Measurement Error in Survey Incomes Over Four Decades

• In Event: Inequality and Measurement

Abstract

Survey data increasingly miss dollars for major income sources (Meyer et al. 2015), threatening their reliability as a foundation for research and policy. Previous studies have gone beyond comparisons of aggregates to document measurement error at the individual level, tending to focus on single income sources like retirement income (Bee and Rothbaum 2017) or SNAP (Meyer et al. 2022) over a limited set of states and/or years.

We provide a comprehensive assessment of how individual-level measurement error has changed over time, using data for twelve income sources spanning nearly four decades. We link the Current Population Survey Annual Social Economic Supplement (CPS) from 1984 to 2022 to administrative records for earnings (IRS and SSA), interest and dividends (IRS), retirement income (IRS), AFDC/TANF (HHS and state agencies), Unemployment Insurance (IRS), Social Security (SSA), Supplemental Security Income (SSA), veterans’ disability compensation (DVA), SNAP (state agencies), public and subsidized housing (HUD), Medicaid (CMS), and Medicare (CMS).

For each income source and year, we quantify three key dimensions of measurement error: false negatives (recipients not reporting receipt), false positives (non-recipients erroneously reporting receipt), and dollar misreporting among true reporting recipients. We show how each component contributes to total dollar underreporting and examine which subgroups experience the largest changes in underreporting.

These estimates have major implications for assessing how income-based measures of poverty, inequality, and program effectiveness have changed over time. They also form the basis for imputation models to correct for misreporting in survey data, which we plan to share with the research community.

Authors

• Bruce Meyer, University of Chicago

• Nikolas Mittag, Charles University

• Derek Wu, University of Virginia