Testing the endogenous growth model: public expenditure, taxation, and growth over the long run
Bibliographic record
Abstract
Endogenous growth models, such as Barro (1990), predict that government expenditure and taxation will have both temporary and permanent effects on growth. We test this prediction using panels of annual and period‐averaged data for OECD countries during 1970–95, isolating long‐run from short‐run fiscal effects. Our results strongly support the endogenous growth model and suggest that long‐run fiscal effects are not fully captured by period averaging and static panel methods. Unlike previous investigations, our estimates are free from biases associated with incomplete specification of the government budget constraint and do not appear to result from endogeneity of fiscal or investment variables. JEL Classification: H30, O40 Validation du modèle de croissance endogène: dépenses publiques, fiscalité et croissance à long terme. Des modèles de croissance endogène comme celui de Barro (1990) prédisent que dépenses gouvernementales et fiscalité vont avoir des effets temporaires et permanents sur la croissance. On met cette prévision au test à l'aide de données annuelles et pour certaines moyennes couvrant des sous‐périodes pour les pays de l'OCDE (1970–95) dans le but de départager les effets à court et à long terme. Les résultats valident fortement le modèle de croissance endogène et suggèrent que les effets fiscaux à long terme ne sont pas pleinement capturés par des méthodes utilisant des moyennes ou des méthodes statiques. Contrairement aux résultats d'enquêtes antérieures, les résultats proposés ne souffrent pas de distorsions attribuables à une spécification incomplète de la contrainte budgétaire du gouvernement, et ne semblent pas être l'effet d'écho de l'endogénéité des variables fiscales et de l'investissement.
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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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".