Non-Linearities in Returns to Participation in Grameen Bank Programs

Abstract

This paper studies the benefits of participation in micro-finance programs, where benefits are measured in terms of the ability to smooth the effect of seasonal shocks that cause consumption fluctuations. It is shown that although membership in these programs is an effective instrument in combating inter-seasonal consumption differences, there is a threshold level of length of participation beyond which benefits begin to diminish. Returns from membership are modelled using an Euler equation approach. Fixed effects non-linear least squares estimation of parameters using data from 24 villages of the Grameen Bank suggests that returns to participation, as measured by the ability to smooth seasonal shocks, begin to decline after approximately two years of membership. This implies that membership alone no longer has a mitigating marginal effect on seasonal shocks to per capita consumption after four years of participation. Such patterns suggest that the ability to smooth consumption as a function of length of membership, need not accrue indefinitely in a linear fashion.

Full text

Non-Linearities in Returns to
Participation in Grameen Bank Programs
NIDHIYA MENON
Brandeis University, Waltham, MA, USA
Final version received July 2005
ABSTRACT This paper studies the benefits of participation in micro-finance programs, where
benefits are measured in terms of the ability to smooth the effect of seasonal shocks that cause
consumption fluctuations. It is shown that although membership in these programs is an effective
instrument in combating inter-seasonal consumption differences, there is a threshold level of
length of participation beyond which benefits begin to diminish. Returns from membership are
modelled using an Euler equation approach. Fixed effects non-linear least squares estimation of
parameters using data from 24 villages of the Grameen Bank suggests that returns to
participation, as measured by the ability to smooth seasonal shocks, begin to decline after
approximately two years of membership. This implies that membership alone no longer has a
mitigating marginal effect on seasonal shocks to per capita consumption after four years of
participation. Such patterns suggest that the ability to smooth consumption as a function of
length of membership, need not accrue indefinitely in a linear fashion.
JEL Classification: 012, 016, D12
I. Introduction
Micro-credit programs such as the Grameen Bank’s in Bangladesh have innovatively
tackled a major problem associated with poverty alleviation in less developed
countries. In these countries, vast sections of the population are caught in a ‘poverty
trap’ because their lack of collateralisable assets precludes them from using formal
credit mechanisms, which, in turn, ensures continued poverty. By targeting credit to
those who have no collateral, and by utilising innovative schemes to deliver that
credit, micro-finance has proven to be a means of deliverance (see Appendix for a
description of these programs).
Despite the vast amounts of research on micro-finance organisations, few studies
have attempted to evaluate the long-run benefits of participation, or sought to
understand how behavior of participants evolves over time. Although development
programs provide aid in the immediate short run, their main objective is to ensure
Correspondence Address: Nidhiya Menon, Department of Economics and International Business School,
MS 021, Brandeis University, Waltham, MA 02454, USA. Email: nmenon@brandeis.edu
Journal of Development Studies,
Vol. 42, No. 8, 1379–1400, November 2006
ISSN 0022-0388 Print/1743-9140 Online/06/081379-22 ª2006 Taylor & Francis
DOI: 10.1080/00220380600930705

that benefits are self-sustaining, and that the participants are able to stand on their
own in the future. Programs such as the Grameen Bank meet these objectives more
effectively than others in two ways. First, the interest charged on loans is at market
rates, and thus local banks have a chance to become self-reliant within a couple of
years of operation. Second, Grameen teaches its members how to administer their
resources more effectively, and how to obtain and manage working capital. By doing
so, micro-finance programs assist the poor in achieving the objectives of
consumption smoothing and asset accumulation. Since loans are stipulated for use
in non-agricultural self-employment activities, participating households are better
able to smooth seasonal shocks to consumption. Additionally, through forced
savings, these programs increase the assets of poor households.
The hypothesis in this paper is that experienced members are better able to
withstand seasonal shocks to household per capita consumption. This enhanced
ability to buffer consumption against shocks captures the household’s long run
capacity to survive independently without aid. There are two facets to consumption
smoothing that are of interest. The insurance aspect emphasises the ability to buffer
consumption against exogenous shocks that are not perfectly foreseen. By stressing
the importance of savings, micro-credit programs develop the means of withstanding
unexpected negative shocks. Furthermore, given the lack of storage and the low
levels of income, poor households are unable to smooth consumption even in
response to anticipated seasonality. By diversifying sources of income within the
household (these programs target women) and by requiring that loans be used for
non-agricultural activities, lending programs help to insulate the consumption of
poor households against anticipated seasonal shocks.
Specific reasons for why participation might reduce the household’s cost of
borrowing (and thus facilitate consumption smoothing) are as follows: (1)
Precautionary – experienced members would have accumulated assets over time,
which, may be used in a precautionary role to smooth consumption. (2)
Accumulated assets may also be used as collateral for loans from other sources.
(3) Membership leads to the formation of a ‘reputation’ for more experienced clients.
By demonstrating their ability to meet regular installment payments, experienced
members signal their ability to be good credit risks for other lenders. (4) Since
experienced members have assets that may be used as collateral, their demand for
credit conditional on a particular source is more elastic. This implies that they are
charged a lower interest rate (Iqbal, l988).
Parameter estimates of the model analysed here indicate the presence of declining
returns to consumption smoothing benefits, after a certain number of years of
participation. The theoretical model formulates a time varying household specific
interest rate, where the deviation of this interest rate from the average interest rate
for households in the village captures the higher cost of borrowing faced by a poor
household. Poor households may face a higher cost of borrowing due to a host of
reasons, the most prominent being the lack of collateral. The household specific
interest rate reflects the implicit price of intertemporal resource transfer and may be
affected by social and monetary costs, as well as the household’s reputation in the
village. It thus captures both the physical cost of borrowing as embodied in the
actual interest rate faced by the household, and other social considerations that
might affect how much this household can borrow in that village. Participation in
1380 N. Menon

