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AI and Jobs: The Role of Demand

Bessen

2018NBER Working Paper Series194 citations

Theoretical / conceptualTheoretical model

Automation / RobotsAI (General)General automationAugmentation vs. substitution

Abstract

Artificial intelligence (AI) technologies will automate many jobs, but the effect on employment is not obvious.In manufacturing, technology has sharply reduced jobs in recent decades.But before that, for over a century, employment grew, even in industries experiencing rapid technological change.What changed?Demand was highly elastic at first and then became inelastic.The effect of artificial intelligence on jobs will similarly depend critically on the nature of demand.This paper presents a simple model of demand that accurately predicts the rise and fall of employment in the textile, steel and automotive industries.This model provides a useful framework for exploring how AI is likely to affect jobs over the next 10 or 20 years.

Summary

Bessen develops a theoretical model with preference distributions to explain the inverted-U pattern of employment in automated industries, validating predictions against historical data from US textiles (1810-1995), steel (1860-1982), and automotive (1910-2007) to analyze how demand elasticity mediates automation's employment effects and inform predictions about AI's labor market impact.

Main Finding

Employment in automated industries follows an inverted-U pattern determined by demand elasticity: when price elasticity exceeds 1, automation increases employment; when it falls below 1, jobs are lost. Historical estimates show demand elasticity fell from 2.13 to 0.02 (textiles), 3.49 to 0.16 (steel), and 6.77 to 0.15 (automotive), accurately predicting employment trajectories (R-squared > 0.982).

Primary Datasets

US Census Historical Statistics (employment, production); Bureau of Labor Statistics Current Employment Situation

Secondary Datasets

Parker and Klein (1966) agricultural productivity data; industry-specific labor productivity and price indices

Key Methods: Theoretical model with lognormal preference distributions; historical validation using manufacturing employment, productivity, and price data from textiles, steel, and automotive industries (1810-2011)
Sample Period: 1810-2011
Geographic Coverage: United States
Sample Size: Three manufacturing industries tracked over 100+ years (textiles: 1810-1995, steel: 1860-1982, automotive: 1910-2007)
Level of Analysis: Industry, Occupation
Occupation Classification: 1950 census occupations (discussed)
Industry Classification: SIC codes (2211, 2221, 3312)

Notes

NBER Working Paper 24235 [Claude classification]: Paper uses historical manufacturing data to calibrate and validate a theoretical model, then applies the framework to discuss AI's likely employment effects over 10-20 years. Model assumes labor-augmenting technical change and competitive markets. Emphasizes that AI's employment impact depends critically on whether automation is partial vs. complete and on demand responsiveness in affected markets. [Claude classification]: Paper uses historical manufacturing data to calibrate and validate a theoretical model, then applies the framework to discuss AI's likely employment effects over 10-20 years. Model assumes labor-augmenting technical change and competitive markets. Emphasizes that AI's employment impact depends critically on whether automation is partial vs. complete and on demand responsiveness in affected markets. [Claude classification]: Paper uses historical manufacturing data to calibrate and validate a theoretical model, then applies the framework to discuss AI's likely employment effects over 10-20 years. Model assumes labor-augmenting technical change and competitive markets. Emphasizes that AI's employment impact depends critically on whether automation is partial vs. complete and on demand responsiveness in affected markets. [Claude classification]: Paper uses historical manufacturing data to calibrate and validate a theoretical model, then applies the framework to discuss AI's likely employment effects over 10-20 years. Model assumes labor-augmenting technical change and competitive markets. Emphasizes that AI's employment impact depends critically on whether automation is partial vs. complete and on demand responsiveness in affected markets. [Claude classification]: Paper uses historical manufacturing data to calibrate and validate a theoretical model, then applies the framework to discuss AI's likely employment effects over 10-20 years. Model assumes labor-augmenting technical change and competitive markets. Emphasizes that AI's employment impact depends critically on whether automation is partial vs. complete and on demand responsiveness in affected markets. [Claude classification]: Paper uses historical manufacturing data to calibrate and validate a theoretical model, then applies the framework to discuss AI's likely employment effects over 10-20 years. Model assumes labor-augmenting technical change and competitive markets. Emphasizes that AI's employment impact depends critically on whether automation is partial vs. complete and on demand responsiveness in affected markets. [Claude classification]: Paper uses historical manufacturing data to calibrate and validate a theoretical model, then applies the framework to discuss AI's likely employment effects over 10-20 years. Model assumes labor-augmenting technical change and competitive markets. Emphasizes that AI's employment impact depends critically on whether automation is partial vs. complete and on demand responsiveness in affected markets. [Claude classification]: Paper uses historical manufacturing data to calibrate and validate a theoretical model, then applies the framework to discuss AI's likely employment effects over 10-20 years. Model assumes labor-augmenting technical change and competitive markets. Emphasizes that AI's employment impact depends critically on whether automation is partial vs. complete and on demand responsiveness in affected markets. [Claude classification]: Paper uses historical manufacturing data to calibrate and validate a theoretical model, then applies the framework to discuss AI's likely employment effects over 10-20 years. Model assumes labor-augmenting technical change and competitive markets. Emphasizes that AI's employment impact depends critically on whether automation is partial vs. complete and on demand responsiveness in affected markets. [Claude classification]: Paper uses historical manufacturing data to calibrate and validate a theoretical model, then applies the framework to discuss AI's likely employment effects over 10-20 years. Model assumes labor-augmenting technical change and competitive markets. Emphasizes that AI's employment impact depends critically on whether automation is partial vs. complete and on demand responsiveness in affected markets. [Claude classification]: Paper uses historical manufacturing data to calibrate and validate a theoretical model, then applies the framework to discuss AI's likely employment effects over 10-20 years. Model assumes labor-augmenting technical change and competitive markets. Emphasizes that AI's employment impact depends critically on whether automation is partial vs. complete and on demand responsiveness in affected markets.