Performance analysis of Microfinance Institutions of India.
This is a study of Microfinance Institutions-MFIs of India. It includes analysis of MFIs of India. This study includes analysis of performance of microfinance institutions with reference to both financial and non-financial ways. Performance of microfinance institutions is measured using four parameters, which are sustainability/profitability, outreach, operational and financial efficiency. Data is taken of 99 Microfinance Institutions of India from the Microfinance Information Exchange for a period of 11 years. Variables of this study are both in absolute and relative terms. The endogenous variables are Return on Assets and Return on Equity for sustainability, Number of Borrowers per Staff Member for operational efficiency, Cost per Borrower for financial efficiency, and Number of Active Borrowers for outreach. Panel data analysis is done after checking the assumptions of the model. Hausman Test is applied to find out the suitability of Fixed or Random Effect Model. Both random and fixed effect were found suitable for application. In addition to this descriptive analysis of the variables is also done. The results show that most of the variables used in the study are significant in outreach model; other than rank, financial revenue to assets ratio, portfolio at risk, deposits, and capital to assets ratio all other variables are significant in case of sustainability using ROA model and same variables are found insignificant in ROE model except financial expense to assets ratio; in financial efficiency model both significant and insignificant variables are found; and in case of operational efficiency all variables are found significant.
Keywords: Microfinance; Return on Assets, Return on Equity; Panel Data; India.
This research is about the analysis of Microfinance Institutions-MFIs operating in India. The analysis of Microfinance Institutions is done with reference to their performance in the areas of outreach, profitability and sustainability, and efficiency including both financial and operating efficiencies. Most of the bigger MFIs of India are included in the research, which constitute about 80 percent of the total capital of microfinance in the country. Analysis is done using ratios and absolute figures of 11 years. Although, microfinance institutions are of different types and have varied scope of operations but some measures are common and can be applied to all types of MFIs to assess their performance.
History of microfinance can be traced back to eighteenth century in Europe, in India and China it is even older (Seibel, 2005). 1970s were the period of micro credit revolution in which Grameen Bank of Bangladesh presented a new model (Sengupta & Aubuchon, 2008). In 1990s came the microfinance revolution in which the major contributor was Consultative Group to Assist the Poor-CGAP (Gregorian, 2007). In 1960s some experimentation was done to find out the approach of microfinance providers and to set the approach of MFIs. Subsidized financing and flexible repayment plans were not very successful and sustainable institutions approach developed, which is still prevailing. Viability, Sustainability and Self-reliance are the terms used to make MFIs effective (Robinson, 2001).
Looking in the history of microfinance in India it is seen that microfinance developed in the late sixties when banks were nationalized and then in mid-seventies when Regional Rural Banks-RRBs were formed. Cooperatives formed for microfinance initially were not successful due to corruption and mismanagement. Integrated Rural Development Program-IRDP of India was at one time the largest microfinance program of the world, in terms of the fund base and number of beneficiaries; performance of IRDP went down due to many factors such as political influence and poor management; later institutions like SEWA bank, Annapurna Mahila Mnadal, and Working Women's Forum filled the gap; in late nineties cooperatives were reformed and Mutually Aided Cooperative Societies-MACS was formed; National Bank for Agriculture and Rural Development-NABARD, Small Industries Development Bank of India-SIDBI and Rshtriyu Mahila Kosh-RMK provide large amounts to several Self-Help Groups-SHGs (The World Bank, 2007).
Netscribes (2009) stated that there are three forms of MFIs in India having their own philosophies i.e. non-profit organizations including trusts, societies and companies formed under section 25 of company law of India; mutual benefit groups including Self Help Groups, co-operative societies; and for profit organizations including non-banking financial corporations.
