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
 USDA: quality standards are based on measurable
attributes that describe the value and utility of the
product
Importance of Grading
 Differentiates commodity goods
 Sends price signals
 Facilitates trade
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)
Price Signaling
Feuz, Ward, and Schroeder
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
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
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.
Beef Tenderness Valuation
First Trial
 Consumers not aware of
tenderness
 Steaks labeled
 Blue for probably tough
 Red for guaranteed tender
Second trial
 Consumers told tenderness
 Steaks labeled
 Probably tough
 Guaranteed tender
Lusk et al.
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)
Beef Tenderness Valuation Results
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
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
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.
Chalfant et al.
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.
Grading Experiment
 Human grading errors
 Class will grade two beverages
 Compare results to see variation in quality perceptions
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.
Worst
Best
Beverage Grading Scale
 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.
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
Computer Vision System
 Digitization: Process of
converting pictorial
images into numerical
form
 Image processing and
analysis are core of
computer vision
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
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.