Iyengar, S. Lelkes, Y. LEvendusky, M. Malhotra, N. Westwood, S.J., 2019. “The Origins and Consequences of Affective
Polarization in the United States”
➔ Introduction
◆ Divided nation
● Political elites, Congress, are increasing disagree on policy issues
◆ New type of division ⇒ American citizens dislike and distrust other party members
● Both sides say the others are hypocritical, selfish, and closed-minded
➔ Affective Polarization: An Outgrowth of Partisan Social Identity
◆ US partisanship = identifying as a Democrat or a Republican
◆ Divided in i n group, our party, o r o ut group, the opposing party
◆ Partisanship
● Powerful identifier
○ Acquired at young age and rarely changes
○ Political campaigns
◆ Focus exclusively on the affective dimension of polarization
➔ How Do We Measure Affective Polarization?
● Three main techniques : survey self-reports of partisan affect, implicit or subconscious tests of
parisian bias and behavioral measures of interpersonal trust and group favoritism or discrimination
based on partisan cues
◆ Survey Self-Reports
● “Feeling thermometer” question
● From 1980 to 2016 Graph
○
● Marriage rates of the same party have increased over time
○ especially if a person is vocal about politics
◆ Implicit Measures
● major limitation to survey-based indicators of partisan affect is that they are reactive and
susceptible to intentional exaggeration/suppression based on normative pressures
● These tests are harder to manipulate than explicit self-reports
● In survey questions, approx. 70% of Democrats and Republicans show a bias in favor of their party
● implicit bias is less pronounced than explicit bias, 91% of Republicans and 75% of Democrats in
the same explicitly evaluated their party more favorably
● Shows when explicit political preferences are weak, underlying implicit preferences drive political
decision making
◆ Behavioral Measures
● Introduce economic games as a platform for documenting the extent to which partisans are willing
to endow or withhold financial rewards from players who either share or do not share their partisan
affiliation. Using both the trust game and the dictator game, this work measures partisan bias as the
difference between financial allocations to co partisans and opposing partisans. Results show that
co partisans consistently receive a bonus while opposing partisans are subject to a financial penalty.
○ Proves that driving ordinary citizens to reward co partisans and penalize opposing
partisans
➔ Origins and Causes of Affective Polarization
◆ When most Democrats are liberals and most Republicans are conservatives, co-partisans are less likely to
encounter conflicting political ideas and identities (Roccas & Brewer 2002) and are more likely to see non
identifiers as socially distant.
◆ She has shown that those with consistent partisan and ideological identities became more hostile toward the
out party without necessarily changing their ideological positions, and those who have aligned religious,
racial, and partisan identities react more emotionally to information that threatens their partisan identities or
issue stances.
◆ People who are more polarized to one party will watch news that the party supports, which could arguably
make extreme attitudes even more extreme
◆ Another thing found is even if partisan news or other identity-consistent information heightens affective
polarization, few people may actually limit their exposure to sources representing a particular identity or
ideology
◆ Internet usage ⇒ plays a definite role in the growth of polarization
● Discussion of peers
◆ Mainstream media
● 20% more stories about polarization than there were at the turn of the 21st century
◆ Political campaigns
● Increase tensions
● 50-150% were more polarized on election day than a year earlier
○ Political advertisements → especially negative ones
◆ Portray the other side as an “existential threat”
➔ The NonPolitical Consequences of Affective Polarization
◆ People would be more comfortable with dating or married someone from their own party
● Americans who would be somewhat or very unhappy if their child married someone of the opposite
party has increased by about 35 percentage points over the last 50 years, with Republicans
especially sensitive to cross party marriage
○ 80.5% of married couples share a party identification
● Online dating websites
◆ Friendships with other party’s people
● 64% of Democrats and 55% of Republicans say that they have a few or no close friends from the
other party
◆ Jobs
