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<strong>The</strong> <strong>Impact</strong> <strong>of</strong> Micr<strong>of</strong>inance on Household Welfare:
Case Study <strong>of</strong> a Savings Group in Lao PDR
by
Kongpasa Sengsourivong
Master <strong>The</strong>sis
Department <strong>of</strong> Regional Cooperation Policy Studies
Graduate School <strong>of</strong> International Cooperation Studies
Kobe University
Filed July 2006

<strong>The</strong> <strong>Impact</strong> <strong>of</strong> Micr<strong>of</strong>inance on Household Welfare:
Case Study <strong>of</strong> a Savings Group in Lao PDR ∗
Kongpasa Sengsourivong
July 2006
∗ Copyright © 2006, Master <strong>The</strong>sis submitted to the Department <strong>of</strong> Regional Cooperation Policy Studies,
Graduate School <strong>of</strong> International Cooperation Studies, Kobe University, Japan .<strong>The</strong> views and
interpretations in this paper are those <strong>of</strong> the author and do not necessarily reflect the position <strong>of</strong> Kobe
University. E-mail: s_kongpasa@yahoo.com.

ACKNOWEDGEMENTS
I am indeed indebted to my academic adviser, Associate Pr<strong>of</strong>essor Koji KAWABATA
for his insightful comments and support. I also would like to sincerely thank my two
former academic advisers, Associate Pr<strong>of</strong>essor Fumiharu MIENO who provided me with
valued guidance, feedback and financial support for the follow-up survey in Laos, and
Pr<strong>of</strong>essor Hiroshi UENO who provided me <strong>of</strong> tremendous support during my first
semester at the Graduate School <strong>of</strong> International Cooperation Studies. I am very grateful
to the critical questions and invaluable comments from both Pr<strong>of</strong>essor Seiichi FUKUI
and Pr<strong>of</strong>essor CHEN Kuang-hui during the time <strong>of</strong> being examination committee.
Contribution from Pr<strong>of</strong>essor TAKAHASHI Motoki is gratefully acknowledged. I also
wish to thank Dr. Shalini MATHUR for her assistance in editing and structuring my
thesis.
In addition, I would like to express my gratitude to the Lao Women’s Union,
especially Ms. Sysay LEUDEDMOUNSONE (<strong>The</strong> President <strong>of</strong> Lao Women’s Union),
Ms. Boualone VONGDALASENE (the President <strong>of</strong> Lao Women’s Union at the
Vientiane Capital), Ms. Saikham SENGKA (the President <strong>of</strong> Lao Women’s Union at
Naxaithong city), and Ms. Khampane NAOVALARD (the Vice President <strong>of</strong> Lao
Women’s Union at Naxaithong city) for their advice and support throughout the surveys.
Thanks also to the Women and Community’s Empowering Project, particularly Mr.
Khanthone PHAMUANG (the Manager <strong>of</strong> the Women and Community’s Empowering
Project), and the other staff <strong>of</strong> the project for their support and cooperation during the
survey and for providing me with data on savings groups.
ii

Furthermore, I am obliged to my former boss, Mr. Huw LESTER, Senior
Manager <strong>of</strong> PricewaterhouseCoopers (Laos) Ltd, and Mrs. Rae DUNSTAN who edited
and pro<strong>of</strong> read my paper. Special thanks to my beloved sister and brother, Ms.
Fongchinda SENGSOURIVONG and Mr. Apisid SENGSOURIVONG, for their
invaluable support and comments; and to my friends, Ms. Chanmany VONGLOKHAM,
Mr. Paliphonepheth BOUARAVONG, Ms. Phoummaly SIRIPHOLDEJ, Ms. Visany
PHOMSOMBATH, and the students <strong>of</strong> the faculty <strong>of</strong> economics and business
management (FEBM) from the National University <strong>of</strong> Laos, for kindly helping me to
conduct surveys. I would also like to thank my friend, Mr. Bounthone SOUKAVONG,
Economic Lecturer at FEBM, for kindly providing the students to help me conduct my
survey. My gratitude to my friends, Mr. Santisouk PHOUNESAVATH and Mr. Buavanh
VILAVONG for their invaluable comments; to Ms. Chansathith CHALEUNSHINH,
Research Officer <strong>of</strong> National Economic Research Institute, for providing me relevant data
and arranging <strong>of</strong> the surveys and to Ms. Daravanh VONGVIGIT, for kindly helping me
on data processing. Many thanks to my Vietnamese friend, Ms. Nguyen Thi Thuy Vinh,
for her helpful support on data processing and technical aspects <strong>of</strong> the econometric
s<strong>of</strong>tware. In working through this research, I also have leant to appreciate positive
externalities through discussions with my colleagues. I am thankful to them.
Moreover, I would like to thank Japan International Cooperation Agency (JICA)
and Japan International Cooperation Center (JICE) for the financial support during study
in Japan. Finally, I would like to truly thank my beloved parents, my dear wife and my
cute daughter for always supporting me.
SENGSOURIVONG, Kongpasa. Kobe, Japan, July 2006.
iii

EXECUTIVE SUMMARY
Many countries recognize that micr<strong>of</strong>inance can play an important role in
economic development as one <strong>of</strong> the tools for poverty reduction. Access to financial
services is a major issue for both rural and urban areas <strong>of</strong> Laos. Consequently, the
government <strong>of</strong> Laos recognizes that access to rural finance and micr<strong>of</strong>inance could be
one <strong>of</strong> the major tools for poverty alleviation and places micr<strong>of</strong>inance activities as one <strong>of</strong>
the priority programs for the agriculture and forestry sector in order to promote
sustainable growth and poverty eradication under the National Growth and Poverty
Eradication Strategy. Many studies have examined the relationship between micr<strong>of</strong>inance
and economic development, but until now, only two key studies were undertaken in Laos.
While these two studies made an important contribution to this subject, they had some
shortcomings as they did not correct for problems <strong>of</strong> the self-selection and endogenous
program placement.
This paper surmounts these issues by adopting the methods used by Coleman
(1999) to estimate the effects on household welfare or outcomes by the participation in
the savings group. <strong>The</strong> survey was conducted in six villages in 2005 - 2006, in a semi-
urban area <strong>of</strong> Laos. All these villages had savings groups which were in operation for
various lengths <strong>of</strong> time. Of these, three villages had savings groups operating for more
than a year, and are called “old” savings groups. <strong>The</strong> remaining three villages also started
operating savings groups but these were in operation for less than a year. <strong>The</strong>se are called
“new” savings groups. In the six villages, villagers were allowed to self-select to be
iv

savings group members or nonmembers. <strong>The</strong> survey sample included members and
nonmembers in the six villages. Members who experienced benefits from joining the
savings group by either obtaining a credit or receiving a dividend are called the
“treatment” group, and those who have not benefited from the groups are called the
“control” group. All members <strong>of</strong> the “control” group were relatively new members with
an average membership <strong>of</strong> 2.2 months. Hence, the effects on savings group members in
the treatment group can be compared with the savings group members in the control
group. In addition, differences in the length <strong>of</strong> time that savings group program has been
available to members in both treatment and control groups is taken into account to obtain
more precise impact estimates. Inclusion <strong>of</strong> nonmembers in all six villages allowed for
the use <strong>of</strong> village fixed effect estimation to control the possibility that the order in which
these six villages had savings group program placement is endogenous.
With this kind <strong>of</strong> survey design, the impact <strong>of</strong> savings group program on
household outcomes can be straightforwardly estimated. <strong>The</strong>se positive outcomes include
increase in household house value, household livestock production income, household
agriculture production income, household rental expenses, and household education
expenses. <strong>The</strong> results illustrate that the savings group participation has large positive and
significant effects on all <strong>of</strong> these outcomes, except household yearly income from
agriculture. However, this can largely be explained by issues relating to the robustness <strong>of</strong>
the data for this indicator. In short, the participation <strong>of</strong> savings group can increase
household asset, household income from self-employment activities and support the
education <strong>of</strong> children.
v

Consequently, this paper’s findings have several important implications. Firstly,
the large positive impact savings group has on household asset suggest that micr<strong>of</strong>inance
programs may improve household status in terms <strong>of</strong> wealth. Secondly, the positive
significant effects <strong>of</strong> the savings group on productivity, particularly livestock and
agriculture in terms <strong>of</strong> rental on rice fields, suggest that the savings group program may
be a viable strategy for the poverty eradication. This is consistent with the National
Growth and Poverty Eradication Strategy (NGPES) (2004: 65) <strong>of</strong> the Government <strong>of</strong> Lao
PDR which recognizes the importance <strong>of</strong> micr<strong>of</strong>inance and has placed it as the one <strong>of</strong> the
high priority projects for the agriculture and forestry development plan. Thirdly, the great
positive influence <strong>of</strong> the savings group program on household education expenses
suggests that micr<strong>of</strong>inance program may be one viable strategy to reach the millennium
development goals in terms <strong>of</strong> education.
vi

CHAPTER TITLE
TABLE OF CONTENTS
vii
PAGE
Title Page i
Acknowledgements ii
Executive Summary iv
Table <strong>of</strong> Contents vii
List <strong>of</strong> Tables ix
1 INTRODUCTION 1
1.1 Issues 1
1.2 Research Topic and Objective 5
1.3 Hypothesis 5
1.4 Methodology 6
1.5 Structure <strong>of</strong> the Paper 7
2 MICROFINANCE IN LAOS 8
2.1 Country Brief 8
2.2 Micr<strong>of</strong>inance in Laos 9
3 LITERATURE REVIEW 19
3.1 <strong>Impact</strong> Studies <strong>of</strong> Micr<strong>of</strong>inance on Different Economic and Social
Indicators
19
3.2 Methodology 26
4 ANALYTICAL FRAMEWORK 31
4.1 <strong>The</strong>oretical Framework 31
4.2 Model Specification and Methodology 36
5 SURVEY DESIGN 46

5.1 Survey Design 47
5.2 Data Description 51
6 EMPIRICAL RESULTS 52
6.1 <strong>Impact</strong> on Household House Asset 55
6.2 <strong>Impact</strong> on Self-Employment Activities 59
6.3 <strong>Impact</strong> on Education 64
7 CONCLUSION 67
REFERENCES 71
APPENDIX A 80
Table 1: Descriptive Statistics for Variables <strong>of</strong> Whole Sample Size 80
Table 2: Descriptive Statistics for Variables by Treatment Group 84
Table 3: Descriptive Statistics for Variables by Control Group 88
Table 4: Descriptive Statistics for Variables by Nonmember 92
Table 5: <strong>Impact</strong> <strong>of</strong> Savings Group on Household House Value - GLS 96
Table 6: <strong>Impact</strong> <strong>of</strong> Savings Group on Yearly Self-Employment Income
from Livestock – GLS
100
Table 7: <strong>Impact</strong> <strong>of</strong> Savings Group on Household Yearly Self-
Employment Income from Agriculture – GLS
103
Table 8: <strong>Impact</strong> <strong>of</strong> Savings Group on Household Monthly Rental
Expenditure – GLS
106
Table 9: <strong>Impact</strong> <strong>of</strong> Savings Group on Household Monthly Educational
Expenditure – GLS
109
APPENDIX B 112
Case study: Savings Groups in Naxaithong City 112
viii

TABLE TITLE
LIST OF TABLES
ix
PAGE
2.1 Current Practices in Laos 16
5.1 Sample Size 49

1.1 Issues
CHAPTER 1
INTRODUCTION
Micr<strong>of</strong>inance is significant source <strong>of</strong> finance for poor, lower income people in
developing countries. It provides the funding for these people to run their micro-business
and to smooth their household’s consumption. Poor, lower income people have
difficulties in obtaining finance from formal financial institutions such as commercial
banks, due to barriers such as high collateral requirements and complicated application
procedures (Yunus, 2001; and Hulme &Mosley, 1996). However, there is strong demand
for small-scale commercial financial services (for both credit and savings) among the
economically active poor in developing countries. <strong>The</strong>se and other financial services help
low-income people improve household and enterprise management, increase productivity,
smooth income flows, enlarge and diversify their microenterprises, and increase their
incomes (Robinson, 2001). <strong>The</strong>se effects were evident from a number <strong>of</strong> impact studies
<strong>of</strong> micr<strong>of</strong>inance. Based on the recent studies on this subject, micr<strong>of</strong>inance has significant
impact on income, expenditure, assets, educational status, health as well as gender
empowerment.
This positive impact <strong>of</strong> micr<strong>of</strong>inance on income was confirmed in studies
undertaken by Hulme and Mosley (1996); Mckernan (2002); Khandker et al. (1998);
Copestake et al. (2001); Sichanthongthip (2004); Shaw (2000); Mosley (2001); and
Copestake (2002).
1

Research by Pitt and Khander (1996 and 1998); and Khandker (2003) found that
micr<strong>of</strong>inance could increase household expenditure. Morduch (1998), however, argued
that the eligible households that participated in the micr<strong>of</strong>inance programs have strikingly
less consumption levels than the eligible households living in villages without the
programs.
<strong>The</strong> positive impact <strong>of</strong> micr<strong>of</strong>inance on household assets was confirmed in studies
by Montgomery et al. (1996); Pitt and Khandker (19996 and 1998); Mosley (2001); and
Coleman (1999 and 2002). However, Mckernan (2002) found an inverse relationship
between participation in program and household assets. Mckernan also found that
households with fewest assets benefit most from participating in a program.
Research by Chowdhury and Bhuiya (2004); Holvoet (2004); and Pitt and
Khandker (1996 and d1998) found that micr<strong>of</strong>inance has a positive effect on education.
Similarly, Chowdhury and Bhuiya (2004); Pitt and Khandker (1996); and Pitt et al.
(1999) revealed that micr<strong>of</strong>inance has a positive impact on health. Furthermore, Hashemi
et al. (1996); and Pitt and Khandker (1998) noted that micr<strong>of</strong>inance has positive effect on
women empowerment. However, there exists a counter argument that microcredit
programs inflicted extreme pressure on women by forcing them down to meet difficult
loan repayment schedules (Goetz and Gupta, 1996).
Beside the micr<strong>of</strong>inance impact on the indicators mentioned above, Kyophilavong
and Chaleunsinh, (2005), found that the behavior <strong>of</strong> the village savings group members
was changed as a result <strong>of</strong> participating in a program. While previously savings were kept
in the form <strong>of</strong> gold, livestock, jewelry, deposits in the bank and savings at home,
members now saved in the savings group.
2

<strong>The</strong> Lao People’s Democratic Republic (Lao PDR) is one <strong>of</strong> the poorest countries
in East Asia in terms <strong>of</strong> an estimated per capita income <strong>of</strong> US$ 390 in 2004 (World Bank
Vientiane Office, 2006). Laos is classified by the United Nation as a Least Developed
Country (LDC). According to the World Bank Vientiane Office (2006), 71 per cent <strong>of</strong>
Lao population lived on less than US$2 a day, and 23 per cent on less than US$1 a day in
2004. In the same year, 34 per cent <strong>of</strong> the population lived under the national poverty
line; infant mortality was 82 per 1,000 live births; and life expectancy was approximately
55 years.
Consequently, the government <strong>of</strong> Laos recognizes that access to rural finance and
micr<strong>of</strong>inance could be one <strong>of</strong> the major tools for poverty alleviation and places
micr<strong>of</strong>inance activities as one <strong>of</strong> the priority programs for the agriculture and forestry
sector in order to promote sustainable growth and poverty eradication under the National
Growth and Poverty Eradication Strategy (NGPES) (2004). Since 1987, the broad
approach <strong>of</strong> micr<strong>of</strong>inance, including in kind and in cash revolving role fund, has been
implemented by numerous development projects which includes those <strong>of</strong> government.
However, the micr<strong>of</strong>inance sector is still relatively new in Laos. Although donors have
made a significant investment in the last few years in micr<strong>of</strong>inance programs, the sector
is developing very slowly. About one million economically active people potentially
require access to formal or semi-formal micr<strong>of</strong>inance services. However, almost three
quarters can not reach them. Approximately 300,000 people recently accessed loan and
savings services. Only 21 per cent have access to microcredit from the formal sector. 33
per cent are dependant on the semi-formal sector and project initiatives and the rest 46
3

per cent are obtaining financial service from the informal sector (Micr<strong>of</strong>inance Capacity
Building and Research Programme, 2005).
Very few empirical studies have conducted to examine the effect <strong>of</strong> micr<strong>of</strong>inance
on individual, household, community, or institutional levels in Laos and to test whether
or not micr<strong>of</strong>inance is one <strong>of</strong> tools for poverty reduction. One empirical study <strong>of</strong> Lao
micr<strong>of</strong>inance on Saithani case 1 by Sichanthongthip (2004) showed a positive impact <strong>of</strong>
microcredit on income level <strong>of</strong> individual borrower. <strong>The</strong> study involved evaluating the
impact <strong>of</strong> a micr<strong>of</strong>inance program (village savings group) in a semi-urban area <strong>of</strong> Laos
using a questionnaire to collect primary household data from members <strong>of</strong> the
micr<strong>of</strong>inance program at two points <strong>of</strong> time (before and after borrowing) 2 .
Sichanthongthip reported the results <strong>of</strong> the impact on income by applying econometric
analysis. However, he did not control for selection bias in the sample. Another study
which was not aware with such bias is the study by Kyophilavong and Chaleunsinh
(2005) who estimated the impact <strong>of</strong> a village savings group in a semi-urban area <strong>of</strong> Laos
by conducting a survey for both members and nonmembers <strong>of</strong> the village savings group.
<strong>The</strong>y presented only the means comparison <strong>of</strong> many impact indicators for both members
and nonmembers. Without the correction <strong>of</strong> the selection bias problem, the results may
overestimate the impact <strong>of</strong> the program.
This study will attempt to evaluate the impact <strong>of</strong> micr<strong>of</strong>inance programs in Laos
by correcting for the bias described above. This study also selected savings groups in
Naxaithong city as a case study. <strong>The</strong> city is located in a semi-urban area <strong>of</strong> Vientiane, the
1 Saithani case is derived from that the Saithani Small and Rural Development Project (Saithani Project)
which was established in 1996. <strong>The</strong> Project is a cooperative management between Lao Women’s Union
and the Foundation for Integrated Agricultural Management (FIAM) (Sichanthongthip, 2004:21).
2 Data before the borrowing was collected by respondent recall.
4

Capital <strong>of</strong> Laos, and the program established as part <strong>of</strong> the Women and Community’s
Empowering Project. <strong>The</strong> location <strong>of</strong> the study was selected because most <strong>of</strong> the savings
groups are located in and around the capital. Results <strong>of</strong> a recent survey found that most <strong>of</strong>
the 357 savings groups operating in Laos were located in and around Vientiane
(Micr<strong>of</strong>inance Capacity Building and Research Project (2003) cited in Chaleunsinh,
2004:7).
1.2 Research topic and objective
Many studies examining the impact <strong>of</strong> micr<strong>of</strong>inance at the household level,
enterprise level and macroeconomic level have shown a positive impact <strong>of</strong> micr<strong>of</strong>inance
on two sets <strong>of</strong> indicators – economic and social indicators – in both developing and
developed countries. However, such studies have not been widely conducted in Lao PDR.
<strong>The</strong>refore, this paper will address this gap by examining the impact <strong>of</strong> micr<strong>of</strong>inance to
household welfare or outcomes in Lao PDR. More specifically, it will investigate the
impact <strong>of</strong> savings groups at the household level in a semi-urban area <strong>of</strong> Laos.
<strong>The</strong> primary objective <strong>of</strong> a savings group in Laos is to improve the living status <strong>of</strong>
borrowers and their families to bring them out <strong>of</strong> poverty. <strong>The</strong> main purpose <strong>of</strong> this paper
will be to evaluate the success <strong>of</strong> savings group against their primary objective.
1.3 Hypothesis
Most <strong>of</strong> poor people and lower income people join micr<strong>of</strong>inance program in Laos
because they can access credit with specified interest rate which is lower than that
obtained from the informal money lender. <strong>The</strong>y can, thus, save money. <strong>The</strong>refore, it is
5

hypothesized that members with a long-term participation on the savings group may have
better quality <strong>of</strong> life in terms <strong>of</strong> wealth, income and expenses.
1.4 Methodology
To achieve the research objective, the author adopted the survey design and
research methodology <strong>of</strong> Coleman (1999) taking into account bias for self-selection and
endogenous program placement which was not corrected in the previous studies relating
to Laos. <strong>The</strong> author conducted a survey <strong>of</strong> 251 households in six villages in a semi-urban
area <strong>of</strong> Laos – Naxaithong district which is located 16 kilometers from Vientiane, the
Capital <strong>of</strong> Laos. Of these, three villages had savings groups operating for more than a
year, and are called “old” savings groups. <strong>The</strong> remaining three villages also started
operating savings groups but these were in operation for less than a year. <strong>The</strong>se are called
“new” savings groups. In the six villages, villagers were allowed to self-select to be
savings group members or nonmembers. <strong>The</strong> survey sample included members and
nonmembers in the six villages. Members who experienced benefits from joining the
savings group by either obtaining a credit or receiving a dividend are called the
“treatment” group, and those who have not benefited from the groups are called the
“control” group. All members <strong>of</strong> the “control” group were relatively new members with
an average membership <strong>of</strong> 2.2 months. Hence, the effects on savings group members in
the treatment group can be compared with the savings group members in the control
group. In addition, differences in the length <strong>of</strong> time that savings group program has been
available to members in both treatment and control groups is taken into account to obtain
more precise impact estimates. Inclusion <strong>of</strong> nonmembers in all six villages allowed for
6

the use <strong>of</strong> village fixed effect estimation to control the possibility that the order in which
these six villages had savings group program placement is endogenous.
1.5 Structure <strong>of</strong> the paper
This paper is organized as follows:
• Chapter 2 presents an overview <strong>of</strong> micr<strong>of</strong>inance in Lao PDR.
• Chapter 3 presents a literature review <strong>of</strong> previous studies on the impact <strong>of</strong>
micr<strong>of</strong>inance on economic and social indicators.
• Chapter 4 discusses the theoretical framework and identifies the empirical
modeling and estimation methods to be used for this study.
• Chapter 5 describes the survey design and data.
• Chapter 6 outlines the results <strong>of</strong> empirical analysis.
• Chapter 7 summarizes the results and draws policy implications.
7

CHAPTER 2
MICROFINANCE IN LAOS
Many countries realize that micr<strong>of</strong>inance can play an important role in economic
development as one <strong>of</strong> the tools for poverty reduction. <strong>The</strong> government <strong>of</strong> Laos also
recognizes that role and has placed micr<strong>of</strong>inance activities as one <strong>of</strong> the priority programs
for the agriculture and forestry sector in order to promote sustainable growth and poverty
eradication under the National Growth and Poverty Eradication Strategy (NGPES). This
chapter will outline the general background on the development <strong>of</strong> micr<strong>of</strong>inance in Laos,
provide an overview <strong>of</strong> the current state <strong>of</strong> play in Laos in this area.
2.1 Country Brief
<strong>The</strong> Lao People’s Democratic Republic (Lao PDR) is one <strong>of</strong> the poorest countries
in East Asia with an estimated per capita income <strong>of</strong> US$ 390 in 2004 (World Bank
Vientiane Office, 2006). Laos is classified by the United Nation as a Least Developed
Country (LDC). According to the World Bank Vientiane Office (2006), in 2004, 71 per
cent <strong>of</strong> the Lao population lived on less than US$2 a day, and 23 per cent on less than
US$1 a day. In the same year, 34 per cent <strong>of</strong> the population lived under the national
poverty line; infant mortality was 82 per 1,000 live births; and life expectancy was
approximately 55 years.
In 2004, Laos had a population <strong>of</strong> around 5.8 million and a land area <strong>of</strong> 236,800
square kilometers. <strong>The</strong> country is characterized by a high degree <strong>of</strong> geographic, cultural
8

and linguistic diversity with very poor infrastructure. Moreover, villages, particularly
those populated by ethnic minority groups, tend to be extremely isolated and practice
subsistence agriculture. Laos is also a landlocked country which is located in the center
<strong>of</strong> the Mekong region, bordered by Thailand, Vietnam, Southern China, Cambodia and
Myanmar, <strong>of</strong> which, the first three are experiencing rapid economic growth. Nevertheless,
Laos has significant economic potential because <strong>of</strong> its rich natural resources (such as
forestry, minerals and hydro-electric power) and its proximity to major Asian economies.
Agriculture is the major economic sector contributing 51 per cent <strong>of</strong> GDP and employing
80 percent <strong>of</strong> the labor force. <strong>The</strong> industry sector accounts for 23 per cent and services
for 26 per cent (World Bank Vientiane Office, 2006).
2.2 Micr<strong>of</strong>inance in Laos
Since 1987, the broad approach <strong>of</strong> micr<strong>of</strong>inance in Laos (including in kind and in
cash revolving role fund) has been implemented by numerous development projects
which include those initiated by the government. However, the micr<strong>of</strong>inance sector is still
relatively new in Laos. Although donors have made a significant investment in the last
few years in micr<strong>of</strong>inance programs, the sector is developing very slowly. About one
million economically active people potentially require access to formal or semi-formal
micr<strong>of</strong>inance services. However, almost three quarters cannot reach them. Approximately
300,000 people recently accessed loan and savings services. Only 21 per cent have access
to microcredit from the formal sector, 33 per cent are dependant on the semi-formal
sector and project initiatives and the rest 46 per cent are obtaining financial services from
the informal sector (Micr<strong>of</strong>inance Capacity Building and Research Programme, 2005).
9

