Competition and Mutual- ism among Early Tele

Competition and Mutualism among Early Telephone Companies
William P. Barnett
Glenn R. Carroti
University of
California, Berkeley
In an exploratory study of the early telephone industry,
we search for evidence of competition and mutualism between legally autonomous companies. Neighboring companies are found to have both types of interdependencies,
although their exact nature depends on organizational
form. Companies in separate geographical locations are
found to be competitive with each other, regardless of organizational form. The two prevalent organizational
forms in the industry at this time each apparently flourished in distinct niches and were symbiotically related.
The findings are interpreted within a community ecology
framework.*
One of the more controversial features of organizational
ecology concems its depiction of the environment. Traditionally, ecological theory has conceived of environments as
completely exogenous from organizations (Hawley, 1968).
More recent formulations continue this tradition: environments are seen as exogenous conditions that select for and
against organizations with particular characteristics (Hannan
and Freeman, 1977). The challenging view comes from those
who argue that it makes little sense to separate organizations
from their environments. Organizational environments have
been claimed to be controlled by organizations (Perrow,
1986), enacted by organizations (Weick, 1979), or tightly coupled with organizations (Granovetter, 1985),
© 1987 by Cornell University.
0001-8392/87/3203-0400/S1.00,
The research reported here was supported
in part by the Institute of Industrial Relations, University of California, Berkeley.
Other support was provided by the AT&T
Fellowship in Telephone History (to Barnett) and by National Science Foundation
Grant #BNS-8700864 (to Carroll, while he
was a Fellow at the Center for Advanced
Study in the Behaviors! Sciences. Palo
Alto), We are extremely grateful to Roy
Atwood for providing access to the data
analyzed here and to Robert Lewis, Robert
Garnet, and Mildred Ettlinger at the AT&T
Histohcal Archives for their advice and assistance. Jacques Delacroix, Claude S.
Fischer, Michael T. Hannan, Heather Haveman, Y. Paul Huo, Anand Swaminathan,
and Nancy Brandon Tuma made helpfu!
comments on earlier drafts. Ross Boylan,
Jonathan Leonard, David Levine, Will
Mitchell, and Dan Ouan made useful suggestions about the empirical analyses.
1
We restrict our discussion to the generic
fomis of interdependence, competition
and mutualism. While the models we use
t>elow allow asymmetric interdependence,
we do not. for reasons of conceptual
clarity, cast the theoretical discussion in
terms of ali possible painftilse combinations of interdependence. See Brittain and
Wholey (1988) and Pianka (1978) for discussion of these topics.
Discussion of this issue often fails to recognize that the two
positions are not necessarily at odds with each other. Organizations can have tight connections with some elements in
their environments and yet at the same time be subject to the
exogenous conditions of other aspects of the environment.
The issue is thus an empirical one of determining where to
draw the "environmental" boundaries for any particular organization or set of organizations.
One guideline that researchers might use in addressing this
matter involves looking at the similarity of outcomes within a
group of organizations. For example, if a group of organizations shows shared fates, then it probably does not make
sense to treat members of the group as part of the selection
environment of any particular organization in the group. Instead, the whole group should be analyzed with respect to its
common environment. Hannan and Freeman (1977:
960-961) argued that such an approach can accommodate
many of the concerns of the challengers: ", . . large dominant
organizations can create linkages with other large and powerful ones so as to reduce selection pressures. If such moves
are effective, they alter the pattern of selection. In our view
the selection pressure is bumped up to a higher lever. So instead of individual organizations failing, entire networks fail,"
By the ecological conception, shared fates among organizations indicate interdependence. When organizations negatively affect one another, they are competitive. When they
enhance each other's viability, organizations are mutualistic.*'
Organizational interdependence can exist at several levels:
between individual organizations, between populations of organizations, and between communities of organizations. For
the most part, current organizational researchers think only of
the organizational level.
400/AdmJnJstrative Science Quarterly, 32 (1987): 400-421
Competition and Mutualism
Our goal in this paper is to show that higher level interdependencies can make a real difference. Toward this end, we
study the processes of competition and mutualism among
companies in the early telephone industry. Our analysis examines organizational interdependence in models of organizational mortality, growth, and technological capability. Among
the findings, we show that mutualism existed among individual organizations, while communities of organizations
show indirect evidence of competition. The study therefore
supports the claims of those who denounce a strict separation of individual organizations from their environments. At
the same time, however, it shows that the approach advocated by Hannan and Freeman (1977) can overcome these
objections.
ORGANIZATIONAL INTERDEPENDENCE
In ecological theory, organizations are thought to be interdependent when they affect each other's fate. Interdependence
has been studied with respect to a variety of outcomes. For
some research problems, the relevant outcome is growth and
decline in organizational size (see Hannan and Freeman,
1978; McPherson, 1983; McPherson and Smith-Lovin, 1988).
For other problems, organizational foundings and deaths
better reflect interdependence. We expect that telephone
companies affected one another's size and life chances, and
so we focus on both types of outcomes.
Competition among organizations can be either direct or diffuse (Hannan and Freeman, 1988b). Direct competition
occurs between pairs of organizations, each identifiable to the
other. Most often, direct competition is thought of as the result of a zero-sum contest between organizations that require
the same resource. By contrast, diffuse competition occurs
among many organizations, with competitors largely anonymous to one another. It results also from limited common resources.
Like competition, mutualism can be either direct or diffuse.
Direct mutualism occurs, for example, when organizations
with complementary abilities cooperate to the benefit of both.
Although it is less commonly discussed, diffuse mutualism
also occurs, as when organizations with similar characteristics
enhance each other's institutional legitimacy (Hannan and
Freeman, 1987).
Hawtey (1950: 209), drawing heavily from Durkheim (1933),
described two distinct bases for mutualism: commensalism,
defined as positive interdependence based on supplementary
similarities; and symbiosis, which is positive interdependence based on complementary differences. The most
common example of symbiosis is the mutual benefit resulting
when different types of producers transact in markets. Commensalism is often the result when similar organizations work
together in concerted political action.
Mutualism is important to the ecology of organizations because it creates organizational communities—networks of organizations that exist with unit properties of their own
(Hawley, 1986). For example, manufacturers in many industries engage in ongoing symbiotic transactions with upstream
suppliers and downstream distributors, while also main401/ASQ, September 1987
taining commensal relations with similar companies through
trade associations. An organizational network of this sort can
have vital implications for its members. The strength of a
successful organization in one community, compared to its
competitors in another, results in part from the strength of
that organization's mutualistic partners and the manner in
which its community is organized. Consequently, organizational communities may be viewed as competing with each
other, although at a high tevel of analysis.
