This paper presents an estimation of the impact of microcredit on income poverty, following a quasi-experiment specific...
The impact of microcredit on income poverty. An endogeneity-corrected estimation in urban Mexico Miguel Niño-Zarazúa1 Summary This paper presents an estimation of the impact of microcredit on income poverty, following a quasi-experiment specifically designed to control for endogeneity and selection bias in the context of urban Mexico. Although we find impacts on income poverty, the magnitude of the effect is marginal and only significant among moderate poor. We find no evidence of impacts on extreme poverty. The evidence points to a link between poverty impacts and lending technology. Rigid screening and monitoring devices used by group lending contracts generate a utility cost of borrowing that undermines the efforts of poverty alleviation. Keywords: endogeneity; selection bias; microcredit; urban poverty; Mexico
Introduction Microcredit has become an instrument for poverty alleviation widely used in the developing world. The Microcredit Summit campaigners have recently reported that about 3316 microcredit programs were reaching more than 133 million borrowers worldwide (Daley-Harris 2007), despite the fact that the existing empirical evidence on the impact of microcredit remains inconclusive, and methodologically contested (see Morduch and Haley 2002 for a review). Following global trends, the Mexican government launched in 2001 the National Programme for Financing the Microentrepreneur (PRONAFIM), a state revolving loan fund established with US $100 million to support, through capital subsidization, the expansion of an infant microcredit sector in the country. The role of PRONAFIM was perceived as complementary to a wider financial reform that under a new regulatory framework organises the overall non-banking system. The number of microcredit programs supported by PRONAFIM rapidly increased, from just eleven in 2001 (receiving US $7.9 million) to 80 by the end of 2005, recipients of more than US $26 million2. PRONAFIM was launched under the general proposition that access to credit, per se, is a significant determinant of increasing household income and thus, alleviating poverty. However, most empirical studies that have tested such proposition focus on the rural context and suffer, with a few
1
exceptions, from endogeneity and selection bias, and those that have successfully controlled for these estimation constraints (e.g. Pitt and Khandker 1998a; Coleman 1999) follow methodological approaches that are difficult to replicate in the urban context, where many microcredit programs in the developing world actually operate. This paper presents an alternative methodological approach to control for endogeneity and selection bias in the impact analysis of microcredit, using data from a quasi-experiment operationalised in the context of urban Mexico. Our methodology, which is discussed in section one, also allows the evaluation of potential differences between different lending technology regarding poverty impacts, particularly when the utility cost of borrowing is included in the analysis. The econometric estimation procedure discussed in section two is formulated to test for the underlying assumptions of no endogeneity and selection bias. Section three examines the proposition regarding microcredit as a significant determinant of an income rise, whereas section four analyses poverty impacts. Section five concludes with some policy recommendations. 1.
Research design
We designed a type of quasi-experiment that is often referred to as a non-equivalent, post test-only quasi-experiment (Campbell and Stanley 1966), in which two groups of households are sampled: treatment and control. Data was collected at the household level to address, as suggested by Hulme (2000) in this journal, the fungibility problem. A major problem that emerges with the nonequivalent, post test only quasi-experiment, referred hereafter as quasi-experiment, is that these two groups may differ in important ways that influence the decision of borrowing and thus, the outcome of interest. In other words, there might be unobservable factors related to e.g. individual efforts, abilities, preferences and attitudes towards risk that affect the selection process and thus the income variable. We refer to this potential problem as a demand-related bias. A fundamental assumption here is that participation in a microcredit program is always voluntary. Another potential selection problem emerges from the implicit nature of credit markets. Even if we
2
had a control group willing to take risks and borrow from a microcredit organization, we may still face selectivity discrimination made by the lender or group members that screen out applicants who, for instance, are not residents of the neighbourhood where the microcredit program operates. We refer to this potential problem as a supply-related bias. In this sense, the selection process, I i , is defined by two components: 1) the decision of a household of whether or not to participate in a microcredit program ( I1* ), and 2) the decision of lenders (or group members) of whether or not to accept the applicant ( I 2* ) (see figure 1). INSERT FIGURE 1 ABOUT HERE Although we cannot observe households that choose either to participate or not, and households that are either accepted or rejected by the lender, i.e. I = I1 + I 2 , we can specify the distribution of households that have self-selected to participate in the credit program, and have been accepted by the lender, i.e. I = I1 ⋅ I 2 , with a time-variance difference that accounts for the length of membership. Consequently, those households who had self-selected to participate in a credit program and had been accepted by the lender, and therefore were actively participating in the credit program were eligible to be sampled as members of the treatment group. Similarly, those households who had self-selected to participate in a credit program and had been accepted by the lender, but had not received a loan by the time the quasi-experiment was conducted, were eligible to be sampled as the members of the control group. We also followed a geographical criterion, i.e. we operationalised the quasi-experiment among households living in the same neighborhood, in areas with a minimum level of socio-economic homogeneity, where the comparison between treatment and control groups was reasonable. By following this criterion, it was possible to hold constant factors such as infrastructure, costs of inputs, and local prices that could otherwise cause an endogeneity problem. A high population density in poor urban areas makes possible to follow this approach. As a result, selection bias and
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endogeneity are assumed to be controlled through the process of data collection itself. We test for such assumptions in the econometric procedure presented in section 2. Given the homogeneity of household characteristics, a sample survey was the preferred type of data collection (Babbie 1990; Keppel 1991). The sampling strategy was implemented using a multistage procedure in the form of clusters (Fink and Kosecoff 1985): first, we had access to a list of program participants (both treatment and control) from three case-study organizations (the clusters), and who lived in the selected areas. Participants with loan in arrears were included in the list. In the second stage, both treatment and control groups were selected at random. The survey was administrated face-to-face employing, as instrument of data collection, a semi-structuredinterview format, which embodied a combination of methods in a dominant-less dominant design form, and where a simultaneous triangulation was straightforwardly implemented (Morse 1991)3. In the end, we surveyed 148 households: 55 participating at Community Financial Services (Fincomun) and living in San Miguel Teotongo, a neighborhood located to the eastern periphery of Mexico City; 46 participating at Centre for the Assistance of the Microentrepreneur (CAME) and living in the Chalco Valley, one of the most densely populated municipalities in the country located to the eastern periphery of the Metropolitan area of Mexico City; and 47 participating at Programs for Women (Promujer) and living in Tula City and the surrounding areas, a locality about two hours from Mexico City. Thus, we have three locations, one for each organization. Unlike most microcredit programs operating in Mexico, Fincomun mostly relies on individual lending, and demands, as a result, physical collateral and guarantees as enforcement mechanisms. CAME, on the other hand, employs a credit-only village banking approach, and consequently exploits joint liability as enforcement device. Similarly, Promujer use a credit-plus village banking approach that combines credit with education and training as part of the services provided (see table 1 for more details). INSERT TABLE 1 ABOUT HERE
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2.
