(bu eva) s-fernandez-kranz, santalo_effect, product market competition, csr, strategy

8/12/2019 (Bu Eva) S-Fernandez-Kranz, Santalo_Effect, Product Market Competition, CSR, Strategy

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When Necessity Becomes a Virtue:

The Effect of Product Market Competition

on Corporate Social Responsibility

DANIEL FERNANDEZ-KRANZ

Department of EconomicsIE Business School

Mara de Molina 11Madrid 28006, Spain

[email protected]

JUAN SANTALO

Department of StrategyIE Business School

Mara de Molina 11Madrid 28006, [email protected]

We test whether Corporate Social Responsibility (CSR) is driven by strategicconsiderations by empirically studying the link between competition andfirms social performance. We find that firms in more competitive industrieshave better social ratings. In particular, we show that (i) different marketconcentration proxies are negatively related to widely used CSR measures;(ii) that an increase in competition due to higher import penetration leads tosuperior CSR performance; (iii) that firms in more competitive environmentshave a superior environmental performance, measured by firm pollution levels;and (iv) that more product competition is associated to a larger within-industryCSR variance. We interpret these results as evidence that CSR is strategicallychosen.

1. Introduction

Corporate Social Responsibility (CSR) has been advocated as a keycomponent of the social contract between business and society, but thepurpose of this contract is still the subject of much debate betweentwo opposing views: the altruistic view and the strategic view of CSR.

This research project has been partially funded by Madrid Regional Governmentsresearch Grant #S2007/HUM-0413 and the Spanish Ministry of Educations research

Grants #SEJ2007-67582-C02-01 and #ECO2009-07237. The authors thank Luis Diestre forhis priceless help with the data collection process.

C 2010, The Author(s)Journal Compilation C 2010 Wiley Periodicals, Inc.Journal of Economics & Management Strategy, Volume 19, Number 2, Summer 2010, 453487


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454 Journal of Economics & Management Strategy

According to an altruistic approach to CSR, firms are willing to sacrificeprofits for the social interest (Elahuge, 2005). Instead, the strategicview asserts that firms engage in profit maximizing CSR (Baron, 2001;

McWilliams and Siegel, 2001) and that companies do well by doinggood. Both views coexist without a clear consensus as to whether firmsperform responsibly because they can afford it or because corporatevirtue is profitable.

There is a vast empirical literature devoted to the issue ofdiscerning whether CSR is indeed associated with superior financialperformance but with inconclusive results. Margolis and Walsh (2003)in their survey of this literature come across 109 empirical studies andreport that almost half of them, 54, provide evidence of a positive

association between CSR and financial performance, whereas onlyseven studies find a negative relationship. Among the other, 28 studiesreport nonsignificant relationships whereas 20 display a mixed setof findings. Yet, even if the sheer force of numbers indicate that the(almost) absolute majority of studies find a positive relationship, thiscannot be considered evidence in favor of the strategic view of CSR. Apositive correlation between CSR and profits could indeed be drivenby CSR providing companies with some sort of strategic advantagethat reflects on the firms bottom line. But it is also consistent with

CSR being propelled by the existence of economic rents that managerschoose to divert to social initiatives rather than returning them to firmowners either with or without their approval. Overall, the literature hasbeen quite ineffective in establishing causality in the observed linkagebetween CSR and financial performance (see also Griffin and Mahoney,1997, as well as Margolis and Walsh, 2003, for a complete literaturereview).

In this paper, we propose a different approach to the testing ofcausality in the CSR-financial performance debate. Rather than studying

the CSR-profits linkage, we investigate the effect of product marketcompetition on CSR levels and variance. If altruismeither of managersor firm ownersis the driving force that pushes forward CSR initiatives,then tougher competition should reduce the rents available for diversionto CSR and therefore it should lead to a nonincreasing firm social perfor-mance. On the contrary, if CSR strategies are mainly undertaken becausetheir usefulness for companies in search of competitive advantages, thena more competitive environment could lead to an enhanced use of CSRas a way to either better differentiate, gain access to new markets, or

achieve a better fit between the firms products or services and consumerpreferences in some unique market niches.

In this paper, we provide strong evidence that firms operatingin more competitive industries are more socially responsible, a resultwe view as consistent with the strategic theories of CSR. We do not


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Corporate Social Responsibility 455

claim that CSR is never altruistically motivated. In fact, CSR couldbe altruistically motivated and at the same time strategically chosento serve the interests of the firm. We view our results as indicative

of the importance of the strategic motives for CSR. More precisely,our results suggest that strategic CSR models have a better fit withthe observed competitionCSR linkage than altruism-based theoreticalmodels.

We perform three distinct empirical tests to support this claim.First, we show how increases in market concentration measures arenegatively associated with the CSR ratings devised by an independentinvestment company. This result holds true when controlling for a vari-ety of observed and unobserved firm characteristics and it holds when

we consider both firm social strengths and firm social concerns indepen-dently. Second, we provide evidence that an increase in competition dueto higher import penetration leads to superior CSR performance. Third,we find that firms in more competitive environments have a superiorenvironmental performance, measured by firm pollution levels. Allthese results are not only statistically significant, but they also revealeffects of economic significance. In particular, our estimates suggestthatif all else is constantdoubling competition in the marketplacewould increase the CSR ratings of an average company by between

184% and 800%.Furthermore, we also investigate the linkage between the intensity

of market competition and the variance of CSR ratings across firms. In-tuitively, if CSR is undertaken by companies as a differentiation device,then as competition intensifies we should see that some companieschoose to improve their social performance whereas other companiesin the same sector will keep it constant or even diminish it, because theeffectiveness of CSR as a differentiation device depends on the relativesocial performance vis-a-vis competitors (Baron, 2006). Consistent with

this argument, we report evidence that shows a positive associationof within-industry CSR variation and the intensity of product marketcompetition.

The rest of the paper is structured as follows: Section 2 reviews theliterature on altruistic and strategic CSR, Section 3 provides a theoreticalframework for the competitionCSR linkage. Section 4 characterizes thedata and the variables used in our empirical analysis. We describe theempirical strategy in Section 5, whereas in Sections 6 and 7 we show theempirical evidence of a positive relationship between competition and

CSR with different proxies of both constructs. Section 8 looks at severalrobustness tests, Section 9 investigates the linkage between marketcompetition and within-industry CSR variance, whereas Section 10concludes.


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2. Literature Review: The Altruistic

and Strategic Motives for CSR

One stream of literature asserts that firms engage in profit maxi-mizing CSR (Baron, 2001; McWilliams and Siegel, 2001; for a reviewsee McWilliams et al., 2006) and therefore companies do well bydoing good. From the consumer side, CSR practices may increaseprofits because they directly increase consumer willingness to payfor the firm product (Waddock and Graves, 1997; Reinhardt, 1998;McWilliams and Siegel, 2001; Bagnoli and Watts, 2003; Baron, 2006,2008); prevent consumer boycotts (Micheletti, 2003); or they credi-bly signal to the consumer the unobserved high quality of the firm

products (Feddersen and Gilligan, 2001; McWilliams and Siegel, 2001;Fisman et al., 2006; Siegel and Vitaliano, 2007). Good CSR stan-dards can help to attract more or cheaper sources of capital fromaltruistic investors or investors that consider that socially responsi-ble actions signal the high quality of the management team (GraffZivin and Small, 2005; Vogel, 2005, chapter 3; Mackey et al., 2007).Responsible firms may also benefit from improved employee moraleand retention (Vogel, 2005, chapter 3; Turban and Greening, 1997).Finally, businesses could use CSR as a means to preempt more costly

regulatory actions (Segerson and Miceli, 1999; Maxwell et al., 2000),to avoid taxes (Hansen, 1999; Schmelzer, 1999) and even to influenceregulations in such a way that their competitors face higher costs thanthe firms practicing CSR (McWilliams et al., 2002).

On the contrary, the altruistic theories of CSR (e.g., Aupperleet al., 1985; Baron, 2006) consider that CSR policies involve necessarilysacrificing firm profits for the social interest (Elahuge, 2005). Firms thatperform responsibly incur a competitive disadvantage because theyexperience costs that should be avoided or that should be borne by third

parties (Waddock and Graves, 1997). Furthermore, altruistically drivenfirms abstain engaging in certain lucrative courses of action becausethey do not wish to profit from unethical or heinous actions (Rosenet al., 1991).

