**The \$700 Billion Bailout:**

**A Public-Choice Interpretation**

Carlos D. Ramírez[^1]

Department of Economics

George Mason University

Fairfax, VA 22030-4444

and

Center for Financial Research

Federal Deposit Insurance Corporation

Washington, DC 20639

Abstract

On September 29, 2008, the House of Representatives voted to reject HR
3997 (known as the original \$700 Billion Bailout Bill). On October 3,
the House reversed course and voted to approve the Emergency Economic
Stabilization Act of 2008 (EESA). This paper applies a political voting
model to these two House votes---the rejection of the bill on September
29 and its passage on October 3. Both economic conditions and PAC
contributions matter in explaining the two votes, but their effect is
attenuated by legislator's power. PAC contributions from the American
Bankers Association appear to matter for explaining the legislators who
switched. The role of ideology in explaining either the September 29 or
October 3 vote is limited.

*I. Introduction*

In the United States, major pieces of legislation often are enacted
during tumultuous periods. In banking and finance, for example, the
extensive New Deal reforms were passed during the Great Depression of
the 1930s. Unsurprisingly, the severity of the current economic downturn
has provided new impetus for fast and drastic lawmaking activity. A
clear example is the recently enacted Emergency Economic Stabilization
Act (EESA), more commonly known as the \$700 Billion "Bailout" Act.
Although at this point it is hard to discern the act's full
consequences, one can safely argue that this piece of
legislation---which attempts to restore confidence in the banking system
and reestablish normal levels of lending activity among banks and
between banks and the general public---is one of the most far-reaching
in recent U.S. history. It allows the Treasury Secretary to take direct
equity positions in financial institutions. It provides for the Treasury
Secretary to purchase "troubled assets" from financial institutions.[^2]
And it temporarily increased FDIC deposit insurance from \$100,000 to
\$250,000.[^3]

This legislation became law on October 3, 2008. However, the process of
enactment was not smooth. Four days earlier, on September 29, the House
of Representatives had voted to reject a previous version of the bill
(HR 3997), and within minutes the stock market saw one of the largest
and most precipitous declines in its recent past, with the Dow Jones and
the Standard & Poor's 500 index falling almost 9 percent in that one
day.

This unusual path to enactment can be exploited for clues that may
illuminate one of the oldest issues in political economy, and still a
hotly debated one: to what extent do private interests (interest groups)
influence public legislation? The "surprise" element of the September 29
House vote allows us to identify which group within the financial sector
industry gained relatively more from the eventual passage of this
legislation on October 3. After identifying the groups in the financial
sector industry that had a stake in the bill, the paper then
investigates the extent to which Political Action Committee (PAC)
contributions from these groups influenced the House votes. In addition
to PAC contributions, it examines the effect of economic conditions at
the congressional-district level (unemployment, foreclosures) on these
votes, after controlling for key legislator attributes and
characteristics.

Although EESA was passed only recently, it has already been the subject
of research in the media as well as in academia. On October 5, just two
days after its enactment, Campante and Chor (2008) blogged about the two
votes, the one on September 29 as well as the one on October 3. They
report that the most crucial factor in determining the outcome of the
vote was a legislator's ideological position. In addition, they find
that the proportion of financial sector employment in a legislator's
congressional district enhanced the chances that the legislator would
have voted in favor of the bill. They do not find evidence that economic
conditions (unemployment rate, prevalence of subprime loans) influenced
voting patterns.

Although Campante and Chor's results reveal important patterns, they are
not without problems. For example, the authors did not consider the role
of financial-sector PAC contributions in the votes---an important
omission because the financial sector can easily be identified as a
"winner" in this legislation. Furthermore, some of the variables
Campante and Chor use to control for economic conditions are measured at
the state level, not at the congressional-district level. Hence, the
failure to find statistical significance for these particular variables
may be due to mismeasurement problems rather than to the possibility
that the variables are inherently unimportant determinants.

Mian, Sufi, and Trebbi (2008) investigate factors that explain the two
votes as well. They find that PAC contributions from the financial
industry predict votes in favor of EESA. In addition, just like Campante
and Chor (2008), they find that ideology was instrumental: conservative
representatives were less likely to vote in favor of the first bill and
less likely to change their minds and vote for the second bill.

This paper adds to the literature in two important respects. First,
whereas previous research emphasizes the role of ideology in the vote,
this paper highlights the role of a previously unused variable that
Knowlegis researchers have recently developed---a legislator's "power"
index, a variable that measures the effectiveness of a legislator in
advancing an agenda.[^4] This is an important variable to consider
because, as discussed in more detail below, it alters the mechanism
through which PAC contributions influenced the vote: the marginal effect
of PAC contributions changes with the power of the legislator. Power
also alters the mechanism through which economic conditions influenced
the vote.

Second, exploiting the fact that the September 29 vote was a surprise,
this paper identifies groups within the financial sector industry that
were more likely to gain from the passage of the legislation and then
uses PAC contributions from these groups, along with power, to
investigate why legislators voted the way they did on September 29 and
why 58 of them changed their minds and voted "Yea" on October 3, after
having voted "Nay" just four days earlier.

I find that PAC contributions from the financial industry, and
particularly from big banks (institutions with over \$100 billion in
assets), were influential variables. However, their effect is nonlinear:
the marginal effect of PAC contributions on the probability of voting in
favor of the bill increases with the power of the legislator. I also
find that the influence of unemployment and the number of foreclosures
in the legislator's congressional district is nonlinear; in particular,
I find that the influence of unemployment and foreclosures on the
probability of voting in favor weakens with the power of the legislator.
In a sense, therefore, to some extent power immunizes legislators from
economic conditions in their district.

I use a regression similar to the one used to examine the September 29
and October 3 votes to investigate why a certain number of
representatives changed their minds between September 29 and October 3.
The results indicate that contributions from the American Bankers
Association (ABA) were particularly influential and increased the
likelihood that relatively weak, "inexpensive" legislators would switch
their vote.

Section II discusses in more detail the market's reaction to the
September 29 vote. It also investigates the extent to which different
sectors within the financial services industry were affected by EESA.
Section III discusses the empirical methodology as well as the data.
Section IV presents and discusses the results, and Section V concludes.

*II. The Market's Reaction*

The outcome of the vote on the bailout bill on September 29 was
officially announced at 2:07 p.m. Legislators, however, are typically
given a 15-minute grace period when casting a vote. Thus, sometime
around 1:50 p.m. it became known that the bill had failed. Nearly
immediately, the market reacted. This is shown in Figure 1, which
displays the September 29 minute-by-minute percentage changes in the
Standard & Poor's 500 index. As the figure shows, at around 1:50 p.m.
the market reacted with a decline of nearly 1 percent.[^5] The panic
continued throughout the rest of the afternoon. By the end of the day,
the market had lost nearly 9 percent, one of the largest one-day
percentage drops in history.[^6]

Without a doubt, the outcome of the bailout vote came as a surprise to
the market. Because the market's reaction was both instantaneous and
dramatic, it is a clear indication of which sector was hurt the most by
the rejection of the bill and, by implication, which sector stood to
gain the most if the bill were enacted. In this section, I analyze the
stock market reaction as it affected three key groups: (1) large banks
(banks with assets over \$100 billion in assets); (2) banks represented
by the American Bankers Association (ABA)---mostly small and
medium-sized financial institutions; and (3) real estate interests (real
estate investment trusts, organizations representing home builders,
realtors, etc.). There are good reasons for suspecting that the gains
that would have accrued from this bill would probably have been
disproportionately distributed among these three financial sub-sectors.
Thus, the market's reaction can be expected to bear on the three groups
disproportionately as well.

The "too-big-to-fail" doctrine implies that large banks would have
benefited from the rescue disproportionately more than smaller financial
institutions. Moreover, many of these large institutions were explicitly
identified as the largest recipients of the \$700 billion bailout fund
in the form of direct equity investments.[^7] Thus, I expect the stock
market surprise on September 29 to have adversely affected the stock
price of large banks disproportionately more.

For banks in the second group, those represented by the ABA, my
expectations about the stock market's reaction are different. The reason
for the difference is that a provision the ABA lobbied strongly for---an
increase in the FDIC's coverage of insured deposits from \$100,000 to
\$250,000---was not included in the September 29 bill, and therefore ABA
banks were not seen as having had as much at stake in the first bill as
the big banks. Accordingly, I expect the stock of ABA-banks to have
suffered relatively less on September 29. The provision the ABA wanted
was, however, an amendment that was included in the October 3 version of
the bill (which ultimately became law) It is important to point out,
however, that on October 1 the Senate had approved an amended version of
the bill that contained the deposit insurance provision. Thus, I expect
the stock of ABA-banks to have gained considerably by that date, and not
necessarily by October 3, when the second House vote took place because
by that date, the provision was no longer news.

The third group represents interests from the real estate sector
(National Association of Home Builders, National Association of
Realtors, real estate investment trusts). Because part of the objective
of the bailout bill was to purchase "toxic" mortgage assets, one could
see this group competing for a share of the \$700 billion pie. For this
group, it might be more beneficial for the government to allocate a
larger share of the bailout money for the purchase of these toxic assets
(rather than use the money to provide direct liquidity to banks) in
order to stabilize real estate prices and perhaps even resume the volume
of real estate activity that this market enjoyed before the meltdown (a
rather optimistic scenario).

To determine how the stock prices of these three groups reacted to the
events of September 29 (the House's failure to pass the bill) and
October 1 (the Senate's approval of the amended version of the bill), I
computed daily returns for the week of September 26 to October 3 for a
random sample of large banks (banking organizations with more than \$100
billion in assets), a random group of ABA-represented banks, a random
sample of real estate investment trusts (whose stocks are actively
traded), and a control sample of stocks in the Dow Jones or Standard &
Poor's 500 indices. The total portfolio contains 66 stocks.[^8]

Table 1 presents the regression results of the pseudo-event study.
Regression 1 tests the hypothesis that the stock market reaction was
abnormal on September 29 and October 3. It also examines whether the
reaction for financial stocks was different from the reaction for the
market as a whole. Regressions 2, 3, and 4 test the same specification,
but broken down by the three relevant financial groups: Big Banks
(Regression 2), ABA Banks (Regression 3), and Real Estate interests
(Regression 4). The influence of each of these groups is tested
separately in order to quantify the group's stock reaction relative to
the market and not relative to one another.

