The Political Economy of Clean Air Legislation

Environmental and Resource Economics11: 119–134, 1998. 119c 1998Kluwer Academic Publishers. Printed in the Netherlands.

The Political Economy of Clean Air LegislationAn Analysis of Voting in the U.S. Senate on Amendments to the 1990 CleanAir Act

MARK L. BURKEY 1 and GAREY C. DURDEN21Department of Economics, Duke University, Durham, NC 27708, USA;2Department ofEconomics, John A. Walker College of Business, Raley Hall, Appalachian State University, Boone,NC 28608, USA

Accepted 4 April 1997

1. Introduction

One of the most important functions of a government is to regulate activitieswhich generate negative externalities. Economic theory has a lot to say about theefficient levels of externality controls, but economists have yet to come to a firmconsensus on how to privately obtain such levels.1 Thus, in most situations thegovernment must, through a bureaucratic or legislative process, decide both levelsof stringency and implementation strategies for externality regulation. This makesthe understanding of how regulatory decisions are made an important issue forstudy.

Much research in political science and economics has attempted to explainvoting patterns among members of legislative bodies. In this paper we extend theexisting analysis in three ways. First, we address the subject of voting on air qualityregulation by the U.S. Senate. A subject of great importance and significance, suchvotes have not previously been the focus of much empirical investigation. Second,we develop an arguably more correct and effective methodology for measuring andunderstanding the ideological preferences of individual Senators, as revealed bytheir voting patterns on 1990 amendments to the Clean Air Act. Third, we applythe minimum chi-square methodology for estimating the determinants of Senatorvoting patterns on the issue.

In Section 2, the economic theory of regulation is elaborated as it is specificallyrelated to 1990 senate voting on amendments to the Clean Air Act. In Section 3,we provide a brief literature review, focusing on the principal-agent model andhow voting patterns are influenced by campaign contributions, constituent socio-economic characteristics, and individual legislator ideology. In Section 4 we presenta very simple model of the principal-agent relationship which underlies legislativevoting behavior. In this section (supplemented by information in an appendix) we

120 MARK L. BURKEY AND GAREY C. DURDEN

introduce a new methodology for creating a proxy variable to represent legislatorideology, comparing the new method with those previously used. Section 5 pro-vides a chronological background on clean air legislation, and Section 6 discussesthe data and proxy variables used for the empirical estimations. Section 7 containsa presentation and evaluation of three empirical techniques, including one not pre-viously used, the minimum chi-square method which, we argue, is both appropriateand easily interpretable. This claim is based upon the fact that the dependent vari-able, SCORE, is neither continuous nor dichotomous, but ordered and categorical,constructed from votes on amendments to the Clean Air Act. Section 8 summarizesthe empirical results and Section 9 presents conclusions.

2. Theory

The economic theory of regulation (Stigler 1971) explores to what extent variousinterest groups (constituents, PACs, political parties, etc.) affect political outcomes.This is closely linked to the rational interest theory of legislative voting, (i.e., Barro1973) which attempts to explain why legislators may be affected by special interestpressures. The discussions below will focus upon the economic theory of legislativevoting as applied to clean air regulation.

Some authors assume that the goal of legislators is to maximize either general“political support” or number of votes received at the polls (Wright 1993). Neitherof these goals seems sufficient in and of itself. From principal-agent theory, econo-mists should take the view that legislators are rational, utility-maximizing agentswho will work for their constituents and for important interest groups if monitored,but will also shirk (sometimes vote against the wishes and expectations of interestgroups and constituents). Political support will be supplied as demanded but onlyto the extent that such support will increase total utility. This means that, whenreasonably assured of re-election, a given legislator may indulge own preferences,which may cost votes and other forms of support. A legislator makes choices thataffect his own utility as well as the probability of reelection.

When studying an issue such as air quality, we can imagine that it is representedas a continuous variable on a 0–1 scale. For example, we could consider 0 to beno air quality regulation at all, and 1 to be a level of regulation which amounts toan absolute ban (Durden et al. 1991) on anything which reduces air quality. Howdoes a legislative body decide what position along this continuum to occupy?

The analysis undertaken here will focus on the decisions of individual legisla-tors. Suppose that a given legislator has a personal, preferred level of air quality,I0

(see Figure 1). He (she) would like to vote in such a way as to achieve an air qualitylevel as close toI0 as possible. However, own preferences must be balanced withthose of majority preference constituencies on the issue.