The differentiating factors of MFIs in India are legal model, loan repayment structure, mode of interest rate calculation, product offering and legal structure: Indian MFIs with reference to lending models are classified as individual and group lenders, of the two group-lending is more popular; group lending models are of two types i.e. Self Help Groups model and Joint Liability Group-JLG/Solidarity Group model; in JLG model the most popular models in India are Grameen Bank model and Association for Social Advancement-ASA and Individual lending model which is very similar to retail individual loans; differentiating factors of MFIs in India include loan repayment structures, MFIs using JLG model have weekly or fortnightly repayment plans, whereas SHGs model have monthly repayment plans, some MFIs in giving loans to traders take repayment on daily basis and in case of agricultural loans the payment are also linked with the crops sale and cash realization; in case of the third differentiating factor i.e. interest rate, generally MFIs using JLG model charge 12-18% flat interest rates on the amount of loan, whereas in case of SHGs interest rates vary from 18-24% on reducing balance method. Some MFIs also charge processing fee from the borrowers; products of microfinance in India are many but most of the MFIs only offer micro credit facility, only a few MFIs offer savings/thrift, insurance, pension, and remittance facilities. Finally, with reference to legal structure the differentiation is of three types in India, 'Not for Profit' organizations including Societies (Bandhan and Rashtrya Sewa Samithi), Public Trusts (Shri Kshetra Dhamasthala Rural Development Project, and Community Development Center), and Non-profit Companies (Indian Association for Savings and Credit and Cashpor Micro Credit); Mutual Benefit organizations including Registered Cooperatives and Mutually Aided Cooperative Societies-MACS; and 'For-profit' organizations including Non-banking Financial Corporations-NBFC (Bhartiya Samruddhi Financials Limited, Share Microfinance Limited, SKS Microfinance Limited, and Spandans Sphoorthy Financials Limited), Producer Corporations (Sri Vijaya Visakha Milk Producers Company Limited), and Local Area Banks (Krishna Bhima Samurdhi Local Area Bank) (CRISIL, 2009).
In 1972 a group of women formed and organization by the name of Self Employed Women's Association-SEWA, with the objective of "strengthening its members' bargaining power to improve income, employment and access to social security"; to address poverty and to bring women from the eternal debt they formed the bank called Mahila Sewa Cooperative Bank and is providing microfinance to millions of people in India (Shri Mahila Sewa Sahkari Bank Ltd., 2006).
Nawaz (2010) gathered data of 204 MFIs of 54 countries to see the performance of these MFIs with reference to what is called the mission drift in microfinance i.e. mainly focusing from poverty alleviation to institutional sustainability. There are studies in which researchers analyzed the performance of MFIs with reference to the two main issues i.e. sustainability and outreach (Cull, Demirguc-Kunt, & Morduch, 2007), (Hudon & Traca, 2011), and (Merslan & Strom, 2007). Cull, Demirguc-Kunt, and Morduch (2007) used data of 124 MFIs selected from 49 countries; they used Oprational Self-Sufficiency-OSS and Retrun on Assets to determine sustainability, other variables in the form of ratios related to outreach were taken. The paper of Cull, Demirguc-Kunt, and Morduch (2007) includes OLS and descriptive analysis of ratios. The results showed high interest rates lead to low profits; trade-off between profitability and serving the poor exists; when better-off customers are served the benefit of cost cut diminishes.
Assefa, Hermes, and Meesters (2010) indicated that performance of MFIs is measured in the areas of outreach, repayment, efficiency and financials. This article indicates that competition among MFIs resulted into deterioration in the performance. The data was of three sixty two MFIs taken from seventy three countries over a period of fifteen years i.e. 1995-2009.
Cull, Kunt, and Morduch (2007) in financial performance and outreach analysis microfinance banks of the world used indicators such as financial self-sufficiency, operational self-sufficiency, return on assets, for-profit status, age of MFI, size of loan portfolio, average loan size to GNP per capita, nature of MFI, capital costs to assets, real gross portfolio yield, labor cost to assets, loans to assets, donations to loan portfolio, average loan size, women borrowers, and average loan size to GNP per capita of the poorest 20%. They took data of one twenty four MFIs in forty nine developing countries from 1999-2002 and applied the given indicators to evaluate the performance. Lafourcade, Isern, Mwangi, & Brown (2005) conducted a research for MIX in the region of Sub Saharan Africa in which they used Outreach (both breadth and depth (*)) including Borrowers Number of active borrowers with loans outstanding, GLP, unadjusted Percentage of Women Borrowers Number of women borrowers/number of borrowers, Average Loan Balance per Borrower GLP/number of borrowers, Average Loan Balance per Borrower/GNI per Capita Average loan balance per borrower/GNI per capita, Savers Number of savers with passbook and time deposit accounts, Savings Total value of passbook and time deposit accounts, Average Savings Balance per Saver, Savings/Number of savers; Financial Structure including Savings-to-Liabilities Ratio Total savings/total liabilities, Capital-to-Asset Ratio Total equity/total assets; Financial Performance including Return on Assets (ROA) Net operating income, net of taxes/average total assets, Return on Equity (ROE) Net operating income, net of taxes/average total equity, Operational Self-Sufficiency (OSS) Financial revenue/(financial expense + net loan loss provision expense + operating expense), and Financial Revenue Ratio Financial revenue/average GLP; Efficiency and Productivity including Operating Expense/GLP Operating expense/average GLP, Cost per Borrower Operating expense/average number of borrowers, Cost per Borrower/GNI per capita Cost per borrower/GNI per capita, Cost per Saver Operating expense/average number of savers, Cost per Saver/GNI per capita Cost per saver/GNI per capita, Borrowers per Staff Member Number of borrowers/number of personnel, and Savers per Staff Member Number of savers/number of personnel; Portfolio Quality including PAR > 30 days.