micro-finance programs is hypothesized to reduce the deviation of the household
specific interest rate from the average village interest rate.
A natural method to test this hypothesis is to examine the ability of participants to
smooth per capita consumption across seasons. Following Zeldes (1989) and Foster
(1995), an Euler equation incorporating the cost of borrowing faced by the
household, is adopted to study inter-seasonal consumption differentials. The
deviation of the household interest rate from the average village interest rate is a
function of the length of membership in a credit program. If the change in per capita
household consumption between seasons is negatively correlated to the length of
membership, it follows that experienced members are better able to smooth seasonal
shocks. To allow for non-linear effects in the returns to participation, a quadratic
approximation is used to model the length of membership in a credit program.
A fixed effects non-linear least squares estimation of an equation that relates
changes across seasons in household per capita consumption expenditure to length
of membership, changes in prices, preferences, and the cost of borrowing,
demonstrates that membership length is inversely related to the change in seasonal
per capita food consumption. Data from 24 villages of the Grameen Bank indicate
that although participation does have a mitigating effect on seasonal shocks to
consumption, the magnitude of such effects begins to diminish over time. Estimates
from the model suggest that the effects reach a maximum at two years of
membership, and begin to decline thereafter. This does not imply that consumption
volatility increases for experienced members, merely that participation no longer has
the same mitigating influences as before.
The layout of the paper is as follows. Section II provides details on the model that
is estimated. Section III summarises the data, and Section IV discusses issues
involved in the estimation. Results are reported in Section V and Section VI provides
various robustness checks of the estimates obtained. Section VII concludes with
policy implications.
II. Model
The presence of credit constraints is tested by deriving an Euler equation from a
dynamic utility maximisation model. This Euler equation relates consumption
changes to changes in prices and the interest rate, under the assumption that no
borrowing constraints exist.
Consider a household that maximizes the expected value of a time separable
lifetime utility function. In each time (season) period t, household iin village j
chooses the quantity of per capita consumption C
ijt
to solve the following:
Max EtX
T1
k¼0
bkUðCijt þkÞ
subject to an asset update:
Aijtþk¼ð1þrijtÞðAijtþk1ÞþYijtþkPjtþkCijtþk8k
where bis the discount rate, E
t
is the expectations operator conditional on
information available as of time t,Tis the end of the household’s horizon, r
ijt
is the
Grameen Bank Programs 1381

interest rate faced by the household, A
ijt
are household assets, Y
ijt
is household
income, and P
jt
are prices in village jat time t. The first order conditions for the
above problem are Euler equations of the following form:
Et
U0ðCijtþ1Þ
U0ðCijtÞ¼1
bð1þrijtÞ
Pjtþ1
Pjt
ð1Þ
Assuming a CRRA utility function:
UðCtÞ¼C1a
t
1a
and substituting its first derivative into Equation (1) yields:
Cijtþ1
Cijt

a
bð1þrijtÞPjt
Pjtþ1
¼1þe0
ijtþ1
where e0
ijtþ1is the expectational error. Defining R
ijt
¼(1 þr
ijt
) and substituting into
the above implies:
ln Cijtþ1
Cijt

¼1
alnðbRijtÞþln Pjt
Pjtþ1

lnð1þe0
ijtþ1Þ

ð2Þ
Following Zeldes (1989), it can be shown that lnð1þe0
ijtþ1Þ¼lnð1þeijtþ1Þþ1
2s2
eijtþ1.
Substituting in Equation (2), we obtain:
ln Cijtþ1
Cijt

¼1
alnðbRijtÞþln Pjt
Pjtþ1

þeijtþ1

ð3Þ
where eijtþ1¼ lnð1þeijtþ1Þþ1
2s2
eijtþ1

. In order to analyse the cost of borrowing
faced by a household, the term that captures the difference between the household
interest rate and the average interest rate in the village needs to be incorporated.
Approximating 1
alnðbRijtÞto its first order Taylor series expansion about the village
average interest rate R
jt
gives:
ln Cijtþ1
Cijt

¼1
alnðbRjtÞþðrijt rjtÞ1
Rjt
ln Pjtþ1
Pjt

þeijtþ1

where r
jt
is the average interest rate in village jat time t,andr
ijt
is the interest rate
faced by household iin village jat time t, Simplifying, we obtain:
Dln Cijtþ1¼g0þg1ðrijt rjtÞþg2Dln Pjtþ1þnijtþ1ð4Þ
where ‘D’ denotes changes over time, that is, Dln C
ijtþ1
¼(ln C
ijtþ1
–ln C
ijt
)
and Dln P
jtþ1
¼(ln P
jtþ1
–ln P
jt
). Furthermore, g0¼1
aln bRjt;g1¼1
aRjt ;g2¼1
a;and
1382 N. Menon

nijtþ1¼1
aeijtþ1. Those households that are better able to engage in credit based
consumption smoothing (that is, with small (r
ijt
7r
jt
)) do not experience large
deviations in their cost of borrowing (relative to the rest of the village). Thus for
them, changes in per capita consumption are primarily governed by changes in
prices, preferences, and the average village interest rate.
We hypothesise that the deviation between the average village interest rate and the
household interest rate depends on the length of time the household has been a
member of a credit program. Membership reduces the cost of borrowing since those
who have been participants for longer periods of time would have accumulated the
means to minimise the effect of seasonal shocks.
1
The (r
ijt
7r
jt
) term captures the
extent to which the household specific interest rate differs from the average village
interest rate, where the latter reflects the effect of average village and time (season)
shocks. For those who do not participate, (r
ijt
7r
jt
) picks up the full effect of average
village and time (season) shocks. For those who do participate, the effect of the
average village–time (village–season) shock is dampened by the length of member-
ship variable. This is captured in the following specification:
g0þg1ðrijt rjtÞ¼FðDijt Þmjt ð5Þ
F(D
ijt
) denotes that the household’s cost of borrowing is a function of D
ijt
where D
ijt
represents the duration of membership (of household iin village jat time t)ina
credit program, and m
jt
is a village–time (season) dummy that captures average
interest rate change. Where
2
FðDijtÞmjt ¼ðedtDijt Þmjt ;g0þg1ðrijt rjtÞ¼ðedtDijt Þmjt .
Adjusting the time subscript appropriately and substituting into Equation (4):
Dln Cijtþ1¼ðedtþ1Dijtþ1Þmjtþ1þg2Dln Pjtþ1þnijtþ1ð6Þ
Variables such as characteristics of the household head as well as the quantity of
land owned by the household also play a role in reducing the cost of borrowing.
These are represented by XC
ijtþ1(the ‘C’ superscript denotes that these are the X
ijtþ1
in
the equation where change in consumption is the dependent variable (Equation (6))),
and may be included in (6) in a similar manner to the inclusion of D
ijtþ1
.
Non-Linear Returns to Participation
The measure of returns to program participation in this paper is the household’s
ability to minimise fluctuations in per capita consumption expenditure across
seasons. In order to ascertain whether these returns become smaller over time, we
estimate a quadratic counterpart of Equation (6):
Dln Cijtþ1¼ed1tþ1Dijtþ1þd2tþ1D2
ijtþ1þbcXC
ijtþ1

|fflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl}
A
mC
jtþ1þg2Dln Pjtþ1þnijtþ1ð7Þ
This quadratic approximation allows consumption-smoothing benefits to decline
over time (a cubic functional form was initially estimated, but the quadratic model is
used since the effect of the cubic term in the cubic functional form was insignificant).
Grameen Bank Programs 1383

