Government‐spending multipliers and the zero lower bound in an open economy
Bibliographic record
Abstract
Abstract This paper assesses the size of the government‐spending multiplier in an open economy when the zero lower bound (ZLB) on the nominal interest rate is binding. In a theoretical framework, in a closed economy, other authors have shown that when the nominal interest rate is binding the government‐spending multiplier can be very large (close to four). Their theory helps illuminate the government‐spending multiplier in the ZLB, but it is difficult to match that theory with the data. We argue that, in an open economy, another channel exists for the crowding‐out effect via the real exchange rate. For an open economy, the government‐spending multiplier is not large owing to the appreciation of the real exchange rate, induced by the appreciation of aggregate demand that follows the increases in government spending. To test the robustness of our open economic model, we conduct the same analysis in a corresponding closed economy model. The result from our closed economy model confirms the result obtained in the other work. Our theoretical results are consistent with the results obtained in the empirical literature, which uses the vector autoregressive method and the structural vector autoregressive approach to measure the impact of government‐spending shock on the real gross domestic product and revealed that the government‐spending multiplier tends to be lower in open economy.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".