A Welfare Economic Analysis
of the Impact of Microfinance in Bangladesh
AKM Ghulam Hussain
Department of Economics
University of Dhaka
Nigar Nargis
Department of Economics
University of Dhaka
April 30, 2008
Correspondence: Nigar Nargis, Assistant Professor, Department of Economics,
University of Dhaka, Nilkhet, Dhaka-1000, Bangladesh, Phone: (880) 2-8011151, Fax:
(880) 2-8615583, E-mail: nnargis2100@yahoo.com.
Abstract
Using data of about 2700 households in Bangladesh from a longitudinal survey for the
period of 1998-2004, this study undertakes a welfare economic analysis of the impact of
microcredit program in Bangladesh. First, the welfare dominance approach shows that
aggregate welfare of the rural households improved over the period of 1998-2004.
Household income increased across all income percentiles for all of regular, occasional
and non-participant groups. Regular participants experienced the lowest welfare gain and
non-participants gained most. By the abbreviated social welfare function approach, on the
other hand, the welfare outcome appears ambiguous. The reallocation of labor resource
from farming to non-agricultural self-employment opportunities resulted in income and
productivity growth and poverty fell across all groups by MFI participation status.
Average work hour, however, did not increase leaving the status quo of pervasive
underemployment in the rural economy of Bangladesh. Moreover, both between and
within group inequality increased.
When we break down the sample of households by the status of eligibility to participate
in a microcredit program, we find that eligible participants performed no better than the
eligible non-participants and non-eligible participants fared far worse than non-eligible
non-participants. It appears that the contribution of microcredit program participation lies
in helping the households at the lower side of the economic strata to keep up with the rest
of the society possessing comparable initial endowments. On the higher side, it appears to
even detract from their growth potential. Thus this study finds evidence against the
popular belief that microcredit is instrumental to uplifting of the rural poor to a higher
economic status. There is no denying the fact that poverty would have been alleviated at a
faster pace if it was accompanied by income and productivity growth with greater
employment opportunities and reduced inequality.
1
I. Introduction
The micro credit program in Bangladesh began its journey in the late 1970s and assumed
maturity by the mid-1990s. The following decade has seen replications of the program in
different parts of the country so numerously that any village without such interventions
can hardly be found. The history of microfinance revolution in the last quarter of a
century is marked with successes and failures which have been recorded in a wide array
of literature1. These studies so far focused on the absolute impact of microfinance in
terms of consumption increase (Khadakar, 2001), poverty reduction (Khandakar,
Hossain, 1988) and improvement in social indicators such as women empowerment
(Matin, 1996). However, none of these studies investigated income growth, employment
generation, poverty alleviation and income equalization across the microcredit participant
and non-participant groups over time irrespective of their eligibility status for the
participation in microcredit program. As such, the overall change in the welfare status of
rural households in Bangladesh in the wake of microcredit revolution is yet to be
analyzed.
In this paper, we first take an overview of the aggregate welfare economic change over
time aiming at a more complete understanding of the role microcredit can potentially play
in welfare improvement in rural Bangladesh. Against this background, we then undertake
the welfare analysis across microcredit participant and non-participant groups by their
eligibility status in an attempt to isolate the marginal contribution of microcredit in
welfare improvement.
2
This paper addresses the following questions. First, how have the participants and the
non-participants of the microcredit program shared the overall economic growth
performance? Second, to what extent has the micro credit program stimulated selfemployment activities and income generation, thereby leading to poverty reduction, of its
beneficiaries relative to the non-participants? Third, has this growth been accompanied
by increased (or reduced) inequality, both between and within participant and nonparticipant groups?
The paper is organized in eight sections. Section II lays out the analytical framework for
the study. Section III describes the income growth experience of rural households, first
overall and then by participation status in the microcredit program. Section IV
decomposes household income to trace out the source of income growth. Section V
investigates the accompanying growth and redistribution of employment among wage
employment, farming, non-farm agricultural activities and non-agricultural selfemployment activities. Section VI reports the resultant state of poverty and inequality
associated with the observed income and employment growth. Section VII presents the
welfare analysis by the eligibility status of rural households and identifies the marginal
contribution that microcredit plays in this respect. The paper summarizes its findings and
concludes in Section VIII
3
II. Analytical framework
The data used for the analysis comes from a panel of about 2700 households that was
collected during 1997-2004 in four waves to evaluate the socio-economic impact of
microcredit in a quasi-experimental set-up involving comparison between program and
control villages (dispersed throughout the country) and between participants and nonparticipants in the program villages (Zohir et al. 2001; Rahman et al. 2005).1 Household
participation status was defined in terms of current membership of either member of a
household with microfinance institutions (MFIs). The households were grouped into
eligible participants, eligible non-participants, non-eligible participants and non-eligible
non-participants. As the criterion for eligibility varies across MFIs, the ownership of 50
decimals or less of cultivable land was used to define eligibility.
