Impact Evaluation Methods in Public Economics: A Brief Introduction to Randomized Evaluations\nand Comparison with Other Methods
Bibliographic record
Abstract
Recent years have seen a large expansion in the use of rigorous impact evaluation techniques. Increasingly, public administrations are collaborating with academic economists and other quantitative social scientists to apply such rigorous methods to the study of public finance. These developments allow for more reliable measurements of the effects of different policy options on the behavioral responses of citizens, firm owners, or public officials. They can help decision makers in tax administrations, public procurement offices, and other public agencies design programs informed by well-founded evidence. This paper provides an introductory overview of the most frequently used impact evaluation methods. It is aimed at facilitating communication and collaboration between practitioners and academics by introducing key vocabulary and concepts used in rigorous impact evaluation methods, starting with randomized controlled trials and comparing them with other methods ranging from simple pre-post analysis to difference-in-differences, matching estimations, and regression discontinuity designs.
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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 | 0.009 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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; both teacher heads agree on what is shown here.
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".