.

05

0.04

1.

07

0.52

1.

89

0.00

0.

01

0.01

0.

94

0.24

Pr

ob >

F

0.19

02

0.39

57

0.15

22

0.83

94

0.30

18

0.47

10

0.16

99

0.97

39

0.90

81

0.93

78

0.33

32

0.62

18

Obs

erva

tions

2,

811

2,81

1 73

3 2,

226

2,81

1 2,

811

727

2,20

8 5,

316

5,31

6 1,

400

4,25

6 R

2 0.

010

0.03

2 0.

0158

0.

0373

0.

029

0.06

1 0.

0696

0.

0522

0.

016

0.02

3 0.

0141

0.

0492

N

ote:

Rob

ust c

lust

er p

-val

ues

are

in p

aren

thes

es; S

tand

ard

erro

rs a

re c

lust

ered

at c

ente

r lev

els (

187

cent

ers)

; ***

p <

.01,

**

p <

.05,

* p

< .1

. Cov

aria

tes:

age

, hou

seho

ld si

ze,

mar

ital s

tatu

s, ye

ars o

f sch

oolin

g, a

nd c

ity d

umm

ies.

# F-

test

–

=

0.

(§ )We

also

con

duct

Tob

it es

timat

es fo

r the

se d

epen

dent

var

iabl

es a

nd th

e re

sults

are

sim

ilar (

avai

labl

e on

requ

est).

140

Tab

le 4

.5: I

mpa

ct o

f G&

B tr

aini

ng o

n do

mes

tic v

iole

nce

for

mar

ried

wom

en

(1)

(2)

(3)

(4)

(5)

(6)

Po

st tr

eatm

ent

Sing

le d

iffer

ence

D

oubl

e di

ffer

ence

V

AR

IAB

LES

Phys

ical

vi

olen

ce

Psyc

holo

gica

l vi

olen

ce

Phys

ical

vi

olen

ce Ps

ycho

logi

cal

viol

ence

Ph

ysic

al

viol

ence

Psyc

holo

gica

l vi

olen

ce

T1

-0.0

880*

0.

0934

-0

.087

5*

0.09

21

-0.0

349

0.10

9

(0.0

872)

(0

.325

) (0

.088

3)

(0.3

30)

(0.6

19)

(0.2

52)

T2

-0.1

28**

-0

.067

9 -0

.127

**

-0.0

702

-0.0

937

0.19

4*

(0

.038

9)

(0.4

67)

(0.0

402)

(0

.456

) (0

.187

) (0

.094

2)

T1 ×

tim

e

-0

.059

2 -0

.027

8

(0.4

76)

(0.8

37)

T2 ×

tim

e

-0

.037

5 -0

.268

*

(0.6

84)

(0.0

888)

C

onst

ant

0.14

0 0.

114

0.13

7 0.

112

0.37

1***

0.

253*

*

(0.1

37)

(0.3

54)

(0.1

46)

(0.3

65)

(1.5

7e-0

5)

(0.0

350)

F-

test

# 0.

49

1.94

0.

48

1.95

0.

06

1.83

Pr

ob >

F

0.48

40

0.16

48

0.49

11

0.16

43

0.80

46

0.17

80an

d

Obs

erva

tions

2,

891

2,89

7 2,

891

2,89

7 5,

470

5,48

2 R

2 0.

020

0.03

9 0.

020

0.03

9 0.

017

0.02

1 N

ote:

Rob

ust c

lust

er p

-val

ues

are

in p

aren

thes

es; S

tand

ard

erro

rs a

re c

lust

ered

at c

ente

r lev

els (

187

cent

ers)

; ***

p <

.01,

**

p <

.05,

* p

< .1

. Cov

aria

tes:

age

, hou

seho

ld si

ze,

mar

ital s

tatu

s, ye

ars o

f sch

oolin

g, a

nd c

ity d

umm

ies.

# F-

test

–

=

0.

141

4.6 List Experiment Analysis

Survey data are necessary to empirically study household domestic violence. Our estimated

results show the promising impact of G&B training on reductions in household physical

domestic violence. However, it may be that women underreport the sensitive issues related.

Because these questions related to domestic violence were asked directly, women could lie or

refuse to answer, leading to biased results. In this section, we use the “list experiments” survey

technique to reexamine the impact of the training on household physical domestic violence.

Moreover, we compare the results between direct questioning and list experiment on reporting

domestic violence among women.

4.6.1. List Experiment Design In the list experiment, a sensitive question is asked indirectly so is the respondent is more likely

to reveal a truthful answer. Respondents have a chance to report their sensitive behavior without

allowing the interviewers to identify their responses. The list experiment is designed by adding a

sensitive item with a list of other non-sensitive items (baseline list). Glynn (2013) suggests that it

is necessary to limit ceiling effects and the biases related to these effects and minimize variance

of the estimator when designing a list experiment.

Ceiling effects occur when an interviewee would answer yes honestly to all non-sensitive

items (Kuklinski et al., 1997) and thus would not perceive privacy protection to honestly report

their responses to the sensitive items. Consequently, the respondents are still likely to

underreport their responses. To limit the ceiling effects and control privacy protection, high and

low prevalence of non-sensitive items should be avoided (Kuklinski et al., 1997, Tsuchiya et al.,

2007, Glynn, 2013).

To limit ceiling effects and minimize the variance of the estimator, we use the negative

correlation approach which Glynn (2013) proposes to design a baseline list. The negative

correlation between the responses to baseline items can also help keep the list relatively short.

Using two pairs of items is better to achieve these objectives. We propose our base list as

follows:

142

1. I have money in savings account

2. My household does not have television

3. I prefer local fruits over Chinese fruits

4. I usually buy pears

In items 1 and 2, whereas having money in savings account is more likely for high-

income women, not having television is more likely to for low-income women (in the context in

Vietnam). Both items will be true for a small number of women. Similarly, the third and fourth

items are paired to be negatively correlated. Women who usually consume local fruits are not

likely to buy pears because most pears sold in northern Vietnam are imported from China. In

summary, we expect few women to answer all four items positively, and biases due to ceiling

effects should be minimized. Moreover, most respondents are unlikely to notice the negative

correlation designed. Consequently, it is less likely to induce underreporting.

To conduct the list experiment, we randomly chose half the women from the midline

survey to a base group that received the following question and presented the base list:

“Please tell me with how many of the following statements you agree. I don’t want to know

which ones, just how many.”

Separately, another randomly chosen half of female clients was randomized to a

treatment group and received an identical question and the baseline list but with the following

sensitive item appended.

“I’m often hit by my spouse”

We presented the order of non-sensitive and sensitive items randomly to respondents. If

women in the treatment group answer with fewer than five items, they gain privacy protection.

We then estimate the true proportion of women often hit by their spouses by subtracting the

average responses among the treatment group from the average responses among the base group

(a difference-in-means estimator).

4.6.2. Estimated Results Table 4.6 reports the observed data from the list experiment. The data of list experiment is only

collected in the midline. The sample size is 3,822 women, of which 1,900 women were in the

treatment groups for the physical domestic violence item. The results show that the responses are

143

well distributed and there are few responses in the extreme cases (0 and 4 for the base group; 0

and 5 for the treatment group). Thus, we do not have problems of ceiling effects in our data.

Table 4.6: Observed data from the list experiments

Base group Treatment group Response values Frequency Percentage Frequency Percentage

0 38 1.98 38 2 1 539 28.04 410 21.58 2 1,123 58.43 1,116 58.74 3 219 11.39 241 12.68 4 3 0.16 94 4.95 5 1 0.05

Total 1,922 100 1,900 100

For direct questioning of physical domestic violence, we used one out of four questions of

partner violence toward women in the midline survey: “how often did your spouse push, slap,

beat or hit you?”(with answer scale from 0 = Never; 1 = Rarely; 2 = Sometimes; 3 = Often; 4 =

Very often). In our data, all of respondents provide the answers of this question with a scale

ranging from 0 to 2. To make it easier for us to compare the proportion results between the list

experiment and direct questioning, we constructed a dummy variable of physical domestic

violence for direct questioning. The violence dummy takes a value of 1 if respondents say their

spouses rarely or sometimes hit them and 0 if their spouses never did these acts.

Table 4.7 reports the results of the physical domestic violence between list experiment

and direct questioning. The results for the whole sample in column (3) in Panel 1 show that 17

percent of women agree with the sensitive item “I’m often hit by my spouse”. Direct questioning

reveals only 5.3 percent of women who report that their spouses rarely or sometimes hit them.

Similarly, only 7.7 percent, 4 percent, 3 percent of women reported on direct questioning about

intimate partner physical violence, whereas list randomization results in estimates of 12.7

percent, 23.2 percent and 17.4 percent for groups C, T1, and T2, respectively. Table 4.8 reports

the results of Z-tests for difference in proportions. The results indicate that in all cases, the

estimated proportions between list experiments and direct questioning on spouses’ physical

violence against women are statistically significantly different.

144

Column (3) of Panel 2, 3, and 4 compares the results of physical domestic violence based

on list experiments for groups C, T1, and T2. The results show that the percentages of women

who were often hit by their male spouses were 12.7 percent, 23.2 percent and 17.4 percent for

groups C, T1, and T2, respectively. On the basis of the list experiment results, we conclude that

women in both treated groups T1 and T2 experience more partner physical violence than those in

group C. This finding contradicts the results reported in Section 4.5 with regard to the impact of

G&B training on physical domestic violence in the ITT estimates, which show that the training

helps reduce the household physical domestic violence. The results of Z-tests in the lower part of

Table 4.8 also confirm this contradicted effect. Proportions of women in groups T1 and T2

experiencing partner physical violence are higher than those in group C. These differences are

statistically significant. Moreover, invited women in the groups with men more often report

about their partner physical violence than those in the treated groups without men. Again, these

differential effects are statistically significant.

In short, while the estimated results are in line with our expectation in the theory of

change that the training helps reduce household domestic violence, the findings of the list

experiment show the contradicted impact. In particular, trained women in both groups T1 and T2

reported more often about partner physical domestic violence than those in the control groups. In

addition, women in the groups with invited men experienced more physical domestic violence

than those in the treated groups without men. The results from the list experiment may support

our hypothesis in the potential risks of offering business training to female clients and their

husbands. In this case, the training may lead to increased intra-household conflicts.

145

Table 4.7: Results of list experiment and direct report on household physical domestic violence

List experiment Direct report (1) (2) (3) (4)

Variable Treatment group Base group Diff-in-means Violence dummy

Panel 1: N 1900 1922 3826 All Mean 1.971579 1.797086 0.1744926*** 0.053319 Std. Err. 0.0181338 0.0151091 0.0235796 Std. Dev. 0.7904329 0.6623919 0.224699 Panel 2: N 802 815 1618 Group C Mean 2.008728 1.880982 0.1277466*** 0.076638 Std. Err. 0.0286945 0.0245382 0.0377111 Std. Dev. 0.8126181 0.7005221 0.266098 Panel 3: N 662 665 Group T1 Mean 2.007553 1.77594 0.231613*** 1328 Std. Err. 0.0313098 0.0251643 0.0401496 0.040663 Std. Dev. 0.8055802 0.6489276 0.197582 Panel 4: N 436 442 880 Group T2 Mean 1.848624 1.674208 0.1744157*** 0.029546 Std. Err. 0.034059 0.0278408 0.0439323 Std. Dev. 0.7111738 0.5853192 0.169426

Notes: *** p < .01, ** p < .05, * p < .1

146

Table 4.8: Proportion comparisons of household physical domestic violence

Diff-in-proportion Std. Err. Z-test

List experiment and direct report

pro (all) listexp – pro (all) direct .1211736*** .0071331 16.68

pro (C) listexp – pro (C) direct 0.051109*** 0.010614 4.8

pro (T1) listexp – pro (T1) direct 0.19095*** 0.012786 14.35

pro (T2) listexp – pro (T2) direct 0.14487*** 0.014021 10.04

List experiment prop(T1) – prop(C) 0.103866*** 0.014249 7.39

prop(T2) – prop(C) 0.046669*** 0.015262 3.17

prop(T1) – prop(T2) 0.057197*** 0.017266 3.23 Notes: *** p < .01, ** p < .05, * p < .1

4.7 Conclusion and Discussion

In this chapter, we test the impact of providing G&B training on gender outcomes for female

microfinance clients in Vietnam. In addition, we examine whether inviting husbands to the

training results in any additional impact on female gender outcomes. Although the midline

survey took place only six months after the completion of the entire training, we do find some

promising short-term impacts of the training on gender outcomes. In particular, we find strong

evidence that the training leads to significant improvements in gender knowledge of invited

women. In addition, the training has limited positive impact on non-cognitive, business-related

skills, especially on self-esteem and trust behavior for invited women. These limited

improvements in these skills are potential factors that could strengthen women’s fallback

positions in household bargaining process. Because these non-cognitive skills are related to

personal perceptions, we cannot expect invited women to have “sudden” changes or

improvements of these skills a short time after the training. It may take longer for women to

build up their gender competence, and gradual improvement of these non-cognitive skills is more

likely in the long run.

147

Most recent RCTs report that providing business training does not lead to improvements

in female empowerment (Giné and Mansuri, 2011, Karlan and Valdivia, 2011) or attitude

changes toward domestic violence and gender relations. In contrast to existing literature, we

provide some new evidence that the training improves women’s household bargaining power on

major expenditure decisions and reduces the levels of physical domestic violence within families

for married women. To some extent, our results are in line with Kim et al. (2007), who show that

integrating microfinance services with gender and health training significantly improves female

empowerment and reduces intimate partner violence.

Because partner physical violence against women is a sensitive issue, women are more

likely to underreport abuse, which could lead to biased estimates. To avoid this limitation, we

use the list experiment technique to estimate the impact of the training on physical domestic

violence. The list experiment estimates provide conflicting results with ITT estimates: the invited

women report partner physical violence more often than those in the control group. Furthermore,

the list experiment results in much higher statistically significant proportions of women who

report domestic violence than those in direct questioning. The results from the list experiment

estimates raise a concern that the potential risks of the training may outweigh the benefits of the

training on reductions of household domestic violence.

Our study does not find strong evidence of additional impact of the training on female

gender outcomes if husbands are also invited to attend the training, except with regard to effects

on trust behavior. For other outcome variables, such as gender knowledge or female

empowerment on major household expenditure decisions, the average impact of the training is

greater when husbands were also invited, though the effects are not statistically significant. Our

results are somewhat in line with Allen et al. (2010), who also do not find evidence that the

including husbands in microfinance solidarity groups improved women’s bargaining power. As

we discussed in the previous chapter, a possible reason for this result is the relatively low

attendance of husbands in combination with small effect sizes due to the short consideration

period. Husband attendance could influence female empowerment in the long run.

Although the ITT estimates do not show significant additional impact of inviting

husbands on reducing physical domestic violence, the list experiment suggests a contradicting

result. The aggregate information of the list experiment suggests that women in the treated group

148

with men are more likely to report household physical violence than those in the treated group

without men. This differential effect is statistically significant.

However, we recommend caution when comparing the impact results of training on

household domestic violence using ITT and list experiment estimates. When we estimate the

impact of the training on household domestic violence using different econometric techniques,

we try to control for other individual characteristics that also influence the outcomes. Although

the list experiment can yield more accurate response to sensitive survey questions, a drawback of

this technique is that it provides only aggregate information. The anonymity of the method

makes it impossible to examine the relationship between the behavior and individual

characteristics.

On the basis of the midline analyses, we conclude that adding a gender component to the

business training is relevant. Moreover, integrating business skills and gender perspectives seems

appropriate to promote gender quality and female empowerment. Our studies show that G&B

training improved several gender outcomes such as gender knowledge, noncognitive skills, and

female empowerment, signaling the relevance of the gender component in the training and the

importance of integrating gender perceptions and business skills.

We provide several suggestions for further research. First, this study measures women’s

non-cognitive, business-related skills using surveys. Previous studies show that to some extent,

the results of surveys are correlated with behavioral experimental games; therefore, we suggest

that further research could use some experimental behavioral games to corroborate our results

and provide more precise information about behavior changes. Second, we distinguish two

groups of outcomes, business and gender outcomes, when examining the impact of G&B

training. It would be worthwhile for further research to investigate the extent to which gender

outcomes influence female-owned business outcomes. Third, the anonymity of list experiment

does not report the relationship between the behavior and individual characteristics. Thus,

breaking the analysis of the list experiment into subgroups defined by individual characteristics

could provide more room to explore this sensitive behavior and individual characteristics. We

leave this for further research.

149

Appendices

Appendix 4.1: Descriptions of outcome variables Variables Expected

sign Description Time of

measurement Gender knowledge + Sum of correct answers of gender

(4 questions) midline

Personal changes Locus of control – Sum of two points on the Rotter

scale. The higher the scores, the greater the external locus of control

baseline and midline

Self-esteem + Sum of five-point Rosenberg scale. The higher the scores, the higher the self-esteem

baseline and midline

Trust Trust friends + Trust someone who is not a relative

(close friend) 1 = Yes and 0 = No baseline and midline

GSS Trust + 1= “most people can be trusted” 0 = “ you can’t be too careful in dealing with people”

baseline and midline

GSS Fairness + 1= “most people would try to be fair” 0= “try to take advantage of you if they got the chance”.

baseline and midline

GSS Helpfulness + 1= “most of the time people try to be helpful” 0= “they are mostly just looking out for themselves”

baseline and midline

Trust strangers + “You can’t count on strangers anymore”(1 = disagree, 0= agree)

baseline and midline

Female empowerment Bargaining power on household decisions Major expenditure decisions

+ First component of principal component analysis that related to who makes decision on asking for a loan; durable item purchases; health expenditures; saving for farming or business activities and households; house purchases, improvement or repair; where to invest surplus money; and how to assist family members in financial matters.

baseline and midline

150

Appendix 4.1: Descriptions of outcome variables (cont.)

