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Future of Microfinance

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Spandana Case Study: An Analysis

This essay is based on the first randomized Impact evaluation of Microfinance. Section 1 explains the methodology of the study, Section 2 presents the data analysis and results, Section 3 presents the Caveats to be considered while interpresting the results, Section 4 presents some conclusions derived from the study and Section 5 talks about how some of the problems identified with respect to MFIs can be addressed.

In a paper named “Miracle of Microfinance: Evidence from a randomized evaluation”, Abhijit Banerjee, Esther Duflo, Rachel Glennerster and Cynthia Kinnan analyze the first randomized evaluation of the impact of introducing the standard microcredit group-based lending product in a new market. This study also follows the households over the longest period of any evaluation (three to 3.5 years after the introduction of the program in their areas) to trap impacts not only in the short run but also over the medium run. The results of this study compel us to think about the functional difference that the microfinance initiatives are making in the target community and whether it’s time to address this sphere of developmental finance with less fancy and more caution. SECTION 1: METHODOLOGY OF THE STUDY

Baseline Survey A baseline neighbourhood survey (2005) was conducted to assess baseline conditions such as household composition, education, employment, asset ownership, expenditure, borrowing, saving, and any businesses currently operated by the household or stopped within the last year. However, due to the absence of census and the urgent need to collect information for stratification before Spandana began operations, every nth house in the area was chosen such that there were 20 households per area, instead of a random selection from a household list (total 120 areas). Since there was a lack of rigour in this sampling, baseline sample was used only for stratification, descriptive analysis and for area-level characteristics and dropped from subsequent analysis. Sampling Sixteen out of the 120 areas were dropped out of the study due to dominance of migrant-worker households. The remaining 104 areas were grouped into pairs of similar neighbourhoods based on average per capita consumption and per-household debt, and one of each pair was randomly assigned to the treatment group for opening of an MFI branch by Spandana. Between 2006 and 2007 Spandana began its operations in the treatment area. Endline sample: A comprehensive census of each area in 2007 revealed low rates of MFI borrowing even in treatment areas. The endline sample, thus, consisted of households whose characteristics suggested high likelihood of having borrowed: households who had resided in the area for at least three years and contained at least one woman aged 18 to 55. Spandana borrowers identified in the census were

oversampled, and the results presented below correct for this oversampling so that the results are representative of the population as a whole. The overall sample size for the endline survey was 6,864 households. End line Survey 1 Fifteen to 18 months after the introduction of microfinance in each area, a comprehensive household survey was conducted in an average of 65 households in each neighborhood, for a total of about 6,850 households. In the meantime, other MFIs had also started their operations in both treatment and comparison households, but the probability of receiving an MFI loans was still 8.8 percentage points higher in treatment areas than in comparison areas (27.1% borrowers in treated areas versus 18.3% borrowers in comparison areas). Endline Survey 2 Two years after this first endline survey, we surveyed the same households once more. By that time, both Spandana and other organizations had started lending in the treatment and control groups, so the fraction of households borrowing from microcredit organizations was not significantly different (38.5%) in treatment and 33% in control). But households in treatment groups had larger loans and had been borrowing for a longer time period. This second survey thus gives us an opportunity to examine some of the longer-term impacts of microcredit access on households and businesses.

To estimate the impact of microfinance becoming available in an area on likely clients, there is a focus on intent-to-treat (ITT) estimates1; that is, simple comparisons of averages in treatment and comparison areas, averaged over borrowers and non-borrowers. Interpreting the tables: Each column of each table reports the results of a regression of the form

 Yia = α + β × Treatia + X’a γ + εia where, Yia is an outcome for household i in area a,  Treatia is an indicator for living in a treated area, and
   β is the intent-to-treat effect X is a vector of control variables, calculated as area-level baseline values: area population, total businesses, average per capita expenditure, fraction of household heads who are literate, and fraction of all adults who are literate.

 Standard errors are adjusted for clustering at the area level and are in parentheses.

An intention-to-treat (ITT) analysis of the results of an experiment is based on the initial treatment assignment and not on the treatment eventually received. This helps avoid non-random attrition of participants from the study and allows us to account for dropouts in the study.

