International Journal of Industrial Organization16 (1998) 139–168

Sunk costs and regulation in the U.S. pesticide industry1*Michael Ollinger , Jorge Fernandez-Cornejo

Economic Research Service, U.S. Department of Agriculture, 1301 New York Ave., NW, Washington,D.C., 20005, USA

Abstract

This paper examines the impact of sunk costs and market demand on the number ofinnovative companies, the U.S. market share of foreign-based firms, and merger choice inthe U.S. Pesticide Industry. Results are consistent with Sutton’s (1991) view of sunk costsand market structure in that rising endogenous sunk research costs and exogenous sunkpesticide product regulation costs and declining demand negatively affect the number offirms in the industry, have a stronger negative impact on the number of smaller firms, andencourage foreign-based firm expansion. 1998 Elsevier Science B.V.

Keywords: Sunk costs; Pesticide regulation; Market structure

JEL classification: L11

1. Introduction

Sutton (1991) proposes that sunk costs, market demand, and the toughness ofprice competition determine market structure. He observes that sunk costs caneither be exogenous or endogenous. Exogenous sunk costs, such as the capitalcosts of production, are incurred by all entrants to an industry and depend on thenature of the underlying technology. Endogenous sunk costs, such as advertising

*Corresponding author.1 The authors are economists at the United States Department of Agriculture, Economic Research

Service, 1800 M Street, NW, Washington, D.C., 20036. Some of this work was completed at the Centerfor Economic Studies at the Bureau of the Census.

0167-7187/98/$19.00 1998 Elsevier Science B.V. All rights reserved.PII S0167-7187( 96 )01049-1

140 M. Ollinger, J. Fernandez-Cornejo / Int. J. Ind. Organ. 16 (1998) 139 –168

and research and development, are choice variables and therefore vary amongfirms.

Many economists have studied the strategic and technological attributes thataffect market structure. Schmalensee (1986), for example, considers a model ofadvertising competition. He suggests that, if demand within an industry does notchange, an increase in advertising causes firm output to rise and the industry toconsolidate. Other researchers, such as Gilbert (1989), contend that sunk exit costsincrease the costs of failed entry and encourage incumbents to react moreaggressively to firm entry. Additionally, Bresnehan and Reiss (1991) show a directlink of changes in demand and changes in the number of incumbent firms. Finally,Klepper and Graddy (1990) provide evidence of how an industry’s life cycle canimpact consolidation of that industry.

The U.S. pesticide industry over the 1972–89 period provides an example ofhow research and regulatory costs and demand conditions affect an industry’smarket structure. Specifically, as health and environmental testing (regulatory)costs increased from about 17.5% to 47% of total research costs over the 1972–89period, the number of pesticide firms undertaking research and development(innovative pesticide firms) dropped from 33 to 19; the U.S. market share held byforeign-based companies rose from 18% to 43%; and, the percent of U.S. firmsales from foreign markets rose from about 23 percent in 1974 to 60 percent in1989 (Table 1). Much of the structural change during the 1970s and 1980s tookthe form of sales by major domestic producers of their pesticide operations to evenlarger pesticide firms. Among the most newsworthy of these sales were those ofthe pesticide divisions of Shell, Stauffer, and Union Carbide to DuPont, ICI, andRhone Poulenc, respectively. Companies with smaller pesticide operations, such asPPG, Mobil, and Pennwalt, were even more dramatically affected. The number ofthese small pesticide operations dropped from 16 in 1972 to 6 in 1989 (Table 1).

Studies of regulation have found that EPA regulation affects small plants morestrongly than large plants and nonunionized plants more strongly than unionizedones (Pashigian, 1984). Grabowski et al. (1978) found that regulation negativelyaffects innovation. Thomas (1990), Ollinger and Fernandez-Cornejo (1995) foundthat regulation has a more negative effect on innovation in small firms than onlarge ones. However, previous studies of regulation have not established a linkagebetween regulation and research expenditures, nor have they demonstrated theimpact of both regulation and research expenditures on market structure.

In this paper, we examine the impact of regulatory costs on market structure.Extending Sutton’s (1991) view to a regulated product market, we hypothesizethat a rise in exogenous sunk regulatory costs increases the minimum amount ofrevenue a firm requires to recover its sunk costs. As a result, firms raiseendogenous sunk research expenditures in order to increase demand for theirproducts. Applying this hypothesis to the pesticide industry, we expect thecombination of rising product regulation, higher research expenditures, anddeclining industry demand to negatively affect the number of pesticide firms

M. Ollinger, J. Fernandez-Cornejo / Int. J. Ind. Organ. 16 (1998) 139 –168 141

Table 1aThe U.S. pesticide industry

Year Number Foreign firm Regulatory Four firm PercentInnovative Firms U.S. market costs to concentration American

cresearch ratio ratio firm salesdAll Small Large abroad

1972 33 16 17 18 18.0 0.496 n.a.1973 34 17 17 16 19.0 0.501 n.a.1974 34 17 17 20 18.0 0.484 23 (54)1975 36 18 18 20 20.0 0.487 28 (53)1976 36 18 18 21 33.0 0.478 39 (56)1977 36 18 18 20 31.0 0.441 40 (56)1978 36 18 18 22 29.0 0.421 42 (55)1979 36 18 18 21 35.0 0.407 38 (54)1980 34 16 18 21 29.0 0.394 39 (60)1981 34 16 18 21 27.0 0.378 42 (60)1982 33 15 18 21 30.0 0.372 44 (64)1983 32 14 18 21 31.0 0.392 44 (64)1984 29 10 19 23 28.0 0.402 45 (56)1985 28 9 19 28 34.0 0.385 46 (64)1986 27 8 18 29 39.0 0.380 48 (62)1987 23 8 15 36 40.0 0.454 53 (64)1988 23 8 15 38 41.0 0.466 56 (55)1989 19 6 13 43 47.0 0.483 60 (n.a.)aConsists of companies which had agricultural chemical research and development expenditures andwere pesticide firms over the 1972–89 period. The starting date is either the first year in which the firmwas identified by Eichers (1980), Kline and Company surveys (1974–91), Census of Manufacturingdata, or four years prior to the introduction of a firm’s first new product as reported in Aspelin andBishop (1991).bForeign firm U.S. market share includes the production by foreign owned plants in the U.S., plus valueof imports into the U.S. market by foreign owned companies.cRegulation costs refer to the value of all spending on health and environmental testing as reported inthe National Agricultural Chemical Association annual survey (1972–89) and EPA publications.dPercentage of sales by American firms that is produced overseas is in parentheses.

(industry size). Additionally, since regulation has been found to favor large firmsover small ones (Bartel and Thomas, 1987; Thomas, 1990), sunk regulation andresearch expenditures should favor large firms over small ones. Similarly, sinceinternational firms have a greater number of market outlets from which to generaterevenues (Teece, 1982), sunk regulatory costs and research expenditures shouldfavor international firms over strictly domestic ones. Finally, we hypothesize thatfirms that survive the industry consolidation have greater profitability and havelower sunk costs associated with regulatory fines and penalties than those that exit.

The remainder of this paper proceeds in the following way: first, we presentbackground information on pesticide regulation and industry changes. In Section 3we summarize previous work on the impact of sunk costs and demand on marketstructure. In Section 4 we present our theoretical model and apply it to the case of

142 M. Ollinger, J. Fernandez-Cornejo / Int. J. Ind. Organ. 16 (1998) 139 –168

product regulation in the pesticide industry. In Section 5 we present empiricalmodels to test the hypotheses of the effects of sunk costs and demand on thenumber of innovative firms, smaller versus large firms, and their expansion intointernational markets. In Section 6 we examine motives for mergers. In Section 7we discuss our estimation procedures. In the last two sections we give the resultsand conclusions.

2. Regulation and industry changes

Under the 1948 Federal Insecticide, Fungicide, and Rodenticide Act (FIFRA),Congress required that all pesticides for sale in interstate commerce be registeredagainst the manufacturers’ claims of effectiveness and that manufacturers indicatetoxicity on the product’s label. Congress gave the United States Department ofAgriculture (USDA) responsibility for enforcing these regulations.

Pesticide regulation passed into a new era with the transfer of regulatoryjurisdiction to the Environmental Protection Agency (EPA) in 1970, and in the1972 amendment to FIFRA. Under the new amendment to FIFRA, Congress gavethe EPA responsibility for reregistering existing pesticides, examining the effectsof pesticides on fish and wildlife safety, and evaluating chronic and acute toxicity.

Implementation of the 1972 FIFRA mandate came about gradually. Thephysical change in jurisdiction and staffing at the EPA in 1970 involved thetransfer of people from the USDA and the FDA. Thus, many of the early testingprocedures for the FIFRA legislation were based on what regulation personnel inthese two agencies had done previously. More significantly, ambiguities existed inthe 1972 FIFRA amendment, with clarification not forthcoming until the prom-ulgation of the 1978 amendment to FIFRA.

