Changing Labour Market Dynamics in Australia: Skill Shortages, Job Transitions, and Artificial Intelligence Technology Adoption

Dawson, Nikolas Jack

Changing Labour Market Dynamics in Australia: Skill Shortages, Job Transitions, and Artificial Intelligence Technology Adoption

Dawson, Nikolas Jack

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Publication Type:: Thesis
Issue Date:: 2021

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Field	Value	Language
dc.contributor.author	Dawson, Nikolas Jack
dc.date.accessioned	2021-10-10T23:32:41Z
dc.date.available	2021-10-10T23:32:41Z
dc.date.issued	2021
dc.identifier.uri	http://hdl.handle.net/10453/150972
dc.description	University of Technology Sydney. Faculty of Engineering and Information Technology.	en_US.UTF-8
dc.description.abstract	The Australian labour market is in the midst of significant structural changes. Emerging technologies, such as Artificial Intelligence (AI), are changing the demands for skills and tasks within jobs. Additionally, the economic crisis induced by COVID-19, in conjunction with other factors, have accelerated these adjustments. Three major issues for the Australian labour market are (1) skill shortages, (2) job transitions, and (3) AI adoption at the firm-level. This thesis by compilation addresses each of these issues in a series of four standalone papers. The first paper puts forward a range of indicators to detect skill shortages from a time series dataset of online job advertisements (ads). The second paper develops a machine learning model that accurately predicts skill shortages from job ads data and employment statistics. The third paper conducts an in-depth case study of the journalism jobs crisis in Australia, examining both the changes in labour demand (using job ads) and labour supply (using employment statistics) from 2012-2020. Last, the fourth paper develops a novel method to measure the similarity between sets of skills from real-time job ads data. These similarity measures are then combined with other labour market variables to build a 'Job Transitions Recommender System' that accurately predicts transition pathways between occupations, validated against a longitudinal household survey. The same skills set similarity method is then used to construct a measure of new technology adoption in labour markets, showcasing AI.	en_US.UTF-8
dc.format	Thesis (PhD)
dc.language.iso	en_US	en_US.UTF-8
dc.relation	https://opus.lib.uts.edu.au/bitstream/10453/150972/2/02whole.pdf
dc.rights	The author owns the copyright in this thesis including all reproduction and reuse rights for the work. The work may not be altered without the permission of the copyright owner. Attribution is essential when quoting or paraphrasing from this thesis.
dc.rights	au.edu.uts.lib/ppc
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Changing Labour Market Dynamics in Australia: Skill Shortages, Job Transitions, and Artificial Intelligence Technology Adoption	en_US.UTF-8
dc.type	Thesis
utslib.copyright.status	open_access	*

Abstract:

The Australian labour market is in the midst of significant structural changes. Emerging technologies, such as Artificial Intelligence (AI), are changing the demands for skills and tasks within jobs. Additionally, the economic crisis induced by COVID-19, in conjunction with other factors, have accelerated these adjustments. Three major issues for the Australian labour market are (1) skill shortages, (2) job transitions, and (3) AI adoption at the firm-level. This thesis by compilation addresses each of these issues in a series of four standalone papers. The first paper puts forward a range of indicators to detect skill shortages from a time series dataset of online job advertisements (ads). The second paper develops a machine learning model that accurately predicts skill shortages from job ads data and employment statistics. The third paper conducts an in-depth case study of the journalism jobs crisis in Australia, examining both the changes in labour demand (using job ads) and labour supply (using employment statistics) from 2012-2020. Last, the fourth paper develops a novel method to measure the similarity between sets of skills from real-time job ads data. These similarity measures are then combined with other labour market variables to build a 'Job Transitions Recommender System' that accurately predicts transition pathways between occupations, validated against a longitudinal household survey. The same skills set similarity method is then used to construct a measure of new technology adoption in labour markets, showcasing AI.

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