This website uses cookies.

By clicking the "Accept" button or continuing to browse our site, you agree to first-party and session-only cookies being stored on your device to enhance site navigation and analyze site performance and traffic. For more information on our use of cookies, please see our Privacy Policy.

  1. Home
  2. ASSA Annual Meeting
  3. 2025
  4. Program
  5. Technology and the Changing Nature of Work
« Back to Results

Technology and the Changing Nature of Work

Paper Session

Friday, Jan. 3, 2025 10:15 AM - 12:15 PM (PST)

Hilton San Francisco Union Square, Union Square 17 and 18
Hosted By: American Economic Association
  • Chair: Zara Contractor, Middlebury College

Technology and Labor Displacement: Evidence from Linking Patents with Worker-Level Data

Leonid Kogin
,
MIT Sloan School of Management
Dimitris Papanikolaou
,
Kellogg School of Management, Northwestern University
Larry Schmidt
,
MIT Sloan School of Management
Bryan Seegmiller
,
Kellogg School of Management, Northwestern University

Abstract

We develop measures of labor-saving and labor-augmenting technology exposure using textual analysis of patents and job tasks. Using US administrative data, we show that both measures negatively predict earnings growth of individual incumbent workers. While labor-saving technologies predict earnings declines and higher likelihood of job loss for all workers, labor-augmenting technologies primarily predict losses for older or highly-paid workers. However, we find positive effects of labor-augmenting technologies on occupation-level employment and wage bills. A model featuring labor-saving and labor-augmenting technologies with vintage-specific human capital quantitatively matches these patterns. We extend our analysis to predict the effect of AI on earnings.

The Short-term Effects of Generative Artificial Intelligence on Employment: Evidence from an Online Labor Market

Xiang Hui
,
Washington University in St. Louis
Oren Reshef
,
Washington University in St. Louis
Luofeng Zhou
,
New York University

Abstract

Generative Artificial Intelligence (AI) holds the potential to either complement workers by enhancing their productivity or substitute them entirely. We examine the short-term effects of the recent release of the large language models (LLM), namely ChatGPT and other image-based models, on the employment outcomes of freelancers on a large online platform. We find that freelancers in highly affected occupations suffer from the introduction of generative AI, experiencing reductions in both employment and earnings. We find similar effects studying the release of other image-based, generative AI models. Exploring the heterogeneity by freelancers’ employment history, we do not find evidence that high-quality service, measured by their past performance and employment, moderates the adverse effects on employment. In fact, we find suggestive evidence that top freelancers are disproportionately affected by AI. These results suggest that generative AI reduces overall demand for workers of all types, and has the potential to narrow gaps among workers.

Technological Change and Insuring Job Loss

Carter Braxton
,
University of Wisconsin-Madison
Bledi Taska
,
SkyHive

Abstract

We examine the role of technological change in shaping insurance to the unemployed. We integrate technological change, occupation choice, and employment risk into a Bewley-style economy to examine the optimal combination of public insurance transfers and retraining subsidies for unemployed workers. We find that technological change introduces a motive for a utilitarian government to introduce retraining subsidies as part of an optimal policy for unemployed workers.

 The Effect of Software Adoption on Skill Demand and Wage Inequality

Zara Contractor
,
Middlebury College
Bledi Taska
,
SkyHive

Abstract

We study how firms change their demand for workers of different skill sets when they adopt additional software varieties. We construct software adoption events from job posting data compiled by Lightcast and use a latent variable strategy to estimate the causal impacts of these adoptions. After an adoption, analytic and social skill requirements for the job using the software increase by 0.8 and 1.1 percentage points, respectively, and the number of vacancies also rises by 30%. We then embed these effects in an equilibrium model of software adoption and occupation sorting amongst white-collar occupations, and find that falling software prices increase inequality both within and between occupations. The upskilling effects of software drive the increase in within-occupation inequality by restricting labor from moving to higher wage software jobs

Discussant(s)
Erin L. Wolcott
,
Middlebury College
Enghin Atalay
,
Federal Reserve bank of Philadelphia
Aakash kalyani
,
Federal Reserve Bank of St. Louis
Sebastian Steffen
,
Boston College
JEL Classifications
  • O3 - Innovation; Research and Development; Technological Change; Intellectual Property Rights
  • J3 - Wages, Compensation, and Labor Costs