2. 1
The ideological and political clashes that resulted from the financial crisis of 2008 and
the subsequent economic bailout have been at the forefront of American politics. The $700
billion government bailout of financial institutions and was the major cause of the rise of the Tea
Party, who claimed that “government spending is out of control,” as well as the Occupy Wall
Street movement which argued that “government is corrupt and controlled by corporations.” The
crisis, of course, had deep roots. Irresponsible mortgage and investing practices by large
financial institutions starting in the Clinton years resulted in the sub-prime mortgage crisis in
2008. Toxic investments in a troubled housing market had a malicious domino effect on the U.S.
economy. The investment banking firm, Lehman Brothers, became insolvent on September 15
from these risky practices, causing a financial panic in the stock market that had the potential to
foster a severe bank run. The dominoes continued to fall to various financial markets, when the
inevitability of the collapse of the credit market prompted government officials to take action.
In response to the financial crisis, Treasury Secretary, Henry Paulson, announced his
Troubled Asset Relief Program (TARP) on September 20. Known as the Paulson Proposal,
TARP proposed that the Treasury would buy up to $700 billion of Mortgage Backed Securities
owned by large financial institutions, in hopes of stabilizing financial markets by directly
reducing the uncertainty in the financial market and banking system. Four days later, President
Bush addressed the nation on primetime television to rally public support for Paulson’s Proposal.
In Congress, the TARP proposal became a bill called the Emergency Economic Stabilization Act
of 2008 (EESA). The bill was taken up expeditiously for a vote in the House on September 29,
but it failed 205-228. That same day, investors responded to the failed bailout, and the Dow
Jones Industrial Average had its largest single-day point drop in its history.1
The media’s
1 The Dow Jones Industrial Average incurred its largest point drop for a single day in its history, plummeting almost 800
points. < http://money.cnn.com/2008/09/29/markets/markets_newyork/index.htm>
3. 2
reporting was starting to become frightening, warning that people’s savings and 401k
investments were at risk of being lost. After being passed in the Senate on October 1, the EESA
was sent back to the lower chamber for a revote on October 3. This time, the EESA passed the
House comfortably, 263-171. Between the first and second votes on the $700 billion bailout, 58
members of the House of Representatives changed their vote from “nay” to “yay.”
Why did these 58 members change their vote? Did interest group pressure in the form of
PAC contributions get to them and cause them to change their mind? Was it pressure from the
President, or were members in fine tune with how their constituency viewed the bailout and
chose to alter their initial decision to prevent electoral backlash? The purpose of this paper is to
examine these questions, and to determine the underlying factors that caused these 58 MCs to
change their vote. In their highly influential research on voting behavior in Congress, Matthews
and Stimson (1978) found a change in congressional voting behavior to be an evolutionary
process. In the case of the EESA, 58 members changed their initial position on a highly public
vote in a span of four days; here, voting change was quite drastic. Kingdon (1973) posits that
when faced with a contentious voting decision, MCs will consult their “field of forces” in
looking for cues in how to vote. The forces of constituency, leadership, and interest group
pressure were all at play on this issue. This $700 billion bank bailout was one of the major
causes of the Tea Party and Occupy Wall Street movements, the two most important political
movements of the 2000’s. The publicity of the bill and the massive implications it has for
political science scholarship and modern American politics makes this pair of votes essential to
understand. This paper provides a unique opportunity to understand the forces at play on one of
the most public bills of the 21st
century, in which tales of corruption were plentiful. Two popular
movies have been made which discuss the corruption of moneyed interests on the contents of the
4. 3
EESA (HBO’s Too Big to Fail and Charles Ferguson’s Inside Job). Untangling the causality of
why these 58 MCs changed their vote will give insight to the strength of interest group,
constituency, and leadership pressures in the United States Congress.
In this paper I argue that campaign contributions from financial services PACs and
constituency pressures were significant causes of this legislative vote switch. I find
circumstantial evidence, at best, to support my theory on constituency pressure and PAC
contributions having a significant effect on members changing their votes. However, counter to
expectations, I find that switching is associated with members who received relatively small
amounts of PAC contributions.
Congressional Voting Decisions
Understanding legislative voting decisions requires us to consider that they occur within a
framework of limited time and information (Weisberg, Heberlig, and Campoli 1999). Legislators
must vote on hundreds of motions per year, so it is impossible for them to understand the full
implications of each vote they cast. As politicians are careerists, their main priority is reelection
(Kingdon 1977; Mayhew 1974), so MCs must take into careful consideration how a vote could
be used against them in the next election cycle. Yet the reelection incentive only takes us part of
the way toward understanding roll call decisions.
Because of the large quantity of votes a MC must cast without sufficient time to carefully
consider the consequences of their action, they must have some form of a voting strategy. Some
motions voted on by the members are familiar issues that have come up for a vote many times
throughout their careers. For these common issues, members tend to consider the long-term
forces of district partisanship and ideology when making their decision (Weisberg, Heberlig, and
Campoli 1999). Early in their career, they will develop a voting pattern that is consistent with the
5. 4
policy preferences of their constituency. After a few election cycles in which they maintain their
seat, they have no reason to change their voting behavior, as their self-interested goal of
reelection is satisfied. A change in voting behavior may earn the MC the distinction of being a
“flip-flopper” or out of touch with his/her constitutents, so their voting behavior is constrained
by their “voting histories” (Kingdon 1977).
Legislative voting patterns also appear to be a function of the stability of background
factors such as party, ideology, and constituency (Asher and Weisberg 1978). If these factors
remain stable, which, for the most part they do, then a MC will display consistency in roll-call
selection. Thus, stability rather than change characterizes congressional voting behavior; vote
changing in Congress being an evolutionary process rather than a drastic one (Asher and
Weisberg 1978, 391). Sudden change in voting patterns may be attributed to membership
replacement, member conversion, and partisan change in the presidency, rather than an
unexpected deviation in opinion or preferences.