In all regressions we see the "September 29" indicator variable being
negative and statistically significant. This confirms what Figure 1
displays---that there was a largely negative market surprise. By
contrast, the "October 1" indicator variable is generally not
statistically significant (except for the last regression, where the
significance level is only at the 10 percent). In Regression 1 the
interaction term "Sep. 29 \* Financials" is negative and statistically
significant, while the "Oct. 1 \* Financials" is slightly positive and
significant. This indicates that financial firms in general suffered
disproportionately more than the market on September 29 and benefited
slightly more on October 1.

The results in Regressions 2, 3, and 4 indicate that the negative
reaction observed on financial firms (in Regression 1) is largely driven
by the large banks and not by ABA banks or the real estate sector. The
interaction term "Sep. 29 \* Big Banks" is negative and relatively
large, while the same interaction term is either positive (for the real
estate sector) or statistically indistinguishable from zero (for ABA
banks). Hence, it is possible to conclude that the market interpreted
the September 29 vote as bad news in general, but particularly worse for
the largest financial institutions.

As noted above, the interaction term "Oct. 1 \* Financials" in
Regression 1 is positive and statistically significant. A glance at the
same coefficients in the regressions 2, 3, and 4 reveals that the
portfolio of ABA banks largely drives this gain. The "Oct. 1 \* ABA"
coefficient is positive and significant, while the same coefficients are
either negative or statistically zero in the other two financial groups.
This suggests that the October 1 version of the rescue bill was
perceived to be good news for banks represented by the ABA. These
results are consistent with expectations: as pointed out above, the ABA
saw the inclusion of the amendment that temporarily increased FDIC
deposit insurance coverage as a major achievement.

The next section tests in more detail how these three groups influenced
the outcomes of the September 29 and October 3 votes, and especially how
they influenced the legislators who changed their minds in those four
days.

*III. Data and Empirical Methodology*

The empirical specification used here is motivated by the existing
literature on legislative voting.[^9] These theories highlight the role
of three broad categories as key determinants of congressional voting:
(1) legislator characteristics, (2) constituent characteristics and
economic conditions, and (3) PAC contributions. Table 2 presents the
summary statistics for all the variables included in the regression,
split by these three categories. Below is a more detailed description of
the variables included in the model.[^10]

*III. A --Legislator Characteristics*

Legislator characteristics include tenure, party affiliation, ideology,
and power ranking. Previous literature has highlighted the role of these
variables in helping to explain how legislators vote. Tenure, for
example, is included to control for seniority; in addition,
representatives closer to retirement may face a smaller cost from
deviating from their constituents' interests and may, instead, have a
higher incentive to vote according to their own preferences (Bronars and
Lott, 1997; Munger and Dougan, 1989; Munger, Grier, and Roberts, 1991).
As for party affiliation, representatives often vote along party lines,
so the inclusion of party affiliation controls for the effects of party
discipline.

The role of ideology in explaining legislative voting behavior has often
been highlighted (see, for example, Poole and Rosenthal, 1997, and
Levitt, 1996). Mian, Sufi, and Trebbi (2008) as well as Campante and
Chor (2008) find that ideology was one of the most important
determinants of the votes on both September 29 and October 3. Hence,
this variable is also included here. A commonly used ideology index is
Poole and Rosenthal's (1997) DW-Nominate scores. The more negative the
score, the more "liberal" the legislator is deemed to be. Analogously,
the higher (and the more positive) the score, the more "conservative"
the legislator.

In addition to tenure, party affiliation, and ideology, this paper also
examines the effect of a newly constructed variable that quantifies
legislative power. "Power ranking" is the Knowlegis rating index
developed for each legislator, and it is based on several criteria that
measure the power and effectiveness of a legislator in advancing an
agenda. The index generally falls in the 0 to 100 range, with a higher
number indicating more power.[^11] According to the team that developed
this index, it is based on four key criteria: (1) position of
legislator, which captures the influence of seniority, committee
assignments, and leadership positions; (2) indirect influence, which
measures how effective a legislator is in pushing an agenda; (3)
legislative activity, which tracks the influence of legislators in
shaping bills through amendments; and (4) earmarks, which tracks how
much funding (pork, really) a legislator brings to his or her
district.[^12]

A legislator's "Power Ranking" can be considered a measure of his or her
ability to advance an agenda. As explained in more detail below, the
inclusion of this variable is critical for determining the "supply
curve" of legislation; the marginal effect of a dollar of PAC
contributions, for example, is likely to be influenced by a legislator's
ability (power) or seniority (Munger and Denzau, 1985; and Kroszner and
Stratmann, 1998).

*III. B --Constituent Interest and Economic Conditions*

When explaining congressional voting behavior, the existing literature
also typically highlights the role of constituent interests.[^13] In
general, these variables include district-level characteristics such as
the distribution of the population by educational level (i.e.,
percentage of population with some minimum level of educational
achievement), by ethnicity (i.e., the proportion of African Americans,
Hispanics, etc.), and even by income level (i.e., proportion of the
population whose income is above some threshold level). For the
particular votes with which we are concerned, however, the constituent
interest variables that are likely to be more relevant are those that
measure current economic conditions more directly. After all, it was
widely accepted at the time the votes took place that the cause of the
current financial and economic downturn originated in the housing
market. To capture more directly the influence of economic conditions, I
include the 2007 level of unemployment and the number of foreclosures
between January and July 2008 in each congressional district. Table 2
includes basic statistics for these two variables.

*III. C --PAC Contributions*

The role of PAC contributions in shaping legislation has also been
extensively studied, but the question of whether these contributions
matter "a little" or "a lot" is still not fully resolved (Ansolabehere,
de Figueiredo, and Snyder, 2003). Nonetheless, because we can easily
identify the "winners" and "losers" of the bill passed on October 3 (see
Section II above), and the winners (within the financial services
industry) were large banks, ABA-banks, and possibility the real estate
sector, while the losers were the taxpayers, it seems pertinent to
investigate the extent to which PAC contributions from the financial
services industry mattered in the outcome of the votes on both September
29 and October 3.

The inclusion of PAC contributions in voting regressions allows one to
examine the extent to which contributions affect votes. This perspective
assumes an explicit directional effect from contributions to votes. It
implies that contributions can be seen as a strategic investment that
helps to shape legislation favorable to the interests of the donor.
However, an important issue that challenges the interpretation of PAC
contributions in voting regression models is the likely simultaneity
between PAC contributions and legislative votes. That is, it is possible
that votes affect contributions. This reverse effect (votes affecting
contributions) implies that donors are simply rewarding legislators for
voting in the manner donors would have preferred (Bronars and Lott,
1997; Stratmann, 2002).

In this paper, this simultaneity challenge is mitigated by two key
factors. First, the PAC contribution data are for the period January to
September 2008. Since the first vote took place on September 29, it is
hard to argue that the contributions were made after the vote. Instead,
it is more likely that contributions were made before the first vote
took place, and certainly that they were made before the second one.
Second, as already noted, the September 29 vote appears to have been a
surprise. The stock prices of the largest financial institutions plunged
dramatically on the afternoon of the vote. Thus, it is hard to argue
that contributions were made as a reward for the vote.

*III. D -- Dependent Variable*

I analyze legislators' voting patterns on three key votes: the vote on
H.R. 3997 (the September 29 vote), the October 3 vote by the whole
House, and the October 3 vote by those who changed their minds between
September 29 and October 3---the so-called switchers. Basic statistics
on these votes are presented in the summary statistics in Table 2. Table
3 tabulates the September 29 and October 3 votes against each other.

*IV. E -- Regression Specification*

The basic regression is the simple linear specification model:

$$V_{i} = \alpha_{0} + \alpha_{1}\left( \overline{\text{LC}_{i}} \right) + \alpha_{2}\left( \overline{\text{EC}_{i}} \right) + \alpha_{3}\left( \overline{\text{PAC}_{i}} \right) + e_{i}$$

where $V_{i}$ is the vote that legislator from Congressional District
$i$ casts, $\overline{\text{LC}}$ represents the vector of legislator
characteristics, $\overline{\text{EC}}$ represents the vector of
economic conditions, and $\overline{\text{PAC}}$ represents PAC
contributions. $V_{i}$ is dichotomous in nature: it equals 1 if the
representative voted in favor of the bill (or changed his or her mind),
0 otherwise. Hence, all estimations are performed using probit
regressions.[^14]

Although the basic specification is very popular, it may be too
restrictive in the sense that it forces all covariates to influence the
outcome of the vote linearly. Conceivably, however, the probability of
voting in favor of the bill changes with legislator's attributes. For
example, the effect of PAC contributions on the probability of voting in
favor of the bill may be sensitive to how powerful the legislator is or
how ideologically conservative. To capture possibly nonlinear effects, I
introduce the following interaction terms:

$$V_{i} = \beta_{0} + \beta_{1}\left( \overline{\text{LC}_{i}} \right) + \beta_{2}\left( \overline{\text{EC}_{i}} \right) + \beta_{3}\left( \overline{\text{EC}_{i}} \cdot \overline{\text{LC}_{i}} \right) + \beta_{4}\left( \overline{\text{PAC}_{i}} \right) + \beta_{5}\left( \overline{\text{PAC}_{i}} \cdot \overline{\text{LC}_{i}} \right) + e_{i}$$

*IV. Empirical Findings*

*IV. A --The September 29 Vote*

Table 4 presents the votes on H.R. 3997 (the September 29 vote). The
first two regressions display the results of the basic linear
specification (Regression 1) and the specification that includes both
the "power" variable and the variables that control for economic
conditions in a linear fashion (Regression 2). According to regressions
1 and 2, the most important variables in explaining the vote are
legislator attributes. In Regression 1, for example, "Tenure" is
positive and statistically significant. This suggests that more-senior
legislators were more likely to vote in favor of the bill. The "Party"
and "DW Nominate" coefficients do not appear to be important in
explaining the vote. However, their lack of statistical significance is
partly due to the fact that party and ideology are correlated.[^15] In
contrast to the results reported in Mian, Sufi, and Trebbi (2008), PAC
contributions from the financial industry do not appear to be
statistically important in the linear specification model.

