Minimum wage and employment: Escaping the parametric straitjacket

Stefano Cabras, Jan Fidrmuc and Juan de Dios Tena Horrillo

No 2016-17, Economics Discussion Papers from Kiel Institute for the World Economy (IfW Kiel)

Abstract: Parametric regression models are often not flexible enough to capture the true relationships as they tend to rely on arbitrary identification assumptions. Using the UK Labor Force Survey, the authors estimate the causal effect of national minimum wage (NMW) increases on the probability of job entry and job exit by means of a non-parametric Bayesian modelling approach known as Bayesian Additive Regression Trees (BART). The application of this methodology has the important advantage that it does not require ad-hoc assumptions about model fitting, number of covariates and how they interact. They find that the NMW exerts a positive and significant impact on both the probability of job entry and job exit. Although the magnitude of the effect on job entry is higher, the overall effect of NMW is ambiguous as there are many more employed workers. The causal effect of NMW is higher for young workers and in periods of high unemployment and they have a stronger impact on job entry decisions. No significant interactions were found with gender and qualifications.

Keywords: BART; causal inference; regression approach; matching regression (search for similar items in EconPapers)
JEL-codes: C11 C14 C23 J3 J4 (search for similar items in EconPapers)
Date: 2016
New Economics Papers: this item is included in nep-eur
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http://www.economics-ejournal.org/economics/discussionpapers/2016-17
https://www.econstor.eu/bitstream/10419/130748/1/857684159.pdf (application/pdf)

Related works:
Journal Article: Minimum wage and employment: Escaping the parametric straitjacket (2017)
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:ifwedp:201617

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