Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.758158
Title: Essays on microfinance repayment behaviour : an evaluation in developing countries
Author: Huang, Guan
ISNI:       0000 0004 7430 9377
Awarding Body: University of Reading
Current Institution: University of Reading
Date of Award: 2018
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Abstract:
Microfinance research concerns addressed in this thesis relate to: the associations between the individual characteristics of borrowers and the probabilities of being in delinquent or default; the determinants for the financial awareness of interest repayment; and the application and comparison of modern missing data techniques (Multiple Imputation, Maximum Likelihood Estimation, and Predictive Mean Matching) with incomplete loan book data. The thesis emphasises credit scoring issues that affect repayment performance and revolves around three empirical chapters that seek to address the above research concerns. Survey and loan book data from individuals in 51 MFIs across 27 developing countries. The data were compiled by the MFIs and collected by Micro Finanza Rating. Varied micro-econometric techniques (ordinary least squares, Logit regression, Tobit regression, Two-Part model, Double-Hurdle model, Box-Cox transformation, and three missing data imputation methods: Multiple Imputation, Maximum Likelihood Estimation, and Predictive Mean Matching) are used depending on the hypotheses being considered in each chapter. The main findings are: engaging in agriculture is related to a lower probability of default that measured by the amount of arrear in general; besides, the association between agriculture and the length of delayed repayment is insignificant; previous access to micro-finance has positive association with the financial awareness of the clients who lived in urban areas; in addition, previous access to saving service has positive effect on the clients with at least primary education; when the missing microfinance data is semi-continuous, PMM outperforms MI and ML in most simulations; for binary or ordinal categorical data, PMM performance surpass MI and ML only when the sample sizes of data are large, the missing rates are low, and the missing mechanism is MAR. The thesis suggests the following recommendation both for management of MFIs and government, we need to: make financial services for poor farm households and farm-related business more attractive to the MFIs; financial awareness can be improved by access to microfinance services, hence extra learning programmes may be unnecessary; Two-Part Model should be applied to credit scoring; and PMM imputation is the best technique to be applied to deal with the missing data issues and improve data quality in microfinance.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.758158  DOI: Not available
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