Individual and Aggregate Information E ects in the 2006 US Senate Election
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Individual and Aggregate Information Eects in
the 2006 US Senate Election∗
†
Mattias Nordin
Preliminary and incomplete, please do not cite
August 24, 2011
Abstract
Despite much research scholars still disagree whether voters have enough
information to cast votes in accordance with their own preferences. Using
the 2006 Common Cooperative Election Study I try to answer this ques-
tion. From the survey I know the respondents' opinions on a number of
dierent issues as well as their knowledge on how their senators voted on
those issues. Together with actual senator roll-call vote data this provides
the ideal setting for testing whether more knowledgeable voters are more
likely to evaluate their senators based on their actual behavior in o ce.
In order to make causal inference on the eect of information I use the
mismatch between the local TV markets and the states as a way of iden-
tifying knowledge of the senators roll-call votes. The results suggest that
low-informed voters make systematic errors in their vote decision.
1 Introduction
A striking feature of developed democracies is how little information voters actu-
ally have about politics. Furthermore, information is not randomly distributed
in the electorate, certain groups of voters have more information than others.
While this has been discussed extensively in the literature it still remains unclear
how it aects the electoral process and, ultimately, government policy. Broadly
speaking there are two dierent strands of literature on this subject. The rst
one discusses whether voters have enough knowledge of politics to cast an in-
formed vote. The second one takes voters as exogenous and instead focuses on
how politicians respond to the actions of the voters. Politicians who care about
reelection will want to target the voters who are more likely to reward/punish
them based on their actions. If some, uninformed, voters don't observe who
is responsible for government policy but other, informed, voters do then the
reelection-motivated politician will want to slant policy towards the informed
voters. This is the prediction in the models by, for instance, Baron (1994) and
∗ I am grateful for comments and suggestions made by Henrik Jordahl, Eva Mörk and James
Snyder.
† Department of Economics and Uppsala Center for Fiscal Studies, Uppsala university, Box
513, SE-751 20 Uppsala. E-mail: Mattias.Nordin@nek.uu.se
1Grossman and Helpman (1996). Also along this line Strömberg (2004) argues
that the new deal spending in the US in the 1930s was targeted to voters with
radio access and Besley and Burgess (2002) show that Indian states are more
likely to provide disaster relief in areas with a high level of news circulation.
These papers rely on the assumption that high-informed voters are better
than low-informed voters at basing their vote decision on expected policy out-
come. While this may seem like a natural conclusion some scholars (see for
instance Popkin, Gorman, Phillips, and Smith 1976; McKelvey and Ordeshook
1986; Conover and Feldman 1989; Lupia 1994) have argued that while voters
may be ignorant on speci c policy issues they are still able to vote as if they
are informed by using informational shortcuts or cues such as party identi ca-
tion or candidate gender and occupation that serves as proxies for actual policy
positions. In this way uninformed voters may still be able to vote as they would
have voted under full information.
Furthermore, several scholars, following Condorcet (1785), have argued that
even though voters make errors they will cancel out when votes are aggregated
(see Wittman 1989; Converse 1990; Page and Shapiro 1992). If voters are equally
uninformed regardless of policy preferences then, when votes are aggregated,
the voting result may be the same regardless of whether voters are informed
or not. On the other hand, if some groups are more informed than others the
composition of politicians, and therefore policy, may be biased towards these
groups.
Bartels (1996), Carpini and Keeter (1996), Althaus (1998, 2003) all empiri-
cally estimate whether informed and uninformed voters vote dierently. Specif-
ically, they assume that voters with similar background characteristics have
similar interests. The dierence in vote outcome between the informed and un-
informed voters, conditional on their observed covariates, is interpreted as an
information eect. They then simulate what the vote result would have been
had all voters been fully informed. The conclusion in all these papers is that
the aggregate outcome would dier substantially if all voters were informed. In
this paper I investigate a similar question but with the dierence that I, using
the 2006 CCES survey on the US Congress election, observe the actual policy
preferences of the voters. Ansolabehere and Jones (2010), using the CCES sur-
vey, show that citizens' do hold their representatives accountable based on their
beliefs of the senators' roll-call voting record in o ce. I add to their ndings by
showing how citizens' beliefs interact with their preferences when they evaluate
their senators' behavior in o ce. Furthermore, using the mismatch between the
local TV markets and the states as an instrument for citizen knowledge I argue
that I am able to have a causal interpretation of the estimates.
