POLLUTERS AND COLLECTIVE ACTION: THEORY AND EVIDENCE*

Richard Damania University of Adelaide

Per G. Fredriksson

Southern Methodist University

Thomas Osang‡ Southern Methodist University

March 30, 2004

Abstract We suggest a new perspective on firms’ ability to organize collective action. We argue that industries that face a greater number of regulations have an easier time forming a lobby group and sustaining joint lobbying efforts. In particular, firms in industries that are pollution intensive, and therefore incur abatement costs, face an extra policy issue compared to other industries. The prediction that emerges from the theory is that more polluting industries should have greater levels of lobbying contributions. Using U.S. manufacturing sector data, we find empirical support for this hypothesis. JEL Codes: D70, Q28. Keywords: Lobby group formation, political economy, protection, regulation. * We are grateful to two helpful referees, Toke Aidt, Brian Copeland, Bouwe Dijkstra, Christian Hilber, Angeliki Kourelis, Daniel Millimet, Felix Oberholzer-Gee, Barkley Rosser, Jr., Kevin Siqueira, and session participants at the Public Choice Society Meetings in San Antonio, at the European Public Choice Society Meetings in Paris, and at the EAERE Annual Conference in Southampton, for useful discussions, comments and suggestions, and to Kishore Gawande for sharing his data and providing additional comments. Earlier versions of the paper were circulated under the title “Trade Policy, Polluters, and Collective Action: Theory and Evidence.” The usual disclaimers apply. ‡ Correspondence: Thomas Osang, Department of Economics, Southern Methodist University, Dallas, TX, 75275; Tel (214) 768-4398; E-mail: tosang@mail.smu.edu

2

POLLUTERS AND COLLECTIVE ACTION: THEORY AND EVIDENCE*

Abstract We suggest a new perspective on firms’ ability to organize collective action. We argue that industries that face a greater number of regulations have an easier time forming a lobby group and sustaining joint lobbying efforts. In particular, firms in industries that are pollution intensive, and therefore incur abatement costs, face an extra policy issue compared to other industries. The prediction that emerges from the theory is that more polluting industries should have greater levels of lobbying contributions. Using U.S. manufacturing sector data, we find empirical support for this hypothesis.

3

I. INTRODUCTION

The seminal theory by Olson (1965) predicts that industries with fewer firms have a greater ability to

undertake collective action. They organize cooperative political action more easily because greater

concentration lowers the cost of political action.1 The empirical evidence is inconclusive, however.

Andres (1985), Masters and Keim (1985), Heywood (1988), and Humphries (1991) find positive

effects of industry concentration on the probability of making political action contributions (PAC) (see

also McKeown (1994)). Pittman (1976) find that concentrated industries generate greater contribution

levels. Grier et al. (1991) find an inverted-U shaped relationship between the level of PAC formation

and industry concentration, with a maximum political participation rate occurring at a four-firm

concentration ratio around 0.45. Esty and Caves (1983) and Zardkoohi (1985) report ambiguous effects

of concentration on PAC contributions.2

In this paper we suggest an alternative perspective on firms’ ability to organize collective action

which, to our knowledge, has been ignored so far. The novel argument is that industries that face

multiple regulations (a greater number of policy issues) find it easier to overcome collective action

problems and sustain lobbying. In particular, we focus on the difference between firms in polluting and

clean industry sectors. Using a simple repeated game framework similar to Spagnolo (2000), we argue

that firms in industries that are naturally polluting (e.g., due to their input requirements), and therefore

incur pollution abatement costs, will face an additional policy battle compared to other industries,

1 A large literature has extended this theory in various directions. However, Grossman and Helpman (1994) argue that the literature suffers from a lack of attention to the issue of lobby group organization, and Persson and Tabellini (2000, p. 175) find that “The major problem is that we lack a precise model of the process whereby some groups get politically organized and others not. This is a difficult question to which there is still no satisfactory answer.” 2 Most previous studies of PACs examine the allocation and timing patterns of PACs (see, for example, Snyder (1990) and Stratmann (1992, 1995)), as well as the effects of PACs (see, for example, Salamon and Siegfried (1977) and Stratmann (1991)). A different strand of the literature argues that the importance of corporate PAC contributions in US politics is relatively small, that PAC contributions are not equivalent to bribes or a proxy for lobbying activity (see Milyo et al. (2000) and Ansolabehere et al. (2002, 2003)). These authors argue that PAC contributions primarily buy access to legislators or are a form of consumption good with little effect on legislation.

4

everything else equal.3 This enables such industries to sustain greater cooperation and lobbying. This is

because firms seeking to form a lobby group face a free-riding problem due to a limited amount of

“enforcement power” available to punish deviation and ensure cooperation. Firms that face multiple

areas of regulation have an advantage in the formation of lobby groups because they have a greater

amount of enforcement power available to reallocate between policy issues. When joint lobbying gives

large gains in environmental policy, this surplus can be reallocated to trade policy, for example. Free

riding behavior on trade policy lobbying may thus more easily be disciplined. The prediction that

emerges from our theoretical model is that polluting industries are relatively less affected by the free-

riding problems involved in organizing political action, and we thus expect the level of political

contributions to be higher in these sectors.4

We evaluate this prediction using a cross-section data set of U.S. manufacturing industries. Our

empirical model builds on a multiple-equation model by Gawande and Bandyopadhyay (2000), who

test the well-established theory of Grossman and Helpman (1994) on the pattern of protection (their

theory takes lobby group formation as given). We augment Gawande and Bandyopadhyay’s model

with an additional equation for environmental policy stringency.5 The empirical results lend support to

our theory. Industry political action committee (PAC) contributions, and thus the level of lobby group

cooperation, are greater in industries with larger pollution abatement costs. This result is robust to

several measures of lobby group formation and environmental policy.

3 Spagnolo (2000) models (theoretically) issue linkage in international agreements. He does not discuss lobby group formation, or the determination of trade protection or environmental policies. See also Bernheim and Whinston (1990) and Conconi and Perroni (2001). 4 This conclusion is based on the assumption that the interests of firms in an industry coincide sufficiently. In the current context this requires that regulations are sufficiently severe so as to induce firms to lobby for less stringent regulations. Clearly in cases where either firms are highly heterogenous, or regulations have differing impacts on different firms, there will be no incentive to lobby for the same policy changes. 5 Gawande and Bandyopadhyay (2000), as well as Goldberg and Maggi (1999), find support for the predictions made by the model of Grossman and Helpman (1994). See also Gawande (1997, 1998). See Gawande and Krishna (2001) for a recent survey of this literature.

5

The present paper contributes to the recent literature on the formation of lobby groups. In the

area of pollution taxation, Damania and Fredriksson (2000) argue that collusive industries may more

easily form lobby groups that oppose such taxes. Using a related set-up, Damania and Fredriksson

(2003) discuss the effect of (exogenous) trade liberalization on environmental policy formation when

lobby group formation is endogenous. Pecorino (1998) and Mitra (1999) discuss the formation of trade

lobby groups.6 Neither of these papers explore the relationship between collective action and the

number of policy instruments, however.7

Empirical work is severely lacking in this area, although some related work does exists. Our

paper complements Grier et al. (1994) who argue that industries which potentially may benefit from

government assistance contribute more in corporate PAC contributions, but are hindered by collective

action problems. Pittman (1988) shows that the level of federal regulations (primarily measured as the

level of capital expenditures on pollution abatement induced by EPA-regulations) significantly

determines campaign contributions. To our knowledge, no study addresses the influence of the number

of regulations on the degree of political action, however.

