Panel Paper: Poverty in the U.S. Using the Comprehensive Income Dataset

Friday, November 8, 2019
Plaza Building: Concourse Level, Plaza Court 4 (Sheraton Denver Downtown)

*Names in bold indicate Presenter

Bruce Meyer1, Derek Wu1 and Carla Medalia2, (1)University of Chicago, (2)U.S. Census Bureau

We examine poverty in the U.S. addressing income under-reporting by using groundbreaking data linking nearly all transfers and tax credits to two major household surveys.

This paper provides new estimates of poverty in the United States using a groundbreaking set of linked survey and administrative data, which is part of a larger project at the Census Bureau to develop a Comprehensive Income Dataset. The administrative data cover earnings and asset income from Internal Revenue Service tax records and transfer income for a myriad of safety net programs including Social Security, Supplemental Security Income, Supplemental Nutrition Assistance Program, Unemployment Insurance, Veterans’ Benefits, Public Assistance, housing assistance, Medicare, Medicaid, Special Supplemental Nutrition Program for Women, Infants, and Children, and energy assistance. We link these data to the Current Population Survey, the source of official poverty and inequality statistics and the Survey of Income and Program Participation, the most comprehensive survey of income sources in the U.S. Linking the administrative data to the surveys is vital given that a large and rising share of benefits and other income sources is not recorded in the surveys. Using these linked data, we examine the extent to which misreporting of various survey income sources biases the reported poverty status of households. We document how our knowledge of the demographics of poverty is changed by the improved data. We also improve measurement of the resources of the low-income population, showing how these estimates diverge from those calculated using the survey data alone. These results will provide a prototype for the combination of survey, program and tax data that will improve poverty and income measurement.