The econometric estimation procedure
To begin with the impact analysis, our exposition considers the case where household i decides to participate in a credit program. The amount of credit is exogenously determined by the lender L, who sets up a maximum threshold according to level of program participation. The lender is expected to exploit several screening, incentive and enforcement devices to deal with the problems of moral hazard and adverse selection that are related to borrowers’ behavior (Hoff and Stiglitz 1990). Some of these devices are, inter alia, progressive lending, compulsory savings schemes and periodical repayment schedules in group meetings. Given the nature of fragmented credit markets, the demand for credit is rationed by the lender (Stiglitz and Weiss 1981), and endogenously determined by household characteristics such as the stock of human capital, individual preferences and attitudes towards risks. Our primary interest is to estimate the impact of microcredit on the outcome of interest, which is observed through the income variable, Yi . Thus, we consider the following model:
Yi = X i β + I iδ + ui
(1)
where X i is a vector of household characteristics and I i is a dichotomous variable with value
I = 1 if household i is a program participant, I = 0 otherwise. The model measures the impact of program participation by the coefficient of the parameter estimate, δ . Note that the variable I i cannot be treated as exogenous if a problem of selection bias is anticipated. Initially, we consider a specification equation in the form:
Y1i = X1i β1 + I i δ + u1i (for program participants)
(2)
Y2i = X2i β 2 + u2i (for non participants)
(3)
I1* = Z1γ 1 − ε1
(4)
I 2* = Z 2γ 2 − ε 2
(5)
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where I i is defined, as illustrated in figure 1, by I1* and I 2* . However, since we only observe household i self-selecting to participate in a credit program and being accepted by the lender, then we can specify the distribution of households that are accepted to participate in the program ( I 2* ) and then estimate the parameter γ 2 , if such households have previously self-selected ( I1 = 1 ). Maddala (1999) suggests to define I 2* over a sample population, e.g. identify households living in the same neighborhood, and then analyze the model from the truncated sample, where the parameters γ 1 and γ 2 can be estimated by maximizing a likelihood function, e.g. Probit or Tobit. Thus, the observed Yi is defined as Yi = Y1i if I i = 1 , and Yi = Y2i if I i = 0 , where the participation decision function is given by I i* = Z iγ = ε i . Since treatment and control groups were sampled to satisfy condition I = I1 ⋅ I 2 , i.e. both groups are participants with a time-variance difference that accounts for the length of membership, then the following specification can be used:
Y1i = Xi β1 + u1i (for treatment group)
(6)
Y2i = Xi β 2 + u2i (for control group)
(7)
and
E Y1i I i = 1 − E Y2i I i = 0 = X i ( β1 − β 2 ) + σ *
φ ( Z iγ ) +V Φ ( Z iγ )
(8)
where σ * = (σ 2ε − σ 1ε ) ; φ ( ⋅) and Φ ( ⋅) are the density of the distribution function and the cumulative distribution function of the standard normal, respectively, and E (V ) = 0 . If we encounter a selection problem, then σ * > 0 . In other words, households with comparative advantages will benefit more from a microcredit program than disadvantaged households. Although the selection problem is assumed to be controlled through the process of data collection, we need to test this assumption. In order to do so, we follow a Heckit estimation procedure (Heckman 1979) with an identifying instrumental variable (IV)4.
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2.1
The Heckman procedure with I i as endogenous regressor
This Maximum Likelihood method follows the model:
Yi = X i β y + I iδ + uiy
(9)
I i = X i β I + Z iγ + uiI
(10)
where X i is a vector of household characteristics, and Z i , the identifying instrument. Z i is an observable variable distinct from those in X i that affect I i but not the outcome of interest Yi conditional on I i . In other words, the instrument must be partially correlated with I i , i.e. the coefficient on Z i must be nonzero, γ ≠ 0 , so Cov ( Z i , uiI ) ≠ 0 , while Z i must be uncorrelated with
Yi , so Cov( Z i , uiy ) = 0 , where the projected error, E(uiy ) = 0 is uncorrelated with Z i . Selecting an appropriate instrument becomes a crucial and complex task for the estimation. The Heckit procedure allows testing for the assumption of no self-selectivity by estimating the inverse Mills ratio, λ ( ⋅) ≡
φ ( ⋅) Φ (⋅)
, resulting from the relationship between the density of the
distribution function, φ ( ⋅) , and the cumulative distribution function of the standard normal, Φ ( ⋅) . As suggested by Heckman (1979), we can estimate consistently the parameters β I and γ by exploiting the properties of the first stage Probit estimation and then get the estimated inverse Mills ∧
ratio, λ . In the second stage we obtain the parameters β y and δ from Ordinary Least Squares (OLS) with the inverse Mills ratio added to the regressors as follows:
Yi = X i β y + Liθ y + I iδ + λ M + uiy
(11)
We have included in (11) a vector of credit market characteristics, Li , which captures the effects of other credit agents such as banks, moneylenders and rotating savings and credit associations (ROSCAS) that compete with microcredit programs (see table 12 for details about independent
7
variables). The rationale behind incorporating Li relies on the principle that if we do not control for the effects of such agents on Yi , then the parameter δ may be inconsistent, i.e. we could wrongly attribute some outcomes to the microcredit program when in fact come from, for example, ROSCAS. The two-stage Least Square (2SLS) procedure yields consistent estimates in the parameter of interest δ (Wooldridge 2002) where M and λ are the inverse Mills ratio and its parameter estimate, respectively. A simple way of testing for self-selectivity is under the null hypothesis of no selection bias, H 0 : λ = 0 , using the usual 2SLS t statistic. If λ ≠ 0 , then the selection problem is significant. 2.2
Selecting the instrumental variable
It is a common practice among microcredit programs to demand, as a screening device, periodical repayment schedules, often in compulsory group meetings. Group meetings can be highly timeintensive and potentially costly for program participants. In the beginning we considered an observable variable with computational values that varied from household to household and which reflected the heterogeneity of the utility cost of program participation. We computed this variable by estimating the cost of transportation per credit cycle CiT , that captures the geographical characteristics of the accessibility to credit, in addition to the opportunity cost of borrowing, CiO , as a proxy of the income forgone per credit cycle for attending periodical group meetings. We transformed this variable into logarithmic form, in order to test for the underlined assumptions of no correlation between the identifying instrument and the income variable as follows: LGINCOMEPC = β1AVEDUi + β2HOWNERi + β3HESTATEi + β4TIMEBUSi + β5WWORKERi + β6DEPENDRATIOi + β7AGEi + β8WOMANi + β7MARITALi + θ1ROSCASi + θ2FORMALCREDITi + θ3MONEYLENDERi + δLGMAXCREDITi + γLGCOSTBORROWPCi We find that, in the case of Fincomun, the coefficient γ of LGCOSTBORROWPC reported p-values
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of the t statistic that reject the null H 0 : γ = 0 at 5% level of significance (see table 2), throwing out any possibility of using this variable as the identifying instrument in the impact estimation for the three institutions participants in the study as a whole5. INSERT TABLE 2 ABOUT HERE We
also
tried
to
derive
the
identifying
instrument
from
the
first
component
of
LGCOSTBORROWPC, i.e. the logarithm of the cost of transportation per credit cycle, log CiT . The argument relies on the idea that there is a correlation between program participation and accessibility that emerges from two sources: 1) Microcredit programs impose, due to transaction costs, lending restrictions to households living faraway from the branch. Regarding this particular issue, the Managing director of Fincomun mentioned in an interview that a fundamental principle for the organisation was to operate in a geographical radius that did not exceed a journey of 30 minutes walking or by public transport to house of the borrower. 2) A process involving an individual choice, where households reporting high transaction and opportunity costs of participation would either have high incentives to borrow the largest amount of credit available, in order to compensate these costs, or may simply decide to drop out or not to participate in the first place. Our survey collected information on the cost of transportation; however there were substantial missing values in the dataset that reflected the individual choice of walking to attend periodical group meetings. For that reason, we decided to explore the attributes of the time dimension that capture the information about the distance from the residence (or businesses) of program participants to the branch, as a proxy of credit accessibility. Data about the time that participants spent since they left home (or business) until they arrived at the branch was computed and weighted when public transport was used in order to capture the
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time that they would have consumed if they had walked to the branch6. We coded this identifying instrument as DISTANCE. When the reduced form equation (11) was estimated with DISTANCE as identifying instrument for each microcredit program, the p-values of the t statistic for the coefficient γ rejected the null of H 0 : γ = 0 , i.e. it reflected the statistically significance correlation between accessibility and participation; however, when we included Zi in equation (11), the parameter estimate γ accepted the null of no correlation against the income variable, Yi (see table 3). As a result we were able to use DISTANCE as the identifying instrument for the Heckit procedure. INSERT TABLE 3 ABOUT HERE One of the reasons for choosing the Heckit procedure is due to its structural qualities. On the one hand, it enables us to test for the assumption of no selection bias by exploiting the non-linearity properties of the inverse Mills ratio (coded in the regression equation as MILLS). As discussed above in section 1, we operationalised the quasi-experiment in a way to control for selection bias; however, we still need to test for the assumption of no selectivity problem, H 0 : λ = 0 , allowing I i ^
to be endogenous by using the 2SLS t statistic for λ . On the other hand, the Heckit procedure allows us to test for the quality of the instrumental variable and the robustness of the estimation. In order to do so, the identifying instrument DISTANCE is included in (11) alongside with the other exogenous variables, including the inverse Mills ratio. The identification is achieved by exploiting the properties of the inverse Mills ratio that result from the non-linear relationship of the exogenous variables in the reduced form equation (11). After running the identification equation, the coefficients of the endogenous explanatory variable in the estimation equations as well as the Mills ratio for each organization under study remained stable (see table 4)7. The consistency of the results confirm the robustness of DISTANCE as the instrumental variable, and allow us to accept the null of no self-selectivity, confirming that
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we followed an appropriate methodological procedure during the process data collection. INSERT TABLE 4 ABOUT HERE 3.