This section will briefly describe the development <strong>of</strong> micr<strong>of</strong>inance in terms <strong>of</strong>
micr<strong>of</strong>inance providers 3 which consist <strong>of</strong> the formal, semiformal and informal sectors.
<strong>The</strong> information is sourced from Enterplan (2003).
2.2.1 Formal sector
<strong>The</strong> formal banking system in Laos consists <strong>of</strong>:
• <strong>The</strong> Bank <strong>of</strong> Laos (the central bank);
• Three state-owned banks (the Banque pour le Commerce Exterieur Lao
(BCEL), Lao Development Bank (LDB) 4 , and the Agricultural Promotion
Bank (APB));
• Three joint venture banks (Joint Development Bank, Lao-Viet Bank and
Vientiane Commercial Bank);
• Six foreign commercial banks with branch <strong>of</strong>fices and one foreign
commercial bank with a representative <strong>of</strong>fice (FCBs) 5 .
<strong>The</strong> headquarters <strong>of</strong> each bank are located in Vientiane. APB is the largest bank
in term <strong>of</strong> branch network in Lao PDR. It has one head <strong>of</strong>fice in Vientiane, 18 branches
in the 17 provincial capitals and Vientiane Capital City, and 55 sub-service units at the
district level. <strong>The</strong> head <strong>of</strong>fice <strong>of</strong> LDB is also located in Vientiane and it has 17 branches
(one in each province). BCEL has three branches. Lao-Viet Bank has one branch in
3 This section borrows extensively from Enterplan (2003).
4 It was merged from two state-owned banks: Lao May Bank and Lane Xang Bank.
5 Public Bank (Malaysia), Bangkok Bank, Bank <strong>of</strong> Ayudhya, Krung Thai Bank, Siam Commercial Bank,
Thai Military Bank (all Thai Bank) all <strong>of</strong> which have branch <strong>of</strong>fices in Vientiane. Standard Chartered Bank
has a representative <strong>of</strong>fice and only provides <strong>of</strong>fshore guarantee facilities for overseas clients doing
business in Laos.
10

Champasack province. <strong>The</strong> rest (JVBs and FCBs) have no branches and no provincial
outreach, and only operate in the area around Vientiane municipality.
Practically, only one formal financial institution, APB 6 , has a large outreach and a
true track record in rural finance. APB was established by Decree in 1992 (Decree 92/PM
1992) to assist the development <strong>of</strong> the agricultural sector in Laos. Since March 2000,
APB has been largely under the provision <strong>of</strong> the Decree on Commercial Banks
(Decree02/PR 2000) after operating under that separate mandate. In April 2002, APB had
about 130,000 borrowers. Of these, about 123,000 were in groups (group lending). <strong>The</strong>
activities <strong>of</strong> APB have focused on the provision <strong>of</strong> subsidized loans which use funds from
the government and donors.
2.2.2 Semiformal sector
<strong>The</strong> semiformal financial sector in Laos is poorly developed. In this sector,
activities may be classified in two categories: micr<strong>of</strong>inance initiatives and illustrative
International Non Government Organization (INGO) initiatives.
A. Micr<strong>of</strong>inance Initiatives
Many micr<strong>of</strong>inance initiatives provide micr<strong>of</strong>inance in urban and rural areas <strong>of</strong>
Laos through program such as the Micr<strong>of</strong>inance Project, Cooperative de Credit de
Soutien aux Producteurs (CCSP), Project de Developpement Rural du District de
Phongsaly (PDDP), the Rural Development Cooperative (RDC), and Credit Union Pilot
Project.
6
Lane Xang Bank before merging with Lao May Bank also provided micro lending with a focus on microtraders
(Bank <strong>of</strong> the Lao PDR, 2002: 31).
11

(1) <strong>The</strong> Micr<strong>of</strong>inance project
<strong>The</strong> United Nations Capital Development Fund (UNCDF) and the United Nations
Development Programme (UNDP) with cooperation <strong>of</strong> the Government <strong>of</strong> Laos launched
the micr<strong>of</strong>inance project in late 1997. This project ran micr<strong>of</strong>inance activities in
Oudomxay and Sayaboury Provinces using the group lending methodology. It also
includes the Sihom Project Savings and Credit Scheme (SIPSACRES) – an urban credit
and savings cooperative founded under UNDP project in 1995. However, the UNDP and
UNCDF did not provide funding for this project after 2002. Now, the Lao government
manages the project 7 . While data for 2002 is unavailable, estimated data for 2000 showed
that the micr<strong>of</strong>inance project serves about 3,525 clients in total in October 2000 (United
Nations Capital Development Fund, 2001).
(2) Cooperative de Credit de Soutien aux Producteurs (CCSP)
CCSP was established in 1996 on the initiative <strong>of</strong> a group <strong>of</strong> Lao entrepreneurs. It
consists <strong>of</strong> nine member-owned cooperatives. Microcredit provided to their clients in the
vicinity <strong>of</strong> Vientiane is for the purposed <strong>of</strong> agriculture, handicraft, small industry and
services. CCSP also accepts savings from member and issues loan to members secured by
group guarantee and compulsory savings. In September 2002, there were about 1,000
active savers and about 650 active borrowers in this initiative.
(3) Project de Developpement Rural du District de Phongsaly (PDDP)
PDDP was formed in 1997. It runs village banks for farmers in 57 villages in
Phongsaly city with the support from Agence Francais Developpement (AFD). PDDP
also requires compulsory savings from their members and leverages these with AFD
7 For the ones operating in Sayaboury and Oudomxay provinces are recently under the provincial
government authorities. SIPSACRES is currently under the supervision <strong>of</strong> Finance Department, Vientiane
Capital.
12

funds. In March 2003, about 2,350 borrowers received a loan from PDDP. In this
initiative, loans are provided for cash crops, livestock, handicraft and petty trade and it
applies a joint liability system for loan guarantee.
(4) Rural Development Cooperative(RDC)
RDC began running its activities in August 2001 when it received the funds from
the Vientiane Municipal Development Fund. Its operation is implemented in thirty
villages in Vientiane Capital City. Its services include both credit and savings. Loans are
issued for different kinds <strong>of</strong> economic activities such as agriculture, handicraft and trade.
<strong>The</strong>se loans require physical collateral and a 20 per cent savings deposit for guarantee.
RDC had between 400 and 500 borrowers in 2003.
(5) Credit Union Pilot Project
<strong>The</strong> Credit Union Pilot Project is one <strong>of</strong> the subprojects <strong>of</strong> Asian Development
Bank TA cluster 3413 – LAO: Rural Finance Development. In March 2003, the savings
and credit unions (SCUs) were established in Vientiane, Savanakhet and Luang Prabang
Provinces. <strong>The</strong> three pilot credit unions have licensing as a condition <strong>of</strong> the ADB
Banking Sector Reform Programme Loan (BSRPL).
B. Illustrative INGO initiatives
In addition to the micr<strong>of</strong>inance initiatives, there are about 1,600 village revolving
funds (VRFs) that have been supported by donors and NGOs (Bank <strong>of</strong> the Lao PDR,
2002: 31).
International Non Government Organizations (INGOs) use village revolving
funds (VRFs) to widely provide financial services in the rural areas. <strong>The</strong>se organizations
13

tend to focus on the operation in the areas which are not covered by banks including the
APB. In these areas, villagers can only access to credit from friends, family members,
and moneylenders. Around 35 INGOs and a few bilateral and multilateral agencies are
supporting rural development projects in Laos. <strong>The</strong>se projects have set up cash or in-kind
funds for villagers as part <strong>of</strong> their integrated programmes. Assistance is provided for
agricultural equipment, gravity water and irrigation supplies, seeds, rice banks for food
security, animal banks, and medicinal drug fund. <strong>The</strong> projects depend on subsidized
credit and when this credit finishes when the project ends. <strong>The</strong>re is rarely any local
resource mobilization. As a result <strong>of</strong> the above, a substantial number <strong>of</strong> projects are not
sustainable.
<strong>The</strong> Lao Women’s Union (LWU) is a key intermediary for INGOs rural financial
projects. LWU was established in 1955 and is a mass organization which has been
implemented at three levels – district, provincial and national. LWU operates in every
village through the country. It is active in a significant share <strong>of</strong> INGOs projects as an
intermediary, an organizer, and as the Lao Government partner. Due to its reach into the
villages, LWU has frequently been the implementing agent or partner for INGO-
supported projects.
In addition to LWU, there are three more mass organizations 8 and five
government <strong>of</strong>fices 9 at district and provincial level to run micr<strong>of</strong>inance programs
throughout the country (Micr<strong>of</strong>inance Capacity Building and Research Programme,
2005).
8
Lao Youth Union, Lao Front for National Construction Office and Federation <strong>of</strong> Trade Union.
9
Agriculture Office, Planning and Investment Office, Health Office, Labour and Social Welfare Office,
and Finance Office.
14

In addition, there are many NGOs running micr<strong>of</strong>inance initiatives in Laos
including CARE, CONSORTIUM, CUSO, Mennonite Central Committee (MCC), Save
the Children Australia (SCA).
2.2.3 Informal sector
This sector comprises moneylenders, rotating savings and credit schemes which
locally known as Houai (daily, weekly, and monthly), traders and rich farmers.
According to Bagchi et al. (2002), inter-household loans are important among rural
households and most <strong>of</strong> these loans are made in-kind. Nevertheless, rotating savings and
credit (Houai) is a significant source <strong>of</strong> loan money for investment or emergency needs in
the urban or semi-urban areas.
Moneylender
According to the Bank <strong>of</strong> the Lao PDR (2002), the role <strong>of</strong> moneylenders in both
urban and rural areas is estimated to be quite important – approximately 50 per cent <strong>of</strong>
rural villages appear to have access to money lender services. Most pr<strong>of</strong>essional
moneylenders operate close to the markets and lend for quick turnover activities.
According to pr<strong>of</strong>essional observation, rates have been stable for the last few years
ranging from 10 and 30 per cent per month with lower interest rates in urban areas due to
competition. Supplier credit for agricultural inputs is rare. Some foreign suppliers across
the Mekong River accept precious metals as collateral for merchandise purchases.
As described above, there are many different approaches to micr<strong>of</strong>inance in Laos.
Bagchi et al. (2002) analyzed each micr<strong>of</strong>inance practice in Laos and provide
commentary on each type <strong>of</strong> initiative. <strong>The</strong>se comments are summarized in Table 2.1.
15

Table 2.1: Current practices in Laos 10
No. Approach/
Implementing agencies
Limitations/ Challenges
Models GoL/Ministry/ Bank INGOs Bi and or
Provincial
Multilateral
Authority/
Mass
organization
agency
1 Institutional √ √ • Not targeting poor people
methodology
• Not flexible enough
(solidarity
methodology
focused on
institutional
sustainability)
• Limited outreach
• Rural households do not have
access to financial services
2 Credit union √ √ • Lack <strong>of</strong> confidence <strong>of</strong> people
as a depositor
• Limited outreach
• Rural households do not have
access to financial services
• Lack <strong>of</strong> skilled human
resources
3 Direct bank
√ • Accessibility is difficult
operation
because <strong>of</strong> lots <strong>of</strong> paper work
and formality
• Lack <strong>of</strong> confidence <strong>of</strong> people
as a depositor
• Limited outreach
• Rural households do not have
access to financial services
• Lack <strong>of</strong> skilled human
resources
• Does not disclose its
financial information or audit
results to the public
4 Co-operative √ • Limited outreach
• Rural households do not have
access to financial services
5 Self help group √ √ √ • Not properly designed
• Ownership is big challenge
• Most <strong>of</strong> the time village elite
people have easy access to
funds
• Often poorer people
excluded from the benefits
• Highly subsidized
10 Table 2.1 is sourced from the market research study done by Bagchi et al. (2002).
16

No. Approach/
Implementing agencies
Limitations/ Challenges
Models GoL/Ministry/ Bank INGOs Bi and or
Provincial
Multilateral
Authority/
Mass
organization
agency
6 Village or √ √ √ √ • Not properly designed
community
• Ownership is big challenge
bank
• Most <strong>of</strong> the time village elite
people have easy access to the
fund
• Often poorer people excluded
from the benefits
• Emphasis on credit rather than
savings
• Sustainability is questioned
7 Informal kinds √ √ √ • Mainly introduced for food
(cereal and
security
animal bank)
• No cash transactions made
initiatives
• Not financial services
• Pushing back towards the non
cash economy
• Highly subsidized
• Very informally organized
• Very limited option for local
resources mobilization
8 Village √ √ √ • Ownership is a question
Revolving Fund
• Often poorly designed
• Most <strong>of</strong> the time village elite
people have easy access to funds
• Often poorer people excluded
from benefits
• Emphasis on credit rather than
savings
• Highly subsidized
• Leadership is a crucial factor
• Sustainability is questioned
Source: Annex 5 <strong>of</strong> Bagchi et al. (2002).
Although the practice <strong>of</strong> micr<strong>of</strong>inance in Laos is still new and at an early stage <strong>of</strong>
development, this study aims to examine the influence <strong>of</strong> micr<strong>of</strong>inance on household’s
outcomes. <strong>The</strong>refore, the rest <strong>of</strong> the paper will conduct an in-depth investigation <strong>of</strong> the
17

effects <strong>of</strong> savings groups on household welfare or outcomes to determine whether or not
the household’s status improves with access to micr<strong>of</strong>inance.
18

CHAPTER 3
LITERATURE REVIEW
Most existing studies on the impact <strong>of</strong> micr<strong>of</strong>inance examine two sets <strong>of</strong>
indicators 11 – economic and social indicators – at different levels 12 . Despite the variation
in the methods used and the results <strong>of</strong> studies conducted in various countries, the main
impact <strong>of</strong> micr<strong>of</strong>inance impact is on change in income, expenditure, assets, educational
status, health as well as gender empowerment. <strong>The</strong> studies that have examined the impact
<strong>of</strong> micr<strong>of</strong>inance on these indicators are discussed below.
3.1 <strong>Impact</strong> studies <strong>of</strong> micr<strong>of</strong>inance on different economic and social indicators
3.1.1 <strong>Impact</strong> studies <strong>of</strong> micr<strong>of</strong>inance on income
<strong>The</strong> effect on income has been analyzed at the individual, household and
enterprise levels. Hulme and Mosley (1996), conducted various studies on different
micr<strong>of</strong>inance programs in numerous countries, and found strong evidence <strong>of</strong> the positive
relationship between access to a credit and the borrower’s level <strong>of</strong> income. <strong>The</strong> authors
indicated that the middle and upper poor received more benefits from income-generating
credit initiatives than the poorest. McKernan (2002), moreover, evaluated three
significant microcredit programs in Bangladesh and discovered that the pr<strong>of</strong>it for self-
employed activities <strong>of</strong> households can be increased by program participation. <strong>The</strong>se
11 Economic indicators are normally measurements for micr<strong>of</strong>inance impact as income, level and patterns
<strong>of</strong> expenditure, consumption and assets. Social indicators to measure the impact <strong>of</strong> micr<strong>of</strong>inance became
popular in the early 1980s as educational status, access to health services, nutritional levels, anthropometric
measures and contraceptive use, for example (Hulme, 2000).
12 Hulme (2000) identified levels <strong>of</strong> assessment in different units as individual, enterprise, household,
community, institutional impacts and household economic portfolio such as households, enterprise,
individual and community.
19

programs were also examined at the village-level impacts in the study <strong>of</strong> Khandker et
al.(1998) which showed that they have positive impact on average households’ annual
income, especially in the rural non-farm sector. Copestake et al. (2001), estimated the
effect <strong>of</strong> an urban credit programme – a group-based microcredit programme – in Zambia,
and found that microcredit has a significant impact on the growth in enterprise pr<strong>of</strong>it and
household income in case <strong>of</strong> the borrowers who have received a second loan.
Sichanthongthip’s study (2004) also pointed to a positive impact <strong>of</strong> microcredit on the
income level <strong>of</strong> individual borrowers. This can be seen from the higher monthly income
earned after the member accessed credit, in the empirical study <strong>of</strong> Lao micr<strong>of</strong>inance on
Saithani case. Shaw (2000) studied two micr<strong>of</strong>inance institutions (MFIs) in Southeastern
Sri Lanka and showed that the less poor clients’ microbusiness that accessed loans from
micr<strong>of</strong>inance programs could earn more income than those <strong>of</strong> the poor do. Mosley (2001)
evaluated the impact <strong>of</strong> loans provided by two urban and two rural MFIs on poverty in
Bolivia. He found that the net impact <strong>of</strong> micr<strong>of</strong>inance from all institutions, at the average
level, was positive in relation to borrowers’ income, even though that net impact for
poorer borrowers might be less than the net impact on richer borrowers. Copestake
(2002) conducted the case study <strong>of</strong> the Zambian Copperbelt, applying the village bank
model to investigate the effect on income distribution at the household and enterprise
levels. <strong>The</strong> study showed that the impact on income distribution depends on who obtains
the loan, who move on to larger loans and who exits the program: group dynamics was
also an important factor. As he discovered, “Some initial levelling up <strong>of</strong> business
incomes was found, but the more marked overall effect among borrowers was <strong>of</strong> income
polarization.”(Copestake, 2002: 743).
20

3.1.2 <strong>Impact</strong> studies <strong>of</strong> micr<strong>of</strong>inance on expenditure
Expenditure is another indicator to measure the impact <strong>of</strong> micr<strong>of</strong>inance. Pitt and
Khandker (1996 and 1998) estimated the effect <strong>of</strong> microcredit obtained by both males
and females for the Grameen Bank and two other group-based microcredit programs in
Bangladesh on various indicators. <strong>The</strong>y showed that the clients <strong>of</strong> the programs could
gain from participating micr<strong>of</strong>inance programs in many ways. It can be seen that income
per capita consumption could be increased by accessing a loan from a microcredit
program such as the Grameen Bank. Khandker (2003) also conducted research on the
long-run impacts <strong>of</strong> micr<strong>of</strong>inance on household consumption and poverty in Bangladesh
by identifying types <strong>of</strong> impact in six household’s outcomes as outlined bellow:
• Per capita total expenditure;
• Per capita food expenditure;
• Per capita non-food expenditure;
• <strong>The</strong> incidence <strong>of</strong> moderate and extreme poverty;
• Household non-land assets
<strong>The</strong> author found that the micr<strong>of</strong>inance effects <strong>of</strong> male borrowing were much weaker
than the impact <strong>of</strong> female borrowing and there was decrease in return to borrowing all the
time. Moreover, he noted that the impact on food expenditure was less pronounced than
the one on non-food expenditure. Besides, he showed that the poorest gained benefits
from micr<strong>of</strong>inance and micr<strong>of</strong>inance had a sustainable impact in terms <strong>of</strong> poverty
reduction among program participants. In addition, the author discovered that there was
spillover effect <strong>of</strong> micr<strong>of</strong>inance to reduce poverty at the village level. In contrast, the
impact was less noticeable in reducing moderate rather than extreme poverty. Morduch
21

(1998), however, argued that the eligible households that participated in these three
micr<strong>of</strong>inance programs have strikingly less consumption levels than the eligible
households living in villages without the programs.
3.1.3 <strong>Impact</strong> studies <strong>of</strong> micr<strong>of</strong>inance on wealth
A further indicator <strong>of</strong> the impact micr<strong>of</strong>inance is wealth. Montgomery et al.
(1996) examined the performance and impact <strong>of</strong> two micr<strong>of</strong>inance programs in
Bangladesh. <strong>The</strong>y found that there were positive impacts <strong>of</strong> a microcredit program, such
as the Bangladesh Rural Advancement Committee’s (BRAC’s) Rural Development
Program (RDP), on both enterprise and household assets. Clearly, even though total value
<strong>of</strong> household assets had a slight increase after the borrowers obtained last loans, there had
significant increase in the value <strong>of</strong> productive assets. Pitt and Khandker (1996 and 1998)
also noted that the microcredit had a positive impact on women’s non-land assets. Mosley
(2001) also pointed out that there was positive impact <strong>of</strong> micr<strong>of</strong>inance on asset levels. He
further stated that accumulation <strong>of</strong> asset and income status was generally highly
correlated, which led to extreme correlation between income poverty and asset poverty.
Coleman (1999) investigated the impact <strong>of</strong> a village bank on borrower welfare in
Northeast Thailand. He found that there was a slight impact <strong>of</strong> program loans on clients’
welfare. However, he discovered that the village bank had a positive and significant
impact on the accumulation <strong>of</strong> women’s wealth, particularly landed wealth but this result
included bias from measured impact (discussed in methodology below). In contrary to the
positive results, Mckernan (2002) found an inverse relationship between participation in
22

program and household assets. Mckernan also found that households with fewest assets
benefit most from participating in a program.
3.1.4 <strong>Impact</strong> studies <strong>of</strong> micr<strong>of</strong>inance on educational status
Many impact studies <strong>of</strong> micr<strong>of</strong>inance have focused on educational status.
Chowdhury and Bhuiya (2004), studied the impact <strong>of</strong> a micr<strong>of</strong>inance program, BRAC
poverty alleviation program, in Bangladesh, and found that both member and non-
member groups <strong>of</strong> BRAC had improved in educational performance. However, the
BRAC member households benefited much more than poor non-member households.
Furthermore, girls gained more than boys. Holvoet (2004) investigated the effects <strong>of</strong>
micr<strong>of</strong>inance on childhood education by examining two micr<strong>of</strong>inance programs in South
India – one with direct bank-borrower credit and another one with group mediated credit.
<strong>The</strong> author showed that loans to women, through women’s groups, had a significant
positive impact on schooling and literacy for girls, whereas it remained mainly
unchangeable in the case <strong>of</strong> boys. However, in case <strong>of</strong> direct individual bank-borrower
lending, there was no improvement in educational inputs and outputs for children. Pitt
and Khandker (1996) found that a credit to the participants provided by a micr<strong>of</strong>inance
institution like the Grameen Bank, could grow school enrolment for children. <strong>The</strong>y found,
for example, that in case <strong>of</strong> the Grameen Bank and Bangladesh Rural Development
Board’s (BRDB) Rural Development RD-12 program, credit lending to women had a
significantly positive impact on schooling for boys (Pitt and Khandker, 1998).
23

3.1.5 <strong>Impact</strong> studies <strong>of</strong> micr<strong>of</strong>inance on health
Indicators related health issues are also applied as proxies to examine the impact
<strong>of</strong> micr<strong>of</strong>inance. Chowdhury and Bhuiya (2004) found that micr<strong>of</strong>inance program, led to
a good improvement in child survival and nutritional status. Pitt and Khandker (1996)
also noted that there was a rise in contraceptive use and decrease in fertility in case <strong>of</strong> the
participants obtaining a credit provided by the Grameen Bank. However, there was no
evidence to prove that an increase in contraceptive use or a decrease in fertility resulted
from the participation <strong>of</strong> women in group-based credit programs. But fertility reduction
was observed and contraceptive use slightly increased in case <strong>of</strong> men’s participation (Pitt
et al., 1999).
3.1.6 <strong>Impact</strong> studies <strong>of</strong> micr<strong>of</strong>inance on empowerment
Micr<strong>of</strong>inance also leads to the empowerment <strong>of</strong> women. Hashemi et al. (1996)
studied two main micr<strong>of</strong>inance programs in Bangladesh, the Grameen Bank and the
Bangladesh Rural Advancement Committee (BRAC). <strong>The</strong>y noted that the participation <strong>of</strong>
the programs had important positive impacts on eight different dimensions <strong>of</strong> women’s
empowerment:
• Mobility,
• Economic security,
• Ability to make small purchases,
• Ability to make larger purchases,
• Involvement in major household decisions,
24

• Relative freedom from domination by the family (especially, women’s
ownership <strong>of</strong> productive assets),
• Political and legal awareness,
• Participation in public protest and political campaigning.
In another study, Pitt and Khandker (1998) found that the behavior <strong>of</strong> poor households
was significantly changed in case <strong>of</strong> women’s participation in the program credit, in
Bangladesh. It, for example, could be seen that every 100 additional taka credit provided
to women by the microcredit programs, namely the Grameen Bank, BRAC and BRDB,
increased yearly expenditure for household consumption by 18 taka, whereas that
provided to men from the same programs grew yearly household consumption
expenditure by 11 taka. However, there exists a counter argument that microcredit
programs inflicted extreme pressure on women by forcing them down to meet difficult
loan repayment schedules (Goetz and Gupta, 1996).
Besides the micr<strong>of</strong>inance impact on the indicators mentioned above, one study
tried to examine how the savings group in Laos affects the behavior <strong>of</strong> member <strong>of</strong> a
village savings group. It showed that the behavior <strong>of</strong> the village savings group members
was changed as a result <strong>of</strong> participating in a program. While previously savings were kept
in the form <strong>of</strong> gold, livestock, jewelry, deposits in the bank, and savings at home,
members now saved in the savings group (Kyophilavong and Chaleunsinh, 2005).
Different methodologies have been adopted to analyze the impact <strong>of</strong> micr<strong>of</strong>inance
programs. <strong>The</strong>se are discussed in the next section.
25

