Market Embeddedness and Corporate Instability: The ... - CiteSeerX

SOCIAL SCIENCE RESEARCH ARTICLE NO.

26, 419–441 (1997)

S0970601

Market Embeddedness and Corporate Instability: The Ecology of Inter-industrial Networks Ilan Talmud and Gustavo S. Mesch Department of Sociology and Anthropology, University of Haifa, Mount Carmel, Haifa 31905, Israel This paper studies the effect of inter-industrial control on the survival of Israeli firms. It examines how relations between environments modify relations within environments. More specifically, this paper studies how the social organization of the competition at the aggregate level (industry) affects outcomes at the individual (firm) level. Using trade data and national accounts statistics, we specify and test the hypothesis that an industry’s corporate instability is negatively associated with its social capital (comprised from its market embeddedness and political embeddedness). Our findings show that industry’s corporate instability within industries is associated with the industry’s structural embeddedness in the network of input–output transactions, the degree of control it has over its external transactions, and its political leverage. r 1997 Academic Press

MARKET EMBEDDEDNESS IN THE SOCIAL STRUCTURE OF THE COMPETITION This paper studies the effect of inter-industrial dependency on the survival of Israeli firms.1 The first part of this paper presents the analytical research problem, using models of social capital and structural models of exchange. The second section reviews the differences between two approaches to social capital, deriving several testable hypotheses considering the effect of industry’s social capital. Then structural determinants of industry’s corporate instability are estimated. Finally, we discuss the findings and their implications for research on the relations between environments and business organizations.

The first author thanks the Koret Foundation for financing the project. Both authors are grateful to Yitshak Haberfeld, Shin-Kap Han, Michael Harrison, Robert Faulkner, Vered Krauss, Haya Stier, and two anonymous reviewers for their valuable comments and suggestions. We appreciate the technical assistance provided by Bina Ben-Dor and Daphna Caspi. Address reprint requests to Ilan Talmud, Department of Sociology and Anthropology, University of Haifa, Mount Carmel, Haifa 31905, Israel. E-mail: [email protected]. 1 This paper reports findings of the first stage of a larger project. The method used here is a comparative static approach to instability between two years, 1976 and 1986. We hope to analyze in the second stage, firms’ survival using a dynamic model (i.e. event-history analysis) 419 0049-089X/97 $25.00 Copyright r 1997 by Academic Press All rights of reproduction in any form reserved.

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1. SOCIAL CAPITAL AS A STRUCTURAL SOURCE OF MARKET INEQUALITIES A. Economic Treatment of Market Imperfection and Rent Despite the fact that contemporary economics of industrial organization deals with structured inequality between producers, its baseline is still the theory of perfect competition. According to the paradigm of perfect competition, supply and demand are freely negotiated. Moreover, this theory holds that to the extent to which the market is perfect, buyers and sellers are numerous, small, and anonymous, and commodities can be divided infinitely. As a result, trade is assumed to be performed under conditions of equity on individual dyadic base, no single agent has a noticeable influence over price by his or her conduct, and there are no exit or entry barriers for producers. Market outcomes, then, are a product of individuals’ gaming behavior. One consequences of this individualistic perspective of economic theory is a theoretical disregard with the conduct of observable market. As a consequence of this idealist and individualist bias, only a few leading economists paid systematic theoretical attention to rent-creation and rent-seeking behavior.2 Rent is defined as an economic profit that is earned when competition is not free or markets are imperfect (such as under monopoly). Moreover, even those economists who pay tribute to the phenomenon assume an ideal reality of perfect competition, where there are no socially structured inequalities. Hence, imperfect competition and rent creation are considered as ‘‘deviant cases.’’ The existence of monopoly is explained as a function of scale economy, production advangate, or intellectual property right. The economic theory usually does not discuss cases stemming from structural rigidities and social capital.3 Consequently, rent, which is the outcome of market power, is assumed to be a temporary situation (Sorenson, 1996). Neo-classical theorists, have thus failed to perceive that the real market operates under intrinsic structural barriers to ‘‘free’’ competition in the form of social capital and political institutions, which are inherent forces of the economic rivalry. B. The Sociology of Competition, Social Capital, and Rent By contrast to the individualist ‘‘baseline’’ approach of neo-classical theory, sociologists treat the market, like any other social areana, as socially structured. Consequently, in sociology, social capital is regarded as pervasive and inevitable. Social capital is any aspect of social organization that provides comparative advantage to one actor or more.4 Rent, which is an outcome of the operation of 2 Prominent exceptions are Alfred Marshall, Krueger (1974), and Bhagwati (1982). See Sorenson (1996) for a recent extension of the theory of rent into the field of social stratification. 3 Moreover, contemporary economic theory maintains that even under a monopoly control, market position can be ‘‘contestable’’ (Baumol, Panzar, and Willig, 1986), as another potential producer could offer the holder of the monopoly the discounted value of its monopoly return. 4 This definition is slightly different from Coleman’s (1994: 1970).