The sample households were broadly classified into three groups on the basis of
individual affiliation with MFIs:
(i)
regular participants: participants in any microcredit program in all four rounds
of the longitudinal survey;
(ii)
occasional participants: who may drop out for a period but may rejoin;
(iii)
non-participants: those who never participated in any microcredit program
over the observation period.
For the present analysis, we maintain this classification throughout the paper. Within the
participant categories, the convenience of occasional participation as opposed to the
persistent dependence on microcredit is expected to result in better performance of
1
However, no control villages could be discerned when the same villages were revisited in 2004, as is
expected given the rapid rate of expansion of the microfinance institutions (MFIs) in rural Bangladesh.
4
occasional participants than the regular participants. We have considered the changes
over two surveys conducted in 1997-98 and 2004 to allow a considerable length of time
to elapse between the beginning and the end of the observation period such that the long
term welfare impacts are accounted for.
The welfare impact of changes in income distribution can be captured using the welfare
dominance approach due to Hadar and Russell (1969) and Saposnik (1981). As the
theorem says, “… one distribution X first-order-dominates another distribution Y for the
class of anonymous, increasing social welfare functions if and only if the income of the
person in each rank in X is at least as great as the income of the person with the
corresponding rank in Y and strictly greater someplace” (Fields 2005).
By the axiom of anonymity of individuals in the income distribution, this theorem does
not require that the same person be followed over time. What the welfare dominance
method suggests is that we make welfare comparison between the beginning and the end
of the observation period undergoing some program intervention on the basis of the
comparison of income percentiles for example. If income increases at every percentile,
we can conclude that welfare has improved. Since this approach involves overall welfare
comparison, we have used total household income for determining the implied rank
dominance.
For a deeper understanding of the rural labor market outcomes due to microcredit
program participation, we couch the analysis in the comparative static framework
5
following Fields (2005). Fields studied theoretically the welfare consequences of labor
market policy changes in modern-versus-traditional sector or urban-versus-rural sector
environment of the Harris-Todaro model. On the other hand, the present analysis is
limited to the rural sector characterized by traditional land based or dependent
employment vis-à-vis growing self-employment opportunities, presumably created by the
extensive network of the MFIs in the country.
In a paradoxical labor market where per capita income is one of the lowest in the world
but reported official unemployment rate is no more than 5%, it is clearly the extent of
underemployment rather than the unemployment rate that should be its health indicator.
Thus the criterion of welfare judgment regarding employment performance is that the
increase in the number of employed should be accompanied by growth of work intensity
which would be reflected in the growth in annual average working hour per person.
Given wage rate, average annual hours worked can, therefore, be used as a proxy of
effective employment. This two-dimensional employment growth should cause
household labor earnings to grow and poverty to fall. If the process of all these
simultaneous changes takes place with lower inequality of income, we are able to
conclude unambiguously that social welfare has increased.
The class of social welfare functions that summarize these welfare judgments is named
abbreviated social welfare function, which can be formally expressed as follows in the
present context:
W = f (household labor earnings; annual average work hour; poverty; inequality).
6
Each of the argument in the welfare function is a vector of three elements representing
the corresponding outcomes for three groups of the population: regular, occasional and
non-participants of the microcredit program. So f i j would denote the conditional partial
derivative that represents the change in W in response to a given change in the i th
argument for the j th group (j = regular, occasional, non-participant). The spill-over
effects of microcredit program enjoyed by the non-participants are included in their
corresponding partial derivative.
The welfare evaluation criteria can be written in the order of the arguments in the welfare
function as:
f1 j > 0 ;
f 2j > 0 ;
f 3j < 0 ;
f 4j < 0 .
