Six Randomized Evaluations of Microcredit: Introduction and Further Steps †

By Abhijit Banerjee, Dean S. Karlan, Jonathan Zinman. 2015

860 citations recorded by the source corpus.

Causal evidence on microcredit impacts informs theory, practice, and debates about its effectiveness as a development tool. The six randomized evaluations in this volume use a variety of sampling, data collection, experimental design, and econometric strategies to identify causal effects of expanded access to microcredit on bor rowers and/or communities. These methods are deployed across an impressive range of locations—six countries on four continents, urban and rural areas—borrower characteristics, loan character istics, and lender characteristics. Summarizing and interpreting results across studies, we note a consistent pattern of modestly positive, but not transformative, effects. We also discuss directions for future research. (JEL D14, G21, I38, O15, O16, P34, P36)

Subfields: Economics and Econometrics, Management Information Systems, Urban Studies

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Citations in the corpus

Micro-entrepreneurship and subjective well-being: Evidence from rural Bangladesh (2019, background): “An influential multicountry study of the effects of microcredit on socio-economic outcomes such as income, consumption, child schooling and female empowerment, reports very modest positive effects at best (Banerjee et al., 2015).”
Impact of Village Savings and Loan Associations: Evidence from a cluster randomized trial (2016, methodology): “…ﬁ nance for which several randomized impact studies have been made showing limited effects (see Banerjee (2013) for a review of micro ﬁ nance, and Banerjee et al. (2015c) for a review of microcredit), rigorous impact evaluations of VSLAs that address endogenous program placement and selection…”
Generating Employment in Poor and Fragile States: Evidence from Labor Market and Entrepreneurship Programs (2015, background): “43See Banerjee et al. (2015) and Bandiera et al. (2013).”
A theory of financial inclusion and income inequality (2020, background): “…the income of poor and low-income households is consistently and significantly more positive than that of (micro-) credit (Brune et al. 2011; Dupas and Robinson2013 ; Karlan, Ratan, and Zinman 2014; Banerjee, Karlan, and Zinman 2015; Banerjee et al.2015 ; Demirguc-Kunt, Klapper, and Singer 2017).”
The impact of microcredit on agricultural technology adoption and productivity: Evidence from randomized control trial in Tanzania (2020, methodology): “…of microcredit by conducting randomized control trials (RCT) (Angelucci et al. 2015; Attanasio et al. 2015; Ausburg 2015; Banerjee et al. 2015 a; Banerjee et al. 2015 b; Crépon et al. 2015; Karlan and Zinman 2009; 2010; 2011; 2018; Tarozzi et al. 2015) and by quasi-experimental methods (Kaboski…”
Fintech, financial inclusion and income inequality: a quantile regression approach (2020, background): “Theory suggests that financial market imperfections – such as information asymmetries and transaction costs – prevent poor people from escaping poverty by limiting their access to formal financial services (Banerjee and Newman 1993; Galor and Zeira 1993; World Bank 2014).”
Changes in Social Network Structure in Response to Exposure to Formal Credit Markets (2021, neutral): “…level outcomes such as consumption, investment or business proﬁt (Angelucci et al ., 2015; Attanasio et al ., 2015; Augsburg et al ., 2015; Banerjee et al ., 2015b, 2015a; Cr´epon et al ., 2015; Tarozzi et al ., 2015) (see Meager, 2015 for a meta-analysis), but also with our ﬁndings in…”