The rest of the paper is structured as follows. Section 2 discusses the 2006
CCES survey and the empirical strategy. Section 3 discusses the baseline re-
sults showing that uninformed voters are signi cantly worse at evaluating their
senators. Section 4 addresses the issue of causality by instrumenting the infor-
mation the voters have about their senator by the mismatch between the local
TV market and the state. Speci cally, some voters live in a media market pri-
marily located in a dierent state. I argue that this lead to exogenously less
information for these voters about their senators which allows for IV estimations
of the information eect. The IV estimations show an even larger information
eect than the OLS estimations do. Finally, section 6 concludes.
22 Data and empirical strategy
To test whether U.S. voters have enough information to evaluate their senators
according to their own preferences I use from the 2006 Common Cooperative
Election Study (CCES). The CCES is a survey ideally suited to test the hy-
pothesis for several reasons. It is a large survey with around 36500 respondents
which allows for precise estimates. Furthermore, it contains questions on the
respondents opinions on several policy issues that the senators had been voting
on. The key is that it also contains a question on how the respondents think
their senators voted on these issues. When adding actual roll-call voting data
this provides the ideal setting to test whether voters have enough information
to make correct vote decisions.
The respondents were faced with the following statement:
As you know, Senators and Representatives in Washington regularly
have to decide how to vote on issues aecting the country. We'd like
to ask you about how you would vote on some of these same issues
as well as how you think your representative voted.
The issues asked about was on the funding of stem cell research (H.R. 810),
withdrawal of troops from Iraq (S.Amdt. 4320), giving more opportunities for
illegal immigrants to become legal citizens (S. 2611), increasing the minimum
wage (H.R. 3058), reducing the capital gains tax (H.R. 4297) and implementing
the Central America Free Trade Agreement (H.R. 3045).
1
Let kisj be a variable that takes on value 1 if the respondent would vote as
the senator on issue j and 0 if the respondent would vote the opposite way. Fur-
thermore, let bisj take on value 1 if the respondent knew what the senator voted
for and 0 if s/he did not know or answered incorrectly.
2 For each respondent
I only use the issues for which s/he did express on opinion and which her/his
senator took part of the vote. I denote that number of issues nis .
I de ne two variables of interest:
Pnis Pnis
j=1 kisj j=1 bisj
SAMEis := , KNOWis := . (1)
nis nis
That is, the variable SAMEis measure the share of the issues that the respondent
agrees with the senator on. Similarly, KNOWis measure the share of issues
which the respondent knows how the senator voted on. What I want to study
is how these two variables aect the approval rating of the senator. We would
expect SAMEis to be positively related to the approval of the senator; the better
the preferences of the respondent correspond to the senator's voting record
the more likely it is that the respondent would approve of the job the senator
does. Furthermore, we would expect this relationship to be stronger the more
knowledge, KNOWis , the respondent has of the senator.
To measure the approval of the senator I use a question in the survey where
the respondents were asked whether they approved of the way their senators
1 There was also one additional question in the survey on banning of late-term abortions.
However the that was voted on in the 108th congress (20032004) which means the rst-time
senators elected in 2004 did not take part of the vote. Therefore I exclude that question.
2 A respondent who did not answer the question is coded as not knowing what the senator
voted for.