Without any ad hoc functional specification of the abbreviated social welfare function, its
partial derivatives can be identified with the change of outcome over time conditional on
the participation status of households or individuals. The sign of the change in W can be
ascertained provided all the changes are aligned in the same direction. It should be noted
that while increasing income increases welfare unambiguously, increasing work hour
may not necessarily do so. This is because there is a disutility from work arising from
lower time for leisure. Nevertheless, we maintain the criterion that the partial derivative
for work hour is positive in view of the fact that in an economy with severe
underemployment and income shortfall from the minimum requirements, increase in
work hour leads to increasing welfare.
7
The ranking of these outcomes across the groups would indicate whether one group is
doing better than the other, in general. Their relative performance can then be attributed
to the participation status once we break down the analysis by the eligibility status of the
participant and non-participant households. Thus a normative evaluation of the impact of
microcredit program on the rural labor market conditions in Bangladesh can be
performed.
III. Growth in household income2
The annual income of households is calculated by adding the sales value of annual
household production of crops, livestock and fishery, earnings from wage employment,
non-agricultural self-employment, and other sources such as service, help from relatives,
relief, food for education, remittance, house rent, etc. The average annual household
income grew at an annual compound rate of 3.88% to rise from Tk.48,195 in 1998 to
Tk.60,546 in 2004 (Table 1). Comparing the percentiles of annual household income
between 1998 and 2004, we find that growth occurred across all the income strata. This
change in household income distribution is shown in Figure 1a by the upward shift in the
curve of annual household income percentiles. In particular, the median income increased
from Tk.36,008 to Tk.41,550 marking an annual compound rate of growth of 2.41%.
This rank dominance of the income distribution in 2004 over that in 1998 implies that the
income of the household in each rank in 2004 is greater than the income of the household
with the corresponding rank in 1998 for every percentile. In other words, the household
that ranks poorest in each distribution has a higher real income in 2004 than in 1998, and
2
All the income figures are converted to 2004 constant prices.
8
likewise for the second poorest household, the third, and so on. It in turn implies that
poverty diminished in 2004 compared to 1998.
The remarkable growth over 1998-2004 was, however, not uniform across the income
distribution of the sample households. It concentrated at the upper half of the households,
peaking for the top 1% of the income distribution and exponentially decreasing up to the
bottom income stratum (Figure 1b). It suggests that income inequality increased in the
rural economy during the period under observation.
Two questions are pertinent as far as of the role of microcredit is concerned. First, how
this unequal growth performance was shared by the regular, occasional, and nonparticipants of the microcredit program. In other words, what the contribution of between
group inequality was to the observed unequal household income growth. Second, to what
extent this unequal growth is a result of within group inequality in income.
Comparing the annual average household income levels by participation status, we find
that non-participants ranked highest followed by the occasional and then the regular
participants at both the beginning and the end of the observation period (Table 1). The
same order is observed in the gain in average income levels over the period under
observation. The gain experienced by non-participants is twice as high as that of regular
participants. As a consequence, the average income gap widened between these groups
over time.
9
While all the three groups of households across all the income percentiles experienced
growth in household income, the overall growth performance seems to have been
primarily driven by growth at the top quartile of households as shown in Figure 2a.
Among the three groups, regular participants experienced the least increase. In order to
have a closer look at the growth differences across groups, we have blown up the figure
from 1st to 75th percentiles for both years in Figure 2b. It shows that the growth level
curves of occasional and non-participants crisscrossed each other at several points below
the 75th percentile suggesting similar income gain of this two groups at the lower three
quartiles. At the top quartile, however, non-participants dominated.
IV. Decomposition of household income
To figure out what stunts the growth of household income for regular participants
compared to occasional- and non-participants, we resort to the decomposition of total
household income by two broad sources—endogenous labor income and exogenous
income. By endogenous labor, we mean engagement of household members in wage
employment, household agricultural activities including farming and non-farm
agricultural activities, and non-agricultural self-employment activities within their
locality. Income from this source is likely to depend on, among others, household
participation status in any microcredit program. Exogenous income, on the other hand,
comes largely from foreign remittance, besides services outside the locality, rental
income from assets, and transfers. This is the unearned income accruing to the
individuals living in the household and is unlikely to depend on their MFI participation
status.
10
As shown in Table 1, the growth of endogenous labor income of regular participants fell
short of that for the other two groups of households. As a result, despite beginning with
the higher average of the three, regular participant households ended up with the lowest
average endogenous labor income by 2004. Thus it appears that the growth in
endogenous labor income was not equalizing across participation status of households. In
terms of average exogenous income, regular participants began from an initially
disadvantageous position and maintained it through 2004 as shown in Table 1. Nonparticipant households took the lead in both endogenous labor income and exogenous
income growth.
