Faculty of Management Technology
Working Paper Series
Microcredit, Human Capital, and Personal Income Distribution: Empirical Evidence from Greater Cairo
by Heiko Fritz Guenter Lang
Working Paper No. 30
May 2012
Microcredit, Human Capital, and Personal Income Distribution: Empirical Evidence from Greater Cairo by Heiko Fritz Guenter Lang May 2012
Abstract Providing the poor access to finance, microcredit is supposed to alleviate poverty and can be expected to reduce income inequality in developing countries. Drawing on a primary survey of 670 borrower households in Cairo in 2010, we run a number of cross-sectional regressions and simulations to assess the actual impact of microcredit. The paper yields three main findings. First, microloans have a clear positive impact on earnings. Second, the earning potential of male borrowers is significantly higher than of female borrowers which may be attributed to different levels of human capital. Third, microcredits increase income inequality in the population of borrowers as relatively rich borrowers disproportionally gain from improved access to finance.
JEL classification G21, O15, O16
Keywords Microfinance, impact of microcredit, inequality, earnings function
Heiko Fritz German University in Cairo Faculty of Management Technology Al Tagamoa Al Khames 11835 New Cairo – Egypt
[email protected]
Guenter Lang German University in Cairo Faculty of Management Technology Al Tagamoa Al Khames 11835 New Cairo – Egypt
[email protected]
1. Introduction Pioneered by Muhammad Yunus and the Grameen Bank in the early 1980s in Bangladesh, microcredit has become a major instrument of policies aiming at sustainable poverty alleviation in the developing world. The main idea of microcredit is a non-conventional design of the loan contract that allows the lender to reduce transaction costs in small scale lending significantly. Providing the poor with access to financial services improves their opportunities to become self-employed; enhances the scope and scale of their economic activities; and potentially increases their productivity and their income. This may eventually allow them to escape the poverty trap. If this materialised, a side effect of a microcredit system operating at sufficiently large scale is the reduction in income inequality, a phenomenon prevalent in many developing economies. Evaluating the impact of microcredit is important for three reasons. First, though the largest microfinance institutions (MFI) are nowadays commercially funded for-profit institutions, numerous MFI are still non-governmental non-profit institutions absorbing substantial amounts of development assistance. Donors need to demonstrate their stakeholders, often taxpayers in high income countries, that their funds have been used in a responsible way given the intended aim. Second, microcredit comes in a huge variety of institutional settings. Given that the dynamics of creative destruction works only in an environment of competitive markets, the identification and promotion of best practices requires rigorous impact analysis across different settings. Third, microfinance competes with other programs of development assistance for scarce funds, such as educational programs, infrastructure investment, or agricultural support. Impact studies are needed to identify the most efficient use of funds. With this backdrop, the aim of this paper is to assess the impact of microcredit on the borrowers’ earning potential and on the income distribution in the population of borrowers. Using a sample of 670 households in Greater Cairo, comprising both microborrowers and new loan applicants, we run a number of cross-sectional regressions to estimate the effect of three sets of independent variables on the income of individuals in the sample. The explaining variables capture the level of human capital, the type of economic activity as well as characteristics of the microloan. We find that the magnitude of the income-boosting effect of the microloan critically depends on the human capital of the borrower. Due to the uneven distribution of human capital, microloans tend to increase income inequality in the population of microborrowers. The remainder of this paper is organised as follows. The next section sketches out the causal relations between microfinance on the one hand and the level as well as the distribution of borrowers’ income on the other hand. In addition, related empirical findings will be reviewed. Section three describes the dataset used in this study and discusses whether microlenders in Greater Cairo actually reach the poor, obviously a precondition for poverty alleviation. Being the core of our study, section four estimates a Mincer-earnings function in order to assess the impact of microcredit on the earning potential of the borrowers in our sample. Moreover, the effect of microcredit on the income distribution in the population of borrowers will be
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simulated. The last section draws conclusions and makes policy recommendations for the design of effective microlending schemes. 2. Micro-credit, poverty, and income distribution The main task of the financial system is to channel funds from savers to borrowers in order to improve the allocation of capital in the economy. Market imperfections impede the financial system in doing so at zero cost. Most notably, asymmetric information cause problems of adverse selection and moral hazard in financial transactions. Coping with these require screening the quality of the transaction partner, monitoring his behaviour, and enforcing the contract on a reneging partner. These activities incur transaction costs, the minimisation of which calls for efficient institutions. In this respect, financial markets seem to have a comparative advantage in wholesale finance while financial intermediaries prove superior in retail finance. However, neither markets nor conventional intermediaries, such as banks, provide an institutional design apt to cope efficiently with asymmetric information in small-scale transactions with poor clients. Hence, the poor’s demand for loanable funds is largely