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on Microfinance |
| By: | Chowdhury, Shyamal (University of Sydney); Smits, Joeri (Harvard Kennedy School); Sun, Qigang (Yale University) |
| Abstract: | Do constraints to technology adoption vary by behavioral traits? We randomize 150 villages in Bangladesh into being offered standard microcredit, loans with a grace period, the choice between those two contracts, and control. No discernible average effects are detected on the adoption of mechanized irrigation, hybrid seeds, and chemical fertilizers. However, credit access enhances technology adoption among present-biased farmers, whose output and profits increase. These effects are driven by the standard contract and choice villages, as present-biased farmers select out of the grace period contract. This suggests offering commitment and screening applicants on present bias to enhance agricultural technology adoption. |
| Keywords: | microfinance, technology adoption, time inconsistency, Bangladesh |
| JEL: | O13 O33 Q14 Q16 |
| Date: | 2020–08 |
| URL: | http://d.repec.org/n?u=RePEc:iza:izadps:dp13590&r=all |
| By: | Wu, Benjian; Cui, Yi |
| Keywords: | Community/Rural/Urban Development, Agricultural Finance, Institutional and Behavioral Economics |
| Date: | 2020–07 |
| URL: | http://d.repec.org/n?u=RePEc:ags:aaea20:304649&r=all |
| By: | Ding, Zhao; Jiang, Yuansheng |
| Keywords: | Institutional and Behavioral Economics, Agricultural Finance, Research Methods/Statistical Methods |
| Date: | 2020–07 |
| URL: | http://d.repec.org/n?u=RePEc:ags:aaea20:304308&r=all |
| By: | Simplice A. Asongu (Yaounde, Cameroon); Nicholas Biekpe (Cape Town, South Africa); Danny Cassimon (University of Antwerp, Belgium) |
| Abstract: | The present research extends Lashitew, van Tulder and Liasse (2019, RP) in order to understand the greater diffusion of mobile money innovations in Africa. To make this assessment, a comparative analysis is engaged between sampled African countries and the corresponding sampled developing countries. Three main types of predictor groups are used for the study, namely: demand, supply and macro-level factors. The empirical evidence is based on Tobit regressions. The tested hypothesis is confirmed because from a comparative analysis between African-specific estimates and those of the sampled countries, not all factors driving mobile money innovations in Africa are apparent in the findings of Lashitew et al. (2019). An extended analysis is also performed to take on board the concern of multicollinearity from which, the best estimators from the study are derived. Comparative findings from correlation analysis show that an African specificity is largely traceable to the ‘unique mobile subscription rate’ variable. An in-depth empirical analysis further confirms an African specificity in the outcome variables (especially in the mobile used to send/receive money) which, may be traceable to informal sector variables not documented in Lashitew et al. (2019). Scholarly and policy implications are discussed. |
| Keywords: | Mobile money; technology diffusion; financial inclusion; inclusive innovation |
| JEL: | D10 D14 D31 D60 O30 |
| Date: | 2020–01 |
| URL: | http://d.repec.org/n?u=RePEc:abh:wpaper:20/032&r=all |