Complex-task biased technological change and the labor market

Abstract

In this paper we study the relationship between task complexity and the occupational wageand employment structure. Complex tasks are defined as those requiring higher-order skills, such as the ability to abstract, solve problems, make decisions, or communicate effectively. We measure the task complexity of an occupation by performing Principal Component Analysis on a broad set of occupational descriptors in the Occupational Information Network (O*NET) data. We establish four main empirical facts for the U.S. over the 1980-2005 time period that are robust to the inclusion of a detailed set of controls, subsamples, and levels of aggregation: (1) There is a positive relationship across occupations between task complexity and wages and wage growth; (2) Conditional on task complexity, routine-intensity of an occupation is not a significant predictor of wage growth and wage levels; (3) Labor has reallocated from less complex to more complex occupations over time; (4) Within groups of occupations with similar task complexity labor has reallocated to non-routine occupations over time. We then formulate a model of Complex-Task Biased Technological Change with heterogeneous skills and show analytically that it can rationalize these facts. We conclude that workers in non-routine occupations with low ability of solving complex tasks are not shielded from the labor market effects of automatization.

Searching through the Haystack:The Relatedness and Complexity of Priorities in Smart Specialization Strategies

“…The highest value is observed for physicists and astronomers and the lowest for vehicle cleaners. As Caines, Hoffmann, and Kambourov (2017) show, this variable positively correlates with wage levels and growth, underlining the economic benefits of more complex occupations. We use the 2019 Standard Occupational Classification to International Standard Classification of Occupations (SOC-to-ISCO) crosswalk to connect the 968 SOC occupations to 424 four-digit ISCO occupations and make the measure compatible with European data.…”

“…These include the "ability to abstract, solve problems, make decisions, or communicate effectively" (Caines, Hoffmann, and Kambourov 2017, 1). In total, Caines, Hoffmann, and Kambourov (2017) Large values imply that the occupation requires a higher ability to abstract, solve problems, communicate, and make decisions. The highest value is observed for physicists and astronomers and the lowest for vehicle cleaners.…”

Rethinking the measurement of occupational task content

“…Usually, however, these auxiliary axes do not serve a well-defined purpose. Only Caines et al (2017) formulate a theory of task complexity in relation to routine-intensity and technological change. In most other analyses, the auxiliary task dimension does not add much of substantive interest.…”

Labour market polarisation revisited: evidence from Austrian vacancy data

“…Several studies suggest that methods used to analyse polarisation patterns need further refinement. For example, Caines et al (2017) introduce a measure of task complexity and demonstrate that, based on this measure, routine intensity does not significantly predict wages and wage growth. Furthermore, Salvatori (2018) shows that the changing the skill mix acts as an important driver for occupational polarisation in the UK.…”

“…However, several research groups recently indicated that these studies need refinement (e.g. Alabdulkareem et al 2018;Caines et al 2017;Salvatori 2018), noting that the traditional occupation-based approaches may neglect the relevance of specific skills and skill bundles, which can potentially be used to explain patterns of labour market polarisation. This aspect is also highlighted by Deming and Kahn (2018) who use vacancy data and exploit the detailed job descriptions to demonstrate that the demand for skills is heterogeneous within occupations, industries and across geographic locations.…”