● Democrats that filled out an application were 2.4% more likely to receive a callback than
Republican resumes
● Republicans that filled out an application were 5.6% more likely to receive a callback than
Democrat resumes
➔ Decreasing Affective Polarization
◆ Party-stereotypical groups ⇒ not accurate
● Correcting these stereotypes could possibly reduce affective polarization
◆ See both party members as Americans
● That’s what they have in common and typically reduces tension
◆ Downplaying politics in everyday life events
◆ Two important limitations
● Will not be easy to reduce tension
◆ Build on insights of intergroup contact theory
● Examine constructive engagement between Democrats and Republicans and reduction of
polarization
➔ Conclusion
◆ Agenda items
● Little to no research identifying the mechanisms underlying affective polarization
● Literature has yet to specify the conditions under which partisans are motivated by either in-group
favoritism or out-group animosity
● Encourage researchers to explore the role of sorting as a potential mediator of affective polarization
● Partisanship appears to now compromise the norms and standards we apply to our elected
representatives, and even leads partisans to call into question the legitimacy of election results, both
of which threaten the very foundations of representative democracy
Barrios, J.M., and Hochberg, Y. 2020, “Rick perception through the lens of politics in the time of the covid-19 pandemic”
➔ Introduction
◆ Risk of infection
● Determined by their own havior and externalities imposed on them by others with which they have
no choice but to interact
● Where people get their news from → changes attitudes
➔ Exhibit 1 Graphs + Explanations
◆
● This exhibit plots the national average trends for each of our outcomes of interest over the first few
months of 2020 against the cumulative number of confirmed COVID19 cases in the United States.
In Panel A we plot the average search share for COVID-19 on google (left panel) as well as search
share for unemployment benefits related terms (right panel). In Panel B we plot the average daily
level of our two social distancing behaviors. In the left panel we plot the daily average of the
percentage change in distance traveled in the county (relative to the pre-COVID period), while in
the right panel we plot the daily average of the percentage change in visits to non-essential business
in the county (relative to the pre-COVID-period). A red vertical line marks March 16 the day that
the federal guidelines for social distancing were announced.
➔ Exhibit 2 Graphs + Explanations
◆
● Panel A plots our two measures of search share on the Trump VS in the 2016 election in each of the
Nielsen DMAs. The left panel uses COVID-19 search shares while on the right we use the search
share for unemployment. Each of the plots control for the log number of confirmed cases,
population density, income per capita, population, the day of the week, the number of days since
the first case in the DMA.
Panel B : Changes in Search Shares around Confirmed Cases and Political Polarization
◆
● Panel B provides a multivariate analysis on changes in search share with respect to COVID cases.
The dependent variable is the log search share for COVID-19 (column 1-3) and Unemployment
terms (column 4-6). In columns (1) and (4) we regress the search shares on the Log Number of
confirmed COVID cases including DMA fixed effects. In columns (2) and (5) we interact the
number of cases with the Trump Vote Share in each of the DMAs and include DMA specific linear
trends. Finally, in columns (3) and (6) we replace the vote share with an indicator for High Trump
Vote share DMAs (DMA is in the upper quartile of DMAs in trump vote share). Standard errors are
clustered by DMA and are reported in parenthesis.
Panel C: Event Studies: Changes in Search Shares around Confirmed Cases and Deaths for High and Low Trump Vote
Share Areas
◆
● Panel C plots abnormal search share for COVID-19 relative to 5 days before the first confirmed
case of COVID-19 in a DMA (left panel) and the first COVID-19 death (right panel). These
estimates are done for high (red) and low (blue) Trump vote share DMAs. The estimates are
obtained by estimating an OLS where the daily log search share is regressed on event time
dummies. Each specification controls for DMA time invariant characteristics like population,
per-capita income and density. We also control for calendar time trends via day fixed effects.
Moreover, in the first death event study we also control for time since the first confirmed case.
