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Learning from Ricardo and Thompson: Machinery and Labor in the Early Industrial Revolution, and in the Age of AI

Acemoglu, Johnson

2024NBER Working Paper Series5 citations

Theoretical / conceptualTheoretical model

LLM / Generative AIAutomation / RobotsAugmentation vs. substitutionGeneral automationHuman-AI collaborationAlgorithmic management

DOI: 10.3386/w32416

Abstract

David Ricardo initially believed machinery would help workers but revised his opinion, likely based on the impact of automation in the textile industry.Despite cotton textiles becoming one of the largest sectors in the British economy, real wages for cotton weavers did not rise for decades.As E.P. Thompson emphasized, automation forced workers into unhealthy factories with close surveillance and little autonomy.Automation can increase wages, but only when accompanied by new tasks that raise the marginal productivity of labor and/or when there is sufficient additional hiring in complementary sectors.Wages are unlikely to rise when workers cannot push for their share of productivity growth.Today, artificial intelligence may boost average productivity, but it also may replace many workers while degrading job quality for those who remain employed.As in Ricardo's time, the impact of automation on workers today is more complex than an automatic linkage from higher productivity to better wages.

Summary

Acemoglu and Johnson use historical wage and employment data from British cotton textiles (1770-1850) to develop a theoretical framework explaining when automation benefits or harms workers, combining Ricardo's insights on productivity and labor demand with Thompson's analysis of workplace power dynamics, with direct application to understanding contemporary AI impacts on labor markets

Main Finding

During the early Industrial Revolution (1800-1820s), automation in cotton textiles displaced handloom weavers whose real wages fell by more than half despite massive productivity gains, with little compensatory employment growth elsewhere; this historical pattern illustrates how automation can reduce labor demand and shift power to capital, lessons directly relevant to contemporary AI deployment

Primary Datasets

Historical cotton textile wages and automation data

Secondary Datasets

Parliamentary Select Committee reports on handloom weavers (1835); historical accounts from Hammond and Hammond (1919); Thompson (1966); raw cotton import data from Chapman (1904) and Mitchell (1984)

Key Methods: Historical analysis of wage and price data; theoretical framework combining Ricardo's productivity analysis with Thompson's power dynamics; descriptive statistics from archival sources
Sample Period: 1770-1840; contemporary parallels
Geographic Coverage: UK; US
Sample Size: Industry-level data covering cotton textile sector (168,000 workers in 1788 to 336,000 in 1820); economy-wide wage indices; handloom weavers peaked at 240,000 workers
Level of Analysis: Occupation, Industry, Country
Occupation Classification: None
Industry Classification: None

