Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/240412 
Year of Publication: 
2020
Series/Report no.: 
WWZ Working Paper No. 2020/03
Version Description: 
Version October 2020
Publisher: 
University of Basel, Center of Business and Economics (WWZ), Basel
Abstract: 
This paper presents a new approach to estimate the green potential of occupations. Using data from O*NET on the skills that workers possess and the tasks they carry out, we train several machine learning algorithms to predict the green potential of U.S. occupations classified according to the 6-digit Standard Occupational Classication. Our methodology allows existing discrete classications of occupations to be extended to a continuum of classes. This improves the analysis of heterogeneous occupations in terms of their green potential. Our approach makes two contributions to the literature. First, as it more accurately ranks occupations in terms of their green potential, it leads to a better understanding of the extent to which a given workforce is prepared to cope with a transition to a green economy. Second, it allows for a more accurate analysis of differences between workforces across regions. We use U.S. occupational employment data to highlight both aspects.
Subjects: 
green skills
green tasks
green potential
supervised learning
labor market
JEL: 
C53
J21
J24
Q52
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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