Trade and Domestic Production Networks
Bibliographic record
Abstract
We use Belgian data with information on domestic firm-to-firm sales and foreign trade transactions to study how international trade affects firm efficiency and real wages. The data allow us to accurately construct the domestic production network of the Belgian economy, revealing several new empirical facts about firms' indirect exposure to foreign trade through their domestic suppliers and buyers. We use this data to develop and estimate models of domestic production networks and international trade. We first consider a model of trade with an exogenous network structure, which gives analytical solutions for the effects of a change in the price of foreign goods on firms' production costs and real wages. To examine how gains-from-trade calculations change if buyer-supplier links are allowed to form or break in response to changes in the price of foreign goods, we next develop a model of trade with endogenous network formation. We take both models to the data and compare the empirical results to those we obtain using existing approaches. This comparison highlights the relevance of data on and modeling of domestic production networks in studies of international trade.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".