Micro-Evidence on Product and Labor Market Regime Differences between Chile and France
Bibliographic record
Abstract
Institutions, social norms and the nature of industrial relations vary greatly between Latin American and Western European countries.Such institutional and organizational differences might shape firms operational environment in general and the type of competition in product and labor markets in particular.Contributing to the literature on estimating simultaneously product and labor market imperfections, this paper quantifies industry differences in both types of imperfections using firm-level data in Chile, a non-OECD member under the considered time period, and France.We rely on two extensions of Hall's econometric framework for estimating price-cost margins by nesting three labor market settings (perfect competition or right-to-manage bargaining, efficient bargaining and monopsony).Using an unbalanced panel of 1,737 firms over the period 1996-2003 in Chile containing unique data on firm-level output price indices and 14,270 firms over the period 1994-2001 in France, we first classify 20 comparable manufacturing industries in 6 distinct regimes that differ in the type of competition prevailing in product and labor markets.We then investigate industry differences in the estimated product and labor market imperfections.Consistent with differences in institutions and in the industrial relations system in the two countries, we find important regime differences across the two countries.In addition, we observe cross-country differences in the levels of product and labor market imperfections within regimes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".