What Drives Lending Interest Rates in the Microfinance Sector?

Transcription

1 What Drives Lending Interest Rates in the Microfinance Sector? Taking into account the potential benefits formal lending could provide, there is a growing interest among policymakers to formulate policies that may help increase the participation of the poor in formal credit markets. One of the challenges to achieve such goal has been the high real interest rates financial institutions charge to micro and small enterprises. Further, these high interest rates affect the probability that financing spurs growth. In lieu of this, governments have tried to induce a reduction in such rates but have had little success. One of the possible reasons for such failure is the lack of empirical studies that show what the main determinants of such interest rates are. Using a cross section-time series panel data for microfinance institutions operating in Africa, Asia and America, we examine in this paper which factors may help explain the interest rate that microfinance institutions charge. JEL: G21, G28 Keyword: lending interest rates, microfinance Pablo Cotler 1 Deyanira Almazan August, Economics Department, Universidad Iberoamericana. pablo.cotler@uia.mx 1

2 What Drives Lending Interest Rates in the Microfinance Sector? I. Introduction Taking into account the potential benefits formal lending could provide, there is a growing interest among policymakers to formulate policies that may help increase the participation of the poor in formal credit markets. From a macroeconomic point of view, studies such as Beck, Demirguc-Kunt and Levine (2007) reveal the importance of greater financial leverage: it affects in a disproportional manner the income of the poorest 20% of the population. Thus not only does it help to reduce poverty, it scales down inequality as well. However, at the micro-level positive results are not easy to find. Papers by Pitt and Khandker (1998), Morduch (1998), Banerjee and Duflo (2004) and Alexander- Tedeschi and Karlan (2006) show the methodological difficulties to assess the impact of financial access. Furthermore, according to Armendariz and Morduch (2005) the existing studies suggests that those showing the strongest impacts are also those with the largest methodological flaws, while those which are cleanest methodologically generally show little or no positive impact. Certainly this is puzzling given the growth of micro lending institutions across countries. Cotler and Woodruff (2008) report the results of an experiment done in Mexico where a group of firms received funding (the treatment group) and another group did not (the control group) over a period of eighteen months. Taking all the necessary precautions to minimize biases due to sample design and attrition, they find that sales, inventories and fixed assets relatively grew in the treatment group. However, the relative change in profits once the loan and interest were paid- was not always significant from a statistical point of view. Hence, the impact of such experiment may be considered ambiguous. 2

3 One possible reason why a relative increase in sales does not boost relative earnings may be that the interest rate paid for was too high: the lending institution with which Cotler and Woodruff (2008) worked during their research was charging a real annual interest rate of 43%. But Mexico is not a unique case in this regard. According to the Mix Market database, the annual lending interest rate charged by microfinance institutions during the period was on average 42% in Africa and in Latin America and 35% in Asia. Since annual inflation rates during those years in all three continents were around 7%, real interest rates paid by microfinance clients were high. Thus, it is not surprising that one of the most discussed issues in microfinance is the high interest rates these institutions charge. As Gonzalez (2010) asks, are high micro credit interest rates not a sign that these institutions that proclaim development objectives are in fact gouging the poor? Since interest rates affect the probability that financing spurs growth, it would be useful to know what factors cause these high interest rates. Do they closely follow the interest rates that financial institutions pay for their funding? Or are the operating costs the main cause? Or perhaps, these high interest rates are just a result of a lack of competition in the credit market. To answer such questions, our paper will examine which are the main determinants of the lending interest rates that small firms face in less developed countries. For this purpose, this paper will be divided into four additional sections. In the first one we provide a literature review. The second one describes the database we consulted and the methodology we used. Then in the third section, we present and discuss our findings. Finally in the last section we conclude. II. Literature Review When analyzing interest rates, the first aspect which comes to mind is whether they follow a behavioral pattern consistent with how much competition financial firms face. While the structure-conduct-performance theory suggests that greater competition among lending institutions should bring interest rates down, the informational problems that surround credit 3

