Understanding adolescent economic behavior: A novel empirical approach using genetic data
Bibliographic record
Abstract
This thesis has two main foci. First, it seeks to outline how recent findings from the genetics and neuroscience literature can enrich both the empirical and theoretical understanding of economic behavior. Second, it presents models of health/education investment in teens as well as a special case of the Becker-Murphy model of smoking that treats adolescence separately. To address the hypotheses generated by these models, a unique and rich longitudinal dataset of 1000 high school students that includes detailed information on smoking and genetic markers is analyzed. Two staged least squares models that use genetic markers as instruments are used to infer causality from health to academic achievement. The main findings from this thesis are: (1) Genetic markers possess good statistical properties as instrumental variables that can yield rich insights when included in estimation. (2) The impact of health status on educational outcomes varies greatly by gender. This difference becomes more striking once endogeneity of health outcomes is accounted for. (3) Co-morbid health conditions complicate results and present an additional hurdle for empirical researchers. (4) Among boys with ADHD and girls who are diagnosed as obese, cigarette smoking is correlated with higher GPA, although low sample size may bias those results. (5) Models of addiction do not take into account changes in the adolescent brain that can impact addictive behavior. (6) Though data limitations make it impossible to empirically test the predictions of the smoking model, future data sets should allow for such analysis.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".