direct estimation of synergy.pdf

Direct Estimation of Synergy:A New Approach to the

Diversity-performance Debate

Rachel Davis L. G. ThomasStern School of Business, New York University, 7-63 Management Education Center, 44 West 4th Street,

New York, New York 10012School of Business, Emory University, lid Rich Building, 1602 Mizell Drive, Atlanta, Georgia 30322

This study examines the linkages between relatedness and synergy in the context of diver-sification among U.S. pharmaceutical firms for the period 1960-1980. Rather than assume(as in the entropy, Herfindahl and concentric indices of diversification) that the levels of synergygenerated by different related combinations of business units are identical, we estimate synergydirectly using a modified version of the concentric index. In addition to estimating synergyusing capital market performance of the firm as a whole, we examine the effects of nondrugdiversification on the innovative productivity of firms' pharmaceutical divisions alone.

Our two main findings are that production relatedness, such as that between drugs andchemicals, in fact did not imply synergy over the period of our study; and that the patterns ofsynergy for different types of relatedness shifted over time with the industry life cycle.{Diversification; Synergy; Industry-Evolution; Pharmaceuticals)

1. IntroductionIf diversification strategy is to succeed it is imperativethat synergy, or super-additivity' in valuation of busi-ness combinations, be achieved. The failure to achieveexpected synergies in large part accounts for the mixedsuccess of diversification strategy among U.S. firms(Amit and Livnat 1988, Porter 1987). As Reed andLuff man (1986) have written, "while the benefits ofsynergy are truly legendary.. . as every student knows,those particular benefits show an unshakable resolvenot to appear when it becomes time for their release."

Given that many combinations of businesses fail toachieve synergies, it is important for both researchersand corporate strategists to be able to identify reliable

' By synergy, we mean super-additivity in valuation of business com-binations. In simpler terms, synergy means that the valuation of acombination of business units exceeds the sum of valuations for standalone units.

Economies of scope (cost sub-additivity) would produce synergy,but so also would revenue-side advantages of combinations.

and straightforward proxies for synergy. In the strategicmanagement literature, the most widely used proxy ofsynergy for two businesses is their relatedness (Wrigley1970, Rumelt 1974), or the presence of similar activitiesand shared resources at various points of the valuechain. While there is an ongoing debate as to how tobest measure relatedness, increasingly many researchers(notably Montgomery 1982, Palepu 1985) have usedshared SIC codes as proxies of relatedness betweenbusinesses. These SIC linkages effectively act as proxies(of relatedness) for another proxy (of synergy). How-ever, SIC linkages measure relatedness very narrowly,at only a single point in the value chain, that is, pro-duction.^ It is this narrow equation of synergy and pro-duction (output-based) relatedness in this latter stream

^ Production or output similarities are defined as including similaritiesin raw materials and/or manufacturing technology.

4-digit SIC categories "define an industry as a grouping of estab-lishments primarily engaged in the same or similar lines of economic

1334 MANAGEMENT SCIENCE/VOI. 39, No. 11, November 1993 0025-1909/93/39n/1334$01.25Copyright 1993, The Institute of Management Sciences

DAVIS AND THOMASDirect Estimation of Synergy

of literature (SIC-based studies) that is the analyticaltarget of this paper.

Our study examines relatedness and synergy in theU.S. pharmaceutical industry during the period 1960-1980. Our two main findings are that production relat-edness in fact does not necessarily imply synergy, andthat the actual relatedness-synergy linkages shift dra-matically, but predictably, over time. For example, wefind that the drug-chemical linkage, primarily produc-tion relatedness, is dissynergistic^ during most of theperiod of our study. In contrast, the drug-health prod-ucts linkage, a marketing-based relatedness, becomessynergistic by the late 1970s. These changes are con-sistent with the product life cycle of the pharmaceuticalindustry.

The outline of this paper is as follows. Section 2sketches expected synergies in the pharmaceutical in-dustry, and their development over time with particularemphasis on the documented dissynergies betweenchemicals and pharmaceuticals. Section 3 presents theanalytical framework of the paper, along with a briefreview of the literature. Section 4 discusses the dataand estimation methodologies, while 5 presents theestimation results. Concluding remarks are given in 6.

2. Synergy in the PharmaceuticalIndustry

This study examines diversification in the U.S. phar-maceutical industry during 1960-1980. We have chosen

activity. . . . In the manufacturing division, the line of activity isgenerally defined in terms of the products made, raw materials con-sumed or manufacturing process used (SIC Manual, 1957, p. 431).

In the SIC Manual, 1987 (p. 645), this definition is loosened, in-dicating that the industry classification does not "follow any singleprinciple, such as the use of the products, market structure, the natureof the raw materials, etc. "

We can surmise from the above that regardless of the criteria used,the SIC definition of "industry" is not based on criteria such as mar-keting (e.g., packaged goods industries) or human resource skills (e.g.,high technology industries).^ Dissynergy will result when related diversification strategies resultin internal transaction costs outweighing the achieved benefits. Jonesand Hill (1988) point out that diversification increases the bureaucraticcost of managing the enlarged organization; there will be a declinein performance unless the additional bureaucratic costs are offset byincreases in operating efficiency.

this industry for two reasons. First, the present form ofcompetition in the global pharmaceutical industryemerged only in the 1950s. Thus, a study covering theperiod 1960-1980 will encompass several differentphases of the industry life cycle. Second, this industryhas been extensively studied by management scholars,economists and others, and we, therefore, have a goodgrasp of the bases for strategic success in this industry(Cool and Schendel 1987, 1988; Department of Com-merce 1986; Grabowski et al. 1978; Hill and Hansen1991; Hansen 1979; NAE 1983; Schwartzman 1976;Temin 1980; Thomas 1990). Presented below is a briefdescription of the pharmaceutical industry.

