This paper compares and contrasts the use of four "short-cut" methods for identifying poor households: (i) the poverty probability method; (ii) OLS regressions; (iii) principal components analysis; and, (iv) quantile regressions. | Assessing alternative poverty proxy methods in rural Vietnam Linh Vu and Bob Baulch* Abstract This paper compares and contrasts the use of four ‗short-cut‘ methods for identifying poor households: (i) the poverty probability method; (ii) OLS regressions; (iii) principal components analysis; and, (iv) quantile regressions. After evaluating these four methods using two alternative criteria (total and balanced poverty accuracy) and representative household survey data from rural Vietnam, we conclude 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 sub-populations, or in years when household surveys are not available. We then test the performance of the poverty probability method with different poverty lines and using an alternative household survey, and find it to be robust. * Assistant Professor, University of Economics and Business, Vietnam National University, Ha Noi and Lead Economist, Prosperity Initiative CIC, Hanoi. The authors thank John Marsh and an anonymous reviewer for helpful comments on an earlier version of this paper. 1 I. Introduction In most developing countries, it is only feasible to conduct detailed household surveys every few years using relatively small samples of households. The results of these surveys can usually only be disaggregated to the regional or provincial level, and cannot be disaggregated for many population groups that are of interest to policy makers (for example, specific occupations or ethnic groupings). However, government and donor agencies often require that poverty should be monitored on an annual basis for specific administrative or project areas, or require that projects demonstrate their impact on specific groups or occupations. Poverty measurement using household surveys is also difficult, expensive and time consuming, requiring that detailed information is collected on .
