PRIVATE ENVIRONMENTAL ACTIVISM AND THE SELECTION AND RESPONSE OF FIRM TARGETS

Transcription

1 PRIVATE ENVIRONMENTAL ACTIVISM AN THE SELECTION AN RESPONSE OF FIRM TARGETS Mchael J. Lenox* Assocate Professor of Strategy Fuqua School of Busness uke Unversty PO Box urham, NC Tel: (919) Fax: (919) Charles E. Eesley octoral Student Sloan School of Management Massachusetts Insttute of Technology 50 Memoral rve, E Cambrdge, MA Tel: (857) Fax: (617) Abstract Envronmental actvsts are ncreasngly resortng to prvate strateges such as boycotts and protests focused on changng ndvdual frms behavor. In ths paper, we examne actvsts use of such prvate poltcs to engender frm complance wth actvst objectves. We begn by developng a smple theoretcal model of an actvst campagn then we develop a seres of propostons n response to these questons. We then derve from these propostons a set of emprcal hypotheses based on a set of observable features of frms. Fnally, we test our hypotheses usng a unque dataset of envronmental actvst campagns aganst frms n the Unted States from Ths paper flls an mportant need n the lterature as one of the frst emprcal attempts to examne the prvate poltcal strateges of actvsts and has mportant mplcatons for the burgeonng lteratures on ndustry self-regulaton and the non-market strateges of frms. JEL Classfcatons: L20, L31, L10, Q20 Keywords: non-market strategy, envronmental management, ndustry self-regulaton RUNNING HEA: Prvate Envronmental Actvsm raft date: September 30th, 2006 raft. Please do not quote or cte wthout author s permsson. * Correspondng author 1

2 PRIVATE ENVIRONMENTAL ACTIVISM AN THE SELECTION AN RESPONSE OF FIRM TARGETS 1. Introducton Envronmental actvsts are ncreasngly eschewng tradtonal publc strateges such as lobbyng legslatures to acheve ther objectves and resortng to prvate strateges focused on changng ndvdual frms behavor (Baron 2003; Baron and ermeer 2005). Through boycotts, protests, and cvl suts, actvsts can force frms to nternalze negatve envronmental externaltes and motvate frms to comply wth ther demands absent any nterventon by the state. Increasngly actvsts are vewng such prvate poltcs (Baron 2003) as more effectve than dealng wth large bureaucratc publc nsttutons. Actvsts have a number of tactcal weapons n ther arsenal. Some tactcs, such as cvl suts, may mpose a drect fnancal oblgaton on the frm f successful, not to menton the tme and effort necessary defendng oneself n court. Others, lke protests, boycotts, and letter wrtng campagns may mpact consumers wllngness to pay for frm products and servces. All may be costly to the extent they drect fnte manageral attenton away from more productve concerns. Overall, these tactcs may damage the general reputaton of the frm makng t more dffcult for the frm to secure supplers and buyers ncludng attractng talented employees who gan utlty from workng for socally responsble employers. In ths paper, we nvestgate a number of mportant questons for both actvsts and frms concernng prvate poltcal campagns: 1) what determnes the aggressveness of a campagn? 2) what ncreases the lkelhood that a frm wll be targeted by a campagn? 3) what drves the probablty that a frm wll comply wth an actvst s demands? We begn by developng a 2

3 smple theoretcal model of an actvst campagn. From ths model, we develop three propostons n response to the three questons posed above. We then derve from these propostons a set of emprcal hypotheses based on a set of observable features of frms. Fnally, we test our hypotheses usng a unque dataset of envronmental actvst campagns aganst frms n the Unted States from Ths paper flls an mportant need n the lterature as one of the frst emprcal attempts to examne the prvate poltcal strateges of actvsts and the non-market response strateges of frms. The research has mportant mplcatons, n partcular, for the burgeonng lterature on ndustry self-regulaton (Maxwell, Lyon, and Hackett 2000; Kng & Lenox 2000). The prvate poltcal strateges explored n ths paper have been cted as a potentally mportant lever to encourage frms to regulate ther behavor beyond that requred by law,.e., to self-regulate (Arora & Cason 1995; Lenox 2006). Ths paper provdes gudance to understandng when the prvate poltcs of actvsts are lkely to be successful and whch frms are lkely to be subjected to such pressures. As such, t provdes gudance on the lmts and opportuntes for such campagns to motvate self-regulatory behavor n frms. 2. A Model of Prvate Envronmental Actvsm In ths secton, we develop a model of prvate actvsm n the sprt of Baron & ermeer (2005). Central to our model s that actvsts stage campagns aganst frms. These campagns consst of a demand/request for acton on the frm s part ( x ) and a threat/promse of punshment/rewards for falure to comply/complance (η). We wll assume that the request ( x ) s a pre-determned preference of the actvst. Many actvst groups are founded wth certan ssues as ther focus. For example, Rverkeeper s an envronmental advocacy organzaton 3

4 concerned wth polluton of the Hudson Rver n New York State. Thus, for a specfc campagn, we assume that the actvst s man decsons are whom to target and what threat/promse to make. To smplfy our model, we wll focus on threats of punshment for falure to comply. In some nstances, actvsts may reward frms for complance by provdng postve publc relatons and commtments not to target the frm n the future. However, our nvestgaton of envronmental actvst actons n the Unted States found that at the heart of vrtually all actons s a threat of punshment. Punshments nclude consumer boycotts of frm products, cvl lawsuts, and protests as well as more bengn actons such as letter wrtng campagns and proxy votes n shareholder meetngs (Strckland, Wles, & Zenner 1996). We propose a three stage model. In the frst stage, actvsts choose a frm for whom to target. Gven a request and target, the actvst then decdes n the second stage on the level of harm/punshment to threaten the frm. In practce, harm s sometmes gven at the outset of a campagn, e.g., a request wll be made and a boycott wll be waged smultaneously. In such cases, t s useful to thnk of the threat as the contnuaton of harm and the reward for complance as the dscontnuaton of harm. For example, boycotts and protests can be canceled and lawsuts can be dropped. In the thrd stage, frms decde whether or not they wll comply wth the request at the center of the actvst s campagn. We begn wth the utlty actvsts gan from wagng campagns. Actvsts gan utlty out of prvate campagns aganst frms dependng on whether targeted frms comply to demands or not: actvst j ( x, η, ) p u + (1 p )( u c ( η) ) 0 U = (1) j 4

