Market Structure and Innovation

SOCIAL SCIENCE RESEARCH ARTICLE NO.

27, 1–21 (1998)

SO970608

Market Structure and Innovation* Holly J. Raider Columbia University Corporate innovation is often argued to result from, and be encouraged by, market competition. The tradition is to test such arguments with market concentration data; firms with stronger control over their market can afford the long-run investment of R&D. Network theory has broadened our understanding of how competition is a function of structure within and beyond a market. Producers earn high profit margins to the extent they are few and their customers and suppliers are many and disorganized. The first condition is measured by the traditional concentration ratio for a market. The second condition is measured by the network constraint of suppliers and customers. My purpose in this paper is to describe how our understanding of the market competition effect on innovation is enriched when the concentration measure of market competition tradition in economics is replaced with the network model of market competition now prevalent in sociology. Results indicate that markets facing competitive environments, in high constraint market positions, show greater R&D intensity and faster rates of innovation than do industries facing less competitive pressure. These effects persist even with controls for technological opportunity and appropriability, in contrast to traditional models using only concentration data to measure competition. r 1998 Academic Press Key Words: innovation; market structure; research and development; structural holes; networks

With the advent of industrial laboratories, industrial firms became an important if not dominant source of innovative activity (Noble, 1977). Although individual inventors (Jekwes, Sawers, and Stillerman, 1958) and university scientists (Henderson, Jaffe, and Trajtenberg, 1995) are important sources of invention, industrial research and development (R&D) is often essential to consummating a marketable innovation. The prominent role of firms in fostering technological change

* An earlier version of this paper was presented at the 1995 Annual Meetings of the American Sociological Association. I thank Ron Burt, Greg Janicik, Richard Nelson, Bhaven Sampat, and two anonymous reviewers for productive comments on earlier versions of this manuscript. Additionally, I gratefully acknowledge use of the Yale data on Research and Development. Address correspondence and reprint requests to Holly Raider, 195 North Harbor Drive #5001, Chicago, IL 60601; e-mail: [email protected]. 1 0049-089X/98 $25.00 Copyright r 1998 by Academic Press All rights of reproduction in any form reserved.

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prompts researchers to examine the organizational contexts facilitating innovation. Corporate innovation is often argued to result from and be encouraged by (lack of ) market competition. Schumpeter’s (1942) conjecture regarding the link between firm size and creative destruction ignited a copious literature on market structure and innovation in which interindustry differences in innovation are ascribed to competitive differences across industries. This tradition, in economics and industrial organization, uses market concentration data to index market competition. Unfortunately, a half-century of research has not resulted in consensus. Further, when institutional factors such as appropriability and technological opportunity are taken into account, market competition effects become secondary. In this paper, I provide argument and evidence for using a network model of competition to understand industry differences in innovation and R&D investment. Network theory has broadened our understanding of how competition is a function of internal and external market structure. We know that firms earn high profit margins to the extent they are in industries with few other producers and transact with many, disorganized buyers and suppliers. I hypothesize that the same autonomy from market competition which facilitates high profit margins also provokes corporate investment in R&D and accelerates the rate of innovation. The result is a more robust measure of market structure which is also amenable to advances in understanding the innovation process as involving network forms of organization (Powell, Koput, and Smith-Doerr, 1996; Robertson and Langlois, 1995) as well as upstream suppliers and downstream users (von Hippel, 1988). THE ‘‘SCHUMPETERIAN’’ HYPOTHESES In Capitalism, Socialism, and Democracy (1942), Schumpeter suggested that large firms, firms facing few competitors and able to charge monopolistic, or at least oligopolistic prices, are associated with the kind of research, development, and innovation that drives technological change and thereby capitalist change. This argument challenged prevailing notions regarding the virtues of free market capitalism and so it is not surprising that much empirical research ensued. However, this prolific market structure and innovation literature can only best be described ‘‘inconclusive’’ (Cohen and Levin, 1989). In addition to Cohen and Levin’s thorough examination, reviews of this literature are widely available: Scherer (1992) makes tractable the diversity of theoretical approaches to Schumpeter’s conjecture and ample precis are offered by Kamien and Schwartz (1982) and Baldwin and Scott (1987). The research tradition that followed Schumpeter’s argument examines two so-called Schumpeterian hypotheses regarding technological advance: innovation increases with firm size and innovation increases with market concentration. Though Schumpeter’s original theoretical argument concerns firm-level actors, scholars have focused on more aggregate industry data largely as a consequence of inadequate firm-level data to meaningfully conduct statistical tests at that level of analysis. Industry level data on innovative inputs, such as R&D spending or

