Job training and inequality

Benecchi, Andrea (2020) Job training and inequality. PhD thesis, University of Glasgow.

Abstract

This thesis is composed of three chapters. After a brief introduction, the first chapter discusses the definition of on-the-job training, reviews the literature, and reports empirical analyses for the specific case of UK. I decompose training participation and study its evolution in the last 20 years for specific sub-groups of workers, providing new compelling evidence.

The second chapter finds empirical evidence in favour of a relation between training and wage inequality between workers with different education level. On this basis, a dynamic general equilibrium (DGE) model with on-the-job training is developed and calibrated to match UK data. I use the framework to study the redistributional effects of training subsidies. The model is intentionally simple, to allow for a better understanding of the dynamics of macroeconomic variables after policy changes.

The third chapter proposes a more articulated general equilibrium model which features training externalities and distortionary income taxes. I present evidence that motivates the use of this framework, and its underlying assumptions. Thus, I calibrate the model to replicate the salient characteristics of the UK economy and I employ it to evaluate the welfare effects of policy reforms on training. The main contributions of my work are summarised in the conclusions.

• Item Type: Thesis (PhD)
• Qualification Level: Doctoral
• Keywords: economic policy, job training, inequality, wages, human capital, general equilibrium, fiscal policy.
• Subjects: H Social Sciences > HB Economic Theory
• Colleges/Schools: College of Social Sciences > Adam Smith Business School > Economics
• Supervisor's Name: Malley, Prof. James and Angelopoulos, Dr. Konstantinos
• Date of Award: 2020
• Depositing User: dr Andrea benecchi
• Unique ID: glathesis:2020-81349
• Copyright: Copyright of this thesis is held by the author.
• Date Deposited: 12 May 2020 08:15
• Last Modified: 31 Aug 2022 10:40
• Thesis DOI: 10.5525/gla.thesis.81349
• URI: https://theses.gla.ac.uk/id/eprint/81349