Nothing Special   »   [go: up one dir, main page]
IDEAS home Printed from https://ideas.repec.org/p/dra/wpaper/201301.html
   My bibliography  Save this paper

A Bayesian approach to labour market modelling in dynamic microsimulation

Author

Abstract
The paper presents a novel approach to modelling labour market processes in dynamic microsimulation. The method combines and integrates Bayesian simulation based estimation and simulation of the dependent variables. The approach is applied to a dynamic panel model for hourly wage rates for Danish employees using a large panel data set with 17 years of data for 1995 to 2011. The wage rate model and a parallel model for annual work hours are currently being implemented in SMILE (Simulation Model for Individual Lifecycle Evaluation), a new dynamic microsimulation model for the Danish household sector. The application benefits from the richness of Danish administrative panel data. Nevertheless, the results and the approach have several features that should be of interest to micro-simulators and others. Indeed, the model features both an extraordinarily comprehensive list of dependencies and a rich dynamic structure. Together, these features contribute to ensure that simulations produce realistic cross-sectional distributions and interactions as well as inter-temporal mobility – the key determinants of the quality of a dynamic microsimulation model. In addition to the 'usual' socio-demographic variables (gender, age, ethnicity, experience, education etc.), the dependencies include a more novel set of variables that represent a person’s labour market history, secondary school grade and social heritage (represented by the parent’s education level). The dynamic model structure includes a lagged dependent variable, an auto-correlated error term with a mixed Gaussian distribution for the white noise component, an individual random effect with a mixed Gaussian distribution and permanent effect of a person’s first wage after leaving the education system. The estimation sample is identical to the simulation sample, which allows us to use the same historical detail as well as estimated individual effects – i.e. random effect components – for the simulation of future wage rates. The Bayesian estimation method handles missing observations for the dependent variable – due to either non-employment or temporary non-participation – by treating missing observations as latent variables that are simulated alongside the Bayesian iterations. As a byproduct, the estimations produce model consistent latent wage rates for the unemployed that are useful for labour supply analysis.

Suggested Citation

  • Hans Bækgaard, 2013. "A Bayesian approach to labour market modelling in dynamic microsimulation," DREAM Working Paper Series 201301, Danish Rational Economic Agents Model, DREAM.
    • Handle: RePEc:dra:wpaper:201301
    Note: Conference paper for the 4th General Conference of the International Microsimulation Association
    as

    Download full text from publisher

    File URL: http://www.dreammodel.dk/SMILE/N2013_05.pdf
    File Function: First version, 2013
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Martin Browning & Mette Ejrnæs & Javier Alvarez, 2010. "Modelling Income Processes with Lots of Heterogeneity," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 77(4), pages 1353-1381.
    2. Geweke, John & Keane, Michael, 2000. "An empirical analysis of earnings dynamics among men in the PSID: 1968-1989," Journal of Econometrics, Elsevier, vol. 96(2), pages 293-356, June.
    Full references (including those not matched with items on IDEAS)

