Some Economic Consequences of Improving Mathematics Performance
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it