Frank, D; Henderson, D.R., 1992.pdf

Agricultural & Applied Economics Association

Transaction Costs as Determinants of Vertical Coordination in the U.S. Food IndustriesAuthor(s): Stuart D. Frank and Dennis R. HendersonReviewed work(s):Source: American Journal of Agricultural Economics, Vol. 74, No. 4 (Nov., 1992), pp. 941-950Published by: Oxford University Press on behalf of the Agricultural & Applied Economics AssociationStable URL: http://www.jstor.org/stable/1243192 .Accessed: 03/09/2012 10:58

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Transaction Costs as Determinants of Vertical Coordination in the U.S. Food Industries

Stuart D. Frank and Dennis R. Henderson

Vertical coordination is a more comprehensive concept than vertical integration, capturing market, contractual, and ownership coordination. Williamson suggests that transaction costs motivate the use of nonmarket arrangements to vertically coordinate production. This paper presents a vertical coordination index incorporating industry input-output relationships and nonmarket arrangements. In an econometric analysis, the vertical coordination index is utilized to examine transaction cost effects on food industry vertical linkages. Empirical results support the hypothesis that transaction costs are a primary motivation to vertically coordinate via nonmarket arrangements. Results also suggest the vertical coordination index is more robust than traditional vertical integration measure.

Key words: contracts, ownership integration, spot markets, transaction costs, vertical coordination.

Many economic factors affect an industry's vertical organization. Both Coase and Williamson (1975, 1979) examine factors affecting the organization of production systems in a market- hierarchy framework. In such a framework, the organizational criterion is minimization of production and transaction costs (Williamson 1979). Williamson (1979, p. 233) suggests the use of various administered vertical exchange arrangements is motivated by transaction costs, stating that "if transaction costs are negligible, the organization of economic activity is irrelevant."

Vertical organization is traditionally consid- ered in the context of vertical integration. How- ever, vertical integration is only one mode of vertical structure. Vertical coordination is a more comprehensive concept, capturing not only vertical integration but the entire "process by which

the various functions of a vertical value adding system are brought into harmony" (Marion 1976, p. 180). Vertical coordination encompasses all means of harmonizing vertically interdependent production and distribution activities, ranging from spot markets through various types of contracts to complete integration.

Several studies (Mighell and Jones; Marion 1976; Knoeber) qualitatively examine vertical coordination antecedents and implications in the food sector. These studies casually link transaction costs to vertical coordination. Other researchers empirically examine transactional inefficiencies effects either on vertical integration or long-term contracts (Joskow, Levy). Such empirical analyses do report transaction cost linkages to vertical organization. However, no study uses a single measure of vertical coordination reflecting the full range of options between spot transactions and integration.

We introduce here a vertical coordination index and use it to examine transaction cost effects on U.S. food manufacturing industries' vertical coordination. Our empirical analysis suggests transactional inefficiencies encountered by food industries promotes increased utilization of nonmarket vertical arrangements. Most influential transaction cost factors are those related to uncertainty, input supplier concentration, asset specificity, and internalization costs.

Stuart D. Frank is an economist with the Agricultural Cooperative Service, U.S. Department of Agriculture. Dennis R. Henderson is a professor, Department of Agricultural Economics, The Ohio State University.

This article is based upon research conducted as a part of North Central Region research project NC-194, entitled "The Organiza- tion and Performance of World Food Systems: Implications for U.S. Policies."

The views expressed in this paper are the authors' and do not necessarily reflect the views or policies of the U.S. Department of Agriculture or Ohio State University.

The authors extend their appreciation to the anonymous Journal referees for constructive comments on earlier drafts of this paper.

Review coordinated by Steven Buccola.

Copyright 1992 American Agricultural Economics Association

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942 November 1992 Amer. J. Agr. Econ.

Administered Coordination

Administered coordination involves the transfer of certain rights from one firm to another by various coordinating mechanisms other than spot markets. Transferring rights involves a consolidation of control by the contractor or integrator.

Administered coordination arrangements include both contracts and tacit arrangements in addition to vertical integration. Tacit arrangements (for example, providing technical exper- tise and advice or increased credit) allow firms some control over vertically interdependent enterprises owned by others (Blois). However, these arrangements offer less control than that typi- cally conveyed by contracts.

Williamson (1979) provides insight into the structure of contracts within the vertical coordination process. Using Macneil's contract law classifications, he advances three contractual classifications: classical, neoclassical, and relational. Classical contracts are based on a set of legal rules with formal documents and self- liquidating transactions. Neoclassical contracts involve longer-term arrangements that do not cover all contingencies, but include additional governance structures (for example, arbitra- tion). Relational contracts are agreements in principle, which circumscribe the contracting parties' relationship, including tacit as well as explicit arrangements.

