This site is undergoing review. Some annotations were human-generated, some AI-generated — all are being verified.
Back to papersSummary Main Finding Notes Quantitative model; Note: Hubmer (2023) actual paper is 'The Race Between Preferences and Technology' (Econometrica). Title may be confused with Acemoglu & Restrepo (2022).
[Claude classification]: Paper metadata mismatch: provided metadata suggested 'Task-Based Automation' by Hubmer (2023), but actual paper content is 'Automation and Rent Dissipation' by Acemoglu and Restrepo (NBER WP 32536, revised October 2025). The paper includes extensive robustness checks distinguishing efficiency-enhancing vs inefficient rents, alternative task measures, and Monte Carlo validation of the GMM estimation procedure.
[Claude classification]: Paper metadata mismatch: provided metadata suggested 'Task-Based Automation' by Hubmer (2023), but actual paper content is 'Automation and Rent Dissipation' by Acemoglu and Restrepo (NBER WP 32536, revised October 2025). The paper includes extensive robustness checks distinguishing efficiency-enhancing vs inefficient rents, alternative task measures, and Monte Carlo validation of the GMM estimation procedure.
[Claude classification]: Paper metadata mismatch: provided metadata suggested 'Task-Based Automation' by Hubmer (2023), but actual paper content is 'Automation and Rent Dissipation' by Acemoglu and Restrepo (NBER WP 32536, revised October 2025). The paper includes extensive robustness checks distinguishing efficiency-enhancing vs inefficient rents, alternative task measures, and Monte Carlo validation of the GMM estimation procedure.
[Claude classification]: Paper metadata mismatch: provided metadata suggested 'Task-Based Automation' by Hubmer (2023), but actual paper content is 'Automation and Rent Dissipation' by Acemoglu and Restrepo (NBER WP 32536, revised October 2025). The paper includes extensive robustness checks distinguishing efficiency-enhancing vs inefficient rents, alternative task measures, and Monte Carlo validation of the GMM estimation procedure.
[Claude classification]: Paper metadata mismatch: provided metadata suggested 'Task-Based Automation' by Hubmer (2023), but actual paper content is 'Automation and Rent Dissipation' by Acemoglu and Restrepo (NBER WP 32536, revised October 2025). The paper includes extensive robustness checks distinguishing efficiency-enhancing vs inefficient rents, alternative task measures, and Monte Carlo validation of the GMM estimation procedure.
[Claude classification]: Paper metadata mismatch: provided metadata suggested 'Task-Based Automation' by Hubmer (2023), but actual paper content is 'Automation and Rent Dissipation' by Acemoglu and Restrepo (NBER WP 32536, revised October 2025). The paper includes extensive robustness checks distinguishing efficiency-enhancing vs inefficient rents, alternative task measures, and Monte Carlo validation of the GMM estimation procedure.
[Claude classification]: Paper metadata mismatch: provided metadata suggested 'Task-Based Automation' by Hubmer (2023), but actual paper content is 'Automation and Rent Dissipation' by Acemoglu and Restrepo (NBER WP 32536, revised October 2025). The paper includes extensive robustness checks distinguishing efficiency-enhancing vs inefficient rents, alternative task measures, and Monte Carlo validation of the GMM estimation procedure.
[Claude classification]: Paper metadata mismatch: provided metadata suggested 'Task-Based Automation' by Hubmer (2023), but actual paper content is 'Automation and Rent Dissipation' by Acemoglu and Restrepo (NBER WP 32536, revised October 2025). The paper includes extensive robustness checks distinguishing efficiency-enhancing vs inefficient rents, alternative task measures, and Monte Carlo validation of the GMM estimation procedure.
[Claude classification]: Paper metadata mismatch: provided metadata suggested 'Task-Based Automation' by Hubmer (2023), but actual paper content is 'Automation and Rent Dissipation' by Acemoglu and Restrepo (NBER WP 32536, revised October 2025). The paper includes extensive robustness checks distinguishing efficiency-enhancing vs inefficient rents, alternative task measures, and Monte Carlo validation of the GMM estimation procedure.
[Claude classification]: Paper metadata mismatch: provided metadata suggested 'Task-Based Automation' by Hubmer (2023), but actual paper content is 'Automation and Rent Dissipation' by Acemoglu and Restrepo (NBER WP 32536, revised October 2025). The paper includes extensive robustness checks distinguishing efficiency-enhancing vs inefficient rents, alternative task measures, and Monte Carlo validation of the GMM estimation procedure.
[Claude classification]: Paper metadata mismatch: provided metadata suggested 'Task-Based Automation' by Hubmer (2023), but actual paper content is 'Automation and Rent Dissipation' by Acemoglu and Restrepo (NBER WP 32536, revised October 2025). The paper includes extensive robustness checks distinguishing efficiency-enhancing vs inefficient rents, alternative task measures, and Monte Carlo validation of the GMM estimation procedure.
Task-Based Automation: Implications for Wages and Inequality
Hubmer
2023NBER Working Paper No. 325369 citations
Theoretical / conceptualCausalTheoretical model
Automation / RobotsRoutine task changeAugmentation vs. substitutionGeneral automationGender
This paper develops a task-based model of automation that incorporates labor market imperfections and worker rents. It shows that when automation targets tasks where workers earn rents (wages above opportunity cost), it generates rent dissipation that can offset productivity gains. Using US data from 1980-2016, the authors estimate that automation accounts for 52% of the rise in between-group wage inequality, with one-fifth of this effect (10 percentage points) due to rent dissipation. The paper finds that inefficient rent dissipation offset 60-90% of the productivity gains from automation, leaving only 0.3-1.3% net TFP growth. The framework distinguishes between efficiency-enhancing rents (efficiency wages, match-specific capital) and inefficient rents (insider bargaining, labor regulations).
Automation accounts for 52% of the rise in between-group wage inequality in the US since 1980, with one-fifth (10 percentage points) due to rent dissipation from targeting high-rent tasks. Inefficient rent dissipation offset 60-90% of productivity gains from automation, leaving only 0.3-1.3% net TFP growth and 0.45-1.95% consumption growth from automation over 1980-2016.
- Key Methods
- Structural estimation using GMM to identify propagation and rent-impact matrices from observed wage changes and task displacement patterns. Uses revealed comparative advantage in routine tasks to measure automation exposure. General equilibrium model with task-based production, discrete worker groups, and labor market imperfections.
- Sample Period
- 1980-2016
- Geographic Coverage
- US
- Sample Size
- 500 demographic groups defined by education (5 categories) × gender (2) × age (10 five-year bins) × race (3) × nativity (2), tracked over 1980-2016
- Level of Analysis
- Occupation, Task
- Occupation Classification
- SOC
- Industry Classification
- N/A
- Replication Package
- Yes