JOINT USC FBE FINANCE SEMINAR and
Bibliographic record
Abstract
We show that differences in the quality of labor relations across countries can help to explain cross-country variation in ownership concentration. Controlling for minority share-holder protection, countries in which labor relations are hostile tend to have more concen-trated ownership than countries in which labor relations are cooperative. Union strength, labor regulation, and the political orientation of countries, on the other hand, appear not to matter for ownership concentration. Based on Crouch’s (1993) argument that cross-country differences in the quality of labor relations can be attributed to the presence or absence of state-church conflicts in the 19th century, which in turn can be attributed to a country’s religious affiliation, we instrument labor relations using religion in 1900. The result remains the same. We also find qualitatively similar results when examining postwar Canadian and Italian ownership and strike time series data, which show a remarkably strong correlation between strike activity and changes in ownership concentration over time.
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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 | 0.000 | 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 | 0.000 | 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) | 0.009 | 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".