Williamson argues that increases in transaction complexity, frequency, and uncertainty, ac- companied by idiosyncratic investments, result in a shift in the coordination structure from classical to neoclassical to bilateral and finally to unilateral relational contracts. One party typi- cally becomes dominant in this progression.

Mighell and Jones discuss several administered arrangements for vertical coordination in the food sector. They specify three general contract types: market specification, production management, and resource providing. These contract types follow the progression of increasing dominance by one party, characteristic also of Williamson's classification.

A Measure of Vertical Coordination

A specification that includes both ownership and contractual relationships for vertically interdependent firms or industries provides a more complete measure of vertical coordination than traditional vertical integration measures. Such a specification should include consolidation of

control as well as degree of interdependency among firms and industries.

Traditional industry structure studies employ variations of the value-added-to-sales ratio in order to calculate vertical integration. However, this ratio is influenced by the firm's profitability and position in the production process. Hence, the ratio is undesirable for use in industry cross- section analysis. Moreover, the ratio does not capture the firm's partial consolidation of control through contracts and other agreements.

A second vertical integration measure examines industry linkages through production functions. Maddigan advances such a measure, capturing industry input-output interdependencies. Through aggregate production functions, interdependencies are expressed by physical input- output coefficients.

Maddigan's Vertical Industry Connection (VIC) index is a viable starting point for measuring vertical coordination. Her index exploits the interactions of the Leontief input-output model. In the Leontief framework, each x,; in input-output matrix X is the optimal value of industry i's output used as an input by industry j.

The input-output matrix is manipulated to form the initial component of the Vertical Coordina- tion (VC) index's up- and down-stream interdependent linkages matrix. Two matrices, A and B (equations (1) and (2)), capture all net production interrelationships for an industry's input-output linkages:

(1) A = I - [xj/(zj - xi1)] + [y1 and

(2) B = [xi,(zi - xii)] - [yj] - I where I = identity matrix, r x r;

xij = value of ith industry's output used as an input to the jth industry; i, j 1 ...., r;

z = total value of industry j's output, j 1,..., r; Yij = [xii/(z, - xii)] if i = j; 0 if i # j; i, j ... r.

Each element aij of matrix A represents the percentage of the value of industry j's net output contributed by industry i. Each element bij of B represents the percentage of the value of industry i's output supplied to industry j. In short, matrix A represents an industry's up-stream connections and matrix B its down-stream connections. Notationally, inputs are negative as values used in production and outputs are positive.

In order to calculate vertical interdependence

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Frank and Henderson Transaction Costs and Vertical Coordination 943

for each specific industry, two matrices, CK and DK, are defined. Matrices CK and DK capture industry k's primary and secondary interindustry connections. The association of industry k with its interdependent industries is determined by the flow of net production. These matrices are constructed using the rows and columns of matrices A and B, specifically the columns of A and rows of B. Matrices CK and DK are represented by (3) cij as()s(j) i,j = 1, ..., n (n

-

r) and

(4) dij = bs(i)s(j) i, j 1,..., n (n r) where s(i) = industries with which industry k is associated, indexed by i;

cij = percentage of the value of industry s(j)'s net output supplied by industry s(i);

dij = percentage of the value of industry s(i)'s net output supplied to industry s(j).

In matrix CK, column j (where j = k), industry k has a primary input relationship with industry i; in column j (where j =A k), industry k has a secondary input relationship with industry i. It is the obverse in matrix DK, where, in row i (i = k), industry k has a primary output relationship with industry j and in row i (i # k), industry k has a secondary output relationship with industry j.

To complete the vertical coordination index, the degree of administrative control that is consolidated by the contractor/integrator must be specified. Administration of vertical interdependencies may be accomplished through ownership and/or a wide variety of contractual relationships. This implies a progressive consolidation of control between the end points of no integration (spot markets) and complete integration. Along the continuum, the contractor/ integrator consolidates increasing degrees of vertical control.