Equation (7) is estimated using a non-linear least squares technique with village-
time fixed effects. Since the data has information on 24 Grameen villages over three
seasons, more than one village–season fixed effect parameter ðmC
jtþ1Þis estimated.
The coefficients d
1tþ1
and d
2tþ1
are the marginal effects of interest. A negative value
for d
1tþ1
would confirm the idea that experienced participants are better able to
smooth consumption. The declining returns hypothesis is supported if d
2tþ1
40.
Consider the term denoted by ‘A’ in Equation (7). Intuitively, ‘A’ denotes the
parameters that dampen the effect of seasonal shocks mC
jtþ1. For those households
that do not participate, D
ijtþ1
¼0. Therefore for such households, A¼ðebCXC
ijtþ1Þ. For
those households that do participate, ‘A’ is as shown in (7). As long as d
1tþ1
is
negative and larger than d
2tþ1
(in absolute terms), participants are better able to
buffer consumption against seasonal shocks. The ability to dampen the effect of
seasonal shocks becomes stronger with length of participation (large value for
D
ijtþ1
).
III. Data
The data used in this analysis were collected from rural Bangladesh during 1991–
92. The estimation sample was drawn from eight Grameen thanas (Grameen is
the only program that operates in these thanas), and participants from three
villages in each of these eight thanas were interviewed. These villages were selected
on the basis of their having had a Grameen program in operation for three or
more years. Since Grameen does not lend to households that own more than half
an acre of cultivable land, this rule is used to classify households in each of the 24
villages as ‘target’ or ‘non-target’. Participants and non-participants among the
target households are then separately identified, and target non-participants as
well as participants are over sampled in the data. The data has information on a
total of 479 Grameen households, of which 420 are target households (own less
than half an acre of cultivable land). Of the 420 target households, 297 are
participants.
Each household is surveyed for three rounds, corresponding to the three major
rice crop seasons in Bangladesh. Data in the first round were collected in December/
January 1991 – the post harvest time of the Aman rice crop. The second round
corresponds to the post harvest time of the Boro crop (April–May 1992), and the
third round corresponds to the post harvest time of the Aus rice crop (August–
September 1992). The Aman rice crop is the largest of the year, and the Aus harvest is
the smallest.
In this paper, Round 1 refers to the Aman season (Season 1), Round 2 to the Boro
season (Season 2), and Round 3 to the Aus season (Season 3).
Table 1 (‘HH’ is a short-form for household in all tables) provides the weighted
means and standard deviations of all the independent variables used in the analysis.
In order to account for the choice based sampling of the data, the means of the
variables are adjusted by weights that correct for the difference between the actual
distribution of households in the villages surveyed, and the distribution of
households in the sample. Length of membership is measured by the number of
years the participant has been a member of Grameen’s lending program. Two
households had members belonging to a program other than the Grameen Bank,
1384 N. Menon

Table 1. Weighted means and standard deviations of independent variables
Independent variable Full sample Target participants Target non-participants Non-participants
Log of decimals of land owned by HH
{
1.4750 (3.3305) 0.8247 (3.1963) 70.6033 (2.5771) 1.7648 (3.3503)
Years of education of HH head 2.2359 (3.2934) 1.9258 (2.6773) 1.7271 (3.1419) 2.3741 (3.5269)
Gender of HH head: ¼1 if male 0.9351 (0.2464) 0.9361 (0.2446) 0.8943 (0.3079) 0.9346 (0.2474)
Age of HH head 39.9144 (12.5267) 42.2872 (11.8579) 37.1526 (12.7676) 38.8569 (12.6794)
Highest value of years of education for a
female in HH
1.5457 (2.8872) 1.2627 (2.1507) 1.1182 (2.4950) 1.6718 (3.1544)
Highest value of years of education for a
male in HH
2.6562 (3.5280) 2.4462 (3.0256) 1.9947 (3.4143) 2.7498 (3.7287)
Dummy for no adult (416 years) female
in HH
0.0142 (0.1182) 0.0070 (0.0832) 0.0313 (0.1743) 0.0174 (0.1308)
Dummy for season 3 (Aus) 0.3333 (0.4716) 0.3333 (0.4717) 0.3333 (0.4720) 0.3333 (0.4718)
Price of coarse grain rice 10.4635 (1.0461) 10.4944 (1.0488) 10.4127 (1.0419) 10.4497 (1.0453)
Difference in the log of price of rice
between seasons 2 and 1
70.0102 (0.0846) 70.0154 (0.0836) 70.0086 (0.0903) 70.0079 (0.0851)
Difference in the log of price of rice
between seasons 3 and 2
70.1260 (0.0757) 70.1244 (0.0764) 70.1275 (0.0735) 70.1267 (0.0754)
Difference in the log price of rice and
wheat flour between season 2 and 1
70.0555 (0.0665) 70.0571 (0.0669) 70.0574 (0.0677) 70.0548 (0.0664)
HH maximum years member program* 1.4147 (2.4223) 4.5890 (2.1117)
HH maximum years member program*
for female participant
0.9805 (1.9828) 3.1806 (2.3986)
HH maximum years member program*
for male participant
0.4360 (1.6702) 1.4144 (2.7690)
Observations 1437 891 369 546
{
1 decimal ¼1/100
th
of an acre.
*Denotes endogenous variable.
Standard errors in parenthesis.
Grameen Bank Programs 1385