For the most part, organizational researchers have not studied
mutualism and competition together. Thus, w e know very
little about when mutualism rather than competition will exist
between organizations. Similarly, w e are ill-informed about
when mutualism will be symbiotic or when it will be commensal. As a consequence, w e know very little about what
gives rise to organizational communities or of how communities themselves compete.*
More fundamentally, our inattentiveness t o mutualism and to
the organizationa! community has sometimes led to the spurious conclusion that competition is random. The reason is
simple enough, but elusive. Diffuse competition among organizations has been characterized as "random," because a rival
cannot be identified for the purposes of organization-level
competitive strategy (Emery and Trist, 1965). In this way, diffuse competition among organizations has been equated to
the atomistic view of competition advocated by economists.
However, diffuse competition at the organization level may
result from direct competition between organizationaf communities. After all, when one organizational community outcompetes another, the organizations involved do not win or
lose one-on-one with some rival. Instead, the overall viability
of organizations in one community is traded off against the
overall viability of those in another community. This is diffuse,
seemingly random competition at the organizational level. But
to each community, taken as a unit, the competition is
nonrandom and direct. There is much to gain, therefore, by
studying interdependence w i t h an eye for both competition
and mutualism and for patterns that may appear at all levels
of analysis.
Organizational Interdependence in the Early
Telephone Industry
W e analyze data on the early telephone industry, most of
which were compiled by Atwood (1984). These data record
the organizational life histories of al! telephone companies
ever appearing in Johnson, Washington, or Iowa counties in
southeast iowa from 1900 to 1917. The early telephone industry of Iowa has several characteristics that make it attractive for the study of organizational interdependence. First, the
telephone industry experienced rapid growth over this period,
especially in rural areas such as Iowa. At the beginning of the
century, roughly 2 percent of all farms in the U.S. had telephones (10 percent of all households did). By 1920, almost 40
percent of farms had telephones, while only 35 percent of
households did (Fischer, 1987). Second, t w o distinct organizational forms operated in the industry during this era; comlee. however. Astiey's (1985) speculative mercially Organized firms and mutually organized companies.
discussion.
The Commercial telephone companies existed primarily to
402/ASQ, September 1987
Competition and IMutualism
make profits and were organized much the same as any
business firm. Some were privately held; others issued stock
to raise capital. These companies operated mostly in small
cities. Table 1 describes the companies studied.
Table 1
The Teiephone Industry in Three Counties of Southeast towa. 1900-1917
Mutual
companies
Operating companies
Mean company size
in telephones
Companies providing
long-distance service
Companies issuing
stock
Companies operating
oniy in one county
Companies operating
oniy in two counties
Companies operating
in all three counties
Organizational deaths
Organizational foundings
Commercial
companies
Total
222
27
249
32
3,172
407
13
23
36
135
18
153
207
19
226
14
5
19
1
51
215
3
10
18
4
61
233
Among the commercial firms in this area was the Iowa Teiephone Company, a part of the Bell System. Because this
company was not a dominant actor in southeast Iowa during
the period under study, we treat it as we do the other commercial firms. For rural areas, this must have been common,
for Bell operating companies were unable to capture more
than one-third of the rural market before the Depression
(Danielian, 1939).
Mutual telephone companies were consumer cooperatives,
organized and solicited primarily by farmers and rural townspeople. These companies raised capital from their nt^embers
and existed in order to provide telephone service without regard for profitability. Fischer (1987:8) provided a rich description:
Typically, a rural mutual system or farmer line v^^as organized by a
group of leading farmers, or a small-tov^^n merchant or doctor, whose
efforts to solicit service from a major commercial company had
failed. For an initial investn:>ent of $15 to $50 and often their time
and materials, roughly 15 to 50 farmers would combine as shareholders in a mutual stock company, receive a telephone, and connection to others in the rural neighborhood. Annual rental fees might
run from S3 to $18 a year, less if the subscriber was a shareholder.
(Shareholders, however, were often assessed for needed capital.) If
the system had a switchboard, a farm wife or daughter typically
served as operator during the daytime. Often, the shareholders arranged a connection to a commercial, or larger mutual, company's
switchboard in town, and through that, to the wider world.
As Figure 1 shows, the mutual organizational form proliferated rapidly in this area during the study period. By 1917,
mutual telephone companies vastly outnumbered commercial
ones.
The dynamics behind the patterns of population growth can
be seen in Figure 2. The sharply irregular patterns of founding
403/ASQ, September 1987
Figure 1. Operating tel^hone companies in three OHinties in Iowa,
1910-1917.
175 n
150-
« 125-1
UJ
Z
5 100
O
Commercial
o
Mutual
u.
O 75-
50H
25-
01900
1905
1910
1915
1920
YEAR
and death suggest the industry was in a period of transition,
or disequilibrium. Appendix A reports the form-specific data
on foundings and deaths.
There are solid reasons for expecting that telephone companies experienced both competition and commensalism.
Figure 2. Foundings and deaths of teiephone companies in three
countim in Iowa, 1900-1917.
70-1
60-
50-
Foundings
Deaths
40-
30-
20-
10-
01905
1900
YEAR
404/ASQ, September 1987
1910
1915
1920
Competition and Mutuafism
Thompson (1967) used the telephone industry as a prime example of the "mediating technology." Organizations with
such a technology are rewarded for connecting people with
as many other people as possible. To do this successfully, organizations use technological systems that are both standardized and extensive. Telephone companies in Iowa could
increase the extensiveness of their systems either by encroaching on the territories of their neighbors (direct competition) or by connecting with each other (direct commensalism).
In fact, companies commonly used either or both strategies
during this period (U,S. Federal Communications Commission,
1938: 130-131). They also joined together in commensal organizations such as the Iowa Telephone Association, an industry association of independent (non-Belt) telephone
companies.
Symbiosis was also likely among these companies, although
as a relationship between organizational forms at the population level. Each organizational form thrived in a different environment. The commercials tended to stay in more urban
areas, where business practices were institutionalized and
markets for capital and labor were close by. As Fischer (1987)
convincingly demonstrated, Bell and the larger commercial
companies had little enthusiasm for expanding into the rural
farm areas. This left the rural telephone market open for the
mutual companies, which flourished in these areas due to
strong customer loyalty,^ Thus, the overall market for telephones was apparently divided between the two forms. What
likely made the division symbiotic were the many interconnections between companies of the two forms. Although
these interconnections were often fraught with conflict (Atwood, 1984), enough succeeded to make it possible for each
form to provide service to the other's market. In other words,
by connecting with each other, the two forms extended the
overall system.
Finally, it is possible that community-level competition occurred, Networks of companies, commensally related within
each form and symbiotically related between forms, may
have competed with each other, possibly by encroaching on
each other's territory, by stealing each other's mutual companies, and by price and service competition between the dominant commercials, Fischer (1987) told of how Bell and the
larger independents competed with each other for connections to the mutual lines, Atwood (1984) reported that localized, hierarchical networks did develop in these areas of Iowa,
with commercial companies playing a central, coordinating
role in each. If so, the commercial form was ecologically
dominant in each community, controlling many of the activities of the community and driving the community's competitive relations with other communities (Hawley, 1950).
