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Record W1508714553 · doi:10.34989/swp-2006-32

Governance and the IMF: Does the Fund Follow Corporate Best Practice?

2021· preprint· en· W1508714553 on OpenAlexaff
Eric Santor

Bibliographic record

VenueRePEc: Research Papers in Economics · 2021
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal Financial Crisis and Policies
Canadian institutionsBank of Canada
Fundersnot available
KeywordsCorporate governanceBusinessAccountingFinancial systemFinance

Abstract

fetched live from OpenAlex

The governance challenges facing the International Monetary Fund (IMF) are not simply limited to representation and voice, and the associated question of quota allocation. The author identifies governance issues that hitherto remained largely ignored by the literature and policy-makers alike. Specifically, he examines the governance issues that arise when (i) one or more shareholders hold controlling voting blocks, and (ii) principal-agent problems exist between the Executive Board and the Managing Director. Furthermore, these typical governance issues are compounded by the specific characteristics of IMF governance, such as consensus decision making, the lack of clear fiduciary duty on the part of the Executive Board, and the lack of separation between the Executive Board and the Managing Director. The author then attempts to quantify the extent to which the IMF's governance structure deviates from corporate best practice. Unsurprisingly, he finds that the IMF does not follow best practice. The author offers several proposals for governance reforms, including that the IMF should implement a form of "constrained discretion." Under this framework, the Executive Board would set the objectives and rules for the IMF on an annual basis. The Managing Director and the staff would be free to pursue these objectives, conditional on the rules. These respective reforms would improve accountability and hence the legitimacy of the IMF.

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)0.016
metaresearch head score (Gemma)0.060
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: Empirical · Consensus signal: none
Teacher disagreement score0.016
Threshold uncertainty score0.086

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.060
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0020.008
Scholarly communication0.0120.007
Open science0.0010.002
Research integrity0.0050.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.062
GPT teacher head0.302
Teacher spread0.240 · 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
GenreEmpirical

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

Citations12
Published2021
Admission routes1
Has abstractyes

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