Using Census and Survey Data to Estimate Poverty and Inequality for Small Areas
Alessandro Tarozzi, Duke University
Angus Deaton, Princeton University
Recent years have seen a widespread use of small-area "poverty maps" based on census data enriched by relationships estimated from household surveys that predict variables not covered by the census. These methods are used to estimate putatively precise estimates of poverty and inequality for areas as small as 20,000 households. In this paper we argue that to usefully match survey and census data in this way requires a degree of spatial homogeneity for which the method provides no basis, and which is unlikely to be satisfied in practice. Error variances calculated on the assumption of homogeneity will underestimate mean squared errors and overestimate the coverage of calculated confidence intervals. We use data from the 2000 census of Mexico to construct synthetic "household surveys" and to simulate the poverty mapping process using a robust method of estimation; our simulations show that while the poverty maps contain useful information, their nominal confidence intervals give a misleading idea of precision.