In order to capture the coordination mechanisms of an industry's primary and secondary interactions, we develop matrices EK and FK. Each ej represents industry k's measure of consolidated control of up-stream industry i. Sim- ilarly, each fj represents industry k's consolidated control of down-stream industry j. To measure consolidation, each coordinating structure is assigned a value representing percentage consolidated control. Equation (5) shows the calculation of matrices E and F:

(5) e11 andf1 = > CLghOgh gh g=l h=l

i, j= 1. . ... n(nsr)

where g = number of products produced in each industry, g = 1,...., s;

h = type of coordinating mechanism, h= 1,..., t;

L = for eii, product g's percentage of industry j's input mixture and,

for fj, product g's percentage of industry i's output mixture;

O = assigned value of consolidated control;

N = percentage of production coordinated by each transaction type. With matrices C, D, E, and F, the Vertical Co- ordination index can be calculated. Equation (6) is the generalized formulation of the Vertical Coordination index for industry k:

(6) VCk = 1 1 (Ci)p(Di)P(Ei)P(Fi)P

where C' = column i of industry k's upstream connections matrix;

Di = row i of industry k's downstream connections matrix;

E' = column i of industry k's up-stream control matrix;

Fj = row i of industry k's down-stream control matrix;

p = vector dot product; n = number of industries with which in-

dustry k is interdependent. This specification of Vertical Coordination

(VC) has several desirable properties.' 1. VC increases (decreases) when an input in-

dustry becomes relatively more (less) important by accounting for a larger (smaller) percentage of the total output value of another industry.

2. VC increases (decreases) when relatively more (less) of the output of an industry is used as an input to another industry.

3. VC increases (decreases) as an industry increases (decreases) its number of vertical interactions with other industries.

4. VC increases (decreases) as an industry ex- ercises increased (decreased) up- and/or down-stream consolidated control.

5. The range of VC is between 0 and 1. The Vertical Coordination (VC) index's com-

ponents are influenced, but not skewed, by several factors. Price and quantity changes affect the values of the input-output matrix and argument L in (5). If xj (input-output matrix) or argument L in (5) increases between industries i

' Interested readers may contact authors for mathematical deri- vations of the vertical coordination index properties.

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or j, the importance of the industries to each other increases. However, at the same time, all other linkages of either industry are relatively less important. Thus, VC will increase or decrease de- pending on the relative magnitude of the changes in matrices CK or DK and argument L in (5).

The emphasis of the Vertical Coordination index is on interindustry, not intraindustry, linkages in a vertical system in which industries are categorized according to a recognized scheme (for example, four-digit Standard Industrial Classification, SIC). As firms vertically inte- grate within their defined classification, value added by that industry increases. As value added increases, interindustry input-output vector elements x,; decrease. Therefore, the industry's primary and secondary connections become less important.

The hypothesis to be tested is that the food industries' use of various vertical coordination arrangements is motivated by transactional inefficiencies. Although Williamson's transaction cost paradigm focuses on the firm, our hypothesis is tested using industry data. Firms use several nonmarket arrangements in response to various transactional inefficiencies. The coordination methods used represent those the firms chose in order to minimize transaction costs encountered when organizing productive activities. Assum- ing firms within the same industry classification encounter similar transactional inefficiencies, industry data should be representative of firm actions.

Food Manufacturers' Vertical Coordination

Calculating the Vertical Coordination (VC) measure requires two major components, interindustry linkages and coordination methods for these linkages. First, industry vertical linkages are constructed utilizing the 1982 input-output transactions matrix with the four-digit (SIC) scheme in order to classify firms into industries.2 The transactions matrix incorporates the input-output interdependencies between production agriculture (SIC 0111 to 0291) and food manufacturing (SIC 2,011 to 2,099) industries.3

Second, it is necessary to have data on the use of various coordinating methods in agriculture. While there is no systematic reporting of agricultural contract data, several researchers pro-

vide estimates of contract use consistent with the Mighell and Jones classification. Five coordinating methods are used here: 1) spot markets, 2) market specification contracts, 3) production management contracts, 4) resource providing contracts, and 5) integration.

Food manufacturers' data on utilization of the five coordination methods for procuring agricultural commodities are unavailable. To approximate food manufacturers' up-stream coordinating methods, data on farm sector's down- stream contracting are used. The share of total farm-to-processor product flow of the five coordinating methods is estimated on the basis of previously reported information (table 1). In table 1, for each coordinating method except spot markets (which capture the residual value) the coordination share value SV is

n

(7) SV Ai Bi

where A; is farm commodity i's percentage of total up-stream procurement by each food industry and Bi is the percentage of farm commodity i coordinated by each method. These shares are used for argument N in (5) to calculate the elements of matrix E.

Argument O of (5) captures consolidation of control associated with each coordinating method.4 A decreasing marginality function (concave) is incorporated into the Vertical Co- ordination measure's calculation." That is, the percentage of consolidated control increases at a decreasing rate for each successive coordination method, moving from spot markets (0%) to integration (100%).