Variables Expected sign

Description Time of measurement

Daily needs decisions + Second component of principal component analysis that related to who makes decision on daily decision such food, clothing, and tuition fee choices.

baseline and midline

Bargaining power on business and farming decision Business decision + Principal decision makers on

making the most decisions to manage the main household business activity: 0 = male spouse or other, .5 = both woman and her spouse, 1 = respondent (a woman)

baseline and midline

Farming decision + Principal decision makers on making the most decisions to manage the main household farming activity: 0 = male spouse or other, .5 = both woman and her spouse, 1 = respondent (a woman)

baseline and midline

Domestic violence Physical violence – First component of principal

component analysis from four categories of physical violence

baseline and midline

Psychological violence – First component of principal component analysis from four categories of psychological violence

baseline and midline

151

Appendix 4.2: IV estimates Table 4.1A: Impact of G&B training on gender knowledge

VARIABLES Gender knowledge

Percentage# 0.502 (0.294) Training 0.978*** (0) Observations 3,300 R2 0.236

Notes: Robust cluster p-values are in parentheses; *** p < .01, ** p < .05, * p < .1. Standard errors are clustered at center levels (187 centers); Covariates: age, household size, marital status, years of schooling, and city dummy. #Percentage of total training modules in which an invited husband participated. Table 4.2A: Impact of G&B training on locus of control and self-esteem

(1) (2) (3) (4) Post-treatment Single difference

VARIABLES Locus of control Self-esteem Locus of control Self-esteem Percentage# 0.588 -0.566 0.804 -0.600 (0.356) (0.795) (0.212) (0.777) Training -0.150 0.829 -0.204 0.826 (0.326) (0.115) (0.197) (0.113) Observations 3,207 3,237 2,645 3,164 R2 0.018 0.022

Notes: Robust cluster p-values are in parentheses; *** p < .01, ** p < .05, * p < .1. Standard errors are clustered at center levels (187 centers); Covariates: age, household size, marital status, years of schooling, and city dummy. #Percentage of total training modules in which an invited husband participated.

152

Tab

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: Im

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of G

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(1

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153

Table 4.4A: Impact of on women’s empowerment for married women

(1) (2) (3) (4) (5) (6) (7) (8) Post-treatment Single difference

VARIABLES Major expenditure

decisions

Daily needs

decisions

Business decisions§

Farming decisions§

Major expenditure

decisions

Daily needs

decisions

Business decisions§

Farming decisions§

Percentage# 0.589 0.658 0.312 0.0319 0.498 0.537 0.280 0.0114 (0.222) (0.420) (0.168) (0.818) (0.319) (0.491) (0.164) (0.933) Training 0.0688 0.0278 -0.0357 -0.0570 0.105 0.0575 -0.0448 -0.0516 (0.615) (0.899) (0.492) (0.141) (0.455) (0.783) (0.320) (0.170) Observations 2,775 2,775 725 2,199 2,775 2,775 719 2,181 R2 0.001 0.027 0.049 0.021 0.056 0.088 0.067

Notes: Robust cluster p-values are in parentheses; *** p < .01, ** p < .05, * p < .1. Standard errors are clustered at center levels (187 centers); Covariates: age, household size, marital status, years of schooling, and city dummy. #Percentage of total training modules in which an invited husband participated. §We also conduct IV Tobit estimates for these dependent variables, and the results are similar (available on request). Table 4.5A: Impact of G&B training on domestic violence for married women

(1) (2) (3) (4) Post-treatment Single difference

VARIABLES Physical violence

Psychological violence

Physical violence

Psychological violence

Percentage# 0.160 0.594 0.157 0.596 (0.479) (0.169) (0.486) (0.169) Training -0.128** -0.0679 -0.127** -0.0694 (0.0369) (0.465) (0.0382) (0.458) Training × time 0.0107 Observations 2,854 2,860 2,854 2,860 R2 0.019 0.026 0.020 0.026

Notes: Robust cluster p-values are in parentheses; *** p < .01, ** p < .05, * p < .1. Standard errors are clustered at center levels (187 centers); Covariates: age, household size, marital status, years of schooling, and city dummy. #Percentage of total training modules in which an invited husband participated.

154

Appendix 4.3: Principal component analysis of household bargaining power We composed household decision-making power indices from answers to the question “Who

makes the decision?” on the following issues: asking for a loan; food items; educational

expenditures; clothing items; purchasing durable items; health expenditures; saving for

farming/business activities and households; home purchase, improvement, or repair; where to

invest surplus money; and how to assist family members in financial matters. On the basis of

the results of eigenvalues and parallel analysis, we decide to extract two factors. Based on

(Tabachnick and Fidell, 2007b), p.646), because the factor correlation is approximately .6, we

apply oblique rotation method. Consequently, we have one factor related to major expenditure

decisions and one factor related to daily decisions such as decisions about food, clothing, and

tuition fee.

Appendix 4.4: Questions measuring gender knowledge14 Please select True/False in the following sentences 1 = True; 0 = False; 99. Don’t know; 88. Refused to answer

Fraction of answers 0 1 99 88

1 Men and women should have equal opportunities in enterprise development

5.02 94.93 0.05

2 Only men can launch a new business 77.07 22.93

3 Only women are responsible for the housework and children 68.73 31.27

4 Boys should have more chances to access to education and training than girls 77.05 22.88 0.08

14 Questions are only in the midline surveys

155

Appendix 4.5: Questions measuring non-cognitive skills Locus of control

Please choose one choice (1) or (2) that reflects your beliefs

(Code : 1= option (1); 2= option (2); 3= not (1) or (2); 99. Don’t know; 88. Refused to

answer)

Fraction of answers 1 2 3 88 99

1

1) What happens to me is my own doing

2) Sometimes I feel that I don’t have enough control over the direction my life is taking

Baseline 58.89 39.18 1.93

Midline 57.1 42.57 0.13 0.19

2

When I make plans,

1) I am almost certain that I can make them work

2) It is not always wise to plan too far ahead, because many things turn out to be a matter of good or bad fortune anyhow

Baseline 41.94 54.2 3.86

Midline 57.02 42.79 0.05 0.13

3

1) Getting what I want has little or nothing to do with luck

2) Many times I might just as well decide what to do by flipping a coin

Baseline 77.11 16.02 6.87

Midline 85.55 14.1 0.19 0.16

4

1) Many times I feel that I have little influence over the things that happen to me

2) It is impossible for me to believe that chance or luck plays an important role in my life

Baseline 50.54 44.46 5.01

Midline 44.18 55.42 0.24 0.16

Self-esteem Respond to the following statements by indicating how well those reflect your opinion.

1= Strongly disagree; 2= Disagree; 3= Neither agree nor disagree; 4= Agree; 5= Strongly agree

Fraction of answers 1 2 3 4 5

1 I feel that I’m a person of worth, at least on an equal basis with others

Baseline 4.97 1.13 3.61 82.87 7.42

Midline 1.55 0.19 2.49 90.91 4.86

2 I feel that I have a number of good qualities

Baseline 2.4 2.55 8.19 77.66 9.2

Midline 1.28 1.12 3.47 83.77 10.35

156

3 All in all, I am inclined to feel that I am a failure

Baseline 10.73 56.83 9.98 20.73 1.73

Midline 3.39 63.36 13.21 19.14 0.91

4 I am able to do things as well as most other people Baseline 2.14 4.38 6.09 81.66 5.73

Midline 1.39 1.71 3.95 86.66 6.3

5 I feel I do not have much to be proud of Baseline 9.69 34.87 10.9 41.8 2.73

Midline 3.1 37.76 12.44 44 3.18

6 I take a positive attitude toward myself Baseline 2.81 6.7 7.3 75.79 7.4

Midline 1.41 6.27 5.05 79.34 7.93

7 On the whole, I am satisfied with myself

Baseline 2.63 2.76 4.92 80.6 9.1

Midline 1.33 1.28 3.52 85.75 8.11

8 I wish I could have more respect for myself Baseline 2.4 2.22 5.88 75.94 13.56

Midline 1.33 1.04 6.03 82.98 8.62

9 I certainly feel useless at times Baseline 11.66 60.42 6.83 18.39 2.71

Midline 5.45 68.3 8.84 14.71 2.7

10 At times I thinks I am no good at all Baseline 13.94 41.44 6.76 35.63 2.22

Midline 5.24 52 7 33.16 2.59

Trust 1 Generally speaking, would you say that most people can be trusted or that you have to be

extremely careful in dealing with people? Baseline Midline 1. Most people can be trusted 31.91 47.35 2. You have to be extremely careful in dealing with people 59.81 50.29 3. not choose (1) and (2) 5.88 88. Refuse to answer 0.43 99. don't know 2.4 1.93

2 Do you think most people would try to take advantage of you if they got a chance, or would they try to be fair?

Baseline Midline 1. Would take advantage of you 11.66 15.36 2. Would try to be fair 55.48 72.02 3. not choose (1) and (2) 21.69 88. Refuse to answer 1.41 99. don't know 11.17 11.2

157

3 Would you say that most of the time people try to be helpful, or that they are mostly just looking out for themselves?

Baseline Midline 1. Try to be helpful 50.26 57.56 2. Just look out for themselves 25.79 28.45 3. not choose (1) and (2) 13.75 88. Refuse to answer 1.31 99. don't know 10.21 12.69

4 If I say ‘’You can’t count on strangers anymore’’, you :

Baseline Midline 1= Strongly disagree 15.55 14.82 2= Disagree 9.63 14.98 3= Neither agree nor disagree 12.97 20.95 4= Agree 55 45.77 5= Strongly agree 6.79 3.48 99= don't know 0.05

5 Do you trust someone who is not a relative? (any close friend)

Baseline Midline 0 = No 19.94 14.47 1 = Yes 80.06 85.53

158

Appendix 4.6: Questions measuring female empowerment Code: 0=Spouse; 1=Couple; 2=Self; 3= Other; 99. Don’t know; 88. Refused to answer

Fraction of answers 0 1 2 3 88 99

1

Who makes most decisions about asking for a loan?

Baseline 5.05 86.88 8.07

Midline 3.07 90.1 6.2 0.57 0.03

2

Who makes most decisions about what food items to purchase?

Baseline 1.03 22.7 76.27

Midline 0.82 21.8 76.94 0.41

3

Who makes most decisions about what educational expenditures to make (tuition, etc?)

Baseline 1.84 48.58 49.58

Midline 1.3 38.8 59.41 0.54

4

Who makes most decisions about what clothing items to purchase?

Baseline 1.63 31.11 67.26

Midline 1.08 22.8 75.59 0.51 0.03

5

Who makes most decisions about purchasing durable items? (TV, Fridge, etc.)

Baseline 11.73 80.2 8.07

Midline 10.79 82.5 6.27 0.44

6

Who makes most decisions about what health expenditures to make?

Baseline 1.63 67.65 30.71

Midline 1.8 60.8 37.06 0.32

7

Who makes most decisions about expenses for house purchase, improvement or repair?

Baseline 11.37 80.41 8.22

Midline 15.26 78.7 5.67 0.41

8

Who makes decisions about where to invest surplus money?

Baseline 2.09 67.41 30.5

Midline 7.22 66.1 26.42 0.26 0.03

9

Who makes decisions about how to assist family members in financial matters?

Baseline 2.54 84.76 12.7

Midline 8.11 83.3 8.27 0.29 0.03

10

Who makes most decisions about saving for household?

Baseline 2.42 74.67 22.91

Midline 2.85 70.5 26.24 0.38

11

Who takes most decisions about how to manage the main farming activity?

Baseline 0.89 71.9 27.15 0.06

Midline 3.41 52.86 39.87 3.86

12

Who takes most decisions about how to manage the main business activity?

Baseline 6.04 57.48 36.48

Midline 16.19 39.72 40.84 3.26

159

Appendix 4.7: Questions measuring household domestic violence Sometimes, you and your spouse disagree on major decision, get annoyed about something the other does; or just have conflicts because you and your spouse are in a bad mood or tired or for some other reason.

Would you like tell me how often your spouse did the act listed below in the last 6 months?

(Codes: 0 = Never; 1 = Rarely; 2 = Sometimes; 3 = Often; 4 = Very often ; 99. Don’t know; 88. Refused to answer)

Fraction of answers 0 1 2 3 4 88 99 1a. Verbal aggression

Baseline 59.76 23.28 16.05 0.6 0.3 Midline 63.48 27.44 8.92 0.13 0.03

1b. Physical Assault (Pushed, Slapped , beat or hit with a fist)

Baseline 93.62 4.5 1.54 0.06 0.27

Midline 94.15 5.66 0.19 1c. Threatened and used with an object like sticks, knife, etc.

Baseline 99.64 0.15 0.09 0.12

Midline 99.78 0.19 0.03 1d.Other

Baseline 97.85 1.06 0.88 0.21 Midline 99.75 0.25

Fraction of answers 0 1 2 3 4 88 99

2a. Kept you from seeing your family members or friends

Baseline 98.1 0.91 0.57 0.03 0.06 0.33

Midline 98.77 1.11 0.09 0.03

2b. Insisted on knowing where you are at all times

Baseline 95.01 2.45 1.78 0.67 0.03 0.06

Midline 94.27 2.88 2.47 0.38

2c. Wanted you to ask permission before doing anything

Baseline 86.34 4.29 6.8 2.51 0.06

Midline 88.54 7.38 3.48 0.6

2d. Insulted or humiliated you in front of other people

Baseline 99.55 0.27 0.09 0.03 0.03 0.03

Midline 99.05 0.73 0.19 0.03

2e.Other

Baseline Midline

160

Appendix 4.8: Post-treatment estimates without covariates Table 4.1B: Impact of G&B training on gender knowledge (Post-treatment estimates without covariates)

VARIABLES Gender knowledge T1 1.081*** (0) T2 0.917*** (0) Constant 2.591*** (0) Observations 3,826 R-squared 0.226

Note: Robust cluster p-values are in parentheses; Standard errors are clustered at center levels (187 centers); *** p < .01, ** p < .05, * p < .1.

Table 4.2B: Impact of G&B training on locus of control and self-esteem (Post-treatment estimates without covariates)

(1) (2) VARIABLES Locus of control Self-esteem T1 -0.0238 0.629 (0.781) (0.113) T2 -0.0752 0.629 (0.592) (0.201) Constant 5.467*** 34.71*** (0) (0) Observations 3,702 3,736 R-squared 0.001 0.009

Note: Robust cluster p-values are in parentheses; Standard errors are clustered at center levels (187 centers); *** p < .01, ** p < .05, * p < .1.

161

Table 4.3B: Impact of G&B training on trust (Post-treatment estimates without covariates)

(1) (2) (3) (4) (5) VARIABLES Trust friend GSS

Trust GSS

Fairness GSS

Helpfulness Trust Stranger

T1 0.0411 -0.0195 0.0557 -0.00989 0.114* (0.133) (0.750) (0.178) (0.863) (0.0758) T2 0.00954 -0.0525 -0.0279 0.0302 0.0431 (0.826) (0.502) (0.649) (0.653) (0.607) Constant 0.839*** 0.504*** 0.812*** 0.666*** 0.328*** (0) (0) (0) (0) (0) Observations 3,814 3,652 3,276 3,220 2,950 R-squared 0.003 0.002 0.008 0.001 0.010

Note: Robust cluster p-values are in parentheses; Standard errors are clustered at center levels (187 centers); *** p < .01, ** p < .05, * p < .1.

Table 4.4B: Impact of G&B training on married women’s bargaining power (Post-treatment estimates without covariates)

(1) (2) (3) (4) VARIABLES Major expenditure

decision Daily needs

decision Business decision

Farming decision

T1 0.230** 0.217 0.0349 -0.0501 (0.0209) (0.163) (0.365) (0.138) T2 0.114 0.0211 -0.0126 -0.0678* (0.414) (0.924) (0.823) (0.0968) Constant -0.433*** 0.121 0.621*** 0.691*** (4.33e-07) (0.309) (0) (0) Observations 3,065 3,065 742 2,265 R-squared 0.006 0.005 0.003 0.012

Note: Robust cluster p-values are in parentheses; Standard errors are clustered at center levels (187 centers); *** p < .01, ** p < .05, * p < .1.

162

Table 4.5B: Impact of G&B training on domestic violence for married women (Post-treatment estimates without covariates)

(1) (2) VARIABLES Physical violence Psychological violence T1 -0.0833 0.0930 (0.106) (0.350) T2 -0.145** -0.00388 (0.0104) (0.980) Constant -0.0766* -0.0890* (0.0598) (0.0691) Observations 3,153 3,159 R-squared 0.014 0.002

Note: Robust cluster p-values are in parentheses; Standard errors are clustered at center levels (187 centers); *** p < .01, ** p < .05, * p < .1.

