A Local-Spillover Decomposition of the Causal Effect of U.S. Defense Spending Shocks
Bibliographic record
Abstract
This paper decomposes the causal effect of government defense spending into: (i) a local (or direct) effect, and (ii) a spillover (or indirect) effect.Using state-level defense spending data, we show that a negative cross-state spillover effect explains the existing simultaneous findings of a low aggregate multiplier and a high local multiplier.We show that enlisting disaggregate data improves the precision of aggregate effect estimates, relative to using aggregate time series alone.Moreover, we compare two-step efficient GMM with two alternative moment weighting approaches used in existing research.There is substantial evidence that changes in U.S. national defense spending typically have a positive and less than one-for-one effect on U.S. national output. 1 Yet, estimates of this relationship using regional cross-sectional or panel data find larger effects of a region's defense spending on that region's output.2 The former is an aggregate effect while the latter is a local effect.The local effect of a treatment need not equal the treatment's aggregate effect in the presence of spillovers.For example, if there are negative spillovers across regions, then the local multiplier will likely provide an upwardly biased estimate of the aggregate effect of a policy.3 Our paper reconciles this discrepancy in a unified empirical framework.By leveraging both the cross-sectional and aggregate variation in a panel, we decompose the causal effect of govern- * The analysis set forth does not reflect the views of the Federal Reserve Bank of St. Louis or the Federal Reserve System.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".