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social capital, is depicted as ‘‘any advantage or surplus created by nature or social structure over a certain period of time’’ (Sorenson, 1996, p. 1344). Rent, then, is tightly connected to the social structure of the competition and to economic agents’ social capital. Individual may rationally invest in social capital, aiming to maximize or maintain an established advantageous market position, using all sorts of social investments. As a result, surplus in revenues, derived from social capital (over and beyond what is expected from ‘‘market equilibrium price’’), is sometimes depicted as a result of ‘‘directly unproductive, profit-seeking behavior’’ (Bhagwati, 1982). Weber describes the market as a social arena, where a price is determined by a struggle between parties. Yet, the struggle between parties is influenced by ‘‘a plurality of potential exchange partners,’’ seeking opportunities and power. For Weber, market outcomes are consequences of actors’ rational considerations and power. In other words, the process of competition—that ends up in an exchange (Swedberg, 1994, p. 265)—is a function of a direct power struggle between parties and indirect influence of potential competitors, buyers and suppliers. The social organization of the competition sets up the opportunity space for each party. Weber asserts that economic power is a function of an organization capacity to dictate prices to both exchange partners and competitors (Weber, 1978, pp. 941–943). Collusions, tacit agreements, and oligopolistic structures provide firms, by squeezing profits from a third party (either the supplier or the consumer), with the capacity to operate even at high costs (Baker and Faulkner, 1993). Additionally, Weber treated politics as an independent source of power. In many cases, political embeddedness of the market, institutional regulation and government-sanctioned restrictions on competition permeate the economy, thus augmenting barriers to entry (Levacic, 1987, pp. 118–138). This Weberian insight was further elaborated by the sociological treatment of rational choice and by the structural analysis of markets. First, models of power and exchange stipulate the conditions under which actors control outcomes in systems of exchange. More specifically, models of social exchange have shown that power and social capital are related to availability of direct and indirect resources (Emerson, 1972; Cook et al., 1983; Markovsky et al., 1988). Second, models of resource dependence have argued that discretionary power and organizational control are a function of resource spread over trade partners (Pfeffer and Salancik, 1978). Third, in a similar way to models of resource dependence and to theories of social exchange, network analysis inquires into the impact of relationship structure on interdependencies and inequalities among actors (see Burt and Talmud, 1993). More particularly, in this framework, relationship structure modifies the degree to which an actor controls his or her transactions (Emerson, 1972; Cook et al., 1983; Marsden, 1981; 1983; Burt, 1983; Stokman et al., 1985; Stearns and Mizruchi, 1986; Burt, 1988a; Markovsky, Willer and Patton, 1988; Burt and Carlton, 1989). Further, network models of market operation describe how social capital,


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defined by parameters of imperfect competition, shapes the ability of a rentmaximizing, rational actor to influence price and accumulate resources (Burt, 1983; 1988a; 1992; Burt and Carlton, 1989). C. Social Capital as Market Advantage As an extension of both resource dependence and models of social exchange, Burt shows that entrepreneurial opportunity increases with disconnection between network clusters (Burt, 1992, esp. Chaps. 1, 2). This theory is consistent with Kirzner’s theory of real-world market process and myopia (Kirzner, 1973; 1979). Moreover, Burt implements his theory of ‘‘structural holes’’ in a variety of contexts, including the national business market, precisely operationalizing a key structural dimension of imperfect competition. Burt asserts that dependence is a function of alternative (direct and indirect) opportunities. Structural alternative opportunities for resources are captured by: (1) how resource segments are organized and (2) the manner in which trade partners are connected (Burt, 1982; 1983; 1988a; 1989; 1992). Thus, it is not just the strength of relations, but also the (direct and indirect) structure of those relations that define market power. Social capital, therefore, is a direct function of market imperfection. Rent creation, therefore, is intimately related to an actor’s social capital, derived by its position in a transaction network. 2. TWO PERSPECTIVES ON SOCIAL CAPITAL: THE IMPACT OF SPARSE VERSUS DENSE NETWORKS Different schools have emphasized various dimensions of social capital: cultural capital and symbolic capital, on the one hand (Bourdieu, 1987) versus nets’ closure, actor’s exchange alternative, and collective goods, on the other (Coleman, 1988; 1994). One of the consequences of this heterogeneous use is that in the sociological literature, social capital is vaguely specified. By contrast, in this study we define social capital more narrowly, following Burt (1982; 1983; 1992), as a function of actor’s structural location in the (imperfect) competition. An analytical benefit of this definition is that it is consistent with the logic behind both the rational choice approach to competition, and with the economics of market power. Next, we address the following issue: What kind of social capital is advantageous? (See Fig. 1.) A. Rent As A Result of A Sparse Network Recent literature on social capital and competitive advantage has stressed the role of weak ties and sparse social networks in determining performance in very different settings and levels (Burt, 1992). For examples, job seekers with sparse networks find new jobs through weak ties more quickly than those connected to strong, dense ties (Granovetter, 1973; 1974). Similarly, it was found that firms spreading their ties with unconnected market segments are more profitable than those connecting to ‘‘redundant ties’’ (Burt, 1988a; Burt and Carlton, 1989;


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Two approaches to social capital.

Talmud, 1992; 1994). Coleman (1994, pp. 172–178) suggested that dense network may inhibit economic innovation. In the marketplace and inside organizations, players develop ways to manage their dependence, while protecting their advantage in strategic positions (Galaskiewicz, 1985; Baker, 1990; Garguillo, 1993). Burt found that managers with non-dense networks are ‘‘fast movers’’ in the organizational promotion ladder (Burt, 1992, Chap. 4), and Lin and his associates showed that holders of high status acquire prestigious positions via weak ties (Lin, Ensel, and Vaughn, 1981; Lin, 1982; Lin and Dumin, 1986).5 Additionally, two primary networks concepts: prominence (Burt, 1983b) and centrality-as-betweenness (Freeman, 1979) implicitly assume that ego’s power is a function of his or her association with sparse network clusters. B. Control As A Result of A Dense Network In contrast, other studies on social capital and rent creation have found significant impact of strong ties and dense networks in creating opportunities. Coleman stresses the major benefit of closure in social nets (Coleman, 1988). This line of thought was corroborated in many studies. For example, people occupying unsecured positions, especially under conditions of severe scarcity, use strong ties to find a job (Granovetter, 1982). Similarly, Krackhardt found that trust and political moves within a business firm are facilitated via ‘‘philos’’ relations (Krackhardt, 1992). In a similar way, Lin and his associates found that first job in 5 This mechanism is valid also in the inter-personal arena: families who have unconnected friends have more autonomy in their role performance (Bott, 1957). Weimann found a structural advantage of weak ties and marginal individuals in the process of communication flow (Weimann, 1982, 1983).