As regulatory policy evolved, the EPA mandated additional field test require-ments. Currently, field test requirements can include up to 70 different types oftests that can take several years to complete, and cost millions of dollars. Staffinglevels reflect growing EPA regulatory requirements. It took an average of 54.2EPA pesticide division employees to approve each new pesticide during the1972–75 period. This labor requirement rose to 91.4 pesticide division employees

2for the approval of each new pesticide by the 1986–89 period.The EPA considers chemical pesticides to be toxic substances and thus

regulatory provisions of The Clean Air Act of 1970, Clean Water Act of 1972, theResource Conservation and Recovery Act of 1976 (RCRA), and the Comprehen-sive Environmental Response, Compensation, and Liability Act of 1980 (Super-fund) also apply to them. The Clean Water Act and the Clean Air Act mandates

2 Based on the number of new product registrations (Aspelin and Bishop, 1991) and employmentlevels at the Pesticide Division of the EPA.

M. Ollinger, J. Fernandez-Cornejo / Int. J. Ind. Organ. 16 (1998) 139 –168 143

limits on the discharge of pollutants and specified the type of equipment necessaryfor regulatory compliance. RCRA specifies how organizations should contain anddispose of toxic substances. Superfund legislation stipulates who would paypenalties for existing toxic dump sites and established a trust fund to use to pay fordump site clean-ups.

The pesticide industry made a transition from growth to maturity over the1972–89 period. Between 1966 and 1976, the sales of herbicides, the mostcommonly used type of pesticide, rose from 101 million pounds of activeingredient (a.i.) to 373.9 million pounds of a.i. By 1982, herbicide sales increasedto 455.6 million pounds of a.i. and then rose to only 478.1 million pounds by 1992(Osteen and Szmedra, 1989; Delvo, 1993). In terms of acres treated, farmersapplied pesticides to almost 95% of their corn, cotton, and soybean acreage by1982 and application rates were stable during the 1980s.

After rising during the 1970s, farm sector demand for pesticide inputs droppedduring the 1980s, as farm exports stabilized and farm surpluses encouragedCongress to provide incentives to farmers to reduce their planted acreage. From1970 to 1982, American total grain production rose from 187 to 332 million metrictons. By 1989, however, production had dropped to 283.7 million metric tons.Reflecting this change in circumstances, farm real estate values declined from$304 million in 1982 to $215 billion in 1989 (United States Department ofAgriculture, 1974, 1991).

3. Market structure and sunk costs and industry demand

Many economists have discussed the strategic forces that shape marketstructure. Limit pricing models (Bain, 1949; Sylos-Labini, 1962; Modigliani,1958) suggest that minimum efficient firm size and product demand influenceindustry size. Other models, such as that of Gaskins (1971), propose thatincumbent firms cede market share in order to realize economic profits. Schmalen-see (1986) considers a model of advertising competition in which, under acondition of constant industry demand, an increase in advertising expenditurescauses firm output to rise, and, thus, an industry to consolidate. Aside fromadvertising expenditures and prices, other objects of competition include: sunkcapital (Dixit, 1981), product competition (Dixit and Stiglitz, 1977), and researchand development competition (Dasgupta and Stiglitz, 1980; Dasgupta et al., 1982).Additionally, Stiglitz (1986) points out that competition in research and develop-ment is similar to a contest with a large first prize and small prizes for others, i.e. alarge market share for one firm and small market shares for others.

Many researchers have described the role sunk costs play in strategic behaviorand market structure. Gilbert (1989) defines sunk costs as foregone profits that afirm must bear if it leaves an industry, i.e. the difference between the current valueof an asset and its value in an alternative use. Sunk costs affect entry directly by

144 M. Ollinger, J. Fernandez-Cornejo / Int. J. Ind. Organ. 16 (1998) 139 –168

adding to the costs of a failed entry, and indirectly by altering the incentives ofestablished firms. For example, the Dixit (1981) suggests that, as sunk costs rise,incumbents become more aggressive.

Sutton (1991) asserts that sunk costs are either exogenous or endogenous.Exogenous sunk costs, such as plant capital costs, are determined by the state ofproduction technology. Endogenous sunk costs are nonfungible, discretionaryspending that increase consumers’ willingness to pay for a product by improvingthat product’s quality or enhancing product image. These costs include researchand development or advertising expenditures. Sutton (1991) shows that a rise inexogenous sunk costs requires a firm to increase its revenues, which can only beaccomplished by raising endogenous sunk costs.

Firm specific technology also affects market structure. Demsetz (1973) arguesthat industries become concentrated because firms with lower costs force rivals toexit the industry. Gort and Klepper (1982); Klepper and Graddy (1990) andJovanovic and MacDonald (1994) explain that the life-cycle of an industry followsa path in which failure to innovate causes firm exits.

Economists consider demand conditions as vital to the determination of marketstructure. Bresnehan and Reiss (1991) show that variations in demand affect thenumber of firms. Additionally, the limit pricing model (Bain, 1949; Sylos-Labini,1962; Modigliani, 1958) suggests that an incumbent’s ability to influence entrydepends on minimum efficient scale relative to industry demand.

Although empirical intra-industry studies have been criticized by many econom-ists because they do not provide consistent estimates, Schmalensee (1989) pointsout that they do provide useful stylized facts to guide theory construction. In thisvein, most empirical studies are consistent with Sutton (1991) and the otherstudies described above. They suggest that minimum efficient plant size, capitalcosts, advertising, and industry growth all affect market structure. Additionally,Levy (1985) finds that changes in minimum efficient plant size, capital costs,advertising, and demand growth affect changes in industry concentration.

4. Sunk costs and the pesticide industry

4.1. Sunk costs and the number of innovative pesticide firms

Adapting Schmalensee (1986), (1992) advertising competition model to the caseof research and regulation costs as they impact firms in the pesticide industry, Eq.(1) expresses the hypothesized effect of sunk costs on the profitability of firm i.

Ni j

p 5 (P 2 c )S c O c 2 RD 2 R 2 s, (1)F Gi i e e ij51

where p denotes unit profits; P is price, c is the firm’s cost per unit; S is pesticidei i

M. Ollinger, J. Fernandez-Cornejo / Int. J. Ind. Organ. 16 (1998) 139 –168 145

demand (sales); e is defined as the toughness of competition (how intensely firmscompete on price and nonprice attributes); N is the number of firms; RD isi

endogenous sunk research and development expenditures; R is exogenous sunkregulatory costs; and, s is sunk set-up (capital) costs, which is determined by thestate of production technology.

Well-behaved, symmetric, zero-profit Nash equilibria in c exist for all e.0,i

with the number of firms given by

P(S /(s 1 RD 1 R)) 1 e]]]]]]]N* 5 , (2)1 1 e

and N*→` as S→`; N*→0 as R→`; N*→0 as RD →`; and, non-pricei

competition resembles price competition in the limit.Eq. (2) suggests that a decline in industry demand or a rise research and

development expenditures, regulatory costs, capital costs, or toughness of competi-3tion negatively affects the number of firms.

Regulation costs affecting the pesticide industry arise from product andpollution (clean water and air) regulation. Firms incur product regulation costsduring the new product development cycle and costs for pollution regulationduring pesticide production. Eq. (2) suggests that a rise in regulatory costs causesa decline in the number of firms.

Fixed capital costs are not sunk set-up costs for pesticide firms. Census dataindicates that the production of pesticides takes place in plants that producenumerous other chemicals, suggesting that pesticide production equipment hasmany uses. Accordingly, we exclude sunk set-up costs from further consideration.

Klepper and Graddy (1990) and Jovanovic and MacDonald (1994) providetheoretical models suggesting that the toughness of competition increases withinan industry over its life-cycle. They characterize young industries as having a fewsmall firms, high product prices, and considerable uncertainty about demandcharacteristics. Firm entry increases firm production, causing output to rise andprices to drop. Industry growth continues but at a rate below that of the increase insales per firm. As a result, less efficient innovators and high cost producers mustexit the industry.

4.2. Size effects of sunk costs and demand

Many economists have studied how regulation affects small firms differentlyfrom large ones. Thomas (1990) found that product regulation in the pharma-ceutical industry adversely affected small firms’ research productivity but had littleimpact on the research productivity of large firms. Pashigian (1984) found that

3 This is in agreement with previous research on market structure and demand (Bresnehan and Reiss,1991), sunk costs (Sutton, 1991), and the toughness of competition (Sutton, 1991).

146 M. Ollinger, J. Fernandez-Cornejo / Int. J. Ind. Organ. 16 (1998) 139 –168

environmental regulation of production facilities favored large factories over smallones and capital over labor.

Several economists assert that high research costs, such as those required forchemical pesticides, may favor large firms. Galbraith (1952) suggests that largefirms have greater financial capacity and thus have a greater ability to spread risks.Greene et al. (1977) and Teece (1982) claim that large firms are better able to takeadvantage of their research because they have more market outlets. In addition,Acs and Audretsch (1987) empirically show that large firms have an innovativeadvantage in industries that are capital-intensive and produce differentiated goods.Hence, research costs may have more of a negative impact on smaller firms thanon larger ones.

Liebermann (1990) provides empirical evidence suggesting that small firms arethe most likely companies to exit an industry under declining demand conditions.Additionally, several economists (Demsetz, 1973; Klepper and Graddy, 1990;Jovanovic and MacDonald, 1994) argue that a consolidation occurs in the laterstages of an industry’s evolution because highly successful firms gain market shareat the expense of less efficient rivals. Klepper and Graddy (1990) provideempirical evidence supporting this view. This consolidation suggests that anincrease in the toughness of competition over an industry’s life-cycle has a greateradverse effect on small firms than on large ones.

5. Empirical models of sunk costs and number, size, and internationalscope of firms

5.1. Empirical model of the effect of sunk costs on the number of innovativecompanies

Below, we consider a reduced form empirical model of the effect of sunk costsand demand conditions on the number of innovative firms in the U.S. Pesticide

4Industry.

N 5 b 1 b ALLREG 1 b LPOLLUTE 1 b LRDSALE1 2 3 4

1 b LRESTATE 1 b LSTAGE 1 [ , (3)5 6 t

4 We define an innovative firm as an agricultural pesticide firm that conducts agricultural chemicalresearch and development and either introduced new chemical pesticides over the 1972–89 period asreported in Aspelin and Bishop (1991), has been listed in bi-annual surveys of the pesticide industryconducted by Kline and Company, or is identified in Eichers (1980). All firms that did not reportresearch and development expenditures or, were not identified as pesticide companies from thesesources, were dropped. For further explanation see the data section in the appendix.

M. Ollinger, J. Fernandez-Cornejo / Int. J. Ind. Organ. 16 (1998) 139 –168 147

where N is the number of innovative firms (either a large or small firm); ALLREGis environmental and health testing costs as a fraction of research expenditures;POLLUTE is pollution compliance capital expenditures divided by sales (pollutionabatement costs); LRDSALE is lagged research to sales ratio for either large orsmall firms; LRESTATE is farm sector demand; and, LSTAGE is the stage of theindustry growth cycle, which is a proxy for the toughness of competition. Firmssize is based on 1974 world sales. See Appendix A for a description of thevariables and Appendix B for a description of the data.

Product regulation and pollution abatement costs are exogenous sunk costs.Each should negatively affect the number of innovative pesticide firms if anincrease in sunk costs increases minimum efficient firm size. Greene et al. (1977)note that product regulation testing costs tend to be fixed for each new registeredpesticide. However, pollution abatement expenditures and capital costs vary withthe number of plants that a firm operates. Additionally, Census data reveals thatpesticide plants can be used to produce a variety of other chemicals and thus arenot pesticide industry-specific assets. As a consequence, product regulation costsshould have a much greater impact on the number of innovative firms thanpollution abatement costs.

Research expenditures are endogenous sunk costs and should likewise have anegative impact on the number of innovative firms (Eq. (2)). We divide researchexpenditures by sales in order to control for the size of the product market.

Eq. (2) suggests that industry demand should have a positive influence on thenumber of innovative firms. We use real estate values as a proxy because farm realestate values reflect the long-run expected prices for farm commodities (Tegeneand Kuchler, 1991) and, therefore, affect long-term demand for farm investment(Conley and Simon, 1992).

The toughness of price and nonprice competition should negatively affect thenumber of innovative pesticide firms (Eq. (2)). Klepper and Graddy (1990)provide empirical evidence indicating that a large number of firms exist in theearly stages of an industry’s life cycle, but many are forced to exit later in the

5cycle. This large number of early entrants suggests that there is initial overinvest-ment. The later consolidation and stabilization of industry size results in a decreasein aggregate investment in the industry. Together, the early period of rapidexpansion and the later consolidation suggests that the ratio of capital expendituresto sales drops as the industry life cycle progresses. Thus, the capital expendituresto sales ratio should inversely relate to the stage of the industry life-cycle andshould have a positive effect on the number of firms.

5 Klepper and Graddy (1990) and Jovanovic and MacDonald (1994) provide theoretical modelssuggesting that the toughness of competition over the life-cycle of an industry.

148 M. Ollinger, J. Fernandez-Cornejo / Int. J. Ind. Organ. 16 (1998) 139 –168

5.2. The size effects of sunk costs and demand

Eq. (4) is a full dummy variable regression model in which we use dummyvariables to examine the impact of sunk regulatory costs on larger versus smallerfirms.

N 5 b 1 b ALLREG 1 b LPOLLUTE 1 b LRDSALE7 8 9 10

1 b LRESTATE 1 b LSTAGE 1 b LITTLE 1 b LITREG11 12 13 14

1 b LITPOLLUT 1 b LITLRDS 1 b LITSTATE 1 b LITSTAG15 16 17 18

1 [ , (4)

where LITTLE equals one for the group of small innovative firms and zero for thegroup of large innovative firms; LITREG, LITPOLLUT, LITLRDS, LITSTATE,and LITSTAG are interaction terms between LITTLE and ALLREG, LPOLLUTE,LRDSALE, LRESTATE, and LSTAGE, respectively.

As indicated earlier, product regulation costs should negatively affect smallfirms more than large ones. However, it is unclear whether pollution regulationfavors larger innovative firms over smaller ones because Census data reveal thatpesticides are produced in chemical plants with other chemicals and all pesticidefirms are large chemical producers. Accordingly, innovative pesticide firms withsmall pesticide operations may have large factories and may be less affected byenvironmental regulation and capital costs than innovative firms with largerpesticide operations. In addition, since similar equipment can be used for othertypes of chemical production, pollution abatement equipment is not valueless if aplant ceases pesticide production.

5.3. International expansion and sunk costs and demand

One aspect of firm size of interest to many economists is international scope.Teece (1982) believes that firms with international markets may have a competi-tive advantage over their smaller, strictly domestic rivals because they canintroduce products in more than one country. He maintains that product failures inone country may be product successes in another. Hence, international firms canreduce research and development risks and have a greater capacity to recoverresearch and development expenditures by selling products in several countries.Similarly, international firms have a greater capacity to recover product regulationcosts because data used to gain approval of pesticides in overseas markets may beuseful for registration of a pesticide in the U.S.

If international firms have a competitive advantage because they have access tonumerous geographic markets, a firm with extensive operations overseas but smallor non-existent U.S. operations would gain more from expanding in the U.S. thana firm that had large U.S. operations. Similarly, firms with large U.S. operationsbut a small overseas business would have more to gain from growth overseas than

M. Ollinger, J. Fernandez-Cornejo / Int. J. Ind. Organ. 16 (1998) 139 –168 149

firms already claiming large overseas operations. Accordingly, if complemen-tarities exist in international expansion, then sunk research costs should encouragea rise in the share of pesticide sales by large foreign-based firms in the U.S.Similarly, there should be a rise in pesticide sales abroad by large American firms.

In 1974 foreign-based pesticide firms, such as Bayer, BASF, Sandoz, and ICI,had very small or non-existent U.S. pesticide operations, but had achieved higherworld sales than many of their large American competitors (Table 2). Accordingly,with large portfolios of pesticides, foreign-based firms could sell them in the U.S.market. Consistent with this argument, Ollinger and Fernandez-Cornejo (1995)found that foreign-based firms faced lower innovation costs than did theirAmerican rivals. Also, to the extent that testing data developed for overseasmarkets could be used for U.S. pesticide registration, foreign-based firms mayhave had lower regulatory costs.

Table 2List of Innovative pesticide firms and years active in industry, 1972–89 (size ranking based on ranking

aof worldwide sales)b c b cLargest 19 firms Years Smallest 19 firms Years

American Cyanamid 1972–89 Abbott 1972–83dBASF 1972–89 Buckman 1972–79

Chevron 1972–88 D. Shamrock 1972–87d dCiba-Geigy 1972–89 Fermenta 1972–89

Dow 1972–89 FMC 1972–89DuPont 1972–89 Gulf 1972–84

dHoechst 1975–89 Hercules 1972–84d dICI 1972–89 Hoffman LaRoche 1972–83

Lilly 1972–88 Merck 1972–89dBayer 1972–89 Mobil 1972–81

Monsanto 1972–89 Occidental 1972–83Rohm and Haas 1972–89 Pennwalt 1972–88

d dRhone-Poulenc 1972–89 Phillips 1972–82dSandoz 1972–89 PPG 1972–88

d dSchering 1984–89 Sumitomo 1972–89dShell 1972–86 Uniroyal 1972–89

Stauffer 1972–85 Upjohn 1972–89Union Carbide 1972–86 U.S. Borax 1972–79Velsicol 1972–86 W.R. Grace 1972–83aCovers companies that both had agricultural chemical research and development expenditures andwere pesticide firms over the 1972–89 period.bCompany ranks are based on 1974 world sales.cThe starting date is either the first year in which a company is reported in Survey of Research andDevelopment at the Census Bureau; Kline and Company (1974–90) reports; Eichers (1980), or, fouryears prior to the production of that company’s first new product, as reported in Aspelin and Bishop(1991), whichever was earlier. We assumed four years prior to the first new product because averageproduct development time varied from 7 to 11 years, as reported by the National AgriculturalChemicals Association. The ending date is either 1989 or the year of firm exit from the industry.dForeign-based firms.

150 M. Ollinger, J. Fernandez-Cornejo / Int. J. Ind. Organ. 16 (1998) 139 –168

We use a reduced form model (Eq. (5)) to examine factors that affect the U.S.market share of foreign-based firms (FORSHARE).

FORSHARE 5 b 1 b ALLREG 1 b LPOLLUTE 1 b LRDSALE19 20 21 22

1 b LRESTATE 1 b LSTAGE 1 [ , (5)23 24

where FORSHARE is a foreign-based firm’s U.S. market share. All other variablesare described briefly above. See Appendix A for a more detailed description of thevariables and Appendix B for a description of the data.