Results from the second-stage Heckit: the impact of program participation on household income
We have estimated Yi as the dependent variable in (11) by employing the logarithm of income per capita and three different definitions of income per adult equivalent. The use of adult equivalence scales is generally justified to take into account economies of scale and intra-household resource allocation. The first equivalence factor (IAE1) follows the approach adopted by Rothbarth (1943); the second equivalence factor (IAE2) follows Wagstaff and van Doorslaer (1998), whereas the third equivalence factor (IAE3) follows the OECD modified scales based on Hagenarrs et al, (1998). Results from the income estimation equation are presented in table 5. Since the coefficient of the inverse Mills ratio reveals no evidence of selection bias, we are able to concentrate on the OLS estimation. The parameter estimate δ of the impact variable, I i , reports the difference in the mean log income per adult equivalent of treatment households relative to the control group. The slope coefficients show, as expected, a positive sign for each of the three credit programs; however, the coefficients are only statistically significant different from zero in the case of Fincomun. INSERT TABLE 5 ABOUT HERE In order to calculate the percentage change in income per adult equivalent of treatment households relative to the control group, we take the antilog of the parameter estimate I i and compute
(eδ − 1) × 100 (Halvorsen and Palmquist 1980). For example, if we estimate the antilog of δ for the logarithm of monthly IAE1, we obtain e0.548 = 1.7297 , suggesting that ceteris paribus, the median income per adult equivalent of treatment households at Fincomun was higher than that of the control groups by about 73%. Surprisingly, the parameter δ is positive but not significantly different from zero in the case of CAME and Promujer. In other words, although there might be a positive impact of program participation on the level of income, the empirical evidence does not
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confirm this relationship. Note that δ reports the average impact of program participation; however, it does not take into account the effect of borrowing over time. Treatment households with say five years of membership are expected to report greater impacts than those households with just one or two years of membership. This is in part due to the effects of progressive lending, an incentive device extensively used by microcredit programs. In order to address this issue we extend the Heckman procedure to a Tobit selection equation in section 3.1. 3.1
A Tobit selection equation: the impact of microcredit on household income
We replace the treatment dichotomous variable I i in equation (11) by a continuous variable, Ci , that now measures the amount of credit borrowed during the last credit cycle. We assume that Ci is exogenously determined by the lender L, who defines this maximum threshold according to level of participation in the program. Thus we have the following specification equation:
Ci* = X i β c + Z iγ + uic
(12)
where
Ci = max(0, Ci* ) , i.e. * i
Ci = C Ci = 0
if if
(13)
* i
(14)
* i
(15)
C > 0 (for treatment group) C ≤ 0 (for control group)
and
ui X i ~ Normal (0, σ 2 ) Consequently, Ci takes a maximum value and a lower threshold zero in the form of a censored Tobit model (Tobin 1958) with a Ci > 0 for treatment groups and Ci = 0 for control groups8. In this way we believe to capture a more precise measure of the impact of microcredit. Note that the Tobit model implies that the probability of observing Ci > 0 and Ci = 0 are φ ( ⋅) and p (Ci* < 0) = Φ (0) , respectively, where φ ( ⋅) and Φ ( ⋅) denote the density function and the cumulative density
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function of the standard normal. These assumptions are very similar to those implied in the Heckit, but now the log-likelihood function takes the form:
ln L =
Ci − X i β c X i βc + ∑ ln 1 − Φ σ Ci = 0 σ
∑ − ln σ + ln φ
Ci > 0
(16)
which generates the conditional mean function of the observed dependent variable Ci that is censored at zero for control groups and have disturbances normally distributed, which can be used to estimate the determinants of the level of borrowing by treatment and control groups alike9. This is actually the reason of using a Tobit specification equation. If no censoring had occurred, the Tobit model would be inappropriate (Maddala 1999). We estimate a borrowing function for the level of program participation, which is determined by the marginal effects of the amount of credit borrowed during the last credit cycle, as follows:
Ci = α c + X i β c + Z iγ + Liθ c + uic
(17)
where X i and Li are the same vectors of household and credit market characteristics, respectively derived earlier in equation (11) and Z i is a vector of observable variables distinct from those in X i that affect Ci but not the outcome of interest Yi conditional on Ci that play the role of identifying instruments. α c , β c , γ and θ c are the intercept and the unknown parameters, respectively, whereas uic is the error term, which captures unmeasured household characteristics that determine borrowing levels. The function for the income per adult equivalent, conditional upon the level of program participation Ci takes the form
Yi = α y + X i β y + Liθ y + Ciδ + uiy
(18)
where α y , β y , θ y and δ are the intercept and the unknown parameters respectively, while uiy is the error term reflecting unmeasured determinants of Yi that vary from household to household.
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Since Ci is included as the explanatory variable in (18), we need to identify an instrument, additional to DISTANCE, to control for policy-specifics that affect the amount of credit and not only the accessibility to it. This instrument must satisfy the same conditions as in the Heckit in order to estimate the 2SLS Tobit procedure, the type of method that Amemiya (1984) refers to as Type III Tobit model. We derive this estimation equation as follows:
Yi = α y + X i β y + Liθ y + Ciδ + Riυ + ei
(19)
where Ri and υ are the predicted Tobit residuals and its parameter estimate, respectively, and
ei ≡ uiy − E (uiy Ri ) , where (ei , Ri ) are assumed to be independent of X i , i.e. E (ei X i , Ri ) = 0 . The predicted residuals from the Tobit equation are estimated when Ci ≥ 0 in (17) and then included as another regressor in (19) to yield consistent and efficient estimators (Wooldridge 2003). The null of no selection bias is tested in similar fashion as in the Heckit; however, now the 2SLS heteroskedasticity-robust t statistic on the predicted residuals is used: when υ ≠ 0 , a selection problem is encountered. We identify, as the additional instrumental variable, the length of membership, which is computed as the number of years of program participation and coded as MEMBERSHIP. This variable is related to progressive lending, an incentive device extensively used by microcredit programs to deal with moral hazard and reduce operational costs in the long run. When equation (17) was estimated with DISTANCE and MEMBERSHIP as identifying instruments, the p-values of the t statistic for the coefficient γ for each microcredit program rejected the null of H 0 : γ = 0 , reflecting the statistically significance correlation between the level borrowing, Ci and the two instruments in Z i ; however, when Z i was included in equation (18), the parameter estimate γ accepted the null of no correlation against Yi (see table 6)10. As a result we were able to use DISTANCE and MEMBERSHIP as identifying instruments for the Tobit selection procedure.
