Poverty comparison and household survey design

Poverty comparison - an increasingly important starting-point for welfare analysis - are almost based on household surveys. They therefore require that one be able to distinguish underlying differences in the populations being compared from sampling variation: standard errors must be calculated. So far, this has largely been done on the assumption that the household surveys are simple random samples. But household surveys are more complex than this. We show that taking into account sampling design has a major effect on standard errors for well-known poverty measures: they can 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 at all of sampling design.