The sectoral origins of heterogeneous spending multipliers
Bibliographic record
Abstract
The aggregate spending multiplier crucially depends on the sectoral origin of government purchases. To establish this result, we characterize analytically the response of aggregate output to sector-specific government spending shocks in a tractable production-network economy, showing how it maps into various characteristics of the shocked sector. The response is larger when government spending originates in sectors with a relatively small contribution to private final demand, low markup, high labor intensity, and in those located downstream in the supply chain. We confirm these predictions and evaluate their quantitative relevance within a calibrated multi-sector model of the U.S. economy that embeds several dimensions of sectoral heterogeneity. Leveraging this model, we illustrate how differences in the sectoral composition of purchases across U.S. government levels lead to large variation in the spending multiplier. The latter ranges from 0.47 for federal defense spending, which is relatively concentrated in upstream capital-intensive manufacturing, to 0.82 for state and local spending, which is mainly oriented towards downstream labor-intensive services. Finally, we exploit heterogeneity in the sectoral composition of military spending across U.S. states to provide empirical evidence supporting our theoretical predictions.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".