Disaggregating the effect on profits in manufacturing industries of having imperfectly competitive consumers and suppliers

SOCIAL

SCIENCE

RESEARCH

Disaggregating industries

8,

120-143 (1979)

the Effect on Profits in Manufacturing of Having Imperfectly Competitive Consumers and Suppliers RONALD

S. BURT

Department of Sociology, University of California at Berkeley Casting the American economy as a network of economic exchange relations between firms in sectors, industries are those sectors engaged in manufacturing and are here analyzed as network positions. The structure of an industry’s dollar transactions with suppliers is demonstrated to affect industry profits in a manner distinct from that in which its structure of transactions with consumers affects profits. For the 335 four-digit SIC manufacturing industries corresponding to unique sectors of the 1%7 Input-Output Study, price-cost margins corrected for interindustry differences in capital requirements are regressed over four-firm concentration ratios and various structural indicators of imperfect competition among suppliers versus consumers. The structural indicators are computed from dollar flow coefficients among 492 sectors of the I%7 Input-Output Study. Only one type of product flows from an industry to its consumers, who have no trouble seeing the value of collusion despite the multiple sectors in which their own products are sold; however, products from different sectors may flow to the industry without creating the competition among suppliers that prompts collusion. Industry profits are constrained by suppliers to the extent that firms in the industry purchase supplies from few separate sectors as product markets. In contrast, industry profits are constrained by consumers to the extent that firms in the industry sell to a small number of oligopolistic sectors.

INTRODUCTION

The American economy, like any other economic exchange system, can be represented in an input-output analysis as a network of aggregate I appreciate the facilities made available to me during the analysis at the National Opinion Research Center by J. S. Coleman through National Science Foundation Grant SOC7305504. The concentration ratios used here, their sources, and scores on the groupaffiliation index, Y2, are given in my unpublished doctoral dissertation in sociology, University of Chicago, 1977, entitled: Actors in Structures: Empirical Statics. As part of the ongoing work under the Project in Structural Analysis at the Survey Research Center, University of California at Berkeley, the preparation and distribution of this manuscript was made possible by National Science Foundation Grant SGC77-22938. J. F. Burt and A. Ong assisted in the data processing. Although in no way implicated by my statements here, several individuals have been generous with their comments on earlier drafts: K. Azumi, J. S. Coleman, S. H. Lustgarten, L. E. Preston, and J. A. Wiley. 0049-089x/79/020120-24$02.00/O Copyright All rights

@ 1979 by Academic Press, Inc. of reproduction in any form reserved.

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dollar transactions between firms as actors in sectors of the economy (e.g., Leontief, 1951). Firms in each sector have different levels of purchases from firms in other sectors as suppliers. Firms in each sector have different levels of sales to firms in other sectors as consumers. Aggregating across firms in a sector, a sector is a pattern of relations to each sector of the economy as dollars of sales and from each sector of the economy as dollars of purchases. In network terminology, this pattern of relations defines a sector as a “position” within the economy (cf. Burt, 1976, 1977; White, Boorman, and Breiger, 1976). Following Standard Industrial Classijcation (SIC) terminology, those sectors of the economy that manufacture products are discussed here as “industries.” These manufacturing “industries” are “positions” in the network of economic exchange relations described as an economy. I have elsewhere used the network concept of “structural autonomy” to demonstrate that average levels of profits obtained by firms in an industry are a function of the structure of transactions defining the industry as a network position (Burt, 1978). Evidence on this point is reviewed below. However, the crude structural effects aggregated in earlier analyses for the overall economy obscure a fundamental feature of the manner in which industry profits are affected by the industry’s environment. My purpose here is to demonstrate that the structure of an industry’s transactions with suppliers affects industry profits in a manner quite distinct from the manner in which its structure of transactions with consumers affects profits. Profit differences in American manufacturing industries are analyzed in terms of interindustry differences in imperfect competition as captured by the pattern of transactions defining each industry as a position in the economy. This effect of imperfect competition is disaggregated into its component effects stemming from relations among firms within an industry as well as relations involving firms with their suppliers and consumers in other sectors. For the 335 four-digit SIC industries corresponding to unique sectors of the 1967 Input-Output Study, price-cost margins measuring industry “profits” are regressed over four-firm concentration ratios and various structural indicators of imperfect competition computed from the dollar flow coefficients among 492 sectors of the InputOutput Study. I should emphasize that the analysis here is solely concerned with one of two well-known networks of relations among firms in the economy. The network to be analyzed here is composed of aggregate dollar flow relations among firms in sectors of the economy. Particular patterns of such relations are demonstrated to be detrimental to obtaining profits. As will become apparent from the work reviewed below, this is the network usually studied by economists rather than sociologists or political scientists. A second network of relations among firms is superimposed upon the network of economic relations. Firms create cooptive relations with

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one another. This second network is most often studied by political scientists and sociologists (cf. Evan, 1978). Perhaps the best known cooptive relation is the interlocking directorate (National Resources Committee, 1939; Patman, 1968; Levine, 1972; Allen, 1974) although the literature is growing with consideration of diversification relations such as mergers (Pfeffer, 1972) and joint ventures (Pfeffer and Nowak, 1976). It is not surprising to find evidence that cooptive relations are constructed in response to constraints imposed on firms by the network of economic exchange relations (e.g., Selznick, 1949; Zald, 1967; Pfeffer, 1973; Hirsch, 1975, for specific examples and for general textbook treatments, Katz and Kahn, 1966; Thompson, 1967; Azumi and Hage, 1972, especially pp. 91-100). A precursor to clearly understanding the creation of cooptive relations, however, is a clear understanding of the constraints inherent in the structure of the economic exchange relations from which cooptive relations emerge. The discussion here is directed toward that more primitive understanding. THREE AGGREGATE STRUCTURAL EFFECTS OF IMPERFECT COMPETITION ON PROFITS