Appendix 4.9: CACE estimates Table 4.1C: Impact of G&B training on gender knowledge (CACE estimates)

VARIABLES Gender knowledge P1& 1.292*** (0) P2+ 1.181*** (0) Constant 2.484*** (0) F test# 0.68 Prob > F 0.4104 Observations 3,459 R2 0.214

Notes: Robust cluster p-values are in parentheses; *** p < .01, ** p < .05, * p < .1. Standard errors are clustered at center levels (187 centers); Covariates: age, household size, marital status, years of schooling, and city dummy. &Percentage of total training modules in which invited women in the group T1 participated. +Percentage of total training modules in which invited women in the group T2 participated. # F-test – = 0.

163

Table 4.2C: Impact of G&B training on locus of control and self-esteem (CACE estimates)

(1) (2) (3) (4) Post-treatment Single difference

VARIABLES Locus of control Self-esteem Locus of control Self-esteem P1& -0.0325 0.812* -0.0350 0.798* (0.748) (0.0778) (0.741) (0.0796) P2+ -0.180 0.991 -0.243 0.983 (0.326) (0.117) (0.201) (0.116) Constant 5.761*** 34.50*** 5.569*** 32.54*** (0) (0) (0) (0) F test# 0.64 0.08 1.17 0.09 Prob > F 0.4252 0.7768 0.2801 0.7655 Observations 3,361 3,391 2,775 3,311 R2 0.013 0.016 0.016 0.019

Notes: Robust cluster p-values are in parentheses; *** p < .01, ** p < .05, * p < .1. Standard errors are clustered at center levels (187 centers); Covariates: age, household size, marital status, years of schooling, and city dummy. &Percentage of total training modules in which invited women in the group T1 participated. +Percentage of total training modules in which invited women in the group T2 participated. # F-test – = 0.

164

Tab

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trus

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165

Table 4.4C: Impact of G&B training on women’s empowerment for married women (CACE estimates)

(1) (2) (3) (4) (5) (6) (7) (8) Post treatment Single difference

VARIABLES Major expenditure

decision

Daily needs

decision

Business decision§

Farming decision§

Major expenditure

decision

Daily needs

decision

Business decision§

Farming decision§

P1& 0.267** 0.221 0.0439 -0.0625* 0.279** 0.219 0.0229 -0.0628* (0.0235) (0.222) (0.344) (0.0904) (0.0158) (0.210) (0.598) (0.0810) P2+ 0.0832 0.0270 -0.0428 -0.0676 0.128 0.0630 -0.0543 -0.0613 (0.612) (0.918) (0.509) (0.140) (0.451) (0.802) (0.334) (0.170) Constant -0.756*** 0.435* 0.566*** 0.767*** -0.564*** 0.570** 0.381*** 0.652*** (0.000456) (0.0937) (1.37e-08) (0) (0.00795) (0.0227) (8.09e-

05) (0)

F-test# 1.59 0.61 1.78 0.01 0.98 0.43 1.80 0.00 Prob > F 0.2075 0.4366 0.1820 0.9095 0.3225 0.5114 0.1796 0.9733 Observations 2,781 2,781 722 2,205 2,781 2,781 716 2,187 R2 0.008 0.028 0.027 0.053 0.028 0.055 0.121 0.069

Notes: Robust cluster p-values are in parentheses; *** p < .01, ** p < .05, * p < .1. Standard errors are clustered at center levels (187 centers); Covariates: age, household size, marital status, years of schooling, and city dummy. §We also conduct IV Tobit estimates for these dependent variables, and the results are similar (available on request). &Percentage of total training modules in which invited women in the group T1 participated. +Percentage of total training modules in which invited women in the group T2 participated. # F-test – = 0

166

Table 4.5C: Impact of G&B training on domestic violence for married women (CACE estimates)

(1) (2) (3) (4) Post treatment Single difference

VARIABLES Physical violence

Psychological violence

Physical violence

Psychological violence

P1& -0.101* 0.115 -0.101* 0.114 (0.0983) (0.314) (0.0993) (0.318) P2+ -0.152** -0.0823 -0.150** -0.0847 (0.0389) (0.460) (0.0402) (0.451) Constant 0.133 0.129 0.130 0.126 (0.142) (0.285) (0.153) (0.293) F-test# 0.54 2.00 0.53 2.00 Prob > F 0.4607 0.1571 0.4678 0.1569 Observations 2,861 2,867 2,861 2,867 R2 0.020 0.037 0.021 0.037

Note: Robust cluster p-values are in parentheses; Standard errors are clustered at center levels (187 centers); *** p < .01, ** p < .05, * p < .1. Covariates: age, household size, marital status, years of schooling, and city dummies. # F-test – = 0. &Percentage of total training modules in which invited women in the group T1 participated. +Percentage of total training modules in which invited women in the group T2 participated

167

Chapter 5

Business Training and Intertemporal Consumption:

Experimental Evidence from Vietnam

5.1 Introduction

We study the impact of business training on intertemporal consumption smoothing behavior of

female microfinance clients in Northern Vietnam. Many MFIs now offer such business training,

as recent evidence suggests the impact of microfinance may depend on human capital levels of

borrowers (Karlan and Morduch, 2010, Berge et al., 2011). However, there is only weak and

mixed evidence on whether knowledge training improves financial decisions. One aim of this

paper is to provide new experimental evidence on this essential issue.

To learn about the impact of business training on financial decision-making we combine

a randomized controlled trial (RCT) design, where our respondents are randomly assigned to

treatment arms, with a specific behavioral game – the Convex Time Budget experiment (CTB).

This game, developed by Andreoni and Sprenger (2012), enables the measurement of time

preferences by asking respondents to allocate a fixed budget over two moments in time.15 The

CTB method allows the analyst to relax the assumption of linear utility, and to distinguish

between time preferences driven by time discounting from time preferences due to diminishing

This chapter is co-authored with Robert Lensink and Erwin Bulte

15 Conventionally, time preferences are determined using multiple price lists (MPL). The MPL method involves a sequence of binary choices – receive money now or at some point in the future. The interest rate increases monotonically, and the switching point where individuals flip their choice from sooner to later carries information about intertemporal preferences. Assuming linear utility, the switch point can be used to calculate an individual discount rate.

168

marginal utility of consumption (i.e. the curvature of the utility function). This study is among

the first to conduct the CTB in a developing country setting. The combination of RCT and CTB

implies we can not only rigorously assess the impact of the training – we can also learn about the

mechanism explaining impact (Barrett and Carter, 2010, Camfield and Duvendack, 2014).

The contribution of this study is twofold. First, we make a methodological contribution

by explicitly allowing for suboptimal consumption choices. Rather than assuming that

respondents are fully rational and behave perfectly efficiently, we used revealed behavior in the

CTB experiment to obtain proxies for both the underlying preferences that drive behavior, as

well as deviations from rational choice. We then use the RCT design to explore whether the

business training has an effect on these underlying preferences as well as the extent to which

actual intertemporal consumption choices depart from optimal consumption smoothing.

Second, we test whether the impact of the business training is conditional on the presence

of husbands during the training. Excluding husbands may trigger frustration and invite intra-

household conflicts (Allen et al., 2010) possibly eroding the impact of the training. In addition,

it is expected that the presence of men, who bring their own expertise and experience to the

event, changes the nature and depth of the discussions during the training. While we do not aim

to explain why the presence of men might matter, we provide evidence on the relevance of

inviting husbands to business training organized for female microfinance borrowers.

We obtain several noteworthy results. We first document that, on average, financial

choices are not fully rational. Specifically, we find evidence of over-saving. Most studies assume

that financial education will improve financial decision-making, and raise savings rates (Tustin,

2010, Bruhn et al., 2013, Landerretche and Martínez, 2013). However, savings rates in Vietnam,

as in several other Asian countries, are already very high. According to the World Bank, gross

domestic savings as a percentage of GDP amounted to 31% in 201216. Our second result is that

while business training does not change preferences, they do tend to improve the optimality of

intertemporal consumption choices by stimulating current consumption at the expense of future

16 see http://data.worldbank.org/indicator/NY.GDS.TOTL.ZS

169

consumption. Thirdly, the impact of business training is conditional on the presence of husbands

– their contribution accentuates the impact of the formal training.

This paper is organized as follows. In section 2 we summarize the relevant literature,

focusing on experimental studies of business training and on studies based on the CTB approach

to elicit time preferences. In Section 3, we present a simple model that explains how we model

time preferences and intertemporal consumption smoothing. Section 4 provides details about our

experiment and summarizes our data. In Section 5 we present our results, and try to answer

whether advanced knowledge is the channel via which business training affect intertemporal

consumption smoothing. Section 6 concludes.

5.2 A Brief Survey of the Relevant Literature

The literature on the impact of business training on behavior of microfinance clients has

produced ambiguous results. But our understanding of the impact of such training is now

growing rapidly due to a few recent RCTs, which provide compelling evidence that business

training can help to improve business practices and outcomes. For example, Bjorvatn and

Tungodden (2010) show that business training have a positive effect on business knowledge in

Tanzania, and Drexler et al. (2014) indicate positive effects on management practices of small

businesses in the Dominican Republic. Karlan and Valdivia (2011) do not find strong general

effects, but suggests that business training may have small positive effects on female

microfinance borrowers. Similarly encouraging results are found (Berge et al., 2011, Giné and

Mansuri, 2011, Bruhn et al., 2013). For a recent survey of the various impacts of business

training, see (McKenzie and Woodruff, 2014).

One particular component of human capital amenable to outside interventions is financial

literacy. Financial literacy may be defined as consumers’ awareness, skills, and knowledge,

enabling them make informed, effective decisions about financial resources. Many business

training programs include modules on financial literacy. Giné et al. (2012) and Cohen and

Young (2007) offer evidence that financial literacy is an important determinant of insurance

170

adoption. Cole et al. (2009) suggest financial literacy training induces households in Indonesia to

open a bank account.

Several studies on the impact of business training in general, and financial literacy

training in particular, focus on the effects on savings. It is often assumed that developing

countries are characterized by under-saving, and that training help to reduce knowledge gaps. If

training help to improve financial decision-making, the expectation is that clients will respond by

an increase in savings. However, rigorous evidence on the effects of financial education on

savings is very scarce. One of the few experimental evaluations that finds some small short-term

effects on savings is the financial literacy experiment conducted in Mexico by Bruhn et al.

(2013). Sayinzoga et al. (2013) also demonstrate that financial literacy training affects not only

financial knowledge, but also savings and repayment behavior of microfinance clients in

Rwanda.

A related literature considers the determination of time and risk preferences. While

traditional neo-classical economics assumes that preferences are exogenously determined and

stable (Stigler and Becker, 1977), modern economic theory acknowledges that preferences may

change over time, in response to various factors such as education.17 This could set in motion

complex dynamics. If more patient individuals are more likely to invest in human capital

accumulation (Ghez and Becker, 1975, Fuchs, 1982, Becker and C.B., 1997, Ameriks et al.,

2003, Kirby et al., 2001), and education in turn affects time and risk preferences, then self-

reinforcing patterns can emerge. Schooling may also improve patience, and reduce risky

behavior (Shefrin and Thaler, 1992, Becker and C.B., 1997).

To measure time preferences, Andreoni and Sprenger (2012) propose to use the CTB

experiment (see below for details). To the best of our knowledge, only two other (as yet

unpublished) studies have conducted CTB experiments to elicit time preferences in a developing

countries’ field setting. Yang and Carlsson (2012) investigate intertemporal choices in rural

China, focusing on intra-household bargaining between the spouses. Giné et al. (2012) examine

17 Other factors may also change time and risk preferences. For example, Voors et al. (2012) find that exposure to conflict explains variation in such preferences among a sample of respondents in rural Burundi.

171

the relationship between time preferences under commitment and time-inconsistency in rural

Malawi.

5.3 The Theoretical Model

We now present a simple theoretical model that enables derivation of an empirical specification

that we can use to test the impact of business training on intertemporal consumption choices. We

assume that subjects devise a consumption plan that maximizes utility, subject to an

intertemporal budget constraint. Subjects choose between consumption available at time t, ct , or

an amount ct+k >ct available after k>0 periods. We also assume a constant time-separable utility

function and an exponential discount function:

, (1)

where is the one period discount factor. Following Andreoni and Sprenger (2012), we

assume subjects are faced with a convex budget set, and maximize utility subject to the following

budget constraint:

, (2)

where m is the given (experimental) budget, valued in period t values. In (2), r is the

constant interest rate. Solving the maximization problem and combining first order conditions

yields the following condition for optimal consumption smoothing:

. (3)

This condition states that a rational consumer sets the marginal utility of consumption in

period t equal to the marginal value of consumption in period t+k, appropriately valued. Efficient

consumption smoothing implies that the marginal rate of substitution between consumption at

times t and t+k equals the marginal rate of transformation. This optimality condition is derived

assuming that intertemporal consumption choices can be made without constraints (other than

the budget constraint). In practice, however, this may not be the case, especially in developing

countries where markets often do not work perfectly. For instance, borrowing or savings

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constraints may restrict intertemporal consumption choices, and actual consumption choices

deviate from optimal choices. Condition (3) may also not hold if households behave irrationally

due to knowledge gaps or behavioral biases. Business training may be of particular relevance

since they are assumed to address such knowledge gaps. We aim to explicitly address this issue

in our empirical application, which implies we need to allow for “sub-optimal” intertemporal

consumption choices (rather than assuming that all choices are necessarily the outcome of a

maximization process) in the theory. To this end we introduce a new parameter A:

. (4)

Optimal consumption choices imply A = 1; If A differs from 1, intertemporal

consumption choices are inefficient. Specifically, for A>1 respondents save too much (given the

discount rate, interest rate and their own preferences), and for A<1 respondents save too little.

In order to produce a testable equation, we use a standard Constant Relative Risk

Aversion (CRRA) utility function:

. (5)

for > 0, ≠1, where is the coefficient of relative risk aversion, defined as . Note

where is the CRRA curvature parameter. Tangency condition (4) yields:

. (6)

Taking logs on both sides gives an equation that is linear in k and ln(1+r). After some

manipulation, this yields our main equation for testing:

. (7)

Note that A enters in a constant term in this equation. A disadvantage of the CRRA specification

is that corner solutions, where a subject allocates the full budget to either or , are not

defined. This implies that some observations will be dropped. To probe the robustness of our

results, and to benefit from the full sample for the empirical analysis, we therefore also use a

Constant Absolute Risk Aversion (CARA) specification: , where is the

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constant absolute risk aversion parameter. For this model, the relevant tangency condition

produces the following testable model:

. (8)

Where A again enters in the constant.

Specifications (7) and (8) allow estimation of the “curvature” of the utility function, or

the parameters or (depending on the specification of utility), jointly with the discount factor

and inefficiency parameter A. Note that both CRRA and CARA utility are consistent with

consumption smoothing, or interior solutions. In contrast, assuming risk neutrality (or a linear

utility function) implies optimal consumption would be a corner solution (consume everything

now or later, depending on δ and θ).

Importantly, for our analysis, specifications (7) and (8) do not rule out “inefficient”

consumption smoothing a priori. Consider the constant for the case of CRRA utility and

constant for the case of CARA utility. When intertemporal consumption smoothing is

perfectly efficient, these coefficients should vanish (as ln(1)=0). Below we will estimate (7) and

(8) for our sample of Vietnamese microfinance clients, capturing the “inefficiency terms”

and by adding a constant to the model that is estimated. Andreoni and Sprenger (2012)

estimate the CTB models without constants. Other studies include a constant term, but do not

interpret it. Assuming respondents maximize a CRRA (or CARA) utility function, we interpret

constants that are significantly different from zero as evidence of deviations from fully efficient

or rational choice. We are particularly interested in whether such inefficiencies (if any) co-vary

with attendance of the business training.

5.4 Experimental Context, Design, Data, and Identification

We now explain the experiment, and start by introducing the RCT intended to measure the causal

impact of attending business training. Next, we zoom in on the behavioral game, intended to

174

measure consumption smoothing behavior (our main dependent variable). Finally, we introduce

our data and outline our identification strategy.

5.4.1. The RCT and the Business Training

This section summarizes the experimental design and training intervention which have been

described in detail in Chapter 3. We collaborate with a microfinance institution in Vietnam, the

TYM fund, to evaluate the impact of business training to poor female clients. The TYM fund is

the largest microfinance organization for poor women in northern Vietnam, operating since

1992. Its main mission is to improve the quality of life and the status of poor women and their

families by providing them access to financial and non-financial services. We investigate

training sessions held in Vinh Phuc and Ha Noi, two areas relatively close to TYM headquarters

in Ha Noi. Training provided through TYM is based on the Gender and Entrepreneurship

Together (GET) Ahead for Women in Enterprise Training Package and Resource Kit, designed

by the International Labor Organization (ILO). The program centers on gender equality, general

business skills, strategy training, and client-specific problem solving. The training took place

during nine monthly center meetings (in the period February 2012-November 2012). Each

module requires 45–60 minutes. In addition to the training module, trainers organized

discussions and consultations for the trainees on a weekly basis, lasting about 15–30 minutes.

These discussion sessions were organized during the times that TYM clients came to pay their

debts. Participation in the training was voluntary and free of charge.

The lending centers, averaging some 30 female clients, were assigned randomly to the

treatment and control arms. We randomized this assignment at the lending center level, and used

a cluster sampling approach to reduce the “risk” of spillover effects. The four selected branches

in Vinh Phuc and Ha Noi contain 187 lending centers. Randomization was stratified by lending

branch. We distinguish between two different treatments: lending centers in which male partners

were invited to join the business training (T1), and lending centers in which male partners were

not invited to join the training (T2). The control centers received no additional services, beyond

the regular credit and savings facilities (C).

