Product Recalls, Imperfect Information, and Spillover Effects: Lessons from the Consumer Response to the 2007 Toy Recalls
Bibliographic record
Abstract
In 2007, the Consumer Product Safety Commission (CPSC) issued 276 recalls of toys and other children's products, a sizeable increase from previous years. The overwhelming majority of the 2007 toy recalls were due to high levels of lead content and almost all of these toys were manufactured in China. This period of recalls was characterized by substantial media attention to the issue of consumer product safety and eventually led to the passage of the Consumer Product Safety Improvement Act of 2008. This paper examines consumer demand for toys following this wave of dangerous toy recalls. The data reveal four key findings. First, the types of toys that were involved in recalls in 2007 experienced above average losses in Christmas season sales. Second, Christmas sales of infant/preschool toys produced by manufacturers who did not experience any recalls were about 25 percent lower in 2007 as compared to earlier years, suggesting industry-wide spillovers. Third, a manufacturer's recall of one type of toy did not lead to a disproportionate loss in sales of their other types of toys. And, finally, recalls of toys that are part of a brand had either positive or negative effects on the demand for other toys in the property, depending on the nature of the toys involved. Our examination of the stock market performance of toy firms over this period also reveals industry wide spillovers. The finding of sizable spillover effects of product recalls to non-recalled products and non-recalled manufacturers has important implications for regulation policy.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".