5 where p s the probablty that the targeted frm responds postvely to the demands of the actvst, u s the utlty the actvst gans from frm complance, s the utlty the actvst x u 0 gans from frm non-complance, and c (η ) s the cost of levyng harm on the frm for falng to j comply. We allow for the possblty that the actvst gans utlty from even a non-complant frm ( u 0 ). For example, actvst organzatons may beneft from faled campagns f they x > 0 are perceved as fghtng the good fght. Falure n tself may be used as justfcaton to seek addtonal funds from donors. However, we assume that the actvst gans greater utlty out of the targeted frm complyng than not complyng ( u x u x 0 > ). Otherwse the actvst would have no ncentve to delver rewards or harm (whch are costly) and would prefer to make dle threats f that was credble. A number of factors may mpact the utlty an actvst receves from frm complance (or non-complance). Arguably, the more objectonable are the frm s practces, the greater the utlty the frm wll receve from complance to ts demands. In the envronmental arena, the margnal utlty of complance should be greater for more pollutng frms than for less pollutng frms. In many nstances, ths reflects a smple calculus. If an actvst wshes to curb global warmng, a 10% reducton n greenhouse gas emssons by a more pollutng frm wll lead to a larger absolute reducton as compared wth a less pollutng frm. Other factors play a role as well. Actvsts lkely gan utlty not only from drect changes to frm behavor but from the ablty to attract attenton to causes of concern and to rase funds to ntate future prvate and publc campagns (such as lobbyng government). 1 Large, vsble frms are attractve targets as campagns aganst them are more lkely to garner attenton from the 1 It may also be the case that actvst organzatons suffer from smlar agency problems as for-proft frms. Actvsts may seek personal notorety and prestge by targetng large, vsble frms even at the expense of the organzatons larger objectves. 5

6 meda and the general publc. Such attenton may ncrease the utlty of a campagn regardless of whether the frm comples or not. Furthermore, the margnal utlty of ganng complance may be greater wth large, vsble frms even for smlar gans n mprovement n the underlyng performance attrbute of concern. A smlar reducton n emssons may be more valuable to the actvst f undertaken by a large, vsble frm who attracts publcty. that Gven a request ( x ) and a target (frm ), an actvst wll choose a level of harm (η) such maxu η ( x, η ) actvst j, (2) Takng the frst order condtons and rearrangng terms: Δu x + cj ( η) = 1 p ( η) c '( η) j p '( η) (3) In other words, the actvst wll choose a level of harm such that the gan n utlty to the actvst from the frm respondng postvely plus the cost of delverng harm dvded by the probablty that the frm does not comply s equal to 1) the margnal cost of delverng that harm, dvded by 2) the margnal probablty of the frm respondng postvely to the demand gven the threatened level of harm. We consder each of these latter two factors n turn. The margnal cost of gvng harm, c '( η), s lkely drven by the targeted frm s access to j fnancal and human captal. Eesley and Lenox (2006) propose that the ablty of an actvst to ncentvze a frm to comply wth demands depends on the power of the actvst relatve to the targeted frm power beng defned as access to resources such as fnancal and human captal. On one hand, well-funded actvsts are better able to develop the nfrastructure to ntate and sustan costly actons aganst frms. On the other hand, resource-rch frms may be better able to resst actvst pressure. Frms wth large cash reserves or large human captal reserves are able to 6

7 support dedcated legal and publc relatons staff. They may have the resources to repar reputatons potentally damaged by stakeholder actons. As a result, the margnal cost to an actvst to delver a certan level of harm to a frm s greater, the greater the frm s fnancal and human captal. For our model, we wll assume that 2 c j ( η ) = αη and that j '( η) = 2αη c. The margnal probablty that a targeted frm responds postvely to a promsed reward/harm, p '( η), ultmately depends on whether the beneft of avodng punshment exceeds the absolute value of the operatonal loss assocated wth complyng wth the request. η Δπ (4) where Δ π = π π 0 where π and π are the dscounted future stream of profts that would accrue to the frm x 0 x gven the status quo and complance wth the actvst s request, respectvely. We assume that complance wll lead to an operatonal loss ( Δπ = π π 0 ),.e. complance wll lead to lower profts ndependent of any gan to the frm s reputaton (or loss avodance as captured by η). Otherwse, the frm would have ncentves to comply wth the actvst s request ndependent of any promsed threat or reward and, thus, the actvst s request would be unnecessary. A number of factors may mpact the magntude of operatonal losses facng the frm. The greater the changes requred to comply wth the actvst s request, the greater the cost to the frm all else beng equal. On envronmental matters, more pollutng frms wll lkely face greater costs to acheve some absolute envronmental performance target than less pollutng frms. To meet such targets mght requre small operatonal changes for less pollutng frms, but wholesale x < 0 7

