A Panel Probit Analysis of Roll Call Votes

A Panel Probit Analysis of Campaign Contributions

and Roll Call Votes

by

Gregory Wawro

y

September 2, 1999

Paper prepared for presentation at the 1999 Annual Meeting of the American Political

Science Association.

y

Assistant Professor, Department of Political Science, Columbia University,

[email protected].

1 Introduction

Political scientists have long been concerned with the eects of campaign contributions on

roll call voting. However, methodological problems have hampered attempts to assess the

degree to which contributions aect voting. One of the key problems is that it is dicult

to untangle the eect of contributions from the eect of a member's predisposition to vote

one way or another. That is, political action committees (PACs) contribute to members of

Congress who are likely to vote the way the PACs favor even in the absence of contributions.

A PAC donation to a friendly member might be misconstrued as causing a member to vote

a particular way, when in reality the member would have voted that way to begin with.

It is therefore crucial to account for a member's propensity to vote in a particular way in

order to assess the inuence of contributions. One way that studies have done this is to

use ideological ratings developed by interest groups. This approach is problematic, however,

because the ratings are built from roll call votes and thus will introduce bias if campaign

contributions aect the votes used to compute the ratings. In order to circumvent the prob-

lem of accounting for voting predispositions, I use panel data methods which, unfortunately,

have seen almost no application in political science. These methods enable us to account

for individual specic eects which are dicult or impossible to measure, such as the pre-

disposition to vote for or against a particular type of legislation. To employ these methods,

I build panels of roll call votes on legislation that business and labor groups have indicated

are important for their interests. Using panel data estimators, I determine the eects of

contributions from corporate and labor PACs on the probability of voting \aye" or \nay",

while accounting for members' propensities to vote in particular directions. I nd that con-

tributions have minimal to no eects on roll call votes, while short-term factors including

monthly unemployment and support for the president in the district have substantial eects.

2 Money and Votes

Recent eorts to reform campaign nance laws have been motivated in part by the belief that

campaign contributions have a pernicious eect on the behavior of members of Congress.

1

Reformers claim that money from special interests has eroded the principle of \one person,

one vote" and has dire consequences for representative democracy. Yet an extensive amount

of research has failed to nd consistent results that indicate that campaign contributions

inuence members' behavior. Most of this work has focused on the relationship between

contributions and roll call voting, one of the most important and visible activities that

members of Congress engage in.

1

Despite the amount of resources poured into this research

program, we still lack denitive answers about how contributions aect the way that members

vote.

2

One reason for the lack of denitive answers is the host of methodological problems that

plagues quantitative research on campaign nance. One of the biggest problems is that

groups tend to contribute to members of Congress who are predisposed to vote for them

(Wright 1996, 136-149). Voting predispositions are determined by district interests and

members' personal policy preferences|things that are prohibitively costly to observe for a

large sample of members and a large sample of votes. If members are predisposed to vote in

an interest group's favor and we do not account for this predisposition, then contributions

from that group (or more precisely the group's aliated PAC), will appear as if they have a

big eect on getting the member to vote in the group's favor. Yet this correlation is spurious

and the eect of the contributions will be overstated.

Some method for accounting for voting predispositions is necessary. Unfortunately, mea-

sures of the factors that constitute voting predispositions are incredibly costly to obtain,

especially for a wide range of votes. The costs of obtaining these measures force researchers

to make trade-os. Researchers can choose one or a few votes for which it is relatively easy

to measure relevant constituency interests. But it is not clear how generalizable the results

of these case studies are.

Researchers who choose to examine a larger samples of votes are forced to use more

general measures of constituency interests that are less reliable for measuring the interests

1

For a laundry list of works in this literature see Snyder 1992 or Wright 1996.

2

This has prompted research on the relationship between campaign contributions and other types of

legislative activity, though relationships there remain weak to nonexistent (Hall and Wayman 1989; Box-

Steensmeier and Grant forthcoming; Wawro forthcoming).

2

that members take into account when deciding which way to vote. Most studies have relied

on interest group ratings of members of Congress as proxies for constituency interests and for

the ideological positions of members. However, these measures are problematic because they

are based on roll call votes cast by members. If there is a relationship between campaign

contributions and roll call votes, then we cannot use these scores to account for voting

predispositions. These scores are endogenous to campaign contributions and are therefore

inappropriate for disentangling voting predispositions from contribution eects. Grier and

Munger (1991) claim that voting scores are appropriate measures of voting predispositions,

citing the studies that have failed to nd a relationship between contributions for votes.

