EconPapers    
Economics at your fingertips  
 

Explaining the Labor Share: Automation Vs Labor Market Institutions

Luís Guimarães and Pedro Gil

Labour Economics, 2022, vol. 75, issue C

Abstract: We propose a simple model to assess the evolution of the US labor share and how automation affects employment. In our model, heterogeneous firms may choose a manual technology and hire a worker subject to matching frictions. Alternatively, they may choose an automated technology and produce using only machines (robots). Our model suggests that automation reduces the labor share but increases employment and wages. Furthermore, our model suggests that labor market institutions are unlikely to have played a major role in the fall of the US labor share after 1987. Instead, technological factors are a more promising candidate.

Keywords: Automation; Labor share; Technology choice; Employment; Matching frictions (search for similar items in EconPapers)
JEL-codes: E24 J64 L11 O33 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0927537122000392
Full text for ScienceDirect subscribers only

Related works:
Working Paper: Explaining the labor share: automation vs labor market institutions (2019) Downloads
Working Paper: Explaining the labor share: automation vs labor market institutions (2019) Downloads
Working Paper: Explaining the labor share: automation vs labor market institutions (2019) Downloads
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:labeco:v:75:y:2022:i:c:s0927537122000392

DOI: 10.1016/j.labeco.2022.102146

Access Statistics for this article

Labour Economics is currently edited by A. Ichino

More articles in Labour Economics from Elsevier
Bibliographic data for series maintained by Catherine Liu ().

 
Page updated 2025-03-19
Handle: RePEc:eee:labeco:v:75:y:2022:i:c:s0927537122000392