Fiscal Policy, Quality of Education, and Economic Growth in the Dominican Republic
Bibliographic record
Abstract
This paper depicts three mechanisms through which fiscal policy affects economic growth in the Dominican Republic. The first mechanism rests on the evidence that increasing public expenditures on education leads to the reduction of adolescent fertility and the percentage of young females who neither study or work, which gives rise to increasing the ratio of female to male employees and thus increasing domestic savings and economic growth. The second mechanism consists of increasing social expenditures, which results in decreasing the underground economy and, thus, results in increased productivity, economic growth, and reduced violence. The third mechanism is based on the evidence that increasing education expenditures leads to the reduction of emigration and, thus, to the reduction of remittances, which in turn increases economic growth. The operation of these mechanisms is sustained by Figures that show that the postulated relationships exist in the Dominican Republic. The results imply first, that fiscal policy has important effects that have often been overlooked, such as the reductions in school desertion, the percentages of female and male youth that neither work nor study, and the decrease in informality in the Dominican Republic. And second, a valid development strategy resides in increasing tax revenues to support the expansion of social services.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".