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INSERT TABLE 6 ABOUT HERE Note that the predicted residuals from the second-stage Tobit selection equation presented in table 7 (and coded as RESID) report insignificant levels in the parameter estimates υ , confirming, as in the Heckit procedure, the assumption of no selection bias. In this sense, the evidence suggests that increasing levels of borrowing are a function of policy-specifics that are exogenously determined, and linearly correlated to progressive lending (captured by the length of membership). INSERT TABLE 7 ABOUT HERE Now, in order to confirm the assumption of exogeneity, we exploit the qualities of the Hausman’s procedure (Hausman 1978) by testing under the null hypothesis that the asymptotic covariance matrix of the OLS estimator is not systematically larger than the 2S-Tobit selection equation. In order words, we examine under the null if p lim d = 0 , where d = b 2 S −Tobit − BOLS , whereas under the alternative, p lim d ≠ 0 . Following Greene (2003:83) we compute the Hausman statistic in STATA as follows:
{
−1
} ( Bˆ
H = (bˆ2 S −Tobit − BˆOLS ) ' Est. Asy. Var bˆ2 S −Tobit − Est. Asy. Var BˆOLS
OLS
)
d − bˆ2 S −Tobit → χ 2 (J ) .
The Hausman statistics are χ 2 (13) = 0.24, χ 2 (13) = 0.13, and χ 2 (12) = 2.11 for the sample surveys at Fincomun, CAME and Promujer, respectively. Consequently, we cannot reject the null that the
BˆOLS and bˆ2 S −Tobit are both consistent, and BˆOLS efficient relative to bˆ2 S −Tobit . In this sense, by following a geographical criterion during the process of data collection, we were able to control for potential endogeneity problems, allowing us to concentrate on the OLS results in table 7. The parameter estimate δ of the impact variable, Ci , reports a positive sign for each microcredit program; however, the coefficients were only significantly different from zero in the case of Fincomun. More precisely, the results suggest that a one percent increase in the amount of capital borrowed from Fincomun gives rise to a 0.064% in income per adult equivalent, ceteris paribus. This result is important for two reasons:
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First, it confirms that our findings are in line with the results reported in table 5; however, by substituting Ci for I i we were able to discount the effects of older borrowers on the average impact of program participation. Second, the result confirms the findings reported elsewhere, in relation to small (or insignificant) impacts of microcredit on income, e.g. Morduch (1998) and Coleman (1999). Now, based on the evidence reported by Hulme and Mosley (1996), we consider the following proposition: Proposition 1. Microcredit programs reporting poverty impacts have the largest effects among those closest to, or above, the poverty line. In order to test Proposition 1, we examine in section 4 the relationship between the severity of income deprivation and the impacts of microcredit programs. 4.
The impact of credit on poverty reduction
To begin the discussion, we estimate the incidence of poverty among program participants. We follow Sedesol (2002) criteria in order to derive three different thresholds of income deprivation for urban areas11: 1) a food-based poverty line that identifies the lowest threshold of income required to fulfill the minimum nutritional requirements to have a healthy living. This threshold, referred hereafter as PL1, is computed at 784.5 pesos per month and measures the incidence of extreme poverty. 2) A capabilities-based poverty line that includes to the food-based basket, other components such as health care and education. This threshold, referred hereafter as PL2, is computed at 1507.5 pesos per month. 3) An asset-based poverty line (PL3) that measures the incidence of moderate poverty and is computed at 1881 pesos per month. We also include the World Bank’s US$ 2 a day poverty line for comparative purposes. The use of several critical thresholds of human deprivation is justified for two reasons: firstly, there is a widespread recognition that the conventional World Bank’s poverty lines are too low for the existing domestic prices in Mexico. Secondly, by computing several poverty lines we can analyze how deep microcredit programs reach the poor and measure the magnitude of poverty impacts by
16
levels of deprivation. The estimation of the incidence of poverty and poverty gap are presented in table 8. INSERT TABLE 8 ABOUT HERE We observe a larger poverty incidence among treatment households at CAME and Promujer than at Fincomun; however, only in the case of Fincomun we find, when computing PL3 and PL2, a significant association between treatment and control groups in relation to the incidence of poverty. The estimated poverty gap also shows a longer distance between actual incomes and the poverty line among participants at Fincomun than at CAME and Promujer. Poor borrowers at Fincomun had to cover, on average, an income shortfall of 545 pesos per month in order to cross the PL2, whereas poor borrowers at CAME and Promujer had to cover only 304 and 319 pesos, respectively. The empirical evidence suggests that some programs (in this case Fincomun) might be more effective at reporting poverty impacts but only by lifting those closest to the poverty line (as stated in Proposition 1). Other programs (e.g. CAME and Promujer) might be more effective at having impacts on the poverty gap but by doing so, they report insignificant impacts on the overall incidence. In order to explore this issue, we compute the marginal effects of credit across the poverty lines, using a Probit estimation equation as follows:
PLi = α i + δ Ci + ui
(20)
where PLi is a binary variable that takes the values
1 if i th household is below poverty line PLi 0 otherwise and Ci is the same continuous variable in (19) that measures the amount of credit borrowed in logarithmic form. We have run (20) with PLi adopting different poverty lines and using by default, the definition of income per adult equivalent 1. We have also included, for comparative purposes,
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I i as substitute of Ci , where I i is the dichotomous variable used in the Heckit with value I = 1 for treatment households and I = 0 for control groups. By computing the marginal effects of Ci we are able to estimate the impact of a relative change in the amount of credit borrowed by a poor household on the probability of staying in poverty. Alternatively, by including I i in the Probit equation, we are able to estimate the impact of the choice of a poor household to participate in a microcredit program on the probability of staying in poverty. The results are presented in table 9. INSERT TABLE 9 ABOUT HERE The slope coefficient of Ci reports negative signs when PL2 and PL3 are included as thresholds of income deprivation; however, they are only statistically significant In the case of Fincomun. Other things held constant, the impact of a relative change of x% in the level of borrowing by poor members was a decline of about –0.038x% in the probability of staying below PL2, and the magnitude of this impact was marginally greater (-0.043x%) when PL3 was included as the poverty measure. Although the parameter δ reports negative signs when PL1 is used as the poverty line, the statistical significance is not different from zero. In other words, we do not find evidence of poverty impacts when only the extreme poor are included in the analysis. Unexpectedly, Promujer reports positive signs and statistical significance in the slope coefficient of
Ci when PL1 is used in the estimation equation. This suggests that, other things held constant, a relative change in the amount of credit borrowed by a woman in extreme poverty will increase her probability of staying in that level of deprivation. Although the slope coefficient of Ci shows negative signs, we find no statistical significance to confirm poverty impacts in the case of CAME. These results must be interpreted with caution in view of the relative small number of participants in extreme poverty, although they appear to confirm Proposition 1 regarding larger poverty impacts among those at the upper limits of deprivation. This should not be surprising since, as Hulme and Mosley point out: “those with higher incomes have greater range of investment opportunities,
18
more information about market conditions and can take on more risk than the poorest households without threatening their minimum needs for survival” (1996:109). A question naturally arises at this point: why are there impact differences between microcredit programs even when they operate under similar market conditions? During the process of data collection, a large percentage of borrowers (97.2% in the case of Fincomun, and 78.6% and 100% in the cases of Fincomun, CAME and Promujer, respectively) reported having been engaged in income generating activities that often took place at street markets or premises far away from their place of residence. It appears that the urban poor, unlike in the rural context, often travel long distances in search of livelihoods. Treatment groups at Fincomun, for instance, spent on average 78 minutes in a return journey from their place of work to the branch, and treatment groups at CAME and Promujer travelled 65 and 82 minutes, respectively. Program participants repeatedly expressed their frustration with the rigidity of periodical repayment schedules in compulsory meetings, which are extensively used as monitoring device. CAME and Promujer, for instance, request to attend group meetings on a weekly basis. Since Fincomun relies mostly on individual lending, it does not require periodical meetings but just weekly instalments12. To illustrate this, consider the following examples: Case 1: Mrs T lives in the Chalco Valley and has been member of one of CAME´s village banks for almost 7 years. She sells shoes at street markets that are usually held on particular days of the week. For that reason, she travels from square to square across Mexico City. When she was asked to give her opinion about CAME, she replayed: “I cannot make repayments every week, I don’t have problems with the interest rate but I don’t like when they (credit officers) force us to come every week. I have a business to attend, you know, and it is far away…”(Interview: Int2-11032004). Case 2: Mrs C lives in Tula City in the state of Hidalgo and has been member of Promujer for more than a year. She sells home products with relatives and friends, and at street markets in the surrounding areas. Since she is in charge of childcare (she has three small children), she is able to
19
work only 2 hours per day, although she expressed a desire of spending more time on her business. When she was asked to give her opinion about Promujer, she replayed: “What I don’t like from Promujer is that we have to come every week and wait hours and hours for some comrades that come late. I have lots of problems to be sitting here waiting for them (group-members). And now because of the meetings I cannot pick up my children from school. To be honest with you, I will leave the group as soon as the credit cycle ends…” (Interview: Int5-06042004). Although periodical group meetings are important monitoring mechanisms that reduce informational costs to the lender, they actually transfer these costs, as pointed out by Stiglitz (1990), to the borrower. The income that the urban poor have to forego as the consequence of attending group meetings can be significant. Some readers may ask: if rigid monitoring devices exacerbate the opportunity cost of program participation, why then do participants remain in the program? Credit markets in Mexico are highly fragmented, and as long as interest rates and loan contracts offered by microcredit programs are in better conditions than those of close competitors such as moneylenders, borrowers may simply decide to remain in the program in order to take advantage of progressive lending, which de facto generates micro-rationing13, and minimise this cost. A change in household income will shift the budget constraint to the right and consequently, the opportunity of cost of program participation. In that perspective, we consider the following proposition for empirical investigation: Proposition 2. Microcredit performs as a normal good and after an income effect; the elasticity of demand for microcredit in relation to the opportunity cost of program participation will be greater than one. In order to test Proposition 2, we compute a Tobit model following the same estimation procedure of section 3.1 that takes the form:
Ci = α c + X i β1c + CiO β 2 c + Liθ c + uic
(21)
where Ci measures the amount of credit borrowed and CiO , the opportunity cost of program
20
participation, both expressed in logarithmic form. X i and Li are the same vectors of household and credit market characteristics, respectively, derived earlier in (19). CiO is computed by estimating the time spent in compulsory group meeting plus the time spent on travelling to attend such meetings. The sum was weighted by the estimated earnings forgone, based on actual values reported from income generating activities, and the outcome multiplied by 16 given the number of periodical repayments per credit cycle14. Estimation results, which are presented in Table 10, confirm Proposition 2: a one percent change in the opportunity cost of program participation gives rise to a 1.614 percentage change in the demand for microcredit at Fincomun, ceteris paribus, and this figure was in the order of 1.716 and 1.513 at CAME Promujer, respectively. INSERT TABLE 10 ABOUT HERE Estimation results also reflect the impact of rigid monitoring devices on household income. For example, the opportunity cost of program participation that participants at CAME absorbed was in the order of 1008 pesos and represented, on average, about 17% the capability-based poverty line (PL2), vis-à-vis 886 pesos reported from participants at Fincomun. 5.
Conclusions and policy recommendations
Our results are suggestive for two reasons: 1) although expanding access to credit can be an important instrument in the fight against poverty, design factors can constrain the magnitude of the expected outcome. Our findings point to a linkage between lending technology and poverty impacts. Rigidity in group lending can increase the utility cost of program participation to such levels that undermine the efforts of poverty alleviation. In that context, experimentation should be encouraged, and perhaps facilitated by governmental agencies such as PRONAFIM beyond the narrowed objective of expanding credit access, to improve lending technology and practices. This could be done through a number of possible policy actions that are summarised in table 11. INSERT TABLE 11 ABOUT HERE
21
2) Although we find poverty impacts from individual lending technology, the magnitude of the impacts were only significant at the upper limits of deprivation, where the poor and moderate poor are grouped, suggesting a strong relationship between the level of household welfare and poverty impacts. However, we find no evidence of microcredit impacts on extreme poverty. Our results question the ability of microcredit to reduce extreme poverty in urban areas and call for more research on alternative instruments such as nutrition, literacy and health care, which in combination with credit, can be more effective to avoid extreme deprivation in urban areas. INSERT TABLE 12 ABOUT HERE References Amemiya T. (1984) Tobit models: a survey, in: Journal of Econometrics, Vol. 84 pp. 3-61 Babbie, E. (1990) Survey research methods. Belmont, CA: Wadsworth Campbell, D. and Stanley, J. (1966) Experimental and Quasi-experimental designs for research, in: N.L. Gage (Ed), Handbook of research on teaching, pp. 1-76. Chicago: Rand McNally Coleman B. (1999) The impact of group lending in Northeast Thailand, in: Journal of Development Economics, Vol. 60, pp. 105-141. Daley-Harris, S. (2007) State of the Microcredit Summit Campaign. Report 2007. Fink, A. and Kosecoff, J. (1985) How to conduct surveys: A step-by-step guide. Beverly Hills, CA: Sage Publications Greene W. (2003) Econometric Analysis. Prentice Hall, Fifth edition. Halvorsen R. and Palmquist R. (1980). The interpretation of dummy variables in semilogarithmic equations, in: American Economic Review, Vol. 70 No. 3, pp. 474-5 Hagenaars A. de Vos K. and Zaidi A. (1998). ‘Patterns of poverty in Europe’ in: The Distribution of Welfare and Household Production: International Perspectives, Cambridge University Press Cambridge Hausman J. (1978) Specification test in Econometrics, in Econometrica, 46, pp 1251-1271 Heckman J. (1979) Sample selection bias as a specification error, in Econometrica, Vol. 46, pp, 15361 Hoff, K and Stiglitz, J (1990). Introduction: Imperfect information and rural markets –Puzzles and policy perspectives, in: World Bank Economic Review, No. 4, pp. 235-249
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Hulme, D (2000) Impact Assessment methodologies for Microfinance: theory, experience and better practice, in: World Development, 28(1), pp. 79-98
Hulme D. and Mosley P. (1996) Finance against poverty. Vol. I, II, London: Routledge. Klein L (1961) An introduction to Econometrics, Prentice Hall, Englewood Cliffs, N.J. Keppel, G. (1991) Design and analysis: A researcher’s handbook. Englewood Cliffs, NJ: Prentice Hall. Maddala G. (1999) Limited dependent and qualitative variables in Econometrics. Econometric Society Monographs, Cambridge University Press Morduch J. (1998) Does microcredit really help the poor: new evidence from flagship programs in Bagladesh. Department of Economics and HIID, Harvard University and Hoover Institution, Stanford University. Morduch J. and Haley B. (2002) Analysis of the effects of microfinance on Poverty Reduction. New York University: NYU Wagner working paper No. 1014. Morse J. (1991) Approaches to qualitative-quantitative methodological triangulation, in: Nursing Research, 40(1), pp. 120-123 Niño-Zarazúa, MA (2007). The impacts of microcredit on income poverty, labour and well-being: A quasi-experimental study in urban Mexico. Doctoral thesis. Department of Economics, University of Sheffield. Secretaria de Desarrollo Social (Sedesol) (2002) Medición de la Pobreza. Variantes metodológicas y estimación preliminar, Comité Técnico para la Medición de la Pobreza. Serie: documentos de investigación, julio. Stiglitz, J. (1990) Peer monitoring and credit markets, in: The World Bank Economic Review, Vol. 4, No. 3, pp. 351-366 Stiglitz, J. and Weiss, A. (1981) ‘Credit Rationing in Markets with Imperfect Information’ American Economic Review, 71, pp 393-410 Pitt, M. and Khandker, S. (1998a) Household and intra-household impact of the Grameen Bank and similar targeted credit programs in Bangladesh, in: Journal of Political Economy, Vol. 106, 558-596 Pitt, M. and Khandker, S. (1998b) The impact of group-based credit programs on poor households in Bangladesh: Does the gender of participants matter? in: Journal of Political Economy, Vol. 106, 958-996 Rothbarth, E. (1943). ‘Note on a method of determining equivalent income for families of different composition’, in C. Madge (ed) War-time Pattern of Saving and Spending, Cambridge University Press, Cambridge.