Notes

NBER WP 32416; Annual Review of Economics; automation boosted productivity but not wages for decades [Claude classification]: This is primarily a historical and conceptual paper using Industrial Revolution evidence to develop a theoretical framework for understanding AI impacts. The authors construct a conceptual model combining Ricardo's revised views on machinery with E.P. Thompson's emphasis on power dynamics and working conditions. While it analyzes historical data, it makes no causal claims about those data - rather uses them illustratively. The AI tech focus codes what the paper discusses as contemporary parallels (LLM/Generative AI and Automation/Robots), not the historical technologies studied. Published as NBER WP 32416; also appears in Annual Review of Economics. [Claude classification]: This is primarily a historical and conceptual paper using Industrial Revolution evidence to develop a theoretical framework for understanding AI impacts. The authors construct a conceptual model combining Ricardo's revised views on machinery with E.P. Thompson's emphasis on power dynamics and working conditions. While it analyzes historical data, it makes no causal claims about those data - rather uses them illustratively. The AI tech focus codes what the paper discusses as contemporary parallels (LLM/Generative AI and Automation/Robots), not the historical technologies studied. Published as NBER WP 32416; also appears in Annual Review of Economics. [Claude classification]: This is primarily a historical and conceptual paper using Industrial Revolution evidence to develop a theoretical framework for understanding AI impacts. The authors construct a conceptual model combining Ricardo's revised views on machinery with E.P. Thompson's emphasis on power dynamics and working conditions. While it analyzes historical data, it makes no causal claims about those data - rather uses them illustratively. The AI tech focus codes what the paper discusses as contemporary parallels (LLM/Generative AI and Automation/Robots), not the historical technologies studied. Published as NBER WP 32416; also appears in Annual Review of Economics. [Claude classification]: This is primarily a historical and conceptual paper using Industrial Revolution evidence to develop a theoretical framework for understanding AI impacts. The authors construct a conceptual model combining Ricardo's revised views on machinery with E.P. Thompson's emphasis on power dynamics and working conditions. While it analyzes historical data, it makes no causal claims about those data - rather uses them illustratively. The AI tech focus codes what the paper discusses as contemporary parallels (LLM/Generative AI and Automation/Robots), not the historical technologies studied. Published as NBER WP 32416; also appears in Annual Review of Economics. [Claude classification]: This is primarily a historical and conceptual paper using Industrial Revolution evidence to develop a theoretical framework for understanding AI impacts. The authors construct a conceptual model combining Ricardo's revised views on machinery with E.P. Thompson's emphasis on power dynamics and working conditions. While it analyzes historical data, it makes no causal claims about those data - rather uses them illustratively. The AI tech focus codes what the paper discusses as contemporary parallels (LLM/Generative AI and Automation/Robots), not the historical technologies studied. Published as NBER WP 32416; also appears in Annual Review of Economics. [Claude classification]: This is primarily a historical and conceptual paper using Industrial Revolution evidence to develop a theoretical framework for understanding AI impacts. The authors construct a conceptual model combining Ricardo's revised views on machinery with E.P. Thompson's emphasis on power dynamics and working conditions. While it analyzes historical data, it makes no causal claims about those data - rather uses them illustratively. The AI tech focus codes what the paper discusses as contemporary parallels (LLM/Generative AI and Automation/Robots), not the historical technologies studied. Published as NBER WP 32416; also appears in Annual Review of Economics. [Claude classification]: This is primarily a historical and conceptual paper using Industrial Revolution evidence to develop a theoretical framework for understanding AI impacts. The authors construct a conceptual model combining Ricardo's revised views on machinery with E.P. Thompson's emphasis on power dynamics and working conditions. While it analyzes historical data, it makes no causal claims about those data - rather uses them illustratively. The AI tech focus codes what the paper discusses as contemporary parallels (LLM/Generative AI and Automation/Robots), not the historical technologies studied. Published as NBER WP 32416; also appears in Annual Review of Economics. [Claude classification]: This is primarily a historical and conceptual paper using Industrial Revolution evidence to develop a theoretical framework for understanding AI impacts. The authors construct a conceptual model combining Ricardo's revised views on machinery with E.P. Thompson's emphasis on power dynamics and working conditions. While it analyzes historical data, it makes no causal claims about those data - rather uses them illustratively. The AI tech focus codes what the paper discusses as contemporary parallels (LLM/Generative AI and Automation/Robots), not the historical technologies studied. Published as NBER WP 32416; also appears in Annual Review of Economics. [Claude classification]: This is primarily a historical and conceptual paper using Industrial Revolution evidence to develop a theoretical framework for understanding AI impacts. The authors construct a conceptual model combining Ricardo's revised views on machinery with E.P. Thompson's emphasis on power dynamics and working conditions. While it analyzes historical data, it makes no causal claims about those data - rather uses them illustratively. The AI tech focus codes what the paper discusses as contemporary parallels (LLM/Generative AI and Automation/Robots), not the historical technologies studied. Published as NBER WP 32416; also appears in Annual Review of Economics. [Claude classification]: This is primarily a historical and conceptual paper using Industrial Revolution evidence to develop a theoretical framework for understanding AI impacts. The authors construct a conceptual model combining Ricardo's revised views on machinery with E.P. Thompson's emphasis on power dynamics and working conditions. While it analyzes historical data, it makes no causal claims about those data - rather uses them illustratively. The AI tech focus codes what the paper discusses as contemporary parallels (LLM/Generative AI and Automation/Robots), not the historical technologies studied. Published as NBER WP 32416; also appears in Annual Review of Economics. [Claude classification]: This is primarily a historical and conceptual paper using Industrial Revolution evidence to develop a theoretical framework for understanding AI impacts. The authors construct a conceptual model combining Ricardo's revised views on machinery with E.P. Thompson's emphasis on power dynamics and working conditions. While it analyzes historical data, it makes no causal claims about those data - rather uses them illustratively. The AI tech focus codes what the paper discusses as contemporary parallels (LLM/Generative AI and Automation/Robots), not the historical technologies studied. Published as NBER WP 32416; also appears in Annual Review of Economics.