Second thoughts on the exporter productivity premium
Bibliographic record
Abstract
Abstract Contrary to the prevailing interpretation, this paper shows that the central models of trade with heterogeneous firms (Melitz 2003; Bernard et al. 2003) exhibit ambiguous predictions for the exporter productivity premium. This prospect arises because of differences between theoretical and empirical representations of firm productivity. Instead of marginal productivity, we examine in both models the theoretical equivalent of empirically observable productivity (value‐added per employee). Given the presence of fixed export costs or heterogeneous mark‐ups and trade costs, the observable productivity of exporters in proximity to the export‐indifferent firm turns out to be lower than that of non‐exporters; that is, the productivity distributions overlap. The paper reviews empirical literature that reports non‐positive exporter productivity premia in firm‐level data and discusses implications for empirical research on exporter performance, including learning and the role of non‐parametric regressions (stochastic dominance, quantile regressions), fixed costs, and productivity distributions.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.033 | 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".