Import Churning and Export Performance of Multi‐product Firms
Bibliographic record
Abstract
Abstract This paper analyses the impact of churning in the imported varieties of capital and intermediate inputs on firm export scope and productivity. Using detailed data on imports and exports at the firm‐product‐market level, we document substantial churning in both imports and exports for Slovenian manufacturing firms in the period 1994–2008. On average, a firm changes about one‐quarter of imported and exported product‐markets every year, while gross churning in terms of added and dropped product‐markets is almost three times higher. A substantial share of this product churning is due to simultaneous imports and exports of firms in identical varieties within the same CN‐8 product code (so called pass‐on‐trade). We find that churning in imported varieties is far more important than reduction in tariffs or declines in import prices for firms’ productivity growth and increased export product scope. We also find gross churning has a bigger impact on firm productivity improvements by a factor of more than 10 in comparison with net churning. Both adding and dropping of imported input varieties thus seem to be of utmost importance for firms aiming to optimise their input mix towards their most valuable inputs. These effects are further enhanced when excluding simultaneous trade in identical varieties, suggesting that pass‐on‐trade has less favourable effects on firms’ long‐run performance than regular 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 | 0.001 | 0.006 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".