Heuristic Thinking and Limited Attention in the Car Market
Bibliographic record
Abstract
Can heuristic information processing affect important product markets? We explore whether the tendency to focus on the left-most digit of a number affects how used car buyers incorporate odometer values in their purchase decisions. Analyzing over 22 million wholesale used-car transactions, we find substantial evidence of this left-digit bias; there are large and discontinuous drops in sale prices at 10,000-mile thresholds in odometer mileage, along with smaller drops at 1,000-mile thresholds. We obtain estimates for the inattention parameter in a simple model of this left-digit bias. We also investigate whether this heuristic behavior is primarily attributable to the final used-car customers or the used-car salesmen who buy cars in the wholesale market. The evidence is most consistent with partial inattention by final customers. We discuss the significance of these results for the literature on inattention and point to other market settings where this type of heuristic thinking may be important. Our results suggest that information-processing heuristics may be important even in markets with large stakes and where information is easy to observe.
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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 | 0.003 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".