3Figure 1: Approval rating of senator, by the KNOW variable
4
3
APPROVE
2
1
0 .25 .5 .75 1
SAME
1st quartile 2nd quartile
3rd quartile 4th quartile
were handling their work. They could give four dierent responses: strongly
disapprove somewhat disapprove, somewhat approve and strongly approve. For
simplicity I de ne the variable APPROVEis as linear ranging from 1 to 4.
Figure 1 shows the relationship between senator approval rating, APPROVEis ,
and SAMEis where the knowledge variable, KNOWis , has been split into quar-
3 The result is striking. For the most knowledgeable quartile the average
tiles.
value of the APPROVE variable range from 1.26 for the respondents with com-
pletely opposite preferences from their senator to 3.61 for the respondents who
agree with their senator on all roll-call votes. In contrast to this, the association
between having the same opinions as the senator and approval is much weaker
for the least knowledgeable quartile of the respondents. For the respondents
with opposite preferences from their senator the average value of APPROVE is
2.45 whereas the average value of APPROVE for the respondents with the same
preferences as their senator is 2.90, a moderate dierence.
Figure 1 reveals the basic nding: informed voters' evaluation of their sen-
ators correlate strongly with how well the senators behavior in o ce align with
the respondents' preferences. On the other hand, the senators' roll-call voting
records are only weakly correlated with uninformed voters' approval of their
senators. One should exercise some caution before taking these result literally.
There are several factors that may in uence both the knowledge of the voters
and their preferences. The omission of these factors may bias the result so I
will control for as many of these as possible in a regression model. Speci cally,
I will test the following model:
^ is
APPROVE = µKNOWis +γ SAMEis +δ(KNOWis × SAMEis )+Xis β+νs +εis
(2)
where ^ is
APPROVE is simply APPROVEis standardized to have standard de-
3 For the rst quartile the mean of KNOW is 0.03, for the second the mean is 0.39, for the
third 0.69 and for the fourth 0.92.
4viation of 1. The interaction between KNOWis and SAMEis capture the infor-
mation eect, measured by the parameter δ. The larger is δ the larger is the
information eect. Xis is a vector of controls and νs is a senator-speci c xed
eect.
3 Baseline results
Table 1 shows the results from the estimations of (2). The rst column shows
the estimation of (2) without the control vector Xis or the senator eect, νs . As
can be seen the large sample size in the survey causes the estimates to be very
precise. Because both the SAME and KNOW variables are scaled from 0 to 1
the negative main eect of the KNOW variable suggests that the most knowl-
edgeable respondents are predicted to have around 1.4 standard deviations lower
approval of their senator compared with the least knowledgeable respondents
when they have completely opposite preferences from their senator. The signif-
icantly positive interaction eect means that as the respondents get more like
their senator in preference the approval increases faster the more knowledge-
able the respondents are. Perfectly informed respondents that have the same
preferences as the senator are predicted to have around 1.2 standard deviations
higher approval rating of the senator compared with completely uninformed
respondents.
The main eect of SAMEis is positive which means the senator approval
rating of the least informed voters increase as the number of issues where the
preferences of the respondent and the senator correspond increases. It may seem
odd that this eect exist for the voters with no knowledge of their senator's roll-
call vote record. After all, why would they be more likely to approve of their
senator as the number of issues they have the same preferences on increase if
they don't know that this is the case? One explanation is that they know of other
policy positions of their senator that is correlated with the issues asked about
in the survey. Another possible answer is that they successfully employ the use
of cues such as party identi cation to, at least partly, compensate for their lack
of knowledge. However, it should be clear from this table that low-informed
voters are far from able to completely make up for their lack of knowledge using
informational shortcuts.
In the second column of Table 1, senator eects are added to control for
the fact that some senators are more well-known than others which may aect
both the respondents' knowledge of their voting record as well as their approval
rating for reasons other than that knowledge. In the third column demographic
background characteristics of the voters for sex, race, marital status, age and
education as well controls for ideological background characteristics such as
party identi cation, whether the respondent owns a gun, whether the respondent
considers religion to be important, placement on a liberalconservative scale as
well as whether the respondent approved of the then-president George W. Bush.
