The corporate governance consequences of small shareholdings: Evidence from sovereign wealth fund cross‐border investments
Bibliographic record
Abstract
Abstract Research Question/Issue Existing research on sovereign wealth funds (SWFs) has largely explored how they affect target firm value, overlooking the role they play in corporate governance. This paper examines the impact of SWFs' cross‐border equity acquisitions on targets' corporate governance and the role the institutional environment of SWF countries plays in shaping this impact. Research Findings/Insights We use a difference‐in‐differences approach and find that, on average, SWF investments are negatively related to target firms' corporate governance. This impact holds for small SWF cross‐border equity investments only and is stronger for firms that are weakly governed and for those located in jurisdictions with weak shareholder protection. The negative relation is more pronounced when SWFs' home countries have lower‐quality investor protection, corruption control, governmental effectiveness, and law enforcement than their host countries. We find further that SWF investments are positively associated with target firms' earnings management and negatively associated with investment efficiency. Finally, target firm value is found to decrease after SWF investments. Theoretical/Academic Implications The evidence that SWFs' small equity investments are detrimental to target firms' corporate governance is broadly consistent with the view that SWFs are passive investors. Managers can exploit this passivity, as evidenced by higher earnings management, reduced investment efficiency, and lower firm value. Practitioner/Policy Implications This study has important policy implications for investors, SWF managers, and policymakers. The passive role of SWFs in corporate governance should prompt minority shareholders to look for alternative monitoring mechanisms. Moreover, SWF managers may realize the need to target firms with strong corporate governance at the outset to compensate for the post‐acquisition decline. Host country policymakers may need to condition SWF investments on commitments to improve the corporate governance of investee firms, which would be akin to the performance requirements imposed on inward foreign direct investment (FDI). This is particularly relevant for SWFs from countries with weak institutions that are targeting countries with strong institutions.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".