Contenido y dimensiones de la distribución comercial

The discussion on commercialization in microfinance is quite recent and so far there are only a handful of studies that have analyzed its impact in detail. Perhaps the most well known cross-country study is by Cull et al (2008), which distinguishes institutions by their legal structure. The study concludes that for-profit MFBs are likely to offer larger loans using individual rather than group lending arrangements8_{, have fewer women}

clients, but are more efficient and make higher profits as compared to non-profit MFIs. Schreiner (2001) adds to the list by observing that commercialization has led to an increased preference to lend to urban rather than rural clients.

Given the paucity of data on individual borrowers, especially for cross-country and cross- institution studies, a commonly used proxy for the poverty of microcredit borrowers is average loan size. Smaller loans imply poorer customers, for institutions determine the size of the loan based on the borrower’s capacity to repay (e.g. Cull et al, 2008;

Armendariz and Szafarz, 2009).

My field research in Pakistan largely confirms this. The typical microcredit loan

verification process includes a thorough analysis of the borrower’s existing income, total net worth and number of wage earners in the household. This information helps an institution determine the size of the loan for each borrower. During non-participant observations of the disbursement process, I observed several clients across institutions beg branch managers for higher loans and being refused on the ground that they did not make enough money to be able to repay a loan higher than was offered to them.

8_{Group lending in microcredit involves lending to groups of between 3 to 20 individuals}

simultaneously, with joint liability. The structure has evolved away from this in some cases recently as described in later chapters.

50

But Schreiner (2001) argues that loan size is only one aspect of the lending arrangement and should only be used within a proper context. Other aspects of a loan that Schreiner contends should be analyzed are interest rates, fees, guarantees, and whether the loan is disbursed individually or in groups. This is a valid argument but can only be properly addressed when using a much smaller dataset.

Armendariz and Szafarz (2009), who advocate the use of average loan size to determine depth of outreach, point out that there is more than one reason for loan sizes to go up. The first is what microfinance institutions refer to as “progressive lending”, which means that as clients complete repayment of their existing loans and are able to demonstrate a clean repayment record, the institution moves them up to higher denomination loans. Thus, older clients are likely to have larger loans than more recent clients. By the same token, older institutions are more likely to have larger average loans than newer institutions, assuming their older clients have stayed with them.

Another reason is cross-subsidization, that is, an institution may lend to wealthier clients in order to finance lending to poorer clients (Armendariz and Szafarz, 2009). This makes intuitive sense because it is operationally more efficient to lend to the less poor, in terms of unit cost per loan and the risk profile of the borrower.

These explanations are consistent with the poverty alleviation mission of microfinance institutions. By this logic a dilution of an institution’s social and developmental mission must be defined as the tendency of a microfinance institution to lend to wealthier clients in such a way that it crowds out poorer clients (Armendariz and Szafarz, 2009). When this is observed it can only mean that the profit-making concerns of the institution have

51 won over its developmental objectives.

This implies that average loan size may not be a sufficient measure of depth of outreach. Using an international dataset, Hoepner, Liu and Wilson (2011) show that the

relationship between actual client poverty levels and average loan sizes is weak. Of course, these critiques come from studying cross-country data and the relationship is likely to be more controlled for an intra-country analysis.

Nevertheless, it is important to recognize that average loan size is only a proxy and no match for actual borrower statistics. For instance, gender ratios are important because microcredit is widely considered to be a tool for women’s empowerment. Section II will describe gender related trends across institutional groups in Pakistan, though the data used to construct these trends is not available at the district level and cannot be incorporated into the econometric model.

Similarly, data on the poverty level of borrowers is unavailable at the district level. At the same time socioeconomic disparities between districts can be stark and are likely to be an important determinant of depth of outreach. Therefore, in addition to using average loan size per district, I also estimate the probability of a positive microfinance presence, given the socioeconomic profile of each district. This essentially provides us with a loan supply function.

The Loan Supply Function of MFIs and MFBs

A loan supply function is simply a theoretical model designed to help predict the extent to which different factors impact a lender’s decision to lend. The dependent variable in a loan supply function is usually either the probability an institution will extend a loan

52

(Ravina, 2008), the expected loan size (Ghosh and Tassel, 2008) or some other lending term, such as the interest rate, or a combination thereof (Ang and Willhour, 1976). Mansuri and Jain (2005), and McIntosh and Wydick (2005) argue that determining a microfinance institutions’ objective function is usually much more complex than for a regular profit-maximizing financial institution, as the former has competing objectives to balance off. An objective function defines an individual, group or institution’s

optimization problem, reflecting a tradeoff between expected benefits and costs

(Geoffrion, 1977). While an objective function is not necessarily a loan supply function, the latter can be considered a subset of the former.

Jain and Mansuri (2005), and McIntosh and Wydick’s (2005) analysis demonstrates the tension in the microfinance sector between maximizing profits and client welfare. However, their analyses are limited to NGO MFIs and they do not specifically consider microfinance banks. In the present study, I construct two separate loan supply functions, one for MFBs and another for MFIs using two separate indicators of depth of outreach. The first is the probability that an MFI or an MFB will locate in a particular district, given the district’s socioeconomic profile, and the second is the expected value of the average MFI or MFB loan, again given the district’s socioeconomic profile.

Dimensions of Wellbeing

The socioeconomic profile of each district is created using multiple indicators of

wellbeing. The economist Amartya Sen’s capability approach, which defines poverty as the lack of individual freedom or the opportunity to exercise a set of valued choices (Sen, 1999), is used to select these indicators.

53

The capability approach has had a strong influence on the evolving concept of poverty since the 1990s. It has effectively shifted a single-minded focus on income-based measures of poverty towards a multi-dimensional conceptualization of deprivation. For instance, the United Nation Development Programme’s (UNDP) employs different dimensions of development, namely health, education and living standards, to construct the Human Development Index (HDI). Every year since 1990, HDI rankings for each country have been published in the UNDP’s Human Development Report (HDR). An alternative and relatively newer measure of poverty, also influenced by the capability approach, is the Multidimensional Poverty Index (MPI). Here again the three dimensions of poverty are education, health and living standards, but living standards for the MPI are measured in terms of household assets and conditions, rather than GDP per capita as in the case of the HDI (Bourguignon and Chakravarty, 2003).

To construct each district’s socioeconomic profile I will use the same three dimensions of wellbeing used by the HDI and the MPI. Differences in data availability will, however, influence the choice of particular indicators of health, education and living standards. The section below describes the main trends in microfinance outreach in Pakistan over the past six years.