Suchergebnisse

Accurately measuring government benefit receipt in household surveys is necessary when studying disadvantaged populations and the programs that serve them. The Food Stamp Program is especially important given its size and recent growth. To validate survey reports, we use administrative data on participation in two states linked to the American Community Survey (ACS), the Current Population Survey (CPS), and the Survey of Income and Program Participation (SIPP). We find that 23 percent of true food stamp recipient households do not report receipt in the SIPP, 35 percent in the ACS, and fully 50 percent in the CPS. A substantial number of true non-recipients are also recorded as recipients, especially in the SIPP. We examine reasons for these errors including imputation, an important source of error. Both false negative and false positive reports vary with household characteristics, implying complicated biases in multivariate analyses, such as regressions. We then directly examine biases in common survey-based estimates of program receipt by comparing them to estimates from our combined administrative and survey data. We find that the survey estimates understate participation among single parents, non-whites, and low-income households, and also lead to errors in multiple program receipt, and time and age patterns of receipt.

AbstractRecent research linking administrative to survey data has laid the groundwork for improvements in survey data products. However, the opportunities have not been fully realized yet. In this article, our main objective is to use administrative-survey linked microdata to demonstrate the potential of data linkage to reduce survey error through model-based blended imputation methods. We use parametric models based on the linked data to create imputed values of Medicaid enrollment and food stamp (SNAP) receipt. This approach to blending data from surveys and administrative data through models is less likely to compromise confidentiality or violate the terms of the data sharing agreements among the agencies than releasing the linked microdata, and we demonstrate that it can yield substantial improvements of estimate accuracy. Using the blended imputation approach reduces root mean squared error (RMSE) of estimates by 81 percent for state-level Medicaid enrollment and by 93 percent for substate area SNAP receipt compared with estimates based on the survey data alone. Given the high level of measurement error associated with these important programs in the United States, data producers should consider blended imputation methods like the ones we describe in this article to create improved estimates for policy research.

BackgroundIncome is one of the most important measures of well-being, but it is notoriously difficult to measure accurately. In the United States, income data are available from surveys, tax records, and government programs, but each of these sources has important strengths and major limitations when used alone.
ObjectivesWe link multiple data sources to develop the Comprehensive Income Dataset (CID), a prototype for a restricted micro-level dataset that combines the demographic detail of survey data with the accuracy of administrative measures.
MethodsBy incorporating information on nearly all taxable income, tax credits, and cash and in-kind government transfers, the CID surpasses previous efforts to provide an accurate and comprehensive measure of income for the population of United States individuals, families, and households. We also evaluate the accuracy of different income sources and imputation methods.
ConclusionsWhile still in development, we envision the CID enhancing Census Bureau surveys and statistics by investigating measurement error, improving imputation methods, and augmenting surveys with the best possible estimates of income. It can also be used for policy related research, such as forecasting and simulating changes in programs and taxes. Finally, the CID has substantial advantages over other sources to analyze numerous research topics, including poverty, inequality, mobility, and the distributional consequences of government transfers and taxes.

This is an edited volume reviewing the major means-tested social programs in the United States. Each author addresses a major program or area, reviewing each area's successes and recommending how to address shortcomings through policy change. In general, our means-tested programs do many things well, but some adjustments to each could make the system much more effective. This book provides policymakers with a broad overview of the issues at hand in each program and how to address them