 All regressions are weighted to correct for oversampling of Spandana borrowers and for higher probability of tracking them.  The tables present values for the Treated area  * is significant at the 10% level, ** at the 5% level,*** at the 1% level The following text analyses the impact of microcredit on four crucial parameters: Business outcomes, labour supply, consumption and social outcomes. IMPACT OF MICROCREDIT ON NEW BUSINESSES AND BUSINESS OUTCOMES Probability of target households to start a new business TABLE 1: SELF EMPLOYMENT ACTIVITIES (DATA SET 1) Has started a New Female‐run Has a self‐ business in the businesses new businesses Employment last 12 months Activity 0.009 0.015** 0.0143*** 0.009 (0.006) (0.007) (0.00533) (0.022) ‐0.000 0.00283 ‐0.0047 0.023 (0.010) (0.0135) (0.00622) (0.023)

Endline survey 1 Endline survey 2

Number of self -employment activities 0.021 (0.033) 0.045 (0.040)

Endline Survey 1 results:  In comparison areas, 4.7% of households opened at least one business in the year prior to the survey, compared to 5.6% in treated areas leading to a difference of only 0.9% or 0.009 which is not statistically significant (shown in first column). More new businesses were created in treatment areas overall: 6.8 per 100 households, versus 5.3 per 100 households in control areas leading to a difference of 1.5% or 0.015. With the calculated t-stat equal to 2.142, the difference is significant at the 5% level. As Spandana lends only to women, the marginal businesses tend to be female-operated: when we look at creation of businesses that are owned by women, we find that almost all of the differential business creation in treatment areas is in female-operated businesses–there are 0.014 percentage points more female-owned businesses in treatment than in control areas, an increase of 55%. Nevertheless, overall treatment households are no more likely to have a business and they don’t have significantly more businesses either. (last 2 columns)

Endline Survey 2 results:  There is no significant difference in business creation between the two groups: the point estimate is virtually zero (the 90% confidence interval ranges from 2pp fewer new businesses, to 2.5pp more) For investment outcomes such as new business creation, business assets acquired in the previous year the point estimate is very close to zero (however the standard errors are large).

Probability of investment in durables for a business TABLE 2: SELF EMPLOYMENT ACTIVITIES (DATA SET 2) Investment in last 12 months Endline survey 1 391* (213) Endline survey 2 ‐134 (207)

Endline Survey 1 results: Treatment households invest more in durables for their businesses. Since only a third of households have a business, and most businesses use no assets whatsoever, the point estimate is small in absolute value (Rs. 391 over the last year, or a bit less than a third of the increase in average MFI borrowing in treatment households). The t-statistic is 1.835 and is hence statistically significant at the 10% level. Endline Survey 2 results: By the second endline survey, there is a decrease in investment of durables in the treatment group and the difference between control and treatment group is no longer significant with a t-statistic of -0.65. Impact on general business status (Assets, Revenue, Expenses and Profit) TABLE 3: SELF EMPLOYMENT ACTIVITIES (DATA SET 3) Assets Revenue Expenses Profit (stock) Endline survey 1 598 927 255 354 (384) (1183) (1056) (314) Endline survey 2 1261** 266 ‐530 542 (530) (527) (547) (372) Endline Survey 1 results:  Treatment households have more business assets (although the t-statistic on the asset stock is only 1.56).  The treatment effects on revenues is positive but insignificant. The t-statistic on revenue is just 0.78 and is hence not statistically significant.  The treatment effects on expenses is positive but insignificant. The t-statistic on expenses is just 0.24 and is hence not statistically significant.  There is an insignificant increase in business profits. The point estimate, at Rs. 354 per month, corresponds to a roughly 50% increase relative to the profits received by the average comparison household. This is thus large in proportion to profits, but it represents only a very small increase in disposable income for an average household. The average total consumption of these households is about Rs. 7,000 per month and an increase of Rs. 354 per month in business revenues is certainly not going to change the life of the average person who gets access to microcredit. Endline survey 2 results:  Businesses in treatment areas have significantly larger asset stock, which reflects the cumulative effect of the past years during which they had a chance to borrow and expand.

