Misreporting of Government Transfers: How Important Are Survey Design and Geography?

Abstract

Recent studies linking household surveys to administrative records reveal high rates of misreporting of program receipt. We use the FoodAPS survey to examine whether the findings of these studies of general household surveys using one or two states generalize to a survey with a narrow focus and across many states. First, we study how reporting errors differ from other surveys. We find a lower rate of false negatives (failures to report true receipt) in FoodAPS, likely partly due to the shorter recall period of FoodAPS. Misreporting varies with household characteristics and between interviewers. Second, we examine geographic heterogeneity in survey error to assess whether we can extrapolate from linked data from a few states. We find systematic differences between states in unconditional error rates but no evidence of substantial differences conditional on common covariates. Thus, extrapolating error rates across states may yield more accurate receipt estimates than uncorrected survey estimates.

The Effect of SNAP and School Food Programs on Food Security, Diet Quality, and Food Spending: Sensitivity to Program Reporting Error

“…FP are rates of false positives and FN are rates of false negatives, all in percentages. CDT refers to Courtemanche et al (2017) and MM refers to Meyer and Mittag (2017). Oster (2017) are reported.…”

“…Two other studies, Courtemanche et al (2017) (henceforth CDT) and Meyer and Mittag (2017) (henceforth MM), also study the degree of misreporting in the FoodAPS data set. There are two reasons for differences in our estimated false negative and false positive rates with theirs.…”

Food Access, Program Participation, and Health: Research Using FoodAPS

“…Meyer and Mittag () consider how important survey design was here in minimizing misreporting, concluding that the relatively short recall period probably helped. They also find household characteristics, such as income, employment status, and minority status, all predict misreporting.…”

“…This article speaks to the ongoing debate about whether administrative data should always be the gold standard for correcting measurement error bias. Meyer and Mittag (2019) consider how important survey design was here in minimizing misreporting, concluding that the relatively short recall period probably helped. They also find household characteristics, such as income, employment status, and minority status, all predict misreporting.…”

Estimating the Associations between SNAP and Food Insecurity, Obesity, and Food Purchases with Imperfect Administrative Measures of Participation

“…In this case, they show that the OLS estimator yields the wrong sign if misreporting is endogenous, with the size of the sign‐switching region increasing with the rate of false negatives and decreasing with the true participation rate . Meyer and Mittag () show that the likelihood of misreporting is systematically related to observable characteristics such as income, employment, and geography, suggesting that it is likely related to unobservable characteristics as well. Most researchers using survey data to study SNAP do not account for the possibility of nonclassical measurement error and the few that do so make assumptions akin to random misreporting.…”