Staff Background Paper for the G20 Surveillance Note - Priorities for Structural Reforms in G20 Countries
Bibliographic record
Abstract
provide a powerful lift to growth—both in the short and the long term—if they are well aligned with individual country conditions . These include an economy’s level of development, its position in the economic cycle, and its available macroeconomic policy space to support reforms. The larger a country’s output gap, the more it should prioritize structural reforms that will support growth in the short term and the long term—such as product market deregulation and infrastructure investment. Macroeconomic support can help make reforms more effective, by bringing forward long-term gains or alleviating their short-term costs . Where monetary policy is becoming over-burdened, domestic policy coordination can help make macroeconomic support more effective. Fiscal space, where it exists, should be used to offset short-term costs of reforms. And where fiscal constraints are binding, budget-neutral reform packages with positive demand effects should take priority. Some structural reforms can themselves help generate fiscal space. For example, IMF research finds that by boosting output, product market deregulation can help lower the debt-to-GDP ratio over time. Formulating a medium-term plan that clarifies the long-term objectives of fiscal policy can also help increase near-term fiscal space. With nearly all G-20 economies operating at below-potential output, the IMF is recommending measures that both boost near-term growth and raise long-term potential growth. For example: ? In advanced economies, these measures include shifting public spending toward infrastructure investment (Australia, Canada, Germany, United States (US)); promoting product market reforms (Australia, Canada, Germany, Japan, Korea, Italy) and labor market reforms (Canada, Germany, Japan, Korea, United Kingdom (UK), US); and fiscal structural reforms (France, UK, US). Where there is fiscal space, lowering employment protection is also recommended (Korea). ? Recommendations for emerging markets (EMs) focus on raising public investment efficiency ( India, Saudi Arabia, South Africa), labor market reforms (Indonesia, Russia, Saudi Arabia, South Africa, Turkey), and product market reforms (China, Saudi Arabia, South Africa), which would boost investment and productivity within tighter budgetary constraints particularly if barriers to trade and FDI were eased (Brazil, India, Indonesia). Governance (China, South Africa) and other institutional reforms are also crucial. Where policy space is limited, adjusting the composition of fiscal policy can create space to support reforms ( Argentina, India, Mexico, Russia). ? Some commodity-exporting EMs (Brazil, Russia, Saudi Arabia, South Africa) are facing acute challenges, with output significantly below potential and an urgent need to rebuild fiscal buffers. To bolster growth, Fund staff recommends product market and legal reforms to improve the business climate and investment; trade and FDI liberalization to facilitate diversification; and financial deepening to boost credit flows. IMF advice also aims to promote inclusiveness and macroeconomic resilience. The Fund recommends a targeted expansion of social spending toward vulnerable groups (Mexico), social spending for the elderly poor ( Korea), and upgrading social programs for the nonworking poor (US). Recommendations to bolster macrofinancial resilience include expanding the housing supply (UK), resolving the corporate debt overhang (China, Korea), coordinating a national approach to regulating and supervising life insurers (US), and reforming monetary frameworks (Argentina, China).
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.207 | 0.125 |
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".