In terms of per capita income (total household income divided by household size), regular
and occasional participants did not perform any better in 2004 than in 1998 (Table 2).
Non-participants, on the other hand, added Tk.1,400 to per capita income on average.
However, the comparison of income growth performance in terms of per capita income in
the traditional rural setup in Bangladesh is misleading in the present context. It
understates the welfare gain from income growth for young households with
proportionately larger number of young children compared to the senior households with
larger proportion of working age adults. To address this concern and keeping in view the
presumption that the role of microcredit is mostly reflected in the growth performance of
endogenous labor income, we have looked at the endogenous labor income per working
member of the household. This variable is essentially a measure of individual labor
productivity. By this measure, as shown in Table 2, regular participant households made
the least progress among the three groups. Occasional participants added to their labor
11
productivity by almost twofold and non-participants added by more than threefold of the
productivity gain experienced by regular participants.3
V. Growth and redistribution of employment
In an attempt to understand why microcredit program participants did not experience as
much growth in labor productivity as the non-participants, we investigated the pattern of
allocation and reallocation of their labor resource by type of employment. The annual
average hours worked by individuals in the sample households did not register any
increase between 1998 and 2004—as shown in Figure 3, it changed from 1,289 to 1,285
hours. This low level of annual work hour reflects the severity of underemployment in
the rural sector of Bangladesh.
It is expected that microcredit would widen the scope of self-employment on top of the
existing employment opportunities. So work hour would increase in the aggregate as well
as per working member of a household, especially in the participating households.
Although average work hour did not rise, a remarkable change occurred in the mix of
hours by type of employment—a change that reveals the dynamics of the redistribution of
labor resource occurring between these two years. While the average annual working
hour decreased in wage employment and farming, it remained almost the same in nonfarm agricultural activities and increased significantly in non-agricultural selfemployment activities, such as, trading and micro-enterprises.
3
The greater increase in productivity than in per capita income over 1998-2004, as shown in Table 2, is
partly explained by the slower growth in the number of working members per household than household
size. This is due to the fact that the children born during the six years under observation do not readily turn
into working members whose income generating capacity can be potentially influenced by participation in
a microcredit program. Nor do children demand equal amount of household resources as adults.
12
Similar pattern is observed in the proportion of total annual household work hours to
different types of employment as shown in Table 3. The fall in the proportion of hours in
farming from 15% to 10% in six years with concomitant rise in non-agricultural selfemployment from 38% to 46% indicates that the incentives to withdraw from subsistence
farming and to move towards more rewarding self-employment activities outside the
agricultural sector are at work.
The continual reallocation of labor resource resulted in the proportion of hours employed
in self-employment activities, including both non-farm agricultural and non-agricultural
types, to increase from 0.54 to 0.61 contributing to the overall growth in labor
productivity of rural households. The pattern of inflow of labor to self-employment
activities, both in terms of average annual hours worked per person and the allocation of
hours, is found to be similar across the regular, occasional and non-participants (Table 3
and Figures 4a, 4b, and 4c).
Within group comparison of allocation of work hours reveals two important facts. First,
regular participants worked longer hours than the other two groups, particularly in nonagricultural self-employment. Second, in both years, regular participants allocated the
largest share of their work hours to self employment including non-farm agricultural and
non-agricultural activities. By 2004, they were spending two-thirds of their time in self
employment in contrast to 60% by occasional participants and 56% by non-participants.
This pattern of household labor allocation, along with the fact that their labor productivity
13
remained lower of the three groups, suggests that their marginal return to labor in self
employment activities is low and also experienced slower growth.
VI. Poverty and inequality
Poverty by all of the four measures shown in Table 4 decreased from 1998 to 2004.4 The
incidence of poverty and the rate of movement out of poverty are, however, not uniform
across the MFI participation status. By the head count index, the poverty gap ratio, and
the Sen index, regular participants were the poorest group in both years. By the FosterGreer-Thorbecke index of order 2, although they ranked the least poor of the three groups
in 1998, they turned into the poorest by 2004.
Moreover, regular participants made the slowest progress in the movement out of poverty
and in narrowing the shortfall from the poverty line income. The performance of
occasional participants was close to or even better than the non-participants by this
criterion. The slower rates of growth in income and productivity of regular participants
observed in the earlier sections explain why regular participants did not make as much
progress as the other two groups in poverty reduction.