3.2 Methodology
Empirical studies on the impact <strong>of</strong> micr<strong>of</strong>inance can be distinguished into two
main groups: those that were not concerned about selection bias problem 13 and those that
were. A large number <strong>of</strong> impact studies <strong>of</strong> micr<strong>of</strong>inance programs did not take into
account selection bias. According to Chen’ s review <strong>of</strong> 11 impact studies <strong>of</strong> the Grameen
Bank in Bangladesh, no study corrected the selection bias (Chen (1992) cited in Coleman
1999, p.109). Shaw (2004) also studied two micr<strong>of</strong>inance programs in Sri Lanka, and
used a questionnaire and conducted interviews in one semi-urban and two rural groups.
<strong>The</strong> author presented only median comparisons <strong>of</strong> client incomes among four household
income groups (extreme poor, poor, near-poor and nonpoor), at time <strong>of</strong> their first loan
(June 1994) and at the time the research was conducted (June1999). However, he did not
take into account selection bias. Sichanthongthip (2004) evaluated the impact <strong>of</strong> a
micr<strong>of</strong>inance program <strong>of</strong> the village savings group in a semi-urban area <strong>of</strong> Laos and used
a questionnaire to collect primary household data from members <strong>of</strong> the micr<strong>of</strong>inance
program at two points <strong>of</strong> time (before and after borrowing). He reported the results <strong>of</strong> the
impact on income by applying econometric analysis. On the other hand, he also did not
control for selection bias. Another study which also did not take into account such bias is
the study by Kyophilavong and Chaleunsinh (2005) who estimated the impact <strong>of</strong> a village
savings group in a semi-urban area <strong>of</strong> Laos by conducting a survey for both member and
nonmember <strong>of</strong> village savings group. <strong>The</strong>y presented only a comparison <strong>of</strong> the mean
values <strong>of</strong> many impact indicators for both members and nonmembers.
13 As Holvoet (2004: 32) noted, “Selection bias may occur because <strong>of</strong> nonrandom program placement,
through selection by program staff, or because <strong>of</strong> self-selection by program participants”. For further
details, please read more in Baker (2000), and Greene (2000). For numerous discussions for methods to
correct that bias, please refer to Heckman and Robb (1985), M<strong>of</strong>fitt (1991), and Ravallion (2005).
26

A number <strong>of</strong> researchers, however, have attempted to correct the selection bias.
Hashemi et al. (1996) evaluated the effect <strong>of</strong> rural credit programs in Bangladesh by
undertaking ethnographic research in six villages during the period 1991-94 and
conducting a survey in late 1992. <strong>The</strong> authors classified their sample into four groups
consisting <strong>of</strong>:
• Grameen Bank members,
• BRAC members,
• Nonmembers living in the Grameen Bank villages (who would have been
eligible to join either BRAC or Grameen Bank),
• A comparison group living in villages without the Grameen Bank or
BRAC programs but who would have qualified to join the credit programs.
<strong>The</strong>y also tried to address the possibility <strong>of</strong> selection bias by including nonparticipants
and participants in Grameen Bank villages and comparing them with women living in
villages without microcredit programs.
In contrast, Hashemi et al. (1996) did not control for possibility <strong>of</strong> endogenous
program placement, even though, the authors presented the effects <strong>of</strong> credit program on
eight dimensions <strong>of</strong> empowerment by applying logistic regression models as mentioned
in section 3.1.6 above.
Hulme and Mosley (1996) also tried to solve for selection bias by studying
different credit programs in a number <strong>of</strong> countries. In their study, they included eight
micr<strong>of</strong>inance institutions which provide group lending. Of these eight institutions, two
were used a control group in the case a loan had been approved for participants but they
had not yet received any amount <strong>of</strong> the loan. However, only the means <strong>of</strong> different
27

outcome variables for both treatment and control groups were introduced. <strong>The</strong>re were no
statistical analyses <strong>of</strong> the differences between the two groups. In addition, the possibility
<strong>of</strong> endogenous program placement could not be controlled with their available data
(Coleman, 1999: 109).
Recently, many papers on the evaluation <strong>of</strong> micr<strong>of</strong>inance programs have adopted
an econometric approach and taken account <strong>of</strong> both selection bias and nonrandom
program placement (Pitt and Khandker 1996&1998; Pitt et al. 1999; Coleman 1999 &
2002; Khandker 2003; Khandker et al.1998; McKernan 2002; Morduch 1998). <strong>The</strong>
methodology was applied by Pitt and Khandker (1996&1998) to attempt to correct both
selection bias and nonrandom program placement in group lending, is described as the
below.
<strong>The</strong> authors used survey data <strong>of</strong> the Grameen Bank and two other group lending
programs in Bangladesh (the Bangladesh Institute <strong>of</strong> Development Studies (BIDS) and
the World Bank). <strong>The</strong>y conducted a quasi-experimental household survey <strong>of</strong> 87 villages
in 29 thanas 14 . <strong>The</strong>y sampled randomly for both members and nonmembers from villages
that had a micr<strong>of</strong>inance program. <strong>The</strong>y also randomly chose households from villages
without a program. In this case, credit program availability was applied as an identifying
variable. <strong>The</strong> authors, however, identified that systematic variation will occur between
the two kinds <strong>of</strong> villages because <strong>of</strong> the possibility <strong>of</strong> endogenous program placement.
Thus, village fixed effects estimation was applied to control for unobserved variation
between villages. Nevertheless, households living in program villages, which are
exogenously excluded from the program by program rules which restrict participation for
household with more than 0.5 acres <strong>of</strong> land were principally excluded from membership
14 A thana is the administrative center for numerous villages.
28

consideration by any <strong>of</strong> the three programs, that were sampled for this survey. This might
have resulted from the possibility <strong>of</strong> collinearity between the village-specific dummy
variables, identifying fixed effect, and the availability <strong>of</strong> the program. Many impact
studies then applied a similar methodology. As can be seen from the studies by Pitt et al.
(1999); Khandker (2003); Khandker et al.(1998); McKernan (2002); and Morduch (1998).
But, Khandker (2003) did some further interesting work on the data set. He used the same
data set as Pitt and Khandker (1996) and, then did a follow up survey <strong>of</strong> the same
households in 1998/99 to come with panel data. However, according to Coleman claimed
to the eligibility criteria for membership consideration:
Most group lending programs…do not impose such eligibility criteria. Rather,
they attempt to attract the relatively poor and dissuade the relatively rich from
participating by the small size <strong>of</strong> loans, the high frequency <strong>of</strong> meetings, and the
stigma <strong>of</strong> belonging to a poor person’s credit program. Hence, the method <strong>of</strong> Pitt
and Khandker could not be implemented in most group lending programs.
Moreover, even in the context <strong>of</strong> the three Bangladesh programs they studied,
their survey found that some 18-34% <strong>of</strong> program participants in fact had wealth
that should have excluded them from participating. Hence, the use <strong>of</strong> this
eligibility criterion as a key exclusion restriction may not be appropriate
(Coleman, 1999:110). 15
<strong>The</strong> methodology applied by Coleman (1999) did not require the existence and
enforcement <strong>of</strong> exogenously imposed membership criteria to identify program impact. As
part <strong>of</strong> his study, a unique survey which allows for the use <strong>of</strong> relatively straightforward
estimation techniques was applied for data collection. <strong>The</strong>n, the survey was done four
15 Morduch’s study in 1998 (cited in Coleman 1999: 110) found that in program village, Pitt and Khandker
marked “eligible” for households’ participation <strong>of</strong> the microcredit program as any households in program
village, consisting households that should have been excluded principally. On the other hand, they follow
exactly the rule on the eligibility criteria for marking household as eligible or not eligible in non-program
village. Thus, both “treatment” group and “control” group was not conformed each other, and it led to
overestimated results on program impact.
29

times over the course <strong>of</strong> a year, during 1995-1996, for both members and nonmembers <strong>of</strong>
the village bank in 14 villages in Northeast Thailand. Six <strong>of</strong> those villages were identified
as “control” villages that were recognized to receive NGO support for village bank within
one year after the identification. It means that there was self-selection for villagers in six
control villages as participants had already decided whether or not they want to be
membership <strong>of</strong> the village bank. <strong>The</strong> rest <strong>of</strong> eight villages were “treatment” villages <strong>of</strong>
which seven villages had a village bank for two to four years and one village started its
village bank suddenly after the first survey. <strong>The</strong> comparison between the “old” village
bank members in the eight treatment villages and the “new” village bank members in the
six “control” villages could be undertaken. In addition, the author identified precise
impact estimator as a variation <strong>of</strong> the length <strong>of</strong> time for the program availability in the
treatment villages. When nonmembers in all villages were included in the sample, this
allows for the use <strong>of</strong> village fixed effect estimation to control the possibility that the order
in which these 14 villages received program support is endogenous.
Based on empirical evidence, most studies showed positive impact <strong>of</strong>
micr<strong>of</strong>inance on different dimensions <strong>of</strong> outcomes at different levels, even though they
applied various methodologies. Until now, no study on impact <strong>of</strong> micr<strong>of</strong>inance in Laos
has corrected the selection bias and endogenous program placement. <strong>The</strong>refore, this study
will try to take into account that bias and the author will apply method used by Coleman
(1999) to evaluate the impact <strong>of</strong> a village savings group in a semi-urban area <strong>of</strong> Laos.
30

CHAPTER 4
ANALYTICAL FRAMEWORK
This paper will to examine the impact <strong>of</strong> micr<strong>of</strong>inance program by applying two
sets <strong>of</strong> indicators – economic and social indicators. To test the hypothesis that members
with a long-term participation on the savings group may have better quality <strong>of</strong> life in
terms <strong>of</strong> wealth, income and expenses, the theoretical framework, model specification
and methodology will be discussed in this chapter.
4.1 <strong>The</strong>oretical Framework
In economic perspectives, consumer theory is used to identify the consequences
<strong>of</strong> consumer behavior by maximizing consumer utility. <strong>The</strong>refore, on the basis <strong>of</strong> the
framework <strong>of</strong> consumer theory, the impacts <strong>of</strong> micr<strong>of</strong>inance program can be illustrated
by using the concept <strong>of</strong> utility maximization. To evaluate the effects <strong>of</strong> group-based
credit program participation on household behavior and intra-household resource
allocation, it will consider a simple model that generates an efficiency argument for
targeted credit for the rural poor. This paper will follow the framework that was
developed and used by Pitt and Khandker (1996).
Assume that households <strong>of</strong> size n comprise two working age adults (the male
head and his wife) plus n-2 dependents. <strong>The</strong> households maximize a lifetime utility
function which contains time-specific utility functions <strong>of</strong> the form:
31

Where:
U t = U ( n,
Qi
, H i , li
)
(1)
Qi: a set <strong>of</strong> market goods consumed by household member i,
Hi: a set <strong>of</strong> non-market household-produced goods allocated to member i,
li : leisure time consumed by household member i.
To generalize (1), each <strong>of</strong> the two adult household members, denoted by f and m, needs to
maximizes his (if m) or her (if f) own utility uit,
u it = ui
( n,
Qi
, H i , li
) , i = f,m (2)
Where household social welfare is some function <strong>of</strong> the individual utility
functions U it = U ( u ft , umt
) , it can be represented as
U = u + ( 1−
λ)
u , 0 ≤ λ ≤ 1 (3)
it
λ ft
mt
In this, λ is the weight given to women’s preferences in the social welfare function <strong>of</strong>
household. <strong>The</strong> parameter λ can be thought to represent the bargaining power <strong>of</strong> female
household members relative to males in determining the intra-household allocation <strong>of</strong>
resources. When λ =0, female preferences are given no weight and the household’s social
welfare function is identical to that <strong>of</strong> the males. <strong>The</strong> household-produced goods H
include “household care” activities such as preparation <strong>of</strong> food, childcare, and the
gathering <strong>of</strong> fuel.
32

where,
H mh fh
= H ( L , L , G,
F)
(4)
L mh : time devoted to the production <strong>of</strong> H by males,
L fh : time devoted to the production <strong>of</strong> H by females,
G : a vector <strong>of</strong> market goods used as inputs in the production <strong>of</strong> H
F : a vector <strong>of</strong> technology parameters that affect efficiency in H good production.
Due to socio-cultural factors, relatively few poor women work in the wage labor
market. <strong>The</strong> reservation wage for market work is, therefore, relatively high. In addition to
this preference effect on female wage employment, workers typically must commit to a
full day’s employment even in the spot labor market. If men’s time (or that <strong>of</strong> other
household members) is a poor substitute for women’s time, and if important H-good
outputs, such as child care and food preparation, must be “produced” daily (cannot be
stored), then working a full day may entail foregoing the production and consumption <strong>of</strong>
highly valued H goods. Thus, the non-storability and time-intensity <strong>of</strong> production <strong>of</strong>
household goods H, the indivisibility <strong>of</strong> time allocation in the wage labor market, and
high reservation wages due to cultural impediments to wage employment outside the
home all result in most women being engaged in the production <strong>of</strong> household goods H in
every period to the exclusion <strong>of</strong> employment in market activities. <strong>The</strong>se effects are
magnified if λ is small and male preferences tend to favor certain kinds <strong>of</strong> H-goods
produced on women's time.
However, there are also economic activities that produce goods to sell in the
market that is not culturally frowned upon. <strong>The</strong>se activities produce what we refer to as
Z-goods. <strong>The</strong>se activities permit part-day labor and do not require that production occur
33

away from the home. For many Z-goods a minimum level <strong>of</strong> capital is necessary, even
though many <strong>of</strong> these production activities can be operated at low levels <strong>of</strong> capital
intensity. This minimum is <strong>of</strong>ten the result <strong>of</strong> the indivisibility <strong>of</strong> capital items. For
example, dairy farming requires no less than one cow, and hand-powered looms have a
minimum size. For other activities, such as paddy husking, where the indivisibility <strong>of</strong>
physical capital is not an issue, transaction costs (or the high costs <strong>of</strong> information) take
the place <strong>of</strong> the minimal level <strong>of</strong> operations. In many societies these indivisibilities may
be inconsequential, but among the rural poor <strong>of</strong> many developing countries, including
Laos, household income and wealth is so low that the costs <strong>of</strong> initiating production at
minimal economic levels are quite high. Poverty alleviation programs, such as the Rural
Works Programs, which target households by drawing them into (in-kind) wage labor,
have a comparatively small direct effect on the time allocation and productivity <strong>of</strong>
women. In addition, transportation and other transaction costs in labor markets may be so
high as to make part-day labor un-remunerative. Formally, we represent the production
function for the Z-goods as:
Where
Z mz fz
= Z(
K,
L , L , A,
J )
(5)
L mz : labor time <strong>of</strong> head devoted to the production <strong>of</strong> Z,
L fz : labor time <strong>of</strong> wife devoted to the production <strong>of</strong> Z,
K : capital in Z production,
A : a vector <strong>of</strong> variable inputs
34

J: a vector <strong>of</strong> technology parameters that affect efficiency in Z-good production
(information).
Positive production requires a minimal level <strong>of</strong> capital K, Kmin. <strong>The</strong> production
function (5) can be operated at a non-zero level when Lmz or L fz are zero, but not when
both are zero. For example, although at least one cow is required in the production <strong>of</strong>
milk, any person’s labor can be used to obtain the milk. In other examples, Kmin may
stand for the minimum information which is required to produce and market home
production.
Households maximize lifetime utility subject to a budget constraint that requires
that the present discounted value <strong>of</strong> expenditure on goods and leisure equal the present
value <strong>of</strong> all wealth, defined as assets plus the discounted present value <strong>of</strong> the time
endowments, and the two production function equations (4) and (5). Household ability to
borrow has significant influence on the time path <strong>of</strong> household consumption. Households
having very low levels <strong>of</strong> initial assets as collateral may not be able to borrow to achieve
the minimum capital requirements necessary to operate the Z-good activity. At very low
levels <strong>of</strong> income and consumption, reducing current consumption to accumulate assets
for this purpose may not be optimal because it may seriously threaten health, production
efficiency, and life expectancy, as shown in Gersovitz (1983). <strong>The</strong>refore, for many
households, the Z-good activity is never carried out (and Lfz = 0) and women who do not
work in the wage labor market devote all their time to production <strong>of</strong> the non-market good
H and to leisure.
35

4.2 Model Specification and Methodology
4.2.1 Model Specification
From the model mentioned above, the reduced-form determinants <strong>of</strong> credit
program participation include:
• <strong>The</strong> prices <strong>of</strong> market time,
• <strong>The</strong> price <strong>of</strong> the purchased market good Q,
• <strong>The</strong> prices <strong>of</strong> the market inputs into H-good production including the cost
<strong>of</strong> averting a birth and other determinants <strong>of</strong> fertility,
• <strong>The</strong> prices <strong>of</strong> variable inputs into Z-good production,
• <strong>The</strong> price <strong>of</strong> the capital good,
• Age and education levels <strong>of</strong> the borrower and spouse,
• Access to transfers from non-resident relatives and,
• Village-level characteristics (V).
Whether or not poor households, especially the women, are credit-constrained is a
complex issue. Rashid and Townsend (1994) concluded a detailed study <strong>of</strong> this issue in
the context <strong>of</strong> targeted group-based lending. <strong>The</strong>y recommend that inefficient
consumption and production outcomes may be a result <strong>of</strong> risk, private information,
communications and enforcement difficulties. Significant evidence shows that collateral
and education, the health risks and intermittency <strong>of</strong> employment associated with
childbirth, and cultural barriers result in the limitation <strong>of</strong> women participation in the
formal credit market. Rashid and Townsend note that the evidence does not in itself
imply that outcomes are inefficient if, for example, women have access to other sources
36

<strong>of</strong> finance such as transfers, or if male household members obtain funds for female
household members.
A primary focus <strong>of</strong> this paper is to estimate the impact <strong>of</strong> credit programs on
various household outcomes such as household consumption, household income, and
household asset. <strong>The</strong> author proposes to estimate the conditional demand equation for
each outcome to be investigated, conditioned on the household’s program participation as
measured by the quantity <strong>of</strong> credit borrowed. As noted earlier, the methodology adopted
is that developed by Pitt and Khandker (1996). Consider the reduced form equation (6)
for the level <strong>of</strong> participation in one <strong>of</strong> the credit programs by household i in village j ( C ij ),
where level <strong>of</strong> participation will be taken to be the value <strong>of</strong> program credit
where
C = X β + V γ + Z π + ε
(6)
ij
ij
c
j
c
Xij : a vector <strong>of</strong> household characteristics (e.g., age and education <strong>of</strong> household head),
Vj: a vector <strong>of</strong> village characteristics (e.g. prices and community infrastructure),
Zij : a set <strong>of</strong> household or village characteristics distinct from the X’s and V’s in that they
affect Cij but not other household behaviors conditional on Cij (see below),
β c , γ c , and π are unknown parameters,
c
ε ij : a random error having three components
ε = μ + η + e
(7)
c
ij
j
ij
c
ij
ij
37
c
ij

where
μ : an unobserved village-specific effect,
j
η ij : an unobserved household-specific effect,
c
e ij : a non-systematic error uncorrelated with the other error components or the regressors.
<strong>The</strong> conditional demand for household outcome Yij conditional on the level <strong>of</strong> program
participation Cij is
Y = X β + V γ + C δ + ε (8)
ij
ij
y
y
where β y , γ y and δ are unknown parameters and ε ij is comprised <strong>of</strong>
y
ij
j
j
y
j
y
ε = ( αμ + μ ) + ( θη + η ) + e (9)
where α and θ are parameters (corresponding to correlation coefficients), y
μ j and
are additional village and household-specific errors uncorrelated with μ j and η ij ,
ij
respectively, and y
e ij is a non-systematic error uncorrelated with other error components
y c
or with the regressors. If α ≠0 or θ ≠0 the errors ε ij and ε ij are correlated. Econometric
estimation that does not take this correlation into account will yield biased estimates <strong>of</strong>
the parameters <strong>of</strong> equation (8) due to the endogeneity <strong>of</strong> credit program participation Cij.
4.2.2 Methodology
ij
Econometric estimation <strong>of</strong> this equation (8) will yield biased parameter estimates
y c
if ε ij and ε ij are correlated and this correlation is not taken into account. According to
38
y
ij
y
ij
y
ij
y
η ij

Coleman (1999), two different sources which lead to such correlation are (1) self-
selection into the savings group and (2) nonrandom program placement. He illustrated
sources <strong>of</strong> such correlation that for the first source <strong>of</strong> correlation, by considering a sample
<strong>of</strong> households drawn only from with villages with a savings group 16 , some households
will have selected to be savings group members, and others will have not selected to be
y c
members. ε ij and ε ij will almost definitely be correlated in this sample. For example, if
there are many entrepreneurial households joining savings group, then unmeasured
“entrepreneurship” would affect both the decision to become a member and impact
y c
measures such as income and assets. In contrast, if ε ij and ε ij were negatively correlated,
estimation <strong>of</strong> savings group impact would be biased downward. For example, the
relatively poor may join the savings group more than the rich who might feel stigmatized
in a group for poor people.
Coleman also demonstrated the second source <strong>of</strong> correlation as follows:
Consider another commonly used sample, which includes households <strong>of</strong> village
bank members from some villages and randomly selected households from
villages without a village bank…Now it is possible for ε ij and μ ij to be
correlated across villages if village bank placement is not random. For example, if
some village are viewed as more entrepreneurial or better organized, have more
dynamic leaders and such leadership spills over to effect others’ behavior in the
village, or are simply poorer (e.g., living in flood-prone or drought-prone areas),
16 In original paper <strong>of</strong> Coleman (1999) used word <strong>of</strong> “village bank” but hence use word “savings group” to
conform to this study. Actually, village bank in Coleman’s case and savings group in this study are quite
similar concept because <strong>of</strong> they both are applied from village bank model <strong>of</strong> the Foundation for
International Community Assistance (FINCA) but the village bank is much more dependent on external
fund than internal fund which contrast to the savings group. Please see Appendix B section 1.2.1 for point
<strong>of</strong> view <strong>of</strong> savings group and section 2.2.1 for its source <strong>of</strong> fund.
39

and if NGOs use such criteria to determine village bank placement, then ε ij and
μ ij will be correlated (Coleman, 1999: 113) 17 .
To deal with the case <strong>of</strong> correlation between y
ε ij and
40
c
ε ij
18 , therefore, the author
will follow the method <strong>of</strong> Coleman (1999) 19 to measure the impact <strong>of</strong> savings group on
the household outcomes. As mentioned in section 3.2 <strong>of</strong> chapter 3, Coleman (1999)
conducted a unique survey which permits the estimation <strong>of</strong> an alternative, simpler
specification allowing for the use <strong>of</strong> relatively straightforward econometric techniques to
measures program impact.
By applying Coleman’s method, the author conducted a survey on 251 households
in six villages in semi-urban area <strong>of</strong> Laos (Naxaithong district). Of these, three villages
had savings groups operating for more than a year, and are called “old” savings groups.
<strong>The</strong> remaining three villages had also started operating savings groups that were in
existence for less than a year. <strong>The</strong>se are called “new” savings groups (see table 5.1 <strong>of</strong> the
next chapter). Both old and new savings groups cannot be identified as “treatment”
villages and “control” villages as the case <strong>of</strong> the village banks in Northeast Thailand done
by Coleman (1999). This is a result <strong>of</strong> slight difference in the way funds are managed.
Unlike the village banks in Coleman’s case, savings groups in this case study are much
more dependent on internal funds than external funds (NGOs’support) (see more
17
<strong>The</strong> words <strong>of</strong> “village bank” and the symbols <strong>of</strong> “ ε ij ” and “ μ ij ” used in the paper <strong>of</strong> Coleman (1999)
c
y
are equivalent to the words <strong>of</strong> “savings group” and symbols <strong>of</strong> “ ε ij ” and “ ε ij ” used in this study
respectively.
18
To cope with such correlation, M<strong>of</strong>fitt (1991) suggests three standard procedures: using instrumental
variables, using panel data, and assuming an error distribution <strong>of</strong> the outcome variable without treatment.
For more explanation was mentioned in the paper <strong>of</strong> M<strong>of</strong>fitt (1991: 295-305).
19
Aghion and Morduch (2005) also suggested that the method <strong>of</strong> Coleman (1999) is one <strong>of</strong> the approaches
for impact evaluation to address selection bias.

