Integrating Multiple U.S. Census Bureau Data Assets to Create Standardized Profiles of Program Participants
January 2026
Working Paper Number:
CES-26-01
Abstract
The Foundations for Evidence-Based Policymaking Act of 2018 (Evidence Act) directed federal agencies to systematically use data when making policy decisions. In response, the U.S. Census Bureau established the Evidence Group within its Center for Economic Studies (CES). With an interdisciplinary team of economists, sociologists, and statisticians, the Evidence Group can support the broader federal government in their efforts to use existing data to improve program operations without increasing respondent burden. For federal agencies administering social safety net and business assistance programs in particular, the team provides a no-cost evidence-building service that links program records to Census Bureau data assets and creates a series of standardized tables describing participants, their economic outcomes prior to program entry, and the communities where they live. These tables provide partner agencies with the detailed information they need to better understand their participants and potentially make their programs more accountable and effective in reaching their target populations. In this working paper, we describe the standardized tables themselves as well as the data assets available at the Census Bureau to create these tables, the data files produced by the table production process, and the methodology used to merge and harmonize data on participants and subsequently calculate unbiased and accurate estimates. We conclude with a brief discussion of steps taken to ensure confidentiality and data security. This documentation is intended to facilitate proper use and understanding of the standardized tables by partner agencies as well as researchers who are interested in leveraging these tools to explore characteristics of their samples of interest.
Document Tags and Keywords
Keywords
Keywords are automatically generated using KeyBERT, a powerful and innovative
keyword extraction tool that utilizes BERT embeddings to ensure high-quality and contextually relevant
keywords.
By analyzing the content of working papers, KeyBERT identifies terms and phrases that capture the essence of the
text, highlighting the most significant topics and trends. This approach not only enhances searchability but
provides connections that go beyond potentially domain-specific author-defined keywords.
:
analysis,
economist,
researcher,
statistical,
data census,
census data,
survey,
study,
agency,
respondent,
research,
yearly,
statistician,
federal,
population,
census bureau,
census employment,
census survey,
paper census,
individuals census
Tags
Tags are automatically generated using a pretrained language model from spaCy, which excels at
several tasks, including entity tagging.
The model is able to label words and phrases by part-of-speech,
including "organizations." By filtering for frequent words and phrases labeled as "organizations", papers are
identified to contain references to specific institutions, datasets, and other organizations.
:
Bureau of Labor Statistics,
Social Security Administration,
Center for Economic Studies,
Establishment Micro Properties,
Current Population Survey,
Bureau of Labor,
Decennial Census,
Employer Identification Numbers,
Survey of Income and Program Participation,
Social Security,
Unemployment Insurance,
American Community Survey,
Social Security Number,
Longitudinal Employer Household Dynamics,
Protected Identification Key,
Employment History File,
Social and Economic Supplement,
Quarterly Census of Employment and Wages,
Local Employment Dynamics,
Master Address File,
Disclosure Review Board,
Person Validation System,
Person Identification Validation System,
MAFID,
MAF-ARF
Similar Working Papers
Similarity between working papers are determined by an unsupervised neural
network model
know as Doc2Vec.
Doc2Vec is a model that represents entire documents as fixed-length vectors, allowing for the
capture of semantic meaning in a way that relates to the context of words within the document. The model learns to
associate a unique vector with each document while simultaneously learning word vectors, enabling tasks such as
document classification, clustering, and similarity detection by preserving the order and structure of words. The
document vectors are compared using cosine similarity/distance to determine the most similar working papers.
Papers identified with 🔥 are in the top 20% of similarity.
The 10 most similar working papers to the working paper 'Integrating Multiple U.S. Census Bureau Data Assets to Create Standardized Profiles of Program Participants' are listed below in order of similarity.
