The CEQ Institute at Tulane University works to reduce inequality and poverty through rigorous tax and benefit incidence analysis and active engagement with the policy community. The studies published in the CEQ Working Paper series are pre-publication versions of peer-reviewed or scholarly articles, book chapters, and reports produced by the Institute. The papers mainly include empirical studies based on the CEQ methodology and theoretical analysis of the impact of fiscal policy on poverty and inequality. The content of the papers published in this series is entirely the responsibility of the author or authors. Although all the results of empirical studies are reviewed according to the protocol of quality control established by the CEQ Institute, the papers are not subject to a formal arbitration process. The CEQ Working Paper series is possible thanks to the generous support of the Bill & Melinda Gates Foundation. For more information, visit www.commitmentoequity.org.The CEQ logo is a stylized graphical representation of a Lorenz curve for a fairly unequal distribution of income (the bottom part of the C, below the diagonal) and a concentration curve for a very progressive transfer (the top part of the C).
ABSTRACTThis paper presents a survey of causes and correction approaches to address the "missing rich" problem in household surveys. "Missing rich" here is a catch-all term for the issues that affect the upper tail of the distribution of income: undercoverage, sparseness, unit and item nonresponse, underreporting and top coding. Upper tail issues can result in serious biases and imprecision of survey-based inequality measures. A number of correction approaches have been proposed. A main distinction is between those that rely on within-survey methods and those that combine survey data with information from external sources such as tax records, National Accounts, rich lists or other external information. Within each category, the methods can correct by replacing top incomes or increasing their weight (reweighting). Correction methods can be nonparametric and parametric. This survey aims to help researchers choose appropriate correction strategies and design robustness tests.