717[ Journal of Law and Economics, vol. 58 (August 2015)].docx

717
[ Journal of Law and Economics, vol. 58 (August 2015)]
© 2015 by The University of Chicago. All rights reserved.
0022-2186/2015/5803-0024$10.00
“Can You Hear Me Now?” Exit, Voice, and
Loyalty under Increasing Competition
T. Randolph Beard Auburn University
Jeffrey T. Macher Georgetown University
John W. Mayo Georgetown University
Abstract
Competition works only if poorly performing vendors can be
punished. The
principal vehicle for consumers to discipline ill-performing
firms is to switch
to alternative providers. But switching is not the only
mechanism consumers
have to express disapproval. While some unhappy consumers
may choose to no
longer buy the good or service, other consumers express their
disappointment
through complaining. This article examines Albert O.
Hirschman’s conjecture
that as industries become more competitive, consumers’
complaints give way to
switching. It offers a simple description of the theoretical
relationships among

market structure, quality, and complaints. It then utilizes an
extensive data set
to explore the empirical determinants of consumers’
complaining behavior in
the local-exchange telephone industry. These data overcome the
problem that
complaints can depend on both competition and quality, while
competition also
presumably affects quality directly. The estimations
accommodate this compli-
cation and provide considerable support for Hirschman’s
conjecture.
[E]conomists have refused to consider that the discontented
con-
sumer might be anything but either dumbly faithful or outright
traitorous (to the firm he used to do business with). (Hirschman
1970, p. 31)
1. Introduction
While theoretical studies of market structure and quality are
plentiful, they have
produced few general conclusions. This dearth of interpretations
has persisted
for a variety of reasons. First, quality may not be apparent to
buyers prior to use,
so information (or the lack thereof) can alter customers’
behavior and the con-
The authors gratefully acknowledge seminar participants at the
Institutions and Innovation Con-
ference at Harvard Business School and at the Federal
Communications Commission (FCC) for
helpful comments on earlier manuscript versions. The authors
also received helpful comments from

Chris Borek, Matthew Demartini, Silke Forbes, J. Bradford
Jensen, Nathan Miller, Stanley Nollen,
This content downloaded from 134.053.121.170 on October 29,
2018 12:10:01 PM
All use subject to University of Chicago Press Terms and
Conditions (http://www.journals.uchicago.edu/t-and-c).
718 The Journal of LAW & ECONOMICS
sequent incentive for the equilibrium provision of quality.
Moreover, the rela-
tionship among market structure, quality, and product-
information availability
is itself complex (Jin and Leslie 2003). Second, quality is a
multifaceted concept.
Goods can vary in both horizontal and vertical quality
dimensions, with different
consequences for the relationship between competition and
product character-
istics (Waterson 1989). For instance, whether firms compete in
prices or quanti-
ties affects the provision of quality and the extent of
differentiation (Motta 1993).
Third, scale economies in the provision of goods of different
qualities can cause
market structure to alter cost conditions indirectly, as more
concentrated mar-
kets may allow greater per-firm outputs. Even the simplest
quality-comparison
case under monopoly and perfect competition with constant
returns creates am-
biguity, as the result depends on the relationship between the
average willing-

ness to pay for quality and the corresponding willingness of a
marginal buyer
(Tirole 1988). Fourth, industry conditions may alter the
intertemporal behavior
of firms. For instance, firms may seek to cultivate their
reputations in a dynamic
context, which leads to a host of quality-investment incentives
that are absent in a
static context (Kranton 2003). In sum, the relationship between
quality and mar-
ket structure is likely to vary between industries and to remain
largely a matter
of empirical analysis (see, for example, Goolsbee and Petrin
2004; Crawford and
Shum 2007; Chu 2010).
Yet while empirical analysis may provide a fruitful path, a close
consideration
of the theoretical developments regarding the relationship
between market struc-
ture and quality reveals an underspecification that, if more fully
developed, holds
promising insights. In particular, existing models recognize that
firms that fail to
offer an acceptable level of quality to consumers need to
somehow bear the con-
sequences of that failure. It is primarily through consumers’
actions in response
to their dissatisfaction that this disciplining is supposed to
occur.1 Formal mod-
els of the relationship between market structure and quality,
however, provide
little emphasis on this disciplinary mechanism and instead
represent consumers
as reacting to any alleged breach of equilibrium expectations by
simply switch-

ing away from the offending firm. Consumers’ discipline of
firms is thought to
be least effective under monopoly and presumably increases in
effectiveness as
market structure atomizes. The salubrious story of competitive
markets thus rests
largely on the ability and willingness of informed consumers to
take actions that
discipline ill-behaving firms. For example, if one firm’s price
and/or quality is
unattractive relative to that of rival firms, consumers convey
their displeasure by
fleeing.
————
Karok Ray, Marcia Mintz, John Rust, Dennis Quinn, Rob
Shapiro, Mike Stern, Scott Wallsten, and
Luc Wathieu. The authors appreciate support from the
Georgetown Center for Business and Public
Policy in the McDonough School of Business; the Institute for
Business Innovation at the University
of California, Berkeley; and the Stanford Institute for Economic
Policy Research. Any errors are at-
tributable solely to the authors.
1 The principal alternatives are disciplines imposed by the
threat or realization of regulatory or
legal actions against a firm providing low quality.
2018 12:10:01 PM

Competition and Consumers’ Complaints 719
While the theoretical and empirical power of such switching
behavior (often
termed exit) has received considerable scholarly attention
(Farrell and Klemperer
2007; Farrell and Shapiro 1988), another mechanism by which
consumers ex-
press their dissatisfaction is much less studied. When faced with
unexpectedly
low quality, many consumers complain—most frequently to
offending firms but
also commonly to public (or private) bodies providing
oversight. For example,
Better Business Bureaus in the United States and Canada
reported receiving over
918,000 complaints in 2013 (Better Business Bureau 2013). Just
in the telecom-
munications industry, the Federal Communications Commission
(FCC) received
more than 450,000 complaints between 2003 and 2006
(Government Account-
ability Office 2008).
Despite its prevalence, customers’ complaining behavior has
received consid-
erably less economic consideration than switching behavior.
This lack of scrutiny
is not due to either the scarcity or the economic unimportance
of complaining
but rather is a consequence largely of a practical nature. In
particular, while firms
routinely receive and process consumers’ complaints, the
scrutiny and manage-
ment of such complaint data are almost never shared with
outsiders. Firms are

simply not inclined to publicize their shortcomings.
Consequently, the ability of
researchers to directly observe and study data on complaints is
limited.
In this article, we focus on the complaint process as an essential
part of the
portfolio of options that consumers use to react to service
failures and express
their dissatisfaction. In doing so, we emphasize the role that
market structure
plays as a determinant of complaining behavior—an idea
originally outlined by
Hirschman (1970) in his construct “exit, voice, and loyalty.”
Hirschman posits
that there is a negative relationship between the extent of
competition and the
degree of observed complaining, which arises as dissatisfied
consumers in more
competitive markets are more apt to switch providers rather
than complain to
their incumbent providers. We refer to this relationship as the
Hirschman con-
jecture. Because complaints depend on the quality that firms
offer—and quality
itself may be endogenously determined by market structure—
our principal chal-
lenge is to separate changes in complaining behavior that arise
from changes in
industry structure, as conjectured by Hirschman, from changes
in quality (and
corollary changes in complaining) that arise endogenously with
changes in mar-
ket structure.
Our empirical analysis is able to overcome this challenge

because of the con-
fluence of several fortuitous conditions. First, while firms’
complaint data are
typically unavailable, we draw on complaint data compiled by
the FCC as part
of its regulatory oversight of local-exchange telephone firms.
Second, the data
span a period in which exogenous market structure changes
occurred as a conse-
quence of the Telecommunications Act of 1996 (Pub. L. No.
104-104, 110 Stat. 56
[1996]), a major federal law permitting competition in a
formerly monopolistic
industry. Third, we utilize corollary data compiled by the FCC
to control for the
influence of service quality changes on complaining behavior
separately from the
influence of market structure changes on complaining behavior
over this period.
2018 12:10:01 PM
Our results suggest that the Hirschman conjecture is indeed
correct: increasing
competition results in decreasing voice (that is, complaints).
The rest of this paper is organized as follows. Section 2 frames
the theoreti-
cal analysis by providing background and a review of the extant