In the modern corporation, the origin of this corporate philan-thropy can come either from altruistic managers or directly inspiredby the magnanimity of owner-shareholders. If corporate owners havealtruistic preferences for the social welfare they will commission man-agers to run the firm in a philanthropic manner. A second possibility is

that investors are selfish, and thus they are unwilling to accept lowerreturns in exchange for better firm social performance, but executivespreferences for CSR diverge from those of their principals. Accordingto this interpretation, CSR is simply an additional managerial perk and


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the expression of the traditional agency problem between managersand shareholders as Friedman (1970) pointed out. Along these samelines Vogel (2005) notes that the origins of the modern doctrine of CSR

in the United States are inextricably linked to the popularization ofprofessional managers and the separation of corporate ownership andcontrol at the beginning of the XXth century.

As explained earlier, a large body of empirical literature hastested whether companies do well by doing good. If this was thecase, one could argue that, independently of any additional altruisticmotivation, CSR can indeed be strategic and studied like any otherprofit maximizing strategy. The problem with this approach is that thisliterature has not been successful in establishing causality because a

positive correlation between CSR and financial results is consistentboth with altruistic and strategic theories. In this paper, we take anovel approach to discerning between both models by looking at thecompetitionCSR linkage.

3. Theoretical Framework: Effect of Market

Competition on Altruistic and Strategic CSR

3.1 Effect of Competition on Purely MorallyBased CSR

Under the assumption that philanthropy enters the utility functionof shareholders as a normal good (see plenty evidence of thisin Schervish and Havens, 1998; Nelson, 2001), the total amount ofmoney that shareholders are willing to sacrifice for altruistic reasonsshould increase with firm profits. According to this logic, more intensecompetition in the marketplace decreases the strength of a firmseconomic position and as a result corporations would behave less

philanthropically in more competitive markets.CSR could be the result of an agency problem in which managers

divert shareholders money to their pet socially responsible projects.In this case, the CSR competition-linkage will be the same as itwould be with altruistic shareholders. If managers running companiesthat operate in noncompetitive markets divert part of their rents toimprove firm social performance at the expense of shareholders, thencompetition in the marketplace diminishes the amount of resourcesthat can be diverted to social issues and hence decreases CSR levels.

This relationship holds independently whether the altruism source isthe legitimate virtue of corporate owners or simply agency problemsthat enable managers to divert shareholders money to social causes. In


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fact, note that according to this logic, CSR cannot coexist with perfectlycompetitive markets because selfish investors would always requiremarket returns.

3.2 Effect of Market Competition on Strategic CSR

On the contrary, models of strategically driven CSR are not incompatiblewith a positive relation between competition and socially responsiblebehavior, although the theoretical literature that looks at the relationshipbetween market competition and strategic CSR has found conflictingresults regarding the sign of this linkage. This is not surprising becauseIndustrial Organization economists that have studied theoretically the

effect of competition on other profit maximizing business strategieslike innovation (Aghion and Griffith, 2005) have not found a univocaleffect either. These ambiguous results arise because there are two maineffects of product market competition on a given strategy: the rentdissipation effect and the escape competition effect (Aghion andGriffith, 2005), with opposite signs. On one hand, an increase in productmarket competition reduces the product profit margin and this in turnreduces the marginal return of any generic business strategy. This is thesame logic used by Schumpeter (1943) to defend a negative link between

competition and innovation. According to this rent dissipation effect,product market competition would reduce CSR. This effect dominatesin Bagnoli and Watts (2003), where the cost disadvantage associatedto the provision of the public good has more pronounced negativeeffects for firm profitability in more competitive markets. This negativerelation between market competition and CSR is found both whenBagnoli and Watts (2003) define intense competition as a larger numberof competitors, and also when they consider Bertrand price competitioninstead of the less aggressive Cournot type of models.

Alternatively, in more competitive scenarios, a small advantageacquired by any of the competitors could easily translate in largerincreases in market share. For example, in a Bertrand competition typeof model any business strategy that leads to a small decrease in price(or to a small increase in product quality or attributes, given price)could translate into a 100% market share because consumers wouldswitch massively to firms that offer a better deal. According to thisescape competition effect product market competition would affectpositively firm levels of CSR. Consistent with this reasoning, Fisman

et al. (2006) present a signaling model of corporate philanthropy inwhich the final consumers value products coming from firms thatengage in CSR activities because this credibly signals the firms aversionto sacrificing unobservable quality. Fismanet al. (2006) find that strategic


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CSR activities are more likely to occur in those markets in whichproduct market competition is more intense, the reason being thatthe profitability of a differentiation strategy achieved by partaking

in CSR activitiesthe escape competition effectis larger in thosemarkets in which price competition is strong. In their model, the escapecompetition effect is particularly powerful because CSR is the onlyproduct differentiation device in an otherwise homogenous output.

The opposed sign of these two effects explains why a givenstrategic theory of CSR is unlikely to predict an unambiguous relationbetween the intensity of competition and firms social performanceunless imposing a strict set of assumptions that limit its applicability tothe empirical analysis. Yet, a positive relation can be expected whenever

the escape competition effect dominates the rent dissipation effect.More important, although a negative relation between market

competition and CSR would be consistent both with strategic andaltruistic models of CSR, a positive association is incompatible witha purely altruistic motivation and therefore would provide evidenceof CSR undertaken, at least partially, as a profit maximizing strategy.In this regards, the evidence of a positive linkage that we report laterindicates that the documented positive correlation between CSR andprofits found in the majority of studies in the literature (Margolis

and Walsh, 2003) is driven by CSR boosting profits rather than profitsinducing socially responsible corporate behavior.

So far, we have discussed the relation between competition andCSR without distinguishing between positive and negative socialactions whereas Mattingly and Berman (2006) clearly establish thatpositive and negative social actions are distinct constructs and thereforeshould not be combined in a unique dimension. Furthermore, Creyerand Ross (1996) provide experimental evidence that consumers dorespond to unethical behavior with a demand for lower prices and

that positive social actions have value to consumers only when theyare presented to counteract unethical behavior. However, we claim thatproduct market competition can both decrease negative social actionsand increase the likelihood of corporate virtue.

First, an increase of competition in the marketplace raisesthe expected harm to profits caused by unethical behavior becausethe corresponding decrease in market share would be larger given theimportance of substitutes/competitors. In other words, the escapecompetition effect increases the incentives not to undertake unethical

acts negatively valued by consumers and this implies a negativerelationship between the degree of competition and bad corporate socialbehavior. Similarly, the payoff or the importance of ethical behaviorto compensate past negative social acts will be greater in these same


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situations and therefore we should find a positive linkage betweenincreases in competition and good corporate social behavior. Note,however, that as a second-order effect if competition reduces negative

social actions then there may be less need to undertake positive socialactions, which would imply a negative relation between competitionand ethical behavior. As we will show later, this possibility is notsupported by the data, because all our results point to the samedirection: a positive link between market competition and ethicalbehavior.

3.3 The Effect of Market Competition on CSR-driven

Product Differentiation

A positive association between the intensity of competition in themarketplace and the average level of CSR would be consistent with CSRactions that improve the efficiency of firms either because of a reductionin costs or an increase in productivity. Using CSR to attract cheapersources of capital, to attract and retain talent, to enhance the productivityand morale of the labor force, or to preempt costly regulatory actions areexamples of this. Indeed, in highly competitive markets the survival offirmsoftendependsonhavingtherightcostofcapitalorhavingtheright

team of workers and attracting talent to the firm. However, if firms useCSR as a product differentiation device, then we should see a positivelink between product market competition and CSRvariancerather thanCSR levels. In this context higher competition will lead some firms toinvest in CSR to differentiate but because by definition all firms cannotdifferentiate by doing the same strategy, then we should also see at thesame time some other firms with constant or lower CSR investmentlevels. This intuitive reasoning is coherent with the so-called principleof maximum differentiation to soften price competition reported in the

Industrial Organization literature (Tirole, 1988). This reasoning wouldsuggest that higher levels of market competition should increase withinindustry CSR variance because in equilibrium only some companieswould pursue a CSR-based differentiation strategy.

This positive association between product market competition andCSR variance applies to situations in which CSR is both horizontal andvertical product differentiation. If it is horizontal differentiation and notall consumers agree that CSR is valuable, then more intense productcompetition may induce some companies to look for those consumer

niches in which CSR is highly valued. At the same time other companiesmay pursue a socially irresponsible strategy and focus on offering low-cost goods at low prices to those consumers that do not care aboutsocial performance. Similarly, if CSR constitutes vertical differentiation


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and all consumers agree about the superior value of CSR initiatives,competition will induce some companies to specialize in the uppersegment of the market whereas other companies will get the low end

of the market in a separating equilibrium similar to the one reported inBaron (2006), Fisman et al. (2006), or Tirole (1988).

