A Method of Correcting for Misreporting Applied to the Food Stamp Program

Mittag, Nikolas

doi:10.2139/ssrn.2272239

“…The false negative rates are 11.65% using ADMIN and 11.46% using ALERT, while the false positive rates are 8.39% using ADMIN and 7.83% using ALERT. Interestingly, for both participation measures, the prevalence of false negatives is substantially lower than previously reported by studies using more traditional survey data sets (Mittag, ; Meyer, Mittag, and George ). One possible explanation is that FoodAPS households were asked to consent to having their responses verified.…”

Section: Sensitivity Of Participation and Misreporting Ratescontrasting

confidence: 62%

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

Courtemanche¹,

Denteh

²

,

Tchernis

³

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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“…The evidence in the case of transfer programs suggests that simple imputation methods are unlikely to work. The assumptions these methods rely on are unlikely to hold, because program mismeasurement is non-classical and related to covariates (Mittag 2013, Meyer and Mittag 2014, Meyer, Goerge and Mittag 2015. A third approach is to link administrative records to improve data quality or even replace survey responses, which can address both the problem of misreporting and item non-response.…”

Section: Discussionmentioning

confidence: 99%

Using Linked Survey and Administrative Data to Better Measure Income: Implications for Poverty, Program Effectiveness and Holes in the Safety Net

Meyer

¹

,

Mittag

²

2015

Self Cite

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in ABSTRACTWe 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 qualitative conclusions are likely to apply to other surveys. We link administrative data for food stamps, TANF, General Assistance, and subsidized housing from New York State to the CPS at the individual level. Program receipt in the CPS is missed for over one-third of housing assistance recipients, 40 percent of food stamp recipients, and 60 percent of TANF and General Assistance recipients. Dollars of benefits are also undercounted for reporting recipients, particularly for TANF, General Assistance and housing assistance. We find that the survey data sharply understate the income of poor households, as conjectured in past work by one of the authors. Underreporting in the survey data also greatly understates the effects of anti-poverty programs and changes our understanding of program targeting, often making it seem that welfare programs are less targeted to both the very poorest and middle income households than they are. Using the combined data rather than survey data alone, the poverty reducing effect of all programs together is nearly doubled while the effect of housing assistance is tripled. We also re-examine the coverage of the safety net, specifically the share of people without work or program receipt. Using the administrative measures of program receipt rather than the survey ones often reduces the share of single mothers falling through the safety net by one-half or more. Abstract 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 qualitative conclusions are likely to apply to other surveys. We link administrative data for food stamps, TANF, General Assistance, and subsidized housing from New York State to the CPS at the individual level. Program receipt in the CPS is missed for over one-third of housing assistance recipients, 40 percent of food stamp recipients and 60 percent of TANF and General Assistance recipients. Dollars of benefits are also undercounted for reporting recipients, pa...

show abstract

“…The evidence in the case of transfer programs suggests that simple imputation methods are unlikely to work. The assumptions these methods rely on are unlikely to hold, because program mismeasurement is non-classical and related to covariates (Mittag 2013, Meyer and Mittag 2014, Meyer, Goerge and Mittag 2015. A third approach is to link administrative records to improve data quality or even replace survey responses, which can address both the problem of misreporting and item non-response.…”

Section: Discussionmentioning

confidence: 99%

Using Linked Survey and Administrative Data to Better Measure Income: Implications for Poverty, Program Effectiveness and Holes in the Safety Net

Meyer

¹

,

Mittag

²

2015

Self Cite

View full text Add to dashboard Cite

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in ABSTRACTWe 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 qualitative conclusions are likely to apply to other surveys. We link administrative data for food stamps, TANF, General Assistance, and subsidized housing from New York State to the CPS at the individual level. Program receipt in the CPS is missed for over one-third of housing assistance recipients, 40 percent of food stamp recipients, and 60 percent of TANF and General Assistance recipients. Dollars of benefits are also undercounted for reporting recipients, particularly for TANF, General Assistance and housing assistance. We find that the survey data sharply understate the income of poor households, as conjectured in past work by one of the authors. Underreporting in the survey data also greatly understates the effects of anti-poverty programs and changes our understanding of program targeting, often making it seem that welfare programs are less targeted to both the very poorest and middle income households than they are. Using the combined data rather than survey data alone, the poverty reducing effect of all programs together is nearly doubled while the effect of housing assistance is tripled. We also re-examine the coverage of the safety net, specifically the share of people without work or program receipt. Using the administrative measures of program receipt rather than the survey ones often reduces the share of single mothers falling through the safety net by one-half or more. Abstract 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 qualitative conclusions are likely to apply to other surveys. We link administrative data for food stamps, TANF, General Assistance, and subsidized housing from New York State to the CPS at the individual level. Program receipt in the CPS is missed for over one-third of housing assistance recipients, 40 percent of food stamp recipients and 60 percent of TANF and General Assistance recipients. Dollars of benefits are also undercounted for reporting recipients, pa...

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A Method of Correcting for Misreporting Applied to the Food Stamp Program

Abstract: Using administrative and survey data I show that survey misreporting leads to biases in common statistical analyses. Standard corrections for measurement error cannot remove these biases.

Cited by 11 publications

References 64 publications

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

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

Using Linked Survey and Administrative Data to Better Measure Income: Implications for Poverty, Program Effectiveness and Holes in the Safety Net

Using Linked Survey and Administrative Data to Better Measure Income: Implications for Poverty, Program Effectiveness and Holes in the Safety Net

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