Expertise
Autor, Thompson
2025NBER Working Paper Series4 citations
Theoretical / conceptualCausalTheoretical model
AI (General)Automation / RobotsRoutine task changeAugmentation vs. substitutionGeneral automation
AbstractWhen job tasks are automated, does this augment or diminish the value of labor in the tasks that remain?We argue the answer depends on whether removing tasks raises or reduces the expertise required for remaining non-automated tasks.Since the same task may be relatively expert in one occupation and inexpert in another, automation can simultaneously replace experts in some occupations while augmenting expertise in others.We propose a conceptual model of occupational task bundling that predicts that changing occupational expertise requirements have countervailing wage and employment effects: automation that decreases expertise requirements reduces wages but permits the entry of less expert workers; automation that raises requirements raises wages but reduces the set of qualified workers.We develop a novel, content-agnostic method for measuring job task expertise, and we use it to quantify changes in occupational expertise demands over four decades attributable to job task removal and addition.We document that automation has raised wages and reduced employment in occupations where it eliminated inexpert tasks, but lowered wages and increased employment in occupations where it eliminated expert tasks.These effects are distinct from-and in the case of employment, opposite to-the effects of changing task quantities.The expertise framework resolves the puzzle of why routine task automation has lowered employment but often raised wages in routine task-intensive occupations.It provides a general tool for analyzing how task automation and new task creation reshape the scarcity value of human expertise within and across occupations.
SummaryAutor and Thompson develop a general equilibrium model and novel empirical measures to show that automation affects occupations differently depending on whether it removes expert or inexpert tasks, using U.S. occupational data from 1977-2018 to demonstrate that the same automation can simultaneously replace experts in some occupations while augmenting expertise demands in others
Main FindingAutomation simultaneously replaces experts and augments expertise: removing inexpert tasks raises occupational wages by 18% per standard deviation of expertise increase but reduces employment, while removing expert tasks lowers wages but expands employment by reducing expertise barriers to entry; routine task automation bifurcated expertise demands, raising wages in occupations where routine tasks were inexpert and lowering them where routine tasks were expert
Primary Datasets
Dictionary of Occupational Titles (DOT) 1977; O*NET 2018 (release 23.0); U.S. Census 1980; American Community Survey (ACS) 2018; Current Population Survey (CPS) 1971; Occupational Employment and Wage Statistics (OEWS) 2018; Educator's Word Frequency Guide (1995); Dale-Chall List (1995)
Secondary Datasets
Census Alphabetical Index of Occupations and Industries; Google Ngrams (for validation)
- Key Methods
- General equilibrium theoretical model; novel content-agnostic expertise measure using Standard Frequency Index; word embeddings to match tasks across time periods; cross-sectional and longitudinal regressions; causal identification using routine task automation as instrument
- Sample Period
- 1977-2018
- Geographic Coverage
- United States
- Sample Size
- 303 harmonized Census occupations covering all U.S. civilian employment; approximately 4,000+ individual tasks in DOT 1977 and O*NET 2018
- Level of Analysis
- Occupation, Task
- Occupation Classification
- Census occupation codes (303 harmonized three-digit occupations)
- Industry Classification
- None
NotesNBER Working Paper 33941
[Claude classification]: Novel contribution is the 'expertise framework' distinct from human capital and task models. Uses GPT-4.1 to classify tasks as routine/manual/abstract. Theoretical model assumes hierarchical expertise, occupational task bundling, and automation. Empirical analysis focuses on accounting clerks vs inventory clerks as motivating examples. Four outlier occupations (telephone operators, textile machine operators, etc.) excluded from some employment regressions due to extreme automation/trade impacts.
[Claude classification]: Novel contribution is the 'expertise framework' distinct from human capital and task models. Uses GPT-4.1 to classify tasks as routine/manual/abstract. Theoretical model assumes hierarchical expertise, occupational task bundling, and automation. Empirical analysis focuses on accounting clerks vs inventory clerks as motivating examples. Four outlier occupations (telephone operators, textile machine operators, etc.) excluded from some employment regressions due to extreme automation/trade impacts.