4 market transactions could weaken the former argument. In this regard, some authors predict the existence of a negative correlation between market power and interest rates. Among them, Peterson and Rajan (1995) found that lending institutions who wield greater market power are those with enough resources to invest in relationship lending. Thus, as market power increases, the likelihood that small firms will be granted loans is greater and therefore interest rates should decline. With a different argument, Marquez (2002) and McIntosh and Wydick (2005) arrive to the same conclusion: as competition among financial institutions increase, default risks may follow a similar path and so does interest rates. However, other authors have an opposite view. For example, Boot and Thakor (2000) claim that a relationship orientation helps to partially protect the financial institution from competition. Thereby, higher competition may induce financial firms to reallocate resources towards more relationship lending and therefore smaller firms may face a reduction in the lending interest rates. Thus in the theoretical literature we found two conflicting hypotheses regarding the effects that an increased competition on credit markets for small firms have on interest rates. Furthermore, the structure-conduct-performance framework may be misleading if market constestability is a relevant feature. On the other hand however, if potential borrowers lack formal documentation to certify their income and expenditure flow, it is very likely that financial institutions may need to develop special techniques to assess the risk profile of these potential borrowers. In this scenario, an entrance threat is not necessarily sustainable and could therefore call into question the issue of market contestability and its effects on interest rates. Therefore, it is not clear-cut how is the interaction between entry, competition and interest rates on credit markets. Whether the correlation between market structure and interest rate is positive, negative or null, it is a question to be solved empirically. However, a review of the empirical literature shows that there are conflicting results. On the one hand we refer to Boot and Thakor (2000) and Ongena and Smith (2001), whose works supports the traditional structure-conduct-performance hypothesis. On the other hand, the findings of Petersen and Rajan (1995) and Zarutskie (2003) support an alternative hypothesis. Certainly, the results depend on the methodology being used 4

5 and databases characteristics. For example, Carbó, Rodríguez and Udell (2006) show that the sign of the correlation is sensitive to how market power is assessed. If market power is defined by the Lerner index, their results support the conventional theory: greater market power implies higher interest rates. However, if market power is defined by concentration indexes, their results are the opposite and the conventional theory is discarded. But interest rates are not only determined by real or potential competition; the characteristics of borrowers and lenders also matter. Rosemberg, R., A. Gonzalez and S. Narain (2009) and Gonzalez (2010), suggest that tiny loans with very low default rates require higher administrative expenses which appear not to be offset by economies of scale. According to these authors, these administrative costs are the single largest contributor to interest rates. Further, while microfinance institutions have higher returns on assets than commercial banks, these same authors claim that the search for returns is not an important driver of interest rates. While such hypotheses may be appealing, these authors do not explain how they arrived to such conclusions: there is no information regarding the econometric method being used nor what other explanatory variables were considered nor anything regarding the statistical significance of their results. In a somewhat similar straw, Cull, Demirguc-Kunt & Morduch (2006) examine the determinants of profitability, portfolio at risk and loan size without taking into account competition in the credit market because they claim- the typical proxies for competition have problems of endogeneity and do not necessarily measure how intensive is competition. Using the Mixmarket database for the period , they find among other results- that lending interest rates and capital costs affect the profitability of financial institutions and that these same costs affect the size of average loans. While interesting, their conclusions may need to be reexamined since they are the result of least squares estimations in which it is implicitly assumed among other things- that the estimated coefficients are independent of the size of the institutions and that there is no simultaneity on the decisions taken by managers of these microfinance institutions regarding interest rates, loan size or profitability. Thus for example, managers of these financial institutions may have a profitability goal which allows them for example- to enlarge the portfolio and/or increase the number of borrowers, create more branches and/or 5

6 recruit more loan officers or simply to be financially self-sufficient. Whatever the objective, the need to make a profit and the characteristics of the market niche in which they are, results in an optimal pricing policy comprising a loan size within a time-scale that they are willing to offer. If this framework were to resemble what happens in reality, then it could be more reasonable to jointly estimate a set of equations rather than one equation at the time. III. Data and Methodology Obtaining financial information from institutions involved in microfinance is no easy task in most countries because there is no financial authority that collects it and makes it available to the public. Furthermore, the absence of governmental or organized market supervision means that these entities can freely decide how to measure if they want to do it the variables describing their different sources of income and expenditure. Finally, even if there were an informal consensus on how to measure these variables, that would not necessarily ensure that the information is reliable since it is very likely that accounting deficiencies might exist. To solve these problems we used the information collected by the Microfinance Information Exchange (Mix) 1. Members of this network report their financial results to managers who make sure that the definition and methodology used to define variables is uniform. Thanks to this, we have annual information for the period 2000 to 2008 concerning financial income, the value of the loan portfolio, average loan size, cost of funds, lending interest rates, operating costs, delinquency rates, number of clients, profitability, etc., for 1,299 financial institutions which are located (see table 1) in 84 countries throughout Africa, Asia and Latin America. Given the time span considered and the number of years that these institutions have been reporting their figures, we have an unbalanced cross section-time series panel data with 4,718 observations. INSERT Table 1 Even though the database may have a self-selection bias, is worth using it for several reasons. First of all, it is a conceptually homogeneous database; each variable has the same 6

