Determinants of microcredit repayment in Portugal: analysis of borrowers, loans and business projects View Full Text


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Article Info

DATE

2018-11

AUTHORS

Jorge Mota, António Carrizo Moreira, Cristóvão Brandão

ABSTRACT

Microcredit programs in Portugal represent a unique case for studying the microcredit repayment determinants in a developed country, as it experienced a financial hardship in 2008–2009, with economic and social consequences that led to unemployment crisis. This research examined the determinants of microcredit loan repayment based on a sample of 752 microcredit loans granted in Portugal by the National Association for the Right to Credit, adopting individual lending mechanisms and granting loans through partnerships with several credit institutions. This is the first study to ascertain the influence that a set of factors – grouped into three categories: borrowers’ individual characteristics; loan characteristics; and characteristics of business projects implemented by borrowers – has on the repayment ability of microcredit programs, in a developed country of the Eurozone. Moreover, this is the first study using an ordered logistic regression (OLR) in estimating the determinants of microcredit loan repayment. Similar to previous studies, married borrowers tend to repay loans faster as they tend to be more responsible than single borrowers. Nationality seems to be an issue as foreigners tends to default the repayment loans. Finally, those involved in manufacturing activities perform better than those involved in service activities in repaying their loans. This clearly indicates that in developed countries special attention needs to be provided to minority groups as well as market/supply conditions, which are not normally considered in less favored economic countries. More... »

PAGES

141-171

References to SciGraph publications

Journal

TITLE

Portuguese Economic Journal

ISSUE

3

VOLUME

17

Author Affiliations

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s10258-018-0148-2

DOI

http://dx.doi.org/10.1007/s10258-018-0148-2

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1106705713


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