literature on the
economic dimensions of consumers’ complaining behavior. It
then offers a sim-
ple descriptive model of complaining behavior, in which
complaining (voice)
represents a response to dissatisfaction of an intensity
intermediate between suf-
fering in silence (staying loyal) and exit (switching). The model
highlights the in-
fluence of consumers’ costs of switching providers (as will arise
with changes in
market structure), the costs of complaining, and the role of
service quality on
consumers’ propensities to complain. Section 3 presents an
empirical analysis of
the determinants of observed complaints in the US local-
exchange telephone in-
dustry over the 1999–2006 period. These data are especially
useful for our pur-
poses, as large market structure changes due to entry and
consolidation among
telephone service providers were triggered by the
Telecommunications Act of
1996. Our empirical results indicate a strong effect of market
structure on com-
plaining behavior that is independent of any product quality
effect. Section 4 of-
fers concluding comments and further research suggestions.
2. Theory
2.1. Background and Extant Literature
Hirschman (1970) offers a seminal framework for understanding
the role of
exit, voice, and loyalty that has been applied in a number of

contexts across the
economic, political science, management, and marketing
domains. At the most
general level, Hirschman (1970) seeks to explain the
foundational determinants
of when and why some disgruntled customers exit, some
customers use their
voice, and some customers maintain loyalty. While the
Hirschman framework
has considerable intuitive and general appeal for the study of
complaining, the
provision of empirical insights arising from this framework has
been limited for
several reasons. First, capturing the voice of dissatisfaction in a
systematic way
is often impractical because of difficulties in securing either
cross-sectional or
time-series complaint data. While virtually all firms collect
such data, they are
understandably reluctant to share it. Second, most empirical
studies examine
individual consumers’ characteristics as relevant determinants
of complaining
behavior but neglect other factors that potentially influence this
relationship.2
As a result, more is known about complainers’ characteristics
than processes
that generate complaints or how these processes relate to
industry characteris-
tics. Third, a central proposition of the Hirschman framework
has hitherto been
largely ignored. In particular, a fundamental implication of
Hirschman’s analy-
sis is that there is a relationship between the extent of
marketplace competition

2 For example, marketing research has principally focused on
the role of consumers’ characteris-
tics on the propensity to complain. See Kolodinsky (1995) for a
review.
2018 12:10:01 PM
and the means that consumers use to express their
dissatisfaction with a good
or service. Hirschman (1970, p. 33) indicates that “[t]he voice
option is the only
way in which dissatisfied customers or members can react
whenever the exit op-
tion is unavailable. . . . In the economic sphere, the theoretical
construct of pure
monopoly would spell a no-exit situation, but the mixture of
monopolistic and
competitive elements characteristic of most real market
situations should make
it possible to observe the voice option in its interaction with the
exit option.” It is
this conjecture of Hirschman’s—namely, that moving from
monopoly toward a
more atomistic industry structure leads to reduced voice and
increased exit—that
we propose to test.
Several papers examine consumer dissatisfaction but do not
invoke the

Hirschman framework per se. On the theoretical side, Gans
(2002) develops a
model of customers’ choice and switching behavior in response
to variation
in suppliers’ quality. He finds that the presence of more
competitors increases
customers’ abilities to switch suppliers in response to poor
service, which sub-
sequently creates greater competitive pressures to improve
quality levels. As in
virtually all customer loyalty models, however, the theoretical
framework is con-
strained by examining only the exit and loyalty (but not the
voice) options. On
the empirical side, Oster (1980) provides an economic analysis
of complaining
behavior, examining the determinants of consumers’ complaints
about different
products filed with the Better Business Bureau in New Haven,
Connecticut. The
cross-sectional nature of the data unfortunately does not allow
the relationship
between market structure and complaints to be examined.
Andreasen (1985) ex-
amines consumers’ propensities to complain using patient
survey data on physi-
cian care (subjectively chosen to represent a loose monopoly) at
a single point in
time. Absent both cross-sectional and time-series variation in
industry structure,
he considers only how individual consumers’ characteristics
affect complaint
propensities and thus does not explore the Hirschman
conjecture. Forbes (2008)
uses publicly available passenger complaint data on airline
service (namely, flight

problems and baggage handling) from the US Department of
Transportation to
examine the relationships between complaints and firm quality
and complaints
and the level of expected firm quality. She finds that the
number of complaints
increases when quality decreases and that complaints are
affected by consumers’
expectations about quality. Controlling for actual service levels,
the higher the
consumers’ expectations of quality, the greater the propensity
for consumers to
complain.
In summary, although complaining is ubiquitous in many
markets and the
characteristics of complaining customers have been analyzed in
several indus-
tries, the basic link between market structure and voice posited
by Hirschman
(1970) remains largely unexamined. To motivate such a study,
we turn to a sim-
ple description that characterizes complaining as an
intermediate response to
dissatisfaction. Even in the simplest contexts, we show that the
observed relation-
ship between market structure and voice will likely depend on
the competitive
determination of service quality.
2018 12:10:01 PM

2.2. Complaining Behavior, Market Structure, and Exogenous
Quality
In the economic analysis of quality, goods or services are
generally considered
to be of two types. For the first type, consumers are able to
observe ex ante the
quality of the good or service they are purchasing (namely,
search goods or ser-
vices). For the second type, quality may be determined only ex
post—after pur-
chase of the good or service (namely, experience goods or
services). Models of
search goods and services and experience goods and services
provide a number
of insights into the incentives (or lack thereof) for firms to
provide high quality,
as well as the regulatory or private incentive mechanisms that
may be employed
to promote high-quality offerings (Laffont and Tirole 1993).
But these models
ignore the prospect that consumers may react to
disappointments about quality
by complaining. Given the prominence of customer complaints,
we examine this
question with a simple representation of an experience service
in which consum-
ers make an initial purchase decision only to potentially
discover ex post that the
quality of the service purchased is low. This model is perhaps
the simplest possi-
ble that includes complaining and illustrates the Hirschman
conjecture.

Consider a market composed of a large number (N ) of price-
taking consumers,
each of whom makes a decision whether to purchase a given
service. Consump-
tion of the service provides benefits that depend both on the
consumer’s value or
taste for the service (given by her type t) and on her
consumption experience—
which depends on how well the service works and what actions
or recourse she
(optimally) selects in response to a service failure. For
simplicity, assume that
consumers have unit demands for the service (so marginal
quantity choice is not
analyzed), and there is a single outside composite service.
Each consumer knows her type t, which is a random variable
distributed with
marginal density f(t) and cumulative density F(t) on the interval
[tL, tH]. We in-
terpret t as the value that a consumer attaches to successfully
consuming 1 unit of
the service in question, for which she must pay a price P. This
formulation differs
from that of Shaked and Sutton (1987), for example, in that
differentiated con-
sumers do not consume goods of various qualities with certainty
but rather at-
tempt to consume goods of known characteristics that may,
however, completely
fail to function. The consumer has income M and utility from
the composite ser-
vice U(q), where q is the quantity of the composite service
consumed. Assume
that U is increasing in the composite service q and that the price

of the composite
service is $1 per unit. If a consumer purchases the service, she
obtains a value of t
if the service works—an outcome that occurs with a known
exogenous probabil-
ity θ, where 0 < θ < 1. Thus, 1 − θ represents the probability of
service failure,
which (at this stage) we interpret as a binary event.3
3 Our model assumes implicitly that failure has the same
qualitative effect on each consumer. In
particular, failure deprives consumers of some portion of the
value of the service. As these values
differ between types, the implications of failure are not the
same for everyone. An alternative model
parameterization would allow for differing degrees of failure,
but this would not materially affect the
conclusions if the value of functional service is held equal
between customers. It would be necessary,
however, to respecify the nonpurchase condition. Combining
random values and random failure
effects introduces substantial complexity without corresponding
benefits to insight.
2018 12:10:01 PM
If the service fails, then the consumer responds in one of three
ways. First, the
consumer may remain loyal (that is, does nothing and suffers in

silence, or per-
haps just complains to friends). Second, the consumer may
formally complain
(for example, by filing a complaint with a public regulatory
body). Third, the con-
sumer may exit (for example, by switching to an alternative
vendor). The precise
sequence of events in each of these options is not critical. What
matters instead
is interpreting these actions as having payoffs that are related to
consumer types
in a sensible way. These three options are thus better
understood as shorthand
representations for various consumer responses that presumably
incorporate se-
quential activity. For example, a loyal consumer may engage in
informal (low-
or no-cost) complaining to neighbors or coworkers. Similarly, a
consumer who
switches vendors might do so only at the end of a series of
actions that begins
with informal complaining followed by formal complaining to
public oversight
bodies, studying market information, and so on. For simplicity,
we define a con-
sumer’s utility V in the following simple forms:
V = U(M) if the consumer does not buy the service,
V = t + U(M – P) if the consumer buys the service and the
service works,
V = dt + U(M – P) if the consumer buys the service, the service
fails, and the
consumer remains loyal,
V = bt + U(M – P) – c (where c is the cost of complaining) if
the consumer

buys the service, the service fails, and the consumer selects the
voice re-
sponse, and
V = at + U(M – P) – s (where s is the cost of switching) if the
consumer buys
the service, the service fails, and the consumer selects the exit
response.
We assume that 1 > a > b > d > 0 and that s > c > 0.4 We thus
depart slightly
from Hirschman (1970, p. 40) that “voice tends to be costly in
comparison to
exit” and emphasize instead the often significant costs of
switching (Farrell and
Klemperer 2007).5
The interpretations of these expressions are relatively
straightforward. A con-
sumer who buys a service that fails may respond in different
ways, with the op-
timal response depending on the consumer’s type (or value of
service). A con-
sumer can recapture part of the value attached to the service,
although failure
is always utility reducing: t + U(M - P) > it + U(M - P) ∀ t, i ∈
{d, b, a}. We
interpret consumers’ responses ordered by their degree of
aggressiveness, with
loyalty considered least aggressive, complaint considered
moderately aggressive,
and exit considered most aggressive. While more aggressive
responses are more
4 Our assumed ordering is predicated on two considerations.

