Monday, December 3, 2018
Bruce D. Meyer (Chicago) presents The Use and Misuse of Income Data and the Rarity of Extreme Poverty in the United States (with Victoria Mooers (Chicago) & Derek Wu (Chicago)) at UC-Berkeley today as part of its Robert D. Burch Center for Tax Policy and Public Finance Seminar Series:
Recent research suggests that rates of extreme poverty, defined as living on less than either $2 or $4 per-person per-day, are high and rising in the United States. Meyer re-examines the rate of extreme poverty using the Survey of Income and Program Participation (SIPP), generally thought to have the most accurate survey income data in the U.S. In addition to income, the SIPP provides information on hours worked, assets, hardships, and other household characteristics. He links these data to IRS tax records and administrative program data on the Supplemental Nutrition Assistance Program (SNAP), public and subsidized housing benefits, Supplemental Security Income (SSI), and Old Age, Survivors, and Disability Insurance (OASDI). He finds that more than 90% of the 3.6 million households with survey-reported cash income below $2/person/day are misclassified once we account for in-kind transfers, errors in earnings reports, errors in transfer reports, and substantial assets. Several of the largest misclassified groups appear to be at least middle class based on material hardship, housing characteristics, tax data, and other variables. More than two-thirds of all misclassified households are initially categorized as extreme poor due to errors in cash reports of earnings, asset income, and retirement income. Of the households remaining in extreme poverty, 90% consist of a single individual. An implication of the low recent level of extreme poverty is that it cannot have risen substantially over time or due to welfare reform.