Related papers: Low-skilled Occupations Face the Highest Upskillin…
In this study, the global scientific workforce is explored through large-scale, generational, cross-sectional, and longitudinal approaches. We examine 4.3 million nonoccasional scientists from 38 OECD countries publishing in 1990-2021. Our…
This paper develops a theory-driven automation exposure index based on Moravec's Paradox. Scoring 19,000 O*NET tasks on performance variance, tacit knowledge, data abundance, and algorithmic gaps reveals that management, STEM, and sciences…
We measure human capital using the self-reported skill sets of nearly 9 million U.S. college graduates from professional profiles on LinkedIn. We aggregate skill strings into 48 clusters of general, occupation-specific, and managerial…
Common narratives about automation often pit new technologies against workers. The introduction of advanced machine tools, industrial robots, and AI have all been met with concern that technological progress will mean fewer jobs. However,…
In a rapidly evolving job market, skill demand forecasting is crucial as it enables policymakers and businesses to anticipate and adapt to changes, ensuring that workforce skills align with market needs, thereby enhancing productivity and…
Women and people of color remain dramatically underrepresented among computing faculty, and improvements in demographic diversity are slow and uneven. Effective diversification strategies depend on quantifying the correlates, causes, and…
We document the age-race-gender intersectionality in the distribution of occupational tasks in the United States. We also investigate how the task content of work changed from the early-2000s to the late-2010s for different…
The rapid growth of the digital platform economy is transforming labor markets, offering new employment opportunities with promises of flexibility and accessibility. However, these benefits often come at the expense of increased economic…
Gender imbalances in work environments have been a long-standing concern. Identifying the existence of such imbalances is key to designing policies to help overcome them. In this work, we study gender trends in employment across various…
We study size-based schedulers, and focus on the impact of inaccurate job size information on response time and fairness. Our intent is to revisit previous results, which allude to performance degradation for even small errors on job size…
Automation affects the labour content of work differently across different contexts. Yet, most existing exposure measures assign fixed scores to tasks or occupations, limiting comparisons of automation exposure across countries. We develop…
Computing Education faces significant challenges in equipping graduates with the resilience necessary to remain relevant amid rapid technological change. While existing curricula cultivate computing competencies, they often fail to…
In this paper we conduct a longitudinal analysis of the structure of labour markets in the US over 7 decades of technological, economic and policy change. We make use of network science, natural language processing and machine learning to…
China is the world's second largest economy. After four decades of economic miracles, China's economy is transitioning into an advanced, knowledge-based economy. Yet, we still lack a detailed understanding of the skills that underly the…
In this paper, we compare different methods to extract skill requirements from job advertisements. We consider three top-down methods that are based on expert-created dictionaries of keywords, and a bottom-up method of unsupervised topic…
Many studies demonstrate that there is still a significant gender bias, especially at higher career levels, in many areas including science, technology, engineering, and mathematics (STEM). We investigated field-dependent, gender-specific…
This paper explores the potential impacts of large language models (LLMs) on the Chinese labor market. We analyze occupational exposure to LLM capabilities by incorporating human expertise and LLM classifications, following the methodology…
We provide experimental evidence on how employers adjust expectations to automation risk in high-skill, white-collar work. Using a randomized information intervention among tax advisors in Germany, we show that firms systematically…
Workers without formal credentials experience substantially lower employment rates than their credentialed counterparts, but the extent to which information frictions contribute to these disparities remains unclear. We conducted a…
Which jobs can AI learn to do? We examine this for every occupation in the US economy. Existing indices measure the overlap between AI capabilities and occupational tasks rather than which tasks AI systems can learn to perform, and as a…