Trade Liberalization and Labor Market Institutions
Bibliographic record
Abstract
Abstract While the firm-level distributional consequences of market liberalization are well understood, previous studies have paid only limited attention to how variations in domestic institutions across countries affect the winners and losers from opening up to trade. We argue that the presence of coordinated wage-bargaining institutions, which impose a ceiling on wage increases, and state-subsidized vocational training, which creates a large supply of highly skilled workers, generate labor market frictions. Upward wage rigidity, in particular, helps smaller firms weather the rising competition and increasing labor costs triggered by trade liberalization. We test this hypothesis using a firm-level data set of European Union countries, which includes more than 800,000 manufacturing firms between 2003 and 2014. We find that, for productive firms, gains from trade are 20 percent larger in countries with liberal market economies than they are in coordinated market economies. Symmetrically, less productive firms in coordinated market economies experience significantly smaller revenue losses compared to liberal market economies. We show that both the presence of an institutionalized wage ceiling and the availability of subsidized vocational training are key mechanisms for reducing the reallocation of revenue from unproductive to productive firms in coordinated market economies compared to liberal market economies. In line with our theory, we find that wages and employment in liberalized industries increase differentially across both types of labor markets. Finally, we provide suggestive evidence that trade liberalization triggers a differential demand for redistribution at the individual level across different labor markets, which is in line with our firm-level analysis.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".