7 meaning for each institution and calculations are validated by the methodology used by managers of the Mix. Secondly, very few micro-finance institutions are willing to share their intertemporal experiences, so having such a panel of data may help understand the dynamics of interest rates. Finally, even if the panel of data is not representative of all microfinance institutions, collectively however is very likely that they serve a very large fraction of microfinance customers worldwide. Thus, we consider that this work may be an important step in the empirical literature on the management of microfinance institutions in developing countries. As Tables 2 and 3 show, there are some differences across continents that suggest that correlation between variables may be space-dependant. Overall, microfinance institutions in Africa and America appear to charge higher interest rates. However, while microfinance institutions in Asia charge less that does not imply that they earn a lower return on assets. Clearly, default rates, operating and funding costs may also have some influence. The data shown in Table 3 also suggests that this industry is composed by very firms of heterogeneous size. In this regard, our econometric method needs to examine if our estimated parameters are sensitive to the size of these financial institutions. INSERT Tables 2 and 3 Following Martinez and Mody (2003) we considered four types of variables which might explain the behavioral pattern of interest rates on loans. The first type includes variables which describe loans: the average loan size in real terms and the lending interest rate. This last variable is the portfolio yield (which is defined as all interest and fee revenue from loans) divided by the average gross loan portfolio. Thus, is a weighted average of the interest rate actually received by the financial institution. Even though higher inflation rates may lead to more uncertainty and thereby to higher interest rates, such variable was not included because during the period considered all three continents enjoyed a stable inflation rate 2. In addition to these variables we also have variables that may signal the expected degree of riskiness. For such purpose we 7

8 included as variables the strength of legal rights index and the depth of credit information index, variables that are available in the World Bank dataset. In the second group are variables which summarize two of the costs that these financial institutions must pay: their funding costs and the operational cost per peso lent. If the latter is turned upside down, we could use it as a first approximation to productivity 3. However to measure productivity effectively we must adjust our proxy by the quality of the product. To have a better understanding of this, consider two examples. First, imagine two financial entities which report the same average operational cost but whose delinquency rates differ. In this scenario, the entity with the lowest delinquency rate should be considered more productive. Bearing this in mind, a better proxy for productivity is constructed by adjusting our first proxy (the inverse of operational costs per peso lent) by portfolio delinquency. Now let us consider two financial entities whose average operational cost and delinquency rates are similar, but whose average loan size differs. Taking into consideration that smaller loans carry-out higher operating costs, the entity with the smaller loan size should be considered more productive. For this reason, a third approximation of productivity will be made by adjusting the operational costs per peso lent by the portfolio delinquency rate and by the relative size of loans granted by the institution. Taking into account these examples, two proxies for productivity will be used. The first one makes use of the operational cost per peso lent without adjustments and a second one which adjusts by the delinquency rate and by the relative size of loans. 4 Finally, we considered a third group of variables through which we describe the size of the financial institutions, the earnings they made and the number of years they have been operating. As an indicator of the size we use the value of their financial assets and loan portfolio, both measured in real terms and expressed in logarithms. To measure the profitability of financial transactions we used the return on assets. Regarding age, we used the number of years the institutions has been operating, regardless of when they started to provide information to the Microfinance Information Exchange. 8

9 Regarding the actual or potential state of competition, as explained before, it is difficult to have a good proxy. Further, since we are working with data which varies according to the institution and over time, we would need to have a similar cross section- time series panel data set for a proxy of competition. Since the vast majority of the institutions that comprise our sample operate in local markets and usually compete with informal money lenders, the information required to construct such a variable would be tremendous. Notwithstanding such problems, we followed Kai (2009) and used as proxy for how much competition there is the ratio of microfinance clients to the population with ages between 15 and 64. Finally, we will include dummies for countries and years to consider the possibility of differences across countries and across time. With the use of these data, the hypothesis we are trying to verify is whether we could detect the following functional relationship: I L = F(I F, productivity, roa, Avgloan,) whereas: I L 1>0, I L 2<0, I L 3>0, I L 4<0..(1) Where I L describes the lending rate of interest, I F is the funding rate of interest, roa is the return on assets and Avgloan is the real average loan size. Next to this functional form we describe the sign of the partial derivative we expect to find. Thus for example, we expect to find a positive correlation between the lending (I L ) and the funding interest rate (I F ), being such hypothesis denoted by: I L 1>0. Further, we posit a negative correlation between the lending rate and our proxy for productivity. Thus, as financial firms become more productive (because their operational costs decline or because they are able to achieve lower default risks), they may be able to reduce their lending rate and still reach the same profit rate. However, if these financial firms wish to increase profits they will surely increase their lending rate, thus, I L 3>0. Finally, for firms looking to cover variable costs, a bigger loan size may help them to reduce their lending interest rate: I L 4<0. Further, if higher loans were received by more experienced borrowers then credit risk would decline and thereby interest rates. As explained before, some control variables were included. Among the most important we have a proxy for competition and dummies to consider differences across countries and years. With regard to competition, its correlation with 9

10 interest rates could go as explained before- either way. Thus in a dynamic setting, a stronger competition could lead to lower interest rates as explained by the standard theory- or it could lead to higher rates if microfinance institutions move to new markets or if markets itself became riskier. However, many of the variables which could explain the behavior followed by the lending interest rate (I L ) are clearly endogenous. In particular, the profitability rate and the size of loans are two variables on which the financial institution seeks to influence. Thus, managers of these organizations may have a profitability goal which allows them for example- to enlarge the portfolio and/or increase the number of borrowers, create more branches and/or recruit more loan officers or simply to be financially self-sufficient. Whatever the objective, the need to make a profit and the characteristics of the market niche in which they are, results in an optimal pricing policy comprising a loan size within a time-scale that they are willing to offer. Accordingly we believe that the loan transaction may be described in four steps. First, the financial firm decides how much to charge and what the optimal loan size to offer must be in order to reach its profitability goal. Once known the value of the lending interest rate and the average loan size the financial institution offers, a potential customer decides whether s/he wants to request a loan. Taking into account the credit history of the potential borrower and its income-expenditure stream, the financial institution builds a risk profile of the individual. With this at hand, they decide where to lend or not. While for first time customers the typical microfinance institution does not allow any kind of negotiation related to the loan size, for repeated customers some sort of negotiation is possible but is the financial institution who taking into account its goalsdecides the loan size. Taking this description into account it could be more reasonable to estimate simultaneously the following three equations: 2a. I L = F(I F, proxy for productivity, roa, Avgloan) where: I L 1>0, I L 2<0, I L 3>0, I L 4<0. 2b. roa = G(I L, I F, proxy for productivity, Avgloan) where: roa 1 >0, roa 2 <0, roa 3 >0, roa 4 >0. 2c. Avgloan = H(I L, proxy for productivity, roa, years operating) where: Avgloan 1 <0, Avgloan 2 <0, Avgloan 3 >0, Avgloan 4 >0. 10

11 In this system, equation (2a) is similar to equation (1). With regard to the profitability ratio (roa), equation (2b) suggests that it will be explained by the lending and funding interest rates, the financial firm s productivity and the loan size. As shown by equation (2b), profitability will increase when lending interest rates and/or productivity and/or the loan size increase or when the funding cost declines. Finally equation (2c) describes our hypothesis regarding the loan size. We believe that the lending interest rate will be negatively correlated with the loan size: Avgloan 1<0. This correlation is consistent with a financial firm that wishes to cover variable costs and with potential borrowers that have a downward sloping demand curve. Financial firms in our sample claim that they wish to serve the poor. Therefore we will assume that as firms become more productive they will be able to offer loans of lower size: Avgloan 2<0. However, if they wish to achieve a higher profitability it is likely that all else equal they will offer loans of higher size: Avgloan 3>0. Finally, we have the variable years operating which may affect both the supply and the demand for bigger loans. On the one hand, when microfinance institutions start operations they usually offer loans of small amounts because they do not have much capital or experience and debtors tend to be people without credit history. However, if the supply of loans has dynamic incentives (i.e., the services offered by the institution will increase as the debtor builds his credit history and reputation), is very likely that the loan size will increase through time. On the other hand, if loans have a positive impact on wealth, is possible to assume that the demand for bigger loans will rise. Under these assumptions, we expect to find Avgloan 4>0. Finally, since microcredit is seen as a tool for development we also considered as an explanatory variable a proxy for outreach. IV. Results 1. Individual Estimates 11

12 The first approach to the problem is to estimate equation (1). The results are reported in Table 4 and are based using a generalized least square regression with random effects in which a set of dummies were included to consider the possibility for time and country effects. Insert Table 4 As Table 4 shows 5, the lending interest rate (I L ) follows a behavior in all three continents that is consistent with equation (2a). The dependent variable responds positively to changes in the funding cost (I F ) and to the return of assets (roa), and negatively to the proxy for productivity (lprod). In other words, funding costs, operational costs and the quest for profits help explain the behavior followed by the lending interest rate. In addition to these variables, the size of the average loans (AvgLoan) was also an important explanatory variable and the sign of the correlation is consistent with two possible stories. First, if financial firms were looking to cover variable costs, then a bigger loan size could be correlated with smaller interest rates. On the other hand, if higher loans were received by more experienced borrowers then credit risk would decline and thereby interest rates. With regard to the proxy for competition, results suggest higher competition leads to higher interest rates in Africa and lower in Asia. As explained before, a stronger competition could lead to lower interest rates as explained by the standard theory- or it could lead to higher rates if microfinance institutions moved to new and riskier markets. However, as explained before, such correlation must be taken with caution since it is not clear how good such proxy is. Finally, our proxy for outreach a dummy that takes the value of 0 if the loan size as a percentage of gross domestic product is greater that the median value in the continent- was not statistically significant. Given the heterogeneity in the size of the financial institutions that comprise our sample 6 next we analyzed whether the parameters reported in Table 4 are sensitive to the initial size of the financial institutions. Further, since it is possible that a learning curve may exist regarding the appropriate use of techniques to mitigate information asymmetries, age could also be a factor that may lead to heterogeneous impacts. For this purpose we added -to the set of explanatory 12

