Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/19820
Authors: 
Klump, Rainer
Prüfer, Patricia
Year of Publication: 
2005
Series/Report no.: 
Proceedings of the German Development Economics Conference, Kiel 2005 / Verein für Socialpolitik, Research Committee Development Economics 27
Abstract: 
The UN Millennium Development Goals have recognized poverty reduction as the main goal of global development policy. A comprehensive framework to evaluate the effectiveness of single policy measures and policy packages with respect to poverty reduction is still lacking, though. Policy evaluation is exposed to manifold uncertainties given the dependency of the preferred outcomes on a chosen policy, available information, and policy makers' preferences. We show that Bayesian Model Averaging (BMA) is most valuable in this context as it addresses the parameter and model uncertainty inherent in development policies. Using data for the 61 Vietnamese provinces we are able to ascertain the most important determinants of poverty from a large number of potential explanatory variables.
Subjects: 
Poverty determinants
Vietnam
model uncertainty
Bayesian Model Averaging (BMA)
JEL: 
R11
C11
C52
O18
O53
Document Type: 
Conference Paper

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