The General Equilibrium Effects of Fiscal Policy with Government Debt Maturity
Bibliographic record
Abstract
This paper highlights the importance of accounting for both the maturity structure of government debt and the composition of fiscal instruments when studying the macroeconomic effects of fiscal policy. Using a dynamic stochastic general equilibrium (DSGE) model featuring a debt maturity structure and six exogenous fiscal shocks spanning both the expenditure and revenue sides, we show that long-maturity debt systematically weakens the expansionary effects of fiscal policy under dovish monetary policy, particularly in response to increases in government purchases, government investment, and capital income tax cuts, where long-term financing leads to the significant crowding-out of private activity. In contrast, short-term debt financing yields output multipliers that often exceed unity. The maturity structure also alters the relative efficacy of fiscal instruments: while labor income tax cuts produce the largest multipliers under short-term debt, government purchases become more potent under long-term debt financing. We also show that the stark difference between short- and long-term debt becomes muted under a hawkish monetary regime. Our results have important policy implications, suggesting that the maturity composition of public debt should be carefully considered in the design of fiscal policy, particularly in high-debt economies.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".