unmet by banks leaving potential borrowers credit constrained (Stiglitz and Weiss, 1981). Applying this logic, microfinance is to be understood as the set of institutions framing small-scale financial transactions so that transaction costs are not prohibitive. In the area of microcredit the institutional set-up provided by microfinance institutes (MFI) comprises a variety of different mechanisms (Armendáriz and Morduch, 2010; Morduch, 1999). In particular, joint liability group lending (JLGL) attracted scholarly attention. Ghattak (1999) shows that borrowers in JLGL schemes tend to self-select into groups with fellow borrowers sharing similar risk characteristics. Stiglitz (1990) demonstrates that peer monitoring in borrower groups mitigates moral hazard, and Besley and Coate (1995) emphasise the power of peer pressure as a mechanism of contract enforcement. Thus JLGL both reduces the level of transaction costs and shifts the burden of transaction costs, at least partly, from the lender to the borrower. Together with other mechanisms, including progressive lending and frequent repayment installments, JLGL constitutes an institutional setting that enables MFI to cater small-scale loans at reasonable transaction costs to poor households. Development economics suggest different channels through which access to finance potentially allows the poor to sustainably earn higher incomes. Assuming a positive but diminishing marginal product of any production factor throughout, the standard neoclassical theory maintains that the marginal product of capital is high in an environment where capital is relatively scarce. Accordingly, capital given to a poor household increases the production potential and, ignoring price effects, the earning potential of this household significantly. This view finds empirical support. De Mel et al. (2008) estimate in an elaborate study that the real marginal return to capital in microenterprises is 68 percent per year. Likewise extremely low default rates on loans from informal moneylenders, often subject to an interest rate exceeding 100 percent per year, indicate that the poor can earn an extraordinarily high return on capital (Mallick, 2012).
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Discarding the neoclassical idea of substitutable production factors still allows justifying the expectation of positive and lasting income effects from capital lent to the poor. For instance, in a simple Leontieff production function, capital and labour are complementary inputs and the marginal product of any production factor is either zero or positive and constant depending on the actual factor input ratio. Following the rationale of this model, the poor may find their economic activities dramatically constrained by a lack of capital. In the strict sense, perfect factor complementarity means that zero capital does not even allow the household to earn on working. However, this holds only for self-employment and neglects the option of being wageemployed. Adapting the model of occupational choice of Banerjee and Newman (1993) to incorporate microfinance, Ahlin and Jiang (2008) distinguish three types of occupation: wage-employment, self-employment, and entrepreneurship. Wageemployees work in the least productive jobs and earn subsistence wages. Selfemployed workers own a small-scale business set up with a modest level of capital. Characterised by a medium level of productivity, self-employment pays wages above subsistence. Full-scale entrepreneurs run larger businesses that require more capital than the micro-firms of self-employed and typically employ wage-labourers. Both the productivity and the wage of entrepreneurs are the highest among the three occupations. A microloan allows a wage-employed to become self-employed but it does not provide sufficient capital to become an entrepreneur. According to Ahlin and Jiang (2008) a well functioning system of micro-credit tends to alleviate poverty through a direct channel and an indirect channel. Those borrowers directly increase their income who successfully set up a business and become selfemployed. If the number of self-employed is substantial remaining wage-employees may indirectly benefit from an increase in the wage as entrepreneurs need to pay higher wages to attract them. Either channel tends to improve the income of the poor and, hence, to mitigate income inequality. Note, however, that an extremely (and unrealistically) large outreach of microfinance schemes counteracts this effect as fierce competition between the newly self-employed can drive down their earnings even below the subsistence wage level. The empirical evidence of the impact of microfinance on the economic well-being of borrowers is ambiguous (for recent reviews see Duvendack et al., 2011; Odell, 2010). Surveying a large sample of borrowers in different lending schemes in Bangladesh, Pitt and Khandker (1998) and Khandker (2005) demonstrate that the return to income is between 18 and 20 percent from borrowing, sizeable by any standard. In addition, these studies find that borrowing is associated with an improvement in school enrollment and indicators of nutrition and health care of the children in borrower households. The findings of Coleman (1999; 2006) in Northeast Thailand, likewise widely cited, are in stark contrast to this. The author does not find any positive effect of a microloan on indicators of the material well-being of borrowers. Expenditures of borrowers on healthcare were even lower than of non-borrowers. Methodological differences, though rightfully stressed (e.g. Karlan and Goldberg, 2006), are certainly not the only reason for differing empirical evidence of the impact
