Allowance rates for disability insurance applications vary by race and ethnicity, but it is unclear to what extent these differences are artifacts of other differing socio-economic and health characteristics, or selection issues in SSA's race and ethnicity data. This paper uses the 2015 American Community Survey linked to 2015-2019 SSA administrative data to investigate DI application allowance rates among non-Hispanic White, non-Hispanic Black, non-Hispanic Asian, non-Hispanic American Indian/Alaska Native, and Hispanic applicants aged 25-65. The analysis uses regression, propensity score matching, and inverse probability weighting to estimate differences in allowance rates among applicants who are similar on observable characteristics. Relative to raw comparisons, differences by race and ethnicity in multivariate analyses are substantially smaller in magnitude and are generally not statistically significant.
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Evaluating Race and Hispanic Origin Responses of Medicaid Participants Using Census Data
April 2015
Working Paper Number:
carra-2015-01
Health and health care disparities associated with race or Hispanic origin are complex and continue to challenge researchers and policy makers. With the intention of improving the measurement and monitoring of these disparities, provisions of the Patient Protection and Affordable Care Act (ACA) of 2010 require states to collect, report and analyze data on demographic characteristics of applicants and participants in Medicaid and other federally supported programs. By linking Medicaid records to 2010 Census, American Community Survey, and Census 2000, this new large-scale study examines and documents the extent to which pre-ACA Medicaid administrative records match self-reported race and Hispanic origin in Census data. Linked records allow comparisons between individuals with matching and non-matching race and Hispanic origin data across several demographic, socioeconomic and neighborhood characteristics, such as age, gender, language proficiency, education and Census tract variables. Identification of the groups most likely to have non-matching and missing race and Hispanic origin data in Medicaid relative to Census data can inform strategies to improve the quality of demographic data collected from Medicaid populations.
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The Work Disincentive Effects of the Disability Insurance Program in the 1990s
February 2006
Working Paper Number:
CES-06-05
In this paper we evaluate the work disincentive effects of the Disability Insurance program during the 1990s. To accomplish this we construct a new large data set with detailed information on DI application and award decisions and use two different econometric evaluation methods. First, we apply a comparison group approach proposed by John Bound to estimate an upper bound for the work disincentive effect of the current DI program. Second, we adopt a Regression-Discontinuity approach that exploits a particular feature of the DI eligibility determination process to provide a credible point estimate of the impact of the DI program on labor supply for an important subset of DI applicants. Our estimates indicate that during the 1990s the labor force participation rate of DI beneficiaries would have been at most 20 percentage points higher had none received benefits. In addition, we find even smaller labor supply responses for the subset of 'marginal' applicants whose disability determination is based on vocational factors.
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Non-Random Assignment of Individual Identifiers and Selection into Linked Data: Implications for Research
January 2026
Working Paper Number:
CES-26-06
The U.S. Census Bureau's Person Identification Validation System facilitates anonymous linkages between survey and administrative records by assigning Protected Identification Keys (PIKs) to person records. While PIK assignment is generally accurate, some person records are not successfully assigned a PIK, which can lead to sample selection bias in analyses of linked data. Using the American Community Survey (ACS) and the Current Population Survey Annual Social and Economic Supplement (CPS ASEC) between 2005 and 2022, we corroborate and extend existing findings on the drivers of PIK assignment, showing that the rate of PIK assignment varies widely across socio-demographic subgroups. Using earnings as a test case, we then show that limiting a survey sample of wage earners to person records with PIKs or successful linkages to W-2 wage records tends to overestimate self-reported wage earnings, on average, indicative of linkage-induced selection bias. In a validation exercise, we demonstrate that reweighting methods, such as inverse probability weighting or entropy balancing, can mitigate this bias.
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Reporting of Indian Health Service Coverage in the American Community Survey
May 2018
Working Paper Number:
carra-2018-04
Response error in surveys affects the quality of data which are relied on for numerous research and policy purposes. We use linked survey and administrative records data to examine reporting of a particular item in the American Community Survey (ACS) - health coverage among American Indians and Alaska Natives (AIANs) through the Indian Health Service (IHS). We compare responses to the IHS portion of the 2014 ACS health insurance question to whether or not individuals are in the 2014 IHS Patient Registration data. We evaluate the extent to which individuals misreport their IHS coverage in the ACS as well as the characteristics associated with misreporting. We also assess whether the ACS estimates of AIANs with IHS coverage represent an undercount. Our results will be of interest to researchers who rely on survey responses in general and specifically the ACS health insurance question. Moreover, our analysis contributes to the literature on using administrative records to measure components of survey error.
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Coverage and Agreement of Administrative Records and 2010 American Community Survey Demographic Data
November 2014
Working Paper Number:
carra-2014-14
The U.S. Census Bureau is researching possible uses of administrative records in decennial census and survey operations. The 2010 Census Match Study and American Community Survey (ACS) Match Study represent recent efforts by the Census Bureau to evaluate the extent to which administrative records provide data on persons and addresses in the 2010 Census and 2010 ACS. The 2010 Census Match Study also examines demographic response data collected in administrative records. Building on this analysis, we match data from the 2010 ACS to federal administrative records and third party data as well as to previous census data and examine administrative records coverage and agreement of ACS age, sex, race, and Hispanic origin responses. We find high levels of coverage and agreement for sex and age responses and variable coverage and agreement across race and Hispanic origin groups. These results are similar to findings from the 2010 Census Match Study.