Pesticide firms often register their pesticides in many countries and thus haveregulatory testing data that could lower their U.S. regulatory costs. Additionally,Table 2 indicates that, in terms of world pesticide sales, nine foreign-based firmswere large and only four were small. Accordingly, since foreign-based firms mayhave existing regulatory data and product regulation favors larger firms oversmaller ones (Thomas, 1990), product regulation should encourage foreign-basedfirm expansion.

As observed above, in reference to large firms in general, pollution control costsmay or may not affect a foreign-based company’s U.S. market share because

6pesticide plants can also be used to produce other chemicals. Also, as discussedearlier, international firms may be able to spread research costs over moregeographic markets (Teece, 1982). Thus, a rise in research and developmentexpenditures should increase the U.S. market share of foreign-based firms.Additionally, declining industry demand (Liebermann, 1990) and later stages ofthe industry life-cycle (Klepper and Graddy, 1990) should favor firms with agreater portfolio of pesticide products and thus positively affect foreign-based firmU.S. market share. This implication suggests that the coefficient of the stage of theindustry life cycle should be negative.

6. Regulatory penalties and merger choice

6.1. Motives for mergers

Economists have discussed many motives for mergers. For example, Gort(1969) hypothesizes that mergers occur because outside buyers expect to earn

6 Foreign-based firms can avoid American pollution abatement costs by importing pesticides, whichwould bias the expected positive coefficient on pollution abatement costs upward. However, if therewere an advantage to importing these agricultural chemicals, then both American and foreign-basedfirms would likely import. Yet, NACA data indicates that the U.S. has been a net exporter of pesticidessince 1967. Further, all of the major foreign-based firms are either Japanese or European and thus havestrict pollution control laws in their home markets, making it unlikely that they would find anadvantage in producing abroad and selling it in the U.S.

M. Ollinger, J. Fernandez-Cornejo / Int. J. Ind. Organ. 16 (1998) 139 –168 151

more from a target firm’s assets that do its present owners. However, economistsdiffer as to whether mergers are profitable for acquiring companies. Jensen andRuback (1983) suggest that mergers are profitable but Mueller (1989) reportsmergers to be unprofitable. These economists have examined mergers of all types,but particularly relevant to the pesticide industry are motives for horizontalmergers.

Rival firms have strong incentives to take over competitors. Researchers(Eckbo, 1983, 1985; Stillman, 1983) found that returns from horizontal mergersbenefited the shareholders of both acquiring and target firms. Eckbo (1985) addsthat the mergers do not affect competition in the product market. Dutz (1994)shows that horizontal mergers enable acquiring firms to retire older capacity andthus lower per unit costs. Tremblay and Tremblay (1988) empirically show thatmergers in the beer industry are an efficient way of transferring assets from afailing to a successful firm and that large firms are more likely to be buyers. Inaddition, numerous economists regard market power as a motive for horizontalmergers. Hence, firm profitability, firm size, and market power may encouragehorizontal mergers.

6.2. Empirical merger model

As discussed above, Eq. (2) suggests that higher research and development andregulatory costs cause a reduction in the number of firms. Table 3 indicates that

Table 3Major mergers among firms with pesticides operations, 1972–89

aFirm mergers Year

Alpine Labs /Uniroyal 1979Mobil /Rhone Poulenc 1981Phillips /Uniroyal 1982Gustafason/Uniroyal 1982Olin /Uniroyal 1983Occidental /Sandoz 1983Hercules /Schering 1984Upjohn Chemical /Schering 1985Stauffer / ICI 1985Diamond Shamrock/Fermenta 1985Union Carbide /Rhone Poulenc 1986Velsicol /Sandoz 1986

bChevron/Sumitomo 1989PPG/Chevron 1989

cLilly /Dow 1989aThe first firm in each listing is the firm selling itself and the second company is firm which brought theseller. Merger data comes from Kline and Company and the Wall Street Journal Index.bChevron and Sumitomo formed a joint venture and eventually Sumitomo took full control.cLilly and Dow formed a joint venture with Dow taking majority control.

152 M. Ollinger, J. Fernandez-Cornejo / Int. J. Ind. Organ. 16 (1998) 139 –168

many innovative firms exited the pesticide industry by merging horizontally withrival innovative firms. In Eq. (6) we examine the factors that motivate mergers.

MERG 5 b 1 b PROFIT 1 b WRLDSALE 1 b USSHARE25 26 27 28

1 b EPAPNLTY 1 [ , (6)29

where PROFIT is firm profitability; WRLDSALE is world pesticide sales;USSHARE is U.S. market share; and, EPAPNLTY is firm regulatory penalties.MERG is defined as merger choice. It equals 2 in the year in which a firm buysanother firm; 1 in the years in which a firm neither buys nor is bought by anotherfirm, i.e. it is a status quo firm; and, 0 in the year in which a firm sells itself toanother firm. See Appendix A for a description of the variables and Appendix Bfor a description of the data.

If horizontal mergers are a way of transferring assets from a less efficient to amore efficient producer (Gort, 1969), then a given firm’s profitability shouldpositively influence its decision to buy another firm (be a buyer). If internationalfirms have developed a large portfolio of pesticides that can be sold in the U.S.market, but only claim a small U.S. market share, and some American firms have arelatively large U.S. market share but a weak portfolio of pesticides, thencomplementarities may exist for a merger. Accordingly, high world pesticide salesshould encourage firms to be buyers and large U.S. market shares shoulddiscourage them from buying.

Although all innovative firms must undertake environmental and health testingto gain product approval, expenses related to regulation vary among innovativefirms because some of these firms are penalized for noncompliance withenvironmental and health regulations while other innovative firms completelyavoid regulatory infractions. The penalties for noncompliance include fines forviolations of environmental standards in either the production or distribution ofpesticides. They also include lost revenues from EPA cancellation of pesticideregistrations. These registration cancellations occur when the EPA concludes thatthere are possible harmful health and environmental effects associated withpreviously approved pesticides. Both fines and product cancellations adversely

7affect profitability and consequently should encourage a firm to exit an industry.

7 Pesticide cancellations and environmental penalties are common in the pesticide industry. Sinceeach pesticide firm knows its competitors’ product line and also knows EPA regulatory requirements, itis probable that most firms in the pesticide industry know which types of products and, consequently,which firms will be affected by product cancellations. Additionally, since lawsuits are publicinformation, it is also likely that firms know which other pesticide firms have either suffered or arelikely to suffer regulatory fines. Accordingly, potential product cancellations or regulatory fines wouldlikely reduce the acquisition price of any target firm. Note also that, aside from decreasing currentrevenues, product cancellations and EPA environmental penalties imply a lost opportunity to generaterevenues from research and development and production resources. Presumably, another managementteam would make better use of these resources.

M. Ollinger, J. Fernandez-Cornejo / Int. J. Ind. Organ. 16 (1998) 139 –168 153

7. Estimation

According to Zellner (1962) and Dwivedi and Srivastava (1978), seeminglyunrelated regression (SUR) techniques are not necessary for the case in whichregressors are the same across all equations and there are no theoretical restrictionsfor the regression coefficients. They show that the matrix is the same, and singleequation estimation yields the same results as SUR methods. Hence, we estimatedEqs. (3)–(5) separately. Additionally, we did not include Eq. (6) in the systembecause it covers a different time period than the other two equations and is basedon firm-level rather than industry data.

Our methodology was as follows. We first used ordinary least squares (OLS)adjusted for autocorrelation for the regressions of the factors influencing thenumber of innovative firms (Eqs. (3) and (4)) and foreign-based company U.S.market share (Eq. (5)). Results indicated the need for adjustment for auto-correlation for all regressions. For the foreign-based company market share model,we checked our results with a ‘‘two-limit’’ tobit because the regression wasbounded between zero and one (Maddala, 1984). Results are similar to that of theOLS adjusted for autocorrelation model because the limits are not binding. We donot report the ‘‘two-limit’’ tobit results.

Sutton (1991) argues that research spending is an endogenous sunk cost that isaffected by exogenous sunk costs, such as pesticide product regulation. According-ly, OLS estimates of the parameters of Eqs. (3)–(5) may be inconsistent. Thus inthe first stage of a two stage approach, we purged the dependence of endogenoussunk research expenditures—LRDSALE (research expenditures)—on regulationand other factors by creating an instrumental variable (LRDSALE). We usedindustry research expenditures, growth in real estate values, industry sales growth,and all exogenous variables as instruments for estimation of LRDSALE for Eqs.(3) and (4) and growth in real estate values, industry sales growth, and allexogenous variables as instruments for estimation of LRDSALE in Eq. (5).

In the second stage for Eqs. (3) and (4), we used the Parks method to estimatethe regression because there are two cross-sectional units in the dataset andadjustment for autocorrelation was necessary. The Parks method assumes afirst-order autoregressive error structure with contemporaneous correlation be-tween cross sections. In the second stage of Eq. (5), we adjusted for auto-correlation with the Prais–Winsten method (Prais and Winsten, 1954). We usedthis approach because it does not lose the first observation. Also, Harvey andMcAvinchey (1978) indicate that this approach is a superior way to adjust forautocorrelation when the autoregressive parameter is not large.

In the merger choice model (Eq. (6)), we used a multinomial logit regressionbecause innovative firms can make three independent choices during any year.These choices include either buying another firm, neither a buying nor selling, i.e.a status quo firm, or selling themselves to another firm. We include all innovativefirms over the 1976–89 period. We considered only the 1976–89 period becausethe first merger occurred in 1979.