The coordinating method data we utilize poses potential biases in estimating Vertical Co- ordination. Mighell and Jones' contract classifications do not explicitly incorporate dynamics of many vertical arrangements captured in Wil- liamson's governance scheme. Thus the Mighell and Jones' classifications understate the extent of common or shared control among interdependent firms.

Where there are intermediaries between farmers and food processors, the processor's de-

2 An input-output transactions matrix was provided by Alward.

3 This specification of vertical coordination is dependent upon time-specific input-output coefficients and is not as robust to changes in industry definitions or classifications.

4 Several specifications of the degree of consolidated control associated with various coordinating structures were examined. These included decreasing marginality, constant marginality, and increasing marginality. In a separate analysis of the three relationships, the decreasing marginality specification proved superior (Frank, pp. 38-44 and 61-71).

5 The assigned values of consolidated control are: spot market, 0; market specification, 0.5; production management, 0.8; resource providing, 0.9; and integration, 1.0.

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Table 1. Share of U.S. Food Manufacturing Industries' Farm-Originated Inputs Coordi- nated by Various Methods

Contracts

Production Spot Market management Resource

Industry marketa specification (percentage) providing Integration Meat packing 89.5 7.0 0.0 0.0 3.5 Sausages and other prepared meats 89.3 7.2 0.0 0.0 3.5 Poultry dressing 13.0 0.0 0.0 73.0 14.0 Poultry and egg processing 6.5 0.0 18.5 48.2 26.8 Creamery butter 17.7 81.0 0.0 0.0 1.3 Cheese, natural and processed 17.7 81.0 0.0 0.0 1.3 Condensed and evaporated milk 17.7 81.0 0.0 0.0 1.3 Ice cream and frozen desserts 19.0 70.8 3.6 0.0 6.6 Fluid milk 17.7 81.0 0.0 0.0 1.3 Canned specialties 30.1 6.2 38.8 0.0 24.9 Canned fruits and vegetables 35.4 10.4 30.6 0.0 23.6 Dehydrated fruits, vegetables, and soups 24.9 14.0 33.4 0.0 27.7 Pickles, sauces, and salad dressings 36.9 1.3 40.3 0.0 21.5 Frozen fruits and vegetables 24.9 14.0 33.4 0.0 27.7 Frozen specialties 19.3 14.0 15.7 24.1 26.9 Flour and other mill products 91.7 7.8 0.0 0.0 0.5 Cereal breakfast foods 81.6 13.0 0.0 0.0 5.4 Rice milling 91.5 8.0 0.0 0.0 0.5 Wet corn milling 92.5 7.0 0.0 0.0 0.5 Dog, cat, and other pet food 93.3 6.0 0.0 0.0 0.7 Prepared feeds, n.e.c.b 92.4 7.1 0.0 0.0 0.5 Bread, cake, and related products 40.0 35.0 0.0 0.0 25.0 Cookies and crackers 100.0 0.0 0.0 0.0 0.0 Raw & refined cane and beet sugar 0.0 0.0 69.0 0.0 31.0 Confectionery products 85.0 12.3 0.0 0.0 2.7 Chocolate and cocoa productsc 100.0 0.0 0.0 0.0 0.0 Cottonseed oil mills 82.3 16.7 0.0 0.0 1.0 Soybean oil mills 89.5 10.0 0.0 0.0 0.5 Vegetable oil mills, n.e.c. 89.5 10.0 0.0 0.0 0.5 Animal and marine fats and oils 100.0 0.0 0.0 0.0 0.0 Shortening and cooking oils 100.0 0.0 0.0 0.0 0.0 Malt beverages 93.2 6.0 0.0 0.0 0.8 Malt 92.5 7.0 0.0 0.0 0.5 Wines, brandy, and brandy spirits 32.0 41.0 0.0 0.0 27.0 Distilled liquor, except brandy 92.5 7.0 0.0 0.0 0.5 Bottled and canned soft drinksc 100.0 0.0 0.0 0.0 0.0 Flavoring extracts and syrups, n.e.c.' 100.0 0.0 0.0 0.0 0.0 Canned and cured seafoods 100.0 0.0 0.0 0.0 0.0 Fresh or frozen packaged fish 96.0 3.0 0.0 0.0 1.0 Roasted coffeec 100.0 0.0 0.0 0.0 0.0 Macaroni and spaghetti 11.0 0.0 45.0 0.0 44.0 Food preparations, n.e.c. 79.0 16.0 0.0 0.0 5.0

a Residual values. b n.e.c. means not elsewhere classified. c Based upon input-output transactions matrix, industry had no up-stream linkages to agricultural producers (SIC 0111-0291). Sources: See Frank for complete list.