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5.4.2. The Behavioral Game

One month after the training sessions had been completed, we conducted time preference games

with random samples of females who participated in the RCT. We restricted our sample to

married TYM members. Reflecting the three distinct treatment arms, our sample for the time

preference game again contains three groups: 115 females were randomly selected from T1, 110

females were randomly selected from T2, and 140 females were randomly selected from C.

Importantly, while women from T1 attended the training together with their husbands, they

played the behavioral game alone – as did the respondents from the other two groups. It should

be noted that to simplify the organisation of the games, loan officers invited a random sample of

women who actually followed the training (instead of a random sample of women who were

invited to the training) to join the experiments. Hence, this may imply that women who attended

the artefactual field experiments were the “most interested” women in the sample of women that

were invited to the training.

We use the Convex Time Budget method proposed by Andreoni and Sprenger (2012) to

assess the impact of the business training on intertemporal consumption smoothing. Each

subject received a budget of 80,000 VND (approximately USD4), and was asked to allocate this

endowment between “consumption” and “saving” (so that the money comes available with a

delay of k days). Participants received a return on that part of the endowment that was saved.

Each participant faced 20 convex budget decisions, and we varied the length of the payment

delay (k). For half of these allocations we used a “near future” time frame, where respondents

had to allocate their endowment between early payment (t = 4 days from today) and the delayed

payment (k= 28 days or k=56 days, depending on the question). Hence, t+k equals 32 and 60

days, respectively. In the “far future” time frame respondents had to allocate their endowment

between an early payment (t=32 days from today) and delayed payment (k=28 days or k=56 days

later). Now, t+k equals 60 and 88 days, respectively. In what follows we refer to allocations to

the early payment as “consumption” and allocations to the delayed payment as “saving.”

We refer to each (t, k) combination as a choice set. Each choice set has five different rates

of return, summarized in Table 5.1 for each respondent. The (t, k) combinations were selected to

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avoid weekends. Payments were scheduled during working days at TYM’s office. Since all

female participants are TYM members, they participate in weekly activities involving loan

repayments and depositing of savings at center meetings. TYM is a long-time credible partner,

and the credibility of future payments was not questioned by our respondents.

Table 5.1: Choice sets of experiment

T (start date)

k (delay) (1+r)

daily interest rate APR

Annual rate (%)

4 28 1.01 0.04% 13% 14% 4 28 1.05 0.17% 64% 81% 4 28 1.10 0.34% 124% 196% 4 28 1.30 0.94% 344% 1094% 4 28 1.50 1.46% 532% 2852% 4 56 1.02 0.04% 13% 14% 4 56 1.10 0.17% 62% 78% 4 56 1.30 0.47% 171% 316% 4 56 1.50 0.73% 265% 665% 4 56 1.75 1.00% 367% 1249% 32 28 1.01 0.04% 13% 14% 32 28 1.05 0.17% 64% 81% 32 28 1.10 0.34% 124% 196% 32 28 1.30 0.94% 344% 1094% 32 28 1.50 1.46% 532% 2852% 32 56 1.02 0.04% 13% 14% 32 56 1.10 0.17% 62% 78% 32 56 1.30 0.47% 171% 316% 32 56 1.50 0.73% 265% 665% 32 56 1.75 1.00% 367% 1249%

Before conducting the time-preference games in Vietnam, we organized experimental

pilots with students in the Netherlands, and with TYM members (and husbands) in Vinh Phuc.

The pilots were used to adapt the CTB method to our purpose, and to ensure instructions were

sufficiently clear. After the pilots, we employed and trained 23 loan officers from TYM, who

were subsequently used as instructors during all games. The maximum number of females per

experimental round was also 23, so our respondents had the benefit of one-on-one guidance. The

177

experiments were organized during the weekends. To minimize the risk that information about

the experiment would spread to other females, we organized all experiments for a particular

TYM branch during the same weekend.

Once the women had agreed to participate in the experiments, one of the experimenters

briefly explained the experiment. As mentioned, respondents had to make 20 separate choice

decisions. Each instructor sat next to a respondent, helping her to fill in her choices on a laptop.

For each choice, the instructor informed her about the start date (t), the length of the delay (k),

and the interest rate (r). Next, the respondent had to decide how to allocate the endowment of

80,000 VND between the early consumption and savings option. The instructor then repeated the

preferred allocation, which the respondent had to confirm. After that, the instructor moved to the

next choice-set. This process continued until all 20 allocations had been selected.

To incentivize the allocation process, participants were informed that one (randomly

selected) allocation would actually be paid out. The stakes were quite high. On average, the

payoff equaled approximately the income of 2-3 working days.

Finally, note that the “sooner” period in our experiment refers to 4 days after the

experimental games. Following Giné et al. (2012) we avoid immediate payoffs since this could

induce individuals to choose for the early period just to reduce travel costs. Instead, the

respondent received a voucher specifying the day and amount she could pick up at the TYM

office.

5.4.3. Data

Table 5.2 provides summary statistics of the households in our sample. The average age of TYM

clients in the sample is approximately 46 years; about 60 percent of the sampled women have

completed secondary school; the average household has 5 members; and a small percentage

(around 5.7 percent) of the sampled women is a member of the communist party. Dummy

variables C, T1, T2 indicate that a client belongs to the control group, treatment 1 (with

husbands) or treatment 2, respectively. Since the training was integrated in regular credit center

meetings, the participation of female clients was very high. However, the participation of

husbands in treatment 1 involved genuine (opportunity) costs for men, so their participation

178

varies. On average, invited husbands followed five out of nine training modules. Some ten

percent of the invited husbands did not follow any training module. Imperfect compliance

implies our regression results should be interpreted as an intention to treat (ITT) estimator.

To examine the impact of business training on intertemporal consumption choices, we

also measure business knowledge. In February 2013 we revisited the sampled women and asked

them 10 questions on general business knowledge, 13 questions on financial literacy, 10

questions on marketing and production, 8 questions on accounting skills and 4 questions of

gender issues. We use responses to these questions to construct a range of index scores (by

summing the number of correct answers). These indices are also included in Table 5.2.

Table 5.2: Descriptive statistics

Variable Obs Mean Std. Min Max lnct - lnct+k 4235 -1.2214 1.16015 -5.6285 2.6981 ct – ct+k 6710 -63621 51520.4 -140000 80000 Age 339 45.7156 9.05666 26 68 Secondary school level (1=yes) 339 .6431138 .4791167 0 1 Household size 339 5.04551 1.84543 2 13 Communist party member (1=yes) 339 0.0574 0.23263 0 1 Business knowledge 338 6.748949 1.87122 1 10 Financial literacy 338 10.58198 1.729762 0 13 Combined business and financial literacy 338 17.33093 2.994178 4 22 Training knowledge 338 35.82042 5.828057 15 44 C dummy (1= a client is in control group) 341 0.33988 0.4737 0 1 T1 dummy ( 1= a client is treatment group 1 with inviting husbands) 341 0.33571 0.47227 0 1 T2 dummy ( 1= a client is in treatment group 2 without inviting husbands) 341 0.3244 0.46819 0 1 Husbands in the treatment group 1 joined at least one training module

103 5.456311 2.325379 1 9

Husbands in the treatment group 1 did not join any training modules

12 0 0 0 0

179

Figure 5.1: Mean Experimental Responses over Time

Figure 5.1 plots the average amount allocated to the “early period” against the gross

interest rate (1+r) of each choice. We plot separate points for the two experimental values of t

(i.e., t = 4 or 32 days) and also separate graphs for the two experimental value of k (k = 28 or 56

days). Not surprisingly, for each delay k, the amount allocated to the early payment declines

monotonically with the interest rate. Similarly, for a given interest rate r, increasing the length of

the delay invites the re-allocation of money towards the early period. These results are consistent

with expectations, suggesting the participants understood the experimental instructions.

Table 5.3 provides a further summary of experimental allocations. It summarizes the

allocation of money for different starting dates, delay lengths, and interest rates. Participants

appear to balance consumption between two periods, as expected. For example, when facing an

interest rate of 5 percent, the median participant saves 60,000 VND and consumes 20,000 VND.

However, when the interest rate increases to 10 percent, the median participant saves 70,000

VND and consumes only 10,000 VND. A small share of our respondents (3-4 percent)

0

10000

20000

30000

40000

50000

60000

70000

80000

1 1.2 1.4 1.6 1.8 1 1.2 1.4 1.6 1.8

k = 28 days k = 56 days

t = 4 days t = 32 daysGross Interest Rate (1+r)

Graphs by delayed length

180

consistently display corner solutions, and save nothing. Now consider the interest rate of 50

percent in the near time frame. The median participant saves 80,000 VND when the delay length

is 28 days, but savings decrease to 75,000 VND when the delay length increases to 56 days.

Table 5.3 also reveals important information on individual heterogeneity. On average,

around 63 percent of the participants have no corner solutions in any of the 20 allocation choices.

Hence, 37 percent of our participants have at least consumed or saved their full endowment at

least once. Since this heterogeneity may co-vary with observable characteristics of the female

clients, we include a vector of control variables in our regression models to improve the

precision of our estimates.

Table 5.3: Allocations to later over time and rate of return, in VND

start date

delayed length r mean median sd N

Share corner (later allocation = 80,000VND)

Share corner (later allocation = 0VND)

4 28 0.01 50,908.23 60,000 22,510.55 316 12% 5% 4 28 0.05 55,832.84 60,000 21,042.64 341 18% 4% 4 28 0.1 61,656.89 70,000 18,768.16 341 25% 3% 4 28 0.3 66,703.81 70,000 15,250.18 341 36% 1% 4 28 0.5 71,225.81 80,000 13,645.47 341 54% 1% 4 56 0.02 53,170.89 60,000 23,424.50 316 17% 6% 4 56 0.1 57,621.70 60,000 21,213.91 341 22% 4% 4 56 0.3 63,560.12 70,000 18,591.43 341 32% 2% 4 56 0.5 68,278.59 75,000 15,104.98 341 43% 1% 4 56 0.75 72,102.64 80,000 13,092.14 341 57% 0%

32 28 0.01 52,357.59 60,000 24,289.43 316 17% 8% 32 28 0.05 57,340.18 60,000 22,013.67 341 23% 4% 32 28 0.1 62,126.10 70,000 18,916.04 341 28% 3% 32 28 0.3 67,266.86 70,000 15,678.26 341 40% 1% 32 28 0.5 71,492.67 80,000 14,636.30 341 58% 1% 32 56 0.02 53,528.48 60,000 25,376.58 316 22% 8% 32 56 0.1 59,046.92 65,000 21,879.97 341 28% 3% 32 56 0.3 64,316.72 70,000 18,281.76 341 35% 2% 32 56 0.5 69,659.82 75,000 14,347.22 341 48% 1% 32 56 0.75 72,998.53 80,000 13,297.20 341 63% 1%

181

5.4.4. Identification

We now outline our empirical strategy. First, we will estimate simple OLS models, explaining

variation in consumption smoothing by delay length, the interest rate, treatment dummies, and

interaction terms. We will also include a vector of controls. The CRRA and CARA models,

respectively, read as follows:

(9)

. (10)

In (9) and (10), and are outcome variables for female client i of

choice j at time t and t+k and. As before, k is the delay length of choice j, and r is the interest

rate of choice j. is a dummy variable that takes the value 1 if the client belonged to

the treatment group, are covariates, and is an IIDN(0, σ2) error term. , ,

and . Note , where is the CRRA curvature parameter. For the CARA

models we report , or the constant absolute risk aversion parameter. Observe that and are

“composite parameters”, but that the “relevant” parameters – discount rate and curvature

parameters - can be calculated. Tables 5.4 – 5.7 below present the “calculated” relevant

parameters (Alpha, Year rate, Delta and Rho) in the bottom panel.

The constant terms capture inefficiencies in decision-making (assuming we have

specified the correct utility function). The coefficients of the interaction terms between

treatment and k, and between treatment and ln(1+r) test whether the training affects time

preferences and the curvature of the utility function. The coefficient for the “stand-alone”

treatment dummy tests whether the training affects the degree of inefficient intertemporal

consumption smoothing as this coefficient is simply added to the constant for the relevant

treatment group (accentuating or attenuating our estimate of inefficiency). The OLS models will

182

produce the estimates of the impact of participating in the treatments on consumption smoothing

behavior.

In addition to estimating these OLS models, we also estimate a series of 2SLS models.

Specifically, in the first stage we regress our knowledge index on the treatment dummies, and

then we regress smoothing choices on (predicted) knowledge in the second stage. This approach

allows us to test whether increased knowledge is the channel linking the training to behavioral

outcomes. Carpena et al. (2011) argue that financial literacy may affect financial choices

through various channels, including enhanced product awareness or changed attitudes towards

using financial products and services. Currently, we know very little about the relative

importance of the various channels via which training may affect behavior. If the business

training affects consumption choices through channels other than enhanced knowledge, our IV

approach based on exogenous variation in financial literacy will provide an under-estimate of the

total effect of the training. Or, alternatively, if according to the OLS models there is significant

impact of the training on financial choices, but the same result does not emerge in the 2SLS

models, then we have reason to believe that (part of) the training’s impact is via other channels

than the knowledge one.

While the intervention was randomly assigned at the center level, we only interviewed a

few participants per center (often only one or two). In this case, clustering standard errors at the

center level makes little sense. Instead we report the outcomes of models with and without

clustering of standard errors at the level of individual participants.18 Our preferred specification

is the CRRA model with clustering.

5.5 Results

In Table 5.4 we report OLS models for the CRRA model, based on the full sample (pooled

across all treatments). The dependent variable is the log of current consumption minus the log of

future consumption. We first report regression output for the simple model where we do not

cluster standard errors (columns 1-6). In columns (3-4) we pool participants from the two 18 Since we have only one respondent per lending center, clustering at the level of the individual or at the center level amounts to nearly the same correction.

183

treatments (T1 and T2) and use an aggregate treatment dummy. In columns (5-6) we

differentiate between the treatments where husbands are present, and the one where they were

not, and use two treatment dummies T1 and T2.

In Column (1) we report the results of the most parsimonious specification proposed by

Andreoni and Sprenger (2012). While the interest rate result is consistent with previous work

(and with expectations), the same is not true for the coefficient associated with delayed length

(k). This coefficient is negative, and highly significant – suggesting our Vietnamese

microfinance clients would save more in response to a longer delay in receiving the future

payment. While the parsimonious model does not organize the data from Vietnam as expected,

we obtain a much more sensible picture when we include some basic (demographic) covariates

and a constant as in column (2). Our estimate of the curvature of the utility function is

unaffected, but we now find an annual discount rate equal to 0.777. This discount rate is higher

than those estimated by Andreoni and Sprenger (2012), but in the same ballpark as OLS results

reported by Andreoni et al. (2013). The curvature parameter is estimated at 0.622, which is

lower than curvature parameters estimated by Andreoni and Sprenger (2012) and Andreoni et al.

(2013), but comparable to outcomes based on the Double Multiple Price List approach

(employing Holt and Laury risk measures – see (Andreoni et al., 2013)). We also find that older

people save more, as do members of the communist party.

More interestingly, from our perspective, is that the constant enters significantly and with

a negative sign. In other words, on average our respondents save “too much” relative to their

own preferences – a sign of irrational decision making. In light of the high savings rates in

Vietnam, mentioned above, this is perhaps no surprise.

Does the business training affect consumption smoothing? Column (3) suggests it does.

When including a treatment dummy, we find this dummy enters significantly and with a positive

sign. We find that the constant term for the control group is larger than before (it now takes a

value of -0.581) but that the constant term for those receiving treatment is closer to zero (-0.581

+ 0.213 = -0.368). This implies that treated microfinance clients behave more rational than their

untreated fellows, and reallocate part of their endowment from savings to consumption. Note

184

that the estimated coefficients for the delay variable and interest rate are virtually unaffected by

the inclusion of the treatment dummy.

In column (4) we further probe the impact of the training on behavior by including two

interaction terms: the training dummy times the delay variable or the interest rate. Neither

interaction term enters significantly, suggesting that the training does not affect the preferences

of the participants. In columns (5-6) we distinguish between the treatments with and without

husbands, and include two dummy variables (T1 and T2). The interesting thing to observe in

column (5) is that both treatments reduce the constant term for the relevant sub-group (i.e. both

coefficients are significant and positive). But the coefficient associated with the treatment that

includes husbands appears greater,19 suggesting that women learn more when they have an

opportunity to interact with husbands, who introduce their own expertise and experiences.20 In

column (6) we test whether the treatments affected preferences and include 4 interaction terms

(the product of the treatments and the delay or interest rate variables). This allows us to explore

whether the business training affects the curvature parameter or the annual discount rate. We

find that the interaction terms are not significant. Observe that the coefficient associated with the

basic treatment T2 (excluding women) is no longer significant.

In columns (7-12) we estimate the same models, but now cluster standard errors at the

level of individual respondents. The coefficients are the same, but less precisely estimated.