But this assumes that the models in these analyses have adequately accounted for voting

predispositions, which I claim they have not. Interestingly, several studies that specify

models with interest groups ratings instead of individual votes as the dependent variable

nd statistically signicant eects for contributions (Saltzman 1987; Wilhite and Theilmann

1987; Wilhite and Paul 1989).

Several studies conceive of the problem of measuring a member's propensity to vote in an

interest group's favor as an endogeneity problem (e.g., Chappell 1982; Grenzke 1989), and

employ methods such as two stage least squares to overcome this problem. However, the

equations that they estimate typically include interest group ratings as exogenous variables,

ignoring the potential endogeneity of these scores. It is highly questionable then whether

these studies have corrected for the problems that in part motivate their analyses.

3

Grenzke (1989) argues for a dynamic approach to circumvent the problems of accounting

for voting predispositions. She contends that, since district demographics and a member's

ideology are more or less constant over time, looking at changes over time in contributions

and votes \eliminates the need to measure many control variables" (Grenzke 1989, 3). This

logic is similar to that behind the least squares dummy variable model for panel data (see

Hsiao 1986 29-32), where the panel structure of the data is used to sweep out the individual

xed eects. While Grenzke constructs a panel of members serving continuously between

1973 and 1982, she does not use appropriate panel data methods to account for the individual

3

It is also highly questionable then whether the exclusion restrictions necessary for identication of the

models used in these analysis hold.

3

heterogeneity that she acknowledges exists. Her sampling design also builds in a survivor

bias since it includes only those members who served in every Congress in the analysis.

Since a member's roll call record and campaign contributions are determinants of whether

or not a member gets reelected and hence can serve in every Congress in the analysis, the

statistical analysis is plagued by sample selection problems which render the results highly

suspect. While constructing panels of members is a step in the right direction, analyzing

panels that extend over several Congresses will involve correcting for nonrandom attrition

from the sample, which can be very dicult to do (Baltagi 1995).

I argue that a better way to account for voting predispositions is to employ panel data

techniques to analyze votes and contributions within a Congress/election cycle. These meth-

ods enable us to account for individual specic eects|in particular voting predispositions|

in order to estimate cleanly the eects of explanatory variables. These methods also enable

us to understand more about campaign contributions within an election cycle. Most studies

of campaign nance aggregate contributions over the two-year period between elections. But

this ignores the day-to-day politicking that goes on within an election cycle. Sample attrition

problems are also much less of a concern as relatively few incumbents leave the institution

in the middle of the election cycle, and those who do are likely to leave for reasons (e.g.,

death) that are random to the variables of interest.

McCarty and Rothenberg's work (1996) indicates the importance of analyzing campaign

contributions at shorter intervals. They nd that that long-term agreements involving the

exchange of contributions for favors are not viable. We should expect these kind of exchanges

to take place, if they take place at all, within time periods much smaller than an entire

election cycle or several election cycles. That is, spot markets for contributions and votes

should involve smaller chunks of time than those that are typically examined.

By analyzing contributions and votes at dierent periods within an election cycle we can

better assess the eects of contributions on votes. Panel data methods can also allow us to

examine how members of Congress adjust their voting behavior in response to stimuli during

an election cycle. Determining the responsiveness of members' behavior to changing district

conditions is important to assessing how well members represent their districts. Yet most

studies of roll call behavior do not use ne enough slices of time to determine how members'

4

adjust their behavior to respond to short term changes in district conditions.

Recently, researchers have become more interested in the dynamics of campaign nance

within an election cycle, examining the relationship between contributions and behavior

at ner slices of time (Biersack, Herrnson and Wilcox 1993; Box-Steensmeier 1996; Box-

Steensmeier and Lin 1996; Krasno, Green, and Cowden 1994; Wand and Mebane 1999).

However, analyzing campaign nance within an election cycle requires statistical methods

that are more involved than those typically used. Treating data as panels complicatesmatters

methods-wise, especially when, as in this analysis, the dependent variable of interest is

dichotomous. In the next section I discuss panel data methods that are appropriate when

the dependent variable is dichotomous, focusing on recent innovations that are especially

relevant for analyzing contributions and votes.

