Price and Quality Relationships in Local Service Industries

R. Dennis Murphy

Division of Consumer Protection, Bureau of Economics

Federal Trade Commission

Washington, DC 20580

dmurphy@ftc.gov

(202) 326-3524

Abstract: This working paper presents the findings of research on the relationship between price

and quality in consumer service industries in the Washington, D.C. area. The study relies

primarily upon consumer ratings of service provider quality and other data published in

Washington Consumer’s Checkbook Magazine. The data base includes nineteen service

industries and, in virtually all cases, time series information for price and quality ratings over

several ratings periods since the magazine’s inception in 1976. The results provide interesting

and frequently surprising information on basic price-quality relationships in this sector, and on

the reliability of several non-price “signals” that consumers might use to gauge a service

provider’s probable performance. Specifically, only three of the nineteen industries report

consistently significant positive price-quality correlations, and industries specializing in repair

services frequently display significant negative correlations between price and quality. Further,

possible signaling mechanisms, such as the size of a firm’s Yellow Pages advertisement, or a

firm’s status as a member of a nationwide chain, do not function as indicators of higher quality in

this data set. 

JEL Classification: C10, D83, L15

* I thank Susan Painter, Lynn Carpenter, Carolyn Samuels, Genevieve Fu, and Kata Mihaly

for assistance in data collection and preparation, and two anonymous referees for their valuable

comments. I also gratefully acknowledge the assistance that Jesse Leary provided in resolving

important econometric issues. Finally, I would like to thank staff of Washington Consumer’s

Checkbook Magazine for furnishing me with data on firm size. The views expressed herein are

those of the author and do not represent the views of the Federal Trade Commission or any

individual Commissioner. Any errors or omissions are the responsibility of the author. 
 

1

 The first theoretical treatment of price dispersion as an indicator of market imperfection

appears in Stigler [16]. Stigler treated the initial existence of price dispersion as exogenous, and

employed a simple consumer search model to explain why this condition would persist. Later

treatments have attempted to explain the initial variation in prices. Usually, it is assumed that

consumers differ in the amount of search they are willing to undertake, and that sellers specialize

in serving either high or low-search consumers. Some sellers might, for example, bid for a

particularly convenient location and charge a higher price to consumers who do not wish to

search for a lower price. Other sellers with less advantageous sites will specialize in charging

lower prices to consumers who are willing to search extensively to find the best deal. Entry will

equalize the marginal return to the different strategies, and price dispersion will persist in

equilibrium [4]. 

 

2

 A few of these studies use results from Consumers Research, and one article employed

rankings of running shoes published in Runners World [2].

- 1 -

I. INTRODUCTION

In highly competitive markets characterized by low consumer search costs, we would

expect product price and product quality to be strongly and positively correlated. If consumers

can search easily among competitors and readily assess price and quality, those sellers providing

higher quality will be able to charge a higher price to willing customers. Those failing to provide

quality commensurate with price will lose customers and be forced to lower their price or exit the

market. 

If, however, consumers must incur significant costs to obtain accurate price and/or quality

information, a high correlation between these two product attributes need not exist in

equilibrium.1 Presumably, the higher are search costs, the less likely consumers will be able to

recognize good buys and bad buys before purchase, and the lower will be the correlation between

price and quality. In the extreme case where consumers have no ability whatsoever to discern

variations in quality pre-purchase, and assume simply that all products are of “average” quality, a

variant of Gresham’s Law will in sequential fashion drive higher quality providers from the

market, since they will not be able to charge a sufficient premium to cover their greater

production costs. Average quality will continue to decline until the products left in the market are

of equally low quality. Although there would be a perfect correlation between price and quality

at this degenerative point, this perfect correlation would signify a complete market failure [1].

A. Prior Research on Price-Quality Correlations

The degree of correlation between product price and quality in U.S. product markets has

been explored extensively in a literature that dates back to 1950 [12]. The basic methodology of

these studies is remarkably uniform. Product quality ratings usually are based on tests conducted

by Consumer Reports (“CR”).2

 The authors then calculate a correlation coefficient for these

quality rankings and the list prices or, when available, the actual transaction prices for the tested
- 2 -

products. The results of these studies have also been quite consistent. In general, price and

quality are only weakly correlated, with coefficients usually ranging from .20 to .25 [7]. These

results are construed as evidence that consumer product markets perform "poorly," with one set

of coauthors feeling motivated to title their article "The Chaos of Competition." [10]

This literature suffers from several weaknesses and limitations. Most obviously, the

results can be no more reliable than CR's product ratings. Even if it is assumed that all of the CR

protocols were appropriate and the tests performed competently, CR might not weight the

various performance attributes for complex products such as stereo equipment, dishwashers, or

tires in the same manner as would the average consumer. 

And, if CR did happen to replicate the tastes of average consumers, significant subsets of

buyers might still rank the products differently and willingly pay more for specialized products

that might do relatively poorly in the ratings. Further, CR's ratings are not based on aesthetic

qualities that may be very important to many consumers and that tend to be directly related to

price. 

Finally, sole reliance on CR automatically excludes all of the consumer service and retail

industries that are supplied locally. Published studies of price and quality correlations for local

services are limited to analyses of the legal and optometrical markets, where price advertising has

been severely constrained [3]. We therefore have no systematic examinations of price and

quality relationships in a major sector of the U.S. economy.

B. Current Research

The research reported in this working paper attempts to expand the limited scope and

remedy some of the methodological weaknesses of prior studies of price-quality relationships in

consumer industries. This analysis is made possible by the availability of an extensive data

source for price and quality information for a large number of consumer service industries in the

Washington, D.C. area. Such information has been published regularly by Washington

Consumers' Checkbook since 1976.

Washington Consumers' Checkbook ("WCC") relies on quality evaluations supplied in

response to mail questionnaires sent to all subscribers of Consumer Reports or WCC in the

Washington area. Respondents are asked to rate service providers they have patronized during

the last year as "superior," "adequate” or "inferior" on a variety of performance dimensions, such

as "doing service properly," "starting and completing service promptly," and "overall

performance.” WCC “check rates” the firms that receive particularly high overall satisfaction

ratings. Also reported is the number of consumers who rated each service provider.

The quality ratings are almost always accompanied by price information, usually in the

form of an index. WCC staff contact each of the rated providers and ask for quotes on three or

four standardized jobs. The providers' hourly labor rates are also listed where appropriate.
 

3 Assuming a firm has at least one complaint on file, the employee data can be derived

using the absolute number of complaints and the complaint rate index, both of which are

provided in the ratings. The majority of firms, however, have no complaints, thus precluding any

indirect calculation of employee size. 

 

4 The ratings already contain a very rough indicator of firm size as measured by the

number of respondents rating the various providers. These numbers will, however, be heavily

skewed toward providers who have received high ratings in past issues of WCC, since

respondents to the WCC questionnaire will be far more likely than the general public to have

relied on those prior ratings as a shopping tool. 

- 3 -

Although some establishments refuse to participate in this phase of WCC’s research, ratings

generally are available for 85-90 percent of the rated firms. WCC confers a check rating for price

to the firms with the lowest price index scores.

In addition to price data, WCC customarily contacts local consumer protection offices to

determine the number of complaints on file for each of the surveyed firms. (As discussed below,

the complaint data provide one means of checking the reliability of the WCC survey results.)

Finally, for approximately one-third of the rated industries, WCC gathers information from firms

on the number of principal employees, such as auto mechanics or licensed plumbers. This

information is used to normalize the complaint data for size of firm, and is not published

separately.3

 WCC agreed, however, to provide all available employee data for the most recent

rating period. These data serve as a measure of firm size in the statistical analysis.4 

WCC Magazine is published on a roughly biannual basis, with approximately five or six

industries per issue. For certain industries, such as medical professionals and financial

institutions, WCC does not provide overall quality ratings and/or price index information. For

the current research, suitably complete data were available for 19 service industries. In most

cases, each industry was rated on several occasions over time. These industries are presented in

Table I below, with the number of individual data sets for each industry given in parentheses.
- 4 -

TABLE I

Local Service Industries in Sample

 ________________________________________________________

 

1. Air Conditioning-Heating Repair (5) 11. Locksmiths (3) 

 2. Auto Body Repair (5) 12. Pest Control (4)

3. Auto Mechanical Repair (8) 13. Plumbers (4) 

4. Major Appliance Repair (4) 14. Restaurants (2)

5. Carpet Cleaning (3) 15. Shoe Repair (2)

6. Carpet Sales, Installation (2) 16. Supermarkets (3)

7. Computer Repair (3) 17. Tree Surgeons (1)

8. Drycleaning (2) 18. TV Repair (1) 

9. Electricians (2) 19. Watch Repair (3)

 10. Local Movers (3) 

 ________________________________________________________

 

WCC's approach to quality rankings avoids the major shortcoming of the Consumer

Reports methodology. Since WCC quality ratings are based on actual consumer experience, they

automatically reflect the weights that consumers assign to the various dimensions of firm

performance. Although in some cases consumers may not be able to evaluate certain aspects of a

service provider's competence and honesty as accurately as a trained tester, those ratings might

still capture marked differences in quality among providers.

There is, however, a potentially high price to be paid for the consumer experience

approach to rating service providers. The WCC "quality" ratings are really consumer

"satisfaction" ratings. The ratings essentially measure how happy a consumer is after the service

is completed and the bill is received. The difficulty is that the size of the bill may well influence

the level of consumer satisfaction. Post purchase, most consumers will form a better opinion of,

say, an auto repair establishment that presents a bill for $150 than with one that charges $450,

even if there is no real difference in service quality. 

This potential for bias exists irrespective of whether consumers have any clear knowledge

of what competitors would have charged for the same job (although the magnitude of the bias

would certainly be greater if patrons of the $450 shop were aware that they had paid a $300

premium). Higher bills inflict more pain, and the unfortunate recipients are less likely to reward

providers with high ratings. Thus, depending on the strength of the bias, the WCC ratings could

in theory reveal an inverse relationship between price and satisfaction even when the underlying

relationship between price and true quality was positive. 

Pre-purchase selection bias problems may also affect the satisfaction ratings. 

Consumers who choose to patronize higher cost establishments may also have higher

expectations (and perhaps more complex service problems), and may therefore judge
- 5 -

performance more critically, or be more likely to experience difficulties because of the nature of

their particular service requests. Again, this potential bias in the WCC data may weaken or even

reverse any positive relationship between price and quality that may actually exist in the surveyed

service markets. 

The price data published by WCC have their own potential problems. It would not be

practical or prudent for WCC to gather price information as part of its consumer satisfaction

survey. Even if consumers could remember price information reliably, there would in most

instances be no way to pin down the precise nature and scope of the service that was provided.

Thus, WCC gathers its own information, and is usually limited by time and budget constraints to

soliciting price quotes for a small subset of the wide range of jobs provided by firms in most

service industries. As a result, the price index based on these quotes is reliable only to the extent

that the sampled prices reflect the prices of the services that consumers actually choose from

each provider. As indicated earlier, WCC often publishes hourly labor data to supplement the

price index, which provides a rough consistency check for the analysis.

The above difficulties not withstanding, the WCC ratings provide the most

comprehensive source of information on price and quality in the local service sector that we are

likely to have in the foreseeable future. It would not be economically feasible for a private tester

such as Consumers Union to purchase such services directly and evaluate quality. There are

simply too many providers and the cost of many of the services is too high. Further, unlike many

of the durables tested by CR, services cannot be resold in the used market. The WCC data

therefore deserve attention by researchers. 

II. HYPOTHESES TESTED

A truly rigorous exploration of this subject area would begin with a complete structural

model that would predict the equilibrium correlation between price and quality for firms in a

given service industry based on production cost variables, consumer demand functions, and the

magnitude of consumer search costs. Such a construct would allow us to test specific hypotheses

concerning the absolute value of the correlation coefficient that we should expect in each of the

the sample industries.

This study does not provide such a sophisticated underpinning, both due to the heroic

scale of the theoretical challenges, and because suitable data would almost certainly not be

available for many of the supply and demand variables that would be involved. Absent this

theoretical construct, there is really very little we can say about the absolute correlations we

would expect to see in these industries.

Presumably, none of the service industries included in this study fits the perfectly

competitive model, if only because consumers must incur some search costs to determine firm
 

5 We will ignore for purposes of this discussion the possibly perverse bias introduced by

any interaction between prices and consumer satisfaction that could reveal a seemingly negative

price-quality correlation.

 

6

 Indeed, we would not necessarily expect a simple correlation of one in a perfectly

functioning market. Although quality should increase monotonically with price under such

circumstances, the relationship would not be linear unless the long run marginal cost of

producing additional quality happened to be perfectly linear. 

- 6 -

price and quality. And, in any event, we would not expect even a highly efficient market to

function perfectly at any and every moment in time. Snapshot measurements such as those relied

upon here would always find instances of poor performance that had not yet been punished by the

market. Finally, there are almost certainly measurement errors and other problems in the WCC

price and quality data that could inject random noise and prevent precise measurement of the

actual level of price-quality correlation in the market.5

At the other extreme, it is unlikely that any of the surveyed markets are completely

“imperfect” in the sense that consumers are totally incapable of gathering any price or quality

information whatever. Thus, about all we can predict about the absolute correlation between

price and quality that we might observe from the WCC data for any given industry is that it

should be greater than zero and less than one.6

Fortunately, the WCC data and existing theories from the literature on the economics of

information allow us to test other interesting hypotheses concerning the expected comparative

strength of price-quality correlations across service industries, and to test the accuracy of certain

non-price “signals” that consumers might use to assess the quality of individual providers within

industries. The availability of time series data for most of the sampled industries also permits an

analysis of the stability of a firm’s price and quality ratings over time. Specifically, it is possible

to test whether a firm that receives a high rating for quality in an initial period is likely to “milk”

any reputation gains and lower quality or raise its price in subsequent periods. 

 

A. Price-Quality Correlations Across Industries

The first area for exploration is the pattern of price-quality correlations that we observe

from industry to industry. Theory would predict that these correlations will be highest in those

service markets where consumers can compare the prices and quality of competitors at low cost.

It is relatively easy to specify the characteristics of a service that would determine how costly it

would be for consumers to shop for price. Search will be easiest when the desired service is

standardized, or comprised of standardized subcomponents, and the full scope of the needed

service is known to the consumer before purchase. These characteristics would allow the

consumer to gather price information efficiently by word-of-mouth, telephone, advertisements, or
 

7 Comparative shopping will also be difficult or impractical when service is required on

an emergency basis. There are many examples of service industries that may respond to

emergency situations (e.g. Plumbers, Roofers, Air Conditioning and Heating firms, Auto Repair,

and, for entertainment-challenged families, TV Repair), but this would not be the exclusive or

even primary form of business for such firms. It is therefore difficult to sort industries on this

basis.

- 7 -

on-site inspection. The clearest examples of such services would be drycleaning, carpet cleaning,

restaurants, and supermarkets. 

Almost by definition, consumers cannot directly determine a service provider's quality

until after the service is rendered. Still, there may be wide variance among industries in the ease

with which quality can be evaluated post-purchase. In certain cases, quality can be evaluated

fully by simple inspection or immediate experience. Again, restaurants, carpet cleaning, and

drycleaning score well in this regard. In such instances, the quality reputation of firms would

spread fairly quickly and allow new consumers to locate the desired combination of price and

quality at minimal cost. 

At the other end of the spectrum, comparative price and quality comparisons will be most

difficult where the service is complex and the consumer may not know pre-purchase precisely

which service components will be needed, or post-purchase whether the service had been fully or

honestly rendered. Examples would include certain repair services for automobiles, appliances,

and electronic entertainment items. The full cost of the repair would not be known until the

provider had diagnosed the problem, at which point the consumer may already have incurred

substantial out-of-pocket expenses and might be reluctant to repeat the process with another

establishment.7

 Although consumers could determine post-purchase whether a repair had fixed

the problem, they might not know whether all of the billed services were necessary.

Though not strictly a determinant of search cost, another factor bearing on price-quality

correlation should be the relative frequency with which consumers purchase a given service.

Clearly, consumers will have more opportunity to sample and compare competitors' offerings the

more often they shop. For local services, this means (all else equal) that the market should

punish poorly performing drycleaners more quickly than roofers who offer poor quality for the

money. Although it was hoped that relative frequency of purchase could be quantified using

existing marketing data or information supplied by the principal trade associations for each

sampled industry, such data apparently are not available. Thus, very broad and subjective

assessments of this and other consumer search costs factors are used to test whether price-quality

correlations are in fact higher in low-search-cost industries.

B. Price-Quality Relationships Within Industries

The preceding analysis has focused on informational and purchase pattern considerations

that might explain differing degrees of price-quality correlation across local service industries. It
 

8

 The signaling literature is exceedingly complex, and the various signaling models often

incorporate critical assumptions specifying precise consumer knowledge of firms’ marginal and

fixed costs. If these assumptions are relaxed, a positive relationship between quality and

advertising or other signals need not be reached in equilibrium. In the words of a recent survey

article, “Anything can happen.” [5], p. 34.

- 8 -

would also be of interest to test theories that might predict which types of firms within a given

industry are likely to offer higher levels of quality, or perhaps higher levels of value. That is, in

those markets where consumers do not believe they can rely upon price as an accurate indicator

of firm quality, are there other firm characteristics or practices that consumers can use as a time

and money-saving substitute for extensive search in determining the level of quality and price

that a firm is likely to provide?

1. Signaling Mechanisms 

This issue has been treated extensively in the "signaling" literature, which originated in

1974 with Spence [15] and Nelson [11]. All else equal, it is hypothesized that higher quality

providers will have a greater incentive to signal their advantage through advertising and through

other bonding devices such as nontransferable physical capital (e.g., specialized building designs

and accoutrements), the value of which is lost if consumers are disillusioned with a provider's

quality post-purchase [8, 9]. In such cases, the advertising or other signal need not have any

informational content per se. It is the mere existence and size of the advertising or other

nontransferable physical investment that assures consumers that the firm is trustworthy and

prepared to please customers over the long term.8

In some respects, service industries do not appear to fit the signaling model very well.

First, with the exception of national chains and dealer franchises present primarily in the auto

repair industry (discussed separately below), few of the firms in the industries rated by WCC

engage in broadcast or other major media advertising. Many of the firms serve smaller regional

markets and do not operate at a scale that would justify significant advertising outlays, although

such firms may place ads in smaller regional newspapers and church bulletins. 

In addition, firms in many of the industries under examination provide their services in

the consumer's home. Thus, bonding signals in the form of fancy, imposing office sites would

not prove very effective. Many of the remaining service industries perform repair functions,

where the physical plant is likely to be a basic facility readily transferrable to other uses. There

is, however, one form of advertising in this sector that might perform a signaling function. This

is Yellow Pages advertising.

a.. Yellow Pages Advertising

For many consumers, the Yellow Pages will provide the first and sometimes only step in

the search process. Firms that wish to stand out in the crowd of listings must pay a substantial
 

9

 This information was provided to the author by Marc Rysman from a data set

containing pricing information for almost all Yellow Pages directories in the United States in

1997. See M. Busse and M. Rysman,“Competition and Price Discrimination in Yellow Pages

Advertising” Working Paper Series ES, Number 13, Yale School of Management, June 2001. 

- 9 -

premium for full and half-page ads. Specifically, a full-page ad in the Suburban Maryland

Yellow Pages costs about $42,000 a year.9 Reaching the entire Washington D.C. area with fullpage ads would cost over $100,000 annually. 

Although Yellow Pages advertising does incorporate some of the features of a signaling

mechanism, it is not a particularly strong test of the theory. Specifically, in a community with

high population turnover such as the greater Washington, D.C. region, it is not obvious that the

cost of conspicuous Yellow Pages ads would be so high as to require extensive repeat business to

justify the investment. The payoff from attracting more first-time, and only-time, purchasers

who are new to the region may well be sufficient to warrant such advertising. Indeed, short-term

gains may be high enough even in the absence of high population turnover. 

In addition, heavy outlays for a large Yellow Pages ad would not be a feasible or even

desirable strategy for many firms in the local service sector economy. The service industries at

issue are a mixture of small regional providers who serve only a single jurisdiction or even

neighborhood, and area-wide firms with perhaps many outlets located in Virginia, Maryland, and

the District. Although both types of firms clearly compete with one another, most of the smaller

providers may purposefully limit their scale of operation. Such small, localized firms may have

no interest in running a very large Yellow Pages ad. Rather, their marketing strategy would be to

rely on repeat purchases from current clients and word-of-mouth advertising in their local market

area, and to confine their Yellow Pages presence to a single line listing. (In the statistical

analysis reported below, the relationship between Yellow Page advertising and quality is

explored using both the full sample of firms and a subset of firms that excludes single line

listings.)

b. Franchise and National Chain Status

As mentioned previously, a small subset of the studied industries contains firms that are

franchisees of a national manufacturer or are members of a national chain. Franchised

dealerships are most common in the auto repair sector. There are also several national auto

repair chains represented in the Washington, D.C. area (e.g. Goodyear, Merchant Tire, Sears, and

Jiffy Lube.) National chains or dealerships are present to a lesser extent in the auto body repair,

pest control, and major appliance repair industries.

National chains, such as Sears and Jiffy Lube, advertise extensively and enjoy

considerable brand name recognition. The non-informational component of these advertising

investments can be construed as a pure signal to consumers that the local chain representative
 

10 This is the outcome that Bond et al. [3] observed in their analysis of optometrists

operating in states that did and did not allow price advertising and large chain operations. In

nonrestrictive states, advertisers provided slightly less thorough eye exams than did smaller

nonadvertisers, but charged substantially less for the bundled combination of eye glasses and eye

exams. 

- 10 -

will satisfy its customers, either in terms of the absolute quality of the service or in terms of

quality adjusted for price. 

Franchised car dealerships (e.g. Toyota, BMW, etc.) may benefit from any goodwill

associated with the automobiles they represent, and consumers may assume that dealers have

special expertise in repairing their brands of cars. (It should be noted, however, that the potential

quality clue offered by franchise status does not constitute a pure signal in the economic sense,

since its utility rests on direct information concerning the known quality of the automobile brand

and logical inferences concerning the likely expertise a dealership would gain from specializing

in the repair of a limited number of automobile brands.) 

 The hypotheses that chain or dealership affiliation is a reliable indicator of quality or

value can be readily tested with the WCC data, since such affiliations are obvious from the title

of the firm. Separate zero-one dummy variables were constructed for dealer and chain status,

and included as a right-hand term in regressions using WCC quality ratings as a dependent

variable. 

2. Firm Size

Many consumers may also make quality inferences based on a service provider’s size.

Size is not a pure signal in the sense described above, since size is a highly complex market

outcome rather than a simple short-term investment decision. Further, the relationship between

size and quality (as well as price) is an interesting and important economic issue irrespective of

whether consumers know a firm’s scale of operations or make use of any information they might

have. The fundamental question, of course, is whether large size is in fact a market reward for

superior performance. 

Firms might prosper simply by providing above-average quality at a higher-than-average

price, assuming there was a substantial market for such a combination of price and quality. 

Alternatively, firms may attempt to grow by exploiting any scale economies in such activities as

purchasing, inventorying, certain administrative functions, and scheduling. A priori, it is not

clear what absolute level of quality would be optimal for such firms. Presumably, however, these

providers would attempt to obtain increased market share by passing at least some of their cost

savings through to consumers, and we would therefore expect the price charged for any given

level of service quality to be below the industry average for that quality level.10 The WCC data

allow us to test whether larger firms offer higher quality, and/or whether such firms offer higher

value as measured by consumer quality ratings adjusted for price. 
- 11 -

3. Third Party Certification and Endorsement

Official authorizations, accreditation, or certification from various private or public

sources may provide consumers with very straightforward indicators of firm quality. For the

local services in this data set, such third party certifications are most prevalent in the auto repair

industry. Examples of private endorsements include approval by the American Automobile

Association and certification of mechanics by the National Institute of Automotive Service

Excellence. Consumers might also infer a quality advantage for repair shops that are approved

by local or state governments for safety and/or emissions inspection and testing. The WCC data

contain shop-specific information on such certifications and authorizations, and thus provide one

means of testing their usefulness to consumers as quality clues. 

C. Price and Quality Performance Over Time

As previously discussed, WCC has evaluated most of the 19 industries included in our

sample on several occasions since 1976. The time lapse between evaluations ranges from a

minimum of three years to over a decade for the infrequently rated industries. These various

snapshots in time allow us to evaluate the stability of an individual firm’s price and quality

ratings and to measure any trends in an industry’s overall performance from its initial assessment

to the present. Finally, the availability of multiple data sets for most of the sample industries

provides a consistency check for the statistical analysis of price-quality correlations and certain

other relationships.

III. DETAILED DESCRIPTION OF DATA SET AND VARIABLES

The data set is comprised of 19 local service industries, which represents every industry

with WCC price information and consumer quality ratings. As reported in Table I above, in most

instances separate data sets were constructed for each evaluation an industry has received since

the magazine began publishing in 1976. There are a total of 60 such data sets. 

A typical data set for a given industry would include the following variables taken from

the WCC ratings: (1) a price index for each firm as constructed by WCC; (2) the number of

complaints on file for a given firm at local consumer protection offices, in certain cases adjusted

for the size of firm; (3) the quality rating given by the survey respondents; (4) the number of

respondents rating a firm; and (5) two zero-one dummy variables to indicate whether WCC had

check rated the firm for quality and/or price. 

The respondent quality rating variable requires further elaboration. The survey

instrument used by WCC asks consumers to rate a firm on the basis of “overall performance,”

“doing work properly the first time,” and a variety of other performance dimensions, such as
- 12 -

“letting you know cost early,” “starting and completing work promptly,” and in some cases, even

“neatness.” Unless otherwise noted, all of the results reported below are based on the “overall

performance” category. Although the “doing work properly” category is in some sense the most

focused measure of a firm’s pure competence, excelling in the other dimensions of performance

included in the “overall performance” category should impose costs on a firm, and these

attributes of performance properly should be viewed as components of quality. (In all cases,

however, sensitivity tests were conducted to determine whether an alternative measure of quality

would alter the results substantially.)

 Within each category of firm performance, consumers may rate a firm as “superior,”

“satisfactory,” or “unsatisfactory.” For a majority of industries, WCC reports for at least the

“overall performance” category both the percentage of respondents rating the firm superior, and

the percentage rating the firm either satisfactory or superior. For six of the industries, however,

only the broader “satisfactory or superior” percentage is provided. This omission reduces the

variance in the quality ratings and could reduce any explanatory power the independent variables

might have. 

In addition to the WCC variables, supplementary information was gathered for the most

recent rating period for 14 of the industries to test for any signaling function provided by Yellow

Page advertising. The resulting variable is simply the size of a given firm’s advertisement

measured in square inches, with a one line regular print listing coded as .25 square inches, and a

bold listing coded as .5 square inches. Thus, this variable has a potential range of .25 to 80 for a

full ten-by-eight-inch page. 

The WCC data set was also supplemented in certain cases with information on firm size

as measured by number of employees. WCC was able to supply this information for a total of

seven industries. Finally, dummy variables were created where appropriate to represent whether

a firm was a dealership or a franchisee of a national chain. Certain other dummy variables were

used in the Auto Repair data set, which is discussed separately below.

IV. STATISTICAL METHODOLOGY

For most of the regression analysis reported below, the left-hand variable is the WCC

overall performance rating, which is the percentage of survey respondents rating a firm superior

(or, in some cases, either satisfactory or superior). There are a number of statistical issues

involved in use of this measure as a dependent variable. First, the measure can only vary from

zero (percent) at a minimum to 100 at a maximum. Further, a substantial number of summary

ratings cluster at or near the upper end of the scale. Under such circumstances, ordinary least

squares is not a suitable estimation technique, since the error terms will not be distributed

normally.
- 13 -

In addition, the error terms for observations within any given firm will be correlated,

since there will be variables not included in our analysis that will have an impact on the overall

level of satisfaction ratings for each firm. One such factor could be the politeness and general

demeanor of the staff. Firms faring well in this dimension will receive higher WCC quality

ratings, which means that a regression that did not capture this determinant of satisfaction would

systematically underestimate the quality scores reported by respondents for such firms. 

Finally, the number of respondents rating a particular firm varies widely within industries,

from a minimum of ten to a maximum of 500 or more. Since these ratings are in effect sample

estimates of the true ratings that all customers of that firm would give, the precision of the

estimates increases with the number of ratings. It is appropriate, therefore, to weight firm quality

ratings by the number of individual ratings on which they are based.

All of these econometric issues were addressed by adopting a logit estimation technique

that is structured for use with “grouped” data. In essence, this procedure unfolds the summary

quality rating for a given firm into a series of binary categorical variables, with a zero-one value

generated for each respondent rating used in calculating the overall quality score. Consider, for

example, a firm that receives an overall performance rating of 90, which for most industries

would mean that 90 percent of the consumers reporting on that firm rated it superior. If there

were a total of 10 respondents, our logit technique would generate a series of dependent variables

comprised of nine “one” values and one “zero” value. Each of these dummies would be

associated with the corresponding firm-specific independent variables used in the logit

regression. Thus, firms with the highest number of individual ratings automatically would be

weighted heaviest in the regression estimation procedure. This procedure also adjusts standard

errors to correct for the expected correlation of error terms within firms. 

V. SUMMARY OF PRINCIPAL FINDINGS

A. Overview of Quality Ratings

Table II below presents the average WCC overall quality ratings for the various industries

in the sample for the most recent survey period. The first rating is the average percentage of

respondents that gauged the overall performance of firms in the industry as superior. The second

rating combines the superior percentage with the percentage of respondents that rated a firm as

“adequate.” WCC did not disclose ratings for the superior category in six industries, and in four

other industries did not provide the combined score for superior and adequate. In the case of

restaurants, respondents were asked to rate the quality of the food on a scale of zero to100. The

resulting quality measure therefore is not comparable to the average percentage score ratings for

the other industries. Industries are ranked in ascending order by their average rating in the “%

Superior or Adequate” category (where available). 
- 14 -

Table II 

WCC Quality Ratings for Sample Industries

______________________________________________________________________________

 Year Rated Industry % Superior % Superior

 or Adequate

______________________________________________________________________________

98 Supermarkets 36.0 ------

96 Carpet Installers ----- 80.2

98 Computer Repair ----- 80.2

89 Television Repair ----- 87.7

01 Auto Repair ----- 89.1

96 Appliance Repair ----- 89.8

99 Pest Control 59.6 90.5

98 Auto Body Repair 69.3 90.5

96 Watch Repair 74.5 91.1

98 Plumbers ----- 91.8

97 AC & Heating 70.7 92.9

96 Drycleaners 61.4 94.0

98 Local Movers 69.4 -----

98 Carpet Cleaning 73.7 -----

96 Electricians 78.2 94.4

99 Locksmiths 79.5 95.8

95 Shoe Repair 81.0 -----

99 Tree Experts 79.9 96.5

98 Restaurants ----- 75.4*

______________________________________________________________________________

*Restaurants are rated on a scale of 0-100

______________________________________________________________________________

Table II reveals a few interesting patterns and conclusions. First, using the more sensitive

“% Superior” rating, Washington area customers are clearly not extremely enthusiastic about the

overall quality of local supermarkets. It is not clear whether this reflects an underlying problem,

or merely the extreme familiarity consumers have with the food shopping experience. And, for

whatever reason, local consumers do seem enthusiastic about their choice of shoe repair firm. 

Between these two boundaries, industries specializing in mechanical repair do not fare as well as

the more traditional “guild” industries, such as electricians, locksmiths, and, as mentioned, shoe

repair. 

On an overall basis, the various ratings suggest that the local service sector is functioning

fairly well from an absolute standpoint. An average of 90 percent of consumers rated their

service provider as at least adequate, and an average of 69 percent graded the performance as
- 15 -

superior. At a minimum, these percentages do not suggest a major market meltdown of the

magnitude associated with a “lemons” model outcome. 

B. Consistency of WCC Quality Ratings and Complaint Data 

As discussed, the WCC ratings list the number of complaints on file at local consumer

protection offices for each rated firm. For certain industries, this number is normalized for firm

size, as measured by number of employees. The complaint variable was included in the initial

regression runs to function as a possible consistency check on the WCC quality ratings. Since a

firm’s complaint history is really an indirect measure of quality, it cannot be construed properly

as a true independent variable in any regression that uses the WCC quality ratings as a dependent

variable. It does have the potential, however, to shed some light on the reliability of the

respondent ratings.

In particular, the complaint variable can help determine whether the WCC quality ratings

are hopelessly biased by the possibly perverse impact that a firm’s prices might have on

consumer attitudes toward the firm. As described earlier, this interaction between price and

consumer satisfaction, combined with possible selection biases, might yield a strong negative

correlation between price and quality, even when true quality was in fact directly related to price. 

Consumers presumably take the trouble to write complaint letters over what is perceived

as truly egregious firm behavior. Although these complaints may involve what a consumer feels

is an exorbitant price, complaints should not be generated by the more subtle impact that, say, a

somewhat above-average service repair charge price might have on the disposition of a WCC

respondent rating the overall performance of a firm. Thus, the complaint rate variable should

help flag firms that are genuinely poor performers, and this variable should be negatively

associated with the WCC ratings if these ratings are at all reliable. 

The results indicate that there is in fact a persistent and strong negative correlation

between firm complaint histories and the quality ratings. For the most recent rating periods,

WCC provided some form of complaint data for 14 of the 19 industries. In ten cases, the data 

were adjusted by firm employee size. In seven of these industries, the complaint rate was

negatively and significantly correlated with quality. In the three remaining industries, the

coefficient sign was negative but not significant. For five industries, WCC provided only the

absolute number of complaints a firm had on file. The complaint coefficients for three of these

industries were negative and significant, and insignificant in the remaining two. In the entire

data base, the complaint rate variable was negative and significant in approximately 90 percent of

the industries for which size-adjusted data were provided. These results, though far from

conclusive, do suggest that the WCC ratings are at least flagging the very worst performers.
- 16 -

C. Simple Price-Quality Correlations

In this section, we discuss the threshold question of whether price by itself provides a

reliable indicator of quality as measured by WCC respondent performance ratings. The

regressions reported do not control for any firm characteristic, such as size or status as a

franchisee or dealership, or any signaling mechanism, such as Yellow Pages advertising. The

analysis simply attempts to determine the extent to which consumers can rely upon price alone as

an indicator of firm quality. 

