Does sample design matter for poverty rate comparisons?

Poverty comparisons - an increasingly important starting-point for welfare policy analysis - are almost always based on household surveys. Therefore they require that one be able to distinguish underlying differences in the populations being compared from sampling variation: standard errors must be calculated. This has typically been done assuming that the household surveys are simple random samples. However, household surveys are more complex than this. We show that taking into account sampling design has a major effect on estimated standard errors for well-known poverty measures. In our samples they increase by around one-half. We also show that making only a partial correction for sample design (taking into account clustering, but not stratification, whether explicit or implicit) can be as misleading as not taking any account of sampling design at all.