Despite this, their profits are still not significantly larger, though the point estimate is around 60% of the sample mean (with a t-statistic of around 1.5). The positive increase, nevertheless, is once again concentrated in the top and bottom tails, although it starts being positive a little earlier, at the 85th percentile.

Standardized index of business outcomes TABLE 4: STANDARDIZED INDEX OF BUSINESS OUTCOMES2 Index of business outcome Endline survey 1 0.0365 (0.0188) Endline survey 2 0.0151 (0.0186)

Endline Survey 1 results: Looking at all businesses outcomes taken together, we find a 0.037 standard deviation increase in the standardized index of business outcomes, which is significant at 10% (tstatistic is 1.94) with conventional standard errors but not once the multiple hypothesis testing across different families of outcomes is taken into account (p-value of 0.17). Endline Survey 2 results: By the time of the second endline survey, the increase in business outcome of treatment group moderates and with a t-statistic of just 0.812, the difference between treatment and control group is not significant. Note: This is the ITT estimate, and part of the reason it is low is that few households took advantage of microcredit in the treatment groups (and some did in the control as well). The marginal borrower in the treatment group may also have fewer opportunities than someone who was interested enough to borrow in the control group. This does not rule out that the businesses of some specific groups could have benefited from the loan. To look at this in more detail, we focus on businesses that were already in existence before microcredit started. Impact on businesses that existed before microcredit started TABLE 5: SELF EMPLOYMENT ACTIVITIES (HOUSEHOLDS WITH OLD BUSINESSES) Assets Investment Revenue Expenses Profit Employees Index of (stock) in last 12 dependent months variables 851 1,129 5,417 1,680 2206** ‐0.05 0.0915** (1083) (713) (3763) (3291) (1112) (0.0843) (0.0412) 1,715 ‐1,203 ‐15 -3589.2** 950 ‐0.16 ‐0.0162 (1752) (739) (1502) (1724) (1160) (0.120) (0.0297)

Endline survey 1 Endline survey 2


Presents the coefficient of a "treatment" dummy In a regression on treatment of an index of z‐scores of the outcome variables for each round following Kling, Liebman and Katz (2007)

Endline Survey 1:  For businesses that existed before Spandana expanded, we find an expansion in sales, inputs and investment. The overall business index is significant at 5% (t-statistic is 2.22) and positive. This is so even after correcting for multiple inference (0.09 standard deviation, with a p value of 0.057 after the correction). There is an average increase in profits of Rs. 2,206 in treatment areas, which is statistically significant at 5% (t-statistic is 1.98) and represents more than doubling, relative to the control mean of Rs. 2,000. This increase is not due to a few outliers; however, it is worth nothing it is concentrated in the upper tail (quantiles 95 and above). At every other quantile, there is very little difference between the profits of existing businesses in treatment and control areas. There are 75 businesses above the 95th percentiles, so it is not a handful, but the 95th percentile of monthly profit of existing businesses is Rs. 14,600 (or $1590 at PPP), which makes them quite large and profitable businesses in this setting. The vast majority of the small businesses make very little profit to start with, and microcredit does nothing to help them. The finding that microcredit is most effective in helping already-profitable businesses is contrary both to much of the rhetoric of microcredit and the view of microcredit skeptics.

Endline survey 2:  By the second endline survey, the difference in business outcome between the control and the treatment group becomes insignificant. IMPACT OF MICROCREDIT ON LABOUR SUPPLY TABLE 6: HOURS WORKED OVER THE PAST 7 DAYS BY HOUSEHOLD HEAD AND SPOUSE Total For self- employment For outside activities Endline survey 1 3.176** 2.710* 0.466 (1.421) (1.474) (1.418) Endline survey 2 0.991 1.703 ‐0.712 (1.176) (1.583) (1.488)

Endline survey 1 Endline survey 2

TABLE 7: HOURS WORKED OVER THE PAST 7 DAYS BY TEENAGERS All Girls Boys ‐1.629 ‐2.076** ‐0.026 (1.432) (1.046) (2.065) ‐0.358 0.440 ‐1.387 (1.226) (0.948) (1.521)

Endline Survey 1:   the household head and spouse in treatment households increase their overall labor supply by an average of 3.18 hours (t-statistic 2.235 and significant at 5 level). The increase occurs entirely in the households’ own businesses, and there is no increase in number of hours worked for wages: those hours may be much less elastic, if the households do not fully choose them.