thus for each household, the maximum value for length of program membership is
used in the estimations.
3
Table 2 reports the weighted means and standard deviations of the dependent
variables. The second and third variables in Table 2 (the main dependent variables)
consist of two sets of differences in per capita consumption–Between Seasons 2 and
1, and between Seasons 3 and 2. Per capita consumption is measured by per capita
food expenditure in the week previous to the survey. In these data, expenditure on
food constitutes almost 80 per cent of total expenditure at the household level.
Equation (7) shows that the difference in per capita consumption between Season 2
and Season 1 is a function of Round 2 data, and the difference in per capita
consumption between Season 3 and Season 2 is a function of Round 3 data. The
flowchart in Figure 1 depicts the structure of the data (‘HH’ refers to household).
IV. Estimation
The main aim of micro lending programs is to alleviate poverty. If programs are
deliberately placed in areas that are relatively poorer than others (non-random
program placement), then estimates of the effects of program participation are
necessarily biased. The use of village level fixed effects that capture systematic
differences in attributes across villages, aids in removing this bias. The mC
jtþ1fixed effect
parameters that we estimate in Equation (7) have both a village jand a time (season)
tdimension. The village jdimension controls for non-random program placement.
Individual heterogeneity also needs to be taken into account since those who
choose to participate could be systematically different from non-participants. Once a
credit program is established in a village, participants self-select into groups to
become members of the program. If those who join are more able at managing self-
employment activities, or have higher than average entrepreneurial skill levels as
compared to non-participants, then the estimation of participation effects is biased.
Individual and household level heterogeneity of this type could confound results and
incorrectly attribute to the program those effects that arise from differences in the
nature of household unobservables.
The presence of individual heterogeneity implies that the length of membership
variable (D
ijtþ1
) in Equation (7) is endogenous. Given this, identification of the effect
of membership length requires the use of instrumental variables. If a household is a
target household (owns less than half an acre of land) in a village with the Grameen
program, the household is considered to be eligible and to have the choice to
participate. Where choice is a dummy variable, the interaction of choice and the
exogenous variables ðXC
ijtþ1Þof Equation (7) form the set of identifying instruments.
It is important to note that the exogenous variables of themselves are not the
instruments, the interactions of the exogenous variables with the choice dummy
constitute the instrument set. This is because exogenous variables can have an effect
on length of membership only if the household is eligible and has choice. For
households without choice, length of membership is identically zero. That is:
Dijtþ1¼bDXD
ijtþ1þmD
jtþ1þwijtþ1if choice ¼1
¼0 if choice ¼0
1386 N. Menon

Table 2. Weighted means and standard deviations of dependent variables
Dependent variable Full sample
Target
participants
Target
non-participants Non-participants
Log of per capita food expenditure last
week (expenditure in takas per week)
4.0467 (0.3410) 4.0301 (0.3190) 3.9996 (0.3683) 4.0541 (0.3503)
Difference in the log of per capita food
expenditure last week between seasons 2
and 1 (expenditure in takas per week)
70.0826 (0.3568) 70.0849 (0.3079) 70.0254 (0.3992) 70.0816 (0.3768)
Difference in the log of per capita food
expenditure last week between seasons 3
and 2 (expenditure in takas per week)
70.0902 (0.2588) 70.1010 (0.2390) 70.1128 (0.2610) 70.0854 (0.2672)
Observations 1437 891 369 546
Standard errors in parenthesis.
Grameen Bank Programs 1387

Since length of membership is identically zero for those who do not have choice and
endogenous for participants, the interaction of choice and the exogenous variables
(XD
ijtþ1; the ‘D’ superscript denotes that these are the X
ijtþ1
in the duration of
membership equation immediately above) form the set of instruments. These
instruments affect membership length discontinuously since exogenous variables can
have an effect only if the household owns less than half an acre of land and resides in
a village with a program (that is, if the household has choice). In the following
section, we test the validity of these instruments.
V. Results
Motivational Results
Table 3 reports results of a linear village fixed-effects model where the log of per
capita food expenditure is regressed on (instrumented) length of membership of all
participants (in the interests of brevity, the estimated fixed-effect coefficients are not
reported in Table 3). We acknowledge that this is a selected sample. The purpose of
Table 3 is to provide a cursory check of the relationship between membership length
and food consumption for households that participate in the Grameen program.
Columns (1) and (3) show results for the cubic term of membership duration. Given
the insignificance of the cubic term in Columns (1) and (3), the quadratic form of
Columns (2) and (4) is appropriate. Columns (2) and (4) of Table 3 indicate that
although the predicted length of membership has positive and significant effects on
the log of per capita food expenditure, the square of this variable is negative (except
in Column (4) where the square of length of membership of a female participant is
measured imprecisely). Thus, although membership in the Grameen program
increases food consumption, such returns do not accrue indefinitely in a linear
fashion. There is preliminary evidence in these data that non-linearities in the returns
to participation are present. Most of the other variables in Table (3) have predicted
effects.
Figure 1. Structure of the data
1388 N. Menon

Table 3. Dependent variable: log of per capita food expenditure
Explanatory variable Pooled (1) Pooled (2)
Gender-
stratified (3)
Gender-
stratified (4)
Predicted length of
membership
0.6460** 0.4524**
(0.2087) (0.0789)
Predicted length of
membership squared
70.0651 70.0170
{
(0.0489) (0.0091)
Predicted length of
membership cubed
0.0031
(0.0031)
Predicted length of
membership of male
participant
0.059 0.0693
{
(0.0582) (0.0412)
Predicted length of
membership of male
participant squared
70.01 70.0160*
(0.0251) (0.0075)
Predicted length of
membership of male
participant cubed
70.0005
(0.0021)
Predicted length of
membership of female
participant
0.3281* 0.3123**
(0.1439) (0.1171)
Predicted length of
membership of female
participant squared
0.0046 0.0061
(0.0264) (0.0085)
Predicted length of
membership of female
participant cubed
0.0001
(0.0025)
Log of decimals of
land owned by HH
0.0295** 0.0307** 0.0414** 0.0406**
(0.0039) (0.0037) (0.0090) (0.0083)
Years of education of
HH head
70.0079 70.0085 0.0231* 0.0223*
(0.0062) (0.0061) (0.0117) (0.0112)
Gender of HH
head: ¼1 if male
0.1135** 0.1147** 0.3792** 0.3686**
(0.0398) (0.0398) (0.1095) (0.1008)
Age of HH head 70.0044** 70.0050** 70.0084** 70.0082**
(0.0012) (0.0010) (0.0033) (0.0030)
Highest value of years
of education for a
female in HH
0.0157** 0.0167** 0.0283** 0.0277**
(0.0050) (0.0049) (0.0085) (0.0081)
Highest value of years
of education for a
male in HH
0.0211** 0.0224** 70.004 70.0035
(0.0058) (0.0057) (0.0084) (0.0081)
Dummy for no adult
(416 years) female in HH
1.2465** 1.3373** 1.4753** 1.4179**
(0.1706) (0.1445) (0.5684) (0.5200)
Price of coarse grain rice 0.1215** 0.1245** 0.1066** 0.1056**
(0.0120) (0.0117) (0.0145) (0.0140)
Price of wheat flour 0.0415* 0.0483** 0.0433
{
0.0414
{
(0.0170) (0.0155) (0.0239) (0.0225)
Observations 891 891 891 891
R-squared 0.996 0.996 0.996 0.996
Model includes village-time fixed effect parameters.
Standard errors in parentheses.
{
significant at 10%; *significant at 5%; **significant at 1%.
Grameen Bank Programs 1389

