Job security can never be taken for granted, especially in times of rapid, widespread and unexpected social and economic change. These changes can force workers to transition to new jobs. This may be because new technologies emerge or production is moved abroad. Perhaps it is a global crisis, such as COVID-19, which shutters industries and displaces labor en masse. Regardless of the impetus, people are faced with the challenge of moving between jobs to find new work. Successful transitions typically occur when workers leverage their existing skills in the new occupation. Here, we propose a novel method to measure the similarity between occupations using their underlying skills. We then build a recommender system for identifying optimal transition pathways between occupations using job advertisements (ads) data and a longitudinal household survey. Our results show that not only can we accurately predict occupational transitions (Accuracy = 76%), but we account for the asymmetric difficulties of moving between jobs (it is easier to move in one direction than the other). We also build an early warning indicator for new technology adoption (showcasing Artificial Intelligence), a major driver of rising job transitions. By using real-time data, our systems can respond to labor demand shifts as they occur (such as those caused by COVID-19). They can be leveraged by policy-makers, educators, and job seekers who are forced to confront the often distressing challenges of finding new jobs.
Artificial Intelligence and Skills: Evidence from Contrastive Learning in Online Job Vacancies
Zhejiang University（浙江大学）；Research Center for Regional Coordinated Development, Zhejiang University（浙江大学区域协调发展研究中心）；Center for Research on China Open Economy, Zhejiang University（浙江大学中国经济研究中心）；School of Economics, Zhejiang University（浙江大学经济学院）
Skills or Degree? The Rise of Skill-Based Hiring for AI and Green Jobs
UNESCO（联合国教科文组织）；Oxford Internet Institute（牛津互联网研究院）；Burning Glass Institute（Burning Glass研究所）；University of Oxford（牛津大学）；Humboldt Institute for Internet and Society（洪堡互联网与社会研究所）；Bruegel（布鲁盖尔研究所）；DWG Berlin（柏林DWG）
Remote work expands pathways to upward career mobility
College of Urban and Environmental Sciences, Peking University（北京大学城市与环境学院）；Department of Urban Studies and Planning, Massachusetts Institute of Technology（麻省理工学院城市规划系）；Laboratory for Earth Surface Processes of the Ministry of Education, Peking University（北京大学地表过程教育部重点实验室）
Pay and Beyond: The Evolving Rewards for AI Skills
Oxford Internet Institute, University of Oxford（牛津大学牛津互联网研究所）；Burning Glass Institute（燃烧玻璃研究所）；Institute for New Economic Thinking, Oxford Martin School（牛津马丁学院新经济思想研究所）；Bruegel（布鲁盖尔研究所）
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）
An economic geography dataset of U.S. skill specialization, relatedness, and complexity
Arizona State University（亚利桑那州立大学）；University of Oregon（俄勒冈大学）；Carnegie Mellon University（卡内基梅隆大学）；University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）
The Explore of Knowledge Management Dynamic Capabilities, AI-Driven Knowledge Sharing, Knowledge-Based Organizational Support, and Organizational Learning on Job Performance: Evidence from Chinese Technological Companies
