Farina; Northen; Fearne, Hornibrook, and Dedman). The information obtained through the
QAS may allow the processing firm to better sort the input it buys, to gain a better idea of the
actual quality of the inputs, and to be able to convey assurance to its customers about the
quality of its product.
The topic of this article is relevant in an environment in which quality is variable and
difficult to verify. Many food attributes can be thus classified (see, e.g., Caswell and
Mojduszka; Antle, 1996; and Unnevehr and Jensen). Clearly, if quality is readily observable
by both input buyers and consumers, there is no need for a QAS in the procurement process.
We define a high-quality product as one that is certified as meeting an agreed-upon
standard. The stringency of a selected QAS informs the processor about the proportion of the
purchased input that actually meets the standard. Whether a particular unit of input meets the
standard is unknown. We assume that the quality of the processed output has a direct
relationship to its input counterpart, an assumption that is equivalent to claiming that the
processing technology cannot be used as a substitute for input quality, or that it does so only at
prohibitively high costs. We assume also that there is a one-to-one correspondence between
the amount of input bought and output sold by a processor. Hence, the production technology
works in a Leontief fashion, and the decision on the output rate essentially determines how
much of the agricultural input is needed.
Let the random vector Q denote the vector of imperfectly observable quality attributes.
For tractability we assume that only one quality attribute is of interest. The unconditional
cumulative distribution function of Q that is available in the market is FQ(q)=PrQ(Q≤q)
for all q. This approach accommodates both the case in which the quality attribute or trait is
the production method itself and the case in which the process alters the probability