The Review of Economics and Statistics

VOL. CV

MAY 2023

NUMBER 3

PARTISAN BIAS, ECONOMIC EXPECTATIONS, AND HOUSEHOLD SPENDING

Atif Mian, Amir Suﬁ, and Nasim Khoshkhou*

Abstract—The well-documented rise in political polarization among the
U.S. electorate over the past 20 years has been accompanied by a substantial
increase in the effect of partisan bias on survey-based measures of economic
expectations. Individuals have a more optimistic view on future economic
conditions when they are more closely afﬁliated with the party that controls
the White House, and this tendency has increased signiﬁcantly over time.
Individuals report a large shift in economic expectations based on partisan
afﬁliation after the 2008 and 2016 elections, but administrative data on
spending shows no effect of these shifts on actual household spending.

ECONOMISTS have long believed that economic expec-

tations are crucial to understanding economic activity.
But how do individuals actually form economic expectations?
One line of research in economics examines responses to sur-
vey questions. For example, the University of Michigan Sur-
vey of Consumers asks individuals the following question:
“Looking ahead, which would you say is more likely – that
in the country as a whole we’ll have continuous good times
during the next 5 years or so, or that we will have periods of
widespread unemployment or depression or what?”

Economists typically treat an individual’s answers to these
questions as a reﬂection of the individual’s expectations of
future income growth. The evolution of such expectations
could reﬂect information the household receives on funda-
mental changes in the economy. Alternatively, household be-
liefs about future income growth may reﬂect sentiment, or
changes in expectations that are orthogonal to future eco-
nomic conditions. A large body of research in economics has
focused on these issues (e.g., Barsky & Sims, 2012; Azari-
adis, 1981; Benhabib & Farmer, 1994; Lorenzoni, 2009, and
Angeletos & La’O, 2013). Beyond the academic literature,

Received for publication May 15, 2020. Revision accepted for publication

March 25, 2021. Editor: Shachar Kariv.

∗Mian (corresponding author): Princeton & NBER; Suﬁ (corresponding

author): Chicago Booth & NBER; Khoshkhou: Synchrony Financial.

This research was supported by funding from the Initiative on Global
Markets at Chicago Booth, the Fama-Miller Center at Chicago Booth,
and Princeton University. We thank Tu Cao, Pranav Garg, Seongjin Park,
Jung Sakong, and Xiao Zhang for excellent research assistance. For help-
ful comments, we thank Fernando Alvarez, Bob Barsky, Anthony Fowler,
Matthew Gentzkow, Christian Gillitzer, Guido Lorenzoni, Claudia Sahm,
Jesse Shapiro, Danny Yagan and seminar participants at many places. We
also thank Shachar Kaviv (the editor) and four anonymous referees for
comments and suggestions that improved the study. Any opinions, ﬁnd-
ings, conclusions, or recommendations expressed in this material are those
of the authors and do not necessarily reﬂect the view of any other institution.
A supplemental appendix is available online at https://doi.org/10.1162/

rest_a_01056.

the answers to such survey questions receive widespread cov-
erage from the ﬁnancial press, which likely reﬂects the view
that the answers contain valuable information for predicting
income and spending growth.1

However, research in political science suggests caution
when evaluating responses to surveys on economic condi-
tions because of potential partisan bias. For example, it has
been shown that individuals have a more positive assessment
of current economic conditions when the White House is oc-
cupied by the party they support (e.g., Bartels, 2002). The
idea of a “partisan perceptual screen” has been present in the
literature since the seminal work by Campbell et al. (1960);
Gerber and Huber (2009) summarize the idea succinctly by
writing: “In short, this evidence portrays partisan voters as
individuals who tend to see what they want to see.” A separate
but related line of research in political science documents a
large increase in social and affective polarization across po-
litical parties (e.g., Iyengar, Sood, & Lelkes, 2012; Mason,
2013, 2015; Gentzkow, 2016; Boxell, Gentzkow, & Shapiro,
2017). Political parties are increasingly homogeneous in the
ideology of their members, and partisans show increasing
hostility toward members of the opposite political party. This
line of research suggests that partisan bias in evaluations of
the economy may be growing over time.

In this study, we investigate three related questions. Does
partisan bias inﬂuence an individual’s assessment of future
economic conditions as reported in surveys? If so, has par-
tisan bias in expectations formation risen over time? And
ﬁnally, do movements in economic expectations driven by
partisan bias inﬂuence household spending?

We ﬁnd that partisan bias exerts a signiﬁcant inﬂuence on
survey measures of economic expectations, and this bias is
increasing substantially over time. Using two independent
data sources (the University of Michigan Survey of Con-
sumers and Gallup), we show that individuals who afﬁliate
with the party that controls the White House have systemati-
cally more optimistic economic expectations than those who
afﬁliate with the party not in control. This has been true at
least since the Reagan administration in the 1980s. Further,
the bias is becoming larger over time. For example, Repub-
licans have economic expectations since January 2017 that

1For example, the release of the August 2017 consumer sentiment index
from the University of Michigan was covered by CNBC, the Financial
Times, and the Wall Street Journal.

The Review of Economics and Statistics, May 2023, 105(3): 493–510
© 2021 The President and Fellows of Harvard College and the Massachusetts Institute of Technology
https://doi.org/10.1162/rest_a_01056

D
o
w
n
o
a
d
e
d

f
r
o
m
h

t
t

:
/
/

d
i
r
e
c
t
.

i
t
.

e
d
u
/
r
e
s
t
/

a
r
t
i
c
e
–
p
d

f
/

1
0
5
3
4
9
3
2
0
9
0
0
0
4
/
r
e
s
t
_
a
_
0
1
0
5
6
p
d

b
y
g
u
e
s
t

o
n
0
7
S
e
p
e
m
b
e
r
2
0
2
3

494

THE REVIEW OF ECONOMICS AND STATISTICS

are on average 1 to 1.5 standard deviations more optimistic
than Democrats. The difference was less than one-half a stan-
dard deviation prior to the ﬁrst Obama administration. The
explanatory power of party afﬁliation on economic expecta-
tions, as measured by the R2 from a linear regression, has
risen fourfold from 0.07 to 0.28 from the George W. Bush to
Trump administrations.

How does the rise in partisan bias in economic expecta-
tions affect household spending? To answer this question,
we focus on changes in economic expectations right around
Presidential elections, which give us the “cleanest” estimates
of the pure effect of political outcomes on economic expec-
tations. Following the 2008 and 2016 Presidential elections,
we ﬁnd that individuals supporting the party of the winning
presidential candidate witness a substantial relative rise in
optimism about the economy immediately after the election.
The relative change in economic expectations is particularly
large after the 2016 election. Individuals identifying them-
selves as Republicans see a 1.5 standard deviation increase
in economic optimism from November 2016 to January 2017,
whereas Democrats see a 0.75 standard deviation decline in
economic optimism.

One hypothesis is that economic expectations of partisans
are driven by the party controlling the White House because
the actual economic condition improves for partisans if their
party is in control. We examine county-level and state-level
measures of tax rates, personal income growth, and transfers
around elections, and we ﬁnd little evidence that economic
circumstances change to the beneﬁt of areas supporting the
new President after elections.

We also test this hypothesis using a simple assumption:
individuals living in the same zip code should be similarly
affected by whatever economic factors are associated with
the occupant of the White House. Using the data set from
Gallup, which contains large samples and detailed geographic
identiﬁers, we show that our estimates of partisan bias are
unchanged with the inclusion of zip code by month ﬁxed
effects. For example, Democrats and Republicans experience
sharply diverging views on the economy after the election of
Donald Trump in 2016 even if they live in the same zip code.
As a further test, we examine answers to a question in the
Gallup survey on whether the ﬁrm for which an individual
works is hiring or letting go of employees. We ﬁnd substantial
partisan bias in the answers to these questions right around
elections, and this bias is also unchanged with the inclusion
of zip code by month ﬁxed effects. In other words, after the
election of Donald Trump, a Republican is much more likely
to report that her ﬁrm is hiring workers while a Democrat
living in the same zip code is much more likely to report
that her ﬁrm is ﬁring workers. Taken together, these results
lead us to the conclusion that the sharp relative changes in
economic optimism around Presidential elections are pure
partisan bias as opposed to a response to changes in economic
circumstances of partisans.

To measures the effects on spending, we utilize two types of
data: survey questions where individuals report information

on their spending, and administrative data that records actual
spending at the county and zip code level. We ﬁnd mixed
evidence on spending in the survey questions. In the Michigan
data, we ﬁnd weak evidence of a change in spending patterns
based on questions on whether it is a good time to buy major
household items or a car. In the Gallup data, Republicans
report higher spending after the election of Donald Trump in
2016, but they do not report lower spending after the election
of Barack Obama in 2008.

In the administrative data, we ﬁnd no evidence of a change
in spending driven by changes in economic expectations due
to partisan bias. The evidence for the 2016 election is most
striking. Through October 2017, there is no relative increase
in auto purchases or credit card spending in U.S. counties or
zip codes where individuals voted in the highest proportion
for the Republican candidate, even though the increase in op-
timism on the economy in these areas is large. The overall
evidence on spending leads us to the conclusion that parti-
san bias in economic expectations has little to no effect on
household spending.

Why do movements in survey-based economic optimism
fail to move spending? We consider a number of factors,
and we conclude that the most likely explanation is that
survey-based economic optimism driven by partisan bias re-
ﬂects “cheerleading” instead of actual expectations of income
growth. This is consistent with evidence from Bullock et al.
(2015) and Prior et al. (2015) who ﬁnd that partisan bias in
views on current economic conditions can be reduced consid-
erably by providing survey respondents monetary incentives
for providing more accurate answers.

There is a large body of research in political science eval-
uating the effect of partisan bias on views on the economy
(e.g., Wlezien, Franklin, & Twiggs, 1997; Duch, Palmer, &
Anderson, 2000; Palmer & Duch, 2001; Bartels, 2002; Evans
& Andersen, 2006; Ladner & Wlezien, 2007; Stanig, 2013).
Our research is most closely related to three studies in particu-
lar. Gerber and Huber (2010) examine changes in evaluations
of the economy among partisans before and after the 2006
mid-term election, and they ﬁnd large differences across par-
tisans in how economic assessments are revised immediately
after the election. Gerber and Huber (2009) evaluate a longer
time series of county-level spending responses to Presiden-
tial elections based on the partisan leaning of the county,
and they ﬁnd evidence that counties leaning to the winning
Presidential candidate experience a boost in spending after
the election. However, McGrath (2016) extends the sample in
Gerber and Huber (2009) and examines the previous evidence
in more detail, and concludes that there is no evidence of a
differential partisan effect of Presidential election outcomes
on spending.