3 Panel Data Methods for Dichotomous Dependent

Variables

Though the use of panel data methods for dichotomous dependent variables has become com-

mon in economics, almost no applications exist in political science, despite the availability of

this kind of panel data and questions of interest where these methods are appropriate (and

in some cases, necessary).

4

Granted, panel data with dichotomous dependent variables does

require more sophisticated methods than does panel data with continuous dependent vari-

ables. But standard panel data estimators are now included in popular statistical software

packages, making these methods widely accessible.

The standard panel data model where the dependent variable is dichotomous is

y

it

=

0

x

it

+

i

+ u

it

(1)

where is a vector of parameters to be estimated, x

it

is a vector of explanatory variables,

i

is an individual specic eect, u

it

is a disturbance term, and y

it

is a latent variable which

4

The only use of discrete panel data methods that I am aware of is Beck, Katz, and Tucker's innovative

study (1999), which derives a model for analyzing time series cross section data that allows for the inclusion

of a lagged dichotomous dependent variable.

5

determines the value of the observed variable y

it

according to the following scheme:

y

it

=

n

1 if y

it

> 0

0 if y

it

0

As with cross section data, the two main methods that are employed to estimate (1) are

logit and probit, with the choice of method determined by the distributional assumptions

we make for u

it

. As with panel data with continuous dependent variables, we assume the

i

are either xed or random (i.e., the individual specic eects are either constant param-

eters or they have a distribution with a mean and variance). A key dierence between the

dichotomous and continuous cases is that the choice between xed and random eects in

eect determines the distributional assumptions we will make for the disturbance term, and

consequently, whether we will use logit or probit. For the kind of data analyzed in this paper

where T is small and N is large, likelihood approaches where the xed eects are estimated

along with the coecients on the explanatory variables will generally lead to inconsistent

estimates (Hsiao 1986, 159-161). A method similar to what is typically used for continuous

dependent variables that sweeps away the xed eects is necessary for consistent estimates.

The conditional maximum likelihood estimator developed by Chamberlain (1980) works sim-

ilarly to transformations for the continuous case where the xed eects are removed from the

model so that parameters of interest can be estimated. Unfortunately, the computations for

this method become extremely cumbersome as T grows, making this method unattractive

for the size of T used in this analysis (i.e., T > 10).

Adopting the probit framework by assuming a normal distribution for u

it

can be more

suitable for large T . However, we need to assume random eects instead of xed eects,

as the xed-eects probit model lacks a consistent estimator of . The trade-o is that for

random eects, we must adopt the unappealing assumption that the

i

are independent of

the x

it

, which may not be reasonable. One plus of the random eects estimator is that is

allows us to estimate the eects of time-invariant variables, such as party aliation, which

previous studies of contributions and votes have found to matter. The sequential panel probit

estimator developed by Chamberlain (1984) does allow for correlation between the individual

eect, but requires us to impose a good deal of structure on the relationship between

i

and

x

it

and include only time-varying variables in x

it

.

6

Bertschek and Lechner (1998) discuss several generalized method of moments (GMM)

estimators for the panel probit model that do not require some of the restrictive assumptions

necessary to estimate the panel probit model by maximumlikelihood. They show how several

GMM estimators that appear in the panel data literature can be derived from the same set

of conditional moment restrictions. These various GMM estimators range from a standard

\pooled probit" estimator which essentially ignores the panel structure of the data to more

general, nonparametric models which allow for arbitrary error processes and correlation

between the

i

and the x

it

.

These latter estimators are especially attractive for analyzing campaign contributions

and votes. Allowing for correlation in the disturbances within cross-sectional units over time

seems especially important. Studies have shown that members' voting behavior is very stable

over the long term. The model should allow for persistence in the roll call voting behavior

of individuals in the short term as well. In fact, we might want to specify a dynamic model

which includes lags of previous votes. Persistence in voting patterns implies that the previous

vote on a similar bill would be a good predictor of the way the members will vote on the next

bill in the sequence. Honore and Kyriazidou (1998) derive a logistic model for estimating

models with discrete, lagged dependent variables and individual specic eects, which would

be appropriate here.

