The Micro and Macro of Disappearing Routine Jobs: A Flows Approach
Bibliographic record
Abstract
The U.S. labor market has become increasingly polarized since the 1980s, with the share of employment in middle-wage occupations shrinking over time.This job polarization process has been associated with the disappearance of per capita employment in occupations focused on routine tasks.We use matched individual-level data from the CPS to study labor market flows into and out of routine occupations and determine how this disappearance has played out at the "micro" and "macro" levels.At the macro level, we determine which changes in transition rates account for the disappearance of routine employment since the 1980s.We find that changes in three transition rate categories are of primary importance: (i) that from unemployment to employment in routine occupations, (ii) that from labor force non-participation to routine employment, and (iii) that from routine employment to non-participation.At the micro level, we study how these transition rates have changed since job polarization, and the extent to which these changes are accounted for by changes in demographic composition or changes in the behavior of individuals with particular demographic characteristics.We find that the preponderance of changes is due to the propensity of individuals to make such transitions, and relatively little due to demographics.Moreover, we find that changes in the transition propensities of the young are of primary importance in accounting for the fall in routine employment.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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".