Modelling the effect of Chinese outward investments on its bilateral exports: Motivation for Belt and Road Initiative
Bibliographic record
Abstract
The paper aims to estimate how outward investment activity of China affects its bilateral exports at the aggregate and sectoral level in the period of recovery from the pandemic crisis. Theoretical discussion about the channels of the effect is complemented with empirical analysis (correlation and regression analysis). China’s exports and outward investments relatively its partner’s GNI are used as the main variables together with several control variables. Nonparametric correlation, alternative definitions of foreign direct investments, exclusion of outliers and weighting cases were used for robustness check. Sector-specific models were created for the main exported products. There is a negative non-linear dependence of Chinese exports on distance to markets, which provides a confirmation of proximity-concentration trade off theory. The diminishing negative effect of distance suggests existence of fixed costs of exports regardless the distance, which can be potentially decreased by investment in logistical facilities or trade liberalization. Large China’s outward investment projects lead to a lasting positive effect on China’s bilateral exports: each 1% of partner’s GNI accumulated investments by China in its trade partner increases bilateral exports of China there by 0.05-0.2% of partner’s GNI. But the regularity is irrelevant for exports of fuels, electronic and electrical appliances. This provides a partial support for Kojima's hypothesis and Vernon's product life cycle model (in particular rising labor costs lead to reallocation of some production abroad resulting in structural changes in exports). The general positive effect of free trade areas (additional exports equivalent to 2-3% of partner’s GNI) and the negative effect of a trade partner’s economy size are not robust across time. Multilateral trade balances in partner countries do not affect significantly exports of China. This evidences in favor of high competitiveness of Chinese exports even in protected foreign markets and in strong economies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".