The Impact of Poor Health on Education: New Evidence Using Genetic Markers, National Bureau of Economic Research (NBER) Working Paper No
Bibliographic record
Abstract
This paper examines the influence of health conditions on academic performance during adolescence. To account for the endogeneity of health outcomes and their interactions with risky behaviors we exploit natural variation within a set of genetic markers across individuals. We present strong evidence that these genetic markers serve as valid instruments with good statistical properties for ADHD, depression and obesity. They help to reveal a new dynamism from poor health to lower academic achievement with substantial heterogeneity in their impacts across genders. Our investigation further exposes the considerable challenges in identifying health impacts due to the prevalence of comorbid health conditions and endogenous health behaviors. ∗We are grateful to seminar particpants at the 2005 NBER Summer Institute, University of Toronto and BU/Harvard/MIT Health Economics Seminar for helpful comments and suggestions. We would also like to thank Paul Wylieto for answering our numerous questions about the data employed in the study. Lehrer wishes to thank SSHRC for research support. Rosenquist wishes to thank AHRQ for support. We are responsible for all errors. 1 1
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How this classification was reachedexpand
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 | 0.002 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".