In Regression 2, which includes "power" as well as the variables that
control for economic conditions (unemployment and foreclosures), the
only one that enhances the statistical fit of the regression is power,
with a positive and statistically significant coefficient. This suggests
that power is an important variable. According to Regression 2,
controlling for everything else, powerful legislators were more likely
to vote in favor of the bill. The other covariates are statistically
insignificant.

Regressions 3 and 4 examine the interaction effects, and specifically
the effect of power on economic conditions and on PAC contributions
(Regression 3---Nonlinear influence of power) and the effect of ideology
on the same set of variables (Regression 4---Nonlinear influence of
ideology). The results in Regression 3 indicate that what matters is the
interaction of power with economic conditions and with PAC
contributions. To see this more precisely, consider first the effect of
a change in PAC contributions on the likelihood of voting in favor of
the bill, $R(Y)$. According to Regression 3, the effect is

$$\frac{¶R(Y)}{¶\left( \text{PAC} \right)}\text{=-}0\text{.}\text{071} + 0\text{.}\text{005}\left( \text{Power} \right)$$

This expression indicates that the marginal effect of PAC contributions
increases with the power of the legislator. According to the
coefficients, for PAC to have a positive effect on the probability of
voting in favor, the legislator would have to have a power index of at
least 14.2 (= 0.071/0.005). Given that the median legislator power index
is 16.2, this result suggests that PAC contributions exerted a positive
influence on the vote mostly at the top 50-percentile range of power.
This result is very intuitive, and it is consistent with the
"investment" view of PAC contributions. For PAC contributions to have a
positive effect, the money must go to powerful legislators---those who
can most effectively push an agenda, where the marginal product of PAC
money is highest.

The results in Regression 3 also suggest that power affects how
legislators react to economic conditions. In particular, the effect of
unemployment on the probability of voting in favor of the bill weakens
with the power of the legislator. The estimated effect is

$$\frac{¶R(Y)}{¶\left( \text{UE} \right)} = 0\text{.}\text{181} - 0\text{.}\text{009}\left( \text{Power} \right)$$

According to this, when a legislator's power is low, the probability of
voting in favor of the bill is very sensitive to the unemployment level.
However, as the legislator becomes more powerful, he or she also becomes
more insensitive to economic conditions. This result is also observed
for foreclosures. Evidently, then, power appears to decouple
legislator's sensitivity from constituent interest.

Regression 4 presents the results when ideology is combined with PAC
contributions, unemployment, and foreclosures. The results here are
markedly different. The only interaction effect that is statistically
significant is that between ideology and unemployment. According to the
results, the effect of unemployment on the probability of voting in
favor is higher among the more conservative politicians. This makes
intuitive sense given that, at the time these votes took place,
Democrats controlled Congress: conservative politicians, being in the
minority, evidently felt more vulnerable to constituent needs and
conditions.

Although the interaction between ideology with unemployment is
significant, the other interaction terms are not; neither the effect of
foreclosures nor the effect of PAC contributions on the probability of
voting in favor of the bill appears to be sensitive to ideology. A
comparison of the regression fit suggests that Regression 3 does better
in explaining the vote than Regression 4 does. The pseudo-R2, the
percentage of cases classified correctly, and the ROC curve are all
higher for Regression 3 than for Regression 4. Taken together, all of
this suggests that for this vote, power is more influential than
ideology.

*IV.B--- The October 3 Vote*

Table 5 presents the results for the October 3 vote. To facilitate the
comparison of results, the functional forms used for the September 29
vote (Table 4) are also used for this vote. Thus, Table 5 reports four
probit regressions, the first two examining linear effects and the last
two examining the interaction effects of power (Regression 3) and
ideology (Regression 4). Although the overall results are similar to
those reported in Table 4, there are important differences. In
Regression 1, for example, tenure, which was the only statistically
significant covariate in Regression 1 of Table 4, continues to be
positive, but the magnitude of the effect (and therefore its statistical
significance) declines considerably. More notably, ideology enters
negatively and is statistically significant. Indeed, the effect of
ideology survives the inclusion of power (which continues to be
significant) as well as the inclusion of the covariates that control for
economic conditions (Regression 2). Hence, it is likely that ideological
considerations were relevant for the October 3 vote: conservative
legislators were much less likely to vote in favor of the October 3
bill. This result has been reported in Campante and Chor (2008) and
Mian, Sufi, and Trebbi (2008). Nonetheless, the role of power continues
to be important in this vote.

To examine the relative importance of power and ideology, Regressions 3
and 4 present the interaction effects of these two variables. Power
appears to have a (nonlinear) effect much as it did on the September 29
vote. For example, the effect of unemployment on the likelihood of
voting in favor depends on the power of the legislator. As power
increases, the legislator becomes more immune to the effect of
unemployment conditions.

However, the results for foreclosures are somewhat different in the two
votes. In the October 3 vote, the interaction effect of foreclosures is
positive and statistically significant at the 6 percent level. Thus, for
this vote, power enhances the effect of foreclosures on the vote. It is
tempting to speculate that the combination of events---the outcome of
the September 29 vote received so much media attention, while so much
detrimental housing news was also announced---raised the visibility of
the more powerful legislators in the public forum, thereby enhancing
their sensitivity to levels of foreclosure.

Just as Table 4 reported for the September 29 vote, the influence of PAC
contributions on the October 3 vote appears to be sensitive to the power
of the legislator, with power enhancing the probability of voting in
favor of the bill. Given the estimated coefficients, for PAC
contributions to have a positive effect on the outcome of the vote, the
legislator must have a power index of at least 11.6 (= 0.035/0.003).
This power index level corresponds to the 28th percentile in terms of
ranking, suggesting that approximately 72 percent of legislators were
positively influenced by PAC contributions.

Following the same pattern as in Table 4, Regression 4 in Table 5
reports the interaction effects with ideology. Although regressions 1
and 2 indicate that ideology alone is a statistically important variable
explaining the vote, Regression 4 indicates that the importance of
ideology is sensitive to the inclusion of other covariates. Instead, the
regression suggests that the way ideology influences the vote is by
increasing the sensitivity of unemployment.

It is also worth noting that for this regression, the linear effect of
foreclosures is positive and statistically significant. Thus, regardless
of ideological considerations, foreclosures appear to matter. This
result is consistent with the observation made above that the explosion
of adverse news about housing and foreclosures may have increased the
sensitivity of legislators, especially the more powerful ones, to this
variable. Lastly, it is worth noting that in this regression, PAC
contributions have an effect on the vote similar to the effect they had
on the earlier vote.

*IV.C --The Influence of Specific PACs on the September 29 Vote*

Although one could argue that the financial services industry as a whole
stood to gain from the implementation of this legislation, it is
nonetheless possible that these gains will accrue differently to
different subgroups within the industry. For example, one of the key
differences between the September 29 version of the bill and the version
that finally passed on October 3 is the later bill's inclusion of an
amendment temporarily increasing the coverage of insured deposits by the
FDIC from \$100,000 to 250,000. This amendment was strongly supported by
the American Bankers Association, whose members (mostly small and medium
banks) feared that without it, the likelihood of experiencing bank runs
in the currently distressed financial environment would have been
unacceptably high. It is also worth noting that (as pointed out in the
previous section) the negative stock market reaction to the September 29
vote disproportionately affected the largest financial institutions
relative to small (ABA-represented) banks or even real estate investment
trusts.

For these reasons, it seems prudent to investigate how different groups
within the financial services industry may have influenced the outcome
of the votes. To do so, I obtained PAC contribution data from three
groups within the industry: large banks, the American Bankers
Association, and the real estate sector.[^16]

To test for the influence of these groups on the September 29 and
October 3 vote, I use the same functional form that I used in Regression
3 of Tables 4 and 5. That is, I estimate the nonlinear functional form
with the independent variables in combination with power. The reason for
doing this is that, as pointed out above, PAC contributions from
financial groups did not appear to influence the vote in a
straightforward, linear fashion. Instead, the influence is nonlinear,
working its way to the vote through power (Regression 3, Tables 4 and 5)
rather than through ideology (Regression 4, Tables 4 and 5).

The results are presented in Table 6. For the September 29 vote
regression, power, unemployment, and their interaction term are
statistically significant at the 1 percent level. In particular, the
interaction effect is negative, confirming the result discussed above
that legislators appear to become more immune to economic conditions as
their power increases. The interaction of power and foreclosures is also
negative and statistically significant at the 5 percent level. Thus,
although power appears to weaken the relationship of foreclosures to the
probability of voting in favor, the results are somewhat weaker than
those for unemployment.

This regression also indicates that PAC contributions from the largest
banks appeared to have significantly influenced both votes, but just as
before, in a nonlinear fashion. According to the results, power makes a
legislator's likelihood of voting in favor of the bill more sensitive to
contributions (i.e. there is an interaction effect between power and the
size of the specific PAC contribution). PAC contributions from the
American Bankers Association or from real estate groups did not appear
to be as instrumental as contributions from the largest banks in
explaining why legislators voted in favor of the bill on September 29.

Similar, but not identical, results were obtained for the October 3
vote. The only differences are that power, in its linear term, does not
appear to exert a statistically significant effect. Power affected this
vote via its indirect influence on unemployment, on contributions from
large financial institutions, and to some extent on contributions from
real estate PACs.

*IV. D---Explanation of Why Representatives Switched*

As Table 3 indicates, between the September 29 and October 3 votes, 59
legislators changed their minds---58 of them switching from originally
voting No to then voting Yes, and 1 from a Yes to a No. Why did they
change their minds? Given the stock market reaction on the afternoon of
September 29, one may be tempted to argue that the precipitous decline
in stock prices shocked legislators and jolted some "nay" voters into
voting in favor of the bill. But this begs the question of why it jolted
some and not others. Thus, we still need to investigate whether key
legislator attributes (power or ideology) or contributions from relevant
PACs were instrumental as explanations for the switch.

To investigate this issue, I estimate probit regressions on the
"switchers" (those who changed their minds between September 29 and
October 3). Given the lopsided nature of the switch, I concentrate on
investigating only the No-to-Yes switch. The independent variables are
the same as before, but unemployment or foreclosures are excluded in
order to isolate the effects of legislator attributes and PAC
contributions.[^17] Legislator attributes could be relevant to the
extent that they reveal particular characteristics that make legislators
more or less likely to switch their votes. For example, as Mian, Sufi,
and Trebbi (2008) argue, conservative legislators may be more concerned
about maintaining their reputations among their constituents and thus
may be less inclined to switch their votes. Similar arguments could be
made for the reputational consequences of power, another attribute that
has shown to be at least as important as (if not more important than)
ideology.