First, as a theoretical matter, a > b
(in the presence of s > c) is required for switching behavior to
be present at all. Second, the assump-
tion generates the potential for an observed portfolio of
consumer behaviors consistent with our
empirical observations. See Section 3. However, one can easily
imagine cases in which b > a. The
parameterization assumed here is merely the simplest one that
generates the observed portfolio of
consumer behaviors.
5 We note that in his initial discussion of voice, Hirschman
references not competition but mo-
nopoly, in which the cost of exit is prohibitive. In other words,
the costs of voice and switching
depend on market structure. Alternately, one could assume b >
a, in which case c > s would be not
only allowed but also necessary to generate all three observed
behaviors.
2018 12:10:01 PM
expensive (s > c > 0), they provide consumers with greater
expected recovery
(a > b > d) of service value t. Consumers who value a service
more may thus
find it optimal to respond more aggressively to service failure
than consumers
who value a service less. This assumption is the most

reasonable if consumers’
responses are to be determined solely by a single parameter. It
is this sorting that
makes our conceptualization informative empirically. In our
context, consumers
who buy the service and are satisfied—or do not buy the
service—neither com-
plain nor switch. For consumers who buy the service and
experience a failure,
complaining is a means to an end. That is, we exclude the case
of the rare individ-
ual who enjoys complaining for its own sake.6 We instead argue
that complaining
is an action taken to discipline firms—absent a service failure,
no complaining
occurs. Similarly, no independent utility arises from switching.
We instead argue
that switching (and complaining) takes time and subsequently
presents opportu-
nity costs that reduce the value of the service obtained. In short,
complaining and
switching both use up time and/or resources that could
otherwise be utilized to
obtain value from the service. Further, consumers may
experience psychological
costs from complaining or switching. We incorporate these
factors into the cost
parameters c and s.
From this basic setup, a consumer of type t will not buy the
service whenever
U M U M P t dt bt c at s( ) ( ) ( )max( , , ),> - + + - - -q q1 (1)
so it is sufficient that U(M) > t + U(M - P). For a consumer who
buys a ser-

vice that subsequently fails, her response is governed by her
type and the values
of the parameters a, b, d, c, and s. A number of outcomes are
possible, although
we are guided in our specification by the simple observation
that firms generally
have some consumers who are satisfied, others who are quietly
dissatisfied, others
who complain, and still others who switch. Because of the
linearity of consumer
utilities in consumers’ types, if a consumer of type t prefers to
utilize voice in-
stead of loyalty or exit instead of voice, then any consumer of
higher type would
agree with this bilateral judgment. In other words, preferences
satisfy the single-
crossing property.
We thus complete our specification of restrictions on the value
and cost pa-
rameters by assuming that there exist values t0, t1, and t2 such
that tL < t0 < t1 <
t2 < tH and that consumers with types below t0 do not buy,
consumers with types
between t0 and t1 buy and do not complain (that is, remain
loyal) even when ser-
vice failure occurs, consumers with types between t1 and t2 buy
and complain if
service failure occurs, and consumers with types above t2 buy
and exit if service
failure occurs.
Of particular interest in the parameterization is the restriction
that exit requires
a higher type than voice. This restriction is equivalent to the
requirement that (a

- b)/(b - d) > s/c, which has a fairly natural interpretation. The
expression a -
b measures the additional proportion of value captured by exit
compared with
6 We choose not to explore the possibility of a pure
consumption value of complaining. We also
assume that the benefits of complaining accrue solely to the
complaining party.
2018 12:10:01 PM
voice, while the expression b - d makes the same comparison for
voice and loy-
alty. Their ratio measures the relative additional gain from
switching and must be
compared with (and smaller than) the ratio of the cost of
switching (s) to the cost
of complaining (c) (since we take the cost of remaining loyal to
be 0). In other
words, if switching is cheap relative to complaining, then
complaining will not
be observed, as consumers will immediately transition from
remaining loyal to
switching. Only for certain values of the cost and benefit
parameters would one
observe the pattern of customer responses that we suggest.
Figure 1 provides an illustration of our argument. We include

three curves cor-
responding to the utility obtained by different responses to
failure, along with the
cutoff minimal utility below which no service is purchased.
Since the utilities are
straight lines, their upper envelope is always convex. We
interpret complaining as
a response to service failure lying between loyalty and exit. The
particular inter-
section points for t0, t1, and t2 are defined by the equations
t
U M U M P
d0
=
- -( ) ( )
, (2)
t
c
b d1
=
-
(3)
and
t
s c
a b2
=

-
-
. (4)
Given these considerations, a simple representation of the
extent of complaining
and/or switching obtains whereby magnitudes relate to levels of
dissatisfaction.
Figure 1. Theoretical model
2018 12:10:01 PM
In any given market, the numbers of customers falling into
different categories by
the theoretical frequencies are as follows:
total customers = -N F t[ ( )],1 0 (5)
satisfied customers = -qN F t[ ],( )1 0 (6)
dissatisfied customers = - -( ) ( ) ,[ ]1 1 0q N F t (7)
loyal customers = -
æ
è
ççç

ö
ø
÷÷÷÷-
é
ë
ê
ê
ù
û
ú
ú
( ) ( ) ,1 0q N F
c
d
F t (8)
vocal customers = -
-
-
æ
è
ççç
ö
ø

÷÷÷÷-
æ
è
ççç
ö
ø
÷÷÷÷
é
ë
ê
ê
( )1 q N F
s c
a b
F
c
d
ùù
û
ú
ú
, (9)
and
exiting customers = - -

-
-
æ
è
ççç
ö
ø
÷÷÷÷
é
ë
ê
ê
ù
û
ú
ú
( ) .1 1q N F
s c
a b
(10)
The effects of changes in underlying cost parameters, or in
product quality θ,
on the theoretical frequencies are obtained directly. Our interest
focuses mainly
on complaints. It is easy to see that the number of complaints

rises as s rises, falls
as c rises, and rises as (1 - θ) rises. In particular, if we denote
the number of dis-
satisfied customers that adopt the voice response by π, then we
obtain
¶
¶
= -
-
-
æ
è
ççç
ö
ø
÷÷÷÷ - >
-p q
s
Nf
s c
a b
a b( ) ( ) ,1 01 (11)
¶
¶

= - -
-
-
æ
è
ççç
ö
ø
÷÷÷÷ - +
æ
è
ççç
ö
ø
÷÷÷÷
é
ë
ê
ê
ù
û
-p q
c

N f
s c
a b
a b f
c
d
( ) ( )1 1 úú
ú
< 0, (12)
and
¶
¶
= -
-
-
æ
è
ççç
ö
ø
÷÷÷÷-
æ
è