    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. John Carter Braxton & Kyle F. Herkenhoff & Jonathan Rothbaum & Lawrence Schmidt, 2021. "Changing Income Risk across the US Skill Distribution: Evidence from a Generalized Kalman Filter," Opportunity and Inclusive Growth Institute Working Papers 55, Federal Reserve Bank of Minneapolis.
    2. Manuel Arellano & Stéphane Bonhomme & Micole De Vera & Laura Hospido & Siqi Wei, 2022. "Income risk inequality: Evidence from Spanish administrative records," Quantitative Economics, Econometric Society, vol. 13(4), pages 1747-1801, November.
    3. Meghir, Costas & Pistaferri, Luigi, 2011. "Earnings, Consumption and Life Cycle Choices," Handbook of Labor Economics, in: O. Ashenfelter & D. Card (ed.), Handbook of Labor Economics, edition 1, volume 4, chapter 9, pages 773-854, Elsevier.
    4. Moira Daly & Dmytro Hryshko & Iourii Manovskii, 2022. "Improving The Measurement Of Earnings Dynamics," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 63(1), pages 95-124, February.
    5. Manuel Arellano & Richard Blundell & Stéphane Bonhomme, 2017. "Earnings and Consumption Dynamics: A Nonlinear Panel Data Framework," Econometrica, Econometric Society, vol. 85, pages 693-734, May.
    6. Magnac, Thierry & Pistolesi, Nicolas & Roux, Sébastien, 2013. "Post schooling human capital investments and the life cycle variance of earnings," IDEI Working Papers 765, Institut d'Économie Industrielle (IDEI), Toulouse.
    7. Fatih Guvenen & Fatih Karahan & Serdar Ozkan & Jae Song, 2015. "What Do Data on Millions of U.S. Workers Reveal about Life-Cycle Earnings Risk?," NBER Working Papers 20913, National Bureau of Economic Research, Inc.
    8. Botosaru, Irene, 2023. "Time-varying unobserved heterogeneity in earnings shocks," Journal of Econometrics, Elsevier, vol. 235(2), pages 1378-1393.
    9. repec:spo:wpmain:info:hdl:2441/dc0ckec3fcb29ms9850j92g21 is not listed on IDEAS
    10. Costanza Naguib & Patrick Gagliardini, 2023. "A Semi-nonparametric Copula Model for Earnings Mobility," Diskussionsschriften dp2302, Universitaet Bern, Departement Volkswirtschaft.
    11. Taisuke Nakata & Christopher Tonetti, 2015. "Small Sample Properties of Bayesian Estimators of Labor Income Processes," Journal of Applied Economics, Taylor & Francis Journals, vol. 18(1), pages 121-148, May.
    12. Fatih Guvenen & Fatih Karahan & Serdar Ozkan & Jae Song, 2021. "What Do Data on Millions of U.S. Workers Reveal About Lifecycle Earnings Dynamics?," Econometrica, Econometric Society, vol. 89(5), pages 2303-2339, September.
    13. Hospido, Laura, 2015. "Wage dynamics in the presence of unobserved individual and job heterogeneity," Labour Economics, Elsevier, vol. 33(C), pages 81-93.
    14. Audra Bowlus & Jean-Marc Robin, 2008. "An international comparison of lifetime labor income values and inequality," Working Papers hal-03459796, HAL.
    15. repec:hal:spmain:info:hdl:2441/dc0ckec3fcb29ms9850j92g21 is not listed on IDEAS
    16. Cristóbal Huneeus & Andrea Repetto, 2005. "The Dynamics of Earnings in Chile," Central Banking, Analysis, and Economic Policies Book Series, in: Jorge Restrepo & Andrea Tokman R. & Norman Loayza (Series Editor) & Klaus Schmidt-Hebbel (Series Edi (ed.),Labor Markets and Institutions, edition 1, volume 8, chapter 12, pages 383-410, Central Bank of Chile.
    17. Serdar Ozkan & Jae Song & Fatih Karahan & Fatih Guvenen, 2013. "What Do Data on Millions of U.S. Workers Say About Labor Income Risk?," 2013 Meeting Papers 1271, Society for Economic Dynamics.
    18. Joachim Hubmer, 2018. "The Job Ladder and its Implications for Earnings Risk," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 29, pages 172-194, July.
    19. Alvarez, Javier & Arellano, Manuel, 2022. "Robust likelihood estimation of dynamic panel data models," Journal of Econometrics, Elsevier, vol. 226(1), pages 21-61.
    20. Masakatsu Okubo, 2015. "Earnings Dynamics and Profile Heterogeneity: Estimates from Japanese Panel Data," The Japanese Economic Review, Japanese Economic Association, vol. 66(1), pages 112-146, March.
    21. Malcolm Keswell, 2004. "Non‐Linear Earnings Dynamics In Post‐Apartheid South Africa," South African Journal of Economics, Economic Society of South Africa, vol. 72(5), pages 913-939, December.
    22. L. Hospido, 2012. "Modelling heterogeneity and dynamics in the volatility of individual wages," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 27(3), pages 386-414, April.

    More about this item

    Keywords

    labour market; modelling; dynamic microsimulation;
    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:dra:wpaper:201301. 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: Peter Stephensen (email available below). General contact details of provider: https://edirc.repec.org/data/dreamdk.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.