-
Working PaperDeveloping a Residence Candidate File for Use With Employer-Employee Matched Data
January 2017
Working Paper Number:
CES-17-40
This paper describes the Longitudinal Employer-Household Dynamics (LEHD) program's ongoing efforts to use administrative records in a predictive model that describes residence locations for workers. This project was motivated by the discontinuation of a residence file produced elsewhere at the U.S. Census Bureau. The goal of the Residence Candidate File (RCF) process is to provide the LEHD Infrastructure Files with residence information that maintains currency with the changing state of administrative sources and represents uncertainty in location as a probability distribution. The discontinued file provided only a single residence per person/year, even when contributing administrative data may have contained multiple residences. This paper describes the motivation for the project, our methodology, the administrative data sources, the model estimation and validation results, and the file specifications. We find that the best prediction of the person-place model provides similar, but superior, accuracy compared with previous methods and performs well for workers in the LEHD jobs frame. We outline possibilities for further improvement in sources and modeling as well as recommendations on how to use the preference weights in downstream processing.View Full Paper PDF
-
Working PaperConfidentiality Protection in the Census Bureau Quarterly Workforce Indicators
February 2006
Working Paper Number:
tp-2006-02
The QuarterlyWorkforce Indicators are new estimates developed by the Census Bureau's Longitudinal Employer-Household Dynamics Program as a part of its Local Employment Dynamics partnership with 37 state Labor Market Information offices. These data provide detailed quarterly statistics on employment, accessions, layoffs, hires, separations, full-quarter employment (and related flows), job creations, job destructions, and earnings (for flow and stock categories of workers). The data are released for NAICS industries (and 4-digit SICs) at the county, workforce investment board, and metropolitan area levels of geography. The confidential microdata - unemployment insurance wage records, ES-202 establishment employment, and Title 13 demographic and economic information - are protected using a permanent multiplicative noise distortion factor. This factor distorts all input sums, counts, differences and ratios. The released statistics are analytically valid - measures are unbiased and time series properties are preserved. The confidentiality protection is manifested in the release of some statistics that are flagged as "significantly distorted to preserve confidentiality." These statistics differ from the undistorted statistics by a significant proportion. Even for the significantly distorted statistics, the data remain analytically valid for time series properties. The released data can be aggregated; however, published aggregates are less distorted than custom postrelease aggregates. In addition to the multiplicative noise distortion, confidentiality protection is provided by the estimation process for the QWIs, which multiply imputes all missing data (including missing establishment, given UI account, in the UI wage record data) and dynamically re-weights the establishment data to provide state-level comparability with the BLS's Quarterly Census of Employment and Wages.View Full Paper PDF
-
Working PaperLODES Design and Methodology Report: Methodology Version 7
August 2025
Working Paper Number:
CES-25-52
The purpose of this report is to document the important features of Version 7 of the LEHD Origin-Destination Employment Statistics (LODES) processing system. This includes data sources, data processing methodology, confidentiality protection methodology, some quality measures, and a high-level description of the published data. The intended audience for this document includes LODES data users, Local Employment Dynamics (LED) Partnership members, U.S. Census Bureau management, program quality auditors, and current and future research and development staff members.View Full Paper PDF
-
Working PaperThe LEHD Infrastructure Files and the Creation of the Quarterly Workforce Indicators
January 2006
Working Paper Number:
tp-2006-01
The Longitudinal Employer-Household Dynamics (LEHD) Program at the U.S. Census Bureau, with the support of several national research agencies, has built a set of infrastructure files using administrative data provided by state agencies, enhanced with information from other administrative data sources, demographic and economic (business) surveys and censuses. The LEHD Infrastructure Files provide a detailed and comprehensive picture of workers, employers, and their interaction in the U.S. economy. Beginning in 2003 and building on this infrastructure, the Census Bureau has published the Quarterly Workforce Indicators (QWI), a new collection of data series that offers unprecedented detail on the local dynamics of labor markets. Despite the fine detail, confidentiality is maintained due to the application of state-of-the-art confidentiality protection methods. This article describes how the input files are compiled and combined to create the infrastructure files. We describe the multiple imputation methods used to impute in missing data and the statistical matching techniques used to combine and edit data when a direct identifier match requires improvement. Both of these innovations are crucial to the success of the final product. Finally, we pay special attention to the details of the confidentiality protection system used to protect the identity and micro data values of the underlying entities used to form the published estimates. We provide a brief description of public-use and restricted-access data files with pointers to further documentation for researchers interested in using these data.View Full Paper PDF