To the best of our knowledge, this study is the ﬁrst to show
both the dramatic rise in the effect of partisan bias on survey-
based measures of economic expectations over time, and that
this rise does not appear to affect household spending. In ad-
dition, to the best of our knowledge, this is the ﬁrst study to
evaluate the election of Donald Trump in this context. Much

D
o
w
n
o
a
d
e
d

f
r
o
m
h

t
t

:
/
/

d
i
r
e
c
t
.

i
t
.

e
d
u
/
r
e
s
t
/

a
r
t
i
c
e
–
p
d

f
/

1
0
5
3
4
9
3
2
0
9
0
0
0
4
/
r
e
s
t
_
a
_
0
1
0
5
6
p
d

b
y
g
u
e
s
t

o
n
0
7
S
e
p
e
m
b
e
r
2
0
2
3

PARTISAN BIAS, ECONOMIC EXPECTATIONS, AND HOUSEHOLD SPENDING

495

of the political science literature has focused on assessments
of current economic conditions, whereas our study focuses
on expectations of future conditions. Further, we use a vari-
ety of data sources on economic expectations and household
spending that we believe are new to the literature. We utilize
administrative data on auto sales and credit card spending
at the zip code-monthly level, which we believe is the most
disaggregated administrative spending data in the literature.
The Gallup dataset is signiﬁcantly larger and more compre-
hensive than data sets used in the past to measure economic
expectations; this data set allows us to utilize zip code by
month ﬁxed effects in order to estimate partisan bias more
precisely than has been done in the past. Two closely related
studies were written either contemporaneously or subsequent
to the original version of this study (Gillitzer & Prasad, 2018
and Benhabib & Spiegel, 2019). We will discuss these two
studies in more detail in section VI below.

The rest of this study proceeds as follows. In the next
section, we present the data, our methodology for estimat-
ing voting propensity in Presidential elections, and summary
statistics. Sections II and III show the shift in economic ex-
pectations among partisans from 2000 to 2016. Section IV
examines whether spending changes differentially for parti-
sans after elections. Section VI compares our results to other
research, and section VII concludes.

I. Data, Measurement, and Summary Statistics

A. Data

The two primary data sets used in our analysis are the
Thomson Reuters University of Michigan Survey of Con-
sumers and the Gallup Daily survey by Gallup, Inc. The
Michigan survey is a nationally representative survey of about
500 individuals every month. On average two-thirds of the
individuals surveyed in a month are interviewed a second
time after six months. The remaining third are only surveyed
once. We do not utilize the panel structure of the data, and
so the sample is a repeated cross-section in each month. The
individual level data from Michigan is available from 1978
to 2017. The Gallup Daily data cover about 1,000 individuals
every day, and are available from 2008 to 2017. The Gallup
Daily surveys ask questions related to political, economic,
and well-being topics. We use the Gallup data at the monthly
frequency, leading to approximately 30,000 individuals every
month.

We require two main variables for the purpose of this study:
a measure of an individual’s expectations of the economy go-
ing forward and a measure of an individual’s political partisan
afﬁliation. Both the Michigan and Gallup data contain de-
tailed questions on economic expectations, and we describe
these questions in more detail below. Measuring partisan af-
ﬁliation is more challenging. The Gallup data set, covering
2008 to 2017, contains a question asking the individual’s par-
tisan afﬁliation in almost all surveys. The Michigan survey,
however, has only asked partisan afﬁliation in certain months,

namely, June 1980, January 1984, July 1984, January 1985,
April 1985, May 1985, September through November 2006,
March 2008 through June 2009, March 2010 through Novem-
ber 2010, April 2012, May 2012, September through Novem-
ber 2012; June 2014, June 2015, June through October 2016,
and February and March of 2017.

We also use a number of data sets at the county and zip
code level. The ﬁrst is the share of individuals in the county
voting for the Republican candidate in each presidential elec-
tion, which we purchased from David Leip’s Atlas of U.S.
Presidential Elections website. We also use income and trans-
fers data from the Bureau of Economic Analysis. To measure
spending at the zip code and county level, we utilize two data
sets. First, we use new auto purchases from R.L. Polk. These
data are derived from new car registrations and are based
on the county where the buyer lives. The data are described
in detail in Mian and Suﬁ (2012), and are available from
1998 to 2017. Second, we use a previously unused data set
on credit card spending from Argus Information and Advi-
sory Services, a Verisk Analytics company. Argus specializes
in credit card and deposit benchmarking. The benchmark-
ing data is collected from individual issuers at the account
and transaction level, and then aggregated at the zip code
level to construct an monthly measure of spending through
credit cards. The Argus spending data was constructed in two
rounds. The ﬁrst data pull was in 2014 and covered the years
2006 through 2013. The second data pull was in Decem-
ber 2017 and covered the period from January 2014 through
September 2017. Both the Argus and Polk data are available
at the monthly frequency, which allows us to examine at a
relatively high frequency whether spending tracks changes
in economic expectations around Presidential elections.

B. Measuring Partisan Afﬁliation and Vote Propensity

The Gallup survey, which covers 2008 to 2017, asks the
following two questions to infer party afﬁliation: “In politics,
as of today, do you consider yourself a Republican, a Demo-
crat, or an Independent?” If the individual answers “Repub-
lican” or “Democrat,” no further question on party afﬁliation
is asked. If the individual responds “Independent,” another
party, or refuses, a follow-on question is asked: “As of today,
do you lean more to the Democratic Party or the Republican
Party?” The individual can answer “Democrat” or “Republi-
can” to this question. Our ﬁnal measure of partisan afﬁliation
is Republican if the individual answers either of these ques-
tions “Republican,” and Democrat if the individual answers
either of these questions “Democrat.” The remaining individ-
uals are classiﬁed as Independents. As we show in ﬁgure A1
of the online appendix, the fraction of Republicans (45%),
Democrats (45%), and Independents (10%) according to this
measure has been relatively constant from 2008 to 2017.

For the Michigan survey, in the months in which polit-
ical afﬁliation is asked, we infer political afﬁliation from
two questions. The ﬁrst is: “Generally speaking, do you
usually think of yourself as a Republican, a Democrat, an

D
o
w
n
o
a
d
e
d

f
r
o
m
h

t
t

:
/
/

d
i
r
e
c
t
.

i
t
.

e
d
u
/
r
e
s
t
/

a
r
t
i
c
e
–
p
d

f
/

1
0
5
3
4
9
3
2
0
9
0
0
0
4
/
r
e
s
t
_
a
_
0
1
0
5
6
p
d

b
y
g
u
e
s
t

o
n
0
7
S
e
p
e
m
b
e
r
2
0
2
3

496

THE REVIEW OF ECONOMICS AND STATISTICS

Independent, or what?” The second question is “Do you think
of yourself as closer to the Republican Party or to the Demo-
cratic Party?,” which is asked of people who say “Indepen-
dent” in response to the ﬁrst question. We classify individ-
uals as Republican if they answer either of these questions
“Republican,” and Democrat if they answer either of these
questions “Democrat.” Remaining individuals are classiﬁed
as Independent. In most of our analysis below, we exclude
independents.

C. Measuring Economic Expectations and Spending

in Survey Data

The Michigan Survey is widely cited in the ﬁnancial
press as a measure of consumer economic expectations. The
main reported results from the Michigan Survey are the in-
dex of consumer sentiment (ICS), the index of consumer
expectations (ICE), and index of current economic condi-
tions (CEC). The ﬁrst is a slightly adjusted average of the
latter two. Our main measure of consumer expectations is
the ICE. The ICE is a slightly adjusted average of answers to
the following three questions:

First, “Now looking ahead–do you think that a year from
now you (and your family living there) will be better off
ﬁnancially, or worse off, or just about the same as now?”
The answers are coded in the data as 1 for better off, 3 for
the same, and 5 for worse off. We refer to this as the “my
ﬁnancial situation, 1 year” question, which is coded in the
Michigan survey as PEXP.

The second question is: “Now turning to business condi-
tions in the country as a whole–do you think that during the
next twelve months we’ll have good times ﬁnancially, or bad
times, or what?” The answers are coded as 1 for good times,
2 for good times with qualiﬁcations, 3 for no opinion, 4 for
bad with qualiﬁcations, and 5 for bad times. We refer to this
question as the “Country business conditions, 12 months”
question, which is coded in the Michigan Survey as BUS12.
The third question is the one mentioned in the introduction:
‘Looking ahead, which would you say is more likely – that
in the country as a whole we’ll have continuous good times
during the next 5 years or so, or that we will have periods of
widespread unemployment or depression or what?” The an-
swers are coded exactly the same as the 12 months question.
We refer to this question as the “Country business conditions,
5 years” question, which is coded in the Michigan Survey as
BUS5.

The ICE is the following average of these three questions:

ICE = PEXP + BUS12 + BUS5

4.1134

+ 2.0.

For ease of interpretation, we rescale all four of these vari-
ables to be mean zero and standard deviation one for the
entire 2000 to 2017 sample. We also invert the ordering
so that higher numbers are associated with more optimistic
assessments.

There are ﬁve other questions from the Michigan Survey
we utilize in the analysis below. The Current Economic Con-
ditions index is a slightly adjusted average of the answer
to two different questions meant to capture how people feel
about the current economy. The ﬁrst is: “We are interested in
how people are getting along ﬁnancially these days. Would
you say that you (and your family living there) are better off or
worse off ﬁnancially than you were a year ago?” The second
is: “About the big things people buy for their homes–such as
furniture, a refrigerator, stove, television, and things like that.
Generally speaking, do you think now is a good time or a bad
time for people to buy major household items?” The latter
question is a component of the CEC, and it also serves as an
independent measure of household spending views which we
refer to as the “major household items” question.

The other household spending question relates to car pur-
chases. It is: “Speaking now of the automobile market – do
you think the next 12 months or so will be a good time or a
bad time to buy a vehicle, such as a car, pickup, van, or sport
utility vehicle?” We refer to this as the “car” question.

