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Growth of incomes of borrowers always exceeds

that of control group. Increase in borrowers’

income larger for better-off borrowers.

Positive benefi ts, but no statistical tests for

differences reported.

5 per cent of participant households removed

from poverty annually. Additional consumption

of 18 taka for every 100 taka of loan taken out

by women.

Positive impact of program participation on

total weekly expenditure per capita, women’s

non-land assets and women’s labor supply.

Strong effect of female participation in Grameen

Bank on schooling of girls.

Credit programs can change village attitudes and

other village characteristics.

No evidence of program impact. Village bank

membership no impact on asset or income


Chen and Snodgrass


Coleman (2004)

Park and Ren (2001)

Duong and Izumida


Kaboski and

Townsend (2002)

India (SEWA bank)

Thailand (village


China (NGOs,

government programs,

mixed NGO–

government programs)

Vietnam (VBA 84

per cent of total

lending), VBP, PCFs,

commercial banks,

public funds)

Thailand (production

credit groups, rice

banks, women’s groups,

buffalo banks)

Control group from same

geographic area

Double difference estimation

between participants and nonparticipants

and villages with and

without micro-fi nance program

(i) Probit estimation of

participation and eligibility

for each type of program; (


ordinary least squares and

instrumental variable estimation

of impact of micro-credit on

household income

Tobit estimation of (i)

participation in rural credit

market; (ii) behavior of lender

toward credit-constrained

household and (iii) weighted least

square estimation for impact on

output supply.

Two–staged least squares test

of micro-fi nance impact on

asset growth, probability of

reduction in consumption in bad

years, probability of becoming

moneylender, probability of

starting business and probability

of changing job. Separate

estimation according to type and

policies of MFI

Average income increase rose for bank’s clients

in comparison with control group. Little overall

change in incidence of poverty, but substantial

movement above and below poverty line.

Programs are not reaching the poor as much as

they reach relatively wealthy people. Impact is

larger on richer committee members rather than

on rank-and-fi le members.

In NGO and mixed programs the better-off even

if eligible (for mixed programs) are excluded

from participation. In the government program

the better-off are both eligible and more likely to

participate. Impact estimation fi nds evidence of

positive impact of micro-credit on income.

Poor have diffi culties in accessing credit facilities:

livestock and farming land are determinants of

household participation; reputation and amount

of credit applied for are determinants of credit

rationing by lenders. Impact estimation shows

positive correlation between credit and output

Production credit groups and women’s groups

combined with training and savings have positive

impact on asset growth, although rice banks and

buffalo banks have negative impacts. Emergency

services, training and savings help to smooth

responses to income shock. Women’s groups help

to reduce reliance on moneylenders.

1) Non-parametric test of

stochastic dominance of average

monthly consumption of

members and non-members

2) Maximum likelihood test of

micro-credit membership on

vulnerability, consumption and

household characteristics.

1) Basic consumption-smoothing

test on household’s ability to

perform daily living activities

2) State dependence tests of basic

regression (

relative male–female

earning, physical job, savings)

3) Test of geographical proximity

to fi nancial institutions on

consumption smoothing

Members are poorer than non-members.

Programs are more successful at reaching

poor, but less successful at reaching vulnerable.

Poor vulnerable are effectively excluded from


Signifi cantly positive correlation between

household’s consumption and measure of health.

Wealthier households are better insured against


Households that live far from fi nancial

institutions suffer more from sudden reduction in


Amin et al. (2003)

Gertler et al. (2003)

Bangladesh (Grameen

Bank, BRAC, ASA)

Indonesia (Bank

Rakyat Indonesia,

Bank Kredit Desa,

commercial banks)

Table 7.2 (continued)

Study Coverage (in Asia only) Methodology Results


Khandker (2003)

Pitt et al. (2003)

Bangladesh (Grameen


Bangladesh (BRAC,

BRDB, Grameen


1) Fixed effect Tobit estimation

of borrowing dependent on land

and education endowments of


2) Panel data fi xed effects,

instrumental variable estimation

to defi ne long-term impact of

micro-fi nance borrowing on

expenditure, non-land assets and

moderate and extreme poverty

Maximum likelihood estimation

controlling for endogeneity of

individual participation and of

the placement of micro-fi nance

programs. Impact variables are

health of boys and girls (arm

circumference, body mass index

and height-for-age)

Households who are poor in landholding and

formal education tend to participate more.

