Does Exporting Improve Matching? Evidence from French Employer-Employee Data
Bibliographic record
Abstract
Does opening a market to international trade affect the pattern of matching between firms and workers?And does the modified sorting pattern affect welfare?This paper answers these questions both theoretically and empirically in three parts.We set up a model of matching between heterogeneous workers and firms where variation in the worker type at the firm level exists in equilibrium only because of the presence of search costs.When firms gain access to the foreign market their revenue potential increases.When stakes are high, matching with the right worker becomes particularly important because deviations from the ideal match quickly reduce the value of the relationship.Hence exporting firms select sets of workers that are less dispersed relative to the average.We then document a novel fact about the hiring decisions of exporting firms versus non-exporting firms in a French matched employer-employee dataset.We find that exporting firms feature a lower type dispersion in the pool of workers they hire.The matching between exporting firms and workers is even tighter in sectors characterized by better exporting opportunities as measured by foreign demand or tariff shocks.In a calibrated general equilibrium version of the model we show that trade opening increases welfare by more when search costs are high, pointing to an additional source of gains from trade.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".