Taking advantage of institutional weakness? Political stability and foreign subsidiary survival in primary industries
Bibliographic record
Abstract
Purpose This paper aims to investigate the extent to which locating primary industry subsidiaries in politically unstable countries impacts their survival. The authors argue that foreign multinational enterprises in less stable political environments can shape policies that are impactful on the costs of operating in primary industries and avoid compliance with more stringent policies at home. Design/methodology/approach Using a sample of 753 primary sector investments of Japanese multinational enterprises during the period 1986 to 2013, the authors conduct a parametric survival analysis of the relationship between political stability and subsidiary survival. Findings Political instability has a slight, curvilinear relationship with subsidiary survival, such that both high and low stability are associated with lower exit hazard, while moderate levels of stability increased exit hazard. This nonlinear relationship is stronger for efficiency-seeking subsidiaries, and weaker for market-seeking subsidiaries. Originality/value This research contributes to the debate around the pros and cons of globalization by examining the extent to which firms benefit by offshoring primary sector investments to avoid more costly legal requirements at home. The results suggest that this non-market strategy should be mitigated through appropriate policy measures and provides evidence that those policies already implemented are effective.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".