Perfect competition within the American economy would, in the long run, equate profits across each industry by allowing little or no difference between the marginal cost of production in an industry and the selling price for the industry’s output. Empirically, however, price usually exceeds cost and price-cost margins are well above zero. Further, pricecost margins are not equally greater than zero for all manufacturing industries. Firms in some industries are able to charge prices further in excess of production costs than are firms in other industries. Let Y,, be an indicator of industry profits obtained by correcting price-cost margins in each industry for interindustry differences in capital requirements. A score of SO on Y,, for industry j means that about half of each dollar of sales by firms in industryj can be treated as “profit” in the sense of being income above and beyond necessary costs of production.’ Instead of ’ As introduced by Collins and Preston (1968, pp. 13-17, 54-57), raw price-cost margins are computed by dividing the extent to which value added exceeds labor costs by total sales. For industryj: PCM, = (VA, - ,5,)/W,, where value added (VA), Labor costs (L), and value of shipments (VS, the total sales by firms in an industry) are taken from the 1967 Census of Manufactures. The dollars of income given by the difference between value added and labor costs are to some extent due to interindustry differences in capital requirements in production; higher than average capital requirements necessitating higher than average differences between VA and L. Although previous studies have controlled for interindustry differences in capital requirements by specifying one additional independent variable in each regression equation, namely, capital output ratios computed as the ratio of gross book value of depreciable assets in industry j over total volume of sales by industry j; CR, = ASSETS, /VS, (e.g., Collins and Preston, 1%9; Rhoades, 1973, 1974; Rhahlzadeh-Shirazi, 1974; Lustgarten, 1975), the analysis here has taken the more conservative position of completely

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being low and equal for all industries, these corrected price-cost margins computed for the 1967 census data on the 335 industries considered here have a mean of .26 and a SD of .OS. The lowest margin is for firms in the “tobacco stemming and redrying” industry (SIC category 2141, yjo = .04). The highest margin is for firms in the “toilet preparations” industry (SIC category 2844, yjo = .58). Attributing these interindustry differences in Y, to imperfect competition, three aggregate structural effects are documented.” removing from price-cost margins interindustry differences in capital requirements. Corrected price-cost margins are computed as residuals in a linear regression of raw price-cost margins over capital output ratios so that t&corrected price-cost margin for industryj, yj,, is given as: y, = PCMJ - .077(CRj - CR), where .077 is the unstandardized regression coefficient of PCM over CR for the 335 industries considered here, 5 is the mean capital output ratio for all 335 industries, and CR, is taken from the 1970 Survey ofManufacturis as the ratio of ASSETS, over VSj. Z Throughout this analysis, manufacturing industries corresponding to sectors of the Input-Output Study will be treated as network positions. This treatment produces a conservative bias in estimating later equations. The conservative bias is due to several sectors, distinguished in the Input-Output Study being in fact structurally equivalent (cf. Burt, 1976, p. %, 1977b, p. 111; White et al., 1976, pp. 737-739). Production activities are so closely distinguished empirically in the sectors corresponding to four-digit SIC categories that a single sector can be specified as multiple sectors. The possibilities of collapsing input-output sectors is discussed at length by Kaysen and Turner (1959, pp. 295-331) in regard to the four-digit SIC categories. Their basic point is that sectors are sometimes classified so finely that substitutable and complementary commodities are placed in separate categories [although the 1967 Input-Output Study makes an effort to collapse those categories distinguishing such goods, e.g., beet sugar (SIC category 2063) and cane sugar (SIC category 2061) are combined in the sugar sector]. Further, a cluster analysis of the positions of sectors in the 99-category 1%3 Input-Output Study demonstrated that even at the 99-category levellet alone the 484-category level used here-multiple sectors distinguished in the study were in fact structurally equivalent (social distances between sectors as positions were cluster analyzed, cf. Burt, 1976). Thus oversegregation of sectors can be expected to artificially inflate the effective concentration ratios within the sectors oversegregated since increasingly narrow production activities are increasingly likely to be performed by a single firm. These upwardly biased concentration ratios within sectors can then be expected to downwardly bias estimates of parameters involving concentration ratios. The regression of profits over Y, will be biased toward zero since oversegregated sectors will have lower profit margins than would be expected from their inflated concentration ratios. The regression of profits over Y, and its component structural indicators in Table 1 that use concentration ratios (Y,, Y,, Y,, and YJ will be biased toward zero since sectors having extensive transactions with over segregated sectors will have higher profit margins than would be expected from the inflated concentration ratios in the sectors with which they have extensive transactions. The most direct method of dealing with this bias would have been to collapse the 1967 Input-Output Table into structurally unique sectors by combining sectors clustered together as structurally equivalent. Concentration ratios could then be estimated for the combined sectors (e.g., the regression coefficients for Y,, Y,, and their interaction are higher for an analysis of two-digit SIC categories than is the case in the analysis of four-digit categories here, cf. Burt, 1978). Such data massage, however, would have meant a departure from the data base most often used in analyzing the impact of market structure or profits. I have therefore taken the

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The Effect of Oligopoly One effect stems from imperfect competition among firms within an industry. This effect is justified in the economics literature in terms of monopolies being able to escape the constraint of supply and demand so as to raise their selling price well in excess of production costs (cf. Stigler, 1964; Shepherd, 1970, Chaps. 2 and 3). The level of imperfect competition among firms within an industry is usually operationalized as the level of concentration of sales in the M largest firms in the industry. For example, a four-firm concentration ratio of .50 for an industry means that the four largest firms in the industry account for half of the total sales by all firms in the industry. A concentration ratio close to 1 means that there are four or fewer competitors in an industry. Not surprisingly, there is replicated evidence that interindustry differences in concentration are positively associated with differences in profits (cf. Collins and Preston, 1968, 1969).3 This association is replicated here. The correlation between corrected price-cost margins (YJ and four-firm concentration ratios (Y,) as given in the 1967 Census of Manufactures for the 335 industries considered here is positive and significant (Y = .243).” The Effect of Group-Affiliation Industries with different levels of imperfect competition among firms in each industry are themselves linked to other industries as suppliers and consumers. To the extent that a fixed pattern of transactions characterizes an industry (as is the case under the usual input-output assumption that production of a type of good requires fixed proportions of goods from each other sector of a economy), then differential imperfect competition in other industries can be problematic for obtaining profits in any one industry. A second structural effect of imperfect competition on profits therefore stems from differential levels of transactions with imperfectly option of building on existing work while acknowledging some unknown conservative bias in the coefficients to be presented. 3 Although this coefficient appears to be robust over studies of industries as a general population in the United States, it is not constant across types of industries. For example, Collins and Preston (1%9) find that the association between concentration and profits is higher for consumer goods industries than for producer goods industries. Even more striking Porter (1974) finds that among consumer goods industries, the direct effect of concentratior on profits is positive for nonconvenience goods but negative for convenience goods (the latter being goods with low unit price and to which the consumer would be expected to want easy access, e.g., meat and dairy products, soft drinks, tobacco products, etc.). These fine distinctions are ignored here since the focus is on a general model disaggregating the effect on profits of patterns of transactions with suppliers versus consumers. 4 Four-firm concentration ratios are used here rather than eight(or more)-firm ratios since manufacturing industries have been disaggregated to the level of four-digit SIC categories and require fhte distinctions in concentration (cf. Collins and Preston, 1%8, Table 11, 1%9; Rhoades, 1973, 1974; Lustgarten, 1975).