We would have observed a greater rate of reduction in poverty if it were accompanied by
reduction in inequality. The shifting out of the Lorenz curve drawn on the basis of the
distribution of per capita income in Figure 5, however, indicates that inequality increased
between 1998 and 2004. The overall Gini coefficient increased from 0.38 in 1998 to 0.40
in 2004 indicating rise in inequality (Table 5). The within group Gini coefficients show
4
These measures are based on total income per capita.
14
that inequality increased among both regular and non-participants, more so among nonparticipants than among regular participants. It did not change much among occasional
participants. However, it is not clear at this point what role microfinance plays in the
observed trend in inequality. It can be understood using the decomposition of Gini
coefficient into between- and within-group coefficients, as shown in the lower panel of
Table 5.
The between-group inequality measure represents what the degree of inequality would
have been if the total income of each group were equally distributed among the group
members.5 Thus this part of inequality is attributable to the segregation of population into
different groups, by microfinance intervention in this case.
As shown in Table 5, about one-third of overall inequality in income can be explained by
inequality within the groups of regular, occasional and non-participants in both years.
The contribution of between-group inequality, on the other hand, increased from 13.5%
in 1998 to 20.9% in 2004. Thus it appears that microcredit intervention did not contribute
to bridging the inequality between participants and non-participants.
As far as labor market outcome is concerned, we can specifically investigate the between
group inequality of labor productivity. The emphasis on labor income per working
individual allows us to abstract from the effects of changes in household composition or
any redistributive policy on household income (Burtless 1999). This way the impact of
5
The measure of between-group inequality is obtained by assigning the group mean per capita income to
all individuals within the group and calculating the inequality of that smoothed income distribution.
15
microcredit intervention on inequality through self-employment generation can be
isolated. To this end, we calculate group-wise average of productivities, assign the
average productivity to each individual within the corresponding group, and plot Lorenz
curve on the basis of the group mean so that within group inequality is smoothed out. The
resulting Lorenz curve is shown in Figure 6, which displays two kinks dividing the
population into the three comparison groups under consideration. Regular, occasional and
non-participant groups are represented in order of their average productivity level. The
slope of each line segment represents the average productivity of the corresponding
group. The movement of the curve over 1998-2004 away from the line of equality
implies increasing inequality of labor productivity between groups, which in turn
contributes to the increase in overall inequality of household income distribution
reflected in Figure 5.
VII. Welfare economic analysis by eligibility status
With a view to identifying the net welfare impact attributable to microcredit program
participation, we compare the income and employment outcomes across participants and
non-participant households that are comparable by eligibility status. For eligible
households, total household income and endogenous labor income do not vary
significantly across participants and non-participants in either year, although differences
exist in exogenous income levels (Table 6a). Of particular importance is the growth in
endogenous labor income that appears to be not much different by participation status—
Tk.6,568 for regular participants, Tk.7,187 for occasional participants and Tk.6,007 for
16
non-participants.6 In contrast, among non-eligible households, non-participants
experience significantly larger growth in endogenous labor income—three times as much
as for regular participants and 1.4 times as much as for occasional participants (Table 6b).
Similarly, when we compare labor productivity, we observe no significant difference
between regular, occasional and non-participants among the eligible households-Tk.1,490 for regular participants, Tk.1,839 for occasional participants and Tk.1,457 for
non-participants (Table 7). Non-eligible non-participants, in contrast, performed far better
than the non-eligible regular and occasional participants (Table 7).
Taking ownership of cultivable land as the proxy for initial resource endowment and
assuming that eligible households are alike in that respect, we can infer that the
contribution of microcredit to income and productivity growth is negligible. For noneligible households, the outcome is even worse. It is the dispersion in the performance of
non-eligible participants and non-participants that is contributing to the increase in the
between group inequality observed in the previous section.
The productivity growth across groups with increased between group inequality suggests
that the efficiency gain in the allocation of labor resource was not redistributive. This
finding contrasts the observation by Armendáriz and Morduch (2005, p.34) that provision
of access to financial services to the poor can both enhance their opportunities relative to
the non-poor and increase aggregate productive efficiency. The optimism, spelled out by
6
The hypothesis that these differences in average endogenous labor income over time are equal across the
comparison groups cannot be rejected at 1% level of significance.
