Local knowledge, formal evidence, and policy decisions
Bibliographic record
Abstract
How do policymakers value advice from local experts versus formal evidence from impact evaluations when making policy decisions? Using a discrete choice experiment conducted in collaboration with the World Bank and Inter-American Development Bank, we show that policymakers were willing to accept a program that had a 5.0 percentage point smaller estimated effect on enrollment rates if it were recommended by a local expert. They also preferred programs supported by evidence from a different region over programs supported by local evaluations only if the former had a 5.8 percentage point higher estimated impact. These premiums are large, surpassing the effects of many programs aimed at improving enrollment rates. This highlights the substantial weight that policymakers place on local evidence. • Policymakers and policy practitioners prefer programs with a local impact evaluation. • They also prefer programs recommended by local experts. • These preferences often outweigh differences in estimated treatment effects.
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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 | 0.134 | 0.322 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".