There is also a question regarding views on government
economic policy. This speciﬁc question is: “As to the eco-
nomic policy of the government – I mean steps taken to ﬁght
inﬂation or unemployment – would you say the government
is doing a good job, only fair, or a poor job?” We refer to this
as the “government economic policy” question. Finally, there
is a question focused on the income growth of the individ-
ual in particular: “During the next 12 months, do you expect
your income to be higher or lower than during the past year?”
As with the expectations variables, all ﬁve of these measures
are re-scaled to be mean zero and standard deviation one for
the entire sample. We also invert the ordering so that higher
numbers are associated with more positive assessments.

The main measure of economic expectations in the Gallup
data is the following question: “Right now, do you think that
economic conditions in this country, as a whole, are getting
better or getting worse?” The potential answers are “getting
better,” the “same,” or “getting worse.” We utilize two other
questions in the Gallup survey related to economic condi-
tions. One is a measure of current conditions: “How would
you rate economic conditions in this country today – as ex-
cellent, good, only fair, or poor?” The other is a measure of
employer job growth: “Now thinking more generally about
the company or business you work for, including all of its
employees. Based on what you know or have seen, would
you say that in general your company or employer is: hir-
ing new people and expanding the size of its workforce? Not
changing the size of its workforce? Or letting people go and
reducing the size of its workforce?” Once again, the measures
are rescaled to be mean zero and standard deviation one for
the entire sample, and we invert the ordering so that higher
numbers are associated with more positive assessments.

The Gallup survey also contains measures of household
spending, and we use two in particular. The ﬁrst is a measure
of nondurable household spending: “we’d like to ask you
about your spending yesterday, not counting the purchase

D
o
w
n
o
a
d
e
d

f
r
o
m
h

t
t

:
/
/

d
i
r
e
c
t
.

i
t
.

e
d
u
/
r
e
s
t
/

a
r
t
i
c
e
–
p
d

f
/

1
0
5
3
4
9
3
2
0
9
0
0
0
4
/
r
e
s
t
_
a
_
0
1
0
5
6
p
d

b
y
g
u
e
s
t

o
n
0
7
S
e
p
e
m
b
e
r
2
0
2
3

PARTISAN BIAS, ECONOMIC EXPECTATIONS, AND HOUSEHOLD SPENDING

497

FIGURE 1.—AVERAGE ECONOMIC EXPECTATIONS BY PARTISAN AFFILIATION, BY PRESIDENTIAL TERM

This ﬁgure presents the average economic expectations in the Gallup data set (left panel) and Michigan data set (right panel) by partisan afﬁliation and by Presidential term. Party afﬁliation is measured directly from
the individual’s response to the survey. We also report the absolute value of the difference between the two. For Presidential election years, November, December, and January are excluded.

of a home, motor vehicle, or your normal household bills.
How much money did you spend or charge yesterday on all
other types of purchases you may have made, such as at a
store, restaurant, gas station, online, or elsewhere?” The other
question we utilize is: “At this time, are you cutting back on
how much money you spend each week, or not?” For the
latter measure, we invert the ordering so that higher numbers
are associated with not cutting back, and we standardize the
variable to be mean zero and standard deviation one. Table
A1 in the appendix contains the summary statistics of the
sample.

II. Partisan Bias and Economic Expectations: Long Run

We begin with an analysis of partisan bias in economic
expectations over the long run. To show the increasing effect
of partisan bias on economic expectations, we compare the
average outlook of individuals based on their partisan af-
ﬁliation over time in ﬁgure 1. For this ﬁgure, we exclude
November, December, and January of Presidential election
years to focus on the long-run partisan bias as opposed to the
short-run effects right around elections.

The left panel utilizes the Gallup data. During the George
W. Bush administration, Democrats on average reported sig-
niﬁcantly lower economic expectations than Republicans,
with the absolute value of the difference being 0.4. During the
two Obama administrations, the ordering ﬂips, with Republi-
cans reporting more pessimistic economic expectations. The
absolute value of the difference increases substantially during
the two terms. During the Trump administration, the ordering
once again ﬂips, and the absolute value of the difference is
greater than one standard deviation.

The right panel shows similar results in the Michigan data,
although we are able to go back to the Carter and Rea-
gan administrations given data availability. The difference
in economic expectations between Democrats and Republi-
cans during the last year of the Carter administration is almost
zero. The difference is quite large during the Reagan admin-
istration. The difference becomes larger from the George W.
Bush administration through the second Obama administra-
tion, but it then jumps substantially during the Trump ad-
ministration. In the Michigan data since February 2017, Re-
publicans report economic expectations that are almost 1.5
standard deviations higher than Democrats.

Table 1 reports estimates of a regression version of this
ﬁgure. More speciﬁcally, the estimated γt from the following
equation are reported in table 1:

(cid:2)

αt +

Xit =

γt ∗ αt ∗ Repit

+ (cid:2)it ,

(1)

where αt are indicator variables for each Presidential adminis-
tration and Repit is the party afﬁliation of survey respondent
i during Presidential administration t. The estimates of γt
provide us the difference in economic expectations between
Republicans and Democrats during administration t.

In both the Gallup data (column 1) and the Michigan data
(column 4), the gap between Republicans and Democrats in
economic expectations has been growing substantially over
time. For the Gallup data where we have large samples, we
can reject the hypothesis that the size of the absolute differ-
ence is constant since the George W. Bush administration. In
the Michigan data, we do not have the same statistical power.
Nonetheless, we can reject the hypothesis that the absolute
value of the difference in economic expectations between

D
o
w
n
o
a
d
e
d

f
r
o
m
h

t
t

:
/
/

d
i
r
e
c
t
.

i
t
.

e
d
u
/
r
e
s
t
/

a
r
t
i
c
e
–
p
d

f
/

1
0
5
3
4
9
3
2
0
9
0
0
0
4
/
r
e
s
t
_
a
_
0
1
0
5
6
p
d

b
y
g
u
e
s
t

o
n
0
7
S
e
p
e
m
b
e
r
2
0
2
3

498

THE REVIEW OF ECONOMICS AND STATISTICS

TABLE 1.—PARTISAN BIAS IN ECONOMIC EXPECTATIONS, BY PRESIDENTIAL TERM

Gallup: economy getting better

Michigan: index of consumer expectations

(1)

(2)

(3)

(4)

(5)

0.364***
(0.004)
−0.708***
(0.005)
−0.886***
(0.005)
1.092***
(0.006)
1,057,280
0.196

0.000
0.000
0.000
None

0.373***
(0.005)
−0.700***
(0.005)
−0.871***
(0.005)
1.113***
(0.007)
1,057,280
0.230

0.380***
(0.006)
−0.709***
(0.006)
−0.872***
(0.006)
1.115***
(0.008)
1,057,280
0.381

0.000
0.000
0.000
County × month

0.000
0.000
0.000
ZIP × month

0.094
(0.070)
0.457***
(0.047)
0.452***
(0.031)
−0.487***
(0.025)
−0.561***
(0.033)
1.291***
(0.051)
16,002
0.134

0.373
0.074
0.000
None

0.482***
(0.061)
−0.527***
(0.049)
−0.582***
(0.061)
1.198***
(0.097)
14,006
0.532