 

Interestingly, we do not find the increase in teenagers’ labor supply that is sometimes feared to be a potential downside of microfinance and that was found in the Bosnia study (as the adolescents are drawn into the business by their parents). Teenage girls work about two hours less per week in treatment than control areas, and this difference is significant ( t-statistic is -1.98 and is hence significant at 5% level) Given that there is an increase among adults and a decrease among teens, the overall index is, not surprisingly, close to zero and insignificant.

Endline Survey 2:  By endline 2, as control households have started borrowing, the difference between treatment and control disappears. IMPACT OF MICRO CREDIT ON CONSUMPTION TABLE 8: CONSUMPTION (PER CAPITA PER MONTH) Durables NonFood Health Education Temptation durables goods 10.24 17.08* ‐6.50 ‐9.34 -2.86 ‐0.78 -8.817* (37.22) (9.90) (31.81) (10.18) (9.74) (8.59) (4.89) ‐48.83 1.25 -45.45 ‐11.20 ‐21.01 10.40 ‐10.07 (51.53) (8.579) (46.92) (17.88) (14.95) (12.74) (6.61) Total EL1 EL2

Festivals Home durable good index ‐11.76* ‐0.051 (6.80) (0.057) 5.62 ‐0.013 (3.52) (0.043)

Endline Survey 1  There is no significant difference in total household expenditures–either total or non-durable– per adult equivalent, between treatment and comparison households. The point estimate is essentially zero in both cases and we can reject at the 5% level the null hypothesis that there was a Rs. 85 per month increase in total consumption per adult equivalent and Rs. 56 in nondurable consumption (about 6% of the average in control for consumption, and 4% for nondurable consumption) increase. Hence, enhanced microcredit access does not appear to be associated with any meaningful increase in consumption after 15 to 18 months. Of course, this may partly be due to the fact that relatively few people borrow, and that some in the control group borrow from another MFI. There are shifts in the composition of expenditure: column 2 shows that households in treatment areas spent a statistically significant Rs. 17.08 more per capita per month, or Rs. 205 per capita over the last year, on durables than did households in comparison areas3. While there was no detectable change in non-durable spending otherwise; the increase in durable spending by treatment households was essentially offset by reduced spending on “temptation goods” and festivals. Together, the average drop in consumption in temptation goods and festivals is Rs. 21 per capita per month.


This is probably an underestimate of the total effect of loans on durable purchases, since this study’s measure would miss anyone who borrowed more than a year before the survey (the survey was 15 to 18 months after the centers opened) and immediately bought a durable with the loan.

Endline Survey 2      At the time of the second endline, the effects on both total per capita spending and total per capita non-durable spending are negative with t-statistics around 1. Spending on temptation goods is still lower by about Rs.10 per month, similar to endline 1, though the effect is now insignificant. The effect on festivals is now positive but insignificant. There is also no difference on average in durable goods spending in endline 2. Given that the main difference between treatment and control households at endline 2 is that treatment households have been borrowing for longer, this suggests that, in the second cycle, households in the treatment seem to just repeat the first cycle with another durable (of roughly the same size), while households in the control group also acquire a durable. IMPACT OF MICROCREDIT ON SOCIAL OUTCOMES LIKE CHILD LABOUR, EDUCATION AND WOMEN EMPOWERMENT A finding of many studies of household decision-making is that an increase in women’s bargaining power leads to an increase in investments in children’s human capital4. TABLE 9: SOCIAL EFFECTS Share of children Hours worked per Share of teenagers Index of aged 5‐15 in school child aged 5‐15 over (aged 16‐20) in women’s the past 7 days school independence/ empowerment Girls Boys Girls Boys Girls Boys ‐0.021 0.008 0.000 ‐0.004 ‐0.012 0.006 0.007 (0.027) (0.021) (0.003) (0.003) (0.010) (0.012) (0.023) 0.019 0.006 0.001 ‐0.005 0.008 0.004 ‐0.011 (0.025) (0.021) (0.002) (0.003) (0.011) (0.012) (0.021)