4. Data Description and Variable Construction

4.1 Sample Construction

In this paper we combine two data sets: the KLD data set, withinformation on the CSR behavior of firms, and Compustat, which

provides accounting statement information of public companies. TheKLD data set comes from Kinder, Lyndenberg and Domini (KLD), afirm that rates the social performance of public companies with thepurpose of facilitating the integration of environmental, social, andgovernance factors into investment decisions. In comparison to otherCSR indicators, KLDs ratings are based on the assessment of expertsoutside the focal firm, and therefore the KLD ratings are more objectivethan accounts of companies self-reported CSR activities as used inother indexes. Waddock (2003, p. 369) asserts that KLD data is the de

facto research standard at the moment for measuring CSR in scholarlyresearch. Furthermore, the KLD data has been found to be consistentwith other commonly used measures of CSR. For example Sharfman(1996) found that the KLD data correlated very well with the 1991Fortunereputation score of 300 corporations and the holdings list of 11ethical funds.

The KLD data covers the period 19912005 and considers sevenbroad areas of CSR: community relations, corporate governance, em-ployee relations, diversity, the environment, human rights, and product

quality and safety. Then, within each of the seven areas, KLD analyzesthe behavior of companies on various aspects or activities, differen-tiating between good or socially responsible behavior (KLD calls thisa strength) and bad social behavior (KLD calls this a concern). Thenumber of firms in the KLD sample experiences a substantial increasein the year 2001 since from that year onward, KLD added CSR ratingsfor all firms belonging to the Russell 1000

RIndex. From 2003 onward

KLD additionally reports CSR data on all those companies belongingto theRussell 2000

RIndex.

In our empirical analysis later, we exclude firms in theDomini 400Social Index (DS400) to avoid selection bias caused by KLD selectingspecific types of corporations. By doing this we lose 1,014 firm year observations. However, in nonreported results, we replicate our


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specifications including companies in the Domini 400 Social Index, andqualitatively obtain the same results.

Throughout the years KLD has changed the number of activities

that it scrutinizes. In particular, many of the activities rated in years19942005 were not rated in 19911993, and vice versa. For this reasonyears 19911993 are excluded from the analysis. We also dropped thoseratings that appear in some years but not in others, and this resultedin the exclusion of an average of two strengths and three concerns peryear. Finally, those observations that had no match in the Compustatdata set were left out, which led to the exclusion of 2,870 firm yearobservations.

In total, we keep 12,933 firm year observations with an average

of 56 CSR ratings for every firmyear. The resulting unbalanced panelconsists of 3,630 firms, each of them staying an average of 3.56 yearsin our sample. However, the distribution of the number of years in thesample is bimodal, because larger firms in the SP500 stay an average of8.71 years. An important element of our empirical strategy that weexplain later is the inclusion of firm fixed effects. To have enoughwithin-firm time variability we run regressions using only firms thatare present in the data set for a minimum of 5 years, which reduces oursample to 6,206 firm year observations. To have comparable results

across specifications, we impose the exact same sample restriction inall the analyses. We have considered other thresholds for the minimumnumber of years in the data set and the results are practically identical.

4.2 Dependent Variables: Measures of CSR

Performance

We follow the literature (see, e.g., Siegel and Vitaliano, 2007) andwe compute the difference between total strengths (STR) and total

concerns (CON) across all different CSR areas to get an aggregatemeasure of CSR, ACSR, for each firm and year. The analysis later alsostudies the linkage with market competition of concerns and strengthsseparately because as Creyer and Ross (1996) and Mattingly and Berman(2006) argue, consumers may value asymmetrically positive rather thannegative CSR-related events. Note that adding raw KLD scores acrossdomains over weights some domains and underweights others becausethe maximum number of strengths and concerns is not equal acrossdomains. Although for better comparison with previous literature, we

report the regressions done with the raw KLD scores as dependentvariables, we have run alternative models using standardized scoresand the results were unchanged.

The ACSR values range from 9 to 12 and the standard deviationacross firms is 1.98. Because of the characteristics of the KLD scores,


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27% of the observations have an ACSR score equal to 0, whereas thispercentage jumps to 37% in the case of the concerns (CON) and 41% inthe case of the strengths (STR). Interestingly, as noted by Mattingly and

Berman (2006) there is a positive correlation between social strengthsand concerns (see Table II).

4.3 Independent Variables: Measures of Product

Market Competition

We construct three different market competition proxies. First, we con-struct the HirschmanHerfindahl Index (HHI) for each industry definedat the six-digit NAICS code level. This is a standard index of industry

concentration used profusely in the standard Industrial Organizationliterature that is computed by adding the square of the market share ofall players operating in an industry a given year. We compute total salesin industry i by considering all Compustat nondiversified companiesactive solely in industryias well as those divisions of diversified firmsthat report industry i as their primary sector of activity. This is thesame measure of concentration used by Arora and Cason (1995) whenstudying the determinants of participation in voluntary environmentalregulation programs.

Note that this HHI is constructed using exclusively informationfrom public companies in Compustat, and therefore it is an upward-biased estimator of the real HHI. However, it constitutes a good proxyfor industry concentration because larger firms are usually public andthis limits the importance of the bias. Furthermore, a large number ofsmall local private companies may not be competing with large nationalor international companies even if they operate in the same NAICS code.For this reason, our in-sample concentration index might be a betterproxy of the relevant extent of market competition for public companies.

The only alternative would consist in using the HHI reported by the UScensus. However, the US census only provides concentration indexesfor manufacturing industries and this data is only available one out ofevery 5 years. This would complicate the analysis with firm or industryfixed effects, which are so essential to our empirical strategy.

The number of competitors in the same industry is taken asanother proxy for the intensity of competition, again defined at the six-digit NAICS code level. As earlier, both divisions of diversified firmsand nondiversified firms active in the same sector are considered as

competitors.For the last measure of competition, we utilize measures of

import penetration and industry tariff protection obtained from theJohn Romalis US Tariff Database 19892001 files, the TradeStats ExpressNational Trade Data, and the US Industry Annual Accounts data


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section in the Bureau of Economic Analysis of the US Department ofCommerce as reported in Xu (2006). With it, we construct measures ofimport penetration and industry tariff protection, covering a total of

21 manufacturing industries (three-digits NAICS level) for the period19942001.

A large percentage of firms in our sample (43.23%) are diversified,and as such they operate in more than one industry. The correspondingmarket competition proxy for these companies is constructed as theweighted average of the market competition in all the industries inwhich the company operates, where the weights are given by thepercentage of firm sales in each industry.

All three competition variables display a significant degree of

variation across industries, across firms, and across time. For example,for the case of the HHI, its mean is 0.20 and its standard deviation acrossfirms is 0.19 and 0.06 across time. All three-market competition proxiescorrelate negatively with firm profitability (see Table II).

4.4 Controls

It might happen that firms subject to more competitive environmentsincrease their expenditure in R&D or advertisement in addition to

undertaking CSR initiatives. In this case, omitting controls such as R&Dand advertising expenditures would lead to an overestimation of theimpact of competition on CSR. With this in mind, we follow the adviceof McWilliams and Siegel (2000) and all our specifications have controlsfor R&D intensity (R&D) and advertising intensity (ADVER), defined asR&D expenditures over sales and advertising expenditures over sales.Because companies are not forced by the SEC to report advertisingand R&D expenditures, there are a large percentage of companies thathave missing values for these two variables in Compustat. Instead of

dropping those observations, we follow the standard practice and assignthem a value of zero and at the same time create two dummies, onefor each variable, that have a value equal to 1 if the company reportseach respective type of expenditures, and zero otherwise. These twodummies are included in all the specifications in which these controlsare used.

We are interested in exploring whether market competition couldhave an effect on CSR other than decreasing the excess resourcesavailable to invest in socially responsible behavior. According to an

altruistic point of view, all the effect of market competition on CSRratings should go through its negative effect on profits. Thus, once thiseffect is taking into account market competition should have a zeroeffect on firm social performance or a negative effect if competition has


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a negative effect on future profits and this induces firms to reduce itsresponsible behavior. We take this effect into account by including as acontrol variable firm accounting profits (PROFITS), defined as the firms

operating profits, in all our specifications.We are also concerned about the possibility of a spurious correla-

tion between CSR and competition due to a size effect. This can happenif CSR has economies of scale and firm size varies with competition(e.g., in less competitive markets firms have a larger size). Also, largecompanies are more likely to have a greater public visibility and, as aresult, larger companies may have higher chances to qualify for anyof the strengths and concerns considered by KLD. For these reasons, acontrol for firm size (ASSETS) is included, computed as the log of book

value of firms assets.We report descriptive statistics and correlations of the variables

we employ in the empirical analysis in Tables I and II. Firms operatingin manufacturing industries, NAICS code between 31 and 33, arethe largest group in our sample followed by firms competing in theservice sector, NAICS 5192. Across time the proportion of firms inmanufacturing decreases, from 51% in the interval 19941997 to 36%in 20022005; whereas the percent of companies in the service sectorincrease from 19% to 34% during the same period of time. This time

variation in the sector composition of our sample is partially due tothe changing universe in the KLD sample reported earlier. The lateradded indexes that have a different industry mix because they includemuch smaller companies. In addition, the sector combination of theindexes captured in the whole sample period varies across time. This isclear in the case of S&P 500 in which the proportion of manufacturingcompanies is decreasing. Note also that the mean values of the controlvariables change considerably across time. The effect of any potentialtime biases in our estimations is taken into account by including year

dummies in all our specifications.

