How Much Should Consumers Be Willing to Pay for

Information About Consumer Products?

Michael Finke, Mona Ismail, Peng Chen, Chandrika Jayathirtha, Hui Wang,
Sun-Young Park and Sherman Hanna, The Ohio State University(1)

Consumers relying on the notion that you get what you pay for
assume that competitive markets will dictate price variation
according to product attributes. In a perfect world we would
be able to shop for all consumer goods as we do for, say, a
melon in a grocery store. We would inspect for bruises and
blemishes and squeeze or sniff for ripeness, and we’d choose
a melon that is, to us, just right. For most consumer goods,
however, we must rely on product design, brand names,
packaging, the advice of salesmen, the advice of friends and,
for a few products, the recommendations of independent
product researchers, in our attempt to find the VCR or
bathroom scale which fits our budgets and our expectations.
The consumer’s lack of success in achieving optimal product
choice is well documented (e.g., Geistfeld, 1988; Snider &
Ziporyn, 1992; Morris & Bronson, 1969). The usual
prescriptions for this type of problem are government
consumer protection and/or consumer education. Consumers
are overwhelmed by information about thousands of products
(Snider & Ziporyn, 1992, pp. 6-7) and may lack the time or
cognitive ability to evaluate products. Consumer Reports
provides ratings of some products, but with a very limited
budget and long publication lead time, the information is of
limited usefulness (Snider & Ziporyn, 1992, pp. 190-191).
American consumers will soon have access to computerized
information over telephone or cable TV lines, and expert
systems software has the potential for providing timely,
individualized advice on choosing products and services. All
of this will cost money, as any product evaluation represents
a large fixed cost that may be economical if spread out over a
large number of consumers. This article represents an effort to
estimate the value of information about price and quality to
consumers. The results show that there is a potential for
supporting product evaluation systems, as the average value of
information to consumers might be as much as $59 for a
sample of 48 products rated by Consumer’s Union in its 1995
Annual Buying Guide.

Maynes (1991, p. 494) identified three types of losses
consumers face because of lack of information:

1. The dollar loss incurred by paying more than necessary
for the level of quality purchased;

2. The loss (or its dollar equivalent) incurred by obtaining
less quality than was possible for the price paid;

3. The loss arising from the fact that the consumer was
unable to discern, and hence purchase, the price-quality
combination representing his/her desired trade-off
between quality and price.

Maynes’ concept of the Perfect Information Frontier (PIF)
can illustrate each type of loss. If an overall measure of
quality is available and all of the models (varieties) in a
particular market are mapped on a price-quality graph, the PIF
is the set of points for which each level of quality may be
purchased at the lowest possible price. In general, the PIF will
include the lowest price specimen (model/seller combination)
and the highest quality specimen. Figure 1, a price-quality
map for 486DX/2 66 mhz computers (Consumers Union,
1995, pp. 224-225), is an example of the PIF and Maynes’
three types of losses. Based on the prices and the overall
quality ranking in the article, a consumer would only consider
3 models of the 10 listed: A, B and J. Model A is the highest
quality product, model J is the lowest price product, and model
B represents a lower price and/or higher quality than any of the
other models. If a consumer enters this market completely
ignorant of quality and prices, he or she may choose at
random. Paying a price higher than the minimum price may
result in higher quality, but if the consumer chooses model F
(at $3,079) there would be an excess price of $1,030, because
model B with higher quality could have been purchased at
only $2,049. A consumer choosing at random in this market
would, on the average, pay $324 more than necessary to obtain
a particular level of quality. Obviously most consumers do not
start with complete ignorance. On the other hand, this excess
measure of the cost of ignorance does not include
Maynes’ (1991) loss types 2 and 3. For instance, in Figure 1,
if a consumer buys model I rather than B, there is an excess
price of $40, but there is also a substantial loss in quality
(Maynes’ loss type 2). Lacking complete information would
make it unlikely that the consumer would make the optimal
tradeoff between price and quality (Maynes’ loss type 3).
Therefore, even though our measure of mean excess price is
biased upwards in terms of Maynes’ loss type 1, it may not be
a bad estimate of the total cost of ignorance, and therefore the
potential value of information, to the consumer. Ratchford and
Gupta (1991) also calculated mean consumer losses, although
with a more complex procedure and with different

Figure 1. Example of Perfect Information Frontier.


The mean excess price for the 48 product categories ranged
from $1 (for caulk, with a mean price of $3.44) to $342
(computers, with a mean price of $59.) Relative loss,
measured as the ratio of mean excess price to mean price
ranged from 3% to 61%, with a mean of 26%. Two
regressions were run to estimate the relationship between the
mean price for each product and (a) the mean loss in dollars,
and (b) the mean loss as a percent of price. Figure 2 shows the
predicted relationships of each of these to mean price. The
predicted loss in dollars increases with mean price, while the
predicted loss as a percent of price decreases with mean price.
Low price products tend to have high percentage losses. Table
1 shows the mean excess price and percentage loss by product

The high mean relative loss (26% of price) suggests that
consumers may benefit from more information about price and
quality. Consumers aware of the benefits this information
would provide should be willing to pay for information that
would reduce the loss from buying a product overpriced for its
quality. If 100,000 consumers paid $5 for current product
information, an information service could have $500,000 for
putting together price and quality information and
implementing expert systems software to make the information
easily accessible and understandable to the average American
consumer. It may be possible for a private information service
to provide information about prices and quality levels of
products to consumers, make a profit, and at the same time
help consumers make more efficient choices.

Figure 2. Relationship Between Mean Excess Price and
Average Price of Product Category.

Table 1. Mean Excess Price by Product Category and for
Selected Products.

Product Category # of products Mean Excess Price($) Mean % Loss
Audio & video gear 10 74 20
Photography 2 112 26
Major appliances 7 68 14
Small appliances 7 47 37
Misc. home items 9 16 31
Heating,cooling,etc. 7 52 39
Bicycle helmets. 1 25 43
Health(blood pres.,scales) 2 18 39
IBM-compatible computers 1 324 13
Lawn mowers 2 69 27


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1. All are graduate students in the Family Resource Management Department, except for Hanna, who is a professor. 1787 Neil Ave., Columbus, OH
43210-1290. Phone: 614-292-4584. FAX: 614-292-7536. Internet: