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Labor market entry and earnings dynamics: Bayesian inference using mixtures‐of‐experts Markov chain clustering

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  • Sylvia Frühwirth‐Schnatter
  • Christoph Pamminger
  • Andrea Weber
  • Rudolf Winter‐Ebmer

Abstract

This paper analyzes patterns in the earnings development of young labor market en- trants over their life cycle. We identify four distinctly di®erent types of transition patterns between discrete earnings states in a large administrative data set. Further, we investigate the e®ects of labor market conditions at the time of entry on the probability of belonging to each transition type. To estimate our statistical model we use a model-based clustering approach. The statistical challenge in our application comes from the di±culty in extending distance-based clustering approaches to the problem of identify groups of similar time series in a panel of discrete-valued time series. We use Markov chain clustering, proposed by Pam- minger and FrÄuhwirth-Schnatter (2010), which is an approach for clustering discrete-valued time series obtained by observing a categorical variable with several states. This method is based on ¯nite mixtures of ¯rst-order time-homogeneous Markov chain models. In order to analyze group membership we present an extension to this approach by formulating a prob- abilistic model for the latent group indicators within the Bayesian classi¯cation rule using a multinomial logit model.
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  • Sylvia Frühwirth‐Schnatter & Christoph Pamminger & Andrea Weber & Rudolf Winter‐Ebmer, 2012. "Labor market entry and earnings dynamics: Bayesian inference using mixtures‐of‐experts Markov chain clustering," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 27(7), pages 1116-1137, November.
  • Handle: RePEc:wly:japmet:v:27:y:2012:i:7:p:1116-1137
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    Cited by:

    1. Sylvia Kaufmann, 2014. "K-state switching models with time-varying transition distributions – Does credit growth signal stronger effects of variables on inflation?," Working Papers 14.04, Swiss National Bank, Study Center Gerzensee.
    2. Kaufmann, Sylvia, 2015. "K-state switching models with time-varying transition distributions—Does loan growth signal stronger effects of variables on inflation?," Journal of Econometrics, Elsevier, vol. 187(1), pages 82-94.
    3. Beatrice Brunner & Andreas Kuhn, 2010. "The Impact of Labor Market Entry Condition on Initial Job Assignment, Human Capital Accumulation, and Wages," NRN working papers 2010-15, The Austrian Center for Labor Economics and the Analysis of the Welfare State, Johannes Kepler University Linz, Austria.
    4. Sylvia Frühwirth-Schnatter & Stefan Pittner & Andrea Weber & Rudolf Winter-Ebmer, 2016. "Analysing Plant Closure Effects Using Time-Varying Mixture-of-Experts Markov Chain Clustering," Economics working papers 2016-10, Department of Economics, Johannes Kepler University Linz, Austria.
    5. Brunner, Beatrice & Kuhn, Andreas, 2010. "The Impact of Labor Market Entry Conditions on Initial Job Assignment, Human Capital Accumulation, and Wages," IZA Discussion Papers 5360, Institute of Labor Economics (IZA).
    6. Gregor Zens, 2018. "Bayesian shrinkage in mixture of experts models: Identifying robust determinants of class membership," Papers 1809.04853, arXiv.org, revised Jan 2019.
    7. Jan Vávra & Arnošt Komárek, 2023. "Classification based on multivariate mixed type longitudinal data with an application to the EU-SILC database," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 17(2), pages 369-406, June.
    8. Beatrice Brunner & Andreas Kuhn, 2010. "The impact of labor market entry conditions on initial job assignment, human capital accumulation, and wages," IEW - Working Papers 520, Institute for Empirical Research in Economics - University of Zurich.
    9. Sylvia Frühwirth-Schnatter, 2011. "Panel data analysis: a survey on model-based clustering of time series," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 5(4), pages 251-280, December.
    10. Deguilhem, Thibaud & Berrou, Jean-Philippe & Combarnous, François, 2017. "Using your ties to get a worse job? The differential effects of social networks on quality of employment: Evidence from Colombia," MPRA Paper 78628, University Library of Munich, Germany.
    11. Frühwirth-Schnatter, Sylvia & Pamminger, Christoph & Weber, Andrea & Winter-Ebmer, Rudolf, 2014. "When Is The Best Time To Give Birth?," Economics Series 308, Institute for Advanced Studies.
    12. Christoph Pamminger & Regina Tüchler, 2011. "A Bayesian Analysis of Female Wage Dynamics Using Markov Chain Clustering," NRN working papers 2011-04, The Austrian Center for Labor Economics and the Analysis of the Welfare State, Johannes Kepler University Linz, Austria.
    13. Roy Costilla & Ivy Liu & Richard Arnold & Daniel Fernández, 2019. "Bayesian model-based clustering for longitudinal ordinal data," Computational Statistics, Springer, vol. 34(3), pages 1015-1038, September.
    14. Gregor Zens, 2019. "Bayesian shrinkage in mixture-of-experts models: identifying robust determinants of class membership," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 13(4), pages 1019-1051, December.
    15. Laura C. Dawkins & Daniel B. Williamson & Stewart W. Barr & Sally R. Lampkin, 2020. "‘What drives commuter behaviour?': a Bayesian clustering approach for understanding opposing behaviours in social surveys," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(1), pages 251-280, January.
    16. Marco Berrettini & Giuliano Galimberti & Saverio Ranciati, 2023. "Semiparametric finite mixture of regression models with Bayesian P-splines," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 17(3), pages 745-775, September.
    17. Murat K. Munkin, 2022. "Count Roy model with finite mixtures," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(6), pages 1160-1181, September.
    18. Winter-Ebmer, Rudolf & Weber, Andrea & Frühwirth-Schnatter, Sylvia & Pamminger, Christoph, 2014. "When Is The Best Time To Give Birth - Career Effects Of Early Birth Decisions," CEPR Discussion Papers 10132, C.E.P.R. Discussion Papers.
    19. Daniel Fernández & Richard Arnold & Shirley Pledger & Ivy Liu & Roy Costilla, 2019. "Finite mixture biclustering of discrete type multivariate data," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 13(1), pages 117-143, March.

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