Industry Boundaries and PerformanceChapter 5

p. 80

5.1 Introduction

In the previous chapter I used stochastic structure analysis to demonstrate that the

organizational position construct has empirical validity in the context of inter-industrial

exchange. In this chapter, I demonstrate how the identification of industrial positions

affects the conclusion of a substantive organizational theory. Burt’s theory of structural

autonomy (1980, 1982, 1983) is exemplary with respect to its theoretical clarity and the

methods employed in its empirical validation. That said, as it relies very heavily on the

idea of organizational positions, empirical analyses of structural autonomy are inherently

quite sensitive to how the boundaries of these positions are defined.

5.2 Positional Boundaries and Competitive Dynamics

In Chapter 1 I reviewed a wide range of theories that invoke the organizational

position metaphor. These theories make a convincing argument that organizational

positions as intermediate-level social structures should play a significant role in shaping

the competitive dynamics of organizations. Of these, Burt’s model of structural

autonomy (1980, 1982) captures both the dynamics of competition within an

organizational position as well as the competitive dynamics that arise out of exchange

relations between these positions. Although this model is quite specific with respect to

how the dynamics within and between these positions should be analyzed, it is equivocal

with respect to how the boundaries between these positions should be determined. This

section will outline the major features of the structural autonomy model, and use existing

research to generate a set of propositions about how the boundaries of these positions

might be determined.

p. 81

The principal argument forwarded by Burt (1980, 1982) about competition is that

certain positions within an exchange structure are advantageous because these positions

are associated with higher autonomy. Higher autonomy allows actors that occupy these

positions to have wider discretion in decision-making, and, as a result, to enjoy a number

of advantages including (but not limited to) higher profitability. Burt identifies two

sources of autonomy: one derives from competitive dynamics within a position, and

another derives from competitive dynamics between positions. Within a position,

autonomy derives from a high degree of centralized decision-making or oligopoly (Burt

1980: 896; Burt 1982: 272). To the extent that actors are certain of the decisions that

their competitors will make, they are able to choose from a wider range of options than

they would if they had to respond to competition. Burt’s second source of autonomy

relates to the ability of exchange partners (customers and suppliers) to co-ordinate and

limit decision-making opportunities for a given actor. Actors whose exchange relations

are concentrated in a small number of customers and suppliers are subject to a high

degree of constraint, particularly to the extent that these customers and suppliers are

characterized by highly coordinated decision-making or oligopoly conditions.

Measuring the constraint placed on an organizational actor by its customers and

suppliers requires information about the levels of exchange between positions as well as

information about the degree of oligopoly Oj of each position j in a given system. In

order to determine the level of constraint faced by an actor in a given position, it is first

necessary to determine the dependence of one position in the system on another. A

position i is dependent on a position j to the extent that exchange from position i is

concentrated in position j. Following this logic, Burt (1992: 51) defines dependence pij as

p. 82

€

pij =zij + z ji(ziq + zqi)q≠ i∑

, (5.1)

where zij represents the level of exchange directed from a position i to a position j. Burt

(1992: 64) uses this expression, in turn, to define the constraint faced by an actor in

position j from actors in position I

€

cij = pij + piq pqjq≠ i≠ j∑

2

Oj . (5.2)

He goes on to aggregate this measure of relational constraint into an actor-level measure

of aggregate constraint Ci measured as

€

Ci = cijj≠ i∑ . (5.3)

In most empirical investigations of market structure, oligopoly Oj is operationalized as

the degree of concentration within an industry as an organizational position, typically

measured using a four-firm concentration ratio.

Burt argues that the autonomy of an organization is determined by the constraint

and concentration of the position that it occupies, and demonstrates that the autonomy of

an organizational position is reflected in industry-wide profitability. To this end, he

p. 83

proposes that high structural autonomy should be associated with the following three

conditions (Burt 1982: 272)

1) High centralization among occupants of an organizational position as reflected

by a high oligopoly measure Oj

2) Diverse relations with other organizational positions that are not characterized

by oligopoly as reflected in a low aggregate constraint measure Ci.

