Abstract

We present a framework for understanding the effects of automation and other types of technological changes on labor demand, and use it to interpret changes in US employment over the recent past. At the center of our framework is the allocation of tasks to capital and labor—the task content of production. Automation, which enables capital to replace labor in tasks it was previously engaged in, shifts the task content of production against labor because of a displacement effect. As a result, automation always reduces the labor share in value added and may reduce labor demand even as it raises productivity. The effects of automation are counterbalanced by the creation of new tasks in which labor has a comparative advantage. The introduction of new tasks changes the task content of production in favor of labor because of a reinstatement effect, and always raises the labor share and labor demand. We show how the role of changes in the task content of production—due to automation and new tasks—can be inferred from industry-level data. Our empirical decomposition suggests that the slower growth of employment over the last three decades is accounted for by an acceleration in the displacement effect, especially in manufacturing, a weaker reinstatement effect, and slower growth of productivity than in previous decades.

Keywords

ProductivityLabour economicsAutomationProduction (economics)EconomicsTask (project management)Capital (architecture)Technological changeLabor demandIndustrial organizationMicroeconomicsWageEngineeringEconomic growthManagementMacroeconomics

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Publication Info

Year
2019
Type
article
Volume
33
Issue
2
Pages
3-30
Citations
1748
Access
Closed

Social Impact

Social media, news, blog, policy document mentions

Citation Metrics

1748
OpenAlex
103
Influential
1532
CrossRef

Cite This

Daron Acemoğlu, Pascual Restrepo (2019). Automation and New Tasks: How Technology Displaces and Reinstates Labor. The Journal of Economic Perspectives , 33 (2) , 3-30. https://doi.org/10.1257/jep.33.2.3

Identifiers

DOI
10.1257/jep.33.2.3

Data Quality

Data completeness: 81%