13 variables used in Table 4- new variables that are created by multiplying those used in Table 4 by the initial value of each institution s assets and its size. Insert Table 5 While the sign of the estimated parameters of all variables included in table 4 remain constant and were statistically significant, the parameters for most of the multiplicative variables were not statistically significant for Africa and America. Thus, in these two continents our hypothesis could not be rejected: I L 1>0, I L 2<0, I L 3>0, I L 4<0 (see equation 2a). In the case of Asia, our results suggest that impacts are not homogenous: the initial size of the financial institution and the number of years it has been operating, matter. Notwithstanding such heterogeneity, the impact of each independent variable over the lending interest rate across the distribution of age and size has a sign that is consistent with our hypothesis. Thus, even if we assumed heterogeneous impacts, our hypothesis stated in equation (1) or (2a) appear to hold. 2. Simultaneous Estimates However, results reported in Tables 4 and 5 may be misleading if reality is better described by a system of equations in which interest rates, loan size and profitability are jointly determined. Thus, managers of these organizations must have a profitability goal regardless of whether it is distributed among owners- which enables them for example- to increase the loan portfolio and/or the number of borrowers, expand the number of branches and/or the number of loan officers or simply to be financially self-sufficient. Whatever their objective, profitability and characteristics of their market niche are essential for them to adopt an appropriate pricing policy and a loan size within the time-scale that they are willing to offer. Thus we believe that the loan transaction may be described in several steps. First, the financial firm decides how much to charge and what the optimal loan size to offer must be in order to reach its profitability goal. Once known the value of the lending interest rate and the average loan size the financial institution offers, a potential customer decides whether s/he wants 13

14 to request a loan. Taking into account the credit history of the potential borrower and its incomeexpenditure stream, the financial institution builds a risk profile of the individual. With this at hand, they decide where to lend or not. While for first time customers the typical microfinance institution does not allow any kind of negotiation related to the loan size, for repeated customers some sort of negotiation is possible but is the financial institution who taking into account its goals- decides the loan size. So, the financial institution sets the price and negotiates the size of the loan within a range so that it may achieve the profitability goal drawn at the beginning. Taking this description into account it could be more reasonable to estimate a system of three equations that are simultaneously solved. For such purpose next we used a simultaneous equation estimation (three stage least squares regression) that considers the existence of three endogenous variables (the lending interest rate, the average size of loans and the profitability on assets) that are jointly estimated. As explained before, our joint hypothesis may be described by the following set: 2a. I L = F(I F, proxy for productivity, roa, Avgloan) where: I L 1>0, I L 2<0, I L 3>0, I L 4<0. 2b. roa = G(I L, I F, proxy for productivity, Avgloan) where: roa 1 >0, roa 2 <0, roa 3 >0, roa 4 >0. 2c. Avgloan = H(I L, proxy for productivity, roa, years operating) where: Avgloan 1 <0, Avgloan 2 <0, Avgloan 3 >0, Avgloan 4 >0. Insert Table 6 As Table 6 show, all parameters of equation (2a) have the same sign as those reported in table 4, which were in line with our hypothesis. The only exception is with regard to the correlation between the lending rate and the average loan size: with this new approach the parameters are no longer statistically significant. Further, if we compare results reported in tables 4 and 6, the correlation between lending rates and funding costs and between lending rates and productivity do not vary in a meaningful manner. With regard to the other dependent variables (return on assets (roa) and average loan size (Avgloan), results appear consistent with equations (2b) and 14

15 (2c), with the only exception being the correlation between productivity and average loan. Thus, contrary to our hypothesis, an increase in productivity leads to an increase in the average loan. Thereby, higher productivity is not used by financial firms to reduce their loan size; it is used to reduce lending interest rates. While results shown in Table 6 are consistent with our main hypothesis, they share one problem with those of table 4: the assumption of homogeneity. As explained before, age and initial size could matter and therefore impacts must not be assumed to be homogenous. In this regard, coefficients shown in table 7 allowed for the existence of simultaneity and the possibility for facing heterogeneous effects whose origin is the different years that these financial institutions have been operating and their initial size as measured by its financial assets. Insert table 7 As this table shows, impacts on lending interest rates appear to be heterogeneous: the initial size of the institution and the years it has been operating matter. Notwithstanding such result, our hypotheses stated on equations (2a-2c) are validated for almost all the distribution of sizes and ages of financial institutions. Once all interactions are considered, we find (see Table 8 for an example) that lending interests may be affected by changes in the funding cost, in the productivity of financial institutions and by the number of years these institutions have been operating, being these correlations consistent with our hypotheses stated in equation (1) or (2a). However, changes in these variables may also affect the profitability of financial institutions and in turn affect the likelihood that poor people may have access to loans. Taking into account the figures in Table 8, a reduction in the funding cost appears to be a mechanism to consider since it helps to reduce the lending rate and at the same time leads to an increase in profitability. However, this is likely to be a short lived policy in lieu of the distortions it creates. The other candidates are policies that may help increase the productivity of these financial institutions since they lead to reductions in lending rates and increases (with the exception of Africa) in profitability. 15