The reported PAC contribution measures did not change between September
29 and October 3, but a tremendous amount of lobbying activity occurred
between those two dates. It is plausible that the volume of lobbying
activity is proportional to PAC contributions. If Representative X
received \$10,000 from the ABA, while Representative Y received nothing,
it is plausible to argue that the ABA, in its efforts to influence
legislative outcomes, will target Representative X more frequently than
Representative Y in subsequent lobbying activity. Thus, the level of PAC
contributions could be used as a proxy for the connections between
legislators and contributors, and hence as a proxy for the effects of
lobbying.

Table 7 presents the regression results. Regression 1 indicates that,
from a statistical standpoint, ideology and power are the more relevant
characteristics. According to this regression, both ideologically
conservative legislators and powerful legislators were less likely to
change their minds. Mian, Sufi, and Trebbi (2008) as well as Campante
and Chor (2008) note that ideology is an important determinant of the
switchers. The results in Regression 1 indicate that, in addition to
ideology, power seems to matter as well. In the statistical sense, none
of the other covariates appears to matter in the linear specification.

To ascertain the relative importance of power and ideology, I test
separately the role of these two variables in combination with PAC
contributions. Regressions 2 and 3 report the regression estimates.
According to Regression 2, power matters, but as discussed above, the
effect works through PAC contributions. The estimated effect is

$$\frac{\partial Ρ\left( \text{switch} \right)}{\partial\left( \text{PAC} \right)} = 0\text{.}\text{028} - 0\text{.}\text{001}\left( \text{power} \right)$$

This suggests that the effect of PAC contributions on the probability of
switching declines with power. In other words, the more power a
legislator has, the less he or she is likely to be induced by PAC
contributions to switch. Viewed from the "investment" perspective of PAC
contributions, this result intuitively makes sense. To the extent that
the level of contributions is a signal for lobbying, it appears to be
the case that lobbyists, in their effort to try to convince legislators
to switch, resorted to contacting the "cheapest," least powerful ones
first, leaving the more expensive, powerful ones for last. The point
estimates suggest that legislators with a power index of less than 28
(which is about 80 percent of them) were positively influenced by PAC
contributions.

Regression 3 suggests that ideology did not affect the sensitivity of
the likelihood of switching with respect to PAC contributions. In other
words, the likelihood that contributions would induce switching was not
a function of ideology. Although ideology enters negatively and
significantly as a linear covariate, its interaction term with PAC
contributions is not statistically different from zero. Evidently, then,
a legislator's power has a greater effect on the passage of legislation
than the legislator's ideology.

To identify which PAC group had the largest effect on the probability of
switching, I redo Regression 2 of Table 7 (which measures the nonlinear
effects of power) for the three different PAC groups: large banks, the
ABA, and real estate interests. Table 8 presents the results. Of the
three PAC groups, the role of one---the ABA---in influencing legislators
to switch stands out. Thus, apparently the ABA was instrumental in
getting enough legislators to switch. This result is very consistent
with expectations. As pointed out above, the American Bankers
Association lobbied very strongly to include as an amendment a provision
that would increase deposit insurance coverage from \$100,000 to
\$250,000. This result is also consistent with the fact that on October
1, when the Senate version of the bill (a version that contained the
amendment) was passed, the stock price of ABA-represented banks reacted
positively, whereas the stock price for the other two groups (large
banks, real estate groups) either did not increase or declined.

*V. Concluding Remarks*

This paper addresses three important questions regarding the recently
enacted Emergency Economic Stabilization Act of 2008: (1) To what extent
did the financial services industry benefit from this legislation? (2)
Were there sectors within this industry that benefited relatively more?
(3) Why did the House fail to pass the bailout bill on September 29,
only to reverse course and pass it on October 3rd?

The answer to the first question is, to put it bluntly, a lot. The
market reaction to the vote on September 29 indicates that the outcome
of the vote was an unpleasant surprise: with the market as a whole
suffering a drastic decline that day, the decline in the financial
services industry's stocks was twice as large as the decline for the
whole market. The negative effects of the failure to pass on September
29 can be used as a gauge of the value of this bill when it finally
passed on October 3.

To address the second question, I investigated the extent to which stock
returns from three groups within the financial services industry (big
banks, ABA banks, and the real estate sector) reacted to the September
29 vote. The results show that the largest financials institutions also
had the largest stake in the outcome of this vote. The stock market
decline in the big-bank portfolio was particularly severe on that
day---a drop of nearly 30 percent. Banks represented by the ABA (mostly
small and medium sized institutions) did not suffer the same fate as the
big banks did on September 29. While their stock also declined, their
decline was comparable to the fall off in the market that day. Instead,
I find that ABA-represented banks appear to have benefited from the
modified version of the bill, which was approved by the Senate on
October 1: the stock market reaction for the ABA portfolio was positive
that day. This last result is consistent with expectations, for the ABA
lobbied strongly for the inclusion of the deposit insurance increase
amendment.

The analysis done here provides an answer to the third question as well.
The bill did not pass on September 29 because, despite significant
pressure from the financial sector, the real side of the economy had not
suffered commensurately with the financial side. That is, neither
unemployment nor foreclosures were "high enough" in a sufficient number
of congressional districts. The regressions show that legislators who
switched their vote between September 29 and October 3 likely caved in
to the lobbying efforts of the American Bankers Association. Hence, in
all probability the October 3 vote would have failed again had it not
been for the pressure exerted by the ABA.

**References**

Ansolabehere, Stephen, John M. de Figueiredo, and James M. Snyder. 2003.
"Why Is There So Little Money in U.S. Politics?" *Journal of Economic
Perspectives* 17, no. 1:,105--30.

Bronars, Stephen G., and John R. Lott. 1997 "Do Campaign Donations Alter
How a Politician Votes? Or, Do Donors Support Candidates Who Value the
Same Things That They Do?" *Journal of Law and Economics* 40, no. 2:
317--50.

Campante, Filipe and Chor, Davin. 2008. "The \$700 Billion Question: Why
Did the House Change Its Mind?" In *Dani Rodrik's Weblog*, October 5,
2008,
<http://rodrik.typepad.com/dani_rodriks_weblog/2008/10/the-700-billion-question-why-did-the-house-change-its-mind.html>.

Kau, James B., and Paul H. Rubin. 1982. *[Congressmen, Constituents and
Contributors]{.underline}.* Boston, MA: Martinus Nijhoff Publishing.

Kroszner, Randall S., and Thomas Stratmann. 1998. "Interest-Group
Competition and the Organization of Congress: Theory and Evidence from
Financial Services' Political Action Committees." *American Economic
Review* 88, no. 5: 1163--87.

Levitt, Steven D. 1996. "How Do Senators Vote? Disentangling the Role of
Voter Preferences, Party Affiliation, and Senator Ideology." *American
Economic Review* 86, no. 3: 425--41.

Mian, Atif, Amir Sufi, and Francesco Trebbi. 2008. "The Political
Economy of the U.S. Mortgage Default Crisis." NBER Working Paper No.
14468.

Munger, Michael C., and Arthur T. Denzau. 1985. "Legislators and
Interest Groups: How Unorganized Interests Get Represented." *American
Political Science Review* 80, no. 1: 89--106.

Munger, Michael C., and William R. Dougan. 1989. "The Rationality of
Ideology." *Journal of Law and Economics* 32:213--39.

Munger, Michael C., Kevin Grier, and Brian Roberts. 1991. "The
Industrial Organization of Corporate Political Activity." *Southern
Economic Journal* 57:727--38.

Peltzmann, Sam. 1984. "Constituent Interests and Congressional Voting."
*Journal of Law and Economics* 27:181--210.

Poole, Keith T., and Howard Rosenthal. 1997. *[Congress: A
Political-Economic History of Roll Call Voting]{.underline}.* Oxford:
Oxford University Press.

Snyder, James M. 1990. "Campaign Contributions as Investments: The U.S.
House of Representatives, 1980--1986." *Journal of Political Economy*
98, no. 6: 1195--1227.

Stratmann, Thomas. 2002. "Can Special Interests Buy Congressional Votes?
Evidence from Financial Services Legislation." *Journal of Law and
Economics* 45, no. 2: 345--73.

---------. 2005. "Some Talk: Money in Politics, A (Partial) Review of
the Literature" *Public Choice* 124, nos. 1--2: 135--56.

White, Halbert A. 1980. "Heteroskedasticity-Consistent Covariance Matrix
Estimator and a Direct Test for Heteroskedasticity." *Econometrica*
48:817--38.

**Figure 1**

**S&P 500 on September 29, 2008**

![](media/image1.png){width="6.520833333333333in"
height="4.7243055555555555in"}

**Table 1**

Stock Market Returns: Pseudo-Event Study

  -------------- -------------- ------- --------- -------------- ------- --------- -------------- ------- --------- -------------- ------- ---------
                 Regression 1                     Regression 2                     Regression 3                     Regression 4           

                 Coef           Std Err P-value   Coef           Std Err P-value   Coef           Std Err P-value   Coef           Std Err P-value

  September 29   -0.059\*\*\*   0.005   0.000     -0.084\*\*\*   0.010   0.000     -0.130\*\*\*   0.032   0.000     -0.135\*\*\*   0.030   0.000

  October 1      -0.006         0.004   0.140     0.014\*        0.008   0.082     0.000          0.009   0.991     0.015          0.010   0.107

  Sep 29 \*      -0.131\*\*\*   0.050   0.009                                                                                              
  Financials                                                                                                                               

  Oct 1 \*       0.035\*\*      0.016   0.025                                                                                              
  Financials                                                                                                                               

  Sep 29 \* Big                                   -0.307\*       0.166   0.065                                                             
  Banks                                                                                                                                    

  Oct 1 \* Big                                    -0.014         0.038   0.714                                                             
  Banks                                                                                                                                    

  Sep 29 \* ABA                                                                    0.017          0.042   0.677                            

  Oct 1 \* ABA                                                                     0.066\*\*\*    0.023   0.005                            

  Sep 29 \*                                                                                                         0.078\*\*      0.032   0.016
  REITs                                                                                                                                    

  Oct 1 \* REITs                                                                                                    -0.033\*\*     0.016   0.039

  Constant       0.006          0.005   0.254     0.006          0.005   0.243     0.006          0.005   0.255     0.006          0.005   0.256

  R^2^ within    0.227                            0.289                            0.181                            0.183                  

  Num            396                              396                              396                              396                    
  Observations                                                                                                                             
  -------------- -------------- ------- --------- -------------- ------- --------- -------------- ------- --------- -------------- ------- ---------

Notes: Dependent variable is the difference of the log of the daily
stock price, adjusted for dividends and splits for the week of September
26 to October 3, 2008. The independent variables are as follows:
"September 29" and "October 1" are indicator variables. The rest of the
variables are interaction terms for different groups of stocks:
financial stocks, real estate investment trust (REIT) stocks, American
Bankers Association (ABA) stocks, and "big banks" stocks (banks that are
over \$100 billion in assets). All regressions include fixed effects.
"Coef" is the coefficient estimate. "Std Err" means (robust) standard
errors. "P-value" is the significance level.