ççç
ö
ø
÷÷÷÷
é
ë
ê
ê
ù
û
ú
ú
<
p
q
N F
s c
a b
F
c
d
0. (13)
Similar expressions are available for all categories of
consumers. We are now
able to examine the probable effects of changes in competition

on consumers’
complaining behavior. For reasons of practicality and
correspondence with
Hirschman’s conjecture, we focus primarily on the
consequences of changes in
market structure on the costs of switching providers (s) and on
firms’ service
quality (θ).
Equation (11) represents the pure Hirschman conjecture in our
framework. As
a general matter, the cost of switching to another provider is
plausibly declining
in the degree of market competition for several reasons. First,
switching costs are
plainly infinite in pure monopoly. Second, greater competition
increases the like-
lihood that a disappointed buyer is located close to an
alternative seller, either
2018 12:10:01 PM
geographically or metaphorically. (An exception to this general
supposition could
arise if vendor-specific equipment or operating-system
incompatibilities are im-
portant.) Third, firms in competitive markets offering
subscription services often
take steps to make switching cheaper for (rival firms’)

customers by, for exam-
ple, paying some switching costs or handling the porting of
accounts to the new
provider. Incumbents can of course also make it more costly for
customers to
switch to competitive vendors, so the net effects are unclear.
Fourth, it is presum-
ably easier to leave one seller for another when the first has a
small market share
and all others collectively have a large market share. While all
of these effects are
plausible and suggest that more competition can reduce
switching costs, it is clear
that switching costs may not generally be monotonic in market
structure every-
where. For example, while an oligopoly must offer lower
switching costs than a
monopoly, the comparison between oligopoly and atomistic
competition is less
clear. Hirschman (1970) repeatedly refers to markets exhibiting
a combination of
monopolistic and competitive elements, so the limiting case of
perfect competi-
tion may solely be of theoretical interest.
The effect represented in equation (11), however, generally
cannot be inde-
pendently observed. This is because of the confounding effect
represented in
equation (13), which illustrates the impact of service quality on
complaints. In
the simple representation outlined here—and in the
commonsense view—com-
plaints are less likely when service quality is high, ceteris
paribus. However, the
relationship between competition and product quality—even of

the vertical
sort—is theoretically ambiguous. Models in the literature can be
parameterized
to produce either a positive or a negative relationship between
competition and
vertical product quality. This ambiguity implies that, for any
given industry or
market, the nature of the relationship can only be resolved
empirically. While it
is generally assumed that competition encourages quality and
that firms lacking
competition often provide mediocre or poor service, this issue
remains one that
requires practical study.
If an increase in competition increases (average) quality in the
market, then
the analysis implies that the numbers of complaints will decline.
This outcome is
because increased competition lowers switching costs (so more
consumers switch
at the expense of complainers), while higher product quality
results in fewer dis-
satisfied buyers in every category. A potential difficulty arises,
however, when de-
creases in market concentration trigger reduced product quality.
In this unlikely
but not theoretically impossible case, the number of complaints
might increase if
the effects of reduced quality overwhelm the reduced switching
costs. This out-
come implies that any conclusion regarding the validity of
Hirschman’s conjec-
ture on voice and market structure must be based on an analysis
capable of iden-
tifying and separating these effects.

Finally, it is clearly possible that changes in market structure
might also be
thought to affect other parameters in the simple analysis given
here. We have
largely ignored the potential effect of a change in market
structure on the costs
of using voice, for example. Changes that increase this cost will
discourage com-
2018 12:10:01 PM
plaints, which will lead to more switching and more silent
suffering. It is not clear
what effect market structure might have on these costs, but it is
plausible that the
existence of some sort of scale economies in customer service
could give the mo-
nopoly a cost advantage in fielding service calls. Such an effect,
if it existed, would
work against any finding in favor of Hirschman’s conjecture.
It is therefore apparent that the competitive process adds
complexity and idio-
syncrasy to the role of customers’ complaints. Changes in the
level of competi-
tion might alter the costs of voicing complaints and/or
switching but at the same
time may also increase or decrease product quality, which leads

to changes in
complaint flows. The empirical challenge then is to tease apart
these potentially
distinct economic phenomena. It is to this task that we now
turn.
3. Empirics
3.1. Empirical Setting
Our empirical setting is the telecommunications industry—a
sector whose in-
dustrial organization has evolved significantly over time. Along
with the 1984
AT&T divestiture, the Telecommunications Act of 1996
represents a significant
watershed in the transformation of the industry from a
monopoly environment
to the more competitive industry of today. In particular, the act
represented the
first full-throated endorsement of industry competition. The act
provided not
only a rhetorical embrace of competition but also language that
established the
means by which new entrants (competitive local-exchange
carriers; CLECs)
could directly compete against incumbents (local-exchange
carriers; LECs) for
the patronage of residential and business customers.
The 1996 Telecommunications Act envisioned that new entrants
would com-
pete as resellers of local-exchange services, purchase unbundled
network ele-
ments, or become full facilities-based providers of local-
exchange telephone

service. Given the substantial expense associated with full
facilities-based entry,
CLECs predominantly entered either as resellers or as
purchasers of unbundled
network elements in the wake of the act and, consequently,
required the use of
LEC-owned facilities in order to compete. While general
principles to guide new
entrants’ access to incumbent LECs’ facilities were part of
federal law, the de-
tailed implementation of access was left predominantly to
individual states. In
the first instance, CLECs were required to negotiate with
incumbent LECs over
the terms and conditions of access. In the event that the parties
could not come
to terms, individual state public-utility commissions (PUCs)
were charged with
establishing appropriate rates, terms, and conditions. As CLECs
largely failed to
reach terms with incumbent LECs in the wake of the act, state
PUCs arbitrated
access terms on their behalf. Consequently, over the 1996–98
period CLECs were
immersed in administrative proceedings instead of marketplace
competition, and
their combined market share remained under 1 percent.
Significant entry into the
local-exchange marketplace began in earnest post-1999,
however, with CLECs
capturing market share vis-à-vis incumbent LECs over the
1999–2006 period.
2018 12:10:01 PM

Three policy elements precipitated by the Telecommunications
Act provide
a useful setting in which to test the relationship between
consumers’ complain-
ing behavior and marketplace competition. First, the act
represents a relatively
clean shift in local-exchange telephone service from a monopoly
environment
to a competitive environment. This policy change thus offers a
quasi-natural-
experiment setting in which the impact of industry structure
changes on com-
plaining behavior can be tested.7 Second, competition that
emerged in the wake
of the act was far from uniform, as considerable geographic
variation in both the
number of new entrants and the extent of market share captured
by new entrants
resulted. We take advantage of not only intertemporal changes
in the intensity
of competition but also geographic variations in the intensity of
competition to
isolate the effects of market structure changes on complaining
behavior. Third,
an enduring feature of the telecommunications industry is its
distinct customer
types. As business customers are often considerably larger than
residential cus-
tomers, we are able to test the effects of market structure
changes for these two

consumer types using separate econometric models.
Finally, two fortuitous characteristics of the data on the
telecommunications
industry facilitate our empirical analysis. First, the study of
complaining behav-
ior has been limited by the highly proprietary nature of the data
on complaints.
Because LECs are overseen by state and federal regulatory
agencies, however, the
FCC collects data on complaints. Second, and as described in
Section 2, the emer-
gence of competition can theoretically affect numbers of
complaints either di-
rectly by altering consumers’ costs for switching providers or
indirectly by alter-
ing marketplace quality levels. The FCC also collects data on
perceptions of LECs’
customer-service quality. These data allow us to identify the
effects of changes in
quality on complaining behavior separate from the effects of
industry structure
on complaining behavior as conjectured by Hirschman.
3.2. Data
Our data are drawn from the FCC’s electronic Automated
Reporting Manage-
ment Information System (ARMIS) filing system.8 We utilize
the FCC’s Service
Quality Report (Report 43-05) and Customer Satisfaction
Report (Report 43-06)
in our empirical analysis. Each report spans the years 1996–
2006 and contains
data on LECs. We utilize the years after 1998 for both
conceptual and practical

reasons. As described above, new entrants were largely engaged
in administrative
proceedings rather than marketplace competition during 1996–
98. As a practi-
cal matter, moreover, the de minimis presence of new entrants
resulted in the
FCC withholding market-share data in a large number of states,
which severely
7 The “quasi” modifier is necessitated by the prospect that
changes in market structure and com-
petition may generate changes in the quality—in turn, altering
changes in propensities to complain.
We account for this indirect effect in the empirical analysis.
8 For more information and data available via the Automated
Reporting Management Informa-
tion System (ARMIS), see FCC, ARMIS Data Descriptions
(http://www.fcc.gov/encyclopedia/armis
-data-descriptions-1).
2018 12:10:01 PM
http://www.fcc.gov/encyclopedia/armis-data-descriptions-1
http://www.fcc.gov/encyclopedia/armis-data-descriptions-1
truncates the available data over this 2-year period. Finally,
while the industry
structure has continued to evolve since 2006, the FCC altered
the categorization

of CLECs in 2007, thereby creating an unfortunate
incompatibility with the ear-
lier data collected. We accordingly focus our empirical analysis
on 1999–2006,
the period in which the industry experienced its most prominent
changes as
competition emerged. The data include observations on all
reported incumbent
local- exchange telephone carriers in the FCC’s database.
Because of corporate re-
organizations, mergers, and spinoffs, the data constitute an
unbalanced panel of
between 172 and 196 companies (observations), depending on
the year.
The FCC’s Service Quality Report provides data on customers’
complaints
made to state PUCs.9 Once state PUCs receive these
complaints, they inform the
relevant LECs so that service issues may be resolved. The LECs
are required to
report these complaints to the FCC on or before April 1 of each
year, which tallies
them in its ARMIS database. Our focus is on service-quality
complaints, which
pertain to service, installation, and repair but not to billing,
operator service
providers, or 900 and 976 numbers (see FCC 2007). The Service
Quality Report
separates complaints by residential and business customers.
Figure 2 displays
average reported service-quality complaints across LECs by
customer type over
1999–2006. Following an initial spike, average residential
customer and business
customer complaints decline over this time period. Figure 2 also