The results of the simple quality-price regressions for the most recent ratings period are

reported in Table III. The coefficients were obtained by grouped logit analysis using the WCC

quality rating as the dependent variable. P values for the price coefficients appear in parentheses.

TABLE III

 Quality-Price Correlations in Most Recent Rating Period 

 ______________________________________________________________

 Industry Coefficient Sign 

 for Price 

 (P-value)

 ______________________________________________________________

 Carpet Cleaning Positive (.009) 

 Carpet Installers Positive (.090)

 Dry Cleaning Positive (.102)

 Local Movers Positive (.216)

 Pest Control Positive (.000)

 Restaurants Positive (.000)

 Supermarkets Positive (.000)

 Tree Experts Positive (.403)

 Auto Body Shops Negative (.548)

 Air Conditioning-Htg. Negative (.034)

 Appliance Repair Negative (.000)

 Auto Mechanical Repair Negative (.000)

 Computer Repair Negative (.078)

 Electricians Negative (.001)

 Locksmiths Negative (.003)

 Plumbers Negative (.007)

 Shoe Repair Negative (.131)

 Television Repair Negative (.307)

 Watch Repair Negative (.732) 

 ______________________________________________________________
 

11 Such an outcome might occur under the extreme assumptions that consumers never

purchase a service more than once and that absolutely no quality information is available prepurchase. In that event, some firms might successfully pursue a strategy of high price and low

quality, since consumers seeking high quality might specifically target firms quoting the highest

price for a service, and by assumption such firms would never be punished for failing to deliver

the expected quality. Once these assumptions are relaxed to allow for at least some repeat

purchases and limited availability of quality information, it is once again difficult to understand

how the high price-low quality strategy could persist so consistently in so many industries over

such a long period of time. 

- 17 -

As hypothesized, those industries with particularly low search costs and relatively frequent

purchase generally display significant positive correlations between price and quality. These are

Carpet Cleaning, Restaurants, Supermarkets, and, just missing significance at the .10 level, Dry

Cleaning (P=.102). In addition, price and quality are also positively correlated for Carpet

Installers and Pest Control. The most striking feature of Table III, however, is the relentless

procession of negative correlations in the eleven repair industries, seven of which are significant. 

The picture does not change substantially when results for all of the ratings periods are

considered. Table IV provides a detailed listing of the observed price-quality correlations for each

of the 60 data sets in the sample. Table IV reveals significant positive correlations in

ten data sets, representing seven industries. Of these industries, however, only Carpet Cleaning,

Drycleaners, and Restaurants are consistently positive over time. Supermarkets reports a highly

significant positive price-quality correlation in the two later ratings periods, but is insignificant in

1979. Significant negative price-quality correlations can be observed in 22 data sets, accounting

for ten industries, and 28 sets display no significant price-quality correlation.

 The widespread occurrence of negative price-quality correlations is difficult to rationalize

using any rigorous theory of market performance. Even in the presence of extremely high search

costs, there are no a priori grounds for expecting price to serve as a perverse indicator of quality.

Rather, we would simply expect a great deal of noise with no systematic relationship between

price and quality. If we are to believe the results reported above, firms apparently prosper by

choosing a strategy of high prices and poor performance. It is implausible that even serious

market imperfections would perpetually reward such a strategy.11 Thus, we certainly cannot

dismiss the hypothesis that the WCC data are biased due to the previously discussed interaction

between price and consumer satisfaction with firm performance. 

One alternative hypotheses for certain industries in the sample is that the simple quality

and price regressions fail to control for relevant firm cost variables, particularly higher rental 
- 18 -

Table IV

Quality-Price Correlations for All Industries and Ratings Periods

______________________________________________________________________________

 

 Industry Year Sign P Value Observations

______________________________________________________________________________

 AC & Heating Contractors 97 negative .034 119

92 negative .273 106

87 negative .428 78

82 negative .518 61

77 positive .972 44

 Appliance Repair 96 negative .000 46

91 negative .002 43

85 negative .012 52

78 negative .228 60

 Auto Body Repair 98 negative .548 120

95 negative .422 106

90 negative .393 90

85 negative .191 105

82 negative .041 71

 Auto Mech. Repair 01 negative .000 482

97 negative .000 444 

94 negative .000 431 

91 negative .000 380

88 negative .000 354

85 negative .000 310

81 negative .000 289

76 negative .004 148

 Carpet Cleaning 98 positive .009 33

94 positive .425 31

87 positive .727 30

 Carpet Sales, Installation 96 positive .090 24

89 negative .723 39

 

 Computer Repair 98 negative .078 27

94 positive .248 31

89 negative .929 14
- 19 -

Table IV (Continued)

 Industry Year Sign P Value Observations

______________________________________________________________________________

 Drycleaners 96 positive .102 211

90 positive .038 232

 Electricians 96 negative .001 36

90 negative .036 21

 Locksmiths 99 negative .003 25

94 negative .499 27

86 negative .587 33

 Movers 98 positive .216 28

92 negative .720 29

81 positive .051 25

 Pest Control 97 positive .000 51

93 positive .477 37

86 negative .292 46

77 negative .030 25

 Plumbers 95 negative .007 134

89 negative .045 81

83 negative .019 97

77 positive .165 58

 Restaurants 98 positive .000 672

95 positive .000 718

 Shoe Repair 95 negative .131 95

88 positive .213 96

 Supermarkets 01 positive .000 8

92 positive .079 7

79 positive .997 16

 Television Repair 89 negative .307 36

 Tree Experts 99 positive .403 29

 

 Watch Repair ` 96 negative .691 41

91 negative .181 30

80 negative .002 52

______________________________________________________________________________
 

12 These industries were Auto Body Repair, Auto Mechanical Repair, Drycleaners, Shoe

Repair, and Watch Repair. Due to resource constraints and the strong positive price-quality

correlation already evident, data were not collected for Restaurants. 

- 20 -

costs in more convenient and/or affluent locations. Consumers presumably would willingly pay

more for a tune up in a shop or dealership located close to work or home than one in an industrial

park in an outer suburb. Thus, the simple correlations potentially suffer from a missing demand

variable in the form of convenience, and a missing supply variable in the form of rental costs. This

defect would affect a fairly small number of the sample industries, since so many of the services

studied here are performed in the home and do not involve consumer visits to provider sites.

In an attempt to remedy this problem, census household income data by census tract were

collected and used as an independent variable in a separate price equation for the relevant

industries.12 Although direct rental cost information by, say, zip code would have been preferable

to the income variable, these data were not available. It was hoped that the income data would be

sufficiently correlated with rentals to provide a satisfactory substitute. For whatever reason,

however, none of the price equations produced significant results, and no subsequent attempt was

made to estimate a reduced form equation that would control for convenience and rental costs. 

D. Tests of Signaling Theories and the Performance of Larger Firms

Our data set allows tests of several signaling mechanisms, and also a test for whether firm

size is associated with quality. The variables and hypothesized outcome for size of Yellow Pages

ad, dealership status, and franchisee status have been discussed earlier. In addition, the Auto

Repair and Pest Control data sets permit certain other signaling tests that will be discussed

separately. Results were obtained from grouped logit regressions that used only the independent

variable under examination as a predictor of quality. This specification is appropriate to test the

hypothesis that consumers can rely upon the variable of interest in isolation as a signal of quality. 

See Appendix A for a presentation of the full regression results underlying the discussion in this

section.

1. Yellow Pages Advertising

Data on the size of Yellow Pages ads were collected for the most recent rating period for

14 of the 19 industries. This variable was used in several specifications to test for robustness and

any extreme value problems that could be introduced by the very wide range in values (from .25

to 80), and the possibility that one or two firms with full page ads might by chance carry

particularly high or low quality ratings. Specifically, additional regressions were run with the

Yellow Pages variable in log form, and subsets of firms were tested to determine whether any

relationship between quality and advertising was limited to advertisements above or below a

certain size. In particular, runs were always made excluding firms with one-line listings. As
- 21 -

discussed earlier, such firms might deliberately have chosen to limit their scale of operation and

would never have considered signaling quality using a larger Yellow Pages ad. 

The results provide little support for a Yellow Pages signaling function. A significant

positive coefficient was found for only four of the 14 industries. In seven industries, the Yellow

Pages variable was negatively and significantly associated with quality. There was no significant

relationship in the remaining three industries. Simple regressions were also run to investigate

whether the size of an ad might signal higher value as measured by the ratio of the quality rating

to the price index. None of these regressions supported this hypothesis. Indeed, a significant

negative correlation between Yellow Pages ad size and value was found in five industries.

 2. Dealership and Franchisee Status

In two industries, Auto Mechanical Repair and Auto Body Repair, numerous firms in the

sample were franchised dealerships for the major auto companies. In four industries (the two auto

repair industries, Local Movers, and Pest Control), many of the firms were franchisees for

national chains, such as Maaco, Sears, United Van Lines, and Orkin Exterminating. In these

industries, dummy variables were used to test whether consumers rated such firms higher than

independent firms. The results were uniformly negative. The coefficients on these variables were

always negative, and usually highly significant. (The results for Auto Mechanical Repair will be

presented in more detail below.) Although franchised dealerships consistently charged more than

non-dealers, the prices of national chains did not differ significantly from those of independent

providers.

3. Firm Size

The firm size variable is of interest for two reasons. First, it clearly would be interesting

to see whether larger firms might have attained their size advantage because they provided

superior quality. Second, it is of interest to determine whether larger firms are realizing scale

economies that might be passed on to consumers in lower prices for a given level of quality. For

this data set, the answer to both questions is a resounding no. WCC provided employee size

information for seven industries, and in all seven industries there is a very significant negative

correlations between quality and size. Nor did larger firms provide greater value in terms of

quality adjusted for price. The coefficient on the value variable was negative in all cases, and

significantly so in five industries.

4. Warranties

One of the industries–Pest Control–provides a unique opportunity to test whether the

length of warranty protection that firms provide signals higher reliability, or whether the warranty

functions purely as indemnification in the event of actual product failure. The WCC ratings for

pest control firms contain a detailed breakdown of firm charges and terms for a pest eradication

treatment for a typical house. WCC lists the initial charge for the treatment, the time period
- 22 -

during which the firm will provide a free follow-up treatment, and the charge for treatment after

the free follow-up warranty has expired. The warranty varies widely in the sample, from zero days

to a full year. Further, as is the case for almost all industries, WCC provides customer ratings for a

firm’s ability to do the work properly on a first try, as well as an overall performance assessment. 

If firms offering longer follow-up warranties were more competent and diligent, so that

consumers could rely on the warranty period as an indicator of quality, one would expect that such

firms would receive higher ratings for doing the initial treatment properly. This is not the

relationship we observe in the Pest Control data set. The rating for doing work properly tends to

be negatively and significantly associated with the length of the follow-up treatment warranty. 

5. Certification

Finally, the Auto Repair data set contains information that can be used to test whether

consumers can rely on various forms of certification and authorization as indicators of quality.

WCC indicates for each auto repair establishment whether it is approved by the American

Automobile Association, whether the firm employs at least one ASIE technician, and whether it

is licensed to perform state safety inspections. The first two variables provide straightforward

quality certification tests. The hypothesized coefficient for state safety inspection status is less

clear, since many repair establishments may question the profitability of such inspections, and

may decline participation for reasons unrelated to provider competence. 

Certification by AAA is positively but not significantly correlated with the WCC ratings

in the seven auto repair data sets. The ASIE variable, on the other hand, is frequently negative

and significant, and never positive and significant. State Safety Inspection facilities consistently

fare worse in the quality rankings.

As presented in Appendix A on pp. 43-44, the most dramatic result from the simple

regression runs is the strength of the negative correlation between the dealership and chain

variables and overall performance. In simple numeric terms, without controlling for any other

variables, dealerships on average received an overall satisfaction score of 80.6 in the 1997

ratings, 12 points below the rating of 92.6 for independent shops. Chains, with an average rating

of 76.9, scored even lower than dealerships.

Firm size also displays a highly significant negative correlation with quality. Since

dealerships and chains tend to be larger than independent shops, it might be concluded that the

size variable is merely serving as a proxy for dealership and chain status. As shown in Table V

below, however, multivariate regression analysis demonstrates that the significant negative

coefficient on firm size persists when dealership and chain status are also included as

independent variables. 

Further, this negative association between size and quality or between chain status and

quality cannot be attributed to any interaction between price and the consumer satisfaction
- 23 -

ratings. Price is not correlated with either firm size or chain status in this data set. (Dealerships,

however, charged significantly higher prices than independents or chains.) Thus, the negative

coefficients for the size and chain variables apparently should be accepted at face value.

TABLE V

Full Regression Results For Auto Repair 1997

______________________________________________________________________________

Dependent Variable = % rating firm superior or adequate

Logit estimates Number of firms = 441.00 

 LR chi2(7) = 872.69

 Prob > chi2 = 0.0000

Log likelihood = -6423.8417 Pseudo R2 = 0.0636

------------------------------------------------------------------------------

 supq | Coef. Std. Err. z P>|z| [95% Conf. Interval]

-------------+----------------------------------------------------------------

 chain97 | -1.31156 .097349 -13.47 0.000 -1.502361 -1.12076

 dealer97 | -.9997785 .0806641 -12.39 0.000 -1.157877 -.8416797

 Price97 | .0046225 .0015918 2.90 0.004 .0015025 .0077424

 Size7 | -.0147877 .0034198 -4.32 0.000 -.0214904 -.008085

 ASIE97 | -.8662402 .2126259 -4.07 0.000 -1.282979 -.4495012

 AAA97 | .189219 .0550878 3.43 0.001 .0812489 .297189

 Safety97 | -.3748802 .0612289 -6.12 0.000 -.4948866 -.2548739

 Constant | 3.403598 .2461795 13.83 0.000 2.921095 3.886101

------------------------------------------------------------------------------

______________________________________________________________________________

 It should also be noted that the price index variable is positively and significantly

correlated with quality in Table V, whereas the coefficient was negative and highly significant

when used as a single predictor. The primary explanation for the shift in signs is the explicit

accounting for dealer performance in this regression. As discussed, dealers tend to be more

expensive and do considerably worse in the ratings than independents. Thus, price acts partially

as a proxy for dealership status when used alone as a predictor of quality. 

The positive correlation in the full regression cannot, however, be interpreted as an

indicator of market efficiency. The reversal in sign merely indicates that we have identified and

controlled for one possible source of poor industry performance. On the other hand, the

seemingly poor performance of dealers may itself be an artifact of possibly strong selection

biases associated with dealership status. For example, if consumers seek out dealers for

particularly complicated repair tasks, the probability of service problems inevitably would be

higher and reflected in the lower satisfaction ratings. 
- 24 -

E. Stability of Price and Quality Ratings Over Time

 In this section, we first explore any overall trends in industry quality performance since

WCC began its ratings in 1976. (The price data are virtually always normalized, with an average

price within any industry assigned a value of 100, and therefore are not suitable for trend

analysis.) We then turn to the individual firm level and analyze the stability of firm performance

through ratings periods. In particular, we test whether firms that are singled out for a check

rating for price or quality tend to maintain a check rating in subsequent periods.

1. Overall Industry Trends 

It is of some general interest to determine whether overall performance in the sample

service industries has improved over the years, due perhaps to technological improvements or

even to the impact of the WCC ratings themselves. There is, unfortunately, a potentially

incestuous influence in the WCC data that will tend to improve average satisfaction scores

irrespective of any actual increase in quality among all the firms in the industry. Once an

industry has been evaluated in an issue of WCC, readers of the magazine can be expected to

patronize firms that do well in quality and/or price. In the subsequent rating period, firms that

scored poorly initially will tend to receive fewer ratings as readers shift their allegiances to more

favored providers, and some will drop out of the sample entirely due to inadequate response

rates. Thus, there should be a built-in upward trend in the average quality ratings. 

With this proviso, we present in Table VI the results of a simple trend analysis obtained

by regressing the average overall satisfaction scores for a given industry on a time counter. In

some cases, the number of ratings periods is very low and the resulting “trend” is not particularly

interesting. For what they are worth, the results show that, for the 16 industries with more than

one WCC rating period, overall satisfaction increases significantly in nine, decreases

significantly in four, and displays no trend in three. 

TABLE VI

Trends in Service Industry Consumer Satisfaction Over Time

______________________________________________________________________________

Significant Upward Trend (P<.10): Auto Body Repair, AC & Heating Contractors, Major

Appliance Repair, Auto Mechanical Repair, Drycleaning (2 periods), Electricians (2 periods),

Locksmiths, Shoe Repair (2 periods), Watch Repair. 