gree of up-stream control (represented by the farm sector's down-stream coordination data) is biased downward by the amount of the processor-in- termediary linkage. In addition, if the first han- dler is also a food processor, integration will be understated to the extent that internal transfer of procured farm commodities exceeds farmer- processor integration. Many food manufactur-

ing industries also procure a portion of their inputs from other food processing industries. However, the food manufacturing industries' up-stream coordinating method data are not available. Further, the F-matrix in the Vertical Coordination index (equation 6) cannot be calculated because of the unavailability of food manufacturers' down-stream coordinating data.

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In the absence of conceptually desirable data, use of available data understating true magnitudes should not diminish the underpin- nings of our transaction cost and vertical coordination linkage estimates. Arguably, these linkages would be more pronounced absent such bias.

Food Manufacturers' Transaction Costs

Variables we use to measure the food manufacturing industries' transaction costs are presented below. Transactional inefficiencies are grouped into four general categories: uncertainty, concentration, idiosyncratic investments, and internalization costs.

Uncertainty

Convention holds that uncertain demand/supply causes firms to rely more on nonmarket coordination methods. When transactions are conducted under uncertainty, it can become very costly or impossible to anticipate all contingencies. In this event, alternative means of coordination that attenuate uncertainty may be more desirable. To measure uncertainty of food manufacturers' input supply, we use the percentage change in farm output supply between 1981-1982 (PCFS). If current farm supplies are expected to fall short of demand, food manufacturers are motivated to use nonmarket coordination methods to attenuate supply uncertainty. A negative PCFS coefficient corresponds to an increase in vertical coordination activity.

To measure unanticipated demand uncertainty, we employ the variance of the residual of the log of food industry sales regressed on a time trend (Levy). (8) LFISk= a + 3 TT + k = I ton where LFISk is the log of food industry k's sales and TT is a time trend.

Concentration

As the number of buyers and sellers in a market diminishes, small-number bargaining problems become more prevalent. To reduce potential of opportunistic behavior when few firms bargain, firms may utilize nonmarket coordinating meth-

ods. We employ several variables to proxy the number of current and future market partici- pants. Williamson (1979, p. 260) states, "as ge- neric demand grows and the number of supply sources increases, exchange that was once transaction-specific loses this characteristic and greater reliance on market mediated governance is fea- sible." The number of potential firms in each industry is represented by anticipated demand growth (ADG); this is the time trend coefficient in (8). The motivation to coordinate vertically via nonmarket coordination methods diminishes as future demand increases.

To capture the food industry's concentration as a buyer of inputs, we use the food manufacturing industries' four firm concentration ratio (CR4). We also develop two variables capturing seller (input supplier) concentration, one each for the farm output and food manufacturing industries. Variable FSGC is a farm commodity's GINI coefficient weighted by that commodity's net contribution as a supplier to each food industry.6 Similarly, variable FUSC is the weighted four-firm concentration ratio of food manufacturing industries supplying inputs to another food manufacturing industry (for example, meat packing industry supplying inputs to the sausage and prepared meats industry).

Idiosyncratic Investments

Firms producing specialized or differentiated products or those with highly intensive technical production processes may have idiosyncratic investments. A firm at one stage may be able to appropriate quasi-rents from another firm with idiosyncratic investments (Klein, Crawford, and Alchian). In order to attenuate opportunistic behavior, firms increasingly will use nonmarket vertical coordination. To capture differential characteristics and asset specificity, we use the food industry's advertising-to-sales ratio (AS) and research and development expenditures-to- sales ratio (RD).7

6 With the absence of farm production concentration data at the local level, we use available data at a more aggregate level, which potentially understates production concentration. At the limit, na- tional GINI's are no larger than local GINI's, and often less. There- fore, the downward bias provides a conservative estimate of what would be revealed absent such bias. The farm industry GINI coefficient is calculated from Lorenz curves based on the ratio of cumulative percentage of output to cumulative percentage of farms in each size classification, using Census of Agriculture data.

7 Advertising expenditures were provided by Rogers, and research and development expenditures are reported in Scherer.

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Costs of Administered Vertical Coordination

The same transactional inefficiency factors pro- moting nonmarket coordination also limit the extent of internalization. Firms will internalize transactions up to the point where market costs of an activity equal the cost of internalization. Firm characteristics affecting internalization costs may include: firm specialization, capital intensity, and flow economies. To proxy these characteristics we use the industry specialization ratio (SPCR), capital-to-sales ratio (KS), and a food production dispersion index (FPDI).

