Regulating Gasoline Prices: Experimental Evidence' presented by Dr Prof Justus Haucap on 24 Jan 2013
Bibliographic record
Abstract
If retail gasoline prices are to be regulated, is it better to allow only one price increase per day (while price cuts are always possible) as in Austria, impose a maximum retailer mark-up as in Luxembourg , or allow only one daily price change (either up or down) as in Western Australia? Indeed, is price regulation the best way to increase consumer welfare in retail gasoline markets characterised by oligopoly market structures, frequent interactions, highly transparent prices, rather inelastic demand and collusive behaviour? A recent German inquiry backed suspicions of tacit collusion and suggested adoption of regulatory price rules for gas stations similar to those implemented in Austria, parts of Australia, Luxembourg or parts of Canada. Whilst intuition suggests the proposed rules will raise consumer welfare, theoretical evidence is mixed and empirical evidence rare. In this seminar, Justus Haucap discusses evidence obtained using an experimental gasoline market in a laboratory experiment which suggests that these rules tend to decrease rather than increase consumer welfare. Whilst no rule tends to induce lower retail prices, some rules are less harmful than others.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".