0.553
0.479
0.000
County × month

Republican afﬁliation

× Carter

× Reagan

× Bush W 2

× Obama 1

× Obama 2

× Trump

Observations
R2
P values of F tests

Obama 1 + Bush W 2 = 0
Obama 2 − Obama 1 = 0
Trump + Obama 1 = 0

This table presents estimates of economic expectations by partisan afﬁliation by Presidential administration (t). Equation (1) from the text is the exact speciﬁcation. For Presidential election years, November,
December, and January are excluded. Oster (2019) test statistic for coefﬁcient on Republican × Trump comparing columns 1 and 3 (null β∗ = 0, Rmax = 1.5R): δ = 3.18. * p < 0.1, ** p < 0.05, *** p < 0.01. Heteroskedasticity-robust standard errors clustered at the county level are in parantheses. Republicans and Democrats was the same in the Trump ad- ministration and the previous administrations.2 Does the growth in partisan bias reﬂect the fact that Presi- dents increasingly cater to their base in terms of actual eco- nomic policy? One test of this hypothesis is to examine indi- viduals living in the same county or zip code. The underlying assumption is that the economic circumstances of individu- als living in the same county or zip code should be simi- larly affected by actions taken by the President. In columns 2 and 3 of table 1, we show that inclusion of county by month or zip by month ﬁxed effects has almost no effect on the partisan gap in economic expectations. For example, as shown in column 3, the inclusion of zip code by month ﬁxed effects doubles the R2, but has almost no effect on any of the estimates of partisan differences. Following the logic in Al- tonji, Elder, and Taber (2005) and Oster (2019), this suggests that the partisan bias we estimate is not due to omitted vari- able bias in exposure to differential economic policies due to who controls the White House. The inclusion of county by month ﬁxed effects in the Michigan data also has a minimal effect on the estimated coefﬁcients, despite boosting the R2 considerably.3 Formally, the Oster (2019) technique is implemented in comparing the estimated coefﬁcient on the Republican afﬁli- 2In the appendix, we estimate a univariate linear regression relating eco- nomic expectations to partisan afﬁliation. Figure A2 reports the R2 from each regression. The explanatory power of partisan afﬁliation has increased by four times from the George W. Bush administration to the Trump ad- ministration. 3The most detailed geographic measure in the Michigan data is county, and this information is only available from 2000 onward. ation X Trump indicator variable in columns 1 and 3 of table 1. The test is implemented under the assumptions that the maxi- mum R2 is 150% of the observed R2 in column 3, and that the null hypothesis is a true coefﬁcient of zero. The test statistic for δ is 3.18, which implies that unobservables would have to be more than three times as important as observables in order for the true coefﬁcient to be zero. III. Partisan Bias and Economic Expectations: Around Elections Partisan bias in economic expectations has been rising over time, but what is the effect of this bias on actual household spending? The long run analysis in section II is not well- suited to answer this question. Over the long run, household spending in more Republican versus Democrat areas may change for reasons completely unrelated to partisan bias. To identify the spending effect of shifts in economic expecta- tions due to partisan bias, we focus on changes in economic expectations right around Presidential elections. A. Shifts Around Elections Figure 2 presents the average economic expectations for Republicans and Democrats around the 2016 and 2008 Presi- dential elections. The results for the 2016 election are similar for both the Gallup and Michigan data. Prior to the election, there is almost no pretrend in economic expectations among Republicans or Democrats. From November 2016 to Jan- uary 2017, Republicans see a 1.5 standard deviation increase in their expectations, and Democrats see a 0.75 standard l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . e d u / r e s t / l a r t i c e - p d f / / / / 1 0 5 3 4 9 3 2 0 9 0 0 0 4 / r e s t _ a _ 0 1 0 5 6 p d . f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 PARTISAN BIAS, ECONOMIC EXPECTATIONS, AND HOUSEHOLD SPENDING 499 FIGURE 2.—ECONOMIC EXPECTATIONS AROUND THE 2008 AND 2016 ELECTIONS, BY PARTISAN AFFILIATION l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . e d u / r e s t / l a r t i c e - p d f / / / / 1 0 5 3 4 9 3 2 0 9 0 0 0 4 / r e s t _ a _ 0 1 0 5 6 p d . f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 This ﬁgure presents the average economic expectations in the Gallup data set (left panels) and Michigan data set (right panels) by partisan afﬁliation around the 2008 (bottom panels) and 2016 (top panels) elections. deviation decline in their expectations. We see a similar pat- tern for the 2008 election, with Democrats experiencing a relative increase in economic optimism following the elec- tion of Barack Obama. However, the relative shift in optimism is smaller and happens less quickly.4 This may in part be due to higher ex ante likelihood of an election win by Barack Obama in 2008 relative to the ex ante likelihood of an elec- tion win by Donald Trump 2016. The Trump victory in 2016 was less expected and therefore represents more of a surprise to individuals. How does the large relative shift in economic expectations based on partisanship around the 2008 and 2016 presidential elections compare to other elections? To answer this ques- tion, we estimate regressions for each year, where the year is centered on November. We call these “pseudoyears” as they run from June of one calendar year to May of the next calendar year (November being the sixth month of a “pseu- doyear”). For example, the 2008 pseudoyear runs from June of 2008 to May of 2009. For each pseudoyear y, we estimate the following regression (where we exclude the subscript y for ease of exposition): 4In ﬁgure A3 of the appendix, we plot the same ﬁgure for the 1984 and 2012 elections. There is almost no relative change in economic expectations around the 1984 or 2012 elections. Xim = m=May(cid:2) m=June αm ∗ dm + γ0 ∗ Repi m=May(cid:2) + m=June,m(cid:3)=Oct γm ∗ (dm ∗ Repi) + νim, (2) where dm is an indicator variable for month m, m = 0 is the “omitted” month which is October, αm represents month ﬁxed effects, and γm are the coefﬁcients of interest that measure the relative shift in economic expectations around the election for those who identify with the Republican Party. We have a set of coefﬁcients γm for each pseudoyear in the sample. The left panel of ﬁgure 3 shows estimates of these γm coefﬁcients for each pseudoyear for the Gallup sample, which runs from 2008 to 2017. The right panel of ﬁgure 3 shows the estimates using the Michigan data from 2008 and 2016, which are the only two years for which there is sufﬁcient data on partisan afﬁliation of respondents. For both panels, the elec- tion pseudoyear coefﬁcients are shown with a bold line with a different pattern and different markers. To help illustrate statistical signiﬁcance, we also plot the coefﬁcients of γm for the nonelection years, which we keep in gray thin lines with no markers. The coefﬁcients γm should be interpreted as the 500 THE REVIEW OF ECONOMICS AND STATISTICS FIGURE 3.—PARTISAN SHIFT IN ECONOMIC EXPECTATIONS AROUND PRESIDENTIAL ELECTIONS This ﬁgure presents coefﬁcient estimates of γm for each pseudo year y (June to May) from the following speciﬁcation: Xim = (cid:3)m=May m=June αm ∗ dm + γ0 ∗ Repim + (cid:3)m=May m=June,m(cid:3)=Oct γm ∗ (dm ∗ Repim ) + νim. The coefﬁcients plotted can be interpreted as the relative change in economic expectations for those afﬁliated with the Republican Party around each Presidential election. The thin gray lines plot γm for nonelection years. relative shift in consumer expectations among Republicans around October of each year. The gray lines can be thought of as “placebo” tests; they reﬂect the relative change in eco- nomic expectations among Republicans but in nonelection years. As both panels of ﬁgure 3 show, the size of the relative shift in economic expectations among Republicans in 2016 is unprecedented. In terms of magnitude, being afﬁliated with the Republican Party leads to a two standard devia- tion relative increase in economic expectations from October to December 2016. There is no evidence of a pretrend, and the relative optimism endures to May 2017. The results for 2008 and 2016 are similar for the Gallup and the Michigan data. To test statistical signiﬁcance in a regression framework, we estimate the following speciﬁcation: Xiym = αm + αm ∗ Repiym (cid:2) + αy + αy ∗ Repiym + [βy ∗ Posty] y=08,12,16 (cid:2) y=08,12,16 + [γy ∗ Posty ∗ Repiym] + (cid:2)iym, (3) where Xiym is the measure of economic expectations, αm are month of year indicator variables, αy are pseudoyear indi- cators (i.e., June to May), and Posty is an indicator variable for November to May of pseudoyear y. The coefﬁcients of interest are the γy for each election year. The coefﬁcients γy measure the differential change in outcome X during pseu- doyear y for Republicans in the six months after each election. We interact the Republican measure with both year indicator variables and month of year indicator variables to control for any relative patterns in seasonality or annual trends. The coefﬁcient estimates of βy and γy are reported in table 2. Economic magnitudes are easy to interpret as the left hand side variables all have a mean of zero and a standard deviation of one. As columns 1 through 3 show, there is a substantial relative shift in economic expectations for Republicans fol- lowing the 2008, 2012, and 2016 election using the Gallup data. In terms of magnitudes, the shift is largest for the 2016 election, followed by 2008, and then 2012. The inclusion of zip code by month ﬁxed effects has almost no effect on the coefﬁcient estimates, despite a doubling of the R2. The Oster (2019) test statistic when comparing the coefﬁcient estimate on Republican afﬁliation X Post 2016 election in columns 1 and 3 is 3.46, which implies that selection on unobservables relative to observables would have to be substantial for the true coefﬁcient to be equal to zero. Columns 4 and 5 report estimates from the Michigan data set. The sample sizes are much smaller, but the results are qualitatively similar. The effect for the 2016 election is larger in the Michigan data set than in the Gallup data set. Further, the 2008 and 2012 effects are closer in size. As before, in- clusion of county by month ﬁxed effects signiﬁcantly boosts the R2 of the regression, but the coefﬁcient estimates on par- tisanship are almost identical.5 5In table A2 of the online appendix, we explore changes in the answers to other questions from the Gallup and Michigan survey around elec- tions, including evaluation of current economic conditions and government l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . e d u / r e s t / l a r t i c e - p d f / / / / 1 0 5 3 4 9 3 2 0 9 0 0 0 4 / r e s t _ a _ 0 1 0 5 6 p d . f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 PARTISAN BIAS, ECONOMIC EXPECTATIONS, AND HOUSEHOLD SPENDING 501 TABLE 2.—PARTISAN BIAS IN ECONOMIC EXPECTATIONS AROUND PRESIDENTIAL ELECTIONS Gallup: economy getting better Michigan: index of consumer expectations Post-2008 election Post-2012 election Post-2016 election Republican afﬁliation × Post-2008 election × Post-2012 election × Post-2016 election Observations R2 FE (1) 0.140*** (0.007) 0.039*** (0.008) −0.397*** (0.009) −0.353*** (0.009) −0.062*** (0.010) 1.241*** (0.012) 1,020,159 0.176 None (2) 0.141*** (0.007) 0.042*** (0.008) −0.401*** (0.010) −0.355*** (0.010) −0.063*** (0.011) 1.242*** (0.012) 1,020,159 0.210 County × month (3) 0.133*** (0.009) 0.030** (0.010) −0.415*** (0.011) −0.358*** (0.012) −0.054*** (0.013) 1.247*** (0.015) 1,020,159 0.370 ZIP × month (4) −0.012 (0.058) 0.103 (0.092) −0.816*** (0.076) −0.246** (0.085) −0.158 (0.135) 2.025*** (0.107) 15,789 0.156 None (5) −0.054 (0.109) −0.015 (0.164) −0.778*** (0.140) −0.303 (0.168) −0.131 (0.233) 1.948*** (0.205) 15,789 0.549 County × month This table presents estimates of how economic expectations change differentially around Presidential Elections for individuals based on their party afﬁliation. Equation (3) from the text is the exact speciﬁcation. Oster (2019) test statistic for coefﬁcient on Republican × Post 2016 election comparing columns 1 and 3 (null β∗ = 0, Rmax = 1.5R): δ = 3.18. * p < 0.1, ** p < 0.05, *** p < 0.01. Heteroskedasticity-robust standard errors clustered at the county level are in parantheses. TABLE 3.—PARTISAN BIAS AND SURVEY MEASURES OF SPENDING AROUND ELECTIONS Post-2008 election Post-2012 election Post-2016 election Republican afﬁliation × Post-2008 election × Post-2012 election × Post-2016 election Observations R2 Log spending yesterday −0.122*** (0.011) 0.092*** (0.011) −0.016 (0.010) 0.007 (0.017) −0.033* (0.016) 0.058*** (0.015) 968,295 0.018 Gallup Survey Michigan Survey Log spending yesterday, with ZIP × month FE Not cutting back spending Not cutting back spending, with ZIP × month FE Good time to buy a car Good time to buy major HH items −0.110*** (0.015) 0.091*** (0.014) −0.016 (0.013) 0.002 (0.022) −0.017 (0.020) 0.062*** (0.018) 968,295 0.263 0.018 (0.017) −0.150*** (0.017) −0.137*** (0.021) 0.357*** (0.022) 299,393 0.028 0.041 (0.031) −0.166*** (0.031) −0.139*** (0.040) 0.390*** (0.043) 299,393 0.504 0.136* (0.065) 0.048 (0.094) −0.230** (0.082) −0.040 (0.096) −0.141 (0.143) 0.319** (0.120) 15,298 0.035 −0.089 (0.064) 0.009 (0.085) −0.243** (0.075) −0.155 (0.092) −0.027 (0.140) 0.190 (0.113) 15,251 0.076 This table presents estimates of how spending as measured in survey questions changes differentially around Presidential Elections for individuals based on their party afﬁliation. Equation (3) from the text is the exact speciﬁcation. * p < 0.1, ** p < 0.05, *** p < 0.01. Heteroskedasticity-robust standard errors clustered at the county level in parentheses. B. Actual Economic Conditions? One hypothesis is that a partisan truly is better off econom- ically when the White House is controlled by the party she favors. In this case, it would not be accurate to call the relative change in economic optimism around an election a partisan “bias.” The results above using zip code by month ﬁxed ef- fects are difﬁcult to reconcile with this alternative hypothesis. The coefﬁcient estimates of partisan bias are almost identical with and without the inclusion of zip code by month ﬁxed effects. Under the relatively weak assumption that changes economic policy. We also split out the three components of the index of consumer expectations from the Michigan survey. In table A3 of the online appendix, we exploit the panel dimension in the Michigan Survey by iso- lating the sample to individuals that are in the data set more than once, and including individual ﬁxed effects. in the economy will affect individuals living in the same zip code similarly, it is difﬁcult to argue that the opposite reac- tions of Republicans and Democrats living in the same zip code are due to actual economic conditions changing based on party afﬁliation. This is supported by the Oster (2019) test statistics reported in tables 2 and 3. We also evaluate this alternative hypothesis in table A4 of the appendix. More speciﬁcally, table A4 reports estimates of equation (3) where the left hand side variable is the employer hiring measure. The results show strong partisan bias in the answer to this question, especially after the 2016 election. Furthermore, the results are almost identical when including county by month or even zip code by month ﬁxed effects. This implies that after the election of Donald Trump in 2016, a Democrat reports his employer is less likely to hire work- ers while a Republican living in the same zip code reports his l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . e d u / r e s t / l a r t i c e - p d f / / / / 1 0 5 3 4 9 3 2 0 9 0 0 0 4 / r e s t _ a _ 0 1 0 5 6 p d . f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 502 THE REVIEW OF ECONOMICS AND STATISTICS FIGURE 4.—SURVEY MEASURES OF SPENDING AROUND ELECTIONS l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . e d u / r e s t / l a r t i c e - p d f / / / / 1 0 5 3 4 9 3 2 0 9 0 0 0 4 / r e s t _ a _ 0 1 0 5 6 p d . f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 This ﬁgure presents coefﬁcient estimates of γm for each pseudoyear y (June to May) for the exact same speciﬁcation described in ﬁgure 3, but replacing the left hand side variable with answers to questions on whether it a good time to buy major household items or a car in Michigan survey (top panels) and questions on past and future spending behaviors in Gallup survey (bottom panels). The thin gray lines plot γm for nonelection years. employer is boosting hiring. Under the assumption that peo- ple living in the same zip code tend to work for similar in- dustries or employers, the Democrat and Republican answers cannot both be correct. In the online appendix (ﬁgures A4 and A5), we examine county and state level data on transfers, tax rates, and personal income growth around the 2000 and 2008 elections. We ﬁnd little evidence that counties or states supporting the winning candidate see a disproportionate im- provement in any of these measures. IV. Does Partisan Bias Affect Household Spending? A. Survey Data Evidence We begin our investigation of the effect of partisan bias on household spending by exploring answers to questions on spending in the Gallup and Michigan surveys. Figure 4 exam- ines the answers to these questions by presenting coefﬁcient estimates from equation (2), where we use the spending ques- tions in the Gallup and Michigan survey as the left hand side variable. The top two panels of ﬁgure 4 report results for the Michi- gan questions on whether it is a good time to buy major household items or a car. The Michigan survey shows some evidence that Republicans witnessed a relative change in answers to spending questions after Presidential elections. The bottom two panels examine Gallup measures of house- hold spending. Here we see a stronger effect, especially for the 2016 election. For both the spending yesterday question and the question of whether individuals are cutting back spending, Republicans see a relative increase in reported spending. In table 3, we examine the corresponding regressions for ﬁgure 4. Columns 1 and 2 evaluate the Gallup question on total spending yesterday. Consistent with ﬁgure 4, Republi- cans see a relative increase in their reported spending after the election of Donald Trump in 2016 of about 6%. How- ever, there is no effect after the 2008 election. Recall that there was a sizable relative decline in economic expectations for Republicans after the 2008 election; there is no corre- sponding relative decline in spending. For the cutting back spending question, we see large effects after both the 2012 PARTISAN BIAS, ECONOMIC EXPECTATIONS, AND HOUSEHOLD SPENDING 503 FIGURE 5.—AUTO PURCHASES AND CREDIT CARD SPENDING AROUND 2008 AND 2016 ELECTIONS l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . e d u / r e s t / l a r t i c e - p d f / This ﬁgure presents spending in counties around Presidential elections. To construct the plots below, we ﬁrst index auto sales and credit card spending in a county to be 100 in October prior to the election, and then estimate the following regression for each month around the election: spendingindexedcm = αm + γm ∗ RepVoteSharec + νcm. Where RepVoteSharec is the two-party share voting for the Republican candidate in the county. The plotted lines below represent predicted values for RepVoteSharec (Republican county) given this estimation. = 0 (Democratic county) and RepVoteSharec = 1 / / / 1 0 5 3 4 9 3 2 0 9 0 0 0 4 / r e s t _ a _ 0 1 0 5 6 p d . f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 and 2016 election.6 For both spending measures, the inclu- sion of zip code by month ﬁxed effects does not affect the post 2016 election results. The last two columns of table 3 examine the Michigan survey questions. The statistical precision is lower in these speciﬁcations given the smaller sample sizes. There is some evidence that Republicans report in surveys higher spending after the 2016 election and lower spending after the 2008 and 2012 election. B. Administrative Data Evidence A drawback to these survey questions is that they do not capture actual household spending. One obvious concern is that the same partisan bias that affects measures of economic expectations could also inﬂuence spending reported in a survey. To measure the response of actual spending, we turn to data on auto purchases and credit card spending at the county level and zip code level. Moving from the individual level to the broader geography level requires us to construct geo- graphic measures of partisan afﬁliation. At the county level, we measure partisanship of the county using the total votes for the Republican candidate in the county divided by the total votes for either the Republican or Democrat, which we refer to as the two-party vote share for the Republican. We measure this for the nearest election for each county. We focus on new auto purchases and credit card spending around the 2016 and 2008 elections in ﬁgure 5. To create this ﬁgure, we ﬁrst index the spending measure to be 100 in October of the Presidential election year in question for each county. We then estimate for each month the following county-level cross-sectional regression: spendingindexedcm = αm + γm ∗ RepVoteSharec + νcm. Observations in this regression are weighted by total pop- ulation of the county.7 Using the estimates from this 6The cutting back spending question was ﬁrst asked in 2009, so we do not have the estimate for the 2008 election. 7Regressions based on geographical areas are always weighted by a geo- graphical area’s population given that there are more observations observed 504 THE REVIEW OF ECONOMICS AND STATISTICS FIGURE 6.—REPUBLICAN VOTE PROPENSITY, AUTO PURCHASES, AND CREDIT CARD SPENDING This ﬁgure presents coefﬁcient estimates of γm for each pseudo year y (June to May) from the following speciﬁcation: Ln(Scm ) = (cid:3)m=May m=June αm ∗ dm + γ0 ∗ RepVoteSharec + (cid:3)m=May m=June,m(cid:3)=Oct γm ∗ (dm ∗ RepVoteSharec ) + νcm. The coefﬁcients plotted can be interpreted as the relative change in spending for those counties most strongly supporting the Republican candidate around each Presidential election. The thin gray lines plot γm for nonelection years, where RepVoteSharec is based on nearest election year. speciﬁcation, we predict auto sales or credit card spending in = 0 and each month around the election for RepVoteSharec = 1. In this manner, we estimate the evolu- RepVoteSharec tion of spending in a county where all voters vote for Demo- crat (“Democratic counties”) and where all voters vote for the Republican (“Republican counties”). As ﬁgure 5 shows, there is little evidence of a larger rise in auto purchases or credit card spending in counties that voted for Donald Trump in 2016. While there appears to be a larger Christmas shopping bump in November 2016 for Republican counties, the size of the November bump is almost identical in November 2015, which suggests that Republican areas con- sistently spend more in November, a result we conﬁrm below. This null result is in stark contrast to the strong rise in opti- mism on the economy among those most likely to vote for Donald Trump, which is shown above in ﬁgure 3 in section III. The strong relative rise in optimism among those living in more Republican counties does not appear to translate into higher auto purchases or credit card spending. We also do not see a noticeable relative change in auto purchases or credit card spending after the 2008 election, despite the large rela- tive decline in economic optimism among Republicans. In ﬁgure 6, we estimate the county-level version of equa- tion (2) from section III above. More speciﬁcally, for each pseudoyear y, we estimate the following regression: in geographic areas with more individuals, and as a result the average error term across geographical areas is highly heteroskedastic. In particular, the assumption is that the variance of the error term is larger in geographic areas with fewer people. Following Solon, Haider, and Wooldridge (2015) we ex- amine the squared predicted residuals from unweighted speciﬁcations and ﬁnd that indeed geographical areas with a smaller population have larger squared residuals. Ln(Scm) = m=May(cid:2) m=June αm ∗ dm + γ0 ∗ RepVoteSharec m=May(cid:2) + m=June,m(cid:3)=Oct γm ∗ (dm ∗ RepVoteSharec) + νcm, (4) where dm is an indicator variable for month m, m = 0 is the “omitted” month which is October, αm represents month ﬁxed effects, and γm are the coefﬁcients of interest that measure the relative shift in log spending (Ln(S)) around the election for counties with a higher vote share for the Republican can- didate (RepVoteShare). Observations in these regressions are weighted by total population in the county. We estimate equa- tion (4) for both auto purchases and credit card spending. We only have data for credit card spending from 2006 onward, and so the analysis for credit card spending is focused only on the 2008, 2012, and 2016 elections. There is little evidence in ﬁgure 6 of a sharp change in spending patterns for Republican-leaning counties around any of the elections. If anything, there may be some evidence that auto spending actually rose more for Republican-leaning counties after the 2008 election. For credit card spending, Republican-leaning counties tend to see a stronger spike in spending every November and December, but there is no evi- dence that 2008 or 2016 were special relative to the nonelec- tion years. In table 4, we formally test the statistical signiﬁcance of the patterns shown in ﬁgures 5 and 6. More speciﬁcally, we estimate the following speciﬁcation: l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . e d u / r e s t / l a r t i c e - p d f / / / / 1 0 5 3 4 9 3 2 0 9 0 0 0 4 / r e s t _ a _ 0 1 0 5 6 p d . f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 PARTISAN BIAS, ECONOMIC EXPECTATIONS, AND HOUSEHOLD SPENDING 505 TABLE 4.—PARTISAN BIAS AND ADMINISTRATIVE MEASURES OF SPENDING: COUNTY LEVEL TABLE 5.—PARTISAN BIAS AND ADMINISTRATIVE MEASURES OF SPENDING: ZIP LEVEL Economy getting better (1) 0.131*** (0.039) 0.116*** (0.030) −0.410*** (0.031) −0.319*** (0.072) −0.165** (0.057) 1.284*** (0.056) 213,593 0.254 Log auto sales (2) −0.103 (0.322) −0.040 (0.312) −0.239 (0.259) 0.082 (0.247) 0.013 (0.212) 0.101 (0.556) 0.024 (0.517) 0.022 (0.460) −0.015 (0.422) 0.006 (0.345) 645,626 0.279 Log credit card spending (3) −0.067 (0.250) 0.013 (0.244) 0.035 (0.199) 0.018 (0.447) 0.011 (0.419) −0.001 (0.324) 401,096 0.354 Post-2000 election Post-2004 election Post-2008 election Post-2012 election Post-2016 election Republican vote share × Post-2000 election × Post-2004 election × Post-2008 election × Post-2012 election × Post-2016 election Observations R2 This table presents estimates of the differential response of household spending for Presidential elections based on the county-level vote share for the Republican candidate in the nearest Presidential election. Equation (5) in the text is the exact speciﬁcation. Counties in the regressions are weighted by their total population as of 2008. * p < 0.1, ** p < 0.05, *** p < 0.01. Heteroskedasticity-robust standard errors in parentheses. Ln(Scym) = αm + αm ∗ RepVoteSharecym + αy + αy ∗ RepVoteSharecym (cid:2) [βy ∗ Posty] + + y=00,04,08,12,16 (cid:2) y=00,04,08,12,16 [γy ∗ Posty ∗ RepVoteSharecym] + (cid:2)cym, (5) where Scym is either new auto purchases or credit card spend- ing, αm are month of year indicator variables, αy are pseu- doyear indicators (i.e., June to May), and Posty is an indicator variable for November to May of pseudoyear y. As before, the coefﬁcients of interest are the γy for each election year. The coefﬁcients γy measure the differential change in log spend- ing after the election for counties that more heavily favored the Republican candidate in the election in question. Obser- vations in these regressions are weighted by total population in the county. Before examining the spending measures, we begin in column 1 by estimating equation (5) using our measure of economic expectations from the Gallup data set averaged at the county-month level. We want to ensure that aggre- gating to the county-month level from the individual-month level does not reduce power signiﬁcantly when it comes to relative movements in economic expectations. As column 1 Economy getting better (1) 0.121*** (0.011) 0.010 (0.010) −0.451*** (0.013) −0.350*** (0.018) −0.036* (0.016) 1.294*** (0.020) 761,920 0.177 Log auto sales (2) −0.271*** (0.007) 0.048*** (0.007) −0.030*** (0.008) 0.005 (0.014) −0.014 (0.013) −0.008 (0.014) 1,247,741 0.063 Log credit card spending (3) −0.068*** (0.011) 0.037** (0.012) 0.051*** (0.011) 0.012 (0.017) −0.007 (0.018) −0.027 (0.018) 1,160,055 0.050 Post-2008 election Post-2012 election Post-2016 election Republican afﬁliation × Post-2008 election × Post-2012 election × Post-2016 election Observations R2 This table presents estimates of the differential response of household spending for Presidential elections based on the zip-level average Republican Party afﬁliation. Equation (5) in the text is the exact speciﬁcation. Zip codes in the regressions are weighted by the number of respondents in the Gallup data set. * p < 0.1, ** p < 0.05, *** p < 0.01. Heteroskedasticity-robust standard errors clustered at the county level in parentheses. shows, the relative shift in economic optimism using county- month-level data with Republican vote share as the measure of partisanship leads to similar coefﬁcient estimates as seen in the individual-month-level data (compare with column 1 of table 2). Yet despite this large relative shift in economic expecta- tions based on partisan afﬁliation in Republican leaning coun- ties, we see no relative change in auto purchases or credit card spending in columns 2 and 3. The evidence does not support the view that changes in expectations driven by who wins the White House affects actual spending. In table 5, we estimate equation (5) at the zip code–month level. We do not have zip code–level vote shares; as a result, we use the Gallup data to measure partisanship at the zip- code level. These data are available only after 2007. For ev- ery year, we measure a zip code’s partisan leaning using the fraction of individuals afﬁliated with the Republican Party in the Gallup data divided by the total number of respon- dents in the Gallup data afﬁliating with either the Republi- can or Democratic Party. Zip codes in these regressions are weighted by the total number of respondents in the Gallup data. The results at the zip code level are broadly similar. First, there are similar relative shifts in economic optimism around elections based on the partisan leaning of the zip code. Sec- ond, there is no noticeable effect on auto purchases or credit card spending. For example, a zip code in which only Re- publicans live witnesses a 1.3 standard deviation increase in economic optimism after the election of Donald Trump in 2016, but if anything new auto purchases and credit card spending are reduced in the six months after the election. The zip code level results also provide the most power for an assessment of how precise the null effect on spending is. A one standard deviation increase in the Republican share l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . e d u / r e s t / l a r t i c e - p d f / / / / 1 0 5 3 4 9 3 2 0 9 0 0 0 4 / r e s t _ a _ 0 1 0 5 6 p d . f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 506 THE REVIEW OF ECONOMICS AND STATISTICS of a zip code (0.39) leads to a sizable 0.5 standard deviation shift in optimism after the 2016 Presidential election. How large an effect on spending can we rule out? To take an extreme calculation, we use the largest coefﬁcient for the Re- publican * post 2016 election indicator variable that is within the 95% conﬁdence interval from the estimation. The largest coefﬁcient within the conﬁdence interval is 0.021 and 0.004 for auto sales and credit card spending, respectively. The es- timates imply that we can be conﬁdent with 95% probability that the effect of a one standard deviation increase in the Re- publican share of a zip code (0.39) on the change from 2016 to 2017 in log auto purchases and log credit card spending is smaller than 0.71% and 0.44%, respectively. To put this into perspective, the standard deviation of the change in log auto sales and log credit card spending from 2016 to 2017 across zip codes is 21.1% and 4.8%, respectively. Zip codes that have a higher Republican share experience a large increase in economic expectations after the 2016 election, but even the largest estimate on spending within the 95% conﬁdence interval is estimated to be close to zero. V. Discussion of Results A. Why the Null Result on Spending? The macroeconomics literature focusing on economic ex- pectations questions in the Michigan Survey generally uses the answers to these questions as a measure of the expected income growth of the individual answering the question. For example, in Barsky and Sims (2012), innovations to answers of the “Country business conditions, 5 years” question re- ﬂect innovations to an individual’s perceived growth rate of the economy. As a result, this literature argues that changes in the answers to these questions should be expected to pre- dict consumption growth, even if the changes in the answers to these questions are driven by “sentiment” or noise that is unrelated to the actual income growth of the individual. The cross-sectional approach presented here shows that partisan afﬁliation of an individual has a large effect on the answers to these questions in the aftermath of Presidential elections. This large effect appears to be largely orthogonal to actual future income realizations, as shown in section IIIB. While individuals afﬁliated with the winner of the Presiden- tial election display more optimism in their answers to sur- vey questions, they do not appear to change their household spending, at least according to administrative measures. Why might this be? One potential explanation is that indi- viduals focus on the expected income growth at the national level instead of at the individual level when answering the survey questions. It could be that partisans are more opti- mistic about the overall economy when their preferred can- didate wins, but they are not more optimistic about their own economic situation. This explanation can be partially assessed using more in- dividualized questions in the Gallup and Michigan Surveys. For example, the Michigan survey includes questions con- cerning an individual’s own ﬁnancial situation and an indi- vidual’s own income growth. The Gallup survey asks a ques- tion about an individual’s own employer’s hiring decisions. In the cross-section of respondents taken in any given time period, the answers to the national questions and the answers to the individualized questions are very highly correlated, a fact shown in table A5 in the online appendix. This suggests that an individual’s view on the national economic situation is highly correlated with their view on their personal economic situation. Furthermore, as shown in tables A2 and A4 in the appendix, there is also evidence of substantial partisan bias when using the more individualized questions. In the Gallup survey, the results using questions about the expected hiring behavior of an individual’s own employer shows substantial partisan bias. The results for the Michigan survey are somewhat weaker us- ing the more individualized questions, but they are still large and statistically signiﬁcant for the 2016 election