V. Empirical Strategy

In our empirical analysis we run a set of linear regressions to estimatethe relation between CSR and market competition. Although we use asimultaneous specification, as other empirical studies of the relationshipbetween CSR and profits (Fisman et al., 2006) and the type of goods

(Siegel and Vitaliano, 2007), our results are robust to lagging theindependent variables 1 year. We do not include this estimation toavoid flooding the paper with tables but the results are available fromthe authors upon request.


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Tabl

eI.

SummaryS

tatistics

19941997

19982001

20022005

Min

Max

SD

Manufacturing(NAICS3133)(%)

51.60

45.61

36.57

Services(NA

ICS5192)(%)

19.11

22.50

34.77

Trade(NAICS4245)(%)

9.61

10.92

8.78

Othersector(%)

19.69

20.96

19.88

Averagesales(MM$)

6,592.34

7,763.89

3,586.13

0.66

328,213.00

14,108.83

Averageassets(MM$)

12,947.34

17,247.94

9,400.02

7.95

1,494,037.00

54,315.48

Averagepro

fits(MM$)

1,225.55

1,427.23

672.53

5,7

43

69,104.00

2,984.94

Averageadv

ertisingexpense(MM$)

379.33

307.44

131.01

0

5,917.00

507.91

AveragetotalCSRscores(ACSR)

0.598

0.465

0.346

9.00

12.00

1.98

AverageKLDtotalstrengths(STR)

2.155

2.123

1.178

0

14.00

1.72

AverageKLDtotalconcerns(CON)

1.556

1.658

1.524

0

13.00

1.49

AverageHH

I

0.221

0.203

0.191

0.01

1

0.19

Averagepla

yers

79.504

124.180

155.95

1

830

181.98

Averageimports(%)

17.815

21.027

2.80

74.80

12.62

AverageR&

Dexpense(MM$)

290.25

316.73

133.22

0

12,183.00

668.19

HHIistheHerfindahlIndex,calculatedatthesix-digits

NAICSindustrylevelandaveragedove

rindustriesifafirmisdiversified.

Playersreprese

ntisthenumberofcompetitorsatthesix-digitsNAICSindustrylevelandaverag

edoverindustriesifafirmisdiversified.

AllMin,Max,andSDnumbersarecalculatedatthelevelofthefirm.


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TableII.

CorrelationC

oefficients

Sales

Assets

Profits

Advertising

R&

D

ACSR

STR

CON

HHI

Player

Import

Sales

1.00

Assets

0.51

1.00

Profits

0.77

0.80

1.00

Advertising

0.67

0.72

0.75

1.00

R&D

0.56

0.64

0.67

0.70

1.0

0

ACSR

0.01

0.07

0.06

0.18

0.1

6

1.00

Strengths-ST

R

0.39

0.31

0.42

0.54

0.4

9

0.68

1.00

Concerns-CON

0.47

0.25

0.40

0.45

0.3

6

0.53

0.24

1.00

HHI

0.06

0.01

0.03

0.09

0.0

2

0.01

0.01

0.03

1.00

Players

0.10

0.05

0.01

0.08

0.0

4

0.08

0.01

0.12

0.52

1.00

Imports(%)

0.16

0.16

0.18

0.20

0.0

1

0.04

0.02

0.09

0.06

0.03

1.00

Significantat1%;

Significantat5%;Significanta

t10%.

HHIistheHer

findahlIndex,calculatedatthesix-digits

NAICSindustrylevelandaveragedoverindustriesifafirmisdiversified.

Playersreprese

ntisthenumberofcompetitorsatthesix-digitsNAICSindustrylevelandaverag

edoverindustriesifafirmisdiversified.


16/35


Note that even if the proxies of market competition, COMP, are byits nature defined at the industry-level, COMP has firm-level variabilitygiven that each diversified firm has potentially a distinct value for

COMP. Because both STR and CON are left-censored variables, we fitTobit models whenever using these as dependent variables, whereaswe run standard OLS regressions when using ACSR.

When using a Tobit specification we cannot introduce firm fixed ef-fects (Wooldridge, 2002) and this is why in all Tobit specifications belowindividual firm fixed effects are substituted by industry dummies. Theseindustry dummies represent six-digit NAICS codes in those regressionswith HHI and PLAYERS as independent variables and instead three-digit NAICS codes for those regressions with IMPORTS as proxy of

market competition (we are consistent with the industry definition usedin each competition variable).

The inclusion of industry dummies is important because of twoseparate reasons. First, as Nickell (1996) points out market share-based measures of market power (such as the HHI) have little valuewhen utilized in a cross-section but instead are more reliable whenexploiting the explanatory power of their time variability as we dowhen introducing industry dummies. Second, Siegel and Vitaliano(2007) show how CSR levels vary systematically across industries

because they report that companies selling experience goods are moresocially responsible than firms selling search goods. Adding industrydummies avoids biases caused by the presence of unobserved industrycharacteristics that could be correlated at the same time with CSR levelsand market competition proxies. We also estimate regressions with firmfixed effects to take into account generic unobserved firm heterogeneity.

Note that we cannot run regressions with industry dummies andfirm fixed effects at the same time because this would require havingenough number of firms that switch industries during the 10-year

period. This does not happen for any of the companies in our sample.Besides, industry fixed effects will also capture time invariant industryeffects.

A potential drawback of our analysis is the potential endogeneityof HHI and PLAYERS and standard reverse causation arguments. Inparticular, it could happen that CSR strategies modify market structurerather than being the result of competition in the marketplace. Thiscould be the case if, for example, the CSR strategies implemented byincumbents act as an entry barrier and reduce the level of competition in

a mechanism similar to the one described in Sutton (1991) for R&D andAdvertising. This issue is addressed by using an exogenous source ofmarket competition, as is the level of industry trade barriers. We followXu (2006) and Guadalupe (2007) using the time variation of tariffs as


17/35


instruments for import penetration across three-digit manufacturingindustries. As seen later, the results will not change qualitatively whenemploying this exogenous measure of market competition.

The use of import penetration provides another test of a differentkind. It allows us to be confident that any measurement error in thesocial performance variables is not affecting our results. That is, eventhough there is a clear possibility that we are measuring the level ofCSR with error, this will bias our results only if the measurement erroris correlated with the competition variables. The fact that our resultsare robust to changes in the competition variable suggests that thereis no correlation between CSR measurement error and the degree ofcompetition (or that CSR measurement error is equally correlated with

all different competition indicators, which is rather unlikely).In the specifications later we use industry tariffs as instrument for

import penetration. The objective is to use an exogenously determinedmeasure of competition, that is, one that is not affected by the intensity ofCSR in the industry. Because the level of tariffs tends to vary for reasonsnot strictly related to CSR adoption by firms (e.g., trade agreements orpolitical reasons), it seems reasonable to use as proxy of competition thatpart of imports penetration that is related to changes in tariff levels.Moreprecisely, this instrumental variables (IV) approach implies a two-steps

estimation procedure. In the first step, import penetration is regressedagainst industry tariffs and the rest of explanatory variables. In this firs-stage nonreported estimations, industry tariffs always had a negativeimpact on import penetration, and the coefficients were significant atthe 1% level suggesting that indeed import tariffs are good instrumentsfor import penetration. In the second step, CSR is regressed againstthe fitted values of import penetration and the rest of right-hand-sidevariables. The results of this second stage are reported later.

VI. Preliminary Evidence on the CSR-Market

Competition Linkage

We start by showing some anecdotal evidence regarding the relationshipbetween product market competition and the CSR measures. Table IIIdisplays the connection between changes in market concentrationasmeasured by the change in the HHIand changes in CSR over a 12-year period from 1994 to 2005. The first panel in Table III looks at this

relationship at the firm level, whereas panel B takes industry, six-digitNAICS, as the unit of analysis.

The evidence presented in Table III clearly suggests a positiverelationship between the change in market competition and CSR. Those


18/35


Table III.

