The monetary effects arising from stochastic resource revenues and the subsidization of financial intermediation in resource rich developing economies
Bibliographic record
Abstract
In this paper we develop a simple macro model to analyze a set of monetary issues that can arise when monetary, fiscal, and development policies become integrated. This is done for the specific case where a government finances a significant portion of its spending from resource revenues that are subject to external shocks and where the ability to alter both spending and taxes is limited in the short run. One concrete example is that of oil rich developing economies that frequently finance both government operations and development plans through the sale of oil on world markets (subject to stochastic price and/or exchange rate shocks). Here monetary consequences arise through the government budget constraint and to the extent that the central bank manages its exchange rate (Obstfeld, 1982). A second way that monetary policy becomes intermingled with government policy is when a country feels that its growth or development potential is held back by imperfectly functioning internal capital markets (see Levine, 1997). In such cases, the central bank may choose to supplement internal capital markets by monetizing the loans made by the government to encourage higher levels of private or quasi-private domestic investment. These two issues are explored for their effects on price level and inflation rate stability. To analyze the conditions underlying monetary stability, the classic paper of Leeper (1991) is modified to allow the government to own the revenue stream associated with the natural resource and to use this revenue to fund some portion of government services. Both to simplify presentation and set the stage for later application, we characterize the resource revenue as oil revenue. In addition to analyzing the effects that stochastic oil revenues may create for the government’s budget and balance of payments
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".