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competitive suppliers and consumers. Those industries with imperfectly competitive suppliers and consumers would be expected to have low price-cost margins; consumers effectively keeping prices low and suppliers effectively keeping costs high. This effect has been considered in the economics literature predominantly in terms of imperfect competition among consumers using the concept of vertical integration (coordination of firms in separate industries, cf. Stigler, 1951) and countervailing power (coordination of buyers against oligopolistic sellers, Galbraith, 1952). Empirical tests of this second effect have been conducted using the 1963 and 1967 Input-Output Studies.5 Let b be the proportion of all sales by firms in industry j that are transacted with firms in industry i. Brooks (1973) computes a measure of imperfect competition among an industry’s consumers by weighting the ~ij for industry j by the levels of concentration in each industry i. This measure roughly corresponds to the measure of concentration within an industry and has been discussed as a measure of buyer concentration. For example, if firms in industry j only sold goods to firms in industries i and k where Lo = .2 and Lo = .8 and where competition was lower in industry k than in i as reflected in respective concentration ratios ofy,, = . 1 and yil = 5, then buyer concentration for industry j would be: (.2)(.5)+(.8)(.1) = .18. A value of 1 for this measure would indicate that the industry sold all of its goods to one sector and that sector had only four or fewer competing firms within it. As expected, buyer concentration is negatively correlated with interindustry differences in income at the two-digit SIC category level (Brooks, 1973; Clevenger and Campbell, 1977) and negatively correlated with inter-industry differences in price-cost margins at the four-digit level (Lustgarten, 1975). Lustgarten (1975) further demonstrates that simply selling goods to a small number of separate sectors is negatively associated with price-cost margins by summing the squared b for industry j across all industries i to obtain an index that is high to the extent that industry j sells all its goods to a small number of different sectors. Taking a social network approach, the above effects are subsumed under Simmel’s (1922) hypothesis of freedom resulting from conflicting “group affiliations.” An individual’s freedom from constraint by others increases as he has increasing numbers of conflicting group affiliations to balance against one another rather than an affiliation with a single group. Developing Simmel’s hypothesis in the context of a concept of “structural autonomy,” I have elsewhere proposed the following “group affiliation” index as a crude measure of the extent to which industry j has transactions 5 This ratio of measure measure

ignores work measuring vertical integration in a manufacturing industry as the value added over value of shipments (e.g., Tucker and Wilder, 1977). Such a of imperfect competition due to vertical integration is inappropriate here since the is closely related to the dependent variable, price-cost margins (cf. Footnote 1).

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with few competitive supplier/consumer industry is “affiliated” (Burt, 1978):

S. BURT

sectors as groups with which the

where M is the 492 sectors of the Input-Output Study, zo: is the dollars of sales by sector i to industry j as given in the 1967 Input-Output Study, and yil is the four-firm concentration ratio or an approximation to such a ratio for sector i.6 Note that concentration in industry j is excluded since it is properly captured under the effect of oligopoly. Note also that the index includes the two indexes discussed in the economics literature; buyer concentration (the sum across all sectors i of the term yjl (ZulI;%ji) which equals the sum of yil$) and having few separate sectors as consumers (the sum across all sectors i of the term z&ji)2 which equals the sum of 4). The index y, in addition considers relations linking industry j with its suppliers. The closer to 0 that Yj2 is, then the more competitive are industry j’s suppliers and consumers. When industry j buys all its suppliers from a single sector and sells all its output to a single comsumer sector where there are four or fewer firms in either sector, then yj2 will equal its maximum value of 2. As expected, the correlation between 6 Of the 494 sectors in the 1%7 Input-Output Study, two have been excluded; “inventory valuation adjustment” and “value added.” The former is deleted since it represents interests neither opposed to nor in support of a particular industry’s interests. The latter is deleted since there is no information readily available on concent!ration in the labor sector for each sector of the economy. Implicit in the analysis here is the assumption that the concentration in these labor sectors equally affects all manufacturing industries considered. Of the remaining 492 sectors in the Input-Output Study, 10 are purely consumer sectors composed predominantly of government industries. For manufacturing industryj, the dollar flow coefficient from sector i to industry j+, equals 0 if sector i is one of these 10 pure consumer sectors. In computing y, here, I have made several assumptions in order to approximate concentration in each of the 492 sectors considered. Where manufacturing industry i in the InputOutput Study corresponds to more than one four-digit SIC category, Y,, is taken as the average concentration in each SIC category weighed by the proportion of sales it contributes to the input-output sector. For nonmanufactming industries, concentration ratios have been approximated from industry size distribution data which underestimate four-firm concentration ratios since all firms in size categories are assumed to be of equal size. The approximations of four-firm concentration ratios used here are listed in Appendix C of my doctoral dissertation (see acknowledgement footnote). Also given there are the methods used to approximate each concentration ratio baaed on published data in: Census ofAgriculture, Census of MineralIndustries, Census of Construction Industries, Census of Transportation, Census of Business, and Census of Manufactures. In addition to these approximations from census data, two assumptions were made regarding sectors for which no data were available: (i) retail and final consumption sectors were assumed to be perfectly competitive so that to three digits, yi, is equal to 0 and (ii) all government sectors were assumed to be perfectly monopolistic so that yil equals 1. The problem of estimating concentration ratios for structurally equivalent sectors is discussed in Footnote 2.

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price-cost margins and the group affiliation index for the 335 considered here is negative and significant (v = - .291).