Whether any or all of these patterns of interdependence existed in the early telephone industry is an empirical question.
We address it here with an exploratory approach, since we
find each of these scenarios theoretically plausible, and there
is no previous research of this kind,
3
Modeling Organizational Interdependence
llrdn,owa•S^XZu%Tti
rd™'°S'^"XZ%Tt
Following
g Hannan and Freeman (1988a, 1988b), we first
ff
analyzed interdependence by modeling the effects
off
Corporate Archives.
405/ASQ, September 1987
organizational population density on the death rates of Individual organizations. Hannan and Freeman argued that the
number of organizations of a given form, or population density, is crucial to interdependence. They contended that as
density begins to increase, mutualism results: "increasing
density will lower [mortality] rates by increasing the legitimacy of the form (and of populations using this form). Low
density also hampers attempts at coordinated political action
to protect and defend claims of the population or of some of
its members. Increases in numbers alleviate these problems.
Growth in numbers gives force to claims of institutional
standing and also provides economies of scale in political and
legal action" {Hannan and Freeman, 1988bK
But the nature of interdependence changes as a population
continues to increase in size. "At high density, competitive
interactions intensify. Growth in numbers increases the likelihood and intensity of both direct competition between pairs
of organizations and diffuse competition among all (or many)
of them. Individual organizations can easily avoid direct competition with others for members and other scarce resources
when there are few organizations in the system. As the
number of potential competitors grows, avoidance becomes
more difficult" (Hannan and Freeman, 1988b). Combined with
the mutualism argument, the empirical implication is a Ushaped effect of population density on the death rates of organizations. In a consistent set of findings, Hannan and
Freeman (1988b) showed such a relationship holding across
populations of national labor unions, semiconductor manufacturing firms, and newspaper publishing organizations.*
To distinguish between direct and diffuse interdependence
among these companies, we calculated separate density
measures according to geographic proximity. The "local density" of a company's environment is defined as the number of
companies operating within the same county as that company. A positive relationship between local density and the
death rates would be consistent with direct competition between neighboring companies. The other measure, "non-local
density," is defined as the number of companies operating in
counties where that company does not operate. A positive
relationship between non-local density and the death rate
suggests that an organization faces diffuse competition from
firms operating outside its service areas. Such competition
among non-neighboring companies would be indirect evidence of direct competition between organizational communities.
Other, less positive evidence has come
from other organizational populations in
studies by Tucker et al. (1988) and .Delacroix, Swaminathan, and Sdt (1987).
However, neither of those studies covers
a time frame nearty as long as Hannan and
Freeman's. For that matter, nor does the
present study.
The measurement of local and non-local density on the basis
of counties is, of course, not perfect. But it is, we believe,
defensible on empirical grounds. Moreover, the historical accounts of the industry at this time suggest that political
boundaries of all kinds were important barriers to expansion
—companies sought regulatory approval and monopoly rights
from the governing political bodies. Consistent with this
claim is the strong empirical association between the number
of telephone companies in a state and its number of political
units, shown by Carroll, Delacroix, and Goodstein (1988).
Organizational mortality has the advantage of being a relatively unambiguous phenomenon to measure. Operationally,
406/ASQ, September 1987
Competition and Mutualism
we used the statistical construct known as the instantaneous
rate of death. In formal terms, it is defined as
+ Af)/Af]
where qj is the probability of death between two discrete
time points. Using the rate of death as the operational dependent variable implies exactly other more intuitive constructs such as the expected organizational lifetime, an
inverse function of the rate (see Tuma and Hannan, 1984).
In order to model the effects of organizational and environmental characteristics on the death rate, we used the Gompertz model of the death rate,
r-it) = exp[pX^(f)]exp[-/tl
where r^it) is the rate of death for organization j, Xj is a vector
of independent variables measured for each organization for
each year over its lifetime, and -y is the coefficient of organizational age. This specification constrains the predicted rate
to be non-negative (a desirable feature, since negative rates
are meaningless) and separates age dependence from the effects of the Independent variables (see Carroll, 1983), Note
that the model is nonlinear. This means that the absolute effect of each independent variable varies over its range. Furthermore, each variable affects the rate multiplicattvely, so
care must be taken in interpreting these estimates. To estimate this model, we used Tuma's (1979) maximum-likelihood
program, RATE, The procedures used in this program have
the advantage of incorporating into the likelihood calculations
what is know about firms that do not die during the observation period. In this way, the right-censoring problem inherent
in these data is overcome.
ANALYSIS
Organizational Characteristics
The variables used in the analysis are defined in Appendix B.
Table 2 shows maximum-likelihood estimates of various organizational factors on the death rate, using the Gompertz
model. Model (1) estimates age dependence in the absence
of any other controls. The significant negative effect indicates
that the death rate of these companies declines with age, a
finding widely observed in organizational populations and
know as the liability of newness (Freeman, Carroll, and
Hannan, 1983).
Models (2) and (3) introduce the effects of dummy variables
for whether each organization was commercial or mutual, issued stock, or offered long-distance service. As one would
expect, the long-distance providers have lower death rates, as
do companies financed by stock issue. Two other effects of
the long-distance and stock variables are noteworthy. First,
these controls reduce the size of the age effect; it is no
longer statistically significant. Second, with these controls,
commercial firms show a statistically significant higher
death rate than mutuals.
Models (4) and (5) show that the natural logarithm of company size has a statistically significant negative effect on the
rate of death. The effect of size makes the long-distance variable not significant, suggesting that the earlier long-distance
407/ASQ, September 1987
Table 2
Maitfmuni-UkeHhood Estimatm of the Effects of Org«nizati(Hial Chnractaristics on the Death Rate*
Independent
variables
Constant
(1)
(2)
(3)
-3.376*
(.21001
-3.422*
(.2144)
.4599
(.3470)
-2.907*
(.2349)
1.633*
(.4561)
-1.068*
(.2672)
-1.650*
(.5697)
Commercial
Stock
Long-distance
Log size in
telephones
Log market
share
Left-censored
Age
Df
-.0763*
(.0285)
7.85
1
Models
(4)
-1.807*
(.3842)
1.545*
(.4341)
-.8261*
(.2777)
~ .3308
(.6326)
-.5042*
(.1501)
(5)
(6)
i7)
-1.721*
(.3411)
1.415*
(.3644)
- .8283*
(.2754)
4.292
(3.344)
1.109*
(.3992)
-.9546*
(.2803)
-1.701*
(.3455)
1.520*
(.4709)
-.8380*
(.2763)
-.5450*
(.12611
-1.086*
(.3269)
.5679
(.3159)
- .5569*
(.1305)
-.0789*
(.0286)
-.0488
(.0297)
-.0385
(.0294)
- .0373
(.0291)
9.43
2
37.61
4
48.73
5
48.45
4
.0017
(.0350)
51.61
5
-.1966
(.5716)
- .0342
(.0304)
48.57
5
* p < .05.
• Standard errors are in parentheses.
effect was spurious. Model (6) includes the effect of log
market share in Model (5), but it does not improve the model.
Model (7) includes in Model (5) a dummy variable equal to 1
for all companies that were founded before 1900, when the
observation period begins. Such left-censored companies
might have lower death rates, since they are already "survivors" at the point when the study begins. However, this
variable also is not significant. Consequently, we used Model
(5) as the baseline with which to search for competition and
mutualism.
Density Dependence
Table 3 shows re-estimates of Model (5) with various specifications of population density. Models (8) through (11) include
density terms measured for all companies in the sample,
making no distinction by organizational form. The main effects
of total density are not significant here—only the squared
term shows a significant effect. Although the negative sign
on this term might indicate mutualism, the lack of a significant main effect casts doubt on the credibility of this interpretation.
Models (10) and (11) are specified with local and non-local
density measured separately. Local density is never significant. Non-local density is significant, but only with a quadratic
specification. The positive main effect of non-local density in
Model (11) indicates competition, not from neighboring companies, but from companies in other counties. The negative
squared term suggests a self-damping density effect, with
competition increasing at a decreasing rate as the number of
companies in other counties grows.
Models (12) through (16) distinguish between the densities of
the two organizational forms. Model (12) includes for each organizational form the main effects of local and non-local den408/ASQ, September 1987
Competition and Muftiaiism
Table 3
Maidmum-Ukalihood Estimates of the Effects of Population Density on the Death Rate*
Independent
vanables
(8)
(9)
00)
01)
Models
02)
03)
04)
05)
06)
Constant
-2.504* - 12,57* - 2 - 3 5 1 " --8,738* -9-577* -9,577" --11 -37* - 9 , 0 8 1 " -8.735"
(.9894) (3.844) (3,102) (3,586)
(.9819) (6-014)
(3-896) (3.433) (2-683)
1.543*
1.157"
Commercial
1.553*
1.297*
1,284"
1,131"
1.131"
1.103*
1.179*
(.3923)
(.3936) (.3902) (.3958) (.3906) (.3961)
(.3886) (.3991) (.3941)
Stock
-.7869" -.9843* -.8040*- -1-043" -1.089" -1.089* -1,144" -1.086" -1.062"
(.2834) (,2796) (-2855) (-2797) (.2797)
(,2798)
(.2783) (,2795) (-2834)
Log size
- .5572" - ,5256* - .5700* --5513" -,5435" - -5435" - .5478* - ,5499" - .5442"
(,1261)
(.1304) (.1339) (.1349)
(.1248) (-1283) (,1241) (-1327) (-1333)
Total density
.0050
.1480
(-0764)
(.0058)
(Total density)2/i000
- .4898*
(-2428)
.0072
Non-locai density
.1821"
(-0063) (.0683)
(Non-local density)2/100[
--9179"
(.3361)
,0000 -.0106
Local density
(.0086) (-0579)
(Local density)2/i 000
-.1859
(.5094)
.2707"
.2707
.2084*
.2749"
,3415*
Non-local density.
commercials
(.0850) (,3604)
(-0901) (,0826) (.0852)
.0000
(Non-local density.
(17.57)
commercials)2/1000
,0198
Non-local density.
,0198
.0198
.1312
.0240"
(.0674) (.0119) (,0112)
mutuals
(.0119) (.0119)
(Non-local density.
- .6568
(,3757)
mutuals)=/1000
,2671"
,1811
.1485
.3465*
Local density,
,2671"
(.0850) (-0850)
(.0966) (,3616) (-0925)
commercials
6386
(Local density.
(18.93)
commercials)^/! 000
.0075
-,0119
.0069 -,1310"
Local density,
.0075
(.0164) (.0138) (,0632)
mutuals
(.0136) (-0142)
1 -437"
(Local density.
mutuals)2/1000
(.6690)
.0113
Age
- ,0504
-.0157 - .0479 -.0143
.0112
,0136
.0112
.0128
(.0329)
(,0352) (.0329) (.0340) (.0361) (,0361)
(-0357) (.0361) (,0358)
X^
Df
49.28
5
56.67
6
49,88
6
61,76
8
65,84
8
65.84
9
69,24
9
65.96
9
70,40
9
* p < .05.
• Standard errors are in parentheses
sity. Al! density coefficients are positive in these models,
indicating competitive effects. However, only the effects of
commercial density, both local and non-local, are statistically
significant.
Models (13) through (16) re-estimate Mode! (12). each time
with a different squared density effect included. Models (13)
through (15) do not improve statistically over Mode! (12).
Model (16), however, shows statistically significant effects for
al! density measures when a quadratic term for mutual local
density is included. This model clearly improves over Mode!
(12). Hence, this mode! is the best specification of the effects
of organizational characteristics and population density on the
death rates.
The non-local density effects in Model (16) are positive for
both mutual and commercial density. This indicates diffuse
409/ASQ, September 1987
competition generated by each organizational form. The nonlocal density coefficient for the commercial companies is fifteen times larger than that for the mutuals. This suggests that
an individual commercial company generated dramatically
stronger diffuse competition than an individual mutual company, especially since these effects are exponential. However, mutual companies were far more numerous. Thus, the
mutual organizational form as a population actually generated
stronger competitive pressure than that implied by these coefficients. To see this, compare the multiplicative effect of
each density measure evaluated at its mean: death rates are
increased about thirty times when commercial non-local density is at its mean and about ten times higher when mutual
non-local density is at its mean,* Based on these calculations,
the commercial form, as a population, generated diffuse
competition about three times stronger than did the mutual
form.
Perhaps the most interesting estimates in Model (16) are
those for local density. The local density effect for commercial companies is positive, indicating that these companies
were responsible for direct competition. The effect of local
density for mutual companies, however, is nonmonotonic and
requires more careful interpretation. Figure 3 plots the predicted effect of mutual local density in terms of the multiplier
of the rate. Note that the prediction curve bends, changing
direction within the range of the observed data. This means
that as the number of mutuals in any company's immediate
surroundings increased, that company's death rate decreased. Hence, while the mutuals competed with companies
Figure 3. Estimated affect of the number of mutiial companies in the
same county on the death rate. (Vertical lines enclose the observed
range.)