[Claude classification]: Novel contribution is the 'expertise framework' distinct from human capital and task models. Uses GPT-4.1 to classify tasks as routine/manual/abstract. Theoretical model assumes hierarchical expertise, occupational task bundling, and automation. Empirical analysis focuses on accounting clerks vs inventory clerks as motivating examples. Four outlier occupations (telephone operators, textile machine operators, etc.) excluded from some employment regressions due to extreme automation/trade impacts.
[Claude classification]: Novel contribution is the 'expertise framework' distinct from human capital and task models. Uses GPT-4.1 to classify tasks as routine/manual/abstract. Theoretical model assumes hierarchical expertise, occupational task bundling, and automation. Empirical analysis focuses on accounting clerks vs inventory clerks as motivating examples. Four outlier occupations (telephone operators, textile machine operators, etc.) excluded from some employment regressions due to extreme automation/trade impacts.
[Claude classification]: Novel contribution is the 'expertise framework' distinct from human capital and task models. Uses GPT-4.1 to classify tasks as routine/manual/abstract. Theoretical model assumes hierarchical expertise, occupational task bundling, and automation. Empirical analysis focuses on accounting clerks vs inventory clerks as motivating examples. Four outlier occupations (telephone operators, textile machine operators, etc.) excluded from some employment regressions due to extreme automation/trade impacts.
[Claude classification]: Novel contribution is the 'expertise framework' distinct from human capital and task models. Uses GPT-4.1 to classify tasks as routine/manual/abstract. Theoretical model assumes hierarchical expertise, occupational task bundling, and automation. Empirical analysis focuses on accounting clerks vs inventory clerks as motivating examples. Four outlier occupations (telephone operators, textile machine operators, etc.) excluded from some employment regressions due to extreme automation/trade impacts.
[Claude classification]: Novel contribution is the 'expertise framework' distinct from human capital and task models. Uses GPT-4.1 to classify tasks as routine/manual/abstract. Theoretical model assumes hierarchical expertise, occupational task bundling, and automation. Empirical analysis focuses on accounting clerks vs inventory clerks as motivating examples. Four outlier occupations (telephone operators, textile machine operators, etc.) excluded from some employment regressions due to extreme automation/trade impacts.
[Claude classification]: Novel contribution is the 'expertise framework' distinct from human capital and task models. Uses GPT-4.1 to classify tasks as routine/manual/abstract. Theoretical model assumes hierarchical expertise, occupational task bundling, and automation. Empirical analysis focuses on accounting clerks vs inventory clerks as motivating examples. Four outlier occupations (telephone operators, textile machine operators, etc.) excluded from some employment regressions due to extreme automation/trade impacts.
[Claude classification]: Novel contribution is the 'expertise framework' distinct from human capital and task models. Uses GPT-4.1 to classify tasks as routine/manual/abstract. Theoretical model assumes hierarchical expertise, occupational task bundling, and automation. Empirical analysis focuses on accounting clerks vs inventory clerks as motivating examples. Four outlier occupations (telephone operators, textile machine operators, etc.) excluded from some employment regressions due to extreme automation/trade impacts.
[Claude classification]: Novel contribution is the 'expertise framework' distinct from human capital and task models. Uses GPT-4.1 to classify tasks as routine/manual/abstract. Theoretical model assumes hierarchical expertise, occupational task bundling, and automation. Empirical analysis focuses on accounting clerks vs inventory clerks as motivating examples. Four outlier occupations (telephone operators, textile machine operators, etc.) excluded from some employment regressions due to extreme automation/trade impacts.
[Claude classification]: Novel contribution is the 'expertise framework' distinct from human capital and task models. Uses GPT-4.1 to classify tasks as routine/manual/abstract. Theoretical model assumes hierarchical expertise, occupational task bundling, and automation. Empirical analysis focuses on accounting clerks vs inventory clerks as motivating examples. Four outlier occupations (telephone operators, textile machine operators, etc.) excluded from some employment regressions due to extreme automation/trade impacts.