**Table 2**

**Summary Statistics by Category**

  --------------- ------------------ ------- -------- -------- --------- -------- ---------
  Category        Variable           No.     Median   Mean     Std. Dev. Min      Max
                                     Obs.                                         

  Legislator      Vote on HR 3997    425     0        0.476    0.499     0        1
  Attributes                                                                      

                  Switch to "Yea"    426     0        0.133    0.340     0        1
                  after "Nay"                                                     

                  Age                426     58       57.48    9.68      32       85

                  Tenure             426     12       12.39    8.654     1        54

                  Democrat           426     1        0.540    0.498     0        1

                  DW Nominate        426     -0.196   0.026    0.509     -0.922   1.364

                  Power Index        425     16.2     19.41    12.698    -2.59    97.3

  PAC             Finance PAC, Total 426     44,000   86,091   107,393   0        797,185
  Contributions                                                                   

                  Big Bank PAC       426     2,250    8,774    18,003    0        167,650

                  American Bankers   426     3,000    3,762    3,774     0        12,500
                  Assoc.                                                          

                  Real Estate PAC    426     8,500    8,424    5,247     0        31,249

  Constituent     Unemployment rate  426     4.693    4.851    1.202     2.35     12.1
  Economic        2007                                                            
  Conditions                                                                      

                  No. Foreclosures,  426     797      1,322    1,444     59       7,167
                  Jan to Jul 2008                                                 
  --------------- ------------------ ------- -------- -------- --------- -------- ---------

*Note:* For sources and definitions, see Appendix 1.

**Table 3**

**September 29 Votes Tabulated** against October 3 Votes

  ---------------- ------------- ------------- ------------- -------------
                                 October 3                   
                                 Vote                        

                                 Nay           Yea           Total

  September 29     Nay           170           58            228
  Vote                                                       

                   Yea           1             204           205

                   Total         171           262           433
  ---------------- ------------- ------------- ------------- -------------

**Table 4**

**The** September 29 Vote

<table>
<colgroup>
<col style="width: 6%" />
<col style="width: 13%" />
<col style="width: 6%" />
<col style="width: 6%" />
<col style="width: 6%" />
<col style="width: 6%" />
<col style="width: 6%" />
<col style="width: 6%" />
<col style="width: 6%" />
<col style="width: 6%" />
<col style="width: 6%" />
<col style="width: 6%" />
<col style="width: 6%" />
<col style="width: 6%" />
</colgroup>
<tbody>
<tr class="odd">
<td></td>
<td>Variable</td>
<td colspan="3"><em>Reg. 1: Basic Specification</em></td>
<td colspan="3"><em>Reg. 2: Power and Econ. Conditions</em></td>
<td colspan="3"><em>Reg. 3: Nonlinear Influence of Power</em></td>
<td colspan="3"><em>Reg. 4: Nonlinear Influence of Ideology</em></td>
</tr>
<tr class="even">
<td></td>
<td></td>
<td>Coefficient</td>
<td>Std. Error</td>
<td>P-value</td>
<td>Coefficient</td>
<td>Std. Error</td>
<td>P-value</td>
<td>Coefficient</td>
<td>Std. Error</td>
<td>P-value</td>
<td>Coefficient</td>
<td>Std. Error</td>
<td>P-value</td>
</tr>
<tr class="odd">
<td rowspan="4"><p>Legislator</p>
<p>Attributes</p></td>
<td>Tenure</td>
<td>0.120***</td>
<td>0.030</td>
<td>0.000</td>
<td>0.014</td>
<td>0.039</td>
<td>0.717</td>
<td>0.040</td>
<td>0.040</td>
<td>0.329</td>
<td>0.114***</td>
<td>0.031</td>
<td>0.000</td>
</tr>
<tr class="even">
<td>Party</td>
<td>0.127</td>
<td>0.155</td>
<td>0.415</td>
<td>-0.045</td>
<td>0.162</td>
<td>0.781</td>
<td>0.089</td>
<td>0.071</td>
<td>0.215</td>
<td>0.077</td>
<td>0.158</td>
<td>0.626</td>
</tr>
<tr class="odd">
<td>DW Nominate</td>
<td>-0.170</td>
<td>0.157</td>
<td>0.280</td>
<td>-0.153</td>
<td>0.155</td>
<td>0.323</td>
<td></td>
<td></td>
<td></td>
<td>-0.784</td>
<td>0.561</td>
<td>0.162</td>
</tr>
<tr class="even">
<td>Power</td>
<td></td>
<td></td>
<td></td>
<td>0.016***</td>
<td>0.004</td>
<td>0.000</td>
<td>0.046*</td>
<td>0.027</td>
<td>0.088</td>
<td></td>
<td></td>
<td></td>
</tr>
<tr class="odd">
<td rowspan="6"><p>Economic</p>
<p>Conditions</p></td>
<td>UE 2007</td>
<td></td>
<td></td>
<td></td>
<td>0.009</td>
<td>0.023</td>
<td>0.699</td>
<td>0.181***</td>
<td>0.045</td>
<td>0.000</td>
<td>0.000</td>
<td>0.022</td>
<td>0.988</td>
</tr>
<tr class="even">
<td>Power*UE 2007</td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td>-0.009***</td>
<td>0.002</td>
<td>0.000</td>
<td></td>
<td></td>
<td></td>
</tr>
<tr class="odd">
<td>Foreclosures</td>
<td></td>
<td></td>
<td></td>
<td>-0.007</td>
<td>0.028</td>
<td>0.810</td>
<td>0.088</td>
<td>0.059</td>
<td>0.138</td>
<td>-0.006</td>
<td>0.028</td>
<td>0.822</td>
</tr>
<tr class="even">
<td>Power*Foreclosures</td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td>-0.006**</td>
<td>0.003</td>
<td>0.036</td>
<td></td>
<td></td>
<td></td>
</tr>
<tr class="odd">
<td>DWNom*UE 2007</td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td>0.088**</td>
<td>0.044</td>
<td>0.046</td>
</tr>
<tr class="even">
<td>DWNom*Foreclosures</td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td>0.073</td>
<td>0.055</td>
<td>0.185</td>
</tr>
<tr class="odd">
<td rowspan="3">PAC</td>
<td>Finance PAC</td>
<td>0.029</td>
<td>0.018</td>
<td>0.110</td>
<td>0.000</td>
<td>0.018</td>
<td>0.992</td>
<td>-0.071***</td>
<td>0.026</td>
<td>0.006</td>
<td>0.034*</td>
<td>0.020</td>
<td>0.096</td>
</tr>
<tr class="even">
<td>Power*Finance PAC</td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td>0.005***</td>
<td>0.002</td>
<td>0.002</td>
<td></td>
<td></td>
<td></td>
</tr>
<tr class="odd">
<td>DWNom*Finance PAC</td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td>-0.035</td>
<td>0.035</td>
<td>0.326</td>
</tr>
<tr class="even">
<td rowspan="4"><p>Regression</p>
<p>Statistics</p></td>
<td>Pseudo-R-squared</td>
<td colspan="3">0.089</td>
<td colspan="3">0.122</td>
<td colspan="3">0.172</td>
<td colspan="3">0.105</td>
</tr>
<tr class="odd">
<td>Percent Correct</td>
<td colspan="3">62.59</td>
<td colspan="3">65.80</td>
<td colspan="3">65.80</td>
<td colspan="3">64.24</td>
</tr>
<tr class="even">
<td>ROC Curve</td>
<td colspan="3">0.703</td>
<td colspan="3">0.732</td>
<td colspan="3">0.756</td>
<td colspan="3">0.714</td>
</tr>
<tr class="odd">
<td>Number of Obs.</td>
<td colspan="3">425</td>
<td colspan="3">424</td>
<td colspan="3">424</td>
<td colspan="3">425</td>
</tr>
</tbody>
</table>

Notes: Dependent Variable is the vote on HR 3997. The independent
variables are defined as follows: "Tenure" is defined as the log of
(2009-first year of congressperson tenure). "Party" equals 1 if
congressperson is Democrat, 0 otherwise. DW Nominate is the DW Poole and
Rosenthal (1997) ideology score. Power is the congressperson's power
ranking. Economic Condition variables: "UE 2007" is the 2007
unemployment rate level at the congressional-district level.
"Foreclosures" is the number of foreclosures in the congressional
district from January to July 2008. "Finance PAC" represents total
contributions from the financial services industry. The reported
standard errors are the robust (White-1980 corrected) standard errors.
The Regression statistics are as follows: "Pseudo R-sq" is the
McFadden's Pseudo R-squared; "Percent correct" is the count R-squared,
or the proportion of correctly classified observations; "ROC Curve" is
the Receiver Operating Characteristic curve; "Number of. Obs." is the
number of observations.

