A Simple Approach to Testing the Potency of Government Purchases to Stimulate Aggregate Demand
Bibliographic record
Abstract
Abstract: The paper proposes a new approach to testing the potency of government purchases to stimulate the economy by testing a set of conditions implied by the Ricardian Equivalence (RE) proposition that a typical household incorporates the government's budget constraint into its own. These conditions are as follows: (1) private consumption, income, and government purchases form a “levels relationship”; and (2) considering consumption as the dependent variable, the coefficients of income and of government purchases are 1 and -1. The last restriction is also implied by the hypothesis that consumption and government purchases are perfect substitutes, however, so the proposed approach cannot distinguish between the perfect substitutability and the RE hypotheses. This restriction is thus referred to in the paper as the hypothesis of direct or ex ante full crowding out. If it holds, then the multiplier of government purchases is zero. Using US quarterly data, 1947.1-2012.1, the results suggest that a “levels relationship” exists and that the coefficient of government purchases is about -0.4 and significantly below -1, thus leading to the conclusion that government purchases stimulate aggregate demand and output.
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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 | 0.013 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".