Significant Downward Trend: Local Movers, Pest Control, Plumbers, Restaurants.

No Significant Trend: Carpet Cleaning, Carpet Installers, Computer Repair.

______________________________________________________________________________
- 25 -

2. Individual Firm Performance Over Time

Of considerably greater interest than overall industry trends is the issue of whether firms

that are singled out for special recognition by WCC for either price or quality tend to repeat their

superior performance in the subsequent ratings period. It is possible that some firms might

attempt to exploit a quality check rating by, say, lowering costs and quality while maintaining or

even increasing price. Similarly, a firm spotlighted for particularly low prices might seek short

run gains by increasing prices in the hope that a substantial number of consumers acting on the

rating might not realize that the firm had lost its price advantage. 

Ideally, we would like to track firm performance shortly after the ratings appeared in

order to see the initial impact on what might prove to be short term firm behavior. Because of

the long time lag between ratings periods, however, the WCC ratings only allow a check after

several years have elapsed. We therefore are limited to assessing any possible direct or indirect

long term impact of the ratings on firm performance. 

This issue was explored using probit analysis. Specifically, we tested whether firms that

were check-rated for price or quality in a given rating period have a greater-than-average

probability of being check-rated again in the subsequent period. For any industry with N rating

periods, N-1 probits were run so that all subsequent ratings were considered in sequence. 

In order to give more intuitive meaning to the probit coefficients, the results are reported

in terms of marginal probabilities, which show the difference in the probability that a check rated

firm vs. a non-check rated firm will receive a check rating in the subsequent period. That is, a

marginal probability of .42 indicates that a firm check rated for, say, price in the first period will

be 42 percentage points more likely than a non-check rated firm to receive a price check rating in

the next period. 

The probit analysis provides a more rigorous and meaningful test of any tendency for

firms to “milk” reputation effects than would simple comparisons of a firm’s price index score or

its quality rating between periods. For any given rating period, some firms may tend to score

well on the quality survey or price index purely for stochastic reasons. By luck of the draw, an

otherwise average firm might find that it had alienated or pleased a particularly large number of

consumers who happened to have received the WCC survey form. Similarly, WCC might by

happenstance have chosen a mix of service tasks for its price index that particularly advantaged

or disadvantaged a firm because of atypical circumstances. A plumbing contractor, for example,

might have been short on skilled personnel temporarily on the day WCC requested an estimate

for some nontrivial task, and may have quoted a noncompetitive price. 

When such firms were rated again in the next period, such stochastic events would tend to

even out, and the firms’s ratings would tend to “regress to the mean.” Thus, to some extent, we

would always expect to find on average that firms check rated for, say, quality in 1992 would

score somewhat more poorly in 1998. Similarly, an average firm in 1992 might benefit from
- 26 -

chance events and earn a check rating in 1998. We could not conclude from this pattern,

however, that check rated firms tend to milk their reputations and lower quality. 

The probit analysis controls for such random fluctuations and shows more clearly whether

there are real differences in the propensity for check rated and non-check rated firms to score

well in the subsequent period. Still, the results do not lend themselves to an unambiguous test of

the milking hypothesis. In the example above, a significant marginal probability of .42 would

indicate that there is no pervasive tendency for firms to shirk once a check rating is achieved. 

But it would not reject the hypothesis that some firms behave in this manner. 

Tables VII presents the probit results for the price and quality ratings. In the column

labeled Marginal Probability, the first number represents the marginal probability that firms

check rated in the earliest ratings period will repeat their performance in the next rating period. 

The second number is the marginal probability based on the third vs. second rating period, and so

forth. Thus, for an industry that WCC has rated on N occasions, there will be N-1 entries in this

column. (The various marginal probabilities were not generated when the initial probit equation

showed no significant difference between the probabilities that check rated and uncheck rated

firms would be check rated in the next period. Two industries, TV Repair and Tree Experts, do

not appear in Table VII because WCC has rated member firms only once. Restaurants and

Supermarkets are excluded because WCC did not check rate firms for either price or quality. 

Thus, the total number of industries in Table VII is 15.) 

Table VII shows that firm quality performance is considerably more consistent over

timethan price performance. This is not surprising given inevitable measurement errors and

other problems in the WCC price index data set. Nevertheless, the price probits reveal a fair

amount of consistency in firm ratings. For four industries, the marginal probabilities for all

rating periods are significant, and usually highly so. There are mixed results in five industries,

with significant marginal probabilities in six periods, six periods with insignificant results, and

one period with insufficient observations to permit probit analysis. In one industry, Auto Body

Repair, all periods were insignificant. For the remaining four industries, there were either an

insufficient number of observations or firms were not check rated for price.

The consistency of firm quality performance is much more impressive. Eleven industries

report all marginal probabilities significant in all periods where sample sizes permit probit

analysis. One industry, Watch Repair, displayed mixed results. Three industries with small

sample sizes report no periods with significant results.
- 27 -

TABLE VII

Probability That Firms Check Rated For Price or Quality Will Be Check Rated in

Subsequent Evaluation Period

______________________________________________________________________________

Industry* Period Marginal P Value Marginal P Value 

 Probability, Probability, 

 Price Check Quality Check

______________________________________________________________________________

Auto Body Repair 82-85 ----- >.10 .645 .000

85-95 ----- >.10 .353 .005

95-98 ----- >.10 .536 .000

AC & Heating Contractors 77-82 ----- >.10 Insufficient Observations

82-87 ----- >.10 .477 .000

87-92 .334 .010 .423 .000

92-97 .450 .000 .350 .020

Appliance Repair 78-85 .485 .025 .484 .000

85-91 .440 .047 .423 .009

91-96 .590 .001 .409 .025

Auto Mechanical Repair 76-81 ---- NA** .550 .000 

81-85 .325 .000 .543 .000 

85-88 .330 .000 .492 .000

88-91 .341 .000 .650 .000

91-94 .449 .000 .657 .000

94-97 .367 .000 .555 .000

 

Carpet Cleaners 87-94 ---- >.10 Insufficient Observations

94-98 .620 .013 .340 .024

Carpet Installers 89-96 Insufficient Observations ---- >.10

Computer Repair 89-94 Insufficient Observations Insufficient Observations

94-98 .576 .045 .420 .080

Drycleaners 90-96 No Price Check Variable .430 .000
- 28 -

 TABLE VII (Cont.)

Probability That Firms Check Rated For Price or Quality Will Be Check Rated in

Subsequent Evaluation Period

______________________________________________________________________________

Industry Period Marginal P Value Marginal P Value 

 Probability, Probability, 

 Price Check Quality Check

______________________________________________________________________________

Electricians 90-96 Insufficient Observations .500 .041

Local Movers 81-92 Insufficient Observations ---- >.10 

92-98 Insufficient Observations Insufficient Observations

Locksmiths 86-94 ---- >.10 Insufficient Observations

94-99 .420 .070 ---- >.10

Pest Control 77-86 ---- >.10 Insufficient Observations

86-93 ---- .10 Insufficient Observations

93-97 .544 .009 .681 .000

Plumbers 77-83 .260 .009 .561 .000

83-89 .250 .050 .268 .020

89-95 .174 .100 .467 .000

Shoe Repair 88-95 .451 .001 .283 .041

Watch Repair 89-91 Insufficient Observations ---- >.10

91-96 No Price Check Variable .470 .002

______________________________________________________________________________

*Only one ratings period available for TV Repair and Watch Repair. No Price Check or Quality 

 Check variables available for Supermarkets. 

**Firms not check rated separately for price in 1976 ratings.

______________________________________________________________________________
- 29 -

VII. CONCLUSIONS

The results of this Working Paper paint a very mixed picture of the economic

performance of the consumer service sector in the Washington D.C. area. Judging strictly from

the absolute levels of satisfaction reported by respondents to the Washington Consumer

Checkbook questionnaires, consumers appear reasonably happy with the quality of the services

they are receiving. Although the data do not allow any rigorous conclusions in this regard, the

observed average satisfaction levels indicate that the service sector has avoided anything

resembling a “lemons” equilibrium, where only poor quality is provided. 

Market efficiency appears much lower, however, when analyzed more formally using the

degree of correlation between price and quality as a benchmark. Only a few industries display

positive price-quality correlations that are statistically significant and consistent over time. Of

even more concern, the plurality of industries report consistently significant negative pricequality correlations. This outcome is contrary to any accepted economic theory of markets, and

also fails on common sense grounds. There is no reason to believe that the market would

perpetually reward the worst performing firms in an industry. 

Thus, it is difficult to reject that conclusion that there are data problems afoot.

Specifically, respondents to the WCC survey may to some extent be allowing their satisfaction

with the price that is charged for a service color their appraisal of the quality of that service. In

effect, consumers may be providing “value” rankings rather than absolute quality rankings. This

could explain why the WCC quality ratings frequently appear to fall as price increases.

Any such interaction between price and quality ratings cannot, however, explain other

results in our study that are inconsistent with certain theories posited in the literature on the

economics of information. Irrespective of price, firms that advertise intensively by running large

Yellow Pages displays do not perform better in the WCC ratings, and frequently do more poorly

than firms that just run simple one-line listings. 

 In addition, consumers cannot rely on the relative size of a firm as a clue to probable

performance. On average, larger firms score lower than smaller firms on quality, and do not

differ significantly on price. Nor does affiliation with a national chain or status as a franchised

dealership for a manufacturer signal higher quality. Indeed, in the automobile industry, such

firms score much lower in consumer satisfaction than do independent service establishments. 

Finally, in the one industry were data are available, the length of warranty protection offered for a

service does not serve as a signal of higher quality.

Our analysis of time series data for firm price and quality performance shows a fairly high

level of consistency in the ratings, particularly for quality. Firms that WCC singles out for

quality check rating in one rating period display a much higher probability of receiving a check

rating in the next period than do firms that initially are not check rated. Thus, in the long run, it
- 30 -

does not appear that most firms “milk” any reputation advantage from the WCC ratings by

lowering quality. 

Overall conclusions are difficult, particularly because of the potentially serious bias

introduced by an interaction between price and the WCC quality rankings. It does appear,

however, that consumers in the Washington D.C area have a higher probability of reporting a

favorable quality assessment to Washington Consumer Checkbook, if they patronize smaller

independent service providers and rely on word-of-mouth reputation rather than Yellow Pages

displays, or other indirect clues such as firm size, affiliation with a national chain, or status as a

franchised dealership.
- 31 -

REFERENCES

1. AKERLOF, GEORGE A., (1970), “The Market for ‘Lemons’: Quality Uncertainty and the

Market Mechanism,” Quarterly Journal of Economics 84, 488-500.

2. ARCHIBALD, ROBERT B., CLYDE A HAULMAN, and CARLISLE E MOODY, JR.,

(1983), "Quality, Price, Advertising, and Published Quality Ratings," Journal of Consumer

Research 9, 347-356.

3. BOND, RONALD S., ET AL., (1980), “Effects of Restrictions On Advertising and

Commercial Practices In the Professions: The Case of Optometry,” Staff Report to the Federal

Trade Commission, Washington, D.C.

4. BUTTERS, GERARD R., (1977), "Equilibrium Distribution of Sales and Advertising Prices,"

Review of Economic Studies 44, 465-491.

5. CAVES, RICHARD E., and DAVID P. GREENE, (1996), “Brands’ Quality Levels, Prices,

and Advertising Outlays: Empirical Evidence on Signals and Information Costs,” International

Journal of Industrial Organization 14, 29-52.

6. GERSTNER, EITAN, (1985), “Do Higher Prices Signal Higher Quality?,” Journal of

Marketing Research 22, 209-215.

7. GEISTFELD, LOREN V., (1988), "The Price Quality Relationship: The Evidence We Have,

The Evidence We Need," The Frontier of Research in The Consumer Interest, ACCI: 143-172. 

8. IPPOLITO, PAULINE M., (1990), “Bonding and Nonbonding Signals of Product Quality,” 

Journal of Business 63, 41-60.

9. KLEIN, BENJAMIN, and KEITH B. LEFFLER, (1981), "The Role of Market Forces in Assuring

Contractual Performance," Journal of Political Economy 89, 615-641.

10. MORRIS, RUBY TURNER, and CLAIRE SEKULSKI BRONSON, (1969), "The Chaos of

Competition Indicated by Consumer Reports," Journal of Marketing 33, 26-34.

11. NELSON, PHILLIP, (1974), "Advertising as Information," Journal of Political Economy 81,

729-754.

12. OXENFELDT, ALFRED R., (1950), "Consumer Knowledge: Its Measurement and Extent,"

Review of Economics and Statistics, 32, 300-314.
- 32 -

13. ROTHCHILD, MICHAEL, (1973), “ModelsofMarketOrganizationwith ImperfectInformation:

A Survey,” Journal of Political Economy 81, 1283-1308.

14. ROTHCHILD, MICHAEL, and JOSEPH STIGLITZ, (1976), “Equilibrium in Competitive

Insurance Markets: An Essay on the Economics of Imperfect Information,” Quarterly Journal of

Economics 90, 629-650.

15. SPENCE, MICHAEL, (1974), "Competitive and Optimal Responses to Signals: An Analysis of

Efficiency and Distribution," Journal of Economic Theory 7, 296-332.

16 . STIGLER, GEORGE J., (1961), "The Economics of Information," Journal of Political Economy

69, 213-225. 

 
- 33 -

APPENDIX A 

DETAILED REGRESSION RESULTS

This appendix presents the principal regression results for the most recent ratings period

for all industries in the data set. As explained in the main text (see pp. 13-14), the regressions

that use the WCC quality ratings as a dependent variable employ a form of logit estimation

suitable for use with grouped data. In essence, this procedure unfolds the summary quality rating

for a given firm into a series of binary categorical variables, with a zero-one value generated for

each respondent rating used in calculating the overall quality score. 

Consider, for example, a firm that receives an overall performance rating of 80. For most

industries this would mean that 80 percent of the consumers reporting on that firm rated it

superior in overall performance. If there were a total of 10 respondents, the logit program would

generate a series of dependent variables comprised of eight “one” values and two “zero” values. 

Each of these dummies would be associated with the corresponding firm-specific independent

variables used in the logit regression. Thus, firms with the highest number of individual ratings

automatically would be weighted heaviest in the regression estimation procedure. The estimation

technique also adjusts standard errors to reflect the expected correlation of error terms among the

observations for any given service provider.

The discussion first focuses on regressions that include each of the principal independent

variables separately as predictors of the WCC quality score. These bivariate regressions test

whether consumers can rely on the attribute in question in isolation as a signal of quality. For the

Yellow Pages and firm size variables, regression results are also shown testing the hypothesis

that consumers can rely upon the size of a Yellow Pages ad or the size of a firm to signal value,

as measured by the WCC quality score divided by the WCC price index. 

 For those industries with a full complement of independent variables, results are then

reported for multivariate regressions that reveal more precisely any independent explanatory

power that the various variables might have in predicting firm quality. These results are not,

however, directly relevant to the primary signaling hypothesis in question, since such theories do

not posit that consumers will consciously or unconsciously control for other factors when

viewing a single firm attribute as a possible signal of quality. 

Air Conditioning and Heating Contractors

(1997)

This data set includes both the Yellow Pages and employment size variables. Regression

1 documents that price and quality are negatively correlated in this industry. In Regression 2,

quality and firm size are shown to be negatively correlated at a very high level of significance. 

Regression 3 shows a positive but insignificant relationship between quality and size of Yellow

Pages advertising. Regression 4 indicates that consumers cannot expect a firm with a large
- 34 -

Yellow Pages ad to offer better value in terms of quality per dollar. There is actually a

significant negative correlation between these two variables. Regression 5 reveals a negative but

insignificant correlation between value (the quality rating adjusted for price) and the size of the

firm.

Regression 6 discloses a significant positive relationship between Yellow Pages

advertising and quality when firm size and price are also included as independent variables. As

is shown in Regression 7, the Yellow Pages variable is positively correlated with price and firm

size. Because these two variables are in turn negatively correlated with quality, Yellow Pages

functions as a partial proxy for price and size in Regression 3 and loses much of its independent

positive correlation with quality. 

