Production Networks and International Fiscal Spillovers
Bibliographic record
Abstract
This paper analyzes the impact of fiscal spending shocks in a dynamic, multi-country model with international production networks.We first derive a decomposition of the effects of a fiscal spending shock on the GDP of any country.This decomposition defines the response as the sum of a Direct, Income, and Price effect.The Direct Effect depends only on structural parameters and is independent of assumptions about monetary policy, wage setting, or capital mobility, while the Price Effect is zero in the aggregate across countries.We apply this decomposition to an analysis of fiscal spillovers in the Eurozone, using the production network structure from the World Input Output Database (WIOD).We find that fiscal spillovers from Germany and some other large Eurozone countries may be large, and within the range of empirical estimates.Without international production network linkages, spillovers would be only a third as large as predicted by the baseline model.Finally, we explore the diffusion of identified government spending shocks at the sectoral level, both within and across countries, using an empirical measure of the response, based on the theoretical decomposition.The empirical estimates are strongly consistent with the theoretical model.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".