Unfortunately, these models are not quite as easy or convenient to estimate as those who

derived them claim! Though I continue to attempt to estimate these models, for this paper

I report results obtained from a standard random eects probit estimator and hope that the

restrictive assumptions it forces one to make do not tax too severely the reader's willingness

to suspend disbelief .

4 Data and Model Specication

In order to use panel data methods we need observations on votes of members of Congress

over several time periods. While any congressional session oers hundreds of roll call votes

to create panels, some votes will be more relevant to PACs' interests than others. If PACs

do attempt to inuence votes with contributions, then obviously they should target votes

7

that are more salient to them. Some studies of roll call voting and contributions select a few

pieces of legislation that they deem relevant for particular interest groups and then examine

how contributions from PACs aliated with the groups aect the votes on that legislation

(e.g., Chappell 1982; Wright 1985). But this approach has potential problems. These studies

look at very narrow votes, and it is not clear that the results will generalize to other votes. It

is dicult to tell whether researchers have picked the right votes in terms of PACs' interests

or that they have selected the right PACs to examine.

To get around these problems, I examine votes cast on legislation deemed important to

particular groups' interests by the groups themselves. As stated above, previous studies

of campaign contributions and roll call votes have used interest group ratings to account

for voting predispositions. This study uses interest group ratings in a very dierent way

by analyzing the votes that interest groups select to construct their ratings.

5

The fact

that interest groups have chosen to include these votes in the computation of their ratings

indicates that these votes are central to the kind of policy outcomes that the groups' favor

(Fowler 1982).

I examine the votes selected by two interest groups|the U.S. Chamber of Commerce

(USCC) and the American Federation of Labor-Congress of Industrial Organizations (AFL-

CIO). The votes that the USCC selects for its ratings \reect a `pro-business slant which

does not necessarily mean conservative' " (Fowler 1982, 404). The AFL-CIO ratings consist

of votes \that `aect working people who are not necessarily union members.' The non-

union component of the scores depends on support for social welfare issues such as food

stamps or education" (Fowler 1982, 404). Though the votes that constitute the scores are

perhaps broader than we would like, they are good indicators of the issues that corporate

and labor PACs would want to inuence. These groups' ratings are typically cited in proles

of members of Congress (see Politics In America and The Almanac of American Politics),

5

Interestingly, though the stas of interest groups who produce ratings have expressed \some skepticism

at the use to which scholars put their ratings," they thought the ratings \have their greatest impact on the

distribution of campaign funds, because they provide a simple test of support or opposition" (Fowler 1982,

403). This suggests that the particular votes that make up the ratings should be especially important when

particular PACs make their contribution decisions.

8

and have been used extensively in previous studies of campaign nance.

Some may question the reliability of using particular votes from the ratings for measur-

ing groups' interests. Groups may select votes strategically in order to to produce scores

that match their preconceived notions about what members' scores should be based on past

reputations, or to reward \friends" and punish \enemies." However, Fowler (1982) con-

ducted interviews with sta members of these organization in which they denied picking

votes strategically.

Groups may also select votes strategically in order to maintain a constant average rating

over time. While, as Groseclose, Levitt, and Snyder (1999) point out, this can limit the

usefulness of the ratings for analyses across Congresses, such strategic selection of votes

should not aect this analysis since we are examining the votes that make up a rating within

a Congress.

The AFL-CIO and USCC publish the votes that they use to rate members of Congress,

including descriptions of the votes and explanations as to which vote is the \right" one

for supporting the groups' positions.

6

The legislation selected for computing the scores

represents a good mix of the types of legislation on which members vote. Some votes are

on very general pieces of legislation, such as budget bills and constitutional amendments

for balancing the federal budget. Others concern very narrow topics, such as raising the

minimum wage and requiring rest periods for ight attendants. While most votes are for

nal passage of substantive legislation, some votes are for amendments to bills and others

are over matters of procedure that have substantive implications.

Considering the interests that the USCC and the AFL-CIO represent, I analyze how

contributions from corporate and labor PACs relate to votes on the legislation selected by

these groups' for their voting scores.