Imai, Gaila, Thapa, Annim, and Gupta (2012) developed a model of find out the financial performance of MFIs of the world. They used Macro level indicators such as GDP per capita, share of domestic credit to GDP; Characteristics of MFIs such as size, age, and legal status; and Institutional factors such as political stability, voice and accessibility, control on corruption, and rule of law. As this research was from both macroeconomic and institutional perspectives so the variables used are unique, which are generally not used by other researchers.
Hermes, Lensick and Meesters (2011) conducted MFIs analysis to find out the relationship between outreach and efficiency using stochastic frontier analysis and found that there is a negative relationship between the two. Armendariz and Labie in The Handbook of Microfinance (2011) has written that social, financial and efficiency indicators should be used to measure the performance of MFIs.
The details of the variables and the formulae of variables are also given below the equations. The best measure to find out the outreach of MFIs is with reference to the number of active borrowers, more borrowers mean more outreach. The following are the probable models to be developed in the light of this research, first in the area of outreach in FEM and REM respectively:
noab = [[beta].sub.1i] + [[beta].sub.2it] nod + [[beta].sub.3it]nowb + [[beta].sub.4it]adpd + [[beta].sub.5it]alpb + [[beta].sub.6it]adpdpc + [[beta].sub.7it]alpbpc + [[beta].sub.8it]glp + [[beta].sub.9it]dep +[[mu].sub.it]
noab = [[beta].sub.1i] + [[beta].sub.2it] nod + [[beta].sub.3it]nowb + [[beta].sub.4it]adpd + [[beta].sub.5it]alpb + [[beta].sub.6it]adpdpc + [[beta].sub.7it]alpbpc + [[beta].sub.8it]glp + [[beta].sub.9it]dep +[[epsilon].sub.it] + [[mu].sub.it]
Profitability and Sustainability
In the area of profitability and sustainability two models are anticipated, one with reference to return on total assets and the other with reference to return on equity. These two models are given under, both with FEM and REM respectively:
roa = [[beta].sub.1i] + [[beta].sub.2it]assets + [[beta].sub.3it]bor + [[beta].sub.4it]car + [[beta].sub.5it]der + [[beta].sub.6it]dep + [[beta].sub.7it]rank + [[beta].sub.8it]pfar + [[beta].sub.9it]pflita + [[beta].sub.10it]wor + [[beta].sub.11it]ygpf + [[beta].sub.12it]fear + [[beta].sub.13it]oear + [[beta].sub.14it]frar +[[mu].sub.it]
roa = [[beta].sub.1i] + [[beta].sub.2it]assets + [[beta].sub.3it]bor + [[beta].sub.4it]car + [[beta].sub.5it]der + [[beta].sub.6it]dep + [[beta].sub.7it]rank + [[beta].sub.8it]pfar+[[beta].sub.9it]pflita+ [[beta].sub.10it]wor + [[beta].sub.11it]ygpf + [[beta].sub.12it]fear + [[beta].sub.13it]oear + [[beta].sub.14it]frar + [[epsilon].sub.it] + [[mu].sub.it]
The same model will also be used with return on equity as dependent variable as given under again with FEM and REM respectively:
roe = [[beta].sub.1i] + [[beta].sub.2it]assets + [[beta].sub.3it]bor + [[beta].sub.4it]car + [[beta].sub.5it]der + [[beta].sub.6it]dep + [[beta].sub.7it]rank + [[beta].sub.8it]pfar + [[beta].sub.9it]pflita + [[beta].sub.10it]wor + [[beta].sub.11it]ygpf + [[beta].sub.12it]fear + [[beta].sub.13it]oear + [[beta].sub.14it]frar + [[mu].sub.it]
roe = [[beta].sub.1i] + [[beta].sub.2it]assets + [[beta].sub.3it]bor + [[beta].sub.4it]car + [[beta].sub.5it]der + [[beta].sub.6it]dep + [[beta].sub.7it]rank + [[beta].sub.8it]pfar + [[beta].sub.9it]pflita+ [[beta].sub.10it]wor+ [[beta].sub.11it]ygpf+ [[beta].sub.12it]fear + [[beta].sub.13it]oear + [[beta].sub.14it]frar + [[epsilon].sub.it]+ [[mu].sub.it]
