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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".