The potential role of government debt management offices in monitoring and managing contingent liabilities
Bibliographic record
Abstract
As poor management of contingent liabilities has led to significant losses for governments, many now seek to manage them in a more prudent and systematic fashion. Some governments have given the Debt Management Office (DMO) an important role in managing contingent liabilities (CL) risks, often in close coordination with the Budget Office. The latter can promote budget transparency and discipline, while the DMO can contribute with sovereign risk quantification and management, and together they can contribute to the government's design of a general contingent liability policy. The examples of Sweden, New Zealand, Denmark, Canada, and Colombia show how the offices in charge of managing the risks from the country's debt have extended their scope to also monitor and manage risks from contingent liabilities. These examples may be useful for countries seeking to improve the monitoring and management of their contingent liabilities. This paper is divided into six sections, including the introduction. Section two briefly reviews the reasons why governments have CL in the first place, and concludes that many countries will have liabilities of this type, although with varying degrees of exposure. The three main levels of CL management are analyzed in section three, namely: general policy; budgetary transparency and discipline; and financial risk management. Section four analyzes the institutional arrangements for managing CL, section five presents country examples where a country's debt management office (DMO) plays an active role, and finally, some brief conclusions are presented in section six.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".