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 policy analysis. Drawing on several examples, especially that of cigarette demand, we review the potential advantages and challenges of synthesizing econometric evidence on the price-responsiveness of consumer demand. We argue that the overarching goal of research synthesis in this context is to provide policy-relevant evidence for broad brush conclusions and propose three main criteria to select among research synthesis methods. We also contribute a new empirical exercise that puts the results of previous research synthesis to the test. In particular, we ask whether the “best” consensus estimates of the price-elasticity of smoking help predict trends in smoking from 1995 to 2010. The demographics of the smoking population in our baseline year predict a downward trend in smoking even if cigarette prices remained constant. Average cigarette prices, however, more than doubled in real terms by 2010. We find that the observed declines in smoking over this period are considerably smaller than smoking demographics combined with prior consensus elasticity estimates would predict. Our results suggest that these consensus estimates may have systematically overestimated the price responsiveness of cigarette demand.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.333 | 0.761 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.028 | 0.019 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.014 | 0.017 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".