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employment of scientific personnel are simply more accessible. As a result, the market structure and innovation literature grew out of the convention of using aggregate, industry-level data to examine the Schumpeterian hypotheses— usually using market concentration indices such as concentration ratios and the Herfindahl index. It is worth noting that Schumpeter did not write explicitly about market concentration and innovation, but because concentrated markets tend to be comprised of large firms, the argument is often attributed to him (Cohen and Levin, 1989).1 There are several reasons to expect a positive link between firm size/market structure and innovation. These are arguments for innovative activity to be higher when firms operate from a position of strength. The first reasons concern organizational qualities of large firms in monopolistic and oligopolistic industries; they tend to have more working capital to invest in large scale R&D than do smaller firms and larger firms have a better ability to implement and market new inventions (Nelson, 1959). The greater likelihood of marketing the results of basic research is an incentive for large, particularly diversified firms to engage in such research programs. This absorptive capacity of firms, empirically documented by Cohen and Levinthal (1990), is well illustrated at 3M, recently with their microreplication technology for which diverse commercial applications resulted from one core technology (Stewart, 1996, p. 97). The second reason concerns the postinnovation incentive to invest in R&D. In addition to being better able to bring new innovations to market, large firms in concentrated markets expect greater returns on innovation owing to larger market share, the ability to coordinate with competitors, and better predictability at the industry market level. There are also reasons to expect the firm size/market structure link to be just the opposite—that small firms are more innovative. From an organizational standpoint, small firms can cultivate the kind of free, creative thinking argued necessary for innovation, while larger firms are more susceptible to inflexibility and bureaucratic rigidity. Moreover, large firms are more likely to be vested in current technological competencies and have more to lose by innovation generally and by radical innovation in particular. Small firms, then, have greater postinnovation incentive to innovate because they have (marginally) more to gain by ‘‘creative destruction’’ which jolts the market. A cache of research notwithstanding, empirical examinations of the relationship between market concentration and innovation have yielded little in the way of conclusive results. Amidst debate over measurement, estimation, and control variable issues, as well as over the theoretical issues briefly outlined in the preceding paragraphs, there seems to be some agreement that the empirical functional form of the market structure–innovation relationship is a nonmonotonic ‘‘inverted-U’’ shape: The rate of innovation and R&D investment increases 1 More precisely, Schumpeter suggested that the locus of innovation is large corporate R&D labs (rather than independent inventors). The interpretation in the literature that this implies a continuous relationship between firm size and innovation is just that, an interpretation. Nelson, Peck, and Kalachek (1967) further elaborate this distinction.

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FIG. 1. ‘‘Inverted-U’’ association between rate of innovation and concentration. Four firm concentration data from the 1972 Census of Manufactures. Rate of Innovation data from the Yale Survey on R&D in which managers evaluated the rate of process and product innovation in their line of business.

at low levels of concentration and decreases at highest levels of concentration (for example, Scherer, 1967; Levin, Cohen, and Mowrey, 1985). Arguably, it is more a compromise than a consensus, as the empirical association is rather weak (Fig. 1). Despite the empirical morass, the link between market structure and innovative activity is intuitively appealing: market conditions in which firms operate affect incentives to invest in R&D, the ability to innovate, and the ability to commercialize inventions. Concentration measures alone have proven dissatisfactory in predicting innovation and R&D investment, I maintain, because they ignore market structure outside a given industry classification, such as upstream and downstream market contexts. Firms face varying degrees of competitive pressure from their buyer and supplier markets which can affect the incentive to innovate. With the exception of a few analyses prompted by Galbraith’s (1952) notion of countervailing power (e.g., Lustgarten, 1975, and Farber, 1981), researchers remain committed to concentration-based measures of market structure. INNOVATION AND STRUCTURAL AUTONOMY I depart from existing economic and industrial organization approaches to the market structure–innovation question by using network competition theory to explain interindustry differences in innovative activity. The sociology of markets, and network models in particular, offer a theoretical frame for modeling the broader competitive context in which industries operate. In general, network theories explain social or economic phenomena by the content or the pattern of an actor’s relations. Network applications have been used to understand diverse

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kinds of economic action and market behavior such as interlocking corporate directorates (Burt, 1983; Mizruchi, 1992), corporate philanthropic behavior (Galaskiewicz and Burt, 1991), transactions in securities markets (Baker, 1984; 1990), alliance formation (Gulati, 1996), and markets in general (White, 1981; Leifer and White, 1988). Network theory has broadened our understanding of how competition is a function of structure within and beyond a market. The network competition model described in this paper is from structural hole theory (Burt, 1992). A structural hole is a gap in a social structure, a disconnection among actors. Actors are favorably positioned when they span a structural hole, meaning they are connected to other actors who themselves are not connected. This brokerage or gatekeeping location in the social structure is a position of competitive advantage because it offers the opportunity to access diverse information, to control the transfer of information between disconnected parties, and to identify and broker transactions between otherwise disconnected parties. These positions also enable the broker to exert leverage between disconnected parties by creating competition between them or by playing them off one another. Actors in these positions are said to be structurally autonomous because of the independence they enjoy as a matter of their position among other actors in the system. Similarly, actors tied to few, densely interconnected alters are constrained; they have few if any brokerage opportunities and they lack the information benefits of accessing diverse social and economic worlds. The structural hole model applies at the industry level of analysis using transactions between industries to operationalize relations (see Burt, Ch. 3). At the industry or market level of analysis, structural autonomy embodies two properties: the extent to which producer firms are organized within an industrial market and the extent to which producer firms buy from and sell to many, disorganized industrial markets. In other words, firms in an industrial market are autonomous to the extent they transact with competitive markets but compete in a noncompetitive industry. Figure 2 is used to illustrate kinds of market conditions. Each large white circle containing gray dots represents an industry classification, the gray dots inside the circles representing firms in that industry. Solid lines connecting white circles represent volume of sales between industries. The diagram depicts two industries as concentrated, industries A and C; more than 90% of all business in each A and C is captured by the four largest firms (five firms realize 100% of sales in hypothetical industries A and C). The firms within A and C are coordinated; they have contact with one another as illustrated by the dashed black lines connecting them. Contact among the firms may be express, such as a joint venture linkage, or tacit: firms are visible to one another, can predict the others’ behavior, and can coordinate market behavior through price leadership or collusion. In contrast, industries B, E, and F are least concentrated and most closely approximate a free market where coordination and predictability are difficult. This intraindustry organization component of structural autonomy is measured by the

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FIG. 2.