Constituents can be divided into two basic preference groups: those who favorstrong controls and those who have opposing preferences. A legislator must notdisplease a majority of voters (nor financial supporters) too much or reelection may

THE POLITICAL ECONOMY OF CLEAN AIR LEGISLATION 121

Figure 1.

be jeopardized. Thus, a legislator with personal preference levelI0 may support airquality levelI1 when the constituent majority preference level is greater thanI0.The actual support level,I1 may be net of own and constituent majority preferencesor may be precisely what the constituent majority desires, depending on the relativeintensities of preference and the importance of this issue to reelection probability.

In the U.S. a legislator needs a lot of money to be reelected, so campaigncontributions are an important element of the legislative process. Most campaignsare centered around television commercials, mass mailings, and other costly formsof advertising. Political Action Committees (PACs) provide financial contributionsin a way that reflects the preferences of interest groups on air quality controls andmany other issues.2 Since PACs will not likely continue to financially support alegislator who votes contrary to their wishes, pro-control PACS may further affectthe observed support pattern of a relatively anti-control legislator, changing it fromI1 to I2.

In effect, the political support optimization problem has three dimensions: Ownpreferences, preferences of organized constituencies and preferences of PAC con-tributors. What we hope to determine in this paper is the extent to which eachof these dimensions simultaneously affects legislative voting patterns of individ-ual Senators on 1990 amendments to the Clean Air Act. Several techniques areused with proxies for the different influences on support for (against) air qualitycontrols. The measure of legislative support (the dependent variable in empiricalestimations) is constructed from votes on amendments so as to represent the 0–1preference continuum described earlier.

3. Brief Literature Review

How legislative support levels are determined with respect to clean air legislationis fundamentally related to several questions which we shall try to answer, boththeoretically and empirically:

1. What is the role of money (campaign contributions) from political action groups(PACs)?

2. What role does ideology play in shaping each legislator’s voting pattern?3. How closely do the voting patterns of legislators reflect the expected prefer-

ences of large constituent subgroups as identified by relevant socioeconomiccharacteristics?

With respect to the influence of money, some researchers (Silberman and Dur-den 1976; Durden et al. 1991; Neustadtl 1990; Stratmann 1991; and Wilhite andTheilmann 1987) find that campaign contributions do significantly affect voting


behavior. Coates (1993) finds that contributions only marginally influence votingbehavior but, consistent with what has been suggested elsewhere (Durden et al.1991) PAC contributions received may simply be a function of a legislator’s exist-ing position on an issue. However, it is the opinion of Coates that contributionshave little, at least direct, effect on voting.

With respect to the second question, do legislators sometimes display votingpatterns that are quite different from those expected (shirk)? When a legislatorvotes in ways that are contrary to expectations, given known PAC and constituentpreferences, most researchers invoke personal ideology as the primary cause.Although many unobserved factors could cause apparent shirking3 (e.g. hiddenbribes, logrolling, missing variables), the label of ideology seems a reasonablepossibility. The question then becomes, in theory and fact, whether and in whatform will a legislator choose to exercise personal preferences?

The measurement of ideology has been a focus of inquiry since Kau and Rubinfirst empirically addressed the topic (Kau and Rubin 1979). They found, contraryto expectations, that ideology appeared to help explain voting by congressmenon primarily economic issues. Peltzman (1984) argues that the inclination forlegislators to shirk with respect to their constituents’ preferences and vote accordingto their own ideology is more apparent than real. On social policy issues, Peltzmanfinds evidence of ideological voting, but believes that, by and large, a simpleprincipal-agent model without shirking explains political behavior well enough torelegate ideological shirking to a “sideshow”.4

The relationship between PAC contributions and voting is really much morecomplicated than what our simple models are able to capture. Neustadtl (1990)contains an interesting discussion of this complexity, which includes frank discus-sions with PAC directors as to what their motives are. As the title of an article byGrenzke (1989) suggests, “PACs and the Congressional Supermarket: The Cur-rency is Complex.” Grenzke found that contributions from 120 PACs representingabout 10 large interest groups generally do not have much direct effect on spe-cific votes in the U.S. House of Representatives. Rather, the money is partly forincreased “access” to a legislator, and partly an attempt to influence election out-comes. Perhaps the best way to view the relationship between money and votesis that money sometimes slides across the iceberg tip revealing momentarily thegreat political power that organized groups can yield.