in particu- lar. These results suggest that partisan bias applies strongly even to an individual’s expectations of their own economic situation, and yet there is scant evidence that this affects ad- ministrative measures of spending. Another potential reason for the null result on administra- tive spending is a wedge between desired and actual spending by individuals. This could occur, for example, if borrowing constraints are important. While this is a possibility, recall that there is a decline in economic expectations for those sup- porting the losing candidate in Presidential elections, and we do not see a relative decline in spending for this group. It is dif- ﬁcult for borrowing constraints to explain why those becom- ing more pessimistic do not decrease spending—borrowing constraints do not prevent an individual from reducing pur- chases. In fact, Democrats after the 2016 election are more likely to tell Gallup that they will cut back spending (see col- umn 3 of table 3), and yet we do not see actual evidence of a cutback in spending in the administrative data. Furthermore, table A6 in the online appendix repeats the analysis of table 5 on the sample of zip codes that have an average adjusted gross income in the top quartile of all zip codes as of 2010. These high income zip codes are less likely to face severe borrowing constraints, and yet the core results are almost identical. B. An Alternative Setting: The 2006 to 2007 Decline in House Prices The fact that shifts in economic expectations driven by partisan bias do not seem to affect administrative measures of spending raises the question: Do shifts in economic ex- pectations as measured in surveys ever correlate with actual household spending? Perhaps these shifts in expectations are always random noise with little relevance for actual economic outcomes? We already have evidence from Barsky and Sims (2012) that “unexplained movements in the responses to forward- looking questions from the Michigan Survey of Consumers l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . e d u / r e s t / l a r t i c e - p d f / / / / 1 0 5 3 4 9 3 2 0 9 0 0 0 4 / r e s t _ a _ 0 1 0 5 6 p d . f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 PARTISAN BIAS, ECONOMIC EXPECTATIONS, AND HOUSEHOLD SPENDING 507 FIGURE 7.—COMPARING 2016 ELECTION TO 2007 DECLINE IN HOUSE PRICES This ﬁgure presents scatter-plots of county-level data relating the change in economic expectations and auto sales to the decline in house prices from 2006 to 2007. Only counties with at least 5 surveyed respondents in the pre- and post-shock period are included, and counties are weighted by the total number of individuals surveyed. have powerful predictive implications for the future paths of macroeconomic variables.” In aggregate analysis, move- ments in economic expectations as measured in the Michigan survey are related to future income and consumption growth. But perhaps the cross-sectional variation in survey responses is rarely if ever correlated with cross-sectional changes in household spending? To examine this question, we focus on an alternative eco- nomic shock: the initial decline in aggregate house prices from 2006 to 2007 in the United States. This shock offers a promising source of cross-sectional variation across U.S. counties in exposure to a fundamental shock, and it there- fore serves as a useful counter-example where we should expect to ﬁnd an effect on both economic expectations and household spending. More speciﬁcally, there is a great deal of variation across U.S. counties in the degree to which house prices fell during the 2006 to 2009 period (e.g., Mian, Rao, & Suﬁ, 2013). Also, total employment declined more in counties seeing a sharper decline in house prices (e.g., Mian & Suﬁ, 2014), and there are long-lasting effects on income for the individuals living in these counties (Yagan, 2019). Finally, there is a strong positive correlation across counties between house price growth from 2006 to 2007 and house price growth from 2007 to 2008. In hindsight, we know indi- viduals living in counties where house prices began to fall in 2007 experienced a sharp decline in subsequent income and employment growth. So how did their expectations react? We cannot measure the decline in house prices for a given individual in the Michi- gan survey, and so we conduct all of the analysis in this section at the county level. We measure economic expectations in the pre-period from 2004 to 2006. This was a period of economic expansion when house prices rose nationally. Beginning in 2007, house prices began to fall in the United States. Further, they began to fall quite dramatically in some counties. We measure economic expectations in the post period using sur- vey responses of a county in 2007. We purposefully do not include 2008 because it was a year of dramatic national eco- nomic events and it was the year that Barack Obama became President. Both of these factors would likely affect economic expectations for reasons unrelated to house price growth. As a result, 2007 is a clean year for measuring cross-sectional variation across counties in exposure to house price declines during the Great Recession. Given the smaller samples in the Michigan survey, we only keep counties that have at least ﬁve individuals surveyed both in the pre- and post-period. As the left panel of ﬁgure 7 shows, counties seeing a rel- ative decline in house prices also report a relative decline in the index of consumer expectations. There is substantial variation across counties in house price growth from 2006 to 2007, with some counties seeing declines of 20 to 30 percent. Individuals living in those counties report a more pessimistic economic outlook. As already mentioned, these individuals did in fact experience a relatively worse recession after 2007. In this case, survey respondents changed their economic ex- pectations in a predictable way given the fundamental shock they received. Furthermore, as the right panel shows, auto purchase growth from 2006 to 2007 in a county is strongly correlated with house price growth from 2006 to 2007 in a county. So in the case of the house price growth shock, we see that vari- ation across counties in a fundamental shock to economic prospects is correlated with the change in economic expecta- tions in the county. And this variation is also correlated with actual spending. In table 6, we show coefﬁcients from univariate county- level regressions to conﬁrm the robustness of the patterns shown in ﬁgure 7. In the regressions, we keep all counties where we have at least one survey respondent in the pre- and post-periods. We weight each county in all regressions with l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . e d u / r e s t / l a r t i c e - p d f / / / / 1 0 5 3 4 9 3 2 0 9 0 0 0 4 / r e s t _ a _ 0 1 0 5 6 p d . f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 508 THE REVIEW OF ECONOMICS AND STATISTICS TABLE 6.—AN ALTERNATIVE SHOCK: HOUSE PRICE GROWTH FROM 2006 TO 2007 (cid:3) ICE 04–06 to 07 (1) 1.228*** (0.290) 0.035 (0.025) 714 0.025 (cid:3) Major HH items 04–06 to 07 (2) (cid:3) Car 04–06 to 07 (3) Auto sales growth, 06 to 07 (4) 1.169*** (0.258) 0.025 (0.026) 703 0.025 0.461 (0.290) 0.029 (0.024) 708 0.004 0.592*** (0.045) 0.005 (0.004) 714 0.360 Credit card spending growth, 06 to 07 (5) 0.190*** (0.039) 0.049*** (0.004) 714 0.060 House price growth, 06 to 07 Constant Observations R2 This table presents county-level regressions relating house price growth from 2006 to 2007 to the change in economic expectations and spending. In column 2, we focus on responses in the Michigan survey to the question of whether now is a good time to buy a major household item, and in column 3 we focus on the question of whether now is a good time to buy a car. All speciﬁcations are weighted by the number of respondents to the Michigan survey in the county, which is highly correlated with the total population of the county. * p < 0.1, ** p < 0.05, *** p < 0.01. Heteroskedasticity-robust standard errors in parentheses. the number of survey respondents to the Michigan survey in the county. As column 1 shows, the change in economic expectations and house price growth in a county from 2006 to 2007 are positively correlated. Columns 2 through 5 show that all of our measures of household spending are also correlated with the underlying house price growth shock. When there is a true shock to economic fundamentals, economic expectations and actual household spending react as would be predicted in most economic models. C. Partisan Cheerleading So what is different about changes in economic expecta- tions driven by partisan bias? We believe the evidence shown in this study is most consistent with the idea that answers to questions on economic expectations that reﬂect partisan bias are driven mostly by partisan cheerleading as opposed to a se- rious assessment of future income growth. This is consistent with the lessons from political science and social psychology as illustrated by Iyengar et al. (2012); Mason (2013); and Ma- son (2015). For example, Mason (2015) writes, “a partisan behaves more like a sports fan than like a banker choosing an investment . . . the connection between partisan and party is an emotional and social one, as well as a logical one.” Indi- viduals feel elation after their “team” wins the White House. They report in surveys that the economy will improve. How- ever, given the lack of a spending response, the answers to survey questions appear to reﬂect cheerleading rather than a true shift in actual economic expectations. Two recent studies are relevant for understanding the fail- ure of shifts in economic expectations to affect administrative measures of spending. Both Bullock et al. (2015) and Prior et al. (2015) ﬁnd evidence that partisan bias in views on cur- rent economic conditions can be reduced considerably by providing survey respondents monetary incentives for pro- viding more accurate answers. Prior et al. (2015) conclude based on this ﬁnding that “many partisans interpret factual questions about economic conditions as opinion questions, unless motivated to see them otherwise. Typical survey con- ditions thus reveal a mix of what partisans know about the economy, and what they would like to be true.” It may be the case that monetary incentives would yield more accurate answers to questions on economic expectations that would perhaps more strongly correlate with current spending. We view this as a fruitful avenue for future research. VI. Comparison with Recent Research In a study made public subsequent to the original version of this study, Benhabib and Spiegel (2019) use an alternative political measure to capture changes in economic expecta- tions related to political events. In particular, their study uti- lizes state-level data from 2006 to 2016, and it constructs a variable for each state-quarter which is the fraction of U.S. Congressional delegates from the state that is from the same party as the sitting President, which the authors call congpres. The primary measure of economic expectations in their study is the country business conditions in 5 years question from the Michigan survey (BUS5). In appendix section A2, we discuss the Benhabib and Spiegel (2019) in more detail, and we show that the congpres instrument is not a statistically ro- bust predictor of the change in economic expectations around elections. This makes it difﬁcult to compare the results of this study with those in Benhabib and Spiegel (2019). A contemporaneous study by Gillitzer and Prasad (2018) examines how shifts in economic expectations due to Fed- eral elections in Australia affect household spending. They also ﬁnd large shifts in economic expectations around Federal elections based on the party supported by the individual in the survey (see in particular their ﬁgure 3). They ﬁnd an effect of shifts in economic expectations around elections on survey measures of spending on automobiles or major household