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Tobin J. (1958) Estimation of relationships for limited dependent variables, in: Econometrica, Vol. 26, pp. 24-36 Wagstaff and van Doorslaer (1998) Income-related inequalities in health: some international comparisons, in: Journal of Health Economics, Vol 16, Issue 1, pp. 93-112 Wooldridge J. (2002) Econometric analysis of cross section and panel data. London: MIT Press.
Notes
1 Contact at: University of Sheffield. 9 Mappin Street, S1 4DT Sheffield, United Kingdom. Tel: +44 114 222 3343, Email:
[email protected] 2 For more details about the microcredit sector in Mexico, see Niño-Zarazúa (2007). 3 For details about the instruments of data collection, contact the author at
[email protected]. 4 See Wooldridge (2002), Greene (2003) and Maddala (1999) for a detailed discussion on the properties of the identifying instrument. 5 We tested the γ ≠ 0 condition in (11) and (12) by computing a heteroskedasticity-robust t statistic after OLS estimation. 6 In order to estimate these weights, we asked programme participants about the approximate distance they travel to attend group meetings. 7 We adopted Lawrence Klein’s rule of thumb (1961), to test for potential problems of collinearity. We did not find evidence of collinearity. 8
Since we have a data-censoring case demanding the variable
Ci* to follow a homoskedastic normal
distribution, we use a logarithmic transformation in our estimation strategy to make this assumption more reasonable. 9 For further details on the derivation of the conditional mean functions, see Greene (2003). 10 We repeated Klein’s rule of thumb (1961) to test DISTANCE and MEMBERSHIP for potential problems of collinearity. We did not find collinearity problems. 11 The Consumer Price Index was used to deflate the levels of prices in 2004 12 Fincomun has recently opened an account at HSBC where borrowers can deposit their weekly instalments. This initiative has benefited borrowers by reducing both transaction and opportunity costs. 13 Micro-rationing reflects a situation in which even those with access to credit are still credit constrained. 14 We acknowledge the possibility of being overestimating the opportunity cost of borrowing if other household members cooperate with the borrower and work in the business while attending group meetings. We find that a large percentage of borrowers (47%) worked alone, and about half of those were single mothers having their business as the only source of household income. If we take into account the risk associated to the income loss, and if group meetings are arranged during the prime selling hours of the business, then we could be underestimating this cost.
24
Population
Households self-selecting to participate in a credit program
Households accepted by the lender (or group members) to participate
Household participants that have received a credit (TREATMENT GROUP)
(Ι 2 = 1)
(Ι1 = 1)
Households self-excluding to participate in a credit program (Ι1
= 0)
Households rejected by the lender (or group members) to participate
(Ι 2 = 0)
Household participants that have not received a credit (CONTROL GROUP)
Figure 1. The selection process for program participation. Adapted from Maddala (1999)
25
Table 1. Characteristics of the case-study microcredit programs Information corresponding to 2004 Characteristics FINCOMUN Type of organisation Year of establishment Founders No of branches Personnel Lending methodology Interest rate (per annum) Borrowers (000) Women borrowers Gross loan portfolio (000 MEX$) Average outstanding loan (000 MEX$) Loan loss reserve ratio Area under study
CAME
PROMUJER Non-Governmental Organisation 2001
Credit Union
Non-Governmental Organisation
1994
27 339 Individual lending
1991 Foundation for Community Assistance, belonging to the Archdiocese of Mexico 5 580 Credit-only village-banking
21 45 Credit-plus village-banking
72%
60%
72%
25.8 60%
40 80%
11.8 100%
169,725
58,000
13,739
6.6
1.5
2.1
2.7% San Miguel Teotongo, in the Metropolitan area of Mexico City
1.8% Chalco Valley, in the Metropolitan area of Mexico City
2.9% Tula City and the surrounding areas in the state of Hidalgo
Juan Diego Foundation, a catholic group
Pro-Mujer International
Screening devices 16 to 24 weekly instalments at Fincomun officers or HSBC branches 10 as % of loan The use of palm pilots for screening loan applications
10-12 as % of loan
12 to 24 weekly or fortnightly instalments in compulsory group meetings 10-12 as % of loan
No
No
Yes, through joint liability
Yes, through joint liability
No
No
On loan defaults and interest in arrears. Legal actions.
On absence and late attendance to group meetings. Interest in arrears and legal actions
On absence and late attendance to group meetings. Interest in arrears and legal actions
Progressive lending
Additional loans for a maximum of 50% of previous credit
Additional loans based on a fixed loan schedule and compulsory savings. Upper loan limits at 20,000 pesos
Additional loans based on a fixed loan schedule and compulsory savings.
Others
Voluntary savings products and certificates of deposits
Life Insurance to cover loan balance. Revolving fund.
Training in financial literacy, business development and health care
Periodical repayment schedules Compulsory savings Others Enforcement devices Guarantees Physical collateral Penalties
Yes, two guarantees Yes, with a value twice the credit size.
16 weekly instalments in compulsory group meetings.
Incentive devices
Table 2. Identifying equations on functional form Logarithm of the cost of borrowing (LGCOSTBORROWPC) as identifying instrument Dependent variable in (10): logarithm of the maximum amount of credit borrowed (LGMAXCREDIT) Dependent variable in (11): logarithm of monthly income per adult equivalent 1 in pesos of 2004 (LGINCOMEPC) FINCOMUN CAME PROMUJER Eq. (10) Eq. (11) Eq. (10) Eq. (11) Eq. (10) Eq. (11) LGCOSTBORROWPC 1.574 0.325 1.705 0.082 1.458 0.055 (21.18)*** (2.05)** (10.74)*** (0.62) (14.61)*** (0.50) Observations 55 55 46 46 47 47 R-squared 0.44 0.49 0.41 Absolute value of t statistics in parentheses * significant at 10%; ** significant at 5%; *** significant at 1%
26
Table 3. DISTANCE as identifying instrument Dependent variable in (10): logarithm of maximum amount of credit borrowed (LGMAXCREDIT)† Dependent variable in (11): logarithm of monthly income per capita in pesos of 2004 (LGINCOMEPC) FINCOMUN CAME PROMUJER Eq. (10) Eq. (11) Eq. (10) Eq. (11) Eq. (10) Eq. (11) DISTANCE 0.028 -0.000 0.073 0.005 0.066 -0.005 (1.88)** (0.09) (2.15)** (0.94) (1.92)* (1.57) Absolute value of t statistics in parentheses * significant at 10%; ** significant at 5%; *** significant at 1% † The Heckman procedure transforms LGMAXCREDIT into a dummy variable for treatment group = 1 if Ii > 0.
