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 two alternative household surveys, and find it to be robust.