CREAT: Census Research Exploration and Analysis Tool

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• Working Paper

  
  
  Finding Needles in Haystacks: Multiple-Imputation Record Linkage Using Machine Learning
  

  November 2021

  Authors: Kristin McCue, John M. Abowd, Matthew D. Shapiro, Trivellore Raghunathan, Margaret C. Levenstein, Joelle Abramowitz, Dhiren Patki, Ann M. Rodgers, Nada Wasi, Dawn Zinsser

  Working Paper Number:

  CES-21-35

  This paper considers the problem of record linkage between a household-level survey and an establishment-level frame in the absence of unique identifiers. Linkage between frames in this setting is challenging because the distribution of employment across establishments is highly skewed. To address these difficulties, this paper develops a probabilistic record linkage methodology that combines machine learning (ML) with multiple imputation (MI). This ML-MI methodology is applied to link survey respondents in the Health and Retirement Study to their workplaces in the Census Business Register. The linked data reveal new evidence that non-sampling errors in household survey data are correlated with respondents' workplace characteristics.
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• Working Paper

  
  
  Optimal Probabilistic Record Linkage: Best Practice for Linking Employers in Survey and Administrative Data
  

  March 2019

  Authors: Kristin McCue, John M. Abowd, Matthew D. Shapiro, Trivellore Raghunathan, Margaret C. Levenstein, Joelle Abramowitz, Dhiren Patki, Ann M. Rodgers, Nada Wasi

  Working Paper Number:

  CES-19-08

  This paper illustrates an application of record linkage between a household-level survey and an establishment-level frame in the absence of unique identifiers. Linkage between frames in this setting is challenging because the distribution of employment across firms is highly asymmetric. To address these difficulties, this paper uses a supervised machine learning model to probabilistically link survey respondents in the Health and Retirement Study (HRS) with employers and establishments in the Census Business Register (BR) to create a new data source which we call the CenHRS. Multiple imputation is used to propagate uncertainty from the linkage step into subsequent analyses of the linked data. The linked data reveal new evidence that survey respondents' misreporting and selective nonresponse about employer characteristics are systematically correlated with wages.
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• Working Paper

  
  
  Effects of a Government-Academic Partnership: Has the NSF-Census Bureau Research Network Helped Improve the U.S. Statistical System?
  

  January 2017

  Authors: Lars Vilhuber, John M. Abowd, Daniel Weinberg, Jerome P. Reiter, Matthew D. Shapiro, Robert F. Belli, Noel Cressie, David C. Folch, Scott H. Holan, Margaret C. Levenstein, Kristen M. Olson, Jolene Smyth, Leen-Kiat Soh, Bruce D. Spencer, Seth E. Spielman, Christopher K. Wikle

  Working Paper Number:

  CES-17-59R

  The National Science Foundation-Census Bureau Research Network (NCRN) was established in 2011 to create interdisciplinary research nodes on methodological questions of interest and significance to the broader research community and to the Federal Statistical System (FSS), particularly the Census Bureau. The activities to date have covered both fundamental and applied statistical research and have focused at least in part on the training of current and future generations of researchers in skills of relevance to surveys and alternative measurement of economic units, households, and persons. This paper discusses some of the key research findings of the eight nodes, organized into six topics: (1) Improving census and survey data collection methods; (2) Using alternative sources of data; (3) Protecting privacy and confidentiality by improving disclosure avoidance; (4) Using spatial and spatio-temporal statistical modeling to improve estimates; (5) Assessing data cost and quality tradeoffs; and (6) Combining information from multiple sources. It also reports on collaborations across nodes and with federal agencies, new software developed, and educational activities and outcomes. The paper concludes with an evaluation of the ability of the FSS to apply the NCRN's research outcomes and suggests some next steps, as well as the implications of this research-network model for future federal government renewal initiatives.
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