7

Some may be concerned that there is too much

6

The USCC's votes are listed in the annual publication \How They Voted." The AFL-CIO's votes

are available in its annual \Report on Congress." The publications were obtained directly from the

AFL-CIO and USCC. Electronic versions of the AFL-CIO voting records are available for recent years

at http://www.aflcio.org/vrecord/index.htm.

7

The data on contributions comes from the FEC's itemized contribution les. These les enable us to

build monthly panels of contributions from corporate and labor PACs. The data on roll call votes come from

the ICPSR's United States Congressional Roll Call Voting Records, 1789-1996.

9

heterogeneity within business and labor groups and that this could lead to null results

because lumping together contributions from all corporate or all labor PACs makes these

measures of contributions too noisy. However, the alternative is to make arbitrary decisions

about which PACs' contributions should be included. For now, I include contributions from

all PACs within the labor and corporate classes, though in the future I plan to check the

robustness of the results by selecting contributions from narrower classes of PACs (e.g., the

AFL-CIO's PAC and its state aliates.)

It is not clear what the time frame would be for spot markets for votes and contributions.

The contributions could come before the vote, right after the vote, or be spread out around

the vote. I examine how contributions from particular interests given in the same month

that a vote took place aect the probability of voting in a direction that is consistent with

those interests.

The selection of a month as the time frame for contribution-vote spot markets has some

methodological implications. Some months have no votes while other months have multiple

votes. For the months where there were multiple votes I selected one of the votes based on

whether it appeared to appeal more narrowly to labor or business interests. Of course, the

results should be checked to make sure they are robust to the selection of the votes.

Since some months do not have votes that are selected by the AFL-CIO and USCC, the

standard assumption of panel models that the time periods are of equal length is violated.

This assumption is of particular importance to the panel probit model since is identied

only up to scale. That is, we maximize the likelihood function with respect to =

u

, where

u

is the variance of u

it

. With time periods of dierent length it may be unreasonable to

assume that

u

is the same across time periods. A heteroskedasticity-type correction may

be necessary. For example, if we assume that

u

t

= t

v

u

, where t

v

is the number of days

between votes, then dividing (1) through by

p

t

v

would correct for the dierent lengths of

periods. Another option would be to employ one of the nonparametric GMM estimators

derived in Bertschek and Lechner 1998. Though I plan to implement a correction and try

alternative estimators to check the robustness of the results reported below, in this paper I

report only the results obtained from the simple panel probit model.

The model specication includes separate variables for labor and corporate PAC con-

10

tributions given in the same month in which a vote was designated as being important to

business or labor interests. I include contributions from competing interests in the same

equation to assess whether contributions from one interest aect whether a member votes

for or against the opposing interest. That is, for the votes that the AFL-CIO has designated

as being important to labor interests, I assess whether contributions from corporate PACs

inuenced members to vote against those interests, and vice versa. These should give us a

sense about how competition among dierent interests plays out with respect to campaign

contributions.

In addition to contributions from the dierent interests, I include other variables which

may aect the way that a member votes. I include the monthly unemployment rate in the

district. This objective measure of the local economy might have an eect on whether a

member votes a pro-labor or pro-business stance. As short-term unemployment increases,

members may become more sensitive to the interests of labor and be more likely to cast

votes in the direction favored by labor.

8

I also include a measure of how support for other political gures might aect a member's

vote. Most studies of voting and campaign contributions include some measure of the vote

shares of the presidential candidates in previous elections as a measure of district ideology.

The president is one political actor that members take cues from when deciding how to

vote (Matthews and Stimson 1975). Yet members should weigh the cues they get from

the president by the level of support for the president in the district, and support for the

president in districts can vary dramatically over an election cycle. The vote share that the

president received three years ago may not be a good indicator of how the district views

him now, and thus will not be a good indicator of how district support for the president

should aect a member's vote. We can use monthly presidential approval ratings to get a

better measure of how support for the president in the district will provide cues for how a

member should vote to please the district. I interact the monthly approval rating for the

president with the previous vote share for the president in the district. Since the president

8

District-level unemployment gures were calculated from county-level gures by weighting by population.

The data were obtained from the Bureau of Labor Statistics. The unemployment rates are seasonally

adjusted.

11

does not take positions on all votes, the president will not serve as a cue-giver for all votes.