3) The simultaneous co-occurrence of the first two conditions as reflected in a

negative interaction between oligopoly Oj and constraint Ci.

This theory of structural constraint is quite specific with respect to how relations

within a position and between positions should affect the autonomy of organizational

actors. However, the concepts of constraint and concentration central to this theory are

highly dependent on how the boundaries of organizational positions are determined. The

degree of concentration faced by an organization in a given position is dependent on the

characteristics of the other organizations in that position. More dramatically, the

constraint faced by an organization in a given position is not only affected by the other

organizations with which it shares a position, but also by how the boundaries of all other

organizational positions in the system are defined.

Consider the example of the iron ore mining industry (NAICS 212210). In the

context of two-digit NAICS industrial sectors, iron ore mining firms are considered as

part of the mining sector. The four-firm concentration ratio for this sector is 40.07%, the

highest concentration level of any of the two-digit industrial sectors (mean = 11.03%,

p. 84

stdev = 23.30%). When iron ore mining firms are considered at the three-digit level, they

are considered to be a part of the mining (except oil and gas) industrial sub-sector. The

four-firm concentration ratio for this sector is only 28.77%, much closer to the mean

concentration level for three-digit sub-sectors (mean = 19.10%, stdev = 29.63%). In the

context of the highly detailed six-digit national industries, this industry faces a

concentration level of 37.46%, even closer to the mean concentration level in this context

(mean = 32.50%, stdev = 25.67%). Although a few large firms can drive the sensitivity

of concentration ratios to industry classifications (in this case, a small number of very

large oil firms), this industry serves as a clear example of how this process operates

across industries.

The effects of boundary definition are even more dynamic in the measurement of

the constraint faced by firms in a given organizational position. In the context of two-

digit industrial sectors, iron ore mining firms face a constraint of 0.0295, somewhat high

among two-digit sectors (mean = 0.0243, stdev = 0.082). At the three-digit level, these

firms face a constraint of 0.0212, somewhat low compared with other three-digit sub-

sectors (mean = 0.245, stdev = 0.0286). At the six-digit level, however, these firms face

a constraint level of 0.1554, extremely high compared with other six-digit industries

(mean = 0.0362, stdev = 0.0444). These shifts cannot be explained simply by the

inclusion or exclusion of a small number of large and influential firms from an

organizational position, since they depend critically on the characteristics of the positions

that the focal industry is connected to through exchange.

The extent to which concentration and constraint can be affected by the definition

of the boundaries of organizational positions raises the possibility that the mechanisms of

p. 85

structural autonomy may be dependent on where these boundaries are located. Prior

research on the effect of concentration and constraint on autonomy and performance has

been at best equivocal in offering rationales for the selection of one set of boundaries

over another. Early research in this area (Burt 1980, 1982, 1983) restricted attention to

the manufacturing sector and used four-digit SIC codes to determine industrial

boundaries. Subsequent research focused on the broader American economy, including

but not limited to the manufacturing sector (Burt 1988, 1992; Burt and Carlton 1989),

used two-digit SIC codes to determine the boundaries of industries. While each of these

studies addressed a different facet of the mechanisms of structural autonomy, none of

these offers a specific theoretical rationale for the choice of boundary locations. Given

the absence of well-developed theoretical rationales for this choice in the existing

literature, the remainder of this section will develop three hypotheses about boundary

selection implied by the extant research on organizational positions and their effects.

-0.5

0

0.5

1

1.5

2

2.5

3

3.5

4

2-Digit 3-Digit 4-Digit 5-Digit 6-Digit

ConcentrationConstraint

Figure 5.1 – Concentration and Constraint for Iron Ore Mining (z-scores)

p. 86

5.2.1 Boundary-Independent Effects

One interpretation of the existing literature on the effects of organizational

positions on organization outcomes is that these effects are independent of the

designation of boundaries between organizational positions. Although it would be

difficult to support the strong form of this proposition—that any aggregation of

organizations into positions would produce these effects—somewhat weaker versions of

this claim are implied by several empirical studies and theoretical proposals. In one such

study, Burt (1983: 10-11) notes that he analyzes industry effects at both the two-digit and

four-digit SIC levels, finding and reporting results at both of these levels of analysis.

