Human and physical capital: Welfare and income effects of government spending
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Insufficient physical and human capital are major impediments to development and poverty reduction in low-income countries. Given the financial constraint, should the governments emphasize physical capital or human capital investment and what instruments should they use? This paper addresses these questions using a dynamic stochastic general equilibrium model with human capital and two-sectors: formal and informal. The model is estimated using data from India. Results demonstrate that investment subsidy is the most effective instrument in raising the GDP and aggregate welfare followed by government physical capital investment. However, both these policies mainly benefit the rich. The government investment in schooling and tuition subsidies targeted at poor are the most effective instruments for raising the employment income and welfare of poor. Using government physical capital investment in combination with either government schooling investment or tuition subsidy targeted at poor, the government can raise the GDP and income and welfare of both rich and poor.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it