Air Pollution Effect on Expatriate Assignments: An Interactive Perspective on MNEs and Expatriates
Bibliographic record
Abstract
Despite the devastating effects of air pollution on human health, it is still unknown how local air pollution affects multinational enterprises (MNEs) and their employees. Building upon the micro-foundation of Transaction Cost Economics (TCE), we propose expatriate deployment as an interactive contractual process between MNEs and expatriates. We contend that because local air pollution in the host country causes expatriate candidates’ reluctance to relocate, MNEs incur additional transaction costs in expatriate deployment. Our empirical analysis, using samples of Japanese-owned subsidiaries in China, shows that local air pollution in the host country negatively affects expatriate assignments in subsidiaries. We further find that the effect of air pollution is contingent on several external and internal factors. Specifically, the negative effect of local air pollution on expatriate assignments is mitigated when MNEs have more experience in the local environment. Yet, the negative effect of air pollution is more pronounced when MNEs belong to low-pollution industries and when local talent is the most available.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".