Blockmodel analyses of organizations (DiMaggio 1986; Gerlach 1992) in which

researchers arbitrarily choose a level of aggregation of firms into positions also are

consistent with the weak version of this claim. In these cases, a set of plausible

aggregations is suggested, either by a predetermined scheme or by the results of a

blockmodel analysis (White, Breiger, and Boorman 1976). The selection of one or more

of these possibilities without reference to a specific theoretical justification is only

consistent with the logic that the predicted effects of position on an organizational

outcome would result from any of the identified choices. These observations about the

implicit logic underpinning studies that define organizational positions in this arbitrary

way lead to the following proposition:

Proposition 1: Concentration and constraint will significantly affect the

industry profitability at all levels of industrial aggregation.

p. 87

5.2.2 Organization-Specific Niches

A second interpretation of the existing literature on organizational positions is that

aggregated industries simply reflect approximations of phenomena that fundamentally

take place at the level of individual organizations. In this perspective, organizations

derive the benefits of position through their location in some metric space. These

theories allow for the possibility that more than one organization could be located in

exactly the same location, or for all organizations in a system to be assigned to one of a

small set of positions. That said, these theories are fundamentally about the location of

organizations in space, rather than the assignment of organizations to discrete categorical

positions. Podolny, Stuart, and Hannan give a clear example of such a perspective in that

they define niches at the level of the organization (1996: 663). They argue that

organizational life chances are affected by the extent to which the niche of a focal

organization overlaps with the niches of other organizations.

An alternative approach would have been to attempt to assign organizations to a

discrete set of positions, based on the similarity of their technological niches. In his

analysis of changes in the structure of American industry, Burt (1988) similarly chooses

to directly analyze the structural distances between individual two-digit SIC code

industries rather than choosing to aggregate them into groups based on their similarities

(1988: 362). Although neither of these studies examines the effect of position on

autonomy, both highlight the possibility that structural autonomy may operate most

clearly at the organizational level, rather than at the level of organizations aggregated into

industries. If this is the case, then analyses of the effect of constraint and concentration

p. 88

on autonomy and profitability should be most powerful when they are based on the most

fine-grained partitioning of industries, leading to the following proposition:

Proposition 2: Concentration and constraint will have their most

significant effect on industry profitability at the most fine-grained level of

industrial aggregation.

5.2.3 Stochastic Structure

The previous propositions are never explicitly stated in existing organizational

research on the effects of discrete positions on organizational outcomes. Rather, they

express a set of implicit assumptions that must be made in order to draw conclusions

from research on categorical structure that does not explicitly test the validity of a

particular choice of boundary locations. These propositions suggest, respectively, that

the definition of boundary locations is not important for the analysis of structural effects

on organizations, or that there is no need to define boundaries or discrete organizational

positions at all. An alternative not considered by either of these propositions is that

discrete positions do, in fact, play a role in shaping organizational outcomes, and that the

determination of the boundaries of these positions is in fact critical in determining the

effects of structural autonomy.

The fundamental principle underlying this perspective is that individual actors

involved in industrial exchange do not make decisions about buying and selling from

every possible individual organization, but are rather influenced by a set of beliefs,

cognitive processes, and institutions that reflect a collective knowledge about a discrete

p. 89

set of organizational positions. The logic of bounded rationality (March and Simon

1958) suggests that individual managers involved in making production decisions would

be challenged, at best, to consider every organization in a national economy as a

possibility when making decisions about which organizations to buy from and which to

sell to. Social psychologists who study the use of schemas in categorization (e.g. Fiske

and Taylor 1991: 106) provide further evidence that individuals do, in many cases, use

categorical thinking to resolve this complexity. If individual actors in organizations are,

in fact, collectively aware of a set of discrete organizational positions, and if their buying

and selling behavior is affected by this awareness, then the set of organizational positions

identified in Chapter 4 should be most consistent with the mechanisms of structural

autonomy.

