A multiple criteria decision aid analysis of government performance and efficiency in the OECD
Bibliographic record
Abstract
In the 2010 Toronto summit, the leaders of the G-20 countries agreed on the implementation of urgent fiscal consolidation plans, following the expansionary policies adopted to curb the recessionary effects of the financial crisis. Unprecedented cuts in public expenditures have taken place, particularly in the European Union periphery, reviving the discussion on the optimal size of the public sector. Supporters of fiscal restraint defend that bigger governments tend to be more inefficient, its opponents assume that government size determines the effectiveness of its performance. However, the social and economic impacts from contractionary fiscal policies ultimately depend on the level of public sector efficiency. Relatively inefficient governments have more scope to consolidate without compromising social welfare. In this paper, we adopt a multi criteria decision aid technique, not previously employed for the assessment of complex macroeconomic performances, and employ the ELECTRE TRI-C outranking method to categorize OECD countries on a set of criteria representing the quality of their public sectors. We then compare the obtained classifications with the share of each government?s expenditures on GDP, to identify distinct levels of efficiency. Our analysis suggests that many countries exhibit a margin for efficiency gains, attenuating the social and economic effects of fiscal consolidation policies.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".