Bibliographic record
Abstract
This paper investigates differences in productivity by destination market of firms exports. The total factor productivity (TFP) is used as measure of productivity. The productivity differences by export destination are estimated using multilevel approach considering the first destination country of the firm’s exports as the second level group of the model. The analysis is based on a dataset that provides comparable cross-country data of manufacturing firms in seven European countries (Austria, France, Germany, Hungary, Italy, Spain and the United Kingdom). The results are as follows. Productivity differs from market to market and, thus, it gives support to the expectations derived from Chaney’s model (2008). The estimates confirm that non-exporters are, on average, the less productive. On the contrary, the European firms that export to China and India register the highest positive difference. A positive difference also exists for firms that export to the USA and Canada. On the contrary, there is no relevant TFP difference for firms exporting to the EU-15 area. The difference is positive but slight for the Other Asian countries and Other EU countries, while it is negative for Other areas, Other non EU countries and Central and South America. Among firm-specific characteristics only size and sector membership help to explain the productivity differences by destination market and the role of size is by far the most dominant factor.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".