Proposition 3: Concentration and constraint will have their most

significant effect on industry profitability at the level of industrial

aggregation implied by the exchange behavior between industries.

5.3 Evaluating the Performance Propositions

5.3.1 Data Sources

In order to test the propositions about the effect of boundary selection on

structural autonomy, I used the data described in Chapter 4 about industrial exchange and

industry categories. Additionally, I collected data about the concentration and

profitability of this same set of industries. These data sources are described below.

p. 90

Industry Concentration Data

Data on industrial concentration for the industries identified in the input-output

accounts are not available for a single source. The U.S. Census Bureau publishes four-

firm industry concentration ratios for all manufacturing industries, as well as a number of

non-manufacturing industries1. However, there are a small number of industries for

which the census bureau does not publish these data. In these cases, firm-level data was

collected from Ward’s Business Directory of U.S. Private and Public Companies – 46th

Edition. This directory published firm-level revenue data for a wide variety of six-digit

NAICS industry classifications. While firm-level data tends to exaggerate industry

concentration (Burt 1992: 90), using concentration estimates based on these data seemed

preferable to eliminating these industries from the study.

Industrial Performance Data

Previous studies of the effect of market concentration and constraint on industrial

performance have measured industrial performance using the price-cost margin (Collins

1 The U.S. Census Bureau publishes these data for the manufacturing (31-33) sector inthe document titled Concentration Ratios in Manufacturing 1997. The census bureaualso publishes a series of documents with these data for the utilities (22), wholesale trade(42), retail trade (44-45), transportation and warehousing (48-49), information (51),finance and insurance (52), real estate and rental and leasing (53), professional, scientific,and technical services (54), administrative support, waste management and remediation(56), educational services (61), health care and social assistance (62), arts, entertainmentand recreation (71), accommodation and food services (72) and other services (exceptpublic administration) (81) sectors. Coverage was not provided for the agriculture (11),mining (21), construction (23), and management of companies and enterprises (53)sectors. Additionally, coverage was not provided for the religious, grant-making, civic,professional, and similar organizations (813) sub-sector, the newspaper, book, anddirectory publishers (5111), elementary and secondary schools (6111), junior colleges(6112) and colleges, universities and professional schools (6113) industry groups, or theveterinary services (54194) NAICS industry.

p. 91

and Preston 1968, 1969). The price-cost margin of an industry is based on the total sales

of that industry and measures of the extent to which that industry adds value in its

production process. Data for total industry output, employee compensation, indirect

business taxes and non-tax liabilities and other value added are published in the input-

output accounts for each of the industry groupings identified therein.

5.3.2 Modeling Structural Autonomy in a Market

A number of approaches have been taken to modeling the effects of constraint and

concentration on structural autonomy and industrial profitability. Early studies of market

constraint (Burt 1980, 1982, 1983, 1988) modeled the autonomy Ai of an industry i as a

linear function of constraint, concentration, and their interaction

Ai = α + βoOi + βcCi + βxXi + εi, (5.4)

where Oi measures the oligopoly of an industry, Ci measures market constraint, Xi

measures the interaction between oligopoly and constraint and εi corresponds to the

residual. Of these studies, only the study of changes in market structure (Burt 1988)

included non-manufacturing industries. Non-manufacturing industries have significantly

higher profitability than their manufacturing counterparts. As a result, subsequent

models introduced a control variable for manufacturing industries (Burt 1992). In this

study, the nonlinearity of these effects was treated with some rigor, and a number of non-

linear variants of the model in Equation 5.4 were considered. Burt reports finding

stronger results for multiplicative models based on the logarithms of these coefficients,

p. 92

and in particular a model in which the sense of concentration was inverted prior to

multiplication to match the sense of constraint (Burt 1992: 94). Following this logic, I

estimate the following model

log(Ai)= α + βolog(Oi) + βclog(1-Ci)+ βxlog(1-Ci)log(Oi) + βdDi + εi, (5.5)

where Di is a dummy variable indicating whether industry i is a manufacturing industry.

