Budget Rules and Resource Booms and Busts: A Dynamic Stochastic General Equilibrium Analysis
Bibliographic record
Abstract
This paper develops a dynamic, stochastic, general-equilibrium model to analyze and derive simple budget rules in the face of volatile public revenue from natural resources in a low-income country like Niger. The simulation results suggest three policy lessons or rules of thumb. When a resource price change is positive and temporary, the best strategy is to save the revenue windfall in a sovereign fund and use the interest income from the fund to raise citizens’ consumption over time. This strategy is preferred to investing in public capital domestically, even when private investment benefits from an enhanced public capital stock. Domestic investment raises the prices of domestic goods, leaving less money for government to transfer to households; public investment is not 100 percent effective in raising output. In the presence of a negative temporary resource price change, however, the best strategy is to cut public investment. This strategy dominates other methods, such as trimming government transfers to households, which reduces consumption directly, or borrowing, which incurs an interest premium as debt rises. In the presence of persistent (positive and negative) shocks, the best strategy is a mix of public investment and saving abroad in a balanced regime that provides a natural insurance against both types of price shocks. The combination of interest income from the sovereign fund, transfers to households, and output growth brought about by public investment provides the best protective mechanism to smooth consumption over time in response to changing resource prices.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".