Bibliographic record
Abstract
This paper considers an endogenous growth model with public capital and heterogeneous agents. Heterogeneity is due to differences in discount factors and inherent abilities. This allows us to closely approximate the 2007 U.S. income and wealth distributions. Government expenditures, including public investment, are financed through a progressive income tax along with a flat tax on consumption. Three revenue-neutral fiscal policy reforms are considered: (i) an increase in the degree of progressivity of the tax schedule that reduces the after-tax income distribution Gini coefficient to its lowest value over the period 1979–2009, (ii) a reduction in the progressivity ratio that causes the Gini coefficient of the wealth distribution to come close to 1, and (iii) an increase in the fraction of output allocated to public investment that has the same positive impact on the growth rate as reform (ii). It is shown that increasing investment in public capital is the only type of policy that simultaneously enhances growth and reduces both types of inequality (income and wealth). We also find that the public-investment-to-output ratio that maximizes social welfare crucially depends on the elasticity of the labor supply. With a more elastic labor supply the optimal ratio is 4.40%, whereas with a less elastic labor supply it is 5.53%.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".