A potential problem with the estimates is that information may be correlated
with preferences which in turn aects senator approval rating. Speci cally, more
informed respondents do in general have more polarized political views. That
is, the higher the values of KNOW, the more likely the SAME variable is to be
closer to either 0 or 1. It is possible that the reason for this is not that informed
and uninformed respondents hold dierent underlying preferences, but rather
5Table 1: Information eect on senator approval rating and reelection votes
(1) (2) (3) (4)
KNOWis -1.397*** -1.301*** -1.276*** -1.144***
(0.0190) (0.0198) (0.0216) (0.0361)
SAMEis 0.461*** 0.609*** 0.643*** 0.459***
(0.0240) (0.0245) (0.0264) (0.0387)
KNOWis × SAMEis 2.153*** 2.041*** 2.006*** 2.106***
(0.0308) (0.0310) (0.0334) (0.0512)
Individual control variables No No Yes No
Senator eects No Yes Yes Yes
N 63427 63427 56325 63427
Standard errors, shown in parenthesis, have been adjusted allow for cluster
eects on the individual level. *, ** and *** denote signi cance on the
10, 5 and 1 percent level respectively.
that informed voters are better at stating coherent policy preferences given their
underlying preferences. Therefore, we would like to keep the underlying pref-
erences constant when estimating equation (2). While underlying preferences
are unobserved, this can still be accomplished by using the fact that each re-
spondent is asked about two senators which facilitates the possibility to use
individual xed eects. This utilizes the dierence in knowledge the respondent
has of the two senators and estimates the eect this has on the approval rating
while removing the underlying unobserved preferences. The estimate from the
xed eect regression is shown in column 4 of Table 1. As can be seen, the use
of senator eects, individual controls or indvidual xed eects does not alter
the estimates in any signi cant way.
4 Media market coverage
The results so far rely on the assumption that there is no omitted variable
aecting both knowledge of senators as well as the senator approval rating. To
test this I have controlled for a large set of demographic and ideological control
variables as well as utilized within-respondent variation. Nevertheless, we may
still have doubts of whether the results so far really capture a causal eect of
information. To be able to make this causal inference I will use the local TV
market coverage as an instrument for information. Speci cally, some citizens get
their local TV from a media market that is primarily located in another state.
It seems reasonable that the local TV stations' news coverage would primarily
focus on the senators from which the majority of their viewers come from. This
would lead the viewers in other states within the same TV market to receive
exogenously less information about their senators.
This type of identi cation has been used before in the literature. An-
solabehere, Snowberg, and Snyder (2006) use this to study whether the increase
in the incumbency advantage in U.S. elections can be explained by increasing
6TV exposure for the incumbent. Snyder and Strömberg (2010) utilize the mis-
match between congressional districts and the newspaper market to study the
knowledge of voters and the behavior of the incumbents.
To know which TV stations are available in dierent geographic locations I
use the designated market areas (DMAs) as de ned by The Nielsen Company. A
DMA region is a group of counties in which the local television stations dominate
total hours viewed. In total there are 210 DMAs in the US. As long as news are
driven by consumer demand and consumers are more interested about news of
their own senator, we would expect more news coverage of the senators in the
primary state (following Ansolabehere, Snowberg, and Snyder (2006) these are
called in-state senators) than of the senators in the others states within that
particular media market (out-state senators).
As an example, The PittsburghPA market is primarily located within Penn-
sylvania. However, the market also covers two of the northern counties of West
Virginia as well as one county of Maryland. Since an overwhelming majority
of the citizens in this media market lives in Pennsylvania we would expect the
local news stations to primarily cover the senators from Pennsylvania and give
much less coverage to the other senators. This would cause the voters living in
this media market in West Virginia and Maryland to get much less information
about their senators.
