|
on Microfinance |
| By: | Rolando Gonzales Martínez (Instituto Bayesiano para la Investigacion & Desarrollo); Patricia Aranda Blanco (Instituto Bayesiano para la Investigacion & Desarrollo) |
| Abstract: | Gonzales et al. (2017) developed a quasi-experimental Bayesian spatial model to assess the social impact of microcredit at municipal level in Bolivia and found that microfinance had a positive impact on poverty reduction and empowerment of women in this country. Furthermore, the paper discusses the findings within the context of the guidelines of Bolivia’s Financial Services Law and Patriotic Agenda 2025. |
| Keywords: | Bayesian Methods, Microcredit, Spatial Statistics, Matching |
| JEL: | C11 C31 G21 |
| Date: | 2017–12 |
| URL: | http://d.repec.org/n?u=RePEc:efp:wpaper:2017-1&r=all |
| By: | Abdelkrim Araar; Yesuf Mohammednur Awel; Jonse Bane Boka; Hiywot Menker; Ajebush Shafi; Eleni Yitbarek; Mulatu Zerihun |
| Abstract: | This study evaluates the impact of business-development-support programs (credit, training, and a combination of both) on the performance of micro- and small enterprises (MSEs) in Ethiopia. Using 2015 Ethiopian urban survey data and employing endogenous-switching regressions for multiple treatments, we document a positive and significant effect of credit, training, and a combination of training and credit on MSEs. Our results highlight the heterogeneity in treatment effects between women- and men-owned MSEs: women-owned businesses do not benefit from access to treatments. Our results suggest that improving the performance of MSEs requires fine-tuned interventions that meet the specific needs of men and women who own small businesses rather than one-size-fits-all programs. |
| Keywords: | Treatment effects, MSEs, Ethiopia |
| JEL: | C31 J16 M21 |
| Date: | 2019 |
| URL: | http://d.repec.org/n?u=RePEc:lvl:pmmacr:2019-13&r=all |
| By: | Davide Viviano |
| Abstract: | The empirical analysis of experiments and quasi-experiments often seeks to determine the optimal allocation of treatments that maximizes social welfare. In the presence of interference, spillover effects lead to a new formulation of the statistical treatment choice problem. This paper develops a novel method to construct individual-specific optimal treatment allocation rules under network interference. Several features make the proposed methodology particularly appealing for applications: we construct targeting rules that depend on an arbitrary set of individual, neighbors' and network characteristics, and we allow for general constraints on the policy function; we consider heterogeneous direct and spillover effects, arbitrary, possibly non-linear, regression models, and we propose estimators that are robust to model misspecification; the method flexibly accommodates for cases where researchers only observe local information of the network. From a theoretical perspective, we establish the first set of guarantees on the utilitarian regret under interference, and we show that it achieves the min-max optimal rate in scenarios of practical and theoretical interest. We discuss the empirical performance in simulations and we illustrate our method by investigating the role of social networks in micro-finance decisions. |
| Date: | 2019–06 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1906.10258&r=all |