Wage Indexation and Jobs. A Machine Learning Approach

Gert Bijnens, Shyngys Karimov and Jozef Konings

No 643831, Working Papers of VIVES - Research Centre for Regional Economics from KU Leuven, Faculty of Economics and Business (FEB), VIVES - Research Centre for Regional Economics

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:vivwps:643831

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