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the university system is typically found through strong ties (Lin, Ensel, and Vaughn, 1981; Lin, 1982; Lin and Dumin 1986). Also, the adoption of a new product and the reception of technical innovation are usually affected by cohesive networks and communication flow through strong ties (Katz and Lazarsfeld, 1955; Menzel and Katz, 1955; Rogers, 1963; Rogers and Kincaid, 1981; but see Burt, 1987). New business information and opportunities are enhanced through cohesive contacts (Aldrich and Zimmer, 1986; Gilad and Kaish, and Ronen, 1989). Other examples are relevant here: product prices are fixed through tight social networks (Faulkner, 1983; Faulkner and Anderson, 1987; Baker and Faulkner, 1993); and securities’ price fluctuations are moderated to the extent that the dealers have ‘‘clique’’ relations among them (Baker, 1984). By contrast, Tam showed that strong mentorship ties do not produce economic advantage among American lawyers (Tam, 1996). The effect of strong ties seems to be a conditional one. For example, it is evident that, while male managers are promoted via entrepreneurial networks which are composed mainly of non-redundant ties, female middle managers are typically promoted through strong ties with a strategic ally (Burt, 1992, Chap. 4). At another level, Burt (1992) and Han (1993) revealed that, contrary to the logic behind the theory of resource dependence. Those American firms having high recourse dependence on the government survive at higher rates. These analytically related findings—on female inside management and on state-dependent firms—are highly meaningful because they indicate that to the extent that an economic domian is under ‘‘uncertainty,’’6 embedding economic transactions in strong relations, or constructing the economic deal in terms of strong ties, could serve as remedies for survival. The literature on vertical integration also supports the ‘‘strong tie’’ perspective. To the degree that transaction costs are excessive, and to the extent that uncertainty plays an important role in the transaction, embedding relations in a hierarchical organization can be a beneficial strategy for gaining an advantageous position in the market (Hennart, 1988; Williamson, 1994).7 In fact, forming close networks ties can be an alternative strategy for strictly vertically integrated hierarchy (Powell, 1990). The network form of organizing lowers monitoring costs and, at the same time, enable the firms a more flexible exit in times of changing preferences (Powell, 1990; Uzzi, 1996). Network form of interorganizational embeddedness is particularly suitable strategy for purposes such as: maintaining access to reliable and non-standard information, trade in repeated 6 The reality of management and the operation of political organs such as states involve political dimensions that augments uncertainty (DiMaggio and Powell, 1990). It is very difficult to assess who is a ‘‘good manager’’; this is the reason why the ‘‘ceiling glass’’ effect takes place for women managers. Similarly, as governments do not compete with one another over local suppliers, and because it is reasonable to assume that in the institutional sector the terms for vying are less clear and competitive, firms’ operation in a highly politically-embedded domains stems from negotiated institutional environment. 7 Transaction-cost economics focuses mainly on dyadic interface between organizations, while network theory conceptualizes leverage and control as generated by a triad structure of the trade. Both perspectives, nonetheless, emphasize the relationship management of the actors.


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transactions, while also preserving other opportunities at hand (Geertz, 1978; Powell, 1990; Uzzi, 1996). C. A Suggested Integration It seems that there is no such thing as a universal optimal network structure (dense or sparse). Coleman asserts that ‘‘social relationships that constitute social capital for one kind of productive activity may be impediments for another’’ (Coleman, 1994, p. 177). We argue that each form of control (dense network versus sparse network) is beneficial only under certain conditions. Indeed, it seems that different conditions modify distinct networks’ strategies. Both Ibarra (1992) and Krackhardt (1992) argued and showed that a combination of autonomy and cohesive networks are used, depending on the structural context of advantage and constraint. Similarly, studying entrepreneurial success in a network organization, Gabbay has found that corporate turnover is predicted both by structural autonomy and network density (Gabbay, 1997). Gabbay asserts that the successful manager has a dual advantage: non-redundant business opportunities are provided to him or her by non-connectivity among alters, while trust and cohesion are provided by strong ties with some other colleagues. Gabbay found evidence that network density (indicating average strength of relations) and structural autonomy are two different dimensions of successful entrepreneurial opportunity structure (Gabbay, 1997). In a similar way, Uzzi found in his study of the New York apparel industry that organizational failure increases to the degree that the focal firm’s network tends to be comprised of either all arm-length ties (i.e. ‘‘under-embedded,’’ disconnected, non-redundant contractors), or all embedded ties (i.e., ‘‘over-embedded,’’ dense, semi-integrated contractors). Uzzi found out that organizational survival is positively associated with a ‘‘mix model’’ network, which provides a combined advantage. On the one hand, market embeddedness provide benefits such as: trust, joint problem-solving arrangement, complex adaptation, reduced bargaining and monitoring costs. On the other hand, arms-lengths contacts facilitate the firms with new and novel information outside the immediate ties (Uzzi, 1996; 1997). In sum, the conditional approach to social control argue that the logic behind network form is contingent. 3. EXAMINING INSTABILITY IN NETWORK MODELS It is evident that the effect of corporate social capital on corporate turnover is conditional. Despite the dramatic stability across markets, Burt (1992) and Han (1992) found a relatively high volatility of firms within markets which was affected by industry’s network position. Yet, similar to the above-mentioned Gabbay’s assertion (Gabbay, 1997), they established that different structures of dependencies determine the level and direction of corporate survival. They found that organizational instability increases with inter-industrial dependence on other business. In contrast, high dependance on the government decreases firms’ instability (Burt, 1992, Chap. 6; Han, 1992).