Estimates of this model typically have measured autonomy Ai as the price-cost

margin associated with a given industrial group. Collins and Preston (1968, 1969)

introduced this as a measure of industry-wide profitability, and it has become a widely

accepted measure. The price-cost measure PCMi is a ratio that describes the extent to

which an industry is able to generate value based on a given level of sales, and is

measured as

€

PCMi =VAi − LiVSi

, (5.6)

where VAi is a measure of the total value added, Li is a measure of labor costs, and VSi is

a measure of the total industry output of industrial position i2. Market constraint Ci is

determined from the input-output accounts data, according to Equation 5.3. The levels of

2 Values for total value added and labor costs are reported with the input-output accountsdata. There are three categories of value added – compensation of employees (V00100),indirect business tax and non-tax liability (V00200) and other value added (V00300).Compensation of employees represents labor costs, so the value of the numerator inEquation 3.6 is simply the sum of indirect business tax and non-tax liability and othervalue added.

p. 93

market exchange zij in Equation 5.1 are determined by the dollar value of purchases from

organizations in industrial position i by organizations in industrial position j. Industry

oligopoly Oi is measured as the estimated four-firm concentration ratio for industrial

position i. The four-firm concentration ratio CRi is the ratio sum of the sales of the four

establishments with the highest sales in an industrial position divided by the amount of

total sales generated by that position. These data are taken from the industry

concentration data published by the U.S. Census Bureau for those industries for which it

reports industry concentration, and estimated from firm-level data from Ward’s Business

Directory in all other cases3.

Table 5.1 presents the means, minima, maxima and standard deviation statistics

for each of these measures at each of the levels of aggregation modeled in this study.

These results yield some insight into how different levels of aggregation of markets can

change the perception of what activity in this market looks like. Most noteworthy is the

extent to which the relative number of manufacturing industries accounted for is higher at

more finely grained levels of aggregation. The total output from the manufacturing

sector in 1997 represents 28.13% of the output from all industries. However,

manufacturing represents only one (5.3%) of the 19 two-digit sectors (5.3%) and 336

(72.4%) of the 464 six-digit national industries. Whether this means that manufacturing

industries are overrepresented in fine-grained NAICS classifications or underrepresented

3 A regression analysis comparing the industry concentration measures derived fromWard’s Business Directory of U.S. Private and Public Companies to the industryconcentration measures published in the census indicated that there might have beensignificant sample bias in the Ward’s Business Directory data. Specifically, this directoryseems to be much more likely to have information on a firm if it had higher sales. As aresult of this, a procedure was developed to estimate industry concentration from thisfirm-level data based on the assumption that larger firms were more likely to be sampled.The details of this estimation procedure are described in Appendix B.

p. 94

Table 5.1 – Descriptive Statistics for Structural Autonomy MeasuresMean Min Max Std dev

2-digit sectors (N=19) Price-cost margin (log(Ai)) -1.601 -2.793 -0.517 0.604 Concentration (log(Oi)) -2.836 -4.661 -0.089 1.386 Constraint (log(1-Ci)) -0.045 -0.137 -0.021 0.026 Concentration x constraint (log(1-Ci)log(Oi)) 0.129 0.002 0.377 0.084 Manufacturing 0.053 0 1 0.229

3-digit sub-sectors (N=72) Price-cost margin (log(Ai)) -1.916 -6.908 -0.049 1.079 Concentration (log(Oi)) -2.171 -4.872 0 1.324 Constraint (log(1-Ci)) -0.045 -0.611 -0.010 0.077 Concentration x constraint (log(1-Ci)log(Oi)) 0.078 0 0.835 0.118 Manufacturing 0.291 0 1 0.458

3- and 4-digit multi-level industries (N=138) Price-cost margin (log(Ai)) -1.999 -6.908 -0.049 0.965 Concentration (log(Oi)) -1.996 -4.872 0 1.098 Constraint (log(1-Ci)) -0.054 -0.610 -0.011 0.079 Concentration x constraint (log(1-Ci)log(Oi)) 0.098 0 0.936 0.147 Manufacturing 0.558 0 1 0.498