-
Working PaperThe Creation of the Employment Dynamics Estimates
July 2002
Working Paper Number:
tp-2002-13
View Full Paper PDF
-
Working PaperDesign Comparison of LODES and ACS Commuting Data Products
October 2014
Working Paper Number:
CES-14-38
The Census Bureau produces two complementary data products, the American Community Survey (ACS) commuting and workplace data and the Longitudinal Employer-Household Dynamics (LEHD) Origin-Destination Employment Statistics (LODES), which can be used to answer questions about spatial, economic, and demographic questions relating to workplaces and home-to-work flows. The products are complementary in the sense that they measure similar activities but each has important unique characteristics that provide information that the other measure cannot. As a result of questions from data users, the Census Bureau has created this document to highlight the major design differences between these two data products. This report guides users on the relative advantages of each data product for various analyses and helps explain differences that may arise when using the products.2,3 As an overview, these two data products are sourced from different inputs, cover different populations and time periods, are subject to different sets of edits and imputations, are released under different confidentiality protection mechanisms, and are tabulated at different geographic and characteristic levels. As a general rule, the two data products should not be expected to match exactly for arbitrary queries and may differ substantially for some queries. Within this document, we compare the two data products by the design elements that were deemed most likely to contribute to differences in tabulated data. These elements are: Collection, Coverage, Geographic and Longitudinal Scope, Job Definition and Reference Period, Job and Worker Characteristics, Location Definitions (Workplace and Residence), Completeness of Geographic Information and Edits/Imputations, Geographic Tabulation Levels, Control Totals, Confidentiality Protection and Suppression, and Related Public-Use Data Products. An in-depth data analysis'in aggregate or with the microdata'between the two data products will be the subject of a future technical report. The Census Bureau has begun a pilot project to integrate ACS microdata with LEHD administrative data to develop an enhanced frame of employment status, place of work, and commuting. The Census Bureau will publish quality metrics for person match rates, residence and workplace match rates, and commute distance comparisons.View Full Paper PDF
-
Working PaperAccess Methods for United States Microdata
August 2007
Working Paper Number:
CES-07-25
Beyond the traditional methods of tabulations and public-use microdata samples, statistical agencies have developed four key alternatives for providing non-government researchers with access to confidential microdata to improve statistical modeling. The first, licensing, allows qualified researchers access to confidential microdata at their own facilities, provided certain security requirements are met. The second, statistical data enclaves, offer qualified researchers restricted access to confidential economic and demographic data at specific agency-controlled locations. Third, statistical agencies can offer remote access, through a computer interface, to the confidential data under automated or manual controls. Fourth, synthetic data developed from the original data but retaining the correlations in the original data have the potential for allowing a wide range of analyses.View Full Paper PDF
-
Working PaperEstimating the U.S. Citizen Voting-Age Population (CVAP) Using Blended Survey Data, Administrative Record Data, and Modeling: Technical Report
April 2023
Working Paper Number:
CES-23-21
This report develops a method using administrative records (AR) to fill in responses for nonresponding American Community Survey (ACS) housing units rather than adjusting survey weights to account for selection of a subset of nonresponding housing units for follow-up interviews and for nonresponse bias. The method also inserts AR and modeling in place of edits and imputations for ACS survey citizenship item nonresponses. We produce Citizen Voting-Age Population (CVAP) tabulations using this enhanced CVAP method and compare them to published estimates. The enhanced CVAP method produces a 0.74 percentage point lower citizen share, and it is 3.05 percentage points lower for voting-age Hispanics. The latter result can be partly explained by omissions of voting-age Hispanic noncitizens with unknown legal status from ACS household responses. Weight adjustments may be less effective at addressing nonresponse bias under those conditions.View Full Paper PDF
-
Working PaperGradient 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.View Full Paper PDF
-
Working PaperEstimating the Graduate Coverage of Post-Secondary Employment Outcomes
September 2025
Working Paper Number:
CES-25-61
This paper proposes a new methodology for estimating the coverage rate of the Post-Secondary Employment Outcomes data product (PSEO), both as a share of new graduates and as a share of total working-age degree holders in the United States. This paper also assesses how representative PSEO is of the broader population of college graduates across an array of institutional and individual characteristics.View Full Paper PDF
