Heterogeneous wage structure effects: a partial European East-West comparison
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- 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.
- Wager, Stefan & Athey, Susan, 2017. "Estimation and Inference of Heterogeneous Treatment Effects Using Random Forests," Research Papers 3576, Stanford University, Graduate School of Business.
- 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.
- Knaus, Michael C. & Lechner, Michael & Strittmatter, Anthony, 2018. "Machine Learning Estimation of Heterogeneous Causal Effects: Empirical Monte Carlo Evidence," IZA Discussion Papers 12039, Institute of Labor Economics (IZA).
- Lechner, Michael & Knaus, Michael C. & Strittmatter, Anthony, 2018. "Machine Learning Estimation of Heterogeneous Causal Effects: Empirical Monte Carlo Evidence," CEPR Discussion Papers 13402, C.E.P.R. Discussion Papers.
- Knaus, Michael C. & Lechner, Michael & anthony.strittmatter@unisg.ch, 2018. "Machine Learning Estimation of Heterogeneous Causal Effects: Empirical Monte Carlo Evidence," Economics Working Paper Series 1817, University of St. Gallen, School of Economics and Political Science.
- Michael C. Knaus & Michael Lechner & Anthony Strittmatter, 2018. "Machine Learning Estimation of Heterogeneous Causal Effects: Empirical Monte Carlo Evidence," Papers 1810.13237, arXiv.org, revised Dec 2018.
- Daniel Jacob, 2021. "CATE meets ML -- The Conditional Average Treatment Effect and Machine Learning," Papers 2104.09935, arXiv.org, revised Apr 2021.
- 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.
- Michael Knaus & Michael Lechner & Anthony Strittmatter, 2017. "Heterogeneous Employment Effects of Job Search Programmes: A Machine Learning Approach," Papers 1709.10279, arXiv.org, revised May 2018.
- Lechner, Michael & Strittmatter, Anthony & Knaus, Michael C., 2017. "Heterogeneous Employment Effects of Job Search Programmes: A Machine Learning Approach," CEPR Discussion Papers 12224, C.E.P.R. Discussion Papers.
- Knaus, Michael C. & Lechner, Michael & Strittmatter, Anthony, 2017. "Heterogeneous Employment Effects of Job Search Programmes: A Machine Learning Approach," IZA Discussion Papers 10961, Institute of Labor Economics (IZA).
- Knaus, Michael C. & Lechner, Michael & Strittmatter, Anthony, 2017. "Heterogeneous Employment Effects of Job Search Programmes: A Machine Learning Approach," Economics Working Paper Series 1711, University of St. Gallen, School of Economics and Political Science.
- 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.
- Nicole Fortin & Thomas Lemieux & Sergio Firpo, 2010. "Decomposition Methods in Economics," NBER Working Papers 16045, National Bureau of Economic Research, Inc.
- 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.
- 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.
- 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".
- Susan Athey & Stefan Wager, 2021.
"Policy Learning With Observational Data,"
Econometrica, Econometric Society, vol. 89(1), pages 133-161, January.
- Susan Athey & Stefan Wager, 2017. "Policy Learning with Observational Data," Papers 1702.02896, arXiv.org, revised Sep 2020.
- 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.
- 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.
- Daniel Jacob, 2021. "CATE meets ML," Digital Finance, Springer, vol. 3(2), pages 99-148, June.
- Anna Baiardi & Andrea A. Naghi, 2021. "The Value Added of Machine Learning to Causal Inference: Evidence from Revisited Studies," Papers 2101.00878, arXiv.org.
- 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.
- Kayo MURAKAMI & Hideki SHIMADA & Yoshiaki USHIFUSA & Takanori IDA, 2020. "Heterogeneous Treatment Effects of Nudge and Rebate:Causal Machine Learning in a Field Experiment on Electricity Conservation," Discussion papers e-20-003, Graduate School of Economics , Kyoto University.
- 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.
- 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.
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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
NEP fields
This paper has been announced in the following NEP Reports:- NEP-LMA-2023-06-19 (Labor Markets - Supply, Demand, and Wages)
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