16 Insert Table 8 With regard to productivity our data suggests that there is some kind of regional pattern (see figure 1). Since our proxy of productivity was build using operational costs, such variable should depend on how difficult (and thereby costly) is for loan officers to reach their target clients. Such difficulty not only depends on the quality of infrastructure but maybe more important on the geographic characteristics of the country in which microfinance institutions operate. In this regard, a biodiversity index 7 could help capture the variability of a country s territory in terms of height and climate. A higher value of this index may signal more heterogeneity and may be used as a proxy for higher costs for loan officers to reach customers. Overall this index whose range goes from 0 to 100-, took a value once inputted the value for each of the countries in our sample- of in America, in Asia and 4.75 in Africa. On the other hand because economies of scale may exist, a measure of population density should be considered as a second explanatory variable. Since it is almost impossible to have a proxy for each local market in which each financial institution participates, we used the country population density as an aggregate measure. Taking into account the possibility of economies of scale, a higher population density may lead to lower operational costs. On the other hand however, a higher population density also may lead to more competition among borrowers and thereby to higher costs as a mechanism to reduce default rates. In this sense, it is not clear what sign should take the correlation between productivity and population density. However, it may be necessary to included it since the costs associated with a higher biodiversity may be offset may a higher population density. For the year 2007, population density in Asia was of 85 people per square kilometer, 44 in Africa and 28 in America. Insert Figure 1 These geographic proxies and others describing institutional factors should be useful to explain the differences in productivity. Regarding the latter, as Gonzalez (2007) explains, technology use and management quality and commitment to efficiency should be variables to 16

17 consider. However, there are no good proxies for them and lending methodology was not considered since it is an endogenous variable. Lacking good proxies, we used the initial size of financial institutions and the number of years they have been operating as indicators of their human capital and their position in the learning curve. We expect to find a positive correlation between these variables and our proxy for productivity. With these variables at hand, in table 9 we report the results of regressing productivity against the initial size of these financial institutions, the number of years the institution has been operating, a biodiversity index, the population density for the country and one that arises from multiplying biodiversity with population density. As this table shows, age and initial size of the institution matter. Taking into account all interactions, the correlation between productivity and age and productivity and size, was positive in all continents with the exception for the biggest and oldest 10% of financial institutions. On the other hand, density appears to matter but we have conflicting signs that suggest that the two hypothesis regarding productivity and population density may not be discarded. Finally, biodiversity appears to matter only for African and American microfinance institutions: there, as expected, an increase in biodiversity leads to lower productivity. Taking these independent variables as some sort of structural variables, we could analyze how big is the effort put by the different microfinance institutions to increase their productivity (and thereby help to lower lending rates). If we used for each continent the average value of age and the average size of their assets in 2008 of microfinance institutions and the values for the biodiversity index and population density we could forecast with the use of the estimated parameters of Table 9- the average value of productivity in each continent. This could be considered the structural productivity. If we then compare these estimated values with the observed values for productivity, we could then measure the effort that microfinance institutions have put to become more efficient. Using this measurement, a microfinance institution would become more productive as its observed value is higher than the structural productivity here estimated. 17

18 Our results suggest that in the case of Asia and Africa, microfinance institutions have achieved an average productivity that is above the structural one. For Africa, their observed productivity is 8 % above the structural one, and in the case of Asia, it is 63% above. In the meantime, in Latin America microfinance institutions have an observed productivity that is 38% below the structural value. If we used the same parameters and repeat our forecast for the biggest 75% of the microfinance institutions, we would find a similar pattern. In Africa and Asia, the observed productivity is 26% and 74% above the structural productivity. However for America the numbers keep showing a lack of productivity since the observed value is 19% below its structural value. Thus, it appears there is plenty of room in our sample of Latin American microfinance institutions to increase its productivity and thereby according to our results- to reduce the lending rate. V. Conclusion As usually happens with empirical work, the results may depend on the methodology used. Here, we first used individual regressions and then a system of equations that are simultaneously solved and within these two methods we allow for either homogenous or heterogeneous effects. Further, we have a dataset that includes microfinance institutions belonging to different countries and continents. Notwithstanding such variations, many we find that the lending interest rate is negatively correlated with the productivity of financial institutions and with the number of years these institutions have been operating and positively correlated with the funding costs. One of the reasons that gave rise to this paper was that the probability that financing spurs growth is weakened if interest rates are extremely high. Therefore, given our results, are there any guidelines regarding how to induce a reduction in the nominal lending rate? Since a reduction in such rate could lead financial institutions to stop offering loans to small firms, is there a way through which such interest rate reduction does not lead to such outcome? Given our results, two policies stand out. The first one is reducing the funding cost. However, economic 18