Before 1945, the industry was essentially noninno-vative, with most firms selling different brands of acommon product set (NAE 1983). Chemical firms weresuppliers of bulk drugs, made up in pharmacies, andwere prominent among the infrequent innovators ofnew drug products. Thus, chemicals and pharmaceu-ticals shared both production and innovation synergiesin this basically noninnovative period (Temin 1980).This competitive environment changed drastically afterWorld War II, with the emergence of a new form ofcompetition through innovation of fundamentally newproducts. Not only did innovation increase in strategicimportance relative to production, but the process ofinnovation needed to discover new pharmaceuticalproducts steadily changed after the 1950s, beginning ata level of simple screening in the 1950s (the so-called"dope and hope" approach) and ending with explicitdesigning of compounds through molecular biology inthe 1980s (NAE 1983, Department of Commerce 1986,Teitelman 1989).

As the required innovation skills in pharmaceuticalsevolved and became more specialized, the character ofresearch shifted from a chemical to a biomedical basis.In a very similar manner, the strategic importance ofpharmaceutical marketing increased over time andchanged in nature. The stringent market-access regu-lations put into place by the 1962 Amendments to theFood, Drug, and Cosmetic Act fundamentally shiftedthe marketing skills needed for strategic success in thisindustry towards the ability to convince academic med-ical experts of the comparative efficacy and superiorityof specific drugs (Thomas 1990). Gaining acceptancefrom the medical community for new drug products

MANAGEMENT SciENCE/Vol. 39, No. 11, November 1993 1335


required extremely sophisticated marketing skills, aboveand beyond any innovation skills.^ Further, as the extentand expense of innovation costs increased, rapid mar-keting rollouts and large market shares for new productsbecame essential to recoup innovation costs and gavemarketing additional strategic significance. In summary,the complexity, distinctiveness, and expense of both in-novation and marketing skills steadily increased after1950, and particularly after 1962 in the U.S.

In this historical context, the significance of cost-basedsynergies from the continuing production relatednessof pharmaceutical and chemical industries steadilydwindled in strategic importance for the following rea-sons: (a) production costs accounted for a small andcontinually decreasing share of total costs in the phar-maceutical industry; and (b) competition in the phar-maceutical industry is primarily differentiation-basedrather than cost-based. Thus, the drug-chemical linkagebecame dissynergistic as declining strategic benefits wereoutweighed by the bureaucratic costs of managing thelinkage (Jones and Hill 1988, Hill and Hansen 1991).

With the evolution of the pharmaceutical industry,synergies between chemicals (SIC codes 2810, 2860)and Pharmaceuticals (SIC 2830), primarily productionin origin, have steadily declined over the industry lifecycle, as production declined in strategic importance.Meanwhile, synergies between pharmaceuticals andhealth care products (SIC codes 3830, 3840, 3850,2020), primarily marketing and to a lesser extent in-novation in origin, have increased over time as inno-vation and marketing have become more strategicallysignificant and interrelated.'

* The importance of marketing skills in the pharmaceutical industryis effectively illustrated by the Zantac-Tagamet rivalry. It is commonlyargued in the pharmaceutical industry that there are only truly minormedical differences between Zantac (the leading anti-ulcer medicationsold by Glaxo) and Tagamet (the first-mover which is now a distantsecond in sales). Glaxo's triumph with Zantac in overtaking Tagametis due more to sophisticated marketing skills than to innovation.' Both Drugs and Chemicals fall in the same 2-digit SIC category(Drugs SIC 2830 and Chemicals SIC 2810, 2860). By contrast. HealthProducts includes a number of different 2-digit SIC categories: Surgical,Medical, Dental, Ophthalmic, Laboratory and X-ray Equipment, allfalling in 2-digit SIC-3800 (Instruments and Related Products). Aswell as other types of hospital supplies from Baby Formula (2020) toHospital Gowns (2381), Cottonwool and Dressing (2241). Using the

The strategic importance of the drug-chemical dis-synergy should not be underestimated. Hill and Hansen(1991) found that pharmaceutical firms had few op-portunities for related diversification in SIC 2800(Chemical and Allied Products), and that attempts inthis direction were not associated with value creation.Furthermore, the existence of drug-chemicals dissynergyprovides strong explanation for the nonentry of mostU.S. chemical firms into pharmaceuticals (refer Table1), despite the fact that pharmaceuticals are among themost profitable industries in the U.S. (Comanor 1986).

3. Analytical FrameworkThere are two approaches to the measurement of re-latedness in the literature. Existing measures of relat-edness have tended to be described as categoric (Wrigley1970, Rumelt 1974) or continuous (Montgomery 1982,Palepu 1985); however, the critical difference betweenthem is not in the scale of their data but rather in theirapproaches, judgmental versus mechanistic, to deter-mining relatedness.