Endline Survey 1  Despite a reduction in teenage girls’ labour supply, the share of children (t-statistic is -0.78 for girls, 0.38 for boys and hence insignificant) or teenagers (t-statistic is -1.2 for girls, 0.5 for boys and hence insignificant ) who are enrolled in school remains unchanged. There is no difference in the number of hours worked by girls or boys aged 5 to 15. Because there are many possible proxies for women’s empowerment, and many “social” outcomes we use the approach of Kling et al. (2007) to test the null hypothesis of no effect of microcredit on “social outcomes” against the alternative that microcredit improves social outcomes. We construct an equally weighted average of z-scores for the 16 social outcomes; this method gives us maximal power to detect an effect on social outcomes, if such an effect is present. There is no effect on the index of social outcomes (point estimate .007 standard deviations). The t-statistic is 0.304 which is statistically insignificant.

 


Thomas, 1990 and Duflo, 2003

Endline Survey 2  Nothing major changes by endline 2. The effect of microfinance access on the index of women empowerment is still very small (indeed, slightly negative) and insignificant, and anything but a small effect can still be ruled out. Recall that we are comparing households who, by EL2, are equally likely to borrow: the main difference by EL2 is that households in the treatment group have had greater access to microfinance for the first 18 months; this may limit power to detect differences in the social outcomes at the community level.

This suggests that there is no prima facie evidence that microcredit leads to important changes in household decision-making or in social outcomes.

 First, the difference in microfinance take-up between treatment and control areas is low, even by the first endline, which raises two issues: it lowers power and precision (though there are a number of significant effects), and it means that the impact of microcredit detected is driven by the marginal borrowers–those who do not borrow when the cost of doing so is high (because they have fewer MFIs to choose from or do not want to change neighborhoods), but do borrow when that cost is lower. Second, the evaluation was run in a context of very high economic growth, which could have either decreased or increased the impact of microfinance. Third, this is the evaluation of a for-profit microfinance model, and not-for-profit microfinance lenders may have larger positive effects if their interest rates are kept low. Fourth, as Spandana does not provide any complementary services such as business training or sensitivity education, the study is an assessment of the pure impact of providing loans to women who may or may not use them for their own businesses (though Spandana does believe that this is what the money will be used for eventually), and there is an expansion in womenowned businesses. Fifth, the study took place in “marginal” neighborhoods–those Spandana was indifferent about working with at the outset and the impacts may have been different in the neighborhoods they chose to exclude from the randomization5.

  

The results nevertheless are consistent with three studies that look at similar programs in different contexts6.

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Heckman, 1992 Crépon et al. (2013): Study of MFI Al Amana’s impact in rural Morocco , Angelucci, Karlan and Zinman (2013) : Study of MFI Compartamos’ impact in Mexico, Tarozzi et al. (2013) : study of microcredit impact in Ethiopia

SECTION 4: CONCLUSION In this segment, I will draw conclusions based on the above analysis and try to back them by reasons. The demand for microloans is not universal With the constant emphasis on increasing accessibility and amount of micro loans, it appears that demand for micro loans in the target community is significant and relatively inelastic. The results from our study, on the other hand, present a different picture. At the end of three years only 38% of households, with relatively high propensity to take up a loan, actually borrowed. This result is not exclusive to the Spandana study. Similar results have been observed in the randomized evaluations in Morocco7, Mexico8 and a study in rural South India9. There is a fairly low take-up rate in all these different contexts despite the incidence of high levels of informal borrowing and the purported benefits of microcredit over these alternative forms of borrowing. In all cases, except when the randomization was among those who had already expressed explicit interest in microcredit, only a minority of “likely borrowers” ended up borrowing. Reasons:  For a borrower to take up a microcredit, his /her realized rate of return on investment needs to be greater than the interest rate to service the loan. Thus, a possible reason for this could be that even though microbusinesses have a high marginal rate of return, most households do not have a project with the rate of return of atleast 24%, the APR (annual percentage rate) on a Spandana loan, which in turn does not allow them to start a profitable venture using the micro loans extended by the MFI. Another reason why people might prefer to borrow from friends, relatives, or moneylenders could be due to the greater flexibility those sources provide, despite costs such as higher interest (from moneylenders) or embarrassment (when borrowing from friends or relatives).

