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State dependence and wage dynamics: a heterogeneous Markov chain model for wage mobility in Austria

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  • Andrea Weber

    () (Institute for Advanced Studies)

Abstract

The behaviour of individual movements in the wage distribution over time can be described by a Markov process. To investigate wage mobility in terms of transitions between quintiles in the wage distribution we apply a mixed effects panel estimation method suggested by Honoré and Kyriazidou (2000). This method of mobility measurement is robust to data contamination like all methods that treat fractiles. Moreover it allows for the inclusion of exogenous variables that change over time. We apply the estimator to a set of individual data form the Austrian social security records and find that disregarding unobserved heterogeneity greatly underestimates wage mobility. Simulated earnings profiles show that women are less mobile than men and have a tendency to be stuck in the lower part of the wage distribution.

Suggested Citation

  • Andrea Weber, 2002. "State dependence and wage dynamics: a heterogeneous Markov chain model for wage mobility in Austria," 10th International Conference on Panel Data, Berlin, July 5-6, 2002 D2-2, International Conferences on Panel Data.
  • Handle: RePEc:cpd:pd2002:d2-2
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    References listed on IDEAS

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    1. Moshe Buchinsky & Jennifer Hunt, 1999. "Wage Mobility In The United States," The Review of Economics and Statistics, MIT Press, vol. 81(3), pages 351-368, August.
    2. Cowell, Frank & Schluter, Christian, 1998. "Income mobility : a robust approach," LSE Research Online Documents on Economics 2210, London School of Economics and Political Science, LSE Library.
    3. Machin, Stephen, 1996. "Wage Inequality in the UK," Oxford Review of Economic Policy, Oxford University Press, vol. 12(1), pages 47-64, Spring.
    4. Levy, Frank & Murnane, Richard J, 1992. "U.S. Earnings Levels and Earnings Inequality: A Review of Recent Trends and Proposed Explanations," Journal of Economic Literature, American Economic Association, vol. 30(3), pages 1333-1381, September.
    5. Chamberlain, Gary, 1984. "Panel data," Handbook of Econometrics,in: Z. Griliches† & M. D. Intriligator (ed.), Handbook of Econometrics, edition 1, volume 2, chapter 22, pages 1247-1318 Elsevier.
    6. Bo E. Honoré & Ekaterini Kyriazidou, 2000. "Panel Data Discrete Choice Models with Lagged Dependent Variables," Econometrica, Econometric Society, vol. 68(4), pages 839-874, July.
    7. Magnac, Thierry, 2000. "Subsidised Training and Youth Employment: Distinguishing Unobserved Heterogeneity from State Dependence in Labour Market Histories," Economic Journal, Royal Economic Society, vol. 110(466), pages 805-837, October.
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    Citations

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    Cited by:

    1. Ayllón, Sara & Fusco, Alessio, 2017. "Are income poverty and perceptions of financial difficulties dynamically interrelated?," Journal of Economic Psychology, Elsevier, vol. 61(C), pages 103-114.
    2. Biewen, Martin, 2004. "Measuring State Dependence in Individual Poverty Status: Are There Feedback Effects to Employment Decisions and Household Composition?," IZA Discussion Papers 1138, Institute for the Study of Labor (IZA).
    3. 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.
    4. Ayllón, Sara, 2009. "Modelling state dependence and feedback effects between poverty, employment and parental home emancipation among European youth," Working Papers 10, VATT Institute for Economic Research.
    5. 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.
    6. Martin Biewen, 2009. "Measuring state dependence in individual poverty histories when there is feedback to employment status and household composition," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(7), pages 1095-1116.
    7. Sara Ayllón, 2015. "Youth Poverty, Employment, and Leaving the Parental Home in Europe," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 61(4), pages 651-676, December.
    8. Fruhwirth-Schnatter, Sylvia & Fruhwirth, Rudolf, 2007. "Auxiliary mixture sampling with applications to logistic models," Computational Statistics & Data Analysis, Elsevier, vol. 51(7), pages 3509-3528, April.
    9. Sylvia Frühwirth-Schnatter & Christoph Pamminger, 2009. "Bayesian Clustering of Categorical Time Series Using Finite Mixtures of Markov Chain Models," NRN working papers 2009-07, The Austrian Center for Labor Economics and the Analysis of the Welfare State, Johannes Kepler University Linz, Austria.

    More about this item

    Keywords

    Wage mobility; Markov process; fixed effects panel estimation;

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • J31 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Wage Level and Structure; Wage Differentials
    • J60 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - General

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