17
Aghion, Caroli and Peñalosa (1999) a few years ago that microcredit intervention might
have started off the virtuous circle in which redistributive policy can be used to reduce
inequality, which in turn would accelerate growth and thereby automatically induce
further reductions in inequality, seem to have not come true.
VIII. Summary and Conclusion
Using data of about 2700 households in Bangladesh from a longitudinal survey for the
period of 1998-2004, this study undertakes a welfare economic analysis of the impact of
microcredit program in Bangladesh. First, the welfare dominance approach shows that
aggregate welfare of the rural households improved over the period of 1998-2004.
Household income increased across all income percentiles for all of regular, occasional
and non-participant groups. Regular participants experienced the lowest welfare gain and
non-participants gained most. By the abbreviated social welfare function approach, on the
other hand, the welfare outcome appears ambiguous. The reallocation of labor resource
from farming to non-agricultural self-employment opportunities resulted in income and
productivity growth and poverty fell across all groups by MFI participation status.
Average work hour, however, did not increase leaving the status quo of pervasive
underemployment in the rural economy of Bangladesh. Moreover, both between and
within group inequality increased.
When we break down the sample of households by the status of eligibility to participate
in a microcredit program, we find that eligible participants performed no better than
eligible non-participants and non-eligible participants fared far worse than non-eligible
18
non-participants. It appears that the contribution of microcredit program participation lies
in helping the households at the lower side of the economic strata to keep up with the rest
of the society possessing comparable initial endowments. On the higher side, it appears to
even detract from their growth potential. Thus this study finds evidence against the
popular belief that microcredit is instrumental to the uplifting of the rural poor to a higher
economic status. There is no denying the fact that poverty would have been alleviated at a
faster pace if it was accompanied by income and productivity growth with greater
employment opportunities and reduced inequality.
The findings from this study lead us to enquire why participation in microcredit program
is so widespread and persistent despite the insignificant contribution it makes in
enhancing the economic wellbeing of the participants in comparison to the nonparticipants with respect to income, productivity and employment growth, and income
equalization. It clearly asks for further research to find out whether the contribution of
microcredit is limited to the improvement of economic well-being in the short run
through consumption smoothing, sans investment in gainful economic activities.
19
References
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Hadar, J., and W. Russell. 1969. “Rule for ordering uncertain prospects.” American
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20
Zohir, S. 2004. “NGO sector in Bangladesh: An overview.” Economic and Political
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21
Notes:
1.
2.
All
Mean
Std Err.
N
Mean
Std Err.
N
Mean
Std Err.
N
Mean
Std Err.
N
1998
43,240
988
774
47,369
1159
1,174
55,642
1871
758
48,195
758
2,706
2004
51,537
1387
774
59,162
1470
1,174
73,322
2808
758
60,546
1062
2,706
12,351
17,680
11,793
Growth
8,297
1998
37,543
871
776
36,699
817
1,164
37,520
1239
745
37,033
541
2,683
2004
43,102
1158
773
44,486
1102
1,155
46,431
1879
725
44,208
745
2,648
7,176
8,910
7,787
Growth
5,560
Endogenous labor income
1998
14,406
1288
316
22,653
1516
548
33,256
2244
410
23,701
1011
1,276
N represents the number of sample households that reported household income from any source both in 1998 and 2004.
Mean is calculated by dropping the top and bottom 1% observations.
Nonparticipants
Occasional
participants
Participation
status
Regular
participants
Household income
2004
20,262
1684
344
29,513
1630
587
47,384
2973
451
32,485
1236
1,378
22
8,784
14,128
6,860
Growth
5,856
Exogenous income
Table 1: Growth in annual household income (Taka in 2004 constant prices) by MFI participation status and source, 19982004.
Mean
Std.Err.
Mean
Std.Err.
Mean
Std.Err.
Occasional
participants
Nonparticipants
All
8,585
49
9,905
121
8,370
70
9,060
57
11,305
155
8,582
72
474
1,400
212
Total annual income per capita
1998
2004
Growth
7,652
7,688
36
65
77
Note: Mean is calculated by dropping the top and bottom 1% observations.
Mean
Std.Err.
Regular
participants
Participation status
14,559
117
15,870
305
14,103
168
1998
14,400
179
16,220
140
18,739
422
15,644
190
Productivity
2004
15,247
192
848
23
1,661
2,869
1,541
Growth
Table 2: Growth in total annual income per capita and productivity (Taka in 2004 constant prices) by MFI participation
status, 1998-2004.
