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Record W2759669773 · doi:10.1177/0974929217714673

Public–Private Partnerships in Developing Countries

2016· article· en· W2759669773 on OpenAlexafffund
Duncan J. Melville

Bibliographic record

VenueReview of Market Integration · 2016
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicPublic-Private Partnership Projects
Canadian institutionsUniversity of Toronto
FundersUniversity of Toronto
KeywordsDeveloping countryProcurementBusinessFinancePublic infrastructureDeveloped countryPrivate sectorEconomic growthEconomicsMarketing

Abstract

fetched live from OpenAlex

First used in developed markets, public–private partnerships (or PPPs) are being increasingly used to deliver critical infrastructure projects within developing countries. The success in developed markets is, however, unlikely to be easily transferrable to developing markets, and the usefulness of the contractual framework unpinning PPPs in such countries is worth questioning. In particular, a number of important developmental questions need to be answered. Are developing countries’ economic objectives best achieved through PPPs? Can developing country’s institutions support successful PPP procurement? Does a pipeline of PPP projects in a developing country ensure the growth of high-skilled jobs in the country? By exploring the experiences of PPP procurement in Chile, this article draws the conclusion that it would be in the best interest of developing countries to require domestic or local involvement within PPP consortiums, either through domestic ownership or in domestic/foreign construction partnering. Such local involvement is most likely to ensure the development of domestic engineering and construction companies and mitigate the potentially negative effects of an infrastructure market dominated by foreign influence. PPPs have been lauded for providing the ‘best of both worlds’ of private and public involvement. But the complex contractual structuring, sophisticated financing and robust institutional support involved, make PPPs an inaccessible tool for many developing countries. Outside of Australasia, Europe and North America, Chile has enjoyed some of the greatest success in promoting infrastructure development through PPPs. Since 1991, Chile has completed more than 50 PPPs, totalling over US$12 billion in capital investment in its roads, hospitals, ports and electricity system, and has been held out as a model for other less developed nations to follow (Hill, 2011, p. 189). What institutional prerequisites do developing countries need before PPPs become a viable option for infrastructure procurement? What can developing countries learn from Chile’s experiences with PPPs? From a developmental perspective, what could Chile have improved in designing its PPP programme? Split into three parts, this article seeks to answer each of these questions. ‘PPP Overview’ outlines relevant definitions, the various PPP contractual structures, which prerequisites make PPPs most effective and how PPPs encourage competition. ‘The Chilean PPP Case Study’ explores in greater detail the history of PPPs in Chile, the country’s institutional framework and some of the key outcomes from its concessions programme. Finally, ‘The Case for Domestic Involvement’ focuses on a noteworthy omission from the Chilean PPP model, requirements for local involvement. It is the author’s view that other developing countries will enjoy longer term benefits from PPPs by establishing a stance supporting the meaningful involvement of domestic companies and should, therefore, encourage PPPs not only for the public–private collaboration but also for the domestic–foreign cooperation they can foster.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.019
Threshold uncertainty score0.065

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0040.005
Scholarly communication0.0060.005
Open science0.0010.009
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0190.001

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.

Opus teacher head0.103
GPT teacher head0.291
Teacher spread0.188 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations13
Published2016
Admission routes2
Has abstractyes

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