Illustrative market structure.

concentration ratio for a market and is consistent with oligopoly theory (e.g., Caves, 1992). The second component of structural autonomy refers to the organization beyond the industry classification: the structure of relations within and between an industry’s trading partners. Industries are more autonomous to the extent their transactions are distributed across many, disorganized, disconnected buyers and suppliers. This condition sets industry A apart from C; though similarly oligopolistic as C, A is more structurally autonomous than C because A sells to many (four industries), disorganized (B, E, and F are comprised of a large number of uncoordinated firms), and disconnected markets (B, C, E, and F are not interconnected, except indirectly through A). Alternatively, C is more constrained than A because C’s transactions are concentrated in a few industries which are fairly well internally organized and are interconnected. Because A’s trade is distributed across several markets, the loss of business in any particular market has only a small marginal effect, while C is much more dependent on each of its few trade partners. Buyer oligopoly in A, D, and G reduces C’s command of market pricing. C’s brokerage opportunities are limited by the interconnection among its trading partners. Further, the tie between G and D is a constraint on C because G and D can share information about C. For example, in negotiating terms of trade with C, D knows about G’s transactions with C (price, performance, etc.) which puts C at a disadvantage. Following structural hole theory, firms in A operate from a position of strength which enables them to earn a higher rate of return through favorable terms of trade, brokerage opportunities across other industries, and the ability to withstand the loss of any given trading partner. The positive effects of structural autonomy on economic performance are

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documented in several empirical analyses. For American markets, Burt (1983, 1988) observes the positive performance–autonomy relation in both manufacturing and nonmanufacturing industries. The association between the strength of market position and profits is also evidenced in foreign markets including Japan (Yasuda, 1993), Germany (Ziegler, 1982), and Israel (Talmud, 1994). Although structural hole theory describes the information and control benefits of structurally autonomous actors, much of Burt’s work on the profitability of autonomous markets focuses on the control benefits of these positions, emphasizing as the information benefits in this setting the enhanced ability of a market to coordinate when its producers are few and the virtue of transacting with disconnected buyers and suppliers such that information about terms of trade is discrete. Yet the information benefits of accessing diverse and disconnected social worlds afforded to entrepreneurial individuals (Burt, 1992, Ch. 4, and more recently in Burt, 1997) should also accrue to firms and other aggregate actors (see, for example, Rosenthal’s (1996) finding that TQM teams with large, entrepreneurial networks are more likely to be recognized for excellence). The information benefits of industries in structurally autonomous positions extends, I suggest, to facilitate R&D and innovation by making industries so positioned aware of opportunities for adapting technologies used in one market to meeting the needs of customers in other markets. By transacting with many markets, industries access diverse information not only about terms of trade but about technological competencies, know-how, and technological needs of diverse buyers and suppliers. Because structurally autonomous industries connect otherwise disconnected markets, they are uniquely positioned to observe and act on these technological opportunities. Industries which form a unique juncture between other markets, such as industry A in Fig. 2, are positioned for crossfertilization which facilitates the innovation process. I use the structural hole network model of competition to understand industry differences in innovative activity. I expect autonomous firms, firms in positions of market strength and information advantage, to evidence greater innovative activity. Though the model is grounded in network theory, the network argument regarding market power—that market predictability, leverage, and profitability in transactions encourage innovation—is consonant with that of the Schumpeterian hypotheses. I use two indicators of innovative activity to explore the relationship between structural autonomy and innovation: R&D intensity and rate of innovation. R&D intensity is used to indicate inputs to R&D while rate of innovation indicates outcomes of the process. The term ‘‘innovative activity’’ is intended to refer to both. Accordingly, I expect: Industries in a position of structural autonomy (high concentration, low constraint) are expected to have higher investment in R&D. Industries in a position of structural autonomy (high concentration, low constraint) are expected to have a higher rate of introduction of new innovations.

