Effects of Multiple Dimensions of Intangible Distance on Agro-food Exports: Evidence from China
Bibliographic record
Abstract
Intangible distance may play a role as both trade barriers and competitive advantages in cross-border trade. Moreover, for agro-food products, intangible distance reflects the discrepancy between eating habits of the importing and exporting countries, and thus affects agro-food trade also as “eating-habit distance”. This paper investigates the effects of four dimensions of intangible distance on China’s agro-food exports, namely, cultural distance, institutional distance, distance in education, and distance in industrial development. A panel data of 78 countries covering the period 2002-2016 is used, and an extended gravity model is employed. We control for the effects of quality or level of institution, education, and industrial development of China and its trading partners to distinguish the “quality effects” from “distance effects” and to test the robustness of the results. To explore the (possible) different effects of intangible distance on different categories of agro-food products, we consider not only the total agro-food exports, but also the individual samples of the four agro-food categories classified according to the Harmonized System codes. We find that all these dimensions of intangible distance influence China’s agro-food exports significantly, at least for certain categories of agro-food products. Distance in institution, education, and industrial development function as measures of trade costs, whereas cultural distance functions more like a reflection of competitive advantage. Furthermore, when the distance in institution, education or industrial development increases in favor of the importing countries, the negative effects of intangible distance are partly neutralized by the importers’ improved level of institution, education or industrialization.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".