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Minimum wage and employment: Escaping the parametric straitjacket

Listed author(s):
  • Cabras, Stefano
  • Fidrmuc, Jan
  • de Dios Tena Horrillo, Juan

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.

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Paper provided by Kiel Institute for the World Economy (IfW) in its series Economics Discussion Papers with number 2016-17.

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Date of creation: 2016
Handle: RePEc:zbw:ifwedp:201617
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  1. Allegretto, Sylvia & Dube, Arindrajit & Reich, Michael & Zipperer, Ben, 2013. "Credible Research Designs for Minimum Wage Studies," Institute for Research on Labor and Employment, Working Paper Series qt3hk7s3fw, Institute of Industrial Relations, UC Berkeley.
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  10. Hristos Doucouliagos & T. D. Stanley, 2009. "Publication Selection Bias in Minimum-Wage Research? A Meta-Regression Analysis," British Journal of Industrial Relations, London School of Economics, vol. 47(2), pages 406-428, 06.
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