Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/278091 
Authors: 
Year of Publication: 
2023
Citation: 
[Journal:] Journal for Labour Market Research [ISSN:] 2510-5027 [Volume:] 57 [Issue:] 1 [Article No.:] 6 [Year:] 2023 [Pages:] 1-24
Publisher: 
Springer, Heidelberg
Abstract: 
Using German survey and expert data on job tasks, this paper explores the presence of omitted-variable bias suspected in conventional task data derived from expert assessment. I show expert task data, which is expressed at the occupation-level, introduces omitted-variable bias in task returns on the order of 26-34%. Motivated by a theoretical framework, I argue this bias results from expert data ignoring individual heterogeneity rather than fundamental differences on the assessment of tasks between experts and workers. My findings have important implications for the interpretation of conventional task models as occupational task returns are overestimated. Moreover, a rigorous comparison of the statistical performance of various models offers guidance for future research regarding choice of task data and construction of task measures.
Subjects: 
Expert vs survey task data
Individual heterogeneity
Omitted-variable bias
JEL: 
C18
J24
J31
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article

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