Micro-fi nance helps to reduce extreme

poverty much more than moderate poverty

(18 percentage points as compared with 8.5

percentage points over 7 years). Welfare impact

is also positive for all households, including non–

participants, as there are spillover effects.

Signifi cantly positive effect of female credit on

height-for-age and arm circumference of both

boys and girls. Borrowing by men has either

negative or non-signifi cant impact on health of


Programs in a number of countries are considered, including the Grameen

Bank in Bangladesh and the Bank Rakyat Indonesia (BRI). In general a

positive impact is found on borrower incomes of the poor (1988–92) with

on average an increase over the control groups ranging from 10–12 per

cent in Indonesia, to around 30 per cent in Bangladesh and India (Hulme

and Mosley, 1996, Table 8.1). Gains are larger for non-poor borrowers,

however, and within the group the poor gains are negatively correlated

with income. Despite the breadth of the study and its use of control group

techniques, it has been criticized for possible ‘placement’ bias, whereby

micro-fi nance programs may be drawn to better placed villages, so that

part of the advantage relative to the control group may be due to this more

favorable location. Further, the quality and accuracy of some of the data,

particularly in relation to the representative nature of the control groups,

has been questioned (Morduch, 1999: 1600).

Another major early initiative that has provided some of the fi rmest

empirical work involved the surveys conducted in the 1990s by the

Bangladesh Institute of Development Studies (BIDS) and the World

Bank; these provided the data for several major analyses, such as Pitt and

Khandker (1998). Khandker (1998) summarizes a number of different

studies conducted in Bangladesh using the 1991/92 survey and focuses on

three major micro-fi nance programs, including the Grameen Bank and the

Bangladesh Rural Advancement Committee (BRAC). Methodologically

impact is assessed using a double-difference approach between eligible and

ineligible households (with holdings of land of more than half an acre

making households ineligible) and between program and non-program

villages. After controlling for other factors, such as various household

characteristics, any remaining difference was attributed to the micro-fi nance

programs. The study draws a number of conclusions, but the main one is

that the programs had a positive effect on household consumption, which

was signifi cantly greater for female borrowers. On average a loan of 100 taka

to a female borrower, after it is repaid, allows a net consumption increase

of 18 taka. In terms of poverty impact it is estimated that 5 per cent of

participant households are pulled above the poverty line annually.

Khandker (2003) follows up this earlier work by employing panel data.

He uses the BIDS–World Bank survey conducted in 1998–99 that traced the

same households from the 1991–92 survey.7 He fi nds apparently strong and

positive results. Whilst borrowing by males appears to have no signifi cant

impact on consumption, that by females, who are the dominant client group,

does have a positive impact. From this analysis a 100 taka loan to a female

client leads to a 10.5 taka increase in consumption (compared with 18 taka

in the earlier analysis). Allowing for the impact of higher consumption

on poverty gives estimates of poverty impact. It is estimated that due to

participation in micro-fi nance programs, moderate poverty among program

participants decreased 8.5 percentage points over the period of seven years,

and extreme poverty dropped about 18 points over the same period.8 He

also fi nds evidence of positive spillovers on non-program participants in

the program villages with the impact greater for those in extreme poverty.

Poverty for non-participants is found to decline by 1 percentage point due

to the programs whilst extreme poverty declines by nearly 5 percentage

points. This impact is due solely to female borrowing.

The same data set has also been used to identify health impacts as opposed

to income changes. Pitt et al. (2003) fi nd that credit going to females has a

large and signifi cant impact in two out of three health measures for children.

Male borrowing has no such effect. For example, a 10 per cent increase

in credit to females increases the arm circumference of daughters by 6.3

per cent. A 10 per cent increase in female credit on average increases the

height of girls by 0.36 cm annually and of boys by 0.50 cm. The relations

are stronger for daughters than sons. Hence in Bangladesh micro-credit

and improved family health appear to be related.

These results from Bangladesh are strong and positive and probably

are the clearest evidence there is that micro-fi nance is working in the way

intended to bring sustained relief from poverty. However, a couple of caveats

are in order. First, the accuracy of the original results as presented in Pitt

and Khandker (1998) has been disputed on the grounds that the eligibility

criteria of low land holdings was not strictly enforced in practice. In a

reworking of the results focusing on what are claimed to be more directly

comparable households no impact on consumption from participation in a

program is found (Morduch, 1999: 1605).9 Second, in the BIDS–World Bank

survey data the ‘ultra poor’ (defi ned as those with less than 0.2 acres of land)

form nearly 60 per cent of participants and the likelihood of participation is

strongly and negatively associated with level of land holding. Nonetheless,

how much is borrowed depends principally on the entrepreneurship of

households, so that the charge that the risk-averse very poor will benefi t

proportionately less has not been totally dispelled. Furthermore, the panel

data reveal a relatively high drop-out rate of around 30 per cent, indicating

that there were problems for many households.

