Impact Investing as a Safeguard Against Institutional Hazards
Bibliographic record
Abstract
The growing practice of “impact investing” – investing for both pecuniary (financial) and non-pecuniary (social, and environmental) outcomes – has attracted increasing attention in recent years. However, questions remain on the outcomes of impact investments, especially in high-risk countries. The international business and strategy literature establishes that country risk from institutional hazards negatively impacts foreign investments. Leveraging a novel unique hand-collected dataset of impact investments globally, we theorize and empirically test the role of impact investment as a safeguard against institutional hazards. Impact investment may mitigate the adverse effects of institutional hazards through three mechanisms: (1) by reducing the cost of capital, (2) by catalyzing further investments, and (3) by fostering capacity-building to improve institutional environments. These mechanisms help firms manage uncertainties involved in foreign investment and reduce incentives to exit high-risk countries. Analyzing 794 U.S. firms and their subsidiaries in 79 countries over the 2000 to 2015 period, we find empirical support for our assertions.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".