indicates that
average reported residential customer complaints considerably
outpace average
reported business customer complaints—a relatively
unsurprising result given
marked size differences in residential customers versus business
customers.
The FCC’s Customer Satisfaction Report provides data on
customers’ satisfaction
levels. The LECs are required to report to the FCC annual
customer- satisfaction
survey results for residential and business customers based on
customer-
service and business procedures related to installations, repairs,
and business of-
fices. We incorporate these data as a proxy for service quality.
Figure 3 displays
average reported satisfaction levels across LECs by customer
type for 1999–2006.
Residential customers’ perceived quality of local-exchange
telephone service un-
dulates, while business customers’ perceived quality of local-
exchange telephone
service consistently improves after an initial drop over the
observed time period.
Annual versions of the FCC’s Local Competition Reports
provide data on
CLEC competition. While no ideal measure of the extent of
competition exists,
we utilize two commonly used proxies. First, the reports
provide the number of
CLECs operating in each state.10 Prior to 2005, the FCC
collected data only from
those CLECs with at least 10,000 switched access lines in a

particular state. Be-
ginning in 2005, all CLECs regardless of size were required to
report these data.11
Second, the reports provide the aggregate market share held by
CLECs in each
9 While complaints are also made directly to the FCC, they
represent a small fraction of the com-
plaints received. We therefore utilize the more granular data
afforded in the complaints made to
local regulatory agencies.
10 See FCC, Local Telephone Competition and Broadband
Deployment (http://www.fcc.gov/wcb/
iatd/comp.html).
11 We account for this change in our empirical estimation.
2018 12:10:01 PM
http://www.fcc.gov/wcb/iatd/comp.html
http://www.fcc.gov/wcb/iatd/comp.html
state.12 Figure 4 displays the average number of CLECs and the
average CLEC
market share across states over 1999–2006. The average number
of CLECs in-
creased from five to nearly 40, and the average CLEC market
share increased
from 4 to 18 percent over this time period.

3.3. Empirical Strategy and Variables
According to theory, increasing competition may affect
complaints both di-
rectly by altering the costs for consumers to switch providers
(the Hirschman
conjecture) and indirectly via changes in service quality that
may accompany
changes in the level of competition. Our empirical analysis
seeks to separately
identify these channels. In particular, we seek to determine if,
after controlling for
changes in quality that may accompany changes in market
structure, the separate
influence on complaining behavior conjectured by Hirschman
exists. To do so,
we exploit geographic and temporal differences in competition
in the wake of the
Telecommunications Act.
12 Because individual competitive local-exchange carrier
(CLEC) market shares are competitively
sensitive, the FCC reports only aggregate CLEC market shares
in each state-year. If only a small
number of CLECs are present in a state-year (especially in
1999) the FCC withholds CLEC market-
share data but provides data on the number of CLECs. In these
instances, we first calculate average
CLEC market share in a state-year once aggregate market-share
data are reported (say, in 2000)
using the data on number of CLECs. We then use this average
CLEC market share to backcast ag-
gregate CLEC market share in the unreported year using the
average CLEC market share and the
number of CLECs. This approach allows for a more complete

data panel. As a robustness check, we
estimated models with the smaller set of raw data provided by
the FCC and confirm that no substan-
tive differences obtain.
Figure 2. Average yearly number of complaints
2018 12:10:01 PM
We use several variables to conduct our analysis. The term
Complaintsijt rep-
resents the natural logarithm of the number of complaints
received by incumbent
LEC i in state j during year t.13 We separately identify and
measure complaints for
residential (REZ) and small-business (BIZ) customers, and we
estimate models
separately for these customer types.
We use two measures of competition. The term CLEC Countijt
is the natural
logarithm of the number of CLECs competing in the area served
by incumbent i
in state j during year t. Because the FCC reports the number of
CLECs at the state
(rather than service-territory) level, we derive our competition
count measure as
the number of CLECs facing an incumbent LEC relative to the
largest incumbent

LEC operating in the state-year. We thus assume that the
number of competitors
facing an incumbent LEC is proportional to its share of lines
relative to the larg-
est incumbent LEC.14 The term CLEC SoMijt represents the
market share held by
CLECs competing in the area served by incumbent i in state j
during year t.
13 We experimented with several alternative specifications of
the dependent variable, but no sub-
stantive changes to the empirical results reported below obtain.
14 In our measure, CLEC Countijt equals CLEC Countjt ×
(Linesijt /Max Linesjt), where Linesijt is the
number of (residential or business) lines held by incumbent
local-exchange carrier (LEC) i in state
j at time t and Max Linesjt is the (residential or business) line
count of the largest LEC in state j at
time t. We also examined two alternative competition-count
measures as robustness tests. We first
substituted Sum Linesjt (the summation of lines in state j at
time t) for Max Linesjt, which effectively
proxies for the number of competitors an incumbent LEC faces
by its share of total lines in that
state-year. We then used CLEC Countjt (the logged statewide
count of competitors). Neither of these
alternative competition-count measures substantively alters the
results or conclusions. For similar
measurement approaches to competitive entry, see Abel (2002)
and Greenstein and Mazzeo (2006),
Figure 3. Average yearly percentage of satisfied customers
2018 12:10:01 PM

We account for service quality using three customer-satisfaction
measures,
which provide the percentage of either residential or business
customers sur-
veyed that report satisfaction in installations, repairs, and
business- office ser-
vices for incumbent LEC i in state j in year t. We create a
composite residen-
tial customer- satisfaction measure (REZ Pct Satijt) and a
composite business
customer- satisfaction measure (BIZ Pct Satijt) by averaging
across the three mea-
sures.15
We control for incumbent LEC size using the natural logarithm
of the number
of residential lines (REZ Linesijt) or business lines (BIZ
Linesijt) in service. This
approach permits the data to flexibly reveal the relationship of
complaint levels
to firm size, instead of having it imposed as would occur by
using the number
of complaints per line as the dependent variable. To account for
the FCC’s 2005
change in reporting on competitors, we include a dummy
variable (Post 2004t)
set equal to one for years 2005 and 2006 and zero otherwise.
Our analysis also includes time-varying state-level measures of

population
(Populationit), per capita income (Per Capita Incomeit), and the
percentage of
which use new-competitor counts, and Economides, Seim, and
Viard (2008), which uses new-entrant
market share.
15 Slightly less than 3 percent of the residential customer-
satisfaction observations (19 of 668) are
at 100 percent, while slightly more than 3 percent of the small-
business customer-satisfaction obser-
vations (22 of 668) are at 100 percent. Accordingly, ceiling
effects are not binding for the subsequent
empirical analysis. We also experimented with different
permutations (for example, the total of the
aggregate satisfaction measure) as robustness checks. The
results are substantially invariant to those
reported.
Figure 4. Average yearly number and market share of
competitive local-exchange carriers
2018 12:10:01 PM
Democrat PUCs (Pct Dem PUCit). As described below, these
variables serve as
first-stage instruments in an analysis that permits the level of
competition and

the level of consumer satisfaction to be determined
endogenously.
3.4. Sample Statistics
Table 1 provides summary statistics for the unlogged dependent
and inde-
pendent variables. The average annual number of residential
customers’ and
business customers’ complaints per company, respectively, over
the entire sam-
ple are roughly 131 and 18. These numbers indicate that
residential customers’
complaints are far more prevalent than business customers’
complaints, but sub-
stantial heterogeneity is nevertheless observed for each
variable. The number of
CLECs and CLEC market share also demonstrate significant
heterogeneity. Some
states have no CLECs or are characterized by limited CLEC
market share, while
other states have up to 70 CLECs in operation. Residential and
business custom-
ers similarly demonstrate significant variation in their
satisfaction with installa-
tions, repairs, and business-office operations, via the aggregate
measures used in
the baseline empirical estimations.
Table 2 provides correlation statistics for the variables. We find
significant pos-
itive correlations between numbers of residential and business
complaints, be-
tween numbers of residential and business lines, and between
numbers of (res-
idential and business) complaints and (residential and business)

lines. There are
also moderate negative correlations between numbers of
(residential and busi-
ness) complaints and (residential and business) aggregate
customer-satisfaction
levels related to installations, repairs, and business-office
operations. The number
of CLEC competitors is negatively correlated with the number
of both residen-
tial and business complaints (although pairwise significance is
achieved only for
the latter), while the market share held by CLEC competitors is
negatively and
significantly correlated with the number of residential and
business complaints.
Table 1
Summary Statistics
Variable Mean SD Min Max
REZ Complaints 130.93 401.83 .00 4,982.00
BIZ Complaints 17.84 60.86 .00 1,096.00
REZ Lines 577,471.50 1,594,666.00 .00 40,200,000
BIZ Lines 282,657.50 706,988.70 .00 6,745,436
CLEC Count 19.75 16.68 .00 70.00
CLEC SoM 12.76 7.13 .00 46.00
REZ Pct Sat .92 .03 .77 1.00
BIZ Pct Sat .91 .03 .77 1.00
Per Capita Income 30,860.02 4,675.17 20,053 57,746
Population 8,449,827.00 7,812,824.00 479,602 36,000,000
Pct Dem PUC .436 .288 .000 1.000
2018 12:10:01 PM