Thus, I multiply the approval-vote share interaction by a variable that indicates whether the

president took a position on the vote and whether that position is consistent with labor or

business interests. If the president's position is consistent with the relevant interest group,

then I multiply this variable by 1. If the president took the opposite position, I multiply

this variable by 1. If the president took no position, I multiply this variable by 0. The

coecient on this variable should be positive.

I also include two time-invariant variable that might aect the way a member votes. I

include a dummy variable which equals one if the member is aliated with the Republican

party and equals zero otherwise. Republicans are generally more likely to be pro-business and

anti-labor, so I expect a positive sign on this coecient for the USCC votes and a negative

sign for the AFL-CIO votes. The second time-invariant variable pertains to the member's

committee assignments. In his analysis of committee outliers, Krehbiel (1991) found that the

Education and Labor Committee had a pro-labor bias, while the Appropriations committee

had an anti-business bias, as measured by AFL-CIO and USCC ratings.

9

For the AFL-

CIO votes, I include a dummy variable that indicates whether or not the member serves on

Education and Labor (renamed Economic and Educational Opportunities in the 104th). For

the USCC votes, I include a dummy variable that indicates whether or not the member serves

on Appropriations. The Education and Labor dummy should have a positive coecient,

while the Appropriations dummy should have a negative coecient.

The dependent variable is whether or not the member voted in the direction favored by

the USCC or the AFL-CIO. The sample of members includes those who sought reelection

to the House. The behavior of members who seek to return to the House is likely to dier

from those who retire or seek to obtain other oces. The stakes for individuals who run for

reelection in terms of contributions and votes are much higher for those individuals who do

not seek reelection. Members not seeking reelection are more likely to miss roll call votes

and pay less attention to their districts (Herrick, Moore, and Hibbing 1994).

10

9

These nding are conrmed for the most part in Groseclose 1994.

10

It is conceivable that including only those members who sought reelection introduces serious selection

bias if decisions to retire are correlated with the variables of interest. A member who strays too far from her

12

I estimated the probability of voting in the direction favored by the group for roll call votes

in the 102nd and 104th Congresses and contributions given during the 1992 and 1996 election

cycles, respectively. Examining these two Congresses gives us faith in the generalizability of

our results, since Congress was controlled by dierent parties and considered very dierent

agendas in these two Congresses. For purposes of comparison, I estimated a pooled probit

model, which ignores individual specic eects, in addition to the random eects probit

estimator. With each set of estimates I report a likelihood ratio test of the hypothesis

H

0

: = 0, where =

2

2

+1

, which indicates whether or not accounting for individual eects

matters (i.e., whether or not we are gaining anything by estimating a random eects model

instead of the pooled probit model).

Table 1 reports the results for votes included in the AFL-CIO's voting score for the 102nd

Congress. The pooled probit estimates indicate that an increase in labor PAC contributions

increases the probability of casting a pro-labor vote, while an increase in corporate PAC

contributions increases the probability of voting against labor. However, once we account for

individual specic eects, only the result for labor PAC contributions holds; the coecient on

the corporate PAC contributions variable is no longer statistically signicant. The coecient

on the Education and Labor Committee dummy variable also loses its statistical signicance

in the random eects probit model.

The inferences for the other variables are the same for the pooled probit and random

eects models. The random eects probit estimate of the coecient on the unemployment

rate is much larger than that estimated by pooled probit. The coecient on the presidential

cue variable changes sign in the predicted direction once we account for individual eects,

though the size of the standard error prevents us from saying with any degree of condence

that this eect is in fact positive. The party variable is statistically signicant and has the

expected sign. The likelihood ratio test overwhelmingly reject the null that = 0, indicating

that the panel level variance component is important.

To determine the marginal eects of the variables, I simulated the probabilities of voting

constituency when casting roll call votes or who has diculty raising funds for reelection may retire rather

than face a dicult reelection bid. As always, the robustness of the results reported in this paper should be

checked against this sample selection decision.