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of microfinance. It rather seems that the specific set-up of the microlending scheme, i.e. the selection mechanism of borrowers, the design of the loan contract and the specific use of mechanism aiming to align the interest of borrower with the lender, as well as internal dynamics and power relations in borrower groups account for the different performance of the programs. Empirical studies of the effect of microcredit on income distribution are numbered, very heterogeneous in the research design, and contradictory in their findings. In what follows we discuss the findings of cross-country studies using macroeconomic data, cross-sectional studies using household data; and a qualitative study. Using a broad cross-country sample including both high-income and developing countries Beck et al. (2007) study the effect of financial development measured as the ratio of private credit to GDP on poverty and on the income distribution. The authors show that, after correcting for GDP growth, an improvement in financial development is associated with poverty alleviation and with a reduction in income inequality as financial development disproportionally boosts income in the poorest quintile of the population. Financial development is associated with growth in income of the poorest quintile. This income growth of the poorest is to 40 percent due to a reduction in income inequality and to 60 percent due to a stimulation of aggregate economic activity. This partly contradicts the findings of Honohan (2008) who argues that, though strongly correlated, there is no evidence for a causal link between access to finance and poverty alleviation. Moreover, though elucidating, both studies do not focus on problems of financial access in developing countries. The sample of countries comprises all countries on which the authors could gather data including a large number of high and middle income countries. Likewise the indicator of access to finance – the ratio of private credit to GDP and the number of holders of a bank account in total population – addresses the quality of formal finance only. In a cross-country analysis including 61 developing countries Kai and Hamori (2009) study the effect of the level of development of a country’s microfinance system measured by the number of borrowers and the number of MFI operating in the country on the income equality. The authors show that the intensity of microfinance is significantly associated with more equal income distribution. They conclude that sustaining a system of microfinance is an effective policy of income redistribution. Providing a cross-country analysis of 11 Central African countries, Tchouassi (2011) basically confirms these findings. Using panel data from 2002 and 2004 of about 2,700 households in rural areas, Cuong et al. (2007) study the impact of the microfinance program of the Vietnam Bank for Social Policies (VBSP) on borrower households and communities. While the authors find evidence for poverty alleviation of microcredit, the effect of the program on income inequality, though statistically significant, is economically negligible. Drawing on a comprehensive set of panel data from Thailand, Shah (2010) makes use of the Million Baht Program (MBP) as a methodologically sound quasi-experiment. Initiated in 2001 the MBP transferred 1 million baht into a village fund from which
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the households of the village could get microloans. The author analysed the effect of the loans on income inequality on both the household level as well as the level of villages. On the household level, loan ownership has a moderate positive effect of the income of borrowers. At the same time, however, the variance of household income in the community of borrowers increases significantly. Both effects neutralize each other with respect to the income distribution. On the village level, the total number of households with loans is positively associated with increasing income inequality, i.e. as more households in the village obtain loans, income inequality tends to increase. Copestake (2002) highlights a dynamic effect that works via commercialization in conjunction with the group dynamics in borrower groups. According to his argument, microfinance institutes striving for profitability seek to have strong borrower groups increasing the probability of smooth repayment. At the same time borrowers in joint liability group lending schemes are interested to ally with strong borrowers only. This puts increasing pressure on weak or failed borrowers who ultimately leave the scheme. There is quite a risk for them to end up being poorer than before joining the microfinance scheme. Clearly this mechanism exerts a polarizing effect increasing inequality. 3. The data Our analysis uses a dataset created by the Microfinance Research Group (MRG) of the German University in Cairo. In June and July 2010, the MRG conducted a questionnaire-based survey of a sample of 670 households in different areas of Greater Cairo predominantly populated with poor households. The sample comprises veteran microborrowers as well as applicants for a microloan that had been approved but not yet disbursed by the MFI. The sample was stratified to reflect the share of members in JLGL schedules (as opposed to individual lending schedules); the share of women (as opposed to men); and the share of borrowers from MFI (as opposed to banks) in the total number of urban microborrowers in Egypt. Within each stratum a random sample of households was surveyed. In the survey the MRG cooperated with four MFI supplying loans in both individual and group lending schemes and one commercial bank addressing only individual borrowers through a microfinance window. Table 1 provides a descriptive statistics of the variables. The impact of microfinance on poverty and income distribution depends on whether microcredit is actually allocated to the economically active poor excluded from formal finance, or whether funds are deviated from this target group. Figure 1 plots the ratio of the loan size to the annual income of the borrower and the amount of money on which a member in the borrower household lives per day. The plot allows for three findings. First, the members of about one third of the households in our sample live on less than USD 1.25 per day and are thus classified extremely poor. In another third of the households, the members live on more than USD 1.25 but less than USD 2 per day. According to the classification of the Worldbank these households are moderate poor.
