How the Timing of Grade Retention Affects Outcomes: Identification and Estimation of Time-Varying Treatment Effects
Why this work is in the frame
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Bibliographic record
Abstract
In many countries, grade retention is viewed as a useful tool for helping students who fall behind in their achievement. We show how the effect of grade retention varies by abilities, by timing of retention and as time since retention elapses. While existing studies of grade retention also recognize the importance of studying variation by abilities and timing, the existing methods are not well-equipped to deal with the possibility that students retained at different grades differ in unobservable abilities (dynamic selection) and the effects of retention also vary by the student's abilities and the time at which the student is retained. We extend existing factor analytic methods for identifying treatment effects to control for dynamic selection in our time-varying treatment effect setting. This approach can be understood as a hybrid between a control function and a generalization of the fixed effects approach. Applying our method to nationally-representative, longitudinal data, we find evidence of dynamic selection into retention and that the treatment effect of retention varies considerably across grades and unobservable abilities of students. Our strategy can be applied more broadly to many time-varying or multiple treatment settings.
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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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it