The Effect of Interaction

between Oligopoly and Group Affiliation

There is reason to expect that the above two direct, additive effects operate conjointly so as to give rise to a third structural effect as an interaction effect; the simultaneous absence of competition both within an industry and among the industry’s suppliers and consumers. As a baseline, profits should be low when there is high competition within an industry (Y, low) and high imperfect competition among the industry’s suppliers and consumers (group affiliation, YZ, high). When either competition within the industry decreases (Y, goes up) or competition among the industry’s suppliers/consumers increases (group affiliation, Y,, goes down), then profits in the industry should increase through the above direct effects. When competition is already low within the industry, however, changes in competition among the industry’s suppliers/ consumers can have a different association with profits. Since the industry is already an oligopoly, high imperfect competition among the industry’s suppliers/consumers is likely to prevent the industry from being able to keep prices above production costs as would be expected in an oligopoly. With high concentration in an industry, in other words, increasing competition among the industry’s suppliers/consumers should have an increasingly positive effect on industry profits. Similarly with low competition among an industry’s suppliers and consumers, increasing concentration within the industry should have an increasingly positive effect on profits. This interaction effect has been demonstrated in economics for consumers. Lustgarten (1975) demonstrates that the association between profits and Brooks’ measure of buyer concentration at the four-digit SIC category level is negative for highly concentrated industries (Y, greater than .40) and zero for industries with low concentration ratios (Y, less .30). The interaction effect has been demonstrated for consumers and suppliers elsewhere (Burt, 1978). The correlation between corrected price-cost margins and an interaction term measuring the extent to which industry concentration is high-while it is low among the industry’s suppliers/consumers, (Y, - Y,) (YZ - Y,), is positive and significant for the the 335 manufacturing industries considered here (r = .130).’ ’ Althauser (1971) demonstrates that interaction variables expressed as the product of raw scores on their component variables have spuriously high correlations with their component variables to the extent that the means of the component variables differ from zero. This collinearity between interaction variables and their component variables produces a conser-

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SUPPLIER FROM CONSUMER TRANSACTIONS

The above three aggregate effects make no distinction between imperfect competition among an industry’s suppliers versus its consumers. Given its assumption of atomistic firms outside an industry, the traditional analysis of profits vis-a-vis concentration ratios completely ignores imperfect competition among both consumers and suppliers. Brooks (1973) and Lusgarten (1975, 1976) expand this perspective by introducing the effect on profits of imperfect competition among an industry’s consumers. Suppliers, however, are ignored. The network analysis of profits as an indicator of structural autonomy (Burt, 1978) goes one step further in considering the effects on profits of imperfect competition among both consumers and suppliers, however, the group affiliation index Y, makes no distinction between these two types of transactions linking an industry to other sectors. Clevenger and Campbell (1977, pp. 65-66) do distinguish supplier and consumer transactions by computing separate values of Brooks’ concentration index for sectors as consumers (BCO) versus sectors as suppliers (SCI), however, no explanation is offered for their finding that industry income has the expected negative association with BCO but no association with SCI.s While the same structural factors should produce imperfect competition among an industry’s suppliers or consumers, the manner in which imperfect competition so produced affects industry profits can be expected to differ for suppliers as opposed to consumers. The reason for expecting a difference here is due to suppliers and consumers constraining profits in fundamentally different manners. Suppliers constrain profits by keeping an industry’s direct production costs high. Consumers constrain profits by keeping an industry’s selling prices low. Imperfect competition among firms in sectors of the economy affects opposite ends of an industry’s production process when it operates through other sectors as suppliers versus consumers. At these polar opposite ends of a production process, competition is among firms in different mixtures of “product markets:” the pool of potential buyers for a specific product or good. When constraining profits in an industry vative bias in estimating the regression coefficient for the interaction term when controlling for differences in the component variables. To the extent that component variables are uncorrelated, this conservative bias can be eliminated by expressing component variables as deviations from their means. Concentration ratios (Y,) for the 335 manufacturing industries here are correlated only weakly with Y, and the other structural indicators’to be considered (cf. Table 2). In order to obtain an unbiased estimate of the interaction effect on profits of imperfect competition within an industry conjointly with imperfect competition among the industry’s suppliers and/or consumers, component variables in interaction terms are expressed as deviations from their means rather than as raw scores. 8 This finding is replicated here and is consistent with the argument for disaggregating the effect on industry profits of imperfect competition among suppliers versus consumers (cf. Footnote 22).

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through prices paid by consumers, imperfect competition operates within a single product market-the market for goods produced by the industry. If there are only a few independent buyers in the market, each buyer can have a significant impact on market price. This impact in turn promotes collusion among the buyers so as to keep market price low. When constraining profits in an industry through direct costs charged by suppliers, in contrast, imperfect competition operates across multiple product markets-the multiple markets for goods required as inputs by, the industry. Here again, if there are few independent sellers of the goods required as inputs by an industry, each seller can have a significant impact on price and is accordingly encouraged to act in collusion with other sellers so as to keep market prices high as in industry’s direct costs. As an industry’s suppliers, however, firms in separate product markets are unlikely to come into direct competition except where the markets are for substitutable goods. Collusion among suppliers is particularly limited therefore by the extent to which suppliers are in different product markets. This reasoning can be condensed into a single sentence: Only one type of product flows from any given industry to its consumers, who have no trouble seeing the value of collusion despite the multiple sectors in which their own products are sold; however, products from different sectors may flow to the industry without creating the competition among suppliers that prompts collusion.” DISAGGREGATING

IMPERFECT COMPETITION AND CONSUMERS

AMONG SUPPLIERS

The group-affiliation index, Y2, describes the aggregate lack of competition among an industry’s suppliers and consumers. The above argument suggests that this index should be disaggregated so as to separate relations with suppliers from relations with consumers; especially as those relations indicate the extent to which multiple product markets supply an industry. Table 1 presents six structural indicators that capture the disaggregated aspects of the group affiliation index for consumers versus suppliers. The dollar flow coefficients in the 1967 Input-Output Study have been used to estimate the proportion of all sales by industryj that are sold to sector i (L~J and the proportion of all purchases by industry j that are purchased from firms in sector i (oil). The three indicators Y,,Y, and Y, describe aspects of imperfect competition among industry j’s consumers, Y,, Y, and Y, describe the same aspects of imperfect competition as it occurs among industryj’s suppliers. Measuring an absence of dispersed transactions, Y4 and Y, describe the extent to which industryj tends to have transactions s This sentence is based on a wording suggested by an anonymous reviewer for Social Science Research.

i

Yj3 = CbfILtJl Yj7

= C[O?jl i

yj4 = ~[L2%l

Imperfect competition across sectors (undispersed transactions)

YjS = Yp,4

Imperfect competition within sectors (concentrated sectors)

Suppliers Versus Consumers”

a Summation is across all 492 sectors i in the 1%7 Input-Output Study (see Footnote 6 excluding i = j, imperfect competition within sector i is given by the four-Iirm concentration ratio or an approximation to such a ratio, )li,, and where zii is the dolIars of sales by sector i to sector j, ~ii = z&&za) = the proportion of all &es by industry j that are sold to sector i, and oij = zrj/Zizu) = the proportion of all purchases by industry j that are purchased from sector i.

Among suppliers

6; 0 Among consumers

Imperfect competition across and within sectors (Undispersed transactions with concentrated sectors)

TABLE 1 Structural Indicators of Imperfect Competition among Industryj‘s

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with only a small number of different sectors as product markets such that there is imperfect competition across consumer and supplier sectors respectively.lO Indicators Y, and Y, measure the extent to which industryj tends to have extensive transactions with highly concentrated sectors such that there is imperfect competition within the industry’s consumer and supplier sectors respectively. l1 Finally, Y, and Y, measure the extent to which firms in industry j have undispersed transactions with highly concentrated sectors such that there is imperfect competition both within and across the industry’s consumer and supplier sectors, respectively. All of the indicators in Table 1 vary between 0 and 1 and should have negative correlations with profits obtained by firms in an industry. The groupaffiliation index, Yz, is the sum of indicators Y, and Y, for an industry. Do the disaggregated indicators measure different aspects of competition among an industry’s suppliers and consumers? Correlations and standard deviations for the aggregate and disaggregate indicators of imperfect competition are given in Table 2. As was observed by Lustgarten (1975, p. 128) for 1963 data, four-firm concentration (Y, ) has low, positive correlations with imperfect competition among consumers (Y3, Y,, Yd. l2 Also as observed by Lustgarten, an absence of dispersion in transactions with other sectors is negatively correlated with concentration in those sectors, although the correlation is much stronger for consumer sectors than for supplier sectors (rd5 = - .342 versus r,* = ml .077r There are many visible differences in the correlations of the disaggregated indicators with the aggregate indicators Y, and Y,, however, the most direct test of multidimensionality in imperfect competition as measured by the indicators in lo An absence of dispersion in transactions with consumers, Y,, corresponds in content to Lustgarten’s (1975) index of sector dispersion, DSPH. Instead of being stated in terms of two-digit SIC categories as is DSPH, however, Y,~ is stated in terms of the full set of 492 buyers for industry j in the Input-Output Study. ‘I Concentration of consumers, Y,, corresponds to Brooks (1973) and Lustgarten’s (1975) index of buyer concentration. Concentration of suppliers, Y,, corresponds in concept to Clevenger and Campbell’s (1977) extension of Brooks’ measure to suppliers. As pointed out by Guth, Schwartz, and Whitcomb (1976, 1977). Y, (and accordingly Y, for suppliers), overestimates levels of concentration among consumers as a single group of buyers, however, their modifications of the index both ignore structural differentiation among sectors by only considering the m largest sectors (all consumers are grouped together versus all sellers in an industry) and require substantial truncation of the sample size from over 300 to about 50 manufacturing industries (cf. Guth et al. 1976, pp. 489-490, 1977, pp. 242-243, and the response by Lustgarten, 1976, pp. 492-493, to the former). The purpose here is not to accurately measure a condition among consumers/suppliers equivalent to a concentration ratio for firms within an industry. Rather, it is to assess the impact on profits of interindustry differences in alternative structural indicators of imperfect competition among consumers/ suppliers. Therefore, the original measures have been retained without change. I2 This correlation is higher in the analysis of two-digit SIC industries where the 335 observations here are aggregated into 20 observations (cf. the bias discussed in Footnote 2 and the findings by Brooks, 1973; Clevenger and Campbell, 1977; Burt, 1978).

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TABLE 3 Restricted Factor Analyses Demonstrating the Inadequacy of a Model Specifying One Dimension of Imperfect Competition or a Mode1 Specifying Two Dimensions of Imperfect Competition: One for Consumers and the Other for Suppliers” One-dimension Factor Indicators Yl Y3 y4 Yb YC3 Y, YB Lack of fit statistics

model Error

loadings

variances

.080 .249

.994 .938

.143

.980

.141 l.ooo .526 .683

.980 .ooo ,723 .533

x2 (12) = 729.81

Two-dimension model Factor loadings Error Consumers .031 l.CKkI .164 .734 .ooo* .ooo* .ooo*

Suppliers .072 .ooo* .oofYJ .ooo* 1.ooo 526 .683

variances 292 .oca .ooo ,461 .ooo .723 .533

x* (10) = 475.53

a The overall indicator of imperfect competition among suppliers and consumers, Y, has been deleted from each mode1 since it is a linear composite of Y, and Y,. All variables are standardized to unit variance. b These factor loadings have been a priori forced to be zero so that one factor is reflected in consumer indicators while the other is reflected in supplier indicators (cf. Joreskog, 1969). The correlation between the consumer and supplier factors is .249.

Table 2 is to fit a confirmatory factor analytic model to the moments in the table. Table 3 presents statistics on two models fit to the data in Table 2. Table 3 demonstrates the statistical inadequacy of a model specifying either a single dimension of imperfect competition (2 (12) = 729.81, p < .OOl) or two correlated dimensions of imperfect competition; one forced to be reflected in the structural indicators for consumers, the other forced to be reflected in the structural indicators for suppliers (2 (10) = 475.53, p < .OOl). Since the data are not multivariate normal, the estimates in Table 3 are not maximum likelihood (cf. Joreskog, 1969). Nevertheless, the lack of fit for a one or two dimensional model is so great that is seems safe to argue that the indicators in Table 2 measure imperfect competition among suppliers and consumers as a multidimensional concept. The question now is whether or not they have different correlations with industry profits. ANALYSIS

OF PROFITS IN TERMS OF THE STRUCTURAL INDICATORS AS ALTERNATIVES

Table 4 presents correlations between the structural indicators and raw versus corrected price-cost margins. l3 Table 5 presents results for seven regression equations each of which specifies three independent variables as predictors of Y,,: (i) concentration ratios (YI), (ii) one structural indica13 Raw price-cost margins (cf. Footnote 1) are presented since they are readily comparable to past research. Raw and corrected margins are correlated, r = .97.

