Correcting for misreporting of government benefits

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985998%3A_____%2F16%3A00463769" target="_blank" >RIV/67985998:_____/16:00463769 - isvavai.cz</a>

Výsledek na webu

—

DOI - Digital Object Identifier

—

Jazyk výsledku

angličtina

Název v původním jazyce

Correcting for misreporting of government benefits

Popis výsledku v původním jazyce

Recent validation studies show that survey misreporting is pervasive and biases commonnanalyses. Addressing this problem is further complicated, because validation data are usuallynconvenience samples and access is restricted, making them more suitable to document thannto solve the problem. I first use administrative SNAP records linked to survey data to evaluatencorrections for misreporting that have been applied to survey data. Second, I develop anmethod that combines public use data with an estimated conditional distribution from thenvalidation data. It does not require access to the validation data, is simple to implement andnapplicable to a wide range of econometric models. Using the validation data, I show that thisnmethod improves upon both the survey data and the other corrections, particularly fornmultivariate analyses. Some survey-based corrections also yield large error reductions, whichnmakes them attractive alternatives when validation data do not exist. Finally, I examinenwhether estimates can be improved based on similar validation data, to mitigate that thenpopulation of interest is rarely validated. For SNAP, I provide evidence that extrapolationnusing the method developed here improves over survey data and corrections withoutnvalidation data. Deviations from the geographic distribution of program spending are oftennreduced by a factor of 5 or more. The results suggest substantial differences in programneffects, such as reducing the poverty rate by almost one percentage point more, a 75 percentnincrease over the survey estimate.

Název v anglickém jazyce

Correcting for misreporting of government benefits

Popis výsledku anglicky

Recent validation studies show that survey misreporting is pervasive and biases commonnanalyses. Addressing this problem is further complicated, because validation data are usuallynconvenience samples and access is restricted, making them more suitable to document thannto solve the problem. I first use administrative SNAP records linked to survey data to evaluatencorrections for misreporting that have been applied to survey data. Second, I develop anmethod that combines public use data with an estimated conditional distribution from thenvalidation data. It does not require access to the validation data, is simple to implement andnapplicable to a wide range of econometric models. Using the validation data, I show that thisnmethod improves upon both the survey data and the other corrections, particularly fornmultivariate analyses. Some survey-based corrections also yield large error reductions, whichnmakes them attractive alternatives when validation data do not exist. Finally, I examinenwhether estimates can be improved based on similar validation data, to mitigate that thenpopulation of interest is rarely validated. For SNAP, I provide evidence that extrapolationnusing the method developed here improves over survey data and corrections withoutnvalidation data. Deviations from the geographic distribution of program spending are oftennreduced by a factor of 5 or more. The results suggest substantial differences in programneffects, such as reducing the poverty rate by almost one percentage point more, a 75 percentnincrease over the survey estimate.

Druh

O - Ostatní výsledky

CEP obor

—

OECD FORD obor

50202 - Applied Economics, Econometrics

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2016

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů