The effect of political elections at home on the internationalization of state‐owned multinationals from emerging countries
Bibliographic record
Abstract
Abstract Research Summary The literature on the internationalization of state‐owned enterprises in emerging countries usually implicitly assumes continuity in the provision of key resources by home country governments. This assumption, however, does not necessarily hold in the presence of political elections in democratic emerging countries. Drawing from the resource dependence theory and the literatures on election‐induced uncertainty and investment irreversibility, we study how political elections in emerging countries affect the internationalization of multinationals with state indirect ownership. Using a sample of 89 Brazilian multinationals from 2000 to 2012, we find that these state‐owned multinationals are less likely to internationalize during elections than multinationals with fully private ownership. When they internationalize, they choose investments that provide them with more flexibility than those chosen by their private counterparts. Managerial Summary Political elections in democratic emerging countries regularly create considerable policy uncertainty and opportunism in policymaking. This affects the provision of government resources that state‐owned enterprises (SOEs) depend on for their internationalization strategy. Our results show that Brazilian multinationals with state indirect ownership are less likely to internationalize in an election year, and when they do, they implement more flexible strategies than fully private multinationals. These findings suggest that SOEs need to include the timing of political elections in their long‐term international strategic planning. Our study also stresses the pros and cons of the SOE‐government relationship. Whereas the link to the state can offer SOEs a way to access valuable government resources, it may constrain their managerial autonomy in international strategy decisions during political elections.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".