Abstract
AbstractParametric 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.
Highlights
The most characteristic feature of the literature on the causal impact of the minimum wage on employment is the general lack of consensus. Neumark and Washer (2007) compile an extensive survey of previous research and conclude that the minimum wage exerts an adverse impact on employment of low-skilled workers and a non-significant impact on total employment
We use the UK Labor Force Survey to estimate the causal impact of the UK national minimum wage (NMW) on employment using a non-parametric Bayesian modelling approach known as the Bayesian Additive Regression Trees (BART ) that was originally developed by Chipman et al (2010) and applied to the analysis of causal inference by Hill (2011), Sparapani et al (2016), Tan et al (2016) and others
To establish a benchmark to compare our results against, we report the results of a probit model as specified in Equations (3) and (4), where job entry and job exit are functions of the dummy variable for the treatment along with a set of covariates (Table 2)
Summary
The most characteristic feature of the literature on the causal impact of the minimum wage on employment is the general lack of consensus. Neumark and Washer (2007) compile an extensive survey of previous research and conclude that the minimum wage exerts an adverse impact on employment of low-skilled workers and a non-significant impact on total employment. We use the UK Labor Force Survey to estimate the causal impact of the UK national minimum wage (NMW) on employment using a non-parametric Bayesian modelling approach known as the Bayesian Additive Regression Trees (BART ) that was originally developed by Chipman et al (2010) and applied to the analysis of causal inference by Hill (2011), Sparapani et al (2016), Tan et al (2016) and others. Both study the impact of the NMW by applying the difference-in-difference technique to the UK Labor Force Survey data, and find that the NMW does not have a significant adverse effect on employment Unlike these papers, we do not consider a specific year’s increase in the minimum wage but take into account all NMW changes since its introduction in 1999. By comparing these two dates, we can determine the precise age of each respondent on the day of the survey. We know whether a particular individual is below or above the age threshold at which they become eligible for a different (higher) NMW rate
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
