Learning Occupational Task-Shares Dynamics for the Future of Work
This addresses the challenge of technological unemployment and workforce adaptation for policymakers and labor economists, though it is incremental as it applies existing modeling techniques to new data.
The paper tackles the problem of how AI and automation are changing occupational task demands by analyzing a decade of online job postings, finding that big data and AI have significantly risen in high-wage occupations since 2012 and 2016, and uses an ARIMA model to predict future task demands for workforce retraining.
The recent wave of AI and automation has been argued to differ from previous General Purpose Technologies (GPTs), in that it may lead to rapid change in occupations' underlying task requirements and persistent technological unemployment. In this paper, we apply a novel methodology of dynamic task shares to a large dataset of online job postings to explore how exactly occupational task demands have changed over the past decade of AI innovation, especially across high, mid and low wage occupations. Notably, big data and AI have risen significantly among high wage occupations since 2012 and 2016, respectively. We built an ARIMA model to predict future occupational task demands and showcase several relevant examples in Healthcare, Administration, and IT. Such task demands predictions across occupations will play a pivotal role in retraining the workforce of the future.