pairs plot of the rates of changes of loan amounts compared against the rates of changes of macroeconomic variables reported by the world bank for the country of Kenya. Figure 3 of Change of Loan Amount vs. Macro Counterparts in Kenya If the rate of change between loan amounts are significantly correlated with known macroeconomic variables that would lend credence to the assumption that they represent a useful source of information. From a cursory view of Figure 3 there does appear to be a positive relationship. It is also obvious that the microloans have much greater variation than do the macroeconomic variables. In order to test for significance a hypothesis test of correlation is conducted with the results given in Table 1. Table 1 Test of Correlation of Rates of Change R 0.356301 N 35 Alpha .05 Test Statistic 2.19 Result Reject Null Hypothesis (R =0) The result of the test demonstrates statistical significance between the two variables at 95% confidence. This observation supports the use of changes in loan amounts as a proxy for economic conditions. The sample correlation of .35 suggests that the two variables are not strongly related. This is to be expected as microloans are targeted at the lower echelons of an economy as opposed to the country level concentration that the world bank reports. There is also friction concerning the size of the loans as MFIs have varying practices in determining and changing loan sizes over time. However, using averages on a time series loan amount changes offers a positive first effort at measuring the performance of micro economic sectors. It is these sectors that a ground commander may have the best chance of influencing. Methodology The approach used to leverage the microfinance data will rely on the dynamics of the dollar size of loans. In order to infer portfolio performance a major assumption used is that a loan amount (aggregate not individual) will follow a distribution. Cursory log normal observation of the loan size by sector given in figure 4 support the distribution assumption as a stylized fact.
pairs plot of the rates of changes of loan amounts compared against the rates of changes of macroeconomic variables reported by the world bank for the country of Kenya. Figure 3 of Change of Loan Amount vs. Macro Counterparts in Kenya If the rate of change between loan amounts are significantly correlated with known macroeconomic variables that would lend credence to the assumption that they represent a useful source of information. From a cursory view of Figure 3 there does appear to be a positive relationship. It is also obvious that the microloans have much greater variation than do the macroeconomic variables. In order to test for significance a hypothesis test of correlation is conducted with the results given in Table 1. Table 1 Test of Correlation of Rates of Change R 0.356301 N 35 Alpha .05 Test Statistic 2.19 Result Reject Null Hypothesis (R =0) The result of the test demonstrates statistical significance between the two variables at 95% confidence. This observation supports the use of changes in loan amounts as a proxy for economic conditions. The sample correlation of .35 suggests that the two variables are not strongly related. This is to be expected as microloans are targeted at the lower echelons of an economy as opposed to the country level concentration that the world bank reports. There is also friction concerning the size of the loans as MFIs have varying practices in determining and changing loan sizes over time. However, using averages on a time series loan amount changes offers a positive first effort at measuring the performance of micro economic sectors. It is these sectors that a ground commander may have the best chance of influencing. Methodology The approach used to leverage the microfinance data will rely on the dynamics of the dollar size of loans. In order to infer portfolio performance a major assumption used is that a loan amount (aggregate not individual) will follow a log normal distribution. Cursory observation of the loan size by sector given in figure 4 support the distribution assumption as a stylized fact.
MicrofinanceArticle.docx