Main Results
Table 4 reports the results from the non-linear village fixed-effects estimation of
Equation (7) in Round 2 (estimates in Round 3 were insignificant), for the pooled
and gender-stratified models (in the latter, separate coefficients are allowed for the
length of membership of male and female participants). Both the pooled and gender-
stratified models include village–time fixed effect parameters, but these are not
reported in order to conserve space.
4
The dependent variable in Table 4 is the
difference in log per capita food expenditure between Season 2 and Season 1, and the
main right hand side variable of interest is the predicted (instrumented) length of
membership. An F-test that the identifying instruments (interactions of choice and
the exogenous variables) are jointly zero is strongly rejected (F(30, 897) ¼8.33,
Prob 4F¼0.00000). Thus our instruments are valid.
Table 4. Dependent variable: difference in log food expenditure b/w seasons 2 and 1
Explanatory variable Pooled (1) Gender-stratified (2)
Predicted length of membership 70.7318**
(0.2563)
Predicted length of membership
squared
0.1854**
(0.0598)
Predicted length of membership
of male participant
70.6117*
(0.2594)
Predicted length of membership
of male participant squared
0.1565**
(0.0567)
Predicted length of membership
of female participant
70.7327*
(0.2994)
Predicted length of membership
of female participant squared
0.2786*
(0.1313)
Log of decimals of land owned
by HH
70.0343 0.0022
(0.0407) (0.0343)
Years of education of HH head 0.6345 0.6769
{
(0.4152) (0.3943)
Gender of HH head: ¼1 if male 71.1942** 70.4222
(0.3582) (0.4361)
Age of HH head 70.0369** 70.0178
{
(0.0112) (0.0099)
Highest value of years of education
for a female in HH
0.2107** 0.2117**
(0.0389) (0.0373)
Highest value of years of education
for a male in HH
70.7226
{
70.7713
{
(0.4152) (0.3953)
Dummy for no adult (416 years)
female in HH
0.5753 0.3129
(0.5828) (0.5911)
Difference in the log of price of rice
between seasons 2 and 1
70.4513
{
70.5771
{
(0.2722) (0.3301)
Observations 479 479
R-squared 0.329 0.3370
Model includes village-time fixed effect parameters.
Standard errors in parentheses.
{
significant at 10%; *significant at 5%; **significant at 1%.
1390 N. Menon

Columns (1) and (2) of Table 4 show that (instrumented) membership has a
significant effect on smoothing inter-seasonal consumption changes. Note that
although the coefficient on the linear term is negative (in keeping with the hypothesis
that experienced members face smaller deviations in seasonal consumption), the
coefficient on the squared term is significant and positive, indicating the presence of
diminishing returns. Estimates from the gender-stratified and pooled models suggest
that the maximum effect of participation is at about two years. After approximately
four years, the length of membership variable has a substantially reduced mitigating
effect on seasonal shocks in both models.
Table 4 also reports that male-headed households experience smaller consumption
fluctuations across seasons, as do households with older heads. As expected,
households with educated males experience reduced deviations in inter-seasonal
consumption. Unexpectedly, higher levels of female education increase consumption
differences across seasons. This counter-intuitive result may be explained by the fact
that households with highly educated females are also comparatively more rich. Such
households may spend on luxury food items that are available on a seasonal basis.
The price of rice variable has the expected effect in Table 4.
It is important to note that although the length of membership variable produces little
dampening effects on seasonal shocks after four years, the household’s cost of borrowing
need not increase since other exogenous variables (such as gender and age of household
head) still play a mitigating role. In other words, it is not the case that after approximately
four years of membership, participants (again) experience increased volatility in their
consumption patterns. These data suggest that after a certain threshold, membership in
the Grameen program (as measured by the length of participation variable) succeeds in
reducing the vulnerability of the poor to seasonal shocks.
VI. Support for Results
Influence of Village–Level Heterogeneity
As opposed to village–time (village–season) shocks, patterns of inter-seasonal food
consumption may be influenced by village–level differences alone. This implies that
in the context of the model, membership should be interacted with a village dummy
instead of a village–season dummy.
As noted before, the m
jt
parameters of Equation (7) capture both village (j) and
season (t) variations. Since the m
jt
parameters have a village and a time dimension,
they already allow for village-level heterogeneity. However, it is possible to model a
less general form by interacting membership with village dummies alone (m
j
). This is
relatively more restrictive, since the m
j
do not allow for seasonal effects. If the mC
jtþ1
parameters of Equation (7) include the m
j
as a special case, then we expect few
significant changes to the main results of Table 4. That is, length of membership
should still have negative effects on inter-seasonal consumption changes, and these
effects should decline over time.
Table 5 shows that our main results hold when membership is interacted with only
village dummies, as opposed to village-time dummies. The dependent variable in
Table 5 is the difference in food consumption between Seasons 2 and 1 in Round 2,
and the difference in food consumption between Seasons 3 and 2 in Round 3. Thus in
Grameen Bank Programs 1391

