IDEAS home Printed from https://ideas.repec.org/a/spr/empeco/v18y1993i3p543-56.html
   My bibliography  Save this article

Two Aspects of Labor Mobility: A Bivariate Poisson Regression Approach

Author

Listed:
  • Jung, Robert C
  • Winkelmann, Rainer

Abstract

The study introduces a distinction between two types of labor monthly. Direct job-to-job changes (which are assumed to be voluntary) and job changes after experiencing an unemployment spell (assumed to be involuntary). Exploiting the close relationship between those two phenomena, we adopt a bivariate regression framework for our empirical analysis of data on male individuals in the German labor market. To account for the non-negative and discrete nature of the two counts of job changes in a ten year interval, a new econometric model is proposed: the Bivariate Poisson regression proves to be superior to the univariate specification. Further, the empirical content of distinguishing between two types of mobility is subject to a test, and in fact, supported by the data: The hypothesis that both measures are observationally equivalent can be rejected.

Suggested Citation

  • Jung, Robert C & Winkelmann, Rainer, 1993. "Two Aspects of Labor Mobility: A Bivariate Poisson Regression Approach," Empirical Economics, Springer, vol. 18(3), pages 543-556.
    • Handle: RePEc:spr:empeco:v:18:y:1993:i:3:p:543-56
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    Citations

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


    Cited by:

    1. Eugenio Miravete, 2014. "Testing for complementarities among countable strategies," Empirical Economics, Springer, vol. 46(4), pages 1521-1544, June.
    2. Greene, William, 2007. "Functional Form and Heterogeneity in Models for Count Data," Foundations and Trends(R) in Econometrics, now publishers, vol. 1(2), pages 113-218, August.
    3. FOUARGE Didier & MUFFELS Ruud & PAVLOPOULOS Dimitris & VERMUNT Jeroen K., 2007. "Who benefits from a job change: The dwarfs or the giants?," IRISS Working Paper Series 2007-16, IRISS at CEPS/INSTEAD.
    4. Caparros, A. & Navarro, M.L., 2005. "Factors Affecting Quits and Layoffs in Spanish Labour Market," Applied Econometrics and International Development, Euro-American Association of Economic Development, vol. 5(4).
    5. M Ataharul Islam & Rafiqul I Chowdhury, 2017. "A generalized right truncated bivariate Poisson regression model with applications to health data," PLOS ONE, Public Library of Science, vol. 12(6), pages 1-13, June.
    6. Antonio Caparrós Ruiz & Mª. Lucía Navarro Gómez, 2002. "Factors affecting quits and layoffs in Spain," Economic Working Papers at Centro de Estudios Andaluces E2002/16, Centro de Estudios Andaluces.
    7. Bermúdez i Morata, Lluís, 2009. "A priori ratemaking using bivariate Poisson regression models," Insurance: Mathematics and Economics, Elsevier, vol. 44(1), pages 135-141, February.
    8. Lluis Bermúdez i Morata, 2008. "A priori ratemaking using bivariate poisson regression models," Working Papers XREAP2008-09, Xarxa de Referència en Economia Aplicada (XREAP), revised Jul 2008.
    9. Bauer, Thomas K. & Million, Andreas & Rotte, Ralph & Zimmermann, Klaus F., 1998. "Immigration Labor and Workplace Safety," IZA Discussion Papers 16, Institute of Labor Economics (IZA).
    10. Najla Qarmalah & Abdulhamid A. Alzaid, 2023. "Zero-Dependent Bivariate Poisson Distribution with Applications," Mathematics, MDPI, vol. 11(5), pages 1-16, February.
    11. Su Pei-Fang & Mau Yu-Lin & Guo Yan & Li Chung-I & Liu Qi & Boice John D. & Shyr Yu, 2017. "Bivariate Poisson models with varying offsets: an application to the paired mitochondrial DNA dataset," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 16(1), pages 47-58, March.
    12. Rajib Dey & M. Ataharul Islam, 2017. "A conditional count model for repeated count data and its application to GEE approach," Statistical Papers, Springer, vol. 58(2), pages 485-504, June.
    13. Tzougas, George & Makariou, Despoina, 2022. "The multivariate Poisson-Generalized Inverse Gaussian claim count regression model with varying dispersion and shape parameters," LSE Research Online Documents on Economics 117197, London School of Economics and Political Science, LSE Library.
    14. Begoña Álvarez & Daniel Miles, 2003. "Gender effect on housework allocation: Evidence from Spanish two-earner couples," Journal of Population Economics, Springer;European Society for Population Economics, vol. 16(2), pages 227-242, May.
    15. Marco Alfò & Giovanni Trovato, 2004. "Semiparametric Mixture Models for Multivariate Count Data, with Application," CEIS Research Paper 51, Tor Vergata University, CEIS.
    16. A. Colin Cameron & Per Johansson, 2004. "Bivariate Count Data Regression Using Series Expansions: With Applications," Working Papers 9815, University of California, Davis, Department of Economics.
    17. Atella, Vincenzo & Deb, Partha, 2008. "Are primary care physicians, public and private sector specialists substitutes or complements? Evidence from a simultaneous equations model for count data," Journal of Health Economics, Elsevier, vol. 27(3), pages 770-785, May.
    18. William Greene, 2007. "Correlation in Bivariate Poisson Regression Model," Working Papers 07-14, New York University, Leonard N. Stern School of Business, Department of Economics.
    19. Chen, Yulong & Ma, Liyuan & Orazem, Peter F., 2023. "The heterogeneous role of broadband access on establishment entry and exit by sector and urban and rural markets," Telecommunications Policy, Elsevier, vol. 47(3).
    20. George Tzougas & Despoina Makariou, 2022. "The multivariate Poisson‐Generalized Inverse Gaussian claim count regression model with varying dispersion and shape parameters," Risk Management and Insurance Review, American Risk and Insurance Association, vol. 25(4), pages 401-417, December.
    21. Miravete, Eugenio, 2009. "Multivariate Sarmanov Count Data Models," CEPR Discussion Papers 7463, C.E.P.R. Discussion Papers.

    More about this item

    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:spr:empeco:v:18:y:1993:i:3:p:543-56. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.