CREAT: Census Research Exploration and Analysis Tool

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Viewing papers 1 through 3 of 3

• 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

  
  
  SYNTHETIC DATA FOR SMALL AREA ESTIMATION IN THE AMERICAN COMMUNITY SURVEY
  

  April 2013

  Authors: Joseph W. Sakshaug, Trivellore Raghunathan

  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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