202-328-5000 www.rff.orgEvaluating “Cash-for-Clunkers”: Program Effects on Auto Sales and the Environment
Bibliographic record
Abstract
“Cash-for-Clunkers ” was a $3 billion program that attempted to stimulate the U.S. economy and improve the environment by encouraging consumers to retire older vehicles and purchase more fuelefficient new vehicles. We investigate the effects of this program on new vehicle sales and the environment. Using Canada as the control group in a difference-in-differences framework, we find that the program increased new vehicle sales by about 0.36 million during July and August of 2009, implying that approximately 45 percent of the spending went to consumers who would have purchased a new vehicle anyway. Our results suggest no gain in sales beyond 2009 and hence no meaningful stimulus to the economy. In addition, the program will reduce CO2 emissions by only 9 to 28.4 million tons, implying a cost per ton ranging from $91 to $288 even after accounting for reduced criteria pollutants. Key Words: stimulus, Cash-for-Clunkers, auto demand, CO2 emissions
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.723 | 0.304 |
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".