Despite the dominant undercurrent of stability that characterizes MCs’ voting patterns,
there are examples of deviations in voting patterns and some explanations for this behavior have
been offered. Members will alter longstanding voting behavior when they are subject to
redistricting (Stratmann 2000) or when they switch parties (Hager and Talbert 2000; Nokken
2000). Congressional leadership has also been found to cause members to switch their initial
positions on legislation (King and Zeckhauser 2003; Burden and Frisby 2004). One study
exploring the changing of positions between pre-vote whip counts and the actual vote on
NAFTA legislation in 1993 reveals the switch can be explained by how much a member’s
constituency would benefit from the legislation’s passing as well as electoral support in the form
of side payments from President Clinton (Box-Steffensmeier, Arnold, and Zorn 1997).
6. 5
The long-term voting strategies work well with a great majority of bills on which a
member votes. However, sometimes government must deal with specific short-term issues that
arise on which long-term factors, such as ideology and partisanship, are not as useful of an
indicator in making a voting decision. Kingdon (1973) provides insight into how Congressmen
make their voting decisions on short-term issues. According to Kingdon’s theory, the MC will
decide whether or not the issue at hand is controversial. If it is not controversial, they will “vote
with the herd,” or follow the legislative consensus within their party. However, if the vote is
controversial, members will look for “voting cues” from their “perceptual field of forces”
(constituency, fellow Members of Congress, interest groups, party leaders) in how their vote
might be interpreted. They will find a balance between these cues, and make a calculated voting
decision that will best serve their self-interest.
Another voting strategy for short-term issues is the issue framing approach, where MCs
are provided with different ways to “frame” their argument that justify their voting decisions to
their constituents on a particular bill (Weisberg, Heberlig, and Campoli 1999, 387). Members
can then choose which of these different frames will work best with their constituency. This
seems a logical strategy in the case of the bailout, as a switch in a member’s vote could be
explained as helping to save the American economy from going into a depression.
In the case of the Emergency Economic Stabilization Act of 2008 (EESA), Members
were like to have faced many different cross-pressures within Kingdon’s “field of forces.” If no
government economic intervention occurred in a timely manner, many parts of the economy
would start to see major repercussions. Given that MCs are self-interested actors, whose focal
underlying goal is reelection, they were likely aware of the possible electoral consequences of
coming out on the wrong side of this issue. Were the EESA not to pass, more financial
7. 6
institutions would follow Lehman Brother’s lead and become insolvent, bringing large amounts
of layoffs to the financial sector of the economy. Members who represent districts with a large
percentage of employment in the financial industry would understand this notion, and were likely
to switch their vote.
H1: The larger a member’s district financial services employment percentage, the more
likely they were to vote yea on the EESA.
H2: The larger a member’s district financial services employment percentage, the more
likely they were to switch their vote on the EESA.
The housing market took a much talked about dive in the months following the subprime
mortgage crisis in 2008. Housing markets in areas of the country that experienced a real estate
“bubble” were hit hardest. However, if the bailout had not gone through and stabilized financial
markets, the damage to the housing market would likely have been exponentially worse.
Members, being self-interested actors with the main goal of reelection, were likely aware of their
district’s housing market vulnerability. Thus, I posit that MCs from previously high growth real
estate markets were likely to support the EESA. I also expect that these factors may have caused
initially hesitant members to switch their vote if they thought their constituency supported its
passing.
H3: The more that a member’s district was experiencing a housing bubble, the more
likely they were to vote yea on the EESA.
H4: The more that a member’s district was experiencing a housing bubble, the more
likely they were to switch their vote on the EESA.
Besides a large decrease in the value of their homes, constituents had other economic
interests that were sincerely threatened by the financial collapse in 2008. The banks that held
8. 7
citizens’ life savings, including 401k retirement plans, were inevitably going to become insolvent
if the US government did not intervene in the economy. These banks are also responsible for
giving loans to people and businesses of all sizes. If the large banks became insolvent, then a real
credit crisis would have come into fruition, and the average citizens would likely see his/her life
savings evaporate into thin air. Business in America would have come to a halt due to a lack of
credit. Seemingly, constituents from all districts had a vested interest in the timely passing of the
EESA. If their constituent support for the EESA was high and a MC voted against the bill on
both roll calls, a likely result of this situation would be trouble at the ballot box for the next
election, which was one month away.
H5: The more that a member’s district supported the bailout, the more likely they were to
vote yea on the EESA.
H6: The more that a member’s district supported the bailout, the more likely they were to
switch their vote on the EESA.
These factors capture the volatile context in which MCs cast their vote. Considering the
imminent threat the financial crisis posed to financial institutions, I also posit that campaign
contributions from PACs representing financial services had a major influence in the outcome of
the bailout.
PAC Contributions and Roll Calls
The link between campaign contributions from PACs and roll call votes is a contentious
subject in political science. Research claiming no statistical relationship exists (e.g., Wright
1985; Grenzke 1989; Owens 1986; Rothenberg 1992) is made ambiguous by research claiming
that a statistical relationship does exist (e.g., Ashford 1986; Stratmann 1991; Fleisher 1993;
Fellows and Wolf 2004). In their meta-analysis of every paper ever published that studies the
9. 8
relationship PAC contribution and roll-call voting, Roscoe and Jenkins (2005) found the
methodology of testing this relationship to vary widely and that positive findings may be the
result of model specifications. The dominant criticism of this literature rests with the inherent
endogeneity issues that plague contributions and roll-calls. PACs have been found to contribute
to candidates who are already predisposed to voting in their favor on legislation important to
them (Wright 1996; Hall and Wayman 1990), so just because a MC votes a certain way does not
mean that the PAC money the member received caused that specific voting behavior. A MC may
be inclined to vote in favor of a PAC’s policy positions without contributions from the PAC
because their long-term stable “field of forces,” such as party, ideology, and constituency
preferences, lead them to vote that way regardless of PAC contributions. However, a meaningful
and statistically significant relationship between contributions and votes has been found under
specific circumstances. Feldstein and Melnick (1984) and Grenzke (1990) argue that campaign
contributions affect voting decisions when the lawmaker has faced or is facing a close reelection.