0.16
0.16
0.13
0.13
0.46
Wage employment
Farming
Non-farm
agricultural selfemployment
Non-agricultural
self-employment
0.36
0.32
0.27
Type of employment
Occasional
participants
Regular
participants
0.30
0.20
0.18
0.31
0.38
0.16
0.15
0.30
0.53
0.13
0.08
0.27
NonAll
Regular
participants households participants
1998
0.45
0.16
0.09
0.30
Occasional
participants
0.38
0.18
0.12
0.31
0.46
0.15
0.10
0.29
24
NonAll
participants households
2004
Table 3: Allocation of total annual household work hours by type of employment, 1998-2004.
Table 4: Measures of poverty by MFI participation status, 1998-2004.
Participation status
Regular participants
Change
Occasional Participants
Change
Non participants
Change
All
Year
1998
2004
1998
2004
1998
2004
1998
2004
Change
Head
count
index
76.79
71.70
-5.10
72.88
64.05
-8.83
66.29
57.41
-8.87
72.19
64.43
-7.76
Poverty
gap ratio
31.87
28.54
-3.33
31.89
25.73
-6.17
29.28
23.67
-5.61
31.24
25.96
-5.27
Sen index
40.89
36.76
-4.13
40.79
33.38
-7.41
38.14
30.77
-7.37
40.18
33.64
-6.54
FGT(2)
16.47
14.22
-2.25
17.25
13.10
-4.15
16.52
12.42
-4.09
16.88
13.23
-3.65
Notes:
1. The poverty line income in 1998 is Tk.652 per capita per month and in 2004 it is Tk.733 per capita per
month (Rahman et.al, 2005).
2. FGT(2) represents Foster-Greer-Thorbecke index of order 2.
Table 5: Decomposition of Gini coefficient into between and within group
components
Gini coefficient
All
Regular participants
Occasional participants
Non-participants
1998
2004
0.3781
0.3235
0.3737
0.4242
0.4011
0.3451
0.3679
0.4618
0.1297
(34.3%)
0.0512
(13.5%)
0.1972
(52.1%)
0.1345
(33.5%)
0.0840
(20.9%)
0.1826
(45.5%)
Decomposition of Gini coefficient
Within Group
Between group
Residual
Notes: 1. The method of decomposition is followed from Sen and Foster (1997), pp.149-156.
2. The percentage contributions of within and between group inequality to the overall Gini
coefficient are shown in parentheses below the decomposed Gini coefficients.
25
45,125
914
46,813
2329
45,456
1398
9,441
10,453
10,351
Household income
2004
Growth
44,499
8,157
1416
Endogenous labor income
1998
2004
Growth
31,889
38,456
6,568
825
1217
483
482
28,772
35,959
7,187
718
1039
619
612
26,944
32,951
6,007
1206
1509
333
326
29,499
36,007
6,508
508
695
1,437
1,419
1998
12,705
1320
169
17,371
1841
236
23,604
3161
144
16,802
1105
547
Exogenous income
2004
Growth
15,878
3,173
1852
192
23,417
6,045
2322
273
28,072
4,467
3275
189
21,903
5,101
1388
650
Participation
status
Regular
participants
26
Household income
Endogenous labor income
Exogenous income
1998
2004
Growth
1998
2004
Growth
1998
2004
Growth
Mean
56,527
64,538
8,011
47,160
51,265
4,104
18,763
26,992
8,229
Std Err.
2542
3164
1836
2354
3150
3284
N
293
292
292
147
154
Occasional
Mean
61,981
74,877
12,896
45,618
54,482
8,864
26,894
35,346
8,451
participants
Std Err.
2206
2673
1466
2113
2273
2290
N
542
538
538
309
310
NonMean
71,799
97,739
25,940
45,699
57,710
12,012
39,305
62,889
23,584
participants
Std Err.
2940
4906
1926
3190
3138
4533
N
410
410
397
269
262
All
Mean
63,913
79,954
16,042
45,975
54,746
8,771
29,508
43,588
14,080
Std Err.
1493
2101
992
1485
1618
2073
N
1,244
1,240
1,226
722
726
Notes: 1. N represents number of households. 2. Eligibility is defined by the ownership of less than or equal to 50 decimals of cultivable land.
Table 6b: Growth in annual household income by MFI participation status and source for non-eligible households as of 1998.