Regression 1: Dependent Variable = %rating firm superior

 Independent Variable = WCC price index

Logit estimates Number of firms = 119

 Wald chi2(1) = 4.49

 Prob > chi2 = 0.0340

Log likelihood = -4318.1521 Pseudo R2 = 0.0047

 (standard errors adjusted for clustering on idno97)

------------------------------------------------------------------------------

 | Robust

 supq | Coef. Std. Err. z P>|z| [95% Conf. Interval]

-------------+----------------------------------------------------------------

 price97 | -.0098648 .0046538 -2.12 0.034 -.0189861 -.0007435

 _cons | 2.038521 .4890684 4.17 0.000 1.079964 2.997077

------------------------------------------------------------------------------

Regression 2: Dependent Variable = %rating firm superior

 Independent Variable = number of employees

Logit estimates Number of firms = 134

 Wald chi2(1) = 17.73

 Prob > chi2 = 0.0000

Log likelihood = -4936.9785 Pseudo R2 = 0.0085

 (standard errors adjusted for clustering on idno97)

------------------------------------------------------------------------------

 | Robust

 supq | Coef. Std. Err. z P>|z| [95% Conf. Interval]

-------------+----------------------------------------------------------------

 employ97 | -.0082284 .0019541 -4.21 0.000 -.0120584 -.0043984

 _cons | 1.168227 .0950933 12.29 0.000 .9818473 1.354606

------------------------------------------------------------------------------
- 35 -

Regression 3: Dependent Variable = %rating firm superior

 Independent Variable = size of Yellow Pages ad 

Logit estimates Number of firms = 115

 Wald chi2(1) = 0.17

 Prob > chi2 = 0.6767

Log likelihood = -4624.8634 Pseudo R2 = 0.0002

 (standard errors adjusted for clustering on idno97)

------------------------------------------------------------------------------

 | Robust

 supq | Coef. Std. Err. z P>|z| [95% Conf. Interval]

-------------+----------------------------------------------------------------

 yp97 | .0013026 .003124 0.42 0.677 -.0048203 .0074255

 _cons | .9231844 .1079349 8.55 0.000 .711636 1.134733

------------------------------------------------------------------------------

Regression 4: Dependent Variable = size of Yellow Pages ad

 Independent Variable = value (quality/price)

 Source | SS df MS Number of firms = 102

---------+------------------------------ F( 1, 100) = 13.86

 Model | 5610.21758 1 5610.21758 Prob > F = 0.0003

Residual | 40478.981 100 404.78981 R-squared = 0.1217

---------+------------------------------ Adj R-squared = 0.1129

 Total | 46089.1985 101 456.328698 Root MSE = 20.119

------------------------------------------------------------------------------

 yp97 | Coef. Std. Err. t P>|t| [95% Conf. Interval]

---------+--------------------------------------------------------------------

 value | -45.44601 12.20733 -3.723 0.000 -69.66501 -21.22702

 _cons | 57.25293 11.50368 4.977 0.000 34.42995 80.07591

------------------------------------------------------------------------------

Regression 5: Dependent Variable = number of employees

 Independent Variable = value (quality/price)

 Source | SS df MS Number of firms = 117

---------+------------------------------ F( 1, 115) = 1.66

 Model | 401.425903 1 401.425903 Prob > F = 0.2001

Residual | 27798.822 115 241.728887 R-squared = 0.0142

---------+------------------------------ Adj R-squared = 0.0057

 Total | 28200.2479 116 243.105585 Root MSE = 15.548

------------------------------------------------------------------------------

employ97 | Coef. Std. Err. t P>|t| [95% Conf. Interval]

---------+--------------------------------------------------------------------

 value | -10.79503 8.376937 -1.289 0.200 -27.38812 5.798074

 _cons | 26.18363 8.028892 3.261 0.001 10.27994 42.08732

------------------------------------------------------------------------------
- 36 -

Regression 6: Dependent Variable = %rating firm superior

 Independent Variables = number of employees, size of Yellow

 Pages ad, WCC price index

Logit estimates Number of firms = 101 

 Wald chi2(3) = 14.36

 Prob > chi2 = 0.0025

Log likelihood = -3994.811 Pseudo R2 = 0.0143

 (standard errors adjusted for clustering on idno97)

------------------------------------------------------------------------------

 | Robust

 supq | Coef. Std. Err. z P>|z| [95% Conf. Interval]

-------------+----------------------------------------------------------------

 employ97 | -.0141104 .0048582 -2.90 0.004 -.0236323 -.0045885

 yp97 | .0089862 .0033431 2.69 0.007 .0024337 .0155386

 price97 | -.0148654 .004524 -3.29 0.001 -.0237323 -.0059986

 _cons | 2.647565 .4989876 5.31 0.000 1.669567 3.625562

------------------------------------------------------------------------------

Regression 7: Dependent Variable = size of Yellow Pages ad

 Independent Variables = number of employees, WCC price index,

 %rating firm superior

 Source | SS df MS Number of firms = 101

---------+------------------------------ F( 3, 97) = 12.87

 Model | 13071.5361 3 4357.1787 Prob > F = 0.0000

Residual | 32845.053 97 338.608794 R-squared = 0.2847

---------+------------------------------ Adj R-squared = 0.2626

 Total | 45916.5891 100 459.165891 Root MSE = 18.401

------------------------------------------------------------------------------

 yp97 | Coef. Std. Err. t P>|t| [95% Conf. Interval]

---------+--------------------------------------------------------------------

employ97 | .4527038 .1165674 3.884 0.000 .2213497 .6840579

 price97 | .5658379 .1158479 4.884 0.000 .3359119 .7957638

 super97 | .1546826 .1265388 1.222 0.225 -.0964618 .405827

 _cons | -61.42468 17.26251 -3.558 0.001 -95.68598 -27.16337

------------------------------------------------------------------------------

Auto Body Repair

 (1998)

 The simple two-way regressions for the 1998 Auto Body Repair data set reveal no

significant association between price and quality (Regression 1), and highly significant negative

correlations between quality and firm size (Regression 2), dealer status (Regression 6), and chain

status (Regression 7). (There is only one national chain represented in this data set). Regression 3

reveals no significant relationship between quality and the size of Yellow Pages advertising, and

Regression 4 shows that the Yellow Pages variable is not significantly correlated with the value

variable (WCC quality score/WCC price index). In Regression 5, there is a highly significant

negative correlation between value and size of firm.
- 37 -

With all of the independent variables included in Regression 8, dealer status and chain

status continue to display highly significant negative coefficients. Firm size and Yellow Pages

advertising lose significance, however, and price remains insignificant. Further investigation

revealed that the dealer dummy variable and firm size are positively correlated (r=.42). Thus,

when firm size is used as the only regressor, it functions partially as a proxy for dealer status in

predicting firm quality. Taken together, the results for the full regression and simple regressions

indicate that dealer status is a more powerful predictor of firm quality than is firm size. 

In Regression 9, Yellow Pages advertising is removed in order to increase the sample size. 

(A number of smaller firms rated by WCC could not be located in the Yellow Pages listings.) In

the expanded set, firm size achieves a high level of significance, although the z score for dealer

status is once again higher than for firm size, and the positive coefficient on the price variable

attains significance. 

Regression 1: Dependent Variable = %rating firm superior

 Independent Variable = WCC price index

Logit estimates Number of firms = 120

 Wald chi2(1) = 0.36

 Prob > chi2 = 0.5480

Log likelihood = -1935.1868 Pseudo R2 = 0.0004

 (standard errors adjusted for clustering on idno98)

------------------------------------------------------------------------------

 | Robust

 supq | Coef. Std. Err. z P>|z| [95% Conf. Interval]

-------------+----------------------------------------------------------------

 price | -.0065402 .0108856 -0.60 0.548 -.0278756 .0147953

 _cons | 1.584347 1.106101 1.43 0.152 -.5835702 3.752264

Regression 2: Dependent Variable = %rating firm superior

 Independent Variable = number of employees

Logit estimates Number of firms = 138

 Wald chi2(1) = 15.09

 Prob > chi2 = 0.0001

Log likelihood = -2218.0717 Pseudo R2 = 0.0155

 (standard errors adjusted for clustering on idno98)

------------------------------------------------------------------------------

 | Robust

 supq | Coef. Std. Err. z P>|z| [95% Conf. Interval]

-------------+----------------------------------------------------------------

 employ98 | -.0558852 .0143885 -3.88 0.000 -.0840862 -.0276842

 _cons | 1.439325 .1341409 10.73 0.000 1.176414 1.702237

------------------------------------------------------------------------------
- 38 -

Regression 3: Dependent Variable = %rating firm superior

 Independent Variable = size of Yellow Pages ad

Logit estimates Number of firms = 95

 Wald chi2(1) = 0.79

 Prob > chi2 = 0.3730

Log likelihood = -1882.6538 Pseudo R2 = 0.0009

 (standard errors adjusted for clustering on idno98)

------------------------------------------------------------------------------

 | Robust

 supq | Coef. Std. Err. z P>|z| [95% Conf. Interval]

-------------+----------------------------------------------------------------

 yp | -.0083301 .0093499 -0.89 0.373 -.0266556 .0099954

 _cons | 1.04312 .1049185 9.94 0.000 .8374834 1.248756

------------------------------------------------------------------------------

Regression 4: Dependent Variable = size of Yellow Pages ad

 Independent Variable = value (quality/price)

 Source | SS df MS Number of firms = 95

---------+------------------------------ F( 1, 93) = 1.29

 Model | 130.662894 1 130.662894 Prob > F = 0.2586

Residual | 9404.25158 93 101.120985 R-squared = 0.0137

---------+------------------------------ Adj R-squared = 0.0031

 Total | 9534.91447 94 101.43526 Root MSE = 10.056

------------------------------------------------------------------------------

 yp | Coef. Std. Err. t P>|t| [95% Conf. Interval]

---------+--------------------------------------------------------------------

 value | -6.909842 6.078726 -1.137 0.259 -18.98099 5.161304

 _cons | 12.17246 4.41782 2.755 0.007 3.399545 20.94537

------------------------------------------------------------------------------

Regression 5: Dependent Variable = number of employees

 Independent Variable = value (quality/price)

 Source | SS df MS Number of firms = 120

---------+------------------------------ F( 1, 118) = 25.21

 Model | 558.897059 1 558.897059 Prob > F = 0.0000

Residual | 2615.85086 118 22.1682276 R-squared = 0.1760

---------+------------------------------ Adj R-squared = 0.1691

 Total | 3174.74792 119 26.6785539 Root MSE = 4.7083

------------------------------------------------------------------------------

employ98 | Coef. Std. Err. t P>|t| [95% Conf. Interval]

---------+--------------------------------------------------------------------

 value | -12.46466 2.482447 -5.021 0.000 -17.38058 -7.548738

 _cons | 16.65288 1.776893 9.372 0.000 13.13414 20.17161

------------------------------------------------------------------------------
- 39 -

Regression 6: Dependent Variable = %rating firm superior

 Independent Variable = dealer status (dealer = 1)

Logit estimates Number of firms = 138

 Wald chi2(1) = 52.86

 Prob > chi2 = 0.0000

Log likelihood = -2187.0624 Pseudo R2 = 0.0293

 (standard errors adjusted for clustering on idno98)

------------------------------------------------------------------------------

 | Robust

 supq | Coef. Std. Err. z P>|z| [95% Conf. Interval]

-------------+----------------------------------------------------------------

 dealer | -.9553177 .131402 -7.27 0.000 -1.212861 -.6977746

 _cons | 1.164308 .0762207 15.28 0.000 1.014918 1.313697

------------------------------------------------------------------------------

Regression 7: Dependent Variable = %rating firm superior

 Independent Variable = chain status (chain = 1)

Logit estimates Number of firms = 138

 Wald chi2(1) = 53.28

 Prob > chi2 = 0.0000

Log likelihood = -2242.4496 Pseudo R2 = 0.0047

 (standard errors adjusted for clustering on idno98)

------------------------------------------------------------------------------

 | Robust

 supq | Coef. Std. Err. z P>|z| [95% Conf. Interval]

-------------+----------------------------------------------------------------

 chain| -1.216412 .1666463 -7.30 0.000 -1.543033 -.8897911

 _cons | .9481478 .0802853 11.81 0.000 .7907916 1.105504

------------------------------------------------------------------------------

Regression 8 Dependent Variable = %rating firm superior

 Independent Variables = dealer status, wcc price index, size of 

 Yellow Pages ad, number of employees, 

 chain status

 

Logit estimates Number of firms = 95

 LR chi2(5) = 131.31

 Prob > chi2 = 0.0000

Log likelihood = -1546.322 Pseudo R2 = 0.0407

------------------------------------------------------------------------------

 supq | Coef. Std. Err. z P>|z| [95% Conf. Interval]

-------------+----------------------------------------------------------------

 dealer | -1.085687 .1257825 -8.63 0.000 -1.332216 -.8391573

 price | .0057761 .0067361 0.86 0.391 -.0074264 .0189786

 yp | .0026465 .006309 0.42 0.675 -.0097188 .0150119

 employ98 | -.0178601 .0101569 -1.76 0.079 -.0377674 .0020471

 chain | -1.538303 .3354031 -4.59 0.000 -2.195681 -.8809248

 _cons | .7754602 .6555708 1.18 0.237 -.5094349 2.060355
- 40 -

Regression 9: Dependent Variable = %rating firm superior

 Independent Variables = dealer status, WCC price index, 

 number of employees, chain status

Logit estimates Number of firms = 120

 LR chi2(4) = 176.25

 Prob > chi2 = 0.0000

Log likelihood = -1847.7438 Pseudo R2 = 0.0455

------------------------------------------------------------------------------

 supq | Coef. Std. Err. z P>|z| [95% Conf. Interval]

-------------+----------------------------------------------------------------

 dealer | -.956223 .1047231 -9.13 0.000 -1.161477 -.7509695

 price | .010555 .0059654 1.77 0.077 -.0011369 .0222469

 employ98 | -.0289016 .0080113 -3.61 0.000 -.0446034 -.0131997

 chain | -1.4128 .2672092 -5.29 0.000 -1.93652 -.8890793

 _cons | .4006843 .5855493 0.68 0.494 -.7469713 1.54834

------------------------------------------------------------------------------

Auto Mechanical Repair

(1997)

The Automobile Mechanical Repair data set is the largest in the sample, both in terms of

the number of firms and the number of possible predictors of firm quality. The results for the full

set of independent variables for 1997 have already been discussed in the main text on page 24. The

results shown below are for the simple two-way regressions and a regression using size of Yellow

Pages advertising as the dependent variable. 

Regression 1 reports that price functions as a perverse indicator of quality in this industry.

The price coefficient is negative and highly significant. Regression 2 reveals that firm size (as

measured by number of employees) is also inversely related to quality. Indeed, all of the remaining

signaling variables--size of Yellow Pages ad, dealer status, and chain status–display negative

coefficients ( Regressions 3, 6, and 7). Regressions (not shown) using dummy variables for

various certifications revealed significant negative coefficients for employment of ASIE-certified

technicians, and designation as a state safety inspection site, and a positive but insignificant

coefficient for certification by the American Automobile Association. Regression 4 reveals a

significant negative correlation between size of Yellow Pages ad and value, which is measured by

the ratio of the WCC quality score to the WCC price index. In Regression 5, there is a much

stronger negative correlation between the value variable and size of firm. 
- 41 -

Regression 1: Dependent Variable = %rating firm superior or adequate

 Independent Variable = WCC price index

 

Logit estimates Number of firms = 444

 Wald chi2(1) = 30.05

 Prob > chi2 = 0.0000

Log likelihood = -6770.2166 Pseudo R2 = 0.0131

 (standard errors adjusted for clustering on comno)

------------------------------------------------------------------------------

 | Robust

 supq | Coef. Std. Err. z P>|z| [95% Conf. Interval]

-------------+----------------------------------------------------------------

 pricin97 | -.0159178 .0029039 -5.48 0.000 -.0216093 -.0102262

 _cons | 3.679106 .3079504 11.95 0.000 3.075534 4.282678

------------------------------------------------------------------------------

Regression 2: Dependent Variable = %rating firm superior or adequate

 Independent Variable = number of employees

Logit estimates Number of firms = 515

 Wald chi2(1) = 55.66

 Prob > chi2 = 0.0000

Log likelihood = -7610.5535 Pseudo R2 = 0.0311

 (standard errors adjusted for clustering on comno)

------------------------------------------------------------------------------

 | Robust

 supq | Coef. Std. Err. z P>|z| [95% Conf. Interval]

-------------+----------------------------------------------------------------

 employ97 | -.0429339 .0057547 -7.46 0.000 -.0542129 -.0316549

 _cons | 2.583182 .0872403 29.61 0.000 2.412194 2.75417

------------------------------------------------------------------------------

Regression 3: Dependent Variable = %rating firm superior or adequate

 Independent Variable = size of Yellow Pages advertisement

Logit estimates Number of firms = 321

 Wald chi2(1) = 1.11

 Prob > chi2 = 0.2913

Log likelihood = -4669.489 Pseudo R2 = 0.0004

 (standard errors adjusted for clustering on comno)

------------------------------------------------------------------------------

 | Robust

 supq | Coef. Std. Err. z P>|z| [95% Conf. Interval]

-------------+----------------------------------------------------------------

 yp97 | -.0030682 .0029076 -1.06 0.291 -.0087671 .0026306

 _cons | 2.154566 .0848792 25.38 0.000 1.988206 2.320926

------------------------------------------------------------------------------
- 42 -

Regression 4: Dependent Variable = size of Yellow Pages ad

 Independent Variable = value (quality/price) 

 Source | SS df MS Number of firms = 278

---------+------------------------------ F( 1, 276) = 3.23

 Model | 1338.41317 1 1338.41317 Prob > F = 0.0733

Residual | 114319.277 276 414.200277 R-squared = 0.0116

---------+------------------------------ Adj R-squared = 0.0080

 Total | 115657.69 277 417.536786 Root MSE = 20.352

------------------------------------------------------------------------------

 yp97 | Coef. Std. Err. t P>|t| [95% Conf. Interval]

---------+--------------------------------------------------------------------

 value | -10.3485 5.756882 -1.798 0.073 -21.68147 .9844806

 _cons | 19.89839 5.525921 3.601 0.000 9.02008 30.77669

------------------------------------------------------------------------------

Regression 5: Dependent Variable = number of employees

 Independent Variable = value (quality/price)

 Source | SS df MS Number of firms = 444

---------+------------------------------ F( 1, 442) = 143.80

 Model | 6026.86996 1 6026.86996 Prob > F = 0.0000

Residual | 18524.6993 442 41.9110844 R-squared = 0.2455

---------+------------------------------ Adj R-squared = 0.2438

 Total | 24551.5693 443 55.4211496 Root MSE = 6.4739

------------------------------------------------------------------------------

employ97 | Coef. Std. Err. t P>|t| [95% Conf. Interval]

---------+--------------------------------------------------------------------

 value | -16.59809 1.384129 -11.992 0.000 -19.31838 -13.8778

 _cons | 24.0596 1.299164 18.519 0.000 21.50629 26.6129

------------------------------------------------------------------------------

Regression 6: Dependent Variable = %rating firm superior or adequate

 Independent Variable = dealer status

Logit estimates Number of firms = 515

 Wald chi2(1) = 137.69

 Prob > chi2 = 0.0000

Log likelihood = -7531.3521 Pseudo R2 = 0.0412

 (standard errors adjusted for clustering on comno)

------------------------------------------------------------------------------

 | Robust

 supq | Coef. Std. Err. z P>|z| [95% Conf. Interval]

-------------+----------------------------------------------------------------

 dealer97 | -1.098344 .0936021 -11.73 0.000 -1.281801 -.9148873

 _cons | 2.591357 .0745284 34.77 0.000 2.445284 2.73743

------------------------------------------------------------------------------
- 43 -

Regression 7: Dependent Variable = %rating firm superior or adequate

 Independent Variable = chain status

Logit estimates Number of firms = 515

 Wald chi2(1) = 14.35

 Prob > chi2 = 0.0002

Log likelihood = -7831.9797 Pseudo R2 = 0.0029

 (standard errors adjusted for clustering on comno)

------------------------------------------------------------------------------

 | Robust

 supq | Coef. Std. Err. z P>|z| [95% Conf. Interval]

-------------+----------------------------------------------------------------

 chain97 | -.6145223 .1622175 -3.79 0.000 -.9324628 -.2965818

 _cons | 2.021323 .0574306 35.20 0.000 1.908761 2.133885

------------------------------------------------------------------------------

Carpet Cleaning

(1998)

The only variables in this data set are price and size of Yellow Pages advertisement. Price

proves to be positively correlated with quality (Regression 1). Yellow Pages advertising displays

an insignificant negative correlation with quality in Regression 2, and with value (quality adjusted

for price) in Regression 3. 

Regression 1: Dependent Variable = %rating firm superior or adequate

 Independent Variable = WCC price index

Logit estimates Number of firms = 33

 Wald chi2(1) = 6.79

 Prob > chi2 = 0.0092

Log likelihood = -893.30575 Pseudo R2 = 0.0470

 (standard errors adjusted for clustering on comidno)

------------------------------------------------------------------------------

 | Robust

 supq | Coef. Std. Err. z P>|z| [95% Conf. Interval]

-------------+----------------------------------------------------------------

 price98 | .030564 .0117299 2.61 0.009 .0075738 .0535543

 _cons | -1.467002 1.046967 -1.40 0.161 -3.519019 .5850145

------------------------------------------------------------------------------
- 44 -

Regression 2: Dependent Variable = %rating firm superior or adequate

 Independent Variable = size of Yellow Pages advertisement

Logit estimates Number of firms = 38

 Wald chi2(1) = 0.91

 Prob > chi2 = 0.3395

Log likelihood = -1129.8918 Pseudo R2 = 0.0024

 (standard errors adjusted for clustering on comidno)

------------------------------------------------------------------------------

 | Robust

 supq | Coef. Std. Err. z P>|z| [95% Conf. Interval]

-------------+----------------------------------------------------------------

 yp98 | -.0046769 .0048966 -0.96 0.340 -.0142742 .0049203

 _cons | .7946941 .3790674 2.10 0.036 .0517356 1.537653

------------------------------------------------------------------------------

Regression 3: Dependent Variable = size of Yellow Pages Ad

 Independent Variable = value (quality/price)

 Source | SS df MS Number of firms = 27

---------+------------------------------ F( 1, 25) = 2.55

 Model | 1285.4712 1 1285.4712 Prob > F = 0.1230

Residual | 12611.6723 25 504.466893 R-squared = 0.0925

---------+------------------------------ Adj R-squared = 0.0562

 Total | 13897.1435 26 534.50552 Root MSE = 22.46

------------------------------------------------------------------------------

 yp98 | Coef. Std. Err. t P>|t| [95% Conf. Interval]

---------+--------------------------------------------------------------------

 value | -19.90948 12.47226 -1.596 0.123 -45.59657 5.777614

 _cons | 34.77875 13.80935 2.518 0.019 6.337861 63.21964

------------------------------------------------------------------------------

Carpet Sales and Installation

(1996)

WCC respondents were asked to rate carpet firms both for the quality of the sales advice

given in the showroom and for the quality of any installation services that were provided. This

data set does not have any information on firm size. Regressions 1 and 2, respectively, show a

significant positive correlation between price and the quality of sales service, and a positive but

insignificant correlation between the size of a firm’s Yellow Pages ad and respondent satisfaction

with the sales services. 

With respect to the quality of installation services, Regression 3 reveals no significant

relationship between price and quality. Note, however, that the sample size for installation

services is considerably smaller than for sales services (24 vs. 36). Regression 4 shows a positive

but insignificant association between the size of Yellow Pages ads and satisfaction with installation

(P=.124). Further regression analysis (not shown) detected no significant relationship between
- 45 -

Yellow Pages ad size and price, or for quality adjusted for price, using either the sales service or

installation service satisfaction measure. 

Regression 1: Dependent Variable = %rating firm adequate or superior 

 for sales services

 Independent Variable = WCC price index 

Logit estimates Number of firms = 36 

 Wald chi2(1) = 2.88

 Prob > chi2 = 0.0896

Log likelihood = -279.14817 Pseudo R2 = 0.0191

 (standard errors adjusted for clustering on idno96)

------------------------------------------------------------------------------

 | Robust

 supq | Coef. Std. Err. z P>|z| [95% Conf. Interval]

-------------+----------------------------------------------------------------

 price96 | .0316688 .0186556 1.70 0.090 -.0048956 .0682331

 _cons | -1.001684 1.894259 -0.53 0.597 -4.714365 2.710996

------------------------------------------------------------------------------

Regression 2: Dependent Variable = % adequate or superior, sales services 

 Independent Variable = size of Yellow Pages ad

Logit estimates Number of firms = 42

 Wald chi2(1) = 0.46

 Prob > chi2 = 0.4966

Log likelihood = -314.43372 Pseudo R2 = 0.0021

 (standard errors adjusted for clustering on idno96)

------------------------------------------------------------------------------

 | Robust

 supq | Coef. Std. Err. z P>|z| [95% Conf. Interval]

-------------+----------------------------------------------------------------

 yp96 | .0120109 .0176681 0.68 0.497 -.0226181 .0466398

 _cons | 2.197738 .2713814 8.10 0.000 1.665841 2.729636

------------------------------------------------------------------------------

Regression 3: Dependent Variable = %adequate or superior, installation

 Independent Variable = WCC price index

Logit estimates Number of firms = 24

 Wald chi2(1) = 0.04

 Prob > chi2 = 0.8391

Log likelihood = -249.35669 Pseudo R2 = 0.0001

 (standard errors adjusted for clustering on idno96)

------------------------------------------------------------------------------

 | Robust

 supq | Coef. Std. Err. z P>|z| [95% Conf. Interval]

-------------+----------------------------------------------------------------

 price96 | .0023001 .0113312 0.20 0.839 -.0199086 .0245088 

 _cons | 1.345761 1.146281 1.17 0.240 -.9009087 3.592431

------------------------------------------------------------------------------
- 46 -

Regression 4: Dependent Variable = %adequate or superior, installation

 Independent Variable = size of Yellow Pages ad

Logit estimates Number of firms = 28

 Wald chi2(1) = 2.33

 Prob > chi2 = 0.1268

Log likelihood = -278.00221 Pseudo R2 = 0.0063

 (standard errors adjusted for clustering on idno96)

------------------------------------------------------------------------------

 | Robust

 supq | Coef. Std. Err. z P>|z| [95% Conf. Interval]

-------------+----------------------------------------------------------------

 yp96 | .0190109 .0124501 1.53 0.127 -.0053908 .0434127

 _cons | 1.369419 .1885602 7.26 0.000 .9998473 1.73899

------------------------------------------------------------------------------

Computer Repair

(1998)

This data set contains only two independent variables--price and Yellow Pages ad size. In

Regression 1, price is negatively associated with quality. In Regression 2, no significant

correlation is found between quality and the size of a firm’s Yellow Pages ad. Further analysis (not

shown) revealed a direct and significant correlation between the Yellow Pages variable and price,

and no correlation between Yellow Pages ad size and value as measured by quality adjusted for

price.

Regression 1: Dependent Variable = %rating firm adequate or superior

 Independent Variable = WCC price index

Logit estimates Number of firms = 27

 Wald chi2(1) = 3.10

 Prob > chi2 = 0.0781

Log likelihood = -420.23779 Pseudo R2 = 0.0152

 (standard errors adjusted for clustering on comidno)

------------------------------------------------------------------------------

 | Robust

 supq | Coef. Std. Err. z P>|z| [95% Conf. Interval]

-------------+----------------------------------------------------------------

 price98 | -.0208608 .0118402 -1.76 0.078 -.0440672 .0023455

 _cons | 3.439096 1.14026 3.02 0.003 1.204227 5.673964

------------------------------------------------------------------------------
- 47 -

Regression 2: Dependent Variable = %rating firm adequate or superior

 Independent Variable = size of Yellow Pages ad

Logit estimates Number of firms = 18

 Wald chi2(1) = 0.79

 Prob > chi2 = 0.3749

Log likelihood = -396.03579 Pseudo R2 = 0.0109

 (standard errors adjusted for clustering on comidno)

------------------------------------------------------------------------------

 | Robust

 supq | Coef. Std. Err. z P>|z| [95% Conf. Interval]

-------------+----------------------------------------------------------------

 yp98 | .02605 .0293559 0.89 0.375 -.0314865 .0835865

 _cons | .9515454 .2741594 3.47 0.001 .4142029 1.488888

------------------------------------------------------------------------------

Drycleaners

(1996)

The drycleaners data set is one of the largest in the sample, although it does not contain

information on firm size. In Regression 1, the price coefficient is positive but just misses

significance (P=.102). No significant correlation is found between the size of a firm’s Yellow

Pages ad and quality in Regression 2. Regression 3 shows that price and Yellow Pages ad size are

positively correlated at a high level of significance. There is no relationship between the Yellow

Pages variable and value as measured by the WCC quality score divided by the WCC price score

(results not shown).

Regression 1: Dependent Variable = %rating firm superior

 Independent Variable = WCC price index

Logit estimates Number of firms = 211 

 Wald chi2(1) = 2.68

 Prob > chi2 = 0.1018

Log likelihood = -2696.4198 Pseudo R2 = 0.0015

 (standard errors adjusted for clustering on comidno)

------------------------------------------------------------------------------

 | Robust

 supq | Coef. Std. Err. z P>|z| [95% Conf. Interval]

-------------+----------------------------------------------------------------

 price96 | .0043811 .0026777 1.64 0.102 -.0008671 .0096293

 _cons | .0386459 .2725427 0.14 0.887 -.495528 .5728199

------------------------------------------------------------------------------
- 48 -

Regression 2: Dependent Variable = %rating firm superior

 Independent Variable = size of Yellow Pages ad 

Logit estimates Number of firms = 188

 Wald chi2(1) = 2.14

 Prob > chi2 = 0.1434

Log likelihood = -2423.9233 Pseudo R2 = 0.0013

 (standard errors adjusted for clustering on comidno)

------------------------------------------------------------------------------

 | Robust

 supq | Coef. Std. Err. z P>|z| [95% Conf. Interval]

-------------+----------------------------------------------------------------

 yp96 | .0521381 .0356334 1.46 0.143 -.017702 .1219782

 _cons | .4273209 .0758077 5.64 0.000 .2787405 .5759013

------------------------------------------------------------------------------

Regression 3: Dependent Variable = size of Yellow Pages ad

 Independent Variable = WCC price index

 Source | SS df MS Number of firms = 188

---------+------------------------------ F( 1, 186) = 16.60

 Model | 50.4561121 1 50.4561121 Prob > F = 0.0001

Residual | 565.497013 186 3.04030652 R-squared = 0.0819

---------+------------------------------ Adj R-squared = 0.0770

 Total | 615.953125 187 3.29386698 Root MSE = 1.7436

------------------------------------------------------------------------------

 yp96 | Coef. Std. Err. t P>|t| [95% Conf. Interval]

---------+--------------------------------------------------------------------

 price96 | .023579 .005788 4.074 0.000 .0121605 .0349975

 _cons | -1.571738 .59892 -2.624 0.009 -2.753288 -.3901887

-------------------------------------------------------------------------------

Electricians

(1996)

 The data set for electricians is fairly small, but does contain information on firm size as

measured by number of employees. Regression 1 reveals a strong negative correlation between

price and quality. Further investigation (not shown) detected no correlations between price and

any of the other independent variables. Thus, price is not acting as a proxy for, say, firm size. In

Regression 2, firm size and quality are not significantly correlated, although there is a significant

negative association between quality and size of Yellow Pages ad in Regression 3. Regression 4

shows that the size of a Yellow Pages ad cannot be used as a signal of value. The Yellow Pages

variable is negatively correlated with value (as measured by the WCC quality score divided by the

WCC price index). In Regression 5, the value variable is also negatively correlated with firm size,

but the relationship is not significant.
- 49 -

Regression 1: Dependent Variable = %rating firm superior

 Independent Variable = WCC price index

Logit estimates Number of firms = 36

 Wald chi2(1) = 10.87

 Prob > chi2 = 0.0010

Log likelihood = -414.3513 Pseudo R2 = 0.0314

 (standard errors adjusted for clustering on comidno)

------------------------------------------------------------------------------

 | Robust

 supq | Coef. Std. Err. z P>|z| [95% Conf. Interval]

-------------+----------------------------------------------------------------

 price96 | -.0164893 .0050023 -3.30 0.001 -.0262937 -.006685

 _cons | 2.993674 .5343611 5.60 0.000 1.946345 4.041002

------------------------------------------------------------------------------

Regression 2: Dependent Variable = %rating firm superior

 Independent Variable = number of employees

Logit estimates Number of firms = 35

 Wald chi2(1) = 1.65

 Prob > chi2 = 0.1996

Log likelihood = -383.62685 Pseudo R2 = 0.0061

 (standard errors adjusted for clustering on comidno)

------------------------------------------------------------------------------

 | Robust

 supq | Coef. Std. Err. z P>|z| [95% Conf. Interval]

-------------+----------------------------------------------------------------

 employ96 | -.0323265 .0252028 -1.28 0.200 -.0817232 .0170701

 _cons | 1.596658 .1849404 8.63 0.000 1.234182 1.959135

------------------------------------------------------------------------------

Regression 3: Dependent Variable = %rating firm superior

 Independent Variable = size of Yellow Pages ad

Logit estimates Number of firms = 40

 Wald chi2(1) = 9.05

 Prob > chi2 = 0.0026

Log likelihood = -449.60778 Pseudo R2 = 0.0287

 (standard errors adjusted for clustering on comidno)

------------------------------------------------------------------------------

 | Robust

 supq | Coef. Std. Err. z P>|z| [95% Conf. Interval]

-------------+----------------------------------------------------------------

 yp96 | -.0224215 .007454 -3.01 0.003 -.0370311 -.007812

 _cons | 1.622553 .1432273 11.33 0.000 1.341832 1.903273

------------------------------------------------------------------------------
- 50 -

Regression 4: Dependent Variable = size of yellow pages ad

 Independent Variable = value (%superior/WCC price index)

 Source | SS df MS Number of firms = 36

---------+------------------------------ F( 1, 34) = 5.19

 Model | 1636.75634 1 1636.75634 Prob > F = 0.0291

Residual | 10714.695 34 315.13809 R-squared = 0.1325

---------+------------------------------ Adj R-squared = 0.1070

 Total | 12351.4514 35 352.898611 Root MSE = 17.752

------------------------------------------------------------------------------

 yp96 | Coef. Std. Err. t P>|t| [95% Conf. Interval]

---------+--------------------------------------------------------------------

 value | -24.25008 10.64073 -2.279 0.029 -45.87465 -2.625506

 _cons | 33.12038 9.236143 3.586 0.001 14.35028 51.89048

------------------------------------------------------------------------------

Regression 5: Dependent Variable = number of employees

 Independent Variable = value (%superior/ WCC price index)

Source | SS df MS Number of firms = 32

---------+------------------------------ F( 1, 30) = 1.20

 Model | 31.6064178 1 31.6064178 Prob > F = 0.2822

Residual | 790.768582 30 26.3589527 R-squared = 0.0384

---------+------------------------------ Adj R-squared = 0.0064

 Total | 822.375 31 26.5282258 Root MSE = 5.1341

------------------------------------------------------------------------------

employ96 | Coef. Std. Err. t P>|t| [95% Conf. Interval]

---------+--------------------------------------------------------------------

 value | -3.87234 3.536306 -1.095 0.282 -11.09444 3.349761

 _cons | 8.745292 3.044999 2.872 0.007 2.526574 14.96401

------------------------------------------------------------------------------

Locksmiths

(1999)

The 1999 data set for Locksmiths is very small and lacks a Yellow Pages advertisement

size variable. When this WCC rating was published, staff resources were no longer available to

collect the relevant Yellow Pages data. In addition, WCC did not provide firm size data for

Locksmiths. In Regression 1, the WCC price index is seen to be negatively correlated with the

WCC quality index.
- 51 -

Regression 1: Dependent Variable = %rating firm superior 

 Independent variable = WCC price index

Logit estimates Number of firms = 25

 Prob > chi2 = 0.0031

Log likelihood = -280.46412 Pseudo R2 = 0.0175

 (standard errors adjusted for clustering on idno99)

------------------------------------------------------------------------------

 | Robust

 supq | Coef. Std. Err. z P>|z| [95% Conf. Interval]

-------------+----------------------------------------------------------------

 price99 | -.0459347 .0155294 -2.96 0.003 -.0763717 -.0154977

 _cons | 5.978987 1.594268 3.75 0.000 2.854279 9.103695

------------------------------------------------------------------------------

Major Appliance Repair

(1996)

This data set includes variables for price, firm size and size of Yellow Pages ad. As is

evident in regressions 1-3, all of these variables are strongly and negatively associated with quality. 