Operational efficiency is with reference the achievement of ultimate objective of MFIs i.e. servicing the borrowers and financial efficiency is with reference to the cost incurred by an MFI in servicing a borrower. Operational efficiency is measured using the following equation in terms of Number of Borrowers per Staff Member as given in the following equation:
nobpsm = [[beta].sub.1i] + [[beta].sub.2it] noab+ [[beta].sub.3it]nowb+ [[beta].sub.4it]nod+ [[beta].sub.5it]dep+ [[beta].sub.6it]person + [[beta].sub.7it]wor + [[beta].sub.8it]glp+ [[mu].sub.it]
nobpsm = [[beta].sub.1i] + [[beta].sub.2it] noab+ [[beta].sub.3it]nowb+ [[beta].sub.4it]nod+ [[beta].sub.5it]dep+ [[beta].sub.6it]person + [[beta].sub.7it]wor + [[beta].sub.8it]glp + [[epsilon].sub.it] + [[mu].sub.it]
Financial efficiency is measured in terms of cost per borrower. Financial efficiency is related to the efficiently utilizing the funds towards providing loans to the borrowers. Higher financial efficiency leads to the saving funds for more lending, hence maximizing the amount available for lending. The relevant ratios are operating expenses to assets ratio, financial expenses to assets ratio, financial revenue to assets ratio, yield on gross portfolio, write off ratio, return on equity and return on assets. The following are the equations related to financial efficiency with reference to cost per borrower:
cpb = [[beta].sub.1i] + [[beta].sub.2it] oear+ [[beta].sub.3it]fear+ [[beta].sub.4it]frar+ [[beta].sub.5it]ygpf+ [[beta].sub.6it]wor + [[beta].sub.7it]roa + [[beta].sub.8it]roe+ [[mu].sub.it]
cpb = [[beta].sub.1i] + [[beta].sub.2it]oear+ [[beta].sub.3it]fear+ [[beta].sub.4it]frar+ [[beta].sub.5it]ygpf+ [[beta].sub.6it]wor + [[beta].sub.7it]roa + [[beta].sub.8it]roe+ [[epsilon].sub.it] + [[mu].sub.it]
The performance of MFIs is tested using panel data regression analysis indicating the impact of independent variables on selected dependent variables in the four areas of outreach, sustainability, efficiency and compliance. To apply panel data analysis all assumptions are checked.
In the current study many variables were not linear or normally distributed. Due to such problems variables are transformed by taking log (log) of them. These transformations have sorted out the problems of basic assumptions of data required for analysis.
Assumptions of the Models
The assumptions are important to approximate the exact association among variables. These assumptions consist of normality tests which are checked by histograms, linearity is checked by normal probability plot, though linearity and normality is not considered a serious problem in panel data analysis. Multicoliearity is checked by VIF test, autocorrelation is checked by Correlogram and Heteroskedasticity is checked by Heteroskedasticity tests and graphs. Five models are considered in study for each sector. The brief detail and graphs of these assumptions and models are discussed in this section. The residuals are considered distributed normal if the Jarque-bera value is less than round off to 6, value of Skewness is round off to zero and value of kurtosis is near to 3. Linearity is checked by normal probability plot. The histograms and normal probability plots of all variables are checked and data are found normally distributed and linear.Multicoliearity is checked using Variance Information Factor-VIF and the cutoff value of VIF is taken as 10. The results indicate that the there is no problem of multicoliearity. Heteroskedasticity is checked using scattered diagram. The charts indicated no serious problem of Heteroskedasticity.