Conway (1991), Wright (1985), and Mutch (1988) found that PAC contributions have a tangible
effect when the issue at hand is non-partisan and non-ideological
I argue that under certain circumstances, campaign contributions from PACs can
influence a lawmaker’s vote on a bill. Firstly, the industry that the PACs represent must have a
serious interest in an issue. On September 29, 2008, after the first vote in the House on the
bailout failed to pass, the Dow Jones Industrial Average, the S&P 500, and the NASDAQ
composite all recorded one of their worst days.2
The entire financial industry was in a panic. I
2 The Dow Jones Industrial Average, the S&P 500, and the NASDAQ are all indicators of how
well the stock market is doing. On September 29, 2008, the day the first House vote on the
2008 EESA failed, The Dow dropped 777 points, which was the largest single-day point
drop in its history; the S&P 500 dropped 8.8%, the seventh worst day in its history; and the
NASDAQ composite plummeted 9.1%, the third worst day in its history.
10. 9
assume that PACs representing finance, insurance, and real estate interests were extremely
interested in the 2008 EESA. Without some kind of government intervention in the economy,
any business related to the financial, real estate, and insurance industry would surely see major
repercussions. The conditions surrounding an issue have to give a lawmaker an appropriate
frame that they can use for “political cover” in explaining why they voted the way they did to
their constituents. The severe economic circumstances could provide this political cover to
unsure members, while switching their vote will allow them to reward their career sponsors.
Congressmen realize that a large campaign war chest is essential for a successful Congressional
career, so members who receive large quantities in contributions do not want to lose an important
part of their donor base, but at the same do not want to suffer electoral consequences for
supporting corporations. The latter part of this notion is solved with a logical frame for
constituents about the severity of the economic circumstances and wanting to ensure their
constituents do not lose their life savings.
H7: The more contributions a member receiver from PACs representing finance,
insurance, and real estate interests, the more likely they were to vote yea on the EESA.
H8: The more contributions a member received from PACs representing finance,
insurance, and real estate interests, the more likely they were to switch their vote on the
EESA.
Data and Methodology
Dependent Variables
I test my hypotheses on data from the 110th
Congress. My unit of analysis is individual
members of the House of Representatives during the 110th Congress. As discussed, the two
Congressional votes this paper analyzes were four days apart. After the EESA failed in the
11. 10
House, the Senate amended the bill and passed it without drastically altering the content.3
The
2008 election was 34 days away from the first vote and 30 days away from the second.
Seemingly, the same forces that were at play in the first vote were at play in the second vote.
These two voting decisions can be seen as separate but inherently connected voting decisions by
actors whose political circumstances cannot have changed drastically in the period between
decisions. This notion leads me to utilize a bivariate probit model, in which the error terms of
each model are correlated. In this statistical technique, I will able to infer the interrelatedness of
the factors affecting members’ voting decisions on the EESA across votes. I model the
probability of MCs voting YES on these two votes as functions of constituency and interest
group factors:
Y*
1 = X1β1 + ε1
Y*
2 = X2β2 + ε2
where Y*1 and Y*2 stand for the first and second roll call voting decision, XB represents the
independent variables of the two models and their respective coefficients. The correlation of the
error terms is given by the parameter ρ.4
I can incorporate an understanding that unobserved
factors are at least partially, if not mainly, responsible for some influence on the decision making
on both votes. This is statistically accomplished by assuming that the error terms are distributed
as a standard bivariate normal distribution:
[ε1,ε2] ~ φ2(0,0,1,1,ρ)
3 The Senate passed the EESA overwhelmingly 74-25. The bill that they passed contained the same content of the
original bill (the $700 billion purchase of toxic assets of large financial institutions), adding a $150 billion small
business tax break and an increase in the amount of deposits in banks that are insured by the Federal Deposit
Insurance Corporation. http://www.nytimes.com/2008/10/02/business/02bailout.html?_r=0v
4 If a bivariate probit model suggests a ρ value of 0, then there is no correlation between the error terms, and the
bivariate probit is identical to independent probit models. If the ρ is high or low (it ranges from -1 to 1), this will
indicate that factors not present on the right-hand side of the equation that is inherently related to both voting
decisions on the EESA. Failing to account for the influence and correlation of unobserved independent variables will
produce an analysis that will not take factors not included as IVs into consideration, producing conclusions that are
unarranted
12. 11
(e.g. Greene 1997; Zorn 2002).
The two dependent variables in the bivariate model are measures of the first and second
votes on the EESA, which occurred on September 29, 2008 and October 3, 2008, respectively.
This information was obtained from Thomas, the website for the Library of Congress.5
There
were 433-recorded votes on the initial bill and 434 on the subsequent bill.6
Members who voted
no were coded as a 0 and members who voted yes were coded as a 1. The first vote failed by a
relatively slim 23 votes, with 205 “yays” and 228 “nays”, while the second vote passed
comfortably 263-171. About 33% of Republicans and 60% of Democrats voted to pass the
bailout the first time around, which increased to about 46% of Republicans and 73% of
Democrats the second time around. Table 1 and Table 2 display the relative frequencies of party
and vote on the first and second roll calls; 33 Democrats and 25 Republicans voted no on the
initial vote and yes on the subsequent vote. Jim McDermott from Illinois is the only member
who switched their vote from yes to no on the EESA. I include him in my bivariate probit
models but leave him out of the model with switching votes as the dependent variable.7
Table 3
displays a cross tabulation of party and members who switched.
[TABLE 1 HERE]
[TABLE 2 HERE]
[TABLE 3 HERE]
5 The exact URLs for this information is http://clerk.house.gov/evs/2008/roll681.xml and
http://clerk.house.gov/evs/2008/roll674.xml
6 There are 435 members in the House of Representatives, but 433 votes recorded for the first vote on the EESA and
434 votes recorded for the second vote on the EESA. Ohio’s eleventh district was not represented for either vote
because the member holding this seat, Representative Stephanie Tubbs Jones, died on August 20th
, 2008, almost two
months before the roll call vote. Her seat was not filled until the next election cycle in November 2008. Jerry Weller
of Illinois missed the initial vote due to “a family matter”.
http://news.medill.northwestern.edu/chicago/news.aspx?id=99339
7 I later construct a model of switching directly, using a measure of whether or not the member changed their vote
on the EESA as the dependent variable. Rep. McDermott is excluded from that model.
