Assessing Alternative Poverty Proxy Methods in Rural Vietnam

This paper compares and contrasts the use of four “short-cut” methods for identifying poor households: the poverty probability method; ordinary least squares regressions; principal components analysis; and quantile regressions. After evaluating these four methods using two alternative criteria (total and balanced poverty accuracy) and representative household survey data from rural Vietnam, it is concluded that the poverty probability method—which can correctly identify around four-fifths of poor and non-poor households—is the most accurate “short-cut” method for measuring poverty for specific subpopulations, or in years when household surveys are not available. The performance of the poverty probability method was then tested with different poverty lines and using an alternative household survey, and found to be robust.