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Quality and Prices

Quality and Prices. Matt Herrington Katie Teague Iddrisu Yahaya. What is Quality Grading . Brosnan : the sum of all those attributes which can lead to the production of products acceptable to the consumer when they are combined

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Quality and Prices

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  1. Quality and Prices Matt Herrington Katie Teague IddrisuYahaya

  2. What is Quality Grading Brosnan: the sum of all those attributes which can lead to the production of products acceptable to the consumer when they are combined USDA: quality standards are based on measurable attributes that describe the value and utility of the product

  3. Importance of Grading Differentiates commodity goods Sends price signals Facilitates trade

  4. Commodity Differentiation • Corn • Color, lbs./bu., damaged kernels and foreign material (GIPSA) • US No. 1-4 • Beef • Carcass maturity, marbling, lean color and texture • Prime, Choice, Select, and Standard (USDA AMS)

  5. Price Signaling Feuz, Ward, and Schroeder

  6. Trade Facilitation • Reduces transaction costs • Reduces searching and testing costs • Eliminates confusion • Buyers and sellers know exact product specifications • Ex: futures prices all quoted for specific standards

  7. How does Grading Work? • A third party organization (usually USDA) sets standards which must be met • USDA inspectors sample grains and grade carcasses • Assign quality grades based on assessment

  8. Grading Example • Warner-Bratzler (WB) shear force test • Indicates beef tenderness • Developed into commercial processing system • 90% accuracy • Segregates into 3 categories • Guaranteed tender, intermediate tender, and probably tough Lusk et al.

  9. Beef Tenderness Valuation Second trial Consumers told tenderness Steaks labeled Probably tough Guaranteed tender First Trial • Consumers not aware of tenderness • Steaks labeled • Blue for probably tough • Red for guaranteed tender Lusk et al.

  10. Beef Tenderness Valuation Surveyed consumers Sampled 2 different types of steaks Participants responded to questions regarding preference Given a free Blue (probably tough) steak Asked to indicate WTP to upgrade to Red (guar. tender)

  11. Beef Tenderness Valuation Results

  12. Grading Errors • Blending • Creates a greater homogeneous good • Important aspects may not be captured • Some commodities are not graded • Grades may not capture consumer attributes

  13. Grading Errors Cont’d • Inaccuracies in current methods • Human inspection is highly subjective • High labor costs • Inconsistent and highly variable • Resistance to change • Bureaucratic system • No one wants to lose

  14. Grading Errors Cont’d • Asymmetric Grading Errors • Lower quality product receives higher quality grading • Grading errors do not occur equally • Example: • California Prune Grading • Larger portion of small prunes graded as large Chalfant et al.

  15. Chalfant et al.

  16. Prune Example Results Producer is paid less than true market value for all product that is graded correctly But is paid more for the product that is sorted into a higher grade This causes lower quality products to be overvalued and thus overproduced Chalfant et al.

  17. Grading Experiment • Human grading errors • Class will grade two beverages • Compare results to see variation in quality perceptions

  18. Grading Experiment – DISCLAIMER • Participation in this experiment is COMPLETELY VOLUNTARY • Applying the “Las Vegas Theory” to this experiment: • What happens in class stays in class • i.e., please don’t tell Dr. Lambert.

  19. Beverage Grading Scale Best Worst 5 – Full bodied with a smooth finish, rich, complex flavors, very pleasant. 4 – Fresh, additional complexity, lingering head. 3 – Balanced, clean and clear, smooth finish, adequate flavor – The “Average” Beverage. 2 – Very lean, lacking complexity or taste. 1 – Overly aggressive, hazy, bitter, may be skunky or dirty, course aftertaste.

  20. Future of Quality Grading • Computer Vision Systems • Time and cost effective • Consistent • Non-destructive • More focus on Food Safety • UV scanning for e. Coli and other bacteria • As technology increases, more focus on attributes demanded by end-users

  21. Computer Vision System • Digitization: Process of converting pictorial images into numerical form • Image processing and analysis are core of computer vision

  22. Grading Summary • Differentiates commodities, sends price signals, facilitates trade • Grades are imperfect • Some attributes are not graded • Grading errors persist • Grading systems future is in Computer Vision Systems • Eliminates some inconsistencies

  23. References Brosnan, T., and D.W. Sun "Improving quality inspection of food products by computer vision--a review." Journal of Food Engineering 61 (2004):3-16. Chalfant, J.A., J.S. James, N. Lavoie, and R.J. Sexton "Asymmetric grading error and adverse selection: lemons in the California prune industry." Journal of Agricultural and Resource Economics (1999):57-79. Feuz, D.M., C.E. Ward, T.C. Schroeder“Fed Cattle Pricing: Grid Pricing Basics”Division of Agricultural Sciences and Natural Resources, Oklahoma State University, 2003 Lusk, J.L., J.A. Fox, T.C. Schroeder, J. Mintert, and M. Koohmaraie "In-store valuation of steak tenderness." American Journal of Agricultural Economics (2001):539-50.

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