In some cases a large share of the population in a DMA live in a dierent
state from the primary state. This would likely lead the TV stations to also
cover the senators from that state. For instance, almost the entire state of New
Jersey is included in the New York City media market. It seems unlikely that
the media will neglect to report on their senators since New Jersey constitutes
a large part of the media market. To get around this problem I exclude media
markets in which less than 2/3 of the population live within the primary state of
the media market.
4 Figure 2 shows the distribution of in-state (light gray areas)
5
and out-state (dark gray areas) counties in the US. The white areas belong
to counties that are excluded due to the criteria above. As the gure shows
there are, naturally, signi cantly fewer out-state counties than there are in-state
counties. Furthermore since out-state counties are smaller on average there are
fewer respondents from these counties. In total 51303 of the observations belong
to in-state counties and only 2210 belong to out-state counties.
6 Therefore the
precision of the estimates may be problematic. Furthermore the estimated eect
will, as always with IV, be a local estimate not necessarily representative of the
entire sample.
The question is to what extent coverage is devoted to in-state senators in-
stead of out-state senators. To get a measure of this I searched the websites of
all the local TV stations that are a liates of one of the four major networks
(ABC, CBS, FOX or NBC) in the 68 markets in which more than 2/3 of the
population live in in-state counties but where there exist at least one out-state
county. For each TV station (in total 243 stations) I searched for mentions of
4 Ansolabehere, Snowberg, and Snyder (2006) use the same threshold.
5 For convenience, Alaska and Hawaii are excluded in the map but, naturally, their are no
out-state counties in these states since they do not border any other state. However, in Alaska
their are some counties that do not belong to any media market.
6 For purposes of the IV estimation this excludes the observations where the APPROVE
variable contains a missing value.
7Figure 2: In-state and out-state TV coverage
-
each senator (except the senators from Alaska and Hawaii).
7 Since some local
TV stations have limited web pages I excluded the TV stations that had less
than 100 mentions of all the senators combined. I then calculated the share of
hits covering in-state senators, out-state senators and senators from states with
no overlap of the media market. Finally I took the average of these shares for all
the TV stations in each media market to be the estimated coverage of senators.
Figure 3 shows the result. As can be seen the in-state senators receive much
more coverage than the out-state senators. On average each in-state senator
receives more than 15% of all the mentions of any of the 96 senators on the TV
websites. On the other hand the average out-state senator receives less than
3% of all the mentions. I argue that this disparity in coverage should lead to
more information about their senators for voters in in-state counties. It is worth
noting that while the out-state senators receive little coverage it is still more
than the average senator from a state that does not overlap with the media
market at all (labelled unmatched senators). This is not surprising since there
is some part of the media market that is located in the state served by the
out-state senator.
4.1 Does television coverage increase political knowledge?
So far I have shown that in-state senators receive more coverage by the local
TV-stations. In order to use the mismatch between media markets and states
as a way of identifying information eects we also need to show that those who
live in in-state counties know more about their senators than those in out-state
counties. To test for that I de ne a dummy variable, IN_STATEis , that takes
on value 1 if the respondent lives in an in-state county and 0 otherwise. I then
regress this variable on the knowledge the respondents have on their senators
roll-call voting record, KNOWis .
Table 2 shows the result. In the rst column I also control for senator xed
eects. As can be seen, respondents living in in-state counties have more knowl-
edge about their senators roll-call voting record. On average, this increases the
7 To search the websites I used the search engine Bing. The searches took place the 20th
of August 2010.
8Table 2: Local television medias' eect on political knowledge
KNOWis KNOW_MAJis
(1) (2) (3) (4) (5) (6)
In-state media 0.0599*** 0.0394*** 0.0329*** 0.0139 0.000676 -0.000135
(0.0107) (0.0109) (0.0112) (0.0127) (0.0129) (0.0131)
9
Senator eects Yes Yes Yes Yes Yes Yes
County controls No Yes Yes No Yes Yes
Individual controls No No Yes No No Yes
N 53382 53382 47280 40584 40584 36070
Standard errors, adjusted to allow for cluster eects on the individual level, are shown in parenthesis.
