IDEAS home Printed from https://ideas.repec.org/p/cen/wpaper/15-18.html
   My bibliography  Save this paper

Modeling Endogenous Mobility in Wage Determiniation

Author

Listed:
  • John M. Abowd
  • Kevin McKinney
  • Ian M. Schmutte

Abstract

We evaluate the bias from endogenous job mobility in fixed-effects estimates of worker- and firm-specific earnings heterogeneity using longitudinally linked employer-employee data from the LEHD infrastructure file system of the U.S. Census Bureau. First, we propose two new residual diagnostic tests of the assumption that mobility is exogenous to unmodeled determinants of earnings. Both tests reject exogenous mobility. We relax the exogenous mobility assumptions by modeling the evolution of the matched data as an evolving bipartite graph using a Bayesian latent class framework. Our results suggest that endogenous mobility biases estimated firm effects toward zero. To assess validity, we match our estimates of the wage components to out-of-sample estimates of revenue per worker. The corrected estimates attribute much more of the variation in revenue per worker to variation in match quality and worker quality than the uncorrected estimates.

Suggested Citation

  • John M. Abowd & Kevin McKinney & Ian M. Schmutte, 2015. "Modeling Endogenous Mobility in Wage Determiniation," Working Papers 15-18, Center for Economic Studies, U.S. Census Bureau.
  • Handle: RePEc:cen:wpaper:15-18
    as