Round 2, the dependent variable of Table 5 is the same as that in Table 4 (main
results). As is clear from Column (1), length of membership has a negative effect
(significant at the 11.8 per cent level) which becomes smaller over time. In the
gender-stratified model of Column (2), estimates of the length of participation of
male members are consistent with our main results. For female participants, length
of membership has the correct sign although it is measured with error. Hence as
expected, results of the pooled and gender-stratified models of Table 5 are broadly
consistent with the hypothesis of this study. Other variables in Table 5 have
predicted effects.
5
Table 5. Dependent variable: difference in log food expenditure b/w Seasons 2 and 1 in
Round 2 and b/w Seasons 3 and 2 in Round 3
Explanatory variable Pooled (1) Gender-stratified (2)
Predicted length of membership 70.2765
(0.1768)
Predicted length of membership squared 0.0911*
(0.0436)
Predicted length of membership of male
participant
70.7719**
(0.1863)
Predicted length of membership of male
participant squared
0.0394*
(0.0175)
Predicted length of membership of female
participant
79.2068
(64.0407)
Predicted length of membership of female
participant squared
70.1599
(0.1140)
Log of decimals of landowned by HH 70.0082 0.9667
(0.0276) (6.3779)
Years of education of HH head 0.2596
{
0.5471
(0.1400) (0.6344)
Gender of HH head: ¼1 if male 70.5412* 3.1182
(0.2610) (23.2978)
Age of HH head 70.0128
{
0.2787
(0.0069) (1.8245)
Highest value of years of education for a
female in HH
0.1741** 70.0471
(0.0303) (1.8267)
Highest value of years of education for a male
in HH
70.3308* 70.8393
(0.1405) (1.1305)
Dummy for no adult (416 years) female
in HH
0.5513 712.8684
(0.4720) (96.4555)
Difference in the log price of rice and wheat
flour b/w seasons 2 and 1 in round 2 and b/w
seasons 3 and 2 in round 3
0.9341** 0.9574**
(0.1489) (0.1388)
Dummy for season 3 (Aus) 74.2458 73.1851
(4.2593) (4.1308)
Observations 958 958
R-squared 0.24 0.195
Model includes village fixed effect parameters.
Standard errors in parentheses.
{
significant at 10%; *significant at 5%; **significant at 1%.
1392 N. Menon

Functional Form of Length of Participation
The theory in Section II derives from a model in which the length of participation
variable is interacted with village–season shocks. This allows us to gauge the extent
to which membership in the Grameen program dampens the effect of seasonal
shocks to per capita food consumption. The interaction of membership and village–
time shocks is an intuitive way to study consumption smoothing behavior among
Grameen participants.
However, it is possible to estimate the effect of membership separately from its
interaction with village–time shocks. Although it does not directly follow from the
model of consumption smoothing derived in Section II, length of participation may
be entered as a separate term in Equation (7).
6
Table 6 reports the results of the
estimation where membership is entered separately in the model of Table 4. As is
clear from both the pooled model of Column (1) and the gender-stratified model of
Column (2), length of participation variables that are entered separately (that is, not
interacted with village–time shocks) have little effect on inter-seasonal consumption
changes. That length of membership acts to smooth consumption by buffering
seasonal shocks is evident from the first two terms of Column (1) and the first four
terms of Column (2). In support of our hypothesis, these terms in Table 6 show that
membership length (interacted with village–season shocks) reduces inter-seasonal
consumption variation. Again, there is strong evidence that such returns to
participation decline with time in the program.
Simultaneous Estimation of Both Sets of Consumption Changes
The theoretical model of Section II indicates that the change in consumption
between Seasons 2 and 1 is a function of Season 2 (Round 2) variables. Hence, the
difference in consumption between Seasons 2 and 1 is a function of Village–season 2
dummies (that is, dummies for the 24 Grameen villages in Season 2). Similarly, the
difference in consumption between Seasons 3 and 2 is a function of village–season 3
dummies (dummies for the 24 Grameen villages in Season 3). If we consider only one
set of inter-seasonal consumption changes (as is done in the main results of Table 4),
then the model includes village–time dummies that are specific to only one season
(Season 2 in Table 4, since the dependent variable is the change in consumption
between Seasons 2 and 1).
7
It is possible to measure the effect of two dummies (since Bangladesh has three
rice-based seasons) if both sets of consumption changes are estimated simulta-
neously, Table 7 reports the results of this estimation (although dummies for both
Season 2 and Season 3 are included in the model, these are not reported in the
interests of brevity). As evident, the pooled and the gender-stratified models in
Season 2 have the same effects as before, with membership length reducing inter-
seasonal consumption changes. Consistent with results obtained above, there is
evidence that benefits from membership decline over time. With the simultaneous
estimation of both sets of consumption changes, length of participation in Season 3
(in Column (1) of the pooled model) has the expected influence on changes in
consumption between Season 3 and Season 2 (this was not the case before). There is
also evidence that returns to participation decline over time. However, the effects of
Grameen Bank Programs 1393

membership length in Season 3 are still measured imprecisely in the gender-stratified
model (Column (2)). To summarise, the simultaneous estimation of both sets of
dependent variables which allows for two seasonal dummies provides results that are
consistent with the main findings of this study.
Table 6. Dependent variable: difference in log food expenditure b/w Seasons 2 and 1
Explanatory variable Pooled (1) Gender-stratified (2)
Predicted length of membership 70.7864**
(0.2658)
Predicted length of membership squared 0.1983**
(0.0621)
Predicted length of membership of male
participant
70.0560*
(0.0241)
Predicted length of membership of male
participant squared
0.0011*
(0.0005)
Predicted length of membership of female
participant
70.0689**
(0.0252)
Predicted length of membership of female
participant squared
0.0022**
(0.0008)
Log of decimals of land owned by HH 70.0358 70.001
(0.0421) (0.0353)
Years of education of HH head 0.5914 0.6967
{
(0.3682) (0.4022)
Gender of HH head: ¼1 if male 71.1574** 70.4172
(0.3618) (0.4520)
Age of HH head 70.0359** 70.0189
{
(0.0114) (0.0103)
Highest value of years of education for a
female in HH
0.2216** 0.2267**
(0.0403) (0.0403)
Highest value of years of education for a
male in HH
70.6822
{
70.7970*
(0.3685) (0.4036)
Dummy for no adult (416 years) female
in HH
0.5056 0.2294
(0.6012) (0.6075)
Predicted length of membership
@
70.0306
(0.0273)
Predicted length of membership squared
@
0.006
(0.0083)
Predicted length of membership of male
participant
@
0.0001
(0.0043)
Predicted length of membership of male
participant squared
@
0.000001
(0.0001)
Predicted length of membership of female
participant
@
0.0003
(0.0035)
Predicted length of membership of female
participant squared
@
70.0001
(0.0001)
Difference in the log of price of rice between
seasons 2 and 1
@
70.4720
{
70.5870
{
(0.2728) (0.3301)
Observations 479 479
R-squared 0.333 0.341
@
Variable entered separately in model in additive form.
Model includes village-time fixed effect parameters. Standard errors in parentheses.
{
significant at 10%; *significant at 5%; **significant at 1%.
1394 N. Menon