Finally, virtually all studies, including this one, find that appropriately struc-tured proxy variables for constituent characteristics will generally affect legislativevoting. Here again, the linkages are complex and many times variables are com-promised by multicollinearity. Proxies used to represent the interests of constituentgroups should therefore be chosen carefully both to reflect theoretical linkages andto avoid redundancy.


4. The Simple Empirical Model

As previously noted, this paper uses 1990 Senate votes on amendments to theClean Air Act to test the rational interest theory of legislative voting, as influencedby money, interest group pressures and the personal ideologies of legislators.The model is a commonly used one, based on the assumption that a principal-agent relationship exists between legislators and their constituents.5 Underlyingthe model is the recognition that, for most issues, “constituents” refers to the morefinancially powerful and/or better organized subgroups within an overall populationof voters. Thus, independent variables are designed to measure both direction andintensity of the preferences of the various constituent interests.

For clean air legislation, there are two opposing interest group types, thosewhose members have a stake in making controls less stringent in order to advancedirect economic goals (generally, business interests) and those who wish to ensurea particular though not necessarily specific level of air cleanliness (generally,environmental interests). As earlier suggested, a legislator’s ideology also playssome part in the process, although isolation and measurement of this influence hasproven difficult.

Carson and Oppenheimer (1984) give one of the best general discussions ofthe problems associated with estimating models using an ideology variable. Weborrow from their discussion in what follows. A simple mathematical expressionof the model to be estimated is:

V = f(P;D;C; I) (1)

whereV is a senator’s position on an issue as revealed by observed voting pattern.This is a function of party membership (P ), voter interests from a legislator’s stateor district (D), campaign contributions (C), and own ideology (I). If we assume alinear specification, we have

V = a0 + a1P + a2D + a3C + a4I + �: (2)

Proxies for all variables except ideology are relatively straightforward and will bediscussed in Section 6.

SinceI is unobservable, many techniques have been tried to correctly measurethe effect of this “missing” variable. Some have simply used interest group ratingsas a measure of ideology. These ratings are constructed for various groups bycalculating a percentage “correct”, which is the number of times that the legislator’svote reflected the interest group’s preferences divided by total votes on the issue.Such ratings are compiled by Americans for Democratic Action (ADA), Americansfor Constitutional Action (ACA), the League of Conservation Voters (LCV), andothers. Ratings are based on approximately 10 selected issues each year, and areconstructed on a scale of 0 to 100. We submit that ratings compiled in this waycannot properly be used as an independent estimator in voting-pattern regressions


because they basically constitute another measure of the dependent variable, bothbeing composed of votes.

As Kau and Rubin (1979), Peltzman (1984), and Kalt and Zupan (1984) haveshown, ideological group ratings are highly correlated with constituent character-istics and interests. Kalt and Zupan (1984; 1990) and Hird (1993) recognize thecorrelation problem, forgoing the use of ADA and ACA ratings if favor of envi-ronmental and ideological rating scores which have been purged of constituent andinterest group influences. This method involves estimating equations with environ-mental and ideological ratings as dependent variables and a range of theoreticallylinked independent variables as regressors. The process assumes that residualsfrom such equations must reflect ideological preferences of legislators, since otherinfluences have been explained. For example, if the method is used to estimateeach Senator’s score as compiled by the League of Conservation Voters, the resultis:

LCV = a0 + a1P + a2D + a3Cf+a4I + �g; (3)

where LCV is the “correct” voting percentage according to League members andthe other regressors are those from Equation (2). The residual will bea4I + �, anestimate of own ideology scaled bya4, and this proxy is used as a regressor inEquation (2).

Since ideology does not have a known scale, this scaling is not a problem.However, as Carson and Oppenheimer (1984) suggest, the fact that the ideologymeasure so determined includes� is troubling. Carson and Oppenheimer suggestobtaining the residuals from two or more such equations and averaging them toreduce the effect of the error terms. In this paper we further lessen the problem ofthe included error term by using three equations to estimate ideology, which is thenused in a fourth equation to estimate the parameters of the Senator voting patternmodel. This procedure is detailed in the appendix to this study.