19 theory and history shows that the manipulation by governments to reduce this price is likely to be a short lived policy in lieu of the distortions it creates. The other candidates are policies that may help increase the productivity of these financial institutions since they lead to reductions in lending rates and increases (with the exception of Africa) in profitability. How could productivity be raised? This is not an easy question. Institutional factors such as technology use and management quality and commitment to efficiency should be variables to consider. Further, it should depend on how difficult (and thereby costly) is for loan officers to reach their target clients. Such difficulty not only depends on the quality of infrastructure but maybe more important on the geographic characteristics of the country in which microfinance institutions operate. Since there are no good proxies for those institutional factors, we relied on the initial size of financial institutions and the number of years they have been operating as indicators of their human capital and their position in the learning curve. On the other hand, regarding geographic conditions we used a biodiversity index to capture the variability of a country s territory in terms of height and climate. Taking these independent variables as some sort of structural variables we find that contrary to what happens in Asia or Africa- in Latin America the value of productivity predicted by its fundamentals is above its observed value. Thus, there seems to be room in our sample of Latin American microfinance institutions to increase its productivity and thereby according tour results- to reduce the lending rate. There is no easy answer as to how to do this. Taking into account the limitations of our data, one venue to explore would be to encourage the creation of bigger microfinance institutions. Another possibility that is beyond this work would be to analyze how the most productive microfinance institutions operate. 19

20 Bibliography Alexander-Tedeschi, Gwendolyn and Dean Karlan Microfinance Impact: Bias from Dropouts. Unpublished manuscript, Department of Economics, Yale University. Armendáriz de Aghion, Beatriz and Jonathan and Morduch Microfinance. 1rst ed. Cambridge, MA: The MIT Press. The Economics of Banerjee, Abhijit and Esther Duflo Do Firms want to Borrow More? Testing Credit Constraints Using a Directed Lending Program. Unpublished manuscript, Department of Economics, MIT. Beck, T., A. Demirguc-Kunt y R. Levine (2007), Finance Inequality and the Poor. Journal of Economic Growth, vol. 12(1), pp Boot, A. y A.Thakor (2000), Can relationship banking survive competition? Journal of Finance, Vol. 55: Carbó, S. F. Rodriguez y G. Udell (2006), Bank Market Power and SME Financing Constraints. Working Paper 237/2006 Funcas. Cotler, P. y C. Woodruff (2008), The Impact of Short-term Credit on Microenterprises: Evidence from the Fincomun-Bimbo Program in Mexico, Economic Development and Cultural Change, Vol. 56(4): Cull, R., A. Demirguc-Kunt and J. Morduch (2006), Financial performance and Outreach: A Global Analysis of Leading Microbanks. World Bank Policy Research Working Paper Gonzalez, A. (2007), Efficiency Drivers of Microfinance Institutions (MFIs): The case of Operating Costs. MicroBanking Bulletin, Issue 15. Gonzalez, A. (2010), Analyzing Microcredit Interest Rates. Mix Data Brief No.4, 20

21 Kai, H. (2009), Competition and Wide Outreach of Microfinance Institutions. Personal RePEc Archive Munich Marquez, R. (2002), Competition Adverse Selection, and Information Dispersion in the Banking Industry. The Review of Financial Studies, Vol. 15: Martinez, S. y A. Mody (2003), How Foreign Participation and Market Concentration Impact Bank Spreads: Evidence from Latin America. Journal of Money, Credit and Banking, Vol. 36 (3): McIntosh, C. and Wydick, B. (2005) Competition and microfinance Journal of Development Economics 78, Morduch, Jonathan Does Microfinance Really help the Poor? New evidence from Flagship Programs in Bangladesh. Unpublished manuscript, Department of Economics Harvard University. Ongena, S. y D. Smith (2001), The duration of bank relationships. Journal of Financial Economics, Vol. 61: Petersen, M. y R. Rajan (1995), The Effect of Credit Market Competition on Lending Relationships. Quarterly Journal of Economics, Vol. 110: Pitt, M. y S. Khandker (1998), The Impact of Group-Based Credit Programs on Poor Households in Bangladesh: Does the Gender of Participants Matter? Journal of Political Economy. Vol. 106(5): Rosemberg, R., A. Gonzalez and S. Narain (2009), The new Moneylenders: are the poor being exploited by high Microcredit Interest Rates. CGAP Occasional Paper No. 15. Zarutskie, R. (2003), Does bank competition affect how much firms can borrow? New evidence from the U.S, en Proceedings of the 39th annual conference on Bank Structure and Competition, Federal Reserve Bank of Chicago, pp