**Table 5**

**T**he October 3 Vote

<table>
<colgroup>
<col style="width: 6%" />
<col style="width: 13%" />
<col style="width: 6%" />
<col style="width: 6%" />
<col style="width: 6%" />
<col style="width: 6%" />
<col style="width: 6%" />
<col style="width: 6%" />
<col style="width: 6%" />
<col style="width: 6%" />
<col style="width: 6%" />
<col style="width: 6%" />
<col style="width: 6%" />
<col style="width: 6%" />
</colgroup>
<tbody>
<tr class="odd">
<td></td>
<td>Variable</td>
<td colspan="3"><em>Reg. 1: Basic Specification</em></td>
<td colspan="3"><em>Reg. 2: Power and Econ. Conditions</em></td>
<td colspan="3"><em>Reg. 3: Nonlinear Influence of Power</em></td>
<td colspan="3"><em>Reg. 4: Nonlinear Influence of Ideology</em></td>
</tr>
<tr class="even">
<td></td>
<td></td>
<td>Coefficient</td>
<td>Std. Error</td>
<td>P-value</td>
<td>Coefficient</td>
<td>Std. Error</td>
<td>P-value</td>
<td>Coefficient</td>
<td>Std. Error</td>
<td>P-value</td>
<td>Coefficient</td>
<td>Std. Error</td>
<td>P-value</td>
</tr>
<tr class="odd">
<td rowspan="4"><p>Legislator</p>
<p>Attributes</p></td>
<td>Tenure</td>
<td>0.056*</td>
<td>0.029</td>
<td>0.056</td>
<td>-0.018</td>
<td>0.037</td>
<td>0.628</td>
<td>0.010</td>
<td>0.038</td>
<td>0.790</td>
<td>0.060**</td>
<td>0.030</td>
<td>0.047</td>
</tr>
<tr class="even">
<td>Party</td>
<td>-0.035</td>
<td>0.155</td>
<td>0.820</td>
<td>-0.167</td>
<td>0.155</td>
<td>0.293</td>
<td>0.122*</td>
<td>0.066</td>
<td>0.067</td>
<td>-0.017</td>
<td>0.159</td>
<td>0.915</td>
</tr>
<tr class="odd">
<td>DW Nominate</td>
<td>-0.331**</td>
<td>0.158</td>
<td>0.037</td>
<td>-0.315**</td>
<td>0.156</td>
<td>0.045</td>
<td></td>
<td></td>
<td></td>
<td>-0.372</td>
<td>0.523</td>
<td>0.477</td>
</tr>
<tr class="even">
<td>Power</td>
<td></td>
<td></td>
<td></td>
<td>0.013***</td>
<td>0.004</td>
<td>0.001</td>
<td>-0.015</td>
<td>0.025</td>
<td>0.559</td>
<td></td>
<td></td>
<td></td>
</tr>
<tr class="odd">
<td rowspan="6"><p>Economic</p>
<p>Conditions</p></td>
<td>UE 2007</td>
<td></td>
<td></td>
<td></td>
<td>-0.003</td>
<td>0.022</td>
<td>0.877</td>
<td>0.133***</td>
<td>0.043</td>
<td>0.002</td>
<td>-0.017</td>
<td>0.022</td>
<td>0.425</td>
</tr>
<tr class="even">
<td>Power*UE 2007</td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td>-0.007***</td>
<td>0.002</td>
<td>0.001</td>
<td></td>
<td></td>
<td></td>
</tr>
<tr class="odd">
<td>Foreclosures</td>
<td></td>
<td></td>
<td></td>
<td>0.007</td>
<td>0.026</td>
<td>0.795</td>
<td>-0.077</td>
<td>0.049</td>
<td>0.117</td>
<td>0.104**</td>
<td>0.043</td>
<td>0.017</td>
</tr>
<tr class="even">
<td>Power*Foreclosures</td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td>0.004*</td>
<td>0.002</td>
<td>0.059</td>
<td></td>
<td></td>
<td></td>
</tr>
<tr class="odd">
<td>DWNom*UE 2007</td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td>0.028*</td>
<td>0.015</td>
<td>0.068</td>
</tr>
<tr class="even">
<td>DWNom*Foreclosures</td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td>-0.019</td>
<td>0.028</td>
<td>0.493</td>
</tr>
<tr class="odd">
<td rowspan="3">PAC</td>
<td>Finance PAC</td>
<td>0.024*</td>
<td>0.015</td>
<td>0.099</td>
<td>0.008</td>
<td>0.016</td>
<td>0.600</td>
<td>-0.035</td>
<td>0.023</td>
<td>0.128</td>
<td>0.028*</td>
<td>0.015</td>
<td>0.068</td>
</tr>
<tr class="even">
<td>Power*Finance PAC</td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td>0.003**</td>
<td>0.001</td>
<td>0.028</td>
<td></td>
<td></td>
<td></td>
</tr>
<tr class="odd">
<td>DWNom*Finance PAC</td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td>-0.019</td>
<td>0.028</td>
<td>0.493</td>
</tr>
<tr class="even">
<td rowspan="4"><p>Regression</p>
<p>Statistics</p></td>
<td>Pseudo-R-squared</td>
<td colspan="3">0.082</td>
<td colspan="3">0.106</td>
<td colspan="3">0.127</td>
<td colspan="3">0.095</td>
</tr>
<tr class="odd">
<td>Percent Correct</td>
<td colspan="3">67.84</td>
<td colspan="3">69.88</td>
<td colspan="3">68.24</td>
<td colspan="3">69.25</td>
</tr>
<tr class="even">
<td>ROC Curve</td>
<td colspan="3">0.693</td>
<td colspan="3">0.722</td>
<td colspan="3">0.727</td>
<td colspan="3">0.709</td>
</tr>
<tr class="odd">
<td>Number of Obs.</td>
<td colspan="3">426</td>
<td colspan="3">425</td>
<td colspan="3">425</td>
<td colspan="3">426</td>
</tr>
</tbody>
</table>

Notes: Dependent Variable is roll-call vote number 681, the October 3
vote. The independent variables are defined as follows: "Tenure" is
defined as the log of (2009-first year of congressperson tenure).
"Party" equals 1 if congressperson is Democrat, 0 otherwise. DW Nominate
is the DW Poole and Rosenthal (1997) ideology score. Power is the
congressperson's power ranking. Economic Condition variables: "UE 2007"
is the 2007 unemployment rate level at the congressional-district level.
"Foreclosures" is the number of foreclosures in the congressional
district from January to July 2008. "Finance PAC" represents total
contributions from the financial services industry. The reported
standard errors are the robust (White-1980 corrected) standard errors.
The Regression statistics are as follows: "Pseudo R-sq" is the
McFadden's Pseudo R-squared; "Percent correct" is the count R-squared,
or the proportion of correctly classified observations; "ROC Curve" is
the Receiver Operating Characteristic curve; "Number of. Obs." is the
number of observations.

**Table 6**

**The** Influence of Specific PACs on the September 29 and October 3
Votes

+-------+--------------+-------+------+------+-------+------+------+
|       | Variable     | *Sept |      |      | *Oc   |      |      |
|       |              | ember |      |      | tober |      |      |
|       |              | 29    |      |      | 3     |      |      |
|       |              | Vote* |      |      | Vote* |      |      |
+-------+--------------+-------+------+------+-------+------+------+
|       |              | C     | Std. | P-v  | C     | Std. | P-v  |
|       |              | oeffi | E    | alue | oeffi | E    | alue |
|       |              | cient | rror |      | cient | rror |      |
+-------+--------------+-------+------+------+-------+------+------+
| Legis | Tenure       | 0.000 | 0    | 0    | -     | 0    | 0    |
| lator |              |       | .041 | .997 | 0.009 | .038 | .816 |
|       |              |       |      |      |       |      |      |
| Attri |              |       |      |      |       |      |      |
| butes |              |       |      |      |       |      |      |
+-------+--------------+-------+------+------+-------+------+------+
|       | Party        | 0.056 | 0    | 0    | 0.102 | 0    | 0    |
|       |              |       | .073 | .445 |       | .069 | .140 |
+-------+--------------+-------+------+------+-------+------+------+
|       | Power        | 0     | 0    | 0    | 0.004 | 0    | 0    |
|       |              | .087\ | .023 | .000 |       | .023 | .866 |
|       |              | *\*\* |      |      |       |      |      |
+-------+--------------+-------+------+------+-------+------+------+
| Eco   | UE 2007      | 0     | 0    | 0    | 0     | 0    | 0    |
| nomic |              | .182\ | .044 | .000 | .139\ | .046 | .003 |
|       |              | *\*\* |      |      | *\*\* |      |      |
| Condi |              |       |      |      |       |      |      |
| tions |              |       |      |      |       |      |      |
+-------+--------------+-------+------+------+-------+------+------+
|       | Power\*UE    | -0    | 0    | 0    | -0    | 0    | 0    |
|       | 2007         | .010\ | .002 | .000 | .008\ | .002 | .001 |
|       |              | *\*\* |      |      | *\*\* |      |      |
+-------+--------------+-------+------+------+-------+------+------+
|       | Foreclosures | 0.086 | 0    | 0    | -     | 0    | 0    |
|       |              |       | .062 | .164 | 0.077 | .055 | .168 |
+-------+--------------+-------+------+------+-------+------+------+
|       | Power\*      | -0.00 | 0    | 0    | 0.004 | 0    | 0    |
|       | Foreclosures | 6\*\* | .003 | .044 |       | .003 | .136 |
+-------+--------------+-------+------+------+-------+------+------+
| PAC   | Big Bank PAC | -0.   | 0    | 0    | -     | 0    | 0    |
|       |              | 023\* | .014 | .100 | 0.018 | .013 | .165 |
+-------+--------------+-------+------+------+-------+------+------+
|       | Power\*Big   | 0.00  | 0    | 0    | 0.00  | 0    | 0    |
|       | Bank PAC     | 2\*\* | .001 | .017 | 1\*\* | .000 | .035 |
+-------+--------------+-------+------+------+-------+------+------+
|       | ABA PAC      | -     | 0    | 0    | -     | 0    | 0    |
|       |              | 0.021 | .015 | .139 | 0.002 | .013 | .910 |
+-------+--------------+-------+------+------+-------+------+------+
|       | Power\*ABA   | 0.000 | 0    | 0    | -     | 0    | 0    |
|       | PAC          |       | .001 | .526 | 0.000 | .001 | .474 |
+-------+--------------+-------+------+------+-------+------+------+
|       | Real Estate  | -     | 0    | 0    | -     | 0    | 0    |
|       | PAC          | 0.016 | .025 | .527 | 0.032 | .022 | .149 |
+-------+--------------+-------+------+------+-------+------+------+
|       | Power\*Real  | 0.001 | 0    | 0    | 0.00  | 0    | 0    |
|       | Estate PAC   |       | .001 | .233 | 2\*\* | .001 | .042 |
+-------+--------------+-------+------+------+-------+------+------+
| Regre | Pseu         | 0.173 |      |      | 0.139 |      |      |
| ssion | do-R-squared |       |      |      |       |      |      |
|       |              |       |      |      |       |      |      |
| Stati |              |       |      |      |       |      |      |
| stics |              |       |      |      |       |      |      |
+-------+--------------+-------+------+------+-------+------+------+
|       | Percent      | 65.79 |      |      | 67.78 |      |      |
|       | Correct      |       |      |      |       |      |      |
+-------+--------------+-------+------+------+-------+------+------+
|       | ROC Curve    | 0.758 |      |      | 0.738 |      |      |
+-------+--------------+-------+------+------+-------+------+------+
|       | Number of    | 418   |      |      | 419   |      |      |
|       | Obs.         |       |      |      |       |      |      |
+-------+--------------+-------+------+------+-------+------+------+

