Causal Relationship among Education Expenditure, Health Expenditure and GDP: A Case Study for Bangladesh
Bibliographic record
Abstract
This paper investigated the causal relationship among health expenditure, education expenditure and GDP for Bangladesh. First we present the extension form of the augmented Solow Growth model by including education expenditure and health expenditure as education and health capital. In our empirical study we used time series data for the period 1990 to 2009. From the ECM methodology we found that an including of health and education expenditure as an investment in health and education capital improve the significance of the coefficient of human and physical capital in the growth model for Bangladesh. Secondly, we find out the causal relationship among these variables by Var Granger Causality test. From the empirical study we found the existence of bidirectional causality from education expenditure to GDP and also from education expenditure to health expenditure and only unidirectional causality is obtained from health expenditure to GDP. This paper will provide a significant policy guideline to the policy maker.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 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".