items (see in particular their ﬁgures 7 and 8). Their results on actual spending, however, are more mixed. The short-run evidence they ﬁnd using actual auto purchase data is similar to the ﬁndings presented in this study. In particular, for both Australian elections, there is no relative difference in the evo- lution of auto sales from the two quarters before the election to two quarters after the election based on the vote share of the postal code.8 As in our analysis, Gillitzer and Prasad (2018) 8See in particular their ﬁgure 9. Gillitzer and Prasad (2018) do not present regression estimates and statistical signiﬁcance for the estimates in their ﬁgure 9, but based on the ﬁgure there does not appear to be a short-run effect from two quarters before the election to two quarters after the election. l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . e d u / r e s t / l a r t i c e - p d f / / / / 1 0 5 3 4 9 3 2 0 9 0 0 0 4 / r e s t _ a _ 0 1 0 5 6 p d . f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 PARTISAN BIAS, ECONOMIC EXPECTATIONS, AND HOUSEHOLD SPENDING 509 ﬁnd a large and immediate effect of elections on economic expectations, but no effect on actual auto purchases in the six months following the election. For both the 2007 and 2013 election, Gillitzer and Prasad (2018) ﬁnd longer-run effects on auto purchases that begin three quarters after the election. We discuss the Gillitzer and Prasad (2018) in more detail in appendix section A2.9 Finally, the results are closely related to the ﬁndings in McGrath (2016). McGrath (2016) focuses on county-level taxable sales data from 19 states, and relates this proxy for household spending to county-level partisanship as measured by presidential vote shares for the Democratic Party. She eval- uates the 1996 to 2012 Presidential elections, and she ﬁnds no statistically signiﬁcant effect of Presidential elections on differential spending based on the county’s partisanship. While McGrath (2016) focuses on household spending, this study shows both partisan bias in economic expectations and the response of spending. The partisan bias result is cru- cial given that this an important underlying mechanism that would lead to a change in spending in the macroeconomics lit- erature. Furthermore, the administrative measures of spend- ing used in this study cover the entire United States and are available at a more reﬁned geographical area (zip code versus county). In addition, this study evaluates the 2016 election, which is notable given the massive effect on partisan bias in economic expectations following the election of Donald Trump. Overall, the results provide further support to the view that partisan bias does not have an effect on adminstra- tive measures of spending, even when partisan bias surges after the 2016 election. VII. Conclusion The well-documented rise in political polarization among the U.S. electorate has been accompanied by a substantial in- crease in the effect of partisan bias on survey-based measures of economic expectations. However, the shift in survey-based measures of economic expectations induced by partisan bias does not appear to affect household spending. For example, despite the enormous relative increase in economic optimism among Trump supporters after November 2016, there is little evidence in administrative data sets of a relative increase in spending by Republicans since the election. 9Gillitzer and Prasad (2018) cites an older version of this study in which partisan afﬁliation was imputed for the Michigan survey. In this study, par- tisan afﬁliation is measured using answers to survey questions, just as in the data set used in Gillitzer and Prasad (2018). There is no difference between the two studies in this regard. Gillitzer and Prasad (2018) also argue that the null result found in this study is due to the fact that the auto vehicle registration data is for households, businesses, and governments, whereas the auto vehicle registrations data used in Gillitzer and Prasad (2018) is for households only. It is important to emphasize that both studies ﬁnd no effect on auto purchases from the two quarters prior to the election through the two quarters after the election. Furthermore, this study also ﬁnds a null re- sult using an administrative measure of household spending based on credit card data. The data set in Gillitzer and Prasad (2018) does not contain an administrative measure for a broader set of consumer spending. Overall, the results are most consistent with the idea that partisan bias in the answers to survey questions reﬂect par- tisan “cheerleading” as opposed to a serious assessment of future individual income growth, at least when it comes to actual spending decisions. Interestingly, a recent study by Meeuwis et al. (2020) ﬁnds evidence that individuals from more Republican zip codes tend to shift their ﬁnancial wealth portfolios toward equity, although the average effect is only half a percent relative to Democratic zip codes. However, conditional on an investor deciding to rebalance, the effects are substantially larger. Their study suggests that partisan bias in economic expectations may show up in ﬁnancial portfolio allocations, even if it has little to no effect on actual spending. Nonetheless, the results presented here suggest that re- searchers and practitioners should exercise caution in us- ing survey-based measures of economic expectations as true measures of an individual’s actual economic expectations. Partisan bias is increasingly polluting these measures. Our results also suggest that perhaps measures of economic ex- pectations in the aggregate are becoming less powerful in predicting consumption or income growth given the rise of partisan bias. This is a fruitful avenue for future research in our view. REFERENCES Altonji, Joseph G., Todd E. Elder, and Christopher R. Taber, “Selection on Observed and Unobserved Variables: Assessing the Effectiveness of Catholic Schools,” Journal of Political Economy 113:1 (2005), 151–184. 10.1086/426036 Angeletos, George-Marios, and Jennifer La’O, “Sentiments,” Econometrica 81:2 (2013), 739–779. 10.3982/ECTA10008 Azariadis, Costas, “Self-Fulﬁlling Prophecies,” Journal of Economic The- ory 25:3 (1981), 380–396. 10.1016/0022-0531(81)90038-7 Barsky, Robert B., and Eric R. Sims, “Information, Animal Spirits, and the Meaning of Innovations in Consumer Conﬁdence,” The American Economic Review 102:4 (2012), 1343–1377. 10.1257/ aer.102.4.1343 Bartels, Larry M., “Beyond the Running Tally: Partisan Bias in Political Perceptions,” Political Behavior 24:2 (2002), 117–150. 10.1023/A: 1021226224601 Benhabib, Jess, and Mark M. Spiegel, “Sentiments and Economic Activity: Evidence From US States,” The Economic Journal 129:618 (2019), 715–733. 10.1111/ecoj.12605 Benhabib, Jess, and Roger E. A. Farmer, “Indeterminacy and Increasing Returns,” Journal of Economic Theory 63:1 (1994), 19–41. 10.1006/ jeth.1994.1031 Boxell, Levi, Matthew Gentzkow, and Jesse M. Shapiro, “Is the Inter- net Causing Political Polarization? Evidence From Demograph- ics,” National Bureau of Economic Research Technical Report (2017). Bullock, John G., Alan S. Gerber, Seth J. Hill, Gregory A. Huber, “Par- tisan Bias in Factual Beliefs About Politics,” Quarterly Journal of Political Science 10:4 (2015), 519–578. 10.1561/100.00014074 Campbell, Angus, Philip E. Converse, Warren E. Miller, and Donald E. Stokes, The American Voter (New York: John Wiley & Sons, 1960). Duch, Raymond M., Harvey D. Palmer, and Christopher J. Anderson, “Het- erogeneity in Perceptions of National Economic Conditions,” Amer- ican Journal of Political Science 44:4 (2000), 635–652. 10.2307/ 2669272 Evans, Geoffrey, and Robert Andersen, “The Political Conditioning of Eco- nomic Perceptions,” The Journal of Politics 68:1 (2006), 194–207. 10.1111/j.1468-2508.2006.00380.x Gentzkow, Matthew, “Polarization in 2016,” Toulouse Network of Infor- mation Technology white paper (2016). l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . e d u / r e s t / l a r t i c e - p d f / / / / 1 0 5 3 4 9 3 2 0 9 0 0 0 4 / r e s t _ a _ 0 1 0 5 6 p d . f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 510 THE REVIEW OF ECONOMICS AND STATISTICS Gerber, Alan S., and Gregory A. Huber, “Partisanship and Economic Be- havior: Do Partisan Differences in Economic Forecasts Predict Real Economic Behavior?,” American Political Science Review 103:03 (2009), 407–426. 10.1017/S0003055409990098 ——— “Partisanship, Political Control, and Economic Assessments,” American Journal of Political Science 54:1 (2010), 153–173. 10.1111/j.1540-5907.2009.00424.x Gillitzer, Christian, and Nalini Prasad, “The Effect of Consumer Senti- ment on Consumption: Cross-Sectional Evidence From Elections,” American Economic Journal: Macroeconomics 10:4 (2018), 234– 69. 10.1257/mac.20160244 Iyengar, Shanto, Gaurav Sood, and Yphtach Lelkes, “Affect, Not Ideol- ogy a Social Identity Perspective on Polarization,” Public Opinion Quarterly 76:3 (2012), 405–431. 10.1093/poq/nfs038 Ladner, Matthew, and Christopher Wlezien, “Partisan Preferences, Elec- toral Prospects, and Economic Expectations,” Comparative Political Studies 40:5 (2007), 571–596. 10.1177/0010414005285758 Lorenzoni, Guido, “A Theory of Demand Shocks,” The American Economic Review 99:5 (2009), 2050–2084. 10.1257/aer.99.5.2050 Mason, Lilliana, “The Rise of Uncivil Agreement: Issue Versus Behavioral Polarization in the American Electorate,” American Behavioral Sci- entist 57:1 (2013), 140–159. 10.1177/0002764212463363 ——— “I Disrespectfully Agree: The Differential Effects of Partisan Sort- ing on Social and Issue Polarization,” American Journal of Political Science 59:1 (2015), 128–145. 10.1111/ajps.12089 McGrath, Mary C., “Economic Behavior and the Partisan Perceptual Screen,” Quarterly Journal of Political Science 11:4 (2016), 363– 383. 10.1561/100.00015100 Meeuwis, Maarten, Jonathan A. Parker, Antoinette Schoar, and Duncan I. Simester, “Belief Disagreement and Portfolio Choice,” National Bureau of Economic Research Technical Report (2020). Mian, Atif, and Amir Suﬁ, “The Effects of Fiscal Stimulus: Evidence From the 2009 Cash for Clunkers Program,” The Quarterly Journal of Economics 127:3 (2012), 1107–1142. 10.1093/qje/qjs024 ——— “What Explains the 2007–2009 Drop in Employment?,” Economet- rica 82:6 (2014), 2197–2223. 10.3982/ECTA10451 Mian, Atif, Kamalesh Rao, and Amir Suﬁ, “Household Balance Sheets, Consumption, and the Economic Slump,” The Quarterly Journal of Economics 128:4 (2013), 1687–1726. 10.1093/qje/qjt020 Oster, Emily, “Unobservable Selection and Coefﬁcient Stability: Theory and Evidence,” Journal of Business & Economic Statistics 37:2 (2019), 187–204. Palmer, Harvey D., and Raymond M. Duch, “Do Surveys Provide Rep- resentative or Whimsical Assessments of the Economy?,” Political Analysis 9:1 (2001), 58–77. 10.1093/oxfordjournals.pan.a004865 Prior, Markus, Gaurav Sood, Kabir Khanna, “You Cannot be Serious: The Impact of Accuracy Incentives on Partisan Bias in Reports of Eco- nomic Perceptions,” Quarterly Journal of Political Science 10:4 (2015), 489–518. 10.1561/100.00014127 Solon, Gary, Steven J. Haider, and Jeffrey M. Wooldridge, “What are We Weighting for?,” Journal of Human Resources 50:2 (2015), 301– 316. 10.3368/jhr.50.2.301 Stanig, Piero, “Political Polarization in Retrospective Economic Evalua- tions During Recessions and Recoveries,” Electoral Studies 32:4 (2013), 729–745. 10.1016/j.electstud.2013.05.029 Wlezien, Christopher, Mark Franklin, and Daniel Twiggs, “Economic Per- ceptions and Vote Choice: Disentangling the Endogeneity,” Political Behavior 19:1 (1997), 7–17. 10.1023/A:1024841605168 Yagan, Danny, “Employment Hysteresis from the Great Recession,” Jour- nal of Political Economy 127:5 (2019), 2505–2558. 10.1086/701809 l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . e d u / r e s t / l a r t i c e - p d f / / / / 1 0 5 3 4 9 3 2 0 9 0 0 0 4 / r e s t _ a _ 0 1 0 5 6 p d . f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3
Download pdf