Nurturing Infrastructure Investments in Emerging Markets and Africa: Notes from Washington, Beijing and Riyadh
Bibliographic record
Abstract
The investment choices of large asset owners such as pension funds, sovereign wealth funds and endowments, are, to a large extent ‘guided’ and pre-determined by the systematic use of old- fashioned indices or benchmarks designed by a small set of Anglo- American ‘index providers’ – most notably MSCI, formerly known as Morgan Stanley Capital International (MSCI). These companies and the rather conformist investment consultants promoting their indexes tend to be unwittingly biased in favor of liquid assets in rich, developed countries e.g. the archetypal MSCI All Country World Index (ACWI) clearly encourages asset owners to allocate 55% percent (or more) of their overall (equity) assets to the United States and 8% to Japan – whereas these two ageing nations only represent 15% and 4% of the world economy respectively (in real terms i.e. based on purchasing-power-parity or PPP). These unfair, deeply ingrained biases have de facto forced Northern Hemisphere asset owners to over-allocate capital to the US, Germany, Japan etc., thus sucking-up much needed financial resources from the rest of the world and hurting the long-term economic interests of Asia, Africa and Latin America. The paper also explores the geo-economic and financial implications of the US-China rivalry from the perspective of long-term asset owners and the G20 Saudi Arabia Presidency in relation to the increasingly 'Asianized World Economy.' The accelerating 'Sino-American Race' could benefit pivot-nations like Kenya, Egypt, Morocco, Ivory Coast, Senegal, Angola, Madagascar in Africa, and Estonia, Romania, Cyprus, Israel, Saudi Arabia, Vietnam, Cambodia, Malaysia in the Eurasia Pacific area – which will be courted like never before by Washington, Brussels and Beijing – thus eventually adding massive public funding and risk insurance resources to the rising private capital flows coming from sophisticated asset owners based in Canada, Scandinavia, Holland, Australia and Singapore (“Pension Superpowers”).
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".