Innovative financial intermediation and long term capital pools for infrastructure
Bibliographic record
Abstract
Purpose Developing countries are increasingly looking to private sector investment for infrastructure development. Successful development of private infrastructure projects, however, depends on adequate availability of long-term debt to complement private sector equity. As domestic bond markets in many emerging countries are not very deep, availability of long-term debt funding for infrastructure has been limited. Recently, a new form of financial intermediation has emerged in India with the creation of infrastructure debt funds (IDFs) to create capital pools for long-term debt funding. This paper aims to analyse the effectiveness of IDFs for financing infrastructure projects. Design/methodology/approach This paper uses a case study approach. The case studies were written using both secondary and primary information. Secondary information was obtained from various sources such as policy papers, websites and other published sources. Primary information was obtained from interviews with the top management of three IDFs. Information obtained from multiple sources was triangulated for consistency and correctness. Findings IDFs have emerged as an effective intermediation mechanism for attracting long-term capital by offering a new investment product with appropriate risk-adjusted returns. For the fund seekers, IDFs are able to provide long-term capital at lower rates and higher flexibility. Unlike commercial banks, IDFs are able to add value to the projects apart from funding by periodic monitoring of the projects. Practical implications Creating new forms of financial intermediation can help in reducing the financing gap for infrastructure projects, especially in emerging countries. Originality/value IDFs have been analysed from a perspective of financial intermediation. The effectiveness of IDFs in bridging the funding shortfall has been evaluated from multiple perspectives.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".