Estimating the Effects of Government Spending Through the Production Network
Bibliographic record
Abstract
We estimate the effects of government spending along the supply chain using disaggregated U.S. government procurement data.We first identify sectoral public spending shocks and combine them with input-output tables to measure upstream and downstream exposure through the production network.We then estimate panel local projections and find that sector-specific government purchases have sizable effects both in industries that receive procurement contracts and industries across the supply chain.Employment increases significantly in recipient industries and in sectors supplying intermediate inputs to these industries, while employment decreases downstream.The response of prices and wages suggest higher intermediate-input demand by recipient industries translates into higher intermediate-input prices across the network, accounting for the crowding out of downstream employment.We then estimate the aggregate implications of sectoral shocks and the influence of sectoral heterogeneity using a granular instrumental variable approach.Consistent with existing models, we find that aggregate effects are higher when recipient sectors are more downstream, have stickier prices, and when the government accounts for most of the recipient's total sales.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.006 | 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".