Information, Preferences, and Household Demand for School Value Added
Bibliographic record
Abstract
This paper examines the roles that information and preferences play in determining whether households choose schools with high value added. We study Romanian school markets using administrative data, a survey, and an experiment. The administrative data show that, on average, households could select schools with 1 s.d. worth of additional value added. This may reflect that households have incorrect beliefs about schools' value added, or that their preferences lead them to prioritize other school traits. We elicit households' beliefs and find that they explain less than a fifth of the variation in value added. We then inform randomly selected households about the value added of the schools in their towns. This improves the accuracy of households' beliefs and leads low-achieving students to attend higher-value added schools. We next estimate households' preferences and predict their choices under the counterfactual of fully accurate beliefs. We find that beliefs account for 18 (11) percent of the value added that households with low-(high-) achieving children leave unexploited. Interestingly, for households with low-achieving children, the experiment seems to have affected both beliefs and preferences. This generates larger effects on choices than would be predicted via impacts on beliefs alone.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".