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TABLE 4 Correlations between Structural Indicators and Profits Measured as Raw and Corrected Price-Cost Margins’ Raw price-cost General indicators Y, Y* Consumers only Y3 Y4 Y5 Suppliers only Y6 Y, Y* Mean SD

margins

Corrected price-cost margins

.2744 -.2717

.2430 - .2910

- .2630 - .0274 -. 1943

- .2837 + .0438 -.2387

-.I407 -.3964 .0801

-. 1468 - .3876 .0529

.2641 .0835

.2641 .0811

a Margins are detined in Footnote 1.

tor of imperfect competition among suppliers/consumers (YJ, and (iii) an interaction term measuring the extent to which Y, is high while Yk is low (Y, - Y,)(P, - Yd. The first and third coefficients are expected to be positive while the second one should be negative. This expected pattern of coefficients is illustrated using the group affiliation index, Y2, as Yk (row 1 of Table 5). Here, as in all the other equations, concentration ratios are a TABLE 5 Regression of Corrected Price-Cost Margins (Y,,) over Alternative Structural Indicators (Yz through Y& in Interaction with Concentration Ratios (Y,) Structural indicators (Yk) in interaction with concentration General indicator Y, Consumers only Y3 Y4 Y5 Suppliers only YE3 Y, Y8

Structural indicators in equation

RZ

Y,

.162

.102 (5.38)

- .224 (5.62)

.264 (1.70)

.165 ,082 ,175

.099 (5.28) ,092 (4.66) .108 (5.73)

- .277 (5.77) .022 (1.16) -.I33 (5.46)

.532 (2.68) -.257 (2.78) ,458 (4.30)

.087 .208 .073

.096 (4.88) .088 (4.83) .093 (4.67)

-.288 (2.71) - .278 (7.88) .046 (0.93)

,150 (0.38) .123 (0.71) .473 (2.22)

a f-Ratio in parentheses, df = 330.

Y,4

v, - ~,)‘Ci;, - Yk)

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significant predictor of profits where the regression coefficient for the 1967 data is similar to its estimate in previous data, approximately ,I. There is a significant negative effect on profits from imperfect competition among suppliers/consumers as captured by Y,. Beyond their direct, additive effects, Y, and Y2 have a significant interaction effect on profits at the .05 level demonstrating that high profits are obtained by those industries having high concentration ratios conjointly with highly competitive suppliers and consumers. When each of the disaggregated indicators is substituted into the equation as Yk, however, inconsistent estimates of the effect on profits from competition among suppliers or consumers are obtained. The general indicator of imperfect competition among consumers (YJ yields the expected regression coefficients while the same indicator for suppliers (Ys), although yielding a significant negative direct effect on profits, has an insignificant interaction with concentration ratios (Y,). While concentration among consumers (YJ is the best predictor of profits using the consumer indicators as alternatives (Y,,Y, and Y, yielding respective squared multiple correlations of ,165, .082, and .175), dispersion in transactions (YJ is the best predictor of profits using the supplier indicators as alternatives (Y,,Y,, and YB yielding respective squared multiple correlations of .087, .208, and .073). Further, concentration among suppliers (YJ has no significant direct effect on profits although in interaction with concentration ratios, low concentration among suppliers has a positive effect on profits. Worst of all, dispersion in transactions with consumers (YJ has no significant direct effect on profits and has a negative effect on profits in interaction with interindustry differences in concentration. l4 ANALYSIS

OF PROFITS IN TERMS OF THE STRUCTURAL INDICATORS AS COMPLEMENTS

Table 5 demonstrates that the structural indicators of imperfect competition among suppliers and/or consumers are not alternatives. When considered as alternatives, they yield inconsistent estimates of the effects of imperfect competition on profits. When the narrowly defined indicators Y3 through Y, are considered in aggregate form as Y,, however, the expected effects of imperfect competition on profits are obtained (row 1 of Table 5). In order to determine the extent to which these aggregate effects are due to each of the narrowly defined indicators, corrected price-cost margins (YJ could be regressed over 13 independent variables: concentration ratios (Y,), the six indicators of imperfect competition among consumers I4 This negative interaction effect from dispersion in transactions with consumers is in accord with Lustgarten’s (1975, p. 129) finding that his index of dispersion (DSPH) was the only indicator of imperfect competition among consumers that failed to have a stronger negative effect on profits in highly concentrated industries in comparison to industries with low concentration ratios.

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or suppliers (Y3 through Ys), and the six interaction variables between concentration ratios and each of the indicators ( Y2 being excluded since it is a perfect linear composite of Y, and Y6). In other words, for industry j: s Yjo = mo + TlYjl

+

mg.jk

c

+ (1)

k=3 8 c k=3

nTTk*(Yjl

-

yl)

@k

-

Yjk)

+

r)j,

where r/j is a residual term with the usual expectations and a normal distribution. Such an analysis, however, misses the fact that Y3 through Y, are not independent variables but rather are indicators of imperfect competition among suppliers and consumers. Estimates of the unique direct effect of each indicator on profits are not of concern here as much as are estimates of the unique contribution each indicator makes to the aggregate effects on profits of imperfect competition among suppliers and consumers. In other words, the concern here is with the contribution indicators make to three unobserved variables with unit variance that in turn determine profits in industry j: YjO

=

P

+

PlY*jl

+

P2Y*j2

+

P3Y*j3 + 77j7

(2)

where the three unobserved variables corresponding to the three aggregate effects discussed earlier are defined by the indicators as:15 Y~I = WsJYjl = (4*6WYj1,

s

Yj*,

=

YZ3=

1 k=3 8

Ytijk,

c

yk*bj,

-FL)

6k

-

YJk),

k=3

so that YT is Y, standardized to unit variance, y; is an unobserved variable of the extent to which industries have imperfectly competitive I5 To the extent that there is one dimension of imperfect competition among suppliers and consumers affecting an industry’s profits, -ykshould equal 7% fork equal 3 through 8. This constraint was imposed on the estimation such that eight independent coefficients of effect were specified; one effect from Y,, six ys from Y, through Ys, and one p as a coefficient of proportionality between direct and interaction effects from Ys through Y8 (cf., Joreskog, 1973, for details on estimation under constraint). The six equality constraints on the ys resulted in a significant lack of fit so the constrained reduced form coefficients are not presented here. The strongest contradictions to the equality constraints are the dispersion indicators; among consumers an absence of dispersion has a low direct effect and a high interaction effect, among suppliers an absence of dispersion has a high direct effect and no interaction effect (see Y, and Y,, respectively, in Table 6).