13. 12
Independent Variables
To test my PAC contributions hypothesis, I use the amount of campaign contributions
received from PACs representing finance, insurance, and real estate interests each member of the
House received for the 2008 election cycle as an independent variable (between January 3, 2007
and January 3, 2009). I obtained this information from the Center for Responsive Politics’
website, which displays campaign contribution information obtained from the Federal Election
Commission. The votes these paper study were a month away from the 2008 election. Using the
contribution amount from the previous election cycle before the vote is more common in the
contributions literature because a majority of the studies look at the effect of PAC contributions
on multiple votes (Roscoe and Jenkins 2005). However, because of the proximity of the votes
studied in this paper and the next election cycle, I use the contribution amount for the
contemporaneous election cycle. This variable has a wide range from $0 to $955,095. The
independent variable has a mean of $98,578, a median of $51,539, and a standard deviation of
$121,799.8
A histogram of contributions is displayed in Figure 1. Only 13% of members
received more than $200,000 in contributions, illustrating a left-skewed distribution for PAC
contributions as well as the fact that big time money was going to a small minority of members.
My expectation is that the more contributions Members received from financial services PACs,
the more likely they were to switch their vote on the EESA.
[FIGURE 1 HERE]
Measuring constituency influence has been one of the more troublesome problems in
Congressional voting literature (Kuklinski 1979). The previous discussion on the severity of the
8
Because my original independent variable has such large values, I found it necessary to modify it. This new
variable is the independent variable divided by 100,000. Using the modified independent variable in my model will
provide for a model with results that are much simpler to both understand and communicate. I will be able to infer
the effect a $100,000 increase in contributions has on the probability of a member changing their vote from no to yes
on the bailout.
14. 13
financial panic and how much worse the economy would have become, had no government
intervention occurred, leads me to use two district-level economic measures and one public
opinion poll – constituency influence measures that would have been most affiliated with the
subprime mortgage crisis - as independent variables. The subprime mortgage crisis came about
because a small drop in housing prices had a chain reaction on assets of large financial
institutions from risky investing, mortgage, and loan practices. Large financial institutions were
becoming insolvent causing the stock market to go into a panic. On the same day the first vote
failed to pass through the House on September 29, the stock market experienced a $1.2 trillion
loss in market value. The consequences were so bad for financial markets that General Electric
was concerned that they wouldn’t have enough credit to run their day-to-day operations. People’s
401k and other retirement funds were being threatened as the largest banking and financial
institutions (who were coincidentally partially responsible for causing the financial crisis) were
experiencing major problems. If the President and Congress didn’t do something to stabilize
markets, then surely the economy would have worsened drastically. The two parts of the
economy that would have seen the most severe implications if the revote in the House on the
bailout failed were financial services employment and the housing market. Being up for
reelection every two years, I assume that House Members are finely in tune with the economic
characteristics of their district, and how it relates, politically and electorally, to their voting
behavior on bills that are important to the financial lives of their constituents. Thus, I expect
members representing 1) districts with large financial employment sectors and 2) districts that
experienced inflated prices in the housing market to be more likely to switch their vote on the
bailout. Members knew if their district’s economy would get sufficiently worse if the EESA did
not pass on the first revote, and those that thought their district would get hit the hardest were the
15. 14
ones that switched their vote from no to yes. Additionally, I assume that members are aware of
their constituency’s opinion on important public policies, so I expect members whose districts
were more supportive of the bailout more likely to switch.
The percentage of total district workforce in the financial services industry was coded for
each representative of the 110th
Congress. This measure is from a 2007 American Community
Survey 1-year estimate, accessible by district for the 110th
Congress on the American Census
website.9
The change in district median price of a house from 2005-2007 is also coded for each
MC.10
I view large increases in median price of a home between 2005 and 2007 to be an
indicator of being caught in the inflationary housing bubble, and thus Members representing
these constituencies are more at risk of facing electoral backlash if they did not vote to pass the
EESA. The 2005 and 2007 measures are from 2005 and 2007 American Community Survey 1-
year estimates available by district. I expect that the larger a Member’s district financial services
sector and the larger the differential in median price of a home, the more likely a Member is to
switch their vote on the EESA. The third independent variable measuring constituency influence
is support for the bailout in the member’s district. This information comes from the 2008
Cooperative Congressional Election Study. Over 40,000 individuals stemming from all
congressional districts were surveyed. The question asked respondents whether they supported or
opposed the bill. The answers were aggregated to the district level to provide a percentage of
total respondents in the district who supported the bill. This variable has a mean of 21.47 and a
median of 20.34 with a standard deviation of 7.21. Clearly, support for this bill was low
9
This measure is percent of total workforce in the district that is in the “finance and insurance, real estate and rentals
and leasing” industries. This was found using “The American Fact Finder” on the website for the census,
census.gov. This tool conveniently allows a plethora of information to be obtained by Congressional district for
several Congresses, one of which was the 110th
Congress.
10
The exact equation for this variable is (median price in 2007) – (median price in 2005). I expect a higher
differential in housing prices to be associated with being in the housing bubbles, and thus associated with an
increased likelihood of switching. These measures were found using the American Fact Finder.
16. 15
throughout the United States, as only two districts’ support for the EESA rose above 50%. Figure
2 displays a histogram of district bailout support. The EESA was a direct government
intervention in the economy, so should be seen as more agreeable to the liberal ideology rather
than the conservative ideology. Thus, district bailout support can be interpreted as a district’s
policy preference. My expectation is that the more a member’s district supports the bailout, the
more likely he/she is to switch their vote on the EESA.
