Equilibrium Policy Experiments and the Evaluation of Social Programs
Bibliographic record
Abstract
This paper makes three primary contributions. First, we demonstrate the usefulness of general equilibrium models as tools with which to draw policy implications for policies implemented in practice only as small-scale social experiments. Second, we illustrate the usefulness of social experiments as a tool to evaluate equilibrium models. In particular, we calibrate our model using only data on an experimental control group and from general data sets, and then use it to predict (in partial equilibrium) the outcomes experienced by an experimental treatment group. We find that it predicts these outcomes remarkably well. Third, we apply our methodology to the evaluation of the Canadian Self-Sufficiency Project (SSP), a policy providing generous financial incentives for Income Assistance (IA) recipients to obtain stable employment. This policy is similar to many other policies designed to "make work pay" currently under debate or in place in the US, the UK and elsewhere. Our results reveal several important feedback effects associated with the SSP policy; taken together, these feedback effects reverse the cost-benefit conclusions implied by the partial equilibrium experimental evaluation.
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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 | 0.043 | 0.118 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".