While our estimates for the effect of varying the delay and interest rates are still very significant

(at the 1% level throughout), the same is not true for some other key results. For example, the

constant term is now only significant at the 10% level in columns (9-12). While the treatment

dummy is significant at the 10% level in column (9), it ceases to be significant when also

including interaction terms (column 10), which may reflect collinearity. Nevertheless, and

19 However, T1 and T2 do not differ significantly from each other, as is indicated by a Wald test: T1 = T2, F(1, 4171) = 1.55, Prob > F = 0.2129. 20 To better understand this latter effect, we organized focus group discussions with six groups of female borrowers who participated in the trainings (three T1 groups and three T2 groups). We invited six female clients from each group to join the discussion. These focus group discussions confirmed that women appreciated the contribution of husbands to the training, as they could easily link the content of the trainings to their own experiences in practice. We also interviewed 1,311 women from T1 groups as part of another study, and 95 percent of the respondents agreed with the proposition that “due to the attendance of husbands, the discussions during the trainings were more interesting.”

185

across the columns, we continue to find evidence that consumption smoothing in the experiment

is inefficient, and that this inefficiency is attenuated by the training (especially the treatment

where husbands are present—see columns 11 and 12).

186

Table 5.4 : OLS estimates – CRRA ( Dependent variable: lnct -lnct+k)

VARIABLES (1) (2) (3) (4) (5) (6) Delayed length (k) -0.0148*** 0.00417*** 0.00413*** 0.00337* 0.00415*** 0.00338* (0) (0.000809) (0.000848) (0.0816) (0.000811) (0.0813) Interest rate (ln(1+r)) -2.807*** -2.648*** -2.642*** -2.530*** -2.644*** -2.530*** (0) (0) (0) (0) (0) (0) Age -0.00829*** -0.00823*** -0.00819*** -0.00838*** -0.00835*** (2.03e-05) (1.95e-05) (2.12e-05) (1.60e-05) (1.70e-05) Secondary school (1=yes)

0.0116 0.00825 0.00847 0.00826 0.00864

(0.739) (0.813) (0.808) (0.812) (0.804) Household size -0.0136 -0.0145* -0.0145* -0.0142* -0.0142* (0.101) (0.0777) (0.0768) (0.0842) (0.0841) Communist (1=yes) -0.373*** -0.404*** -0.403*** -0.391*** -0.391*** (8.50e-07) (4.18e-08) (4.54e-08) (1.99e-07) (1.93e-07) Treatment 0.213*** 0.197* (2.79e-10) (0.0583) Treatment × k 0.00115 (0.645) Treatment × ln(1+r) -0.172 (0.400) T1 0.238*** 0.284** (1.60e-09) (0.0202) T2 0.184*** 0.0982 (6.77e-06) (0.428) T1×k 3.00e-05 (0.992) T2×k 0.00247 (0.406) T1× ln(1+r) -0.252 (0.301) T2× ln(1+r) -0.0871 (0.720) Constant -0.447*** -0.581*** -0.572*** -0.576*** -0.567*** (2.93e-05) (8.25e-08) (2.84e-06) (1.09e-07) (3.62e-06) alpha 0.644*** 0.622*** 0.622*** 0.605*** 0.622*** 0.605*** (0) (0) (0) (0) (0) (0) Year rate -0.854*** 0.777*** 0.770*** 0.627 0.773*** 0.628 (0) (0.00853) (0.00869) (0.157) (0.00849) (0.156) delta 1.005*** 0.998*** 0.998*** 0.999*** 0.998*** 0.999*** (0) (0) (0) (0) (0) (0) Observations 4,235 4,180 4,180 4,180 4,180 4,180 Clusters R-squared 0.150 0.158 0.158 0.158 0.158

Notes: (a) Annual discount rate calculated as ; (b) Robust p-value in parentheses from columns 1-6, cluster p-value in parentheses from columns 7-12; (c) *** p<0.01, ** p<0.05, * p<0.1

187

Table 5.4: OLS estimates – CRRA ( Dependent variable: lnct -lnct+k (Cont.)

VARIABLES (7) (8) (9) (10) (11) (12) Delayed length (k) -0.0148*** 0.00417*** 0.00413*** 0.00337** 0.00415*** 0.00338** (0) (5.78e-05) (6.42e-05) (0.0335) (5.76e-05) (0.0333) Interest rate (ln(1+r))

-2.807*** -2.648*** -2.642*** -2.530*** -2.644*** -2.530***

(0) (0) (0) (0) (0) (0) Age -0.00829 -0.00823 -0.00819 -0.00838 -0.00835 (0.188) (0.184) (0.186) (0.182) (0.183) Secondary school (1=yes)

0.0116 0.00825 0.00847 0.00826 0.00864

(0.917) (0.941) (0.939) (0.941) (0.938) Household size -0.0136 -0.0145 -0.0145 -0.0142 -0.0142 (0.588) (0.555) (0.555) (0.564) (0.565) Communist (1=yes) -0.373* -0.404* -0.403* -0.391* -0.391* (0.0901) (0.0541) (0.0546) (0.0678) (0.0675) Treatment 0.213** 0.197 (0.0434) (0.111) Treatment×k 0.00115 (0.576) Treatment× ln(1+r) -0.172 (0.586) T1 0.238* 0.284* (0.0568) (0.0518) T2 0.184 0.0982 (0.148) (0.499) T1×k 3.00e-05 (0.990) T2×k 0.00247 (0.298) T1× ln(1+r) -0.252 (0.483) T2× ln(1+r) -0.0871 (0.821) Constant -0.447 -0.581* -0.572* -0.576* -0.567* (0.137) (0.0583) (0.0614) (0.0616) (0.0651) alpha 0.644*** 0.622*** 0.622*** 0.605*** 0.622*** 0.605*** (0) (0) (0) (0) (0) (0) Year rate -0.854*** 0.777*** 0.770*** 0.627* 0.773*** 0.628* (0) (0.000809) (0.000865) (0.0686) (0.000815) (0.0684) delta 1.005*** 0.998*** 0.998*** 0.999*** 0.998*** 0.999*** (0) (0) (0) (0) (0) (0) Observations 4,235 4,180 4,180 4,180 4,180 4,180 Clusters 310 306 306 306 306 306 R-squared 0.562 0.150 0.158 0.158 0.158 0.158

Notes: (a) Annual discount rate calculated as ; (b) Robust p-value in parentheses from columns 1-6, cluster p-value in parentheses from columns 7-12; (c) *** p<0.01, ** p<0.05, * p<0.1

188

In Table 5.5 we estimate the same models, but now use the CARA specification. The

dependent variable is the difference between consumption and saving (rather than log values), so

corner choices are not omitted and the number of observations increases. Across all relevant

columns we again find evidence of inefficient consumption smoothing, or over-saving, as all

constant terms are significant and of negative sign. Consistent with the regression results in

Table 5.4, we find that the treatments do not affect preferences, as the interaction terms enter

insignificantly. And also consistent with earlier results, we find some evidence that the training

reduces inefficiencies in consumption smoothing behavior – especially when husbands are

present. This is evident from columns (3) and (5) when we do not cluster standard errors, and

from column (11) when we do cluster at the level of individual respondents. However, the

CARA results are statistically weaker than the CRRA results discussed above. This is evident

from column (9), where the generic training dummy is not significant, and from column (11)

where the basic training dummy (excluding husbands) is not significant either.21

21 Now the Wald test (T1=T2) indicates that T1 differs significantly from T2: Column (5) in Table 5: F(1, 6601) = 17.22, Prob > F = 0.0000.

189

Table 5.5: OLS estimates – CARA (Dependent variable: ct –ct+k)

VARIABLES (1) (2) (3) (4) (5) (6) Delayed length (k) -508.0*** 161.7*** 161.7*** 105.3* 161.8*** 105.3* (0) (2.76e-05) (2.71e-05) (0.0815) (2.62e-05) (0.0816) Interest rate (ln(1+r))

-178,122*** -171,822*** -171,801*** -167,709*** -171,831*** -167,711***

(0) (0) (0) (0) (0) (0) Age 30.60 34.67 34.97 8.712 9.030 (0.584) (0.534) (0.531) (0.876) (0.871) Secondary school (1=yes)

-1,677 -1,874* -1,876* -1,854* -1,858*

(0.120) (0.0815) (0.0812) (0.0844) (0.0839) Household size 1,068*** 1,069*** 1,068*** 1,075*** 1,074*** (0.000110) (0.000103) (0.000104) (9.23e-05) (9.31e-05) Communist (1=yes) -4,538* -5,225** -5,228** -4,068* -4,070* (0.0562) (0.0261) (0.0259) (0.0848) (0.0845) Treatment 4,812*** 2,598 (2.85e-06) (0.427) Treatment×k 84.94 (0.276) Treatment× ln(1+r) -6,166 (0.293) T1 7,430*** 5,887 (1.36e-09) (0.127) T2 2,024* -875.8 (0.0933) (0.820) T1×k 74.50 (0.422) T2×k 96.08 (0.294) T1× ln(1+r) -7,219 (0.303) T2× ln(1+r) -5,170 (0.455) Constant -38,137*** -41,355*** -39,897*** -40,245*** -38,789*** (0) (0) (0) (0) (0) rho 5.61e-06*** 5.82e-06*** 5.82e-06*** 5.96e-06*** 5.82e-06*** 5.96e-06*** (0) (0) (0) (0) (0) (0) yearrate -0.647*** 0.410*** 0.410*** 0.257 0.410*** 0.258 (0) (0.000288) (0.000284) (0.114) (0.000276) (0.114) delta 1.003*** 0.999*** 0.999*** 0.999*** 0.999*** 0.999*** (0) (0) (0) (0) (0) (0) Observations 6,710 6,610 6,610 6,610 6,610 6,610 Clusters R-squared 0.355 0.357 0.357 0.359 0.359 Notes: (a) Annual discount rate calculated as ; (b) Robust p-value in parentheses from columns 1-6, cluster p-value in parentheses from columns 7-12; (c) *** p<0.01, ** p<0.05, * p<0.1

190

Table 5.5: OLS estimates – CARA (Dependent variable: ct –ct+k) (Cont.)

VARIABLES (7) (8) (9) (10) (11) (12) Delayed length (k) -508.0*** 161.7*** 161.7*** 105.3* 161.8*** 105.3* (0) (4.21e-07) (4.20e-07) (0.0646) (4.10e-07) (0.0647) Interest rate (ln(1+r))

-178,122*** -171,822*** -171,801*** -167,709*** -171,831*** -167,711***

(0) (0) (0) (0) (0) (0) Age 30.60 34.67 34.97 8.712 9.030 (0.873) (0.856) (0.854) (0.963) (0.962) Secondary school (1=yes)

-1,677 -1,874 -1,876 -1,854 -1,858

(0.645) (0.604) (0.604) (0.607) (0.607) Household size 1,068 1,069 1,068 1,075 1,074 (0.274) (0.272) (0.272) (0.268) (0.268) Communist (1=yes) -4,538 -5,225 -5,228 -4,068 -4,070 (0.529) (0.459) (0.459) (0.569) (0.569) Treatment 4,812 2,598 (0.160) (0.541) Treatment×k 84.94 (0.212) Treatment× ln(1+r) -6,166 (0.546) T1 7,430* 5,887 (0.0722) (0.226) T2 2,024 -875.8 (0.616) (0.863) T1×k 74.50 (0.359) T2×k 96.08 (0.191) T1× ln(1+r) -7,219 (0.558) T2× ln(1+r) -5,170 (0.662) Constant -38,137*** -41,355*** -39,897*** -40,245*** -38,789*** (0.000345) (0.000143) (0.000258) (0.000200) (0.000362) rho 5.61e-06*** 5.82e-06*** 5.82e-06*** 5.96e-06*** 5.82e-06*** 5.96e-06*** (0) (0) (0) (0) (0) (0) yearrate -0.647*** 0.410*** 0.410*** 0.257* 0.410*** 0.258* (0) (3.46e-06) (3.45e-06) (0.0834) (3.39e-06) (0.0835) delta 1.003*** 0.999*** 0.999*** 0.999*** 0.999*** 0.999*** (0) (0) (0) (0) (0) (0) Observations 6,710 6,610 6,610 6,610 6,610 6,610 Clusters 341 336 336 336 336 336 R-squared 0.728 0.355 0.357 0.357 0.359 0.359

Notes: (a) Annual discount rate calculated as ; (b) Robust p-value in parentheses from columns 1-6, cluster p-value in parentheses from columns 7-12; (c) *** p<0.01, ** p<0.05, * p<0.1

191

To probe the mechanism linking the training to changes in consumption smoothing, we

also estimated a series of IV models. Since business training is intended to improve (business)

knowledge, we ask whether they affect financial decisions via a reduction in knowledge gaps.

We first regress four complementary knowledge indices on the training dummy (and included

exogenous variables), and then regress consumption smoothing on predicted knowledge. Second

stage results are reported in Tables 5.6 and 5.7. First stage results are in the Appendix 5.1 and

Appendix 5.2. Not surprisingly, perhaps, the instruments are consistently highly relevant –

attending the training improves business knowledge. Moreover, and consistent with the OLS

results discussed above, it appears as if the impact of the training on knowledge is greater when

husbands are present.

We again report results for models without and with clustering of the standard errors. For

CRRA utility, we find across all columns that (i) the coefficients associated with the delay

variable and interest are plausible and of the right sign, (ii) the constant term is consistently

significant and negative (suggesting over-saving), and (iii) the knowledge proxies are always

significant and positive – attenuating the inefficiency implied by the constant term. In other

words, one mechanism via which training have an impact on consumption smoothing is via the

transfer of knowledge.22 The unclustered results in Table 5.7 are very similar, but from columns

(9-16), it is evident that we do not find this result when assuming CARA utility and clustering

standard errors.

22 We have also estimated these models with interaction terms, but found these never entered significantly. Hence, as before, there is no evidence that enhanced knowledge affects preferences for intertemporal consumption smoothing.

192

Tab

le 5

.6: I

V e

stim

ates

– C

RR

A (

Dep

ende

nt v

aria

ble:

lnc t

-lnc t+

k )

VA

RIA

BLE

S (1

) (2

) (3

) (4

) (5

) (6

) (7

) (8

) B

usin

ess k

now

ledg

e 0.

0941

***

0.

0936

***

(4.7

3e-0

9)

(5

.42e

-09)

Fina

ncia

l lite

racy

0.22

4***

0.20

3***

(2

.12e

-08)

(5.3

3e-0

8)

Com

bine

d bu

sine

ss a

nd fi

nanc

ial l

itera

cy

0.06

63**

*

0.06

64**

*

(4

.75e

-09)

(4.1

9e-0

9)

Tr

aini

ng k

now

ledg

e

0.03

05**

*

0.03

05**

*

(3

.66e

-09)

(3.2

4e-0

9)

Del

ayed

leng

th (k

) 0.

0040

8***

0.

0036

5***

0.

0039

5***

0.

0040

1***

0.

0040

7***

0.

0036

7***

0.

0039

5***

0.

0040

1***

(0.0

0125

) (0

.004

83)

(0.0

0172

) (0

.001

36)

(0.0

0125

) (0

.004

26)

(0.0

0172

) (0

.001

36)

Inte

rest

rate

(ln(

1+r)

) -2

.616

***

-2.5

58**

* -2

.599

***

-2.6

07**

* -2

.616

***

-2.5

64**

* -2

.599

***

-2.6

07**

*

(0)

(0)

(0)

(0)

(0)

(0)

(0)

(0)

Age

-0

.009

51**

* -0

.006

71**

* -0

.008

68**

* -0

.008

87**

* -0

.009

50**

* -0

.006

79**

* -0

.008

68**

* -0

.008

87**

*

(1.2

4e-0

6)

(0.0

0119

) (1

.03e

-05)

(5

.16e

-06)

(1

.26e

-06)

(0

.000

911)

(1

.04e

-05)

(5

.30e

-06)

Se

cond

ary

scho

ol le

vel (

1=ye

s)

0.00

0877

-0

.012

0 -0

.002

92

-0.0

0326

0.

0008

63

-0.0

110

-0.0

0292

-0

.003

25

(0

.980

) (0

.746

) (0

.935

) (0

.926

) (0

.981

) (0

.763

) (0

.935

) (0

.927

) H

ouse

hold

size

-0

.016

4*

-0.0

173*

* -0

.016

7**

-0.0

189*

* -0

.016

4*

-0.0

169*

* -0

.016

7**

-0.0

189*

*

(0.0

524)

(0

.041

0)

(0.0

461)

(0

.023

3)

(0.0

526)

(0

.043

7)

(0.0

459)

(0

.023

1)

Com

mun

ist p

arty

mem

ber (

1=ye

s)

-0.4

26**

* -0

.441

***

-0.4

31**

* -0

.399

***

-0.4

26**

* -0

.434

***

-0.4

31**

* -0

.399

***

(2

.35e

-08)

(5

.34e

-08)

(2

.52e

-08)

(9

.19e

-08)

(2

.42e

-08)

(5

.45e

-08)

(2

.41e

-08)

(9

.01e

-08)

C

onst

ant

-1.0

12**

* -2

.852

***

-1.5

56**

* -1

.481

***

-1.0

10**

* -2

.629

***

-1.5

58**

* -1

.480

***

(0

) (7

.37e

-11)

(0

) (0

) (0

) (1

.18e

-10)

(0

) (0

) A

lpha

0.

618*

**

0.60

9***

0.

615*

**

0.61

6***

0.

618*

**

0.61

0***

0.