[FIGURE 2 HERE]
My first control variable is a measure of each member of the House of Representative’s
ADA score for 2008 as a proxy for ideology.11
My second control variable is political party;
Republicans were coded as a 0 and Democrats coded as a 1. In the 110th
Congress, there were
198 Republicans (45.62%) and 236 Democrats (54.38%). ADA score and party are imperative
control variables because they allow my model to control for members’ ideological
predispositions. These dispositions have been found to be the chief causal factors in
congressional voting behavior (Kingdon 1977), so adding them allows me to better estimate the
effect of constituency and interest group pressure on the legislative vote switch. For a bill that
includes a $700 billion bailout, my expectation is that more liberal members and Democrats were
more likely to vote yes on the EESA. I also added 2006 two-party vote share is added as a
control variable.12
I expect members with more comfortable electoral margins of victory to be
more likely to switch. Presidential support scores for the 110th
Congress were additionally added
11 This measure was obtained from the website of Americans for Democratic Action. ADA scores for the 110th
Congress would not be a valid measure if the EESA were one of the votes included in their scoring. Fortunately, the
EESA is not one of those votes. For each vote, Americans for Democratic Action classified which position was the
“liberal” position. The ADA score is the percentage of times a member voted in accord with the Americans for
Democratic Action’s self-declared liberal positions during the 110th
Congress. ADA scores range from 0-100; 0
indicating a perfectly conservative voting record and 100 indicating a perfectly liberal voting
recordhttp://www.adaction.org/media/votingrecords/2008.pdf
12 I took the member’s two-party vote share in the 2006 House elections, disregarding the political party that came
in second place. In other words, if the runner up in a race belonged to a minor party that is not the Democratic or
Republican party, they were still included in the two-party vote share.
17. 16
as a control, allowing my analysis to better understand the influence of constituency and interest
group pressure on the legislative vote switch keeping in mind that President Bush took a very
public stance in pushing for the bailout to pass. My expectation is that the more a MC has
supported President Bush in the past, the more likely they were to vote yes on both votes. The
summary statistics for all independent and control variables used in the analysis can be viewed in
Table 4.
[TABLE 4 HERE]
Results
I begin my analysis by estimating a bivariate probit model, using the two votes on the
EESA as dependent variables. The results of this model are presented in Table 5. The ρ estimate
is an estimated correlation coefficient of the dependence between the two voting equations. A
value of .968 is very large and easily reaches statistical significance, signifying that these two
voting decisions are highly correlated. The factors that caused the initial voting behavior were
similarly causing the subsequent decision. The likelihood ratio test indicates that the bivariate
probit model is superior to independent probit estimates These two values provide substantial
evidence that the factors that influenced a MC to affirm the bailout the first time, highly likely to
influence his/her vote the second time around. My model predicts 62.35% of cases correctly but
only predicts outcomes in which members voted yes both times (1,1) or not both times (0,0).
Interestingly, the bivariate model does not predict a single switch in voting decisions on the
EESA; thus my model never predicts switching (0,1) to be a MC’s most likely outcome.
[TABLE 5 HERE]
My controls seem to be extremely influential predictors in voting yes on both votes,
displaying the strong influence that ideology, party, and support for President Bush had on MCs
18. 17
voting decisions. For both votes, these three factors have p-values of .002 or below. In the
literature, party and ideology are found to be the most powerful influences on congressional
voting behavior (e.g. Kingdon 1977; Weisberg, Heberlig, and Campoli 1999). Finding that a
liberal ideology and being a Democrat are the strongest predictors of MCs voting yes on a $700
billion bailout is consistent with the general conclusions of voting scholarship. Finding that
previous legislative support for the president to be such a strong predictor of vote choice is more
interesting. Seemingly, if a MC supported the president on previous legislation, then he/she
supported this legislation as well by voting yea on both roll calls. Margin of victory in the 2006
election is statistically unrelated to the voting decisions on the EESA.
Financial services PAC contributions have a positive and significant influence on both
voting decisions, but a reduction in both the positivity of the coefficient and significance of the
p-value between the two decisions is observed. However this is an extremely small change.
Contributions appear to have had a larger effect on the initial voting decision in comparison to
the second, providing evidence to refute my PAC contributions hypothesis that members who
received more money were more likely to change their vote. I will revisit this in more detail in
subsequent models. District bailout support is positive and significant for both votes, with a
marginal increase in correlation and strength on the second vote. The coefficient was increased
.005 across votes, indicating district bailout support had an incrementally stronger influence on
the latter voting decision. District housing market value differential between 2005-2007 is
significant for both votes, but in the opposite direction from what was hypothesized. Seemingly,
members whose districts were not in the housing bubble were more likely to vote yes on both
roll calls. Percentage of the district that is employed in the financial services industry is an
insignificant predictor of the first voting decision, but a positive and highly significant predictor
19. 18
of the second voting decision. This brings substantive credit to my theoretical claim that the
severe decline of the economy in the days between votes on the EESA and the inevitable fallout
of the financial services industry that would have gone along with it had the EESA not passed,
significantly influenced members from districts with high employment numbers in the industry
to change their vote from no to yes.
To make more definitive inferences about my theory, I calculate predicted probabilities
of members voting (Yes, Yes) and (No, Yes) for each independent variable from its minimum to
its maximum value. This calculating is located in Table 6. I include these two predicted
probability equations and leave out the other outcomes as predicted probabilities because this
paper theorizes and is concerned with why members changed their vote from no to yes on the
EESA. The outcome of (Yes, No) is not included in my analysis because only one member out of
434 acted this way, and the outcome of (No, No) is not included because this paper is not
concerned with why members voted yes on both votes. Because I employ a bivariate probit
model, I can calculate the joint probability of each of these outcomes as a function of the
observed factors in the model. The probability of voting “yea” on the first vote and “yea” on the
second is equated as:
( ) ( )ρββ ,ˆ,ˆ1,1Pr 221122 xxyyi Φ===
where the symbols on the right side of the equation in parentheses stand for the linear prediction
of the MC’s respective initial and subsequent voting decisions and ρ is the two correlated error
estimate. Φ2 represents the bivariate normal cumulative distribution function. I also equate the
predicted probabilities for an outcome in which a member voted “nay” the first time and “yay”
the second time:
( ) ( ) ( )ρβββ ,ˆ,ˆˆ1,0Pr 22112222 xxxyyi Φ−Φ===
20. 19
where Φ stands for a univariate normal distribution function.
