What Can We Learn from Charter School Lotteries
Bibliographic record
Abstract
We take a closer look at what we can learn about charter schools by pooling data from lottery-based impact estimates of the effect of charter school attendance at 113 schools. On average, each year enrolled at one of these schools increases math scores by 0.08 standard deviations and English/language arts scores by 0.04 standard deviations. There is wide variation in impact estimates. To glean what drives this variation, we link these effects to school practices, inputs, and characteristics of fallback schools. In line with the earlier literature, we find that schools that adopt an intensive “No Excuses” attitude towards students are correlated with large gains in academic performance, with traditional inputs like class size playing no role in explaining charter school effects. However, we highlight that “No Excuses” schools are also located among the most disadvantaged neighborhoods in the country. After accounting for performance levels at fallback schools, the relationship between the remaining variation in school performance and the entire “No Excuses” package of practices weakens. “No Excuses” schools are effective at raising performance in neighborhoods with very poor performing schools, but the available data have less to say on whether the “No Excuses” approach could help in nonurban settings or whether other practices would similarly raise achievement in areas with low-performing schools. We find that intensive tutoring is the only “No Excuses” characteristic that remains significant (even for nonurban schools) once the performance levels of fallback schools are taken into account.
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.
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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".