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Data and Methods The unit of analysis is the industry line of business. R&D intensity is the ratio of investment in R&D to sales and is calculated from 1976 Line of Business data reported by the Federal Trade Commission (FTC). The rate of innovation variable is constructed from the Yale Survey on R&D, which was administered by Klevorick, Levin, Nelson, and Winter in 1983. In the Yale study, 650 industry respondents from 130 manufacturing industry classifications evaluated several aspects of the innovation and R&D environment in their line of business.2 Respondents were asked to evaluate on a seven-point scale the rate at which both new or improved processes and products were introduced in their line of business since 1970. Thus the rate of innovation variable can range between 2 and 14, 2 indicating extremely slow rate and 14 indicating extremely rapid rate of introduction of new innovations. Further details of the survey method and sample frame used in the Yale survey are available elsewhere (Levin, Klevorick, Nelson, and Winter, 1987). Because it is instructive to compare the traditional concentration model with network autonomy model of market structure, I use analyses reported in Levin et al. (1985) as a baseline and use indicators constructed consistently with their research. Structural autonomy. There are two pieces to the structural autonomy of an industry: organization within the industry and organization among buyers and suppliers. Organization within the market is the extent to which the market is an oligopoly, and is measured using the four firm concentration ratio available in the 1972 Census of Manufactures published by the Department of Commerce. The second piece of structural autonomy is the organization of an industry’s buyers and suppliers and the lack of interconnection among those buyers and suppliers. The 1972 input-output table of American markets distributed by the U.S. Department of Commerce in the Survey of Current Business provides volume of transaction data for the population of SIC industry classifications. The input–output table is treated as a matrix of relations from which industry interconnection is calculated. Related use of input–output tables is found in Carter (1970) and especially Leontief (1986[1966]). Because it is easier to measure extant relations than nonrelations, and because doing so enables one to consider varying strengths of relations (rather than connection as a dichotomous variable), a ‘‘positivistic’’ measure is used. The measure of interconnection is network 2 The data contain 130 observations on industries at various levels of aggregation; standard industrial classification (SIC) industries: 68 4-digit, 30 groups of 4-digit, 28 3-digit, two groups of 3-digit, one group of 3- and 4-digit, and one 2-digit SIC Line of Business Classifications. These are mutually exclusive aggregations. The constraint variable is calculated at the 2-digit SIC classification as is the price-cost margin, all other variables are at the same level of aggregation as the 130 SIC observations. Aggregating up to the 2-digit SIC level to conform with the constraint variable results in only 42 cases with considerable loss of information. Aggregating the concentration variables to the 2-digit SIC level, thereby making it correspond to the aggregate constraint variable, does not alter the general pattern of results for Models 1–19, though the more aggregate concentration variable does result in somewhat weaker effects for the concentration variable in the models. Models reported in Tables 2–5 vary from 121 to 130 observations due to missing data.

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constraint. The constraint measure (Ci ) is an index of the extent to which (focal) industry i conducts a large volume of its business with organized, interdependent markets k (see Burt, 1992, Ch. 3): Ci 5

oW O, ik

k

i5 / k

k

where Wik is producer market i’s direct and indirect dependence on supplier/buyer market k and Ok is the extent to which supplier/buyer market k is oligopolistic (k’s four firm concentration ratio).3 Ci ranges from 0 to 1 with 1 being maximum constraint. Control variables. Effects of market concentration on innovative activity have not been robust when institutional factors are taken into consideration (Cohen and Levin, 1989; Levin et al., 1985). Two classes of institutional factors are argued to affect the incentives of firms to invest in and support innovation: appropriability and technological opportunity (von Hippel, 1982; Geroski, 1990). Appropriability refers to the ability of innovators to earn returns on R&D investment. Where firms are unable to recover the costs of R&D or to earn rents on innovation, there is little incentive for them to invest in this activity in the first place. Patenting may be the most familiar protection mechanism, but trade secrets, and first-to-market advantages are other common means to appropriate returns on investment in R&D. Appropriability is operationalized as the highest score received in the Yale Survey for the effectiveness of six appropriation mechanisms. Again, these variables are constructed consistently with those used in Levin et al. (1985). The six appropriation mechanisms are (1) patents to prevent duplication, (2) patents to secure royalties, (3) trade secrets, (4) lead time (first to market), (5) moving quickly down the learning curve, and (6) superior sales or service. Respondents were asked to rate (on a scale of 1 to 7, 1 being not effective, 7 being very effective as a means of appropriation) the six mechanisms separately for products and processes, a total of 12 ratings. For each industry, the highest score across the 12 ratings was taken as an indicator of the effectiveness of appropriation.4 Finally, 3 W is an index of the extent to which industry i conducts a large volume of its business with ik industry k, the extent to which industry k is organized, and the extent to which industry k is connected to the other industries q with whom i transacts, calculated as: (pik 1 [SqPiqPqk 2Ok ], q Þ i Þ k, where pik is the proportion of transactions (volume of sales) i conducts with k, PiqPqk summed over all q captures the connection between i’s other trading partners and k, Piq and Pqk are, respectively, the proportion of transactions i conducts with q and q conducts with k, and Ok is the extent to which industry k is organized (the extent to which k is itself an oligopoly). The constraint measure (Wik ) is summed over all k to calculate the aggregate constraint on each market i, a summation which ranges from 0 to 1, 1 being maximum constraint. 4 I also considered aggregations of the 12 scores. The correlation between the highest score and the sum of scores is 0.574. For the R&D intensity dependent variable, using the sum of scores index in Model 4 (in place of the effectiveness score reported in Table 2) is not significant (t test 5 0.674) and does not affect the level of effects for other variables in the model. For the rate of innovation dependent variable, the sum of scores is a significant predictor (t test 5 2.747, as compared to t 5 1.4 for the effectiveness predictor reported in Model 9, Table 3) and increases the significance level of constraint (t test 5 2.5, as compared with t test 5 2.1). In other words, the alternative measure of appropriability does not change the main result—the strong effects of network constraint persist, even in models