[TABLE 6 HERE]
The model predicting a MC voting in affirmation on both roll calls is in the upper table.
My model predicts that an average member receiving $0 in financial services PAC contributions
has a 67% chance of voting yes the first time and sticking with this decision on the second vote,
and a 98% chance of voting yes consecutively if they received about $1 million in contributions,
an increase of about 30 percentage points. An extremely liberal member is 98 percentage points
more likely to vote yes both times compared to an extremely conservative member, and a
Democrat is 56 percentage points more likely to vote positively than a Republican. Clearly,
liberal Democratic members are heavily predicted to vote in affirmation on both decisions,
especially in contrast to conservative Republicans. In understanding this and the fact that these
probabilities reflect an average Democratic Member of the House, it is clear that the PAC
contributions percentages are inflated by the strong connection between ideology and party with
the legislative voting decision. I will later construct a graph using this joint probability model
that will enable me to better infer the difference between the effects of contributions had on
members of both parties. Constituency support for the bailout has a relatively meaningful
prediction, estimating that a Member representing districts with maximum bailout support of
53% is 88% likely to have voted yes twice on the EESA. Districts with large housing price
differentials were, surprisingly, 29 percentage points less likely to have voted this way. This is
consistent with my observation of this same relationship in the MLE model, and is in direct
conflict with my hypothesized expectation. Members whose constituencies were not
experiencing a housing bubble were more likely to vote yes on either vote. The size of a district
financial services sector seems to have a significant effect on the voting decisions of members on
21. 20
the bailout, as a member representing districts with a maximum value of this variable are 25
percentage points more likely to have affirmed the bill twice. The forces in this model that have
the largest influence on voting behavior on the bailout are my controls ideology, party, and
presidential support score, indicating that being a Democrat, having a more liberal ideology, and
being highly supportive of Bush were the strongest and most meaningful factors in MCs voting
calculus. This, along with the meaningful constituency variables in this probabilistic model,
support scholarship that finds party, previous voting behavior, and constituency factors to be the
most prevalent forces in a member’s “field of forces” (e.g. Kingdon 1977; Weisberg, Heberlig,
and Campoli 1999).
The table at the bottom of Table 6 displays my joint probabilistic model that a MC
switched his/her vote from no to yes between votes. The results for PAC contributions show that
a member who receives an additional $1 million is about 9 percentage points less likely to
initially vote no on the EESA and to alter their decision on the subsequent vote. Substantially,
members who received less money from financial services PACs were more likely to switch.
This provides evidence that my hypothesized claim that members who received more money
were more likely to switch is wrong; the same relationship in the opposite direction has been
proved. Constituency support for the bailout has too small of a difference between the
probabilities of switching votes at a minimum value of support and a maximum value of support
to provide any evidence for my theoretical claims. District housing market has a marginal 5
percentage point differential in the likelihood of switching votes between the minimum and
maximum values, indicating that the higher the differential in housing prices in a member’s
district, the more likely the MC is to, but only marginally so. I observe an almost identical
relationship between the minimum and maximum values of district financial services
22. 21
employment. Ideology seems to not have had a major effect on the vote switchers on the EESA,
2 percentage points being the difference in the likelihood of a very conservative member and a
very liberal member changing their voting decision between votes. However, Republicans were
11 percentage points more likely than a Democrat to have changed their vote. The estimation for
a strong supporter of President Bush to have switched votes is 0. In the model predicting yes on
both votes, an ardent supporter of Bush is 99.9% likely to have affirmed the bill twice. This is a
clear indication that Bush’s supporters in Congress supported his public stance in strong support
of the EESA, and showed it by voting for it twice.
Figure 3 displays a graph of the influence of financial services PAC contributions on the
probability of both a Republican and Democratic Representative voting yes on both votes on the
EESA. The graph clearly illustrates that PAC contributions had a much larger effect on
Republicans’ voting behavior on the EESA than Democrats. As the probabilistic curve for
Republicans moves from $0 in contributions to $1 million, they are estimated to be a substantial
70 percentage points more likely to have voted in affirmation twice in a row, while the same
prediction for Democrats sees a 30 percentage point increase in this likelihood. The slope for
Republicans is static across the low end of contributions, until between $400,000 and $500,000
in contributions, when the slope increases drastically and substantially. Small amounts of PAC
contributions seem not to have had a meaningful effect on Republican MCs’ voting decisions on
the EESA, but large amounts of contributions did have a meaningful effect on the way they
voted. The two graphs are observationally equivalent at about $700,000 in PAC contributions,
indicating that when big money is involved, the voting decisions of Republicans and Democrats
on the bailout cannot be differentiated. The large gap between the probability of a Democrat and
23. 22
of a Republican voting yes twice when they received $0 in contributions is a clear indication of
the mighty influence of party on these voting decisions.
[FIGURE 3 HERE]
To provide an alternative estimation strategy for modeling vote switching, I estimated a
third model that uses switching votes as the dependent variable, displaying my independent
variables’ influence on the likelihood of members switching their vote from no to yes on the
EESA.13
PAC contributions is significant, but surprisingly, in the opposite direction from
hypothesized, indicating that members who received less financial services PAC contributions
were more likely to switch. This is in direct conflict with my theoretical expectation that higher
amounts of contributions were a likely cause of MCs switching their vote. The only other
significant factor in this model is district housing price differential. However, with a p-value of
.095 and a coefficient that is very close to zero, inflationary housing prices had an extremely
marginal, if any, influence on members switching their votes on the EESA. No other
hypothesized or control factor is significant in this model. Some of the p-values associated with
the variables were extremely high, such as ideology (.407) and bailout support (.412). This
model also displays a very small R2
estimate of .025, which is clear indication that my observed
factors account for very little in the decision to switch votes on the bailout. Indeed, the overall
model only reaches statistical significance at the .10 level (Wald χ2
= 13.46, p = .0970), and my
model does not predict a single switch. Yet, in this model, one factor seems to be substantially
correlated with vote switching, PAC contributions, but it is in the opposite directing from what I
hypothesized.
