Some Economic Consequences of Improving Mathematics Performance
Bibliographic record
Abstract
In this report, we examine how improving mathematics performance has economic consequences through raising high school graduation rates. We investigate the link between higher mathematics achievement in school and subsequent human capital and labor market outcomes. We then predict the effect of improving math skills in grades 8 and 10 on the yield of high school graduates per age cohort. Improved mathematics achievement would most likely raise high school completion rates substantially, with especially strong impacts for lower socioeconomic groups and most minorities. We then present the lifetime economic consequences from a higher yield of high school graduates. In particular, we reviewed the impact on income and tax revenues, social productivity, and reductions in the costs of public health, crime, and public assistance. These lifetime consequences are calculated as gains to the individual students (private), as gains to the taxpayer (fiscal), and as gains to society (social). We simulate the total magnitude of these economic benefits if mathematics achievement in the U.S. were raised to equal that of other developed countries in the OECD, Canada, and a high performer, Finland. Finally, we review the evidence on interventions that have demonstrated effectiveness in improving mathematics achievement in high schools and middle schools. Although this evidence is somewhat sparse, we identify several effective interventions and estimate their costs. Given the substantial economic benefits from raising mathematics skills in high school, these interventions have very high benefit-cost ratios.
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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 | 0.008 |
| 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) | 0.011 | 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".