5. Background Information on Clean Air Legislation in the U.S.

The U.S. Federal Government has been involved in air pollution control legislationsince 1955, when Congress, with a $5 million appropriation, directed the PublicHealth Service to begin research on the topic.6 In 1963, authorized expenditureswere increased to $95 million for fiscal years 1964–1967 and, in 1965, the legisla-tion was extended to include the control of hydrocarbon and monoxide automobileexhaust emissions. The Air Quality Act of 1967 broadened the scope of enforce-ment, and much tougher emission control provisions were passed as the Clean AirAct in 1970. The 1970 Act was amended in 1977, making it the nation’s most com-prehensive and complex set of environmental laws. The debate and various votesin the House and Senate were focused on the clash between industrial interests(mostly auto producers) and environmental groups.


Thirteen years passed before the Act was amended again when, in 1990, after aseries of all-night negotiating sessions, a decade of legislative gridlock dissolved,and the House and Senate conferees ratified the basic ingredients of a major newset of air quality regulations. It was Republican President George Bush who madethe difference, pushing for the legislation and making last-minute compromisesthat resulted in passage.

At this point in time, air quality regulations, like strip-mining controls, minimumwage laws and the like, are accepted by most as socially (politically) necessary.The current thrust, as is noticeable in the 1990 legislation, is to decide how strictthe controls will be and how stringently they will affect allowable pollutant levelsfrom automobiles, industrial and manufacturing processes, and the like. The basicconflict remains the tradeoff between the degree of control and consequent cleanli-ness of the air versus the jobs, incomes and taxes to be derived from the productionprocesses which generate pollutants.7 How these conflicts are to be legislativelyresolved and how the acceptable tradeoffs are determined constitutes the basis forthis study.

6. Data and Proxy Variables

The dependent variable, SCORE is constructed using 18 Senate votes on amend-ments which would clearly have strengthened or weakened the air quality bill. Thecontents of the 18 votes are summarized and categorized in Table I. SCORE is ona scale of 0 to 100:8

SCORE= 100� (vj=Vi) (4)

wherevj is (the number of yes votes on amendments to strengthen controls) + (thenumber of no votes on amendments to weaken controls) andVi is the total numberof amendments voted on by each Senator (following Kalt and Zupan 1984).

The distribution of this variable turns out to be a good measure of variouspositions in policy space. There always exists the danger that the observed votingpatterns give a measure which is truncated at one or both ends; that is to say, thatmany senators desire a position in policy space that is either negative (a stronganti-regulation stance) or above 100 (a strong pro-air quality stance). The variableSCORE exhibits no bunching at either extreme. All senators voted to strengthen(or not weaken) on at least three bills, and only five senators voted to strengthen (ornot weaken) on all of the bills. Thus, truncation does not appear to be a problem.

The simple model is operationalized for estimation purposes by selecting proxyvariables designed to reflect the theoretical connections between Senatorial voting,interest group preferences, and personal ideology. A proxy which attempts tomeasure the influence of affected businesses and other interests preferring weakercontrols should be signed negatively. A proxy for an environmental or other interestgroup preferring stronger controls should be signed positively.


Table I. Description of Amendments and Motions used to Compute SCORE; If SenatorVoted Yes on Amendment to Strengthen Controls, yes = 1; If Voted No on Amendment toWeaken Controls, no = 1.

Vote # Description

s30 Breaux, D-La, motion to kill the Glenn, D-Oh, amendment to strike a provisionwhich would remove the authority of EPA and the states to regulate emissions atfacilities targeted by the NRC (rejected 66-31): No vote supports power of EPA,No = 1.

s31 Baucus, D-Mt, motion to kill Lautenberg, D-NJ, amendment to reduce oxygencontent required for certain fuels (passed 53-44): Yes vote increases oxygen contentrelative to noxious gasses, Yes = 1.

s32 Mitchell, D-Maine motion to kill Symms, R-Id amendment to prohibit importingproducts not complying with bill’s air quality standards (passed, 81-16): Symmsamendment would make legislation more difficult to pass, Mitchell amendmentthus pro-control, Yes = 1.

s33 Gore, D-Tn, amendment to phase out fluorocarbons, establish other pro-environment provisions (passed 80-16): Yes = 1.

s34 Mitchell, D-Me motion to kill Lautenberg, D-NJ amendment to require regulationof mobile sources of pollution (passed, 65-33): No = 1.

s35 Mitchell, D-Me motion to kill Wirth, D-Co amendment to add stricter emissionsstandards; gov’t use clean fuels in smoggiest cities; require cleaner fuel in high-ozone cities (passed 52-46): Yes = 1.

s37 Mitchell, D-Me motion to kill Kerry, D-Ma amendment that would have given thegovernment the ability to closely regulate cities which fail to meet pollution targets(passed, 53-46): No = 1.