154 M. Ollinger, J. Fernandez-Cornejo / Int. J. Ind. Organ. 16 (1998) 139 –168

We used the multinomial approach because choice probabilities depend only onindividual characteristics (Maddala, 1984). We used a three choice multinomialrather than the two binary choice logit models employed by Tremblay andTremblay (1988) because the multinomial approach enables one to use all

2information. We report the results, including the chi-square (V ) statistic, in Table6.

8. Results

As discussed in Appendix A, the sample of innovative firms was split in twoequal groups based on 1974 world sales. The regression then traces the ex-periences of these two groups from 1972 to 1989. There are 18 observations ineach of these two cross sectional units of the data set.

Eq. (2) suggests that higher sunk costs encourage market consolidation andhigher demand positively affects industry size. The Two Stage Least Squaresestimates of the number of innovative firms (Eq. (3)) support this view (Table4—Models 1 and 2). The research cost to sales ratio—an endogenous firm-levelsunk cost—and environmental and health testing costs as a fraction of industryresearch (pesticide product regulation)—an exogenous firm-level sunk cost—negatively affect the number of innovative firms. The coefficient on the stage ofthe industry’s life-cycle is positive, suggesting that an earlier stage of theindustry’s life-cycle permits the entry of more firms. Pollution abatement costs andindustry demand have no significant effect on the number of innovative firms.Both have the hypothesized signs, however.

To test the differential effects on large and small firms, we used a full dummyvariable regression model, as described in Eq. (4). Results (Table 4—Models 3and 4) indicate that pesticide product regulation has a significantly negative effecton the number of innovative firms. Further, the dummy variable interaction termsuggests that pesticide product regulation has twice as great a negative impact onsmall innovative firms as on large ones. Research expenditures also negativelyaffect the number of pesticide firms but have no significantly greater impact onsmall firms than on large ones. Industry demand positively affects the number ofpesticide firms but has no greater impact on small firms than on large ones.

A positive coefficient on LSTAGE indicates that early stages of the industry’slife-cycle affect the number of firms. Our results indicate that the industry’slife-cycle has no overall significant impact on the number of firms but does have asignificantly positive differential effect on small innovative firms (Table 4—Models 3 and 4). This life-cycle effect on small innovative firms is four timesgreater than on large ones. Results also indicate that pollution abatement costshave no significant overall impact and no differential effect on the number ofinnovative firms (Table 4—Models 3 and 4).

Results of the foreign-based company’s U.S. market share regression are also

M. Ollinger, J. Fernandez-Cornejo / Int. J. Ind. Organ. 16 (1998) 139 –168 155

Table 4Two stage least squares estimation of the number of innovative pesticide companies, 1972–89(t-statistics in parentheses)

Variable Model 1 Model 2 Model 3 Model 4

INTERCEPT 21.6*** 20.28*** 19.2*** 17.24***(7.74) (8.20) (7.21) (7.31)

LRDSALE 217.7* 215.83* 221.8** 218.72*(21.80) (21.63) (22.07) (21.73)

ALLREG 225.1*** 224.0*** 216.8*** 215.15***(24.75) (24.59) (24.42) (23.88)

LPOLLUTE 279.3 2 2128.1 –(21.02) (21.29)

LSTAGE 39.2** 30.75** 23.7 12.44(2.35) (2.10) (1.32) (0.79)

LRESTATE 0.81 1.00 2.04** 2.29**(1.09) (1.31) (2.40) (2.74)

LITTLE – – 2.57 5.95(0.33) (1.10)

LITLRDS – – 28.43 25.34(20.34) (20.24)

LITREG – – 222.3*** 224.58***(22.85) (23.15)

LITPOLLUT 217.1(0.90)

LITSTAG – – 95.1** 96.53***(2.37) (2.92)

LITSTATE – – 21.11 21.84(20.57) (21.12)

Observations 36 36 36 362ADJ. R n.a. n.a. n.a. n.a.

Model 1 and 2: dependent variable is the number of large or small innovative firms; without dummyvariables included. Models 3 and 4: dependent variable is the number of large or small innovativefirms; with all dummy variables included. See Appendix A Table A.1 for complete definitions.***5significant at 1% level.**5significant at 5% level.*5significant at 10% level.

consistent with Sutton (1991). They are reported in Table 5 and show that researchexpenditures and pesticide product regulation positively affect foreign-basedcompany’s expansion in the U.S., while industry demand negatively affects it. Alater stage of the industry life cycle encourages expansion of foreign-based firmmarket share. The research and product regulation cost results are also consistentwith Sutton (1991); demand results are consistent with Liebermann (1990); andthe stage of the industry life-cycle results agree with Klepper and Graddy (1990).Pollution abatement costs have no effect on foreign-based firm expansion.

We estimated three other slightly different variations of the foreign-based firmmarket share regression model. Significance levels do not change under any of the

156 M. Ollinger, J. Fernandez-Cornejo / Int. J. Ind. Organ. 16 (1998) 139 –168

Table 5Two stage least squares regression of foreign-based company market share of the U.S. pesticideindustry, 1972–89 (t-statistics in parentheses)

Variable Model 1 Model 2 Model 3 Model 4

INTERCEPT 0.29** 0.22** 0.13 0.38***(2.34) (2.67) (2.85) (7.51)

ALLREG 0.77*** 0.72*** 0.59*** 0.97***(7.14) (5.17) (5.32) (11.23)

LPOLLUTE 20.96 – – –(20.24)

LRDSALE 1.07** 1.13** 1.63** –(2.93) (2.20) (4.68)

LSTAGE 20.92** 20.67** – 21.55***(22.67) (22.67) (24.34)

LRESTATE 20.12*** 20.10*** 20.08** 20.13***(23.66) (24.65) (24.45) (26.82)

Observations 18 18 18 182ADJ. R 0.95 0.92 0.91 0.89

Dependent variable5Foreign-based company market share of U.S. pesticide market. See Appendix ATable A.1 for details.Model 1: entire model; Models 2, 3, and 4: various versions of Model 1.See Appendix A Table A.1 for complete definitions.***5significant at 1% level.**5significant at 5% level.*5significant at 10% level.

other model specifications. As with the model of the number of pesticidecompanies, the magnitude of the coefficient for the regulation term is consistentfor each model.

As large foreign-based firms expand their U.S. market share, one would alsoexpect large American firms to have similar advantages overseas. Table 1 offerssome support to this assumption, indicating that American firms increased theirshare of sales from foreign markets from 22 to 60% of yearly total sales over the1974–89 period.

To check the robustness of our variables in Eqs. (3)–(5), we tested pesticideproduct regulation variables with different lag structures. Additionally, we usedother measures of farm sector demand, such as farm assets. Results indicate thatthese variables had similar effects in all of the models to those variables reportedabove.

We also tested other variables found by Levy (1985) to have an effect on marketstructure. We found that past levels of industry growth, industry concentration,advertising intensity, and minimum efficient plant size, which Levy (1985) hadfound to affect industry concentration, had no impact on the number of innovativepesticide firms or on foreign-based firm U.S. market share.

M. Ollinger, J. Fernandez-Cornejo / Int. J. Ind. Organ. 16 (1998) 139 –168 157

The foreign-based company U.S. market share regression and the number ofinnovative firms model suggest that the factors that caused small innovative firmsto exit the market were also those that enabled foreign-based companies to expandtheir U.S. market share. For example, a 6% increase in pesticide product regulationcosts causes two small companies and one large company to exit the pesticideindustry, and also expands foreign-based firm U.S. market share by about 4%.

The result that pollution abatement expenditures did not affect the total numberof firms, and had no differential effects on small firms versus larger ones is notinconsistent with Sutton (1991) and Pashigian (1984). Census data indicates thatpesticide firms both produce pesticides and other chemicals in their plants, whichsuggests that pollution abatement expenditures are transferable. Additionally,pollution regulatory costs are plant-level rather than firm-level sunk costs. Allpesticide firms have large, diversified chemical production operations. Some largefirms may produce pesticides in small chemical plants and small firms mayproduce pesticides in large chemicals plants. Hence, pollution abatement expendi-tures may not affect the number of pesticide firms and may not give large pesticidefirms a competitive advantage over smaller ones.

Results for three variants of the merger choice model are reported in Table 6.For Case 1, MERG is defined as zero in the year in which the merger takes placefor firms that sell themselves (sellers); two in the year of a purchase for firms thatpurchase other firms (buyers); and one for all status-quo situations. The status-quosituations include firms that are neither buyers or sellers, buyers during years inwhich they do not purchase another firm, and sellers during years in which they donot sell themselves.

Defining the merger year as the year in which a merger takes place is somewhatarbitrary because decisions to buy or sell may have been made prior to the mergeryear and the actual merger could have occurred after the actual transaction.Accordingly, for Case 2, MERG is defined as zero for sellers in the year in whichthe transaction takes place and the year before; two for buyers in the year of thetransaction and the years before and after the transaction; and, one for allstatus-quo situations.

One may also regard certain types of innovative firms as companies that arelikely to be buyers, sellers, or status-quo companies. Accordingly, in Case 3,MERG is defined as zero for sellers in all years; two for buyers in all years; and,one for all status-quo firms.