explanation in Appendix B under section 2.2.1). It means that some savings group
members can obtain a credit in short time (two or three days) after the establishment <strong>of</strong>
savings group by depending on internal fund (savings from members). Or some new
savings group members can access a loan quickly after self-selecting to become a
member 20 . However, there were many villagers who had already self-selected to become
members <strong>of</strong> savings groups, but had not obtained any credit from savings groups yet.
This was the case in both old and new savings groups, particularly among old members 21
in the old savings groups who were with the savings group for more than a year. This
may be result <strong>of</strong> their lack <strong>of</strong> demand for credit because they may have enough funds to
run their activities or to smooth their consumption. <strong>The</strong>ir motivation for participating in
the savings group may have been to benefit from the yearly dividends they could expect
from their monthly deposit with the savings groups.
<strong>The</strong> classification above allows the author to set a “treatment” group in which
savings group members have gained benefit from joining the savings group by either
obtaining a credit or receiving a dividend. Similarly, a “control” group in which savings
group members have not benefited from the savings groups can be identified. <strong>The</strong> control
group <strong>of</strong> savings group households would presumably, on average, share the same
unobservable characteristics (such as entrepreneurship, gender attitude, etc.) as the
treatment group <strong>of</strong> savings group members (Coleman, 1999 and Mosley, 1997). In both
20 It is normally one or two day(s) to get a loan after becoming a member <strong>of</strong> savings group if the savings
group has enough funds and the member meet the requirements for borrowing.
21 13.98 percent <strong>of</strong> member sample (n=93) in old savings groups has never borrowed from the savings
groups since they have been a savings group member. <strong>The</strong>ir membership ages varied from 11.5 months to
35.5 months.
41

old and new villages, both members and nonmembers were surveyed. With this survey
design, equation (8) can be replaced by a single impact equation 22 as follows:
Where
Y = X α + V β + M γ + T δ + υ
(10)
ij
ij
Yij ,Vj , and Xij are defined as before
j
ij
ij
Mij : a membership dummy variable equal to 1 if household ij self-selects into the
savings group, and 0 otherwise.
Tij : a dummy variable equal to 1 if a self-selected member has already had gained
benefit from savings groups 23 , and 0 otherwise.
δ : measures the average impact <strong>of</strong> the savings group on Yij.
<strong>The</strong> membership dummy variable Mij can be thought <strong>of</strong> as a proxy for the
unobservable characteristics that lead households to self-select into the savings group.
For example, it captures the unobserved variables that led to the correlation across
households between y
ε ij and
42
ij
c
ε ij . <strong>The</strong> variable Tij which measures availability <strong>of</strong> the
savings group to members who have self-selected is exogenous to the household. This is
unlike the amount borrowed (which may not be exogenous with respect to the village, as
discussed below).
According to Coleman’s (1999) specification, the variable Mij which captures
unobservable household characteristics, can eliminate the correlation between Tij and y
ε ij
22 This equation was adopted from the study <strong>of</strong> Coleman (1999).
23 This is slightly different from Coleman’s case (1999) which he used access to program credit as
distinguish between control and treatment village but this study, the use <strong>of</strong> a gain from participation <strong>of</strong>
savings group by either obtaining a credit or receiving a dividend is to be the proxy for distinguishing
between control and treatment group.

due to self-selection at the household level. In addition, if the order in which villages
have savings group program placement is random with respect to unobserved village
characteristics, then efficient and unbiased estimates can be obtained with Vj as a vector
<strong>of</strong> specific village characteristic affecting Yij. However, if the order is not random with
respect to unobservable village characteristics, then using specific village characteristics
as regressors will lead to a biased estimate <strong>of</strong> impact. This is a difference on the second
source <strong>of</strong> bias discussed above. While in this study does not control villages and
treatment villages, the problem <strong>of</strong> non-random program placement is resolved by the
author dividing the six villages which have own savings groups in terms <strong>of</strong> different
times <strong>of</strong> establishment (that is, three old and three new savings groups). However, the
order in which villages had program support to establish a savings group may not be
random. For example, if the most dynamic villages established a savings group with the
y
program support before less dynamic villages, then Tij and ε ij will be positively
correlated and δ will be biased. One method to eliminate this bias is through village
fixed effects estimation (Coleman, 1999). If the order <strong>of</strong> savings group placement is
random 24 , however, then village fixed effects estimation is still unbiased, but less
efficient than using specific village characteristics as regressors.
<strong>The</strong> empirical model in (10) can be improved upon by recognizing that some
treatment members have received benefit from joining savings groups longer than others.
In the six villages, the age <strong>of</strong> the savings group differed from one month to three years.
24 Un<strong>of</strong>ficial information obtained from the Project’s staff and the District Lao Women’s Union at
Naxaithong city noted that the villages which have three old savings groups were target villages. Before the
establishment <strong>of</strong> savings groups in those villages, the project proponents came to the villages to discuss the
possibility <strong>of</strong> savings group establishment with local authorities <strong>of</strong> those villages. By contrast, the local
authorities in the villages with the “new” savings groups, themselves had approached the project
proponents and requested them to establish a micr<strong>of</strong>inance program in their villages.
43

Of these six villages, there were members who classified to the treatment group and have
their membership ages varied as the age <strong>of</strong> the savings group. Giving that the cumulative
amount that a member can borrow grows over the life <strong>of</strong> the savings group, and that a
member can receive a dividend every twelve months, one would expect to see greater
impact in villages with older savings group. Hence the empirical model estimated, which
is based on the one used by the Coleman (1999), is as follows:
Y = X α + V β + M γ + MAMT δ + μ
(11)
ij
ij
j
ij
44
ij
Where μij is error representing unmeasured household and village characteristics
that determine outcomes; the treatment dummy variable Tij is replaced by MAMTij, the
numbers <strong>of</strong> months the savings group has been operating in the village. In other word,
MAMTij can be thought <strong>of</strong> as the number <strong>of</strong> months participants have gained benefits
from participation <strong>of</strong> savings groups. MAMTij is zero for members in control group and
for nonmembers in the six villages. <strong>The</strong>refore, MAMTij is a more precise measure <strong>of</strong>
program availability than Tij. Now, δ measures the impact per month <strong>of</strong> program
availability. Like the equation (10), if the order <strong>of</strong> program placement is random with
respect to unobservable village characteristics, then efficient and unbiased estimates can
be obtained with Vj as a vector <strong>of</strong> specific village characteristics. If program placement,
however, is not random with respect to unobservable village characteristics, then MAMTij
and
y
ε ij can be eliminated with village fixed effects. This specification (11) is
considerably easier to estimate (if Yij is uncensored, then OLS is appropriate), according
to Coleman (1999).
ij

<strong>The</strong> regression results <strong>of</strong> the equation (11) by employing Coleman’s (1999)
methodology, to evaluate the impact <strong>of</strong> savings group on household outcomes will be
presented in chapter 6. Before moving to that, survey method and data will be described
in the next chapter.
45

CHAPTER 5
SURVEY DESIGN
Many micr<strong>of</strong>inance programs provide financial services to the poor and lower
income people in urban and semi-urban areas <strong>of</strong> Laos. <strong>The</strong> Women and Community’s
Empowering Project (WCEP) is one <strong>of</strong> the many programs which launched micr<strong>of</strong>inance
programs in semi-urban areas <strong>of</strong> Laos. <strong>The</strong> project provides micr<strong>of</strong>inance through a
“savings group” in three districts in Vientiane, the capital <strong>of</strong> Laos. One <strong>of</strong> these three
cities was selected for this case study (see Appendix B for the overview <strong>of</strong> the project and
this case study). <strong>The</strong> reason this case study was conducted in the Vientiane area is due to
the fact that most <strong>of</strong> savings groups operate in and around the capital. It can be seen that
<strong>of</strong> the 357 savings groups throughout the country that are being monitored by seven
agencies 25 , a significant number <strong>of</strong> savings groups are running in Vientiane Capital City.
Most <strong>of</strong> the groups in Vientiane are operating with the technical support <strong>of</strong> two main
bilateral projects – “Small and Rural Development Project for Women” and “Capacity
Building Project for Women and Community,” – between the Central Lao Women’s
Union, the Foundation for Integrated Agricultural Management (FIAM) and Community
Organizations Development Institute: Thailand (CODI) (the postal survey conducted by
the Micr<strong>of</strong>inance Capacity Building and Research Project (2003), cited in Chaleunsinh,
25 Seven agencies consist <strong>of</strong> District Lao Women’s Union, District Lao Youth’s Union, District Planning
Office, District Social Welfare Office, District Finance Office, District Agriculture and Forestry Office and
branches <strong>of</strong> Agriculture Promotion Bank (Chaleunsinh, 2004).
46

2004:7). This section discusses how the survey conducted and provides a description <strong>of</strong>
the data used for this study.
5.1 Survey design 26
In September 2005, a survey was conducted <strong>of</strong> 251 households in six villages in
the Naxaithong district which have their own savings group. At the time <strong>of</strong> survey, three
villages had just established “new” savings groups. Two <strong>of</strong> these were three months old,
one was existent for just one month (see table 5.1). Other three villages had “old” savings
groups ranging in existence from just over one year to almost three years. <strong>The</strong>se were
selected from the list <strong>of</strong> savings groups provided by the project administers <strong>of</strong> projects.
Chose to the three “new” savings groups selected for this study under distance condition.
It means that the three old savings groups were not far from the three new savings groups
– under 15 Kilometer. Both members and nonmembers <strong>of</strong> the village savings group were
encouraged to interview. Households which had a least one person as a member <strong>of</strong> a
savings group were selected for interview and households which has no one joining the
savings group were also chosen for interview as shown in table 5.1. A random sampling
method was not employed because a quasi-census survey <strong>of</strong> member and nonmember
households was conducted. Both old and new savings groups included two kinds <strong>of</strong>
members:
26 <strong>The</strong> method for survey design was the one used by Coleman (1999) for conducting survey in 14 village
banks in Northeast Thailand as mentioned in section 3.2 <strong>of</strong> Chapter 3. However, the case <strong>of</strong> savings groups
Laos are mostly depended on internal fund which mobilized money from member deposit to lend out to
their members (see more explanation in Appendix B under section 2.2.1). <strong>The</strong>refore, hence could not
identified the new village savings groups to be as “Control Villages” and the old village savings groups as
“Treatment Villages”; however, there are “Control” group and “Treatment” group for both old and new
village savings groups as identified in this section, which are quite different from the Coleman’s case.
47

• Members who had gained some benefit from the savings groups by either
obtaining a credit or receiving a dividend from the savings group since
they had been members. <strong>The</strong>se members are classified as the Treatment
group.
• Members who were identified as Control group, are an inversion <strong>of</strong> the
treatment group.
Questionnaires were done in three sets. One was for households in the six villages, which
were interviewed for both members and nonmembers. <strong>The</strong> questionnaire conducted for
this study is a replication <strong>of</strong> Hulme and Mosley (1996). A similar study was conducted
for Saithani case <strong>of</strong> Lao micr<strong>of</strong>inance by Sichathongthip (2004) and it was also
reproduced by Mosley (2001) in the study <strong>of</strong> micr<strong>of</strong>inance and poverty in Bolivia.
However, the questionnaire was slightly modified in some parts to be more suitable and
relevant for the purposes <strong>of</strong> this study. <strong>The</strong> household questionnaire contained data on
household characteristics, assets, income, expenditure, deposits and borrowing. Some
data on household assets, income, expenditure were asked for the current period (at
survey date <strong>of</strong> September 2005) as well as recall period (5 years before conducting the
survey). In each household which have a member <strong>of</strong> savings group, an adult or head <strong>of</strong>
household, who knows almost everything about the household finances, was invited for
interview 27 . <strong>The</strong> same method was done for households which had no members <strong>of</strong> the
savings group.
27 Most interviewees were women (wives) because, in Lao custom, most wives know most about income
and expenses <strong>of</strong> their household. As Sheck-Sandbergen and Choulamany-Khampoui (1995: 91) noted about
women in Laos, “Women are generally good at financial management and accounting because <strong>of</strong> their
social and economic experience in managing the household finances and the local economy: they are the
sellers, buyers, traders, middle-women and entrepreneurs” (quoted by Kunkel and Seibel 1997:116).
48

Table 5.1: Sample size
No. Savings groups
Established
date <strong>of</strong>
savings
group
Number
<strong>of</strong>
household
in village
Number
<strong>of</strong>
household
in savings
group
49
Number <strong>of</strong>
nonmember
household
<strong>of</strong> savings
group Sample size by:
Member
in
treatment
group
Member in
control
group
Total
<strong>of</strong>
sample
size
Nonmember
I. Old savings group in:
1 Nakountay village 01-Oct-02 215 186 29 39 3 19 61
2 Huannamyene village 12-Jun-03 353 217 136 34 1 13 48
3 Dongluang village 01-Apr-04 184 75 a
109 16 0 8 24
Sub-total
II. New savings group in:
752 478 274 89 4 40 133
4 Phonekeo village 15-Jun-05 95 80 15 8 19 6 33
5 Phonesavanh village 01-Jun-05 123 56 67 19 9 17 45
6 Sisavard village 10-Aug-05 59 53 6 15 20 5 40
Sub-total 277 189 88 42 48 28 118
Grand total 1,029 667 362 131 52 68 251
Note: a) this figure is assumed equal to the number <strong>of</strong> families in the savings group.
Source: Author’s survey data, September 2005 and March 2006.

<strong>The</strong> second questionnaire was for collecting village information including
characteristics <strong>of</strong> the villages such as the presence <strong>of</strong> schools and prices <strong>of</strong> goods in each
village. This interview was conducted with the head <strong>of</strong> the village and members <strong>of</strong> the
group committee at the village level.
<strong>The</strong> last questionnaire was for general information on savings group. It included
questions regarding the number <strong>of</strong> the group members, sources <strong>of</strong> funds, group deposit
balances, deposits and credit methods, and methodology for solving bad debts (in case).
This interview was conducted in depth for the group committee at the village level.
In September 2005, the household surveys were administered by third year
students <strong>of</strong> the faculty <strong>of</strong> economics and business management from the National
University <strong>of</strong> Laos, together with the author. <strong>The</strong>se students were trained on how to
conduct a survey before interviewing the villagers. During survey period, the author was
the leader <strong>of</strong> the team and supervised all the students to ensure that correct information
was obtained. In addition, the author conducted the village surveys as well as in-depth
interviews with the group committees. Finally, the author conducted a follow up survey
in March 2006. This survey was targeted at collecting more data on village characteristics
and additional data on deposits, credits and other areas which the previous surveys had
not covered. Information for the last survey was mostly obtained from the group
committee and gathered by in-depth interviews with the chief <strong>of</strong> Lao Women Union at
Naxaithong district. In addition, secondary data from project documents (summary report,
progress report, savings group manual and other) were obtained from the project, Lao
Women Union at Municipality level and group committee.
50

5.2 Data description
<strong>The</strong> data used in this analysis is taken from the author’s survey in September 2005
and March 2006 as described above. All variables used for this study were drawn from
the studies <strong>of</strong> Coleman (1999 and 2002); Hulme and Mosley (1996); Pitt and Khandker
(1996 and 1998); and from Annex 1 <strong>of</strong> Gaile and Foster (1996). <strong>The</strong> main dependent
variables which represent proxies for the household’s outcomes are classified in three
main parts: household monthly expenditure, household yearly income and household
assets. Household monthly expenditure consists <strong>of</strong> two main categories: household
monthly food expenditure and household monthly non-food expenditure. Household
yearly income is classified to two main parts: household yearly self-employment income
and household yearly non self-employment income. Household assets comprise
household owned land and house assets, and household owned non-land and house assets.
Independent variables are mainly characteristics <strong>of</strong> households and villages but
the variable <strong>of</strong> most interest for this study is the number <strong>of</strong> months a person has been a
member <strong>of</strong> a savings group. Household characteristic variables are age, gender, education
level, household size, for example. Village characteristic variables consist <strong>of</strong> distance to
market, price <strong>of</strong> goods, having a school and being near a river, for example. More details
<strong>of</strong> data description are shown in the Appendix A: table 1-4.
51

CHAPTER 6
EMPIRICAL RESULTS
This chapter presents and interprets the results <strong>of</strong> estimating a single impact
equation (11) for a wide variety <strong>of</strong> household outcomes. <strong>The</strong>se outcomes include
household house asset value 28 , household annual self employment income from livestock,
household annual self employment income from agriculture, household monthly rental
expenditure, and household monthly educational expenditure 29 . Before moving to those
results, some particular innovations and specifications for estimation <strong>of</strong> the equation (11)
will be briefly described below.
As mentioned in chapter 4, this study applies the analytical method used by
Coleman (1999). Four innovations borrowed from his study have been incorporated in
the equation (11) for measuring the impact <strong>of</strong> the savings group:
1) It allows for use <strong>of</strong> village fixed effects 30 ;
2) It identifies a control group <strong>of</strong> members who have not benefited from the
savings groups and then surveys “treatment” members, “control” members
and nonmembers, thus allowing for the use <strong>of</strong> a member dummy variable
28
This value stands for value <strong>of</strong> house with land (not empty land or land for rice field and crop plant).
Barnes (1996:4) noted that house asset is one <strong>of</strong> the physical assets representing for the wealth <strong>of</strong>
household.
29
For other proxies representing for household outcomes as shown in Appendix A-table 1-4 are not
reported in this paper. According to the econometric exercise, those outcomes do not show significant
correlation with the regressor <strong>of</strong> interest (months <strong>of</strong> savings group membership).
30
This was used by Pitt and Khandker (1996&1998); Pitt et al. (1999); Khandker et al. (1998); Mckernan
(2002); Coleman (1999&2002).
52

to be a proxy for unobservable differences between members and
nonmembers;
3) It uses the value <strong>of</strong> land owned by household five years before this survey
was conducted to be a proxy for initial household wealth 31 ;
4) It uses the length <strong>of</strong> time that the savings group has been in a village to
obtain more precise impact measures.
In addition, this study uses four specifications from Coleman (1999) to illustrate
the importance <strong>of</strong> the four innovations to correct the bias resulting from self-selection and
endogenous program placement. <strong>The</strong> four specifications are described below:
1) First is the “correct” specification which use village fixed effects and
include a member dummy variable and variable for land value owned by
the household five year before this survey. As mentioned in chapter 4,
fixed effects estimates will be consistent (and unbiased if the dependent
variable is uncensored) but possibly inefficient.
2) Second is the specification which is identical to the first but this includes a
vector <strong>of</strong> specific village characteristics 32 that one might expect to
influence the dependent variables. If the order <strong>of</strong> program placement is
random, and the author was fortunate enough to choose all the relevant
31 Proportion <strong>of</strong> land value is 91.38 per cent <strong>of</strong> household wealth in this sample and land value is an
excellent proxy for wealth which is similar to Coleman’s (1999) case. This land value comes from the
value <strong>of</strong> empty land or land for agriculture or any land which is not for house building. According to
Coleman (1999), collecting data on land owned five years before the surveys is relatively easy. Because
land transactions tend to be large and important for a household, yet relatively infrequent, households can
easily recall land transactions made in the previous five years, and land owned five year earlier (and its
value) can be deduced. Of course, collecting similar data on other assets is not feasible without surveying
over several years.
32 <strong>The</strong>se village characteristics include dummy variable for that village has either a pig pond which has
water throughout the year or be near river; dummy variable for that village has paved road or near main
road (Km 13 road); dummy variable for that village has primary school grade5; a distance from village to
main market; a price <strong>of</strong> traditional chicken (Gailard) per Kg; and daily wage for construction.
53

observable village characteristics, then the estimation <strong>of</strong> nonfixed effects
(“village characteristics” or VC) model will be efficient and consistent
(and unbiased if the dependent variable is uncensored). If the order <strong>of</strong>
program placement is not random, or the author chose the wrong village
characteristics, then the estimation <strong>of</strong> this model will be biased and
inconsistent.
3) Third is “naive” estimates which ignore the first two innovations. For
example, the naïve estimates use specific village characteristics rather than
fixed effects, and they leave out the member dummy variable.
4) Fourth is “super-naive” estimates which are identical to the third but also
ignore the third innovation by leaving out the variables for land value
owned five years before the survey 33 .
In this study, assuming that dependent variables (household outcomes) are
uncensored, the method <strong>of</strong> ordinary least squares (OLS) is applied for estimating the
equation (11). In addition, the White test is applied to test for heteroskedasticity which
lead to unbiased estimators <strong>of</strong> OLS. <strong>The</strong>n, the generalized least squares estimation (GLS)
is used to correct heteroskedasticity, called weighted least squares (WLS) estimations
(Wooldridge, 2003: 268-276).
<strong>The</strong> regression result <strong>of</strong> the equation (11) which covered all above methods will
be discussed bellow under categories <strong>of</strong> the impact on household house assets, self-
employment activities and education.
33 According to Coleman (1999), the naïve models correspond to the models most commonly used to
measure impact, which ignore selection bias, endogenous program placement, and prior wealth.
54

6.1 <strong>Impact</strong> on household house asset
To measure the impact <strong>of</strong> savings group participation on household house assets,
all four specifications, mentioned above, were applied to the equation (11). Four sets <strong>of</strong>
regression results for impact <strong>of</strong> savings group on household house asset value,
corresponding to four specifications, are presented in Table 5 (Appendix A). Jointly
significant test <strong>of</strong> explanatory variables shows strongly significant evidence at the 1 per
cent level for all four methods regressions. All four specification regressions show that
the savings group has positive and significant impact on household asset value. It can be
seen that both fixed effect estimation and nonfixed effect estimation demonstrate reliable
explanatory <strong>of</strong> the monthly savings group membership with large and significance at the
5 per cent level (292,097; p = 0.0493) and at the 10 per cent level (292,097; p = 0.0522)
respectively. In contrast, the regression results <strong>of</strong> both naïve and super-naïve models
which ignore the first two and the first three innovations respectively, also show
significant impact with large proportion <strong>of</strong> coefficients <strong>of</strong> the month <strong>of</strong> savings group
member at the 1 per cent level as (335,021; p = 0.0066) and (358,260; p = 0.0041)
respectively. <strong>The</strong>se results are in contrast with those <strong>of</strong> the study <strong>of</strong> Coleman (1999 &
2002) that showed insignificant effects <strong>of</strong> village bank on house value.
Table 5 (Appendix A) shows that the coefficient <strong>of</strong> the member dummy variable
in the household house asset value is insignificant (1,481,225; p = 0.6816), indicating that
unobservable differences between members and nonmembers (such as entrepreneurship,
preferences, etc.) are <strong>of</strong> little consequence. This is consistent with the result <strong>of</strong> the
coefficient on member dummy variable and the house value in the fixed effect model
found by the Coleman (1999 & 2002). <strong>The</strong>refore, in the fixed effect model, there is no
55

correlation between the member dummy variable and the household house asset value.
Hence, there is no correlation <strong>of</strong> selection bias from unobservables.
In addition, the coefficient on the value <strong>of</strong> household owned land 5 years ago is
positive but it is statistically insignificant at least at the 10 per cent level against a two-
side alternative by employing the first three specifications. However, if we look at P-
value level, the fixed effects and nonfixed effects estimations show those coefficients as
(0.068769; p = 0.1683) and (0.068769; p = 0.1710) respectively, implying that those
coefficients are acceptable at the 16.83 per cent and 17.1 per cent level. It means that the
initial wealth <strong>of</strong> household could slightly increase the value <strong>of</strong> household house asset.
Nevertheless, the coefficient <strong>of</strong> female-owned land value 5 years ago is positive and
statistically significant at the 1 per cent level to women’s wealth in the study <strong>of</strong> Coleman
(1999).
One might expect the women who are the head <strong>of</strong> a household to participate more
actively in the savings group than women who are not the head <strong>of</strong> household. However,
the coefficient on this variable and the household house asset value in all four
specifications regressions is insignificant at least 10 per cent level. This may be explained
by the fact that 52 per cent <strong>of</strong> women who are the household head are either single,
widowed, divorced. This implies that there was no male adult in the households to help
with any household activities, particularly economic activities. According to Kunkel and
Seibel (1997:106), in Lao Loum group 34 , there is a division <strong>of</strong> labor, but not fixed,
34 As Kunkel and Seibel (1997:6) note, “<strong>The</strong> ethnography <strong>of</strong> Laos comprises the lowland Lao Loum who
grow paddy in the river valleys along the Mekong and its tributaries but also include the upland Tai Dam,
who are non-Buddhist; the upland Lao <strong>The</strong>ung many <strong>of</strong> which practice slash-and-burn agriculture in the
hills above the valleys and on the mountain slopes; and the mountain-top Lao Soung who have long
resisted government efforts at resettlement and the substitution <strong>of</strong> new for old cash crops.” In this case
study, all sample come from Lao Loum group.
56

etween the two sexes in households for doing household work, agriculture work,
livestock, collection <strong>of</strong> forest products and handicrafts. Many <strong>of</strong> roles <strong>of</strong> women and men
are overlap.
However, women who do or run family activities, especially economic activities,
(or are entrepreneur) have positive significant increase <strong>of</strong> household house asset value. It
can be seen that the coefficient <strong>of</strong> gender variable is positive and significant in relation to
household house asset value in all four estimation models. For example, there is positive
correlation between gender variable and household house asset value (14,021,407; p =
0.093), corresponding to the fixed effect estimation model (see Appendix A: Table 5).
Education level is highly significant in all four estimation models at the 5 per cent
level. For example, in case <strong>of</strong> fixed effect estimation model, the coefficient on maximum
education by individuals is large and significant (854,694; p = 0.0442). If education’s
years can be considered as a proxy for human capital, then this result likely stands for the
complementarities <strong>of</strong> human capital and physical capital in production (Coleman,
1999:120). Coleman (1999) also discovered the positively significant correlation between
education level for both females and males and women’s wealth but they were shown in
case <strong>of</strong> the “super-naive” estimation model only.
Age <strong>of</strong> the individuals is totally positive significance at the 5 per cent level,
corresponding to the four specification models. In case <strong>of</strong> the fixed effect estimation
model, for example, the coefficient on the age <strong>of</strong> individuals is largely significant
(312,418; p = 0.023). It could be interpreted that the older <strong>of</strong> the individuals in the
households who participate in the savings group, the more significant is the increase in
their household house asset value. This result was also found by Coleman (1999). He
57

