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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".