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appropriation is more difficult in industries in which competitors can easily duplicate an innovation. This is controlled for using an indicator of the duplication time for major innovations in each industry. Respondents in the Yale Survey rated the length of time it would take for a competitor to duplicate a major innovation. This ranges from 1 (less than six months to duplicate) to 6 (timely duplication is not possible). A second set of variables is used to control for technological opportunity effects. In part, the opportunity for firms to translate knowledge inputs into innovative outputs depends on the strength of available inputs (Klevorick, Levin, Nelson, and Winter, 1995). As in Levin et al. (1985), I include six technological opportunity variables to indicate available knowledge inputs: (1) science base— the importance of basic and applied sciences to the industry’s technology, the knowledge contribution of (2) government laboratories, (3) material suppliers, (4) equipment suppliers, and (5) users. Again, respondents to the Yale Survey rated (on a scale from 1 to 7, with 7 being most relevant) the knowledge contribution of these sources. The sixth technological opportunity variable is an indicator of industry maturity: (6) new plant, property and equipment, measured as the percent of new plant, property and equipment installed within 5 years prior to reporting in the Federal Trade Commission’s Line of Business (1974). Table 1 contains a correlation matrix, means, and standard deviations of variables used in the analyses. Results Classic concentration. The ‘‘inverted-U’’ nonlinearity between market concentration and both rate of innovation (Fig. 1) and R&D investment is evident in these data. The variables for concentration and its square account for this curve; the negative coefficients for concentration squared capture the downward slope. OLS results reported in the first columns of Tables 2 and 3 show the inverted-U nature of the association between concentration and innovative activity. Table 2 displays models where R&D intensity is the dependent variable and Table 3 displays models where rate of innovation is the dependent variable. While the inverted-U shape best fits the distribution of R&D intensity across levels of concentration, neither concentration nor its square are significant in Model 1 (Table 2). Concentration and its square are both significant ( p , .05) predictors of rate of innovation (Model 6, Table 3). Add network constraint. The second column of Tables 2 and 3 report the classic concentration models extended to include the constraint measure (Ci ). These are the basic structural autonomy models: combining a term for intraindustry organization (concentration) with the constraint term (Ci ) to capture effects of buyer– supplier coordination. Constraint is a significant predictor of R&D intensity controlling for appropriability and technological opportunity, while those for market concentration do not. Further, given no theoretical reason why one measure is more externally valid than the other, I use the highest of the 12 ratings in the interest of comparability with Levin et al. (1985).

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Innovation rate R&D intensity Concentration Concentration square Constraint Alter markets Density New property Science base Material suppliers Equipment suppliers Users Government entities Appropriability Duplication time Price–cost margin

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b

a

8.01 1.98

0.02 0.02

.382 2.011 .133 2.032 .097 .188 .283 2.240 2.448 .250 .399 .199 .250 .173 .209 .324 2.001 .176 2.210 .204 .267 .125 .286 .133 .112 2.002 .172 .041 .094

2

3

43.44 20.06

.974 .127 2.103 .072 2.106 2.116 2.154 .009 2.133 .001 .044 .106 2.134

Number of cases: 121 (listwise correlation). Total observations: 130.

Mean Standard Deviation

1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16.

1

22.86 19.72

.102 2.073 .037 2.127 2.169 2.166 .039 2.144 2.019 .004 .093 2.135

4

0.05 0.05

2.398 .388 2.163 .148 2.033 .031 .115 .027 .091 .113 2.247

5

7

66.05 8.96

0.05 0.01

2.971 .087 .103 .074 .064 2.068 .096 .014 .012 2.177 .164 2.007 2.009 2.011 .043 2.085 .066 .136 2.119

6

9

10

11

38.50 9.92

6.18 0.88

4.56 1.03

4.83 0.91

.171 .146 .070 2.059 2.072 .252 2.036 .003 .166 2.036 2.017 .149 .037 .038 .055 .164 2.071 2.021 .028 .060 2.168 .043 .120 .021 2.012 2.219

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TABLE 1 Correlations,a Means,b Standard Deviationsb

4.00 1.06

.290 .020 .217 .021

12

14

2.77 1.16

6.00 0.66

.050 .110 2.008 .068 .093

13

3.77 0.92

.021

15

0.12 0.05

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HOLLY J. RAIDER TABLE 2 Determinants of Research and Development Intensity 1

2

Market structure Four firm concentration ratio 0.001 (1.6) 0.000 (1.3) Square of four firm concentration ratio 20.000 (21.3) 20.000 (21.0) 0.083 (4.4) Constraint (log) c Technological opportunity % New plant property, equipment (5 yrs) Science base a Material suppliers a Equipment suppliers a Users a Government laboratories/ agencies a Appropriability environment Effectiveness of appropriability a,b Duplication time (months) Other Price–cost margin Constant 0.005 0.035 N 123 123 0.028 0.159 R2

3

4

5

0.000 (0.7)

0.000 (0.4)

0.000 (0.4)

20.000 (20.2)

0.000 (0.2) 0.008 (4.5)

0.000 (0.1) 0.009 (4.9)

0.000 (2.9) 0.001 (3.5) 0.000 (3.4) 0.003 (1.5) 0.002 (0.9) 0.002 (1.0) 0.000 (0.0) 0.001 (0.4) 0.000 (0.3) 20.004 (22.2) 20.003 (22.2) 20.003 (21.7) 0.004 (2.5) 0.002 (1.2) 0.002 (1.1) 0.003 (2.4)

0.004 (3.3)

0.004 (3.3)

0.002 (0.6) 0.001 (0.9)

0.002 (0.7) 0.002 (1.2)

0.001 (0.5) 0.002 (1.2)

20.044 121 0.290

0.058 (1.8) 20.012 121 0.418

20.009 121 0.400

T-tests shown in parentheses. Models 1 and 3 are a reanalysis of models reported in Levin et al. (1985). a Measured on a 7 point scale. b Highest score reported for effectiveness of six appropriation mechanisms for either processes or products. c The log of constraint is the best fit.

