Economics of SNAP Using Administrative Data
Sunday, Jan. 6, 2019 10:15 AM - 12:15 PM
- Chair: Ilya Rahkovsky, USDA Economic Research Service
The Impact of SNAP Work Requirements
AbstractOur paper examines the impact of SNAP work requirements on the labor supply of participants and on overall participation in SNAP. We perform a regression discontinuity analysis of the impact of work requirements for able bodied adults without dependents (ABAWDs) on labor supply and participation, exploiting the fact that the work requirement applies only to individuals under 50 years old. Using a novel dataset containing ABAWD work requirement waiver information merged with SNAP administrative records and the American Community Survey (ACS) data, we find the work requirements have a close to null impact on labor force participation and the number of hours worked, but do find that the work requirements reduce participation in SNAP. There is some evidence that those with worse job prospects are especially less likely to participate in SNAP as a result of the work requirements. We find little evidence that childless adults respond to the work requirements by claiming disability.
The Implications of Misreporting for Longitudinal Studies of SNAP
AbstractResearchers studying a variety of important economics, nutrition, and health topics use survey data containing information on SNAP participation. In order to study the dynamics of SNAP participation or recognizing possible selection bias in cross-sectional estimators, many researchers use longitudinal estimators to estimate the causal effects of SNAP. However, misreporting of SNAP participation is common in survey datasets, and bias from misreporting can be larger for longitudinal estimators. In an analysis of data combining newly compiled administrative datasets on SNAP participation from nine states and covering the years 2005-2015 with individual records from the CPS ASEC survey, we confirm findings in previous studies of substantial misreporting and find evidence that the misreporting is not done at random. Additionally, we examine bias caused by misreporting in a longitudinal estimators and find severe bias, much greater in magnitude than bias caused by misreporting in cross-sectional estimators. We find that a longitudinal conditional distribution estimator may be an attractive solution for researchers using public use survey datasets.
Precision in Measurement: Using SNAP Administrative Records to Evaluate Poverty Measurement
AbstractPolicy leaders today look to quality data and statistics to help inform and guide programmatic decisions. As a result, assessing the quality and validity of major household surveys in capturing accurate program participation is essential. One method for evaluating survey quality is to compare self-reported program participation in surveys to administrative records from the program itself. In this paper, we are interested in understanding two issues. First, how closely do Supplemental Nutrition Assistance Program (SNAP) participation and benefit amounts align between self-reported survey responses and other source data on program participation? Second, how does replacing household survey self-reported SNAP values with alternative source records for SNAP change poverty measurement in the Supplemental Poverty Measure (SPM)? We find that 46 percent of SNAP recipients (according to administrative records) do not report receipt in self-reported survey responses and 36 percent of SNAP recipients are not estimated to receive benefits in a microsimulation model. This results in a SPM rate that is 0.4 percentage points lower when state SNAP administrative records are used instead of survey self-reported SNAP receipt and 0.9 percentage points lower when estimates from a microsimulation model are used instead of survey self-reported SNAP receipt.
University of Kentucky
University of Georgia
USDA Economic Research Service
USDA Economic Research Service
- D0 - General
- I0 - General