Separating the wheat from the chaff: A disaggregate analysis of the effects of public spending in the US
Bibliographic record
Abstract
Abstract In this paper, we undertake a systematic disaggregate analysis of the effects of government spending on economic activity in the US. The level of disaggregation we consider is the highest available and is unprecedented in the empirical literature. More specifically, public consumption and public investment are decomposed into various subcategories, which are measured at the levels of the federal (defence and non‐defence), and state and local governments. For each subcategory, we estimate a structural vector autoregression that identifies public spending shocks through the conditional heteroskedasticity of the structural disturbances, thus relaxing the identifying restrictions commonly used in the literature. Our analysis reveals significant heterogeneity in the effects of public spending shocks on output both across subcategories and government levels. Shocks to spending on durables and structures are found to have the largest and most persistent effect on aggregate output, with a peak multiplier that exceeds 1. Our results also suggest that there is little association between the size of a given category of public expenditures and the magnitude of its effect on output. Résumé Séparer le bon grain de l’ivraie : une analyse désagrégée des effets des dépenses publiques aux États‐Unis. Dans ce texte, on entreprend une analyse désagrégée des effets des dépenses gouvernementales sur l’activité économique aux États‐Unis. Le niveau de désagrégation considéré est le plus grand possible et est sans précédent dans la littérature empirique. Plus spécifiquement, la consommation et l’investissement publics sont décomposés en diverses sous‐catégories qui sont mesurées pour le gouvernement fédéral (défense et non‐défense), ainsi que pour les gouvernements des états et locaux. Pour chaque sous‐catégorie, on estime un vecteur autorégressif structurel qui identifie les chocs de dépenses publiques à travers l’hétéroscédastictité conditionnelle des perturbations structurelles, pour ainsi relâcher les restrictions d’identification communément utilisées dans la littérature. L’analyse révèle une hétérogénéité significative dans les effets des chocs de dépenses publiques sur la production agrégée selon les sous‐catégories et les niveaux de gouvernement. Les chocs dans les dépenses sur les biens durables et les structures tendent à avoir les effets les plus grands et les plus persistants sur la production agrégée avec un effet multiplicateur qui excède 1. Les résultats suggèrent qu’il y a peu de rapport entre la taille d’une catégorie donnée dans les dépenses publiques et l’ampleur de l’effet sur la production agrégée.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".