(Model 2), but is significant for rate of innovation only under a weak criterion for significance (t test 5 1.8, p , .10). For both dependent variables, however, the coefficients for constraint are large (relative to concentration and its square in the respective models), positive, and increase explained variance. However, the positive coefficients for constraint are in contrast to the direction suggested by hypotheses 1 and 2. I expected innovation to be higher where firms operate in more autonomous, less constrained positions. Instead, innovative activity is higher when faced with higher constraint. I will return to this issue of directionality in the discussion section. Add control variables and omit network constraint. As noted earlier, the association between concentration measures of market structure and innovative activity is no longer significant when factors in the technological environment are controlled (see Cohen and Levin, 1989, for a thorough review). This is first done without the constraint measure so as to compare the controls model with and without the constraint parameter. In Model 3 (Table 2) and Model 8 (Table 3), the

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MARKET STRUCTURE AND INNOVATION TABLE 3 Determinants of Rate of Innovation (Rate of Introduction of New Processes and Products since 1970’s) 6

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Market structure Four firm concentration ratio 0.089 (2.5) 0.084 (2.4) Square of four firm concentration ratio 20.082 (22.3) 20.077 (22.1) 0.408 (1.8) Constraint (log) c Technological opportunity % New plant property, equipment (5 yrs) Science base a Material suppliers a Equipment suppliers a Users a Government laboratories/ agencies a Appropriability environment Effectiveness of appropriability a,b Duplication time (months) Other Price–cost margin Constant 6.013 7.470 N 130 130 0.050 0.074 R2

8

9

10

0.045 (1.2)

0.040 (1.1)

0.044 (1.2)

20.031 (20.8) 20.026 (20.7) 20.029 (20.8) 0.464 (2.1) 0.574 (2.5)

0.037 (2.2) 0.344 (1.7) 0.357 (2.0) 0.364 (1.9) 0.331 (1.9)

0.040 (2.5) 0.294 (1.5) 0.389 (2.2) 0.384 (2.0) 0.221 (1.2)

0.036 (2.1) 0.291 (1.5) 0.402 (2.3) 0.453 (2.3) 0.220 (1.2)

0.046 (0.3)

0.091 (0.6)

0.079 (0.5)

0.426 (1.5) 0.420 (1.4) 0.360 (1.3) 20.074 (20.4) 20.061 (20.3) 20.073 (20.4)

23.915 127 0.220

6.566 (1.8) 22.250 127 0.268

21.951 127 0.248

T-tests shown in parentheses. Models 6 and 8 are a reanalysis of models reported in Levin et al. (1985). a Measured on a 7 point scale. b Highest score reported for effectiveness of six appropriation mechanisms for either processes or products. c The log of constraint is the best fit.

model is expanded to include the appropriability and opportunity factors described in the Data and Methods section. It is here that concentration alone fails to significantly predict either R&D intensity or rate of innovation. Instead, technological opportunity has the most significant effect: industry R&D intensity is greater for industries with relatively new infrastructure (plant, property, and equipment), and where users and government laboratories and agencies are important sources of knowledge (the negative coefficient for importance of equipment suppliers is curious). Neither appropriability environment variable is significant. Similar results yield for rate of innovation: concentration and its square are no longer significant, nor are the appropriability variables. All but the government laboratory variable in technological opportunity are important predictors, effects are significant for new infrastructure and material suppliers as a source of knowledge. Consistent with earlier findings, concentration is no longer a strong predictor of innovative activity when controlling for technological opportunity and appropriability.

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Add network constraint. Models 4 and 9 expand the previous controls models to the network model. For both R&D intensity and rate of innovation, constraint is a significant predictor despite the use of appropriability and opportunity institutional controls. So, unlike concentration alone, the significant constraint effects persist even in models controlling for appropriability and technological opportunity. Further, adding the constraint variable to the equations does not alter the general effects level for most of the control variables from what they were in the models using just concentration (Models 3 and 8).5 Discussion The structural autonomy models better explain industry differences in innovative activity than concentration alone. While significant, the constraint component of structural autonomy has a positive coefficient for both R&D intensity and rate of innovation. My interpretation is that firms in constrained positions—which trade with fewer, coordinated buyers and suppliers—actually have higher rates of innovation and R&D intensity than firms which face weaker customers/supplier environments. This relationship is illustrated in Fig. 3. Industries are decomposed to compare innovative activity across three levels of competitive environments. Industries face light pressure when they transact with many, disorganized buyers and suppliers (below average Ci ) and severe pressure when they transact with few, coordinated buyers and suppliers (above average Ci ). Fifty percent of industries with above average R&D intensity (thin dotted line) face severe upstream and downstream market contexts, compared to 21% which face weak buyer/supplier pressure. Similarly, 75% of industries with above average rates of innovation face adverse market contexts (thick dotted line at the top of Fig. 3) and 83% of markets with either above average rates of innovation and R&D intensity depend on few, well-organized buyer and supplier markets (thick line at the top of Fig. 3). In sum, innovative activity is highest when the competitive context is most severe. This finding is counter to my expectation that firms which operate from industry positions of strength will evidence greater innovative activity than more disadvantaged firms. I predicted that structural autonomy would be positively associated with innovative activity just as industry profits are positively associated with both. Correlations reported in Table 1 display the positive association between pretax profits (the price–cost margin, measured as the ratio of volume of shipments less labor costs to total sales) and the rate of innovation (0.041), R&D intensity (0.094) and its negative association with industry constraint (20.247). Contrary to my initial prediction, I find firms that face strong, oligopolistic buyers and suppliers have higher rates of innovation and R&D investment. This implies that firms relatively unconstrained in their supplier and customer transactions have less incentive to invest in innovation, perhaps owing to a diversity of customers which enables them to exploit existing technological capabilities. The empirical result implies that firms facing few, concentrated and coordinated 5

F-tests are not reported because the compared equations differ by only one variable.