Further analysis (not shown) revealed no correlation between the Yellow Pages variable and value

as measured by quality adjusted for price. In Regression 4, the value variable is negatively

correlated with firm size (as measured by number of employees). It should be noted that there is a

high degree of direct intercorrelation among the independent variables in this data set. This can be

seen in Regression 5, where none of the variables displays a significant coefficient when all are

included in the same regression predicting quality. 

Regression 1: Dependent Variable = %rating firm adequate or superior

 Independent Variable = WCC price index

Logit estimates Number of firms = 46

 Wald chi2(1) = 25.84

 Prob > chi2 = 0.0000

Log likelihood = -840.60736 Pseudo R2 = 0.0411

 (standard errors adjusted for clustering on comidno)

------------------------------------------------------------------------------

 | Robust

 supq | Coef. Std. Err. z P>|z| [95% Conf. Interval]

-------------+----------------------------------------------------------------

 price96 | -.0473062 .0093066 -5.08 0.000 -.0655468 -.0290657

 _cons | 7.208831 1.048761 6.87 0.000 5.153297 9.264364

------------------------------------------------------------------------------
- 52 -

Regression 2: Dependent Variable = %rating firm adequate or superior

 Independent Variable = number of employees

Logit estimates Number of firms = 54

 Wald chi2(1) = 23.43

 Prob > chi2 = 0.0000

Log likelihood = -642.45782 Pseudo R2 = 0.0513

 (standard errors adjusted for clustering on comidno)

------------------------------------------------------------------------------

 | Robust

 supq | Coef. Std. Err. z P>|z| [95% Conf. Interval]

-------------+----------------------------------------------------------------

 employ96 | -.0778501 .016082 -4.84 0.000 -.1093703 -.0463299

 _cons | 2.7201 .2109764 12.89 0.000 2.306594 3.133606

------------------------------------------------------------------------------

Regression 3: Dependent Variable = %rating firm adequate or superior

 Independent Variable = size of Yellow Pages ad

Logit estimates Number of firms = 35

 Wald chi2(1) = 6.74

 Prob > chi2 = 0.0095

Log likelihood = -1005.0842 Pseudo R2 = 0.0203

 (standard errors adjusted for clustering on comidno)

------------------------------------------------------------------------------

 | Robust

 supq | Coef. Std. Err. z P>|z| [95% Conf. Interval]

-------------+----------------------------------------------------------------

 yp96 | -.0253057 .0097507 -2.60 0.009 -.0444167 -.0061947

 _cons | 2.66959 .3206881 8.32 0.000 2.041053 3.298127

------------------------------------------------------------------------------

Regression 4: Dependent Variable = number of employees

 Independent Variable = value (quality/price)

regress employ96 value

 Source | SS df MS Number of firms = 43

---------+------------------------------ F( 1, 41) = 8.35

 Model | 111.030719 1 111.030719 Prob > F = 0.0061

Residual | 545.376258 41 13.3018599 R-squared = 0.1691

---------+------------------------------ Adj R-squared = 0.1489

 Total | 656.406977 42 15.6287375 Root MSE = 3.6472

------------------------------------------------------------------------------

employ96 | Coef. Std. Err. t P>|t| [95% Conf. Interval]

---------+--------------------------------------------------------------------

 value | -11.75401 4.068373 -2.889 0.006 -19.97026 -3.537767

 _cons | 14.47038 3.853586 3.755 0.001 6.687909 22.25286

------------------------------------------------------------------------------
- 53 -

Regression 5: Dependent Variable = %rating firm adequate or superior

 Independent Variable = #employees, WCC price index, size of 

 Yellow Pages ad

Logit estimates Number of firms = 23 

 Wald chi2(3) = 2.32

 Prob > chi2 = 0.5093

Log likelihood = -214.21189 Pseudo R2 = 0.0043

 (standard errors adjusted for clustering on comidno)

------------------------------------------------------------------------------

 | Robust

 supq | Coef. Std. Err. z P>|z| [95% Conf. Interval]

-------------+----------------------------------------------------------------

 employ96 | -.024605 .0360032 -0.68 0.494 -.0951699 .0459599

 price96 | -.0134052 .0185372 -0.72 0.470 -.0497375 .0229271

 yp96 | .008471 .0205447 0.41 0.680 -.0317959 .048738

 _cons | 4.198566 1.760559 2.38 0.017 .7479346 7.649198

------------------------------------------------------------------------------

Movers Local)

(1998)

This data set lacks information on firm size, but does include the Yellow Pages ad size

variable. In addition, a dummy variable is used to identify firms that are agents for national

moving company chains. This permits a test of the hypothesis that consumers can rely on a firm’s

status as an agent to signal higher quality. Two price variables are used in the regressions that

follow. The first is the usual WCC price index, which in this sample was only available for 22

firms. The second is a firm’s hourly wage rate for a crew of 3 during peak moving season, which

was available for 26 firms.

Regressions 1 and 2 find no significant correlation between price and quality using either

price measure. In Regression 3, Yellow Page ad size is not associated with quality. Further

analysis (not shown) revealed that the Yellow Pages variable was not related to value as measured

by the WCC quality score divided by the WCC price index or the hourly wage rate variable

Finally, Regression 4 shows that agents for national chains did not receive higher ratings

than independent firms. The coefficient for the agent dummy is negative, although it does not

achieve significance.
- 54 -

Regression 1: Dependent Variable = %rating firm superior

 Independent Variable = WCC price index

Logit estimates Number of firms = 22

 Wald chi2(1) = 0.00

 Prob > chi2 = 0.9951

Log likelihood = -338.99512 Pseudo R2 = 0.0000

 (standard errors adjusted for clustering on comidno)

------------------------------------------------------------------------------

 | Robust

 supq | Coef. Std. Err. z P>|z| [95% Conf. Interval]

-------------+----------------------------------------------------------------

 price98 | .0001009 .0163045 0.01 0.995 -.0318552 .0320571

 _cons | 1.06614 1.616056 0.66 0.509 -2.101272 4.233553

------------------------------------------------------------------------------

Regression 2: Dependent Variable = %rating firm superior

 Independent Variable = hourly wage rate, crew of 3

Logit estimates Number of firms = 28

 Wald chi2(1) = 1.53

 Prob > chi2 = 0.2163

Log likelihood = -397.5641 Pseudo R2 = 0.0100

 (standard errors adjusted for clustering on comidno)

------------------------------------------------------------------------------

 | Robust

 supq | Coef. Std. Err. z P>|z| [95% Conf. Interval]

-------------+----------------------------------------------------------------

 hour398 | .0199081 .0161001 1.24 0.216 -.0116475 .0514638

 _cons | -.8896816 1.643322 -0.54 0.588 -4.110533 2.33117

------------------------------------------------------------------------------

Regression 3: Dependent Variable = %rating firm superior

 Independent Variable = size of Yellow Pages ad

Logit estimates Number of firms = 28

 Wald chi2(1) = 0.11

 Prob > chi2 = 0.7346

Log likelihood = -407.55673 Pseudo R2 = 0.0004

 (standard errors adjusted for clustering on comidno)

------------------------------------------------------------------------------

 | Robust

 supq | Coef. Std. Err. z P>|z| [95% Conf. Interval]

-------------+----------------------------------------------------------------

 yp98 | .0014154 .0041744 0.34 0.735 -.0067662 .009597

 _cons | .991965 .2576976 3.85 0.000 .486887 1.497043

------------------------------------------------------------------------------
 

1 This three-part measure consists of (1) a firm’s estimated charge for the first treatment

of a sample house, (2) the length of period during which the firm will perform a free follow-up

treatment, and (3) the estimated charge for treatment after the free followup period. Various

alternative specifications were constructed by compiling a composite index that weighted the

initial treatment charge by the length of free followup period, and combined this with the charge

after the followup period. All such composite measures were either negatively correlated with

quality or uncorrelated. In any event, it is not clear which, if any, of the tested specifications is

most appropriate, particularly since it is impossible to estimate expected total costs without firmspecific knowledge of the probability that followup treatments will be needed.

- 55 -

Regression 4: Dependent Variable = %rating firm superior

 Independent Variable = agent status (agent=1)

Logit estimates Number of firms = 30

 Wald chi2(1) = 0.37

 Prob > chi2 = 0.5431

Log likelihood = -425.2212 Pseudo R2 = 0.0024

 (standard errors adjusted for clustering on comidno)

------------------------------------------------------------------------------

 | Robust

 supq | Coef. Std. Err. z P>|z| [95% Conf. Interval]

-------------+----------------------------------------------------------------

 agent98 | -.2566645 .42205 -0.61 0.543 -1.083867 .5705383

 _cons | 1.09089 .1886116 5.78 0.000 .7212183 1.460562

------------------------------------------------------------------------------

Pest Control Firms

(1997)

The Pest Control data set contains information on firm size and Yellow Pages ad size, as

well as two other variables that permit testing of signaling hypotheses. The first is a dummy

variable identifying firms that are part of national chains. The second is a variable representing the

length of the warranty period that firms offer for pest control work. This variable is used to test the

hypothesis that consumers can rely on warranty length as a signal of the effectiveness of the initial

treatment. WCC provides two price measures for pest control firms. One is the charge for a

termite inspection with written report. The other is more complex and not easily adapted for use as

a dependent variable.1

 All price regressions reported below employ the termite inspection charge. 

Regression 1 shows a strong positive correlation between consumer satisfaction and price. 

In Regression 2, however, a similarly strong negative correlation surfaces between satisfaction and

firm size and, in Regression 3, between satisfaction and size of Yellow Pages ad. In Regression 4,

there is a significant negative relationship between the Yellow Pages variable and value (quality

divided by price). The value variable also displays a significant negative coefficient when firm

size is used as the dependent variable in Regression 5. Regression 6 reveals a strong negative
- 56 -

correlation between satisfaction and firm status as a member of a national chain. For the final

signaling hypothesis, Regression 7 indicates that consumers cannot rely on the length of the

warranty period for the initial treatment as a predictor of satisfaction. There is a significant

negative correlation between these two variables. 

All of the signaling variables are included as predictors of satisfaction in Regression 8. 

With the exception of size of Yellow Pages ad, all of the variables are significant and retain the

same sign as displayed in the simple two way regression. Further analysis showed that the Yellow

Pages variable is highly correlated with chain status (r=.52), and loses its explanatory power when

both variables are used as predictors. 

Regression 1: Dependent Variable = %rating firm superior

 Independent Variable = termite inspection charge

Logit estimates Number of firms = 51

 Wald chi2(1) = 16.96

 Prob > chi2 = 0.0000

Log likelihood = -1480.7173 Pseudo R2 = 0.0173

 (standard errors adjusted for clustering on comidno)

------------------------------------------------------------------------------

 | Robust

 supq | Coef. Std. Err. z P>|z| [95% Conf. Interval]

-------------+----------------------------------------------------------------

 chgter97 | .0235317 .0057148 4.12 0.000 .0123309 .0347325

 _cons | -.8820023 .3129227 -2.82 0.005 -1.495319 -.2686851

------------------------------------------------------------------------------

Regression 2: Dependent Variable = %rating firm superior

 Independent Variable = number of employees

Logit estimates Number of firms = 55

 Wald chi2(1) = 17.88

 Prob > chi2 = 0.0000

Log likelihood = -1585.6447 Pseudo R2 = 0.0227

 (standard errors adjusted for clustering on comidno)

------------------------------------------------------------------------------

 | Robust

 supq | Coef. Std. Err. z P>|z| [95% Conf. Interval]

-------------+----------------------------------------------------------------

 empls97 | -.07324 .0173194 -4.23 0.000 -.1071855 -.0392946

 _cons | .8631196 .2232701 3.87 0.000 .4255182 1.300721

------------------------------------------------------------------------------
- 57 -

Regression 3: Dependent Variable = %rating firm superior

 Independent Variable = size of Yellow Pages ad

Logit estimates Number of firms = 51 

 Wald chi2(1) = 12.23

 Prob > chi2 = 0.0005

Log likelihood = -1512.4115 Pseudo R2 = 0.0219

 (standard errors adjusted for clustering on comidno)

------------------------------------------------------------------------------

 | Robust

 supq | Coef. Std. Err. z P>|z| [95% Conf. Interval]

-------------+----------------------------------------------------------------

 yp97 | -.0125807 .0035981 -3.50 0.000 -.0196328 -.0055286

 _cons | .792496 .2053677 3.86 0.000 .3899828 1.195009

------------------------------------------------------------------------------

Regression 4: Dependent Variable = size of Yellow Pages ad

 Independent Variable = value (quality/price)

 Source | SS df MS Number of firms = 48

---------+------------------------------ F( 1, 46) = 8.75

 Model | 5469.12685 1 5469.12685 Prob > F = 0.0049

Residual | 28742.3614 46 624.833944 R-squared = 0.1599

---------+------------------------------ Adj R-squared = 0.1416

 Total | 34211.4883 47 727.904006 Root MSE = 24.997

------------------------------------------------------------------------------

 yp97 | Coef. Std. Err. t P>|t| [95% Conf. Interval]

---------+--------------------------------------------------------------------

 value | -22.58214 7.632881 -2.959 0.005 -37.94634 -7.217952

 _cons | 54.88138 10.37279 5.291 0.000 34.00205 75.76072

------------------------------------------------------------------------------

Regression 5: Dependent Variable = number of employees

 Independent Variable = value (quality/price)

 Source | SS df MS Number of firms = 51

---------+------------------------------ F( 1, 49) = 4.45

 Model | 66.4344209 1 66.4344209 Prob > F = 0.0401

Residual | 731.920183 49 14.9371466 R-squared = 0.0832

---------+------------------------------ Adj R-squared = 0.0645

 Total | 798.354604 50 15.9670921 Root MSE = 3.8649

------------------------------------------------------------------------------

 empls97 | Coef. Std. Err. t P>|t| [95% Conf. Interval]

---------+--------------------------------------------------------------------

 value | -2.453904 1.163576 -2.109 0.040 -4.792198 -.1156102

 _cons | 8.672254 1.55833 5.565 0.000 5.540673 11.80383

------------------------------------------------------------------------------
- 58 -

Regression 6: Dependent Variable = %rating firm superior

 Independent Variable = chain status (chain=1)

Logit estimates Number of firms = 57

 Wald chi2(1) = 39.32

 Prob > chi2 = 0.0000

Log likelihood = -1607.7795 Pseudo R2 = 0.0491

 (standard errors adjusted for clustering on comidno)

------------------------------------------------------------------------------

 | Robust

 supq | Coef. Std. Err. z P>|z| [95% Conf. Interval]

-------------+----------------------------------------------------------------

 chain97 | -1.078211 .1719506 -6.27 0.000 -1.415228 -.7411936

 _cons | .721779 .1444339 5.00 0.000 .4386938 1.004864

------------------------------------------------------------------------------

Regression 7: Dependent Variable = %superior, doing work properly

 Independent Variable = length of warranty for initial treatment

Logit estimates Number of firms = 42

 Wald chi2(1) = 3.97

 Prob > chi2 = 0.0462

Log likelihood = -518.83793 Pseudo R2 = 0.0068

 (standard errors adjusted for clustering on comidno)

------------------------------------------------------------------------------

 | Robust

 supq | Coef. Std. Err. z P>|z| [95% Conf. Interval]

-------------+----------------------------------------------------------------

 period97 | -.0015461 .0007756 -1.99 0.046 -.0030662 -.0000261

 _cons | 2.207526 .1784935 12.37 0.000 1.857685 2.557366

------------------------------------------------------------------------------

Regression 8: Dependent Variable = %rating firm superior 

 Independent Variables = price index, firm size, size of Yellow 

 Pages ad, chain status

Logit estimates Number of firms = 48

 Wald chi2(4) = 80.19

 Prob > chi2 = 0.0000

Log likelihood = -1367.2647 Pseudo R2 = 0.0705

 (standard errors adjusted for clustering on comidno)

------------------------------------------------------------------------------

 | Robust

 supq | Coef. Std. Err. z P>|z| [95% Conf. Interval]

-------------+----------------------------------------------------------------

 chgter97 | .0123134 .0065977 1.87 0.062 -.0006179 .0252447

 empls97 | -.057151 .0220553 -2.59 0.010 -.1003785 -.0139235

 yp97 | .0015786 .0034326 0.46 0.646 -.0051493 .0083064

 chain97 | -1.009009 .2715573 -3.72 0.000 -1.541252 -.4767667

 _cons | .453565 .4369342 1.04 0.299 -.4028103 1.30994
- 59 -

Plumbers

(1995)

The 1995 data set for Plumbers contains variables for size of firm and Yellow Pages ad. 

