Skill shortages are a drain on society. They hamper economic opportunities for individuals, slow growth for firms, and impede labor productivity in aggregate. Therefore, the ability to understand and predict skill shortages in advance is critical for policy-makers and educators to help alleviate their adverse effects. This research implements a high-performing Machine Learning approach to predict occupational skill shortages. In addition, we demonstrate methods to analyze the underlying skill demands of occupations in shortage and the most important features for predicting skill shortages. For this work, we compile a unique dataset of both Labor Demand and Labor Supply occupational data in Australia from 2012 to 2018. This includes data from 7.7 million job advertisements (ads) and 20 official labor force measures. We use these data as explanatory variables and leverage the XGBoost classifier to predict yearly skills shortage classifications for 132 standardized occupations. The models we construct achieve macro-F1 average performance scores of up to 83 per cent. Our results show that job ads data and employment statistics were the highest performing feature sets for predicting year-to-year skills shortage changes for occupations. We also find that features such as 'Hours Worked', years of 'Education', years of 'Experience', and median 'Salary' are highly important features for predicting occupational skill shortages. This research provides a robust data-driven approach for predicting and analyzing skill shortages, which can assist policy-makers, educators, and businesses to prepare for the future of work.
The Changing Global Division of Labor in Software: Emergence and Diffusion of New Programming Skills across IT Hubs
Corvinus University of Budapest（布达佩斯科维努斯大学）；Centre for Economic and Regional Studies（经济与区域研究中心）；Complexity Science Hub（复杂性科学中心）；Copernicus Institute of Sustainable Development, Utrecht University（乌得勒支大学哥白尼可持续发展研究所）；Transforming Economies Lab, Interdisciplinary Transformation University Austria (IT:U)（奥地利跨学科转型大学转型经济实验室）
Course-Skill Atlas: A national longitudinal dataset of skills taught in U.S. higher education curricula
University of Pittsburgh（匹兹堡大学）；Chapman University（查普曼大学）；Stanford University（斯坦福大学）；Teachers College, Columbia University（哥伦比亚大学教师学院）；Massachusetts Institute of Technology（麻省理工学院）
Intranational Skill-relevance Model of the Immigrant's Self-selection: Further Evidence of the Stylized Fact from the E-9 Employment Permit System (EPS)
"Generate" the Future of Work through AI: Empirical Evidence from Online Labor Markets
School of Management, University of Science and Technology of China（中国科学技术大学管理学院）；Michael G. Foster School of Business, University of Washington（华盛顿大学迈克尔·G·福斯特商学院）
AI Skills Improve Job Prospects: Causal Evidence from a Hiring Experiment
Oxford Internet Institute, University of Oxford（牛津大学牛津互联网研究院）；Institute for New Economic Thinking, Oxford Martin School（牛津马丁学院新经济思维研究所）；Bruegel（布勒哲尔研究所）；Humboldt Institute for Internet and Society（洪堡互联网与社会研究所）；Copenhagen Center for Social Data Science, University of Copenhagen（哥本哈根大学哥本哈根社会数据科学中心）；Medialab, Sciences Po Paris（巴黎政治学院媒体实验室）
The Urban-Rural Divide in the Age of Artificial Intelligence: Assessing the Effects of Technology and Automation on Regional Labor Markets
Canadian international school（加拿大国际学校）；Vietnam Australia International School（越南澳大利亚国际学校）；Truong Thanh Vietnam Group（长盛越南集团）；Hanoi College of Industry and Trade（河内工贸学院）