Stigler demonstrates that as a firm specializes in a particular product, it vertically disintegrates to more fully capture increased scale economies. Hence, as an industry becomes more specialized, it relies less on integration as a coordination method. To maintain production capacity in a capital intensive environment concomitant with uncertainty, firms will vertically coordinate. Firms also have an incentive to coordinate vertically in order to capture production process flow economies. Physically closer stages of production can more readily capture such economies. We calculate flow economies (FPDI) as the proximity of enterprises with output-input linkages. Equation (9) represents industry k's potential flow economies (FPDI):

m Fr (9) FPDIk = Wc I F - Pi

k = 1 to n where Fc = percentage of farm commodity c produced in region i;

Pk = percentage of processed food k manufactured in region i;

Wc = percentage net contribution of commodity c to food industry k. A negative FPDI coefficient implies an increase in vertical coordination.

Empirical Results

Our analysis examines 42 four-digit SIC food manufacturing industries using 1982 data. Four variations of the Vertical Coordination index are specified: (a) VCI: upstream interdependencies (matrix C, equation (6)), (b) VC2: up-stream consolidated control associated with various coordinating methods (matrix E, equation (6)), (c) VC3: upstream interdependencies and upstream control (matrices C and E, equation (6)), and

(d) VC4: up- and downstream interdependencies and upstream control (matrices C, D, and E, equation (6)). Degree of information incorporated into the four measures progressively increases from VC1 to VC4. Examining these four Vertical Coordination index measures reveals the importance of each component comprising the most comprehensive measure, VC4.

Each OLS regression estimation was analyzed for heteroskedasticity and multicollinearity. To correct for heteroskedasticity one may estimate the coefficients and t-statistics using White's heteroskedastic-consistent covariance matrix. The Belsley-Kuh-Welsch procedure fails to suggest presence of multicollinearity in the explanatory matrix.

Estimated regression coefficients for the vertical coordination variable incorporating only industry up-stream connections (VC1 in table 2, column 1) are not revealing. Farm output supply concentration (FSGC) has a significant positive relationship with food manufacturer's nonmarket vertical coordination. The remaining concentration variables do not significantly affect VC1. Flow economies (FPDI) also are a significant motivating factor behind vertical coordination. However, capital intensity (KS) has a significantly negative relationship with vertical coordination. The remaining administered cost variable, along with the uncertainty and idiosyncratic investment variables, do not significantly influence VC1. The VC1 estimation has a relatively low R2 (0.29) and transaction cost variables fail to significantly explain the variation in vertical interdependencies (F-statistic = 1.12).

Estimated coefficients in the three vertical coordination regressions incorporating nonmarket methods (VC2, VC3, and VC4) generally have expected signs (table 2, columns 2, 3, and 4). Increased demand (UNANT) and input supply uncertainty (PCFS) have significantly positive effects on vertical coordination. Increased input supplier concentration (FSGC and FUSC) also appears to increase nonmarket coordination. However, in the second equation (VC2), food industry concentration (CR4) has a significantly negative influence on nonmarket coordination, while in all three equations anticipated future concentration (ADG) has no effect. Increased research and development activities (RD) significantly influence the need to use nonmarket vertical coordination.

Results are less clear regarding firm and product differentiation (AS). Increased advertising

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Table 2. Transaction Cost Effects On Vertical Coordination

Dependent variables Explanatory variables VCl VC2 VC3 VC4

constant 0.28 1.82 1.45 1.18 (0.70) (3.83) (3.78) (3.22)

PCFS 0.38 -2.44 -1.37 -1.00 (0.90) (4.62) (2.91) (2.44)

UNANT 0.05 -0.50 0.37 0.83 (0.06) (0.57) (0.51) (1.31)

CR4 0.0003 -0.005 -0.003 -0.0008 (0.15) (2.26) (1.25) (0.37)

ADG -0.20 0.33 -0.35 -0.55 (0.15) (0.25) (0.28) (0.46)

FSGC 0.32 0.84 0.99 1.01 (4.03) (6.56) (10.85) (12.06)

FUSC -0.0004 0.006 0.005 0.004 (0.34) (4.39) (4.45) (3.89)

RD 1.87 24.09 16.39 16.96 (0.32) (3.55) (3.23) (3.80)

AS -1.78 3.31 0.05 -2.20 (1.51) (2.51) (0.04) (2.08)

KS -0.46 0.17 -0.19 -0.06 (2.63) (0.76) (0.94) (0.33)