The categoric measurement of relatedness, originatedby Wrigley (1970) and more fully developed by Rumelt(1974), is judgmental in nature as it depends greatly onthe individual researcher's judgement for its execution.In this approach, classification of relatedness is basedon business units being "in some way" related through"common skills, resources, markets, or purpose" (p. 29).The correct use of Rumelt's schema requires that theresearcher have intimate knowledge of each of the manyfirms to be classified, as well as extensive knowledge ofdifferent types of relatedness at all points of the valuechain across all industries. Then, in a manner that isdifficult to specify and to replicate, relatedness is de-termined by the researcher as an implicit weighted av-erage of similarities/dissimilarities at different pointsof the value chain. These informational and judgmentaldemands have led researchers to search for alternativesthat were simpler to use and more straightforward toreplicate.

output-based rationale of relatedness by 2-digit SIC codes, health-products (3800) and drugs (2830) appear to be unrelated, but theyare clearly related on the basis of marketing to hospitals and physicians.

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Table 1 Chemical

25 LargestChemical Firms(Rank-ordered)

Du PontDow ChemicalExxonMonsantoUnion CarbideCelaneseShell OilStandard Oil (Ind)Atlantic RichfieldWR GraceAlliedPhillips PetroleumGulf OilOccidental Petrol.Eastman KodakMobilHerculesAmerican CyanamidRohm & HaasStauffer ChemicalsTennecoTexacoEthyl Corp.US SteelBorden

-Pharmaceutical Business

Nondrug Chem.% Total Sales

99.054.36.6

38.763.2

100.018.610.38.3

50.124.216.89.0

19.718.93.0

100.030.093.288.111.92.6

62.611.533.7


Combinations among

Drug% of Total Sales

0.810.60.00.00.00.00.00.00.00.00.00.00.00.00,00.00.09.00.00.00.00.00.00.00.0

U.S. Firms

25 LargestPharmaceutical Firms

(Rank-ordered)

MerckAmer. Home Prod.Eli LillySmith Kline FrenchJohnson & JohnsonBristol-Myers Co.Warner-LambertPfizerUpjohnSchering-PloughAbbottRichardson-MerrellSearleSquibbWinthrop/SterlingAmerican CynamidSyntexRobinsEaton-NorwichRorerMarionBlockPennwaltCarter-WallaceBaxter

Nondrug Chem.% Total Sales

2.50.00.00.00.00.00.0

15.020.00.00.0

10,010,00,00,0

30.018.30,00,00,00,00.0

50.00.00.0

Drug% of Total Sales

75.031,545,352,118,825,423.025.038.435.025.934,638,723,423.09,0

51.775,015,875,086,023.06.0

34.05,6

Source: COMPUSTAT segment tapes and sample of study.

In contrast to Rumelt's (1974) methodology, the con-tinuous measures of relatedness suggested by Mont-gomery (1982) and Palepu (1985) are mechanistic intheir application. These measures are based on the as-sumption that all relatedness between business units isfully delineated by 2-digit SIC codes. Thus, using thissingle criterion, combinations of business units withinthe same 2-digit code are deemed as related, and there-fore, synergistic, while combinations of units with dif-ferent 2-digit codes are deemed as unrelated, havingneither synergy nor dissynergy. The most commonlyused of these continuous, mechanistically generatedmeasures are the Herfindahl index (Berry 1974, Mont-gomery 1982), the entropy index (Amit and Livnat1988, Jacquemin and Berry 1979, Palepu 1985), and

the concentric index (Caves et al. 1980, Montgomeryand Wernerfelt 1988).

There are three problems with these mechanisticmeasures, and our hypotheses derive from our discus-sion of them. The first problem with the mechanisticapproach is that the SIC consists of aggregations ofbusinesses based only on production or output similar-ity. By production similarity, we mean commonality inthe physical characteristics of the products, raw mate-rials, or manufacturing processes. Similarities are com-pletely ignored for consumer characteristics, marketingpractices, distribution procedures, innovation activities,human resource skills, management technique, etc.^

' Discussed in footnote 2,

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Second, despite the fact that SIC groupings are basedon production and output similarities, SIC code simi-larity fails even to capture the full scope of productionrelatedness in a strategic sense. Business units with SICcodes not falling in the same 2-digit classification maynot only be related, but could in fact be vertically in-tegrated, Davis and Duhaime (1992) address this issueat some length. In an example they show that, SICs3721 and 3664, a combination of businesses commonamong aircraft manufacturers, would be labeled as un-related manufacturing activity under the mechanisticapproach. But SIC 3721 is aircraft manufacturing andSIC 3664 is the manufacture of search, detection, nav-igation and guidance systems. Activity in SIC 3664 pro-vides critical instrumentation used in all types of aircraft.

A third problem with the mechanistic approach isthat the levels of synergy generated by different relatedcombinations of business units are assumed to be iden-tical. All business combinations in the same 2-digit SICcode generate the same level of synergy, while businesscombinations across 2-digit SIC codes generate neithersynergy nor dissynergy. Clearly however, some businesscombinations will be significantly more synergistic thanothers. Indeed some attempted combinations will evenbe dissynergistic (Hill and Hansen 1991, Jones and Hill1988, Porter 1987, Ravenscraft and Scherer 1987), Yetthe mechanistic approach, as effected in the studies citedabove, completely ignores both dissynergy, and varia-tions in synergy over time.

In summing up the above, we propose three key hy-potheses:

HI. Production relatedness, especially as measured by2-digit SIC categories, is not always synergistic.

H2. Dissynergy exists.

H3. Sources of synergy change over time, accentuatingdifferent points of the value-chain at different points in theindustry life cycle.