615*

**

0.61

6***

(0)

(0)

(0)

(0)

(0)

(0)

(0)

(0)

Yea

r rat

e 0.

766*

* 0.

684*

* 0.

741*

* 0.

753*

* 0.

766*

* 0.

687*

* 0.

742*

* 0.

753*

*

(0.0

111)

(0

.023

8)

(0.0

131)

(0

.011

5)

(0.0

111)

(0

.022

0)

(0.0

131)

(0

.011

5)

Del

ta

0.99

8***

0.

999*

**

0.99

8***

0.

998*

**

0.99

8***

0.

999*

**

0.99

8***

0.

998*

**

(0

) (0

) (0

) (0

) (0

) (0

) (0

) (0

) O

bser

vatio

ns

4,13

2 4,

132

4,13

2 4,

132

4,13

2 4,

132

4,13

2 4,

132

R-s

quar

ed

0.12

4 0.

073

0.12

7 0.

140

0.12

4 0.

088

0.12

7 0.

140

Not

es: (

a) A

nnua

l dis

coun

t rat

e ca

lcul

ated

as

; (b

) Rob

ust

p-va

lue

in p

aren

thes

es fr

om c

olum

ns 1

-8, c

lust

er p

-val

ue in

par

enth

eses

from

col

umns

9-

16; (

c) *

** p

<0.0

1, *

* p<

0.05

, * p

<0.1

; (d)

mod

els 1

– 4

and

mod

els 9

– 1

2 us

ed tr

eatm

ent d

umm

y as

inst

rum

ent v

aria

bles

; mod

els 5

– 8

and

mod

els 1

3-16

us

ed T

1 an

d T2

dum

mie

s as i

nstru

men

t var

iabl

es

193

Tab

le 5

.6: I

V e

stim

ates

– C

RR

A (

Dep

ende

nt v

aria

ble:

lnc t

-lnc t+

k )

(Con

t.)

VA

RIA

BLE

S (9

) (1

0)

(11)

(1

2)

(13)

(1

4)

(15)

(1

6)

Bus

ines

s kno

wle

dge

0.09

41*

0.

0936

*

(0

.063

2)

(0

.063

9)

Fi

nanc

ial l

itera

cy

0.

224*

0.20

3*

(0.0

809)

(0.0

919)

C

ombi

ned

busi

ness

and

fina

ncia

l lite

racy

0.

0663

*

0.06

64*

(0.0

629)

(0.0

628)

Trai

ning

kno

wle

dge

0.

0305

*

0.03

05*

(0.0

600)

(0.0

601)

D

elay

ed le

ngth

(k)

0.00

408*

**

0.00

365*

**

0.00

395*

**

0.00

401*

**

0.00

407*

**

0.00

367*

**

0.00

395*

**

0.00

401*

**

(8

.01e

-05)

(0

.000

336)

(0

.000

111)

(8

.69e

-05)

(8

.07e

-05)

(0

.000

330)

(0

.000

110)

(8

.58e

-05)

In

tere

st ra

te (l

n(1+

r))

-2.6

16**

* -2

.558

***

-2.5

99**

* -2

.607

***

-2.6

16**

* -2

.564

***

-2.5

99**

* -2

.607

***

(0

) (0

) (0

) (0

) (0

) (0

) (0

) (0

) A

ge

-0.0

0951

-0

.006

71

-0.0

0868

-0

.008

87

-0.0

0950

-0

.006

79

-0.0

0868

-0

.008

87

(0

.134

) (0

.333

) (0

.177

) (0

.158

) (0

.134

) (0

.319

) (0

.177

) (0

.159

) Se

cond

ary

scho

ol le

vel (

1=ye

s)

0.00

0877

-0

.012

0 -0

.002

92

-0.0

0326

0.

0008

63

-0.0

110

-0.0

0292

-0

.003

25

(0

.994

) (0

.921

) (0

.980

) (0

.977

) (0

.994

) (0

.926

) (0

.980

) (0

.977

) H

ouse

hold

size

-0

.016

4 -0

.017

3 -0

.016

7 -0

.018

9 -0

.016

4 -0

.016

9 -0

.016

7 -0

.018

9

(0.5

24)

(0.5

06)

(0.5

11)

(0.4

51)

(0.5

25)

(0.5

09)

(0.5

10)

(0.4

50)

Com

mun

ist p

arty

mem

ber (

1=ye

s)

-0.4

26*

-0.4

41*

-0.4

31*

-0.3

99*

-0.4

26*

-0.4

34*

-0.4

31*

-0.3

99*

(0

.057

3)

(0.0

751)

(0

.060

1)

(0.0

637)

(0

.057

5)

(0.0

737)

(0

.059

8)

(0.0

636)

C

onst

ant

-1.0

12**

-2

.852

**

-1.5

56**

-1

.481

**

-1.0

10**

-2

.629

**

-1.5

58**

-1

.480

**

(0

.016

3)

(0.0

417)

(0

.018

3)

(0.0

175)

(0

.016

7)

(0.0

429)

(0

.017

6)

(0.0

167)

A

lpha

0.

618*

**

0.60

9***

0.

615*

**

0.61

6***

0.

618*

**

0.61

0***

0.

615*

**

0.61

6***

(0)

(0)

(0)

(0)

(0)

(0)

(0)

(0)

Yea

r rat

e 0.

766*

**

0.68

4***

0.

741*

**

0.75

3***

0.

766*

**

0.68

7***

0.

742*

**

0.75

3***

(0.0

0107

) (0

.002

59)

(0.0

0131

) (0

.001

12)

(0.0

0107

) (0

.002

49)

(0.0

0130

) (0

.001

11)

Del

ta

0.99

8***

0.

999*

**

0.99

8***

0.

998*

**

0.99

8***

0.

999*

**

0.99

8***

0.

998*

**

(0

) (0

) (0

) (0

) (0

) (0

) (0

) (0

) O

bser

vatio

ns

4,13

2 4,

132

4,13

2 4,

132

4,13

2 4,

132

4,13

2 4,

132

Clu

ster

s 30

3 30

3 30

3 30

3 30

3 30

3 30

3 30

3 R

-squ

ared

0.

124

0.07

3 0.

127

0.14

0 0.

124

0.08

8 0.

127

0.14

0 N

otes

: (a)

Ann

ual d

isco

unt r

ate

calc

ulat

ed a

s ;

(b) R

obus

t p-

valu

e in

par

enth

eses

from

col

umns

1-8

, clu

ster

p-v

alue

in p

aren

thes

es fr

om c

olum

ns 9

-16;

(c) *

**

p<0.

01, *

* p<

0.05

, * p

<0.1

; (d)

mod

els 1

– 4

and

mod

els 9

– 1

2 us

ed tr

eatm

ent d

umm

y as

inst

rum

ent v

aria

bles

; mod

els 5

– 8

and

mod

els 1

3-16

use

d T

1 an

d T2

dum

mie

s as

inst

rum

ent v

aria

bles

194

Tab

le 5

.7: I

V e

stim

ates

– C

AR

A (D

epen

dent

var

iabl

e: c

t –c t+

k)

VA

RIA

BLE

S (1

) (2

) (3

) (4

) (5

) (6

) (7

) (8

) B

usin

ess k

now

ledg

e 2,

048*

**

1,

987*

**

(1.6

5e-0

5)

(2

.83e

-05)

Fina

ncia

l lite

racy

4,78

2***

5,56

7***

(1

.96e

-05)

(3.0

6e-0

7)

Com

bine

d bu

sine

ss a

nd fi

nanc

ial l

itera

cy

1,43

4***

1,52

2***

(1

.60e

-05)

(4.7

6e-0

6)

Tr

aini

ng k

now

ledg

e

676.

0***

714.

1***

(1

.60e

-05)

(5.2

8e-0

6)

Del

ayed

leng

th (k

) 15

5.3*

**

154.

5***

15

5.0*

**

155.

3***

15

5.3*

**

154.

3***

15

5.0*

**

155.

3***

(6.1

5e-0

5)

(7.6

3e-0

5)

(6.2

5e-0

5)

(6.0

0e-0

5)

(6.1

2e-0

5)

(8.4

0e-0

5)

(6.3

2e-0

5)

(6.0

4e-0

5)

Inte

rest

rate

(ln(

1+r)

) -1

70,8

72**

* -1

70,7

28**

* -1

70,8

29**

* -1

70,9

31**

* -1

70,8

73**

* -1

70,7

00**

* -1

70,8

24**

* -1

70,9

33**

*

(0)

(0)

(0)

(0)

(0)

(0)

(0)

(0)

Age

25

.61

132.

6**

57.6

8 53

.09

26.3

6 14

6.0*

* 58

.12

53.2

3

(0.6

49)

(0.0

299)

(0

.307

) (0

.348

) (0

.639

) (0

.016

9)

(0.3

04)

(0.3

46)

Seco

ndar

y sc

hool

leve

l (1=

yes)

-2

,008

* -1

,248

-1

,780

-1

,907

* -2

,009

* -1

,112

-1

,762

-1

,897

*

(0.0

639)

(0

.264

) (0

.102

) (0

.078

8)

(0.0

636)

(0

.322

) (0

.106

) (0

.080

4)

Hou

seho

ld si

ze

1,08

3***

91

4.6*

**

1,03

3***

1,

039*

**

1,08

4***

88

5.5*

**

1,02

9***

1,

036*

**

(8

.56e

-05)

(0

.001

06)

(0.0

0017

7)

(0.0

0014

6)

(8.5

1e-0

5)

(0.0

0143

) (0

.000

185)

(0

.000

151)

C

omm

unis

t par

ty m

embe

r (1=

yes)

-5

,728

**

-5,9

51**

-5

,795

**

-5,4

17**

-5

,688

**

-6,2

08**

* -5

,882

**

-5,4

75**

(0.0

155)

(0

.011

7)

(0.0

141)

(0

.020

9)

(0.0

162)

(0

.008

75)

(0.0

128)

(0

.019

6)

Con

stan

t -5

1,76

8***

-9

3,27

2***

-6

4,21

4***

-6

3,32

5***

-5

1,38

9***

-1

02,1

55**

* -6

5,75

3***

-6

4,68

8***

(0)

(0)

(0)

(0)

(0)

(0)

(0)

(0)

Rho

5.

85e-

06**

* 5.

86e-

06**

* 5.

85e-

06**

* 5.

85e-

06**

* 5.

85e-

06**

* 5.

86e-

06**

* 5.

85e-

06**

* 5.

85e-

06**

*

(0)

(0)

(0)

(0)

(0)

(0)

(0)

(0)

Yea

r rat

e 0.

393*

**

0.39

1***

0.

393*

**

0.39

3***

0.

393*

**

0.39

1***

0.

393*

**

0.39

3***

(0.0

0049

7)

(0.0

0058

2)

(0.0

0050

2)

(0.0

0048

8)

(0.0

0049

5)

(0.0

0062

7)

(0.0

0050

6)

(0.0

0049

0)

Del

ta

0.99

9***

0.

999*

**

0.99

9***

0.

999*

**

0.99

9***

0.

999*

**

0.99

9***

0.

999*

**

(0

) (0

) (0

) (0

) (0

) (0

) (0

) (0

) O

bser

vatio

ns

6,55

0 6,

550

6,55

0 6,

550

6,55

0 6,

550

6,55

0 6,

550

Clu

ster

s

R

-squ

ared

0.

350

0.33

9 0.

350

0.35

1 0.

350

0.33

2 0.

350

0.35

1 N

otes

: (a)

Ann

ual d

isco

unt r

ate

calc

ulat

ed a

s ;

(b) R

obus

t p-

valu

e in

par

enth

eses

from

col

umns

1-8

, clu

ster

p-v

alue

in p

aren

thes

es fr

om c

olum

ns 9

-16;

(c) *

** p

<0.0

1, *

* p<

0.05

, * p

<0.1

; (d

) mod

els 1

– 4

and

mod

els 9

– 1

2 us

ed tr

eatm

ent d

umm

y as

inst

rum

ent v

aria

bles

; mod

els 5

– 8

and

mod

els 1

3-16

use

d T

1 an

d T2

dum

mie

s as i

nstru

men

t var

iabl

es.

195

Tab

le 5

.7: I

V e

stim

ates

– C

AR

A (D

epen

dent

var

iabl

e: c

t –c t+

k) (C

ont.)

VA

RIA

BLE

S (9

) (1

0)

(11)

(1

2)

(13)

(1

4)

(15)

(1

6)

Bus

ines

s kno

wle

dge

2,04

8

1,98

7

(0

.197

)

(0.2

09)

Fi

nanc

ial l

itera

cy

4,

782

5,

567

(0.2

03)

(0

.131

)

C

ombi

ned

busi

ness

an

d fin

anci

al li

tera

cy

1,43

4

1,52

2

(0

.195

)

(0.1

70)

Tr

aini

ng k

now

ledg

e

676.

0

714.

1

(0

.195

)

(0.1

72)

Del

ayed

leng

th (k

) 15

5.3*

**

154.

5***

15

5.0*

**

155.

3***

15

5.3*

**

154.

3***

15

5.0*

**

155.

3***

(6.2

9e-0

7)

(6.7

8e-0

7)

(6.4

3e-0

7)

(6.1

5e-0

7)

(6.2

9e-0

7)

(6.8

8e-0

7)

(6.4

5e-0

7)

(6.1

5e-0

7)

Inte

rest

rate

(ln(

1+r)

) -1

70,8

72**

* -1

70,7

28**

* -1

70,8

29**

* -1

70,9

31**

* -1

70,8

73**

* -1

70,7

00**

* -1

70,8

24**

* -1

70,9

33**

*

(0)

(0)

(0)

(0)

(0)

(0)

(0)

(0)

Age

25

.61

132.

6 57

.68

53.0

9 26

.36

146.

0 58

.12

53.2

3

(0.8

94)

(0.5

32)

(0.7

67)

(0.7

85)

(0.8

91)

(0.4

93)

(0.7

65)

(0.7

85)

Seco

ndar

y sc

hool

leve

l (1=

yes)

-2

,008

-1

,248

-1

,780

-1

,907

-2

,009

-1

,112

-1

,762

-1

,897

(0.5

84)

(0.7

47)

(0.6

31)

(0.6

04)

(0.5

84)

(0.7

74)

(0.6

34)

(0.6

06)

Hou

seho

ld si

ze

1,08

3 91

4.6

1,03

3 1,

039

1,08

4 88

5.5

1,02

9 1,

036

(0

.266

) (0

.351

) (0

.287

) (0

.279

) (0

.266

) (0

.363

) (0

.288

) (0

.280

) C

omm

unis

t par

ty m

embe

r (1=

yes)

-5

,728

-5

,951

-5

,795

-5

,417

-5

,688

-6

,208

-5

,882

-5

,475

(0.4

24)

(0.4

04)

(0.4

17)

(0.4

41)

(0.4

28)

(0.3

85)

(0.4

10)

(0.4

36)

Con

stan

t -5

1,76

8***

-9

3,27

2**

-64,

214*

**

-63,

325*

**

-51,

389*

**

-102

,155

**

-65,

753*

**

-64,

688*

**

(0

.000

335)

(0

.033

2)

(0.0

0368

) (0

.003

50)

(0.0

0036

3)

(0.0

179)

(0

.003

02)

(0.0

0294

) R

ho

5.85

e-06

***

5.85

e-06

***

5.85

e-06

***

5.85

e-06

***

5.85

e-06

***

5.85

e-06

***

5.85

e-06

***

5.85

e-06

***

(0

) (0

) (0

) (0

) (0

) (0

) (0

) (0

) Y

ear r

ate

0.39

3***

0.

393*

**

0.39

3***

0.

393*

**

0.39

3***

0.

393*

**

0.39

3***

0.

393*

**

(4

.83e

-06)

(4

.83e

-06)

(4

.92e

-06)

(4

.74e

-06)

(4

.83e

-06)

(4

.83e

-06)

(4

.93e

-06)

(4

.74e

-06)

D

elta

0.

999*

**

0.99

9***

0.

999*

**

0.99

9***

0.

999*

**

0.99

9***

0.

999*

**

0.99

9***

(0)

(0)

(0)

(0)

(0)

(0)

(0)

(0)

Obs

erva

tions

6,

550

6,55

0 6,

550

6,55

0 6,

550

6,55

0 6,

550

6,55

0 C

lust

ers

333

333

333

333

333

333

333

333

R-s

quar

ed

0.35

0 0.

339

0.35

0 0.

351

0.35

0 0.

332

0.35

0 0.

351

Not

es: (

a) A

nnua

l dis

coun

t rat

e ca

lcul

ated

as

; (b

) Rob

ust

p-va

lue

in p

aren

thes

es fr

om c

olum

ns 1

-8, c

luste

r p-v

alue

in p

aren

thes

es fr

om c

olum

ns 9

-16;

(c) *

** p

<0.0

1, *

* p<

0.05

, * p

<0.1

; (d

) mod

els 1

– 4

and

mod

els 9

– 1

2 us

ed tr

eatm

ent d

umm

y as

inst

rum

ent v

aria

bles

; mod

els 5

– 8

and

mod

els 1

3-16

use

d T

1 an

d T2

dum

mie

s as i

nstru

men

t var

iabl

es.