CSR and Market Concentration (HHI); 19942005

Difference from12-Year Average

Differencebetween 2005Value and the

12-Year Average

Panel A. Firm-level data. Only firms present all years in the data set. 1,800 observations

Quartile ACSR HHI Worst five CSRperformers

ACSR HHI

1st (least 0.25 0.10 Fuller Company 4.66 0.13concentrated)

2nd 0.14 0.02 Kroger Co. 4.50 0.02

3rd 0.05 0.01 Albertsons, Inc. 4.16 0.024th 0.33 0.11 Wal-Mart Stores, Inc. 4.00 0.13

Home Depot, Inc. 3.83 0.05

Best five CSR performersUnisys Corporation 3.25 0.21Intel Corporation 3.41 0.01Ecolab Inc. 3.41 0.05Advanced Micro

Devices, Inc.4.33 0.01

General Motors

Corporation

4.58 0.03

Panel B. Industry-level data (six-digits NAICS). 1,356 observations

Worst five CSR performers1st 0.22 0.11 Adhesives and

sealants (325,520)4.66 0.13

2nd 0.12 0.02 Pens, pencils, otheroffice material(339,941)

3.66 0.06

3rd 0.07 0.01 Plastics, resins andelastomers (325,211)

3.58 0.04

4th

0.27 0.12 Grocery stores(445,110) 3.47 0.02

Convrt papr, paprbrd,ex boxes (322,222)

3.41 0.10

Best five CSR performersMotor vehicles and car

bodies (33,611)3.79 0.05

Paperboard mills(322,130)

2.50 0.01

Semiconductor, relateddevices (334,413)

2.13 0.01

Special industrymachinery (333,295)

2.08 0.00

Aircraft (336411) 1.91 0.01

Based on HHI distribution.Notes:Only firms present all years in the data set. CSR is the Corporate Social Responsibility index as reported by KLD.HHI is the Herfindahl Hirshman Index, calculated at the six-digits NAICS industry level and averaged over industriesif a firm is diversified. Worst (best) CSRperformers are the firms (Panel A) or industries (Panel B) that have experiencedthe largest decrease (increase) in ACSR during the 12-year period that goes from 1994 to 2005.


19/35


companies operating in industries in which concentration has lessenedthe most, a decrease of 0.10 in the HHI from a mean of 0.20, are those thathave experienced the largest increase in the ACSR ratingsa rise of 0.25

where the average of ACSR in the sample is just 0.13. At the same time,those companies in sectors in which market concentration has increasedthe most (increase of 0.11 in the HHI) are those with the largest reductionin ACSR ratings (a decrease of 0.33). Also, those companies that haveundergone the largest decrease of ACSR ratings in 2005 respective totheir historical means (Wal-Mart and Home Depot among them) haveall simultaneously encountered an increase in market concentration;whereas those companies (like Intel and GM) that have experienced thelargest increase in the ACSR levels are also those operating in sectors in

which market concentration has diminished.At the industry-level a similar pattern is observed. The quartile

of industries where concentration has fallen the most (a fall of 0.11)presents the largest increase in ACSR levels (an increase of 0.22). On thecontrary, in the quartile of industries in which market concentration hasgrown the most (an increase of 0.12) ACSR levels experience the largestdecrease (0.27 points). Analogously, the worst (best) CSR performersindustries are those that operate in sectors in which concentration hasgone up (down).

7. Empirical Results

7.1 Market Concentration and CSR

Next we investigate the relationship between competition and CSR infully-fledged econometric specifications. Tables IV and V display theresults of a set of regressions with CSR measures as dependent variablesagainst market competition proxies and our control variables. The first

three columns of both tables display estimated coefficients in which theunit of observation is industry-year where one industry is defined asa four-digits NAICS code. On the contrary, columns (4)(9) display theresults of empirical specifications in which the unit of observation isfirmyear. Columns (4)(6) exhibit the results when adding firm fixedeffects and columns (7)(9) show the results when adding industrydummies instead.

In the industry-level analysis, CSR is calculated as the average ofCSR ratings across all nondiversified firms operating in that industry

and given year. The reason for excluding diversified companies inthe industry-level analysis is that it would be problematic to assigndifferent portions of it to each of the industries where a diversified firmoperates. The disadvantage of considering only nondiversified firms is


20/35


Table

IV.

ResultsfortheOLS/TobitMode

ls;CompetitionV

ariable:HHI

OLSIndustryLev

elwithIndustry

FixedEffects(Four-DigitNAICS)

OLSF

irmLevelwithFirmFixed

Effects

OLS/TobitFirmLevelwith

Industry

Dummies(Six-DigitNA

ICS)

ACSR

ST

R

CON

ACSR

STR

CON

ACSR

STR

CON

(1)

(2

)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

Coeff

Coeff

Coeff

Coeff

Coeff

Coeff

Coeff

Coeff

Coeff

Dependentvariable

(SE)

(SE)

(SE)

(SE)

(SE)

(SE)

(SE)

(SE)

(SE)

HHI

0.8

19

0.709

0.1

10

0.5

36

0.5

15

0.0

21

0.5

38

0.4

83

0.4

96

(0.2

55)

(0.2

01)

(0.1

61)

(0.1

98)

(0.1

47)

(0.1

41)

(0.2

74)

(0.2

84)

(0.2

42)

ASSETS

0.217

0.161

0.378

0.096

0.138

0.042

0.057

0.426

0.565

(0.054)

(0.042)

(0.034)

(0.058)

(0.043)

(0.042)

(0.029)

(0.030)

(0.026)

R&D

0.374

0.16

6

0.540

0.035

0.234

0.270

0.695

1.388

0.235

(0.570)

(0.449)

(0.362)

(0.477)

(0.356)

(0.341)

(0.417)

(0.445)

(0.369)

ADVER

0.359

3.32

0

3.680

1.657

0.917

2.574

0.360

0.597

0.226

(2.624)

(0.2.068)

(1.666)

(1.758)

(1.313)

(1.257)

(1.355)

(1.356)

(1.256)

PROFITS

0.000

0.008

0.009

0.001

0.005

0.006

0.005

0.017

0.010

(0.001)

(0.001)

(0.001)

(0.001)

(0.000)

(0.000)

(0.001)

(0.001)

(0.000)

R2

0.789

0.886

0.974

0.751

0.807

0.771

0.415

0.172

0.219

No.ofobser

vations

1,484

1,4

84

1,484

6,206

6,206

6,206

6,206

6,206

6,206

Significanceat

the10%level.

Significancea

tthe5%level.

Significance

atthe1%level.

Pseudo-R

2for

maximumlikelihoodestimation(Likelihood[justaconstant]/Likelihood[fullmod

el]).

AllregressionsincludeyeardummiesanddummiesformissingobservationsonR&DandADVE

RTISING.Robuststandarderrorsarecomputedassumingthatobservationsare

independent

acrossfirmsbut

notwithinfirmsandacrosstime.CSRis

totalstrengths(STR)minustotalconcern

s(CON).PLAYERSisthenumberofcom

petitorsin00s,calculatedastheweightedaverageof

thenumberoffi

rmsineachoftheindustrieswherethefirmoperatesandwheretheweightsaregivenbythepercentageofsalesofthefirm

ineachindustry.ASSETSisthelogofthe

firmsassets.

R&DandADVE

RareR&Dintensityandadvertisingintensity,respectively,calculatedasR&Dandadvertisingexpendituresoversales.PR

OFITSisthefirmsoperatingprofitsin00s(data13).

(1)(3)Nondive

rsifiedfirmsonly.Aggregationtothefour-digitsNAICScodelevel.

Regressionsare

runonlyconsideringfirmspresentinthe

datasetforaminimumof5years.


21/35


TableV.

Resultsforthe

OLS/TobitModels

;CompetitionVariable:PLAYERS

OLSIndustryLe

velwithIndustry

FixedEffects(Fo

ur-DigitNAICS)

OLSF

irmLevelwithFirmFixed

Effects

OLS/TobitFirmLevelwithIndustry

Dummies(Six-DigitNA

ICS)

ACSR

ST

R

CON

ACSR

STR

CON

ACSR

STR

CON

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

Coeff

Co

eff

Coeff

Coeff

Coeff

Coeff

Coeff

Coeff

Coeff

Dependentvariable

(SE)

(S

E)

(SE)

(SE)

(SE)

(SE)

(SE)

(SE)

(SE)

PLAYERS

0.2

63

0.134

0.1

29

0.112

0.1

60

0.0

47

0.0

95

0.0

20

0.1

58

(0.0

88)

(0.0

69)

(0.0

55)

(0.051

)

(0.0

38)

(0.0

36)

(0.0

63)

(0.0

63)

(0.0

55)

ASSETS

0.255

0.1

34

0.389

0.089

0.131

0.041

0.057

0.416

0.562

(0.054)

(0.0

42)

(0.034)

(0.058

)

(0.043)

(0.042)

(0.029)

(0.030)

(0.026)

R&D

0.484

0.0

63

0.547

0.056

0.197

0.253

0.736

1.369

0.145

(0.569)

(0.4

50)

(0.360)

(0.478

)

(0.356)

(0.341)

(0.420)

(0.446)

(0.370)

ADVER

0.180

3.4

57

3.637

1.425

1.176

2.602

0.286

0.113

0.092

(2.625)

(2.0

75)

(1.662)

(1.758

)

(1.312)

(1.257)

(1.355)

(1.346)

(1.255)

PROFITS

0.000

0.0

09

0.008

0.001

0.005

0.006

0.005

0.017

0.010

(0.001)

(0.0

01)

(0.001)

(0.001

)

(0.000)

(0.000)

(0.001)

(0.001)

(0.000)

R2

0.784

0.8

95

0.982

0.750

0.807

0.771

0.415

0.171

0.200

No.ofobser

vations

1,484

1,4

84

1,484

6,206

6,206

6,206

6,206

6,206

6,206

Regressionsare

runonlyconsideringfirmspresentinthe

datasetforaminimumof5years.