Given the limitations of both the judgmental andmechanistic approaches, it is perhaps not surprising thatmany studies of related diversification and performance(achieved synergy) fail to find the hypothesized positivelinkage among U.S. firms (Dubofsky and Varadarajan1987, Jose et al, 1986, Michel and Shaked 1984, and

Rajagopalan and Harrigan 1986), as well as amongBritish firms (Grant et al. 1988, Grant and Jammine1988, Luffman and Reed 1982). These contrarian em-pirical findings would have been predicted by severaltheoretical studies on the relatedness-synergy linkage(Ansoff 1965, Chandler 1962, Dundas and Richardson1980, Jemison and Sitkin 1986, Reed and Luffman 1986,Salter and Weinhold 1978).

In light of the above, we propose a third approach,the direct estimation of synergy, based on a modificationof the concentric index. That index is formally computedas:

N - 1 N

C o n c e n t r i c I n d e x = 2 21=1 y=i+i

(1)

where f, denotes the proportion of corporate assets in-vested in industry I, and Stj is the coefficient of synergybetween industries ; and ;. This index is designed toapproximate the contribution of relatedness to synergyacross all business combinations of the firm. In thestudies by Caves et al, (1980) and Montgomery andWernerfelt (1988) mentioned above, the values for thecoefficient, Sy, were mechanistically imposed and ar-bitrarily assumed to be 1 if industries i and / have thesame 2-digit codes (hence are "related"), and assumedto be 2 if industries i and ; have different 2-digit SICcodes (hence are "unrelated"). These studies also com-puted the concentric index at the 3-digit level,^ We willuse the concentric index in Equation (1), but will replacethe mechanically imposed coefficients Sjj with statisti-cally estimated valuesOLS coefficients on variables{Fi*Fj), such as (% drugs*% chemicals) or (% drugs*%health). When S,y is estimated to be positive, there issynergy between industries i and;', When S^ is negativethere is dissynergy in combination of industries i and

' Caves et al, (1980) and Montgomery and Wernerfelt (1988) over-come an important limitation of the SIC by using 3-digit SIC codesas the basis for relatedness. Many 2-digit categories are very diverseand thus when used as the sole basis for relatedness, relatedness isoften overstated. However, Davis and Duhaime (1992) (discussedabove) show that both 2-digit and 3-digit SICs fail to effectively cap-ture relatedness, as output-based relatedness often goes across 2-digitSIC codes. It is this latter type of relatedness which is not capturedby the SIC (2-digit or 3-digit) on which we focus our attention.

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j . When Sij is zero, there are neither synergies nor dis-synergies. Equation (1) is thus a linear approximationto some true but perhaps more complicated formula forsynergy (super-additivity).

We will estimate the synergy coefficients S,y as follows.For the moment, we will develop our model under cir-cumstances where only physical capital and synergycontribute to the value of the firm, so that we may focuson the role and measurement of diversification. Later,in the empirical section, we will add back into the modelprior to estimation other important factors that contrib-ute to valuation of the fiirm, such as short-term disequi-libria and intangible capital. Consider first operationsin only two industries i andj. Denote the physical assetsinvested in industry i as K, and physical assets in in-dustry / as Kj. These assets are appropriately measuredat replacement cost value. With no short-run industryshocks and no intangible capital, we may approximatethe capital-market value (denoted M) of a diversifiedinvestment in both industries as:

M = (Ki + Kj) +K (2)

where S,; is the estimated coefficient of synergy, and Kis the sum of physical capital in all industries, (K, -I- Kj)in this instance. We must divide the rightmost term in(2) by the sum of all physical capital K to preserve scale;note that if we double both K, and Kj, then M exactlydoubles. Equation (2) is the most simple, direct test forsynergy, and has a natural interpretation in terms ofTobin's Q. Note that M (the market value of assets)represents the numerator of Tobin's Q, while the sumK (the replacement-cost value of assets) represents thedenominator.

For a firm with operations in three industries, thecapital-market value M becomes:

M = (Ki + Kj + KO +K

Kj *Kk* SjkKK r^- (^ ^

and for a firm with operations in N different industries,the capital-market value is:

N-l N / V2 m

Note that the rightmost term in (4) is simply K timesthe concentric index in (1). Both the mechanistic ap-proach and our new approach are identical to this point.From here on, however, the mechanistic approachwould make assumptions as to the expected values foreach Sij (somehow) and using these values compute thedouble-sum of the concentric index. This process con-verts Equation (4) into:

(5)= K*(l + Concentric Index)which, after linearization, may be estimated. In contrast,we propose to make no assumptions as to the S^ coef-ficients and to directly estimate Equation (4), after lin-earization. Note again, that we will have to add in otherfactors that determine capital market value other thanjust physical capital and diversificationfactors suchas short-run disequilibrium and intangible capital. Wewill postpone these extensions of Equation (4) until theempirical section.

In addition to estimation of synergy using capitalmarket performance of the firm as a whole, we proposeto examine the effects of diversification on the inno-vative productivity of the pharmaceutical division alone.As discussed above in 2, the discovery of new drugsis the core of competitive advantage for the pharma-ceutical industry after 1950. If business combinationsof Pharmaceuticals with chemical, health-care, or agri-cultural divisions improve the abilities of firms to dis-cover such new drugs, then this synergy is of largecompetitive importance. Note that this second approachwill only capture the benefits of synergy associated vnththe pharmaceutical division and the innovative stage ofthe value-chain. We model the effects of diversificationon innovative productivity as:

Idrug = 2;#drug

(6)

where Idmg denotes drug innovations and R&Ddrug de-notes pharmaceutical research and development ex-penditures (properly capitalized). The coefficients ofsynergy Pj indicate whether the combination of phar-maceuticals and industry ' / ' improves or worsens the



innovative productivity of drug research. Equation (6)will be adapted and expanded in the data and estimationsection below.