8 changes to physcal plants (lke purchasng new equpment) n order for more pollutng frms to comply. 2 Overall, we assume that there s uncertanty about these operatonal losses and that the actvst does not have full nformaton about them. As such, we adopt a random utlty model when specfyng the utlty of the frm where frms gan both an underlyng value from adoptng (v ) and a random component (ε ). For smplcty, let s assume that f the frm comples wth the actvst s request, the frm s rewarded η,.e., the frm avods punshment. The margnal utlty receved by the frm from complyng wth a request ( x ) therefore s V frm ( x η ) = v + ε = η + Δπ + ε, (5) where ε represents an unobserved utlty (or dsutlty) that the frm receves for complyng. Once agan, we assume that the margnal change n profts due to complance wll be negatve ( Δπ = π π 0 ). Thus, a frm gans utlty by complyng f the reward gven by the x < 0 actvst (or the falure to punsh) exceeds the operatonal losses assocated wth complance ( Δ π ) plus frm specfc utlty (ε ). Gven our random utlty model, the probablty, p, of a targeted frm respondng postvely can be gven by the multplcatve form, The dervatve of whch wth respect to η s, v η + Δπ x p ( η ) = Pr( x = x ) = = (6) v + 1 η + Δπ + 1 ( η) x 1 1 p ' = = (7) ( v + 1) 2 ( Δπ + η + 1) 2 2 Much has been wrtten n recent years about the prospects for frms to realze cost savngs through emssons reductons (see Porter & van der Lnde 1995a, 1995b, and Palmer, Oats, & Portney 1995 for an nterest debate on the matter.) Whle recent emprcal evdence suggests that such wn-wns are possble (Kng & Lenox, 2002), they are unnterestng n ths context as they should obvate the need for actvsts campagns n the frst place. 8

9 Substtutng our specfcatons for the margnal cost of delverng harm and the margnal probablty that a frm comples wth a request nto equaton (3) and solvng for η gves the optmal level of harm for a gven target frm: 2 Δu x η * = ( 1+ Δπ ) + ( Δπ ) 1 (8) α Assumng that α > 0, Δ > 0, and Δπ < 0, we derve the margnal effect of changes n each u x of these parameters: η * η * < 0, α Δu η * > 0, Δπ < 0 (9) PROPOSITION 1. The harm gven s ncreasng as the margnal cost of gvng that harm (α) decreases, as the utlty of ganng complance ( Δ of the frm to comply ( Δ π ) ncreases. u ) ncreases, and the operaton loss Substtutng equaton (8) back nto the probablty equaton (6) and solvng gves us the probablty that a gven frm wll respond postvely to the actvst s request: Δu x p * 1 ( 1 ) = + Δπ + (10) α Assumng once agan that α > 0, Δ > 0, and Δπ < 0, we derve the margnal effect of u x changes n each of these parameters. (Note that the thrd condton only holds for Δπ < 1). x p * p * < 0, α Δu p * > 0, Δπ < 0 (11) PROPOSITION 2. The probablty that a frm comples to a gven request s ncreasng as the margnal cost of gvng that harm (α) decreases, as the utlty of ganng complance ( Δ u ) ncreases, and as the operatonal loss of the frm to comply ( Δ π ) ncreases. 9

10 Ths last fndng s rather unntutve. One may thnk that the larger the potental operatonal loss to the frm, the lower the lkelhood that the frm would comply. However, the larger the potental operatonal loss to the frm, the greater the harm necessary to motvate complance. In our random utlty model, actvsts wll adopt ncreasngly larger harms relatve to the potental operatonal loss. Ths s due to the uncertanty surroundng the frm s actual operatonal loss. To gan complance (and avod payng the full harm), actvsts adopt extreme levels of threatened harm when the potental operatonal loss s large, thus motvatng the frm to comply. Fnally, we consder the lkelhood that a gven frm s targeted by actvsts. The expected utlty of the actvst for a campagn aganst a gven frm s gven by substtutng (8) and (10) nto equaton (1): 2 Δu x U (, *, ) 2 (1 ) 2 ( 1 ) actvst j x η = u + α + Δπ α + Δπ + (12) α Assumng the actvst s constraned n the number of campagns she can wage at any one tme, the actvst wll lkely frst seek to wage a campagn, x, aganst the frm that gves her the greatest utlty: choose actvst j ( x ) max U subject to U* > 0 (13) From equaton (12), one can see that the utlty of the actvst s ncreasng, as the margnal cost of harm decreases and the utlty of complance ncreases. Thus, the probablty that a gven frm wll be targeted should be ncreasng as the cost of harm decreases and the utlty of complance ncreases. 1 2 Pr( target = ) α Pr( target = ) > 0, Δ u > 0 (14) 10

11 PROPOSITION 3. The lkelhood a frm s targeted by an actvst s ncreasng as the margnal cost of gvng harm decreases (α) and as the margnal utlty of ganng complance ncreases ( Δ ). u Note that the relatonshp between the probablty that any gven frm wll be targeted and the operatonal loss of the frm to comply ( Δ π ) s complex. For certan values of α and Δ u, the relatonshp s nonlnear, wth the probablty of beng targeted decreasng at frst as Δ π ncreases, then ncreasng wth Δ π, only to decrease once agan as π Δ ncreases. 3. Emprcal Approach In our model, we develop three propostons concernng 1) the level of harm an actvst wll adopt, 2) the probablty that a frm wll respond postvely to a request and a gven threatened level of harm, and 3) the probablty that a gven frm wll be targeted by an actvst. Our propostons rely on three underlyng constructs: the margnal cost of gvng harm (α), the utlty of ganng complance ( Δ u ), and the cost of the frm to comply ( Δ π ). Unfortunately, these three underlyng constructs are not observable to the econometrcan. However, there are a number of observable factors that lkely nfluence these constructs. As dscussed above, the margnal cost of gvng harm (α) s lkely drven by the targeted frm s access to fnancal and human captal. The greater a frm s reserves of captal to fght actvst actons, the more costly wll t be for the actvst to delver a gven level of harm. In partcular, we propose that the amount of frm cash on hand (Frm Cash) s a good ndcator of the frm s ablty to fght and should be postvely correlated wth the margnal cost of gvng harm. 11

