Correcting for Misreporting of Government Benefits

Mittag, Nikolas

doi:10.1257/pol.20160618

Cited by 40 publications

(55 citation statements)

References 34 publications

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“…Yet, for both conditional error rates, the average variance of the estimation error exceeds the variance of the estimates by a factor of two or more. 26 For researchers who want to obtain national estimates for the entire United States, this raises the question whether the loss of accuracy from using the error-prone survey data or the loss of accuracy from extrapolating across geography from a record linkage study in the presence of the heterogeneity we document is worse (Mittag 2019). To directly assess how this choice affects estimate accuracy and to further quantify the substantive importance of geographic heterogeneity, we conduct a leave-one-out extrapolation exercise.…”

Section: Geographic Heterogeneity In Survey Errormentioning

confidence: 99%

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

Meyer

Mittag

2019

Southern Economic Journal

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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.

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Section: Geographic Heterogeneity In Survey Errormentioning

confidence: 99%

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

Meyer

Mittag

2019

Southern Economic Journal

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“…A recent report by National Academies of Sciences, Engineering, and Medicine (2019) demonstrates that TRIM reduces baseline deep poverty (measured at half the SPM line) among children from 4.9 to 2.9%. Mittag (2019) finds that TRIM overadjusts for underreporting, suggesting that factor alone should move the "correct" TRIM estimate above 3%. Meyer et al (2019) use administrative data to adjust for benefit underreporting and mainly discuss extreme poverty (at $2 a day).…”

Section: Discussionmentioning

confidence: 99%

“…Some detailed studies on the underreporting of income were carried out in the US. Recent articles by Mittag (2019) and Meyer et al (2019) discuss these adjustments. One method of adjustment for underreporting comes from the Urban Institute's TRIM3 model (Wheaton 2008), which adjusts for the underreporting of some kinds of benefits.…”

Section: Discussionmentioning

confidence: 99%

Deep and Extreme Child Poverty in Rich and Poor Nations: Lessons from Atkinson for the Fight Against Child Poverty

Cai

Smeeding

2019

Ital Econ J

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The paper documents child poverty levels and trends using both relative ('deep') and absolute ('extreme') measures in two clusters: Anglo-Saxon high-income countries and upper middle-income countries. We also investigate the influence of different components of household income and other resources on child deep-poverty rates to examine the role of the market and the redistributive effects that materialize through private transfers, public benefits, and tax systems on generating poverty reduction. Overall, middle-income nations have witnessed continuous reductions in their extreme child poverty rates, while mild decreases or fluctuations have been observed in the five high-income nations, with the US highlighted by its relatively high rates of deep and extreme poverty regardless of absolute or relative measures and type of equivalence scale used. Private institutions play a larger role in poverty reduction in middle-income nations compared to its impact on developed nations. The degree of dependence on universal or assistance benefits varies among high-income nation. In the US, universal programs tend to be meager, while Australian social insurance and universal benefit are robust in their fight against deep poverty. Brazil stands out by its overwhelmingly large proportion of social insurance programs that contribute to improvements of its deep child poverty situation, and South Africa's assistance benefit system performs better in lifting children out of deep poverty.

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“…A growing literature documents the problems with relying on survey measures of program participation, which suffer from significant reporting error, when conducting impact evaluations (Meyer, Mok, and Sullivan 2015;Mittag 2016;Nguimkeu, Denteh, and Tchernis 2019). Administrative data are ordinarily assumed to be the "gold standard" to overcoming these econometric challenges, but relatively little evidence exists on the potential problems with administrative records or econometric strategies to address them.…”

Section: Introductionmentioning

confidence: 99%

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

Courtemanche¹,

Denteh

Tchernis

2019

Southern Economic Journal

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Administrative data are considered the “gold standard” when measuring program participation, but little evidence exists on their potential problems or implications for econometric estimates. We explore these issues using the FoodAPS, a unique data set containing two different administrative measures of Supplemental Nutrition Assistance Program (SNAP) participation and a survey‐based measure. We document substantial ambiguity in the two administrative measures and show that they disagree with each other almost as often as they disagree with self‐reported participation. Estimated participation and misreporting rates can be meaningfully sensitive to choices made to resolve this ambiguity and disagreement. We explore sensitivity in regression estimates of the associations between SNAP and food insecurity, obesity, and the healthy eating index. The signs are unchanged across the three measures, and the estimates are mostly not statistically different from each other. However, there are some meaningful differences in the magnitudes and levels of statistical significance of the estimates.

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Correcting for Misreporting of Government Benefits

Cited by 40 publications

References 34 publications

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

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

Deep and Extreme Child Poverty in Rich and Poor Nations: Lessons from Atkinson for the Fight Against Child Poverty

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

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