Estimating the Productivity Selection and Technology Spillover Effects of Imports
Bibliographic record
Abstract
Economists emphasize two channels through which import liberalization affects productivity, one operating between and the other within firms.According to the former, import competition triggers market share reallocations between domestic firms with different technological capabilities (selection).At the same time, imports can also improve firms' technologies through learning externalities (spillovers).We present evidence for a sample of industrialized countries over the period 1973 to 2002.First, in the long run, import liberalization lowers productivity in domestic industries through selection.This finding confirms the prediction of models with firm heterogeneity, including Melitz and Ottaviano (2008), in which unilateral liberalization lowers the profits of domestic relative to foreign exporters.Second, if imports involve advanced foreign technologies, liberalization also generates technological learning that can on net raise domestic productivity.Third, for short time horizons of up to three years, a surge in imports typically raises domestic productivity.Because the number of firms at home and abroad does not change much in the short-run, new competition from foreign firms has a pro-competitive effect.We also find that high entry barriers, especially regulation, slow down the process of market share reallocation between firms.Over-all, the results support models in which trade triggers both substantial selection and technological learning.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".