Both Fixed and Random Effect Models are applied. The results of Hausman Test are used to select the suitable model. The probability of the Hausman Test is less than 0.05 so Fixed Effect model is accepted. The results of the Fixed Effect suggest that logalpb is significant at 10% level of significance with positive value of beta coefficient. Logdep, lognod, lognowb, logglp are significant at 1% level of significance with positive values of beta coefficient. Logadpd, logadpdp, logalpbp are significant at 1% level of significance with negative values of beta coefficient. At 5% level of significance the following model is applied:
noab = -1.654433 + 0.172703 nod+ 0.278958 nowb - 0.266815 adpd - 0.335749 adpdpc - 0.291125 alpbpc + 0.453501 glp+ 0.096884 dep + [[mu].sub.it]
Sustainability using ROA
The results of Hausman Test show that random effect model is accepted. The accepted model represents that logbor, logder and logfear are significant at 5% level of significance with negative values of beta coefficients. Logpflita and logygpf are significant at 5% level of significance with positive values of beta coefficients. Logasset and logwor are significant at 10% level of significance with positive value of beta coefficients. Logoear is significant at 1% level of significance with positive value of beta coefficient. Rank, Logfrar, logpfar, logdep and logcar are insignificant.
roa = - 0.668900 - 0.065884 logbor - 0.050288 logder - 0.075569 logfear + 0.071122 logpflita + 0.0234767 logygpf + 0.150545 logoear + [[mu].sub.it]
Sustainability using ROE
The probability of Hausman Test is more than 0.05 which shows that Fixed Effect model is rejected and random effect model is accepted. The accepted model suggests that logbor, logcar are significant at 1% level of significance with negative values of beta coefficients. Logder is significant at 1% level of significance with positive values of beta coefficient. Logdep is significant at 5% level of significance with negative value of beta coefficient. Logpfar is significant at 10% level of significance with positive values of beta coefficients. The remaining variables are insignificant.
roe = 0.316988 - 0.021835logdep + 0.071122logpflita - 0.081781 logbor - 0.148969 logcar + 0.169008 logder +[[mu].sub.it]
The probability value of Hausman Test is less than 0.05 which shows that Fixed Effect model is accepted showing that logfear is significant at 5% level of significance with negative values of beta coefficients. Logroa is significant at 1%. Logoear, logfrar, logygpf, logwor and logroe are insignificant variables.
cpb = 1.574739 - 0.070062 fear + 0.096851 roa + [[mu].sub.it]
nobpsm = [[beta].sub.1i] + [[beta].sub.2it] noab+ [[beta].sub.3it]nowb+ [[beta].sub.4it]nod+ [[beta].sub.5it]dep+ [[beta].sub.6it]person + [[beta].sub.7it]wor + [[beta].sub.8it]glp+ [[mu].sub.it]
The probability value of Hausman Test is less than 0.05 so Fixed Effect model is accepted. According to the accepted model lognoab and logwor are significant at 5% level of significance but lognoab has positive values of beta coefficient and logwor has negative value of beta coefficients. Lognod, logdep, logperson are significant at1% level of significance with negative values of beta coefficients and lognowb and logglp are significant at 1% level of significance with positive values of beta coefficients. The following is the actual model:
nobpsm = 1.552329 + 0.060885 noab + 0.111322 nowb - 0.350923 nod + 0.181472 dep - 0.214279 person - 0.054106 wor + 0.193889 glp+ [[mu].sub.it]
It is concluded by the results that most of the factors included in the models are significant. Considering the significance of these factors it can be concluded that these are good indicators of performance analysis of MFIs. Number of depositors, number of women borrowers, total deposits and gross loan portfolio has positive relationship and hence need to be increased in order to increase outreach. Only financial expense to assets ratio is found significant in ROA model but not in ROE model, rest of the variables are similar in the two models. In case of financial efficiency only financial expenses to assets ratio and return on assets are significant, showing very few known factors in the model. Finally, in case of operational efficiency all included variables are found to be significant and included in the fitted model.
