Welfare and income effects of tuition subsidies and public investment in schooling
Bibliographic record
Abstract
Evidence demonstrates that poorer individuals have lower human capital attainment than richer individuals. This study constructs and estimates a two-sector dynamic stochastic general equilibrium model with human capital to analyse the welfare and income effects of different types of public schooling expenditure in India. The results show that tuition subsidies that are targeted towards the poor have a significantly positive effect on households’ income and welfare. Although the rich suffer welfare loss, aggregate welfare rises. Public schooling investment and untargeted tuition subsidies have a significantly positive effect on poor households’ income and welfare; however, the effect is smaller relative to tuition subsidies targeted at the poor. Public schooling investment has a larger impact on poor households’ human capital and income. • This paper analyses income and welfare effects of public schooling expenditure. • Estimates a two-sector DSGE model with human capital for India. • Tuition subsidies targeted at poor are the most effective tool to raise income and welfare of poor. • Untargeted tuition subsidies have a smaller impact on the income and welfare of poor. • Public schooling investment has a larger impact on the poor households’ schooling.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".