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TABLE 6 Estimates of the Reduced Form Coefficients in Eq. (1) Regressing Corrected Price-Cost Margins over Structural Indicators” Structural indicators Y, Y3 Y4 YS YbY, Y8

Direct effects (&I .099 (5.57)b -.244 (2.77) .057 (2.38) .004 (0.08) .418 (2.05) -.334 (5.90) -.120 (1.43)

Interaction effects (&*I .245 -.252 .119 .193 - .038 ,145

(0.41) (2.24) (0.55) (0.21) (0.13) (0.38)

a RZ = .323; intercept = .277. * r-Ratio in parentheses, df = 320.

consumers and suppliers, and Yz is an unobserved variable of the extent to which industries are concentrated and have highly competitive consumers and suppliers. Where the /3s in Eq. (2) represent a maximum of effect on profits, the coefficients in Eq. (2) can be computed from an ordinary least-squares estimation of the reduced form coefficients in Eq. (1).16 Table 6 presents least-squares estimates of the reduced form coefficients in Eq. (1). Using the estimates in Table 6, Fig. 1 presents the ps and ys in Eq. (2) as a path diagram. For unobserved variable Y*,, Pr is the change in corrected price-cost margins expected from an increase of 1 SD in Y*,. If the four largest firms in an industry managed to increase their share of sales in the market by .22 (.22 being the SD of Y,), for example, this would be expected to result in a .02 increase in the corrected pricecost margin for the industry (.02 being b,). At an aggregate level of analysis, Table 6 and Fig. 1 correspond to the I6 Let S,, be the dispersion matrix among the six structural indicators Y, through Y,. Let S, be the vector of six covariances between these indicators and YO,corrected price-cost margins. The maximum correlation between Y, and the six indicators is the square root of the scalar; (l/s,$,S;~S,,, where s, is the SD of Y, (cf. Morrison, 1976, chap. 7). Within Eq. (l), however, the regression coefficients given by S;JS, are given as the six reduced form coefficients ?r3through w*,.Let II, be the vector of these six coefficients. The value of&as a maximum effect from an unobserved variable with unit variance constntcted from the direct effects of Y, through Y, is therefore given as: s#XS;I’IJS~)~‘~ = (S$I,,)l~z = 2 .038. The negative root is presented in Fig. 1 since the aggregate effect on profits of imperfect competition among consumers and suppliers is negative. Given & the ys in Eq. (2) are obtained as: -yk = Irx/b,. The y$.s and & are obtained similarly where S, is the vector of covariances between Y* and the six interaction variables and the six reduced form coefficients from the interaction variables, the n*#, are contained in II,. The positive root is presented in Fig. 1 for & since the aggregate effect on profits of the interaction of high concentration with high competition among suppliers and consumers is positive. More detailed discussion of aggregate effects such as & and ps is given by Hauser (1972) and Heise (1972).

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IMPERFECT WITHIN

COMPETITION INDUSTRY

CORRECTED PRICE-COST MARGINS

FIG. 1. Disaggregation of the effects on profits of imperfect competition among suppliers and consumers given interindustry differences in concentration (observed variables are circled, the three unobserved variables have unit variance paths representing covariation among structural indicators are deleted merely to simplify the diagram, imperfect competition within an industry is completely determined by concentration so the path leading from Y, is the inverse of the standard deviation of Y,. XI, is an interaction term given as (Yl-Yl) (Y,-Y,), and a dagger marks coefficients significantly different from zero beyond the .001 level of confidence).

inferences suggested by the general indicator of imperfect competition among suppliers and consumers, Yz, in row 1 of Table 5: (i) There is a significant tendency for interindustry differences in profits to be associated positively with differences in concentration PI = .021 which is significant beyond the .OOl level of confidence, cf. Table 6). (ii) There is a stronger significant tendency for inter-industry differences in profits to be associated negatively with differences in imperfect competition among an industry’s suppliers and consumers & = -.038 which is significant beyond the .OOl level of confidence).” (iii) There is a significant tendency (but lower than the above two effects) for interindustry differences in *’ Under the null hypothesis that there is no direct effect on profits from imperfect competition among suppliers and consumers (i.e., that p2 = 0) Y, should be a function only of concentration ratios and the six interaction variables. Regressing Y, over these seven

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profits to be associated positively with differences in simultaneous high concentration within an industry and low imperfect competition among the industry’s suppliers and consumers- this interaction effect is in addition to the direct, additive effects of imperfect competition (&, = .017 which is significant at approximately the .Ol level of confidence).‘* At a disaggregate level of analysis, Table 6 and Fig. 1 show that imperfect competition among suppliers versus consumers does contribute in distinct manners to the above aggregate effects. Further, the distinct contributions are in accord with the earlier discussion of product markets as a key to understanding how imperfect competition affects profits through suppliers versus consumers. The strongest predictor of industry profits is an absence of dispersion in transactions with suppliers (Y,). lg No other indicator of imperfect competition among suppliers or consumers has close to a similar level of statistical significance. Having to rely extensively on highly concentrated sectors as suppliers (Y, and Y,) has a negligible effect on profits when other of the unique structural indicators are held constant .2u An interpretation effect of Y, on profits is difficult, however, since Y, includes variability due to interindustry differences in an absence of dispersion in transactions with suppliers and such an absence is a strong predictor of profit differences.“’ None of the indicators of imperfect competition among suppliers predictors yields a squared multiple correlation of .117. Given the total squared multiple correlation of .323 in Table 6, the null hypothesis that p2 = 0 has an F-ratio of 16.47 (df = 6,320).

I* Under the null hypothesis that there is no interaction effect on profits from imperfect competition among suppliers and consumers through concentration ratios (i.e., that p3 = 0) Y0 should be a function only of concentration ratios and Y, through Y,. Regressing Y, over these seven predictors yields a squared multiple correlation of .286. Given the total squared multiple correlation of .323 in Table 6, the null hypothesis that & = 0 has anF-ratio of 2.91 (df = 6.320).