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FIG. 3.

Innovative activity highest where competitive pressure most intense.

buyers and suppliers rely on technological change to increase leverage in these markets. Taking profitability into account only strengthens the basic result: industry profitability is a weakly significant determinant of innovative activity (1.8 t test, p , .10, for R&D intensity in Model 5 as well as for rate of innovation in Model 10). Additionally, including industry profitability increases the coefficients and t tests for the constraint variable. Compare 4.9 t test for industry constraint in Model 5 with the 4.5 in Model 4. Similarly, in Table 3, compare the 2.5 t test for constraint when controlling for industry profits (Model 10) with the 2.1 in Model 9 which does not include the price–cost margin. While profitability is positively associated with innovative activity and less constrained industries are also more profitable, it remains that industries facing strong competitive pressure are more committed to innovative activity, as indicated by R&D intensity and observed rates of innovation. Higher profits provide the means to invest, competitive pressure the incentive to dedicate those resources. This is also contrary to my expectation that the information advantages of structurally autonomous positions encourage innovative activity. Is it the case that large networks of otherwise disconnected markets discourage innovative activity?

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This is the antithesis of the structural hole argument, raising the interesting question of whether the structural hole benefits of entrepreneurial individuals extend to aggregate, corporate actors in the form of better incentives and ability to innovate. To probe this issue more directly, I examine the effects of network size on R&D intensity and rate of innovation. I introduce two variables. The first is the network size—the number of ‘‘alter’’ markets in each industry’s transaction network.6 Network size increases the focal market’s volume of information about various technological needs and capabilities of their alters. The second is network density—the extent to which an industry trades with interconnected markets. When trading with densely interconnected markets (markets which trade among themselves, such as C, D, and G in Fig. 2), an industry does not have the information benefit of being the unique bridge across disconnected markets (spanning a market structural hole).7 I expected initially that connecting many, disconnected markets exposes producers in an industry to diverse technological needs and capabilities, positioning them to translate this information into their own innovation outputs. I test for network size and density effects using several models. These are reported in Table 4 (for R&D intensity) and Table 5 (for rate of innovation). When both outcome variables are predicted by market concentration and size (Models 11 and 16), network size is strongly negatively associated with innovative activity, suggesting that exposure to more markets discourages innovative activity. The effect is stronger for R&D intensity (25.3 t test in Table 4) but still significant for rate of innovation (22.3 t test in Table 5). Similarly, density has a strong positive association with both, though more strongly for R&D intensity (4.7 t test in Model 12) than for rate of innovation (2.4 t test in Model 17). These baseline models imply that small, densely interconnected networks facilitate innovative activity. Subsequent models show disparate patterns for the rate of innovation and R&D intensity. When both size and density are included, size is still a significant deterrent to R&D intensity (t test 5 23.0, Model 13), but has little effect on rate of innovation (Model 18). The next set of models repeats the basic market structure model (constraint and concentration), controlling for network size and density. For R&D intensity, constraint is still a significant predictor of investment in R&D (t test 5 2.1), while network size has a significant negative effect (t test 5 22.8) as does density, but under a weaker criteria for significance (t test 5 21.8). For rate of innovation, however, the network size and density coefficients are both positive, though not significant. The final set of 6 An alternative to a count of markets in each industry’s network is the number of nonredundant markets in each network (see Burt, 1992, p. 52). For these data, the correlation between the two measures is 0.9. 7 Network density is the average strength of relations in a focal market’s network. Density 5 [SqSk zqk/max(zqk )]/[N(N 2 1)], q Þ k where zqk is the strength of relation between market i’s alters (q and k) divided by the largest of q’s relations (max zqk ), divided by the number of possible connections in the network [N(N 2 1)]. Thus, density ranges from zero (no business between buyer and supplier markets) to one (equal volume of business among buyers and suppliers).

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MARKET STRUCTURE AND INNOVATION TABLE 4 Determinants of Research and Development Intensity 11

12

Market structure Four firm concentration ratio 0.000 (1.3) 0.000 (1.3) Square of four firm concentration ratio 20.000 (21.1) 20.000 (21.0) Constraint (log) c Network size and density Number of alter markets 20.001 (25.3) Network density 0.934 (4.7) Technological opportunity % New plant property, equipment (5 yrs) Science base a Material suppliers a Equipment suppliers a Users a Government laboratories/ agencies a Appropriability environment Effectiveness of appropriability a,b Duplication time (months) Constant 20.063 20.042 N 123 123 0.215 0.178 R2

13

14

15

0.000 (1.4)

0.000 (1.3)

0.000 (0.3)

20.000 (21.2) 20.000 (21.1) 0.004 (2.1)

0.000 (0.1) 0.005 (2.6)

20.002 (23.0) 20.002 (22.8) 20.002 (22.5) 21.511 (21.8) 21.512 (21.8) 21.297 (21.7)

0.000 (3.3) 0.001 (0.8) 0.000 (0.3) 20.003 (22.2) 0.001 (1.0) 0.004 (3.3)

0.220 123 0.236

0.002 (1.0) 0.001 (1.0) 0.154 121 0.466

0.223 123 0.265

T-tests shown in parentheses. a Measured on a 7 point scale. b Highest score reported for effectiveness of six appropriation mechanisms for either processes or products. c The log of constraint is the best fit.

