Prioritizing Policies for Pro-Poor Growth : Applying Bayesian Model Averaging to Vietnam
Year: 2006
Type: preprint
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.
Authors: Rainer Klump, Patricia Prüfer
Institution: Tilburg University
Language: English
Subfield: Sociology and Political Science
Field: Social Sciences
Domain: Social Sciences
SDG: No poverty
Open Access status: green
OpenAlex ID: W1532042443