Given rising nonresponse rates and concerns about respondent burden, government statistical agencies have been exploring ways to supplement household survey data collection with administrative records and other sources of third-party data. This paper evaluates the potential of property tax assessment records to improve housing surveys by comparing these records to responses from the 2019 American Housing Survey. Leveraging the U.S. Census Bureau's linkage infrastructure, we compute the fraction of AHS housing units that could be matched to a unique property parcel (coverage rate), as well as the extent to which survey and property tax data contain the same information (agreement rate). We analyze heterogeneity in coverage and agreement across states, housing characteristics, and 11 AHS items of interest to housing researchers. Our results suggest that partial replacement of AHS data with property data, targeted toward certain survey items or single-family detached homes, could reduce respondent burden without altering data quality. Further research into partial-replacement designs is needed and should proceed on an item-by-item basis. Our work can guide this research as well as those who wish to conduct independent research with property tax records that is representative of the U.S. housing stock.
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Comparison of Survey, Federal, and Commercial Address Data Quality
June 2014
Working Paper Number:
carra-2014-06
This report summarizes matching of survey, commercial, and administrative records housing units to the Census Bureau Master Address File (MAF). We document overall MAF match rates in each data set and evaluate differences in match rates across a variety of housing characteristics. Results show that over 90 percent of records in survey data from the American Housing Survey (AHS) match to the MAF. Commercial data from CoreLogic matches at much lower rates, in part due to missing address information and poor match rates for multi-unit buildings. MAF match rates for administrative records from the Department of Housing and Urban Development are also high, and open the possibility of using this information in surveys such as the AHS.
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Matching Addresses between Household Surveys and Commercial Data
July 2015
Working Paper Number:
carra-2015-04
Matching third-party data sources to household surveys can benefit household surveys in a number of ways, but the utility of these new data sources depends critically on our ability to link units between data sets. To understand this better, this report discusses potential modifications to the existing match process that could potentially improve our matches. While many changes to the matching procedure produce marginal improvements in match rates, substantial increases in match rates can only be achieved by relaxing the definition of a successful match. In the end, the results show that the most important factor determining the success of matching procedures is the quality and composition of the data sets being matched.
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Evaluating the Use of Commercial Data to Improve Survey Estimates of Property Taxes
August 2016
Working Paper Number:
carra-2016-06
While commercial data sources offer promise to statistical agencies for use in production of official statistics, challenges can arise as the data are not collected for statistical purposes. This paper evaluates the use of 2008-2010 property tax data from CoreLogic, Inc. (CoreLogic), aggregated from county and township governments from around the country, to improve 2010 American Community Survey (ACS) estimates of property tax amounts for single-family homes. Particularly, the research evaluates the potential to use CoreLogic to reduce respondent burden, to study survey response error and to improve adjustments for survey nonresponse. The research found that the coverage of the CoreLogic data varies between counties as does the correspondence between ACS and CoreLogic property taxes. This geographic variation implies that different approaches toward using CoreLogic are needed in different areas of the country. Further, large differences between CoreLogic and ACS property taxes in certain counties seem to be due to conceptual differences between what is collected in the two data sources. The research examines three counties, Clark County, NV, Philadelphia County, PA and St. Louis County, MO, and compares how estimates would change with different approaches using the CoreLogic data. Mean county property tax estimates are highly sensitive to whether ACS or CoreLogic data are used to construct estimates. Using CoreLogic data in imputation modeling for nonresponse adjustment of ACS estimates modestly improves the predictive power of imputation models, although estimates of county property taxes and property taxes by mortgage status are not very sensitive to the imputation method.
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Incorporating Administrative Data in Survey Weights for the 2018-2022 Survey of Income and Program Participation
October 2024
Working Paper Number:
CES-24-58
Response rates to the Survey of Income and Program Participation (SIPP) have declined over time, raising the potential for nonresponse bias in survey estimates. A potential solution is to leverage administrative data from government agencies and third-party data providers when constructing survey weights. In this paper, we modify various parts of the SIPP weighting algorithm to incorporate such data. We create these new weights for the 2018 through 2022 SIPP panels and examine how the new weights affect survey estimates. Our results show that before weighting adjustments, SIPP respondents in these panels have higher socioeconomic status than the general population. Existing weighting procedures reduce many of these differences. Comparing SIPP estimates between the production weights and the administrative data-based weights yields changes that are not uniform across the joint income and program participation distribution. Unlike other Census Bureau household surveys, there is no large increase in nonresponse bias in SIPP due to the COVID-19 Pandemic. In summary, the magnitude and sign of nonresponse bias in SIPP is complicated, and the existing weighting procedures may change the sign of nonresponse bias for households with certain incomes and program benefit statuses.
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Correctional Facility and Inmate Locations: Urban and Rural Status Patterns
July 2017
Working Paper Number:
carra-2017-08
As the incarcerated population grew from the 1980s through the late 2000s, so too did the number of correctional facilities. An increasing number of these facilities have been constructed in rural areas. While research has shown there has been growth in prisons and prisoners in rural areas, there are no recent national-level statistics regarding the urban-rural status of correctional facilities and inmates, the urban-rural status of inmates prior to prison, or an accounting of how many inmates from urban or rural areas are incarcerated in urban and rural facilities. Using 2010 decennial census and Bureau of Justice Statistics' 2004 Survey of Prison Inmates data we describe these patterns. We find that a disproportionate share of prisons and inmates are located in rural areas, while a disproportionate share of inmates are from urban areas. Our research could inform discussions about the potential consequences of Census Bureau residence criteria for inmates.
