Synthesizing Econometric Evidence: The Case of Demand Elasticity Estimates
Bibliographic record
Abstract
Econometric estimates of the responsiveness of health-related consumer demand to higher prices are often key ingredients for risk policy analysis. We review the potential advantages and challenges of synthesizing econometric evidence on the price-responsiveness of consumer demand. We draw on examples of research on consumer demand for health-related goods, especially cigarettes. We argue that the overarching goal of research synthesis in this context is to provide policy-relevant evidence for broad-brush conclusions. We propose three main criteria to select among research synthesis methods. We discuss how in principle and in current practice synthesis of research on the price-elasticity of smoking meets our proposed criteria. Our analysis of current practice also contributes to academic research on the specific policy question of the effectiveness of higher cigarette prices to reduce smoking. Although we point out challenges and limitations, we believe more work on research synthesis in this area will be productive and important.
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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 | 0.395 | 0.799 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.025 | 0.019 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.016 | 0.017 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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; the direct Gemma label and the distilled Codex classifier 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".