Evidence for HI will be provided by estimated coef-ficients of synergy Sjy or Py that are zero or negativedespite production relatedness (same 2-digit SIC codefor industries i and;') or that are positive despite pro-duction unrelatedness. Evidence for H2 will be providedby negative estimated coefficients of synergy Sy or Pjfor some pairings of industries i and;'. With regard toH3, in the context of 2 of our paper above, we expectthat production relatedness becomes less synergisticover time in the pharmaceutical industry as marketingrelatedness becomes more synergistic. Specifically, weexpect that the coefficient of synergy between drugsand chemicals will decline as the coefficient of synergybetween drugs and health products increases.

4. Data and EstimationThe sample for this study consists of 45 major U.S.-owned firms participating in the drug industry. Thesefirms have been identified by FDA approvals of newchemical entities (NCEs), U.S. patent office filings ofbio-affecting compositions, and scholarly studies of thepharmaceutical industry (refer Table 2 for the list ofsample firms).

Data for 'M', constant dollar stock market value ofcommon equity, are taken from the COMPUSTAT tapesand supplemented by the National Stock Summary. Stockprices are deflated by the U.S. GNP deflator.

Data for R&D are a distributed lag of defiated cor-porate expenditures for research and development. R&Dexpenditure data are from the COMPUSTAT tapes,supplemented by corporate inquiries in 5 percent of thecases where there were missing data, and are deflatedby the U.S. National Institutes 'of Health (NIH)biomedical deflator. Weights for the distributed lag arefrom Thomas (1990) (adapted from Wardell et al. 1982and Hansen 1979). The estimates of pharmaceuticalR&D used by this study (when reported accounting datawere not available) were provided by the strategicplanning divisions of two large U.S. drug firms. Thesecorporate estimates were used in approximately 5 per-cent of all observations. The data provided by both firms

Table 2 Sample Firms

1. Abbot2. Alcon3. Allergan4. American Cyanamid5. American Hospital Supply6. American Home Products7. Armour8. Baxter9. Block

10. Bristol Myers11. Calbioghem12. Carter Wallace13. Chattem14. Cooper15. Cutter16. Dow17. Dupont18. (Norwich-) Eaton19. International Rectifier20. Johnson & Johnson21. Key22. Lilly23. Marion

The following sample firms exitedduring the sample period:

AlconAllerganArmourCalbiochemCutter

24. Mallinkrodt25. Mead Johnson26. Merck27. Merrell28. Miles Labs29. Parke Davis30. Pennwalt31. Pfizer32. Plough33. Proctor & Gamble34. Robins35. Rohm & Hass36. Rorer37. Schering38. Searle39. Smith Kline40. Squibb41. Sterling42. Syntex43. Upjohn44. US Vitamin45. Warner Lambert

due to acquisition

Miles LabsMead JohnsonParke DavisPloughUS Vitamins

Virtually all of these firms were small relative to othersample firms, and suffered from diseconomies of scale dueto FDA regulationsee discussion of results in 5.

were highly similar, hence corroborating, and providea complete tabulation for all sample firms back to 1960.

Idrug consists of nonbiological, self-originated, singlenew chemical entities (NCEs); thus drugs discoveredby one firm but marketed by another firm are attributedto the discovering firm, and compound drugs are ex-cluded. Data on Idrug are from DeHaen (various).

Additionally, completely new data on corporate di-versification for the 45 sample firms are collected bythis study, as described below. Most studies that usecontinuous, cross-sectional measures of diversificationmust rely on corporate financial reports of sales by sec-



tor. These reports are problematic because of the verydifferent levels and methods of aggregation employedby different firms. For example, some firms report drugsales as an independent segment, while other firms lumpdrug sales into either a consumer goods segment, achemical segment, or a health care segment. In contrast,this study begins with highly reliable estimates of phar-maceutical sales for each sample firm in each year drawnfrom the proprietary reports of IMS, a commercial med-ical statistics firm (data used with permission). Thuswe know precisely how much of each firm's corporatesales are in pharmaceuticals, for all sample years backto 1960.

The resulting data set has three particular strengths.First, we have a complete set of data back to 1960, en-abling comparisons of the effects of diversification overtwo full decades. A critical limitation for many studiesis their reliance on corporate financial statements thatdo not comprehensively report certain key variables,such as R&D expenditures, until the mid-1970s whenchanges in U.S. laws compelled such reporting. Thisnonreporting of R&D expenditures is especially pro-nounced for smaller firms, which forces either a (sta-tistically dubious) focus on a nonrandom selection offirms that do report data or else a truncation of thestudy time frame to begin only in the mid-1970s. Incontrast, we have a complete set of data back to 1960.

A second strength is that we have a very concretemeasure of the productivity of each pharmaceutical unitin the samplethe number of FDA-approved newdrugs that were discovered. Most studies rely solely onfinancial measures of diversification impact. While ex-amination of financial success is important, and is rep-licated here, it is useful to provide more direct, tangibleconfirmation of hypothesized diversification synergies.While pharmaceutical innovation is not the sole activityof the diversified firms in the sample, it is the core stra-tegic activity of firms in this industry.