Table 4. Robustness of DISTANCE as instrumental variable Endogenous explanatory variable in (11): Logarithm of the maximum amount of credit borrowed (LGMAXCREDIT)† Dependent variable in (11): logarithm of monthly income per capita (LGINCOMEPC) FINCOMUN CAME PROMUJER Eq. (11) on Eq. (11) Eq. (11) on Eq. (11) Eq. (11) on Eq. (11) functional with functional with functional with form DISTANCE form DISTANCE form DISTANCE LGMAXCREDIT 0.591 0.595 0.103 0.088 0.629 0.582 (2.48)** (3.39)*** (0.59) (0.90) (1.98)** (1.88)* MILLS 0.258 0.653 0.089 0.043 -0.053 0.261 (0.58) (1.57) (0.67) (0.15) (0.14) (1.05) DISTANCE 0.002 0.006 -0.006 (0.32) (1.13) (1.06) Absolute value of z statistics in parentheses. * significant at 10%; ** significant at 5%; *** significant at 1% † The Heckman procedure transforms the LGMAXCREDIT variable into a dummy for treatment group = 1 if Ii > 0
Table 6. Identifying instruments for the Tobit selection equation Dependent variable in (17): logarithm of the maximum amount of credit borrowed (LGMAXCREDIT) Dependent variable in (18): logarithm of monthly income per adult equivalent 1 in pesos of 2004 (LGINCOMEPAE1) FINCOMUN CAME PROMUJER Eq. (17) Eq. (18) Eq. (17) Eq. (18) Eq. (17) Eq. (18) MEMBERSHIP 2.235 -0.024 2.074 0.018 5.487 -0.003 (6.80)*** (0.19) (6.78)*** (0.29) (10.36)*** (1.22) DISTANCE 0.060 -0.001 0.058 0.004 0.042 0.340 (2.60)** (0.41) (1.76)* (0.88) (2.84)*** (1.65) Absolute value of t statistics in parentheses * significant at 10%; ** significant at 5%; *** significant at 1%
27
MILLS
LGMAXCREDIT
MILLS
LGMAXCREDIT
MILLS
LGMAXCREDIT
MILLS
LGMAXCREDIT
CAME OLS Heckit 0.126 0.088 (0.81) (0.90) 0.043 (0.15) 0.140 0.099 (0.91) (1.00) -0.010 (0.03) 0.109 0.063 (0.80) (0.68) 0.183 (0.65) 0.095 0.066 (0.71) (0.70) 0.180 (0.63)
28
Robust t statistics in parentheses * significant at 10%; ** significant at 5%; *** significant at 1% † The Heckman procedure transforms LGMAXCREDIT into a dummy variable for treatment group = 1 if Ii > 0 a/ IAE1 follows the approach developed by Rothbarth (1943). b/ IAE2 follows the approach developed by Wagstaff and van Doorslaer (1998). c/ IAE3 follows the OECD modified scales based on Hagenarrs et. al (1998).
of monthly income per adult equivalent 3 in pesos of 2004 (LGINCOMEPAE3) c/
Dependent variable ( Yi in Equation 11): logarithm
of monthly income per adult equivalent 2 in pesos of 2004 (LGINCOMEPAE2) b/
Dependent variable ( Yi in Equation 11): logarithm
of monthly income per adult equivalent 1 in pesos of 2004 (LGINCOMEPAE1) a/
Dependent variable ( Yi in Equation 11): logarithm
logarithm of monthly income per capita in pesos of 2004 (LGINCOMEPC)
Dependent variable ( Yi in Equation 11):
FINCOMUN OLS Heckit 0.553 0.595 (2.53)** (3.39)*** 0.653 (1.57) 0.548 0.588 (2.57)** (3.27)*** 0.671 (1.57) 0.605 0.554 (2.91)*** (3.05)*** 0.676 (1.57) 0.611 0.558 (2.93)*** (3.14)*** 0.661 (1.57)
PROMUJER OLS Heckit 0.110 0.582 (0.73) (1.88)* 0.261 (1.05) 0.102 0.701 (0.67) (2.33)** 0.293 (1.18) 0.067 0.691 (0.44) (2.53)** 0.294 (1.28) 0.065 0.737 (0.43) (2.75)*** 0.311 (1.35)
Endogenous explanatory variable ( I i in Equation 11): Logarithm of the maximum amount of credit borrowed (LGMAXCREDIT) †
Table 5. The impact of program participation on household income Pooled sample OLS Heckit 0.313 0.115 (3.52)*** (1.75)* 0.129 (0.61) 0.315 0.121 (3.59)*** (1.81)* 0.118 (0.08) 0.314 0.111 (3.75)*** (1.74)* 0.226 (1.09) 0.313 0.109 (3.74)*** (1.69)* 0.219 (1.05)
RESID
LGMAXCREDIT
RESID
LGMAXCREDIT
RESID
LGMAXCREDIT
RESID
LGMAXCREDIT
CAME OLS 2S-Tobit 0.014 0.003 (0.80) (0.09) 0.012 (0.41) 0.015 0.003 (0.89) (0.07) 0.014 (0.47) 0.012 0.004 (0.77) (0.14) 0.008 (0.28) 0.010 0.004 (0.69) (0.12) 0.007 (0.25)
PROMUJER OLS 2S-Tobit 0.015 -0.043 (0.83) (0.94) 0.048 (1.30) 0.015 -0.049 (0.79) (1.12) 0.052 (1.46) 0.010 -0.045 (0.56) (1.03) 0.045 (1.28) 0.010 -0.047 (0.55) (1.10) 0.047 (1.34)
29
Robust t statistics in parentheses * significant at 10%; ** significant at 5%; *** significant at 1% a/ Income per adult equivalent 1 follows the approach developed by Rothbarth (1943) b/ Income per adult equivalent 2 follows the approach developed by Wagstaff and van Doorslaer (1998). c/ Income per adult equivalent 3 follows the OECD modified equivalence scale based on the work of Hagenarrs et. al, (1998).
of monthly income per adult equivalent 3 in pesos of 2004 (LGINCOMEPAE3) c/
Dependent variable ( Yi in Equation 18): logarithm
of monthly income per adult equivalent 2 in pesos of 2004 (LGINCOMEPAE2) b/
Dependent variable ( Yi in Equation 18): logarithm
of monthly income per adult equivalent 1 in pesos of 2004 (LGINCOMEPAE1) a/
Dependent variable ( Yi in Equation 18): logarithm
of monthly income per capita in pesos of 2004 (LGINCOMEPC)
Dependent variable ( Yi in Equation 18): logarithm
FINCOMUN OLS 2S-Tobit 0.065 0.070 (2.82)*** (1.41) -0.007 (0.12) 0.064 0.075 (2.88)*** (1.57) -0.014 (0.25) 0.070 0.085 (3.21)*** (1.96)* -0.019 (0.39) 0.070 0.086 (3.24)*** (1.94)* -0.019 (0.39)
Endogenous explanatory variable ( Ci in Equation 17): Logarithm of the maximum amount of credit borrowed (LGMAXCREDIT)
Table 7 The impact of credit on household income Pooled sample OLS 2S-Tobit 0.037 0.044 (3.67)*** (2.38)** -0.008 (0.42) 0.036 0.045 (3.77)*** (2.51)** -0.010 (0.52) 0.037 0.050 (3.91)*** (2.87)*** -0.016 (0.89) 0.037 0.050 (3.89)*** (2.87)*** -0.016 (0.84)
Table 8. Poverty incidence and poverty gap among program participants Figures in percentages FINCOMUN CAME PROMUJER Concept Control Treated Control Treated Control Treated Overall 34.5 65.5 39.1 60.9 44.7 55.3 Asset-based poverty line (PL3) ≤ 1881 pesos per month 73.7** 36.1 77.8 67.9 61.9 53.9 Poverty gap 44.8 39.3 34.1 25.4 23.3 30.6 Depth of poverty (in pesos) 842 738 642 477 439 576 Capabilities-based poverty line (PL2) ≤ 1507.5 pesos per month 63.2*** 27.8 50.0 42.9 33.3 38.5 Poverty gap 38.1 36.2 35.0 20.2 17.5 21.2 Depth of poverty (in pesos) 574 545 527 304 263 319 Food-based poverty line (PL1) ≤ 784.5 pesos per month 15.8 11.1 11.2 0 0 3.9 Poverty gap 43.4 28.2 13.5 0 0 5.1 Depth of poverty (in pesos) 341 221 106 0 0 43 World Bank's poverty line ≤ US$ 2 a day 15.8 8.3 5.6 0 0 0 Poverty gap 33.6 23.7 1.1 0 0 0 Depth of poverty (in pesos) 225 159 7 0 0 0 The statistically significant association in the cross-tabulations are indicated by the Chi-square values for the cell as a whole at 0.001 (*); 0.01 (**); 0.05 (***); and 0.1 (****) levels of significance.
