Retail health clinics (RHCs) are a relatively new type of health care setting and understanding the role they play as a source of ambulatory care in the United States is important. To better understand these settings, a joint project by the Census Bureau and National Center for Health Statistics used data science techniques to link together data on RHCs from Convenient Care Association, County Business Patterns Business Register, and National Plan and Provider Enumeration System to create the Linked RHC (LiRHC, pronounced 'lyric') database of locations throughout the United States during the years 2018 to 2020. The matching methodology used to perform this linkage is described, as well as the benchmarking, match statistics, and manual review and quality checks used to assess the resulting matched data. The large majority (81%) of matches received quality scores at or above 75/100, and most matches were linked in the first two (of eight) matching passes, indicating high confidence in the final linked dataset. The LiRHC database contained 2,000 RHCs and found that 97% of these clinics were in metropolitan statistical areas and 950 were in the South region of the United States. Through this collaborative effort, the Census Bureau and National Center for Health Statistics strive to understand how RHCs can potentially impact population health as well as the access and provision of health care services across the nation.
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Addressing Data Gaps:
Four New Lines of Inquiry in the 2017 Economic Census
September 2019
Working Paper Number:
CES-19-28
We describe four new lines of inquiry added to the 2017 Economic Census regarding (i) retail health clinics, (ii) management practices in health care services, (iii) self-service in retail and service industries, and (iv) water use in manufacturing and mining industries. These were proposed by economists from the U.S. Census Bureau's Center for Economic Studies in order to fill data gaps in current Census Bureau products concerning the U.S. economy. The new content addresses such issues as the rise in importance of health care and its complexity, the adoption of automation technologies, and the importance of measuring water, a critical input to many manufacturing and mining industries.
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Gradient Boosting to Address Statistical Problems Arising from Non-Linkage of Census Bureau Datasets
June 2024
Working Paper Number:
CES-24-27
This article introduces the twangRDC package, which contains functions to address non-linkage in US Census Bureau datasets. The Census Bureau's Person Identification Validation System facilitates data linkage by assigning unique person identifiers to federal, third party, decennial census, and survey data. Not all records in these datasets can be linked to the reference file and as such not all records will be assigned an identifier. This article is a tutorial for using the twangRDC to generate nonresponse weights to account for non-linkage of person records across US Census Bureau datasets.
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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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The Person Identification Validation System (PVS): Applying the Center for Administrative Records Research and Applications' (CARRA) Record Linkage Software
July 2014
Working Paper Number:
carra-2014-01
The Census Bureau's Person Identification Validation System (PVS) assigns unique person identifiers to federal, commercial, census, and survey data to facilitate linkages across and within files. PVS uses probabilistic matching to assign a unique Census Bureau identifier for each person. The PVS matches incoming files to reference files created with data from the Social Security Administration (SSA) Numerical Identification file, and SSA data with addresses obtained from federal files. This paper describes the PVS methodology from editing input data to creating the final file.
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Person Matching in Historical Files using the Census Bureau's Person Validation System
September 2014
Working Paper Number:
carra-2014-11
The recent release of the 1940 Census manuscripts enables the creation of longitudinal data spanning the whole of the twentieth century. Linked historical and contemporary data would allow unprecedented analyses of the causes and consequences of health, demographic, and economic change. The Census Bureau is uniquely equipped to provide high quality linkages of person records across datasets. This paper summarizes the linkage techniques employed by the Census Bureau and discusses utilization of these techniques to append protected identification keys to the 1940 Census.
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Automating Response Evaluation For Franchising Questions On The 2017 Economic Census
July 2019
Working Paper Number:
CES-19-20
Between the 2007 and 2012 Economic Censuses (EC), the count of franchise-affiliated establishments declined by 9.8%. One reason for this decline was a reduction in resources that the Census Bureau was able to dedicate to the manual evaluation of survey responses in the franchise section of the EC. Extensive manual evaluation in 2007 resulted in many establishments, whose survey forms indicated they were not franchise-affiliated, being recoded as franchise-affiliated. No such evaluation could be undertaken in 2012. In this paper, we examine the potential of using external data harvested from the web in combination with machine learning methods to automate the process of evaluating responses to the franchise section of the 2017 EC. Our method allows us to quickly and accurately identify and recode establishments have been mistakenly classified as not being franchise-affiliated, increasing the unweighted number of franchise-affiliated establishments in the 2017 EC by 22%-42%.
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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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Longitudinal Establishment And Enterprise Microdata (LEEM) Documentation
May 1998
Working Paper Number:
CES-98-09
This paper introduces and documents the new Longitudinal Enterprise and Establishment Microdata (LEEM) database, which has been constructed by Census' Economic Planning and Coordination Division under contract to the Office of Advocacy of the U.S. Small Business Administration. The LEEM links three years (1990, 1994, and 1995) of basic data for each private sector establishment with payroll in any of those years, along with data on the firm to which the establishment belongs each year. The LEEM data will facilitate both broader and more detailed analysis of patterns of job creation and destruction in the U.S., as well as research on the structure and dynamics of U.S. businesses. This paper provides documentation of the construction of LEEM data, summary data on most variables in the database, comparisons of the annual data with that of the nearly identical County Business Patterns, and distributions of establishments and their employment by the size of their firms. This is followed by a simple analysis of changes over time in the attributes of surviving establishments, and a brief discussion of turnover (business births and deaths) in the population and gross changes in employment associated with both establishment turnover and with surviving establishments. It concludes with a summary of the strengths and weaknesses of the LEEM.
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The Longitudinal Business Database
July 2002
Working Paper Number:
CES-02-17
As the largest federal statistical agency and primary collector of data on businesses, households and individuals, the Census Bureau each year conducts numerous surveys intended to provide statistics on a wide range of topics about the population and economy of the United States. The Census Bureau's decennial population and quinquennial economic censuses are unique, providing information on all U.S. households and business establishments, respectively.
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Squeezing More Out of Your Data: Business Record Linkage with Python
November 2018
Working Paper Number:
CES-18-46
Integrating data from different sources has become a fundamental component of modern data analytics. Record linkage methods represent an important class of tools for accomplishing such integration. In the absence of common disambiguated identifiers, researchers often must resort to ''fuzzy" matching, which allows imprecision in the characteristics used to identify common entities across dfferent datasets. While the record linkage literature has identified numerous individually useful fuzzy matching techniques, there is little consensus on a way to integrate those techniques within a
single framework. To this end, we introduce the Multiple Algorithm Matching for Better Analytics (MAMBA), an easy-to-use, flexible, scalable, and transparent software platform for business record linkage applications using Census microdata. MAMBA leverages multiple string comparators to assess the similarity of records using a machine learning algorithm to disambiguate matches. This software represents a transparent tool for researchers seeking to link external business data to the Census Business Register files.
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