12 We proposed earler that the utlty of ganng complance ( Δ u ) s drven n part by the sze and vsblty of the targeted frm and ts envronmental performance relatve to smlar frms. In partcular, actvsts should gan greater utlty when larger, more vsble and more pollutng frms comply wth requests. To capture frm sze and vsblty, we measure frms total assets (Frm Assets) and ther advertsng ntensty (Frm Advertsng Intensty), respectvely. Whle alternatve measures of frm sze such as frm sales and frm employees are reasonable, these measures are lkely hghly correlated wth frm assets. Smlarly, alternatve measures of vsblty are possble, but advertsng ntensty arguably reflects the degree a frm s brands are recognzable and has the advantage of beng wdely avalable. Fnally, the cost of the frm to comply ( Δ π ) should be ncreasng n the relatve envronmental performance of the targeted frm. Arguably, t s more costly for more pollutng frms to comply wth actvsts requests. We propose that the frm s toxc emssons are a good proxy for the frm s overall envronmental performance. Whle frm actvty may mpact the natural envronment n a number of ways beyond toxc emssons, we assert that toxc emssons are lkely postvely correlated wth other sources of envronmental mpact (Kng & Lenox, 2002). An nterestng queston s whether we should be measurng emssons on an absolute or relatve bass (.e., relatve to other frms of smlar type and sze). Arguments can be made for ether measure as a major nfluence on both the margnal cost of the frm to comply and the utlty the actvst receves from complance. We leave the queston to emprcal analyss and construct both absolute and relatve measures of toxc emssons (Frm Absolute Emssons and Frm Relatve Emssons, respectvely). Wth these measures n hand, we can estmate a seres of reduced-form specfcatons based on our model. We propose that the level of harm an actvst wll adopt s ncreasng as the 12

13 margnal cost of gvng that harm decreases, as the utlty of ganng complance ncreases, and the cost of the frm to comply ncreases (see Proposton 1). Thus, the greater a targeted frm s emssons, sze, and vsblty and the lesser the frm s cash reserves, the greater should be the harm adopted by the actvst. In other words, ( Frm Emssons, Frm Cash, Frm Assets Frm Advertsng Intensty) η * = f, (15) η * * where * η * η η > 0, < 0, > 0, > 0 Frm Emssons Frm Cash Frm Assets Frm Advertsng Intensty Smlarly, we propose that the probablty that a frm comples wth an actvst s request s ncreasng as the margnal cost of gvng that harm decreases, as the utlty of ganng complance ncreases, and the cost of the frm to comply ncreases (see Proposton 2). Thus, the probablty that a frm comples wth an actvst s request should be greater, the greater the frm s sze and vsblty and the more pollutng the frm and the lesser the frm s captal reserves. ( Frm Emssons, Frm Cash, Frm Assets Frm Advertsng Intensty) p * = f, (16) p * * where * p * p p > 0, < 0, > 0, > 0 Frm Emssons Frm Cash Frm Assets Frm Advertsng Intensty Fnally, we propose that the probablty that a frm s targeted s ncreasng as the margnal cost of gvng that harm decreases, as the utlty of ganng complance ncreases, and the cost of the frm to comply ncreases (see Proposton 3). Thus, the probablty that a frm s targeted should be greater, the greater the frm s sze and vsblty and the more pollutng the frm and the lesser the frm s captal reserves. ( Frm Emssons, Frm Cash, Frm Assets Frm Advertsng Intensty) Pr( t arget = ) = f, (17) where Pr( target = ) Pr( target = ) Pr( target = ) Pr( target = ) > 0, < 0, > 0, > 0 Frm Emssons Frm Cash Frm Assets Frm Advertsng Intensty 4. ata & Measurement To estmate our emprcal models, we constructed a database of prvate envronmental 13

14 actvst campagns drected aganst frms n the Unted States durng the perod ata on actvst campagns were gathered through an exhaustve search of U.S. newspaper artcles and legal actons as recorded n LexsNexs records and was bolstered wth addtonal data from the Investor Responsblty Research Center. 4 We dentfed 552 actvst campagns nvolvng 273 frms and 267 unque actvst groups n the U.S. durng ths perod. The subject of the campagns vared from requests to report emssons of global greenhouse gases to requests to elmnate the dscharge of toxc chemcals. Table 1 ncludes a summary of the requested actons and ssues ncluded n our sample Insert Table 1 about here ependent Varables Fve broad classes of tactcs to nduce harm are ncluded n our sample: lawsuts, protests, boycotts, letter wrtng campagns, and proxy votes 5. Whle we do not drectly We ntally collected data back to We lmt our analyss to the 16-year wndow between to ncrease our confdence that we were able to dentfy the populaton of major actvst actons n any gven year. We found that as we searched back further than twenty years, we could dentfy sgnfcantly fewer actvst campagns. Whle ths may reflect some general tme trend, we were concerned that we were begnnng to mss mportant campagns. ata on protests, boycotts, and letter wrtng campagns were collected from the LexsNexs Academc database of U.S. newspaper artcles rangng from February 10, 1971 to November 25, 2003 (LexsNexs, 2003). We searched usng keywords ncludng: stakeholder, envronmental group, NGO, frm, envronment, and company. Proxy vote data were collected from the Investor Responsblty Research Center (IRRC). ata on cvl lawsuts were collected through the LexsNexs Legal Research database of Federal and State cvl law suts pertanng to envronmental ssues. We searched usng keywords ncludng: stakeholder, envronmental group, NGO, frm, and company. Records were retaned when we could dentfy the actvst group, the frm, and the request. Ths nformaton was avalable n vrtually all records dentfed. The database contans federal and state case law on envronment-related cvl suts, ncludng U.S. Supreme Court, U.S. Courts of Appeals, Federal strct Courts and state courts. Addtonal data were collected from stakeholder groups annual reports and webstes and by contactng offcals from the group when necessary. Proxy votes are ncluded because they are often ntated by actvsts who specfcally buy enough shares to ntate a proxy vote (Strckland, Wles, & Zenner 1996). Ths was renforced by comments va emal from a nun n one of the relgous groups who wshed to reman anonymous yet noted that the Ssters usually try to purchase a few more shares than the mnmum requred to fle a proxy vote. 14