The paper is organized as follows. Section II sets up a stylized model of the lobbying game.

Section III describes the econometric model, and Section IV discusses the data. Sections V and VI

report the empirical results and the sensitivity analysis, respectively, while Section VII concludes.

6 Pecorino (1998) studies free riding in firms’ lobbying for protectionism in a repeated game model. He finds that an increase in the number of firms in an industry does not necessarily imply that free-rider problems increase. Cooperation may be sustained even under perfect competition, i.e. with an infinite number of firms. Mitra (1999) endogenizes the organization of lobby groups using the Grossman and Helpman (1994) model. He employs an industrial organization-endogenous market structure approach to find the equilibrium number of lobby groups. Mitra argues that geographically concentrated industries with large capital stocks, inelastic demand functions, and few capital owners, are more likely to organize. 7 Our work is a complement to the literature on collective action on the demand side (see, e.g., Hamilton (1993)), and to the theory of regulatory “capture” originated by Stigler (1971) and extended by Peltzman (1976) and many others. The latter authors argue that regulations that erect barriers to entry function as outright transfers of wealth, and are therefore demanded by industry. In our paper the existence of environmental regulations are due to industries being “naturally” polluting, and larger transfers may occur due to a greater ability to undertake collective action.

6

II. A THEORETICAL EXAMPLE In order to illustrate our argument, we outline a stylized infinite horizon model with complete

information, which makes use of the framework developed by Spagnolo (2000) in his study of linkages

of environmental and trade policies in international agreements. The model will underline the reasons

why we may expect pollution intensive sectors to have an easier time to sustain lobbying.

We have two industry sectors, k = A, B, which are identical except that sector A’s production is

polluting whereas sector B’s production is non-polluting. Each industry sector has two identical firms,

h = a, b. Thus in total we have four firms, kh. All firms face at a minimum n government policies each

denoted by i, e.g., trade policy, corporate and wage taxes, etc. Because sector A firms are polluting they

in addition encounter an environmental policy, and these firms thus face n+1 government policies. For

simplicity we set n=1, where the common government policy is a trade policy. Thus, policy issue i=1 is

trade policy, and policy issue i=2 is environmental policy.8 Both sectors thus produce tradable goods.

The two firms in each industry sector wish to organize joint trade policy lobbying. However,

they face a Prisoner’s Dilemma since when one firm contributes to the lobbying effort its rival has an

incentive to deviate. The one-period strategic interaction on policy issue i is represented as a Prisoner’s

Dilemma in which each firm may choose to either contribute (Ci), or free ride (defect) (Di). In industry

k, the one-shot policy Prisoner’s Dilemma game is characterized by firm h’s action space

,,,ii

hki DC=Θ ,, BAk ∈ , bah∈ , and policy payoff functions ,R: k

ikhi

2→Θπ where

.kbi

kai

ki Θ×Θ=Θ This generates the following policy payoff matrix for policy i=1 (trade policy) for the

sector k firms, where ,iiii ZNXY >>> and :2 iii XZY <+

8 An alternative would be to assume all sectors have positive levels of pollution, but some sectors produce only non-tradable goods. However, in our data set all sectors are engaged in international trade (see below).

7

Firm b

Ci Di

Ci Xi, Xi Zi, Yi

Firm a Di Yi, Zi Ni, Ni

The two firms in sector A in addition face a similar Prisoner’s Dilemma on policy i=2 (environmental

policy). In sector A, the payoffs from the policy outcomes on both policy issues enter firm h’s

aggregate static profit functions ),,( 21 ππΠ=Π hA ,,bah = which are continuous and twice

differentiable, with ,0/ >∂Π∂ ihA π i=1,2. In sector B, the payoff from the policy outcome on trade

policy alone enters each firm’s aggregate static profit functions ),( 1πΠ=Π hB ,,bah = with

.0/ 1 >∂Π∂ πhB In each period t firm h in sector k maximizes the net present value of profits from policy

outcomes ∑∞

=

− Π=t

thkNPVτ

ττδ ,, where 1<δ is the firms’ discount factor. Our theoretical analysis

ignores the policy maker entirely, for simplicity our focus is on the lobby groups only. We also

disregard the impact on firm behavior of redistributed tariff or pollution tax revenue income in this

analysis.

The problem is analyzed in a supergame, i.e. the firms are assumed to interact over an indefinite

period of time. We let firms use trigger strategies as discussed by Friedman (1971), and we focus on

symmetric stationary lobbying sustained by stationary punishment strategies. First, assume that the

trigger strategies apply only to one separate policy issue at a time. Thus, defection on trade policy is

punished by abandoning all cooperation on trade policy by reverting to the one-shot Nash equilibrium.

In sector A, taking environmental policy (policy 2) as given, the one-period gain from unilateral

defection on trade policy lobbying only is given by

8

),(),( 21211 ππ XYG AAA Π−Π= , (1)

and the cost of defection is given by

)].,(),([1 21211 ππ

δδ NXC AAA Π−Π−

= (2)

In the polluting sector A, joint lobbying on policy issue 1 is sustainable if .CG AA11 ≤ Suppose instead

that the firms in sector A punish a deviator in both policy dimensions. Thus, if a firm deviates, the rival

firm’s retribution strategy is punishment in both trade and environmental policies. In this case the

deviating firm would chose to deviate on both policies (see Bernheim and Whinston (1990)). Then, the

gain from deviation is given by

),(),( 21212,1 XXYYG A Π−Π= , (3)

and the cost of deviation is given by

)].,(),([1 21212,1 NNXXC A Π−Π−

=δ

δ (4)

In this case, joint lobbying on both policy issues is sustainable if .CG A,

A, 2121 ≤ In sector B the optimal

punishment strategy involves deviation on policy 1 (trade policy), and thus joint lobbying requires

.CG BB11 ≤

The focus of our discussion is the fact that firms seeking to form a lobby group may face a

problem of limited enforcement power. The expected short-term gains from deviation from lobbying

may be greater than the long-term gains from cooperation. In this case, lobbying is not sustainable.

However, firms that interact in multiple areas of regulation (policy issues) have an advantage in the

formation of lobby groups due to the greater amount of available enforcement power that can be

reallocated between policy issues. When cooperation gives large gains in one policy area such as

environmental policy, this slack can be reallocated to enforce cooperation in another such as trade

9

policy. The slack of expected gain from cooperation on environmental policy may then be used to

discipline behavior on trade policy, or vice-versa. The slack available in the environmental policy area

will depend on the pollution intensity (pollution abatement costs) of the sector in question, because this

reflects the amount at stake (i.e. the costs of regulation). In this theoretical example we attempt to show

this in the simplest possible manner. Thus, we follow Bernheim and Whinston (1990) by assuming that

sector A firms’ profit functions are linearly separable in policy issues.9

Assumption 1: In sector A, each firm has a profit function that is linearly separable in the two policies

1 and 2, such that ).()( 2211 ππ AAA Π+Π=Π

Thus, firm h in sector A will maximize the net present value of profits by maximizing

∑∞

=

− Π+Π=t

hAhAthANPVτ

τ ππδ )]()([ 2,

21,

1, . Assumption 1 also implies that if the firms in sector A use an

optimal punishment strategy by canceling cooperation on both policy issues in order to deter

deviations, the two separate lobbying games played now collapse into one game. In industry A this

game has a one period static payoff function ).()( 22112,1 ππ AAA Π+Π=Π On the other hand, in industry B

firms lobby on only one policy. The one period payoff function is ).( 111 πBB Π=Π We can now state

the following result.

9 Spagnolo (2000) discusses the consequences of relaxing this assumption. We abstract from further complications in order to focus the example on our main point. Assumption 1 may most likely hold when output is relatively unaffected by the environmental policy instrument, such as a when a technology standard is used to regulate pollution. This is the most commonly used environmental policy instrument in the U.S. Moreover, when a pollution standard and an import quota (assumed given to importers free of charge) are used, no revenues arise. This is consistent with the policies used in our empirical work.

10

Result 1: Under Assumption 1, the firms in the polluting sector A have an equal or greater ability to

sustain cooperation on lobbying than the firms in the clean sector B.

Proof: See Appendix 1.

Cooperation on trade and environmental policy lobbying is sustainable if

≤+ AA GG 21 ,21AA CC + (5)

where subscript 1(2) denotes trade (environmental) policy. We identify four main cases.

Case 1: ≤AG1AC1 and ≤AG2

AC2 . Condition (5) holds and being a polluting industry does not

affect industry A’s ability to sustain lobbying. It is sustainable in any case.

Case 2: >AG1AC1 and >AG2

AC2 . Then

>+ AA GG 21 ,21AA CC + (6)

and cooperation is not sustainable in sector A on any policy issue. Interaction in the environmental

policy arena confers no advantage to industry A.

Case 3a: ≤AG1AC1 and >AG2

AC2 . A polluting industry is able to cooperate on lobbying if

≤− AA CG 22 ,11AA GC − i.e. if the cost of deviation on the trade policy is greater than the gain from

deviation on the environmental policy issue.

Case 3b: ≤AG1AC1 and >AG2

AC2 , where >− AA CG 22AA GC 11 − . The firms in sector A are able

to cooperate on trade policy only, given that the two policies are treated as separate games.

Case 4a: AG1 > AC1 and ≤AG2AC2 . Industry A is able to cooperate on lobbying on both policy

issues due to the existence of environmental regulations, if ≤− AA CG 11AA GC 22 − . The benefit of

cooperation on environmental policy is greater than the gain from deviation on trade policy. In this

case, lobby group formation is feasible only in polluting sectors.

11

Case 4b: AG1 > AC1 and ≤AG2AC2 . If >− AA CG 11

AA GC 22 − . Only environmental policy lobbying

cooperation is possible, given that the two policies are treated as separate games.

In sum, cooperation on both policy dimensions is sustainable as long as the gains from

deviation on policy i (i.e. Ai

Ai CG − , i = 1,2) are no greater than the gains from cooperation on policy j

(i.e. ,GC Aj

Aj − j ≠ i). Thus even if cooperation is not sustainable on policy issue i in isolation, it may be

sustainable on both issues as long is there is some enforcement slack available on policy issue j.

For expositional ease these results have been derived on the assumption that trade and

environmental policy are linearly independent in their effects on profits (Assumption 1). It is

recognized that this assumption may in some cases not hold in practice. However, as shown by

Spagnolo (2000) for the case of issue linkage in international agreements, the results are the same

whether the policy instrument under consideration are linearly interdependent or strategic substitutes.

From the above discussion, it follows that a polluting industry never faces greater difficulties in

organizing lobbying than it would have faced had it been a clean industry. On the other hand, there are

circumstances where it is favorable for lobbying to be subject to environmental regulations, in

particular when extra enforcement power (or slack in gains from cooperation) is available in the

environmental policy area. This extra enforcement power can then be used, e.g., in the trade policy

area.

Note that although the discussion thus far has (for simplicity) been framed in terms of

environmental and trade policies, it also applies also to other policy areas such as taxation, subsidies,

and other forms of regulations. Thus, the results are in reality more general, and we therefore formulate

the following prediction in a more general fashion:

12

Prediction 1: Firms operating in sectors with multiple regulations sustain equal or greater levels of

political contributions compared to firms operating in sectors with only one regulation, ceteris paribus.

This prediction is the subject of our empirical testing in the next section.10 If, as is generally assumed

in the political economy literature, the level of protection is increasing in political contributions, it

follows that firms in polluting industries will obtain greater protection than those in clean industries.11

For expositional ease we have ignored a number of other theoretical issues. Perhaps the most

important of these is the assumption that firms sustain lobbying by the threat of reversion to the one-

shot Nash equilibrium. Abreu (1986) has shown that in general the one-shot Nash equilibrium is not the

harshest available credible punishment. In this paper we have followed most of the supergame

literature and have ignored this issue for simplicity. However, the qualitative results are unlikely to be

affected by this assumption, since the results hinge on the observation that for any given retribution

strategy, enforcement slack in one domain can be used to enforce cooperation on some other issue.

Another problem that we have ignored is that of the renegotiation proofness of the equilibrium. As

noted by Shapiro (1986), the restriction that equilibria be renegotiation proof would shrink the set of

outcomes where lobbying (i.e. cooperation) is sustainable. Thus, so long as renegotiation proof

equilibria exist, this restriction would not alter the fundamental argument that interaction on numerous

issues may facilitate greater cooperation. Finally, it is worth noting that the equilibria identified in this

paper emerge from incentive compatible noncooperative actions of players. Hence the conclusions do

not depend upon the existence of a formal industry lobby group, in much the same way as the tacitly

10 Given Result 1, a polluting industry lobby’s problem is the allocation of the available political funds among policy lobbying areas. The marginal net return to lobbying must be equal across policy instruments. Thus, some positive share of these funds will likely be allocated to lobbying on policies other than environmental policies, for example trade policy. In case the marginal return to lobbying is the greatest in the environmental policy area, the additional funds generated may potentially be fully used in the environmental policy area only, in which case trade policy lobbying would not be affected, however. This is ultimately an issue that is perhaps best resolved empirically.

13

collusive equilibria in repeated games do not presume the existence of a cartel. Our formal analysis

simply reveals that when firms interact in multiple areas of regulation, their contributions to lobbying

activity (which has public good characteristics) will be greater than if they interact on only one policy

issue, irrespective of whether the lobbying occurs through an industry organization or by firms

individually. 12

III. ECONOMETRIC SPECIFICATION

Our primary focus in the empirical work is on the prediction generated by our theoretical example: we

investigate whether environmental regulations and the amount of lobby group contributions are

positively correlated across U.S. manufacturing sectors.