Microfinance has no discernible effect on women empowerment Although microfinance has the ability to empower women, the connection is not straightforward or easy to make. Just handing money to women and giving them access to financial assets and resources creates a new set of challenges for women, who now face extra burden to run a business. Thus a possible reason could be that the money goes directly to women who are often too over-burdened with domestic and childcare duties to become successful small-time entrepreneurs. Another argument is that focusing on women’s empowerment leads to dilution of efficiency and sustainability of MFIs, and this results in reluctance to focus on women’s empowerment when designing their systems and programmes. The biggest cultural constraint on women’s empowerment through microfinance programmes is the culture of patriarchy pervasive throughout Asia. For a woman, it affects the bargaining power and the ability to make decisions on economic issues within the household, ability to make decisions outside the
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Crépon et al. (2013): Study of MFI Al Amana’s impact in rural Morocco Angelucci, Karlan and Zinman (2013) : Study of MFI Compartamos’ impact in Mexico 9 Banerjee et al. 2013

household, control over loans, building of social networks, responsibility for household chores, and power over one’s time and physical and emotional health and energy. Microcredit does not help small businesses in scaling up (improving business outcomes and employment) and their positive impact is concentrated in only the most profitable of the businesses Stiglitz developed a model built on the observation that part of the household labour (women and children) may be essentially non-traded. While there exists a labour market for male labour for women, the outside labor market is largely missing in most of the developing countries, especially in the rural areas. Even where the market for female labor exists, the ‘selling price’ is, in general, much lower than the ‘buying price’, due to the transaction costs that might reflect social norms regarding women’s participation in the formal labor market (like Purdah) along with the usual search, information and monitoring costs (Lopez, 1986, Sadoulet et. al., 1996). The existence of a transaction cost band implies that many households fall within the band, and the female labor endowment becomes non-tradable for such a household (i.e., household specific missing market for female labor). This implies that the shadow wage rate for the non-traded part of the household labor is determined by the complementary resources available to a household, like land. For a poor household with little land, the shadow wage, in the absence of microcredit interventions, is very low, possibly close to zero. The availability of microcredit enables this non-traded part of the labor to be productive. However, as soon as the labour requirement rises above the labour availability in a household, a larger loan (i.e. expansion of the business) is likely to change the household from a net supplier of labor to a net demander of labor from the market. This means that the cost of labor increases discontinuously, as the opportunity cost of labor is now the ‘market” wage rate (in case of a missing market which is effectively infinite, and for a labor market with transaction cost band, it is the buying price which is much higher than the selling price). The household cannot service a high interest loan if it has to hire labor from the market. Thus, a household might not find it profitable to expand its scale of activity beyond a threshold. Monthly consumption (indicator of overall welfare) did not increase for those who had early access to microfinance neither in the short run nor in the long run. This study took place in a dynamic urban environment, in a context of very high growth. Microcredit seems to have played very little part in such a setting but may have had different impacts in other settings10. Nevertheless, microcredit does affect the structure of household consumption. We see households invest in home durable goods and restrict their consumption of temptation goods and expenditures on festivals and parties. They continue to do so several years later, and this decrease is not due to a few particularly virtuous households, but seems to be spread across the sample. Similar declines in these types of expenses are also found in all the other studies. Change in labour Supply choices and intertemporal substitution of income

Households that have access to loans seem to work harder on their own businesses while reducing their labor input elsewhere. Thus, microcredit plays its role as a financial product in an

