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Impact assessment of group-based credit–lending projects with controlled project placement bias and self-selection bias

By: Material type: TextTextDescription: 227 -238 pSubject(s): In: CHAKRABARTI, BHASKAR DECISIONSummary: A large number of microfinance impact assessment studies were conducted in different parts of the world in last two decades, but most of the impact assessment methods were put to questions. There were methodological concerns associated with both external validation and internal validation. Many microfinance impact assessment studies have suffered from issues related to counterfactual selection, project/programme placement bias and self-selection bias. To address those problems, this paper studied some of the important impact assessment frameworks. This study involved extensive literature scanning. Important impact assessment models were analysed and discussed focusing on unit of analysis, selection of impact variables, selection of comparison group and control of self-selection bias and project placement bias. The study concluded that a methodical analysis of counterfactual would help in avoiding self-selection bias and project placement bias
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Holdings
Item type Current library Call number Vol info Status Notes Date due Barcode Item holds
Journal Article Journal Article Main Library Vol 44, No 3/ 5557939JA5 (Browse shelf(Opens below)) Available 5557939JA5
Journals and Periodicals Journals and Periodicals Main Library On Display JRNL/ GEN/Vol 44, No 3/5557939 (Browse shelf(Opens below)) Vol 44, No 3 (07/11/2017) Not for loan September, 2017 5557939
Total holds: 0

A large number of microfinance impact assessment studies were conducted in different parts of the world in last two decades, but most of the impact assessment methods were put to questions. There were methodological concerns associated with both external validation and internal validation. Many microfinance impact assessment studies have suffered from issues related to counterfactual selection, project/programme placement bias and self-selection bias. To address those problems, this paper studied some of the important impact assessment frameworks. This study involved extensive literature scanning. Important impact assessment models were analysed and discussed focusing on unit of analysis, selection of impact variables, selection of comparison group and control of self-selection bias and project placement bias. The study concluded that a methodical analysis of counterfactual would help in avoiding self-selection bias and project placement bias

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