The dependent variable, however, is the less sensitive overall satisfaction measure that combines

superior and adequate ratings. A dummy variable was also constructed to identify members of a

national chain (of which there was only one.) Analysis (not shown) revealed that this variable was

unrelated to consumer satisfaction or to any of the other independent variables.

Regression 1 shows a strong negative correlation between price and quality. Regression 2

reveals a similarly strong negative relationship between quality and firm size. In Regression 3,

there is an even more systematic negative relationship between size of Yellow Pages ad and

quality. Regression 4 shows that Yellow Pages ad size cannot be used to identify firms providing

particularly good value in terms of price-adjusted quality. This reflects a positive correlation

between the Yellow Pages variable and price (r=.390). Value is also negatively correlated with

firm size, as shown in Regression 5. Finally, Regression 6 reveals that the Yellow Pages variable

demonstrates the greatest predictive power in a regression that employs all of the independent

variables. This suggests that Yellow Pages ad size is not merely functioning as a proxy for price or

firm size in this data set. 

Regression 1: Dependent Variable = %rating firm superior or adequate

 Independent Variable = WCC price index

Logit estimates Number of firms = 134 

 Wald chi2(1) = 7.37

 Prob > chi2 = 0.0066

Log likelihood = -1657.9036 Pseudo R2 = 0.0083

 (standard errors adjusted for clustering on comidno)

------------------------------------------------------------------------------

 | Robust

 supq | Coef. Std. Err. z P>|z| [95% Conf. Interval]

-------------+----------------------------------------------------------------

 price95 | -.0229292 .0084459 -2.71 0.007 -.0394828 -.0063755

 _cons | 4.895038 .8762987 5.59 0.000 3.177524 6.612551

------------------------------------------------------------------------------
- 60 -

Regression 2: Dependent Variable = %rating firm superior or adequate

 Independent Variable = number of employees 

Logit estimates Number of firms = 137 

 Wald chi2(1) = 6.24

 Prob > chi2 = 0.0125

Log likelihood = -1758.2104 Pseudo R2 = 0.0078

 (standard errors adjusted for clustering on comidno)

------------------------------------------------------------------------------

 | Robust

 supq | Coef. Std. Err. z P>|z| [95% Conf. Interval]

-------------+----------------------------------------------------------------

 employ95 | -.0140669 .0056328 -2.50 0.013 -.0251069 -.0030269

 _cons | 2.617006 .1228796 21.30 0.000 2.376167 2.857846

------------------------------------------------------------------------------

Regression 3: Dependent Variable = %rating firm superior or adequate

 Independent Variable = size of Yellow Pages ad

Logit estimates Number of firms = 117

 Wald chi2(1) = 13.19

 Prob > chi2 = 0.0003

Log likelihood = -1558.1123 Pseudo R2 = 0.0391

 (standard errors adjusted for clustering on comidno)

------------------------------------------------------------------------------

 | Robust

 supq | Coef. Std. Err. z P>|z| [95% Conf. Interval]

-------------+----------------------------------------------------------------

 yp95 | -.0187058 .0051502 -3.63 0.000 -.0288 -.0086117

 _cons | 2.882208 .1253447 22.99 0.000 2.636536 3.127879

------------------------------------------------------------------------------

Regression 4: Dependent Variable = size of Yellow Pages ad

 Independent Variable = Value (quality/price)

 Source | SS df MS Number of firms = 113

---------+------------------------------ F( 1, 111) = 35.18

 Model | 16631.9631 1 16631.9631 Prob > F = 0.0000

Residual | 52472.1909 111 472.72244 R-squared = 0.2407

---------+------------------------------ Adj R-squared = 0.2338

 Total | 69104.154 112 617.001375 Root MSE = 21.742

------------------------------------------------------------------------------

 yp95 | Coef. Std. Err. t P>|t| [95% Conf. Interval]

---------+--------------------------------------------------------------------

 value | -84.0762 14.17439 -5.932 0.000 -112.1637 -55.9887

 _cons | 93.41068 12.97204 7.201 0.000 67.70572 119.1156

------------------------------------------------------------------------------
- 61 -

Regression 5: Dependent Variable = number of employees

 Independent Variable = value (quality/price)

 Source | SS df MS Number of firms = 133

---------+------------------------------ F( 1, 131) = 11.45

 Model | 2268.99974 1 2268.99974 Prob > F = 0.0009

Residual | 25955.7296 131 198.13534 R-squared = 0.0804

---------+------------------------------ Adj R-squared = 0.0734

 Total | 28224.7293 132 213.823707 Root MSE = 14.076

------------------------------------------------------------------------------

employ95 | Coef. Std. Err. t P>|t| [95% Conf. Interval]

---------+--------------------------------------------------------------------

 value | -28.16574 8.323095 -3.384 0.001 -44.6308 -11.70067

 _cons | 33.88606 7.774728 4.358 0.000 18.50579 49.26632

------------------------------------------------------------------------------

Regression 6: Dependent Variable = %rating firm superior or adequate

 Independent Variable = WCC price index, size of Yellow Pages 

 ad, number of employees

Logit estimates Number of firms = 112 

 Wald chi2(3) = 25.11

 Prob > chi2 = 0.0000

Log likelihood = -1469.8097 Pseudo R2 = 0.0267

 (standard errors adjusted for clustering on comidno)

------------------------------------------------------------------------------

 | Robust

 supq | Coef. Std. Err. z P>|z| [95% Conf. Interval]

-------------+----------------------------------------------------------------

 price95 | -.0129418 .0094567 -1.37 0.171 -.0314765 .0055929

 yp95 | -.0136778 .0045599 -3.00 0.003 -.022615 -.0047407

 employ95 | -.0037818 .0038631 -0.98 0.328 -.0113534 .0037897

 _cons | 4.179633 .9420912 4.44 0.000 2.333168 6.026098

------------------------------------------------------------------------------

Restaurants

(1998)

This very large data set contains consumer evaluations of 672 restaurants in the

Washington D.C. area. Respondents were asked to base their ratings on a scale of 0 through 100, 

and to rate a restaurant on quality of food, quality of service, and on value. Thus, for this one

industry, the quality ratings may not be confounded to any extent with the price ratings, since

consumers were specifically asked to provide a separate rating for price-adjusted quality. WCC

assigned restaurants to one of five price categories. Although the quality variable is truncated at

both ends, the ratings were fairly evenly distributed and did not bunch at the upper truncation point

(100). For these reasons, the regressions were run in ordinary least squares format. 

Price and food quality are very highly correlated in Regression 1. Note that the coefficients

for the four dummy variables (with category 1 the eliminated variable) increase monotonically,

showing that quality increases with each increment in the price grouping. The same pattern

emerges for quality of service in Regression 2, with even higher significance levels for all of the
- 62 -

coefficients. WCC did not collect firm size data for restaurants, and Yellow Pages ad size data

were not collected due to resource constraints and the perceived low probability that such a

variable would be correlated with quality in this industry. 

Regression 1: Dependent Variable = food quality (0-100)

 Independent Variable = price category

Regression with robust standard errors Number of firms = 672

 F( 4, 671) = 25.84

 Prob > F = 0.0000

 R-squared = 0.1608

Number of clusters (idno98) = 672 Root MSE = 5.4838

------------------------------------------------------------------------------

 | Robust

 food98 | Coef. Std. Err. t P>|t| [95% Conf. Interval]

---------+--------------------------------------------------------------------

 p298 | 2.717179 .6798711 3.997 0.000 1.382249 4.05211

 p398 | 4.282356 .7127837 6.008 0.000 2.882801 5.681911

 p498 | 7.125349 .8785988 8.110 0.000 5.400215 8.850483

 p598 | 10.91182 1.548333 7.047 0.000 7.871658 13.95198 

 cons | 71.77 .6033904 118.945 0.000 70.58524 72.95476

------------------------------------------------------------------------------

Regression 2: Dependent Variable = service quality (0-100)

 Independent Variable = price category

Regression with robust standard errors Number of firms = 672

 F( 4, 671) = 37.01

 Prob > F = 0.0000

 R-squared = 0.2062

Number of clusters (idno98) = 672 Root MSE = 5.1995

------------------------------------------------------------------------------

 | Robust

servic98 | Coef. Std. Err. t P>|t| [95% Conf. Interval]

---------+--------------------------------------------------------------------

 p298 | 3.40044 .5966455 5.699 0.000 2.228923 4.571956

 p398 | 5.117487 .6263974 8.170 0.000 3.887552 6.347422

 p498 | 8.467907 .8089955 10.467 0.000 6.87944 10.05637

 p598 | 11.02182 1.557838 7.075 0.000 7.962995 14.08064 

 cons | 68.16 .513327 132.781 0.000 67.15208 69.16792

------------------------------------------------------------------------------
- 63 -

Shoe Repair

(1995)

The Shoe Repair data set for 1995 contains price and quality information for 95 firms. 

Although WCC did not collect information on firm size, the data set does contain a Yellow Pages

ad size variable. Regression 1 shows a negative but not quite significant negative correlation

between price and quality. In Regression 2, the size of a firm’s Yellow Pages ad is positively

associated with quality. Further analysis (not shown) revealed no association between the Yellow

Pages variable and value as measured by the price-adjusted WCC quality rating.

Regression 1: Dependent Variable = %percent rating firm superior

 Independent Variable = WCC price index

Logit estimates Number of firms = 95

 Wald chi2(1) = 2.28

 Prob > chi2 = 0.1310

Log likelihood = -1284.0179 Pseudo R2 = 0.0019

 (standard errors adjusted for clustering on comidno)

------------------------------------------------------------------------------

 | Robust

 supq | Coef. Std. Err. z P>|z| [95% Conf. Interval]

-------------+----------------------------------------------------------------

 price95 | -.0098966 .0065534 -1.51 0.131 -.0227411 .002948

 _cons | 2.555293 .6473709 3.95 0.000 1.286469 3.824116

------------------------------------------------------------------------------

Regression 2: Dependent Variable = %rating firm superior

 Independent Variable = size of Yellow Pages ad

Logit estimates Number of firms = 77

 Wald chi2(1) = 8.83

 Prob > chi2 = 0.0030

Log likelihood = -1135.0913 Pseudo R2 = 0.0084

 (standard errors adjusted for clustering on comidno)

------------------------------------------------------------------------------

 | Robust

 supq | Coef. Std. Err. z P>|z| [95% Conf. Interval]

-------------+----------------------------------------------------------------

 yp95 | .1727471 .0581234 2.97 0.003 .0588274 .2866668

 _cons | 1.401833 .1263968 11.09 0.000 1.1541 1.649566

------------------------------------------------------------------------------
- 64 -

Supermarkets

 (2001)

The Supermarkets data set for 2001 includes only 8 firms, but over 10,000 respondents

provided ratings based on experiences at dozens of individual stores. Regression 1 shows a very

strong positive correlation between price and quality in this industry.

Regression 1: Dependent Variable = % rating store superior

 Independent Variable = WCC price index

Logit estimates Number of chains = 8

 Wald chi2(1) = 116.89

 Prob > chi2 = 0.0000

Log likelihood = -6870.3591 Pseudo R2 = 0.0337

 (standard errors adjusted for clustering on idno)

------------------------------------------------------------------------------

 | Robust

 supq | Coef. Std. Err. z P>|z| [95% Conf. Interval]

-------------+----------------------------------------------------------------

 price | .0413359 .0038233 10.81 0.000 .0338424 .0488294

 _cons | -4.419366 .4628962 -9.55 0.000 -5.326626 -3.512106

------------------------------------------------------------------------------

TV Repair

 (1989)

The 1989 ratings for TV Repair were the last to include a price index for actual repairs. 

The 1991 ratings provide information only on a firm’s charge for a repair estimate. Neither firm

size nor contemporaneous Yellow Pages ad size data could be obtained for the firms in the1989

data set. Regression 1 shows a negative but insignificant correlation between price and the

percentage of respondents rating a firm superior or adequate in overall quality. 

Regression 1: Dependent Variable = %rating firm superior or adequate

Logit estimates Number of firms = 36

 Wald chi2(1) = 1.04

 Prob > chi2 = 0.3072

Log likelihood = -372.66854 Pseudo R2 = 0.0055

 (standard errors adjusted for clustering on idno)

------------------------------------------------------------------------------

 | Robust

 supq | Coef. Std. Err. z P>|z| [95% Conf. Interval]

-------------+----------------------------------------------------------------

 price | -.0089139 .0087299 -1.02 0.307 -.0260241 .0081964

 _cons | 3.061545 .9877203 3.10 0.002 1.125649 4.997442

------------------------------------------------------------------------------
- 65 -

Tree Experts

 (1999)

The 1999 data set for Tree Experts is limited to information on price and quality. WCC

could not provide firm size data, and resources were no longer available to collect Yellow Pages ad

size data. Regression 1 shows no significant correlation between price and quality.

Regression 1: Dependent Variable = %rating firm superior

 Independent Variable = WCC price index

Logit estimates Number of firms = 29

 Wald chi2(1) = 0.70

 Prob > chi2 = 0.4029`

Log likelihood = -505.6894 Pseudo R2 = 0.0031

 (standard errors adjusted for clustering on idno99)

------------------------------------------------------------------------------

 | Robust

 supq | Coef. Std. Err. z P>|z| [95% Conf. Interval]

-------------+----------------------------------------------------------------

 price99 | .0069715 .008334 0.84 0.403 -.0093627 .0233058

 _cons | .6667981 .8065977 0.83 0.408 -.9141043 2.247701

------------------------------------------------------------------------------

Watch Repair

 (1996)

The 1996 data set for Watch Repair contains information on Yellow Pages ad size, but not

firm size. In Regression 1, The WCC quality ratings and the WCC price index are uncorrelated. 

Similarly, in Regression 2, there is no significant relationship between quality and size of Yellow

Pages ad. Further analysis (not shown) failed to find any significant correlation between the

Yellow Pages variable and price or value (quality divided by price). 

Regression 1: Dependent Variable = %rating firm superior

 Independent Variable = WCC price index

Logit estimates Number of firms = 41

 Wald chi2(1) = 0.12

 Prob > chi2 = 0.7321

Log likelihood = -622.39017 Pseudo R2 = 0.0002

 (standard errors adjusted for clustering on idno96)

------------------------------------------------------------------------------

 | Robust

 supq | Coef. Std. Err. z P>|z| [95% Conf. Interval]

-------------+----------------------------------------------------------------

 price96 | -.0018618 .0054376 -0.34 0.732 -.0125193 .0087958

 _cons | 1.371141 .5230825 2.62 0.009 .3459186 2.396364 

------------------------------------------------------------------------------
- 66 -

Regression 2: Dependent Variable = %rating firm superior

 Independent Variable = size of Yellow Pages ad

Logit estimates Number of obs = 37

 Wald chi2(1) = 0.01

 Prob > chi2 = 0.9065

Log likelihood = -707.40833 Pseudo R2 = 0.0000

 (standard errors adjusted for clustering on idno96)

------------------------------------------------------------------------------

 | Robust

 supq | Coef. Std. Err. z P>|z| [95% Conf. Interval]

-------------+----------------------------------------------------------------

 yp96 | -.0093096 .0792308 -0.12 0.906 -.1645991 .1459799

 _cons | 1.191147 .1316236 9.05 0.000 .933169 1.449124

-------------------------------------------------------------------------------