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Figure 1: Poverty versus loan size
Loan size / annual income
1.5
extremely moderate poor poor
1.2
0.9
0.6
0.3 2
R = 0.2613 0 0
5
10
15
20
25
30
35
40
45
50
EGP per person per day
The last third of the surveyed households stated income levels that allow their members to live on more than USD 2 per day and are thus not part of the genuine target group of microfinance. The income figures require some qualification though. In quite a few households the interviewee was not the only bread earner. In these households the income figure reported by the interviewee is likely to underestimate the actual household income. This as well as occasional in kind income not taken into account may explain why members of some households seem to live on 1 or 2 Egyptian Pounds per day, figures well below the subsistence level of daily consumption. Nevertheless, it seems at least half of the microloans reach the target group of the poor or near poor. Second, the relative size of the microloan differs significantly ranging from 5 to 140 percent of the annual income of the borrower. While a loan at the lower end of this spectrum probably does not significantly enhance the opportunity of a poor household to become self-employed, a loan size above 100 percent of the annual income indicates a severe risk of over indebtedness. However, these figures need to be qualified either. As Collins et al (2009) show poor households typically juggle many financial liabilities (and assets) at the same time. Indeed about 5 percent of the households in the survey revealed to have a simultaneous loan from another source than the MFI, and many more probably concealed this information strategically. Thus, on the one hand, total indebtedness of the households may be much higher than our figures indicate. On the other hand our figures are based on the face value of the loan as it had been disbursed by the MFI. The actual book value of the liability of the household, however, decreases linearly throughout the duration of the loan due to repayment in constant weekly or bi-weekly installments. Third, our data show that increasing poverty of borrower households is associated with higher loan to income ratios. Thus, in accordance with the aim of most MFI, microcredit in particular broadens the financial room for maneuver of poor households.
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Table 1: Statistical Description of the Data Variable
mean
std.dev.
min
max
Earnings (monthly individual gross income; EGP)
1,165
1,454
100
11,000
Male (dummy; equals 1 if person is male)
0.18
0.39
0
1
Female (dummy; equals 1 if person is females)
0.82
0.39
0
1
School (years of schooling)
5.9
5.2
0
15
Experience (work experience in years)
18.1
11.5
0
49
Literate (dummy; equals 1 if person can read)
0.56
0.50
0
1
Training (dummy; equals 1 if person received training from MFI)
0.18
0.38
0
1
Shock (dummy; equals 1if person experienced economic shock in observation period)
0.64
0.48
0
1
D0 (dummy; equals 1 if business is in trade)
0.813
0.396
0
1
D1 (dummy; equals 1 if business is in services other than trade)
0.101
0.297
0
1
D2 (dummy; equals 1 if business is in production)
0.070
0.259
0
1
D3 (dummy; equals 1 if business is in farming)
0.008
0.092
0
1
D4 (dummy; equals 1 if business is not in D0 to D4)
0.008
0.083
0
1
Microloan (granted microloans in 1000’s of EGP)
3.56
4.41
0
35.0
Microloans to the male subgroup (1000’s of EGP)
9.63
6.71
1.5
35.0
Microloans to the female subgroup (1000’s of EGP)
2.19
1.88
0
16.5
YSL (years since first microloan has been granted)
2.7
2.6
0
18
n (number of observations)
596
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4. Empirical results and interpretation To assess the real-world impact of microloans on the earnings distribution, a two-step procedure is applied: First, we econometrically estimate an earnings function for our sample of Egyptian individuals, which is – in a second step – disentangled and analyzed with respect to the relationship between microloans and the Gini coefficient. As for step one, we follow the majority of the labor market literature and apply a Mincer (1974) semi-logarithmic form: (1)
ln Ei = α + β ′X i + ε i
i = 1,K, n .
In equation (1), Ei represents monthly gross earnings of individual i, X i is a vector of explaining variables consisting of human-capital variables, industry dummies, and microloan characteristics of individual i, and n is the sample size. The intercept α and the β -vector are unknown coefficients describing the underlying earnings generating process. Different models with different sets of regressors are estimated to check for the robustness of the results. All models use a more or less standard set of human capital variables, but differ with respect to the industry and the microloan specification. Model 4, for example, distinguishes between microloans granted to males versus those granted to females, allowing for gender specific effects of microloans. To ensure functional flexibility of our estimations, the microloan variable – which is in the core of the study – enters the regressions in linear as well as in squared form. ε represents the error term. Based upon the parameters, the marginal effect of microloans on earnings can be derived as ∂ ln Eˆ i ∂Microloan , where Eˆ i are the predicted earnings of person i. Returns to schooling or returns to experience are determined accordingly. Because of the semilogarithmic form of the earnings function, the marginal effect is a relative (percentage) rather than an absolute figure. The second step concentrates on the impact of microloans on earnings inequality which is measured in this study by the Gini coefficient G. Following Sen (1973) and applied to an earnings function, G can be calculated as (2)
G=
n +1 2 − 2 n n E
n
∑ (n + 1 − i )Eˆ , i =1
i
where E is the arithmetic mean of the earnings. In equation (2), individual earnings Eˆ i are ordered by increasing size. Using this approach, G will be in the interval between zero and 1− 1 n , which is sufficiently close to one for a large n (for different approaches how to estimate the Gini coefficient see Xu, 2003).
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We simulate three different scenarios to determine the effect of microloans on G: G0
Gini coefficient given the actual values of the explaining variables
G1 G2 G3
Simulation 1: Gini coefficient if all microloans would be deleted Simulation 2: Gini coefficient if current microloans would be increased by 1% Simulation 3: Gini coefficient if current microloans would be increased by 100%
The simulations G2 and G3 are run separately for males and females, respectively, to account for possible gender differences. Table A1 in the appendix provides the empirical results of the estimated earnings functions. Model 1, which includes only the block of human capital variables, performed very poor and is statistically rejected. All other models are statistically significant, with model 4 showing the best performance. This model allows for gender specific effects of microloans and is used for the following analysis.
With the exception of the Training and the insignificant Literate variable, all estimated parameters show the expected sign. Each additional year of schooling, for example, promises earnings increases by between 2% (model IV) and 3.5% (model I). This is lower than the overview article of Jeminez and Patrinos (2008) is showing for most developing economies, but not far from the specific results for Egypt (Biltagy, 2011; Psacharopoulos and Patrinos, 2004). Also, returns to experience are positive with diminishing returns. Compared to schooling, the economic effect of experience on earnings is smaller. Interestingly, all industry dummies are positive, indicating an advantage of not being in the trade industry, but most parameters are statistically insignificant. Only businesses in the service industry other than trade have a statistically and economically advantage against the trade business. An interesting visualization of the results is the earnings density function f as shown in figure 2. Following Silverman (1986), the earnings density is estimated from the Gaussian kernel
(3)
f (E ) =
n
1 1 ⋅∑ e n ⋅ h i =1 2π
1 E − Eˆ i − 2 h
2
.
For the bandwidth parameter h, Silverman’s rule is applied: 15
4s 5 , (4) h = 3n where s is the standard deviation of the predicted earnings (Silverman 1986, p 48). Figure 2, which is based on the estimations of model 4, shows that the earnings
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density is skewed to the right, implying a large difference between the median and the mean of the predicted earnings. Figure 2: Kernel Density Estimations of the Earnings Function density 0,020
0,015 median 0,010 mean 0,005
0,000 0
500
1000 1500 2000 2500 3000 3500 predicted earnings in L.E.