4-digit industrial groups (N=193) Price-cost margin (log(Ai)) -2.011 -9.307 -0.049 1.310 Concentration (log(Oi)) -1.932 -4.709 0 1.125 Constraint (log(1-Ci)) -0.066 -0.854 -0.010 0.122 Concentration x constraint (log(1-Ci)log(Oi)) 0.095 0 0.952 0.145 Manufacturing 0.446 0 1 0.498

5-digit NAICS industries (N=306) Price-cost margin (log(Ai)) -1.964 -9.307 0.015 1.050 Concentration (log(Oi)) -1.650 -4.709 0 1.085 Constraint (log(1-Ci)) -0.070 -0.854 -0.009 0.113 Concentration x constraint (log(1-Ci)log(Oi)) 0.094 0 1.213 0.147 Manufacturing 0.562 0 1 0.497

6-digit national industries (N=482) Price-cost margin (log(Ai)) -2.072 -9.307 -0.015 1.047 Concentration (log(Oi)) -1.357 -4.709 0 0.989 Constraint (log(1-Ci)) -0.076 -0.950 -0.009 0.118 Concentration x constraint (log(1-Ci)log(Oi)) 0.081 0 1.052 0.118 Manufacturing 0.714 0 1 0.453

p. 95

in coarse-grained NAICS classifications, it is clear that the level of aggregation can have

a substantial effect on how industries are defined. It is also noteworthy that the mean

level of concentration is strongly affected by the level of aggregation, while the level of

market constraint is relatively unaffected. That more coarsely grained industrial

classifications are less concentrated than more finely grained industries is not

surprising—it is more difficult for a small set of firms to dominate the economic activity

of larger populations. It is not self-evident, however, that constraint would be so weakly

effected by the level of aggregation.

Regression Results

Table 5.2 presents ordinary least-squares estimates for the association between

industry profitability and structural autonomy covariates. In aggregate, these results

indicate that, in the market context of 1997, even using NAICS industry definitions,

structural autonomy is strongly affected by concentration and constraint. In the context

of the multi-level industrial grouping identified in Chapter 4, the model explains over

11% of the variance with these four predictors. In particular, the industry concentration

and its interaction with market constraint are robust predictors of industry performance,

yielding a significant (p < 0.05) result at the level of industry groups, NAICS industries,

and national industries, as well as the analysis based on the multi-level industries.

The results of this model also produce substantial evidence that rejects hypotheses

1 and 2. Contrary to the predictions of Proposition 1, it is quite clear from the results that

the effects of concentration and constraint are quite dependent on the level of aggregation

chosen, even if the aggregation scheme applied is broadly consistent with accepted

p. 96

Table 5.2 – Linear Regression Coefficient Estimates for the Effects of IndustryConcentration and Market Constraint on Industry Performance

2-digit 3-digit 3/4-digit 4-digit 5-digit 6-digitIndependent Variables Industry concentration -0.149

(-0.43)-0.271

(-1.99)-0.330

(-3.52)-0.322

(-3.47)-0.154

(-2.36)-0.162

(-2.78) Market constraint -13.919

(-0.50)-3.618

(-0.89)-5.299

(-2.03)-2.589

(-2.56)-0.918

(-1.26)-0.188

(-0.32) Concentration x constraint

-2.088(-0.20)

-2.365(-0.85)

-4.400(-3.03)

-3.103(-3.50)

-2.256(-3.74)

-2.445(-3.99)

Control Variable Manufacturing industry

-0.532(-0.79)

-0.233(-0.74)

-0.043(-0.26)

0.008(0.04)

-0.062(-0.48)

-0.086(-0.76)

Constant -2.352 -2.415 -2.457 -2.517 -2.051 -2.061

N 19 72 137 191 287 458Adjusted R2 -0.044 0.010 0.111 0.070 0.059 0.071Note: t-test scores in parentheses. Coefficients for which p < 0.05 are indicated in boldface.