Significanceat

the10%level.

Significancea

tthe5%level.

Significance

atthe1%level.

Pseudo-R

2for


el]).

AllregressionsincludeyeardummiesanddummiesformissingobservationsonR&DandADVE

RTISING.Robuststandarderrorsarecomputedassumingthatobservationsare

independent

acrossfirmsbut

notwithinfirmsandacrosstime.CSRis

totalstrengths(STR)minustotalconcern

s(CON).PLAYERSisthenumberofcom

petitorsin00s,calculatedastheweightedaverageof

thenumberoffi

rmsineachoftheindustrieswherethefirmoperatesandwheretheweightsaregivenbythepercentageofsalesofthefirm

ineachindustry.ASSETSisthelogofthe

firmsassets.

R&DandADVE

RareR&Dintensityandadvertisingintensity,respectively,calculatedasR&Dandadvertisingexpendituresoversales.PR

OFITSisthefirmsoperatingprofitsin00s(data13).

(1)(3)Nondive

rsifiedfirmsonly.Aggregationtothefour-digitsNAICScodelevel.


22/35


that we lose almost half of our sample of firmyear observations whenwe perform the industry-level analysis. This is why we had to restrictour analysis to higher levels of industry aggregation (namely, four-digit

NAICS instead of six-digit NAICS) to have a sufficiently big number offirm-level observations per industry.1

In all cases, the sign of the coefficients indicates a consistentpattern: more market competition, lower HHI or more PLAYERS, isassociated with superior social responsibility levels. This happens in theindustry-level analysis, but also in the firm-level analysis with industrydummies or firm fixed effects. Five out of the six coefficients of the ACSRvariable are statistically significantcolumns (1), (4), and (7) in Tables IVand V. Furthermore, the coefficients on both competition variables, HHI

and PLAYERS, seem to suggest that competition in the marketplaceis positively associated with both higher CSR strengths and lowerCSR concerns: five out of six coefficients of the competition variablesignificantly affect the number of strengths, whereas for concerns thesame proportion is three out of six. In none of the six specifications wefind that market competition proxies negatively and significantly affectthe number of CSR strengths. Also, there is not a single specification thatshows that market competition has a positive and statistically significanteffect on the number of CSR concerns.

The comparison of the industry-level results with the firm-levelresults provides a test of whether the inclusion of diversified firmsis having an effect in our results. In general, the direction and thesignificance of the coefficients are the same under both specificationsand hence are independent of the inclusion/exclusion of diversifiedfirms.

Regarding the control variables, both ASSETS and PROFITS areconsistently associated with more CSR strengths but also with moreCSR concerns. One explanation could be that larger and more profitable

corporations have a higher visibility and therefore a larger chance ofbeing spotted under the radar of KLD both for positive and negativesocial practices.

The coefficients on the competition variables presented inTables IV and V suggest that the magnitude of the effects of marketcompetition in the magnitude of CSR ratings is quite important. Forexample, the coefficients in column (4) in both tables suggest thatdoubling the concentration ratio/number of players of the industries

1. We have performed the analysis at different levels of industry aggregation andin general we have found similar but noisier results. The 4 digits level of aggregationseems to offer a good balance between the number of industry level observations (291different industries) and the number of firm level observations to construct each industryaggregate (25.9 firms in each industry on average).


23/35


Table VI.

Results for Linear IV Models; Competition

Variable: IMPORTS

IV Firm Fixed EffectsIV with Industry Dummies

(Three-Digit NAICS)

ACSR STR CON ACSR STR CON(1) (2) (3) (4) (5) (6)

Coeff Coeff Coeff Coeff Coeff Coeff Dependent variable (SE) (SE) (SE) (SE) (SE) (SE)

IMPORTS 0.339 0.141 0.198 0.489 0.366 0.123

(0.183) (0.130) (0.114) (0.143) (0.113) (0.072)

ASSETS 0.061 0.160 0.098 0.554

0.294

0.259

(0.164) (0.116) (0.102) (0.082) (0.065) (0.041)R&D 2.114 1.235 0.878 3.168 2.313 0.854

(2.232) (1.589) (1.390) (2.011) (1.593) (1.009)ADVER 0.215 2.868 3.083 4.132 0.276 3.855

(4.811) (3.425) (2.995) (3.096) (2.453) (1.553)PROFITS 0.015 0.003 0.019 0.041 0.069 0.027

(0.010) (0.007) (0.006) (0.0009) (0.007) (0.004)R2 0.791 0.823 .776 .210 0.269 0.353No. of observations 1,202 1,202 1,202 1,202 1,202 1,202

Significance at the 10% level.Significance at the 5% level. Significance at the 1% level.Regression computed with firms in the data set for a minimum of 5 years. Only manufacturing industries (NAICS311339). All regressions include year dummies and dummies for missing observations on R&D and ADVERTISING.Robust standard errorsare computed assuming that observations are independent across firms but not within firmsand across time. CSR is total strengths (STR) minus total concerns (CON). IMPORTS is import penetration calculatedas imports over sales at the three-digit industry level and is calculated as the weighted average of IMPORTS in eachof the industries where the firm operates where the weights are given by the percentage of sales of the firm in eachindustry. IMPORTS is instrumented with industry tariffs. ASSETS is the log of the firms assets. R&D and ADVERare R&D intensity and advertising intensity, respectively, calculated as R&D and advertising expenditures over sales.PROFITS is the firms operating profits in 00s (data 13).

in which a given firm is competing should lead to a decrease of1.07/increase of 0.24 points in the ACSR levels. Because the averageof the ACSR variable is just 0.13, this means that if the company had anaverage ACSR rating, the effect of dividing the industry concentrationby two would imply a decrease of CSR levels of around 800% whereasmultiplying the number of competitors by two would result in anincrease of ACSR ratings of around 184%.

7.2 Import Penetration and CSR

The results in Table VI show the same pattern as before: an increasein product market competition (IMPORTS) causes firms to be moresocially responsible measured by higher ACSR levels. These results


24/35


hold both for the firm fixed effects model and the model with justindustry dummies. As before, market competition measured by changesin import penetration is associated with both more CSR concerns and

less CSR strengths, although the coefficient on CSR strengths is notstatistically significant when using firm fixed effects.

The economic significance of the coefficients reported inTable VI is also important. If the level of import penetration doublesin the industries in which a given firm operates this should translate,ceteris paribus, into an increase of 0.68 points in the level of ACSR usingthe coefficient of column (1) of Table VI or into an increase of 0.98 pointsusing the corresponding coefficient in column (3). If we assume thatthe firm had an average of 0.13 ACSR rating, this means an increase of

around 623% (853%) in the ACSR ratings.

8. Robustness Tests

In this section we perform three different types of robustness tests.First, we disaggregate the CSR measures to see if some KLD domainsdo not follow the pattern that we have described. Next, we consider thepossible endogeneity of profits and apply a methodology to correct for

this potential bias. Third, we investigate the robustness of our resultswhen using a measure of CSR performance that do not depend onthe assessment of KLD experts, in particular, the firm level of toxicemissions. In all three cases, the results we obtain are consistent withthe previous finding that more competition leads to superior socialperformance. Finally, we explore the possibility of potential nonlinearfunctional forms between CSR and market competition.

8.1 Results for Different CSR Areas

We follow Siegel and Vitaliano (2007) and divide the KLD domains intwo groups: public and nonpublic CSR. The group of public combinesthe social responsibility ratings of community relations, environment,human rights, and diversity. Nonpublic CSR consists of the socialperformance indicators of employee relations, corporate governance,and product quality.

The results indicate that disaggregating our social indicatorsdoes not change the sign or the significance of the coefficients. More

competition continues to be significantly associated with more CSR,with more KLD strengths and (weakly) associated with fewer KLDconcerns. All in all, we do not find significant differences across KLDdomains implying that our previous results were not driven by the level


25/35


of aggregation of the social performance indicators. We do not includethis estimation to avoid flooding the paper with tables but the resultsarea available from the authors upon request.