Results for the merger choice decision (Table 6) indicate that in all three cases,status-quo firms and buyers had significantly greater world sales and status-quofirms had significantly lower regulation penalties than did sellers over the studyperiod. Additionally, results for Case 2 and 3 show that buyers had earnedsignificantly higher profits and had incurred lower regulation penalties than hadsellers. In all three cases, results indicate that profits and world sales have positiveimpacts – and U.S. market share and regulation penalties have negative effects.Only in case 3 is the effect significant. The results of all models are consistent with

158M

.O

llinger,J.

Fernandez-C

ornejo/

Int.J.

Ind.O

rgan.16

(1998)139

–168

Table 6Multinomial logit logistic regression of the merger choice equations, 1976–89 (t-statistics in parentheses)

Variable Case 1 Case 2 Case 3

Status-Quo Buyers Status-Quo Buyers Status-Quo Buyers

2.17*** 21.96** 1.27** 21.77*** 22.01*** 22.47***(5.42) (22.21) (4.26) (23.05) (26.15) (26.55)

PROFIT 1.11 2.76 1.26 2.88 0.26 2.99***(1.02) (1.26) (1.53) (1.99) (0.29) (3.22)

WRLDSALE 1.91* 1.92* 1.63* 1.71** 3.65*** 3.47***(1.86) (1.85) (2.49) (2.59) (7.41) (7.05)

USSHARE 20.36 -0.35 20.28 -0.32 -0.83*** -0.89***(21.18) (-1.08) (21.35) (-1.49) (5.74) (-6.12)

EPAPNLTY 23.02*** -4.18 23.00*** -4.92 -9.69** -8.19**(22.99) (-1.06) (23.38) (-1.49) (-2.46) (-2.26)

OBS 424 424 424x 2 27.3*** 50.7*** 264.8***

Case 1: MERG50 for year of the merger for selling firms; MERG52 for year of the merger for buying firms, and MERG51 for all years for firms that are neitherbuyer nor sellers and all years not defined as merger years for seller and buyer firms; Case 2: MERG50 for the year of the merger and the year before the merger forselling firms; MERG52 for the years before and after the merger year and the year of the merger for buying firms, and MERG51 for all years for firms that areneither buyers nor sellers and all years not defined as merger years for seller and buyer firms; Case 3: MERG50 for all years for selling firms; MERG52 for all yearsfor buying firms in all years, and MERG51 for all years for all firms that are neither sellers nor buyers.See Appendix A Table A.1 for complete definitions.*significant at the 10% level.**significant at the 5% level.***significant at the 1% level.

M. Ollinger, J. Fernandez-Cornejo / Int. J. Ind. Organ. 16 (1998) 139 –168 159

Sutton (1991) for regulatory penalties; Gort (1969), Eckbo (1983), (1985) andStillman (1983) for profitability; Tremblay and Tremblay (1988) for firm size;and, Eckbo (1985) for U.S. market share.

Of particular interest is that our results for regulation penalties in connectionwith merger choices are consistent with Sutton (1991), showing that higher sunkregulatory costs encourage firms to exit the industry. An examination of status-quofirms and buyers relative to sellers reveals that a 10% rise in regulation penaltiesmakes it 30% more likely that a status quo firm would be a seller rather than astatus quo firm and 42% more likely that a buyer firm would be a seller rather thana buyer. Similarly, an investigation of buyers relative to status-quo firms indicatesthat a 10% rise in regulation penalties makes it 12% more likely that an innovative

8firm would be a status-quo firm rather than a buyer.When the results reported in Tables 4–6 are compared, they suggest that higher

sunk pesticide product regulation costs and penalties encouraged industry consoli-dation and expansion of foreign-based firm U.S. market share and causedinnovative firms to sell themselves to other innovative firms. In addition, sunkpesticide product regulation costs affected small firms more pronouncedly thanlarge ones. Results also consistently show that sunk endogenous research costsencouraged industry consolidation and the expansion of foreign-based firm U.S.market share.

The merger choice models enable us to characterize selling firms as lessprofitable, less able to cope with a strict regulatory environment, and as smaller insize than were buying firms. Finally, merger activity does not appear to have had asignificant effect on the competitive balance of the industry over the study period.Four and eight-firm concentration ratios changed little over the 1972–89 period(Table 1). Moreover, four-firm concentration ratios for individual pesticideproducts—herbicides, insecticides, and all other pesticide markets—declined.

9. Conclusions

This paper examines the impact of sunk costs and market demand on thenumber of innovative companies, the U.S. market share of foreign-based com-panies, and merger choice in the U.S. Pesticide Industry. Results indicate thatrising endogenous sunk research costs and exogenous sunk pesticide productregulation costs negatively affect the number of companies in the industry and thatsmaller firms are more markedly affected by rises in these costs than are larger

8 In the logit form, log (P /P )5a 1b x and log (P /P )5(a 2a )1(b 2b )x , where1 0 1,0 1,0 i,t 2 1 2,0 1,0 2,0 1,0 i,t

x is a vector of firm characteristics, P is the probability of being a seller, P is the probability ofi,t 0 1

being a status-quo firm, and P is the probability of being a buyer.2

160 M. Ollinger, J. Fernandez-Cornejo / Int. J. Ind. Organ. 16 (1998) 139 –168

ones. Rising sunk research and product regulation costs also encourage foreign-based firm expansion into the U.S. market, and force less profitable innovativefirms to exit the industry. These results are linked. Higher sunk costs encouragefirms to expand their market presence, either domestically and/or internationally,and firms less able to expand their operations suffer a decline in profitability andare forced to exit the industry.

The results of this paper support Sutton’s (1991) view of sunk costs and marketstructure. Higher sunk costs force an industry to consolidate and greater demandencourages industry expansion. The results are also consistent with previousempirical studies of regulation that show that the effects of regulation vary fordifferent industry groups (Pashigian, 1984; Bartel and Thomas, 1987) and thatproduct regulation adversely affects smaller innovative firms more markedly thanlarger ones (Thomas, 1990). Unlike other studies of regulation, ours placesregulation in the broader context of sunk costs and showed that sunk costs ingeneral and regulation costs in particular affect firm survival.

One ironic note concerns current legislative efforts to ban the export of the U.S.9production of pesticides not registered by the EPA. Our findings suggest that such

legislation may be ineffective. Regulation has encouraged foreign-owned com-panies to expand into the U.S. market and may have encouraged the expansion byU.S. innovative firms into overseas markets (Table 1). Since both foreign and U.S.innovative firms have much of their manufacturing capacity overseas, they canavoid exporting nonregistered products by shifting production to these facilities ifproduction is banned in the U.S. Hence, pesticide product regulation may hinderthe potential effectiveness of legislative efforts to ban the export of nonregisteredU.S. pesticides.

Acknowledgements

The authors gratefully acknowledge the help and assistance given by the Centerfor Economic Studies in their research effort. Special thanks go to Sang Nguyen,Arnie Rezneck, Bob McGuckin, David Ryan, and Bob Bechtold. Any findings,opinions, or conclusions expressed here are those of the authors and do notnecessarily reflect the views of either the Census Bureau or the U.S. Department ofAgriculture. This paper contains some information on individual companies. Thesecompanies are not necessarily used in the statistical analysis. Data on theseindividual companies were obtained from publicly available sources and not fromBureau of the Census files.

9 Some legislators fear that nonregistered pesticides may be used by foreign food growers on foodproducts exported to the U.S.

M. Ollinger, J. Fernandez-Cornejo / Int. J. Ind. Organ. 16 (1998) 139 –168 161

Appendix A

Table A.1. Definition of variable definitions governing the effect regulationon industry composition

DefinitionVariable

N The number of large or small innovative firms. Firm size is basedon 1974 world sales. Aninnovative firm is defined as an agricultural pesticide firm that hasresearch and developmentexpenditures. The Survey of Research and Development at theBureau of Census identifiesall firms with agricultural chemical research and developmentexpenditures. This surveyoverstates the number of pesticide companies because the Censusdata includes companiesconducting research on any type of agricultural chemical, includ-ing fertilizers. Accordinglywe use Aspelin and Bishop (1991), which identifies all firms thatintroduced new pesticidesover the 1972–89 period; bi-annual surveys of the pesticideindustry conducted by Kline and Companyand Eichers (1980). The entry year is either four years before thefirm introducedits first new pesticide or the first year it was reported either in theSurvey of Research andDevelopment at the Bureau of Census; Kline and Company(1974–90); or Eichers (1980),whichever came earlier. All firms not identified as pesticidecompanies from these sourceswere dropped. For further discussion see Appendix B.

LRDSALE The one year lag of large or small firms research costs to salesratio. LRDSALE is aninstrumental variable for LRDSALE.

ALLREG One year lag of regulation costs variable. It is defined as the ratioof pesticide researchexpenditures for environmental and health tests to total researchexpenditures. We use a fouryear moving average because firms reach their product commer-cialization decision in thethird year of an eleven year product development cycle (Sharp,1986). Hence a new

162 M. Ollinger, J. Fernandez-Cornejo / Int. J. Ind. Organ. 16 (1998) 139 –168

product can be withdrawn for regulatory reasons at any pointbeginning eight years prior topesticide registration. The ratio of research expenditures forhealth and environmentaltesting to total research expenditures is used because reportedresearch expenditures includeboth regulatory costs and expenditures for new pesticide develop-ment.

LPOLLUTE One year lag of capital expenditures for pollution abatementequipment divided by sales.