evealed that the coefficient on age-sex categories with women age 40 to 59 and women
age 60 and over were positive and significant at the 5 per cent and 1 per cent levels
respectively to women’s wealth. However, his result was shown by the super-naïve
estimation model only.
People, who have run their business or family activities for a long time, have been
increasing their household wealth. More experience in running the business or family
activities, may lead to effectiveness and efficiency in production by the accumulation <strong>of</strong>
knowledge and experience in that field. It can be seen that the coefficient on number <strong>of</strong>
months doing business are significant at the 1 per cent level to household house asset
value, corresponding to the four estimation models (see Appendix A: Table 5).
It is not surprising that the influence <strong>of</strong> the number <strong>of</strong> relatives in the village on
household house asset value is positive and significant at the 1 per cent level for the four
estimation models. For example, the coefficient on the number <strong>of</strong> relatives in the village
to the household house asset value is large and significant (1,552,376; p = 0.0001), in
case <strong>of</strong> the fixed effects estimation model. <strong>The</strong> number <strong>of</strong> relatives represents village
relationships that would help each other when facing serious situations including self-
employment activities, or to share happiness among them.
Having a chief <strong>of</strong> a savings group committee or member <strong>of</strong> the group committee
in household has a large and statistically significant impact on household house asset
value at the 1 per cent level for the four estimation models. It is statistically significant
(12,703,787; p = 0.0026) in case <strong>of</strong> the fixed effect estimation model, for example.
Similarly, Coleman (1999) found large positive correlation between the independent
58

variable <strong>of</strong> having a village chief or assistant chief in the household and women’s wealth
for the first three specifications.
One might expect that living in the village that has paved roads or been a near
main road may improve household status more than living in the village does not have
these advantages. <strong>The</strong> nonfixed effects, naïve and super-naïve models were negative
effect <strong>of</strong> a variable for village characteristic – village has paved road or been near main
road – on household house asset value with statistically significant level at 5 per cent as
shown in Table 5 (Appendix A). This result is somewhat anomalous and difficult to
explain. However, overall, the empirical results show that participation <strong>of</strong> savings group
has been increasing household asset value by employing all four estimation models.
6.2 <strong>Impact</strong> on self-employment activities
<strong>The</strong> effects <strong>of</strong> savings group on self-employment activities are explained by three
impact categories: household annual self employment income from livestock, household
annual self employment income from agriculture, and household monthly rental
expenditure 35 .
6.2.1 <strong>Impact</strong>s on household annual self employment income from livestock
By regressing the equation (11) with the four estimation models, the results <strong>of</strong>
regressions for the effects <strong>of</strong> savings group on household yearly self employment income
from livestock are shown in Table 6 (Appendix A). <strong>The</strong> fixed effect, nonfixed effects,
naïve and super-naive estimates are all positive for the coefficient on the month <strong>of</strong>
35 Most <strong>of</strong> rental expenditure comes from rental <strong>of</strong> self-employment activities such as rental <strong>of</strong> rice field for
planting rice, rental on shop for trading and rental on garage for vehicles fixing service.
59

savings group membership, implying that savings group participation by a member <strong>of</strong> the
household increases the yearly income <strong>of</strong> household livestock production. Only two
estimation models, fixed effects and nonfixed effects, produce statistically significant for
that coefficient at the 10 per cent level, (19,962.47; p = 0.0606) and (19,962.47; p =
0.0640) respectively. In contrast, those results were not significant in the study by
Coleman (1999) <strong>of</strong> the village bank in Northeast Thailand.
<strong>The</strong> size <strong>of</strong> household also has a positive and significant effect on household
annual self employment income from livestock. Table 6 (Appendix A) showed all
positive relationship <strong>of</strong> the coefficient <strong>of</strong> household size to the yearly income <strong>of</strong>
household from livestock production by employing all four estimation models, and these
coefficients are statistically significant at the 1 per cent level. It implies that more
member in the household could increase the annual income <strong>of</strong> household from livestock
production. For example, in case <strong>of</strong> the fixed effects estimation model, increase in
household size by one person could push up the household yearly income from livestock
production by 318,013 Kip.
Household which had a chief or member <strong>of</strong> the savings group committee had a
large increase in the household yearly income from livestock production. It can be seen
that all four estimation models result in the positive coefficients <strong>of</strong> the variable for
household which has chief or member <strong>of</strong> savings group committee, which are statistically
significant at the 1 per cent level as shown in Table 6 (Appendix A).
In addition, Table 6 (Appendix A) shows that the fixed effects estimates produce
positive coefficients on the number <strong>of</strong> civil servants in the household which is significant
at the 10 per cent level (413,843.2; p = 0.0979).
60

6.2.2 <strong>Impact</strong>s on household annual self employment income from
agriculture
<strong>The</strong> estimation <strong>of</strong> savings group impact on household yearly income from
agriculture production is also done employing equation (11) with the four specifications.
Table 7 (Appendix A) shows that all four estimation models result in significant negative
correlation between the month <strong>of</strong> savings group membership and the yearly income <strong>of</strong>
household from agriculture production. <strong>The</strong> coefficients <strong>of</strong> the month <strong>of</strong> savings group
membership in the four models are significant at the 1 per cent level. However, the study
<strong>of</strong> Coleman (1999) did not find any significant relationship between the month <strong>of</strong> village
bank membership and household agriculture production, corresponding to the four
models.
Although there is negative relation between the month <strong>of</strong> savings group
membership and the household income from agricultural self-employment activities, this
result is still ambiguous. It is difficult to conclude that the participation <strong>of</strong> savings group
decreases the household income from the agricultural self-employment activities, because
such a negative relationship <strong>of</strong> the coefficient may have other reasons behind it. One
possibility accounting for this negative relationship is prices <strong>of</strong> agricultural products
which fluctuate throughout the year. Another possibility is related to underestimated real
household income from agricultural self-employment activities. This can be explained as
data <strong>of</strong> the yearly household collecting only from a part <strong>of</strong> agricultural products sold in
the markets that are excludes the value <strong>of</strong> agriculture products for the owner’s household
consumption and those reserved for reproducing (cultivating). <strong>The</strong> annual household
income from the agricultural self-employment activities shown in the regressions,
61

therefore, is lower than the real value <strong>of</strong> the agriculture products. Some sample
households had a zero value <strong>of</strong> household income from the agriculture products because
<strong>of</strong> the non-sale <strong>of</strong> agricultural products (only used for household consumption), even
though they produced those products. This was particularly the case for households who
rented the rice fields for plantation 36 .
Member dummy variable is positive and significant in relation to the yearly
income from household agricultural production. Its coefficient is large and significant
(286,830.8; p = 0.0808) for the fixed effects estimation model and (286,830.8; p =
0.0847) for the nonfixed effects estimation model. This implies that unobservable
differences between member and nonmembers have large consequences. In other word,
being member <strong>of</strong> savings group can increase the yearly income <strong>of</strong> household agricultural
production. In contrast, in the study by Coleman (1999), there is an insignificant relation
between member dummy variable and household agricultural production.
Having a female household head has a negative and significant relationship to the
yearly income <strong>of</strong> household agricultural production, corresponding to the four estimation
models. Its coefficient is statistically significant at the 1 per cent level for all cases <strong>of</strong> the
estimations as shown in Table 7 (Appendix A). This result shows that household head
who is not female crucially increase the annual income <strong>of</strong> household agricultural
production. This may be true because, in rice plantation, heavy tasks like ploughing or
harrowing are mainly men’s work, while it is the women who select the seed, uproot
seedlings, transplant and weed (Kunkel and Seibel, 1997:106).
36 Most <strong>of</strong> them did not sell their rice, only for own consumption, after payment <strong>of</strong> the rental fee to landlord
by some amount <strong>of</strong> rice, regarding to the agreement between landowner and leasee.
62

<strong>The</strong> size <strong>of</strong> the household and the number <strong>of</strong> months doing business has a positive
and significant relationship to the yearly income <strong>of</strong> household agricultural production as
can be seen from the results <strong>of</strong> all four estimation models. <strong>The</strong>ir coefficients are
statistically significant at the 1 per cent level as shown in Table 7 (Appendix A).
<strong>The</strong> number <strong>of</strong> generations that the family has been in the village has a positive
impact on the yearly income <strong>of</strong> household agricultural production. Its coefficient is
significant at the 5 per cent level. It means that more number <strong>of</strong> generations’ family in
village can help agriculture work to push more products, and then household income may
increase.
It is not surprising that the value <strong>of</strong> household owned land 5 years ago or initial
wealth has positive effect on the yearly income <strong>of</strong> household agricultural production
because land is essential capital for agricultural plantation.
Focus on the variable <strong>of</strong> village characteristic, the distance from the village to
main market has a negative effect on the annual income <strong>of</strong> household agricultural
production. This is true in reality because if goods, especially agricultural products, are
located far away from the market (city market), their price are lower than the ones sold in
the main market.
6.2.3 <strong>Impact</strong>s on household monthly rental expenditure
Table 8 (Appendix A) show the regression results <strong>of</strong> the equation (11) on
household monthly rental expenditure corresponding to the fixed effects, nonfixed effects,
naïve and super-naïve models. All four models indicate the positive correlation between
the month <strong>of</strong> savings group membership and the household monthly rental expenditure.
63

Its coefficient is significant at the 5 per cent level for the first three specifications and at
the 1 per cent level for the super-naïve model. This result could be interpreted as showing
that the participation in a savings group leads to increase in household monthly rental
expenditure. In other words, the savings group participation supports the persons who
have no physical assets to run household self-employment activities such as rice
plantation and opening a shop for trading.
Education level <strong>of</strong> individuals in the household also has a positive and significant
impact on household monthly rental expenditure. Its coefficient is statistically significant
at the 5 per cent level for all four estimation models. <strong>The</strong> result could be explained by the
fact that people who achieve high education level could be more confident to do their
business in new ways walkout investing their own capital (especially physical asset).<strong>The</strong>y
conduct their business by renting or leasing assets.
6.3 <strong>Impact</strong> on education
<strong>The</strong> impact <strong>of</strong> savings group on education can be seen from household monthly
educational expenditure. Table 9 (Appendix A) shows the impact <strong>of</strong> savings group on
household monthly educational expenditure by regressing the equation (11) with respect
to the four specifications. <strong>The</strong> month <strong>of</strong> savings group membership has positive relation
to the household monthly educational expenditure. Its coefficient is significant at the 5
per cent level for the four estimation models. This relationship indicates that the
participation in a savings group can increase the monthly educational expenditure <strong>of</strong>
household. It can be seen that being member <strong>of</strong> savings group for one more month could
raise the household monthly education expenditure by 5,670 Kip, in case <strong>of</strong> the fixed
64

effects model, for example. One can say that the people joining savings group can
support for their children’s schooling. However, this result could not be found in the
study by Coleman (1999).
Turning to village characteristic variables, the village which has a primary school
until grade 5 has a positively significant effect on the household monthly education
expenses. Table 9 (Appendix A) shows that the coefficient <strong>of</strong> this variable with
statistically significant level at the 10 per cent, corresponding to the nonfixed effects,
naïve and super naïve models. This result implies that a village which has a primary
school until grade 5 can support more children to continue their schooling until higher
education level.
To conclude, there are many positive impacts <strong>of</strong> savings group participation at
household level. This is seen by applying the fixed effects, nonfixed effects, naïve and
super-naïve estimation models. <strong>The</strong>se household impacts are shown in three categories,
namely household assets, household self-employment activities and education. However,
some theoretically fundamental relationship results might present the adverse effects
from such as the negative relationship between the months <strong>of</strong> savings group membership
and the household income from agricultural self-employment activities. This is more
likely related to data collection issues.
However, this paper has some limitations. First, even though this applied the
generalized least squares estimation (GLS) to correct heteroskedasticity, called weighted
least squares (WLS) estimations, there may be correlation among independent variables.
This may lead to biased estimations <strong>of</strong> the impact on household outcomes. Second, there
may be endogenous issue in variables because this study did not cover a test for the
65

endogeneity. Third, this study did not take into account the spillover effects to
nonmembers in the six villages. Fourth, the values <strong>of</strong> data on household assets, incomes
and expenditures are nominal. Fifth, the use <strong>of</strong> cross-section data to evaluate the impact
<strong>of</strong> the savings group, may show only the short term effect and may not predict the long
term impact <strong>of</strong> savings group which may be possible to ascertain by using panel data.
This is a suggested area for further research.
66

CHAPTER 7
CONCLUSION
Micr<strong>of</strong>inance affects household welfare or outcome through a number <strong>of</strong> channels.
Research studies have showed that micr<strong>of</strong>inance has significant impact on assets, income,
expenditure, educational status, health as well as gender empowerment. In this paper, the
effects on household outcomes by the participation in the savings group in a semi-urban
area <strong>of</strong> Laos were estimated by application <strong>of</strong> the survey design and research
methodology <strong>of</strong> Coleman (1999) taking into account bias for self-selection and
endogenous program placement which was not corrected in the previous studies relating
to Laos. <strong>The</strong> survey sample included members and nonmembers in six villages that have
own savings groups. <strong>The</strong>se savings group were in operation for various lengths <strong>of</strong> time.
Members who experienced benefits from joining the savings group by either obtaining a
credit or receiving a dividend are called the “treatment” group, and those who have not
benefited from the groups are called the “control” group. All members <strong>of</strong> the “control”
group were relatively new members with an average membership <strong>of</strong> 2.2 months. In order
to demonstrate the importance <strong>of</strong> selection bias correction, the paper compared the results
<strong>of</strong> the “correct” empirical specification to those <strong>of</strong> the three other specifications which do
not take account for some or all <strong>of</strong> the bias.
This paper provides estimates <strong>of</strong> the influence <strong>of</strong> participation in the savings
group on household house value, household livestock production income, household
agriculture production income, household rental expenses, and household education
67

expenses by applying four empirical specifications. <strong>The</strong> empirical results illustrate that
the participation in the savings groups has large positive and significant effects on all <strong>of</strong>
these outcomes, except household agriculture production income. This may be explained
by the fluctuation <strong>of</strong> seasonal agricultural prices, and under reporting <strong>of</strong> the real value <strong>of</strong>
household agricultural income. This is because data collected pertain only to income
from agriculture products which were sold in the markets, not those used for self-
consumption.
<strong>The</strong> results <strong>of</strong> this study differ from those <strong>of</strong> Coleman (1999). He did not find
significant impact <strong>of</strong> village bank loan on household outcomes once corrections were
made for self-selection and endogenous program placement. Only the more “naive”
specifications (i.e. without these corrections) significantly overestimate program impact.
This difference in the results between this study and Coleman’s (1999) study may come
from the different contexts <strong>of</strong> these two cases, even though these two cases follow the
same village bank model (see Appendix B: section 2.2.1). Regarding to Coleman’s
results, he provided some explanations as:
Thailand…is a relatively wealthy developing country, with annual GDP growth at
close to 10% for the past two decades, and many villagers already have access to
low-interest credit from financial institutions such as the BAAC 37 . In fact, the
average wealth <strong>of</strong> survey households was 529,586 baht, and average household
low-interest debt, excluding village bank debt, was 31,330 baht, <strong>of</strong> which 9,342
baht was held by women. In such an environment, it should not be surprising that
loans <strong>of</strong> 1,500 to 7,500 baht would have a negligible impact. Indeed, a common
complaint <strong>of</strong> women surveyed was that the size <strong>of</strong> village bank loans was far too
small for them to be productive. <strong>The</strong> village bank model <strong>of</strong> group lending, as
practiced throughout the world, sometimes takes a “one size fits all” approach,
based on a first loan <strong>of</strong> US$50 and a maximum <strong>of</strong> US$300. <strong>The</strong>se limits are in
fact commonly used in much poorer countries in Africa, where they represent a
37
BAAC stands for the semi-statal Bank for Agriculture and Agricultural Cooperatives (Coleman, 1999:
111).
68

lot <strong>of</strong> money to a village household. <strong>The</strong>y are also the limits used in Northeast
Thai villages, and they are arguably too low. On the other hand, the significant
number <strong>of</strong> members who had worked themselves into debt by borrowing without
having identified a productive activity to invest in points to the need for project
field staff to redouble efforts to stress the need to invest the loans productively
(Coleman, 1999:133).
In contrast to Thailand, Laos is one <strong>of</strong> the poorest countries in East Asia with an
estimated per capita income <strong>of</strong> US$ 390 in 2004. Real GDP grew by an annual average
rate <strong>of</strong> 6.3 per cent in the 1990s and was 7 per cent in 2005 (World Bank Vientiane
Office, 2006). In terms <strong>of</strong> access to financial services, about one million economically
active people potentially require access to formal or semi-formal micr<strong>of</strong>inance services.
However, almost three quarters cannot reach them. Approximately 300,000 people
recently accessed loan and savings services. Only 21 per cent have access to microcredit
from the formal sector, 33 per cent are dependant on the semi-formal sector and project
initiatives and the rest 46 per cent are obtaining financial service from the informal sector.
In this survey sample, only 6 per cent and 4 per cent <strong>of</strong> those sample surveyed access
could credit at low interest rates and make deposits with APB respectively (Appendix B:
Table 5-6). <strong>The</strong>se may be factors that lead to the villagers’ need for a credit for
productive purpose from the savings group. This may, therefore, lead to have a
significant impact on their household outcomes.
This paper also has some limitations. First, even though this applied the
generalized least squares estimation (GLS) to correct heteroskedasticity, called weighted
least squares (WLS) estimations, there may be correlation among independent variables.
This may lead to biased estimations <strong>of</strong> the impact on household outcomes. Second, there
may be endogenous issue in variables because this study did not cover a test for the
69

endogeneity. Third, this study did not take into account the spillover effects to
nonmembers in the six villages. Fourth, the values <strong>of</strong> data on household assets, incomes
and expenditures are nominal. Fifth, the use <strong>of</strong> cross-section data to evaluate the impact
<strong>of</strong> savings group, may show only the short term effect and may not predict the long term
impact <strong>of</strong> savings group which may be possible to ascertain by using panel data.
<strong>The</strong> use <strong>of</strong> panel data for conducting a follow up survey for an impact analysis is
recommended area for further study. This will help in dealing with problems such
spillover effects and endogeneity <strong>of</strong> explanatory variables.
However, this paper’s findings have several important implications. Firstly, the
large positive impact savings group has on household asset suggest that micr<strong>of</strong>inance
programs may improve household status in term <strong>of</strong> wealth. Secondly, the positive
significant effects <strong>of</strong> the savings group on productivity, particularly livestock and
agriculture in terms <strong>of</strong> rental on rice fields, suggest that the savings group program may
be a viable strategy for the poverty eradication. This is consistent with the National
Growth and Poverty Eradication Strategy (NGPES) (2004: 65) <strong>of</strong> the Government <strong>of</strong> Lao
PDR which recognizes the importance <strong>of</strong> micr<strong>of</strong>inance and has placed it as the one <strong>of</strong> the
high priority projects for the agriculture and forestry development plan. Thirdly, the great
positive influence <strong>of</strong> the savings group program on household education expenses
suggests that micr<strong>of</strong>inance program may be one viable strategy to reach the millennium
development goals in term <strong>of</strong> education.
70

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£¤¡¾−¦‰¤À¦ó´£¸¾´À¢˜´Á¢¤¦¿ìñ®Á´È¨ò¤Á콧÷´§ö−, £øÈ´õ¡È¼¸¡ñ®¡÷
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Project between Lao Women’s Union and Community Organizations
Development Institute (Thailand)]
£¤¡¾−¦‰¤À¦ó´£¸¾´À¢˜´Á¢¤¦¿ìñ®Á´È¨ò¤Á콧÷´§ö−, 쾨¤¾−¡¾−¥ñ©ª˜¤¯½ªò®ñ©Â£¤
¡¾−¦‰¤À¦ó´£¸¾´À¢˜´Á¢¤¦¿ìñ®Á´È¨ò¤Á콧÷´§ö−: ª÷ì¾ 2002 À«ò¤¡ñ−¨¾ 2005,
(−½£º−͸¤¸¼¤¥ñ−, 2005), £¤¡¾−»È¸´´õì½¹¸È¾¤¦ø−¡¾−¦½¹½²ñ−Á´È¨ò¤ Áì½
¦½«¾®ñ−²ñ©ê½−¾§÷´§ö− (¯½Àê©Äê). [Women and Community’s Empowering
Project, Report on the Implementation <strong>of</strong> Women and Community’s Empowering
Project: October 2002 - September 2005, Vientiane, 2005, Cooperative Project
between Lao Women’s Union and Community Organizations Development
Institute (Thailand)]
¦ø−¡¾¤¦½¹½²ñ−Á´È¨ò¤ì¾¸ ¦.¯.¯ 쾸 Áì½ ¦½«¾®ñ−²ñ©ê½−¾§÷´§ö− (¯½Àê©Äê),
¡÷ È´êɺ−À¤ò− ¡ñ®¡¾−Á¡ÉÄ¢¯ñ−¹¾£¸¾´ê÷¡¨¾¡: 쾨¤¾−¡¾−¦‰¤À¦ó´ Áì½¢½¹¨¾¨
¡÷ú´êɺ−À¤ò− Ã− ¦.¯.¯ 쾸, (−½£º−͸¤¸¼¤¥ñ−, ®Ò´ó¸ñ−êó), ¦ø−¡¾−¦½¹½²ñ−
78

Á´È¨ò¤ ¦.¯.¯ 쾸 Áì½ ¦½«¾®ñ−²ñ©ê½−¾§÷´§ö− (¯½Àê©Äê). [Lao Women’s
Union: Lao PDR and Community Organizations Development Institute: Thailand,
Savings Group and Poverty Reduction: Study on Promotion and Expansion <strong>of</strong>
Savings Group in Lao PDR, Vientiane, n.d, Lao Women’s Union: Lao PDR and
Community Organizations Development Institute: Thailand, (unpublished)]
79

Appendix A
Table 1: Descriptive statistics for variables <strong>of</strong> whole sample size
Variable descriptions
Dependent variables (in Kip unless stated
otherwise):
Mean Median Maximum Minimum Std. Dev. Observations
Household monthly food expenditure 417,481 300,000 2,100,000 20,000 329,570 251
Households monthly rental expenditure 10,267 0.00 1,200,000 0 84,549 251
Household monthly transportation fee
210,553 45,000 3,600,000 0 432,975 251
expenditure
Household monthly educational expenditure 167,807 55,000 3,000,000 0 333,357 251
Household monthly clothing expenditure 136,084 58,333 1,200,000 0 184,019 251
Household monthly medical expenditure 105,106 30,000 2,000,000 0 234,065 251
Household monthly expenditure on
household utensils
95,754 25,000 2,400,000 0 222,659 251
Household monthly other major expenditure 91,748 25,000 3,500,000 0 316,805 251
Household monthly total non-food
817,319 480,000 6,080,000 12,000 998,698 251
expenditure
Household monthly total expenditure 1,234,800 885,000 7,000,000 67,000 1,152,860 251
Value <strong>of</strong> household owned house 33,964,779 20,000,000 287,000,000 0 45,449,824 251
Value <strong>of</strong> household owned land 498,000,000 20,000,000 111,000,000,000 0 7,010,000,000 251
Value <strong>of</strong> household owned land and house 532,000,000 46,000,000 111,000,000,000 0 7,010,000,000 251
Value <strong>of</strong> household owned agriculture asset 2,524,714 0 70,000,000 0 7,606,691 251
Value <strong>of</strong> household owned livestock asset 5,549,922 400,000 220,000,000 0 19,242,134 251
Value <strong>of</strong> household owned other enterprise
asset
459,721 0 20,000,000 0 2,228,895 251
Value <strong>of</strong> household owned savings at house 1,242,315 200,000 107,000,000 0 7,490,676 251
Value <strong>of</strong> household owned other asset 3,458,696 0 190,000,000 0 14,566,528 251
Value <strong>of</strong> household owned non-land and
house asset
13,235,368 4,100,000 338,000,000 0 33,072,800 251
Value <strong>of</strong> household owned total asset 545,000,000 55,270,000 111,000,000,000 300,000 7,020,000,000 251
80

Appendix A
Table 1: Descriptive statistics for variables <strong>of</strong> whole sample size (continued)
Variable descriptions
Dependent variables (in Kip unless stated
otherwise):
Mean Median Maximum Minimum Std. Dev. Observations
Household yearly self employment income
from agriculture
2,419,044 1,000,000 81,500,000 0 5,979,001 251
Household yearly self employment income
from livestock
1,578,175 300,000 30,000,000 0 3,486,485 251
Household yearly self employment income
from handicraft & textile
1,888,122 100,000 25,400,000 0 3,243,976 251
Household yearly self employment income
from trading
3,376,932 0 194,000,000 0 18,386,628 251
Household yearly self employment income
from repairing& fixing service
379,482 0 21,900,000 0 2,459,819 251
Household yearly self employment income 1,648,562 0 190,000,000 0 15,460,301 251
from rice mill & construction
Household yearly self employment income
from vehicle service
301,793 0 40,000,000 0 2,944,454 251
Household total yearly self employment
income
11,592,111 5,475,000 196,000,000 0 25,294,651 251
Household yearly wage & salary income 2,205,797 0 24,000,000 0 3,799,600 251
Household yearly income from remittance 407,822 0 19,068,600 0 1,704,379 251
Household yearly rental income 79,084 0 10,800,000 0 738,221 251
Household yearly monetary items income 887,139 0 213,000,000 0 13,461,641 251
Household yearly other income 21,514 0 3,600,000 0 253,644 251
Household total yearly non-self employment
income
3,601,356 500,000 213,000,000 0 13,979,828 251
Household yearly total income 15,193,467 8,200,000 213,000,000 380,000 28,634,965 251
81