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A. Basic Premise Following this structural tradition, the present study examines the manner in which the social organization of market competition affects firm’s survival. We use data industry level-of-analysis to predict its corporate instability between two time points. Our general postulate is that there is a direct positive association between the extent to which an industry controls its transactions, and the level of its stability.8 In other words, the greater the degree to which an industry controls its trade, the higher the likelihood of an occurrence of corporate stability within it. Next, we attempt to specify and examine the structural modes under which an industry controls its trade. B. Hypotheses Specification H1. An industry is stable to the extent that its organization of ties is efficient. An efficient organization of ties indicates a non-redundency in an industry’s transactions and can serve as a structural leverage for rent appropriation and profit squeezing behavior (Burt, 1992; Talmud, 1992; 1994). H2. Ceteris paribus, an industry is stable to the extent that the transactions density among its trading partners is high. According to the dense network literature (e.g. Lin, Ensel, and Vaughn, 1981; Gabbay, 1997), control is a function of strong ties and may provide a substantial cohesion as a buffer against unstable environment. H3. An industry is unstable to the extent that its alters increase their density between 1976 to 1986. Such an increase indicates a higher ability of alters to control the focal industry’s behavior by their intensified interconnection. The opposite logic suggests that loosing control between alters may provide the focal industry with more leverage and autonomy. H4. An industry is stable to the extent that its firms are politically owned. In light of resource mobilization literature and institutional theories of organizations, then, this proxy reflects an industry’s ability to gain material support from the political sphere. It is a positive function of an industry’s access to the core of the polity (Tilly, 1978; Laumann and Knoke, 1987).9 8 ‘‘Control’’ refers to what Alfred Marshall calls ‘‘the external economy’’ of the firm, and not to its ‘‘internal economy.’’ Parallel thinking is found in resource dependence theory (Pfeffer and Salancik, 1978) and transaction costs economics (Williamson, 1985). In network models of markets, the term signifies the control over external relations, which is a function of inter-industry buying and selling activities and the degree to which each industry is organized (Burt, 1983: 224–235). 9 A recent study on the lobbying power of fifteen Israeli manufacturing industries has shown that change in protectionist policy (tariff setting) is influenced by the extent of the public sector’s ownership in an industry (Kahane, 1992). This variable predicts both net industrial profitability and direct subsidies granted to producers by the state (Talmud, 1992). We combined ownership of establishments by the government and ownership by the Histadrut (Labor Federation) because they have both served the same political and financial functions, and they demonstrated a similar managerial and business performance. This is also consistent with institutional theory. See an elaborate discussion in Talmud, 1992.


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H5. An industry is stable to the degree it is oligopolistic. Oligopoly hampers competition by providing a profit-squeezing capacity (or unproductive rentseeking behavior) by utilizing switching cost, capital requirements, regulations, barriers to entry a new technology, etc. (Caves, 1976; 1982; Porter, 1980: chapter 5; Jacquemin, 1987; Shapiro, 1989). C. Method 1. Data. A. The data used for this analysis are the input–output tables of the Israeli industries, for the years 1977 and 1986, accounting for 41 industries.10 An input–output table describes the Israeli detailed intermediary transactions of commodities and other material traded in factor or input markets. Each input– output industry is an aggregation of structurally equivalent economic establishments, operating in the same environment and using a homogeneous technology. Thus, the specified resource flows at the industry level are aggregate outcomes of individual interactions of firms operating within that industry. These tables describe the inter-industrial transactions in a given fiscal year. In other words, an input–output table describes the amounts of money each industry spent (as a buyer) on purchases from every other Israeli industry, and the sums of funds each industry received (as a seller) from every other industry. The table represents the network of resource flows between and within industrial branches. It describes the direct and indirect inter-industrial linkage of the Israeli economy. The row i in the table represents the industry’s sales, whereas the column j specifies the industry’s purchases. (See Fig. 2.) B. In addition, we used Industry and Craft Survey data (State of Israel, 1981) to calculate the public sector’s size for the 25 manufacturing industries. The Industry and Craft Survey are performed by the Israeli Central Bureau of Statistics, furnishing a highly reliable data set on manufacturing industries’ production attributes. C. Data on industry’ corporate instability were obtained from Dunn and Bradstreat’s trade publications for all Israeli corporations for the years 1976 and in 1986. After adjusting different industrial classification in the I/O tables and in Dunn and Bradstreat’s publications, we calculated dropout rate for each industry. More specifically, we formed a list of the 10 largest firms (according to sales) for every industry, once for 1976, and once for 1986.11 10 The input-output table accounts for 43 industries since 1977 (State of Israel, 1977), due to the United Nation’s directive. The tables comprised business outcomes from structurally-equivalent profit centers, regardless of legal boundaries. Yet, Repairs and Maintenance industry and General Commerce industry are two residual classification categories. These two industries are too heterogenous be comprised of structurally equivalent players. Previous analyses (Talmud, 1992; 1994) have confirmed that these industries are outliers in any analysis. 11 A few words on data choice are needed here. We selected the years 1976 to 1986 simply because there are no input-output data on the Israeli market at this level of aggregation prior to or following those years. For the firms, we chose to focus our attention on the first biggest ten in each industry, because it fits the size of the Israeli economy. A similar analysis conducted on the American market


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FIG. 2.

