Minimum Wage and Employment: Escaping the Parametric Straitjacket

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.