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The Economic Efficiency of Sampling Size: The Case of Beef Trim Revisited

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  • Mark R. Powell
Abstract
A recent paper by Ferrier and Buzby provides a framework for selecting the sample size when testing a lot of beef trim for Escherichia coli O157:H7 that equates the averted costs of recalls and health damages from contaminated meats sold to consumers with the increased costs of testing while allowing for uncertainty about the underlying prevalence of contamination. Ferrier and Buzby conclude that the optimal sample size is larger than the current sample size. However, Ferrier and Buzby's optimization model has a number of errors, and their simulations failed to consider available evidence about the likelihood of the scenarios explored under the model. After correctly modeling microbial prevalence as dependent on portion size and selecting model inputs based on available evidence, the model suggests that the optimal sample size is zero under most plausible scenarios. It does not follow, however, that sampling beef trim for E. coli O157:H7, or food safety sampling more generally, should be abandoned. Sampling is not generally cost effective as a direct consumer safety control measure due to the extremely large sample sizes required to provide a high degree of confidence of detecting very low acceptable defect levels. Food safety verification sampling creates economic incentives for food producing firms to develop, implement, and maintain effective control measures that limit the probability and degree of noncompliance with regulatory limits or private contract specifications.

Suggested Citation

  • Mark R. Powell, 2013. "The Economic Efficiency of Sampling Size: The Case of Beef Trim Revisited," Risk Analysis, John Wiley & Sons, vol. 33(3), pages 385-396, March.
    • Handle: RePEc:wly:riskan:v:33:y:2013:i:3:p:385-396
    DOI: 10.1111/risa.12027
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    References listed on IDEAS

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    1. S. Andrew Starbird, 2005. "Moral Hazard, Inspection Policy, and Food Safety," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 87(1), pages 15-27.
    2. Harry M. Marks & Margaret E. Coleman & C.‐T. Jordan Lin & Tanya Roberts, 1998. "Topics in Microbial Risk Assessment: Dynamic Flow Tree Process," Risk Analysis, John Wiley & Sons, vol. 18(3), pages 309-328, June.
    3. Buzby, Jean C. & Wells, Hodan Farah & Axtman, Bruce & Mickey, Jana, 2009. "Supermarket Loss Estimates for Fresh Fruit, Vegetables, Meat, Poultry, and Seafood and Their Use in the ERS Loss-Adjusted Food Availability Data," Economic Information Bulletin 58313, United States Department of Agriculture, Economic Research Service.
    4. Muth, Mary K. & Karns, Shawn A. & Nielsen, Samara Joy & Buzby, Jean C. & Wells, Hodan Farah, 2011. "Consumer-Level Food Loss Estimates and Their Use in the ERS Loss- Adjusted Food Availability Data," Technical Bulletins 184307, United States Department of Agriculture, Economic Research Service.
    5. Starbird, S. Andrew, 2000. "Designing Food Safety Regulations: The Effect Of Inspection Policy And Penalties For Noncompliance On Food Processor Behavior," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 25(2), pages 1-20, December.
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    Cited by:

    1. Ollinger, Michael & Houser, Matthew, 2020. "Ground beef recalls and subsequent food safety performance," Food Policy, Elsevier, vol. 97(C).
    2. Mayumi Seto & Koichiro Uriu, 2015. "Sample Size Allocation for Food Item Radiation Monitoring and Safety Inspection," Risk Analysis, John Wiley & Sons, vol. 35(3), pages 409-422, March.

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