15 observe the specfc threatened level of harm n a gven campagn, we submt that these broad classes serve as a suffcent proxy to dscern dfferent levels of harm. Arguably, campagns vary from more bengn modes of cvl unrest such as letter wrtng campagns to company offcals to more confrontatonal actvtes such as protests and cvl lawsuts. We may surmse that cvl suts, for example, pose the greatest potental harm due to the drect rsk of fnancal losses mposed by a credble thrd party (the judcary). 6 Boycotts, on the other hand, are lkely to be less effectve unless they are of suffcent sze that they can make a sgnfcant mpact on the sales of the targeted frm. Recevng even several thousand letters durng a successful letter wrtng campagn appears less lkely to mpose an economc burden to a frm than a protest or boycott. We construct a measure of the level of harm threatened by the actvst by assgnng to a campagn a value from one to fve dependng on the adopted tactc. In partcular, a campagn utlzng a proxy vote s assgned a level of harm of 1. Letter wrtng campagns were assgned a value of 2 and boycotts, protests, and lawsuts were assgned values of 3, 4, and 5 respectvely. To estmate our level of harm model, we adopt an ordered probt specfcaton where an underlyng score s estmated as a lnear functon of the ndependent varables and a set of cut ponts: Pr(η = n) = Pr( κ n-1 < β x < κ n ) (18) where x represents a vector consstng of our ndependent varables and controls and κ n represents a cut pont. The ordered probt specfcaton has the advantage of assumng ordnalty but not cardnalty n our rankng. 6 In fact, envronmental suts by actvsts result n greater wealth loss for frm defendants than any other knd of lawsuts (Bhagat, et al., 1998). 15

16 For each campagn, we dentfed whether the targeted frm compled wth the actvst s request. A frm s complance to a request s coded as a one f the targeted frm postvely responded to actvst demands wthn fve-years of ntaton and zero otherwse. 7 If there were multple actons wthn ths wndow, they were all coded as a one f the frm responded. 8 ata for codng the lkelhood of complance was gathered from a search, by company and actvst names, of artcles referencng the campagn usng the LexsNexs Academc database of newspaper artcles. In the case of cvl suts, the LexsNexs Federal and State Cvl Sut database was searched and complance was coded accordng to the fnal dsposton of the sut (e.g., the nature of the settlement). 9 Gven the bnary nature of our complance varable, we adopt a smple probt specfcaton when estmatng the probablty that a frm comples: p * = Φ(β x) (19) where x represents a vector consstng of our ndependent varables and controls and Φ() represents the standard normal dstrbuton. To capture the lkelhood that a frm s targeted, we counted the number of tmes a gven frm was the target of an actvst campagn n a gven year. Snce we have a count varable, we A tmeframe of ths length was chosen n order to gve tme for the acton to take effect (snce the date recorded was when the acton started or was announced) and then to gve tme for the frm to respond. If the frm made a change beyond ths tme frame we concluded that t s too tenuous to attrbute that change to the ntal actvst acton. On average, frms responded wthn 11 months of an actvst acton. Only n three nstances dd we fnd frms actng n congruence wth actvsts requests n a tmeframe greater than 5 years. Includng these three nstances does not have a sgnfcant mpact on our results. As a robustness check, we also estmated models usng tme to acton as the dependent varable and found smlar results. We leave to further analyss of the effects of multple actvst actons to future work. We do attempt to control for these stuatons n our analyss. There were a small number of outcomes that could not be found n LexsNexs. In these cases, a search was performed to fnd a record of the outcome of the acton on the frm s or stakeholder groups webstes and annual reports. Only f one of these sources drectly addressed the outcome of the exact concern rased by the stakeholder acton was the outcome coded postvely. Complance was not coded f these searches yelded nothng. The one excepton to ths rule was for proxy votes. If a proxy vote was resubmtted the followng year, then we felt confdent that the company had not made the requested change. In order to verfy the codng of ths varable, we had two research assstants ndependently code complance followng the same protocol. The codng from these efforts was correlated at 95.21%. 16

17 adopted a negatve bnomal specfcaton. 10 The expected number of tmes targeted gven a set of ndependent varables may be gven by, E[Tmes Targeted t t ] = λ t = exp(β t + ε t + ν ) (20) where β s the coeffcent vector, t represents our set of tme-varant frm characterstcs, and ν and ε t are ndependent random varables. To have a control sample of frms never targeted by actvsts, we reconsttuted our database usng all publc frms n the Unted States n sectors where at least one frm was the target of a stakeholder acton between Frms were culled from the Compustat Annual ataset usng the 4-dgt Standard Industral Classfcaton code to dstngush sectors. Thus, we analyze two datasets. The frst represents campagn data where each observaton represents a specfc request by an actvst group of a specfc frm. The resultng dataset contans 1,092 unque actvst-frm-campagn trplets. 11 These data are used to estmate our models of level of harm and the probablty of complyng. The second dataset contans frm data were each observaton represents a frm-year observaton. The resultng panel dataset ncludes 33,213 observatons of 3,338 frms. These data are used to estmate the frequency that a gven frm s targeted by envronmental actvsts. 4.2 Independent Varables A number of frm level data were gathered from Standard & Poor s Compustat Annual ataset that s based on the Securtes and Exchange Commsson (SEC) flngs of U.S. publc The negatve bnomal model s commonly used for over-dspersed count data lke ours (Grlches et al, 1987). The negatve bnomal model s a generalzed form of a Posson model where an ndvdual, unobserved effect s ntroduced n the condtonal mean (Greene, 2000). We do not adopt a Posson model because the assumpton of constant dsperson appear volated,.e. the mean and varance of the event count are not proportonal. Note that the number of observatons exceeds the number of campagns snce more than one frm or more than one actvst group may be nvolved n any gven campagn. To allay concerns of overconfdence n our model estmates, we present robust standard errors based on clusterng on the campagn. 17