[TABLE 7 HERE]
13 A bivariate probit that models VOTE1 and SWITCHING, shows an insignificant ρ, and the insignificant LR test
indicates that it is appropriate to estimate Pr(SWITCHING) as an independent probit.
24. 23
Figure 4 displays a graph of the influence of financial services PAC contributions on the
probability of a member switching from no to yes on subsequent EESA roll calls. For ease of
analysis, I center these probabilities at .1. Cleary, higher amounts of financial services PAC
contributions have a negative correlation with the likelihood of Members of both parties
switching their voting decision on the EESA. A Republican who receives $1 million in
contributions is about 20% less likely than a Republican who receives $0 in contributions to have
switched their vote, and the likelihood for a Democrat decreases by 13% for the same measures
of financial services PAC money. This evidence undoubtedly refutes my hypothetical
expectation of campaign contributions from financial service PACs to be a meaningful force in
causing members to switch their vote on the EESA. However, these observations do reveal an
empirically supported finding that would surprise many of the critics of the influence of financial
interests in Congress: the MCs who switched their vote on the EESA received small amounts of
contributions from PACs representing financial services interests for the 2008 election cycle.
This finding may result from the fact that those with high financial services PAC receipts already
cast their lot for the bailout on the first vote, and only those who were unbeholden to the PACs
were at play for switching in the second vote. Additional evidence for this claim is supported by
the noticeable change in slope across increasing levels of contributions: for the region from $0 to
around $150,000, in comparison to larger amounts of contributions, the rate of change in
probability of switching votes has a much steeper negative slope for Republicans and Democrats
alike. At this same point of around $150,000, the functions for probabilistic change for
Representative of both parties are observationally equivalent; meaning their respective estimated
change in probability of switching cannot be distinguished from each other.
[FIGURE 4 HERE]
25. 24
Conclusion
This research provides unique insight into the decision outcomes of Congressmen on a
historical piece of legislation. Through the use of several different models, I unfortunately do not
find sufficient evidence to make definitive claims on the forces involved in causing 58 members
of the House of Representatives to change their vote on the EESA, leaving the underlying
reasons for why they changed their minds on such a public issue within a span of four days
enigmatic. However, certain aspects of my findings supplement the general scholastic
understanding of voting behavior in the US Congress.
Using a bivariate probit model to estimate constituency and interest group influence on
the decision to vote yea on subsequent roll calls on the EESA, the evidence I find to support my
constituency influence hypotheses is in disagreement with my univariate model estimating
switching. In my bivariate model, a Representative is found to be more likely to vote yes
between votes if their district is either supportive of the bailout or has a large financial services
industry. Conflicting evidence is found in my univariate model, as these two measures of
constituency influence have no correlation with switching in the univariate probit model.14
I
conclude that district bailout support and size of district financial services sector were not
meaningful factors in the voting calculus made by members on whether or not to switch their
vote on the bailout.15
Similarly, empirical support for inflationary housing prices is weak at best.
14 Only 58 MCs switched their vote on the EESA. Since my univariate probit model uses switching as a dependent
variable, the fact that only 58 members switched give me a small N problem. It is possible that the extremely
statistically insignificant nature of this model is heavily skewed due to the inefficiency of this model.
15 I considered making the argument that the univariate model estimating switching directly is an inefficient way to
model the 58 members who switched their vote, and to discount its results in evaluating my hypotheses and general
theoretical expectations. I did not go this route because even if I trust my bivariate model that constituency bailout
support and size of district financial services industry were meaningful influence in MCs switching their vote, the
results in the joint predicted probability model estimating (No, Yes) shows that when taking these measures from
their minimum to maximum values, it increases MCs likelihood of switching by 5 percentage points. This small
change in probability is similar to the small decrease of p-value and proportionally small increase in coefficients.
26. 25
My measures of constituency influence, in general, do not exhibit enough of an influence on
switching votes to claim that the electoral backlash I had theorized changed members’ votes.
I hypothesized that higher amounts of campaign contributions from PACs representing
financial services interests would increase the likelihood of members switching their vote from
no to yes on subsequent roll calls on the EESA. Surprisingly, I find clear evidence that higher
amounts in contributions are associated with a decrease in the likelihood of switching votes in
all models. This is because the overwhelming majority of members who received noticeable
amounts of contributions already voted yes on the initial vote, so it was impossible for them to
switch their vote from no to yes.16
It was the MCs receiving small amounts in contributions that
switched their vote on this $700 billion bill to save the financial industry. Research on PAC
contributions and roll call voting provides us with fertile ground in understanding the very strong
relationship observed between PAC contributions from financial services interests and voting yes
on both (and each) individual vote on the EESA: PACs contribute heavily to candidates who are
already predisposed to voting in their favor on legislation important to them (Wright 1996; Hall
and Wayman 1990). Members who received large amounts of financial services PAC
contributions did so because of their previous voting history and ideological predispositions, so I
refrain from making any substantial claims of causality between contributions from these PACs
and MCs’ voting behavior on the EESA.
My results suggest that the “same old forces” Kingdon concluded in 1977 to be the most
important political forces on Congressional voting behavior were the most obvious influences at
play in the EESA’s case, thirty years after it was published. Party and previous voting history
were extremely strong predictors on whether or not Representatives would vote yes on the first,
16 Recall that only a singular Representative in the House of Representatives switched their vote from yes to know.
27. 26
second, or both votes. Previous legislative support for President Bush seems to also somehow be
in the equation. Considering the important role the President played in passing it through
Congress, this finding provides evidence of presidential support score to be a plausible measure
of influence from the Executive branch. Although I have credible insight into why members
voted yes on the EESA, the final analysis reveals the underlying reasons for why members
switched their vote a mystery.
