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Measurement error in occupational coding:an analysis on SHARE data


  • Michele Belloni

    () (Department of Economics, University Of Venice C� Foscari)

  • Agar Brugiavini

    () (Department of Economics, University Of Milan, Bicocca)

  • Elena Maschi


  • Kea Tijdens



This article studies the potential measurement errors when coding occupational data. The quality of occupational data is important but often neglected. We recoded open-ended questions on occupation for last and current job in the Dutch SHARE data, using the CASCOT ex-post coding software. The disagreement rate, defined as the percentage of observations coded differently in SHARE and CASCOT, is high even when compared at ISCO 1-digit level (33.7% for last job and 40% for current job). This finding is striking, considering our conservative approach to exclude vague and incomplete answers. The level of miscoding should thus be considered as a lower bound of the �true� miscoding. This highlights the complexity of occupational coding and suggests that measurement error due to miscoding should be taken into account when making statistical analysis or writing econometric models. We tested whether the measurement error is random or correlated to individual or job-related characteristics, and we found that the measurement error is indeed more evident in ISCO-88 groups 1 and 3 and is more pronounced for higher educated individuals and males. These groups may be sorted in occupations that are intrinsically more difficult to be classified, or education and gender may affect the way people describe their jobs.

Suggested Citation

  • Michele Belloni & Agar Brugiavini & Elena Maschi & Kea Tijdens, 2014. "Measurement error in occupational coding:an analysis on SHARE data," Working Papers 2014: 24, Department of Economics, University of Venice "Ca' Foscari".
  • Handle: RePEc:ven:wpaper:2014:24

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    References listed on IDEAS

    1. Jason M. Fletcher & Jody L. Sindelar & Shintaro Yamaguchi, 2011. "Cumulative effects of job characteristics on health," Health Economics, John Wiley & Sons, Ltd., vol. 20(5), pages 553-570, May.
    2. Hartog, Joop, 2000. "Over-education and earnings: where are we, where should we go?," Economics of Education Review, Elsevier, vol. 19(2), pages 131-147, April.
    3. David H. Autor, 2013. "The "Task Approach" to Labor Markets: An Overview," NBER Working Papers 18711, National Bureau of Economic Research, Inc.
    4. David H. Autor & Frank Levy & Richard J. Murnane, 2003. "The skill content of recent technological change: an empirical exploration," Proceedings, Federal Reserve Bank of San Francisco, issue Nov.
    5. Bastian Ravesteijn & Hans van Kippersluis & Eddy van Doorslaer, 2013. "The Wear and Tear on Health: What Is the Role of Occupation?," SOEPpapers on Multidisciplinary Panel Data Research 618, DIW Berlin, The German Socio-Economic Panel (SOEP).
    6. Jeffrey M Wooldridge, 2010. "Econometric Analysis of Cross Section and Panel Data," MIT Press Books, The MIT Press, edition 2, volume 1, number 0262232588, January.
    7. Maarten Goos & Alan Manning, 2007. "Lousy and Lovely Jobs: The Rising Polarization of Work in Britain," The Review of Economics and Statistics, MIT Press, vol. 89(1), pages 118-133, February.
    8. repec:iab:iabjlr:v:46:i:3:p:185-199 is not listed on IDEAS
    9. Autor, David H., 2013. "The "task approach" to labor markets : an overview," Journal for Labour Market Research, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany], vol. 46(3), pages 185-199.
    10. repec:ilo:ilowps:310566 is not listed on IDEAS
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    Cited by:

    1. Gweon Hyukjun & Schonlau Matthias & Steiner Stefan & Kaczmirek Lars & Blohm Michael, 2017. "Three Methods for Occupation Coding Based on Statistical Learning," Journal of Official Statistics, Sciendo, vol. 33(1), pages 101-122, March.
    2. repec:nsr:escoed:escoe-dp-2018-04 is not listed on IDEAS

    More about this item


    occupation; ISCO; disagreement rate; coding software; gender; education;

    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
    • C88 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Other Computer Software
    • J01 - Labor and Demographic Economics - - General - - - Labor Economics: General
    • J21 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Labor Force and Employment, Size, and Structure
    • J82 - Labor and Demographic Economics - - Labor Standards - - - Labor Force Composition

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