18 frms. 12 A frm s cash poston (Frm Cash) was recorded durng the tme an acton was ntated aganst the frm. Smlarly, we measure frm sze usng the frm s total assets durng the tme an acton was ntated aganst the frm (Frm Assets). Alternatve measures such as frm sales and frm employees were hghly correlated wth frm assets and had mnmal effects on our estmates when used n place of Frm Assets. We take the natural logarthm of both of these measures to account for skew. Fnally, Frm Advertsng Intensty was measured as the rato of frm advertsng expendtures to frm assets for the year an acton was taken. 13 To capture the envronmental emssons of the frm, we use data on faclty emssons of toxc chemcals as collected n the Toxc Release Inventory (TRI) by the U.S. Envronmental Protecton Agency. Snce 1987, the EPA has requred all manufacturng facltes wth greater than 10 employees to report emssons of over 250 toxc chemcals. 14 To construct our measure of absolute performance (Frm Absolute Emssons), we calculate the log of a frm s total annual emssons (n lbs.) by calculatng the toxcty-weghted sum of all core chemcals released nto the envronment, treated onste, and transferred offste for each manufacturng faclty of each frm n our sample. 15 To calculate relatve performance (Frm Relatve Emssons), we estmate a quadratc functon between faclty sze and total emssons for each 4-dgt Standard Industry Classfcaton (SIC) code wthn each year usng standard OLS regresson. W t = e αjt s β1jt ln(s)* t s β2jt t e ε jt (18) Vrtually all the frms dentfed as targets of actvst campagns were publcly traded. There were a number of observatons that were mssng for ths varable. So as to not restrct our sample, we substtuted the average advertsng ntensty n a frm s 4-dgt SIC code when data were mssng. The lst of reportable chemcals has been amended a number of tmes over the last ffteen years. To ensure comparablty, we focus on the 246 core chemcals that have consstently been requred to be reported. Facltes only need to report emssons of chemcals f they emt more the 25,000 lbs or use 10,000 lbs. of that chemcal. Chemcal vary greatly n ther toxcty. Smaller releases of more toxc chemcals can jave greater envronmental mpacts than larger releases of more bengn chemcals. To measure the relatve toxcty of emssons, we weghted each chemcal by the nverse of the EPA s Reportable Quantty toxcty scale. 18

19 where W t s aggregate emssons for faclty n year t, s t s faclty sze, α jt,β 1jt, and β 2jt are the estmated coeffcents for sector j n year t, and ε jt s the resdual. We use the estmated functon to predct the emssons of each faclty gven ts sze, ndustry, and year. Then we use the resdual to measure the relatve emssons of each faclty. W * t = e αjt s β1jt ln(s)* β2jt t s t RW t = e ε jt/σ εjt where W * t s predcted emssons for faclty n year t, RW t s the standardzed relatve (19) emssons for faclty n year t, and σ εjt s the standard error of the resdual for the SIC and year par. To create a frm-level measure of relatve emssons, we calculate the mean relatve performance of each of the frm s facltes for each year Controls We nclude a number of controls for potental sources of unobserved heterogenety n our samples. For our estmates based on the campagn dataset, we nclude ndustry-sector and year dummy varables. Recall, that our sample consttutes unque frm-actvst-campagn trplets. We are able to leverage the fact that most actvsts wage more than one campagn and many frms are the target of more than one campagn and nclude frm and actvst dummy varables for all frms and actvsts who are targeted or ntate more than one campagn. For our estmates based on the frm sample, we have a more tradtonal panel and nclude year fxed-effects and frm random effects. Frm random-effects are adopted rather than fxed-effects due to the large number of frms who are never targets and thus would have been removed from our sample gven our negatve bnomal specfcaton. 16 Ths measure has been used by a number of papers n the lterature as a measure of envronmental performance and s hghly correlated wth other ndcators such as splls, accdents, and hazardous waste stes (Kng & Lenox, 2002). 19

20 We also ntroduce a number of varables to control for the unque nature of a campagn. As llustrated n Table 1, actvsts may request a number of dfferent actons from frms. In some nstances, they may request that frms adopt prncples or sgn pledges. For example, the Coalton for Responsble Economes (CERES) has requested a number of frms to adopt a set of prncples outlnng a commtment to the envronmental sustanablty of ther busness operatons. In other nstances, actvst groups request that frms provde nformaton about ther operatons often n the form of ether product labels or detaled reports. Actvsts may request a whole host of operatonal changes from frms from ncreasng the use of recycled materals to the reducton of toxc effluents. The requested actons wthn our database fall nto one of four categores: adopt prncples or pledges, label products or processes, report on operatons, and make operatonal changes. To control for the requested acton, we nclude dummy varables for each of these categores. In addton to the requested acton, campagns vary on the requested ssue. Our database ncludes four major categores of envronmental ssues: polluton, ndustral recyclng, land use / habtat destructon, and greenhouse gas emssons (global warmng concerns). Arguably, the soundness of the scence and the ndvdual rsk assessment of each of these ssues vary sgnfcantly. Whle the envronmental consequences of habtat destructon and polluton are often well understood, there has been less perceved agreement among the general publc (though not among scentsts) about the global warmng consequences of emttng greenhouse gases or the health effects of consumng genetcally modfed organsms. To control for varance across these ssues, we nclude dummy varables ndcatng the requested ssue at the center of a campagn. Fnally, there are many dfferent types of actvst groups represented n our sample 20

21 ncludng tradtonal envronmental advocacy organzatons, ndvdual actvsts, relgous groups, and other non-governmental organzatons where envronmental ssues are not ther sole focus. These types of groups dffer n the degree to whch they are, at least perceved, to be legtmate arbtrators of envronmental ssues (Fneman and Clarke, 1996; Harvey and Schaefer, 2001). One could magne that certan types of groups may favor specfc campagn tactcs and dsavow others. Table 2 presents a summary of the type of tactc chosen by each actvst category. To control for varance ntroduced by actvst type, we nclude dummy varables for each of the major actvst types n our sample (envronmental advocacy organzatons, ndvdual actvsts, relgous groups, and other non-governmental organzatons) Insert Table 2 about here Analyss & Results Tables 3 and 4 present the descrptve statstcs and par-wse correlatons, respectvely, from our two samples. Please note that Frm Absolute Emssons, Frm Cash, and Frm Assets are expressed n natural logs. 17 Of note, approxmately 44% of the frms n our sample compled wth the actvst s request (see Frm Complance to Request). On average, very few frms were targeted by actvsts (see Tmes Frm Targeted n a Year). However, at the extreme, some frms were targeted upwards of 10 tmes n a gven year. As for the correlaton table, of note s the hgh correlaton between Frm Complance to Request and Level of Harm Adopted by Actvst. 17 Some frms aggregate, toxcty weghted emssons were zero. To avod losng those observatons, we added one to each frms absolute emssons before takng the natural log. 21

22 As predcted n equaton (6), the hgher the level of harm threatened, the greater the lkelhood that the targeted frm wll comply wth the request Insert Tables 3 & 4 about here Table 5 presents our estmates for both the level of harm adopted by the actvst and the lkelhood a targeted frm comples to a request gven a campagn. Models 1 through 3 present estmates for our specfcaton of the level of harm adopted. In Model 1, we use our measure of absolute emssons (Frm Absolute Emssons) and control for fxed sector, year, frm, and actvst effects. The model s statstcally sgnfcant and explans approxmately 46% of the varance. As predcted, we estmate postve coeffcents for Frm Absolute Emssons and Frm Assets and a negatve coeffcent for Frm Cash. Surprsngly, we estmate a negatve coeffcent on Frm Advertsng Intensty though we are not confdent that our estmate s dfferent than zero (t-stat = -1.03) Insert Table 5 about here Whle we are confdent (p <0.001) n our estmates of Frm Assets and Frm Cash, we are not confdent n our estmate of Frm Absolute Emssons (t-stat = 0.62). One possblty s that our measure of absolute emssons does not fully capture the decson logc of actvsts. Perhaps, actvsts gan utlty from frm mprovements relatve to other frms wthn the frm s sector rather than absolute mprovements. In Model 2, we replace Frm Absolute Emssons wth our relatve measure of emssons. The model remans statstcally sgnfcant and explans exactly 22

23 the same amount of varance. We fnd a postve, but not sgnfcant, coeffcent on Frm Relatve Emssons. All other coeffcent estmates are smlar to the prevous estmates presented n Model 1. Another possblty for the lack of sgnfcance on the frm emssons coeffcent s that the mpact of emssons on the level of harm adopted s nfluenced by the type of request and actvst. For example, t seems reasonable that dfferent actvst groups favor dfferent levels of harm. In Model 3, we re-estmate Model 2 controllng ths tme for the requested acton, requested ssue, and the actvst type. The model s statstcally sgnfcant and now explans 56% of the varance. Our estmates for Frm Cash, Frm Assets, and Frm Advertsng Intensty are smlar to prevous models. Our estmate for Frm Relatve Emssons, however, ncreases over seven-fold. Whle the estmate s stll not sgnfcant at the p < 0.01 level, we are 95% confdent the estmate s greater than zero f we do not adjust our standard errors due to clusterng on the campagn (Greene 2003). In Models 4 through 6, we turn our attenton to estmates of our model predctng the lkelhood that a frm wll comply to a request. In Model 4, we use our measure of absolute emssons (Frm Absolute Emssons) and control for fxed sector, year, frm, and actvst effects. The model s statstcally sgnfcant and explans approxmately 36% of the varance. As predcted, we estmate a negatve coeffcent for Frm Cash wth a confdence of 99%. Surprsngly, we estmate negatve coeffcents on Frm Absolute Emssons, Frm Assets and Frm Advertsng Intensty though we are not confdent that our estmates are dfferent than zero. In Model 5, we re-estmate our model substtutng Frm Relatve Emssons for Frm Absolute Emssons. Once agan, the model s statstcally sgnfcant and we estmate a sgnfcant, negatve coeffcent for Frm Cash (p < 0.001). We contnue to estmate a negatve 23

24 coeffcent on frm emssons, however, usng Frm Relatve Emssons, we are now confdent that the coeffcent s less than zero. As a further confrmaton, we re-estmate Model 5 ncludng controls for the requested acton, requested ssue, and the actvst type. In Model 6, we contnue to fnd a sgnfcant, negatve coeffcent on frm emssons. The model remans statstcally sgnfcant and all other coeffcent estmates are smlar to prevous models. The model explans approxmately 46% of the varance. We speculate why we estmate a negatve coeffcent on frm emssons n the dscusson secton. Fnally, we turn our attenton to the lkelhood that a frm s targeted. Table 6 presents our estmates of models of the number of tmes a frm s targeted by actvsts n a year. Recall, our sample ncludes all publc frms n ndustry sectors where at least one frm was targeted by actvst campagns durng the perod. We adopt a negatve bnomal specfcaton and nclude year fxed-effects and frm random-effects n all models presented. In Model 7, we estmate a targetng model usng Frm Absolute Emssons as our measure of envronmental performance. As hypotheszed, we estmate postve coeffcents for Frm Absolute Emssons, Frm Assets, and Frm Advertsng Intensty and a negatve coeffcent for Frm Cash. All coeffcent estmates are sgnfcant at the p<0.001 level. In Model 8, we re-estmate Model 7 substtutng Frm Relatve Emssons. The model contnues to be statstcally sgnfcant and we once agan estmate postve, sgnfcant coeffcents for Frm Absolute Emssons, Frm Assets, and Frm Advertsng Intensty and a negatve, sgnfcant coeffcent for Frm Cash Insert Table 6 about here

