Peer-reviewed publications on labor market dynamics, skill prediction, and machine learning applications in economics.
Joseph Fuller, Matt Sigelman, Nik Dawson
An analysis of how companies often fall short in supporting employee development and growth, with evidence-based recommendations for improvement.
View publicationNik Dawson, Sacha Molitorisz, Marian-Andrei Rizoiu, Peter Fray
Longitudinal analysis of 3,698 journalism job ads revealing the industry crisis. Findings include worsening gender inequity, rising demand for journalism skills despite job losses, and COVID-19's compounding effects.
View publicationNik Dawson, Marian-Andrei Rizoiu, Mary-Anne Williams
A machine learning-based job transitions recommender system that predicts career pathway probabilities using skills data from 8+ million Australian job ads. Includes a leading indicator of AI adoption across industries.
View publicationNik Dawson, Marian-Andrei Rizoiu, Benjamin Johnston, Mary-Anne Williams
Machine learning models achieving 83% accuracy in predicting occupational skill shortages using job ads data and employment statistics. Identifies key predictive features including hours worked, education, experience, and salary.
View publicationNik Dawson, Marian-Andrei Rizoiu, Mary-Anne Williams, Benjamin Johnston
A data-driven method to detect skill shortages in real-time using job advertisements. Proposes five key variables: posting frequency, salary levels, education requirements, experience demands, and job posting predictability.
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