Appendix A
Table 1: Descriptive statistics for variables <strong>of</strong> whole sample size (continued)
Variable descriptions Mean Median Maximum Minimum Std. Dev. Observations
Independent variables:
Months as VSG member 8.08 1 36 0 12 251
Does household has a VSG member?(0/1) 0.73 1 1 0 0.45 251
Value <strong>of</strong> household owned land 5 years ago 29,042,629 10,000,000 911,000,000 0 82,223,525 251
Sex <strong>of</strong> household head (female=1) 0.0996 0.00 1 0 0.30 251
Gender <strong>of</strong> respondents (entrepreneur)
(female=1)
0.888 1 1 0 0.32 251
Maximum education <strong>of</strong> respondents
4.90 5 19 0 3 251
(entrepreneur) (years)
Household size 5.51 5 12 1 2 251
Age <strong>of</strong> respondents (entrepreneur) (years) 41 41 75 18 12 251
Number <strong>of</strong> months doing business 161 120 813 1 146 251
Number <strong>of</strong> Generations <strong>of</strong> family in village 0.502 0 6 0 1 251
Number <strong>of</strong> relatives in village 2.42 1 40 0 4 251
Are you member or head <strong>of</strong> the group
0.064 0 1 0 0.24 251
committee? (0/1)
Number <strong>of</strong> civil servant in household 0.267 0 4 0 0.56 251
Number <strong>of</strong> wage employment in HH 1.08 1 5 0 1.19 251
Number <strong>of</strong> school age in HH 1.57 2 6 0 1.26 251
Number <strong>of</strong> dependent on your income in HH 2.64 2 10 0 1.67 251
Village is near river (0/1) 0.434 0 1 0 0.50 251
Village has pig pond which has water
throughout the year (0/1)
0.375 0 1 0 0.48 251
Village has either pig pond which has water
throughout the year or be near river (0/1)
0.566 1 1 0 0.50 251
Village is located in district capital (0/1) 0.096 0 1 0 0.29 251
82

Appendix A
Table 1: Descriptive statistics for variables <strong>of</strong> whole sample size (continued)
Variable descriptions
Dependent variables (in Kip unless stated
otherwise):
Mean Median Maximum Minimum Std. Dev. Observations
Does village have paved road or near main
road (Km 13 road)? (0/1)
0.287 0 1 0 0.45 251
Village has irrigation (0/1) 0.191 0 1 0 0.39 251
Does village have school until secondary
level?(0/1)
0.096 0 1 0 0.29 251
Does village have primary school up to
0.661 1 1 0 0.47 251
grade5? (0/1)
Distance from village to main markets 22 20 33 15 6.84 251
Price <strong>of</strong> one cattle 1,610,359 1,500,000 2,000,000 1,300,000 233,007 251
Price <strong>of</strong> one buffalo 3,260,956 3,250,000 4,000,000 2,500,000 524,051 251
Price <strong>of</strong> pig per Kg 14,596 11,667 25,000 10,000 5,097 251
Price <strong>of</strong> duck per Kg 14,572 15,000 15,000 13,500 617 251
Price <strong>of</strong> traditional chicken (Gailard) per Kg 18,857 19,000 20,000 18,000 896 251
Daily wage for harvesting rice 18,307 20,000 30,000 0 9,339 251
Daily wage for planting rice 27,875 35,000 50,000 0 18,081 251
Daily wage for construction 24,243 25,000 27,500 20,000 2,043 251
Source: Author’s survey data, September 2005 and March 2006.
83

Appendix A
Table 2: Descriptive statistics for variables by treatment group
Variable descriptions
Dependent variables (in Kip unless stated
otherwise):
Mean Median Maximum Minimum Std. Dev. Observations
Household monthly food expenditure 429,160 300,000 2,100,000 20,000 356,530 131
Households monthly rental expenditure 2,061 0 210,000 0 18,594 131
Household monthly transportation fee
209,551 66,000 2,790,000 0 376,340 131
expenditure
Household monthly educational expenditure 201,508 70,000 2,700,000 0 348,466 131
Household monthly clothing expenditure 161,304 100,000 1,000,000 0 194,430 131
Household monthly medical expenditure 119,523 30,000 2,000,000 0 246,709 131
Household monthly expenditure on
household utensils
98,393 25,000 2,400,000 0 241,647 131
Household monthly other major expenditure 119,380 25,000 3,500,000 0 421,754 131
Household monthly total non-food
911,720 615,000 5,900,000 44,500 1,036,465 131
expenditure
Household monthly total expenditure 1,340,880 1,000,000 7,000,000 67,000 1,197,825 131
Value <strong>of</strong> household owned house 36,757,225 25,000,000 267,000,000 500,000 43,616,512 131
Value <strong>of</strong> household owned land 43,986,808 25,000,000 500,000,000 0 66,102,737 131
Value <strong>of</strong> household owned land and house 80,744,034 56,303,000 700,000,000 500,000 94,659,232 131
Value <strong>of</strong> household owned agriculture asset 2,702,595 0 60,000,000 0 7,597,104 131
Value <strong>of</strong> household owned livestock asset 4,906,851 400,000 150,000,000 0 16,750,015 131
Value <strong>of</strong> household owned other enterprise
asset
412,977 0 18,000,000 0 2,081,643 131
Value <strong>of</strong> household owned savings at house 1,078,321 200,000 50,000,000 0 4,602,353 131
Value <strong>of</strong> household owned other asset 4,311,510 0 190,000,000 0 19,478,105 131
Value <strong>of</strong> household owned non-land and
house asset
13,412,254 3,820,000 338,000,000 0 36,561,896 131
Value <strong>of</strong> household owned total asset 94,156,287 62,100,000 720,000,000 1,080,000 111,000,000 131
84

Appendix A
Table 2: Descriptive statistics for variables by treatment group (continued)
Variable descriptions
Dependent variables (in Kip unless stated
otherwise):
Mean Median Maximum Minimum Std. Dev. Observations
Household yearly self employment income
from agriculture
2,537,023 700,000 81,500,000 0 7,571,050 131
Household yearly self employment income
from livestock
1,466,794 500,000 15,000,000 0 2,435,613 131
Household yearly self employment income
from handicraft & textile
2,134,983 500,000 25,400,000 0 3,686,305 131
Household yearly self employment income
from trading
2,664,695 0 194,000,000 0 17,334,078 131
Household yearly self employment income
from repairing& fixing service
552,672 0 21,900,000 0 2,944,155 131
Household yearly self employment income 1,703,733 0 150,000,000 0 13,617,852 131
from rice mill & construction
Household yearly self employment income
from vehicle service
427,481 0 40,000,000 0 3,754,035 131
Household total yearly self employment
income
11,487,380 6,160,000 194,000,000 0 23,500,748 131
Household yearly wage & salary income 2,327,977 0 19,720,000 0 3,842,135 131
Household yearly income on remittance 448,352 0 19,068,600 0 1,945,576 131
Household yearly rental income 102,290 0 10,800,000 0 954,863 131
Household yearly monetary items income 45,802 0 6,000,000 0 524,222 131
Household yearly other income 27,481 0 3,600,000 0 314,534 131
Household total yearly non-self employment
income
2,951,902 400,000 26,068,600 0 4,678,700 131
Household yearly total income 14,439,282 8,520,000 194,000,000 1,000,000 23,505,613 131
85

Appendix A
Table 2: Descriptive statistics for variables by treatment group (continued)
Variable descriptions
Independent variables:
Mean Median Maximum Minimum Std. Dev. Observations
Months as VSG member 15 15 36 1 12.51 131
Does household has a VSG member?(0/1) 1 1 1 1 0.00 131
Value <strong>of</strong> household owned land 5 years ago 25,345,857 12,000,000 300,000,000 0 38,953,728 131
Sex <strong>of</strong> household head (female=1) 0.09 0.00 1.00 0.00 0.29 131
Gender <strong>of</strong> respondents (entrepreneur)
(female=1)
0.92 1.00 1.00 0.00 0.27 131
Maximum education <strong>of</strong> respondents
4.69 5.00 14.00 0.00 3.41 131
(entrepreneur) (years)
Household size 5.65 5.00 11.00 2.00 1.98 131
Age <strong>of</strong> respondents (entrepreneur) (years) 42.29 43.00 68.00 21.00 10.74 131
Number <strong>of</strong> months doing business 155.19 120.00 531.00 1.00 137.00 131
Number <strong>of</strong> Generations <strong>of</strong> family in village 0.50 0.00 6.00 0.00 1.01 131
Number <strong>of</strong> relatives in village 2.27 2.00 12.00 0.00 2.49 131
Are you member or head <strong>of</strong> the group
0.08 0.00 1.00 0.00 0.28 131
committee? (0/1)
Number <strong>of</strong> civil servant in household 0.28 0.00 2.00 0.00 0.54 131
Number <strong>of</strong> wage employment in HH 1.18 1.00 5.00 0.00 1.29 131
Number <strong>of</strong> school age in HH 1.67 2.00 5.00 0.00 1.17 131
Number <strong>of</strong> dependent on your income in HH 2.66 3.00 8.00 0.00 1.60 131
Village is near river (0/1) 0.56 1.00 1.00 0.00 0.50 131
Village has pig pond which has water
throughout the year(0/1)
0.36 0.00 1.00 0.00 0.48 131
Village has either pig pond which has water
throughout the year or be near river (0/1)
0.62 1.00 1.00 0.00 0.49 131
Village is located in district capital (0/1) 0.12 0.00 1.00 0.00 0.33 131
86

Appendix A
Table 2: Descriptive statistics for variables by treatment group (continued)
Variable descriptions
Dependent variables (in Kip unless stated
otherwise):
Mean Median Maximum Minimum Std. Dev. Observations
Does village have paved road or near main
road (Km 13 road)? (0/1)
0.38 0.00 1.00 0.00 0.49 131
Village has irrigation (0/1) 0.26 0.00 1.00 0.00 0.44 131
Does village have school until secondary
level? (0/1)
0.12 0.00 1.00 0.00 0.33 131
Does village have primary school up to
0.74 1.00 1.00 0.00 0.44 131
grade5? (0/1)
Distance from village to main markets 22.75 20.00 33.00 15.00 6.63 131
Price <strong>of</strong> one cattle 1,573,664 1,500,000 2,000,000 1,300,000 231,660 131
Price <strong>of</strong> one buffalo 3,270,992 3,250,000 4,000,000 2,500,000 579,322 131
Price <strong>of</strong> pig per Kg 15,485 11,667 25,000 10,000 5,334 131
Price <strong>of</strong> duck per Kg 14,668 15,000 15,000 13,500 577 131
Price <strong>of</strong> traditional chicken (Gailard) per Kg 18,985 19,000 20,000 18,000 928 131
Daily wage for harvesting rice 18,168 20,000 30,000 0 8,091 131
Daily wage for planting rice 32,664 40,000 50,000 0 17,081 131
Daily wage for construction 23,798 25,000 27,500 20,000 1,970 131
Source: Author’s survey data, September 2005 and March 2006.
87

Appendix A
Table 3: Descriptive statistics for variables by control group
Variable descriptions
Dependent variables (in Kip unless stated
otherwise):
Mean Median Maximum Minimum Std. Dev. Observations
Household monthly food expenditure 435,058 300,000 1,640,000 20,000 354,763 52
Households monthly rental expenditure 39,396 0 1,200,000 0 180,419 52
Household monthly transportation fee
311,404 50,000 3,600,000 0 671,649 52
expenditure
Household monthly educational expenditure 129,619 73,334 870,000 0 182,782 52
Household monthly clothing expenditure 95,556 45,834 510,000 0 110,758 52
Household monthly medical expenditure 88,875 30,000 800,000 0 166,766 52
Household monthly expenditure on
household utensils
104,131 25,000 1,000,000 0 205,913 52
Household monthly other major expenditure 79,058 30,000 700,000 0 143,501 52
Household monthly total non-food
848,039 477,223 5,050,000 44,167 1,004,497 52
expenditure
Household monthly total expenditure 1,283,097 890,000 6,010,000 225,000 1,238,047 52
Value <strong>of</strong> household owned house 33,009,317 15,000,000 287,000,000 100,000 52,921,605 52
Value <strong>of</strong> household owned land 2,210,000,000 15,000,000 111,000,000,000 0 15,400,000,000 52
Value <strong>of</strong> household owned land and house 2,240,000,000 40,750,000 111,000,000,000 100,000 15,400,000,000 52
Value <strong>of</strong> household owned agriculture asset 3,156,410 0 70,000,000 0 10,603,436 52
Value <strong>of</strong> household owned livestock asset 8,011,365 500,000 220,000,000 0 30,604,352 52
Value <strong>of</strong> household owned other enterprise
asset
726,731 0 15,000,000 0 2,342,297 52
Value <strong>of</strong> household owned savings at house 574,519 195,000 5,000,000 0 967,112 52
Value <strong>of</strong> household owned other asset 1,887,392 0 23,944,385 0 5,072,488 52
Value <strong>of</strong> household owned non-land and
house asset
14,356,418 4,950,000 244,000,000 0 35,608,003 52
Value <strong>of</strong> household owned total asset 2,260,000,000 52,800,000 111,000,000,000 348,000 15,400,000,000 52
88

Appendix A
Table 3: Descriptive statistics for variables by control group (continued)
Variable descriptions
Dependent variables (in Kip unless stated
otherwise):
Mean Median Maximum Minimum Std. Dev. Observations
Household yearly self employment income
from agriculture
2,370,000 570,000 15,000,000 0 3,645,969 52
Household yearly self employment income
from livestock
1,580,385 250,000 24,000,000 0 3,852,912 52
Household yearly self employment income
from handicraft & textile
1,703,500 0 10,000,000 0 2,879,942 52
Household yearly self employment income
from trading
3,952,885 0 94,100,000 0 14,868,408 52
Household yearly self employment income
from repairing& fixing service
0 0 0 0 0 52
Household yearly self employment income 19,231 0 1,000,000 0 138,675 52
from rice mill & construction
Household yearly self employment income
from vehicle service
0 0 0 0 0 52
Household total yearly self employment
income
9,626,000 5,050,000 113,000,000 0 17,829,858 52
Household yearly wage & salary income 1,870,000 0 14,400,000 0 3,213,138 52
Household yearly income from remittance 249,816 0 7,995,450 0 1,182,835 52
Household yearly rental income 113,462 0 4,000,000 0 578,378 52
Household yearly monetary items income 38,462 0 2,000,000 0 277,350 52
Household yearly other income 34,615 0 1,800,000 0 249,615 52
Household total yearly non-self employment
income
2,306,355 900,000 14,400,000 0 3,238,564 52
Household yearly total income 11,932,355 6,600,000 121,000,000 1,000,000 19,371,116 52
89

Appendix A
Table 3: Descriptive statistics for variables by control group (continued)
Variable descriptions
Independent variables:
Mean Median Maximum Minimum Std. Dev. Observations
Months as VSG member 0 0 0 0 0 52
Does household has a VSG member?(0/1) 1 1 1 1 0 52
Value <strong>of</strong> household owned land 5 years ago 36,661,946 9,000,000 770,000,000 0 112,000,000 52
Sex <strong>of</strong> household head (female=1) 0.13 0.00 1.00 0.00 0.34 52
Gender <strong>of</strong> respondents (entrepreneur)
(female=1)
0.88 1.00 1.00 0.00 0.32 52
Maximum education <strong>of</strong> respondents
5.27 5.00 13.00 0.00 3.06 52
(entrepreneur) (years)
Household size 5.54 5.00 11.00 2.00 2.16 52
Age <strong>of</strong> respondents (entrepreneur) (years) 38.63 35.50 68.00 18.00 13.53 52
Number <strong>of</strong> months doing business 161.06 110.50 813.00 2.00 162.29 52
Number <strong>of</strong> Generations <strong>of</strong> family in village 0.50 0.00 4.00 0.00 0.98 52
Number <strong>of</strong> relatives in village 2.21 1.00 12.00 0.00 2.82 52
Are you member or head <strong>of</strong> the group
0.10 0.00 1.00 0.00 0.30 52
committee? (0/1)
Number <strong>of</strong> civil servant in household 0.37 0.00 4.00 0.00 0.71 52
Number <strong>of</strong> wage employment in HH 1.10 1.00 4.00 0.00 1.16 52
Number <strong>of</strong> school age in HH 1.83 2.00 6.00 0.00 1.37 52
Number <strong>of</strong> dependent on your income in HH 2.65 2.00 8.00 0.00 1.63 52
Village is near river (0/1) 0.08 0.00 1.00 0.00 0.27 52
Village has pig pond which has water
throughout the year (0/1)
0.42 0.00 1.00 0.00 0.50 52
Village has either pig pond which has water
throughout the year or be near river (0/1)
0.44 0.00 1.00 0.00 0.50 52
Village is located in district capital (0/1) 0.00 0.00 0.00 0.00 0.00 52
90

Appendix A
Table 3: Descriptive statistics for variables by control group (continued)
Variable descriptions Mean Median Maximum Minimum Std. Dev. Observations
Independent variables:
Does village have paved road or near main
road (Km 13 road)?
0.02 0.00 1.00 0.00 0.14 52
Village has irrigation (0/1) 0.02 0.00 1.00 0.00 0.14 52
Does village have school until secondary
level? (0/1)
0.00 0.00 0.00 0.00 0.00 52
Does village have primary school up to
0.44 0.00 1.00 0.00 0.50 52
grade5? (0/1)
Distance from village to main markets 24 18 33 15 7.81 52
Price <strong>of</strong> one cattle 1,678,846 1,750,000 2,000,000 1,300,000 191,830 52
Price <strong>of</strong> one buffalo 3,134,615 3,000,000 4,000,000 2,500,000 298,942 52
Price <strong>of</strong> pig per Kg 11,484 10,833 20,000 10,000 2,303 52
Price <strong>of</strong> duck per Kg 14,356 14,000 15,000 13,500 605 52
Price <strong>of</strong> traditional chicken (Gailard) per Kg 18,288 18,000 20,000 18,000 572 52
Daily wage for harvesting rice 15,769 25,000 30,000 0 12,887 52
Daily wage for planting rice 11,913 15,000 50,000 0 12,764 52
Daily wage for construction 25,769 25,000 27,500 22,500 1,447 52
Source: Author’s survey data, September 2005 and March 2006.
91

Appendix A
Table 4: Descriptive statistics for variables by nonmember
Variable descriptions
Dependent variables (in Kip unless stated
otherwise):
Mean Median Maximum Minimum Std. Dev. Observations
Household monthly food expenditure 381,539 300,000 1,140,000 30,000 247,036 68
Households monthly rental expenditure 3,799 0 150,000 0 19,816 68
Household monthly transportation fee
135,363 7,500 1,500,000 0 260,151 68
expenditure
Household monthly educational expenditure 132,086 32,500 3,000,000 0 385,963 68
Household monthly clothing expenditure 118,490 50,000 1,200,000 0 202,077 68
Household monthly medical expenditure 89,745 24,584 2,000,000 0 253,594 68
Household monthly expenditure on household 84,265 25,000 1,200,000 0 198,019 68
utensils
Household monthly other major expenditure 48,221 20,000 600,000 0 100,743 68
Household monthly total non-food expenditure 611,968 358,833 6,080,000 12,000 899,020 68
Household monthly total expenditure 993,507 702,500 6,230,000 129,000 962,150 68
Value <strong>of</strong> household owned house 29,315,860 19,000,000 267,000,000 0 42,974,388 68
Value <strong>of</strong> household owned land 66,232,290 10,000,000 2,420,000,000 0 294,000,000 68
Value <strong>of</strong> household owned land and house 95,548,151 30,000,000 2,450,000,000 0 299,000,000 68
Value <strong>of</strong> household owned agriculture asset 1,698,971 100,000 30,000,000 0 4,155,598 68
Value <strong>of</strong> household owned livestock asset 4,906,500 200,000 65,500,000 0 10,876,800 68
Value <strong>of</strong> household owned other enterprise asset 345,588 0 20,000,000 0 2,424,959 68
Value <strong>of</strong> household owned savings at house 2,068,912 150,000 107,000,000 0 12,902,080 68
Value <strong>of</strong> household owned other asset 3,017,360 0 23,656,050 0 5,697,910 68
Value <strong>of</strong> household owned non-land and house
asset
12,037,331 4,825,000 131,000,000 0 22,803,727 68
Value <strong>of</strong> household owned total asset 108,000,000 41,215,000 2,460,000,000 300,000 303,000,000 68
Household yearly self employment income from
agriculture
2,229,265 1,500,000 19,000,000 0 3,462,084 68
92

Appendix A
Table 4: Descriptive statistics for variables by nonmember (continued)
Variable descriptions
Dependent variables (in Kip unless stated
otherwise):
Mean Median Maximum Minimum Std. Dev. Observations
Household yearly self employment income from
livestock
1,791,059 175,000 30,000,000 0 4,740,913 68
Household yearly self employment income from
handicraft & textile
1,553,735 0 11,000,000 0 2,515,442 68
Household yearly self employment income from
trading
4,308,603 0 182,000,000 0 22,539,720 68
Household yearly self employment income from
repairing& fixing service
336,029 0 19,200,000 0 2,363,542 68
Household yearly self employment income from 2,788,235 0 190,000,000 0 22,992,377 68
rice mill & construction
Household yearly self employment income from
vehicle service
290,441 0 18,250,000 0 2,214,526 68
Household total yearly self employment income 13,297,368 5,000,000 196,000,000 0 32,677,643 68
Household yearly wage & salary income 2,227,206 0 24,000,000 0 4,153,388 68
Household yearly income from remittance 450,569 0 10,000,000 0 1,550,881 68
Household yearly rental income 8,088 0 400,000 0 51,551 68
Household yearly monetary items income 3,156,941 0 213,000,000 0 25,853,716 68
Household yearly other income 0 0 0 0 0.00 68
Household total yearly non-self employment 5,842,804 450,000 213,000,000 0 25,914,373 68
income
Household yearly total income 19,140,172 8,100,000 213,000,000 380,000 40,947,644 68
Independent variables:
Months as VSG member 0 0 0 0 0.00 68
Does household has a VSG member?(0/1) 0 0 0 0 0.00 68
93

Appendix A
Table 4: Descriptive statistics for variables by nonmember (continued)
Variable descriptions
Independent variables
Mean Median Maximum Minimum Std. Dev. Observations
Value <strong>of</strong> household owned land 5 years ago 30,337,816 5,000,000 911,000,000 0 113,000,000 68
Sex <strong>of</strong> household head (female=1) 0.09 0.00 1.00 0.00 0.29 68
Gender <strong>of</strong> respondents (entrepreneur) (female=1) 0.82 1.00 1.00 0.00 0.38 68
Maximum education <strong>of</strong> respondents
5.01 5.00 19.00 0.00 3.92 68
(entrepreneur) (years)
Household size 5.24 5.00 12.00 1.00 2.25 68
Age <strong>of</strong> respondents (entrepreneur) (years) 41 39 75 19 13.69 68
Number <strong>of</strong> months doing business 172 121 624 1 152.41 68
Number <strong>of</strong> Generations <strong>of</strong> family in village 0.51 0.00 6.00 0.00 1.13 68
Number <strong>of</strong> relatives in village 2.87 1.50 40.00 0.00 5.27 68
Are you member or head <strong>of</strong> the group committee? 0.00 0.00 0.00 0.00 0.00 68
(0/1)
Number <strong>of</strong> civil servant in household 0.16 0.00 2.00 0.00 0.44 68
Number <strong>of</strong> wage employment in HH 0.88 1.00 4.00 0.00 0.99 68
Number <strong>of</strong> school age in HH 1.19 1.00 6.00 0.00 1.28 68
Number <strong>of</strong> dependent on your income in HH 2.59 2.00 10.00 0.00 1.85 68
Village is near river (0/1) 0.47 0.00 1.00 0.00 0.50 68
Village has pig pond which has water throughout
the year (0/1)
0.37 0.00 1.00 0.00 0.49 68
Village has either pig pond which has water
throughout the year or be near river (0/1)
0.56 1.00 1.00 0.00 0.50 68
Village is located in district capital (0/1) 0.12 0.00 1.00 0.00 0.32 68
Does village have paved road or near main road
(Km 13 road)? (0/1)
0.31 0.00 1.00 0.00 0.47 68
Village has irrigation (0/1) 0.19 0.00 1.00 0.00 0.40 68
Does village have school until secondary level?
(0/1)
0.12 0.00 1.00 0.00 0.32 68
94