Estimations based on earnings predictions from model 4
Models 3 and 4 consider the impact of microloans on earnings. According to our results, microloans have a statistically strong, but diminishing impact on earnings. The number of years since the loan has been granted has a positive, but statistically insignificant effect on earnings. As the most interesting result, however, microloans to males have much higher returns than microloans to females. As is shown in figure 3, the marginal return curve for males lies wide above the one for females, as is the threshold where the marginal effect is turning negative. That is, for a given loan size, the impact on earnings is larger for males. However, because of the large differences in actual loan sizes which are around L.E. 10,000 for males and L.E. 2000 for females, the observed marginal effects are the same: Increasing microloans by L.E. 1,000 from the current status would increase earnings by about 10%. Of course, this result is of high political and economic relevance. Microloans to females appear to be dramatically less productive than those to males, and the threshold where no further income increases can be expected is much lower. The reasons behind that surprising result are twofold: First, because of the semilogarithmic form of the earnings function, interactions among the regressors are allowed. Males have a higher human capital stock like years of schooling, allowing them to better exploit the opportunities from loans. And second, there may be discrimination on all levels of the Egyptian society, preventing females from making full use of opportunities. From an allocative perspective, the conclusion is clear, however: A reallocation of microloans from females to males would trigger growth. It is worth to be mentioned that these results are not completely new. For example, they confirm the findings of De Mel et al. (2008), who estimate that additional capital
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is not used productively when owners have six or less years of schooling. Similarly, La Porta and Shleifer (2011) observe that productivity in informal businesses is dramatically lower than in registered ones. They ascribe this to a lack of human capital of managers of informal firms relative to the ones of registered firms. In our sample, about 77.5 percent of the firms in the sample were not registered, where most of the informal firms are managed by females. Figure 3: Marginal Effect of Microloans on Earnings marginal effect on earnings in % current loan sizes (arithmetic means)
30% 20% 10% 0%
males
-10% females
-20%
0
5
10
15
20
Microloan (1,000 EGP)
Estimations based on model 4 and arithmetic means for regressors other than microloans to males and microloans to females, respectively.
Distributional effects of microloans are shown in figure 4 which plots the Lorenz curve. Considering the previous findings, the result is not very surprising: Microloans increase earnings inequality. This result is because of the different sizes of microloans, which range from zero (control group) to L.E. 35,000, and different productivity effects of microloans dependent upon gender, industry, and human capital stocks. Table 3 shows the impact of microloans on the equality measured by the Gini coefficient. In general, microloans have an inequality-increasing effect, which has already been shown in figure 4 and which is confirmed by simulation run 1. The reason is the strong economic impulse of microloans on earnings combined with the fact, that only a part of the population has access to these loans. Individuals with access to microloans can overcome the very flat human capital stock resulting in equally flat incomes. Simulation runs two and three show an interesting gender effect of microloans: Microloans to males tend to increase earnings inequality, whereas microloans to females tend to decrease inequality. This result mirrors the different human capital stocks and the different productivity effects of microloans among males and females. Because males have higher human capital stocks and because their marginal earnings effect is higher, earnings inequality increases. In contrast, higher microloans to females compensate to some degree for their human capital disadvantage, and consequently inequality decreases. Note, however, that the
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economy benefits more from loans to males because of the much stronger productivity effects. Figure 4: Impact of Microloans on Income Distribution - Lorenz Curve
cumulative income share
100% 80% line of uniform distribution
60%
without microloans
40% 20% current microloans 0% 0%
20%
40%
60%
80%
100%
cumulative population share
Estimations based on model 4. Results for the whole population (males and females).
Table 3: Impact of Microloans on Gini Coefficient Simulation run
Description
0
Current microloans
0.287
1
Without any microloans
0.097 0.288
2
Current microloans to males +1% (microloans to females unchanged) Current microloans to males +100% (microloans to females unchanged)
3
Current microloans to females +1% (microloans to males unchanged) Current microloans to females +100% (microloans to males unchanged)
Gini coefficient
0.327
0.287
0.262
Estimations based on model 4 and arithmetic means for regressors other than microloans to males and microloans to females, respectively.
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5. Conclusions This paper aimed at estimating the impact of microloans on the earning potential of borrowers and on the income distribution in the population of borrowers. Our findings can be summarized as follows. First, microloans have a clear positive impact on earnings. Second, the earning potential of male borrowers is significantly higher than of female borrowers which may be attributed to different levels of human capital. Third, microcredits increase income inequality in the population of borrowers as relatively rich borrowers disproportionally gain from improved access to finance. Some policy conclusions can be drawn to make microlending schemes even more effective. Our findings demonstrate that an increase in the average loan size further improves the effect on the income of the microborrower. The business model of many MFI, geared to reducing operational cost in small-scale lending, encounters difficulties in this respect. Promoting a stronger role of conventional financial institutions in microlending, e.g. by means of strategic partnerships between banks and MFI, seems more promising. This may also help informal microenterprises graduate to formal small firms, a challenge mastered only by very few. Moreover, our analysis suggests that the bias of microfinance towards lending to female borrowers can only be justified when the aim is the empowerment of women. If, however, the aim is primarily to alleviate poverty, many current schemes should be reconsidered in two ways. First, more lending programs should be made available to male borrowers either. Second, those schemes exclusively targeting women should be increasingly offered as microfinance plus education programs as our findings indicate complementarities between financial and human capital. In a broader context of the economic development of low income countries, our findings reemphasise that improving access to finance is an important element of a more encompassing set of policies enhancing economic opportunities of the disadvantaged. The effect of microlending alone is limited, an insight all too often ignored by practitioners in NGO riding the microfinance wave. In order to tap its full potential, microlending schemes must be embedded in training and educational programs.