13

in a particular direction across the range of values for the variables in the sample. Figure 1

shows how the probability of voting the AFL-CIO's position increases for the sample values

for the 102nd Congress. The other variables are held constant at their median values and

the individual specic eect is set to its mean value (i.e., set to zero). As the value of labor

contributions goes from the lowest to highest value, the probability of casting a vote for

the AFL-CIO position increases by only about ve percentage points. Thus, at least for a

member of Congress with median values of the explanatory variables, the marginal eect

of labor contributions on the vote is quite small. The marginal eect of the unemployment

rate is quite large, however. Figure 2 shows that, while members with the lowest district

monthly unemployment rates are unlikely to vote the AFL-CIO position, members with high

monthly unemployment rates during a month in which a vote took place are almost certain

to support the labor position. Members' voting behavior appears to be especially sensitive

to monthly unemployment in the district.

11

Table 2 reports the results for USCC roll call votes. Again, the inference that we would

draw from the pooled probit estimates is that both labor and corporate PAC contributions

aect votes, but in the opposite directions. Once individual specic eects are taken into

account, however, only labor contributions have a coecient that is bounded away from

zero (and has the predicted sign). The coecient on the unemployment rate increases

substantially in the panel probit model. The coecient on the presidential cue variable has

the wrong sign in the pooled probit model, but has the expected sign in the panel probit

model.

Figures 1 through 3 show the marginal eects for variables with statistically signicant

coecients reported in Table 2. Again the marginal eect of labor contributions is fairly

small. Members who receive the highest amount of labor contributions are only slightly less

likely to vote the USCC position than are those members who receive no labor contributions.

11

I also estimated a specication that included changes in unemployment. The eects of changes in

unemployment were not nearly as strong. This might mean that the unemployment rate is simply measuring

a district-specic eect and not really indicating local economic conditions. Districts that experience high

unemployment rates might be predisposed to elect pro-labor legislators. Further exploration is necessary in

order to determine the correct inference to be drawn from the results on this variable.

14

The unemployment rate has quite a large impact|members from districts experiencing high

monthly unemployment are almost certain to vote against the USCC position. The marginal

eects of the presidential cue variable are fairly small. Members with the lowest value of the

presidential cue variable are only about one percent less likely to cast a pro-business vote

than those with the highest value.

Table 3 reports a similar pattern for contributions and AFL-CIO votes for the 104th

Congress. While the coecient on labor PAC contributions remains positive and statistically

signicant when we account for individual specic eects, the coecient on corporate PAC

contributions is no longer statistically distinguishable from zero. While the pooled probit

estimate of the coecient on the unemployment rate is not statistically dierent from zero,

the coecient estimated by the random eects model is highly signicant. The eects of the

presidential cue variable and the party variable are signicant and in the expected direction

for both models.

Looking at the marginal eects, we see that the eect of labor contributions is similar in

magnitude for AFL-CIO votes in the 104th to the eect in the 102nd. Members with median

values for the explanatory variables are already very likely to vote the AFL-CIO position,

and labor contributions increase this likelihood only slightly. This suggests that it would be

worthwhile to simulate the probabilities for other values of the explanatory variables, but I

leave this for future work. The marginal eects of the unemployment rate are moderately

large, while the marginal eects for the presidential cue variable are quite large.

Finally, Table 4 reports the results for USCC votes in the 104th Congress. A similar

pattern for contributions appears; while the pooled probit estimates would lead us to believe

that labor and corporate contributions have signicant though opposite eects, the panel

probit estimates indicate that these eects cannot be distinguished from zero. The coecient

on the unemployment rate goes from being statistically insignicant in the pooled model to

being signicant in the panel model. The inferences for the other variables are essentially

the same.

Figure 2 shows that the marginal eect for unemployment is quite small for USCC votes

in the 104th, while Figure 3 shows that the marginal eect for the presidential cue variable

are huge. These eects indicate that members were very sensitive to the standing of the

15

president in their districts, which is somewhat surprising when we consider that the president

was deemed largely irrelevant for the rst session of the 104th. The size of the eect may

be due to the much larger prole that the president had during and after the budget battles

and the government shutdowns.

5 Discussion

The analysis reported in this paper indicates the substantial value of employing panel data

methods for analyzing campaign contributions and roll call votes. Theoretically, results

obtained from the panel models, which account for individual eects, are more reliable

than methods which ignore these eects or use poor or invalid proxy measures. For all

types of votes examined, accounting for individual specic eects mattered in substantive

ways. While corporate PAC contributions always had statistically signicant eects when

individual eects were ignored, they never had signicant eects when individual eects were

taken into account. In several instances, the panel models led us to dierent inferences for

the unemployment and presidential cue variables|inferences that were consistent with what

theory predicted we should nd.