Industries as dealers in a transaction matrix.

2. Variables. Dependent variable: Dropouts is the number of firms that were removed from the list of the top 10 in each industry between 1976 to 1986.12 It indicates the extent to which variations in competitive pressures differentially eliminate ‘‘looser’’ corporations from the top 10 list, and the degree to which instability within the main industrial players is observed, at least among the biggest 430 firms (10 biggest firms in each 43 industries).13 (Han, 1993) picked up the first twenty firms, a choice which—for some Israeli industries—seems meaningless. Alternative cut off points, including standardization of the list size to the entire number of firms operating in an industry are relevant, but, unfortunately, unrealistic due to data limitation. We chose to define dominance in the market based on rank of sales. We had theoretical and empirical reasons for this choice. Theoretically, sales is the most prevalent dimension for various proxies of intra-market power (see Jacquemin, 1987, pp. 48–65). Other dimensions of dominance (numbers of employees, production capacity, export) have more biases. Empirically, sales was the only consistent measure, lacking missing data, across all population of firms and industries. 12 Surely, there could be raised some hypothetical alternative reasons to dropouts. Yet, in the reality of the Israeli oligipolistic market of the 1970s and 1980s, corporate merges and acquisition practice was virtually absent. Similarly, organizational death of one of the ‘‘BIG 10’’ did not occur (unlike the bottom of the list). This reality, especially regarding mergers and acquisition activity, was dramatically changed since the 1990s, where globalized financial markets along with restructured stock market practices were introduced. However we want to remind the reader that the data used in this research is for changes between 1976–1986. Additionally, out of our total 430 corporations, two companies, nested in two different industries, changed their positions between 1976 to 1986 from their original SIC industry into another. These two firms reappear in 1986 as dropouts in their original industries, 1986, and as new entrants in their destination industries. The firms changed their strategic profile precisely because of competitive pressures. 13 The use of additional five alternative measures of industrial stability will be discussed below.


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Independent variables: Contact Efficiency is the proportion of non-redundant contacts out of the total contacts made by an industry. Non-redundancy is the opposite of dependence between industry’s transactions. Contact Efficiency indicates an efficient organization of ties in dealing with other industries, showing the degree to which an industry is strategic.14 Formally, non-redundancy measure of range is defined as Non-redundancy 5

o 31 2 oP i

jq Mjq

q

4

iÞj j Þq ,

,

(1)

qÞi

where the summation is across all of industry i’s N contacts and where (1) Piq is the proportion of i’s industry network effort (in terms of shipments, money and energy) invested in the relationship with industry q. Piq then, expresses the interaction with industry q, divided by the total of industry i’s contacts with other industries, Piq 5 (Ziq 1 Zqi)

⁄o

(Zij 1 Zji),

i Þ j.

(2)

j

(2) miq is the marginal strength of relations from contact j to q. It expresses the ratio between the interaction of j with q, quantity divided by the strongest of j’s relations with any other industry, Mjq 5 (Zjq 1 Zq j)/MAX (Zjk 1 Zkj),

j Þ k,

(3)

where MAX (Zjk 1 Zkj ) is the largest of industry j’s relations with any other industry. mjq varies between zero to unity. This measure expresses the degree to which an industry i has many independent ties. Dependence between i and j signifies a high overlap between i contacts to j contacts. Range is the additive inverse of industry ties dependence. More specifically, this measure estimates the degree of which q is a large proportion of j’s contacts, and i has contact with j. This overlap is subtracted from unity, and the measure sums the results across j’s contacts i. Finally, Contact Efficiency is the ratio Number of non-redundant ties Number of total contacts

.

(4)

14 Non-redundancy and prominence are linked. A prominent industry is sought by many unconnected clusters of industry. Range, therefore, is a component of prominence (see Burt, 1983b, 1989, for a comprehensive review). This link is especially valid in relatively sparse networks which are also composed of asymmetric relations, such as a transaction matrix between input-output industries. While Burt’s measure of non-redundancy can be used as a proxy for total range (or effectiveness), contact efficiency is the ratio of an industry’s non-redundant ties to its total contacts (Burt, 1983). Though network effectiveness (non-redundancy) and network efficiency have non-linear relations (see Burt, 1992, Chap. 1), previous studies on Israeli industrial profitability and political support revealed that contact efficiency is the most powerful and robust predictor of both industry market power and industry political leverage (Talmud, 1992; 1994).


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Proportional Density is a proxy for local cluster’s cohesion in t1 (1976). It measures the degree of transaction density among the trading partners of an industry. It estimates the extent to which alters or contact pairs have some kind of connection with one another. This proportional measure of density varies from zero (no relations between alters) to 1 (every pair of alters are in contact). Whereas network density measures the average marginal strength of relations (relative to the strongest), proportional density indicates the extent to which relations between the focal industry and its trade partners are both direct (between an industry and its alter) and indirect (between alters).