Table 7. Dependent variable: difference in log food expenditure between Seasons 2 and 1 in
Round 2 and between Seasons 3 and 2 in Round 3
Explanatory variable Pooled (1) Gender-stratified (2)
Predicted length of membership – season 2 70.7277**
(0.2207)
Predicted length of membership
squared – season 2
0.1822**
(0.0508)
Predicted length of membership of male
participant – season 2
70.0507**
(0.0185)
Predicted length of membership of male
participant squared – season 2
0.0011**
(0.0003)
Predicted length of membership of
female participant – season 2
70.0609**
(0.0210)
Predicted length of membership of
female participant squared – season 2
0.0019*
(0.0007)
Log of decimals of land owned
by HH – season 2
70.0361 0.0008
(0.0357) (0.0299)
Years of education of
HH head – season 2
0.6194{0.6821*
(0.3474) (0.3464)
Gender of HH head: ¼1if
male – season 2
71.1882** 70.3917
(0.3120) (0.3826)
Age of HH head – season 2 70.0367** 70.0175*
(0.0097) (0.0086)
Highest value of years of education
for a female in HH – season 2
0.2119** 0.2124**
(0.0339) (0.0324)
Highest value of years of education
for a male in HH – season 2
70.7080* 70.7773*
(0.3474) (0.3473)
Dummy for no adult (416 years)
female in HH – season 2
0.5399 0.268
(0.5118) (0.5151)
Difference in the log of price of
rice between seasons 2 and 1
70.4517
{
70.5728*
(0.2372) (0.2878)
Predicted length of membership – season 3 74.1708**
(1.1814)
Predicted length of membership
squared – season 3
0.6679**
(0.1971)
Predicted length of membership
of male participant – season 3
0.188
(0.1946)
Predicted length of membership
of male participant squared – season 3
70.0066{
(0.0038)
Predicted length of membership
of female participant – season 3
70.2373
(0.1562)
Predicted length of membership of
female participant squared – season 3
70.0477
(0.0347)
Log of decimals of land owned
by HH – season 3
70.4880** 70.0851
(0.1811) (0.2254)
Years of education of HH head – season 3 70.0129 0.175
(0.0962) (0.2090)
Age of HH head – season 3 0.0454** 0.0724*
(0.0164) (0.0344)
Highest value of years of education
for a female in HH – season 3
0.5439** 0.9634*
(0.1437) (0.3919)
(continued)
Grameen Bank Programs 1395

Cohort Effects
If the earlier cohort of participants was more able as compared to later cohorts, then
the onset of diminishing returns for experienced members (which is indicative of
easier capital access, greater assets, and so on) could be driven by this characteristic.
Given the nature of the data used in Table 4, conventional tests for cohort effects
cannot be conducted (note that endogeneity due to self-selection is already
controlled for with the use of instrumental variables). But using the education of
the household head as a proxy for ability, it is possible to study the relationship
between ability and length of membership conditioning on village-level effects. A
graph of the lag between program introduction and joining against the household
head’s schooling in Grameen villages that have had the program for more than 8.67
years (the upper 90 per cent of length of time a program has been present in a village)
and those that have had the program for less than 3.5 years (the lower 10 per cent),
provides evidence of ability bias if the distribution of participants who joined first in
either case is higher than those who joined later. If the plot for the distribution of
early participants is higher (as measured with respect to the household head’s
education), then this would suggest that regardless of when a program begins, able
people are the first to join the program. Figure 2 controls for village-level fixed effects
and shows a Lowess smoothed plot of log education of the household head (adjusted
for differences in average schooling) and the lag between program availability and
time of joining (separately for the two groups of villages mentioned above). The lag
variable (plotted along the x-axis) is the gap between the time the program was set up
in the village, and the time that a particular household in that village became a
member. Figure 3 is a clearer view of Figure 2, and shows the data for participants
who joined within the first three and a half years (lag 3.5) of the program’s
operation in the two groups of villages mentioned above. From Figures 2 and 3, it is
evident that there is no consistent pattern in the data to support the claim that high
ability people always join first. Thus diminishing returns to consumption smoothing
Table 7. (Continued)
Explanatory variable Pooled (1) Gender-stratified (2)
Highest value of years of education
for a male in HH – season 3
0.2735** 0.2642
(0.0998) (0.1920)
Dummy for no adult (416 years)
female in HH – season 3
1.9076
(2.7828)
Difference in the log of price of rice
between seasons 3 and 2
0.7860** 0.7644**
(0.1014) (0.0952)
Observations 958 958
R-squared 0.298 0.308
Model includes village-season 2 and village-season 3 dummies.
Standard errors in parentheses.
{
significant at 10%; *significant at 5%; **significant at 1%.
Dummy for no adult female in HH was not identified for the pooled model in season 3.
Gender of HH head for both the pooled and gender-stratified models was not identified in
season 3.
1396 N. Menon

Figure 2. Lag between program availability and joining versus household head’s schooling in
Grameen Thanas
Figure 3. Lag between program availability and joining versus household head’s schooling in
Grameen Thanas
Grameen Bank Programs 1397

benefits for experienced members are not being driven by the fact that earlier cohorts
of participants were more able.
Robustness Check for Length of Membership
In order to ensure that results are being driven by length of membership as opposed
to just participation, several tests were conducted. A dummy for participation was
introduced into the model of Equation (7). With control for participation, both
length of membership and the dummy for participation become insignificant. When
the model is estimated with only the participation dummy, its effect is insignificant.
Hence, results are being driven by the length of membership variable and not by
participation alone.
VII. Conclusion and Policy Implications
This paper examines the presence of non-linearities in consumption smoothing
benefits for experienced participants of micro-lending programs. A fixed effects non-
linear least squares estimation of data from 24 villages of the Grameen Bank
suggests that returns to membership do vary by length of participation. Estimates
from pooled and gender-stratified models indicate that the maximum dampening
effect on seasonal shocks occurs at about two years of participation. This suggests
that after approximately four years of participation, length of membership has a
weak mitigating influence on per capita consumption fluctuations caused by seasonal
shocks. A section of the paper is devoted to conducting various robustness checks.
Our results remain unaltered.
Although this study does not directly link repayment rates to length of
membership and the ability to smooth seasonal shocks, declining consumption-
smoothing returns may provide a possible explanation for why more experienced
members miss installments on outstanding loans (anecdotal evidence suggests that
this is the case). If marginal returns to membership decline over time in the
program, then experienced participants benefit (where benefits are solely measured
in terms of the ability to smooth seasonal shocks) to a lower extent than those who
have recently joined. If such returns drive self-selection into these programs
(Pitt and Khandker (2002) show that consumption poverty in the lean season
motivates self-selection into these programs), then missed payments may result
when marginal returns begin to decline. Again, there could be several reasons for
why experienced members choose to skip installments, and given the analysis here,
we cannot say that declining returns are the only reason for this phenomenon.
However, we suggest that smaller consumption smoothing benefits over time could
provide one possible explanation.
The results of this research have important implications for program structuring.
In order to avoid strategic behavior over time, the lending and repayment terms for
experienced members may need to be different (as compared to those for less
experienced members). Eligibility to join these program (wealth status) and to
remain a participant needs to be re-evaluated at regular intervals, instead of just once
at the very beginning as is now the practice. Although smaller benefits over time
may signal that micro-finance programs are succeeding in making their clients
1398 N. Menon

