Accounting for Growth Effects of Age Structure Transition through Public Education Expenditure: New Macroeconomic Evidence from India
Bibliographic record
Abstract
This article quantifies the growth effects of age structure transition through current public education expenditure. Using the National Transfer Accounts’ (NTA) First Demographic Dividend (FDD) model, growth effects are accounted by the impact of current public education expenditure on economic support ratio (ESR) and labour productivity through human capital investments. The results offer new macroeconomic evidence. Age structure transition reduces the education dependency ratio (EDR) by all levels of education but the highest in the elementary education. This impacts on a long-term decline in enrolment in elementary education where the current gross enrolment ratio (GER) is close to 100 per cent and a decline in current public education expenditure. Other things being equal, the resultant potential savings, or the availability of extra budgetary resources, is a new way of financing the investment requirements for secondary and higher education with the aim of increasing national economic growth through human capital investments. In particular, growth effects are shown to be positive, higher and longer up to 2050, if the current public education spending is reallocated more for the secondary and higher education. Surprisingly, growth effects are explained less by the ESR than labour productivity. This justifies a higher human capital investment to enhance labour productivity for attainment of higher economic growth. The afore- mentioned macroeconomic framework, results and implications are of general relevance for other developing countries in South Asia and elsewhere in the world. JEL Classification: E65, H52, J11
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".