*, ** and *** denote signi cance on the 10, 5 and 1 percent level respectively.0.154
.15
.1
.05
0.028
0.007
0
In−state Out−state Unmatched
Figure 3: Internet coverage of senators
share of questions answered correctly with six percentage points or, expressed
dierently, living in an in-state county increases knowledge of the senator with
0.19 standard deviations. In the second column I include county averages for
age, education, sex, race, income (in logarithmic form) as well as the popula-
tion size (also in logarithmic form) as controls. This causes the point estimate
to drop but the eect is still signi cant. Finally, I also control for the same
individual characteristics as in the previous section.
One concern with the estimates is that whether a county is in-state or out-
state is not randomly assigned. For instance, out-state counties tend to be
less populated and more rural than in-state counties. We could be worried
that the dierence in knowledge about senators is driven by some underlying
characteristic of the out-state counties that, even after controlling for observable
variables, causes the signi cant estimates. Fortunately I can do a placebo-test
for this. If it is the case that it is the mismatch of the television market that
drives the result we would expect state-speci c knowledge to dier between in-
state counties and out-state counties but not general political knowledge. To
test for this I also ran the regression on whether the respondents knew who had
the majorities in the House and the Senate after the election, KNOW_MAJis .
As can be seen in columns 46 this is not signi cant in any speci cation.
This is reassuring since it suggests that the mismatch between the TV markets
and the states is not correlated with general political knowledge but only with
state-speci c political knowledge. The reason the sample size drops is because
the house-majority question was only asked in the post-election survey which not
everyone responded to. One might suspect that the reason the IN_STATEis
variable is signi cant in the rst three columns but not in the last three is
because they are estimating on two dierent samples. However, reestimating
the rst three columns using only the individuals that did not have a missing
value on KNOW_MAJis does not change the results in a signi cant way (results
available upon request).
10Table 3: IV regression on senator approval rating
(1) (2) (3)
KNOWis -1.300** -0.932 -1.206
(0.554) (0.744) (0.955)
KNOWis × SAMEis 3.677*** 3.651*** 4.422***
(0.822) (0.883) (1.093)
SAMEis -0.348 -0.293 -0.784
(0.534) (0.572) (0.724)
Senator eects Yes Yes Yes
County controls No Yes Yes
Individual controls No No Yes
N 53382 53382 47280
Cragg-Donald F-statistic 15.78 6.66 4.53
Standard errors, adjusted to allow for cluster eects on the
individual level, are shown in parenthesis. *, ** and *** denote
signi cance on the 10, 5 and 1 percent level respectively.
4.2 Instrumental variable regressions
The previous section shows the rst-stage eect of the mismatch of the media
markets on the knowledge the respondents have of their senators. The equa-
tion we really want to estimate is (2) but where we instrument KNOWis and
KNOWis × SAMEis with IN_STATEis and (IN_STATEis × SAMEis )
Table 3 shows the result from the IV estimations. As can be seen the in-
teraction between KNOWis and SAMEis is signi cant on the 1 percent level
in all speci cations. In the rst column I only include senator xed eects. In
the second speci cation I include control variables on the county level and in
the third column I control for both county characteristics as well as individual
characteristics.
The main eect of KNOW is similar in size to the OLS estimates in table 1.
