Priority of Governments the World Over—Institutional Investment into Infrastructure
Bibliographic record
Abstract
Abstract Governments desire institutional investment (from pension, insurance, and sovereign wealth funds) in infrastructure rather than bank financing, which is the predominant means of financing infrastructure debt. Institutional investment into infrastructure does not suffer from asset–liability mismatch issues, which is typical of bank financing of infrastructure. Infrastructure investments should also be attractive to institutional investors given that they provide long-term steady returns, which have low correlation with business cycles. For example, pension funds and insurance companies hold more than 90 per cent of the stock of infrastructure bonds in Chile. However, in India, institutional investors have not been investing much in infrastructure. The chapter explores the reasons for this, and also suggests policy measures, including the need for a specialized credit enhancement company for infrastructure bonds, for domestic institutional investors to invest in infrastructure (as foreign, mainly Canadian pension funds, are already major investors in Indian infrastructure). This chapter also discusses the importance of infrastructure projects aligning with the environmental, social, and governance (ESG) norms increasingly being used by these investors for their investments. In this context, the chapter discusses the role of India’s own sovereign wealth fund, the National Investment and Infrastructure Fund (NIIF), to facilitate institutional investment into infrastructure.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".