Bibliographic record
Abstract
One reason to be concerned about income inequality is the idea that people not only care about their own absolute income, but also their income relative to various reference groups (e.g. co-workers, friends, neighbors, relatives, etc.). We use Canadian linked employer-employee data to estimate the casual effect of co-worker pay on a worker's reported job and pay satisfaction. Since worker satisfaction can affect the worker's productivity, organizational commitment, turnover, creativity and innovation, as well as the firm's productivity and profitability, this is an issue that requires more attention and careful examination. In models that control for a rich set of workplace characteristics, we find that coworker pay has a large positive and significant effect on both pay and job satisfaction. In our preferred models with establishment-level fixed effects, the effect of coworker pay on pay satisfaction is half as large, and the effect on job satisfaction completely disappears, suggesting that part (all) of what previous studies attribute to the effect of coworker pay on worker pay (job) satisfaction is driven by unobserved heterogeneity across firms or establishments. Our results also suggest that the effect of coworker pay on worker satisfaction is much stronger for workers who leave their job during the following year. Finally, we find that while coworker pay has a positive effect on pay satisfaction among Canadian-born whites, it has a negative effect among immigrants and Canadian-born visible minorities.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".