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Adjusting imperfect data: overview and case studies


  • Lars Vilhuber


Research users of large administrative have to adjust their data for quirks, problems, and issues that are inevitable when working with these kinds of datasets. Not all solutions to these problems are identical, and how they differ may affect how the data is to be interpreted. Some elements of the data, such as the unit of observation, remain fundamentally different, and it is important to keep that in mind when comparing data across countries. In this paper (written for Lazear and Shaw, 2007), we focus on the differences in the underlying data for a selection of country datasets. We describe two data elements that remain fundamentally different across countries -- the sampling or data collection methodology, and the basic unit of analysis (establishment or firm) -- and the extent to which they differ. We then proceed to document some of the problems that affect longitudinally linked administrative data in general, and we describe some of the solutions analysts and statistical agencies have implemented, and explore, through a select set of case studies, how each adjustment or absence thereof might affect the data.
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  • Lars Vilhuber, 2004. "Adjusting imperfect data: overview and case studies," Longitudinal Employer-Household Dynamics Technical Papers 2004-05, Center for Economic Studies, U.S. Census Bureau.
  • Handle: RePEc:cen:tpaper:2004-05

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    References listed on IDEAS

    1. Jacobson, Louis S & LaLonde, Robert J & Sullivan, Daniel G, 1993. "Earnings Losses of Displaced Workers," American Economic Review, American Economic Association, vol. 83(4), pages 685-709, September.
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    3. Abowd, John M. & Kramarz, Francis, 1999. "The analysis of labor markets using matched employer-employee data," Handbook of Labor Economics,in: O. Ashenfelter & D. Card (ed.), Handbook of Labor Economics, edition 1, volume 3, chapter 40, pages 2629-2710 Elsevier.
    4. Roberto Leombruni & Roberto Quaranta, 2002. "The Unemployment Route to Versatility," LABORatorio R. Revelli Working Papers Series 16, LABORatorio R. Revelli, Centre for Employment Studies.
    5. Anderson, Patricia M. & Meyer, Bruce D., 2000. "The effects of the unemployment insurance payroll tax on wages, employment, claims and denials," Journal of Public Economics, Elsevier, vol. 78(1-2), pages 81-106, October.
    6. Bruno Contini & Claudia Villosio, 2003. "Worker Mobility, Job Displacement and Wage Dynamics: Italy 1985-91," LABORatorio R. Revelli Working Papers Series 24, LABORatorio R. Revelli, Centre for Employment Studies.
    7. John M. Abowd & Patrick Corbel & Francis Kramarz, 1999. "The Entry And Exit Of Workers And The Growth Of Employment: An Analysis Of French Establishments," The Review of Economics and Statistics, MIT Press, vol. 81(2), pages 170-187, May.
    8. Parent, Daniel, 2000. "Industry-Specific Capital and the Wage Profile: Evidence from the National Longitudinal Survey of Youth and the Panel Study of Income Dynamics," Journal of Labor Economics, University of Chicago Press, vol. 18(2), pages 306-323, April.
    9. Gary Benedetto & John Haltiwanger & Julia Lane & Kevin McKinney, 2003. "Using Worker Flows in the Analysis of the Firm," Longitudinal Employer-Household Dynamics Technical Papers 2003-09, Center for Economic Studies, U.S. Census Bureau, revised May 2004.
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    Cited by:

    1. Tanja Hethey-Maier & Johannes F. Schmieder, 2013. "Does the Use of Worker Flows Improve the Analysis of Establishment Turnover? Evidence from German Administrative Data," Schmollers Jahrbuch : Journal of Applied Social Science Studies / Zeitschrift für Wirtschafts- und Sozialwissenschaften, Duncker & Humblot, Berlin, vol. 133(4), pages 477-510.
    2. Chiara Criscuolo & Peter N. Gal & Carlo Menon, 2014. "The Dynamics of Employment Growth: New Evidence from 18 Countries," CEP Discussion Papers dp1274, Centre for Economic Performance, LSE.
    3. Hethey, Tanja & Schmieder, Johannes F., 2010. "Using worker flows in the analysis of establishment turnover : evidence from German administrative data," FDZ Methodenreport 201006_en, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    4. Hijzen, Alexander & Martins, Pedro S. & Schank, Thorsten & Upward, Richard, 2013. "Foreign-owned firms around the world: A comparative analysis of wages and employment at the micro-level," European Economic Review, Elsevier, vol. 60(C), pages 170-188.
    5. John M. Abowd & Kevin L. McKinney & Lars Vilhuber, 2009. "The Link between Human Capital, Mass Layoffs, and Firm Deaths," NBER Chapters,in: Producer Dynamics: New Evidence from Micro Data, pages 447-472 National Bureau of Economic Research, Inc.

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    JEL classification:

    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • C82 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Macroeconomic Data; Data Access
    • J0 - Labor and Demographic Economics - - General


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