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Microfinance Failures and Macro Indicators

Microfinance Failures and Macro Indicators. Denise Phelps dp8096b@american.edu American University School of International Service. Research Question & Research Hypothesis. Research Questions

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Microfinance Failures and Macro Indicators

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  1. Microfinance Failures and Macro Indicators Denise Phelps dp8096b@american.edu American University School of International Service

  2. Research Question & Research Hypothesis • Research Questions Do richer countries experience lower write offs than poorer countries? Is there a correlation between the GDP per capita of a country with microfinance programs and the overall write off rate of that country? • Research hypothesis Countries with higher GDP per capita have more write offs in microfinance programs than lower GDP per capita countries.

  3. Literature Review • Does the Microfinance Lending Model Actually Work? by Rafael Gómez and Eric Santor • Theory: Peer based lending models produce higher repayment rates than individual based lending models. • Findings : • Peer based lending models do produce higher repayment, however the findings were not conclusive as to the aspects of the lending model that attributes to the higher repayment rates. • Write offs are the best indicator of actual loan failures, due to the fact that delinquent loans can be worked out and not actually fail. Up until the point of being written off, Microfinance Institutions attempt to work with the borrower with modifications and other loan programs to keep the loan from failing.

  4. Data • Unit of analysis : Data from 92 Countries was analyzed • Source of the data: Microfinance Information Exchange and World Development Indicators which are reliable • Dependent variable • Y is Write-off Ratios in each country • Level of Measurement is Interval Ratio and represents percentage of loan portfolio that is not recoverable • Independent Variable • X1 is Gross Domestic Product per Capita (GDPPC) – I-R and measured in $USD • X2 is Corruption Index (CI) – I-R and on scale from 0 to 10 with 10 being less corrupt • X3 is Literacy Rate (LR) – I-R and %

  5. Descriptive Statistics • Write off Ratio is positively skewed and unimodal, which means the majority of countries have low write off rates • GDP per Capita is positively skewed and unimodal, which means the majority of countries have a low GDP per Capita

  6. Write-off Ratio and GDP per capita • Ho = There is no association between Write-off Ratio and GDPPC. • H1 = There is an association between Write-off Ratio and GDPPC. • Sampling = t distribution • Alpha = 0.05 • t (critical) = 2 • t (obtained) = -1.519 • Pearsons R2 = .016 • Indicates a weak association, Write-off ratios explain 1.6% of the variation in GDPPC, and there is low homoscedasticity. Therefore, we must accept the null hypothesis.

  7. Correlations Write-off Ratio is not significantly correlated to GDP per capita, Corruption Index, and Adult Literacy Rate. However, each of these indicators are significantly correlated to each other. ** Correlation is significant at 0.01 level (2-tailed) * Correlation is significant at 0.05 level (2-tailed)

  8. Regression AnalysisThe Dependent Variable is Natural log of Write-off Ratio

  9. Collinearity • There is no significant levels of collinearity.

  10. Findings & Policy Implications of the Research • Findings: Did you accept your research hypothesis? • I rejected my research hypothesis because there is no statistical support that GDP per capita, HDI, Corruption Index, or Literacy Rate are indicators of Write-off Ratios in a country. • What are the policy implications of your findings? • Policies should be developed in each microfinance institution that addresses the investors expectations and not based on the country level indicators. For example, MFIs should not be creating loan failure policies based on economic or development indicators in a specific country.

  11. Write-off Ratio and Literacy Rate • Ho = There is no association between Write-off Ratio and Literacy Rate. • H1 = There is an association between Write-off Ratio and Literacy Rate. • Sampling = t distribution • Alpha = 0.05 • t (critical) = 2 • t (obtained) = .619 • Pearsons R2 = -.008 • Indicates a weak association, Write-off Ratios explain .8% of the variation in Literacy Rate, and there is low homoscedasticity. Therefore, we fail to reject and must accept the null hypothesis.

  12. Write-off Ratio & Corruption Index • Ho = There is no association between Write-off Ratio and Corruption Index. • H1 = There is an association between Write-off Ratio and Corruption Index. • Sampling = t distribution • Alpha = 0.05 • t (critical) = 2 • t (obtained) = -.256 • Pearsons R2 = -.011 • Indicates a weak association, Write-off Ratios explain 1.1% of the variation in Corruption Index, and there is low homoscedasticity. Therefore, we fail to reject and must accept the null hypothesis.

  13. Write-off Ratio and HDI • Ho = There is no association between Write-off Ratio and HDI. • H1 = There is an association between Write-off Ratio and HDI • Sampling = t distribution • Alpha = 0.05 • t (critical) = 2 • t (obtained) = .593 • Pearsons R2 = -.007 • Indicates a weak association, Write-off ratios explain .7% of the variation in HDI, and there is low homoscedasticity. Therefore, we fail to reject and must accept the null hypothesis.

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