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Prioritizing policies for pro-poor growth: applying bayesian model averaging to Vietnam

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Author Info
Klump, Rainer
Pruefer, Patricia (Tilburg University, Center for Economic Research)

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Abstract

Pro-Poor Growth (PPG) is the vision of combining high growth rates with poverty reduction. Due to the myriad of possible determinants of growth and poverty a unique theoretical model for guiding empirical work on PPG is absent, though. Bayesian Model Averaging is a statistically robust framework for this purpose. It addresses the existent parameter and model uncertainty by not choosing a single model but averaging over all possible ones. Using data for the 61 Vietnamese provinces we are able to ascertain a prioritization of all used determinants of poverty, growth and of PPG of our large set of explanatory variables.

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Paper provided by Tilburg University, Center for Economic Research in its series Discussion Paper with number 117.

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Date of creation: 2006
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Handle: RePEc:dgr:kubcen:2006117

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Find related papers by JEL classification:
C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Bayesian Analysis
C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation and Testing
R11 - Urban, Rural, and Regional Economics - - General Regional Economics - - - Analysis of Growth, Development, and Changes

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