8.2 Endogeneity of Profits

We are concerned with the possibility that profits are correlated withunobservable variables that also cause CSR. If so, we are not estimatingcorrectly the coefficient of profits and more importantly this could alsoimply that our estimated coefficients of the competition variables arebiased. We address this potential problem by following the procedure ofSiegel and Vitaliano (2007). As a first step we run a regression in which

the dependent variable is market capitalization and the independentvariables are lagged accounting profits and the rest of controls. In thesecond step, the predicted value of market capitalization is included,instead of profits, as a right-hand-side variable in the specifications withCSR as the dependent variable. The drawback of this procedure is thatwe lose 18% of our sample due to missing values for lagged profits.

As earlier we do not include the table with the results to limit thesize of the paper but the tables are also available upon request. Theresults indicate that using fitted profits instead of firm profits changes

very little the earlier results. All the coefficients of the competition vari-ables continue to have the same sign and roughly the same significanceas before. Overall, we interpret these findings as evidence that ourprevious results are not driven by biases caused by the endogeneity ofprofits.

8.3 Results for TOXIC Emissions

Next we repeat the analysis of the previous section using as dependent

variable toxic emissions at the firm level rather than KLD ratings.Pollution levels are good proxies of firm environmental performance,which is one of the seven areas used to evaluate CSR as explainedin Section 3. The main advantage of this CSR proxy consists in theirobjective nature, because firms in SIC codes 20003999 (the entiremanufacturing sector) are legally required by the US EnvironmentalProtection Agency (USEPA) to report emissions for a 582 individuallylisted chemicals in 30 categories into the air, the water, or the ground,once they exceed certain minimum thresholds. The USEPA web page

(http://www.epa.gov/) provides data to obtain waste composition atthe facility level and we have downloaded this information for theperiod 19942005. The procedure to aggregate this facility data to thefirm level and the subsequent matching with financial informationpresents some difficulties. First, because the TRI data provides Dun &


26/35


Bradstreet (D&B) numbers, we match TRI environmental informationwith the D&B million dollar data set. By this, we are able to obtainthe company cusip (cusip is a company identifier commonly employed

in Compustat) for all facilities in the TRI data that belong to a publiccompany. Unfortunately, we only had access to the D&B million dollardata set for the year 20052006. Because of this reason, we are losing allenvironmental information belonging to those companies that were notpublic in 2005.

As King and Lenox (2000) note, there are mistakes in the way inwhich facilities report the D&B number in the USEPA data. Thus, wecheck carefully the matching between TRI data and D&B million dollardata set using additional criteria, other than the D&B number, to identify

a positive match. We follow King and Lenox (2000) and we only matchthose facilities with identical D&B number, zip code, four-digit SIC, andcompany name. Once we have the company cusip for each facility, weare in position to aggregate the environmental information at the firmlevel and match it with the rest of our variables from Compustat.

We construct our measure of firm environmental performance asthe natural logarithm of the total generation of toxic chemical wasteproduced by a firm in a given year also as King and Lenox (2000).This measure includes all toxic waste emissions as well as toxic wastes

that were treated or recycled. We have weighted the toxic releases bytheir relative levels of toxicity as other previous environmental studies(King and Lenox, 2000, 2002; Toffel and Marshall, 2004). We use twodifferent set of toxic weights. The first set we apply was developedby the EPA to serve as a threshold for reporting accidental spills:the reportable quantities (RQ) in the Comprehensive EnvironmentalResponse Compensation and Liability Act (CERCLA). According to thatAct, the most toxic chemicals, like the chemical war agent heptachlor orarsenic compounds, have to be reported if just a pound of the material

is spilled, where spills of the least toxic materials like methanol haveto be reported only if they exceed 5,000 pounds. We use these RQ togroup the different chemicals into the following seven groups: 1 pound(most toxic), 10 pounds, 100 pounds, 500 pounds, 1,000 pounds, 5,000pounds, and 10,000 pounds (least toxic) and weight the toxic emissionsby the inverse of RQ.

Toffel and Marshall (2004) criticize the use of these RQ weightsbecause their discrete scale reduces precision and there is only one RQvalue for each chemical agent regardless of the medium in which it is

released. For this reason, we explore the robustness of the results usinganother set of chemical weights called the Human Toxicity PotentialFactor (HTP) developed by Hertwich et al. (2001). These weightsmeasure toxicity in terms of benzene equivalence (for carcinogens)or toluene equivalence (for noncarcinogens) and have different value


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depending on which medium the toxic is released (air or water). Withthis information, we compute a toxicity-weighted measure of waste foreach facility by adding the number of pounds of each type of chemical

waste emitted by a facility weighted by its level of toxicity. In a finalstep, this information is aggregated to our firm-level measure of TOXICby summing, for each firm, the toxic waste of all its facilities.

We are able to recover information on toxic emissions, TOXIC,for 270 companies in the interval 19942005, amounting to a total of1,281 firm year observations. TOXIC has a mean value of 165,646and a standard deviation of 1,090,541. A 4.6% of the observations takethe value zero because the level of emissions in those cases was belowthe threshold for reporting. As earlier, we fit specification using a Tobit

regression model to take into account the left censoring of the dependentvariable. All measures of toxic emissions are negatively correlated withthe aggregated KLD indicators of environmental social responsibility.These correlations are all statistically significant at the 1% level.

It seems likely that toxic emissions will depend on the level ofoutput produced by each manufacturing company. We introduce adouble control of firm size (ASSETS & SALES) to rule out any spu-rious correlation between our dependent variable and the competitionvariables due to omitting output measures.

Next we repeat our previous analysis with these new dependentvariables. Columns (1)(4) in Table VII show the results for the OLSmodels with firm fixed effects. Columns (5)(8) show the results forthe Tobit models with industry dummies at the six-digits NAICS codelevel and columns (9)(12) show the results for the IV firm fixed effectsmodels. In each specification we run the same regressions for each of thefour dependent variables that measure the level of toxic emissions: TO-TAL TOXIC, NON-CANCER, CANCER-AIR, and CANCER-WATER;where TOTAL TOXIC measures environmental pollution using the RQ

quantities as toxic weights, CANCER-AIR uses as toxic weights the HTPcancer Index assuming all toxic is released in the air, CANCER-WATERuses as toxic weights the HTP cancer Index assuming all toxic is releasedin the water and finally NON-CANCER uses as toxic weights the HTPnoncancer index computed as the average of the toxicity when the toxicis released to air or water.

In total, Table VII presents the results of 12 different regressions. Inall these 12 cases, the sign of the coefficient of the competition variable(HHI, IMPORTS) confirms the earlier results, that is, more competition

is associated with superior environmental performance, in this case,a lower level of toxic emissions. We obtain this result regardless ofthe measure of toxicity that we use and after controlling for firm andindustry fixed effects. In particular, the HHI concentration index is


28/35


29/35


significant and positively associated with pollution levels in six outof eight cases. The only two cases in which HHI is not significant are thetwo specifications in which pollution is measured using CANCER_AIR.

The results with import penetration are weaker because for this variablethe effect is only significant when pollution levels are measured byCANCER_AIR. Nevertheless note that in the specifications with importpenetration as independent variable our sample gets dramaticallyreduced just to 242 observations. This small number of observationsprevents us to run specifications with just industry dummies at the six-digits NAICS levels as we have done in previous specifications earlier.

Overall, in 7 out of 12 models, the coefficients are statisticallysignificant at standard levels. These results are consistent with the

findings of Arora and Cason (1995) that report that firms belongingto unconcentrated industries were more likely to voluntarily engage inpollution reduction programs. We take these results as strong evidenceof the positive relation between competition and environmental perfor-mance. With respect to the variables that proxy for the size of the firm,only the level of sales is significant across the different specifications andnot surprisingly is positively correlated with the level of firm pollution.

Finally, in nonreported results we have also investigated whetherthe competitionCSR linkage follows a nonlinear functional form in the

same manner that Aghion and Griffith (2005) report that the relationshipbetween product market competition and innovation following aninverted U-shape functional form. However, we have been unableto find robust evidence of a quadratic relation between CSR andcompetition (the corresponding tables are available upon request tothe authors).

9. Product Market Competition and CSR Variancewithin Industries

As a final step we investigate whether CSR differentiation strategiesare more likely to be pursued in more competitive markets. For thispurpose, we construct two measures of within industry variation: thestandard deviation of CSR scores within an industry in a given year,and the difference between the 90th and the 10th percentile of the CSRdistribution in each industryyear. Preliminary analysis confirms thatthere is a high degree of CSR differentiation within each industry

(the within-industry standard deviation of total CSR scores is 1.680,compared to its mean which is 0.658).