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Exploring Administrative Records Use for Race and Hispanic Origin Item Non-Response
December 2014
Working Paper Number:
carra-2014-16
Race and Hispanic origin data are required to produce official statistics in the United States. Data collected through the American Community Survey and decennial census address missing data through traditional imputation methods, often relying on information from neighbors. These methods work well if neighbors share similar characteristics, however, the shape and patterns of neighborhoods in the United States are changing. Administrative records may provide more accurate data compared to traditional imputation methods for missing race and Hispanic origin responses. This paper first describes the characteristics of persons with missing demographic data, then assesses the coverage of administrative records data for respondents who do not answer race and Hispanic origin questions in Census data. The paper also discusses the distributional impact of using administrative records race and Hispanic origin data to complete missing responses in a decennial census or survey context.
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2010 American Community Survey Match Study
July 2014
Working Paper Number:
carra-2014-03
Using administrative records data from federal government agencies and commercial sources, the 2010 ACS Match Study measures administrative records coverage of 2010 ACS addresses, persons, and persons at addresses at different levels of geography as well as by demographic characteristics and response mode. The 2010 ACS Match Study represents a continuation of the research undertaken in the 2010 Census Match Study, the first national-level evaluation of administrative records data coverage. Preliminary results indicate that administrative records provide substantial coverage for addresses and persons in the 2010 ACS (92.7 and 92.1 percent respectively), and less extensive though substantial coverage, for person-address pairs (74.3 percent). In addition, some variation in address, person and/or person-address coverage is found across demographic and response mode groups. This research informs future uses of administrative records in survey and decennial census operations to address the increasing costs of data collection and declining response rates.
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Investigating the Use of Administrative Records in the Consumer Expenditure Survey
March 2018
Working Paper Number:
carra-2018-01
In this paper, we investigate the potential of applying administrative records income data to the Consumer Expenditure (CE) survey to inform measurement error properties of CE estimates, supplement respondent-collected data, and estimate the representativeness of the CE survey by income level. We match individual responses to Consumer Expenditure Quarterly Interview Survey data collected from July 2013 through December 2014 to IRS administrative data in order to analyze CE questions on wages, social security payroll deductions, self-employment income receipt and retirement income. We find that while wage amounts are largely in alignment between the CE and administrative records in the middle of the wage distribution, there is evidence that wages are over-reported to the CE at the bottom of the wage distribution and under-reported at the top of the wage distribution. We find mixed evidence for alignment between the CE and administrative records on questions covering payroll deductions and self-employment income receipt, but find substantial divergence between CE responses and administrative records when examining retirement income. In addition to the analysis using person-based linkages, we also match responding and non-responding CE sample units to the universe of IRS 1040 tax returns by address to examine non-response bias. We find that non-responding households are substantially richer than responding households, and that very high income households are less likely to respond to the CE.
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SYNTHETIC DATA FOR SMALL AREA ESTIMATION IN THE AMERICAN COMMUNITY SURVEY
April 2013
Working Paper Number:
CES-13-19
Small area estimates provide a critical source of information used to study local populations. Statistical agencies regularly collect data from small areas but are prevented from releasing detailed geographical identifiers in public-use data sets due to disclosure concerns. Alternative data dissemination methods used in practice include releasing summary/aggregate tables, suppressing detailed geographic information in public-use data sets, and accessing restricted data via Research Data Centers. This research examines an alternative method for disseminating microdata that contains more geographical details than are currently being released in public-use data files. Specifically, the method replaces the observed survey values with imputed, or synthetic, values simulated from a hierarchical Bayesian model. Confidentiality protection is enhanced because no actual values are released. The method is demonstrated using restricted data from the 2005-2009 American Community Survey. The analytic validity of the synthetic data is assessed by comparing small area estimates obtained from the synthetic data with those obtained from the observed data.
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Estimating Record Linkage False Match Rate for the Person Identification Validation System
July 2014
Working Paper Number:
carra-2014-02
The Census Bureau Person Identification Validation System (PVS) assigns unique person identifiers to federal, commercial, census, and survey data to facilitate linkages across files. PVS uses probabilistic matching to assign a unique Census Bureau identifier for each person. This paper presents a method to measure the false match rate in PVS following the approach of Belin and Rubin (1995). The Belin and Rubin methodology requires truth data to estimate a mixture model. The parameters from the mixture model are used to obtain point estimates of the false match rate for each of the PVS search modules. The truth data requirement is satisfied by the unique access the Census Bureau has to high quality name, date of birth, address and Social Security (SSN) data. Truth data are quickly created for the Belin and Rubin model and do not involve a clerical review process. These truth data are used to create estimates for the Belin and Rubin parameters, making the approach more feasible. Both observed and modeled false match rates are computed for all search modules in federal administrative records data and commercial data.
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