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2018 12:10:01 PM
3.5. Empirical Results
In parallel with the development of the theoretical treatment of
the complaint-
generation process discussed above, we examine the empirical
relationship be-
tween industry structure and complaint propensities. Complaints
are modeled as
a function of the degree of competition, the level of firms’
service quality, and a
set of controls. Our initial theoretical specification of
consumers’ complaint pro-
pensities is conditional on an exogenously determined service-
failure process.
The basic form of the model estimated is
Complaints CLEC Comp Pct Satijt ijt ijt ijt i ijt= + + + + +b b
b d g e0 1 2 X , (14)

where CLEC Compijt is measured as either CLEC Countijt or
CLEC SoMijt, Pct
Satijt is calculated using an aggregate measure of customer
satisfaction (REZ Pct
Satijt or BIZ Pct Satijt), Xijt is a vector of controls, β and δ are
parameters to be es-
timated, γi is the set of state fixed effects, and εijt is a random
disturbance term.
Table 3 provides the initial ordinary least squares estimation
results separated
into residential and business customers.16 Model 1 utilizes
CLEC Countijt as the
competition measure; model 2 utilizes CLEC SoMijt as the
competition measure.
The R2-statistics indicate that considerable explanatory power
obtains in all esti-
mations. Likelihood ratio tests confirm statistically significant
explanatory power
from the inclusion of state fixed effects in the econometric
models.
Several noteworthy insights emerge from the estimations. First,
after con-
trolling for other variables including the observed levels of
quality, the results
provide clear support for the Hirschman conjecture. Across both
residential
and business customers the results indicate that increases in
competition nega-
tively and statistically significantly (p < .01) influence observed
complaint lev-
els. This result robustly holds not only for residential and
business consumers
but also with respect to the measures of competition. Second,
while our principal

goal is to identify or refute the Hirschman conjecture, the
analysis also neces-
sarily raises the question of whether changes in competition
following the Tele-
communications Act led to changes in quality that may, in turn,
have affected
the observed level of complaints. The results in Table 3 provide
some initial in-
sights on this issue. In particular, we see that both residential
and business cus-
tomers’ complaints are negatively and highly statistically
significantly (p < .01)
correlated with consumers’ perceived levels of quality.17 The
inclusion of either
REZ Pct Satijt or BIZ Pct Satijt controls for the possible
confound in our identifi-
cation of the Hirschman effect (were we to not control for
observed quality lev-
els) and identifies a separate influence on complaining behavior
attributable to
post-competition changes in quality. Third, we unsurprisingly
observe that the
16 Given the potential for correlations in the errors across our
residential and business equations,
we also estimated the residential and business models via
seemingly unrelated regression as a ro-
bustness check. The results are inconsequentially different than
those reported. We also explored
whether nonlinearities from interactions or squaring terms
added significantly to the power or in-
sights of the model, but these alternatives were found to add
little to the results reported here.
17 See Forbes (2008) for a similar result for the US airline
industry.

2018 12:10:01 PM
level of complaints is related to firm size but more interestingly
find that the elas-
ticity of observed complaints with respect to size is consistently
less than unity.
As the estimations control for consumers’ perceptions of
quality, this declining
propensity to complain may be due to a sense that larger firms
are less responsive
to complaints, and, consequently, consumers are less likely to
complain for any
given level of service failure.
Although the results in Table 3 provide insight into consumers’
complaining
behavior, several considerations potentially cloud the inferences
from these es-
timations. Of particular concern is the possiblity that both the
observed level of
quality and the level of competition are endogenously
determined. As shown in
Hörner (2002), Kranton (2003), and Levhari and Peles (1973),
service quality may
be endogenously driven by changes in competition. Similarly,
observed levels of
competition may themselves be endogenous to prevailing
market conditions. To

account for the possible confounds that may arise with such
endogeneity, we em-
ploy an instrumental variables (IV) approach for the customer-
satisfaction and
competition variables.18 Our search for satisfactory instruments
draws on three
prior findings. First, as market size has been shown to be a
determinant of com-
18 Durbin-Wu-Hausman tests confirm endogeneity in the
residential customer and business cus-
tomer estimations, which suggests that an instrumental variables
approach is warranted.
Table 3
Ordinary Least Squares Results
Model 1 Model 2
REZ
Complaints
BIZ
Complaints
REZ
Complaints
BIZ
Complaints
CLEC Count −.337**
(.086)
−.228**
(.074)

CLEC SoM −.363**
(.077)
−.344**
(.070)
REZ Pct Sat −11.612**
(1.410)
−12.415**
(1.371)
BIZ Pct Sat −8.128**
(1.126)
−8.131**
(1.250)
REZ Lines .960**
(.055)
.769**
(.028)
BIZ Lines .673**
(.045)
.555**
(.022)
Post 2004 .338*
(.134)
.058
(.113)

.118
(.090)
−.007
(.079)
Constant 3.766*
(1.566)
2.650+
(.365)
6.845**
(1.492)
4.128**
(1.277)
F-test 68.48** 59.20** 79.51** 62.26**
R2 .825 .774 .827 .752
Note. Standard errors (in parentheses) are robust and clustered
by firm. All
regressions include state fixed effects. N = 668.
+ p < .10.
* p < .05.
** p < .01.
2018 12:10:01 PM

petitive entry (Abel 2002; Burton, Kaserman, and Mayo 1999;
Bresnahan and Re-
iss 1991), we expect state-level population and per capita
income to correlate with
CLEC entry. These variables are not obviously related to
variations in complaints,
however, which suggests their suitability as instruments.
Second, as market size
has been shown to be a determinant of product quality (Berry
and Waldfogel
2010), we expect state-level population and per capita income
to affect endog-
enous investments in service quality and, subsequently,
customer-satisfaction
levels. Finally, especially in regulated industries such as
telecommunications, lo-
cal regulatory policies as determined in part by the political
composition of reg-
ulatory commissions may affect competition and customer-
satisfaction levels
(Smart 1994; Fremeth, Holburn, and Spiller 2012). With these
considerations in
mind, Populationit, Per Capita Incomeit, and Pct Dem PUCit
serve as first-stage
instruments.
Tables 4 and 5 provide the two-stage least squares results for
the residential
customer and business customer models. Table 4 provides the
second-stage re-
sults that are of primary interest and are very similar to those
presented in Table
3. In particular, we find the presence of both direct effects of
market structure on

complaining behavior and indirect effects on complaints via
quality changes. For
both competition measures and across residential and business
lines, increases in
competition result in statistically significant (p < .01)
reductions in the level of
observed complaining. These findings provide support for the
Hirschman con-
jecture. Estimations that employ the number of CLEC entrants
indicate that the
elasticity of complaints with respect to changes in competition
is −.70 for resi-
dential customers and −.57 for business customers. Holding all
other variables
at their respective means, we find that a 1-standard-deviation
increase in CLEC
entry decreases residential complaints nearly 55 percent and
decreases business
complaints nearly 48 percent. Estimations that employ the
market share of CLEC
entrants indicate that the elasticity of complaints with respect to
changes in com-
petition is −.51 for residential customers and −.54 for business
customers. A
1-standard-deviation increase in CLEC market share decreases
residential com-
plaints more than 27 percent and decreases business complaints
more than 28
percent, when all other variables are held at their respective
means.
Table 5 reports the first-stage results and provides support for
the chosen in-
struments. Overall, F-tests of the joint significance of the
instruments is signifi-
cant at p < .01. Subsequent empirical tests provide confidence

that the variables
employed to correct for endogeneity represent appropriate
excluded instruments.
Sargan-Hansen tests of overidentifying restrictions are not
rejected (Hayashi
2000), which indicates that the excluded instruments are valid.
Kleibergen-Paap
underidentification tests are rejected (Kleibergen and Paap
2006), which indicates
that the instruments are relevant. And redundancy tests are
rejected (Breusch et
al. 1999), which indicates that the instruments are not
redundant.19
19 The specifics of these first-stage results, while not of
primary interest, nonetheless are informa-
tive. For instance, we find that competition is negatively
correlated with population for our mea-
sures of competition that are based on the number of firms in
the market while being positively
2018 12:10:01 PM
3.6. Discussion
The consistent and strong evidence of a negative relationship
between the level
of competition and the propensity to complain provides
empirical evidence con-

sistent with the conjectures described in Section 2. While the
estimations do not
distinguish whether reductions in the propensity to complain
result in more cus-
tomers moving into the loyal or the exit category, the logic of
our theory suggests
that the emergence of competitive alternatives reduces the costs
associated with
switching and thereby shrinks the category of complainers and
expands the cat-
egory of switchers. Telephone industry data also support this
general proposi-
tion, as the number of incumbent LECs’ residential and business
lines declined
by over 43 million during the 2000–2006 period. This decline is
widely attributed
to a combination of customers switching to newly emergent
CLECs, switching to
wireless carriers, and reducing their number of lines (FCC
2008).
We also find that competition-induced changes in quality exert
an indepen-
related to competition measures based on CLEC market share.
This is consistent with the earlier en-
try by the largest CLECs (AT&T and MCI) that resulted in the
capture of significant market shares,
while smaller and more numerous CLECs targeted less populous
states. For the count measure of
competition, we also find, consistent with Abel (2002), that the
political orientation of public-utility
commissions also impacted the number of entrants.
Table 4
Second-Stage Two-Stage Least Squares Results