    Download full text from publisher

    File URL: https://www2.census.gov/ces/wp/2015/CES-WP-15-18.pdf
    File Function: First version, 2015
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. David Card & Jörg Heining & Patrick Kline, 2013. "Workplace Heterogeneity and the Rise of West German Wage Inequality," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 128(3), pages 967-1015.
    2. Andrews, M.J. & Gill, L. & Schank, T. & Upward, R., 2012. "High wage workers match with high wage firms: Clear evidence of the effects of limited mobility bias," Economics Letters, Elsevier, vol. 117(3), pages 824-827.
    3. Susana Iranzo & Fabiano Schivardi & Elisa Tosetti, 2008. "Skill Dispersion and Firm Productivity: An Analysis with Employer-Employee Matched Data," Journal of Labor Economics, University of Chicago Press, vol. 26(2), pages 247-285, April.
    4. John Abowd & Francis Kramarz & Paul Lengermann & Kevin McKinney & Sébastien Roux, 2012. "Persistent inter‐industry wage differences: rent sharing and opportunity costs," IZA Journal of Labor Economics, Springer;Forschungsinstitut zur Zukunft der Arbeit GmbH (IZA), vol. 1(1), pages 1-25, December.
    5. John M. Abowd & Francis Kramarz & David N. Margolis, 1999. "High Wage Workers and High Wage Firms," Econometrica, Econometric Society, vol. 67(2), pages 251-334, March.
    6. Hoff P.D. & Raftery A.E. & Handcock M.S., 2002. "Latent Space Approaches to Social Network Analysis," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 1090-1098, December.
    7. Cory Koedel & Julian R. Betts, 2011. "Does Student Sorting Invalidate Value-Added Models of Teacher Effectiveness? An Extended Analysis of the Rothstein Critique," Education Finance and Policy, MIT Press, vol. 6(1), pages 18-42, January.
    8. Ian M. Schmutte, 2015. "Job Referral Networks and the Determination of Earnings in Local Labor Markets," Journal of Labor Economics, University of Chicago Press, vol. 33(1), pages 1-32.
    9. Combes, Pierre-Philippe & Duranton, Gilles & Gobillon, Laurent, 2008. "Spatial wage disparities: Sorting matters!," Journal of Urban Economics, Elsevier, vol. 63(2), pages 723-742, March.
    10. Jesse Rothstein, 2010. "Teacher Quality in Educational Production: Tracking, Decay, and Student Achievement," The Quarterly Journal of Economics, Oxford University Press, vol. 125(1), pages 175-214.
    11. Krishna, Pravin & Poole, Jennifer P. & Senses, Mine Zeynep, 2014. "Wage Effects of Trade Reform with Endogenous Worker Mobility," Journal of International Economics, Elsevier, vol. 93(2), pages 239-252.
    12. Robert Gibbons & Lawrence F. Katz & Thomas Lemieux & Daniel Parent, 2005. "Comparative Advantage, Learning, and Sectoral Wage Determination," Journal of Labor Economics, University of Chicago Press, vol. 23(4), pages 681-724, October.
    13. Woodcock, Simon D., 2008. "Wage differentials in the presence of unobserved worker, firm, and match heterogeneity," Labour Economics, Elsevier, vol. 15(4), pages 771-793, August.
    14. John M. Abowd & Bryce E. Stephens & Lars Vilhuber & Fredrik Andersson & Kevin L. McKinney & Marc Roemer & Simon Woodcock, 2009. "The LEHD Infrastructure Files and the Creation of the Quarterly Workforce Indicators," NBER Chapters, in: Producer Dynamics: New Evidence from Micro Data, pages 149-230, National Bureau of Economic Research, Inc.
    15. Kosorok, Michael R., 2000. "Monte Carlo error estimation for multivariate Markov chains," Statistics & Probability Letters, Elsevier, vol. 46(1), pages 85-93, January.
    16. Francis Kramarz & Stephen Machin & Amine Ouazad, 2015. "Using Compulsory Mobility to Identify School Quality and Peer Effects," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 77(4), pages 566-587, August.
    17. Schmutte, Ian M., 2014. "Free to Move? A Network Analytic Approach for Learning the Limits to Job Mobility," Labour Economics, Elsevier, vol. 29(C), pages 49-61.
    18. John M. Abowd & Robert H. Creecy & Francis Kramarz, 2002. "Computing Person and Firm Effects Using Linked Longitudinal Employer-Employee Data," Longitudinal Employer-Household Dynamics Technical Papers 2002-06, Center for Economic Studies, U.S. Census Bureau.
    19. Helwege, Jean, 1992. "Sectoral Shifts and Interindustry Wage Differentials," Journal of Labor Economics, University of Chicago Press, vol. 10(1), pages 55-84, January.
    20. Timothy Dunne & J. Bradford Jensen & Mark J. Roberts, 2009. "Producer Dynamics: New Evidence from Micro Data," NBER Books, National Bureau of Economic Research, Inc, number dunn05-1, March.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. John M. Abowd & Francis Kramarz & Sébastien Pérez-Duarte & Ian M. Schmutte, 2018. "Sorting Between and Within Industries: A Testable Model of Assortative Matching," Annals of Economics and Statistics, GENES, issue 129, pages 1-32.
    2. Herrera-Araujo, Daniel & Rochaix, Lise, 2020. "Does the Value per Statistical Life vary with age or baseline health? Evidence from a compensating wage study in France," Journal of Environmental Economics and Management, Elsevier, vol. 103(C).