SPCR -0.0006 -0.02 -0.02 -0.01 (0.16) (4.91) (4.27) (3.80)

FPDI -0.04 0.02 -0.008 -0.03 (1.63) (1.06) (0.34) (1.31)

R2 0.29 0.71 0.78 0.77 F-statistic 1.12 6.72 9.46 9.14 Degrees of freedom 30 30 30 30

Note: Values in parentheses are t-statistics.

has a significantly positive effect in the second equation (VC2) and a negative effect in the third (VC3). Results also indicate firms increasingly use nonmarket coordination to capture flow economies (FPDI) and are less integrated when more specialized (SPCR). However, capital intensity (KS) appears not to affect vertical coordination. The three estimated relations explain at least 71% of total variation in vertical coordination. Each equation provides a strong sta- tistical relationship between transaction costs and vertical coordination (F-statistics significant at the 1% level).

The vertical coordination measure incorporating only up-stream interdependencies (VC1) is statistically not revealing, while the measure of up-stream coordinating methods (VC2) is quite significant. However, the two measures that combine coordinating methods and input-output interdependencies (VC3 and VC4) have the larg- est R2's and F-statistics.

We regressed conventional vertical structure measures on the same transaction cost variables and compared results with the most complete vertical coordination measure (VC4). Maddi-

gan's Vertical Industry Connections (VIC) index captures both up- and downstream interdependencies (table 3, column 1). Only two variables, farm sector concentration (FSGC) and flow economies (FPDI), have expected signs and are statistically significant. Estimated coefficients of idiosyncratic investments (AS) and capital intensity (KS) have negative relationships with VIC and are also statistically significant. The remaining estimated coefficients are not statistically different from zero. Overall, VIC is not revealing. Its estimated equation has low R2 (0.34) and the F-statistic (1.38) is not significant. Vertical coordination (VC4), which adds coordinating methods to VIC, performed con- siderably better. Coordinating methods appear to be important when empirically examining vertical coordination.

The second variable examined is the traditional vertical integration (VI) measure, defined as the value-added-to-sales ratio (table 3, column 2). Input uncertainty (PCFS), concentration (CR4 and FUSC), and idiosyncratic investments (RD and AS) regression coefficients are statistically significant with expected signs.

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Table 3. Comparisons of Transaction Cost Effects On Vertical Coordination and Vertical Integration

Dependent variables Explanatory variables VIC VI VC4

constant 0.06 0.12 1.18 (0.15) (0.53) (3.22)

PCFS 0.49 -0.47 -1.00 (0.84) (1.47) (2.44)

UNANT 0.46 -1.22 0.83 (0.50) (2.43) (1.31)

CR4 0.002 0.001 -0.0008 (1.30) (1.31) (0.37)

ADG -0.54 0.42 -0.55 (0.41) (0.74) (0.46)

FSGC 0.35 -0.06 1.01 (3.63) (0.80) (12.06)

FUSC -0.001 0.003 0.004 (1.07) (4.99) (3.89)

RD 2.31 6.46 16.96 (0.41) (1.84) (3.80)

AS -4.09 2.47 -2.20 (3.64) (2.41) (2.08)

KS -0.30 0.09 -0.06 (1.76) (0.80) (0.33)

SPCR 0.002 0.0001 -0.01 (0.42) (0.03) (3.80)

FPDI -0.05 0.008 -0.03 (1.72) (0.63) (1.31)

R2 0.34 0.65 0.77 F-statistic 1.38 5.05 9.14 Degrees of freedom 30 30 30

Note: Values in parentheses are t-statistics.

Only the demand uncertainty (UNANT) coefficient has a significant and unexpected sign. Sta- tistically, remaining coefficients do not affect vertical coordination. The vertical integration (VI) equation performs well relative to VIC, with an R of 0.65 and F-statistic significant at the 1% level.

Comparing vertical integration (VI) and vertical coordination (VC4) equations is revealing (table 3, columns 2 and 3). The vertical integration (VI) equation has only five statistically significant transaction cost variables with the hypothesized signs, while the most complete vertical coordination measure (VC4) has seven. Vertical coordination measure VC4 is more robust with higher R2 and F-statistics.