LSTAGE One year lag of gross capital expenditures to sales ratio minus thepollution capitalexpenditures to sales ratio. We use this as a measure of the stageof the industry life cyclebecause Klepper and Graddy (1990) characterize the early stagesof industry evolution as atime of overinvestment relative to the size of the market and laterstages as decreasedinvestment relative to market size. Pollution abatement expendi-tures are subtracted in orderto isolate expenditures for production purposes only.

LRESTATE One year lagged real estate values. This is used to reflect long-runfarm sector demand forpesticides, which should be the basis for firm entry /exit decisions.We use it because Conley and Simon (1992)showed that it is a good measure of long term demand fortractors.

LITTLE One for the small innovative group of firms and zero for the largeinnovative group of firms.

LITLRDS Interaction term between LITTLE and LRDSALE. Note,LRDSALE denotes the lag ofresearch expenditures for either the small or large group ofinnovative firms.

LITREG Interaction term between LITTLE and ALLREG.LITPOLLUT Interaction term between LITTLE and LPOLLUTE.LITSTAG Interaction term between LITTLE and LSTAGE.LITSTATE Interaction term between LITTLE and LRESTATE.FORSHARE Sum of U.S. Market shares held by foreign-based companies.

Foreign-based companies arethose firms with central offices outside of the United States.

MERG equals 0 for the year of the merger for selling firms; 2 for year ofthe merger for buyingfirms, and 1 for all years for firms that are neither buyer norsellers and all years not defined

M. Ollinger, J. Fernandez-Cornejo / Int. J. Ind. Organ. 16 (1998) 139 –168 163

as merger years for seller and buyer firms. In an alternativespecification, it equals 0 for theyear of the merger and the year before the merger for sellingfirms; 2 for the years beforeand after the merger year and the merger year for buying firms;and, 1 for all years forfirms that are neither buyers nor sellers and all years not definedas merger years for sellerand buyer firms. Finally, it equals 0 for all years for selling firms;2 for all years for buyingfirms; and, 1 for all years for all firms that are neither sellers norbuyers.

PROFIT One year lagged price cost margins adjusted for sunk costs. Thismeasure of profitabilityreflects both the ability of the firm to command a high price(product quality) and to controloperating costs.PROFIT5((VALADD2COST)/VALADD)2(RD/SALES)where PROFIT equals the price cost margin; VALADD equals thetotal value of shipmentsplus the end of year inventory minus the beginning of the yearinventory minus the cost ofresales; COST includes building rental payments, fuels, materials,purchasedcommunication, purchased electricity, contract work, machinerydepreciation, salaries andwages, plus beginning of period materials and work in processminus end of year materialsand work in process; RD equals research and developmentexpenditures; and, SALES iscompany sales.

WRLDSALE One year lagged world pesticide sales.USSHARE One year lagged U.S. market share.EPAPNLTY The ratio of the sum of fines levied by the EPA for regulatory

violations and sum of lostpesticide sales due to cancellation of firm pesticides, to firm sales.

z5t z5tS DO EPAFINE 1O LOSTSALEz572 i,z z572 i,z]]]]]]]]]]]]]EPAPNLTY 5 (A.1)i,t SALESi,t

where EPAPNLTY is regulation penalties for firm i in year t,i,t

EPAFINE is EPA finesi,z

164 M. Ollinger, J. Fernandez-Cornejo / Int. J. Ind. Organ. 16 (1998) 139 –168

levied on firm i in year z, LOSTSALE is sales lost byi,z, j

company i in the year z that productj was restricted, and SALES is defined as sales by firm i in yeari,t

t.

Appendix B

Data

We used the Bureau of Census Data (Bureau of Census, 1972–89), Aspelin andBishop (1991), Kline and Company surveys (Kline and Company, 1974–91), andEichers (1980) to determine the number of innovative firms in the pesticideindustry over the 1972–89 period. The methodology is discussed in Table A.1.Table 2 contains a complete list of innovative firms in the sample and their sizeranks, which were based on world pesticide sales.

We used Eichers (1980); Aspelin and Bishop (1991) and Kline and Companydata to determine firm entries and exits of the pesticide industry. We also usedEichers (1980) to determine if a company existed in 1967 and 1976. If the firm didnot exist in 1967, we assumed the entry year was either the first year in which thecompany reported research and development expenditures at the Bureau of theCensus; the first year in which it appeared in Kline and Company data (1974–90);or four years prior to the first registered new pesticide of the firm, as reported inAspelin and Bishop (1991), whichever came earlier. We assumed exit years to beyears in which companies sold their agricultural chemicals businesses, or the lastyear in which companies reported research and development expenditures to theBureau of the Census.

We segmented the sample of innovative firms into large and small innovativefirms categories based on 1974 world pesticide sales. The large firm samplecomprises the largest 18 firms and the small firm sample consists of the 18smallest firms. We used Kline and Company data to identify the sales of the toptwenty firms. From this report, the 18 largest plants were selected as the largefirms. The two smallest firms from that group, along with all other innovativefirms were defined to be small.

Company domicile data comes from Moody’s Industrial Manuals. The foreignshare of U.S. sales (FORSHARE) is the sum of United States market sharescommanded by foreign-based firms. The market share data for the United States(USSHARE) is based on Kline and Company data and the value of domesticproduction from the Product File at the Census Bureau. The Kline and Companydata provides U.S. and worldwide pesticide sales estimates for all domesticcompanies, and U.S. sales for foreign-owned companies. These reports areavailable for the 1974–91 period. The Product File contains data on the value of

M. Ollinger, J. Fernandez-Cornejo / Int. J. Ind. Organ. 16 (1998) 139 –168 165

production for single products defined at the five digit SIC level, and miscella-neous production data. Neither the Kline and Company data nor the Product Filedata give true values of U.S. pesticide sales because the Kline and Company dataare based on farmer surveys, and value of production contains exported shipmentsand does not contain imported chemicals.

We estimated sales in the following way. First, we computed the value ofdomestic production from Census Bureau product file SIC 28694 and SIC 2879over the 1972–89 period. We assumed that the Census data reflects U.S. sales ifthe value of pesticide production was greater than the Kline and Company salesfigure minus $20 million and less than Kline and Company estimated sales plus$20 million. If sales were not within these limits, we assumed that the firm waseither an importer or an exporter, and, thus, used Kline and Company data. Aftermaking these adjustments, we computed industry sales. The estimates wereconsistent with industry sales data reported by the National Agricultural ChemicalsAssociation (NACA). Finally, we used these data to compute the total share of theU.S. pesticide market held by foreign based companies and the U.S. market shareheld by all firms.

Data on industry sales; research expenditures for all firms for the 1971–89period; and research costs for small and large companies for 1971 and the1976–89 period came from a NACA annual industry survey and Kline companydata. Since research data for both small and large firms was not available for1972–75, we estimated research for these groups of firms from industry researchand sales data and the research costs to sales ratio for small and large firms in thepost-1975 period. Environmental and health test cost data also come from theNACA survey. These costs were assumed to include all environmental testing,toxicology studies, and EPA registration costs. Non-environmental or health testresearch costs were assumed to be search, synthesis, field testing, and processdevelopment costs.

Industry capital expenditures data came from the Census Bureau files onindustry capital expenditures. Since these costs include all investment expendi-tures, we subtracted pollution abatement costs. These pollution abatement expendi-tures came from the Census Bureau publication entitled Pollution Abatement Costsand Expenditures—Current Industrial Reports. Lagged real estate values data(LRESTATE ) came from the Agricultural Statistics Handbook.t

Merger data for MERG came from Kline and Company and various Wallit

Street Journal Indexes. Table 3 presents a list of pesticide company mergers.We used the Longitudinal Research Database and the Survey of Research and

Development from the Bureau of the Census; U.S. sales data from SRI Internation-al; and Kline and Company data as sources to compute the firm price cost marginadjusted for the research to sales ratio. The Longitudinal Research Databasecontains over 100 factory-specific responses to survey questions on from 55,000 to70,000 establishments for each year from 1972 to 1988. The sample size andreporting variables vary according to the survey mandate.

We used research for agricultural chemicals from the Survey of Research and

166 M. Ollinger, J. Fernandez-Cornejo / Int. J. Ind. Organ. 16 (1998) 139 –168

Development as our measure of research. We supplemented these data withadditional data from Various Annual Reports (1972–89), and from Kline andCompany (1974–91). We estimated research and development expenditures forsome firms in some years from total firm research expenditures during that yearand agricultural chemical research expenditures for the years surrounding thatyear.

Worldwide pesticide sales (WRLDSALE) data came from SRI International;Kline and Company; and from the Product File of the Bureau of the Census.Sources for the United States market share (USSHARE) data were describedabove.

Data on the cost of fines levied against pesticide companies came from variousAnnual Reports, 1972–89. Data on banned product came from Dr. Kent Smith atthe Pesticide Assessment Laboratory of the Agricultural Research Service ofUSDA (Smith, 1991). We derived lost sales data due to regulatory restrictions orproduct bans from data on sales commanded by the banned product during the lastyear prior to the imposition of the ban on it.

References

Acs, Z., Audretsch, D.B., 1987. Innovation, Market Structure, and Firm Size. The Review ofEconomics and Statistics 69, 567–574.

