Does efficiency lead to lower interest rates? A new perspective from microfinance
Ahmad Nawaz1, Marek Hudon2 & Benanzir Basharat3
1Pakistan Institute of Development Economics,2 CEB (SBS-EM), Université Libre de Bruxelles (ULB); CERMi; Burgundy School of Business & 3 International Islamic University, Islamabad
Abstract:
This paper provides first empirical evidence of the role of efficiency in microfinance in the determination of interest rate charged to the clients. Using the data of 291 MFIs in 67 countries worldwide, the results confirm that after controlling for loan size and gender, social efficiency has insignificant impact on interest rate which depicts the fact that as outreach increases, it causes the lending interest to rise. On the other hand, financial efficiency of MFIs has significant positive relation with interest rate even after controlling for outreach, gender and ROA.
Key Words: Microfinance, Public Policy; Performance, Interest rates
JEL Codes: G21, L38, L25,
1. Introduction
The microfinance sector has grown dramatically for the last ten years. The main objective of MFIs is to provide affordable financial services to poor and low income households. Interest charged on loans is justified by MFIs as it is the main source of income for these institutions. MFIs claim that they have to provide financial services to poor people and have to face administrative cost that’s why they charge such high rates[1]. It is generally hypothesized that an effort to improve financial performance results in raising interest rates on loans but at the same time it inversely affects the social performance (Copestake, J. 2007). However, empirical evidence about the role of efficiency in the determination of interest rate in microfinance is relatively scarce in the literature. Cull et al. (2007) using data on 124 institutions in 49 countries provided evidence that raising interest rates to very high levels does not ensure greater profitability and the benefits of cost-cutting diminish when serving better-off customers.
There exist many factors can have either a direct or an indirect impact on microfinance interest rates. As pinned down by Campion et.al (2010), perhaps the most important is the improved operational efficiency—a key driver of lower rates—comes primarily from five sources: competition, reinvestment of profits, learning by doing, pressure from donors and investors on MFIs to be socially responsible, and the absence of interest rate caps.
This paper fills this gap by investigating the determinants of interest rates by incorporating both social and financial efficiency of microfinance. To analyze the financial efficiency, we employ the non-parametric DEA framework which is very common in the banking literature (Athanassopoulos, 1997; Seiford and Zhu, 1999; Camanho and Dyson, 2005). On the other hand, following Nieto et al. (2009), we measure the social efficiency by including number of poor and women borrowers as an output into the DEA framework. Hence, notwithstanding the inclusion of social and financial efficiency in the analysis, empirical evidence has been investigated by employing those efficiencies as drivers of the interest rates in microfinance.
This quality financial information has been generated directly from the Mix Market[2] website for 291 MFIs located in 67 Countries for four years (2005-2008) thus totaling to 1167 observations. Once the financial and social efficiencies have been calculated, the study aims to highlight some key issues in the public policy debate in microfinance. Notwithstanding the MFIs location, status, lending methodology, regulations, which are more socially efficient and which are more financially efficient? The study further investigates some specific hypothesis about the role of efficiency of microfinance in the determination of interest rates in microfinance. We will also test if outreach, the ownership structure, the size and the percentage of women served by the MFIs have an impact on the interest rates.
Our empirical results based on the panel data suggests that social performance of MFIs has significant inverse relation with interest rate but after controlling for loan size and gender, it has positive but insignificant impact on interest rate which depicts the fact as outreach increases, it cause the lending interest to rise. On the other hand, financial performance of MFIs has significant positive relation with interest rate even after controlling for ROA.
The paper is organized as follows. In the next section, to start off, we review the literature on efficiency and public policy in microfinance. In Section 3, we describe the theoretical background of non-parametric financial and social efficiency analysis. We then present in Section 4 the database and some basic descriptive statistic. Section 5 provides financial and social efficiency results. Section 6 highlights the empirical evidence by employing the regression analysis. Finally, a conclusion is given at the end.
2. Efficiency and Public Policy in Microfinance
Although public policy has been largely debated in the microfinance sector, the nexus between interest rates and efficiency of microfinance is largely a neglected area of research. Higher interest rates in microfinance have spurred lot of debate recently in public policy issues in microfinance. Generally the nominal interest rate charged by most MFIs in the all the regions on average falls in the range is 30% to 70% a year. According to the Mix Market database, the annual lending interest rate charged by microfinance institutions during the period 2000-2008 was on average 42% in Africa and 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?”
An empirical study to investigate this issue is of Cotler, P., and (2010) who tried to find out the main determinants of this high interest rate. By applying regression approach, they used the data of 1299 MFIs of 84 countries from Africa, Asia and Latin America. By using ROA as indicator of profitability, average loan size for outreach operating cost for productivity and funding rate of interest as liability of MFIs, he found that both operating cost for small loans and ROA equally significant in determining the high rate of interest. Funding rate is also a contributor of this lending interest rate and on the other hand, age is inversely related with this interest depicting the fact that as these MFIs matures they are better able to control their operating cost and thus to lending rate of interest.
Like the conventional financial institutions, the efficiency and productivity of MFIs has generally been measured by conventional financial ratios or indicators such as staff productivity or operating expense ratio (Balkenhol, 2007). An example is Hudon and Traca (Forthcoming) who use staff productivity. Most new studies on cost efficiency use more sophisticated indicators of efficiency such as data envelopment analysis (DEA) or stochastic frontier analysis (SFA) to calculate this frontier. Hermes et al., (forthcoming) estimate efficiency of 435 MFIs with stochastic frontier analysis and find that outreach and efficiency of MFIs are indeed negatively correlated, what suggests some trade-off between these dimensions. Gutiérrez-Nieto et al. (2007) use DEA to a sample of 30 Latin American MFIs to test twenty-one specifications. They apply principal component analysis to explain efficiency scores by means of four principal components. Their results show different rankings using DEA and more conventional benchmarks and financial indicators.
