Political capabilities, policy risk, and international investment strategy: evidence from the global electric power generation industry
Bibliographic record
Abstract
Abstract Whereas conventional wisdom holds that multinational enterprises (MNEs) invest less in host countries that pose greater policy risk—the risk that a government will opportunistically alter policies to expropriate an investing firm's profits or assets—we argue that MNEs vary in their response to host‐country policy risk as a result of differences in organizational capabilities for assessing such risk and managing the policy‐making process. We hypothesize that firms from home countries characterized by weaker institutional constraints on policy makers or greater redistributive pressures associated with political rent seeking will be less sensitive to host‐country policy risk in their international expansion strategies. Moreover, firms from home countries characterized by sufficiently weak institutional constraints or sufficiently strong redistributive pressures will seek out riskier host countries for their international investments to leverage their political capabilities, which permit them to attain and defend attractive positions or industry structures. We find support for our hypotheses in a statistical analysis of the foreign direct investment location choices of MNEs in the electric power generation industry during the period 1990–1999, the industry's first decade of internationalization. Copyright © 2010 John Wiley & Sons, Ltd.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".