Survey nonresponse and the distribution of income

Abstract

income distribution, poverty and inequality measurement, survey nonresponse,

Top Incomes and Inequality Measurement: A Comparative Analysis of Correction Methods Using the EU SILC Data

“…This approach is inadequate, as it accounts only for inter-regional differences in non-response rates, and not for systematic differences in response probability across units within individual regions. Mistiaen and Ravallion (2003), and Korinek et al (2006Korinek et al ( , 2007 proposed a probabilistic model that uses information on non-response rates across geographic regions as well as information about the distribution within regions. They estimated the response probability of each household, and used the inverse of this estimate to adjust each household's weight.…”

“…One, the logistic function is well-accepted as a robust form to model probabilistic relations. Two, Korinek et al (2006Korinek et al ( , 2007, and Hlasny and Verme (2018) have evaluated alternative forms of g(x ij ,θ) including non-monotonic functions on US and Egyptian data, and have concluded that some of the most parsimonious functions provide very good fit, compared to both uncorrected income distributions and compared to external information on the true degree of inequality in those countries. Three, nonlinear forms of P(x ij ,θ) and g(x ij ,θ) allow for response differences between poorer and richer households in a realistic way.…”

“…For the United States CPS, Korinek et al (2006Korinek et al ( , 2007 used state-level aggregation, because geographic identifiers are consistently reported only at that level whereas county or metropolitan statistical area identifiers are missing for some responding as well as non-responding households. Hlasny and Verme (2017) considered various degrees of geographic aggregation, from the level of 185 metropolitan statistical areas (MSAs) to that of 7 census divisions.…”

“…On the margins, we will report how the addition of regional indicators affects the correction for the unit non-response bias. 4 For the covariates in x ij , Korinek et al (2006Korinek et al ( , 2007 evaluated several variables affecting households' response probability, including income, gender, race, age, education, employment status, household size and an urban-rural indicator. Hlasny and Verme (2018) compared income and expenditures, and indicators for survey rounds.…”

“…The first method, which we call reweighting, attempts to correct the sampling weights of existing observations using information on unit or item non-response rates across demographic cells such as geographical areas (Mistiaen and Ravallion 2003;Korinek et al 2006Korinek et al , 2007. The approach exploits the relationship between response rates and shapes of income distributions across national regions to estimate the gradient of households' response probability by income level.…”