This paper examines sub-state spatial and temporal variation in misreporting of participation in the Supplemental Nutrition Assistance Program (SNAP) using several years of the American Community Survey linked to SNAP administrative records from New York (2008-2010) and Texas (2006-2009). I calculate county false-negative (FN) and false-positive (FP) rates for each year of observation and find that, within a given state and year, there is substantial heterogeneity in FN rates across counties. In addition, I find evidence that FN rates (but not FP rates) persist over time within counties. This persistence in FN rates is strongest among more populous counties, suggesting that when noise from sampling variation is not an issue, some counties have consistently high FN rates while others have consistently low FN rates. This finding is important for understanding how misreporting might bias estimates of sub-state SNAP participation rates, changes in those participation rates, and effects of program participation. This presentation was given at the CARRA Seminar, June 27, 2013
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A METHOD OF CORRECTING FOR MISREPORTING APPLIED TO THE FOOD STAMP PROGRAM
May 2013
Working Paper Number:
CES-13-28
Survey misreporting is known to be pervasive and bias common statistical analyses. In this paper, I first use administrative data on SNAP receipt and amounts linked to American Community Survey data from New York State to show that survey data can misrepresent the program in important ways. For example, more than 1.4 billion dollars received are not reported in New York State alone. 46 percent of dollars received by house- holds with annual income above the poverty line are not reported in the survey data, while only 19 percent are missing below the poverty line. Standard corrections for measurement error cannot remove these biases. I then develop a method to obtain consistent estimates by combining parameter estimates from the linked data with publicly available data. This conditional density method recovers the correct estimates using public use data only, which solves the problem that access to linked administrative data is usually restricted. I examine the degree to which this approach can be used to extrapolate across time and geography, in order to solve the problem that validation data is often based on a convenience sample. I present evidence from within New York State that the extent of heterogeneity is small enough to make extrapolation work well across both time and geography. Extrapolation to the entire U.S. yields substantive differences to survey data and reduces deviations from official aggregates by a factor of 4 to 9 compared to survey aggregates.
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MISCLASSIFICATION IN BINARY CHOICE MODELS
May 2013
Working Paper Number:
CES-13-27
We derive the asymptotic bias from misclassification of the dependent variable in binary choice models. Measurement error is necessarily non-classical in this case, which leads to bias in linear and non-linear models even if only the dependent variable is mismeasured. A Monte Carlo study and an application to food stamp receipt show that the bias formulas are useful to analyze the sensitivity of substantive conclusions, to interpret biased coefficients and imply features of the estimates that are robust to misclassification. Using administrative records linked to survey data as validation data, we examine estimators that are consistent under misclassification. They can improve estimates if their assumptions hold, but can aggravate the problem if the assumptions are invalid. The estimators differ
in their robustness to such violations, which can be improved by incorporating additional information. We propose tests for the presence and nature of misclassification that can help to choose an estimator.
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Errors in Survey Reporting and Imputation and Their Effects on Estimates of Food Stamp Program Participation
April 2011
Working Paper Number:
CES-11-14
Benefit receipt in major household surveys is often underreported. This misreporting leads to biased estimates of the economic circumstances of disadvantaged populations, program takeup, and the distributional effects of government programs, and other program effects. We use administrative data on Food Stamp Program (FSP) participation matched to American Community Survey (ACS) and Current Population Survey (CPS) household data. We show that nearly thirty-five percent of true recipient households do not report receipt in the ACS and fifty percent do not report receipt in the CPS. Misreporting, both false negatives and false positives, varies with individual characteristics, leading to complicated biases in FSP analyses. We then directly examine the determinants of program receipt using our combined administrative and survey data. The combined data allow us to examine accurate participation using individual characteristics missing in administrative data. Our results differ from conventional estimates using only survey data, as such estimates understate participation by single parents, non-whites, low income households, and other groups. To evaluate the use of Census Bureau imputed ACS and CPS data, we also examine whether our estimates using survey data alone are closer to those using the accurate combined data when imputed survey observations are excluded. Interestingly, excluding the imputed observations leads to worse ACS estimates, but has less effect on the CPS estimates.
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BIAS IN FOOD STAMPS PARTICIPATION ESTIMATES IN THE PRESENCE OF MISREPORTING ERROR
March 2013
Working Paper Number:
CES-13-13
This paper focuses on how survey misreporting of food stamp receipt can bias demographic estimation of program participation. Food stamps is a federally funded program which subsidizes the nutrition of low-income households. In order to improve the reach of this program, studies on how program participation varies by demographic groups have been conducted using census data. Census data are subject to a lot of misreporting error, both underreporting and over-reporting, which can bias the estimates. The impact of misreporting error on estimate bias is examined by calculating food stamp participation rates, misreporting rates, and bias for select household characteristics (covariates).
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The Effects of Smoking in Young Adulthood on Smoking and Health Later in Life: Evidence Based on the Vietnam Era Draft Lottery
September 2008
Working Paper Number:
CES-08-35
An important, unresolved question for health policymakers and consumers is whether cigarette smoking in young adulthood has significant lasting effects into later adulthood. The Vietnam era draft lottery offers an opportunity to address this question, because it randomly assigned young men to be more likely to experience conditions favoring cigarette consumption, including highly subsidized prices. Using this natural experiment, we find that military service increased the probability of smoking by 35 percentage points as of 1978-80, when men in the relevant cohorts were aged 25-30, but later in adulthood this effect was substantially attenuated and did not lead to large negative health effects.
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The EITC over the business cycle: Who benefits?