0,75-
O0.50In the Gompertz model, the multiplicative
effect of any one variable on the death
rate, knovi/n as the "multiplier of the rate,"
varies over the range of that variable according to the function explpX], where X
is the independent variable and p is its
estimatai coefficient (see Tuma and
Hannan. 1984), We evaluate the non-local
density effects of Model (16) v\/ith each
density variable equal to its mean value:
10 for the commercials and 94 for the
mutuals. The estimated commercial nonlocal density multiplier is therefore
exp[.3415 X 10] = 30.42, The multiplier
for mutual non-local density is estimated
to be expl.0240 x 94] = 9,54-
0.25-
0.00-1
0
5
10 15 20 25 30 35 40 45 50 55
60 65 70 75 80 85
LOCAL DENSITY OF MUTUAL COMPANIES
410/ASO, September
Competition and MutuaUsm
in other counties, they were mutualistic with their more immediate neighbors. As the local density of mutuals became
increasingly large, however, its effect on the death rates
changed, becoming more competitive at very high levels. In
fact, had the local density term peaked at just a few mutual
companies higher than its maximum of 77, the overall effect
would have become competitive, moving the multiplier above
unity. For immediately neighboring mutual companies, then,
this analysis replicates Hannan and Freeman's (1988b) Ushaped density effect.
Two potential objections to Mode! (16) have also been tested.
First, the possible effects of the organizational size distribution are not included in the model. This might be especially
important because organizations can fill the carrying capacity
of the environment by expanding in size as well as by proliferating, Such a process seems especially likely for an industry
with a mediating technology, since there is a premium on extensiveness. However, in a re-estimation of Model (16) not
shown, total industry size (which is equivalent to density
weighted by size) was not significantly related to the death
rate. Similarly unremarkable was an equation that included a
measure of the sum of the squared deviations of each company's market share from the mean market share, which
when summed with the reciprocal of density is the wellknown Herfindahl index of industry concentration (Adelman,
1969). The specification in Model (16), therefore, cannot be
improved by including measures of the size distribution of the
industry.
The other possible objection to Model (16) is that the estimated density effects may actually be the spurious result of
unmeasured environmental factors. In models not shown
here, we re-estimated Model (16) repeatedly, each time controlling for a different environmental variable. The main effects of each of the following variables failed to show a
statistically significant relationship to the death rate: county
size in square miles, number of farms, indexed farm value,
number of asses per county, number of cattle per county,
number of swine per county, indexed average wage per
worker, number of dwellings, township population, and rural
population. These variables are also not significant when interacted with organizational form. Remarkably, the estimates
of the density model show little sensitivity to these controls.
The viability of these organizations evidently depended on
their own characteristics and on their interdependencies with
other companies, but very little on the resource environment.
Interpretation
Model (11) suggests that only diffuse competition occurred
among these companies, but this finding conceals the more
complex pattern of interdependence that occurred between
organizational forms. Both the mutual and commercial forms
generated diffuse competition, but the commercial form's effect was three times that of the mutual form. The commercials also competed directly with neighboring companies. In
contrast, the mutuals lowered the death rates of their
neighbors, although this effect reversed itself at high density
levels.
These findings are consistent with a hypothesis of community-level competition: networks of mutual and commercial
411/ASQ, September 1987
companies, united as interdependent communities, may have
competed with other such networks. Such high-level competition could explain why both mutual and commercial companies increased the death rates of geographically removed
companies. Furthermore, the stronger diffuse competition
brought on by the commercials supports the idea that they
were the dominant form in these communities, driving the
competitive movement into the resource space of other networks. Locally, the mutual form was nonthreatening, apparently connecting on a regular basis with other companies.
However, at high enough numbers, this mutualism gave way
to direct competition as it became impossible to avoid encroaching on the markets served by other companies in the
community. Finally, the fact that the commercial form atso
competed locally is again evidence of its dominance.
Members of the dominant population hold and wield power
within the community as well as against others (Hawley,
1950: 221).
Ideally, the community competition hypothesis could be
tested directly by analyzing the survival implications of actual
network interconnections. Unfortunately, however, complete
data on interconnections are not available. Thus we are forced
to rely on two kinds of indirect evidence: (1) descriptive accounts drawn from the historical record and (2) statistical
tests of theoretical implications of the community concept.
Atwood's (1984) detailed historical research on this area contains much evidence consistent with the community competition hypothesis. First, these companies did commonly
connect with each other. Atwood (1984) found 98 published
records of such interconnections in these three counties.
Second, interconnections were geographically based and hierarchical, resembling an ecological community. Atwood
(1984: 175) described clearly the way this worked:
Most mutual lines eventually joined a mutual exchange service located in a neighboring toyvn or village. This exchange inevitably
made connections, often grudgingly and frequently interrupted, with
the commercial systems located in the counties' major towns. If a
mutual exchange was large enough, it might also connect with a toll
service on its own. If the mutual exchange was not large, however,
the company would often rely on the commercial system's toll services for long distance connections.
Atwood (1984: 432) also provided a stylized diagram of such
a network. Third, while connecting localized groupings of
companies, the interconnection network was far from comprehensive for these counties (Atwood, 1984: 175). Thus, the
networks were not rationally integrated with each other, as
would be the case if the whole area constituted a single ecological community. Fourth, the history of interconnections in
this area is replete with feuds, disputes, and conflicts of the
sort that would be expected if companies were encroaching
on each other's territory.
Another kind of indirect evidence of the community competition hypothesis relies on two key theoretical implications of
the argument. First, if the two organizational forms did in fact
work together in communities, then each would have played
a distinctly different role in the dynamics of the industry.
Second, such a division of labor would have resulted in symbiosis between the forms, since, as complements, each form
412/ASa September 1987
Competition and Mutudism
would improve the service provided by the other. We investigated the first implication by looking for distinct patterns of
growth for each form. We then looked for symbiosis between
the forms by comparing how each affected the technological
capability of the other.
Organizational Growth
In the more urban, densely populated areas, telephone service expanded intensively within a limited geographic area. In
this environment, the commercial organization was capable of
growing in size as the industry grew. By contrast, industry
expansion was geographically extensive in the more widespread, sparsely populated rural areas where the mutual
companies operated, In this fragmented environment, industry growth occurred primarily by the proliferation of mutual
companies rather than by their individual expansion. Each of
these companies was less capable of growth but distinctly
capable, for both institutional and technical reasons, of
serving a geographically isolated pocket of subscribers. Figure
4 contrasts the different total growth patterns for each form
(contrast with Figure 1). The number of telephones in the
commercial niche expanded rapidly, while the numbers in the
mutual niche grew slowly.
Figure 4. Industry size by organizationai form.
150000140000130000120000-
Commercial Companies
110000-1
^
100000-
X
90000-
S
80000-
Mutual Companies
70000LU
m
D
6000050000400003000020000100001900
10)5
1910
1915
1920
YEAR
White these aggregate pattems of growth clearly show differentiation between the two forms, it remained to be seen
whether indivkiuai companies of the two kinds grew in response to different stimuli. To examine this issue, we modeled tJie growtii rates for organizations of each form
separately, using as a iaaseline nrjodel the power function Sfi
^tX' where S r^resents company size. This functional
form worked wel! in Fischer and Carro!i's (1988) models of
the diffusion of the te!ephone in the entire industry during this
period. We inc!uded in the mode!, as regressors, the variables
used above measuring organizationa! characteristics and population density. Because apparent regu!arities in form growth
and decline may simp!y be the consequence of regression to
the mean (Leonard, 1986), we also included as a regressor
the ratio of average size to organizational size (by form). A
positive coefficient was expected on this term.