However unlike conventional banking institutions, MFIs have a social face too and donors give importance to their social aspect with financial aspect when it comes to disbursement of funds. Therefore, performance of MFIs should not be solely gauged by financial indicators but it should take into account their social performance too i.e. outreach to poor. As argued by Balkenhol (2007) that efficiency should be regarded as a key indicator by donors who should not only focus on financial but also social performance.
In microfinance literature, there exist studies focusing on the financial efficiency but none of them consider the social efficiency aspect. The very first effort in this regard is made by Gutierrez-Nieto et al. (2009), which have focused on financial efficiency of MFIs by adding the analysis of social efficiency of these institutions. Their study was aimed to show the relationship between social and financial efficiency, and the relationship between efficiency and other indicators, such as profitability, age and type of institution such as Non-Governmental Organization (NGO) and non-NGOs. Data of 80 MFIs were taken from Mix market webpage for 2003.Among the 89 MFIs in the study, only 13 of them were found socially efficient than financially efficient, 37 NGOs were more socially efficient than non-NGOs. So, they concluded that MFIs chose between financial efficiency and social efficiency they prefer to be financially efficient so they can better perform their social work.
To fill in the vacuum by incorporating the complete picture, this study goes beyond and examines the relationship between interest rate and social efficiency, in addition to the financial efficiency.
3. Efficiency: Theoretical framework
3.1Financial Efficiency
For the efficiency analysis of the microfinance institutions, a two-stage analysis has been carried out. Data Envelopment Analysis (DEA) approach is used to estimate technical and pure efficiency scores of the MFIs. The advantages of using the DEA technique to gauge efficiency are well documented in the literature. DEA framework can handle multiple outputs and inputs. Thus, in the context of MFIs efficiency analysis, it can incorporate both the outputs of outreach and sustainability along with other inputs into a single framework. Neither has it required any price information for the dual cost function nor parametric functional form for the production function.
Insert Table 1 here
Table 1 depicts the summary of inputs and outputs selected for this study. The main objective of estimating a production function is to explain the quantity of output produced given certain levels of inputs and other relevant factors that might explain the quantity of output produced. In traditional financial literature two models i.e. Production Model and Intermediation Model are popular depending upon what one thinks an institution do. The majority of the studies in banking efficiency literature are based on the input oriented constant returns to scale CCR model (Charnes et al, 1978). In the production model approach, financial institutions are treated as firms that use physical input, employees and expend money in order to obtain deposits, grant loans and collect revenues. We assume the output oriented Production model with variable returns to scale is better suited to microfinance institutions rather than constant returns to scale model. MFIs are indeed interested in increasing outreach i.e. lending loans to poor people which commensurate with not only their social mission but also contributes towards sustainability as well by collecting more revenues from lending. In addition to that they compete in an imperfect economic environment as the markets for MFIs are not as well developed as the conventional banking sector. And they always have restricted amount of money and human resource (Inputs) to spend on unlike commercial banks which can generate money from shareholders. In the context of output oriented model, this essay asks a specific question “By how much the output quantities are proportionally expanded without altering the input quantities used?”
The selection of specifications with correct inputs and outputs in the context of MFIs is very important. Based on the literature, we have selected a few inputs and outputs. This study uses LR-ACE[3] as a general specification where gross loan portfolio and financial revenues are taken as an output and assets, operating costs and number of staff as an input. In addition to that, we have also used specifications L-ACE and R-ACE, where the former put emphasis on granting loan as main objective of MFIs and latter signifies revenue collection as main objective of MFIs. The other specifications used are basically the different combination of treating subsidies as an input and output with the above general specifications.
3.2 Social efficiency
Following Nieto et al. (2009), on top of the traditional variables used in DEA specification, we also include two output variables to calculate the social efficiency of microfinance i.e., variable P which indicates the benefit to the poorest and number of women borrowers. The main purpose of establishing microfinance institutions was to fight against poverty but the problem is how to measure poverty and how these poor can have access to micro credits? ‘Average loan balance per borrower’ is an indicator which is used as a measure outreach, smaller the average balances of the loan, the deeper the reach of the microcredit (Olivares-Polanco (2005), Cull et al., (2007), Roy et al. (2009)). But average loan balance per borrower is criticized as indicator of outreach as it is measured in monetary units and may mean different in different countries depending on their per capita income (Morduch (2000)). Therefore it is taken in relative terms by dividing it by the per capita Gross National Income (GNI pc). According to WDI, “GNI is the sum of value added by all resident producers plus taxes (less subsidies) not included in the valuation of output plus net receipts of primary income from abroad”. For this study, GNI pc is included by calculating it through purchasing power parity (PPP) method. PPP convert GNI pc to international dollars using purchasing power parity rates. It is directly taken from the WDI[4]. PPP method has an advantage that it provides better information of a country's international purchasing power and their relative economic strength. The method provides better information of the living standards of developing countries[5]. By denoting the relative term with K:
K = Average loan balance per borrower/ GNI pc (1)
Higher the value of K, the larger the average loan size in relative term. We assign K the range between 0-1 and thus find a value between 0 and 1 where a value near 0 indicates that the institution lends to the poorest. In order to find the objective of reaching poor (pi) we deduct K-min (K) from 1 as follows:
pi = 1 – (Ki – Min (K)/Range (K)) (2)
Where i is an indicator associated with a particular MFI. Min (K) is the minimum value over all i, while the Range (K) is the maximum value of Ki minus the minimum value of Ki. For every MFI, we multiply p by the number of active borrowers in order to construct the poverty indicator (P).