The Economics of Prozac (Do Employees Really Gain from Employment Protection?)
Bibliographic record
Abstract
Unlike many other contracts, employment contracts are subject to various external administrative procedures governing separations, ranging from compulsory severance payments and advance notice periods (usually seniority based), to collective layoff procedures (usually depending on the firm's size), and other forms of protections against arbitrary dismissal. These external constraints may raise the wellbeing of workers if everything remains constant, but may fail to do so once other economic channels are accounted for. Here, we explore the effect of such legislation on the firm's attitude towards insiders (i.e. protected workers), notably worker monitoring, working environment, and ultimately what we could term harassment. We show that during downturns, harassing workers in order to induce a quit is a substitute for greater dismissal freedom, and that intense monitoring and depreciated working conditions will occur. Thus, a more protected workforce may loose more than it gains from non-pecuniary pressures exerted by the firm. We test these mechanisms using data from a panel of Canadian individuals (the National Public Health Survey) including details on work-related stress and the consumption of various medications, including anti-depressants. By exploiting cross-province differences in employment protection legislation (EPL), we cannot reject the theoretical hypothesis: we even find positive links between individual employment protection and some dimensions of stress, and weaker but positive links between employment protection, depression and the consumption of various psychotropic drugs. Tenure and firm size information from another dataset is then used to generate further variance in EPL by imputation. This confirms the previous results, as well as falsification exercises: family stress for instance is not correlated with regional EPL, while financial stress is negatively correlated with EPL.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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".