models includes the controls for technological opportunity and appropriability. The strong effects of the network structure variables persists for predicting R&D intensity but not rate of innovation. Size appears to be, overall, a deterrent to innovative activity. This supports my moving target suggestion—that it is sufficiently difficult to cater to the diverse needs of many markets that industries so positioned do not compete by innovation. Density, alternatively, has a negative effect on R&D intensity when controlling for both network size and market structure, consistent with the structural hole prediction. However, with the same controls in place, density is positively associated with rate of innovation, supporting the suggestion that actual innovative output is enhanced by interconnected actors who, by virtue of their interconnection are better able to communicate technological needs and competencies, to act in concert to produce new technologies. There are two additional ways to interpret the results. First, the results may be skewed by industries which are severely constrained by sales to the government.

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HOLLY J. RAIDER TABLE 5 Network Size and Density Effects on the Rate of Innovation (Rate of Introduction of New Processes and Products since 1970’s) 16

17

18

19

20

Market structure Four firm concentration ratio 0.082 (2.3) 0.081 (2.3) 0.081 (2.3) 0.080 (2.3) Square of four firm concentration ratio 20.075 (22.1) 20.074 (22.1) 20.074 (22.1) 20.073 (22.0) 0.192 (0.7) Constraint (log) c Network size and density Number of alter markets 20.044 (22.3) 20.000 (20.0) 0.008 (0.1) Network density 56.432 (2.4) 56.37 (0.6) 55.723 (0.5) Technological opportunity % New plant property, equipment (5 yrs) Science base a Material suppliers a Equipment suppliers a Users a Government laboratories/ agencies a Appropriability environment Effectiveness of appropriability a,b Duplication time (months) Constant 9.085 3.180 3.187 3.364 N 130 130 130 130 0.089 0.091 0.089 0.095 R2

0.034 (0.9) 20.022 (20.6) 0.310 (1.2) 20.010 (20.1) 21.609 (0.2)

0.037 (2.1) 0.295 (1.5) 0.367 (2.1) 0.374 (1.9) 0.206 (1.1)

0.097 (0.7) 0.412 (1.4) 22.401 127 0.258

T-tests shown in parentheses. a Measured on a 7 point scale. b Highest score reported for effectiveness of six appropriation mechanisms for either processes or products. c The log of constraint is the best fit.

Industries such as ordnance and aerospace depend almost exclusively on sales to the government, yet invest heavily in R&D. In fact, the importance of government laboratories and agencies as sources of technological opportunity (Table 2) hints at this explanation. Constraint may be negatively associated with innovative activity for industries not constrained by the government. In addition to standard tests for outliers, I explored this possibility in two ways: (1) repeating the analysis with a dummy variable for industries with both a high constraint score and large portion of sales to the government and (2) repeating the analysis with a term for the proportion of industry sales to the government. Neither analysis affected the positive (and significant) constraint coefficient for both dependent variables. A second, and rather preliminary explanation is that coordination within and between buyers and suppliers facilitates innovation. I can imagine two reasons why this is so. First, incentives. Constrained industries use R&D as a strategy to break out of constrained positions—to increase market share, open new markets,

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improve product quality or increase profit margins through process and product innovation. In other words, R&D is a cooptive strategy much like advertising or philanthropy (Burt, 1983). This is also consistent with Podolny and Stuart’s (1995) finding that firms facing ‘‘technological crowding’’ invest more heavily than do those in more sparse technological niches. Second, opportunity. By selling to few, coordinated markets, industries can better know and respond to coherent technological needs. Many, disorganized industries are a moving target, making it difficult for an industry to cater to diverse needs. Further, the dense connections among buyers and suppliers serve as conduits for information transfer (such as know-how and best practices), which facilitates the innovation process (von Hippel, 1988). The argument that interfirm and interindustry relations facilitate the innovation process resonates with emerging evidence on network forms of innovation, such as Powell and colleagues’ thesis on networks of learning in biotechnology (1996). The information paradox supports this conclusion. Markets with access to many, diverse sources of information actually have lower rates of innovation than markets which trade with few other industries. Broad strategy is enhanced when the target is manageable. This aspect of the findings has implications for how organizations come together from different markets to form research-based joint ventures and alliances. Conclusion I hypothesized that industries in a position of structural autonomy (high concentration, low constraint) would have higher investment in R&D and a higher rate of new innovations. Firms in structurally autonomous markets can negotiate favorable terms of trade, broker across other industries, and withstand the loss of any given trading partner and so earn a higher rate of return on investments. Accordingly, firms in structurally autonomous industries were expected to have greater incentive to engage in innovative activity, reflected by higher R&D intensity and higher rates of innovation. Instead, the analysis demonstrates that concentrated industries facing extreme downstream and upstream competitive contexts devote a greater proportion of their resources to research and development and experience higher rates of innovation, suggesting adversity is a motivator for innovative activity. If the Schumpeterian hypotheses are cast as arguments for overall market strength as a predictor of innovative activity, then they warrant reexamination. An important finding of this analysis is the robustness of network position as a predictor of both R&D intensity and rate of innovation. Unlike traditional models of market structure using concentration ratios alone, the network constraint of buyer and suppliers’ markets is a significant predictor of innovative activity even when institutional factors are taken into consideration. The traditional economic and industrial organization models are enriched by the network model of competition, which provides conceptual as well as methodological apparatus with which to study interindustry differences.

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