Model 1 Model 2
REZ
Complaints
BIZ
Complaints
REZ
Complaints
BIZ
Complaints
CLEC Count −.700**
(.161)
−.573*
(.272)
CLEC SoM −.507**
(.090)
−.538**
(.203)
REZ Pct Sat −26.679**
(10.076)
−19.090*
(8.768)
BIZ Pct Sat −20.124*
(10.095)

−5.989
(12.428)
REZ Lines 1.089**
(.104)
.737**
(.050)
BIZ Lines .790**
(.188)
.566**
(.065)
Post 2004 .721**
(.243)
.614*
(.264)
.167
(.120)
.091
(.081)
Constant 17.103
(10.301)
12.983
(11.191)
13.800
(8.915)

2.209
(12.199)
F-test 37.99** 36.74** 64.22** 61.65**
R2 .766 .711 .817 .776
Note. Standard errors (in parentheses) are robust and clustered
by firm. All
regressions include state fixed effects. N = 668.
* p < .05.
** p < .01.
2018 12:10:01 PM
Ta
bl
e
5
Fi
rs
t-
St
ag
e
T
w

o-
St
ag
e
L
ea
st
S
qu
ar
es
R
es
ul
ts
M
od
el
1
M
od
el
2

C
LE
C
C
ou
nt
R
EZ
Pc
t S
at
C
LE
C
C
ou
nt
B
IZ
Pc

t S
at
C
LE
C
So
M
R
EZ
Pc
t S
at
C
LE
C
So
M
B
IZ
Pc

t S
at
Po
pu
la
tio
n
−2
.5
07
**
(.6
89
)
.0
18
(.0
68
)
−1
.8
76
**
(.7

23
)
.0
36
(.0
90
)
1.
64
7*
(.6
81
)
.0
18
(.0
68
)
1.
64
8*
(.6
81
)

.0
36
(.0
90
)
Pe
r
C
ap
ita
In
co
m
e
1.
20
8+
(.6
32
)
−.
15
8*
*

(.0
50
)
1.
01
1
(.6
98
)
−.
12
9*
*
(.0
47
)
−.
39
1
(.6
31
)
−.
15

8*
*
(.0
50
)
−.
39
2
(.6
31
)
−.
12
9*
*
(.0
47
)
A
vg
D
em
P
U

C
−.
23
4*
(.1
06
)
−.
00
5
(.0
08
)
−.
26
1*
(.1
11
)
−.
00
4
(.0
07

)
−.
03
4
(.0
75
)
−.
00
5
(.0
08
)
−.
03
4
(.0
75
)
−.
00
4
(.0
07

)
T
im
e
T
re
nd
.1
33
**
(.0
23
)
.0
05
**
(.0
02
)
.1
24
**
(.0
26

)
.0
07
**
(.0
02
)
.2
31
**
(.0
20
)
.0
05
**
(.0
02
)
.2
31
**
(.0
20

)
.0
07
**
(.0
02
)
R
EZ
L
in
es
.5
54
**
(.0
15
)
−.
00
5*
*
(.0
01

)
.0
00
(.0
06
)
−.
00
5*
*
(.0
02
)
B
IZ
L
in
es
.5
15
**
(.0
14
)

−.
00
5*
*
(.0
01
)
.0
00
(.0
06
)
−.
00
5*
*
(.0
01
)
Po
st
2
00
4

.7
11
**
(.0
51
)
−.
00
4
(.0
04
)
.6
70
**
(.0
57
)
.0
01
(.0
04
)
−.

31
4*
*
(.0
41
)
−.
00
4
(.0
04
)
−.
31
4*
*
(.0
41
)
.0
01
(.0
04
)

C
on
st
an
t
−2
50
.9
76
**
(3
8.
04
9)
−8
.1
76
*
(3
.1
87
)
−2
39

.5
83
**
(4
3.
31
2)
−1
2.
18
7*
*
(3
.2
03
)
−4
79
.6
95
**
(3
3.
86

1)
−8
.1
76
*
(3
.1
87
)
−4
79
.7
50
**
(3
3.
80
6)
−1
2.
18
7*
*
(3

.2
03
)
F-
te
st
18
6.
21
**
17
.7
0*
*
13
2.
74
**
21
.1
1*
*
69
.8

0*
*
17
.7
0*
*
69
.9
1*
*
21
.1
1*
*
R
2
.9
06
.3
99
.8
97
.3
60

.8
36
.3
99
.8
36
.3
60
N
ot
e.
S
ta
nd
ar
d
er
ro
rs
(i
n
pa
re
nt

he
se
s)
a
re
r
ob
us
t a
nd
c
lu
st
er
ed
b
y
fir
m
.
+
p
<
.1

0.
*
p
<
.0
5.
**
p
<
.0
1.
2018 12:10:01 PM
dent effect on complaint levels.20 In particular, we find that
observed levels of
changes in quality as captured by variations in REZ Pct Satijt or
BIZ Pct Satijt are
negatively and statistically significantly related to complaint
levels.21 Consistent
with previous research (Forbes 2008), our results indicate that
higher levels of
consumer satisfaction are associated with lower complaint
levels.

4. Conclusion
Competition works only if poorly performing vendors can be
punished. In
most markets, the principal vehicle for consumers to discipline
ill-performing
firms is to switch to alternative providers of the good or
service. Considerable and
appropriate attention has accordingly been given to the
magnitude of switching
costs that customers face. Switching is by no means the only
mechanism consum-
ers have to express disapproval. While some unhappy customers
may choose to
no longer buy the good or service, other consumers express
their disappointment
through complaining or, as Hirschman (1970) terms it, using
voice. Although
Hirschman describes the many roles that voice plays, his
analysis of its applica-
tion incorporates a famous conjecture concerning its
relationship with market
structure. Noting that under monopoly complaining is the sole
means for buyers
to express dissatisfaction, Hirschman suggests that the use of
voice declines as
markets become more competitive. Although entirely plausible
and inherently
interesting, Hirschman’s conjecture has not previously been
rigorously tested.
As a matter of practice, large-scale studies of complaining
behavior have been
limited by the fact that firms do not readily provide data on
consumers’ com-

plaints. For regulated industries, however, it is often the case
that one key func-
tion of regulatory oversight bodies is to receive and process
customers’ com-
plaints and to adopt appropriate public-policy responses to
them. By drawing
on a large-scale database of complaints recorded by the FCC
regarding local-
exchange service in the United States, we have been able to
explore the relation-
ship between complaint levels and industry structure. The
empirical analysis pro-
vides strong support for the Hirschman conjecture.
While data on complaints are necessary to test Hirschman’s
conjecture, there
20 As a robustness check, we also created quality variables
representing the summation of the per-
centage of satisfied consumers across the installation, repairs,
and business-office categories. Using
these variables in place of the average consumer-satisfaction
variable produces similarly negative
and statistically significant coefficient estimates in the
residential and business models.
21 In separate regressions, we also sought to identify any direct
influence of competition on our
two quality measures. These regressions used our quality
measures as a dependent variable, with
both competition (number or market share) and a set of controls
as independent variables. The re-
sulting estimations provide considerable (but not unanimous)
support for the intuitive proposition
that increases in quality led to higher levels of satisfaction. In
particular, the estimations indicate a

positive and statistically significant effect from CLEC entry
(CLEC CNTijt) on the percentage of res-
idential customers reporting satisfaction (p < .01) and a positive
and statistically significant effect
from CLEC entry on the percentage of business customers
reporting satisfaction (p < .01). Results
for the estimations are more mixed when competition is
measured by the market share of CLECs,
with a statistically insignificant effect from CLEC market share
on the percentage of residential cus-
tomers reporting satisfaction but a positive and statistically
significant effect from CLEC market
share on the percentage of business customers reporting
satisfaction (p < .05).
2018 12:10:01 PM
is an additional and important complication. A large literature
in industrial eco-
nomics suggests several ways in which market structure and
competition could
be linked to endogenous product quality. Product quality,
however, is known to
significantly affect complaining behavior in the expected way.
Changes in market
structure may therefore affect complaint levels through the
quality channel but
also through changes in the use of voice, as suggested by
Hirschman. Our analy-