s38 Adams, D-Wa amendment, research visibility-impairing pollution in national parks(passed 53-46): Yes = 1.

s43 Baucas, D-Mt, motion to kill Nickles, R-Ok amendment allowing states to allowsome pollution that the EPA may otherwise stop (rejected 47-50): No = 1.

s44 Nickles, R-Ok amendment to allow states greater control, bypassing EPA (rejected47-50): since EPA is expected to favor greater enforcement of controls, No = 1.

s48 Baucas, D-Mt motion to kill Daschle, D-S.D. amendment requiring that lower-polluting blended gasoline be used in some cities (defeated 30-69): No = 1.

s49 Chafee, R-R.I. amendment to subject refurbished coal-burning utilities to less-stringent standards (passed 64-33): No = 1.

s50 Chaffee, R-R.I. motion to kill McClure, Id amendment restricting electricityimported from Canada (passed, 57-40): Yes = 1.

s51 Chafee, R-R.I. motion to kill the Murkowski, R-Ak amendment to broaden thedefinition of clean coal technology to allow some aspects of combustion technologyto qualify for certain benefits under the acid rain program (passed 65-31): Yes = 1.

s52 Chafee, R-R.I. motion to kill the Symms, R-Idaho amendment requiring a commu-nity referendum on plant closings caused by excessive emissions (passed 82-15):Yes = 1.

s53 Dole, R-Ks amendment to shift certain aspects of control from EPA to states(rejected 49-51): No = 1.

s55 General Bill: contains all agreed to amendments to the Clean Air Act (passed89-11): Yes = 1.

s324 Clean Air Act Reauthorization Conference Report: Adoption of the conferencereport on the Clean Air Act, sending the legislation to the president (passed 89-10): Yes = 1.


Data on voting and campaign contributions are from the Inter-UniversityConsortium for Political and Social research (ICPSR),9 with some descriptiveinformation coming from the Congressional Quarterly. All socioeconomic proxyvariables are from the Statistical Abstract of the United States or the Green Index,1991–1992.

An independent variable, MONEY, is constructed to represent the effect ofcampaign contributions on voting:

MONEY = fpro environment donationsg

� fpro business donationsg (5)

The environmental donations are those from the Sierra Club, The League of Con-servation Voters, and other environmental PACs. The pro-business donations arefrom PACs identified by Congressional Quarterly as among the top 40 largestcontributors having an interest in weakening the air quality legislation. This vari-able for each senator is generated from data, in units of $1,000, associated witha senator’s most recent re-election or election bid. A positive value for MONEYwould indicate more contributions to a given senator from environmental thanfrom business interests. Thus, a positive sign is expected for the coefficient of thisvariable, since environmental interests prefer stronger controls.

The percentage of the population in a state which lives in an urban area(PERCURB) is intended to capture any effects of air pollution associated withan increasingly concentrated population. The number of days per year on whichurban areas of the state attained unhealthy levels of air pollution (AIRQUAL) isalso included. These two variables are expected have a positive relationship with alegislator’s votes on air quality regulation.

Much of the 1990 legislation concerned the use of burning coal to produceenergy. Two variables which reflect the degree to which states may be affected bythis are the percentage of electricity in a state produced by coal (COALEL), andthe amount of coal produced in a state (COALPRD). Since many senators wereconcerned about the effects of the legislation on employment levels, we includethe unemployment rate in a state (UNEMP). All of these variables should reflecta desire for less environmental regulation, since costs would tend to increase forstates with higher levels of these variables. The degree of organization of laborinterests in a state is proxied by the degree of unionization in manufacturing,MFGUNION. This may increase the amount of concern that a Senator gives toemployment issues, hence a negative sign on the coefficient is expected.

PARTY is a dummy variable coded 1 for the Republican party, 0 otherwise.Since the Republicans are normally more concerned with the economic tradeoffwhen air quality is regulated (lost jobs, revenues, taxes), the PARTY coefficientshould be signed negatively.

The percentage of citizens in a state who have earned a college degree (PER-COL) is intended to proxy the effects of income and education on constituent’s


feelings about air quality. Since higher income should lead to higher demand for airquality (assumed to be a normal good), the expected sign is positive. The propor-tion of the population who are members of an environmentally-related organization(ENVMEM) is a proxy for the underlying desire for air quality in a state. This vari-able is measured as the number of members per 1,000 population. The expectedsign is positive.

