How's the Job? Well-Being and Social Capital in the Workplace
Bibliographic record
Abstract
This paper takes a different tack in addressing one of the fundamental questions in economics: what are the factors that determine the distribution of jobs and wages? In Adam Smith's classic formulation, and in much of the subsequent literature, wage levels have been used to estimate the values of job characteristics ("compensating" or "equalizing" differentials). There are econometric problems with this approach, principally caused by unmeasured differences in talents and aptitudes that enable people of high ability to have jobs with both high wages and good working conditions, thus understating the value of working conditions. We bypass this difficulty by estimating the extent to which incomes and job characteristics influence direct measures of life satisfaction from three large and recent Canadian surveys. The well-being results show strikingly large values for non-financial job characteristics, especially workplace trust and other measures of the quality of workplace social capital. The compensating differentials estimated for the quality of workplace social capital are so large as to suggest that they do not reflect a full equilibrium. Thus the current situation probably reflects the existence of unrecognized opportunities for managers and employees to alter workplace environments, or for workers to change jobs, so as to increase both life satisfaction and workplace efficiency.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".