Modeling Discrete Choice With Response Error: Food Stamp Participation

Bollinger, Christopher R.; David, Martín

doi:10.2307/2965547

Cited by 50 publications

(68 citation statements)

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“…This may be attributable either to lower NHANES response rates among SNAP participants or to underreporting of SNAP participation (46). Factors related to misreporting SNAP participation include sex, marital status, and income level (47). Although this study accounts the effects of these variables, it is still possible that misclassification of SNAP exists.…”

Section: Discussionmentioning

confidence: 98%

Dietary intake and dietary quality of low-income adults in the Supplemental Nutrition Assistance Program

Leung¹,

Ding²,

Catalano³

et al. 2012

The American Journal of Clinical Nutrition

207

190

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Section: Discussionmentioning

confidence: 98%

Dietary intake and dietary quality of low-income adults in the Supplemental Nutrition Assistance Program

Leung¹,

Ding²,

Catalano³

et al. 2012

The American Journal of Clinical Nutrition

207

190

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“…Estimates suggest that the SIPP underreports SNAP receipt by 7% to 19% (Bitler, Currie, and Scholz 2003; Bollinger and David 1997; Cody and Tuttle 2002), which is somewhat lower than the Current Population Survey of the U.S. Census Bureau and the Bureau of Labor Statistics. 28 Analyses suggest that SNAP reporting error is due primarily to SNAP recipients who do not report SNAP benefit receipt, not nonrecipients who report receiving SNAP benefits.…”

Section: Datamentioning

confidence: 92%

“…28 Analyses suggest that SNAP reporting error is due primarily to SNAP recipients who do not report SNAP benefit receipt, not nonrecipients who report receiving SNAP benefits. Using SIPP data matched to administrative data, for example, Bollinger and David (1997) find that nearly all of the SNAP reporting error stems from recipients who do not report receiving benefits (12%); only 0.3% of nonrecipients report receiving SNAP (i.e., report a false positive).…”

Section: Datamentioning

confidence: 99%

How Much Does the Supplemental Nutrition Assistance Program Reduce Food Insecurity?

Ratcliffe

McKernan

Zhang

2011

American J Agri Economics

338

198

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Nearly 15% of all U.S. households and 40% of near-poor households were food insecure in 2009. The Supplemental Nutrition Assistance Program (SNAP) is the cornerstone of federal food assistance programs and serves as the first line of defense against food-related hardship. This paper measures the effectiveness of SNAP in reducing food insecurity using an instrumental variables approach to control for selection. Our results suggest that receipt of SNAP benefits reduces the likelihood of being food insecure by roughly 30% and reduces the likelihood of being very food insecure by 20%.

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“…Insurance claims categorized as “honest” that are actually fraudulent are misclassified. Several approaches to the estimation of logit models based on misclassified data exist in the literature (see, for example, Poterba and Summers, 1995; Bollinger and David, 1997; Hausman, Abrevaya, and Scott‐Morton, 1998). Identifying fraudulent claims is very similar to a problem addressed in the medical and epidemiological literature when discussing treatment effects on recovery of an illness.…”

Section: Introductionmentioning

confidence: 99%

Fraud Detection Using a Multinomial Logit Model With Missing Information

Caudill

Ayuso

Guillén

2005

J of Risk & Insurance

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Recently, Artís, Ayuso, and Guillén (2002, "Journal of Risk and Insurance" 69: 325-340; henceforth AAG) estimate a logit model using claims data. Some of the claims are categorized as "honest" and other claims are known to be fraudulent. Using the approach of Hausman, Abrevaya, and Scott-Morton (1998 "Journal of Econometrics" 87: 239-269), AAG estimate a modified logit model allowing for the possibility that some claims classified as honest might actually be fraudulent. Applying this model to data on Spanish automobile insurance claims, AGG find that 5 percent of the fraudulent claims go undetected. The purpose of this article is to estimate the model of AAG using a logit model with missing information. A constrained version of this model is used to reexamine the Spanish insurance claim data. The results indicate how to identify misclassified claims. We also show how misclassified claims can be identified using the AAG approach. We show that both approaches can be used to probabilistically identify misclassified claims. Copyright The Journal of Risk and Insurance.

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Modeling Discrete Choice With Response Error: Food Stamp Participation

Cited by 50 publications

References 0 publications

Dietary intake and dietary quality of low-income adults in the Supplemental Nutrition Assistance Program

Dietary intake and dietary quality of low-income adults in the Supplemental Nutrition Assistance Program

How Much Does the Supplemental Nutrition Assistance Program Reduce Food Insecurity?

Fraud Detection Using a Multinomial Logit Model With Missing Information

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