22 Table 1 The database Continent # MFIs #Countries # obs. Average number of years operating in 2008 Average # of Years collaborating with Mixmarket 9 >X 6 6 > X 3 3 > X 1 Africa , America , Asia , Total 1, , Table 2 Distribution of some key Data during the period Africa America Asia Real interest rate (%) ROA (%) Loan as % of GDPpc Real interest rate (%) ROA (%) Loan as % of GDPpc Real interest rate (%) ROA (%) Loan as % of GDPpc 1% % % % % % % ,

23 Total Assets ( dollars) Table 3 Distribution of some key data during the period Africa America Asia Funding Operating Total Funding Operating Total Funding Interest Cost per Assets Interest Cost per Assets Interest Rate dollar lent ( Rate dollar lent ( Rate (%) dollars) (%) dollars) (%) Operating Cost per dollar lent 1% % 1, , , % 4, , , % 12, , , % 54, , , % 176, , , % 962, ,927, ,916,

24 Table 4 Dependent variable: Lending Interest Rates África América Asia Ipas 1.41*** 1.09*** 0.79*** Lprod *** *** *** Roa 0.83*** 0.70*** 0.71*** Avgloan *** *** Competition 2.29*** ** Outreach R-square Within between overall N Notes: 1.-* p<.1; ** p<.05; *** p< Estimation Method: Generalized Least squares with random effects. 3.- We included in each regression a constant, a dummy for each year and a dummy for each country. The omitted categories were the year 2000, Benin (Africa), Argentina (America), and Afghanistan (Asia). 24

25 Table 5 Dependent variable: Lending Interest Rates with heterogenous impacts África América Asia Ipas Ipas * initial assets Lprod Lprod * age Lprod * initial assets Lprod*initial assets*age Initial assets*age Roa Roa*initial assets Initial assets Age Avgloan Competition Outreach 2.24** 1.03*** 0.59*** *** *** *** * * * *** *** 0.89*** 0.46*** 0.28** * 0.07*** 0.03** * *** * *** *** R-sq within between overall N Notes: 1.-* p<.1; ** p<.05; *** p< Estimation Method: Generalized Least squares with random effects. 3.- We included in each regression a constant, and a dummy for each country. Dummies for each year were not included since a joint test rejected the need for them. The omitted categories were Benin (Africa), Argentina (America), and Afghanistan (Asia). 25

26 Table 6 Simultaneous Estimations Dependent Variable Africa America Asia iact Ipas 1.21*** 0.78*** 0.90*** Lprod *** *** *** Roa *** 1.41*** Avgloansize Roa Iact *** 0.53*** Ipas *** Lprod *** 0.13*** Outreach * Avgloansize Lprod 0.18*** 0.44*** 0.41*** Age 0.01*** 0.003* 0.007*** Competition *** 1.53*** Outreach *** *** *** N 923 1,118 1,753 Notes: 1.-* p<.1; ** p<.05; *** p< Estimation Method: Three Stage Generalized Least Squares. 3.- We included in each equation a constant, a dummy for each year (with the exception of America as suggested by a joint chi-square test) and for each country. The omitted categories were the year 2000, Benin (Africa), Argentina (America), and Afghanistan (Asia). 26

27 Table 7 Simultaneous Estimations assuming heterogeneity Dependent Variable Africa América Asia iact Ipas 14.39** *** * Ipas*initial size * *** 0.48** Lprod 4.52*** *** 2.55*** Lprod*initial size *** *** *** Lprod*age *** *** *** Lprod*initial size*age 0.02*** 0.60*** 0.01*** Roa *** *** *** Roa*initial size 5.59*** 98.36*** 7.01*** Initial size* age *** *** *** Initial size 0.97*** 23.15*** 0.28*** Age 0.31*** 9.65*** 0.20*** Avgloansize *** 0.11*** Roa Iact * 0.17** Iact*initial size 0.03*** 0.03*** 0.03*** Ipas *** 0.25 Ipas*initial size *** *** Lprod 0.18*** 0.19*** 0.14*** Lprod*age ** *** *** Age 0.003** *** 0.005*** Initial size ** * *** outreach *** 0.03*** Avgloansize Lprod *** 0.44*** Lprod*initial size Initial size 0.08*** 0.11*** 0.08*** Age Competition ** outreach *** *** *** N 922 1,113 1,739 Notes: 1.-* p<.1; ** p<.05; *** p< Estimation Method: Three Stage Generalized Least Squares. 3.- We included in each equation a constant, a dummy for each year and for each country. The omitted categories were the year 2000, Benin (Africa), Argentina (America), and Afghanistan (Asia). 27