7. Estimation Methods

Two estimation methods have normally been used to estimate the model equations,the first of which is linear regression. There are multiple problems with this method,which many authors have remedied using the ordered probit methodology. We hopeto improve upon results from the linear and ordered probit methods by employingtheminimum chi-square method. To the authors’ knowledge, this method, althougharguably superior, has never been used in the analysis of legislative voting patterns.In what follows, the nature, advantages and disadvantages of each model arediscussed, and for comparison, results are given for all three methods.

7.1. LINEAR REGRESSION

The linear regression model would estimate the following type of equation:

SCOREi = �0�i + �i: (6)

Using least-squares on this equation gives rise to several problems. One arisesbecause SCORE is an ordinal, not continuous variable, so that the error term willbe biased because it is correlated withY (McKelvey and Zavoina 1975). Thepredicted values will be a smooth linear function, but the error will depend onthe distance of the predicted point to the nearest level of the ordinal dependentvariable. Because of heteroskedasticity, tests of significance will not be reliable. Asecond problem is that the linear form allows predicted values to be less than zeroor greater than one. Since no observations are possible outside the 0–100 range,the results cannot be properly interpreted.

7.2. ORDEREDPROBIT

In ordered probit models10 it is assumed that there is a true continuous underlyingvariable, but the researcher typically has available only a few observations. SinceSCORE is an ordinal variable taking on several discrete values, the ordered probitmodel is appropriate. For probit estimations, we categorized the values of SCOREinto 9 ascending categories. The methodology estimates the probability that asenator will fall into each particular category. The largest estimated probability fora SCORE category is the predicted voting pattern as influenced by values of theindependent variables.


The biggest difficulty for many researchers is the interpretation of parameterestimates generated by the probit procedure. The most common method is tocompute the marginal effects of a variable on the Cumulative Density Function ofthe model. However, this is only meaningful and comparable to a linear model whenestimating a binary choice (0, 1 dependent variable) probit. As discussed in Greene(1993), the only appropriate procedure in an ordered probit estimation is to computethe marginal effects of a change in an independent variable on the probability ofa given legislator adopting the highest or lowest observed voting category. Sincethis will not facilitate comparison among the techniques, the marginal effectsof independent variable proxies are not computed. However, we do report theestimated betas and their levels of significance.

The reader should note that, except for the dummy variable PARTY, the signsof the betas in an ordered probit will be the reverse of the linear and chi-squaredspecifications.11 For the latter two models, pro-control (anti-control) influenceswill be reflected by a positive (negative) sign. For probit, pro-control (anti-control)will be signed negatively (positively) with the exception of PARTY.

The most important reason for using the probit model is to get accurate testsof significance. However, a more intuitive interpretation of the betas derived isdesired by many researchers. It turns out that there is an appealing way to satisfythis desire and still address the heteroskedasticity problem associated with linearregression. This is accomplished through use of the minimum chi-square method,as suggested in Maddala (1983).

7.3. THE MINIMUM CHI-SQUARE METHOD

If we haveni observations on a 0,1 dichotomous variable (i.e. votes revealingstrength of preference on air quality regulation) for whichmi are observed to betrue(i.e. support for increasing air quality), we can define the empirical probabilitiesas:

p̂i = mi=ni; (7)

wherep̂ is the observed probability.This is precisely the method used to construct SCORE, and is how the interest-

group ratings (ADA, ACA, etc.) are constructed. This expression may be interpretedas providing an estimate of the probability that a given senator will vote for arandomly-selected bill. In large samples ofn for eachI:

p̂i � pi; V ar(p̂i) � pi((1� pi)=(ni)): (8)

Thus, a weighted least squares method can be used to estimate a linear regressionon p̂i, using the weights:

wi = [ni=(p̂i(1� p̂i))]1=2: (9)


This model, called a linear probability minimum chi-square model, is appealingbecause it can account for senators who are not present for all votes (smallerni). Itwill also give accurate tests of significance for the independent variables. Perhapsmost importantly, the betas have a simple intuitive explanation. They give theamount of change in probability (for a change in a given independent variable) thata senator will vote for a randomly-selected air quality bill.

8. Results from the Regressions

The results for the three estimations are shown in Table II. R-squared values for thelinear and chi-square equations are 0.722 and 0.726, with the ordered probit resultsimilar. These numbers suggest a very high degree of explanatory power for themodel. For most of the independent variables the significance levels are also quiteremarkable for this type of study. In all three estimations, MONEY, IDEOLOGY,and PARTY were significant at well above the 99% level. This robustness highlightsthe significance of these variables in air quality legislation.

