Pain at the Pump: The Differential Effect of Gasoline Prices on New and Used Automobile Markets
Bibliographic record
Abstract
The dramatic increase in gasoline prices from close to $1 in 1999 to $4 at their peak in 2008 made it much more expensive for consumers to operate an automobile.In this paper we investigate whether consumers have adjusted to gasoline price changes by altering what automobiles they purchase and what prices they pay.We investigate these effects in both new and used car markets.We find that a $1 increase in gasoline price changes the market shares of the most and least fuel-efficient quartiles of new cars by +20% and -24%, respectively.In contrast, the same gasoline price increase changes the market shares of the most and least fuel-efficient quartiles of used cars by only +3% and -7%, respectively.We find that changes in gasoline prices also change the relative prices of cars in the most fuel-efficient quartile and cars in the least fuel-efficient quartile: for new cars the relative price increase for fuel-efficient cars is $363 for a $1 increase in gas prices; for used cars it is $2839.Hence the adjustment of equilibrium market shares and prices in response to changes in usage cost varies dramatically between new and used markets.In the new car market, the adjustment is primarily in market shares, while in the used car market, the adjustment is primarily in prices.We argue that the difference in how gasoline costs affect new and used automobile markets can be explained by differences in the supply characteristics of new and used cars.
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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 | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".