1o o 2 ⁄

diq N(N21),

i

iÞq

(5)

j

Ddensity is the difference between proportional density in 1986 to the level of proportional density of 1976. Share is the proportion of the four largest firms’ sales in relation to the total industry’s output, and is a accepted index of industrial concentration for an economy comprised mainly of oligopolistic competition (Caves, 1976; 1982; Jacquemin, 1987).15 Political Ownership is the proportion of legal possession of economic establishments by political organs in an industry. It is a proxy for the magnitude of political ties that managers of an industry have to the political structure. The degree of political ownership approximates the managers’ ability to mobilize resources for their industry’s benefit (Tilly, 1985). Previous studies showed that political ownership had a strong positive effect on tariff protection, subsidies, and a direct net effect on profitability (Kahane, 1992; Talmud, 1992; 1994). Manufacturing Industries is a dummy variable used to control the presumed slower adjustment rate of manufacturing firms to enter and to exit industrial activities. It reflects also the higher tendency of this sector to depend on large batches and capital-intensive technology, leading to an increase of the ‘‘optimal establishment size,’’ and thereby elevating the barriers to entry to and exit from the industry. (See Fig. 3.) 3. Findings. Dropout rates of Israeli industries between 1976 to 1986 were regressed on their network characteristics, using unrestrictive OLS models. Descriptive statistics and a pairwise correlation matrix are presented in Table 1. The Ordinary Least Squares results are shown in Table 2.16 Table 2 shows that the way in which transactions are conducted is the most significant factor: contactefficient industries have a notably more advantageous position. Dealing with unlinked industries provides a strategic advantage. An industry is strategic to the extent that it is sought by different fractions of the national economy. An efficient 15 Other measures of intra-industry relations, such as Herfindahl’s index or Thail index of absolute or relative entropy, are more biased, and are useful to estimate market power in a more detailed industrial classification system (see a review in Jacquemin, 1987, pp. 48–65). 16 Coefficient variance inflation (VIF), as a direct index of the extent to which collinearity harms estimates’ precision (Fox, 1991, pp. 252–259), is low, varying between 1.0008 to 1.29.


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A causal model for industrial instability.

organization of ties signifies industrial control of downstream fragmented industries. High contact efficiency indicates a greater ability of an industry to monopolize its position in the national transaction matrix. As a result, its stability increases. To more an industry’s contacts are efficiently organized, the lower its dropout rate. Despite of the relatively low zero-order correlation (r 5 2.18, Table 1), the net effect of contact efficiency increases (b 5 2.40; t 5 22.343).17 Hypothesis H1, then, is strongly supported. Greater control manifested by trading partners leads to loss of power, more dependency, and resulting in higher probability of being dropout from the list. A high dependence on suppliers and consumers creates the inverse conditions of proportional density. Contact efficiency and proportional density measure the extent to which an industry’s trade partners are linked. However, they measure inverse terms: contact efficiency estimates the degree to which an industry has a link with additional clusters of the economy, regardless of its transaction strength. In contrast, proportional density measures the proportion of paired ties between interconnected alters. Empirically, the two variables are moderately negatively correlated (r 5 2.522, Table 1). In spite the fact that Hypothesis H1 regarding contact efficiency was confirmed, we found that another kind of social capital is beneficial as well. Consistent with our expectation in Hypothesis H2, high proportional density also diminishes industrial instability (b 5 2.34; t 5 21.888). We shall see, furthermore, that this is a consistent finding. In addition, we find that change in industry’s proportional density is related to a higher level of its firms’ volatility (b 5 .29; t 5 1.857). 17 The reader should note that data in Table 2 are national accounts rather than sample data. Therefore, routine t-test statistics provide a heuristic device for evaluating the relative magnitude of the effects and probability of measurement error.


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TALMUD AND MESCH TABLE 1 Descriptive Statistics and Zero-Order Correlation Matrix Mean (SD)

Dropouts 5.366 (2.709) Share .631 (.351) Proportional Density .413 (.136) Ddensity 2.001 (.053) Contact Efficiency .881 (.046) Political Ownership* .122 (.094)

(1)

(2)

(3)

(4)

(5)

(1) (2)

.1129

(3)

2.1989

.0426

(4)

.3416

.1166

(5)

2.1811

2.0417

2.5225**

(6)

2.263

2.284

2.237

2.3004 .1766 2.317

.154

Note. N of cases, 41; 1-tailed Signif, ** , .001; * N of cases, 25; see data description in text.

Table 3 examines the extent to which industry’s structural embeddedness (in the national transactions network) and its political embeddedness (in the management of the economy) affect industrial instability. Previous studies have shown that both dimensions of industrial embeddedness are strongly associated with two meaningful dimensions of rent appropriation. In these studies, inequality between producers in profitability and in state direct subsidies was explained by variations in contact efficiency and in political ownership (Talmud, 1992; 1994). Because data on political ownership were available for only 25 manufacturing industries,

TABLE 2 OLS Estimates of Israeli Industrial Instability 1976–1986 Contact Efficiency Proportional Density Manufacturing industries (dummy) Ddensity Share

2.40** (22.343) 2.34* (21.888) .10 (.620) .29* (1.857) .04 (.234)

R 2 5 .26 Note. N 5 41. Standardized effects. t value in parentheses. * Significant at p , .05. ** Significant at p , .01. *** Significant at p , .001.


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TABLE 3 Four Models of OLS Estimates of Israeli Industrial Instability 1976–1986: Manufacturing Industries

Contact Efficiency Proportional Density Political Ownership

Model 1

Model 2

Model 3

Model 4

2.57*** (22.873) 2.49** (22.412) 2.29 (21.621)

2.58** (2.79) 2.39* (21.864)

2.54*** (22.515) 2.44** (22.099)

2.54*** (22.535) 2.47*** (22.133) 2.23 (21.116) .08 (.401) .12 (.615) .38 3.255

Ddensity

.17 (.942)

Share R2 5 BIC a

.36 21.543

.31 13.098

.19 (.978) .31 13.098

* Significant at p , .10. ** Significant at p , .05. *** Significant at p , .01. *** Significant at p , .001. Note. N 5 25. Standardized effects. t value in parentheses. a Baysian Information Coefficient, see text.