sis incorporates this complication—an approach made possible
by the availability
of standardized indices of consumer satisfaction—so that
quality can, in an ap-
proximate sense, be measured directly.
Our findings are strongly supportive of Hirschman’s insight.
Using both single-
equation and IV techniques on a large unbalanced panel of
telephone-service
complaint data, we find evidence that increasing competition
reduces recourse
to voice, holding the quality of the underlying service constant.
These results ob-
tain for both residential and small-business customers. Further,
as documented
in myriad other related literature, service quality matters, with
higher quality re-
sulting in fewer complaints, ceteris paribus.
While our results provide supportive preliminary evidence on
the nature of
the relationship between competition and voice, they also
suggest the possibil-
ity of additional explorations. Several potential refinements of
the theoretical
models are readily apparent and may yield additional insights.
For example, it
seems plausible that as the intensity of competition increases,
the extent of inter-
nal complaint mechanisms utilized by firms may evolve (Fornell
and Wernerfelt
1988). This effect might be best captured by making the
effectiveness of informal
complaints (that is, complaints to the firm rather than a public
oversight body)

a positive function of industry fragmentation. In this case, a
more sophisticated
model that permits firms to optimize across public and private
complaints may
provide insights into both complaints and complaint-
management processes that
are not considered here.
While we have focused on the relationship between complaints
and market
structure, our empirical results convey a relationship between
customers’ satis-
faction and complaining that is worthy of additional
consideration. While firms
are ultimately interested in the level of customers’ satisfaction
with their goods or
services, the level of satisfaction is often not directly
observable—firms most typi-
cally simply observe that customers do or do not complain. Our
analysis suggests,
however, that a slip of considerable and varying size may exist
between the cup of
satisfaction and the lip of complaints. The simultaneous
presence of satisfaction
and complaint data may consequently afford a more detailed
investigation into
this relationship than has heretofore been possible.
Our empirical results similarly raise several managerial and
public-policy con-
siderations. Consider how individual firms assess data that they
receive from
complaining customers. While firms may be tempted to draw
inferences regard-
ing improved customer service or quality from shorter queues of
complaining

customers, such inferences may prove unwarranted. For any
given level of sat-
isfaction, our results indicate customers’ reduced propensities
to complain as
2018 12:10:01 PM
competition grows. Customers are instead apt to move more
quickly from loy-
alty to exit, bypassing voice completely, as the number of
competitors increases.
Firms with internal complaint mechanisms in place—but
without sophisticated
customer-retention metrics—simply cannot conclude that they
are doing better
as the number of customer complaints falls. In terms of public
policy, we sug-
gest that while monopolistic industry structures gave rise to the
establishment
of public complaint mechanisms in many regulated industries,
the emergence of
competition in the telecommunications industry increasingly
gives customers the
ability to express discontent quite apart from that which they
can express to reg-
ulators. In particular, more competition increasingly allows
customers to avoid
the burdens of making complaints—instead allowing them to
rely on the market-

place alternative of exit to punish ill-behaving firms. In the face
of such ultimate
punishment, the merits of public complaint mechanisms are
likely to diminish.
Finally, while our theoretical results and corresponding
empirical analysis seek
to advance understanding of relationships among market
structure, quality, and
complaints, the applicability of our results in other contexts is
worthy of addi-
tional exploration. Our theoretical results stem in part from
various simplifying
assumptions, while our empirical results are set in a single
industry. Additional
theoretical and empirical research may reinforce or provide
additional insights
into the robustness of our results.
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Criminal Justice Policy Review

2014, Vol. 25(2) 159 –184
© 2012 SAGE Publications
Reprints and permissions:
sagepub.com/journalsPermissions.nav
DOI: 10.1177/0887403412463933
cjp.sagepub.com
463933CJP25210.1177/08874034124639
33Criminal Justice Policy ReviewJohnson
1University of Maryland, College Park, MD, USA
Corresponding Author:
Brian D. Johnson, University of Maryland, 2220 Lefrak, College
Park, MD 20742, USA.
Email: [email protected]
Judges on Trial: A
Reexamination of Judicial
Race and Gender Effects
Across Modes of Conviction
Brian D. Johnson1
Abstract
Extant research on the effects of judicial background
characteristics suggests minimal
influence from the race or gender of the sentencing judge in
criminal cases. This raises at
least two possibilities: (1) the combined influence of judicial
recruitment, indoctrination,
and socialization into the judgeship results in a homogenous
body of criminal court

judges; or (2) current approaches to identifying judge effects in
criminal sentencing
have methodological and conceptual flaws that limit their
ability to detect important
influences from judicial background characteristics. The current
article examines this
issue with data from the Pennsylvania Commission on
Sentencing that is augmented to
include information on sentencing judges and criminal court
contexts. It argues that the
mode of conviction shapes the locus of sentencing discretion in
ways that systematically
underestimate judge effects for pooled estimates of
incarceration and sentence length.
The empirical results support this interpretation, especially for
incarceration in trial
cases, where older, female, and minority judges are
substantially less likely to sentence
offenders to jail or prison terms. The article concludes with a
discussion of future
research directions and policy implications for judge effects and
disparity in sentencing.
Keywords
sentencing, judge effects, disparity, mode of conviction, HLM
For decades, criminologists, legal scholars, and policy makers
have been captivated by
continuing attempts to unravel the causes, consequences, and
loci of disparate treat-
ment in criminal sentencing. A voluminous literature has
developed examining the
Article

by guest on January 16, 2016cjp.sagepub.comDownloaded from
http://cjp.sagepub.com/
160 Criminal Justice Policy Review 25(2)
influence that individual offender characteristics, such as race
and gender, exert on
courtroom decision-making outcomes. A limited but related
literature has also emerged
focusing on the influence of courtroom actor background
characteristics in sentencing.
While the former finds continuing evidence of individual
offender disparities, the lat-
ter reports relatively few important influences from judicial
background characteris-
tics (Zatz, 2000). This has led some scholars to conclude that
the collective impact of
formal sentencing innovations, judicial selection and
socialization processes, and
courtroom workgroup pressures for conformity largely nullify
any preexisting differ-
ences tied to judicial background characteristics (Spohn, 1990a;
Steffensmeier &
Britt, 2001; Steffensmeier & Hebert, 1999).
While the advent of sentencing guidelines and the importance of
judicial socializa-
tion should not be overlooked, recent theorizing on courtroom
actor discretion may
provide valuable insights into the importance of judge
characteristics in sentencing.
Contemporary sentencing research emphasizes the fact that
courtroom decision-
making processes are likely to vary depending on whether or not

a case is plea-
bargained or convicted at trial (Alschuler, 1979; Johnson, 2003;
Padgett, 1985; Smith,
1986). If the underlying processes determining sentencing
outcomes are tied to the
mode of conviction, then it may be necessary to disaggregate
sentencing outcomes to
better capture the effects of different decision makers in the
punishment process. In
particular, if the exercise of judicial discretion is most
prominent in the sentencing of
trial cases, as recent research argues (Johnson, 2003), then the
influence of judicial
background factors should be most pronounced for these types
of cases. Given that
guilty pleas account for about 95% of all felony convictions
(Durose & Langan, 2005),
the general lack of significant findings for judge characteristics
in prior research may
simply reflect the high prevalence of pled cases. The present
study investigates this
possibility by examining variations in judge-level predictors of
sentencing for cases
convicted through different types of pleas and trials. Using data
from the Pennsylvania
Commission on Sentencing (PCS), this study evaluates
hypotheses about the relative
import of a variety of judge-level background characteristics
across modes of convic-
tion. Such an investigation holds the potential to clarify past
discrepancies regarding
the importance of judge factors in sentencing, while also
providing useful directions
for future theoretical and policy developments regarding the
identification and control
of court actor discretion and sentencing disparity.

Prior Research and Theorizing
The Importance of Judge Characteristics in Prior Research
Relative to the copious literature on individual offender
attributes, research on the
characteristics of organizational decision makers in criminal
courts is rare. In the past
three decades, only about a dozen studies have incorporated
measures of judge char-
acteristics in the study of criminal punishment. Table 1 provides
a summary of this
research. Although not necessarily exhaustive, it represents the
most recent and
by guest on January 16, 2016cjp.sagepub.comDownloaded from
http://cjp.sagepub.com/
161
T
a
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717[ Journal of Law and Economics, vol. 58 (August 2015)].docx

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