Survey error is known to be pervasive and to bias even simple, but important estimates of means, rates, and totals, such as the poverty and the unemployment rate. To summarize and analyze the extent, sources, and consequences of survey error, we define empirical counterparts of key components of...

We examine the consequences of underreporting of transfer programs in household survey data for several prototypical analyses of low-income populations. We focus on the Current Population Survey (CPS), the source of official poverty and inequality statistics, but provide evidence that our...

We examine the consequences of underreporting of transfer programs for prototypical analyses of low-income populations using the Current Population Survey (CPS), the source of official poverty and inequality statistics. We link administrative data for food stamps, TANF, General Assistance, and...

Measurement errors are often a large source of bias in survey data. Lack of knowledge of the determinants of such errors makes it difficult for data producers to reduce the extent of errors and for data users to assess the validity of analyses using the data. We study the determinants of...

While measurement error in the dependent variable does not lead to bias in some well-known cases, with a binary dependent variable the bias can be pronounced. In binary choice, Hausman, Abrevaya and Scott-Morton (1998) show that the marginal effects in the observed data differ from the true ones...

We document the extent, nature, and consequences of survey errors for receipt of cash welfare and SNAP in three major U.S. household surveys linked to administrative program records. Our results confirm high rates of misreporting of program receipt, particularly failure to report receipt. The...

We document the extent, nature, and consequences of survey errors in cash welfare and SNAP receipt in three major U.S. household surveys. We find high rates of misreporting, particularly failure to report receipt. The surveys inaccurately capture patterns of multiple program participation, even...

Stigma of welfare participation is important for policy and survey design, because it deters program take-up and increases misreporting. Stigma is also relevant to the literature on social image concerns, yet empirical evidence is scant because stigma is difficult to empirically identify. We use...

Measurement errors are often a large source of bias in survey data. Lack of knowledge of the determinants of such errors makes it difficult for data producers to reduce the extent of errors and for data users to assess the validity of analyses using the data. We study the determinants of...