The Macroeconomics of Automation: Data, Theory, and Policy Analysis
Bibliographic record
Abstract
During the last four decades, the U.S. has experienced a fall in the employment in middle-wage, "routine-task-intensive," occupations.We analyze the characteristics of those who used to be employed in such occupations and show that this type of individual is nowadays more likely to be out of the labor force or working in low-paying occupations.Based on these findings, we develop a quantitative, general equilibrium model, with heterogeneous agents, labor force participation, occupational choice, and investment in physical and automation capital.We first use the model to evaluate the distributional consequences of automation.We find heterogeneity in its impact across different occupations, leading to a significant polarization in welfare.We then use this framework as a laboratory to evaluate various public policies such as retraining, and explicitly redistributive policies that transfer resources from those who benefit from automation to those who bear the brunt of its costs.We assess the tradeoffs between the aggregate impact and welfare distributional consequences of such policies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".