28. 27
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31. 30
TABLE 1: Relative Frequency of Party and the 1st
Vote on the EESA.
1st
Vote on the EESA
Party No Yes Total % Voted Yes
Democrats 95 140 235 59.57%
Republicans 133 65 198 32.83%
Total 228 205 433
32. 31
TABLE 2: Relative Frequency of Party and the 2nd
Vote on the EESA.
2nd
Vote on the EESA
Party No Yes Total % Voted Yes
Democrats 63 171 235 73.19%
Republicans 108 91 199 45.73%
Total 171 263 434
33. 32
TABLE 3. Relative Frequency of Party and Vote Switchers on the EESA.
Switched Vote on the EESA
Party No Yes Total % Switched
Democrats 201 34 235 14.47%
Republicans 173 26 199 13.07%
Total 171 263 434
Note: For this table, if a MC voted differently in any capacity on VOTE1 and VOTE2 they were
included as Switchers.
34. 33
TABLE 4. Summary Statistics for Dependent and Independent Variables.
Dependent Variables Mean Std. Dev. Minimum Maximum
Vote 1 .473 .500 0 1
Vote 2 .606 .489 0 1
Switch .138 .346 0 1
Independent Variables
PAC Contributions $98578 $121800 $0 $955,095
Bailout Support 21.473 7.211 6.122 53.125
District Housing Market 31.158 34.037 -111.1 230.9
Financial Employment 7.039 2.283 .700 19
ADA 58.583 36.594 0 100
Party .541 .499 0 1
Presidential Support 40.228 27.224 4 89
Electoral Margin 18.441 13.258 .020 50
35. 34
TABLE 5. Bivariate Probit of the First and Second Votes on Emergency Economic Stabilization
Act, House of Representatives.
1st
Vote on EESA 2nd
Vote on EESA
Variable MLE (SE) MLE (SE)
Constant - 7.919*** 1.285 - 6.812*** 1.058
Financial PAC $ .183*** .055 .146*** .054
Ideology .048*** .008 .044*** .008
Democrat 1.619*** .510 1.210*** .361
Constituency Support .019* .012 .024** .03
Electoral Margin .006 .005 .007 .005
District Housing Market .002 .002 - .002 .001
District Financial Employment .038 .035 .070** .036
Presidential Support Scores .081*** .016 .067*** .014
ρ .968***
Log Likelihood - 376.86
LR χ2 333.50 (16)***
% Predicted Correctly 62.35%
N 433
Note: Robust standard errors are clustered by state.
* p < .10 ** p < .05 *** p < .01, one-tailed
36. 35
TABLE 6. Predicted Probabilities of Decision Outcomes at Each Variable’s Minimum and
Maximum Values.
Pr(y1 = 1; y2 = 1)
Variable At Minimum At Maximum Difference
Financial PAC $ .673 .981 .307
Ideology .012 .994 .982
Democrat .145 .706 .560
Constituency Support .592 .876 .284
Electoral Margin .668 .764 .096
District Housing Market .812 .522 -.290
District Financial Employment .593 .847 .254
Presidential Support Scores .009 .999 .991
Pr(y1 = 0; y2 = 1)
Variable At Minimum At Maximum Difference
Financial PAC $ .092 .002 -.089
Ideology .026 .002 -.024
Democrat .195 .082 -.113
Constituency Support .076 .063 -.013
Electoral Margin .083 .078 -.005
District Housing Market .056 .114 .058
District Financial Employment .045 .102 .057
Presidential Support Scores .042 .000 -.042
Note: The upper table displays predicted probabilities of voting yes on both votes on the EESA.
The bottom table displays predicted probabilities of voting no on the first vote and yes on the
second vote. For all predicted probabilities, independent variables are taken from their minimum
to maximum value, while holding all other values at their mean for continuous variables and at
their mode for dichotomous variables. The mode for party is 1, meaning all probabilities for the
independent variables (besides party) are for Democrats holding all other values at their means.
37. 36
TABLE 7. Probit Model of Legislator’s Probability of Switching Votes.
Variables MLE (SE)
Constant -1.007* .713
Financial PAC $ -.153*** .064
Ideology .001 ..006
Democrat -.387 .420
Constituency Support .003 .013
Electoral Margin .001 .007
District Housing Market .003* .003
District Financial Employment .035 .035
Presidential Support Scores -.006 .008
Log Likelihood -168.097
LR χ2
13.46
Pseudo R2
.025
% Predicted Correctly .525
N 433
Note: Robust standard errors are clustered by state. Jerry Weller of Illinois was not added
because he was the sole MC to switch their vote from a yes to a no, and the focus of this paper is
why members switched from no to yes.
* p < .10 ** p < .05 *** p < .01, one-tailed
38. 37
FIGURE 1. Histogram Displaying Distribution of Campaign Contributions from PACs
Representing Finance, Insurance, and Real Estate Interests to all Members of Congress.
050100150200
NumberofHouseMembers
0 200000 400000 600000 800000 1000000
Total Receipts from Finance, Insurance, and Real Estate PACs
39. 38
FIGURE 2. Histogram Displaying Distribution of Constituent Support for Bailout.
0.02.04.06
Density
10 20 30 40 50
Percentage of Respondents Who Supported Bailout
40. 39
FIGURE 3. Predicted Probability of Voting Yes on Both EESA Votes by Campaign
Contributions from Financial Services PACs and Party.
.2.4.6.81
ProbabilityofVotingYesonBothVotes
0 200,000 400,000 600,000 800,000 1,000,000
Total Receipts from Finance, Insurance, and Real Estate PACs
Democrats Republicans
41. 40
FIGURE 4. Predicted Probability of Switching Vote on EESA by Campaign Contributions from
Financial Services PACs and Party.
0.05.1.15.2
ProbabilityofSwitchingVotetoYes
0 200,000 400,000 600,000 800,000 1,000,000
Total Receipts from Finance, Insurance, and Real Estate PACs
Democrats Republicans