Table VIII presents the results of estimating specifications verysimilar to the ones used throughout this paper but now using as


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Table

VIII.

IndustryVaria

nceAnalysis

OLS-IndustryLeve

lwithIndustry

FixedEffects(Four

-DigitNAICS)

SDStandardDeviationofCSR

OLS-IndustryLevelwithIndustryFixed

Effects(Four-DigitNAICS)PCT9010

PercentileDifferenceofCSR

Tobit-IndustryLevelPCT

9010

PercentileDifferenceof

CSR

ACSR-SD

STR-S

D

CON-SD

ACSR-PCT

STR-PCT

CON-PCT

ACSR-PCT

STR-PCT

C

ON-PCT

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

Coeff

Coeff

Coeff

Coeff

Coeff

Coeff

Coeff

Coeff

Coeff

Dependentvariable

(SE)

(SE)

(SE)

(SE)

(SE)

(SE)

(SE)

(SE)

(SE)

HHI

1.1

79

0.57

6

0.3

58

1.8

75

1.1

35

0.8

51

2.2

13

2.2

06

0.9

41

(0.5

57)

(0.41

0)

(0.4

21)

(0.9

78)

(0.7

28)

(0.7

38)

(0.5

70)

(0.4

57)

(0.5

09)

ASSETS

0.114

0.11

1

0.049

0.367

0.264

0.055

0.301

0.223

0.082

(0.108)

(0.08

0)

(0.082)

(0.191)

(0.142)

(0.144)

(0.087)

(0.070)

(0.078)

R&D

4.615

3.15

2

2.480

9.630

3.302

4.923

1.487

3.032

5.289

(2.309)

(1.70

0)

(1.746)

(4.113)

(3.060)

(3.102)

(2.920)

(2.342)

(2.628)

ADVER

6.407

6.73

2

3.033

14.376

10.241

9.938

0.136

9.183

3.038

(6.576)

(4.84

3)

(4.973)

(10.305)

(7.667)

(7.773)

(6.581)

(5.289)

(5.917)

PROFITS

0.008

0.04

3

0.017

0.053

0.154

0.093

0.107

0.162

0.039

(0.054)

(0.03

9)

(0.040)

(0.082)

(0.061)

(0.062)

(0.052)

(0.043)

(0.046)

ASSETS-SD/PCT

0.199

0.15

7

0.109

0.553

0.398

0.249

0.524

0.500

0.403

(0.118)

(0.08

7)

(0.089)

(0.103)

(0.077)

(0.078)

(0.080)

(0.064)

(0.071)

R&D-SD/PCT

0.547

0.40

2

0.724

0.924

1.060

0.053

0.566

0.648

2.633

(2.261)

(1.66

5)

(1.710)

(1.516)

(1.128)

(1.143)

(1.311)

(1.050)

(1.171)

ADVER-SD/PCT

4.039

1.92

0

1.248

5.470

1.826

1.741

3.674

1.496

3.978

(5.082)

(3.74

3)

(3.843)

(5.137)

(3.821)

(3.874)

(3.781)

(3.029)

(3.401)

Continued


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Table

VIII.

Continued

OLS-IndustryLevelwith

Indus

tryFixedEffects

(Four-D

igitNAICS)SD

StandardDeviationofCSR

OLS-IndustryLevelwithIndustry

FixedEffects(Four-DigitNAICS)

PCT9010PercentileDifferenceof

CSR

Tobit-IndustryLevelPCT

9010

PercentileDifferenceofCSR

ACSR-SD

STR-SD

CON-SD

ACSR-PCT

STR-PCT

CON-PCT

ACSR-PCT

STR-PCTC

ON-PCT

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

Coeff

Coeff

Coeff

Coeff

Coeff

Coeff

Coeff

Coeff

Coeff

Dependentvariable

(SE)

(SE)

(SE)

(SE

)

(SE)

(SE)

(SE)

(SE)

(SE)

PROFITS-SD

/PCT

0.011

0.104

0.060

0.012

0.119

0.108

0.019

0.137

0.107

(0.066)

(0.048)

(0.050)

(0.049)

(0.367)

(0.032)

(0.032)

(0.027)

(0.028)

R2

0.625

0.721

0.602

0.596

0.672

0.556

0.055

0.103

0.080

No.ofobser

vations

517

517

517

517

517

517

517

517

517

Meanofdep

endentvariable

1.680

1.371

1.066

3.044

2.456

1.923

3.044

2.456

1.923

(itsSD

in

parenthesis)

(1.141)

(.949)

(.947)

(2.128)

(1.814)

(1.785)

(2.128)

(1.814)

(1.785)

Significanceat

the10%level.

Significancea

tthe5%level.

Significance

atthe1%level.

Pseudo-R

2for


el]).

Allregressionsincludeyeardummiesanddummiesfor

missingobservationsonR&DandADV

ERTISING.Robuststandarderrorsarecomputedassumingthatobservationsare

independent

acrossfirmsbutnotwithinfirmsandacrosstime.SDis

thestandarddeviationofCSRscoresof

firmsinagivenindustryandyear.PCT9010isthedifferenceinCSRscoresbetw

eenthe90th

andthe10thpercentileofthedistributionoffirmsinanindustryandyear.ACSRistotalstrengths(STR)minustotalconcerns(CON).HHIistheHirschman-HerfindahlIndex,calculatedasthe

weightedaverageofHHIforeachfirmandyearwheretheweightsaregivenbythepercentageo

fsalesofthefirmineachindustry.ASSETSisthelogofthefirmsassets.R&DandADVERare

R&Dintensitya

ndadvertisingintensity,respectively,calculatedasR&Dandadvertisingexpendituresoversales.PROFITSisthefirmsoperatingprofitsin000s(data13).ASSET

S-SD/PCTis

thestandarddeviation(9010percentiledifference)ofAS

SETS.R&D-SD/PCTisthestandarddeviation(9010percentiledifference)ofR&Dintensity.ADVER-SD/PCTisthestandarddeviation

(9010percentiledifference)ofadvertisingintensity.PRO

FITS-SD/PCTisthestandarddeviation(9010percentiledifference)ofprofits.

Nondiversifiedfirmsonly.Aggregationtothefour-digits

NAICScodelevel.

Firmsinthedat

asetforaminimumoffiveyears.


32/35


dependent variable a measure of CSR variance in each industryyearand adding controls that capture other potential sources of variance likesize, profits, R&D, and advertising. The coefficients of the competition

variable indicate that CSR variability across firms is more intense inmore competitive industries, a result that persists regardless of whetherwe consider total CSR, total strengths, or total concerns. We interpretthis as evidence that product market competition is associated not onlyto better levels of firm social performance but also to a wider dispersionof CSR levels within industry. This suggests that companies might beutilizing CSR as a differentiation device.

10. Concluding Remarks

In this paper we provide strong empirical evidence that firms in morecompetitive markets carry out bigger CSR efforts. Our results indicatethat more competition in the marketplace leads to less negative socialimpact and to greater positive social impact initiatives. This has beenreported using measures of both positive and negative social actions.These results are consistent with a strategic view of CSR in whichconsumers, investors, employees, or the government value positively

corporate virtue and as a result a CSR investment can be a profit-maximizing strategy. On the contrary, the positive association betweencompetition and CSR is harder to reconcile with a purely altruisticview of socially responsible corporate initiatives. Although we cannotclaim that all CSR is entirely driven by strategic reasons, the evidenceprovided in this paper indicates that the implications of models based onpurely altruistic motivations of CSR have a worse fit with our estimatesof firm behavior. Note however that we cannot reject all altruisticreasons for CSR because CSR could be morally motivated and at the

same time strategically chosen to serve the interests of the firm.As a limitation, in this paper we do not offer precise answers about

how exactly firms are using CSR to their advantage and as a responseof competitive pressure. The results in Table VIII suggest one route ofexploration, namely that some firms react to an increase in competitionby pursuing a CSR differentiation strategy. Yet there are many otherpotential reasons that could explain why firms behave more responsiblyin more competitive environments. For instance, if employee talentis more valuable in competitive industries then firms may increase

their CSR investment to retain and attract talented employees. Also,competition in the marketplace might be related to the relative facilityof attracting new investors. CSR may become an important deviceto signal the unobserved quality of the management team that helps


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firms to have access to more and cheaper financial resources preciselywhen competition is more intense. All of these are promising venuesfor future theoretical and empirical research to study the underlying

causes behind the positive linkage between competition and CSR thatwe have documented in this paper.

References

Aghion, P. and R. Griffith, 2005,Competition and Growth

(bu eva) s-fernandez-kranz, santalo_effect, product market competition, csr, strategy

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