Wage Indexation and Jobs. A Machine Learning Approach

Gert Bijnens, Shyngys Karimov and Jozef Konings

No 643831, Working Papers of Department of Economics, Leuven from KU Leuven, Faculty of Economics and Business (FEB), Department of Economics, Leuven

Abstract: In 2015 Belgium suspended the automatic wage indexation for a period of 12 months in order to boost competitiveness and increase employment. This paper uses a novel, machine learning based approach to construct a counterfactual experiment. This artificial counterfactual allows us to analyze the employment impact of suspending the indexation mechanism. We find a positive impact on employment of 0.5 percent which corresponds to a labor demand elasticity of -0.25. This effect is more pronounced for manufacturing firms, where the impact on employment can reach 2 percent, which corresponds to a labor demand elasticity of -1.

Keywords: labor demand; wage elasticity; counterfactual analysis; artificial control; machine learning (search for similar items in EconPapers)
Date: 2019-11-27
New Economics Papers: this item is included in nep-big and nep-cmp
Note: paper number 82
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Forthcoming in VIVES Discussion Paper

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Working Paper: Wage Indexation and Jobs. A Machine Learning Approach (2019)
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Persistent link: https://EconPapers.repec.org/RePEc:ete:ceswps:643831

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