    3. Peter Hull, 2018. "Estimating Treatment Effects in Mover Designs," Papers 1804.06721, arXiv.org.
    4. Südekum, Jens & Dauth, Wolfgang & Findeisen, Sebastian, 2016. "Adjusting to Globalization - Evidence from Worker-Establishment Matches in Germany," CEPR Discussion Papers 11045, C.E.P.R. Discussion Papers.
    5. Bødker, Jonas Ehn & Maibom, Jonas & Vejlin, Rune Majlund, 2018. "Decomposing the Exporter Wage Gap: Selection or Differential Returns?," IZA Discussion Papers 11998, Institute of Labor Economics (IZA).
    6. Jinkins, David & Morin, Annaïg, 2018. "Job-to-job transitions, sorting, and wage growth," Labour Economics, Elsevier, vol. 55(C), pages 300-327.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ian M. Schmutte, 2015. "Job Referral Networks and the Determination of Earnings in Local Labor Markets," Journal of Labor Economics, University of Chicago Press, vol. 33(1), pages 1-32.
    2. John M. Abowd & Francis Kramarz & Sébastien Pérez-Duarte & Ian M. Schmutte, 2018. "Sorting Between and Within Industries: A Testable Model of Assortative Matching," Annals of Economics and Statistics, GENES, issue 129, pages 1-32.
    3. Bombardini, Matilde & Orefice, Gianluca & Tito, Maria D., 2019. "Does exporting improve matching? Evidence from French employer-employee data," Journal of International Economics, Elsevier, vol. 117(C), pages 229-241.
    4. Mario Macis & Fabiano Schivardi, 2016. "Exports and Wages: Rent Sharing, Workforce Composition, or Returns to Skills?," Journal of Labor Economics, University of Chicago Press, vol. 34(4), pages 945-978.
    5. Rasmus Lentz & Jean Marc Robin & Suphanit Piyapromdee, 2018. "On Worker and Firm Heterogeneity in Wages and Employment Mobility: Evidence from Danish Register Data," 2018 Meeting Papers 469, Society for Economic Dynamics.
    6. Bastien Drut & Richard Duhautois, 2017. "Assortative Matching Using Soccer Data," Journal of Sports Economics, , vol. 18(5), pages 431-447, June.
    7. Woodcock Simon D, 2010. "Heterogeneity and Learning in Labor Markets," The B.E. Journal of Economic Analysis & Policy, De Gruyter, vol. 10(1), pages 1-69, September.
    8. Koen Jochmans & Martin Weidner, 2019. "Fixed‐Effect Regressions on Network Data," Econometrica, Econometric Society, vol. 87(5), pages 1543-1560, September.
    9. Krishna, Pravin & Poole, Jennifer P. & Senses, Mine Zeynep, 2014. "Wage Effects of Trade Reform with Endogenous Worker Mobility," Journal of International Economics, Elsevier, vol. 93(2), pages 239-252.
    10. Stéphane Bonhomme & Kerstin Holzheu & Thibaut Lamadon & Elena Manresa & Magne Mogstad & Bradley Setzler, 2023. "How Much Should We Trust Estimates of Firm Effects and Worker Sorting?," Journal of Labor Economics, University of Chicago Press, vol. 41(2), pages 291-322.
    11. Andrea R. Lamorgese & Elisabetta Olivieri & Marco Paccagnella, 2019. "The Wage Premium in Italian Cities," Italian Economic Journal: A Continuation of Rivista Italiana degli Economisti and Giornale degli Economisti, Springer;Società Italiana degli Economisti (Italian Economic Association), vol. 5(2), pages 251-279, July.
    12. Bødker, Jonas Ehn & Maibom, Jonas & Vejlin, Rune Majlund, 2018. "Decomposing the Exporter Wage Gap: Selection or Differential Returns?," IZA Discussion Papers 11998, Institute of Labor Economics (IZA).
    13. Godøy, Anna & Huitfeldt, Ingrid, 2020. "Regional variation in health care utilization and mortality," Journal of Health Economics, Elsevier, vol. 71(C).
    14. Jinkins, David & Morin, Annaïg, 2018. "Job-to-job transitions, sorting, and wage growth," Labour Economics, Elsevier, vol. 55(C), pages 300-327.
    15. Jae Song & David J Price & Fatih Guvenen & Nicholas Bloom & Till von Wachter, 2019. "Firming Up Inequality," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 134(1), pages 1-50.
    16. Andrew B. Bernard & J. Bradford Jensen & Stephen J. Redding & Peter K. Schott, 2018. "Global Firms," Journal of Economic Literature, American Economic Association, vol. 56(2), pages 565-619, June.
    17. Barth, Erling & Davis, James C. & Freeman, Richard B. & McElheran, Kristina, 2023. "Twisting the demand curve: Digitalization and the older workforce," Journal of Econometrics, Elsevier, vol. 233(2), pages 443-467.
    18. Bassi, Vittorio & Nyshadham, Anant & Tamayo, Jorge & Adhvaryu, Achyuta, 2020. "No Line Left Behind: Assortative Matching Inside the Firm," CEPR Discussion Papers 14554, C.E.P.R. Discussion Papers.
    19. David Card & Ana Rute Cardoso & Joerg Heining & Patrick Kline, 2018. "Firms and Labor Market Inequality: Evidence and Some Theory," Journal of Labor Economics, University of Chicago Press, vol. 36(S1), pages 13-70.

    More about this item

    Keywords

    Earnings heterogeneity; Mobility Bias; Latent Class Model; Markov Chain Monte Carlo;
    All these keywords.

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:cen:wpaper:15-18. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Dawn Anderson (email available below). General contact details of provider: https://edirc.repec.org/data/cesgvus.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.