I9 The t-ratios in Table 6 are equivalent to tests for the significance of the ys in Fig. 1 where yK represents the standard deviations of change in an unobserved variable resulting from a unit increase in structural indicator Yk. E0 This analysis has not specified labor as a supplier industry for the manufacturing industries due to an absence of information on transactions with differentially concentrated labor groups. Since labor costs are a significant portion of total inputs, the finding that an absence of dispersion in transactions with suppliers has a dominant effect on profits could in part be due to deleting the labor sectors. If concentration among suppliers is only important for profits when it occurs in the labor sector, then the effect on profits of having concentrated supplier sectors (Y,,) and having extensive transactions only with concentrated sectors (Y,J are being underestimated here. 2r The contradictory positive effect of Y,, on profits is being ignored under the assumption that its significant negative effect in Table 5 has been reversed in Table 6 due to a high correlation with Y, (r6, = .526 in Table 2) and the higher correlation of Y, with corrected price-cost margins (rGuo = -. 147 versus r,yo = - .338 in Table 4). The same situation is likely to be responsible for Y, being negligible in Table 6 although it yields the expected coefficients in Table 5, Y, is highly correlated with Y, (rs5 = .734 in Table 2) and Y, has a slightly higher correlation with corrected price-cost margins than does Y, (ray0 = - .284 versus rsyO= - .239 in Table 4).

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has a significant unique effect on profits via an interaction with concentration ratios. As expected, the crucial determinant of profits being constrained by imperfect competition among an industry’s suppliers is the extent to which the industry purchases supplies from many sectors as separate product markets.22 In contrast to transactions with suppliers, transactions with consumers have a significant negative effect on an industry’s profits when firms in the industry have extensive transactions only with concentrated consumer sectors (YJ. The indicator of concentration in consumer sectors (YJ has no unique effect on profits (however, see Footnote 21) and neither concentration in consumer sectors (YJ nor the indicator of extensive transactions only with concentrated consumer sectors (YJ have significant effects on profits via an interaction with concentration ratios. At about the .Ol level of confidence, an absence of dispersion in transactions with consumers (YJ yields effects opposite to those expected. Interindustry differences in profits are associated positively with differences in an absence of dispersion in transactions with consumers and negatively with differences in the simultaneous dispersion in transactions with consumers and high concentration within the industry. These same unexpected findings are obtained when Y, is considered as an alternative indicator in Table 5, so the findings can not be attributed to collinearity among the structural indicators. In short, imperfect competition among an industry’s consumers constrains profits both when consumers are in few separate sectors of the economy and when those sectors are highly concentrated. As expected, the mere fact that an industry’s consumers themselves sell their own product in multiple markets has no direct effect on the industry’s profits. CONCLUSION

Three aggregate effects on industry profits from imperfect competition have been documented by existing studies: (i) An oligopoly effect consists of the increased profits firms in an industry can expect from imperfect competition within the industry. (ii) A group affiliation effect consists of the decreased profits firms in an industry can expect from imperfect 2p This accounts for the lack of association between industry income at a two-digit SIC category level and supplier concentration (SIC) in the Clevenger and Campbell (1977) analysis. Clevenger and Campbell measure vertical integration among suppliers by a measure similar to Y, (cf. Footnote 11). As can be seen in Tables 5 and 6, Y, has no assciation with price-cost margins. If Clevenger and Campbell had measured vertical integration among suppliers as the absence of dispersion in transactions, Y,, .the analysis here suggests that they would have found a strong association between vertical integration among suppliers and industry income. In other words, and as has been argued here, the same structural indicators computed for an industry’s suppliers and its consumers do not have identical effects on industry profits. Supplier transactions constrain industry profits in a manner distinct from that in which consumer transactions constrain profits.

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competition among the industry’s suppliers and consumers. (iii) An interaction effect consists of the increased profits-above and beyond the above direct effects-that firms in an industry can expect from a simultaneous increase in imperfect competition within the industry and decrease in imperfect competition among the industry’s suppliers and consumers. These aggregate effects mask the fundamentally different manners in which industry profits are affected by imperfect competition among the industry’s suppliers versus its consumers. Only one type of product flows from an industry to its consumers, who have no trouble seeing the value of collusion despite the multiple sectors in which their products are sold; however, products from different sectors may flow to the industry without creating the competition among suppliers that prompts collusion. The analysis here demonstrates that industry profits are constrained by suppliers to the extent that firms in the industry purchase supplies from few separate sectors as product markets. In contrast, industry profits are constrained by consumers to the extent that firms in the industry sell to a small number of oligopolistic sectors. The aggregate group affiliation effect on profits is thus not only different for relations with suppliers versus consumers, it is more a consequence of the network pattern of an industry’s transactions with other sectors than it is a consequence of simply having to deal with oligopolistic sectors. As a unique effect on profits, having oligopolistic consumer and/or supplier sectors is negligible; such a condition only lowering profits when it occurs among consumers and then only in conjunction with an industry having few sectors as consumers. The single strongest determinant of industry profits is the extent to which the industry has few separate supplier sectors. REFERENCES Allen, M. P. (1974), “The structure of interorganizational elite cooptation: Interlocking corporate directorates,” American Sociological Review 39, 393-406. Althauser, R. P. (1971), “Multicollinearity and non-additive regression models,” in Causal Models in the Social Sciences (H. M. Blalock, Ed.), Aldine, Chicago. Azumi, K., and Hage, J. (Eds.) (1972), Organizational Systems: A Text-Reader in the Sociology of Orga&nions, Heath, Lexington. Brooks, D. G. (1973), “Buyer concentration: A forgotten element in market structure models,” Industrial Organization Review 1, 151-163. Burt, R. S. (1976), “Positions in networks,” Social Forces 55, 93-122. Burt, R. S. (1977), “Positions in multiple network systems: I, A general conception of stratification and prestige in a system of actors cast as a social topology,” Social Forces 56, 106-131. Burt, R. S. (1978), “Autonomy in a social topology,” Working Paper 11, Survey Research Center, University of California, Berkeley, presented at the annual meetings of the American Sociological Association. Clevenger, T. S., and Campbell, G. R. (1977), “Vertical organization: A neglected element in market structure-profit models,” Industrial Organization Review 5, 60-66.

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