A third strength of the data used for this study is theimproved precision with which diversification is mea-sured here. As mentioned above, most studies that usecontinuous, cross-sectional measures of diversificationrely on corporate financial reports of sales by sector.Different firms employ very different levels and meth-ods of aggregation, lumping a variety of products in the

same segment. By contrast, we start with highly reliableestimates of pharmaceutical sales from the proprietaryreports of IMS. Using this data we are able to disaggre-gate corporate sales by product area with greater ac-curacy, thus providing a more precise picture of diver-sification among pharmaceutical firms.

In addition to these strengths, there are three limi-tations to our data. The first is that we require a proper(replacement cost) value for tangible assets of each firm.For this study we will use the book value of corporateequity, as it is an available stream of data back to 1960.Data on the replacement cost value of equity is availablefrom the financial statements of U.S. firms only afterthe mid-1970s. Re-estimation of our results using re-placement cost data, for those handful of years to 1980where data are available does not change our findings,and thus we regard this limitation as not serious.

A second, similar limitation concerns intangible mar-keting capital. In addition to data for the tangible(physical) assets of each pharmaceutical firm in thesample, we should have at least proxies for corporateintangible capital in research and marketing. We indeedhave data on intangible research capital, and as dis-cussed above these data are especially good. However,data on advertising expenditures by each firm, tradi-tionally used as a proxy for intangible marketing capital,are available only after the mid-1970s. When we re-estimate our results appropriately adding advertisingdata for those few years when such data are available,the estimated effect of greater advertising capital is sur-prisingly negative, though insignificant; our findings onsynergies are unchanged. Actually, the insignificance ofthe advertising variable should not surprise us as thebulk of marketing for pharmaceuticals in the U.S. con-sists of office visits to doctors and medical institutionsby sales staff ("detailing"), rather than advertising. Thusadvertising is a poor proxy for overall marketing ex-penses in this industry, and we are unable to assess theseriousness of this data limitation for our study.

The third limitation is almost universally shared byother diversification studies. When assessing the deter-minants of asset-based financial performance, weshould measure diversification with the shares of cor-porate assefs that are held in various industries. Thesedata are not available to us before the mid-1970s for

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most sample firms, and when available are subject tothe whims of corporate divisional breakouts in financialstatements. Further, the value of each asset is recordedat its historic rate; thus, comparability of value acrossassets at any point of time is limited. To overcome theseimpediments we use the shares of sales of a firm invarious industries to measure diversification. These dataare available for this study, and as discussed above areespecially good.

For estimation, our first goal is to examine the effectsof diversification on the stock market value of commonequity of the 45 sample firms. We must adapt Equation(4) from 2, because the capital market value of thefirm will depend on more than simply physical capitaland diversification, as Equation (4) for expositionalconvenience denotes. Additional sources of marketvalue arise from corporate holdings of intangible capitalin research and marketing, and from short run industryshocks that create either positive or negative rents. In-tangible research capital has been well-computed forthis study, as discussed above. Data for intangible mar-keting capital available for this study are however quitelimited, as also discussed above. As regards short-rundisequilibrium shocks, a common proxy for the capitalmarket effects of these shocks is the growth rate of cor-porate sales, GROW. Rapid, positive growth is regardedas associated with positive short-run shocks that addto the capital market value of the firm, while slow ornegative growth is regarded as associated with the re-verse effects. Adding capitalized R&D expenditures andthe sales growth rate to Equation (4) gives us:

= K*(l + -I- ajGROW

+ 2 2 h*Fi*S,A. (7)Equation (7) is nonlinear in the independent vari-

ables: K, R&D/K, GROW, and the disaggregated com-ponents of the Concentric Index. Taking logarithms ofboth sides of Equation (7), using the approximationthat X log(l + x) when x is small, we have a linearapproximation ^ :

' This procedure and specification is based on the work by Montgomeryand Wemerfelt (1988).

log (M) = flo + fli log(K) +N - l N

+ 2 2 F,*Fy*S,;.

+

(8)

Equation (8) gives us the expected value for log(M)for a given firm in a given year. We regard the depen-dent variable, log(M) as normally distributed aroundthis expected value. We also expect the individual re-alizations of the dependent variable to be independentlydistributed, except perhaps for autocorrelation. Positiveor negative differences between the expected values forlog(M) based on (8) and the actual values may wellrepresent strategic successes or failures uncaptured byour analysis. The effects of such strategic successes andfailures may well persist beyond a single year. Thus,the parameters of Equation (8), including the S;, pa-rameters of the concentric index, will be estimated usingleast squares, corrected within the observations of eachfirm for autocorrelation.

Our second goal for estimation is to examine the de-terminants of the number of new FDA-approved drugsdiscovered by a sample firm. These discoveries, or newchemical entities (denoted NCE), are estimated usingthe following equation:

log(NCE) = bo + bi*log(R&D)2 i'yfy. (9)

We have adapted Equation (6) into Equation (9) inthat we approximate purely pharmaceutical R&D bythe product of total firm R&D and the firm sales inpharmaceuticals (Fdmg). The x = log(l -I- x) approxi-mation is also used.