This result supports a vertical organization measure focusing on interindustry linkages and nonmarket arrangements as opposed to a value- added integration measure employed in cross- section analysis. For instance, vertical coordination (VC4) is influenced by input supplier concentration (FSGC and FUSC), uncertainty (PCFS and UNANT), and factors affecting scale

economies (SPCR and FPDI), whereas vertical integration is affected by input uncertainty (PCFS), concentration (CR4 and FUSC), and idiosyncratic investments (RD and AS). Fur- thermore, two statistically significant variables in the vertical integration (VI) equation (CR4 and AS) are strongly related to profitability and value- added, which are inherent in the traditional value- added-to-sales ratio. Such a result continues to bias industry cross-section vertical integration studies.

Conclusions We have developed a vertical coordination measure that incorporates product flow linkages and coordinating methods utilized between vertically interdependent industries. Empirical analysis supports the hypothesis that transaction costs are a primary motivation for vertically coordinating via nonmarket arrangements. The most influential transaction cost factors are related to uncertainty, input supplier concentration, asset specificity, and scale economies.

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Comparing vertical coordination measures that only capture product flow interdependencies with other specifications that incorporate coordinating methods as well, reveals the importance of nonmarket exchange mechanisms in reducing transactional inefficiencies. Previous industrial organization studies have not examined empirically the combined role of nonmarket exchange mechanisms and vertical integration. Our results demonstrate that an industrial organization variable incorporating coordinating methods and technical input-output interdependencies bridges the market versus ownership coordination di- chotomy. Our vertical coordination measure is acceptable for use in cross-section analysis because it focuses on interindustry coordination.

Transaction cost economics provide consid- erable insight about the determinants of vertical coordination. Great strides are being made in transaction cost economics methodologies, although obstacles remain to be overcome. There are difficulties in observing and quantifying transactional inefficiencies. In order to improve the vertical coordination measure's accuracy, a generally accepted and comprehensive list is needed of definable coordinating methods in the farm and food sectors. With such a list, detailed data could be collected on the use of each coordinating arrangement. Ultimately, we are interested in knowing how vertical coordination hampers or enhances competitiveness, profitability, and economic welfare.

[Received December 1990; final revision received March 1992.]

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Article Contentsp. [941]p. 942p. 943p. 944p. 945p. 946p. 947p. 948p. 949p. 950

Issue Table of ContentsAmerican Journal of Agricultural Economics, Vol. 74, No. 4 (Nov., 1992), pp. 849-1057Front MatterRisk Aversion and Price Risk in Duality Models of Production: A Linear Mean-Variance Approach [pp. 849-859]Production Risk and Optimal Input Decisions [pp. 860-869]Incorporating Risk Aversion into Dynamic Programming Models [pp. 870-878]Optimal Dynamic Hedging Decisions for Grain Producers [pp. 879-888]Earnings and Mobility of Legal and Illegal Immigrant Workers in Agriculture [pp. 889-896]Effect of U.S. Immigration Reform on Labor-Intensive Agricultural Commodities [pp. 897-906]Incentives for Protecting Farm Workers from Pesticides [pp. 907-917]On-Site Time in the Demand for Recreation [pp. 918-925]Measuring Recreation Values with Multiple Destination Trips [pp. 926-933]A Method for Calculating Profit-Neutral Land Set Asides [pp. 934-940]Transaction Costs as Determinants of Vertical Coordination in the U.S. Food Industries [pp. 941-950]Production Subsidy and Countervailing Duties in Vertically Related Markets: The Hog-Pork Case between Canada and the United States [pp. 951-961]Oligopsony Potential in Agriculture: Residual Supply Estimation in California's Processing Tomato Market [pp. 962-972]Supply Analysis in an Oligopsony Model [pp. 973-979]Imperfect Competition in Multiproduct Food Industries with Application to Pear Processing [pp. 980-990]Characteristic Supplies and Demands in a Hedonic Framework: U.S. Market for Cotton Fiber Attributes [pp. 991-1002]Trading-Day Variation: Theory and Implications for Monthly Meat Demand [pp. 1003-1009]Incorporating Data and Theory in Roundwood Supply and Demand Estimation [pp. 1010-1018]The von Liebig Hypothesis [pp. 1019-1028]Estimating the Technology Coefficients in Linear Programming Models [pp. 1029-1039]Erratum: Nonparametric Test of the Expected Utility Hypothesis [p. 1039]CommentThe Econometrics of Damage Control [pp. 1040-1044]

Books ReviewedReview: untitled [pp. 1045-1047]Review: untitled [pp. 1047-1048]Review: untitled [pp. 1048-1049]Review: untitled [pp. 1049-1051]Review: untitled [pp. 1051-1052]Review: untitled [pp. 1052-1053]Review: untitled [pp. 1053-1054]Review: untitled [pp. 1054-1056]

Necrology [p. 1057]Back Matter

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