Bibliographic record
Abstract
Measured aggregate productivity and the income share of top earners are strongly and positively correlated in the Canadian data. Productivity slowdown since the early 2000s was accompanied with a flattening income share of top earners. Motivated by these facts, we study the role of firms' top-paid workers and worker matching in accounting for the slowdown of measured total factor productivity. We first estimate total factor productivity for Canadian firms in the period of 2003-2015, taking into account the assortative matching between top workers and non-top workers. Measured total factor productivity consists of the Hicks-neutral technology and the quality of top workers. Our estimation suggests that measured aggregate total factor productivity declined from 2003 to 2015, in line with that estimated by the statistical agency. The decline of measured productivity is entirely accounted for by the declining quality of top workers, while the Hicks-neutral technology improved. Both the within-firm changes and the cross-firm reallocation of top-worker quality are important in contributing to the decline of overall top-worker quality. We also discuss possible causes of declines in the quality of top workers, e.g., the emigration of top talents as studied in recent literature.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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".