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References Ahlin, Christian and Neville Jiang, 2008: ‘Can Micro-Credit Bring Development?’ Journal of Development Economics, 86(1), 1-21. Armendáriz, Beatriz and Jonathan Morduch, 2010: The Economics of Microfinance, 2nd ed, Cambridge, MA and London: MIT Press. Banerjee, Abhijit V. and Andrew F. Newman, 1993: ‘Occupational Choice and the Process of Development’, Journal of Political Economy, 101(2), 274-298. Beck, Torsten, Asli Demirgüç-Kunt and Ross Levine, 2007: ‘Finance, Inequality and the Poor’, Journal of Economic Growth, 12(1), 27-49. Besley, Timothy and Stephen Coate, 1995: ‘Group Lending, Repayment Incentives and Social Collateral’, Journal of Development Economics, 46(1), 1-18. Biltagy, Marwa, 2011: ‘Optimal Level of Schooling and its Determinants in Egypt Using a Human Capital Model’, International Proceedings of Development and Research, 20, 350-354. Copestake, James, 2002: ‘Inequality and the Polarizing Impact of Microcredit: Evidence from Zambia’s Copperbelt’, Journal of International Development, 14, 743–755. Coleman, Brett E., 2006: ‘Microfinance in Northeast Thailand: Who Benefits and How Much?’, World Development, 34(9), 1612-1638. Coleman, Brett E., 1999: ‘The Impact of Group Lending in Northeast Thailand’, Journal of Development Economics, 60(1), 105-141. Collins, Daryl, Jonathan Morduch, Stuart Rutherford and Orlanda Ruthven, 2009: Portfolios of the Poor. How the World’s Poor Live on $2 a Day, Princeton and Oxford: Princeton University Press. Cuong, Nguyen Viet, David Bigman, Marrit van den Berg and Vu Thieu, 2007: ‘Impact of Micro-credit on Poverty and Inequality: The Case of the Vietnam Bank for Social Policies, Paper submitted to the conference Microfinance: What Do We Know?, University of Groningen, 7–8 December 2007. De Mel, Suresh, David McKenzie and Christopher Woodruff, 2008: ‘Returns to Capital in Microenterprises: Evidence from a Field Experiment’, Quarterly Journal of Economics, 123(4), 1329-1372. Duvendack, Maren, Richard Palmer-Jones, James G. Copestake, Lee Hooper, Yoon Loke and Nitya Rao, 2011: What Is the Evidence of the Impact of Microfinance on the Well-being of Poor People? London: EPPI Centre.
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Ghatak, Maitreesh, 1999: ‘Group Lending, Local Information and Peer Selection’, Journal of Development Economics, 60(1), 27-50. Honohan, Patrick, 2008: ‘Cross-country Variation in Household Access to Financial Services’, Journal of Banking & Finance, 32(11), 2493-2500. Jeminez, Emmanuel and Harry Anthony Patrinos, 2008: ‘Can Cost-Benefit Analysis guide Education Policy in Developing Countries?’ World Bank Policy Research Working Paper 4568, Washington, DC: Worldbank. Kai, Hisako and Shigeyuki Hamori, 2009: ‘Microfinance and Inequality’, Research in Applied Economics, 1(1), 1–12. Karlan, Dean and Nathanael Goldberg, 2006: The Impact of Microfinance: A Review of Methodological Issues, New York, NY: Financial Access Initiative (FAI). Karlan, Dean and Jonathan Zinman, 2011: ‘Microcredit in Theory and Practice: Using Randomized Credit Scoring for Impact Evaluation’, Science, 332, 1278-1284. Khandker, Shahidur R., 2005: ‘Microfinance and Poverty: Evidence Using Panel Data from Bangladesh’, World Bank Economic Review, 19(2), 263-286. La Porta, Rafel and Andrei Shleifer, 2011: ‘The Unofficial Economy in Africa’, NBER Working Paper Series 16821, Washington DC: National Bureau of Economic Research (NBER). Mallick, Dedulal, 2012: ‘Microfinance and Moneylender Interest Rate: Evidence from Bangladesh’, World Development, 40(6), 1181-1189. Mincer, Jacob A., 1974: Schooling, Experience and Earnings, New York: Columbia University Press. Morduch, Jonathan, 1999: ‘The Microfinance Promise’, Journal of Economic Literature, 37(4), 1569-1614. Odell, Kathleen, 2010: Measuring the Impact of Microfinance. Taking Another Look, Washington DC: Grameen Foundation. Pitt, Mark M. and Shahidur R. Khandker, 1998: ‘The Impact of Group-Based Credit Programs on Poor Households in Bangladesh: Does the Gender of Participants Matter?’, Journal of Political Economy, 106(5), 958-996. Psacharopoulos, George and Harry Anthony Patrinos, 2004: ‘Returns to Investment in Education: A Further Update’, Education Economics, 12(2), 111-134. Sen, Amartya, 1973: On Economic Inequality, Clarendon Press: Oxford. Silverman, Bernard W., 1986: Density Estimation, Chapman and Hall: London.
