Evaluating “Cash-for-Clunkers”: Program Effect on Auto Sales, Jobs, and the Environment
Bibliographic record
Abstract
We investigate the effects of “Cash for Clunkers”, a $3 billion economic stimulus program, on new vehicle sales, employment, gasoline consumption, 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 0.39 million during July and August of 2009, while the net increase reduced to 0.25 million from June to December. The difference suggests that, as intended, the program significantly shifted sales to July and August from other months. Nevertheless, the program would result in only 8.58 to 28.28 million tons of CO2 emission reductions, implying a cost per ton ranging from $91 to $301 even after accounting for the benefit of the program in reducing criteria pollutants. In addition, the program is estimated to have created 3, 676 job-years in the auto assembly and parts industries from June to December of 2009. That effect decreased to 2, 050 by May 2010.
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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".