WHAT FACTORS DETERMINE THE LIKELIHOOD OF AN MFI ’S PROFIT STATUS?

SHIYANA A . GUNASEKARA

Microfinance in India: For-Profit vs. Non-Profit

Outline

BackgroundIntroduction and Previous WorksTheory and HypothesisBrief overview of methodologyQualitative Segment

Methodology Findings Conclusion

Quantitative Segment Methodology Findings

Concerns Overall Conclusion Next Steps

Background

Microfinance has been hyped as a magic tool for poverty alleviation

Large unmet demand for credit in India, mainly by women

India is seeing a commercializing trend of MFI’s

Should non-profit and for-profit firms act differently? Microfinance literature says yes and no

Theory and Hypothesis

Diminishing Marginal ReturnsTheories of EfficiencyMicrofinance and its valueHypothesis

Proportion of Female Borrowers – significant (+/-) Parthasarathy (2012)

For-profit (NBFC) is most efficient and therefore can make significant social impact Morduch (2000), Woller et al., (1999)

Qualitative Field Work: The Organizations

Ahmedabad, Gujarat Largest city in Gujarat, 5th largest in India 4.5 lakhs of “slum”-dwelling families reported Great potential Proposed Gujarati mindset

Gujarati women

Not-For-Profit 6500 women, Rs. 4.5 crore doubling and turning into for –

profit next year

For-Profit Non-Banking Financial Company

41,000 women, Rs. 30 crore

Dependent Variables Independent Variables

Profit Status Products and Services Variety and options offered

to the borrowersTransparency

Interaction between high level officers and members

Why Women? Depth of outreach

Non-financial Services Quality of outreach

Qualitative Methodology – Social Impact

Findings

Non-Profit For-Profit

Products and Services

Loan sizes up to Rs. 24,000; contract with a housing financial company

Loan sizes up to Rs. 25,000; required insurance

Transparency Low Medium

Why Women? Easy access, vulnerable; however, targets poorer clients

Easy access, vulnerable

Non-financial Services

Literacy training – signature

Financial literacy training, gaps in member knowledge

Qualitative Conclusion

Differences I expected to see in the social impact of these MFI’s were not there Literature claims great differences between the two Qualitative captured nuances quantitative could not

Dependent Variable Independent Variables

For Profit or Non-Profit MFI Logistic Regression

Financial efficiencySocial impact16 total

Quantitative Methodology

Logged odds (Profit Status) = a +b1(CAR) +b2(D/E) +b3(AvLoan) +b4(ROA) +b5(ROE)+b6(Rev/Assets)+b7(Exp/Assets)+b8(borr/mem) +b9(yieldgrsport)+b10(cost/borr) + b11(OperExp/Assets) +b12(lnAssets) +b13(lnGrossLoanPort) + b14(Equity) +b15(lnBorrowings) +b16(PercWomen) +e

Findings

Coefficients of significant variables

CAR (140.77) .044 significance

Average loan size per borrower/GNI per capita (-41.844) .019 sig

ROA (-417.56) .015 sig

ROE (-36.712) .003 sig

Financial Revenue/Assets (579.86) .013 sig

Financial Expenses/Assets (-559.24) .020 sig

Operating Expense/assets (-512.13) .008 sig

ln Assets (101.84) .038 sig

ln Gross Loan Portfolio (-7.4323) .001

Conclusion

Qualitative My expectations for the MFIs were both confirmed

and challenged Not a lot of differences Limitations – language, respondent bias

Quantitative The odds of an MFI being FP or NP can be predicted

by several factors Percentage of Women Borrowers not significant Limitations – data, model

Next Steps

Investigate “high performing” non-profits that are messing up my regression SKDRDP

Rs. 246,469,072 in assets 196,210,075 borrowers Debt/Equity Ratio: 43.17

BISWA Rs. 21,490,712 in equity

Cashpor MC ROE: 0.5265

Different modeling approach?