- 15 -
Shah, Avni, 2010: ‘Does Microfinance Polarize? Evidence of its Effects on Income Inequality from Rural Thailand’, mimeo. Stiglitz, Joseph E., 1990: ‘Peer Monitoring and Credit Markets’, World Bank Economic Review, 4(3), 351-366. Stiglitz, Joseph E. and Andrew Weiss, 1981: ‘Credit Rationing in Markets with Imperfect Information’, American Economic Review, 71(3), 393-410. Tchouassi, Gérard, 2011: ‘Microfinance, Inequality and Vulnerability: Empirical Evidence from Central African Countries’, Journal of Development and Agricultural Economics, 3(4), 150–156. Xu, Kuan, 2003: ‘How Has the Literature on Gini’s Index Evolved in the Past 80 Years?’ China Economic Quarterly, 2(4), 757-778.
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Appendix, Table A1: Parameter Estimates of the Earnings Function explained variable: ln of monthly earnings model 1 h u m a n c a p i t a l
i n d u s t r y m i c r o l o a n e f f e c t
model 2
model 3
model 4
const
6.2063*** (0.1160)
6.1903*** (0.1147)
6.0448*** (0.1090)
6.0678*** (0.1183)
Schooling
0.0345*** (0.0112)
0.0335*** (0.0111)
0.0219** (0.0103)
0.0197* (0.0103)
Experience
0.0358*** (0.0104)
0.0317*** (0.0103)
0.0190** (0.0096)
0.0200** (0.0096)
Experience2
-0.0006** (0.0003)
-0.0005** (0.0003)
-0.0005** (0.0002)
-0.0006** (0.0002)
Literate
0.0127 (0.1150)
-0.0135 (0.1140)
-0.0801 (0.1049)
-0.0931 (0.1046)
Training
-0.1565* (0.0935)
-0.1354 (0.0923)
-0.0815 (0.0850)
-0.0716 (0.0852)
-0.2276*** (0.0739)
-0.1997*** (0.0736)
-0.1648*** (0.0677)
-0.1534** (0.0675)
0.5377*** (0.1185)
0.3160*** (0.1112)
0.2641** (0.1133)
Shock D1 D2
0.0137 (0.377)
0.0619 (0.1268)
0.0399 (0.1265)
D3
0.4370 (0.3877)
0.5256 (0.3559)
0.5297 (0.3546)
D4
0.1687 (0.3853)
0.1653 (0.3538)
0.1208 (0.3527)
Microloan
0.1491*** (0.0214)
Microloan2
-0.0035*** (0.02134)
Male · Microloan
0.1716*** (0.0230)
Male · Microloan2
-0.0043*** (0.0009)
Female · Microloan
0.1487*** (0.0455)
Female · Microloan2
-0.0070* (0.0038)
YSL n
0.0153 (0.0139)
0.0162 (0.0140)
596
596
596
596
R²
0.082
0.115
0.259
0.268
2
0.073
0.100
0.243
0.249
SSE
439.8
424.0
355.0
350.6
F test
8.8***
7.6***
15.7***
14.2***
R
Standard errors of the parameters in brackets. *, ** and *** represent a level of significance of 10%, 5%, and 1%, respectively
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