The size distribution of these companies is s!<ewed
throughout the period under study. Numerous ana!yses of
s!<ewed size distributions (e.g., Ijiri and Simon, 1977) suggest
that the error term in the model
is iognormally distributed. By transforming this equation to its
natural logarithm, we obtain the equation
logS« =
which is linear in the parameters and includes a normally distributed error term and can therefore be estimated using
linear regression. We estimated the parameters of this model
using ordinary-least-squares techniques on the poo!ed cross
sections of the samp!e. By poo!ing repeated observations on
the same companies, however, the assumption of independence from observation to observation is !i!<ely violated and
thus can lead to biased estimates (Judge et al., 1980), To test
for this possibility, we also estimated a "fixed-effects" form
of this mode!, in which a separate intercept term is included
for each organization (Judge et al., 1980), Another possible
source of bias in estimation is samp!e selection through organizational death. If "shrinking" organizations are more like!y to
die, they will fa!! from the samp!e without their size reduction
affecting the estimates. To accommodate this possibility, we
a!so estimated the mode! using Heckman's (1979) two-stage
method. As Heckman explained, the sample selection bias
due to attrition (organizational death) is equal to u \ , where \
depends on the characteristics of dying organizations and a is
a coefficient capturing the effect of K in the growth model. To
estimate X, we first estimated a probit model of organizational
mortality using the variables in the best death model (16).
Then we estimated the growth model, including the estimate
of X as a regressor. using ordinary-least-squares.
Table 4 reports the estimates of the growth model, using the
three estimation procedures. Each model controls for organizational age. whether a company issued stock, and whether
it offered long-distance service. As one might expect, both
mutual and commercial long-distance providers grew more,
but the other organization-!eve! variables are nonsignificant in
almost ai! specifications. By contrast, the term testing for regression to the mean is positive!y related to growth for both
forms and statlstica!ty significant in a!! but one specification.
These mode!s a!so inc!ude the effects of !agged deaths of
companies of each form. One might expect a positive relationship between lagged death and growth, reflecting the
process of renewal in the industry. If not explicitly controlled,
this renewal process could be captured spuriously in the coefficients of density. Interestingly, we find evidence for such
414/ASQ, September 1987
Competition and IMutualism
Table 4
Models of Growth in Numbw of Telephones for Commercial and Mimial Companies*
Commercials
Mutuals
1
Independent
variables
Constant
Lagged size (S'T)
Stock
Long-distance
Age
(Average size, commercials)/size
OLS
(17)
.2499
(.1945)
.9936*
(.0127)
.0082
(.0437)
.4590(.0833)
- .0024
(.0057)
.0013(.0003)
Fixed
effects
(18)
Sample
selection
(19)
OLS
(20)
.9773*
(.0126)
0613
(.0434)
1399(,0450)
- .0027
(,0058)
.1787
(,1936)
.9952'
(.0126)
.0379
(.0451)
.4850*
(.0763)
-.0018
(.0054)
.0004
(.00023)
Non-local density, mutuals
Local density, commercials
Local density, mutuals
No. of deaths, commercials
No. of deaths, mutuals
.2494*
(.0754)
1.004*
(,0085)
,0197*
(.0102)
,0440*
(.0204)
-.0011
(.0012)
1,015*
(.0079)
.0191
(.0102)
.0473*
(.0204)
- ,0011
(.0012)
,2426*
(.0774)
1,003"
(.0087)
.0166
(.0133)
.044 T
(.0204)
-.0012
(,0012)
,0112*
(.0029)
.0148*
(.0027}
.0119*
(,0034)
-.0091
(.0093)
- .0028*
(,0008)
-.0016
(,0101)
- .0040*
(.0011)
-.0108
(.0096)
- .0028'
(,0008)
- .0037
(.0104)
- ,0030*
(.0011)
- .0074
(.0091)
-.0032*
(.0008)
.0001
(.0100)
- .0037*
(.0011)
- .0054*
(.0026)
-.0010*
(.0002)
- .0043
(,0027)
-.0017*
(,0003)
- .0055'
(,0026)
-.0010*
(.0002)
-.0041
(.0027)
-,0018*
(.0003)
- .0048
(.0030)
-.0010*
(.0003)
-,0038
(.0031)
-.0017*
(.0003)
-,0168
(.0232)
,0063
(.0075)
-.0223
(.0240)
.0044
(.0077)
- .0246
(.0232)
.0039
(,0075)
,2705*
(,1325)
-.0011
(.0062)
.0044*
(.0019)
- .0008
(.0062)
,0045'
(.0019)
- .0009
(.0062)
.0045*
(.0019)
- .0702
(.1909)
.98000
308
,97864
308
.98019
308
.95888
2214
.95865
2214
.95888
2214
X
Number of spells
Sample
selection
(22)
.0012*
(.0003)
(Average size, mutuats)/size
Non-local density, commercials
Fixed
effects
(21)
• p < .05.
* Al! models are of the form S^ = S\i exp[p'X]t, with all independent variables lagged one year. Standard errors are in
parentheses.
a renewal process only for the mutual form, an effect that is
robust across all three specifications. Apparently, mutual
deaths led to growth among other mutual companies in the
next year. The commercials show no lagged death effect, but
deaths do affect the model. In Model (19), the coefficient of
X is positive and significant, indicating positive sample selection bias due to mortality among the commercials. The firms
that do survive in the more hazardous commercial niche tend
to be "growers,"
Both the local and non-local density measures for mutual
companies are negatively related to growth for each organizational form. This finding is robust and suggests strongly that
mutual companies impaired the grov^h of alt other companies, both their neighbors and those in other counties. The
commercial companies, meanwhile, do not appear to have
the same effect. There is a significant negative relationship
between non-local commercial density and mutual growth,
but this effect is not robust.
415/ASO, September 1987
Mutual and commercial companies therefore differed not only
in terms of their formal goals, ownership structures, appeal to
subscribers, and scale of operation, but also in terms of their
dynamics of growth. Each form flourished in and reinforced
the characteristics of a different niche: the mutuals in fragmented, rural areas and the commercials in more densely
populated areas. Such sharp differentiation is consistent with
the community-competition hypothesis.
Long-Distance Service
If these two organizational forms cohered in ecological communities, then the presence of one would likely have increased the ability of the other to provide extensive telephone
service. Operationally, this implies that each fonn would increase the probability that the other offered long-distance
service. Commercial companies would give mutuals access
to sophisticated long-distance trunking equipment, and mutuals would give commercials the ability to reach widespread
and sparsely populated rural areas. To test for this kind of
symbiosis, we modeled the probability of a company providing long-distance service in any given year, using probit
models. Probit estimation generates unbiased and meaningful
(between 0 and 1) predicted values of a probabilistic dependent variable (Maddala, 1983). Table 5 reports these estimates.
