Essays on Automation and Future of Skills

Abstract

The adoption of automation in the labour market introduces a complex array of effectson employment, skill demand, and human capital investment. This thesis encompassesthree distinct but interrelated studies exploring the multifaceted impacts of technologicaladvancement and automation on the labour market, focusing on different types of au-tomation and how it affects human capital investment and the demand for skills.Chapter 1 delves into the differentiated impact of various forms of automation on thehuman capital investment. Utilising data from the international survey of adult skills(PIAAC) across 22 European countries, the study categorises automation based on tasksand technology (e.g., software, robots, AI) and explores how these factors influence work-ers’ decisions to invest in training. The findings reveal a nuanced landscape where theeffect of automation on training varies significantly depending on the type of technologyand the level of a country’s technological readiness. For example, while AI is associatedwith an increase in the investment in human capital, the reverse is true for other automa-tion technologies.Chapters 2 and 3 exploit online job advertisements data in the UK to measure the changesin the employers’ demand for skills. One of the key challenges when using this type datais to correctly identify occupation from the advertisement text. In Chapter 2, I addressthis issue by proposing a novel methodology for classifying occupations using advancedlanguage processing models. To implement it, I used web-scraping to collect data onjob advertisements in the UK. Incorporating job titles and descriptions into the classi-fication process, in particular skill requirements, significantly improves the accuracy ofoccupational classification from job advertisements, opening new areas of labour marketresearch based on job ads.Building on the previous chapter, chapter 3 uses online vacancy data to examine thechanges in the demand for specific skills and the associated wage premiums within andacross occupations, especially during and after the COVID-19 pandemic. The analysisis based on the universe of job advertisements in UK, aggregated by online job plat-form Adzuna. This study uses a novel text classification method I developed by using aLarge Language Model (LLM), specifically the GPT-4 API, to categorise job postings intoskill groups. It finds that workers with ICT and AI skills command significantly higherwages compared to those with interpersonal skills. Interestingly, the study suggests thatCOVID-19 has had no long-term impact on either the demand for specific skills withinan occupation or the wages offered for those skills.Collectively, these studies offer critical insights into the evolving dynamics of the labourmarket in the face of technological change, emphasising the importance of adaptabilityin skill development and the potential that the advanced data analysis techniques offerfor informing policy and practice