self-sufficient, recognition of the fact that the nature of participants changes over
time will help these programs become more cost-effective in the future.
Acknowledgements
I am indebted to Mark Pitt, Andrew Foster, Moshe Buchinsky, Shahid Khandker,
Faruq Iqbal, Jonathan Morduch, Rachel McCulloch, Gary Jefferson, and
Narayanan Subramanian. Thanks a1so to seminar participants at Brown, George-
town, The World Bank, and Brandeis University. I am grateful to two anonymous
referees whose comments have greatly improved the paper. Support from the
Hewlett foundation is acknowledged. I am responsible for all errors that remain.
Notes
1. As noted before, given a poor household’s inability to cope with either kind of shock, the model does
not differentiate between the effects of exogenous unforeseen shocks and anticipated shocks from
seasonality.
2. Note that for those households that face a below average cost of borrowing, (r
ijt
–r
jt
) may be negative.
This is not problematic here since there are practically no such households in these data.
3. Length of membership is not endogenous due to drop out rates. In these data, only two households are
reported to have dropped out of the program between the first and third rounds.
4. We estimate fixed effect parameters for all 24 Grameen villages by excluding the constant term from the
model.
5. The positive sign on the price variable indicates that the demand for rice and wheat are relatively
inelastic. This is not unexpected since rice and wheat are staple food grains in Bangladesh.
6. The model thus becomes:
Dln Cijtþ1¼editþ1Dijtþ1þd2tþ1D2
ijtþ1þbCXC
ijtþ1

mC
jtþ1þd3tþ1Dijtþ1þd4tþ1D2
ijtþ1þg2Dln Pjtþ1þnijtþ1
7. As noted above, when we estimate each set of inter-seasonal consumption changes separately, length of
participation in Season 3 does not have significant effects in reducing consumption fluctuations between
Seasons 3 and 2.
8. In the absence of land ownership, only those households whose assets are less than or equal to the value
of one acre of medium quality land in that area are eligible to participate.
References
Chowdhury, O. H. (1992) Credit in rural Bangladesh, Asian Economic Review, 34(2).
Copestake, J., Bhalotra, S. and Johnson, S. (200l) Assessing the impact of Microcredit: a Zambian case
study, Journal of Development Studies, 37(4), pp. 81–100.
Dadhich, C. L. (197l) Willful default of co-operative credit in Rajasthan, Indian Journal of Agricultural
Economics, 26(4), pp. 453–4.
Flavin, M. (1981) The adjustment of consumption to changing expectations about future income, Journal
of Political Economy, 89(5), pp. 974–1009.
Foster, A. D. (1995) Prices, credit markets and child growth in low-income rural Areas, Economic Journal,
105(430), pp. 551–70.
Iqbal, F. (1988) The determinants of moneylender interest rates: evidence from rural India, Journal of
Development Studies, 24(3), pp. 364–78.
Khandker, S., Samad, H. and Khan, Z. (1998) Income and employment effects of micro-credit programs:
village-level evidence from Bangladesh, Journal of Development Studies, 35(2), pp. 96–124.
Lawrance, E. (1991) Poverty and the rate of time preference: evidence from panel data, Journal of Political
Economy, 99(1), pp. 54–77.
Grameen Bank Programs 1399

Mohan, T. C., Veerasamy, T. and Sivaraman, S. (1985) Bayesian analysis of crop loan overdues in
Tamil Nadu, Indian Journal of Agricultural Economics, 40(4), pp. 549–55.
Pitt, M. M. and Khandker, S. (2002) Credit programs for the poor and seasonality in rural Bangladesh,
Journal of Development Studies, 39(2), pp. 1–24.
Von Pischke, J. D., Yaron, J. and Zander, R. (1999) Why credit project repayment performance declines.
Submitted to Savings and Development.
Wenner, M. (1995) Group credit: a means to improve information transfer and loan repayment
performance, Economic Development and Cultural Change, 32(2), pp. 263–81.
Yaqub, S. (1995) Empowered to default? Evidence from BRAC’s micro-credit programs, Small Enterprise
Development, 6(4), pp. 4–13.
Zeldes, S. P. (1989) Consumption and liquidity constraints: an empirical investigation, Journal of Political
Economy, 97(2), pp. 305–46.
Appendix
In order to understand some of the key features of the system, consider the Grameen
Bank. Only those owning less than half an acre of cultivable land are eligible to
participate in such program,
8
and loans are given to groups of five members at the
same point in time. These loans are marked for use in non-agricultural self-
employment activities. Since default by any one member disqualifies other group
members from access to future loans, participants in a group become co-signatories
for each other. Joint lending leads to peer monitoring, which, in turn, ensures low
default rates.
Another unique facet of such programs is that general loans have to be repaid
within one year, in equal weekly installment payments. Grameen also dispenses
housing and seasonal loans that follow a different schedule. Housing loans, given
their size, can be repaid over the course of several years, whereas seasonal loans have
to be repaid within six months. Collection of these installment payments occurs at
weekly village meetings which are public. Thus, inability to meet that week’s
installment cannot be kept hidden from other people in the village. Given the large
transaction costs of weekly collection, Grameen is considering moving to a
fortnightly schedule such as that followed by the Bangladesh Rural Advancement
Committee (BRAC).
In addition to dispensing loans, Grameen also inculcates values of cleanliness,
hygiene, and the importance of education. These values are embodied in the ‘Sixteen
Decisions’, also called the ‘Social Development Constitution’. Other micro-credit
programs such as those run by BRAC and ASA have similar features, although they
do differ slightly in terms of when installments are collected and in their overall
approach. For example, BRAC invests in rural development projects in addition to
having a lending program. Grameen is more of a ‘pure’ lending organization, but it
has branched out into providing subsidised electricity and telecommunications in the
recent past.
1400 N. Menon