Though labor PAC contributions had statistically signicant eects in three out of the

sets of votes examined, the marginal eects for these variables indicate that contributions

do not have the pernicious eects on votes that reformers are concerned about. Members

are much more responsive to objective economic conditions in the district, as measured by

the district unemployment rate, and the cues they get from the president and his level of

support. These ndings should grant solace to those individuals who are concerned about

the adequacy of democratic representation.

The results presented in this paper should be considered preliminary at this point. Some

work remains to be done so that we can be more condent about the conclusions that

we draw from this analysis. Obviously, one of the biggest shortcomings of this analysis is

the adoption of the assumption that the individual specic eect is uncorrelated with the

explanatory variables. One can easily make an argument that this assumption does not hold.

Preliminary attempts to estimate a xed eects model produced such bizarre results that I

16

do not report them here. Eorts continue to estimate the models that allow for correlation

between the individual eects and for more general error structures.

Another shortcoming is that I do not analyze soft money contributions. In terms of

dollar amounts, these kinds of contributions became especially important during the period

analyzed in this paper and are one of the prime targets of reformers.

12

While these types of

contributions are much more dicult to analyze than are PAC contributions since they must

be traced through party organizations, the substantial increase in soft money contributions,

especially from labor groups, indicates that we are missing a big part of the campaign nance

picture. Future work, should take into account these other sources of money.

12

For data on the recent explosion in soft money contributions, see http://www.tray.com/fecinfo. For

details on recent reform eorts see Katz and Doherty 1998 and Doherty 1998.

17

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Table 1: Probit model results for voting behavior on AFL-CIO roll call votes,

102nd Congress

Variable Pooled probit Random eects probit

ln Labor PAC contributions :064 :022

(:006) (:009)

ln Corporate PAC contributions :049 :008

(:007) (:010)

Unemployment rate 5:654 26:357

(1:102) (1:966)

Presidential cue variable :150 :158

(:100) (:119)

Party 1:605 1:213

(:054) (:099)

Education and Labor Committee :253 :486

(:088) (:297)

Constant 2:262 |

(:119)

ln likelihood 1874:675 1711:923

Standard errors in parentheses. Number of cross-sectional units =

326. Time periods = 13.

Likelihood ratio test of = 0:

2

1

= 711:63 (p < :001).

Table 2: Probit model results for voting behavior on USCC roll call votes,

102nd Congress



(:005) (:007)


(:006) (:008)


(:963) (1:349)

Presidential cue variable :205 :412

(:111) (:129)

Party 1:575 1:022

(:054) (:071)

Appropriations Committee :106 :186

(:062) (:131)

Constant 2:043 |

(:111)

ln likelihood 2427:422 2408:9076




2

1

= 397:37 (p < :001).

Table 3: Probit model results for voting behavior on AFL-CIO roll call votes,

104th Congress



(:007) (:009)


(:007) (:009)


(1:221) (1:411)

Presidential cue variable 1:799 2:406

(:142) (:164)

Party 1:756 2:070

(:061) (:109)

Education and Labor Committee :194 :0458

(:089) (:178)

Constant :844 |

(:092)

ln likelihood 1719:3876 1613:7026




2

1

= 298:09 (p < :001).

Table 4: Probit model results for voting behavior on USCC roll call votes,

104th Congress



(:006) (:006)


(:006) (:006)

Unemployment rate :107 2:646

(:985) (:747)

Presidential cue variable 2:942 3:200

(:130) (:138)

Party 1:317 1:382

(:046) (:057)

Appropriations Committee :0520 :043

(:059) (:082)

Constant :208 |

(:072)

ln likelihood 2770:493 2728:493




2

1

= 92:92 (p < :001).

Figure 1: Eect of Labor PAC Contributions on the Probability of Voting in Favor of the

AFL-CIO and USCC Positions

Figure 2: Eect of Unemployment Rate on the Probability of Voting in Favor of the AFL-CIO

and USCC Positions

Figure 3: Eect of Presidential Cue Variable on the Probability of Voting in Favor of the

AFL-CIO and USCC Positions

A Panel Probit Analysis of Roll Call Votes

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