Is the Quarter of Birth Endogenous? New Evidence from Taiwan, the US, and Indonesia
Bibliographic record
Abstract
Abstract Recent evidence based on US data suggests that the quarter or month of birth (QOB or MOB) may be endogenous, since family characteristics can explain up to 50% of the effects of QOB on the education outcomes and earnings of adult males. In this study, based on a sample of one million Taiwanese siblings, we examine university admission at age 18 as our outcome variable and find that at school entry, the oldest (September born) children are 31–38% more likely to be admitted into university at age 18 than the youngest (August born) children, indicating strong seasonality in university admission. The inclusion of controls for family background is found to explain only a small portion of these effects, particularly for males. Given that such results are at odds with the recent US evidence, we revisit the US Census data and find that when racial differences are properly controlled for in the estimation, even a rich set of family characteristics is capable of explaining only a minor proportion of the QOB effects. Furthermore, using data from the US and Indonesia, we find that seasonal temperature variation is unlikely to be an important contributor to the US‐Taiwan disparity. Our findings imply that the validity of using QOB or MOB as an instrumental variable may be dependent on the population being studied and the sample selected.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".