Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/130748
Authors: 
Cabras, Stefano
Fidrmuc, Jan
de Dios Tena Horrillo, Juan
Year of Publication: 
2016
Series/Report no.: 
Economics Discussion Papers 2016-17
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.
Subjects: 
BART
causal inference
regression approach
matching regression
JEL: 
C23
C11
C14
J3
J4
Creative Commons License: 
http://creativecommons.org/licenses/by/4.0/
Document Type: 
Working Paper

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