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Record W4391177803 · doi:10.1016/j.intfin.2024.101954

Infrastructure financing in Africa

2024· article· en· W4391177803 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of International Financial Markets Institutions and Money · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicPublic-Private Partnership Projects
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsBusinessFinanceGeography

Abstract

fetched live from OpenAlex

• Infrastructure development increases private investment in African infrastructure. • Infrastructure development increases the use of BOO ownership and commercial debt. • Infrastructure development decreases use of equity financing in the capital structure. • Projects in higher income countries use a smaller proportion of equity financing. • These relations show a substitution effect of equity for debt in riskier environments. We explore current development and constraints on infrastructure financing in Africa. We examine how infrastructure development in African countries affects ownership and capital structure choices of private and public–private partnership infrastructure projects. Using data from 33 African countries over 17 years, our findings suggest that infrastructure projects in African countries with better infrastructure development tend to have more private investment, more long-term investment, and they tend to use more debt financing, including more commercial debt, and less equity in their capital structure. For the least developed African countries, where debt financing is scarce, equity investment is vital for infrastructure financing.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.907
Threshold uncertainty score0.395

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.022
GPT teacher head0.260
Teacher spread0.237 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it