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A Bayesian Spatial Propensity Score Matching Evaluation of the Regional Impact of Micro-finance

Author

Listed:
  • Rolando Gonzales

    (Bayesian Institute for Research & Development)

  • Patricia Aranda

    (Bayesian Institute for Research & Development)

  • Joel Mendizabal

Abstract

"A Bayesian Spatial-Propensity Score Matching estimator is proposed to measure the regional impact of microfinance on poverty reduction and women's empowerment. The impact of microfinance in Bolivia was tested with this estimator, using census and household survey data. The results suggest that microfinance was useful for poverty reduction and women’s empowerment at municipality level in Bolivia."

Suggested Citation

  • Rolando Gonzales & Patricia Aranda & Joel Mendizabal, 2017. "A Bayesian Spatial Propensity Score Matching Evaluation of the Regional Impact of Micro-finance," Review of Economic Analysis, Digital Initiatives at the University of Waterloo Library, vol. 9(2), pages 127-153, December.
    • Handle: RePEc:ren:journl:v:9:y:2017:i:2:p:127-153
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    Citations

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    Cited by:

    1. Wei Yang & Le Wang, 2023. "Impact of farmer group participation on the adoption of sustainable farming practices—spatial analysis of New Zealand dairy farmers," Annals of Public and Cooperative Economics, Wiley Blackwell, vol. 94(3), pages 701-717, September.

    More about this item

    Keywords

    Bayesian methods; microfinance; spatial statistics; matching estimators;
    All these keywords.

    JEL classification:

    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models

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