Data and Code for: The Rise of the Machines: Automation, Horizontal Innovation and Income Inequality

Principal Investigator(s): View help for Principal Investigator(s) David Hemous, University of Zurich; Morten Olsen, University of Copenhagen

Version: View help for Version V1

Name	File Type	Size	Last Modified
Data			09/08/2020 06:10:AM
Intermediate			09/08/2020 03:51:AM
code			09/08/2020 03:51:AM
output			09/08/2020 03:54:AM
Edits_stata.do	text/x-stata-syntax	1.2 KB	09/07/2020 11:50:PM
Main_file_for_publication.m	text/x-matlab	7.3 KB	09/07/2020 11:50:PM
README.pdf	application/pdf	398.2 KB	09/07/2020 11:50:PM

Project Citation:

Hemous, David , and Olsen, Morten. Data and Code for: The Rise of the Machines: Automation, Horizontal Innovation and Income Inequality. Nashville, TN: American Economic Association [publisher], 2021. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2021-12-17. https://doi.org/10.3886/E120390V1

Project Description

Summary:

We build an endogenous growth model with automation (the replacement of low-skill workers with machines) and horizontal innovation (the creation of new products). Over time, the share of automation innovations endogenously increases through an increase in low-skill wages, leading to an increase in the skill premium and a decline in the labor share. We calibrate the model to the US economy and show that it quantitatively replicates the paths of the skill premium, the labor share and labor productivity. Our model offers a new perspective on recent trends in the income distribution by showing that they can be explained endogenously.

Scope of Project

Subject Terms: Automation; Directed Technical Change; Income Inequality

JEL Classification:
      E23 Macroeconomics: Production
      E25 Aggregate Factor Income Distribution
      O31 Innovation and Invention: Processes and Incentives
      O33 Technological Change: Choices and Consequences; Diffusion Processes
      O41 One, Two, and Multisector Growth Models

Geographic Coverage: United States

Time Period(s): 1963 – 2012

Data Type(s): aggregate data; census/enumeration data; survey data

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Published Versions

V1 [2021-12-17]

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