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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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The Impact of Unemployment Insurance Extensions On Disability Insurance Application and Allowance Rates
March 2013
Working Paper Number:
CES-13-10
Both unemployment insurance (UI) extensions and the availability of disability benefits have disincentive effects on job search. But UI extensions can reduce the efficiency cost of disability benefits if UI recipients delay disability application until they exhaust their unemployment benefits. This paper, the first to focus on the effect of UI extensions on disability applications, investigates whether UI eligibility, extension, and exhaustion affect the timing of disability applications and the composition of the applicant pool. Jobless individuals are significantly less likely to apply to Social Security Disability Insurance (SSDI) during UI extensions, and significantly more likely to apply when UI is ultimately exhausted. Healthier potential applicants appear more likely to delay, as state allowance rates increase after a new UI extension. Simulations find that a 13-week UI extension decreases SSDI and Medicare costs, offsetting about half of the increase in UI payments; this suggests that the benefits of UI extensions may be understated ' permanent disability benefits are diverted to shorter-run unemployment benefits and, potentially, new jobs, while easing the burden on the nearly insolvent SSDI Trust Fund.
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The Measurement of Medicaid Coverage in the SIPP: Evidence from California, 1990-1996
September 2002
Working Paper Number:
CES-02-21
This paper studies the accuracy of reported Medicaid coverage in the Survey of Income and Program Participation (SIPP) using a unique data set formed by matching SIPP survey responses to administrative records from the State of California. Overall, we estimate that the SIPP underestimates Medicaid coverage in the California populaton by about 10 percent. Among SIPP respondents who can be matched to administrative records, we estimate that the probability someone reports Medicaid coverage in a month when they are actually covered is around 85 percent. The corresponding probability for low-income children is even higher ' at least 90 percent. These estimates suggest that the SIPP provides reasonably accurate coverage reports for those who are actually in the Medicaid system. On the other hand, our estimate of the false positive rate (the rate of reported coverage for those who are not covered in the administrative records) is relatively high: 2.5 percent for the sample as a whole, and up to 20 percent for poor children. Some of this is due to errors in the recording of Social Security numbers in the administrative system, rather than to problems in the SIPP.
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Medicare Coverage and Reporting
December 2016
Working Paper Number:
carra-2016-12
Medicare coverage of the older population in the United States is widely recognized as being nearly universal. Recent statistics from the Current Population Survey Annual Social and Economic Supplement (CPS ASEC) indicate that 93 percent of individuals aged 65 and older were covered by Medicare in 2013. Those without Medicare include those who are not eligible for the public health program, though the CPS ASEC estimate may also be impacted by misreporting. Using linked data from the CPS ASEC and Medicare Enrollment Database (i.e., the Medicare administrative data), we estimate the extent to which individuals misreport their Medicare coverage. We focus on those who report having Medicare but are not enrolled (false positives) and those who do not report having Medicare but are enrolled (false negatives). We use regression analyses to evaluate factors associated with both types of misreporting including socioeconomic, demographic, and household characteristics. We then provide estimates of the implied Medicare-covered, insured, and uninsured older population, taking into account misreporting in the CPS ASEC. We find an undercount in the CPS ASEC estimates of the Medicare covered population of 4.5 percent. This misreporting is not random - characteristics associated with misreporting include citizenship status, year of entry, labor force participation, Medicare coverage of others in the household, disability status, and imputation of Medicare responses. When we adjust the CPS ASEC estimates to account for misreporting, Medicare coverage of the population aged 65 and older increases from 93.4 percent to 95.6 percent while the uninsured rate decreases from 1.4 percent to 1.3 percent.
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The Icing on the Cake: The Effects of Monetary Incentives on Income Data Quality in the SIPP
January 2024
Working Paper Number:
CES-24-03
Accurate measurement of key income variables plays a crucial role in economic research and policy decision-making. However, the presence of item nonresponse and measurement error in survey data can cause biased estimates. These biases can subsequently lead to sub-optimal policy decisions and inefficient allocation of resources. While there have been various studies documenting item nonresponse and measurement error in economic data, there have not been many studies investigating interventions that could reduce item nonresponse and measurement error. In our research, we investigate the impact of monetary incentives on reducing item nonresponse and measurement error for labor and investment income in the Survey of Income and Program Participation (SIPP). Our study utilizes a randomized incentive experiment in Waves 1 and 2 of the 2014 SIPP, which allows us to assess the effectiveness of incentives in reducing item nonresponse and measurement error. We find that households receiving incentives had item nonresponse rates that are 1.3 percentage points lower for earnings and 1.5 percentage points lower for Social Security income. Measurement error was 6.31 percentage points lower at the intensive margin for interest income, and 16.48 percentage points lower for dividend income compared to non-incentive recipient households. These findings provide valuable insights for data producers and users and highlight the importance of implementing strategies to improve data quality in economic research.
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