Automation and the Labour Market from an applied microeconomic perspective

Purpose: The purpose of this PhD proposal is to investigate the effects of automation in the labour market. As with all major technological advancements in history, automation is not only seen as a potential source of (e.g. productivity) benefits but also as threat to received forms of work organisation. One key aim of this PhD project will be to identify the likely channels through which automation will affect work and unemployment, focussing on issues such as such as the extent to which labour and technology are substitutes or complements. In addition, the project will investigate how automation will affect incentives of both workers (e.g. in trying to mitigate individual effects through occupational choices and levels of education) and governments (e.g. in setting out optimal policy responses aimed at offsetting the adverse effects of automation and/or sustaining the required repositioning of countries in the global division of labour).

Methodology: The PhD will seek to investigate the issues above from an applied microeconomic perspective. Utilising a mainly theoretical methodology, the research may also in the latter stages apply empirical methods to test the main testable hypothesis arising from the theoretical analysis.

Expected contribution to academic literature: Whilst the role of automation has been studied in the past, the literature has yet to reach a point where a solid consensus has emerged. Indeed, with a rapid increase of automation being introduced into production processes it remains a puzzle why there still are so many jobs. This proposal will seek to address this puzzle and contribute to this area of research.
Expected Policy Relevance and Impact: Understanding how a government’s fiscal stance and welfare provision may have unintended and hitherto unknown effects on the method at which automation is introduced. This proposal has the potential as such to shed light on effects that affect workers, firms and society at large and thereby provide a platform that could inform policy makers as they design their fiscal, welfare and educational policies in the shadow of automation.