Notes: Dependent Variable is either the September 29 vote or the October
3 Vote. The independent variables are defined as follows: "Tenure" is
defined as the log of (2009-first year of congressperson tenure).
"Party" equals 1 if congressperson is Democrat, 0 otherwise. DW Nominate
is the DW Poole and Rosenthal (1997) ideology score. Power is the
congressperson's power ranking. Economic Condition variables: "UE 2007"
is the 2007 unemployment rate level at the congressional-district level.
"Foreclosures" is the number of foreclosures in the congressional
district from January to July 2008. "Big Bank PAC" represents
contributions from the largest financial institutions (with assets over
\$100 billion). "ABA PAC" represents contributions from the American
Bankers Association. "Real Estate PAC" represents contributions from
real estate groups. See text for a detailed list of PACs included. The
reported standard errors are the robust (White-1980 corrected) standard
errors. The Regression statistics are as follows: "Pseudo R-sq" is the
McFadden's Pseudo R-squared; "Percent correct" is the count R-squared,
or the proportion of correctly classified observations; "ROC Curve" is
the Receiver Operating Characteristic curve; "Number of. Obs." is the
number of observations.

**Table 7**

**Explaining Switchers**

<table>
<colgroup>
<col style="width: 8%" />
<col style="width: 16%" />
<col style="width: 8%" />
<col style="width: 8%" />
<col style="width: 8%" />
<col style="width: 8%" />
<col style="width: 8%" />
<col style="width: 8%" />
<col style="width: 8%" />
<col style="width: 8%" />
<col style="width: 8%" />
</colgroup>
<tbody>
<tr class="odd">
<td></td>
<td>Variable</td>
<td colspan="3"><em>Reg. 1: Basic Specification</em></td>
<td colspan="3"><em>Reg. 2: Nonlinear Influence of Power</em></td>
<td colspan="3"><em>Reg. 3: Nonlinear Influence of Iideology</em></td>
</tr>
<tr class="even">
<td></td>
<td></td>
<td>Coefficient</td>
<td>Std. Error</td>
<td>P-value</td>
<td>Coefficient</td>
<td>Std. Error</td>
<td>P-value</td>
<td>Coefficient</td>
<td>Std. Error</td>
<td>P-value</td>
</tr>
<tr class="odd">
<td rowspan="4"><p>Legislator</p>
<p>Attributes</p></td>
<td>Tenure</td>
<td>-0.020</td>
<td>0.023</td>
<td>0.403</td>
<td>-0.017</td>
<td>0.023</td>
<td>0.457</td>
<td>-0.050**</td>
<td>0.020</td>
<td>0.013</td>
</tr>
<tr class="even">
<td>Party</td>
<td>-0.149</td>
<td>0.112</td>
<td>0.172</td>
<td>0.050</td>
<td>0.039</td>
<td>0.208</td>
<td>-0.191*</td>
<td>0.114</td>
<td>0.083</td>
</tr>
<tr class="odd">
<td>DW Nominate</td>
<td>-0.209**</td>
<td>0.103</td>
<td>0.046</td>
<td></td>
<td></td>
<td></td>
<td>-0.363**</td>
<td>0.169</td>
<td>0.033</td>
</tr>
<tr class="even">
<td>Power</td>
<td>-0.004**</td>
<td>0.002</td>
<td>0.029</td>
<td>0.012</td>
<td>0.008</td>
<td>0.130</td>
<td></td>
<td></td>
<td></td>
</tr>
<tr class="odd">
<td rowspan="3">PAC</td>
<td>Total Finance PAC</td>
<td>0.012</td>
<td>0.008</td>
<td>0.108</td>
<td>0.028**</td>
<td>0.012</td>
<td>0.021</td>
<td>-0.001</td>
<td>0.007</td>
<td>0.896</td>
</tr>
<tr class="even">
<td>Power*Finance PAC</td>
<td></td>
<td></td>
<td></td>
<td>-0.001**</td>
<td>0.000</td>
<td>0.044</td>
<td></td>
<td></td>
<td></td>
</tr>
<tr class="odd">
<td>DWNom*Finance PAC</td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td>0.016</td>
<td>0.014</td>
<td>0.269</td>
</tr>
<tr class="even">
<td rowspan="4"><p>Regression</p>
<p>Statistics</p></td>
<td>Pseudo-R-squared</td>
<td colspan="3">0.035</td>
<td colspan="3">0.027</td>
<td colspan="3">0.031</td>
</tr>
<tr class="odd">
<td>Percent Correct</td>
<td colspan="3">86.82</td>
<td colspan="3">86.82</td>
<td colspan="3">86.62</td>
</tr>
<tr class="even">
<td>ROC Curve</td>
<td colspan="3">0.624</td>
<td colspan="3">0.600</td>
<td colspan="3">0.615</td>
</tr>
<tr class="odd">
<td>Number of Obs.</td>
<td colspan="3">425</td>
<td colspan="3">425</td>
<td colspan="3">426</td>
</tr>
</tbody>
</table>

Notes: Dependent variable is "switch," which equals 1 if congressperson
switched his or her vote from Nay (Sep 29) to Yea (Oct 3), 0 otherwise.
The independent variables are defined as follows: "Tenure" is defined as
the log of (2009-first year of congressperson tenure). "Party" equals 1
if congressperson is Democrat, 0 otherwise. DW Nominate is the DW Poole
and Rosenthal (1997) ideology score. Power is the congressperson's power
ranking. Economic Condition variables: "UE 2007" is the 2007
unemployment rate level at the congressional-district level.
"Foreclosures" is the number of foreclosures in the congressional
district as of July 2008. The reported standard errors are the robust
(White-1980 corrected) standard errors. The Regression statistics are as
follows: "Pseudo R-sq" is the McFadden's Pseudo R-squared; "Percent
correct" is the count R-squared, or the proportion of correctly
classified observations; "ROC Curve" is the Receiver Operating
Characteristic curve; "Number of. Obs." is the number of observations.

**Table 8**

**The Influence of Specific PACs on Switchers**

+-----------+----------------------+-----------+----------+----------+
|           | Variable             | *October  |          |          |
|           |                      | 3 Vote*   |          |          |
+-----------+----------------------+-----------+----------+----------+
|           |                      | Co        | Std.     | P-value  |
|           |                      | efficient | Error    |          |
+-----------+----------------------+-----------+----------+----------+
| L         | Tenure               | -0.007    | 0.024    | 0.777    |
| egislator |                      |           |          |          |
|           |                      |           |          |          |
| A         |                      |           |          |          |
| ttributes |                      |           |          |          |
+-----------+----------------------+-----------+----------+----------+
|           | Party                | 0.060     | 0.041    | 0.157    |
+-----------+----------------------+-----------+----------+----------+
|           | Power                | -0.006    | 0.005    | 0.217    |
+-----------+----------------------+-----------+----------+----------+
| PAC       | Big Bank PAC         | 0.001     | 0.001    | 0.892    |
+-----------+----------------------+-----------+----------+----------+
|           | Power\*Big Bank PAC  | 0.000     | 0.000    | 0.921    |
+-----------+----------------------+-----------+----------+----------+
|           | ABA PAC              | 0.015\*   | 0.008    | 0.062    |
+-----------+----------------------+-----------+----------+----------+
|           | Power\*ABA PAC       | -         | 0.003    | 0.033    |
|           |                      | 0.001\*\* |          |          |
+-----------+----------------------+-----------+----------+----------+
|           | Real Estate PAC      | -0.011    | 0.013    | 0.404    |
+-----------+----------------------+-----------+----------+----------+
|           | Power\*Real Estate   | 0.001     | 0.001    | 0.281    |
|           | PAC                  |           |          |          |
+-----------+----------------------+-----------+----------+----------+
| R         | Pseudo-R-squared     | 0.029     |          |          |
| egression |                      |           |          |          |
|           |                      |           |          |          |
| S         |                      |           |          |          |
| tatistics |                      |           |          |          |
+-----------+----------------------+-----------+----------+----------+
|           | Percent Correct      | 86.87     |          |          |
+-----------+----------------------+-----------+----------+----------+
|           | ROC Curve            | 0.605     |          |          |
+-----------+----------------------+-----------+----------+----------+
|           | Number of Obs.       | 419       |          |          |
+-----------+----------------------+-----------+----------+----------+

Notes: Dependent variable is "switch," which equals 1 if congressperson
switched his or her vote from Nay (Sep 29) to Yea (Oct 3), 0 otherwise.
The independent variables are defined as follows: "Tenure" is defined as
the log of (2009-first year of congressperson tenure). "Party" equals 1
if congressperson is Democrat, 0 otherwise. Power is the
congressperson's power ranking. PAC contribution variables are as
follows: "Big Bank PAC" represents contributions from the largest
financial institutions (those with assets over \$100 billion); "ABA PAC"
represents contributions from the American Bankers Association; and
"Real Estate PAC" represents contributions from real estate groups. See
text for a detailed list of PACs included. The reported standard errors
are the robust (White-1980 corrected) standard errors. The Regression
statistics are as follows: "Pseudo R-sq" is the McFadden's Pseudo
R-squared; "Percent correct" is the count R-squared, or the proportion
of correctly classified observations; "ROC Curve" is the Receiver
Operating Characteristic curve; "Number of. Obs." is the number of
observations.