25 One potental concern s that the dstrbuton of our dependent varable, Tmes Frm Targeted n a Year, s heavly skewed toward zero. Only 8% of the frms n our sample ever have had an actvst campagn drected at them durng the tme perod of our study. Potentally there s some unobserved feature of frms that determnes whether or not they are ever targeted. To ncrease our confdence n the prevous estmates, we reduce the sample to only frms who are targeted and re-estmate Model 8. In essence, ths model captures the extent to whch a frm s targeted gven that t s targeted. In Model 9, we contnue to estmate postve coeffcents for Frm Relatve Emssons, Frm Assets, and Frm Advertsng Intensty and a negatve coeffcent for Frm Cash. The magntude of each coeffcent s smaller than estmated n Model 8 and we are no longer confdent that Frm Relatve Emssons and Frm Advertsng Intensty are greater than zero. Ths s not surprsng gven the reduced sample sze and the strngency of the test. 6. scusson Consstent wth our hypotheses, we fnd that the level of harm threatened by actvsts s ncreasng as frm emssons ncrease, frm cash decreases, and frm assets ncrease (though the evdence wth respect to frm emssons s weak). Furthermore, we fnd that the probablty of frm complance wth a request s postvely and sgnfcantly decreasng wth ncreasng frm cash. Fnally, we fnd that the lkelhood of a frm beng targeted by an actvst s ncreasng wth frm emssons, frm assets, and frm advertsng ntensty and decreasng wth frm cash. Each of these latter estmates was sgnfcantly dfferent from zero. Inconsstent wth our hypotheses, we found that Frm Relatve Emssons had a sgnfcant, negatve mpact on the probablty of a frm respondng postvely to a request. Whle ths result s ntutve on the surface, we had proposed that actvsts would have ncentves 25

26 to rase the level of harm, the more pollutng a frm, ncreasng the lkelhood the frm would respond postvely. One can magne a number of reasons why ths underlyng logc may not hold. For one, f actvsts face a budget constrant they may be unable to credbly threaten extreme amounts of harm. Thus, we may observe that moderately pollutng frms are more lkely to comply than less pollutng frms but that actvsts are unable to mpose the necessary harm to motvate extremely pollutng frms. Alternatvely, the fact that a frm has hgh relatve emssons may reflect that a frm s managers have a preference for resstance and have been resstant to stakeholder demands n the past and contnue to hold a preference for resstance. Surprsngly, we estmate negatve coeffcents for the margnal mpact of Frm Advertsng Intensty on both the level of harm adopted and the lkelhood that frm wll comply wth a request. Whle we are not confdent that any of these coeffcent estmates were sgnfcantly dfferent from zero, t s worthwhle to consder why we consstently estmated negatve coeffcents. One possblty s that advertsng ntensty does not capture the underlyng construct of frm vsblty. Survey responses of consumer famlarty wth a frm s brands s one alternatve measurement strategy (Kng & Lenox, 2000). Another possblty s that advertsng ntensty may represent a source of strength rather than a lablty for frms when t comes to actvst campagns. Smlarly to captal reserves, frms wth strong marketng capabltes may be able to push back and resst actvst demands engagng n publc relatons and refutng the clams of actvsts. Such frms can rase the cost of delverng harm for the actvst and are less lkely to comply wth a request. Our results are robust to a number of specfcatons. Across all our models, we nclude year dummes to control for heterogenety over tme. In our models of harm adopted and complance, we nclude dummes for sector, frm, and actvst to control for unobserved 26

27 heterogenety across each group. In addton, we nclude controls for the nature of acton requested, the ssue requested, and the actvst type. In our model of targetng behavor, we control for stable sources of unobserved heterogenety between frms by makng full use of our panel and ncludng frm random-effects. One potental concern wth our analyss s that our database mght not nclude all actvst campagns. If these unobserved campagns are randomly dstrbuted across the populaton, the falure to nclude them wll not bas our results and ther excluson would smply make t harder to fnd sgnfcant coeffcents. There s reason for concern, however, f our database mssed campagns n a systematc way related to our varables of nterests. Ths seems unlkely though. If there s a bas, most lkely t s that more major or mportant campagns are more lkely to appear n our dataset. Nonetheless, whle campagns that were mnor enough to have not been reported n even local newspapers could have been mssed, our database does contan some very small campagns lmted to one local area. Even f our dataset s based towards well publczed actvst campagns, snce these have the bggest mpact on frms, they should be the ones we are most concerned about. However, we reman cautous that well publczed actvst campagns may be those where more harm s threatened or nflcted and thus the ones that frms are more lkely to respond to. There are a number of opportuntes to advance both the theoretcal model and emprcal analyss presented n ths paper. Prevous work has found that there may be sgnfcant dfferences across actvsts that nfluence the lkelhood that they would adopt a certan level of harm and the lkelhood that they would target certan frms (Eesley & Lenox, 2005; Eesley & Lenox, 2006). Actvsts are motvated by a mx of factors ncludng focused objectves such as brngng about change n targeted frms, but also broader objectves such as attractng attenton 27

28 to ssues, securng resources for the organzaton, and garnerng ndvdual recognton and respect. Some actvsts are unwllng or, at least unlkely, to accept more aggressve form of unrest. For example, n our sample, relgous organzatons such as nunneres are far less lkely to engage n protests and cvl suts (see Table 2). Actvst preferences such as these have nterestng mplcatons for the structure of campagns and the selecton of targets. We assume n our theoretcal model that the topc of the campagn s an exogenous preference of the actvst. An nterestng extenson would be to model ths as a strategc choce. Actvsts lkely have some dscreton n the specfc ssues they campagn and the demands they make. One could magne a whole host of strategc consderatons that may nfluence whch ssues and actons best advance larger actvst objectves. These may be exacerbated by other more personal goals of the actvst such as ndvdual and organzatonal growth and advancement. Complcatng matters further s that campagns may not be ndependent and that frms and actvsts may be playng a mult-stage dynamc game across tme and campagns. Furthermore, as the number of actvst groups s not fxed, new actvst groups may enter or there may be syndcaton of efforts whch would reduce the cost to nflct a level of harm. Baron & ermeer (2005) consder a number of these extensons n ther theoretcal model. We leave emprcal analyss of such factors to future work. 7. Concluson In ths paper, we examne actvsts use of prvate poltcs to engender frm complance wth actvst objectves. Based on our model, we propose that the greater the utlty of complance for the actvst, the greater the operatonal loss for the frm, and the lesser the margnal cost of delverng harm by the actvst, the greater the level of harm threatened and the 28