196

5.6 Conclusions

The recent economic literature on microfinance emphasizes the importance of relevant

training programs to accompany the provision of credit. However, much is unknown about

the role of human capital in consumption and investment choices. To advance the debate, we

organized an RCT in northern Vietnam and examined whether business training affect

intertemporal consumption behavior. To obtain measures of time preferences and

consumption smoothing, we are among the first to use the CTB game in a developing country

context. Another novelty is that we estimate smoothing behavior using regression models

that include a constant term, and propose that a natural interpretation for this constant term is

a measure of inefficiency (or irrationality). A final contribution is our effort to compare the

effects of training treatments with and without husbands.

We demonstrate evidence of inefficient consumption smoothing among our sample of

Vietnamese microfinance clients. Specifically, and somewhat in contrast to “conventional

wisdom” in the literature on underdevelopment, we find these women tend to save too much

at the expense of short-term consumption (relative to their own preferences). Our second

result is that attending business training helps to reduce such inefficiencies. Trained women

behave more “rational” than untrained ones, and we present tentative evidence that this is

(partly) due to the transfer of knowledge. Our third result is that training in which husbands

participate appear more effective in reducing inefficiencies than (standard) treatments from

which men are banned (even if this difference is not significant across regression

specifications). Hence, our results not only support recent attempts to create human capital

among microfinance clients, they also provide a natural suggestion to improve the impact of

such training. Finally, we find no evidence that attending business training is

“transformative” in the sense that the level of impatience of our respondents is affected. We

also find that the curvature of the utility function is unaffected by the training.

It should be noted that while we do not find a significant additional impact of inviting

husbands on our outcomes of interest in Chapter 3 and 4, we find positive effects of

husband’s presence in this chapter. There are several reasons that can explain the results.

First, in chapter 5 we focus on other outcome variables than in Chapters 3 and 4. It may be so

that the effect of the training on intertemporal consumption smoothing of women is affected

197

by the presence of husbands, while the presence of husbands does not affect the impact of the

training on business practices in general. Second, probably more important: the samples in

Chapter 3 and 4, and Chapter 5, on the other hand, differ considerably. In Chapters 3 and 4

the amount of women included in the sample is very big (per survey around 4000) while in

chapter 5 not more than 340 women are included. Moreover, the sample in chapter 5

probably contains the most interested women since the experiments contain a random sample

from women that actually followed the training, and not a random sample from the women

that were invited to the training.

While our interpretation of the empirical results, i.e. that the training reduces

inefficient intertemporal consumption smoothing, is in line with our theoretical model,

alternative interpretations may hold. It may, for instance, be the case that households in

Vietnam do not behave in line with a CARA or CRRA utility function. Other utility functions

may provide first order conditions in which a significant constant can be explained by

rational behavior. Moreover, our results and interpretation may be affected by households’

possibility to trade “outside” the game. That is, when playing the game, some agents may

decide to put all their money in the “consumption” account, but immediately save this money

“outside” the game. If this happens, we incorrectly conclude that the training stimulates

consumption at the expense of savings. In reality it may also be the case according to another

utility function. Further research should explore to what extent our interpretation, or some

alternative explanations are more in line with reality.

198

App

endi

ces

App

endi

x 5.

1: F

irst-s

tage

regr

essio

n of

IV e

stim

ates

- C

RRA

V

AR

IAB

LES

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

Del

ayed

leng

th (k

) -0

.002

28

0.00

0933

-0

.001

35

-0.0

0483

-0

.002

31

0.00

1054

-0

.001

25

-0.0

0456

(0.2

04)

(0.6

07)

(0.6

45)

(0.3

77)

(0.1

99)

(0.5

6)

(0.6

68)

(0.4

03)

Inte

rest

rate

(ln(

1+r)

) -0

.002

81

-0.2

5947

-0

.262

28

-0.3

0187

0.

0001

44

-0.2

733*

-0

.273

16

-0.3

3252

(0.9

85)

(0.1

) (0

.292

) (0

.514

) (0

.999

) (0

.081

) (0

.271

) (0

.471

) A

ge

0.02

014*

**

-0.0

0403

0.

0161

06**

* 0.

0411

88**

* 0.

0203

94**

* -0

.005

22*

0.01

5172

***

0.03

8555

***

(0

) (0

.122

) (0

) (0

) (0

) (0

.045

) (0

.001

) (0

) Se

cond

ary

scho

ol le

vel (

1=ye

s)

-0.0

4964

0.

0363

93

-0.0

1325

-0

.017

62

-0.0

4998

0.

0379

61

-0.0

1201

-0

.014

14

(0

.32)

(0

.485

) (0

.872

) (0

.908

) (0

.316

) (0

.465

0 (0

.884

) 0.

926

Hou

seho

ld si

ze

0.02

6075

**

0.01

5048

0.

0411

23**

0.

1618

33**

* 0.

0256

14*

0.01

7207

0.

0428

2**

0.16

6618

***

(0

.046

) (0

.233

) (0

.042

) (0

) (0

.05)

(0

.168

) (0

.033

) (0

) C

omm

unis

t par

ty m

embe

r (1=

yes)

0.

2826

43**

0.

1817

35*

0.46

4377

**

-0.0

2342

0.

2618

47*

0.27

9117

***

0.54

0965

***

0.19

2458

(0.0

28)

(0.0

52)

(0.0

16)

(0.9

52)

(0.0

43)

(0.0

02)

(0.0

04)

(0.6

18)

Trea

tmen

t 2.

1467

96**

* 0.

9011

69**

* 3.

0479

65**

* 6.

6192

67**

*

(0)

(0)

(0)

(0)

T1

2.10

5978

***

1.09

2317

***

3.19

8296

***

7.04

2994

***

(0

) (0

) (0

) (0

) T2

2.

1938

8***

0.

6806

78**

* 2.

8745

58**

* 6.

1304

91**

*

(0)

(0)

(0)

(0)

Con

stan

t 4.

3988

42**

* 10

.056

1***

14

.454

95**

* 28

.939

18**

* 4.

3908

73**

* 10

.093

42**

* 14

.484

3***

29

.021

9***

(0)

(0)

(0)

(0)

(0)

(0)

(0)

(0)

Obs

erva

tions

41

32

41

32

4132

4,

132

4,13

2 4,

132

4,13

2 C

lust

ers

R-s

quar

ed

0.31

38

0.07

17

0.25

32

0.31

33

0.31

41

0.08

18

0.25

52

0.31

74

Not

es:

(a) D

epen

dent

var

iabl

es in

mod

els (

1), (

5), (

9) a

nd (1

3) a

re b

usin

ess k

now

ledg

e sc

ores

; de

pend

ent v

aria

bles

in m

odel

s (2)

, (6)

, (10

) and

(14)

are

fina

ncia

l lite

racy

sc

ores

; dep

ende

nt v

aria

bles

in th

e m

odel

s (3)

, (7)

, (11

) and

(15)

are

com

bine

d bu

sine

ss a

nd fi

nanc

ial l

itera

cy sc

ores

; de

pend

ent v

aria

bles

in th

e m

odel

s (4)

, (8)

, (12

) and

(1

6) a

re tr

aini

ng k

now

ledg

e sc

ores

(sco

res o

n ge

nera

l bus

ines

s, fin

anci

al li

tera

cy, m

arke

ting,

acc

ount

ing,

pro

duct

ion

and

gend

er k

now

ledg

e);

(b) R

obus

t p-

valu

e in

pa

rent

hese

s fro

m c

olum

ns 1

-8, c

lust

er p

-val

ue in

par

enth

eses

from

col

umns

9-1

6; (c

) ***

p<0

.01,

**

p<0.

05, *

p<0

.1

199

App

endi

x 5.

1: F

irst-s

tage

regr

essio

n of

IV e

stim

ates

– C

RR

A (C

ont.)

VA

RIA

BLE

S (9

) (1

0)

(11)

(1

2)

(13)

(1

4)

(15)

(1

6)

Del

ayed

leng

th (k

) -0

.002

28**

0.

0009

33

-0.0

0135

-0

.004

83*

-0.0

0231

**

0.00

1054

-0

.001

25

-0.0

0456

*

(0.0

11)

(0.3

4)

(0.3

56)

(0.0

79)

(0.0

1)

(0.2

8)

(0.3

88)

(0.0

97)

Inte

rest

rate

(ln(

1+r)

) -0

.002

81

-0.2

5947

-0

.262

28

-0.3

0187

0.

0001

44

-0.2

733

-0.2

7316

-0

.332

52

(0

.986

) (0

.15)

(0

.321

) (0

.523

) (0

.999

) (0

.127

) (0

.299

) (0

.48)

A

ge

0.02

014*

-0

.004

03

0.01

6106

0.

0411

88

0.02

0394

* -0

.005

22

0.01

5172

0.

0385

55

(0

.066

) (0

.705

) (0

.372

) (0

.24)

(0

.061

) (0

.623

) (0

.399

) (0

.266

) Se

cond

ary

scho

ol le

vel (

1=ye

s)

-0.0

4964

0.

0363

93

-0.0

1325

-0

.017

62

-0.0

4998

0.

0379

61

-0.0

1201

-0

.014

14

(0

.807

) (0

.861

) (0

.968

) (0

.977

) (0

.805

) (0

.855

) (0

.971

) (0

.982

) H

ouse

hold

size

0.

0260

75

0.01

5048

0.

0411

23

0.16

1833

0.

0256

14

0.01

7207

0.

0428

2 0.

1666

18

(0

.621

) (0

.764

) (0

.617

) (0

.293

) (0

.627

) (0

.728

) (0

.599

) (0

.276

) C

omm

unis

t par

ty m

embe

r (1=

yes)

0.

2826

43

0.18

1735

0.

4643

77

-0.0

2342

0.

2618

47

0.27

9117

0.

5409

65

0.19

2458

(0.5

8)

(0.6

2)

(0.5

46)

(0.9

88)

(0.6

09)

(0.4

34)

(0.4

73)

(0.9

) Tr

eatm

ent

2.14

6796

***

0.90

1169

***

3.04

7965

***

6.61

9267

***

(0

) (0

) (0

) (0

)

T1

2.

1059

78**

* 1.

0923

17**

* 3.

1982

96**

* 7.

0429

94**

*

(0)

(0)

(0)

(0)

T2

2.19

388*

**

0.68

0678

***

2.87

4558

***

6.13

0491

***

(0

) (0

.01)

(0

) (0

) C

onst

ant

4.39

8842

***

10.0

561*

**

14.4

5495

***

28.9

3918

***

4.39

0873

***

10.0

9342

***

14.4

843*

**

29.0

219*

**

(0

) (0

) (0

) (0

) (0

) (0

) (0

) (0

) O

bser

vatio

ns

4,13

2 4,

132

4,13

2 41

32

4,13

2 4,

132

4,13

2 4,

132

Clu

ster

s 30

3 30

3 30

3 30

3 30

3 30

3 30

3 30

3 R

-squ

ared

0.

3138

0.

0717

0.

2532

0.

3133

0.

3141

0.

0818

0.

2552

0.

3174

N

otes

: (a

) Dep

ende

nt v

aria

bles

in m

odel

s (1)

, (5)

, (9)

and

(13)

are

bus

ines

s kno

wle

dge

scor

es;

depe

nden

t var

iabl

es in

mod

els (

2), (

6), (

10) a

nd (1

4) a

re fi

nanc

ial l

itera

cy

scor

es; d

epen

dent

var

iabl

es in

the

mod

els (

3), (

7), (

11) a

nd (1

5) a

re c

ombi

ned

busi

ness

and

fina

ncia

l lite

racy

scor

es;

depe

nden

t var

iabl

es in

the

mod

els (

4), (

8), (

12) a

nd

(16)

are

trai

ning

kno

wle

dge

scor

es (s

core

s on

gene

ral b

usin

ess,

finan

cial

lite

racy

, mar

ketin

g, a

ccou

ntin

g, p

rodu

ctio

n an

d ge

nder

kno

wle

dge)

; (b

) Rob

ust

p-va

lue

in

pare

nthe

ses f

rom

col

umns

1-8

, clu

ster

p-v

alue

in p

aren

thes

es fr

om c

olum

ns 9

-16;

(c) *

** p

<0.0

1, *

* p<

0.05

, * p

<0.1

200

App

endi

x 5.

2: F

irst-s

tage

regr

essio

n of

IV e

stim

ates

- C

ARA

VA

RIA

BLE

S (1

) (2

) (3

) (4

) (5

) (6

) (7

) (8

) D

elay

ed le

ngth

(k)

2.42

e-05

0.

0001

79

0.00

0203

-3

e-05

2.

24e-

05

0.00

0187

0.

0002

1 -1

.8e-

05

(0

.986

) (0

.898

) (0

.929

) (0

.994

) (0

.987

) (0

.893

) (0

.927

) (0

.997

) In

tere

st ra

te (l

n(1+

r))

-0.0

0364

-0

.031

67

-0.0

3531

0.

0763

55

-0.0

0316

-0

.033

76

-0.0

3692

0.

0732

5

(0.9

73)

(0.7

69)

(0.8

4)

(0.8

17)

(0.9

77)

(0.7

53)

(0.8

33)

(0.8

24)

Age

0.

0130

48**

* -0

.016

77**

* -0

.003

73

-0.0

0111

0.

0134

64**

* -0

.018

58**

* -0

.005

12

-0.0

0379

(0)

(0)

(0.2

78)

(0.8

69)

(0)

(0)

(0.1

37)

(0.5

71)

Seco

ndar

y sc

hool

leve

l (1=

yes)

-0

.111

1***

-0

.206

5***

-0

.317

6***

-0

.485

6***

-0

.111

64**

* -0

.204

15**

* -0

.315

79**

* -0

.482

11**

*

(0.0

05)

(0)

(0)

(0)

(0.0

05)

(0)

(0)

(0)

Hou

seho

ld si

ze

0.00

4541

0.

0372

23**

* 0.

0417

64**

0.

0796

11**

0.

0044

07

0.03

7805

***

0.04

2211

**

0.08

0474

***

(0

.672

) (0

) (0

.011

) (0

.011

) (0

.681

) (0

) (0

.01)

(0

.009

) C

omm

unis

t par

ty m

embe

r (1=

yes)

0.

3434

7***

0.

1936

77**

* 0.

5371

47**

* 0.

5800

97**

0.

3256

16**

* 0.

2710

91**

* 0.

5967

07**

* 0.

6949

6***

(0)

(0.0

04)

(0)

(0.0

25)

(0)

(0)

(0)

(0.0

07)

Trea

tmen

t 2.

1729

9***

0.

9307

42**

* 3.

1037

32**

* 6.

5845

85**

*

(0)

(0)

(0)

(0)

T1

2.13

2039

***

1.10

8305

***

3.24

0344

***

6.84

8044

***

(0

) (0

) (0

) (0

) T2

2.

2161

15**

* 0.

7437

55**

* 2.

9598

69**

* 6.

3071

42**

*

(0)

(0)

(0)

(0)

Con

stan

t 4.

7651

59**

* 10

.719

59**

* 15

.484

75**

* 31

.535

69**

* 4.

7476

74**

* 10

.795

4***

15

.543

08**

* 31

.648

17**

*

(0)

(0)

(0)

(0)

(0)

(0)

(0)

(0)

Obs

erva

tions

65

50

6550

65

50

6550

65

50

6550

65

50

6550

C

lust

ers

R-s

quar

ed

0.31

73

0.09

11

0.26

64

0.31

29

0.31

76

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Samenvatting (Summary in Dutch)

Eerdere literatuur suggereert dat armoede een meerdimensionaal probleem vormt. Daarom is het

verstrekken van alleen microkrediet niet genoeg om arme mensen aan armoede te laten

ontsnappen. Arme mensen dienen toegang te hebben tot een gecoördineerde combinatie van

microfinanciering en andere ontwikkelingsdiensten om aan armoede te ontkomen. Veel studies

suggereren dat het samenvoegen van niet-financiële diensten met microfinanciering belangrijk

kan zijn. Rigoreus onderzoek naar het effect van de combinatie van beide soorten diensten

ontbreekt echter nog steeds. Om deze kloof in het onderzoek te dichten, verstrekt dit proefschrift

nieuwe inzichten in de relevantie van microfinancieringsinstituties (MFIs) die financiële en niet-

financiële diensten combineren.

Het belangrijkste doel van dit proefschrift is het effect te beoordelen van de integratie van

niet-financiële diensten, in het bijzonder bedrijfsontwikkelingsdiensten, met

microfinancieringsdiensten op de prestaties van MFIs en hun clienten. Om dit doel te bereiken

gebruiken we drie benaderingen, waaronder een quasi-experimentele benadering, een

“randomised controlled trial”t (RCT), en een gedragsspel in een veldlaboratorium.

Ten eerste onderzoekt dit proefschrift met behulp van de quasi-experimentele benadering,

de invloed van de combinatie van financiële en niet-financiële diensten op de prestaties van

MFIs met behulp van een globale panel dataset. In het bijzonder bepalen we of MFIs die

gespecialiseerd zijn in financiële diensten betere financiële en/of sociale resultaten behalen dan

de MFIs die zowel financiële als niet-financiële diensten verlenen. Binnen de niet-financiële

diensten onderscheiden we bedrijfsontwikkelingsdiensten, zoals bedrijfsmatige training, en

sociale diensten. We gebruiken secundaire data van 290 extern beoordeelde MFIs uit 61 landen.

