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Estimating Measurement Error in SIPP Annual Job Earnings: A Comparison of Census Bureau Survey and SSA Administrative Data

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  • John Abowd
  • Martha Stinson

Abstract

We quantify sources of variation in annual job earnings data collected by the Survey of Income and Program Participation (SIPP) to determine how much of the variation is the result of measurement error. Jobs reported in the SIPP are linked to jobs reported in an administrative database, the Detailed Earnings Records (DER) drawn from the Social Security Administration’s Master Earnings File, a universe file of all earnings reported on W-2 tax forms. As a result of the match, each job potentially has two earnings observations per year: survey and administrative. Unlike previous validation studies, both of these earnings measures are viewed as noisy measures of some underlying true amount of annual earnings. While the existence of survey error resulting from respondent mistakes or misinterpretation is widely accepted, the idea that administrative data are also error-prone is new. Possible sources of employer reporting error, employee under-reporting of compensation such as tips, and general differences between how earnings may be reported on tax forms and in surveys, necessitates the discarding of the assumption that administrative data are a true measure of the quantity that the survey was designed to collect. In addition, errors in matching SIPP and DER jobs, a necessary task in any use of administrative data, also contribute to measurement error in both earnings variables. We begin by comparing SIPP and DER earnings for different demographic and education groups of SIPP respondents. We also calculate different measures of changes in earnings for individuals switching jobs. We estimate a standard earnings equation model using SIPP and DER earnings and compare the resulting coefficients. Finally exploiting the presence of individuals with multiple jobs and shared employers over time, we estimate an econometric model that includes random person and firm effects, a common error component shared by SIPP and DER earnings, and two independent error components that represent the variation unique to each earnings measure. We compare the variance components from this model and consider how the DER and SIPP differ across unobservable components.

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File URL: ftp://ftp2.census.gov/ces/wp/2011/CES-WP-11-20.pdf
File Function: First version, 2011
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Bibliographic Info

Paper provided by Center for Economic Studies, U.S. Census Bureau in its series Working Papers with number 11-20.

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Length: 61 pages
Date of creation: Jul 2011
Date of revision:
Handle: RePEc:cen:wpaper:11-20

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References

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  1. Angrist, Joshua D. & Krueger, Alan B., 1999. "Empirical strategies in labor economics," Handbook of Labor Economics, in: O. Ashenfelter & D. Card (ed.), Handbook of Labor Economics, edition 1, volume 3, chapter 23, pages 1277-1366 Elsevier.
  2. John M. Abowd & Francis Kramarz & David N. Margolis, 1994. "High-Wage Workers and High-Wage Firms," CIRANO Working Papers 94s-23, CIRANO.
  3. Bound, John & Krueger, Alan B, 1991. "The Extent of Measurement Error in Longitudinal Earnings Data: Do Two Wrongs Make a Right?," Journal of Labor Economics, University of Chicago Press, vol. 9(1), pages 1-24, January.
  4. John M. Abowd & Lars Vilhuber, 2002. "The Sensitivity of Economic Statistics to Coding Errors in Personal Identifiers," Longitudinal Employer-Household Dynamics Technical Papers 2002-17, Center for Economic Studies, U.S. Census Bureau, revised Mar 2003.
  5. Mellow, Wesley & Sider, Hal, 1983. "Accuracy of Response in Labor Market Surveys: Evidence and Implications," Journal of Labor Economics, University of Chicago Press, vol. 1(4), pages 331-44, October.
  6. Abowd, John M & Card, David, 1989. "On the Covariance Structure of Earnings and Hours Changes," Econometrica, Econometric Society, vol. 57(2), pages 411-45, March.
  7. Bound, John & Brown, Charles & Mathiowetz, Nancy, 2001. "Measurement error in survey data," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 5, chapter 59, pages 3705-3843 Elsevier.
  8. Duncan, Greg J & Hill, Daniel H, 1985. "An Investigation of the Extent and Consequences of Measurement Error in Labor-Economic Survey Data," Journal of Labor Economics, University of Chicago Press, vol. 3(4), pages 508-32, October.
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Cited by:
  1. Audra J. Bowlus & Huju Liu, 2012. "The Contributions of Search and Human Capital to Earnings Growth Over the Life Cycle," University of Western Ontario, CIBC Centre for Human Capital and Productivity Working Papers 20122, University of Western Ontario, CIBC Centre for Human Capital and Productivity.
  2. Wojciech Kopczuk & Emmanuel Saez & Jae Song, 2007. "Uncovering the American Dream: Inequality and Mobility in Social Security Earnings Data since 1937," NBER Working Papers 13345, National Bureau of Economic Research, Inc.
  3. Low, Hamish & Meghir, Costas & Pistaferri, Luigi, 2007. "Wage Risk and Employment Risk Over the Life Cycle," CEPR Discussion Papers 6187, C.E.P.R. Discussion Papers.
  4. Katharine Abraham & John Haltiwanger & Kristin Sandusky & James Spletzer, 2009. "Exploring Differences in Employment between Household and Establishment Data," Working Papers 09-09, Center for Economic Studies, U.S. Census Bureau.
  5. Jung, Juergen & Hall, Diane M. Harnek & Rhoads, Thomas, 2013. "Does the availability of parental health insurance affect the college enrollment decision of young Americans?," Economics of Education Review, Elsevier, vol. 32(C), pages 49-65.
  6. Lisa M. Dragoset & Gary S. Fields, 2006. "U.S. Earnings Mobility: Comparing Survey-Based and Administrative-Based Estimates," Working Papers 55, ECINEQ, Society for the Study of Economic Inequality.

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