**Appendix 1**

**Data Sources**

+--------------+--------------+----------------------------------------+
| Variable     | Description  | Source                                 |
+--------------+--------------+----------------------------------------+
| September 29 | House vote   | <http://t                              |
| Vote         | no. 674      | homas.loc.gov/home/rollcallvotes.html> |
+--------------+--------------+----------------------------------------+
| October 3    | House vote   | <http://t                              |
| Vote         | no. 681      | homas.loc.gov/home/rollcallvotes.html> |
+--------------+--------------+----------------------------------------+
| Tenure       | Log          | First year in Congress figures are     |
|              | (2009-first  | from <http://www.govtrack.us/>         |
|              | year in      |                                        |
|              | Congress)    |                                        |
+--------------+--------------+----------------------------------------+
| Party        | Democrat =   | <http://t                              |
|              | 1,           | homas.loc.gov/home/rollcallvotes.html> |
|              | Republican = |                                        |
|              | 0            |                                        |
+--------------+--------------+----------------------------------------+
| DW Nominate  | DW Nominate  | Poole and Rosenthal (1997). Data       |
|              | ideology     | available in                           |
|              | index        | [www.v                                 |
|              |              | oteview.com](http://www.voteview.com/) |
+--------------+--------------+----------------------------------------+
| Power        | Power        | Knowlegis Research. Source:            |
|              | rankings     | <                                      |
|              |              | http://beavoter.org/congressorg/home/> |
+--------------+--------------+----------------------------------------+
| Unemployment | Unemployment | USDA Economic Research Service.        |
| 2007         | rate at the  |                                        |
|              | county level |                                        |
+--------------+--------------+----------------------------------------+
| Foreclosures | Number of    | LoanPerformance ABS Database           |
|              | subprime and |                                        |
|              | Alt-A        | <http://www.loanperformance.com/>      |
|              | foreclosures |                                        |
|              | between      |                                        |
|              | January and  |                                        |
|              | July 2008    |                                        |
+--------------+--------------+----------------------------------------+
| Total        | C            | Center for Responsive Politics         |
| finance PAC  | ontributions | (www.opensecrets.com)                  |
| and          | from January |                                        |
| c            | 2008 to      |                                        |
| ontributions | September    |                                        |
| from         | 2008         |                                        |
| individual   |              |                                        |
| PACs         |              |                                        |
+--------------+--------------+----------------------------------------+
| Stock market | Daily and    | [www.price                             |
| data         | minu         | -data.com](http://www.price-data.com/) |
|              | te-by-minute |                                        |
|              | stock prices |                                        |
+--------------+--------------+----------------------------------------+

**Appendix 2**

**Portfolio of Stocks in Sample**

  -------- -------------------------- ----------- -------- ------ ------ -------
  Number   Name                       Financial   Big Bank REIT   ABA    Other

  1        3M                         0           0        0      0      1

  2        Abbott Labs                0           0        0      0      1

  3        Alcoa                      0           0        0      0      1

  4        Amerisourcebergen CP       0           0        0      0      1

  5        Analog Devices Inc         0           0        0      0      1

  6        AT&T                       0           0        0      0      1

  7        Auto Zone                  0           0        0      0      1

  8        Baxter International       0           0        0      0      1

  9        Black & Decker             0           0        0      0      1

  10       Boeing                     0           0        0      0      1

  11       Bristol-Myers Squibb       0           0        0      0      1

  12       Caterpillar                0           0        0      0      1

  13       Chevron Corp               0           0        0      0      1

  14       Coca Cola                  0           0        0      0      1

  15       DuPont de Nemours          0           0        0      0      1

  16       Exxon Mobil                0           0        0      0      1

  17       General Electric           0           0        0      0      1

  18       Hewlett Packard            0           0        0      0      1

  19       Home Depot                 0           0        0      0      1

  20       IBM                        0           0        0      0      1

  21       Intel                      0           0        0      0      1

  22       Johnson & Johnson          0           0        0      0      1

  23       Kraft                      0           0        0      0      1

  24       McDonalds                  0           0        0      0      1

  25       Merck                      0           0        0      0      1

  26       Microsoft                  0           0        0      0      1

  27       Pfizer                     0           0        0      0      1

  28       Proctor & Gamble           0           0        0      0      1

  29       United Technologies        0           0        0      0      1

  30       Verizon Comm               0           0        0      0      1

  31       WalMart Corp               0           0        0      0      1

  32       Walt Disney                0           0        0      0      1

  33       Federated Investors        1           0        0      0      0

  34       E-Trade                    1           0        0      0      0

  35       Franklin Resources         1           0        0      0      0

  36       American International     1           0        0      0      0
           Group                                                         

  37       Ameriprise Financial       1           0        0      0      0

  38       American Express           1           0        0      0      0

  39       Bank of America            1           1        0      0      0

  40       Citigroup                  1           1        0      0      0

  41       JP Morgan Chase            1           1        0      0      0

  42       BB & T Corp                1           1        0      0      0

  43       Bank of NY Mellon          1           1        0      0      0

  44       Goldman Sachs              1           1        0      0      0

  45       PNC Financial Services     1           1        0      0      0

  46       Wachovia                   1           1        0      0      0

  47       Wells Fargo                1           1        0      0      0

  48       Equity Residential         1           0        1      0      0

  49       Colonial Property Trust    1           0        1      0      0

  50       Mid-America Apartment Comm 1           0        1      0      0

  51       Associated Estates Realty  1           0        1      0      0

  52       American Land Lease Inc    1           0        1      0      0

  53       Home Properties Inc        1           0        1      0      0

  54       Essex Property Trust       1           0        1      0      0

  55       Brookline Bancorp          1           0        0      1      0

  56       Banco Popular Inc.         1           0        0      1      0

  57       Cathy General Bancorp      1           0        0      1      0

  58       Commerce Bancshares Inc    1           0        0      1      0

  59       City Holding Company Inc   1           0        0      1      0

  60       Citizens Republic Bancorp  1           0        0      1      0

  61       Dime Community Bancshares  1           0        0      1      0

  62       East West Bancorp          1           0        0      1      0

  63       First Niagara Financial    1           0        0      1      0
           Group                                                         

  64       Fulton Financial           1           0        0      1      0
           Corporation                                                   

  65       People\'s United Financial 1           0        0      1      0
           Inc                                                           

  66       Prosperity Bancshares      1           0        0      1      0

                                                                         

           Totals                     34          9        7      12     32
  -------- -------------------------- ----------- -------- ------ ------ -------

[^1]: ^\*^ I would like to thank, without implicating, Ed Kane, Vivian
    Hwa, Lee Davison, Jack Reidhill, Thomas Stratmann, and Vedran Vuk,
    as well as participants at the FDIC weekly workshop, the 2008
    Wittenberg University Law and Economics Lecture, and the Mercatus
    Center at George Mason University workshop for their valuable
    comments and suggestions. Particular thanks go to James Einloth and
    Jocelyn Grazal for their help in obtaining some of the data
    underlying this project. Financial assistance from the FDIC's Center
    for Financial Research is gratefully acknowledged. The views
    expressed here do not necessarily reflect the views of the FDIC or
    the Center for Financial Research.

[^2]: Troubled assets are assets backed by mortgages, which in recent
    weeks the media have described as "toxic" assets.

[^3]: The text of this legislation is available online: for relevant
    URLs, see
    <http://www.house.gov/apps/list/press/financialsvcs_dem/press092808.shtml>.

[^4]: This concept is discussed in more detail in Section III.

[^5]: A t-test on the 3-minute window (from 1:49 p.m. to 1:52 p.m.)
    reveals that the sharp decline is statistically significant at the
    less than 1 percent level.

[^6]: This event received a substantial amount of media attention. See,
    for example,
    <http://money.cnn.com/2008/09/29/markets/markets_newyork/index.htm>.

[^7]: See
    <http://www.nytimes.com/imagepages/2008/10/13/business/20081014_BAILOUT1_GRAPHIC.html>

[^8]: Appendix 2 lists the entire sample.

[^9]: The literature on this topic is voluminous. For a recent survey,
    see Ansolabehere, de Figueiredo, and Snyder (2003), and Stratmann
    (2005).

[^10]: Appendix 1 also contains more details, as well as sources.

[^11]: For the current Congress, the reported range is --2.59 to 97.3.
    The negative score assigned to one Republican representative could
    be the result of a recent recalibration in the formula.

[^12]: The Knowlegis methodology appears to be very thorough. According
    to the company's Web site: "Our researchers reviewed thousands of
    media articles, hundreds of bills that passed out of committee and
    through each chamber, as well as the amendments that attempted to
    shape the outcome of legislation. We collected data on the
    leadership, committee, and caucus positions of each Member of
    Congress in their chamber, researched relevant campaign
    contributions, and considered any characteristic or action that
    could contribute to their \"Sizzle-Fizzle\" factor. We also
    integrated earmark data provided by Taxpayers for Common Sense. In
    sum, there are thousands of data points and variables that were
    considered in the 2008 Knowlegis Power Rankings." For a more
    detailed description see
    <http://www.beavoter.org/congressorg/power_rankings/backgrounder.tt>.

[^13]: Early papers include Kau and Rubin (1982) and Peltzmann (1984).

[^14]: The tables report the marginal effects from probit regressions.
    Almost identical results were obtained when logistic regressions
    were performed instead.

[^15]: Excluding either one from Regression 1 leaves the remaining one
    statistically significant. For the sake of brevity, such auxiliary
    regressions are not discussed here.

[^16]: PAC contributions from large banks include contributions from
    Citibank, J. P. Morgan Chase, Goldman Sachs, Morgan Stanley, Merrill
    Lynch, Credit Suisse, and Bank of America. According to the Center
    for Responsive Politics, these represent the largest banking
    organizations among the top 100 PAC contributors overall. This set
    also includes the largest recipients of the bailout funds. For more
    on this, see
    <http://www.nytimes.com/imagepages/2008/10/13/business/20081014_BAILOUT1_GRAPHIC.html>.
    PAC contributions from the real estate sector include contributions
    from the National Association of Realtors, National Home Builders
    Association, and Freddie Mac. These three groups, too, are listed
    among the top 100 PAC contributors overall, according to the Center
    for Responsive Politics.

[^17]: In most specifications unemployment and foreclosures did not
    appear to explain the behavior of switchers in any case. This is not
    surprising. After all, these measures of economic conditions did not
    change between September 29 and October 3.