There are examples of many other studies that are either inconclusive or

provide less convincing results. Coleman (1999) and MkNelly et al. (1996)

both focus on experiences with village banking in Thailand. Coleman (1999)

utilizes data on villages that had participated in village bank micro-fi nance

schemes and those control villages that were designated as participants,

but had not yet participated. This allows a double difference approach

that compares the difference between income for participants and nonparticipants

in program villages with the same difference in the control

villages, where the programs were introduced later. From the results here the

poverty impact of the schemes appears highly dubious. Months of village

bank membership have no impact on any asset or income variables and there

is no evidence that village bank loans were directed to productive purposes.

The small size of loans implies that they were largely used for consumption,

but one of the reasons there is a weak poverty impact is that there was a

tendency for wealthier households to choose to join village banks.

Coleman (2004) uses the same survey data but reconsiders the estimation

strategy to control for this participation by richer households, who tend

to dominate village bank committees. The results of Coleman (2004)

indicate that there is substantial difference between ordinary members

and committee members of village banks. The impact of micro-credits on

ordinary members’ well-being is either insignifi cantly different from zero

or negative. On the other hand, the impact of micro-fi nance programs on

committee members’ income and wealth is positive, implying a form of

program capture by the better-off in the village, even though this group may

not be well-off by national standards. A similar result in terms of rationing

micro-credit in favor of better-off groups or village bank members is found

in Duong and Izumida (2002) in a study of six villages in Vietnam. There

household economic position and prestige in a village, plus the amount of

credit applied for, are the main determinants of how credit is allocated.

MkNelly et al. (1996) evaluated the Freedom from Hunger credit with

education program in Thailand operated through village banks. The results

show positive benefi ts; however although non-participants in non-program

villages are used as controls, there are problems in accepting the results.

No statistical tests are reported, so one cannot judge whether differences

between participants and non-participants are signifi cant. There is also a

potential measurement bias since the staff responsible for the program also

did the interviewing.

Chen and Snodgrass (2001) examine the operations of the Self Employed

Women’s Association (SEWA) bank in India providing low-income female

clients in the informal sector with both saving and loan services. The study

tests for the impact of these services by comparing the bank’s clients against

a randomly selected control group in a similar geographic area. The two

surveys were conducted two years apart. Average incomes rose over time for

all groups – borrowers, savers and the control, although the increase was less

for the latter. In terms of poverty incidence there was little overall change,

although there was substantial ‘churning’, in that amongst the clients of

SEWA there was quite a lot of movement above and below the poverty

line. In interpreting these results, Meyer (2002) argues that the evidence on

the counterfactual – that is what would have happened to the clients in the

absence of the services of SEWA – is not suffi ciently strongly established

to draw any fi rm conclusions on poverty impact.

The smoothing of consumption over time to protect the poor against

adverse shocks is one of the principal objectives of micro-credit. Using

data again for Bangladesh, Amin et al. (2003) compute several measures

of vulnerability.10 They fi nd that the micro-credit participants in the two

villages covered are more likely to be below the poverty line than if they

had been selected at random, so that the programs have reached the poor.

However, the vulnerable are more likely to join a micro-credit program in

only one of the two villages. Further, for the vulnerable below the poverty

line in one village there is no evidence that they are more likely to be

members of a program, and in the other village there is evidence that they

have either chosen not to join or are actively excluded, presumably on the

grounds that they are a poor credit risk. Hence the very poor and vulnerable

do not appear to be reached.

More positive conclusions in terms of the ability of micro-fi nance to

reduce vulnerability are found for Indonesia by Gertler et al. (2003) who

fi nd that access to micro-fi nance helps households smooth consumption in

the face of declines in health of adult family members. Having established

an empirical relationship between health condition and consumption, the

authors test for a relation between access to a fi nancial institution and

consumption shortfalls associated with ill health. Using geographic distance

as a measure of access they fi nd that for households in an area with a BRI

branch, health shocks have no effect on consumption.11 The study does not

differentiate within the group of the poor.