Equation (9) gives us the expected value for NCE fora given firm in a given year. The NCE-dependent vari-able is however clearly extremely nonnormal in distri-bution. The number of NCEs discovered in a given yearis highly skewed, with a vast majority of observationsin each year having NCE value of zero, and the fre-quency of larger values of NCE monotonically andsharply declining. Second, the realized values of NCEare completely discrete, ranging from integers of zeroto at most four. Third, the distribution of NCEs is highlyheteroscedastic, meaning that the variance of NCE is



Strongly positively correlated with the mean for eachobservation. Consider an observation in Equation (9)where the expected value of NCE is extremely small,say because the level of corporate R&D expenditures isquite small. Both mean and variance of NCE for thisobservation will be near zero. Next consider an obser-vation where the expected value of NCE is relativelyhigh, say because the level of corporate R&D expen-ditures is quite large. The highly skewed nature of thediscovery process for new drugs implies that NCE dis-coveries arrive in a extremely irregular pattern, so thatboth mean and variance of NCE will be high.

The distribution for NCE we have just described isessentially Poisson, which is discrete, skewed, and het-eroscedastic, with equality of the mean and variance ofthe distribution. The exact equality of mean and variancerequired by the Poisson distribution however representsa unneeded assumption. For the estimation of this study,we will take NCE as distributed with:

Variance(NCE) = s^*mean(NCE) (10)where s^ is a parameter to be estimated. Note that if s^were exactly equal to unity, then NCE would be dis-tributed exactly Poisson.

Equations (9) and (10) may be estimated using max-imum quasi-likelihoods. The maximum quasi-likelihoodtechnique uses the standard iteratively weighted non-linear least squares routines of common statistical pack-ages (such as SAS) to estimate parameters in Equation(9) above. In other words, we specify Equations (9)and (10) at once, using the variance terms in (10) asweights to estimate parameters in (9). For further de-tails, see Thomas (1990).

5. ResultsThe effects of diversification on the stock market valueof common equity of the 45 sample firms of this studyare estimated using Equation (8) and reported in Table3. The key coefficients are the S^ terms of the estimated,disaggregated concentric index (defined above in Equa-tion (1)). We examine the pharmaceutical firms' diver-sification into four industries: two industries, generalchemicals and agricultural products (predominantlypesticides and veterinary drugs), that have the same 2-digit SIC code (2800) as pharmaceuticals, and two in-

dustries with differing 2-digit SIC codes health-careproducts (3800, 2000), and all "other" industries. Thecomponents of the concentric index that do not pertainto the pharmaceutical industry are suppressed. Thesample is split into four time periods, with the date ofthe 1962 Amendments to the Food, Drug, and CosmeticAct being a natural dividing point. The remaining 18years are divided into three six-year periods. Finally,because there is a such a clear trend over time in mostcoefficients, a pooling of all four time periods is reportedat the far right of Table 3, with appropriate interactionterms with time.

The estimated coefficients reported in Table 3 conformto our hypotheses of shifting patterns of synergy overthe industry life cycle. After 1962, we find strong, sta-tistically significant dissynergy between drugs andchemicals. The linkage between these two industries isonly production relatedness, and as the pharmaceuticalindustry became a mature industry in its own right, anysimilarities at this point of the value chain becameswamped by dissimilarities at other points. In contrast,we find after 1962, strong and significant synergies be-tween drugs and agricultural products, although herethe synergy is probably not due to a production relat-edness either, but rather relatedness at the innovationstage of the value chain. These results are not surprisingas agriculture products consist mostly of pesticides andveterinary pharmaceuticals. Finally, we find a statisti-cally significant dissynergy between drugs and healthcare before 1962 that becomes synergistic by the late1970s. The rise of the drug-health synergy parallels therising relative importance of marketing to health careprofessionals for pharmaceutical firms during the sam-ple period.

The deviations between these findings and the tra-ditional wisdom of the diversification literature are ev-ident, in accordance with our hypotheses. First, pro-duction relatedness is a questionable guide to patternsof synergy. Second, dissynergy exists and is important.Third, the patterns of synergy shift over time with theindustry life cycle, and thus can only be poorly capturedby any static schema.

Two additional empirical findings in Table 3 shouldbe noted briefly. The coefficient on log (equity) is anelasticity of scale, measuring the advantages and dis-


Table 3 OLS

IndependentVariables

Intercept

In (Book value)

R&D/Book value

Growth

Drug*Chemicals

Drug* Health

Drug*Agrlc,

Drug* Other

Np

Estimates of Diversification

1960-1962

-0,17(-0.33)

0.89(19.67)

7.10(6.09)0,72

(2.35)0.40

(0,21)-2.17

(-2,39)-3.02

(-0.96)0.54

(0.65)

0.92790.22

DAVIS A N D THOMASDirect Estimation of Synergy

Contributions to Stocic Market Valuations, U,S, Pbarmaceutical Firms, 1960-1980

Time

1963-1968

0.68(3.21)0.95

(32.99)9,03

(4.87)0.46

(2,72)-2.80

(-2,68)0.51

(-0.85)1.66

(1.25)-0.48

(-0.85)

0,87195

0,26

Periods

1969-1974

-0,09(-0.28)

1.08(30,83)

5.04(3.39)0.34

(1,30)-5,73

(-3.50)1.20

(1.33)6,30

(3.95)1,84

(2.04)

0,86212

0.18

1975-1980

-0.31(-1.13)

1.14(45.92)