Our empirical model builds on the model estimated in Gawande and Bandyopadhyay (G-B)

(2000), which provides structural estimations of the theoretical model of trade policy formation by

Grossman and Helpman (G-H) (1994) (see Goldberg and Maggi (1999) for an alternative test of the G-

H model). The G-H theory generates predictions of trade policy outcomes across industries, taking the

existence of lobby groups across industries simply as given, and ignoring environmental policy. The G-

H theory argues that given the existence of a lobby, the equilibrium lobby group contribution equals the

welfare loss caused by the lobby’s participation in the political equilibrium. In essence, seen in this

light our theory suggests that polluting sectors will have an easier time generating sufficient funds to

compensate the government for any induced trade policy distortions. In addition these firms must also

contribute the funds necessary to compensate the government for the environmental policy distortions

associated with lobbying.

11 It is perhaps useful to note that if this condition is not satisfied in the Grossman-Helpman (1994) model, there is no equilibrium of the lobbying game (see Damania and Fredriksson (2003) for a discussion of this issue). 12 In some cases the lobbying may be administered through industry groups, while in other circumstances firms may directly lobby policy makers. Irrespective of the organizational arrangements the theory predicts that with interaction over multiple regulations, there will be greater lobbying by firms.

14

In G-B’s empirical implementation of the G-H theory (i) lobby group contributions and (ii) the

degree of import penetration, are jointly determined with (iii) the level of protection, thus accounting

for the endogeneity of three variables. Our empirical model extends the literature by adding a fourth

endogenous variable: environmental policy stringency. Our empirical model thus consists of four

equations. Since three of these equations are discussed in detail in G-B, we here restrict ourselves to a

brief overview of each equation and its main features, highlighting the differences to the previous

approach.

The first equation, the Lobby Group Contributions (LGC) equation, explains the lobbying

efforts by firms (CONTRIBUTIONS).13 G-B builds on Vousden (1990) to derive an equation reflecting

the lobby contributions necessary to compensate the government for the deadweight loss from

protection. The equation includes the level of non-tariff barriers (NTB/(1+NTB)), the import elasticity

(ELASTIC), and the import penetration ratio (IMPORTPENETRAT). The greater any of these variables,

the larger the deadweight loss, and the greater the political spending needed to keep the government

indifferent between free trade and protection. In addition, three variables account for the conflict of

interest between downstream users, who oppose upstream protectionism, and upstream firms (see

Olson (1965)). These are the firm concentration in upstream industries (HERFIN) and downstream

industries (DOWNSTREAMHERFIN), as well as the share of upstream producers’ output sold

downstream as intermediate inputs (DOWNSTREAMSHARE). The higher these measures are, the

greater the bargaining power of downstream users, forcing upstream users to make greater political

contributions.

13 Lobbying is here viewed as directed towards lowering the stringency of environmental policy, rather than to change its type.

15

The novel feature of our specification of the LGC equation is the inclusion of pollution

abatement costs (ABATEMENTCOSTS1), as an additional regressor.14 Our theoretical model suggests

that pollution abatement costs and lobby group contributions (lobby group organization effort) by firms

should be positively correlated. When the industry faces a greater number of policy battles, the overall

loss from deviation in lobbying are larger and potentially more enforcement power can be reallocated

to trade policy. The higher the pollution intensity and thus cost of pollution abatement, ceteris paribus,

the more is at stake for a polluting firm, the more enforcement power should be available. Thus the

generation of political funds by an industry lobby group becomes easier.

The NTB equation, determines the level of protection (NTB/(1+NTB)). The equation is the

empirical counterpart of the closed-form solution for the level of protection derived by G-H.15 The

advantage of a strictly theory-driven specification is that the determinants of protection as well as the

interaction terms that should be included in the equation are determined a priori.16

The Environmental Policy Stringency (EPS) equation determines the stringency of

environmental regulation approximated by the level of pollution abatement cost

(ABATEMENTCOSTS1). We include NTB protection as a right-hand side variable in the EPS equation.

If environmental and trade policies are determined by similar political forces [accounting for the same

RHS variables as in (7.2)], the two variables will be negatively correlated. If, on the other hand, policy

14 Pollution abatement costs been used as a proxy for the stringency of environmental policies in a number of empirical studies. See, for example, Levinson and Taylor (2001) who also provide an extensive survey of the literature. 15 Our empirical specification of the NTB equation corresponds to the one estimated by G-B. 16 First, the level of protection depends on the inverse import penetration ratio (IMPORTPENETRAT)-1, interacted with a political organization dummy variable (ORGANIZE), which takes the value of one if a sector is organized politically, and zero otherwise. We expect that the inverse import penetration ratio has a positive effect on protection if the sector is organized since relatively larger domestic industries will make greater lobbying contributions and receive higher levels of protection provided they are organized. Second, the import demand elasticity has a negative effect on the level of protection, due to the greater deadweight loss of taxation associated with a greater elasticity. Finally, our specification also includes two variables that measure the level of protection for intermediate goods used in that industry. The first is a measure of the average tariff level for intermediates (INTERMTARIFF), while the second measures the extent of intermediate goods protection through non-tariff barriers (INTERMNTB). A higher level of protection for firms producing intermediate goods increases the cost of production for final goods producers. To stay competitive with foreign producers, final goods producers at home need higher levels of protection.

16

makers tend to compensate industries for low trade barriers (stringent environmental policy) with less

stringent environmental policy (greater trade barriers), a positive coefficient is expected (see Eliste and

Fredriksson (2002)). We also include political spending by firms as a regressor, since more intense

lobbying should result in less stringent environmental standards. Finally, we include a measure of an

industry’s pollution intensity as an exogenous regressor (INTENSITY).

The final equation explains the level of import penetration (IMPORTPENETRAT).17 Since the

stringency of environmental regulation should affect the competitiveness of domestic firms, we include

a measure of pollution abatement cost in our specification of the Import Penetration equation.

The system of equations that we estimate can thus be written as:

)1

ln(lnlnln 3210 NTBNTBTRATIMPORTPENEHERFINONSCONTRIBUTI+

+++= αααα (7.1)

,

]/)[(/)/(1

243

12

110

εββ

βββ

+++

++=+

−−

INTERMNTBFFINTERMTARI

ELASTICTRATIMPORTPENEORGANIZEDELASTICCONSIMPNTB

NTB

(7.2)

33210 1εδδδδ +++

++= INTENSITYONSCONTRIBUTI

NTBNTBOSTS1ABATEMENTC , (7.3)

4210 1εγγγ +Γ++

++= XOSTS1ABATEMENTC

NTBNTB

CONSIMP , (7.4)

17 Our specification of the Import Penetration equation follows specifications found in previous studies (G-B, Trefler (1993)). Trade theory suggests that import penetration is a function of the level of protection, as well as factor endowments (as predicted by standard comparative advantage models), firm size and industry concentration (as predicted by models with increasing returns), and own- and cross-price elasticities (to control for differing demand conditions). Trefler (1993) estimates an import penetration equation as part of a two-equation system. In addition to the protection measure, Trefler’s import equation includes only variables that measure factor endowments. Our specification includes other variables as well and is therefore more general.

,lnlnlnln 17654 εαααα +++++ 1OSTSABATEMENTCDOWNHERFDOWNSHAREELASTIC

17

where X is a (row) vector of exogenous variables, Γ is the corresponding (column) vector of

coefficients.18 The error terms are assumed to be normal and independently distributed across

observations. A description of all variables used is given in Table A1 in Appendix 2.

In addition to the endogenous left-hand side variables, Ln(CONTRIBUTIONS), NTB/(1+NTB),

IMPORTPENETRAT, and ABATEMENTCOSTS1, the system of equations (7) includes nonlinear

functions of some of these variables on the right-hand side: Ln(NTB/(1+NTB)),

Ln(IMPORTPENETRAT), and Ln(ABATEMENTCOSTS1) in (7.1), and ORGANIZED in (7.2).19 To

account for these non-linearities, we use a modified version of the two-stage least squares estimator

proposed by Kelejian (1971) (see also G-B). In the first stage, we regress each of the endogenous

variables and their transformations on the set of exogenous variables. In addition to the linear terms, we

admit squared terms and a number of cross product terms.20 In the second stage, we estimate (7) but

replace the endogenous right-hand-side variables by their predicted values from the first stage

regressions.

IV. DATA

Our data are at the 4-digit SIC level, and the year is 1983 unless noted differently.21 Summary

statistics are given in Table A2 in the Appendix. All data except for pollution abatement costs and

pollution intensity are from G-B. The following data description is brief since a detailed description of

how each variable was constructed is found there.

18 The following variables are included in X: SCIENTIST, MANAGER, UNSKILLED, CONC4, FIRMSCALE, TARIFF, ELASTIC, CROSSELAST, REALELASTIC. For a description of these variables, see Table A1 in the Appendix. 19 The dummy variable ORGANIZED is a transformation of CONTRIBUTIONS (see Section IV for details). 20 The exact list of cross-product terms is available from the authors upon request. All first-stage estimation results including the results of the over-identification tests are available upon request. 21 Like other empirical papers on endogenous tariff protection we are confined to the use of 1983 data due to the lack of disaggregated data on non-tariff barriers for later years.

18

Political action committee (PAC) contributions cover the four Congressional election cycles

1977-78, 1979-80, 1981-82, and 1983-84 and were constructed by Gawande (1998) from Federal

Election Commission tapes.22 While we acknowledge that individual firms may sometimes have

different political objectives, we follow the extant empirical literature by assuming that the

contributions made by corporate PACs may be assumed to be a public good. This is also consistent

with a large share of the theoretical and empirical literature on the determination of trade and

environmental policies.

Unfortunately, there are no direct measures of EP stringency (such as regulation dummies, etc)

available at detailed levels of dis-aggregation. Instead, we use two indirect measures of pollution

abatement costs, scaled by value-added: capital expenditures (ABATEMENTCOSTS1) and operating

costs (ABATEMENTCOSTS2). Both measures are available from the Pollution Abatement Costs and

Expenditures Survey (U.S. Department of Commerce (1983)). With ABATEMENTCOSTS1 as the EP

stringency measure, the size of the data set is 89, compared to 177 when ABATEMENTCOSTS2 is used.

When using lagged instead of current pollution abatement measures, the sample sizes are 94 and 155,

respectively. Our measure of an industry’s pollution intensity (INTENSITY) is taken from List and Co

(2000) who identify pollution intensity at the two-digit level. We replicate their results at the four-digit

level.

The political organization dummy (ORGANIZED) is based on a regression of PAC

contributions per firm (scaled by value added) on bilateral import penetration by partner country

22 We thus follow, for example, Grier et al. (1994) by using corporate PAC data in a study of the determinants of collective action (see also Goldberg and Maggi (1999) and G-B). The PACs in our data set are associated with individual firms. As pointed out by a helpful referee, PACs organized by corporations are “connected”, as opposed to “unconnected”. Whereas unconnected PACs must pay all of their operation costs from personal contributions from any U.S. citizen, connected PACs may have their operating expenses (staff salaries, lawyers' fees, fundraising costs, etc.) paid by their parent organizations. They can only contribute money to candidates that are solicited from and contributed by employees of the sponsoring organization. Connected PACs cannot legally give or spend corporate resources for a candidate for federal office. All contributions by a PAC to candidates for federal office must be drawn entirely from voluntary personal donations.

19

interacted with 20 two-digit SIC dummies. There are five partner countries: France, Germany, Italy,

Japan, and the UK. 2-digit industries with positive coefficients are considered organized. The union of

all organized industries from the five regressions constitutes the set of organized industries (at the four-

digit SIC level). In the original data set used by G-B, 165 out of 242 industries are organized (68.2%).

As is typical in the empirical literature on endogenous protection, an aggregate NTB coverage

ratio is used to measure the level of protection. The inverse import penetration ratio is measured as

consumption divided by imports and then scaled by 10,000 to account for small values of imports in

some industries. The own- and cross-price elasticity measures are taken from Sheills et al. (1986),

purged of their inherent errors-in-variables problem, and reproduced at the 4-digit SIC level. Industry

characteristics, value added, and the Herfindahl index were constructed from the 1982 Census of

Manufacturing and, if necessary, from various Annual Surveys of Manufactures. Protection levels in

intermediate industries as well as the concentration measure in downstream industries were constructed

from U.S. Input-Output tables.

V. EMPIRICAL RESULTS

Table 1 contains 2SLS estimates of the lobby group contribution (LGC) and the environmental policy

stringency (EPS) equations [Eqs. (7.1) and (7.3), respectively].23 We use notation similar to G-B, as

well as G-B’s convention for reporting significance levels. In particular, estimates with a t-statistic

greater than unity are marked with an asterisk since their inclusion in the regression equation increases

the value of the adjusted 2R . The most striking result of Table 1 is that the coefficient estimate for

ABATEMENTCOSTS1 has the expected positive sign in the LGC equation. This lends support to our

theory. In addition, the precision of the estimated relationship is high – the estimated coefficient is

23 The empirical findings with regard to the NTB equation (7.2) and the Import Penetration equation (7.4) are similar to those reported in G-B and are available from the authors upon request. The coefficient for ABATEMENTCOSTS1 in (7.4) is negative and significant at the 10% level.

20

statistically significant at the 1% level.24 The remaining coefficient estimates in (7.1) all have the same

signs as in G-B.25 In addition, the size of the estimated coefficients is similar as well, with two

exceptions. The coefficient estimates for import penetration, Ln(IMPORTPENETRAT), and

downstream industry concentration, Ln(DOWNHERFIN), are smaller than in G-B and no longer

significant.26 The drop in sample size as a result of the inclusion of ABATEMENTCOSTS1 as a

regressor in (7.1) raises the issue of sample selection bias. Given the total number of four-digit SIC

industries (N=448), even the sample size used in G-B (N=242) is not immune to this problem. More

importantly, most of our coefficient estimates are very close in magnitude to those reported in G-B,

while our t-statistics are often lower. Thus, instead of sample selection bias, the main consequence of

the smaller sample seems to be the efficiency loss of the estimation.

With regard to the EPS equation (7.3), we find that trade barriers and environmental policy

stringency are positively correlated. In addition, PAC contributions and environmental regulation are

24 Estimation of the LGC equation with the pollution abatement measure in levels lead to qualitatively similar results. The coefficient for ABATEMENTCOSTS1 in the LGC equation is positive and statistically significant at the 1% level. Moreover, using a three-equation system instead, with INTENSITY as an exogenous variable in the LGC equation rather than the endogenous ABATEMENTCOSTS1, results in an insignificant INTENSITY coefficient. This may be explained by the fact that the simple correlation between INTENSITY and ABATEMENTCOSTS1 (ABATEMENTCOSTS2) is a relatively low 0.24 (0.35). The results are available upon request. 25 The negative sign of the NTB protection coefficient is contrary to what theory predicts. However, this is not due a potentially strong correlation between our two policy measures. In effect, import protection and ABATEMENTCOSTS1 are only weakly correlated, with a partial correlation coefficient of 0.14. In addition, leaving out the EP stringency measure does not change the sign on NTB protection, as the results in G-B show. Instead of focusing on the sign of this particular coefficient, it is important to note that the elasticity of corporate PAC spending with respect to deadweight loss is positive, with a value of .34 (the elasticity is equal to the sum of the coefficient estimates on IMPORTPENETRAT and ELASTIC plus one half the estimate on NTB/(1+NTB)). This means that a 10% increase in the deadweight loss from protection would lead to an increase in PAC contributions per firm of 3.4%. Furthermore, the implication of the G-H model that lobbying competition with downstream industries will increase lobbying efforts by upstream firms is also confirmed by the data. Both a strong demand by downstream users and greater concentration of downstream industries are found to have a positive effect on the lobbying contributions per upstream firm. However, only the first effect is statistically significant at the 1% level. 26 One potential explanation for this result is that ABATEMENTCOSTS1 may be positively correlated with firm concentration in downstream industries, DOWNHERFIN. However, a partial correlation coefficient of -.017 indicates that this is not the case. Instead, the changes in size and significance level of both coefficient estimates appear mostly driven by the change in sample size (N=89 here versus N=242 in G-B). This can be seen from the fact that when (7.1) is estimated without EPS variable, both variables remain statistically insignificant. Note that the EPS measure is weakly correlated with industry concentration, HERFIN, (r =.14) and somewhat stronger with output usage by downstream industries, DOWNSHARE, (r = .22). However, even with EPS as an additional regressor, both variables still matter for lobby group contributions – they are statistically significant at the 7% and 5% level, respectively.

21

positively correlated, contrary to the expected sign. This may be explained in the following way.

Polluting industries raise greater levels of PAC contributions, but devote these funds primarily to other

policy areas. Finally, industries that are more pollution intensive have higher levels of pollution

abatement expenditures. Except for the intercept, all coefficient estimates are statistically significant at

the 1% level.

To provide an alternative test of our theoretical prediction, we replace pollution abatement

capital expenditures (ABATEMENTCOSTS1) by operating costs (ABATEMENTCOSTS2) as the

measure of environmental policy stringency. One can argue that ABATEMENTCOSTS2 is a better

measure of abatement spending, and thus policy stringency, than ABATEMENTCOSTS1 since it is

easier for firms to identify the environmental portion of their total operating costs than the

environmental fraction of their total capital expenditures, in particular when more and more abatement

involves process changes, or is an integral part of new technologies. In our case, using

ABATEMENTCOSTS2 instead of ABATEMENTCOSTS1 does not lead to qualitatively different results

with regard to our hypothesis (see Table 2). The operating cost measure is positive and significant

around the 10% level in the LGC equation. The size of the estimated relation is different, though. The

substantially smaller coefficient estimate for ABATEMENTCOSTS2 compared to

ABATEMENTCOSTS1 indicates that the impact of environmental regulation on lobby group formation

and protection may only be 1/3 as strong as suggested by the previous measure of policy stringency.

The remaining coefficient estimates in both equations are in line with their corresponding estimates

from Table 1. The overall fit of the LGC estimation regression (.13) is lower than in Table 1 (.36),

despite the larger sample size (N=177 in Table 2 compared to N=89 in Table 1).

22

VI. SENSITIVITY ANALYSIS

We perform a number of sensitivity tests. First, w use lagged instead of current values for both

ABATEMENTCOSTS1 and ABATEMENTCOSTS2. This reduces the system to three equations since

the potential endogeneity of the environmental stringency variable is no longer an issue. We report the

results for the LGC equation in Table 3.27 The results confirm our previous findings about the impact of

environmental stringency on lobby contributions (formation). The positive coefficient estimate on

lagged ABATEMENTCOSTS1 is significant at the 1% level. This not only confirms the prediction of

our theoretical model but also provides indirect evidence for the goodness of our pollution intensity

measure as an instrumental variable for environmental stringency. In contrast to lagged capital

expenditures, lagged pollution abatement operating costs (ABATEMENTCOSTS2) have no effect on

lobby group contributions. Combined with the findings from Table 2, the result implies that most of the

impact of EPS stringency (as measured by ABATEMENTCOSTS2) on lobby group contributions is

contemporaneous.

Second, we investigate a potential endogeneity bias that may arise due to the use of value-added

as both a scale measure in Eq. (7.4) and to standardize lobbying efforts by firm (CONTRIBUTIONS)

and pollution abatement costs (abatement capital expenditures, as well as abatement operating costs).28

We instead use the number of employees per firm (FIRMSCALE2) as an alternative measure of

industry size in Eq. (7.4). Re-estimating the system of equations (Eqs. 7.1-7.4), we find no evidence of

such a bias, as far as our main results are concerned. A comparison between Table 1 and the results

presented in column 1 of Table 4 yields only minor changes in the point estimates in either equation. In

particular, our previous finding of a positive and highly significant effect of pollution intensity

(INTENSITY) on lobby group contributions remains unchanged. Similarly, when we contrast the results

27 The empirical results for the remaining equations [(7.2) and (7.4)] are available from the authors upon request. 28 We thank one of our referees for noticing this potential problem as well as the one addressed in the subsequent paragraph.

23

from column 3 in Table 4 with those reported in Table 2 we once again find no substantive changes in

point estimates or t-statistics. Most importantly, the coefficient estimate of the operating cost abatement

measure (ABATEMENTCOSTS2) remains around 0.3, and the significance level falls only slightly.

Next, we investigate a potential bias of our environmental policy stringency estimates due to the

special status of industries made up of large-scale firms. Due to their size, larger firms may interact

with government agencies in several dimensions. They may therefore be more likely to lobby the

government for reasons such as tax breaks, subsidies, or changes in certain types of (non-

environmental) regulations. To test the robustness of our results, we therefore remove the large-scale

industries from each sample and re-estimate Eqs. (7.1-7.4).29 The results reported in columns 2 and 4 of

Table 4 show that the effect of environmental policy stringency on lobby group contributions remains

positive and significant. In fact, omitting the largest industries from the sample strengthen the results.

The point estimate is about 50% larger in both cases (1.22 and 0.55, compared to 0.82 and 0.32 in

Tables 1 and 2, respectively) and the precision of the estimated relationship has improved markedly. In

addition, the overall fit of the policy stringency equation reported in columns 2 and 4 of Table 4 (Eqn.

7.3) has improved, with an adjusted 2R above 0.3 compared to 0.14 and 0.18 in Table 1 and 2,

respectively. Note also that some coefficient estimates, in particular those controlling for concentration

in downstream industries in the lobby group contribution (LGC) equation, become smaller and

statistically insignificant in column 2. We thus conclude that controlling for the potential additional

interactions between large firms and the government does not alter the main conclusions emerging

from our previous empirical analysis.

29 To be precise, we remove the largest 25% of all industries, as measured by value-added per firm. This reduces the sample using ABATEMENTCOSTS1 from 89 to 63 observations and the sample using ABATEMENTCOSTS2 from 177 to 133 observations. Removal of large-scale industries using employment per firm as measure of size yields quantitatively similar results (results available upon request).

24

Our final robustness checks involve estimations of a number of different specifications for (7)

such as semi-log of (7.1), additional regressors (e.g. the degree of unionization in (7.1) and (7.2)), and a

discrete measure of CONTRIBUTIONS in (7.3). None of these alternative specifications yield results

that are qualitatively different from the results presented in Tables 1 to 3. In effect, if the two measures

of downstream lobbying competition used as regressors in (7.1) are used in (7.3) instead, the coefficient

estimate on Ln(ABATEMENTCOSTS2) in (7.1) is positive and significant at the 1% level (t-value

2.66).

VII. CONCLUSION

This paper proposes a new theory of lobby group formation. We argue that firms in industry

sectors encountering a greater number of policy instruments more easily organize lobby groups due to a

greater amount of available “enforcement power.” When lobby group cooperation gives large gains in

one policy area, this surplus can be reallocated to another policy area. This causes an advantage for

pollution intensive firms. Any slack of expected gain from cooperation on environmental policy may

be used to discipline lobbying behavior on, for example, trade policy. Firms in polluting sectors facing

stiffer environmental policy should therefore be able to raise greater amounts of PAC contributions.

Our theoretical prediction finds support in the data. Sectors with greater pollution abatement

expenditures and thus more stringent levels of environmental protection have significantly greater

levels of PAC contributions. This result is robust to several different variable measures and

specifications. To our knowledge, this is a novel finding in the literature.

25

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28

Table 1: 2SLS Estimates: Lobby Group Contributions and

Environmental Policy Stringency Equations

Lobby Group Contributions

Environ. Policy Stringency

Dep. Var: Ln(CONTRIBUTIONS) ABATEMENTCOSTS1 Ln(HERF) .18*(1.85) Ln(IMPORTPENETRAT) .07(.89) Ln(NTB/(1+NTB)) -.07**(2.85) Ln(ELASTIC) .31*(1.03) Ln(DOWNSHARE) .31**(2.03) Ln(DOWNHERFIN) .16*(1.25) Ln(ABATEMENTCOSTS1) .82**(4.33) NTB/(1+NTB) .02**(2.05) CONTRIBUTIONS .10**(2.85) INTENSITY .01**(2.20) CONSTANT -1.55**(2.73) -.001(.30) N 89 89 K 8 4 Adj. 2R .36 .14 Model F 8.00 5.65 Ln L -108.36 284.53

Note: t-statistics in parenthesis: ** denotes |t|≥ 2; * denotes 2>|t|≥ 1; N: Size of data set; K: Number of regressors.

Table 2: 2SLS Estimates: Lobby Group Contributions and

Environmental Policy Stringency Equations (PAOC/VA)

Lobby Group Contributions

Environ. Policy Stringency

Dep. Var: Ln(CONTRIBUTIONS) ABATEMENTCOSTS2 Ln(HERFIN) .12*(1.63) Ln(IMPORTPENETRAT) .13**(2.09) Ln(NTB/(1+NTB)) -.07**(2.75) Ln(ELASTIC) .45*(1.80) Ln(DOWNSHARE) .25**(2.13) Ln(DOWNHERFIN) .09(.90) Ln(ABATEMENTCOSTS2) .32*(1.60) NTB/(1+NTB) .02*(1.88) CONTRIBUTIONS .21**(3.48) INTENSITY .01**(4.54) CONSTANT -2.90**(6.40) -.000(.007) N 177 177 K 8 4 Adj. 2R .13 .18 Model F 4.68 13.99 Ln L -238.39 455.20

Note: t-statistics in parenthesis: ** denotes |t|≥ 2; * denotes 2>|t|≥ 1; N: Size of data set; K: Number of regressors.

29

Table 3: Sensitivity Analysis I: Lobby Group Contributions with Lagged Environmental Policy Stringency Dep. Var: Ln(CONTRIBUTIONS) Ln(CONTRIBUTIONS)

Ln(HERFIN) .13*(1.24) .18**(2.56) Ln(IMPORTPENETRAT) .09*(1.24) .15**(2.47) Ln(NTB/(1+NTB)) -.09**(3.22) -.08**(3.25) Ln(ELASTIC) .73**(2.07) .57**(2.33) Ln(DOWNSHARE) .27*(1.64) .26**(2.16) Ln(DOWNHERF) .04(.29) .13*(1.28) Ln(ABATEMENTCOSTS1)t-1 .58**(2.63)

Ln(ABATEMENTCOSTS2)t-1 .16*(1.00)

CONST -2.68**(4.42) -3.09**(7.29) N 94 155 K 8 8 Adj. 2R .23 .18 Model F 4.95 5.84 Ln L -123.35 -196.27

Note: t-statistics in parenthesis: ** denotes |t|≥ 2; * denotes 2>|t|≥ 1; N: Size of data set; K: Number of regressors.

Table 4: Sensitivity Analysis II: Alternative Scale Measure and Adjustment for Industry Size

Measure of Scale: Employment

Large Industries Removed

Measure of Scale: Employment

Large Industries Removed

Policy Stringency Measure: ABATEMENTCOSTS1

Policy Stringency Measure: ABATEMENTCOSTS2

Lobby Group Contributions Equation

1 2 3 4

Dep. Var.: Ln(CONTRIB.) Ln(CONTRIB.) Ln(CONTRIB.) Ln(CONTRIB.) Ln(HERFIN) .19*(1.88) .22*(1.92) .13*(1.71) .16**(2.00) Ln(IMPORTPENETRAT) .06(.80) .02(.20) .11*(1.80) .12*(1.73) Ln(NTB/(1+NTB)) -.07**(2.84) -.06*(1.98) -.07**(2.60) -.05*(1.67) Ln(ELASTIC) .32*(1.04) .35(.98) .45*(1.77) .43*(1.47) Ln(DOWNSHARE) .31**(2.06) .17(.88) .25**(2.11) .16*(1.19) Ln(DOWNHERF) .16*(1.25) .05(.38) .08(.80) .10(.85) Ln(ABATEMENTCOSTS1) .80**(4.25) 1.22**(5.33) - - Ln(ABATEMENTCOSTS2) - - .31*(1.51) .55**(2.48) CONSTANT -1.61**(2.83) -.58(.97) -2.95**(6.47) -2.01**(4.22) Adj. R2 .35 .45 .12 .19 Model F 7.82 8.85 4.39 5.31 Ln L -108.75 -75.35 -239.29 -170.40 Environmental Policy Stringency Equation

Dep. Var.: ABATEMENT-COSTS1

ABATEMENT-COSTS1

ABATEMENT-COSTS2

ABATEMENT-COSTS2

NTB/(1+NTB) .02**(2.06) .03**(4.36) .02*(1.81) .02*(1.51) CONTRIBUTIONS .10**(2.81) .08**(3.57) .20**(3.30) .22**(5.73) INTENSITY .005**(2.20) .003*(1.89) .01**(4.55) .01**(4.25) CONSTANT -.001(.29) -.001(.73) .0003(.10) -.0003(.18) Adj. R2 .14 .32 .17 .33 Model F 5.58 11.42 13.58 22.81 Ln L 284.44 243.74 454.69 395.07 N 89 67 177 133

Note: t-statistics in parenthesis: ** denotes |t|≥ 2; * denotes 2>|t|≥ 1; N: Size of data set; K: Number of regressors.

30

Appendix 1

Proof of Result 1: Note that under Assumption 1, when the punishment strategy calls for deviation in

only one policy instrument, sector A firms have ),()( iAii

Ai

Ai XYG Π−Π= and

)]()([1 i

Aii

Ai

Ai NXC Π−Π

−=

δδ , 2,1=i . Thus, cooperation is sustainable if

≤Π−Π )()( iAii

Ai XY )]()([

1 iAii

Ai NX Π−Π

−δδ . (A1)

Rearranging condition (A1) it follows that cooperation is feasible for all values of the discount factor

such that

ii

Aii

Ai

iAii

Ai

NYXY

∆≡Π−ΠΠ−Π

≥)()()()(

δ . (A2)

On the other hand, firms in sector A have the option of using a punishment strategy that entails

reverting to the non-cooperative strategy on both policies after any form of deviation. Given these two

alternatives, a sector A firm’s optimal deviation is a deviation on both policy issues simultaneously.

The gain from defecting unilaterally from lobbying cooperation is given by ,212,1AAA GGG += and the

cost of defection is given by .212,1AAA CCC += Cooperation on trade and environmental policy lobbying

is sustainable if

≤Π−Π∑=

2

1))()((

ii

Aii

Ai XY ∑

=

Π−Π−

2

1)]()([

1 ii

Aii

Ai NX

δδ . (A3)

Rearranging condition (A3) it follows that cooperation is feasible for all values of the discount factor

such that

2,12

1

2

1

)()(

))()((∆≡

Π−Π

Π−Π≥

∑

∑

=

=

ii

Aii

Ai

ii

Aii

Ai

NY

XYδ . (A4)

31

Without loss of generality, suppose that when there is cooperation with only one policy instrument this

involves interaction over policy i=1 (i.e., trade policy). Clearly cooperation is more easily sustained

with interaction over two policies if 2,1∆ < 1∆ . Substituting from conditions (A2) and (A4), and

rearranging this inequality yield

( ))()(())()((

)()()()(

1111

2

1

2211221112,1

NYNY

YXXY

AA

ii

Aii

Ai

AAAA

Π−Π

Π−Π

ΠΠ−ΠΠ−Ω≡∆−∆

∑=

< 0, (A5)

where 0))()()(())()()(( 111122222211 <Π−ΠΠ+Π−ΠΠ=Ω YXNYXN AAAAAA , and the sign of Ω follows

from the assumption that 1,2 )),()(( =∀Π<Π iYX iAii

Ai . It follows that with interaction over two

policy issues, cooperation is feasible over a larger range of values of the discount factor. Hence,

lobbying is easier to sustain in sectors encountering two policies.30 Q.E.D.

30 For a similar method of proving sustainable cooperative outcomes, see Friedman (1990).

32

Appendix 2

Table A1: Descriptions of Variables used in the Econometric Analysis

NTB NTB coverage ratio CONTRIBUTIONS PAC contributions per firm scaled by value added. (IMPORTPENETRAT)-1 Inverse import penetration ratio, scaled by 10000

(=(consumption/imports)/10000)

ELASTIC Own price elasticity of imports, corrected for errors-in-variables ORGANIZED Political organization dummy (=1 if industry is organized, 0 otherwise) INTERMTARIFF Average tariff on intermediate goods used in an industry INTERMNTB Average NTB coverage ratio of intermediate goods used in an industry ABATEMENTCOSTS1 Pollution abatement capital expenditures, scaled by value added ABATEMENTCOSTS2 Pollution abatement operating costs, scaled by value added HERFIN Herfindahl index of firm concentration IMPORTPENETRAT Import penetration ratio: imports/consumption DOWNSHARE Percentage of an industry’s shipments used as intermediate goods in other DOWNHERFIN Intermediate-goods-output buyer concentration FIRMSCALE Measure of industry scale: Value added per firm FIRMSCALE2 Measure of industry scale: Employment per firm CONC4 Four firm concentration ratio UNSKILLED Fraction of employees classified as unskilled SCIENTISTS Fraction of employees classified as scientists and engineers MANAGERS Fraction of employees classified as managerial REALELASTIC Real exchange rate elasticity of imports CROSSELAST Cross price elasticity between domestic and imported goods, corrected for

errors-in-variables TARIFF Post-Tokyo round ad-valorem tariffs (ratio)

gD , g = 1,…4 Dummies for four industry groups: Food processing, Resource intensive, General manufacturing, and Capital intensive

gLK )/( , g = 1,…,4 Capital labor ratio x gD INTENSITY Dummy for Pollution Intensity (=1 if industry is pollution intensive)

33

Table A2: Summary Statistics*

Variable MEAN STD DEV MINIMUM MAXIMUM NTB/(1+NTB) .076 .132 0 .5 CONTRIBUTIONS .0275 .0317 .0017 .2374 (IMPORTPENETRAT)-1 .0133 .0772 .0002 1.0 ABATEMENTCOSTS1 .0057 .011 .0001 .0781 ABATEMENTCOSTS2 .0128 .0207 .0002 .1714 ORGANIZED .6441 .4802 0 1 ELASTIC 1.4865 .3784 .5491 2.1297 INTERMTARIFF .0523 .0245 .0116 .1723 INTERMNTB .2165 .1354 .0226 .6785 HERFIN .0753 .069 .0014 .295 DOWNSHARE .557 .2898 .0122 .9641 DOWNHERF .2325 .1762 .0448 .7065 SCIENTISTS .042 .0421 0 .1667 MANAGERS .1009 .0376 0 .1807 UNSKILLED .0653 .0475 0 .3333 CONC4 .3958 .2114 .0598 .9883 FIRMSCALE .0115 .0183 .0002 .1023 TARIFF .053 .0451 0 .3241 CROSSELAST -.0602 .8437 -1.7941 2.8864 REALELASTIC -.9839 .4795 -2.02 1.9 INTENSITY .3898 .4891 0 1 *All data are from 1983. The sample size is 177 except for ABATEMENTCOSTS1 which for 1983 is available for only 89 four-digit SIC industries.