Importing Political Polarization? The Electoral Consequences of Rising Trade Exposure - May 2016 - Pubdocs.worldbank.org.
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Importing Political Polarization?
The Electoral Consequences of Rising Trade Exposure
David Autor David Dorn
Gordon Hanson Kaveh Majlesi
May 2016
Trade and Politics
Trade and Politics
The impact of trade on US workers has become a touchstone
issue in the 2016 presidential campaign
• Both among Republicans
“I would tax China on products coming in. I would do a tax, and
the tax, let me tell you what the tax should be... the tax should be
45 percent.”
Donald Trump
• and Democrats
“I voted against NAFTA, CAFTA, PNTR with China. I think they
have been a disaster for the American worker.”
Bernie Sanders
Widely Debated Hypothesis: Do the Economic Impacts of
Trade Favor Ideologically Far-Left and Far-Right Politicians?
Trump and Sanders Have a Point
about Trade with China
Background: Rapid Growth of China’s Manufacturing
Exports since 1990...
10
15
China import penetration in US manuf.
8
10
6
percent
4
5
2
0
0
1991 1996 2001 2006 2011
Year
China share of world manufacturing exports
China import penetration in US manufacturing
...Contributed to Decline in U.S. Manufacturing
Economic Impacts of Import Competition from China
• Closure of manufacturing plants (Bernard Jensen Schott ’06),
declines in employment (Acemoglu Autor Dorn Hanson Price ’16;
Pierce Schott ’16) in more trade-exposed industries
• Lower lifetime incomes, greater job churning for workers in more
trade-exposed industries (Autor Dorn Hanson Song ’14)
• Lower employment, higher labor-force exit, higher long-run
unemployment, greater benefits uptake in more trade-exposed local
labor markets (Autor Dorn Hanson ’13)
Impact on Trade Legislation and US Politics
Anti-trade views precede Trump and Sanders
• Congressional representatives from trade-exposed regions are more
likely to support protectionist trade bills (Feigenbaum Hall ’15; Che,
Lu, Pierce, Schott, Tao ’15) and anti-China legislation (Kleinberg
Fordham ’13; Kuk, Seligsohn, Zhang ’15)
• Our work studies whether the impacts of trade exposure extend
beyond voting on trade policy, and affect the ideological
composition of Congress itself
Major Trend in U.S. Politics
Increasing partisanship in the US Congress
• Not due to a shift in vote shares going to the two major parties
• GOP has bicameral majorities, but nat’l vote shares are close to even
• Voter identification with parties has become weaker, not stronger,
though persistence in county voting is greater
• Rather, the change is more polarized behavior among legislators
• Poole-Rosenthal DW-Nominate scores of roll-call votes
• The ideological divide between the parties has been rising since the
mid-1970s and is now at an all-time high
• Although voters haven’t become more extreme, legislators have
• Also visible in polarized speech patterns in Congress (Gentzkow
Shapiro Taddy ’15)
Polarization in Congress: DW-Nominate Scores
Mean Voting Behavior by Party in the House Mean Voting Behavior by Party in the Senate
.8
.8
.6
.6
Mean DW-Nominate Score
Mean DW-Nominate Score
.4
.4
.2
.2
0
0
-.2
-.2
-.4
-.4
1964 1968 1972 1976 1980 1984 1988 1992 1996 2000 2004 2008 2012 1964 1968 1972 1976 1980 1984 1988 1992 1996 2000 2004 2008 2012
Year Year
Democrats Republicans Democrats Republicans
Distribution of Democrats and Republicans on a 10-Item
Scale of Political Values (Among Politically Engaged)
Distribution of Democrats and Republicans on a 10-item scale of political values, by level of political engagement
Among the politically engaged
Among
Source: Pewthe less engaged
Research Center (2014).
Figure 8
As a starting point, we can return to the National Election Study. One of the longest-runningMany Subtleties in Public Opinion (Gentzkow ’16)
1 No growth of extremism on average
• Distribution of views on issues mostly single-peaked, relatively stable
2 Rising correlations: Views across issues; between issues and party
• Less likely for people to hold liberal views on some issues,
conservative views on others
• Presidential votes increasingly predict citizens political views on
taxes, redistribution, social policy, gun control, the environment, etc.
3 Politics has become more personal and hostile
• More likely to see other party’s supporters as selfish and stupid
• 27% of Dems, 36% of Repubs agree: opposite party’s policies “are
so misguided that they threaten the nation’s well-being” (Pew ’14)
• Less tolerant of cross-party marriage!Explaining Polarization
Literature is large but little consensus on causal mechanisms
• Explanations shown to lack empirical support
• Immigration, manipulation of blue-collar voters (Gelman et al. ’08)
• Greater voter segregation, heterogeneity in voter attitudes (Glaeser
Ward ’06, Ansolabehere Rodden Snyder ’08, Abrams Fiorina ’12)
• Gerrymandering, changes in election structure or congressional rules
(McCarty Poole Rosenthal ’09, Barber McCarty ’15)
• Explanations supported by circumstantial evidence
• Tax/regulatory reform (Bartels ’10, Hacker Pierson ’10)
• Stronger ideological sorting of voters by party (Levendusky ’09)
• Overall distribution of voter attitudes hasn’t changed but difference
in distributions between Dem and GOP party members has
• Media partisanship (DellaVigna Kaplan ’07, Gentzkow Shapiro ’11)Subject of this Paper: Trade and Political Outcomes
Has rising trade exposure in local labor markets contributed to
greater political divisions in Congress?
• Anti-incumbency effect
• Incumbents punished for bad outcomes
• Fair (’78), Margalit (’11), Jensen Quinn Weymouth (’16)
• Party-realignment effect
• