Erschienen in:GMU Working Paper in Economics ; No. 19-42
Umfang:
1 Online-Ressource (14 p)
Sprache:
Englisch
DOI:
10.2139/ssrn.3496364
Identifikator:
Entstehung:
Anmerkungen:
Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments December 27, 2019 erstellt
Beschreibung:
Recently, many have predicted an imminent automation revolution, and large resulting job losses. Others have created metrics to predict new patterns in job automation vulnerability. As context to such claims, we test basic theory, two vulnerability metrics, and 251 O*NET job features as predictors of 1505 expert reports regarding automation levels in 832 U.S. job types from 1999 to 2019. We find that pay, employment, and vulnerability metrics are predictive (R^2~0.15), but add little to the top 25 O*NET job features, which together predict far better (R^2~0.55). These best predictors seem understandable in terms of traditional kinds of automation, and have not changed over our time period. Instead, it seems that jobs have changed their features to become more suitable for automation. We thus find no evidence yet of a revolution in the patterns or quantity of automation. And since, over this period, automation increases have predicted neither changes in pay nor employment, this suggests that workers have little to fear if such a revolution does come