Education Spending and Workforce Quality as Determinants of Economic Growth
Bibliographic record
Abstract
Several studies suggest that there is a statistically significant, positive relationship between education expenditures and economic growth in the U.S. states and among developed countries. Conversely, some literature suggests that there is no link between education spending and economic growth. There are several explanations given for the lack of a link between spending and growth including higher levels of taxation dampening growth, spending on administration instead of teaching, and poor student-teacher ratios. Our analysis of county level expenditures and demographics finds that it is not the expenditures that matter but the educational attainment level of the workforce. While a locally provided high school degree is still important, it is no longer an end but a means toward attaining a college degree, potentially obtained outside the jurisdiction. These results imply that local development officials must do more than spend on primary and secondary education; they must attract and/or retain college educated individuals as well. Keywords: education, regional economics, economic development, growth, public finance, workforce ------------------------------------------------------------- Résumé Plusieurs études suggèrent qu'il y a une relation statistiquement positive et significatrice entre les dépenses d'éducation et la croissance économique aux États-Unis et parmi les pays développés. Inversement, certaines études suggèrent qu'il n'y a aucun lien entre les dépenses d'éducation et la croissance économique. Il y a plusieurs explications attribuées à ce manque de liens entre les dépenses et la croissance incluant les hauts niveaux de taxation qui freinent les hausses, les dépenses administratives plutôt qu'en enseignement, et les faibles ratios étudiant-enseignant. Notre analyse des dépenses et de la démographie au niveau du Comté montre que l'important n'est pas les dépenses mais l'atteinte du niveau éducationnel par la force de travail. Alors qu'un diplôme d'école secondaire local est toujours important, ce n'est plus une fin en soi mais un objectif vers l'atteinte d'un diplôme collégial, potentiellement obtenu à l'extérieur de la juridiction. Ces résultats impliquent que les fonctionnaires du développement local doivent faire davantage que dépenser dans l'éducation au primaire et au secondaire; ils doivent aussi attirer et garder des individus éduqués au niveau collégial.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".