6
Each of these terms shows the effect of
di^sity on the probability that a commercial company will offer long-distance service relative to the prtrirability that a
mutual company will. The positive commerdal x mutual density effect, therefore,
predicts that as mutual companies are
greater in number, a commercial company
is increasingly likely to offer long-distance
service, relative to a mutual company. The
negative commercial x commercial density effect indicates that as commercial
corr^anies are more numerous, a commercial company is decreasingiy likely to
offer long-distance service, relative to a
mutual company. This means that higher
commercial density makes mutual companies increasingly likely to provide longdistance service relative to a commercial.
The prcdiabiJity predictions in a probit
model vary over the explanatory variables
according to the cumulative normal distribution, * . Tt^refore, the magnitude of a
variaWe's effect (say, the effect of xtl
varies over the range of x* according to
the standard normal density function,
since a/axt*(x'Pt = *(x'P)P(t (see Maddala, 1983). Intuitively, this means that the
effect of Xk increases as the predicted
prcAability moves up from 0. peaks at p
= .5, and decreases as p approaches 1
asymptotically. Each density effect was
evaluated at its strongest point, where the
prs^cted probability egu^s .5. At Uiis
point, •(x'p) = 1/V2w. which then must
be rruiltipted by the coefficient of density
to detwmine the ap¥)roxrmate effect of a
one-urat increase in density on the pre(Scted probability.
Not surprisingly, commercial companies were more likely
than mutuals to offer long-distance service. Year of founding
shows a curvilinear relationship with providing long-distance
service. This is probably because the earliest telephone companies (left-censored in these data) were commercial firms
and date as far back as the Bell patent monopoly. These firms
tended to patent and manufacture their own equipment and
were some of the first pioneers of long-distance telephony
(Wasserman, 1985). Later entrants into the industry were
able to purchase their equipment from manufacturers that
became numerous in the early years of this century (U.S.
Federal Communications Commission, 1938). However, these
early manufacturers offered only basic equipment, so telephone companies in the early wave of foundings were less
likely to offer long-distance service. As time passed, the
availability of long-distance equipment increased dramatically,
making late entrants more likely to offer such service. The
significant curvilinear effect of year of founding across all
models in Table 5 supports this scenario.
Models (28) through (32) suggest that the mutual and commercial forms were symbiotically related, each improving the
other's chances of providing long-distance service. The cross
effects of density on each form's probability of providing
long-distance service are significant across these models.
Evaluated at its strongest point, the positive commmercial x
mutual density effect indicates that two additional commercial companies increased by 10 percent a mutual's relative
probability of offering long-distance, service. The negative
commercial x commercial density effect indicates that the
mutuats had a similar, but much weaker, effect on a commercial's relative probability of offering long-distance service;
it took about 30 additional mutuals to increase a commercial's
probability by 10 percent.* (But again, recall the greater
416/ASQ, September 1987
Competition and Mutualism
Table 5
Probit Estimates of the l^ovision of Long-Distance Tei^hone Service*
Independent
variables
Constant
Commercial
Stock
Calendar year
(23)
(24)
-1,439* -15,34
(,0662) (13,78)
2,904*
2,920*
(,1068)
(.1049)
- ,0477
(,0769)
,0073
(,0073)
Organizational
age
Year founded
(26)
Models
(27)
(28)
(29)
(30)
(31)
(32)
1,822*
(,0653)
2.931*
(,1078)
17009*
(3685)
2.827*
(,1093)
16931* 17461*
(3688)
(3803)
2,827* 4,389*
(,1096) (1.078)
20774*
(4511)
4.260*
(1,102)
18548*
(3935)
4,366*
(1,091)
20882*
(4686)
4,277*
(1.103)
20875*
(4627)
4,150*
(1.103)
,0449*
(,0065)
,0178*
(,0079)
-17,84*
(3.872)
4.678*
(1.017)
.0071
(,0264)
-17.75*
(3,874)
4,651*
(1,018)
-,0072
(.0280)
-21,87*
(4,815)
5,727*
(1.264)
,0203
(,0604)
-21.89*
(4,856)
5,727*
(1.275)
(25)
(Year founded)^/
1000
Total density.
mutuals
Total density.
commercials
Commercial x
mutual density
Commercial x
commercial
density
,0119
-,0471 -.0626
(.0273)
(,0303)
(,0439)
-18.31* -21,68* -19.38*
(3.995)
(4,735) (4,132)
4.800*
5,667*
5.060*
(1.049)
(1,243) (1,085)
,0004 -,0015
(,0031) (,0034)
-.0195 -,0052
(0317) (,0332)
,0078*
(,0034)
-.1275*
(,0467)
Land
- ,0027 -,0045 -.0019 -.0064
(.0035) (.0035) (,0034) (,0045)
.0046
,0372
,0001
.0251
(,0339)
(,0361) (,0340) (,0427)
.0071*
,0081*
.0073* .0077*
(,0035)
(,0035) (.0035) (,0035)
-,1191* -.1295* -.1207* -,1172*
(.0471)
(,0477) 1,0474) (,0475)
-44.60*
(9,181)
- 5,647*
(1,405)
,0001
(,0001)
Farms
Dwellings
Indexed fami
value
Indexed
average
wage
Township
population
Rural
population
Df
60.41
(34.52)
3.987
(3.427)
10,96*
(4,693)
1,243
(,8573)
2,267
(4,915)
,0888* .0071
(,0155) (.1139)
-,1830* -.9372
(,0486) (,5378)
1204,9
2
1205,6
2
1254,1
2
1311.9
4
1312,4
6
1327,0
8
1371.2
11
1343,4
10
1379.9
10
1383,0
13
*p< ,05.
* Standard errors are in parentheses.
number of mutuals.) Despite differences in scale, the estimates strongly suggest that the two organizational forms
served each other symbiotically. The presence of either form
improved the ability of the other to provide extensive telephone service.
CONCLUSION
We began by suggesting that the environment of an organization could be determined empirically by examining the interdependencies between an organization and other
organizations. The suggestion that such a research strategy
was possible was not intended to mean that it would always
be simple. Indeed, there were strong theoretical reasons to
think that interdependencies might be of different kinds and
at different levels of analysis.
417/ASa September 1987
Our exploratory analysis of the early telephone industry
shows some of the complexities that can be involved in the
study of organizational interdependence. Initially, we found
evidence of organization-ievel diffuse competition amor>g
telephone companies. By distinguishing between the industry's two predominant organizational forms, mutuals and
commercials, we found other evidence of a more complex
population-level interdependence. The mutual form flourished
in geographically dispersed rural areas. This form expanded in
its niche by proliferation in number rather than by growth in
the sizes of individual companies. Mutuals were commensal
with neighboring companies, decreasing their death rates except at very high density levels. With respect to commercial
companies, the mutuals were symbiotic, their presence increasing the probability that the commercials would offer
long-distance service.