Compared with the least knowledgeable respondents, the most knowledgeable
respondents have around 0.91.3 standard deviations lower approval of a senator
with completely opposite preferences. The interaction eect is signi cant on the
1 percent level and its size of 3.74.4 is larger than the OLS-estimates of around
2. This translates into around 22.4 standard deviations higher approval for the
most knowledgeable compared with the least knowledgeable respondents when
SAME are equal to 1, that is, the respondents and the senator have the same
opinions on the roll-call votes. There are reason to exercise some caution in
interpreting the size of the eects. The functional form assumption required
is quite restrictive and between 820 percent of the predicted values from the
regressions fall outside the interval of the APPROVE variable. There is also a
potential weak-instrument concern. Table 3 shows the Cragg-Donald F-statistic
that is used to test the joint signi cance of the two instruments in the two rst
stages (Cragg and Donald 1993). Stock and Yogo (2005) provide rejection rates
where the null hypothesis is that the instrument is weak. For a size-distortion
11of maximum 10 percent
8 the critical F-value is 4.58. As can be seen from table
3 we can reject the null of weak instruments in the rst two columns but not
in the third which means one should exercise som caution in interpreting the
results from that regression.
In order for the exclusion restriction to hold the media coverage of the sen-
ators can not change the preferences of the respondents. This seems natural
since, in a given media market, both in-state respondents as well as out-state
respondents receive the same news. However, Zaller (1992) argues that voters
infer their own policy position from their politicians' positions. Since out-state
respondents get less information about their senator we might suspect that they
can not infer their own policy position from their senator which would lead them
to have preferences that is further away from the senator. That is, there could be
a positive relationship between living in-state and having the same preferences
as the senator.
To test if this is the case I regressed IN_STATEis on SAMEis . The result
can be seen in table 4. In the rst column I just added senator xed eects
and the estimated coe cient is not signi cantly dierent from zero. We might
think that citizens only uses the senator's policy position as a cue if the sen-
ator belongs to the party the respondent identi es with. I therefore introduce
a variable for if the respondent identi es with the party the senator represent,
SAME_PARTY_IDis , that I also interact with the variable IN_STATEis . If
this interaction is positive we might take that as an indication that the respon-
dents infer their own policy position from the senator if the senator belongs
to the party the respondent identi es with. This is shown in the second col-
umn. As can be seen, SAME_PARTY_IDis is strongly positively correlated
with SAMEis as expected. However, neither the main eect of IN_STATEis
nor the interaction is signi cant. The same pattern holds even when including
control variables as shown in columns 3 to 6. This suggests the respondents' do
not change their preferences when they learn their senators' policy positions.
5 Conclusion
In this paper I investigate whether US citizens have enough knowledge of their
senators to evaluate their behavior in o ce based on the citizens own prefer-
ences. Using the 2006 Common Cooperative Election Study I relate senator
approval rating to the preferences of the respondents and senators as well as the
knowledge the respondents have of their senators. The result is that respon-
dents make substantial errors: uninformed respondents are far more likely to
approve of a senator with opposite preferences from themselves and are far less
likely to approve of a senator with same preferences as themselves compared
with informed respondents.
To address the issue of causality I use the mismatch between the local TV
markets and the states as an instrument for voter information. I show that
voters living in a county where the local TV news cover their senator have more
knowledge of their senator compared with voters getting less TV news coverage
of their senator. The result from the IV estimations con rm the OLS ndings;
informed voters are more likely to approve of senators with similar preferences
as themselves.
8 That is, a 5 percent level test has a maximum size of 15 percent.
12Table 4: Regression on having the same preferences as the senator
(1) (2) (3) (4) (5) (6)
IN_STATEis 0.0104 0.0150 0.00546 0.00898 0.0133 0.0202*
(0.00919) (0.0106) (0.00936) (0.0107) (0.0101) (0.0106)
SAME_PARTY_IDis 0.288*** 0.287*** 0.416***
(0.0138) (0.0139) (0.0145)
IN_STATEis × SAME_PARTY_IDis -0.0113 -0.0113 -0.0204
13
(0.0141) (0.0142) (0.0147)
Senator eects Yes Yes Yes Yes Yes Yes
County controls No No Yes Yes Yes Yes
Individual controls No No No No Yes Yes
N 53382 53375 53382 53375 47280 47280
Standard errors, adjusted to allow for cluster eects on the individual level, are shown in parenthesis.
*, ** and *** denote signi cance on the 10, 5 and 1 percent level respectively.References
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