Privatization and Labor Force Restructuring around the World
Bibliographic record
Abstract
Critics of privatization argue that poor labor force restructuring is a key concern and that governments should establish better retrenchment programs. Using new data from a random sample of 400 companies in the world, we test competing theories about the wisdom of retrenchment programs and their effect on prices paid by buyers and re-hiring policies by private owners after privatization. Our results show that adverse selection plagues retrenchment programs carried out by governments before privatization. Controlling for endogeneity, several labor retrenchment policies yield a negative impact on net privatization prices. In confirmation of the adverse selection argument, various types of voluntary downsizing lead to a higher frequency of re-hiring of the same workers by the new private owners. Compulsory skill-based programs are the only type of program that is marginally associated with higher prices and lower re-hiring rates after privatization, but the political and economic costs of this policy may make it somewhat impractical. A qualified do not intervene appears to be the safest bet with respect to labor retrenchment before privatization.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".