Table 9. Probit: the effect of program participation on the probability of staying in poverty Explanatory variables: Ci is the logarithm of the maximum amount of credit borrowed (LGMAXCREDIT).
I i is a dummy variable for treatment groups = 1 Independent variable: Dummy variable = 1 if IAE1 ≤ poverty line a/
FINCOMUN
Coef World Bank poverty line ≤ US $2 a day
∂Φ ∂X
Incidence of extreme poverty PL1 ≤ 784.5 pesos per month
Coef
Incidence of poverty PL2 ≤ 1507.5 pesos per month
Coef
Incidence of moderate poverty PL3 ≤ 1881 pesos per month
Coef
∂Φ ∂X
∂Φ ∂X
∂Φ ∂X
CAME
with I i
with Ci
-0.379 (0.82) -0.074 (0.82)
-0.051 (1.12) -0.009 (1.12)
-0.217 (0.49) -0.046 (0.49)
-0.029 (0.66) -0.006 (0.66)
-0.925 (2.49)** -0.353 (2.49)**
-0.100 (2.58)*** -0.038 (2.58)***
-0.180 (0.47) - 0.071 (0.47)
-0.019 (0.47) -0.007 (0.47)
-0.989 (2.61)*** -0.375 (2.61)***
-0.108 (2.73)*** -0.043 (2.73)***
-0.301 (0.72) -0.099 (0.72)
-0.030 (0.70) -0.010 (0.70)
with I i
with Ci
Robust z statistics in parentheses * significant at 10%; ** significant at 5%; *** significant at 1% a/ Income per adult equivalent 1 (IAE1) follows Rothbarth (1943)
30
PROMUJER with I i
with Ci
Pooled sample with I i
with Ci
-0.350 (0.98) -0.036 (0.98)
-0.419 (1.08) -0.003 (1.08)
0.178 (5.83)*** 0.003 (5.83)***
-0.229 (0.72) -0.031 (0.72)
-0.027 (0.79) -0.003 (0.79)
-0.137 (0.36) -0.051 (0.36)
-0.013 (0.29) -0.005 (0.29)
-0.327 (1.53) -0.127 (1.53)
-0.390 (1.67)* -0.015 (1.67)*
-0.206 (0.55) -0.080 (0.55)
-0.029 (0.64) -0.011 (0.64)
-0.467 (2.15)** -0.178 (2.15)**
-0.055 (2.31)** -0.021 (2.31)**
Table 10 Tobit estimation equation with LGOPPORTCOSTPC as explanatory variable Dependent variable in (21): Logarithm of the maximum amount of credit borrowed (LGMAXCREDIT) FINCOMUN CAME PROMUJER LGOPPORTCOSTPC
1.614 1.716 1.513 (21.10)*** (10.96)*** (13.51)*** Observations 55 46 47 Left-censored 19 18 21 Absolute value of t statistics in parentheses * significant at 10%; ** significant at 5%; *** significant at 1% Table 11: Summary of findings and policy recommendations Findings Policy actions for experimentation Positive and statistically significant elasticity of income per adult equivalent in relation to credit in the case of Fincomun: a one percent increase in credit borrow gives rise to a 0.064% in income per adult equivalent. Although participants at CAME and Promujer also report positive elasticities, the statistical insignificance does not confirm this effect. A significant poverty impact from participants at Fincomun. The magnitude of the impact is linked to the level of household welfare: a one percent change in credit was predicted to reduce the probability of staying in poverty below a capabilities-based poverty line (PL2) by about -0.038% vis-à-vis 0.043% reported from households below an assetbased poverty line (PL3) that measures moderate poverty. No statistical significance was reported from participants at CAME but only an increased probability for extreme poor women borrowing from Promujer of staying below the food-based poverty line (PL1) that measures extreme deprivation.
An elastic demand for microcredit in relation to the opportunity cost of program participation: a one percent change in the opportunity cost of program participation gives rise to a 1.614, 1.1716 and 1.1513 percentage change in the demand for credit by borrowers at Fincomun, CAME and Promujer, respectively.
Incentive devices such as progressive lending can cause micro-rationing, and rigid screening and monitoring devices such as periodical group meetings can exacerbate the utility cost of program participation and undermine potential impacts on poverty alleviation.
Expected benefits
Redefinition of credit targeting that could be facilitated by relaxation of donor conditionality. Additionally, more research on alternative instruments such as nutrition, literacy and health care that in combination of credit, could be more effective to alleviate extreme poverty.
Potential trickle down and wider effects in benefit of the extreme poor.
Re-design rigid screening and monitoring devices that in combination with lending technology (e.g. mobile banking) could cut down time in groupmeetings. Donors could play a role beyond the objective of expanding credit access, and facilitate research, experimentation and dissemination of innovations and best practices.
Reduction in the utility cost of program participation, with positive impacts on household income, poverty and market efficiency.
Removal of upper limits of progressive lending that could be linked to the introduction of individual lending for “graduated” borrowers.
Reduction in microrationing with potential positive effects on client retention and improvements in financial selfsufficiency.
31
Table 12. List of variables Independent variables Contained in
Obs
Mean
S.D.
Min
Max
Years of education If household owns residence = 1 If house is still in construction = 1 Years in business Number of household members with a waged job Dependency ratio (number of children, students and old members / household size) Age of borrower If borrower is woman = 1 If borrower is in a relationship = 1
148 148 148 148 148
7.047 0.682 0.791 5.162 0.547
3.777 0.467 0.408 5.746 0.703
0 0 0 0 0
17 1 1 30 3
148
0.498
0.222
0.125
1
148 148 148
42.189 0.730 0.757
10.846 0.446 0.430
19 0 0
74 1 1
If borrower participates in rotating savings and credit association = 1 If borrower have received loans from institutional lenders = 1 If borrower have received loans from moneylenders
148
0.453
0.499
0
1
148
0.054
0.227
0
1
148
0.095
0.294
0
1
Distance from branch to place of residence or business (in minutes) Years of membership
148
32.365
21.716
10
100
148
1.704
1.944
0
8
Logarithm of the maximum amount of credit borrowed in the last credit cycle Logarithm of the opportunity cost of borrowing per credit cycle Logarithm of income per capita Logarithm of income per adult equivalent 1 Logarithm of income per adult equivalent 2 Logarithm of income per adult equivalent 3 If household’s income is below poverty line 1 = 1 If household’s income is below poverty line 2 = 1 If household’s income is below poverty line 3 = 1 If household’s income is below US $2 a day = 1
148
5.475
4.466
0
10.621
148
3.880
3.204
0
8.006
148 148
7.296 7.452
0.594 0.571
5.438 5.733
8.868 9.055
148
7.724
0.545
6.114
9.315
148
7.895
0.543
6.324
9.512
148
0.068
0.252
0
1
148
0.405
0.493
0
1
148
0.581
0.495
0
1
148
0.047
0.213
0
1
Xi
AVEDU HOWNER HESTATE TIMEBUS WWORKER DEPENDRATIO
AGE WOMAN MARITAL Contained in
Definition
Li
ROSCAS FORMALCREDIT MONEYLENDER Instrumental variables DISTANCE MEMBERSHIP Dependent variables LGMAXCREDIT LGOPPORTCOSTPC LGINCOMEPC LGINCOMEPAE1 LGINCOMEPAE2 LGINCOMEPAE3 POORPL1 POORPL2 POORPL3 POOR2US
32
