Foodborne Outbreaks, Product Recalls, and Firm Learning: A Recurrent Event Survival Analysis of U.S. Meat and Poultry Recalls
Akhundjanov, S. B.; Pozo, V. F.; Thomas, B.
Show abstract
Firms in the food industry may experience more than one contamination incident over time. In the context of food safety, increasing the interval between foodborne outbreaks represents a key objective for both the food industry and public health officials. We demonstrate a systematic approach to analyzing repeated recalls, specifically to evaluate factors influencing the time until the next recall and, more importantly, to identify the extent of organizational learning from inter-event times. An analysis of meat and poultry recalls issued by publicly traded firms in the United States between 1994-2015 indicates that more diversified firms face a lower risk of repeat recalls as firm size expands, compared to firms primarily producing meat and poultry products. The hazard of a recall incident decreases with the severity of the previous recall. Some evidence of firm learning is found, but there is no definitive evidence indicating that a firms ability to prevent recalls improves with the number of foodborne outbreaks it has experienced.
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- Analysis of the early Covid-19 epidemic curve in Germany by regression models with change points 86%
- Estimating lengths-of-stay of hospitalized COVID-19 patients using a non-parametric model: a case study in Galicia (Spain) 86%
- Estimating the Case Fatality Ratio for COVID-19 using a Time-Shifted Distribution Analysis 84%
Similar papers in this journal
- An efficient approach to nowcasting the time-varying reproduction number 86%
- Estimating SARS-CoV-2 transmission parameters between coinciding outbreaks in a university population and the surrounding community 86%
- Causal Estimands for Infectious Disease Count Outcomes to Investigate the Public Health Impact of Interventions 86%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.