Appendix A
Table 4: Descriptive statistics for variables by nonmember (continued)
Variable descriptions
Independent variables
Mean Median Maximum Minimum Std. Dev. Observations
Does village have primary school up to grade5?
(0/1)
0.68 1.00 1.00 0.00 0.47 68
Distance from village to main markets 20.97 20.00 33.00 15.00 6.30 68
Price <strong>of</strong> one cattle 1,628,676 1,500,000 2,000,000 1,300,000 252,645 68
Price <strong>of</strong> one buffalo 3,338,235 3,500,000 4,000,000 2,500,000 535,607 68
Price <strong>of</strong> pig per Kg 15,262 11,667 25,000 10,000 5,310 68
Price <strong>of</strong> duck per Kg 14,551 15,000 15,000 13,500 664 68
Price <strong>of</strong> traditional chicken (Gailard) per Kg 19,044 19,000 20,000 18,000 871 68
Daily wage for harvesting rice 20,515 20,000 30,000 0 7,877 68
Daily wage for planting rice 30,853 35,000 50,000 0 16,678 68
Daily wage for construction 23,934 25,000 27,500 20,000 2,040 68
Source: Author’s survey data, September 2005 and March 2006.
95

Appendix A
Table 5: <strong>Impact</strong> <strong>of</strong> savings group on household house value - GLS.
Independent
variable
Fixed Effects model Nonfixed effects model “Naïve” model “Super-naive” model
Coefficient Std.
Error
Coefficient Std. Error Coefficient Std. Error Coefficient Std. Error
Months as VSG
member
292,097 ** 147,784 292,097 * 149,683 335,021 *** 122,254 358,260 *** 123,456
Does household
have a VSG
member?(0/1)
1,481,225 3,606,033 1,481,225 3,652,365
Sex <strong>of</strong>
household head
(female=1)
-8,395,409 6,494,524 -8,395,409 6,577,969 -8,192,279 6,446,844 -8,324,504 6,502,591
Gender <strong>of</strong>
individuals
(entrepreneur)
(female=1)
14,021,407 ** 6,760,261 14,021,407 ** 6,847,120 14,277,586 ** 6,841,554 10,800,449 * 6,236,708
Maximum
education <strong>of</strong>
individuals
(entrepreneur)
(years)
854,694 ** 422,396 854,694 ** 427,823 875,203 ** 4,152,112 912,698 ** 405,066
Household size 1,037,618 752,418 1,037,618 762,086 1,031,433 761,675 1,141,685 753,676
Age <strong>of</strong>
individuals
(entrepreneur)
(years)
312,418 ** 136,471 312,418 ** 138,225 317,848 ** 136,362 347,865 *** 137,941
96

Appendix A
Table 5: <strong>Impact</strong> <strong>of</strong> savings group on household house value - GLS. (continued)
Independent
variable
Number <strong>of</strong>
months doing
business
Number <strong>of</strong>
Generations <strong>of</strong>
family in village
Number <strong>of</strong>
relatives in
village
Are you
member or head
<strong>of</strong> the group
committee?
(0/1)
Number <strong>of</strong> civil
servant in
household
Value <strong>of</strong>
household
owned land 5
years ago
Fixed Effects model Nonfixed effects model “Naïve” model “Super-naive” model
Coefficient Std.
Error
Coefficient Std. Error Coefficient Std. Error Coefficient Std. Error
29,502 *** 11,177 29,502 *** 11,321 29,811 *** 11,347 33,317 *** 11,108
843,836 1,191,177 843,836 1,206,481 796,147 1,209,852 542,343 1,190,404
1,552,376 *** 387,633 1,552,376 *** 392,613 1,548,088 *** 392,040 1,544,427 *** 381,940
12,703,787 *** 4,169,217 12,703,787 *** 4,222,785 12,597,429 *** 4,225,101 12,995,561 *** 4,189,169
5,058,192 3,566,208 5,058,192 3,612,029 5,216,180 3,483,950 5,351,551 3,463,556
0.0688 0.0498 0.0688 0.0504 0.0697 0.0502
97

Appendix A
Table 5: <strong>Impact</strong> <strong>of</strong> savings group on household house value - GLS. (continued)
Independent
variable
Village has
either pig pond
which has water
throughout the
year or be near
river (0/1)
Does village
have paved road
or near main
road (Km 13
road)? (0/1)
Does village
have primary
school up to
grade5? (0/1)
Distance from
village to main
markets
Price <strong>of</strong>
traditional
chicken
(Gailard) per
Kg
Fixed Effects model Nonfixed effects model “Naïve” model “Super-naive” model
Coefficient Std.
Error
Coefficient Std. Error Coefficient Std. Error Coefficient Std. Error
-44,768,692 36,726,790 -45,892,240 36,782,065 -47,125,621 36,182,599
-17,908,249 ** 7,655,365 -18,358,229 ** 7,784,479 -18,674,677 ** 7,906,479
45,390,649 38,241,483 46,212,441 38,344,587 47,538,868 3,764,1126
583,219 424,715 599,591 416,257 585,607 424,001
-258 1,783 -429 1,846 -171 1,841
98

Appendix A
Table 5: <strong>Impact</strong> <strong>of</strong> savings group on household house value - GLS. (continued)
Independent
variable
Daily wage for
construction
Fixed Effects model Nonfixed effects model “Naïve” model “Super-naive” model
Coefficient Std.
Error
Coefficient Std. Error Coefficient Std. Error Coefficient Std. Error
-1,018 1,542 -886 1,613 -977 1,613
F-statistic= 2.858314 F-statistic=2.858314 F-statistic=3.044732 F-statistic=2.911104
Prob(F-statistic)=
0.000149
Prob(F-statistic)= 0.000149 Prob(F-statistic)= 0.000080 Prob(F-statistic)= 0.000217
R-squared=0.181513 R-squared=0.181513 R-squared=0.181768 R-squared=0.166006
Note: the superscripts *** , ** and * denote rejection at 1 per cent, 5 per cent and 10 per cent critical values.
Source: Author’s survey data, September 2005 and March 2006.
99

Appendix A
Table 6: <strong>Impact</strong> <strong>of</strong> savings group on yearly self-employment income from livestock - GLS.
Independent
variable
Fixed Effects model Nonfixed effects model “Naïve” model “Super-naive” model
Coefficient Std. Coefficient Std. Coefficient Std. Coefficient Std.
Error
Error
Error
Error
Months as VSG
member
19,962 * 10,589 19,962 * 10,725 5,374 6,546 5,948 6,388
Does household have
a VSG member?(0/1)
-557,276 358,369 -557,276 362,974
Sex <strong>of</strong> household
head (female=1)
-300,623 207,914 -300,623 210,585 -368,213 * 204,348 -371,531 * 202,203
Gender <strong>of</strong> individuals
(entrepreneur)
(female=1)
80,203 845,785 80,203 856,652 -6,467 855,017 -84,635 861,953
Maximum education
<strong>of</strong> individuals
(entrepreneur) (years)
12,190 33,842 12,190 34,277 2,017 33,190 4,484 33,169
Household size 318,013 *** 97,539 318,013 *** 98,792 306,585 *** 95,545 310,764 *** 95,568
Age <strong>of</strong> individuals
(entrepreneur) (years)
4,370 10,151 4,370 10,281 1,298 9,339 2,549 9,296
Number <strong>of</strong> months
doing business
-809 818 -809 828 -761 806 -689 792
Number <strong>of</strong>
Generations <strong>of</strong> family
in village
107,168 83,641 107,168 84,716 110,511 78,823 110,934 78,738
Number <strong>of</strong> relatives
in village
46,844 32,868 46,844 33,290 48,340 31,638 49,922 32,272
100

Appendix A
Table 6: <strong>Impact</strong> <strong>of</strong> savings group on yearly self-employment income from livestock – GLS (continued)
Independent
variable
Are you member or
head <strong>of</strong> the group
committee? (0/1)
Number <strong>of</strong> civil
servant in household
Value <strong>of</strong> household
owned land 5 years
ago
Village has either pig
pond which has water
throughout the year
or be near river (0/1)
Does village have
paved road or near
main road (Km 13
road)? (0/1)
Does village have
primary school
grade5? (0/1)
Distance from village
to main markets
Price <strong>of</strong> traditional
chicken (Gailard) per
Kg
Fixed Effects model Nonfixed effects model “Naïve” model “Super-naive” model
Coefficient Std. Coefficient Std. Coefficient Std. Coefficient Std.
Error
Error
Error
Error
1,378,544 *** 483,964 1,378,544 *** 490,183 1,413,605 *** 473,251 1,445,757 *** 479,883
413,843.2 * 249,040.7 413,843.2 252,240.5 389,296 246,407 395,150 244,608
0.0023 0.0029 0.0023 0.0029 0.0023 0.0027
-3,014,492 2,745,199 -2,626,708 2,695,507 -2,669,113 2,678,944
-654,752 684,148 -481,329 656,940 -493,312 659,751
2,453,660 2,861,017 2,144,790 2,823,562 2,186,486 2,806,690
-16,485 54,007 -21,839 53,026 -22,598 52,962
-99.92 117 -39 99 -31.38 99.5
101

Appendix A
Table 6: <strong>Impact</strong> <strong>of</strong> savings group on yearly self-employment income from livestock – GLS (continued)
Independent
variable
Daily wage for
construction
Fixed Effects model Nonfixed effects model “Naïve” model “Super-naive” model
Coefficient Std.
Error
Coefficient Std. Coefficient Std. Coefficient Std.
Error
Error
Error
83 69 39 56 35 56
F-statistic= 2.296443 F-statistic=2.296443 F-statistic=2.281757 F-statistic=2.388660
Prob(F-statistic)=
Prob(F-statistic)=
Prob(F-statistic)=
Prob(F-statistic)=
0.002577
0.002577
0.003352
0.002513
R-squared=0.151228 R-squared=0.151228 R-squared=0.142720 R-squared=0.140397
Note: the superscripts *** , ** and * denote rejection at 1 per cent, 5 per cent and 10 per cent critical values.
Source: Author’s survey data, September 2005 and March 2006.
102

Appendix A
Table 7: <strong>Impact</strong> <strong>of</strong> savings group on household yearly self-employment income from agriculture - GLS.
Independent
variable
Fixed Effects model Nonfixed effects model “Naïve” model “Super-naive” model
Coefficient Std. Coefficient Std. Coefficient Std. Coefficient Std.
Error
Error
Error
Error
Months as VSG
member
-28,206.57 *** 9,286.57 -28,206.57 *** 9,405.89 -19,530.14 *** 7,505.15 -20,080 *** 7,381.74
Does household have
a VSG member?(0/1)
286,830.8 * 163,582.4 286,830.8 * 165,684.2
Sex <strong>of</strong> household
head (female=1)
-541,902 *** 108,493 -541,902 *** 109,887 -512,509 *** 107,429.6 -486,574 *** 107,728
Gender <strong>of</strong> individuals
(entrepreneur)
(female=1)
72,762 129,788 72,762 131,455.9 103,554.6 128,713 74,531 127,055
Maximum education
<strong>of</strong> individuals
(entrepreneur) (years)
-41,017.54 ** 19,608.96 -41,017.54 ** 19,860.9 -39,569.5 ** 19,779.25 -28,725.78 17,799
Household size 74,184.43 *** 25,956.19 74,184.43 *** 26,289.69 74,259 *** 25,928 80,356 *** 25,993
Age <strong>of</strong> individuals
(entrepreneur) (years)
6,792.35 4,806.29 6,792.35 4,868.04 5,987.76 4,768.41 7,609 * 4,585
Number <strong>of</strong> months
doing business
2,310.43 *** 573.26 2,310.43 *** 580.63 2,347.08 *** 579.52 2,375.99 *** 572.196
Number <strong>of</strong>
Generations <strong>of</strong> family
in village
137,675.9 ** 58,040.65 137,675.9 ** 58,786.38 138,985 ** 57,809 127,145.7 ** 56,916
Number <strong>of</strong> relatives
in village
11,847.96 21,677.13 11,847.96 21,955.64 11,319.47 22,153.05 11,529.7 22,208.8
103

Appendix A
Table 7: <strong>Impact</strong> <strong>of</strong> savings group on household yearly self-employment income from agriculture – GLS (continued)
Independent
variable
Are you member or
head <strong>of</strong> the group
committee? (0/1)
Number <strong>of</strong> civil
servant in household
Value <strong>of</strong> household
owned land 5 years
ago
Village has either pig
pond which has water
throughout the year
or be near river (0/1)
Does village have
paved road or near
main road (Km 13
road)? (0/1)
Does village have
primary school up to
grade5? (0/1)
Distance from village
to main markets
Price <strong>of</strong> traditional
chicken (Gailard) per
Kg
Fixed Effects model Nonfixed effects model “Naïve” model “Super-naive” model
Coefficient Std. Coefficient Std. Coefficient Std. Coefficient Std.
Error
Error
Error
Error
536,173.8 563,871 536,173.8 571,115.9 506,895 569,192.6 507,812.4 567,530
82,783.2 118,974.4 82,783 120,503 107,797.9 115,761.5 158,546 105,499
0.001251 *** 0.000365 0.001251 *** 0.00037 0.001144 *** 0.000367
-1,348,716 5,157,754 -1,576,873 5,166,868 -1,521,957 5,156,553
-1,464,704 3,444,240 -1,550,032 3,444,890 -1,536,444 3,437,276
4,407,984 8,395,579 4,576,076 8,394,287 4,510,155 8,377,608
-118,734 ** 59,639.55 -116,338 ** 59,163.12 -116,370 ** 59,044.5
-697.26 998.98 -724.56 999.12 -720.7 996.85
104

Appendix A
Table 7: <strong>Impact</strong> <strong>of</strong> savings group on household yearly self-employment income from agriculture – GLS (continued)
Independent
variable
Daily wage for
construction
Fixed Effects model Nonfixed effects model “Naïve” model “Super-naive” model
Coefficient Std.
Error
Coefficient Std. Coefficient Std. Coefficient Std.
Error
Error
Error
637.56 792.33 663.61 793.01 656.64 791.38
F-statistic= 1.492664 F-statistic= 1.492664 F-statistic=1.537622 F-statistic=1.607735
Prob(F-statistic)=
Prob(F-statistic)=
Prob(F-statistic)=
Prob(F-statistic)=
0.093412
0.093412
0.082866
0.067655
R-squared= 0.103790 R-squared= 0.103790 R-squared=0.100871 R-squared=0.099043
Note: the superscripts *** , ** and * denote rejection at 1 per cent, 5 per cent and 10 per cent critical values.
Source: Author’s survey data, September 2005 and March 2006.
105

Appendix A
Table 8: <strong>Impact</strong> <strong>of</strong> savings group on household monthly rental expenditure - GLS.
Independent
variable
Fixed Effects model Nonfixed effects model “Naïve” model “Super-naive” model
Coefficient Std. Error Coefficient Std. Coefficient Std. Error Coefficient Std.
Error
Error
Months as VSG
member
138.904 ** 60.75 138.904 ** 61.53 144.28 ** 45.69 144.421 *** 45.613
Does household has a
VSG member?(0/1)
175.558 1,355 175.558 1,372
Sex <strong>of</strong> household
head (female=1)
2,153 1,313 2,153 1,330 2,127.22 1,358.16 2,120.69 1,352.03
Gender <strong>of</strong> individuals
(entrepreneur)
(female=1)
514 1,036 513.95 1,049.55 460.495 1,024.501 367.014 1,024.34
Maximum education
<strong>of</strong> individuals
(entrepreneur) (years)
483.38 ** 204.63 483.38 ** 207.25 488.83 ** 207.36 498.64 ** 208.93
Household size -164 439.83 -164 445.48 -163.57 439.61 -155 439.1
Age <strong>of</strong> individuals
(entrepreneur) (years)
-342.71 *** 124.83 -342.71 *** 126.43 -345.779 *** 123.026 -343.272 *** 121.85
Number <strong>of</strong> months
doing business
-9.44 ** 4.09 -9.44 ** 4.14 -9.41 ** 4.008 -9.364 ** 3.985
Number <strong>of</strong>
Generations <strong>of</strong> family
in village
8,346 *** 2,666 8,346 *** 2,700.43 8,417.66 *** 2,626.15 8,413.08 *** 2,616.92
Number <strong>of</strong> relatives
in village
-325 ** 134 -325 ** 135.42 -335.64 *** 131.47 -333.027 *** 130.446
106

Appendix A
Table 8: <strong>Impact</strong> <strong>of</strong> savings group on household monthly rental expenditure – GLS (continued)
Independent
variable
Are you member or
head <strong>of</strong> the group
committee? (0/1)
Number <strong>of</strong> civil
servant in household
Value <strong>of</strong> household
owned land 5 years
ago
Village has either pig
pond which has water
throughout the year
or be near river (0/1)
Does village have
paved road or near
main road (Km 13
road)? (0/1)
Does village have
primary school up to
grade5? (0/1)
Distance from village
to main markets
Price <strong>of</strong> traditional
chicken (Gailard) per
Kg
Fixed Effects model Nonfixed effects model “Naïve” model “Super-naive” model
Coefficient Std. Error Coefficient Std. Coefficient Std. Error Coefficient Std.
Error
Error
-2,927 ** 1,212 -2,927 ** 1,228.03 -2,959.98 ** 1,217.54 -2,912.395 ** 1,204
-1,736 ** 891 -1,736 ** 902.81 -1,734.42 ** 837.19 -1,684.74 ** 824.22
0.0000027 0.0000035 0.0000027 0.0000036 0.0000027 0.0000035
-46,764.79 83,436.6 -46,847.70 84,100.69 -46,844.96 83,916.4
-30,659.31 55,918.17 -30,707.13 56,154.46 -30,713.54 56,031.4
78,514.40 137,373.4 78,573.59 137,835.7 78,567.17 137,533.2
-898.635 942.369 -897.67 932.11 -898.15 930.08
-9.975 16.173 -9.984 16.258 -9.98 16.22
107

Appendix A
Table 8: <strong>Impact</strong> <strong>of</strong> savings group on household monthly rental expenditure – GLS (continued)
Independent
variable
Daily wage for
construction
Fixed Effects model Nonfixed effects model “Naïve” model “Super-naive” model
Coefficient Std. Error Coefficient Std. Coefficient Std. Error Coefficient Std.
Error
Error
8.714 12.837 8.730 12.911 8.72 12.88
F-statistic= 2.851135 F-statistic=2.851135 F-statistic=3.099860 F-statistic=3.309186
Prob(F-statistic)= 0.000155 Prob(F-statistic)=
Prob(F-statistic)=
Prob(F-statistic)=
0.000155
0.000060
0.000032
R-squared=0.181139 R-squared=0.181139 R-squared=0.184452 R-squared=0.184518
Note: the superscripts *** , ** and * denote rejection at 1 per cent, 5 per cent and 10 per cent critical values.
Source: Author’s survey data, September 2005 and March 2006.
108

Appendix A
Table 9: <strong>Impact</strong> <strong>of</strong> savings group on household monthly educational expenditure - GLS.
Independent
variable
Fixed Effects model Nonfixed effects model “Naïve” model “Super-naive” model
Coefficient Std. Error Coefficient Std. Coefficient Std. Error Coefficient Std.
Error
Error
Months as VSG
member
5,670.26 ** 2,768.37 5,670.26 ** 2,803.94 5,961.41 ** 2,328.64 5,975.62 ** 2,323.98
Does household has a
VSG member?(0/1)
7,079.72 29,520.13 7,079.72 29,899.42
Sex <strong>of</strong> household
head (female=1)
-73,346.61 49,733.75 -73,346.61 50,372.75 -71,354.07 49,884.70 -71,423.84 49,472.92
Gender <strong>of</strong> individuals
(entrepreneur)
(female=1)
-21,040.16 29,964.27 -21,040.16 30,349.26 -20,434.61 28,795.59 -16,808.54 28,283.16
Maximum education
<strong>of</strong> individuals
(entrepreneur) (years)
6,287.33 3,930.84 6,287.33 3,981.35 6,318.587 3,875.395 6,305.82 3,879.46
Household size 20,529.96 *** 5,012.84 20,529.96 *** 5,077.25 20,845.82 *** 4,978.39 20,763.81 *** 4,937.83
Age <strong>of</strong> individuals
(entrepreneur) (years)
1,592.31 1,142.47 1,592.31 1,157.15 1,550.24 1,129.29 1,464.46 1,108.08
Number <strong>of</strong> months
doing business
-210.32 ** 97.97 -210.32 ** 99.23 -201.72 ** 100.08 -207.34 ** 97.26
Number <strong>of</strong>
Generations <strong>of</strong> family
in village
-19,403.54 ** 8,778.61 -19,403.54 ** 8,891.41 -19,900.6 ** 8,926.37 -19,117.51 ** 8,493.06
109

Appendix A
Table 9: <strong>Impact</strong> <strong>of</strong> savings group on household monthly educational expenditure – GLS (continued)
Independent
variable
Number <strong>of</strong> relatives
in village
Are you member or
head <strong>of</strong> the group
committee? (0/1)
Number <strong>of</strong> civil
servant in household
Value <strong>of</strong> household
owned land 5 years
ago
Village has either pig
pond which has water
throughout the year
or be near river (0/1)
Does village have
paved road or near
main road (Km 13
road)? (0/1)
Does village have
primary school up to
grade5? (0/1)
Distance from village
to main markets
Fixed Effects model Nonfixed effects model “Naïve” model “Super-naive” model
Coefficient Std. Error Coefficient Std. Coefficient Std. Error Coefficient Std.
Error
Error
-135.896 1,292.505 -135.897 1,309.112 -186.27 1,232.45 -242.82 1,241.64
-19,447.98 76,584.68 -19,447.98 77,568.68 -23,655.88 7,3921.90 -24,936.75 73,712.6
11,323.64 21,117.49 11,323.64 21,388.81 12,310.31 19,502.08 11,545.31 19,684.12
-0.0000758 0.0000979 -0.0000758 0.0000992 -0.0000769 0.0000914
-321,772.9 * 166,552.9 -328,547.7 ** 155,380.5 -327,368.3 ** 154,914.8
-142,432.3 98,762.85 -144,617.5 93,385.55 -144,249.4 93,205.14
408,506 * 223,862.9 413,289.4 * 214,578.9 411,673.4 * 214,079.6
1,380.33 1,975.93 1,476.9 2,101.4 1,454.18 2,092.53
110

Appendix A
Table 9: <strong>Impact</strong> <strong>of</strong> savings group on household monthly educational expenditure – GLS (continued)
Independent
variable
Price <strong>of</strong> traditional
chicken (Gailard) per
Kg
Daily wage for
construction
Fixed Effects model Nonfixed effects model “Naïve” model “Super-naive” model
Coefficient Std. Error Coefficient Std. Coefficient Std. Error Coefficient Std.
Error
Error
-27.08 26.85 -28.12 25.11 -28.35 25.05
16.933 21.342 17.75 19.87 17.94 19.82
F-statistic= 1.670459 F-statistic=1.670459 F-statistic= 1.835944 F-statistic=1.952906
Prob(F-statistic)= 0.045622 Prob(F-statistic)=
Prob(F-statistic)=
Prob(F-statistic)=
0.045622
0.024821
0.017032
R-squared=0.114734 R-squared=0.114734 R-squared=0.118129 R-squared=0.117802
Note: the superscripts *** , ** and * denote rejection at 1 per cent, 5 per cent and 10 per cent critical values.
Source: Author’s survey data, September 2005 and March 2006.
111

Appendix B
CASE STUDY
SAVINGS GROUPS IN NAXAITHONG CITY
This section discusses the savings groups in Naxaithong city as a case study under
the Women and Community’s Empowering Project (WCEP), one <strong>of</strong> the many programs
which launched micr<strong>of</strong>inance programs in the semi-urban areas <strong>of</strong> Laos. Before going to
that point, we will briefly provide an overview <strong>of</strong> WCEP.
1. Overview <strong>of</strong> the Women and Community’s Empowering Project 38
1.1 Background, goal and activities
<strong>The</strong> Women and Community’s Empowering Project (WCEP), formed formally in
October 2002, is cooperation between the Lao Women’s Union (LWU) – a Lao
Government women's organization – and the Community Organizations Development
Institute: Thailand (CODI). <strong>The</strong> project was implemented in three districts in Vientiane,
the capital <strong>of</strong> Laos, namely Pak Ngum, Naxaithong and Sangthong cities which were
selected by LWU because they were the poorest districts (Asian Coalition for Housing
Rights, 2005), for the period from October 2002 to September 2005 with budget <strong>of</strong>
2,650,000 Baht 39 , supported by the Asian Coalition for Housing Rights (ACHR).
<strong>The</strong> goal <strong>of</strong> the project is to improve quality <strong>of</strong> life for women and their families,
and to support the activities for savings, credit and community fund to be a method for
development. Its main activities have been:
38 This section borrows extensively from the Report on the Implementation <strong>of</strong> Women and Community’s
Empowering Project: October 2002 - September 2005, produced by the Women and Community’s
Empowering Project (2005). This report which was originally written in Lao language was translated by the
author.
39 In 2002, average exchange rate was 42.96 Baht per one US dollar (Bank <strong>of</strong> Thailand, 2006).
112