Armendariz, B., & Labie, M. (2011). The Handbook of Microfinance. Singapore: World Scientific Publishing Company Plc. Ltd.
Assefa, E., Hermes, N., & Meesters, A. (2010). Competition and performance ff microfinance insititutions.
CRISIL. (2009). India Top Fifty Microfinance Institutions: A Financial Awareness Initiative of CRISIL. Mumbai, India: CRISIL.
Cull, R., Demirguc-Kunt, A., & Morduch, J. (2007). Financial performance and outreach a global analysis of leading micro banks. The Economic Journal, 117(517), F-107-F-133.
Gregorian, N. (2007). CGAP Client Survey. New York: Fluent.
Hermes, N., Lensink, R., & Meesters, A. (2011). Outreach and efficiency of microfinance institutions. World Developement, 39(6), 938-948.
Hudon, M., & Traca, D. (2011). On the efficiency effects of subsidies in MF: An empirical inquiry. World Development, 39(6), 966-973.
Imai, K. S., Gaila, R., Thapa, G., Annim, S. K., & Gupta, A. (2012). Performance of microfinance insititutions: A macro economic and institutional perspective. Research Insititute for Economics and Business Adminstration.
Lafourcade, A.-L., Isern, J., Mwangi, P., & Brown, M. (2005). Overview of the Outreach and Financial Performance of Microfinance Institutions in Africa. MIX.
Merslan, R., & Strom, R. (2007). Microfinance banks: Ownership, performance and social trade-offs - A global analysis. MPRA, 2063.
Nawaz, A. (2010). Issues in subsidy and sustainaility of microfinance: An impercial investigation. Solvey Brussels School.
Robinson, M. (2001). The Microfinance Revolution: Sustainable Finance for the Poor. Washington DC: The World Bank.
Seibel, H. D. (2005). Does the history matter: The old and new world of microfinance in Europe and Asia. From Money Lenders to Microfinance. Singapore: Asian Institute of Economics, University of Singapore.
Sengupta, R., & Aubuchon, C. P. (2008, January). The microfinance revolution: An overview. Federal Reserve Bank St. Louis Review, 90(1), 9-30.
Shri Mahila Sewa Sahkari Bank Ltd. (2006). About-Origins. Retrieved September 2012, 2012, from Sewa Bank: http://www.sewabank.com/aboutus-origin.htm
The World Bank. (2007). Microfinance in Asia: Toward Financial Inclusion for the Poor. Washington DC: The World Bank.
Muhammad Azhar Ikram Ahmad
Ather Azim Khan[dagger]
Faculty of Commerce, University of Central Punjab, Lahore, Pakistan
(*) The views or opinions expressed in this manuscript are those of the author(s) and do not necessarily reflect the position, views or opinions of the editor(s), the editorial board or the publisher.
([dagger]) Corresponding author E-mail: firstname.lastname@example.org
(*) Breadth is the number of clients served and volume of services, whereas depth is the socioeconomic level of clients that MFIs reach
TABLE 1 Variable Description Variable Description Assets Assets adpd Average deposit balance per depositor adpdpc Average deposit balance per depositor/GNI per capita alpb Average loan balance per borrower alpbpc Average loan per borrower per capita nobpsm Number of Borrowers per Staff Member bor Borrowings car Capital/asset ratio cpb Cost per borrower Operating Expenses/Average der Debt to equity ratio dpsm Depositors per staff member dep Deposits rank Diamonds equity Equity fear Financial expense/ assets frar Financial revenue/ assets Gross Loan Portfolio noab Number of active borrowers nod Number of depositors Account oear Operating expense/ assets oelp Operating expense/ loan portfolio person Personnel pfar Portfolio at risk >. 