For commercial companies, a different pattern was found.
This form expanded by the grov^rth of individuals rather than
by proliferation, and it did so largely in the more densely populated areas. This form appears to have been ecologically
dominant, controlling resources important to the industry,
such as access to larger technological networks. As the dominant form, the commercials were powerful within their communities, their presence increasing the death rates of neighboring companies. However, they were also in a symbiotic
relationship with the mutuals, their presence increasing the
probability that the mutuals would offer long-distance service.
The commercial niche itself was relatively hazardous, but with
high growth returns for the survivors.
The two forms were highly differentiated, both environmentally and in their relations to other companies. When combined with historical accounts and the apparent symbiosis between forms, the evidence supports the assertion that these
forms together formed coherent organizational communities.
Further, the finding that companies of both forms caused
higher death rates among companies outside their local areas
is very difficult to explain without resort to a higher level of
analysis.
Interestingly, the apparent resilience of telephone companies
to environmental factors also supports the community hypothesis. As Hawley (1986: 44) pointed out, with increasing
differentiation (as between these two organizational forms),
members of a community become more dependent on one
another and less dependent on their immediate environment
for resources. That appears to have been the case for telephone companies.
Taken together, these findings agree with those who argue
against a strict conceptual separation of individual organizations and their environments (Weick, 1979; Granovetter,
1985; Perrow, 1986). However, they also show that the
problem of environmental embeddedness can be overcome,
at least as it concerns other organizations. The solution, as
advocated by Hannan and Freeman (1977), is to consider and
investigate higher levels of analysis, including organizational
populations and communities. We have demonstrated that
such an approach is feasible, even within the demands of
systematic empirical research, and that ecological theory provides a powerful means of interpretation.
418/ASQ, September 1987
Competition and Mutualism
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Appendix A: Foundings and Deaths by Organizational Form
Mutual Companies
Foundings
Dissolutions
1900
1901
1902
1903
1904
1905
1906
1907
1908
1909
1910
1911
1912
1913
1914
1915
1916
1917
12
67
39
15
13
5
7
4
17
3
6
a
7
0
3
3
0
0
2
3
7
8
1
1
6
1
5
2
1
2
3
CJl
1938 Proposed Report—Telephone
Investigation. Washington, DC:
U,S, Government Printing Office.
Wasserman, N.
1985 From Invention to Innovation:
Long Distance Telephone
Transmission at the Turn of
the Century, Baltimore: Johns
Hopkins University Press.
CJl
U.S. Federal Communications
Commission
3
1
0
Commercial Companies
Foundings
Dissolutions
8
3
3
1
1
1
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
1
2
1
0
1
2
2
0
1
0
0
0
0
0
0
Appendix B: Definitions of Variables
Company Variables
Data on the following variables are from the State of iowa Executive
Council's Annual Assessments of Telephone and Telegraph Properties (Des
Moines, lA: Iowa State Printer), published annually during the period under
study. Most of these data were originally collected by Roy Atwood (1984).
Mutual telephone companies. These companies were owned by their subscribers. Two designations are found in the tax records, "pure mutual" and
"stock mutual" companies. The iatter form was still owned by its subscribers, but through stock issue. One additional type of company, shown in
the records as "purely private," was also considered in this study to be mutually owned. These companies were normally small point-to-point farmer
tines established, owned, and operated by the subscribers themselves.
Commercial telephone compar^ies. Two types of commercial firms were
shown in the records, "private commercial" and "stock commercial." They
differed over whether they were publicly or privately owned, but all were
business organizations formally operated to make a profit.
Organizational mortality. Companies were considered dead once they no
longer were recorded in the tax records. No distinction was made between
dissolution by merger and outright failure. Mortality was recorded for the final
year of known operation.
Year founded. Companies were considered founded in the first year they appeared in the tax records.
Long-Distance service companies. There existed a hierarchy of service levels
in the industry during this period. "Basic service," consisting of point-to-point
connection and operator-assisted connection, was the first level of service
provided. "Exchange service," both automatic and manual, provided more
comprehensive, although still local connection, "Toll service" provided subscribers with long-distance transmission through special trunks to areas outside their local service area. The long-distance/local distinction in this study
was made according to whether firms offered toll service.
Stock-issuing companies. This distinction was made according to whether or
not stock was issued by a company to finance capital investments. See
"mutual companies" and "commercial companies,"
420/ASQ, September 1987
<k>mpetition and Mutualism
Log organization size. This variabte is the natural logarithm of the number of
telephones recorded for each company for each year it operated. Data were
missing on some companies for some years and were linearly interpolated.
where possible from other years' tax records.
Log organization market share. This variable was constructed by dividing the
size-in-tetephones measure for each company for each year by total number
of telephones for all companies in that year and then computing the natural
logarithm of this ratio.
Organizationai age. Measured in years, this variable was computed by subtracting year of founding from current calendar year for each company in
each year of operation.
Total density. Computed by adding the total number of companies (or the
total number by form) in operation in any or all of Johnson. Iowa, and Washington counties of southeast Iowa during each year of the study.
Local density. Each company reported its counties of operation to the tax authorities in each year of its operation. This variable was computed for each
company in each year by adding the total number of companies (or the total
number by form) in operation in the company's county of operation. For
multi-county companies, the average number in its counties of operation was
computed.
Non-local density. This variable was computed by subtracting local density
from total density for each organization in each year of operation.
Environmental Variables
Data on these variables were found in the decennial census of 1900, 1910,
and 1920 for each of Johnson, Iowa, and Washington counties. Values were
linearly interpolated for intervening yearsLand. Size of each county measured in square milesFarnts. The number of farms in each county. Interpolated values were
rounded to whole numbers.
Indexed farm value. Computed by dividing the total dollar value of all farm
property in each county by the number of farms in that county. After interpolation, values for each year were indexed by the consumer price index for
that year found in U.S. Bureau of the Census (1975).
Number of asses, cattle, and swine per county. Each variable shows the
number of each of these farm animals in each county. Values were interpolated and rounded to whole numbers for intervening years.
Indexed average wage. Computed by dividing the total dollar wages paid in
each county by the number of wage earners in that county, After interpolation, values for each year were indexed by the consumer price index for that
year found in U.S. Bureau of the Census (1975).
Dwellings. The total number of dwellings in each county. Interpolated values
were rounded to whole numbers.
Urban population. This variable does not reflect the standard census definition for urban population, which would exclude most of the townships in
these counties, instead, we added the census figures for township population to the urban population for each county. Interpolated values were
rounded to whole numbers.
Rural population. Computed by subtracting the recorded and interpolated
urban population variable (above) from the recorded and interpolated total
population for each county.
421/ASa September 1987