2,26(2.08)0,47

(1.14)-4.06

(-2.97)2.91

(4,96)4.85

(5.10)0.05

(0.11)

0.93194

0,31

1960-1980

Variable

1,32(4,85)0.95

(27,43)4.33

(3.21)0.33

(2.55)-2.80

(-5.14)-0.86

(-1.46)3,02

(3.64)0.94

(1.33)

Variable*(year-60)

-0.10(-5,14)

0.01(2,49)

-0.07(-1,40)

0.01(0,21)

-0.19(-1.04)

0.12(3.85)0.15

(0.96)-0,05

(-0.75)

0.88693

0.23

Notes: Dependent variable is logarithm of constant-dollar stock market value of common equity for each observation.f-statistics in parentheses.The right-most specification pools all 4 sample periods and 16 independent variables, i.e., 8 base variable and 8 interaction termscomprised of the base variable multiplied by (year-60) for each observation.

advantages of firm size holding diversification strategyconstant. When this coefficient is less than one, thereare diseconomies of scale; when it is greater than onethere are economies of scale. The estimated and statis-tically significant rise in this coefficient documents theincreasing advantages for larger pharmaceutical firmsafter the 1962 Amendments (Thomas 1990), Addition-ally, the significant positive coefficient on the R&D/equity ratio indicates that intangible R&D capital con-tributes to market value along with tangible physicalcapital; note that the movements over time in this coef-ficient are not statistically significant.

The effects of diversification on the innovative pro-ductivity of the 45 sample firms are estimated usingEquation (9) and reported in Table 4. There are two

major sources of agreement between Tables 3 and 4.First, we find that drugs and chenucals are dissynergisticin both tables. Embedding a pharmaceutical divisionwithin a chemical firm reduces the ability of that divisionto discover new drugs, and hence the stock market valueof the firm. The negative and significant coefficients onthe percentage of firm sales in chemicals in Table 4 in-dicate once again the drug-chemical dissynergy. Second,we find an even sharper upward trend in economies ofscale in Table 4, The coefficient on log (R&D) indicatesthe advantage of size, where size here is correctly mea-sured as scale of research activity.

Examining Table 4 we find neither synergy nor dis-synergy for drug innovation with health care or agri-cultural products. We must stress that we do not expect

1344 MANAGEMENT SciENCE/Vol, 39, No, 11, November 1993


Table 4 Maximum Quasilikelihood Estimates of DiversificationContributions to New Cfiemicai Entities Discovered,U.S. Pfiarmaceuticai Firms, 1960-1980

Intercept

Log (R&D)

Log (% Drug)

% Chemicals

% Health

% Agriculture

Chi-squaredDispersion parameterObservations

1960-1962

-1.85(-3.77)

0.30(2.50)1.02

(2.91)2.19

(1.52)0.52

(0.47)0.70

(0.53)38.7

1.34123

Time Periods

1963-1968

-3.09(-5.72)

0.84(5.25)0.60

(2.60)-1.15

(-2.14)0.94

(1.22)0.50

(0.77)65.1

0.84250

1969-1974

-4.11(-5.07)

0.97(4.61)0.63

(2.13)-1.40

(-2.06)0.14

(0.64)0.64

(1.05)74.60.82

230

1975-1980

-7.16(-6.02)

1.49(6.20)0.57

(2.19)-1.55

(-2.27)1.66

(1.86)1.02

(0.97)135.7

0.56211

Notes: Dependent variable is nunnber of new chennical entities (NCEs) intro-duced,f-statistics in parentheses.

the findings of Tables 3 and 4 to be in one-to-one agree-ment, and thus are not surprised when there are dif-ferences between the two tables. In the case of healthcare, the expected synergies during the later sampleyears are probably due to marketing of given innova-tions, not discovery of more innovations. Such nonin-novative synergies will not be captured in Table 4. Inthe case of agricultural products, any drug-agriculturesynergies may well occur in agricultural innovation, notin discovery of new drugs for humans that is the basisfor Table 4. Synergies realized outside the human phar-maceutical industry will not be captured in Table 4.

6. ConclusionThough our study focussed on the historic patterns ofdiversification within the pharmaceutical industry, ourfindings are generalizable to other industries. Our con-tribution to diversification research derives from ourexploration and testing of the critical assumption thatall relatedness is synergistic. We studied the assumption

of relatedness-synergy equivalency as comprised of twoparts: (1) the levels of synergy generated by differentrelated combinations of businesses; and (2) the rela-tionship between relatedness and synergy for variouscombinations of businesses.

Our findings indicate that all types of relatedness arenot synergistic at any given point of time. In fact, overtime certain types of relatedness which were previouslysynergistic become synergy neutral or negative. Weshow that shifts in synergy are not random events,rather they appear to be influenced by industry life cy-cles. For example, chemicals and drugs are related onthe basis of production. This basis for relatedness de-clined in synergy with the maturation of the pharma-ceutical industry. Meanwhile the drugs-health productscombination based on marketing relatedness increasedin synergy.

In addition to the above findings we also show thatthe direct estimation of achieved synergy, as a measureof strategic diversity, overcomes the drawbacks of thejudgmental and mechanistic schema currently in use.Since relatedness is merely an imperfect substitutemeasure for achieved synergy, direct estimates of syn-ergy provide unambiguous and strategically relevantrepresentations of diversification for both researchersand practitioners.

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Accepted by Richard M. Burton; received ]une 1992. This paper has been with the authors 5 months for 2 revisions.

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