30 days pflita Provision for loan impairment/ assets roa Return on assets roe Return on equity nowb Women borrowers wor Write-off ratio Portfolio ygpf Yield on gross portfolio (nominal) VariableFormula Assets Total Assets adpd Total Deposits/Number of Depositors adpdpc Average Deposit Balance per Depositor/GNI Per Capita alpb Gross Loan Portfolio/Number of Active Borrowers alpbpc Average Loan Balance per Borrower/GNI Per Capita nobpsm Number of Active Borrowers/Number of Personnel bor Borrowings car Total Equity/Total Assets Number of Active cpb Borrowers der Total Liabilities/Total Equity dpsm Number of Depositors/Number of Personnel dep Total Value of All Deposit Accounts rank Ranking equity Equity fear Financial Expense/Average Total Assets frar Financial Revenue/Average Total Assets Gross Loan Portfolio noab Number of Borrowers with Loan Outstanding nod Number of Depositors with any type of Deposit oear Operating Expenses/Average Total Assets oelp Operating Expenses/Average Gross Loan Portfolio person Total Number of Staff Members pfar Outstanding balance, portfolio overdue > 30 Days + renegotiated portfolio/Gross Loan Portfolio pflita Impairment Losses on Loans/Average Total Assets roa Net Operating Income - Taxes/Average Total Assets roe Net Operating Income - Taxes/Average Total Equity nowb Number of women borrowers in wor Value of Loans Written-off/Average Gross Loan ygpf Financial Revenue from Loan Portfolio/Average Gross Loan Portfolio TABLE 2 MFIs Corporate Structures NBFIs NGOs Credit Unions Banks Total /Cooperatives Others Pakistan 14 2 0 8 24 India 51 37 7 2 97 Bangladesh 0 30 1 1 32 Total 65 69 8 11 153 TABLE 3 Descriptive Statistics of Variables for MFIs of India Descriptive Statistics Minimum Maximum Mean Statistic Statistic Statistic assets .00 1.70E9 67617720.0294 adpd .00 192.40 33.9830 adpdpc .00 .00 .0000 alpb .00 274.00 93.9357 alpbpc .00 .00 .0000 nobpsm .00 331.00 146.1635 bor .00 2.98E8 12622103.487 car .00 .00 .0000 cpb .00 71.00 13.1587 der -93.05 95.79 6.1192 dpsm .00 444.00 113.3873 dep .00 1.65E9 33759951.899 rank 1.00 5.00 3.5809 equity - 3.99E8 14922996.448 22037595 fear .00 .00 .0000 frar .00 .00 .0000 glp 17709.00 1.01E9 49890226.7304 noab 341.00 6610000.00 514641.6299 nod .00 8359993.00 586595.2083 oear .00 .00 .0000 oelp .00 .00 .0000 person .00 34841.00 2379.3186 pfar .00 .00 .0000 pflita .00 .00 .0000 roa .00 .00 .0000 roe .00 .00 .0000 nowb .00 6371379.00 465646.9265 wor .00 .00 .0000 ygpf .00 .00 .000 Valid N (list wise) Std. Deviation Skewness Kurtosis Std. Std. Statistic Statistic Error Statistic Error assets 2.12535E8 5.049 .121 28.976 .241 adpd 22.01221 2.971 .121 17.226 .241 adpdpc .00000 . . . . alpb 42.15058 .938 .121 1.274 .241 alpbpc .00000 . . . . nobpsm 54.58922 .961 .121 1.179 .241 bor 30301987.130 6.639 .121 51.628 .241 car .00000 . . . . cpb 6.40309 2.879 .121 19.197 .241 der 10.97775 -3.920 .121 58.360 .241 dpsm 102.84203 .317 .121 -.830 .241 dep 1.59279E8 7.730 .121 65.657 .241 rank .98805 -1.908 .121 2.472 .241 equity 50396808.3533 5.080 .121 29.312 .241 fear .00000 . . . . frar .00000 . . . . glp 1.44651E8 4.192 .121 18.299 .241 noab 1334153.97855 3.351 .121 10.369 .241 nod 1647983.17899 3.466 .121 11.137 .241 oear .00000 . . . . oelp .00000 . . . . person 5324.92241 3.359 .121 11.066 .241 pfar .00000 . . . . pflita .00000 . . . . roa .00000 . . . . roe .00000 . . . . nowb 1234796.57502 3.452 .121 11.156 .241 wor .00000 . . . . ygpf .00000 . . . . Valid N (list wise) Source. All calculations are based on data from Microfinance Information Exchange at 5% significance level.
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|Author:||Ahmad, Muhammad Azhar Ikram; Ahmad, Zahid; Khan, Ather Azim|
|Date:||Jan 1, 2014|
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