December 2014
Working Paper Number:
carra-2014-15
In this paper, I examine the impact of the Great Recession on Earned Income Tax Credit (EITC) eligibility. Because the EITC is structurally tied to earnings, the direction of this impact is not immediately obvious. Families who experience complete job loss for an entire tax year lose eligibility, while those experiencing underemployment (part-year employment, a reduction in hours, or spousal unemployment in married households) may become eligible. Determining the direction and magnitude of the impact is important for a number of reasons. The EITC has become the largest cash-transfer program in the U.S., and many low-earning families rely on it as a means of support in tough times. The program has largely been viewed as a replacement for welfare, enticing former welfare recipients into the labor force. However, the effectiveness of the EITC during a period of very high unemployment has not been assessed. To answer these questions, I first use the Current Population Survey (CPS) matched to Internal Revenue Service data from tax years 2005 to 2010 to assess patterns of employment and eligibility over the Great Recession for different labor-force groups. Results indicate that overall, EITC eligibility increased over the recession, but only among groups that were cushioned from total household earnings loss by marriage. I also use the 2006 CPS matched to tax data from 2005 through 2011 to examine changes in eligibility experienced by individuals over time. In assessing three competing causes of eligibility loss, I find that less-educated, unmarried women experienced a greater hazard of eligibility loss due a yearlong lack of earnings compared with other labor-market groups. I discuss the implications of these findings on the view of the EITC as a safety-net program.
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Local Labor Demand and Program Participation Dynamics
November 2016
Working Paper Number:
carra-2016-10
Estimates the effect of fluctuations in local labor conditions on the likelihood that existing participants are able to transition out of the Supplemental Nutrition Assistance Program (SNAP). Our primary data are SNAP administrative records from New York (2007-2012) linked to the 2010 Census at the person-level. We further augment these data by linking to industry-specific labor market indicators at the county-level. We find that local labor markets matter for the length of time individuals spend on SNAP, but there is substantial heterogeneity in estimated effects across local industries. While employment growth in industries with small shares of SNAP participants has no impact on SNAP exits, growth in local industries with creases the likelihood that recipients exit the program. We also observe corresponding increases in entries when these industries experience localized contractions. Notably, estimated industry effects vary across race groups and parental status, with Black Alone non-Hispanic, Hispanic, and mothers benefiting the least from improvements in local labor market conditions.
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Food Security Status Across the Rural-Urban Continuum Before and During the COVID-19 Pandemic
January 2025
Working Paper Number:
CES-25-01
Background: Food security, defined as consistent access to sufficient food to support an active life, is a crucial social determinant of health. A key dimension affecting food security is position along the rural-urban continuum, as there are important socio-economic and environmental differences between communities related to urbanicity or rurality that impact food access. The COVID-19 pandemic created social and economic shocks that altered financial and food security, which may have had differential effects by rurality and urbanicity. However, there has been limited research on how food security differs across the shades of the rural-urban community spectrum, as most often researchers have characterized communities as either urban or rural.
Methods: In this study, which linked restricted use Current Population Survey Food Security Supplement data to census-tract level United States Department of Agriculture Rural-Urban Commuting Area codes, we estimated the prevalence of household food security across temporal (2015-2019 versus 2020-2021) and socio-spatial (urban, large rural city/town, small rural town, or isolated rural town/area) dimensions in order to characterize variations before and during the COVID-19 pandemic by urbanicity/rurality. We report prevalences as point estimates with 95% confidence intervals.
Results: The prevalence of food security was 87.7% (87.5-88.0%) in 2015-2019 and 88.8% (88.4-89.3%) in 2020-2021 for urban areas, 85.5% (84.7-86.2%) in 2015-2019 and 87.1% (85.7-88.3%) in 2020-2021 for large rural towns/cities, 82.8% (81.5-84.1%) in 2015-2019 and 87.3% (85.7-89.2%) in 2020-2021 for small rural towns, and 87.6% (86.3-88.8%) in 2015-2019 and 90.9% (88.7-92.7%) in 2020-2021 for isolated rural towns/areas.
Conclusion: These findings show that rural communities experiences of food security vary and aggregating households in these environments may mask areas of concern and concentrated need.
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The Long-Run Effects of Recessions on Education and Income
January 2017
Working Paper Number:
CES-17-52
This paper examines the long-run effects of the 1980-1982 recession on education and income.
Using confidential Census data, I estimate generalized difference-in-differences regressions that exploit variation across counties in the severity of the recession and across cohorts in age at the time of the recession. I find that children born in counties with a more severe recession are less likely to obtain a college degree and, as adults, earn less income and experience higher poverty rates. The negative effects on college graduation are most severe and essentially constant for individuals age 0-13 in 1979, suggesting that the underlying mechanisms are a decline in childhood human capital or a long-term decline in parental resources to pay for college. I find little evidence that states with more generous or more progressive transfer systems mitigated these long-run effects. The magnitude of my estimates and the large number of affected individuals suggest that the 1980-1982 recession depresses aggregate economic output today.
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Does the Retirement Consumption Puzzle Differ Across the Distribution?
March 2011
Working Paper Number:
CES-11-09R
Previous research has repeatedly found a puzzling one-time drop in the mean and median of consumption at retirement, contrary to the predictions of the life-cycle hypothesis. However, very little is known as to whether these effects vary across the consumption distribution. This study expands upon the previous work by examining changes in the consumption distribution between the non-retired and the retired using quantile regression techniques on pseudo-cohorts from the cross-sectional data of the 1990-2007 Consumer Expenditure Survey. The results indicate that there are insignificant changes between these groups at the lower end of the consumption distribution, while there are significant decreases at the higher end of this distribution. In addition, these changes in the distribution are gradually larger in magnitude when moving from the lower end to the higher end, which is found using several different measures of consumption. Work-related expenditures are instead shown to decrease uniformly across the consumption distribution. This evidence reveals that there is a progressive distributional component to the retirement consumption puzzle.
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