Recall that a primary facet of this analysis was to provide information on theeffects of money, interest groups and own ideology on Senate voting patterns withrespect to clean air regulations. Table II suggests that MONEY (pro-control minusanti-control PAC contributions) plays an important part (supporting the findings ofSilberman and Durden 1976; Durden, et. al. 1991; Wilhite and Theilman 1987) indetermining the nature and content of clean air legislation. MONEY is significant atfar greater than 99% and is correctly signed12 in all three equations. The coefficientof 0.663 on MONEY from the chi-square regression means that approximately$15,000 in net contributions is associated with a 10% change in the proportion ofvotes on air quality (SCORE value).

Does a legislator’s own ideology affect voting patterns? Very much so and inthe appropriate direction, as shown by the coefficients on IDEOLOGY for linear,chi-square and probit specifications. The beta values are large and significant atconsiderably greater than 99%. Recall, however, that this variable may representfactors other than what is normally thought of as “ideology”.

That constituent interest groups affect voting on air quality legislation is alsodemonstrated. Increases in the variables PERCURB, CONSMEM, AIRQUAL wereexpected to increase SCORE (move away from weak toward stronger controls onthe underlying preference continuum) and respective betas are all correctly signed.Only CONSMEM is significant, but at 95% or better. COLLEGE was expectedto be positive, but exhibits the opposite influence, and is significant in the linearand probit specifications. The result may indicate a statistical problem (possiblymulticollinearity) or that, since other variables account for positive changes inSCORE, COLLEGE is left to pick up the influence of anti-government politicalconservatism which may be associated with higher incomes in the U.S. population.

The overall results for the linear and the chi-square regressions are very similar;more related, in fact, than are the chi-square and the probit, which should both be


Table II. Econometric Results.

Variable Name Linear R2 = 0.722 Chi-Sq. R2 = 0.726 Ordered Probit(t statistic) (t statistic) (�2 statistic)

INTERCEPT 96.98�� 75.79�� –4.759��(7.092) (5.480) (15.58)

IDEOLOGY 0.301�� 0.578�� –0.0355��(6.433) (6.472) (20.22)

MONEY (in $1,000’s) 0.584�� 0.663�� –0.0597��(3.149) (3.079) (13.16)

% URBAN (PERCURB) 0.121 0.168 –0.00924(0.889) (1.046) (0.594)

CONSMEM/1,000 POP. 1.881�� 1.097�� –0.177��(2.584) (2.332) (7.35)

DAYS OF POOR AIRQUAL 0.051 0.0885 –0.00365(1.357) (1.635) (0.997)

%ELEC. COAL (COALEL) –0.0352 0.0198 0.00365(–0.618) (0.336) (0.593)

UNEMPLOYMENT% –3.20 –2.412� 0.341��(–2.567) (–1.838) (10.18)

% COLLEGE –1.414� –1.032 0.125��(–1.937) (–1.168) (3.91)

COAL PRODUCTION –0.0883�� –0.0742�� 0.00663��(-2.272) (–2.062) (3.91)

PARTY (1 = REPUBLICAN) –18.27�� –19.894�� –1.82��(–5.716) (–5.986) (33.70)

% MFG UNION 0.164 0.168 –0.0114(1.293) (1.129) (1.13)

�� = 99% confidence �� = 95% confidence � = 90% confidence

more correct.13 The similarity of linear and chi-square is probably due to the factthat the dependent variable, SCORE, is “approximately” continuous. There were22 observed levels of this variable for 89 observations, so heteroskedasticity is nota serious problem.

The probit results differ slightly because of the number of categories used. Wedivided the 22 observed values into 9 categories for the probit, since number ofobservations is not large enough to support a larger number of categories. Thisagglomeration of observed values causes a loss of some information contained inthe dependent variables, and this likely affected the results.


9. Conclusions

The purpose of this analysis has been to provide an understanding of the forceswhich shape Senate voting on externality internalizing legislation. The issue chosenfor analysis is clean air legislation, specifically, Senate voting on 1990 amendmentsto the Clean Air Act. Based on the principal-agent theory of political behavior, asimple empirical model appears to explain voting patterns to a remarkable degree.Three estimation methodologies were used, linear, probit and minimum chi-square,the latter being used for the first time in this type of study.