All
Nonparticipants
Occasional
participants
Mean
Std Err.
N
Mean
Std Err.
N
Mean
Std Err.
N
Mean
Std Err.
N
1998
36,342
893
483
35,105
956
628
36,360
1707
347
35,684
628
1,457
Table 6a: Growth in annual household income by MFI participation status and source for eligible households as of 1998.
Participation
status
Regular
participants
All
Nonparticipants
Occasional
participants
Regular
participants
Participation status
Mean
Std.Err.
N
Mean
Std.Err.
N
Mean
Std.Err.
N
Mean
Std.Err.
N
1998
13,402
210
1138
12,660
188
1394
13,014
390
675
12,886
131
3205
Eligible households
2004
14,892
227
1,242
14,498
213
1507
14,471
395
710
14,615
147
3461
1,729
1,457
1,839
Growth
1,490
Non-eligible households
1998
2004
16,010
16,092
336
357
840
915
15,527
17,204
261
320
1552
1706
18,321
22,087
471
718
983
1,024
16,320
18,069
192
245
3366
3648
Table 7: Growth in annual household income by MFI participation and eligibility status of households as of 1998.
82
27
1,749
3,766
1,676
Growth
Annual household income (2004 prices)
0
50000
100000
150000
200000
250000
300000
350000
400000
0
10
20
30
50
60
1998
2004
Percentile of sample households
40
70
Figure 1a: Shifting of household income distribution, 1998-2004.
80
90
100
28
Growth in annual household income (2004 prices)
0
20000
40000
60000
80000
100000
0
10
20
30
50
60
Percentile of sample households
40
70
80
Figure 1b: Growth in household income (Taka in 2004 constant prices), 1998-2004
90
100
29
Growth in annual household income (2004 prices)
0
20000
40000
60000
80000
100000
120000
140000
160000
0
10
20
30
50
60
Regular
Occasional
70
Non-participants
Percentile of sample households
40
80
90
100
Figure 2a: Growth (Taka in 2004 constant prices) in annual household income by participation status, 1998-2004.
30
0
4000
8000
12000
16000
20000
24000
0
15
Regular
45
Occasional
Non-participants
Percentile of sample households
30
60
75
31
Figure 2b: Growth (Taka in 2004 constant prices) in annual household income at the bottom 75 percentiles by participation
status, 1998-2004.
Growth in annual household income (2004 prices)
Annual average hours worked
0
500
1000
1500
2000
2500
1,584
Wage employment
1,698
451
Farming
634
303
1998
2004
Type of employment
Non-farm agricultural
self-employment
297
Non-agricultural selfemployment
1,775
2,059
All sectors
1,289 1,285
Figure 3: Annual average hours worked by individuals by types of employment, 1998-2004.
32
0
500
1,000
1,500
2,000
2,500
Wage employment
1,627 1,613
446
Farming
589
298
1998
2004
Type of employment
Non-farm agricultural
self-employment
279
(a) Regular participants
Non-agricultural selfemployment
1,863
2,097
All sectors
1,412 1,424
Figure 4: Annual average hours worked by individuals by types of employment and MFI participation status, 1998-2004.
Annual average hours worked
33
Annual average hours worked
0
500
1,000
1,500
2,000
2,500
1,595
Wage employment
1,740
448
Farming
629
306
1998
2004
Type of employment
Non-farm agricultural
self-employment
296
(b) Occasional participants
Non-agricultural selfemployment
1,722
2,050
All sectors
1,291 1,265
34
Annual average hours worked
0
500
1,000
1,500
2,000
2,500
1,527
Wage employment
1,710
460
Farming
690
304
1998
2004
Type of employment
Non-farm agricultural
self-employment
316
(c) Non-participants
Non-agricultural selfemployment
1,722
2,006
All sectors
1,149 1,154
35
Share of total annual household income
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0
0.1
0.2
0.3
Line of equality
0.5
0.6
Share of population
0.4
0.7
0.8
0.9
2004
1998
Figure 5: The Lorenz curves of per capita income in 1998 and 2004.
1
36
Share of endogenous labor income
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0
0.1
Occasional
participants
0.2
0.3
0.5
0.6
Share of population
0.4
Regular
participants
Regular
participants
0.7
0.8
Occasional
participants
Nonparticipants
0.9
2004
1998
Nonparticipants
Figure 6: The Lorenz curves of labor productivity in 1998 and 2004.
1
37