Table 3 presents results only on those 25 industries. Due to the reduction in data points and to show stable findings under slightly different model specifications, we show in Table 3 four models estimating dropout rates in manufacturing industries. In general, the most consistent finding is that contact efficiency and proportional density have the strongest effects on industrial stability (in model 4, for example, b 5 2.54; 2.47, respectively). Political ownership has a more moderate effect (b 5 2.23; t 5 21.116), in the expected direction of Hypothesis H4, while the two other variables (Share and Ddensity) have no effect, or showing only a negligible one.18 Hypothesis H3 and Hypothesis H5, then, are not supported. These results may support the notion that political embeddedness (indexed by political ownership) is also a meaningful dimension in preventing organizational failure, over and above structural embeddedness variables [signified by contact efficiency and proportional density, change of proportional density (Ddensity), and concentration ratio (Share)]. This finding is consistent with the results found by Burt (1992) and Han (1993) on the American markets. It is also consistent with the general view of the economics of industrial organization (Levacic, 1987; 18 One should note, though, that transaction patterns between input–output industries over the years are highly correlated, Pearson correlation ranging from R 5 .89 to R 5 .96 (see Talmud, 1994). This phenomenon was also traced among the American production markets by Ann Carter (1967; 1970) and Burt (1988).


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Caves, 1982) and with the logic behind the sociological institutional theory (DiMaggio and Powell, 1990). Table 3 reports also the Bayesian Information Coefficient (BIC) as a goodnessof-fit statistics for the different regression models. The BIC has recently been generalized for a variety of other statistical procedure besides the standard use of it in the evaluation of long-linear models (Raftery, 1995; see an application in Kraus and Maestracci, 1997). BIC indicates the relative likelihood that a model fits a given data set. It provides a statistical comparison between various model specifications as a function of the number of cases, explained variance, and the number of independent variables in the equation. For the ordinary least square (OLS) models, BIC is calculated as BICi 5 N[ln (1 2 R 2i )] 1 pi[ln (N )],

(6)

where N is the number of cases and p is number of independent variables in the OLS regression model i. When models are compared, the model with the most negative BIC value is the most likely to best fit the data.19 Comparing the four models in Table 3, it is clear that model 1, comprised from three independent variables (Contact Efficiency, Ddensity, and Political Ownership), has the most negative BIC value and is the best fitting model. It is worth noting that Hypothesis H5 was not supported. One reason for that could be the highly oligipolistic nature of the Israeli economy across industries, thereby minimizing variation in concentration ratio between industries. This phenomenon is also manifested in other developed and small countries such as the Netherlands, Japan, and South Korea. One can claim that it is possible to capture corporate instability by other measures of industrial change. Consequently, we attempted to refine our operationalization of corporate instability by introducing five alternative measures of corporate change:20 (1) Spearman’s Rho, which specifies the extent to which the order of a firm’s rank in an industry (as ordered by sale volume) varies from 1976 to 1986, (2) Kendall Tau b, similarly measuring how many ranks a firm moves from 1976 to 1986, (3) a turnover rate, measuring the proportion between firms dropping out the list from 1976 to 1986 to new entrants to the list in 1986 (Han, 1993), (4) a regeneration index (following McNeil and Thompson, 1971, Tables 2, 8), indicating the rate of industrial stability. (It is measured by the rate of ‘‘stayer’’ firms in an industry to ‘‘movers’’ and ‘‘newcomers.’’21), and (5) an 19 A more restrictive requisite to conclude that one model fits better than the other is that the BIC values of two (or more) models will differ in the order of 10 (Raftery, 1995). The difference between models 1, on the one hand, and models 2 and 3, on the other one, also fits this more restrictive condition. 20 We would like to thank Robert Faulkner and an anonymous referee for raising this point. 21 More precisely, the regeneration index is specified as the number of firms present in an industry’s top 10 lists in both 1976 and 1986, divided by the firms present both times plus those present in 1986 only (McNeil and Thompson, 1971, Table 8).


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additional aggregate index of stability, specified as

Relative Range Stability 5

Î

o (1976 Rank 2 1986 Rank)

2

i

,

number of ranks

(7)

where i expresses a firm nested in one of the 43 industries.22 Then, we repeated the five OLS analyses shown above for each of the five indices. Nonetheless, we found that none of these alternative operationalizations of corporate stability is predicted by inter-industrial linkage. Overall, the only predictive variable of the explained variance in these 25 analyses (five analyses for each dependent variable) was the industrial concentration index. In general, inter-industrial linkage variables did not significantly contribute to the explaining power of the model over and above industrial concentration.23 We therefore presented here the results of the model, predicting variations of the more meaningful dependent variable, namely: corporate dropout. 4. DISCUSSION A. Two Conditions of Social Capital This study has examined the ways in which relations between environments modify relations within environments. More specifically, this study has demonstrated how environments and real market players are structurally embedded. We also showed that the social organization at the aggregate level (industry) affects outcomes at the individual (firm) level. We demonstrated that structural embeddedness in the national transaction matrix and political embeddedness in the national economy play an important role in shaping corporate likelihood of durability. Corporate social capital, then, is a function of the industry’s structural location. In other words, this study reveals that the structure of inter-industrial relations lays out the dependence characteristics among individual trade partners, hence determining firms’ capacity to survive under imperfect competition. In concrete network terms, proportional density and contact efficiency are two different, co-exiting, conditions of control over the external transactions of the firm. High contact efficiency and high proportional density differentiate between the marginal actor and the central player and they influence the firms’ stability.24 Contact efficiency and proportional density signifies two contrasting conditions of social capital: the first condition indicates an overall efficiency in an industry’s 22