notions of what industrial categories are. Figure 5.2 presents the t-scores of the

regression estimates for industry concentration, market constraint, and the interaction

between concentration and constraint, the principal independent variables of the model, at

each level of industry aggregation analyzed. The estimates of t-values for these

independent variables across the levels of aggregation provide substantial evidence that

the effects of concentration and constraint on autonomy are not their strongest when

analyzed using the most detailed data available. Rather, the analyses of structural

autonomy based on either four-digit industrial groups or the mixed-level industrial

classification produce the most consistently strong results. These results are inconsistent

with Proposition 2, and suggest that the proposition should be rejected.

p. 97

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

2-Digit 3-Digit 3/4-Digit 4-Digit 5-Digit 6-Digit

Concentration Constraint Concentration x Constraint

Figure 5.2 – t-values for Least-Squares Coefficient Estimates at Varying Levels ofIndustry Aggregation

5.3.3 Stochastic Structure Analyses

Proposition 3 suggests that the failure of the evidence to support Proposition 1 or

Proposition 2 may arise from the possibility that the exchange behavior individual actors

in the system is shaped by an awareness of social structure as represented by a set of

intermediate organizational positions. Burt does not draw this conclusion, but his

assessment (1988) of the structure of the American economy and the changes therein not

only suggests the possibility that some industries might be meaningfully aggregated, but

also outlines the theoretical rationale for doing so.

In analyzing the structure of the American economy, Burt argues (1988: 357) that

boundaries play a significant role in defining markets with respect to industries. To this

end, he argues that the notion of structural equivalence (Burt 1983: 60-63) is a

p. 98

meaningful way to determine the similarity between a given pair of industries (Burt 1988:

358). While he acknowledges that complete structural equivalence is too extreme of

criteria to be used in empirical research, (Burt 1988:359) he does not conclude that any of

the industries in his study should be aggregated based on their level of structural

equivalence (Burt 1988: 362). In a different context, he even notes the possibility that

four-digit SIC codes may distinguish industries too finely (Burt 1983: 62), a possibility

suggested by a number of other streams of research (Kaysen and Turner 1959; Blin and

Cohen 1977). The questions these authors raise and the evidence presented here in

opposition to Propositions 1 and 2 at the very least suggest that the aggregation of

industries into industrial positions by means of structural equivalence is a possibility

worth investigating.

5.4 Conclusions

The analyses presented in this chapter contribute to a convincing case that

empirically assessing the location of industry boundaries in markets is a worthwhile and

relevant task for organizational researchers. In particular, there are consequences both

for descriptive studies that seek to establish the characteristics of industries and the firms

that populate them, and for studies that seek to empirically establish the consequences for

firms located in these social positions.

The first set of analyses in this chapter demonstrates how the structure of markets

can affect the baseline measurement of industry-level indicators. Aggregate industry

measures such as market concentration, constraint and industry performance are based on

where the boundaries of markets are located. While these measures could clearly be

p. 99

manipulated by choosing arrangements of industry boundaries in an explicitly adverse

way, this analysis shows that these measures can be sensitive to the seemingly

uncontroversial choice of level of market aggregation reflected by the number of digits in

an SIC or NAICS code. Given the possible sensitivity of these measures to this choice,

these analyses suggest that such a choice should not be made purely on the basis of the

availability of data, but rather by empirically evaluating the significance of various

market structures.

The second set of analyses demonstrates that stochastic structural analysis can be

used to empirically identify the market structure that most closely corresponds to the

theory and predictions of a substantive theory of organizational processes. The theory of

structural constraint and autonomy is rather explicitly based on a notion of market

structure determined by relational exchange. As such, it would seem that the structure

that should most convincingly produce the hypothesized impact of exchange-based

market positions should be one that actually reflects the regularities implied by these

exchanges. The analysis in this chapter demonstrates that arbitrary market structures that

would not be selected by the stochastic structure analysis method produce effects of

constraint and autonomy on industry performance that are decidedly weaker than those

produced by model of structure that would be selected by the method.