De gegevens betreffen de periode 1998-2007, waarbij de meeste cijfers de periode 2001-2005

betreffen. De resultaten van de Hausman-Taylor schattingstechniek suggereren dat MFIs die

sociale diensten verlenen betere sociale resultaten opleveren, zij het ten koste van hun financiële

resultaten. MFIs die bedrijfsontwikkelingsdiensten aanbieden presteren in dezelfde mate als de

MFIs die specialiseren in financiële diensten.

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Ten tweede, teneinde vertekening door endogeniteit tegen te gaan, hanteert dit

proefschrift een RCT om de invloed van de integratie van microfinanciering en

bedrijfsontwikkelingstraining op bedrijfs- en ‘gender’ uitkomsten voor de vrouwelijke klanten

van een MFI in Vietnam te analyseren. Bovendien beoordelen we of het uitnodigen van de

echtgenoten om deel te nemen aan de training met hun vrouw extra effect heeft op de uitkomsten

voor vrouwen. Deze studie is een van de weinige die een RCT toepassen op een grote steekproef

om de invloed van bedrijfsmatige trainingen te evalueren. Bovendien is dit onderzoek éen van de

eerste die de relevantie onderzoekt van het uitnodigen van de echtgenoten om bedrijfsmatige

trainingen samen met hun vrouw te volgen.

De RCT wordt toegepast op het TYM fonds, dat de grootste

microfinancieringsorganisatie is in Noord Vietnam, en die werkzaam is sinds 1992. We

begonnen met het willekeurig toewijzen van bestaande kredietcentra, aan twee behandelgroepen

en een controlegroep, elk met gemiddeld 30 vrouwelijke klanten. We randomiseerden de training

op het niveau van het kredietcentrum, hetgeen het gevaar van overdrachteffecten vermindert, en

gebruikten een geclusterde steekproef benadering. In de eerste behandelgroup nodigden we

vrouwen en hun echtgenoten uit om mee te doen aan de training als onderdeel van de verplichte

maandelijkse bijeenkomst. In de andere behandelgroep nodigden we alleen vrouwen uit om mee

te doen aan de training. De controlegroepen bleven hetzelfde: hun vrouwelijke klanten namen

alleen deel aan de krediet- en spaaractiviteiten van het TYM fonds.

We gebruikten trainingsmateriaal ontwikkeld door en aangepast van “Gender and

Enterpreneurship Together (GET) Ahead for Women in Enterprise Training Package and

Resource Kit” van de Internationale Arbeidsorganisatie (ILO). We hielden een beginmeting vóór

de interventie met een steekproef van ongeveer 4.000 vrouwelijke klanten en twee opvolgende

metingen na de behandeling om de trajecten van de effecten na te gaan van zowel de korte- als

de lange-termijn effecten van de training. Dit proefschrift gaat alleen in op de beginmeting en de

middelste meting, omdat de analyse van de lange-termijn effecten buiten het tijdsframe van dit

onderzoekproject valt.

Hoewel de middelste meting slechts zes maanden na de afronding van de gehele training

plaats vond, vinden we een aantal veelbelovende korte-termijn effecten van de training op de

bedrijfsuitkomsten van de vrouwen. De training resulteeert in significante verbeteringen in de

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bedrijfskennis en de bedrijfsvoering. Bovendien vinden we dat ‘gender’- en bedrijfsmatige

training een positief effect heeft op de bedrijfsprestaties van door vrouwen geleide bedrijven. Dit

is een eerste bewijs dat het aanbieden van ‘gender’- en bedrijfsmatige trainingen resulteert in

verbeteringen van bedrijfswinsten en winstmarges bij overlevende bedrijven. Wij vinden echter

geen bewijs dat de training de uitkomsten in de landbouw verbetert. Dit is niet vreemd, omdat de

training zich ook niet richtte op landbouwbedrijven. Verder vinden we sterk bewijs dat de

training leidt tot significante verbeteringen in ‘gender’ kennis. De training laat ook een beperkte

positieve invloed zien op de niet-cognitieve bedrijfsgerelateerde vaardigheden van vrouwen.

Verder zijn er indicaties dat de training de onderhandelingsmacht van vrouwen over belangrijke

uitgavenbeslissingen verbetert en de niveaus van fysiek geweld in gezinnen van getrouwde

vrouwen doet afnemen. We vinden echter geen sterke statistisch significante positieve korte-

termijn effecten van de training wanneer de mannen ook uitgenodigd worden om aan de

trainingsbijeenkomsten deel te nemen. Een mogelijke oorzaak hiervoor is de lage

participatiegraad van de mannen, tezamen met de kleine omvang van de effecten (veroorzaakt

door de korte tijdsperiode die beoordeeld is).

Omdat fysiek geweld door de partner gevoelig ligt, zullen vrouwen hierover minder vaak

rapporteren, waardoor vertekende schattingen kunnen optreden. Daarom gebruiken we ook een

kwalitatieve ondervragingstechniek, het zogenaamde ‘list experiment’ om de invloed van de

training op fysiek huiselijk geweld opnieuw te schatten. In tegenstelling tot de antwoorden op de

directe vragen suggereert dit ‘list experiment’ dat de vrouwen die de training volgden vaker

werden geconfronteerd met fysiek geweld dan de vrouwen in de controle groep.

Tenslotte combineren we de data van de RCT met data van een kunstmatig veld-

experiment. We hielden een convex tijdsbudget experiment (Andreoni en Sprenger, 2012) om de

invloed van bedrijfsmatige training op de tijdpreferenties en de consumptie–afvlakking

(smoothing) van vrouwelijke micro-financieringsklanten. We vinden dan dat de financiële

keuzes, gemiddeld, niet volledig rationeel zijn. Met name vinden we bewijzen voor te veel

sparen in de controle groep. Verder indiceren onze resultaten dat, hoewel bedrijfsmatige training

de preferenties niet verandert, het wel de intertemporele consumptiekeuzes verbetert door

huidige consumptie te stimuleren ten koste van toekomstige consumptie. Voor de subgroep van

microfinance leners die participeerden in het veld-experiment vinden we enigszins bewijzen dat

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het effect van bedrijfsmatige training op vrouwen afhangt van de aanwezigheid van de

mannelijke echtgenoten; hun bijdrage versterkt het effect van de formele training.

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Tóm tắt (Summary in Vietnamese)

Các nghiên cứu trước đây chỉ ra rằng nghèo là một vấn đề mang tính đa chiều. Chính vì vậy, việc

chỉ cung cấp các dịch vụ tài chính vi mô (TCVM) không đủ để giúp người nghèo thoát nghèo.

Người nghèo cần được tiếp cận cả dịch vụ tài chính vi mô và các dịch vụ hỗ trợ phát triển khác

để vượt qua tình trạng nghèo đói. Nhiều nghiên cứu đã cho thấy việc kết hợp các dịch vụ phi tài

chính với các dịch vụ TCVM đóng một vai trò quan trọng. Tuy vậy, không có nhiều bằng chứng

xác đáng đánh giá tác động của việc kết hợp giữa hai loại hình dịch vụ này. Để cung cấp thêm

bằng chứng mới về vấn đề này, luận án tập trung nghiên cứu tính hợp lý của việc kết hợp các

dịch vụ tài chính và phi tài chính trong các tổ chức tài chính vi mô (TCVM).

Mục tiêu chính của luận án là đánh giá tác động của việc kết hợp các dịch vụ phi tài

chính, đặc biệt là các dịch vụ hỗ trợ phát triển kinh doanh, và các dịch vụ TCVM đối với hoạt

động của các tổ chức TCVM và khách hàng của họ. Để đạt được mục tiêu này, luận án sử dụng

ba cách tiếp cận bao gồm: phương pháp bán thực nghiệm (quasi-experimental), thử nghiệm đối

chứng ngẫu nhiên (randomized control trial), và thực nghiệm hành vi (a lab in the field

behavioral game).

Trước hết, luận án sử dụng phương pháp tiếp cận bán thực nghiệm để nghiên cứu tác

động của việc kết hợp các dịch vụ tài chính và phi tài chính đối với kết quả hoạt động của các tổ

chức TCVM bằng cách sử dụng bộ số liệu lớn mang tính toàn cầu. Một cách cụ thể, luận án tập

trung phân tích xem liệu các tổ chức TCVM chỉ tập trung chuyên về cung cấp dịch vụ tài chính

có đạt được kết quả hoạt động về mặt tài chính và xã hội tốt hơn so với các tổ chức TCVM cung

cấp cả dịch vụ tài chính và phi tài chính không. Liên quan đến các dịch vụ phi tài chính, luận án

phân biệt hai loại hình dịch vụ phi tài chính bao gồm: các dịch vụ hỗ trợ phát triển kinh doanh (

ví dụ như đào tạo kinh doanh) và các dịch vụ xã hội (ví dụ đào tạo, tư vấn chăm sóc sức khỏe).

Để thực hiện nghiên cứu này, luận án sử dụng bộ số liệu thứ cấp từ 290 tổ chức TCVM được xếp

hạng từ 61 quốc gia. Bộ số liệu cung cấp thông tin cho giai đoạn 1998-2007, tuy nhiên phần lớn

số liệu phân tích tập trung vào giai đoạn 2001-2005. Bằng việc sử dụng phương pháp ước lượng

Hausman-Taylor, kết quả nghiên cứu cho thấy các tổ chức TCVM có cung cấp các dịch vụ xã

hội đạt được hiệu quả trên các chỉ tiêu về xã hội tốt hơn. Tuy nhiên, kết quả hoạt động tài chính

của các tổ chức TCVM này kém hơn so với các tổ chức TCVM chỉ tập trung cung cấp các dịch

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vụ tài chính. Ngoài ra, kết quả phân tích cũng cho thấy, đối với các tổ chức TCVM có cung cấp

dịch vụ hỗ trợ phát triển kinh doanh thì kết quả hoạt động của họ không khác kết quả hoạt động

của các tổ chức TCVM chỉ tập trung chuyên về dịch vụ tài chính.

Thứ hai, để giải quyết các vấn đề sai lệch ước lượng liên quan đến vấn đề biến nội sinh

trong phương pháp bán thực nghiệm ở trên, luận án sử dụng phương pháp thử nghiệm đối chứng

ngẫu nhiên để phân tích tác động của việc kết hợp các dịch vụ tài chính vi mô và hoạt động đào

tạo giới và kinh doanh đối với kết quả hoạt động kinh doanh và các chỉ tiêu về giới cho các phụ

nữ là khách hàng trong một tổ chức TCVM tại Việt Nam. Ngoài ra, luận án còn tập trung phân

tích tác động của việc mời các ông chồng tham gia các buổi đào tạo cùng với các bà vợ đối với

các chỉ tiêu nghiên cứu kể trên. Nghiên cứu trong luận án này là một trong số ít những nghiên

cứu sử dụng phương pháp thử nghiệm đối chứng ngẫu nhiên với một cỡ mẫu lớn để đánh giá tác

động của hoạt động đào tạo kinh doanh. Bên cạnh đó cũng cần nhấn mạnh, nghiên cứu này là

một trong những nghiên cứu đầu tiên đánh giá tính hợp lý của việc mời các ông chồng tham gia

vào hoạt động đào tạo kinh doanh cùng với vợ của họ.

Luận án thực hiện phương pháp thử nghiệm đối chứng ngẫu nhiên tại Tổ chức Tài chính

vi mô TNHH Một thành viên Tình Thương (TYM). TYM là tổ chức TCVM lớn nhất ở khu vực

phía Bắc Việt Nam, tổ chức này bắt đầu hoạt động từ năm 1992 và tập trung cung cấp các dịch

vụ TCVM cho khách hàng là nữ. Thử nghiệm đối chứng ngẫu nhiên được thực hiện bằng việc

phân bổ ngẫu nhiên các cụm tín dụng (mỗi cụm có trung bình khoảng 30 khách hàng nữ) vào hai

nhóm mục tiêu (treatment groups) và một nhóm đối chứng (control groups). Việc phân bổ mẫu

ngẫu nhiên trên cụm tín dụng giúp chúng tôi giảm thiểu rủi ro hiệu ứng lan tỏa (spillover

effects). Ở nhóm mục tiêu thứ nhất, chúng tôi mời cả khách hàng của TYM và chồng của họ

tham gia và hoạt động đào tạo . Ở nhóm mục tiêu thứ hai, chúng tôi chỉ mời khách hàng của

TYM tham gia đào tạo. Các khách hàng của TYM ở nhóm đối chứng vẫn tham gia các dịch vụ

tài chính của TYM như trước đây và không có hoạt động đào tạo giới và kinh doanh. Hoạt động

đào tạo giới và kinh doanh được lồng ghép vào các buổi họp cụm được tổ chức định kỳ hàng

tháng.

Hoạt động đào tạo sử dụng tài liệu được xây dựng dựa trên tài liệu Giới và kinh doanh

(Gender and Entrepreneurship Together - GET Ahead) của Tổ chức Lao động quốc tế. Dự án

nghiên cứu đã tiến hành ba đợt điều tra: điều tra cơ bản trước khi cung cấp hoạt động đào tạo với

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cỡ mẫu hơn 4.000 thành viên và hai cuộc điều tra giữa kỳ và cuối kỳ sau khi kết thúc đào tạo để

phân tích tác động ngắn hạn và dài hạn của việc đào tạo. Do thời gian thực hiện của cả dự án kéo

dài nên kết quả trình bày trong luận án này chỉ tập trung phân tích tác động ngắn hạn của hoạt

động đào tạo.

Hoạt động điều tra giữa kỳ diễn ra sáu tháng sau khi kết thúc hoạt động đào tạo. Mặc dù

thời gian khá ngắn nhưng kết quả phân tích cho thấy một số tác động ngắn hạn đầy hứa hẹn của

việc đào tạo đối với kết quả hoạt động kinh doanh của các chị em phụ nữ. Cụ thể, hoạt động đào

tạo nâng cao đáng kể kiến thức kinh doanh và các phương thức kinh doanh cho chị em phụ nữ.

Số liệu nghiên cứu cũng cho thấy hoạt động đào tạo giúp cải thiện lợi nhuận kinh doanh và lợi

nhuận cận biên của các doanh nghiệp đang hoạt động do nữ làm chủ. Tuy nhiên, hoạt động đào

tạo không cải thiện kết quả hoạt động nông nghiệp, điều này cũng dễ hiểu vì nội dung của

chương trình đào tạo không tập trung vào các hoạt động nông nghiệp.

Bên cạnh đó, kết quả nghiên cứu cũng cho thấy một số tác động tích cực khác của hoạt

động đào tạo như: làm tăng đáng kể kiến thức về giới cho chị em phụ nữ, nâng cao yếu tố nhận

thức có liên quan đến kỹ năng kinh doanh, nâng cao năng lực thương lượng của các chị em phụ

nữ khi tham gia vào việc ra quyết định liên quan đến các khoản chi tiêu lớn trong gia đình và

quan trọng hơn là hoạt động đào tạo giúp làm giảm mức độ bạo lực gia đình đối với phụ nữ đã

lập gia đình. Tuy nhiên, kết quả phân tích không cho thấy có các tác động ngắn hạn có ý nghĩa

về mặt thống kê của việc mời các ông chồng tham gia hoạt động đào tạo cùng với vợ của họ.

Một trong những lý do để giải thích cho kết quả này là do tỷ lệ tham gia hoạt động đào tạo của

các ông chồng thấp, và hiệu ứng của tác động này chưa cao do thời gian phân tích sau đào tạo

khá ngắn.

Một vấn đề cần lưu ý là do bạo lực gia đình đối vớiphụ nữ là một vấn đề nhạy cảm, vì

vậy khách hàng TYM khi tham gia phỏng vấn có nhiều khả năng cung cấp thông tin này không

trung thực , dẫn đến kết quả phân tích có thể bị sai lệch. Chính vì vậy, luận án sử dụng một kỹ

thuật phân tích định tính, thường được gọi là danh sách thử nghiệm (list experiment) để đánh giá

lại tác động của hoạt động đào tạo trong vấn đề bạo lực gia đình lên phụ nữ. Trái ngược với kết

quả từ việc phân tích các câu hỏi trực tiếp từ bảng hỏi, kết quả nghiên cứu với phương pháp danh

sách thử nghiệm cho thấy rằng những phụ nữ có tham gia hoạt động đào tạo đối mặt với bạo lực

gia đình nhiều hơn so với phụ nữ ở nhóm đối chứng.

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Cuối cùng, chúng tôi kết hợp số liệu của thử nghiệm đối chứng ngẫu nhiên và thực

nghiệm hành vi để đánh giá tác động của hoạt động đào tạo lên sự ưu tiên về thời gian và hành vi

chi tiêu của các khách hàng TCVM nữ. Luận án sử dụng phương pháp thực nghiệm hành vi

“convex time budget experiment” của Andreoni và Sprenger (2012). Kết quả nghiên cứu cho

thấy, các khách hàng TCVM nữ có các quyết định tài chính không hợp lý, cụ thể là họ tiết kiệm

quá mức cần thiết. Hoạt động đào tạo không làm thay đổi nhận thức ưu tiên về thời gian nhưng

nó có xu hướng cải thiện sự tối ưu về chi tiêu bằng cách kích thích chi tiêu cho hiện tại trên cơ sở

các chi phí của việc chi tiêu trong tương lai. Trên cỡ mẫu nhỏ của các thành viên tham gia thực

nghiệm hành vi, nghiên cứu cho thấy hoạt động đào tạo có tác động nổi bật đối với nhóm phụ nữ

tham gia đào tạo cùng với các ông chồng.