Legislator ideology, political party, and money (campaign contributions) appearto be the major influences on voting patterns for this particular issue. Other con-stituent characteristics are influential, but only marginally so, when compared tothe major determinants.

We argue that this analysis has resulted in several important contributions. First,of course, is the fact that the theoretical and empirical analyses have shed lighton how legislatures shape environmental regulations. Second is the use and com-parisons of linear, probit and minimum chi-square estimation techniques. Whileminimum chi-square is theoretically correct, all three methods produce similarresults. Finally, we have extended the methodology for creating a variable toapproximate the influence of own ideology on voting patterns of legislators.

Acknowledgement

For valuable comments, the authors thank Tim Perri, Paul Combs, Gordon Brady,Beth Kulas, David Wilson, Rod Duncan, Jim Leitzel, and referees for this journal.

Notes

1. Theoretically, controls should be increased to the point where marginal benefits are equal to mar-ginal costs. Coase (1960) has shown that, when property rights are properly assigned, negotiationamong private parties produces an efficient result. However, because of transaction costs, thiscannot be widely adopted for the internalization of externalities.

2. PAC donations must be reported to the Federal Elections Regulatory Commission and are avail-able to the public.

3. For an excellent review of the literature on shirking, see Bender and Lott (1996).4. Stigler (1972) was also of the opinion that investment and otherwise self-interested motives were

the primary explanations for voting behavior and that these should be pursued in much greaterdetail before devoting much effort to ideological motives.

5. Specific appeal to the notion of rational self-interest as the motive for political behavior is at leastas old as Smith. For modern articulation of the theoretical linkages, see Buchanan and Tullock(1962), Stigler (1971; 1972), Barro (1973), and Peltzman (1976).

6. These discussions are based on information from theCongressional Quarterly Almanac, variousissues.

7. While the 1994 Republican victories in the House and Senate will not likely result in repeal ofthe Clean Air Act, movement toward less stringent controls is a possibility.

8. We create the SCORE variable on a 0–100 scale, rather than a 0–1 scale so that the parameterestimates will reflect a percent change in voting patterns.


9. Files delineating campaign expenditures include:9828: Years 1989–19908987: Years 1985–19868511: Years 1983–19848238: Years 1981–1982

For Roll-Call Voting Results:0004: Congressional Roll-Call Voting

10. McKelvey and Zavoina (1975) and Silberman and Durden (1976) discuss the application ofthe probit model to multi-categorical voting data. The work of the former is also discussed inMaddala (1983).

11. A positive probit coefficient indicates a shift in probability from higher to lower values of thepreference categories, hence increasing the likelihood of a lower value for SCORE.

12. Recall that the ordered probit estimators will be signed the opposite of estimators for the othertwo regressions, with the exception of the dummy variable, PARTY.

13. Because, for the linear specification the error terms are heteroskedastic.

Appendix

The three equations used to estimate the variable IDEOLOGY were:

LCVy = MONEYy + PERCURBy + � � �+ �y

where LCV is the “correct” voting score compiled by the League of Conservation Voters,MONEYy is net PAC contributions computed as explained in equation 4, and PERCURBy

+ � � � + �y is the vector of independent variables and the error term.The IDEOLOGY equations were calculated for the years (y):

1987–1988: An average of the LCV ratings was used for these two years, and campaigncontributions for 1982, 1984, and 1986 were used.

1989: 1989 LCV ratings were used, and campaign contributions from 1983–1984, 1985–1986, and 1987–1988 were used.

1990: 1990 LCV ratings were used. Campaign contributions from 1986 and 1988 wereused, as well as the average contributions from 1984 and 1990. The reason for averag-ing the contributions for 1984 and 1990 is that U.S. senators are elected every 6 years,and by mid-year 1990, a large percentage of contributions for the 1989–1990 electioncycle had been received. To take this into account, but not weight these contributionstoo heavily, the average was used from this and the previous election cycle.

The three values of�y for each of the three above estimations were averaged, and usedas the independent variable IDEOLOGY in the final estimations:

SCORE90 = MONEY90+ PERCURB90+ � � �+ IDEOLOGY+ �90

Notes:1. Data on campaign contributions is only collected once for each two-year election cycle.2. Data for 89 Senators were available this study. Because data were needed for four

years, those Senators who were newly elected in 1988 could not be used.


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Documents

The Political Economy of Clean Air Legislation