Intermediaries in Transaction Networks: Location of Wholesalers' Headquarters and Other Establishments
Bibliographic record
Abstract
Using establishment-level data in Japan, this paper analyzes the role of information and geographical location of establishments, and the location of wholesalers' headquarters and establishments as factors in determining export behavior, especially focusing on regional economies. There are two main findings. First, regarding export probability of wholesalers' establishments, whether headquarters are located in urban areas matters more than the location of the establishments themselves; and the export probability is higher when there are other exporting establishments within the same firm, which suggest that information (exporting know-how held by headquarters and other export establishments within the same firm) is more important than infrastructure (access to trade hubs such as ports). Second, regarding domestic transaction networks between wholesalers and manufacturers, manufacturing firms in rural areas sell to exporting wholesaler firms in distant urban areas for indirect export, but the transaction distance measured between the closest establishments is significantly shorter than the distance between headquarters, at approximately one-third to one-quarter. The number of establishments per wholesaler firm is much larger than that of manufacturers and the distance between establishment and headquarters for wholesalers is much larger than that for manufacturers, which suggests that exporting wholesaler firms in urban areas reduce search costs by setting up other establishments in various regions, from which they search for suppliers.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".