Appendix B
• to establish the savings group and working capital fund;
• to promote community activities with learning promotion, training and
procession provision for community to be able to create activities for self
development such as community development, community welfare
provision, use <strong>of</strong> natural fertilizer, sharing <strong>of</strong> experience;
• to improve the functioning <strong>of</strong> the savings group and its branches by
developing accounting system, and developing administration and
management system for savings group, branch, district fund and central
fund;
• to provide advice on self-employment opportunities as well as provide
training to develop marketing and other related skills
1.2 Savings group
1.2.1 Points <strong>of</strong> view
Savings groups are still a new development in Laos. <strong>The</strong>re are various definitions
<strong>of</strong> a savings group. According to Chaleunsinh defined a savings group as:
A group <strong>of</strong> people who share the same voluntary spirit to help each other in
solving problems on financial liquidity in order to improve solidarity and upgrade
the living standard <strong>of</strong> people within the village. Basically, the savings group is an
association which accumulates savings <strong>of</strong> the group’s members 40 and loans them
to members who need to borrow them and charging them a loan interest rate
which must be higher than the savings’ rate <strong>of</strong> return. In addition, some strong
savings groups also provide member support services, such as vocational training
and establishing production group based on the potential <strong>of</strong> their members
(Chaleunsinh, 2004: 4-5).
40 That must be equal to or higher than the minimum savings the group has established.
113

Appendix B
In addition, according to the savings group manual <strong>of</strong> the Women and
Community’s Empowering Project (n.d, 3), the savings group has to have its own
organizational structure, which consists <strong>of</strong> group committee 41 , member, regulation, and
savings group consultant, received promotion from government authority in each level and
other related parties including benchmark village. More details on the organizational
structure will be discussed in following sections. In the next three sections, most <strong>of</strong> the
information is derived from the savings group manual <strong>of</strong> the Women and Community’s
Empowering Project (n.d) 42 .
1.2.2 Objectives
According to the savings group manual <strong>of</strong> the Women and Community’s
Empowering Project (n.d), the main objectives <strong>of</strong> the savings group are listed down as the
following:
• To reduce poverty and improve living status <strong>of</strong> women and their families;
• To accumulate savings for village working capital fund;
• To help each other and exchange knowledge and experiences <strong>of</strong> economic
activities among members;
• To strengthen women solidarity in a village;
• To improve knowledge and capacity <strong>of</strong> women such as management,
accounting, credit and banking;
41 Normally, the group committee at village level includes 5 people who are villagers (generally being
membership <strong>of</strong> Lao women union at village level) in the village. <strong>The</strong>y take caring every thing in the village
savings group such as doing savings account, loan account, cash book, income and expenses account, and
general ledger.
42 This savings group manual which was originally written in Lao language was translated by the author.
114

Appendix B
• To sustain living status <strong>of</strong> members and villagers and to have good welfare
step by step;
• To be one <strong>of</strong> the implementations <strong>of</strong> economic and social development
plan which the Lao government issues in each period.
1.2.3 Establishment process <strong>of</strong> savings group
<strong>The</strong> steps for establishing a savings group are described as bellows:
Step 1: <strong>The</strong> project proponents meet local authorities at village level to discuss the
possibility <strong>of</strong> savings group establishment in a village. <strong>The</strong>n, these authorities will
consult with their villagers.
Step 2: <strong>The</strong> local authorities at village level submit a list <strong>of</strong> the names who self
select to be membership <strong>of</strong> a savings group 43 ; to the promoter (hence may be the Lao
Women Union at district level or the Project). <strong>The</strong>n, a meeting is held to provide
information about policies <strong>of</strong> the group and to set up the savings group by:
• Election for group committee and group consultant;
• Setting the roles for group committee, group consultant, and member;
• Dividing the responsibilities for both group committee and group
consultant;
• Managing savings group with respect to the rules settled.
Step 3: Group committee and group consultant accept the first set <strong>of</strong> savings
group members. <strong>The</strong> members have to pay their membership fee and deposit money with
43 <strong>The</strong> members included in the list should not be less than 20 people (Lao Women Union: Lao PDR and
Community Organizations Development Institute: Thailand, n.d: 17).
115

Appendix B
the savings group at the deposit day which has been set out. <strong>The</strong> group committee
summarizes a financial statement after receiving the member savings.
Step 4: Operations <strong>of</strong> the savings group’s activities, such as savings and credit
activities, have to follow the rules. <strong>The</strong> group committee can receive a technical assistant
from the promoter (the project) if the committee do not understand how to operate the
activities, during implementation period.
Step 5: Instead <strong>of</strong> savings activities, members can think about how to create
activities, for example activities for earning extra income, welfare activity, environment
and others, with respect to suitability and capacity <strong>of</strong> that local area.
Step 6: <strong>The</strong> promoter <strong>of</strong> the project, the government authority at each level and
other related organization have to follow up and promote and support a savings group to
reach the development objective <strong>of</strong> poverty reduction.
1.2.4 Organizational structure <strong>of</strong> savings group
<strong>The</strong> organizational structure <strong>of</strong> the savings group consists <strong>of</strong> group committee,
savings group members, group consultant, the promoter <strong>of</strong> the project, the government
authority and other related organizations.
A. Savings group member: A person who is selected and accepted to be a
member by the group committee, has attributes as the following:
• Self selection to be membership;
• Harmonious, helpful, kindness;
• Acceptable and agreeable to follow the savings group regulations;
• Rational and acceptable to the majority <strong>of</strong> others;
116

Appendix B
• No gender and age discrimination;
• Be a resident <strong>of</strong> the village.
B. Group committee: A person who received high scores from the members
by election or voting, has attributes like:
• Being a membership <strong>of</strong> savings group and self selection to be group
committee;
• Trusted by others, knowledgeable, skillful and leadership experience;
• Satisfied to do the job for others, honest, faithful, patient;
• Healthy
• Good attitude;
• No discrimination on social class and ethic;
• Accepted by majority <strong>of</strong> members.
C. Group consultant: A person who is a representative <strong>of</strong> the local authority
at the village level or a high respected person in the village or a teacher, is self selected
and accepted by most <strong>of</strong> the members.
D. Savings group regulations: <strong>The</strong> creation <strong>of</strong> regulation with agreement <strong>of</strong>
members and group committee is based on particularity, suitability and capacity <strong>of</strong> a
village or local. <strong>The</strong>refore, there are some different and similar articles in regulations
among different savings groups. However, the summary <strong>of</strong> savings group regulation
should have different articles as:
• Membership criteria;
• Minimum savings per day/per week/ per month.
• Setting date, location and time <strong>of</strong> deposit;
117

Appendix B
• Setting membership fee;
• Setting rules for members who save irregularly
• Conditions for savings and dropping out from the group;
• Setting loan interest rate for members such as loan for emergency sick;
and normal loans (for doing rice field, crop plant, livestock, education
and other);
• Setting conditions for member borrowing such as condition and right
<strong>of</strong> borrowing; and term <strong>of</strong> loan repayment;
• Allocating benefit from total revenue at the year ended such as:
o Dividend payment to members 44 (based on calculation rate <strong>of</strong>
member deposit or share ratio);
o Administration budget such as wage for group committee and
group consultant; savings group expenditure which is savings
group reserve;
o Village development fund;
o Welfare fund;
o Other fund which is beneficial to village, agreed by member and
group committee;
• Setting aging or period <strong>of</strong> group committee and group consultant;
• Regulation can be improved and changed with respect to agreement
and suitability <strong>of</strong> most members.
44 Normally, dividend paid to the member is come from 70% <strong>of</strong> savings group pr<strong>of</strong>it and the rest <strong>of</strong> the
pr<strong>of</strong>it (30%) is allocated to different categories <strong>of</strong> fund and reserve (for particular rate is depended on
practice <strong>of</strong> each savings group), according to author’s survey data (September, 2005).
118

Appendix B
1.2.5 Results <strong>of</strong> the project implementation on savings group
During the period <strong>of</strong> the project, savings groups have been established in number
<strong>of</strong> villages in three target districts. It can be seen from table 1 that there were many
villages demanding to voluntarily establish a savings group in own village. As a result,
there were 24 more savings groups than the number planned. Table 2 shows that about
84% <strong>of</strong> the villages in the three pilot districts have their own savings groups. It means
that savings groups have good outreach to provide financial services for villagers.
Table 1: Number <strong>of</strong> savings groups established during the period from
October 2002 to September 2005
Areas Number <strong>of</strong> Savings Group
Planned number Actual number Variation
Pak Ngum district 17 25 8
Naxaithong district 31 37 6
Sangthong district 17 27 10
Total 65 89 24
Source: Women and Community’s Empowering Project (2005: 42)
Table 2: Number <strong>of</strong> savings group in three districts compared to number <strong>of</strong>
Districts Number <strong>of</strong>
Villages
villages as the statistic <strong>of</strong> July 2005
Number 45 Number <strong>of</strong>
member
119
Savings Groups
Number <strong>of</strong>
families
Deposit
balance in
Kip
Pak Ngum district 53 55 5,201 3,963 3,616,44,500
Naxaithong district 56 38 6,128 3,877 1,674,802,000
Sangthong district 37 30 2,704 2,164 734,514,500
Total 146 123 14,033 10,004 6,025,761,000
Source: Women and Community’s Empowering Project (2005: 43)
45 <strong>The</strong> number in this column is higher than the figures in third column <strong>of</strong> table 1 because it included the
number <strong>of</strong> savings groups implemented before October 2002 as Pak Ngum district with 30 savings groups,
Naxaythong district with one savings group, and Saengthong district with three savings groups (Women
and Community’s Empowering Project, 2005).

Appendix B
Savings groups in the three districts provide different kinds <strong>of</strong> loans to their
members. <strong>The</strong> largest loan balance is loans for planting rice field in all three districts,
followed by credit for trade and other services.
Table 3: Loan balance for the savings groups in each district as April 2005
No. Types <strong>of</strong>
loan
Pak Ngum district Naxaithong district Sangthong district
No. <strong>of</strong> Loan balance No. <strong>of</strong> Loan No. <strong>of</strong> Loan balance
borrowers
borrowers balance borrowers
1 Crop plant 230 314,638,000 151 94,700,000 183 141,550,000
2 Textile 46 387 212,360,000 27 9,080,000 420 253,260,000
3 Trade and
other
services
273 1,304,487,000 144 174,900,000 567 456,380,000
4 Emergency
sick
327 181,916,000 - - - -
5 Sick - - 95 32,800,000 63 30,750,000
6 Emergency - - 34 76,830,000 75 58,340,000
7 Rice field
planting
1,747 1,570,942,300 700 260,749,500 1,016 619,937,000
Total 2,964 3,584,343,300 1,151 649,059,500 2,324 1,560,217,000
Note: “-” means no balance.
Source: Women and Community’s Empowering Project (2005: 45-46)
However, this paper will focus only on savings groups in Naxaithong district as
the case study due to limitation <strong>of</strong> time and budget. <strong>The</strong> following section will discuss
more details about the case study.
2. Savings groups in Naxaithong district
2.1 Background
Naxaithong city, located in the area <strong>of</strong> Vientiane, the capital <strong>of</strong> Laos, is about 16
Km from the capital. A total <strong>of</strong> 56 villages in Naxaithong district are divided to six zones.
46
In Naxaythong district, this type <strong>of</strong> loan is provided borrowers, who do textile <strong>of</strong> Lao skirt, called Tam
Hook.
120

Appendix B
Its population is 57,129 people consisting <strong>of</strong> 28,761 females and there are 10,378 families
(Women and Community’s Empowering Project, 2005: 40-41).
<strong>The</strong> main economic activities in this district are planting rice fields, crop plants,
livestock, textile and trade. According to the Women and Community’s Empowering
Project (2005: 41), the living status <strong>of</strong> population in this district was poor before the
establishment <strong>of</strong> the savings group. This could be seen from low income levels, lack <strong>of</strong>
capital for production, and lack <strong>of</strong> finance for smoothing consumption and other activities.
This led to the selling <strong>of</strong> Green Rice 47 and borrowing money from the informal sector
such as money lenders at high monthly interest rates <strong>of</strong> 10 percent to 30 percent. For
example, in the 38 villages which have savings groups, 423 families sell green rice; 1,202
families obtained a loan from money lender; and 57 families were classified as being <strong>of</strong> a
poor status before the establishment <strong>of</strong> the savings groups. In addition, in the absence <strong>of</strong>
the savings group, the education level <strong>of</strong> the people was limited due to a lack <strong>of</strong> learning
opportunities, especially for women and lack <strong>of</strong> knowledge and ability for management
<strong>of</strong> different activities.
In June 2001, a savings group was implemented in Naxaythong district. Now,
there are 38 village savings groups available in four zones <strong>of</strong> this district as shown in the
table 2.
2.2 Financial services <strong>of</strong> savings groups in Naxaithong District
Savings groups provided some basic financial services such as credit and savings
to their members. <strong>The</strong> following section discusses the findings <strong>of</strong> the survey conducted in
47 As Chanleunsinh (2004:8) defined as, “Green rice selling or Khai Khao Khiew means farmers sell rice at
a discounted price, which is evaluated by the middleman or the traders, before it is harvested. <strong>The</strong>
traders/middle men then come to take the rice after it is harvested”.
121

Appendix B
six <strong>of</strong> these villages. Six villages which the author did field survey in September 2005
and March 2006.
2.2.1 Source <strong>of</strong> fund
<strong>The</strong> two main sources <strong>of</strong> funds for running savings group are internal funds and
external funds 48 . Internal funds come from deposit amounts from member <strong>of</strong> savings
group (see table 4). At beginning <strong>of</strong> each month, normally at the first day <strong>of</strong> the month,
the members have to deposit money to savings groups <strong>of</strong> at least the minimum required
amount 49 which is one <strong>of</strong> the membership rules. <strong>The</strong>re is no interest paid on such deposits
but members receive dividends every twelve months 50 . 70 per cent <strong>of</strong> the savings group
pr<strong>of</strong>it is paid to each member as a dividend. <strong>The</strong> dividend payment to each member is
based on share ratio or proportion <strong>of</strong> deposit <strong>of</strong> each member to total deposit balance <strong>of</strong>
whole savings group. This implies that deposit amount per time from members who
require more dividend tends to be increased every month.
External funds are a s<strong>of</strong>t loan from the central funds which are managed by the
management committee <strong>of</strong> the project. This loan is lent to a savings group the funds <strong>of</strong>
which are not sufficient fund for borrowers. <strong>The</strong> process <strong>of</strong> making a s<strong>of</strong>t loan request
48
Besides the two main sources <strong>of</strong> fund, interest income from loan to members is one source <strong>of</strong> fund for
savings group.
49
Minimum deposit amount is normally 5,000 Kip per month or equivalent to 0.46US dollar as exchange
rate <strong>of</strong> 10,890 Kip per a dollar in September 2005, according to six savings groups <strong>of</strong> this case study. Until
September 2005, there has been no limitation for maximum deposit amount in those six savings group.
However, there is limitation on maximum deposit, up to one million kip per time, in case <strong>of</strong> savings group
in Nakountay village at the time <strong>of</strong> follow up survey on 2 nd March 2006.
50
(a)If members withdraw deposit before dividend payment date, they will not receive any dividend from
their deposit accumulation. (b) If they withdraw at dividend payment date, they will still get dividend from
their deposit, but if they need a sequent loan, they cannot withdraw their deposit. (c) <strong>The</strong>y can withdraw
only at the day <strong>of</strong> withdrawal, not the deposit day, (<strong>The</strong> deposit day is the day to make a plan to request for
withdrawal).
122

Appendix B
starts with members <strong>of</strong> savings group making borrowing plans to their savings groups.
<strong>The</strong> plans are submitted to the committee at zone level, through the fund committee at
district level to the central fund. <strong>The</strong> central fund committee issues the s<strong>of</strong>t loan to the
fund committee at district level. <strong>The</strong>n, the committee at district level will lend to the
members <strong>of</strong> different groups who make borrowing plans with approval <strong>of</strong> the fund
committee at district level (Women and Community’s Empowering Project, 2005: 63-64).
Table 4: Credit balance and source <strong>of</strong> fund in six savings groups as
September 2005
Date <strong>of</strong> the
Members Loan balance
Source <strong>of</strong> fund as
September 2005
savings
in as September
External
group Population savings 2005 Internal fund fund
No. Villages established in village group
Kip
Kip Kip
1 Nakountay 01-Oct-02 1,089 300 167,000,000 158,148,500 -
2 Huannamyene 12-Jun-03 1,710 338 136,200,000 109,031,000 -
3 Dongluang 01-Apr-04 1,048 153 74,700,000 56,499,000 6,000,000
4 Phonekeo 15-Jun-05 532 145 13,112,000 11,967,000 11,000,000
5 Phonesavanh 01-Jun-05 680 74 650,000 650,000 -
6 Sisavard 10-Aug-05 393 121 1,200,000 1,120,000 -
Source: Author’s survey data, September 2005 and March 2006.
In the survey sample, there were two savings groups which had external funds.
One is a savings group in Dongluang village which received a s<strong>of</strong>t loan from the fund
committee <strong>of</strong> Naxaithong district amount <strong>of</strong> six millions Kip with one year term <strong>of</strong> loan
and 2% interest rate per month, on 5 th August 2005. However, the savings group could
123

Appendix B
repay this loan by three months later. A savings group in Phonekeo village was another
one which obtained s<strong>of</strong>t loan from the fund committee <strong>of</strong> Naxaithong city. On the 1 st <strong>of</strong>
August, 2005, this savings group borrowed from the fund committee <strong>of</strong> Naxaithong city
the amount <strong>of</strong> 11 million Kip with a one year term <strong>of</strong> the loan and 3% interest rate per
month. At the time <strong>of</strong> follow up survey in this village, 3rd March 2006, this loan was yet
not repaid.
In addition, as table 4 shows four savings groups are dependent on internal funds
(member deposit) in order to lend to their members. This implies that most savings
groups in Laos are much based on savings mobilization for lending to their members
rather than s<strong>of</strong>t loans or external funds for the project. This is in contrast to the village
bank system in Northeast Thailand <strong>of</strong> the study by Coleman (1999). Even though, this
approach was promoted by Thai NGO, Community Organizations Development Institute:
Thailand (CODI) which follow the “Village Bank” group lending methodology <strong>of</strong> the
Foundation for International Community Assistance (FINCA) 51 (Chaleunsinh, 2004: 7).
This result is consistent with Chaleunshinh that:
“Savings group” operation in Lao PDR, especially in Vientiane Capital, is quite
different from the “Village Bank” system in Northeast Thailand, due to the fact
that most <strong>of</strong> the village savings groups in Laos are currently based on money
mobilized within their villages or internal funds rather than from s<strong>of</strong>t loans from a
project. However, this approach was promoted by the Thai NGOs, Foundation for
Integrated Agricultural Management: FIAM, and Community Organizations
Development Institute: Thailand or CODI, starting in 1997. Both organizations
follow the “Village Bank” group lending methodology <strong>of</strong> the Foundation for
International Community Assistance (Chaleunsinh, 2004:7).
51 Please refer to Ledgerwood (1999: 85) for more explanation <strong>of</strong> Village Bank.
124

Appendix B
2.2.2 Credit
Savings groups lend money to their members on a monthly basis. Normally, the
borrowing day is the day after deposit day. Members who require the loans fill the loan
request form and submit it at the deposit day. <strong>The</strong> group committee collects all the
request forms 52 and summaries the amount <strong>of</strong> loan requested. <strong>The</strong>y then check how much
money the group has at that time 53 . <strong>The</strong> savings group provides different kinds <strong>of</strong> loan as
shown in table 3. In the six savings groups <strong>of</strong> this sample, loan size is range from 100,000
Kip to 5,000,000 Kip with a 5% interest rate per month 54 and 3 to 6 months term <strong>of</strong> loan 55 .
Limitation <strong>of</strong> loan size is dependent on the practices <strong>of</strong> each savings groups. For example,
in the case study <strong>of</strong> the six savings groups, the groups in Dongluang, Huannamyene and
Phonesavanh villages have a minimum loan size <strong>of</strong> 100,000 Kip while other three savings
groups have not settled for that. For maximum loan size, savings groups in Nakountay,
Dongluang and Phonesavanh villages have settled at amount <strong>of</strong> 5 million Kip, one
million Kip and 300,000Kip respectively, whereas the rest three savings groups have not
settled for that but they consider the loan size depending on the borrowing request and
how much the group have money for (deposit amount).
52
Generally, borrowing conditions are settled in different way among savings groups. In case <strong>of</strong> savings
group in Nakountay village, some conditions for member borrowing are: (1) members have to have deposit
amount with the group for guarantee; (2) If they borrow at 500,000kip, TV or freeze can be collateral; if
they borrow amount more than 3,000,000kip, a land certificate can be collateral but it is also depended on
how much volume <strong>of</strong> their deposit with the group; (3) if member has no any things to be guarantee, friends
can be guarantor; (4) If a wife borrows, her husband has to be guarantor and vice versa.
53
Normally, savings group does not keep any money balance with the group after receiving deposit from
members, then the day after, all money has to lend out to members depending on their request and
borrowing conditions. In case <strong>of</strong> deposit balance <strong>of</strong> the group remaining at 1 million Kip, the group
committee will deposit that balance to bank such as Agricultural Promotion Bank, an example <strong>of</strong> savings
group in Nakountay village.
54
Sometimes, interest rate per month is 3%, an example <strong>of</strong> savings group at Nakountay village, it is
depended on what kinds <strong>of</strong> loan.
55
Three new village savings groups have 3 months term <strong>of</strong> loan while three old village savings group
practice on 6 months term <strong>of</strong> loan during the survey time. For example, savings group in Nakountay village,
term <strong>of</strong> loan was 3 months for the period from year 2002 to 2004, but since 2 March 2005, term <strong>of</strong> loan has
been 6 months.
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Appendix B
3. Other financial service providers in Naxaithong district
According to the author’s survey in September 2005, some financial service
providers, apart from the savings group, provide basic financial services, mainly credit
and deposits in the survey area. For credit access, 12% <strong>of</strong> sample survey could obtain a
loan from financial providers. <strong>The</strong> Agricultural Promotion Bank (APB) was the first
financial provider at 6%, followed by money lender at 4%, while other micr<strong>of</strong>inance
providers covered only by 1% <strong>of</strong> sample survey (see table 5). It is interesting that 17% <strong>of</strong>
the control group had access to credit from other financial providers. Of that, APB was
the most dominant financial provider at 12%.
In terms <strong>of</strong> deposit service, about 9% <strong>of</strong> those surveyed in the sample deposited
with other financial providers. APB and other micr<strong>of</strong>inance providers covered by 4% per
each while the 2% was covered by money rotating (see table 6). For control group sample,
8% <strong>of</strong> its sample could access to financial providers which APB and other micr<strong>of</strong>inance
providers shared that percent by half per each.
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Appendix B
Table 5: Loan balance at different sources <strong>of</strong> finance for sample survey in six
villages as September 2005
Mean by group: Sources <strong>of</strong> credit:
Other
APB MFIs b)
Money
Relative
&
lender friend Total
Interest rate per day (%):
All sample (n=251):
0.02 - 0.17 0.167 - 0.2 0.7 - 1 0 - 10
Number <strong>of</strong> borrowers 0.06 0.01 0.04 0.02 0.12 a)
Loan amount (Kip)
Member (n=183):
40,035,438 41,833 39,982 21,978 40,139,231
Number <strong>of</strong> borrowers 0.07 0.02 0.04 0.03 0.15
Loan amount (Kip)
Treatment group(n=131):
54,851,885 57,377 51,833 30,145 54,991,240
Number <strong>of</strong> borrowers 0.05 0.02 0.04 0.03 0.14
Loan amount (Kip)
Control group (n=52):
231,870 80,153 10,687 41,729 364,439
Number <strong>of</strong> borrowers 0.12 - 0.04 0.02 0.17
Loan amount (Kip) 192,452,308
Nonmember (n=68):
- 155,489 962 193,000,000
Number <strong>of</strong> borrowers 0.03 - 0.03 - 0.06
Loan amount (Kip) 161,765 - 8,088 - 169,853
Note: a) One person could access to credit in both APB and relative &friends.
b) This consists <strong>of</strong> savings group <strong>of</strong> Lao Women Union at Naxaithong city, Rural
Development Cooperative Naxaithong and others.
Source: Author’s survey data, September 2005.
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Appendix B
Table 6: Deposit balance at different institutions as September 2005
Mean by group: Deposit balance at different institutions:
Other
APB MFIs Money rotating Total
Interest rate per year (%): 8 - 24 0 - 3
All sample (n=251):
Number <strong>of</strong> depositors 0.04 0.04 0.02 0.09 a)
Deposit balance (Kip) 32,207 12,530 1,801 46,538
Member (n=183):
Number <strong>of</strong> depositors 0.05 0.05 0.02 0.12
Deposit balance (Kip) 30,514 17,186 2,377 50,077
Treatment group (n=131):
Number <strong>of</strong> depositors 0.05 0.06 0.02 0.14
Deposit balance (Kip) 40,840 22,634 3,321 66,794
Control group (n=52):
Number <strong>of</strong> depositors 0.04 0.04 0.08
Deposit balance (Kip) 4,500 3,462 7,962
Nonmember (n=68):
Number <strong>of</strong> depositors 0.01 0.01 0.01
Deposit balance (Kip) 36,765 250 37,015
Note: a) One person deposited with both APB and Money rotating
Source: Author’s survey data, September 2005.
128