Please use this identifier to cite or link to this item: https://ir.iimcal.ac.in:8443/jspui/handle/123456789/3215
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dc.contributor.authorPanda, Debadutta Kumar
dc.date.accessioned2021-08-27T08:41:37Z
dc.date.available2021-08-27T08:41:37Z
dc.date.issued2017-09
dc.identifier.issn0304-0941 (print version) ; 2197-1722 (electronic version)
dc.identifier.urihttps://doi.org/10.1007/s40622-017-0148-6
dc.identifier.urihttps://ir.iimcal.ac.in:8443/jspui/handle/123456789/3215
dc.descriptionDebadutta Kumar Panda, Economics and Strategy, Institute of Management Technology, Survey No. 38, Cherlaguda Village, Shamshabad Mandal, R.R. District, Hyderabad, India
dc.descriptionp.227-238
dc.descriptionIssue Editor – R. Rajesh Babu
dc.description.abstractA 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.
dc.publisherIndian Institute of Management Calcutta, Kolkata
dc.relation.ispartofseriesVol.44;No.3
dc.subjectImpact assessment
dc.subjectGroup-lending
dc.subjectMicrofinance project
dc.subjectControl group
dc.subjectSelf-selection bias
dc.titleImpact assessment of group-based credit–lending projects with controlled project placement bias and self-selection bias
dc.typeArticle
Appears in Collections:Issue 3, September 2017

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