While predicting whether or not a person will default on a loan is nigh unto impossible, modeling the probability that they will incur back-debt by the frequency of missing payments could be a reasonable proxy for this measure. This paper aims to do this by regressing the number of loans that have gone into default during any cycle on a host of measures designed to assess the individual's ability to pay back the loans. The observations have been drawn from a survey of 2,800 households in India. I am interested to see if my logical predictions of which characteristics will cause an individual to fall into default will be proven accurate. The results of this analysis could potentially help microfinance institutions make more-informed decisions regarding whom to provide loans to, in order to minimize their risk factors. Moreover, I am intrigued to analyze the theory that females make better loan candidates than males. On one hand, it does seem logical that their business revenues will be reinvested within the family, engendering more consumption in the community. But will the increase in disposable income from successful businesses affect both genders of borrowers the same?
Tara Fleming. "Examining the Influence of Microfinance on Household Conditions in Hyderabad, India." Proceedings of the New York State Economics Association. vol. 6, October 2013, p. 29-38
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