Annual Reports, 1972–89. Various issues, Various companies.Aspelin, A., Bishop, F., 1991. Chemicals Registered for the First Time as Pesticidal Active Ingredients

Under FIFRA (Office of Pesticide Programs, U.S. Environmental Protection Agency, WashingtonD.C.).

Bain, J., 1949. A Note on Pricing in Monopoly and Oligopoly. American Economic Review 39,448–464.

Bartel, A., Thomas, L.G., 1987. Predation Through Regulation: The Wage and Profit Effects of theOccupational Safety and Health Administration and the Environmental Protection Agency. Journalof Law and Economics 30, 239–264.

Bresnehan, T.F., Reiss, P.C., 1991. Entry and Competition in Concentrated Markets. Journal of PoliticalEconomy 99, 977–1009.

Bureau of Census, 1972–89. Survey of Research and Development, Product Files and LongitudinalResearch Database (U.S. Department of Commerce, Economics and Statistics, Bureau of Census,Washington D.C.).

Conley, D.M., Simon, D., 1992. U.S. Sales Demand for Farm Tractors (Institute of Agriculture andNatural Resources, University of Nebraska Lincoln).

Dasgupta, P., Stiglitz, J.E., 1980. Industrial Structure and the Nature of Innovative Activity. EconomicJournal 90, 266–293.

Dasgupta, P., Gilbert, R., Stiglitz, J.E., 1982. Invention and Innovation Under Alternative MarketStructures: The Case of Natural Resources. Review of Economic Studies 49, 567–582.

Delvo, H., 1993. Pesticide Use Down 3 Percent in 1993, in: United States Department of Agriculture,Agricultural Resources: Inputs Situation and Outlook Report (United States Department ofAgriculture, Washington D.C.) 13–20.

Demsetz, H., 1973. Industry Structure. Market Rivalry, and Public Policy. Journal of Law andEconomics 16, 1–10.

M. Ollinger, J. Fernandez-Cornejo / Int. J. Ind. Organ. 16 (1998) 139 –168 167

Dixit, A., 1981. The Role of Investment in Entry Deterrence. Economic Journal 90, 95–106.Dixit, A., Stiglitz, J., 1977. Monopolistic Competition and Optimal Product Diversity. Review of

Economic Studies 43, 217–235.Dutz, M.A., 1994. Horizontal Mergers in Declining Industries: Theory and Evidence. International

Journal of Industrial Organization 7, 11–33.Dwivedi, T.D., Srivastava, V.K., 1978. Optimality of Least Squares in the Seemingly Unrelated Model.

Journal of Econometrics 6, 391–395.Eckbo, B.E., 1983. Horizontal Mergers, collusion, and stockholder wealth. Journal of Financial

Economics 11, 241–273.Eckbo, B.E., 1985. Mergers and the Market Concentration Doctrine: Evidence from the Capital Market.

Journal of Business 58, 325–349.Eichers, T.R., 1980. The Farm Pesticide Industry, Agricultural Economic Report 461 (United States

Department of Agriculture, Economic Research Service, Washington D.C.).Galbraith, J.K., 1952. American Capitalism (Houghton Mifflim, Boston, MA).Gaskins, D., 1971. Dynamic Limit Pricing: Optimal Pricing Under Threat of Entry. Journal of

Economic Theory 2, 306–322.Gilbert, R.J., 1989. Mobility Barriers and the Value of Incumbency. in: R. Schmalensee, Willig, R.

(Eds.), Handbook of Industrial Organization, Vol. II (North Holland, Amsterdam).Gort, M., 1969. An Economic Disturbance Theory of Mergers. Quarterly Journal of Economics 83,

624–642.Gort, M., Klepper, S., 1982. Time Paths in the Diffusion of Product Innovations. Economic Journal 92,

630–653.Grabowski, H.,Vernon, J., Thomas, L.G., 1978. Estimating the Effects of Regulation on Innovation: An

International Comparative Analysis of the Pharmaceutical Industry. Journal of Law and Economics21, 133–163.

Greene, M.B., Hartley, G.S., West, T.F., 1977. Chemicals for Crop Protection and Pest Control(Pergamon Press, Oxford, England).

Harvey, A.C., McAvinchey, I.D., 1978. The Small Sample Efficiency of Two-Step Estimators inRegression Models with Autoregressive Disturbances, Discussion paper No, 78-10 (University ofBritish Columbia, British Columbia).

Jensen, M.C., Ruback, R., 1983. the Market for Corporate Control. Journal of Financial Economics 11,5–50.

Jovanovic, B., MacDonald, G.M., 1994. The Life Cycle of a Competitive Industry. Journal of PoliticalEconomy 102, 322–347.

Klepper, S., Graddy, E., 1990. The Evolution of New Industries and the Determinants of MarketStructure. Rand Journal of Economics 21, 27–44.

Kline and Company, 1974–1991. The U.S. Pesticide Market, (Kline and Company, Fairfield, NewJersey).

Levy, D., 1985. Specifying the Dynamics of Industry Concentration. Journal of Industrial Economics34, 55–68.

Liebermann, M., 1990. Exit from declining industries: ‘‘shakeout’’ or ‘‘stakeout’’? Rand Journal ofEconomics 21, 538–554.

Maddala, G.S., 1984. Limited-dependent and qualitative variables in econometrics, (CambridgeUniversity Press, Cambridge, England).

Modigliani, F., 1958. New Developments on the Oligopoly Front. Journal of Political Economy 66,215–232.

Moody’s Industrial Manuals, 1972–89. Various issues (Moody’s Investor Service, New York).Mueller, D.C., 1989. Mergers: Causes, Effects, and Policies. International Journal of Industrial

Organization 7, 1–10.National Agricultural Chemicals Association, 1971–89. Pesticide Industry Profile Study, various issues

(National Agricultural Chemicals Association, Washington D.C.).

168 M. Ollinger, J. Fernandez-Cornejo / Int. J. Ind. Organ. 16 (1998) 139 –168

Ollinger, M., Fernandez-Cornejo, J., 1995. Innovation, Regulation, Innovation, and Market Structure inthe U.S. Pesticide Industry, Agricultural Report Number 719, (Economic Research Service, UnitedStates Department of Agriculture, Washington D.C.).

Osteen, C., Szmedra, P., 1989. Agricultural Pesticide Use Trends and Policy Issues, Agricultural ReportNumber 622, (United States Department of Agriculture, Washington D.C.).

Pashigian, B.P., 1984. The Effect of Environmental Regulation on Optimal Plant Size and FactorShares. Journal of Law and Economics 27, 1–28.

Prais, S.J., Winsten, C.B., 1954. Trend Estimators and Serial Correlation, Discussion Paper No. 383(Cowles Commission).

Schmalensee, R., 1986. Advertising and Market Structure. In J.E. Stiglitz, Mathewson, G.F. (Eds.),New Developments in the Analysis of Market Structure (MIT Press, Cambridge, MA) 373–96.

Schmalensee, R., 1989. Inter-Industry Studies of Structure and Performance. In: R. Schmalensee,Willig, R. (Eds.), Handbook of Industrial Organization, Vol. II (North Holland, Amsterdam)952–1009.

Schmalensee, R., 1992. Sunk Costs and Market Structure: A Review Article. The Journal of IndustrialEconomics 40, 125–135.

Sharp, D., 1986. Metabolism of Pesticides—An Industry View, Paper presented at the SixthInternational Congress of Pesticide Chemists, Ottawa, Canada, August 10–15.

Smith, K., 1991. Special Review and Cancellation Update for Pesticides in the United States, January1970 through September, 1991, (United States Department of Agriculture—Agricultural ResearchService, Pesticide Assessment Laboratory, Beltsville, Maryland).

Stillman, R., 1983. Examining Antitrust Policy Towards Horizontal Mergers. Journal of FinancialEconomics 11, 225–240.

Stiglitz, J., 1986. Theory of Competition Incentives and Risk. In: J.E. Stiglitz, Mathewson, G.F. (Eds.),New Developments in the Analysis of Market Structure (MIT Press, Cambridge, MA).

SRI International, 1993. SRI International, Menlo Park, California.Sutton, J., 1991. Sunk Costs and Market Structure, (The MIT Press, Cambridge, MA).Sylos-Labini, P., 1962. Oligopoly and Technical Progress, (Harvard University Press, Cambridge, MA).Teece, D.J., 1982. Towards an Economic Theory of the Multiproduct Firm. Journal of Economic

Behavior and Organization 3, 39–63.Tegene, A., Kuchler, F., 1991. An Error Correcting Model of Farmland Prices. Applied Economics 23,

1741–1747.Thomas, L.G., 1990. Regulation and firm size: FDA impacts on innovation. Rand Journal of Economics

21, 497–517.Tremblay, V.J., Tremblay, C., 1988. The Determinants of Horizontal Acquisitions: Evidence from the

U.S. Brewing Industry. Journal of Industrial Economics 37, 21–45.United States Department of Agriculture, 1974. Agricultural Statistics (United States Department of

Agriculture, Washington, D.C.).United States Department of Agriculture, 1991. Agricultural Statistics (United States Department of

Agriculture, Washington, D.C.).Wall Street Journal Indexes, 1958–90. Various issues, (Dow Jones and Company, New York).Zellner, A., 1962. An Efficient Method for Estimating Seemingly Unrelated Regression and Test of

Aggregation Bias. Journal of the American Statistical Association 57, 348–368.