We thank one of our reviewers for this point. Tables are available upon requests from the authors. Our task here is limited to the inquiry of the role of inter-industrial forces in shaping corporate instability. We do not wish to develop here a general theory of industrial organization and operation, linking intra- to inter-industrial forces. We merely conclude, therefore, that for the proposed study, our dependent variable is the best alternative among the six operationalizations of industrial instability. We speculate that this could be so, precisely because it captures more extreme events of change in corporate ranking, namely: dropouts. 24 We didn’t find any impact of their interaction effects. The tests were performed using Allison’s method (Allison, 1977). 23


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transaction network, while the second circumstance can be interpreted as an industry’s local effectiveness in the organization of economic ties. It seems that industries utilize two seemingly contradictory relational methods to gain an advantage in the marketplace: efficiency and effectiveness. Yet, the precise relations between cluster’s cohesion and ties’ efficiency, and their link with concrete business strategy cannot be assessed using this highly aggregated data set. Industry’s social capital, then, is composed of at least three, presumably tenuous, dimensions: relatively an efficient organization of inter-industrial ties, relatively high cohesion in trade cluster, and strong linkage to institutionalized political sphere. This reasoning is consistent with Uzzi’s finding, derived from a micro level analyses, that ‘‘a firm’s structural location, although not fully constraining, can significantly blind it to the important effects of the larger network structure.’’ Additionally, ‘‘a paradox appears: optimal networks are not composed of either all embedded ties or arm’s-length ties but integrate the two’’ (Uzzi, 1996, p. 694). Gabbay (1997) and Uzzi (1996; forthcoming) claims that each kind of controlled embeddedness ‘‘yield positive returns up to a certain point,’’ and ‘‘embedded networks offer a competitive form of organizing, but possess their own pitfalls.’’ Our interpretation, then, is consistent with Gabbay (1997) and Uzzi (1996; forthcoming). Competitive pressures create complex, dissimilar, and distinct networks forms, rather than a singly optimal embeddedness strategy. Yet, these results still leave us with a theoretical puzzle for future research regarding the precise specification of the boundary conditions under which each form of embeddedness is functional for the corporate organization. B. A Few Issues for the Future 1. Conditional relevance of social capital. A general theoretical puzzle stems from this study (as well as from the related inquiries of Burt, 1992; Han, 1992; Gabbay, 1997; and Uzzi 1996): It seems that ‘‘mix model’’ of embedding economic relations is very beneficial. Yet, the theoretical question remains: under what general practical circumstances ‘‘over-embeddedness’’ [to borrow Uzzi’s terminology (1996)] of strong ties and closed networks is an asset and what are the situations under which it is a liability? What are the conditions under which ‘‘under-embeddedness’’ of unconnected market segments may prove beneficial? We have a partial answer to this question. We speculate that in the general operation of competition between producers, weak ties are beneficial. Yet, in all those circumstances where trust, uncertainty, synergy, and long-term projects are involved, strong ties and cohesion are significantly important. Based on our highly aggregated data, however, we cannot precisely determine the rationality or the fitness of social capital to the immediate economic context25 (though Gabbay (1997) and especially Uzzi (1996) elaborate and show the conditional rational). 25

See Hannan (1970) on problems of aggregation and disaggregation.


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Further studies on the creation and operation of social capital under different structural conditions is highly required. 2. Cross-level analysis. Another way to scrutinize competitive process is to relate market structure, as specified by network models of competitive advantage, to individual firms’ structural attributes, using techniques such as cross-level analyses or Hierarchical Linear Models (Byrk and Raudenbush, 1988; Hox and Kreft, 1994). Using this analytical strategy, the researcher may decompose variations in organizational failure into inter-industry and intra-industry components. 3. Two levels of ‘‘residual analysis.’’ Two important research agendas stem here from ‘‘residual analyses.’’ The first one is mainly interested in agency, while the second revolves around issues of structure. The first raises questions for future research on business strategy, while the second addresses the nature of various environments. A. At the firm level-of-analysis: (1) Given our ability to predict the average industrial instability from network parameters of competitive advantage, what are the organizational characteristics of the ‘‘losers’’ or the ‘‘winners’’ within each industry (i.e., those below and above the average prediction)? (2) How, for example, do inter-industrial relationships differentially modify the ‘‘top 10,’’ on the one hand, and small, peripheral firms, on the other (within each industry)? B. At the industry level-of-analysis: (3) Given our capacity to draw a link between inter-industrial relations and industrial stability, what are the structural characteristics of ‘‘loser’’ and ‘‘winner’’ industries (i.e., those firms situated below and above the regression line)? 4. Political embeddedness with the state and between producers. Further, this study corroborates the general notion that political embeddedness is also key dimension of market operation, over and above structural embeddedness. Yet other questions have also to be examined at both the macro and micro levels. They include the effect of other indices of political capital (as derived by actors’ location in the political organization of economy) on survival. These prospective research questions should include the ways in which the politics of markets— informal political networks, tacit collusions, trade unions, privatization and globalization efforts, institutional regularities, latent financial aid, and tariff protection—influence the survival of the firm. REFERENCES Aldrich, H., and Zimmer, C. (1986). ‘‘Entreprenuership through social networks,’’ in The Art and Science of Entreprenuership (D. L. Sexton, and R. W. Smilor, Eds.), Ballinger, Cambridge, MA. Allison, P. (1977). ‘‘Testing for interaction in multiple regression,’’American Journal of Sociology 83, 144–155. Baker, W. E. (1984). ‘‘The social structure of a national securities market,’’ American Journal of Sociology 89, 775–811. Baker, W. E. (1990). ‘‘Market Networks and Corporate Behavior,’’American Journal of Sociology 96, 589–625.


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