➔ Exhibit 3
Panel A: Social Distancing Behavior and Political Polarization – Trump Vote Share
◆
● Panel A plots our two measures of county Social distancing on the Trump VS in the 2016 election
in each of the counties. The left panel uses the percentage change in the average distance traveled
in the county while on the right panel we examine the percentage change in visits to non-essential
businesses in the county. Each of the plots control for the log number of confirmed cases,
population density, income per capita, population the day of the week, and the number of days
since the first case in the DMA.
◆
● Panel B provides a multivariate analysis on changes in social distancing behavior with respect to
COVID cases. The dependent variable is the percentage change in: distance traveled in the county
(column 1-3) and non-essential visits (column 4-6). In columns (1) and (4) we regress the SD
behavior on the Log Number of confirmed COVID cases including day fixed effects as well as
controls for county population, density, per-capita income and time since the first case. In columns
(2) and (5) we interact the number of cases with the Trump Vote Share in each of the counties while
in columns (3) and (6) we replace the vote share with an indicator for High Trump Vote share
counties (counties is in the upper quartile of counties in trump vote share). Columns (2), (3), (5),
and (6) include county fixed effects. Standard errors are clustered by county and are reported in
parenthesis.
◆
● Panel C provides a multivariate analysis of changes in social distancing behavior around the
adoption of various measures at the state and federal level to motivate the citizenry to engage in
social distancing. Specifically, we focus on the federal regulations to slow the virus, state
regulations that closed schools and businesses, and states adopting mandatory stay at home orders.
On the right panel, we run a multi-variable regression where we regress our two measures of social
distancing on various indicators for the federal and state orders. To examine the differential social
distancing behavior by trump areas, we interact with the indicators, an indicator for High Trump
Vote share counties. Each specification includes controls for the log number of confirmed cases and
county fixed effects. Standard errors are clustered by county. Finally, on the right panel we plot the
cumulative reaction concerning changes in distance traveled for high and low trump counties
around each of the orders along with .95 confidence intervals. These are estimated from the
specification in column 1 but using only the high and low trump counties.
➔ Exhibit 4
◆
● Panel B plots the cumulative change in the percentage change in distance traveled (left panel) and
the percentage change in non-essential visits given an 20% increase in the number of confirmed
after the CPAC announcement in high and low trump vote share counties. We obtain these
estimates by estimating models like those in columns (1) and (2) in Panel C in Exhibit 3.
Specifically, we augment the models by using a Post-CPAC indicator and interacting them with the
base variables. Each plotted estimate includes 95% confidence intervals and standard errors are
clustered at the county level.
◆
● Panel A provides bin scatter plots relating the search share for COVID-19 on the average ratio of
Fox News searches to MSNBC News searches on google in the DMAs during 2019. Each of the
plots control for the log number of confirmed cases, population density, income per capita,
population, the day of the week, the number of days since the first case in the DMA. To examine
the impact of the CPAC event on the relation between our measures and the Fox News ratio, we
partition between pre-CPAC event searches and post-CPAC searches.
➔ Exhibit 5
◆
● Panel A examines the relation between the share of the population over 60 and search share (top
row) and changes in the daily distance traveled (bottom row). For each measure we examine both
the fundamental relation (left column) and the differential effect based on high Trump VS. The
search share panels are measured at the Nielsen DMA level while the daily travel distance change
is measured at the county level. Each of the plots control for the log number of confirmed cases,
population density, income per capita, population, the day of the week, and the number of days
since the first case in the DMA or county.
◆
● Panel B examines the relation between social distancing behavior and the share of the workforce
that is easily done at home (Telework). The Telework measure is obtained from Dingel and Neiman
(2020). They classify the feasibility of working at home for all occupations and merge this
classification with occupational employment counts for the United States in the left column we
examine the fundamental relation while in the right column we examine the differential effect
based on high Trump VS counties. Each of the figures control for the log number of confirmed
cases, population density, income per capita, population, the day of the week, and the number of
days since the first case in the DMA or county.
Chan, S. Hu, and He, K. 2019. “Discerning states’ revisionist and status-quo orientations
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