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Heterogeneous wage structure effects: a partial European East-West comparison

Author

Listed:
  • Olga Takács

    (Corvinus University of Budapest)

  • János Vincze

    (Corvinus University of Budapest and Centre for Economic and Regional Studies)

Abstract

We estimate heterogeneous wage structure effects for country-pairs within the EU by the Causal Forest algorithm, then identify groups of workers with the highest and lowest discrepancies in terms of wage differentials. We find that, in the East-West comparison, age is the most consistently differentiating factor. People over 40 are most adversely treated in the East relative to the West, and especially those who have no tertiary education and work in small or medium-sized enterprises.

Suggested Citation

  • Olga Takács & János Vincze, 2023. "Heterogeneous wage structure effects: a partial European East-West comparison," CERS-IE WORKING PAPERS 2305, Institute of Economics, Centre for Economic and Regional Studies.
    • Handle: RePEc:has:discpr:2305
    as

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    References listed on IDEAS

    as
    1. Fortin, Nicole & Lemieux, Thomas & Firpo, Sergio, 2011. "Decomposition Methods in Economics," Handbook of Labor Economics, in: O. Ashenfelter & D. Card (ed.), Handbook of Labor Economics, edition 1, volume 4, chapter 1, pages 1-102, Elsevier.
    2. Michael C Knaus & Michael Lechner & Anthony Strittmatter, 2021. "Machine learning estimation of heterogeneous causal effects: Empirical Monte Carlo evidence," The Econometrics Journal, Royal Economic Society, vol. 24(1), pages 134-161.
    3. Stefan Wager & Susan Athey, 2018. "Estimation and Inference of Heterogeneous Treatment Effects using Random Forests," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(523), pages 1228-1242, July.
    4. Jan Drahokoupil & Agnieszka Piasna, 2018. "What is behind low wages in central and eastern Europe?," Post-Communist Economies, Taylor & Francis Journals, vol. 30(4), pages 421-439, July.
    5. Iga Magda & François Rycx & Ilan Tojerow & Daphné Valsamis, 2011. "Wage differentials across sectors in Europe," The Economics of Transition, The European Bank for Reconstruction and Development, vol. 19(4), pages 749-769, October.
    6. Jacob, Daniel, 2021. "CATE meets ML: Conditional average treatment effect and machine learning," IRTG 1792 Discussion Papers 2021-005, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    7. Susan Athey & Stefan Wager, 2021. "Policy Learning With Observational Data," Econometrica, Econometric Society, vol. 89(1), pages 133-161, January.
    8. Jonathan M.V. Davis & Sara B. Heller, 2020. "Rethinking the Benefits of Youth Employment Programs: The Heterogeneous Effects of Summer Jobs," The Review of Economics and Statistics, MIT Press, vol. 102(4), pages 664-677, October.
    9. Iga Magda & David Marsden & Simone Moriconi, 2012. "Collective Agreements, Wages, and Firms' Cohorts: Evidence from Central Europe," ILR Review, Cornell University, ILR School, vol. 65(3), pages 607-629, July.
    10. Michael C. Knaus & Michael Lechner & Anthony Strittmatter, 2022. "Heterogeneous Employment Effects of Job Search Programs: A Machine Learning Approach," Journal of Human Resources, University of Wisconsin Press, vol. 57(2), pages 597-636.
    11. Daniel Jacob, 2021. "CATE meets ML," Digital Finance, Springer, vol. 3(2), pages 99-148, June.
    12. Anna Baiardi & Andrea A. Naghi, 2021. "The Value Added of Machine Learning to Causal Inference: Evidence from Revisited Studies," Papers 2101.00878, arXiv.org.
    13. Reshmaan Hussam & Natalia Rigol & Benjamin N. Roth, 2022. "Targeting High Ability Entrepreneurs Using Community Information: Mechanism Design in the Field," American Economic Review, American Economic Association, vol. 112(3), pages 861-898, March.
    14. Daniel Jacob, 2021. "CATE meets ML -- The Conditional Average Treatment Effect and Machine Learning," Papers 2104.09935, arXiv.org, revised Apr 2021.
    15. Kayo Murakami & Hideki Shimada & Yoshiaki Ushifusa & Takanori Ida, 2022. "Heterogeneous Treatment Effects Of Nudge And Rebate: Causal Machine Learning In A Field Experiment On Electricity Conservation," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 63(4), pages 1779-1803, November.
    16. Anna Baiardi & Andrea A. Naghi, 2021. "The Value Added of Machine Learning to Causal Inference: Evidence from Revisited Studies," Tinbergen Institute Discussion Papers 21-001/V, Tinbergen Institute.
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    More about this item

    Keywords

    Wage structure effects; Generalized Random Forest Regression; Conditional average treatment effects; Wage convergence in the EU;
    All these keywords.

    JEL classification:

    • J31 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Wage Level and Structure; Wage Differentials
    • F66 - International Economics - - Economic Impacts of Globalization - - - Labor
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General

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