“The Impact of Microcredit Borrowing on Household Consumption in Bangladesh” 2012 shows that an increase in amount borrowed has a positive and significant impact on per capita household consumption

environment where access is limited, not only to credit but also to saving opportunities. It expands households’ abilities to make different intertemporal choices, including business investment. SECTION 5: ADDRESSING PROBLEMS IN THE EXISTING MICROFINANCE MODEL
Addressing the problem of high interest rates Some policy makers have suggested that interest rate ceilings to ensure access to affordable credit for poor households. However, Charging prices high enough to cover costs is an essential practice for any business enterprise that intends to continue its operations beyond the short-term and hence ceilings are not the solution. Some possible measures to achieve the objective could be: I. Promoting more competitive markets Some countries in the region are already showing encouraging results from their efforts to create a more competitive environment for the microfinance industry. Cambodia has allowed national and international nongovernmental organizations (NGOs) to operate freely in the market, helping them increase their outreach. A legal framework was created for licensing NGO MFIs and helping them transform into regulated financial institutions to offer poor households a wide range of services. Policy makers opted for a “truth in lending law” rather than impose an interest rate ceiling. Together, these measures have resulted in a more competitive industry that, in turn, has led to lower microcredit interest rates. Microcredit interest rates in the country have declined from around 10% per month (on a reducing-balance basis) to 3-4% per month (on the same basis) during the last 5 years. Improving the Physical and Human Infrastructure High operating costs is a root cause of the high interest rates on microcredit. Simple infrastructure improvements-such as road links, bridges, and the reliable supply of electricityand better human infrastructure will have a positive impact on the operating costs of microlenders. Such improvements also expand economic opportunities for poor households. Policy makers also need to pay attention to improving infrastructure that specifically affects the use of new information and communication technology by MFIs, given that such technology can have a potentially significant positive impact on lenders' operating costs. Human infrastructure improvements, particularly in primary and adult education and accounting and auditing will also have a significant bearing on operating costs. For example, better-educated clients reduce costs to the MFIs. Thus, policy makers need to help develop financial literacy among the poor and low-income households. Recognizing Innovation and Efficiency It is essential for MFIs to undertake cost-reducing innovations that can then lead to lower interest rates. One great example of this is the M-pesa project in South Africa which is facilitating financial transactions via mobile telephones. In the most common application, microfinance customers can pay loan installments via telephone by entering a code that transfers funds from a personal account to the bank’s account. This significantly reduces operation and monitoring costs. Similar technologies can be adopted by MFIs.



Tackling low flexibility problem of microloans Microcredit institutions apply rigid and fixed repayment schedules when disbursing micro credit in order to reduce transaction costs and to inculcate discipline for better repayment behavior. Microcredit clients, however, do not typically have smooth income throughout the year. Thus this generates a cash flow disconnect, and given the presumed liquidity constraints of the typical microcredit client, this is resolved either through underinvestment, sale of productive assets, or failure to smooth consumption. Introducing greater flexibility in terms of micro credit can encourage more borrowers to shift from informal sources of borrowing to MFIs, inducing higher demand for micro-credit. An evidence of the same was presented by Erica Field, an associate professor of Economics at Harvard University. Field studied 169 groups of 845 female borrowers in Kolkata who were given loans between $100 and $250 at an annual interest rate of 22%. What Field found was that women who were offered a 2 month grace period in repayment had higher profits and more inventory. In other words, there was a clear business expansion although grace periods brought up the default rate by 12%. Enhancing the efficiency and sustainability of MFIs, while ensuring gender equality and empowerment This can be done through:         Understanding and valuing women’s activities, strategies, priorities and challenges Making information available in language that is user- friendly to women Including women in design plans through participatory research programmes Integrating gender into core group mobilization for savings and credit Giving women access to non-financial services, such as involving them in the application process Recognizing women’s talents and giving them loans in amounts that would help them expand a business and increase its quality through the purchase of superior equipment and materials Including women in value and supply chains so as to promote markets for services used by women Providing smaller loans with quick returns, targeted at productive activity, and making savings and services available in locations that women frequently access

References:       “Miracle of Microfinance: Evidence from a randomized evaluation” (March, 2014) Microfinance and Missing Markets (2006) Understanding and Dealing with High Interest Rates on Microcredit (2006), Asian Development Bank Women’s empowerment and microfinance, An Asian perspective study (2011) Does Flexibility in Microfinance Pay Off? Evidence from a Randomized Evaluation in Rural India (2011) From Microfinance to m-Finance (2007)

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