Survey compliance and the distribution of income

While it is improbable that households with different incomes are equally likely to participate in sample surveys, the lack of data for nonrespondents has hindered efforts to correct for the bias in measures of poverty and inequality. The authors demonstrate how the latent income effect on survey compliance can be estimated using readily available data on response rates across geographic areas. An application using the Current Population Survey for the United States indicates that compliance falls as income rises. Correcting for selective compliance appreciably increases mean income and inequality, but has only a small impact on poverty incidence up to commonly used poverty lines in the United States.Services&Transfers to Poor,Economic Theory&Research,Poverty Impact Evaluation,Poverty Monitoring&Analysis,Health Economics&Finance,Governance Indicators,Poverty Monitoring&Analysis,Safety Nets and Transfers,Inequality,Economic Theory&Research

An econometric method of correcting for unit nonresponse bias in surveys

Past approaches to correcting for unit nonresponse in sample surveys by re-weighting the data assume that the problem is ignorable within arbitrary subgroups of the population. Theory and evidence suggest that this assumption is unlikely to hold, and that household characteristics such as income systematically affect survey compliance. The authors show that this leaves a bias in the re-weighted data and they propose a method of correcting for this bias. The geographic structure of nonresponse rates allows them to identify a micro compliancefunction, which they then use to re-weight the unit-record data. An example is given for the U.S. Current Population Surveys, 1998-2004. The authors find, and correct for, a strong household income effect on response probabilities.Statistical&Mathematical Sciences,Economic Theory&Research,Scientific Research&Science Parks,Science Education,Environmental Economics&Policies

Re-interpreting sub-group inequality decompositions

The authors propose a modification to the conventional approach of decomposing income inequality by population sub-groups. Specifically, they propose a measure that evaluates observed between-group inequality against a benchmark of maximum between-group inequality that can be attained when the number and relative sizes of groups under examination are fixed. The authors argue that such a modification can provide a complementary perspective on the question of whether a particular population breakdown is salient to an assessment of inequality in a country. As their measure normalizes between-group inequality by the number and relative sizes of groups, it is also less subject to problems of comparability across different settings. The authors show that for a large set of countries their assessment of the importance of group differences typically increases substantially on the basis of this approach. The ranking of countries (or different population groups) can also differ from that obtained using traditional decomposition methods. Finally, they observe an interesting pattern of higher levels of overall inequality in countries where their measure finds higher between-group contributions.Inequality,Poverty Impact Evaluation,Governance Indicators,Rural Poverty Reduction,Services&Transfers to Poor

Survey Compliance and the Distribution of Income

While it is improbable that households with different incomes are equally likely to participate in sample surveys, the lack of data for non- respondents has hindered efforts to correct for the bias in measures of poverty and inequality. We demonstrate how the latent income effect on survey compliance can be estimated using readily-available data on response rates across geographic areas. An application using the Current Population Survey for the U.S. indicates that compliance falls as income rises. Correcting for selective compliance appreciably increases mean income and inequality, but has only a small impact on poverty incidence up to commonly-used poverty lines in the U.S

An Econometric Method of Correcting for Unit Nonresponse Bias in Surveys

Past approaches to correcting for unit nonresponse in sample surveys by re-weighting the data assume that the problem is ignorable within arbitrary subgroups of the population. Theory and evidence suggest that this assumption is unlikely to hold, and that household characteristics such as income systematically affect survey compliance. The authors show that this leaves a bias in the re-weighted data and they propose a method of correcting for this bias. The geographic structure of nonresponse rates allows them to identify a micro compliance function, which they then use to re-weight the unit-record data. An example is given for the U.S. Current Population Surveys, 1998-2004. The authors find, and correct for, a strong household income effect on response probabilities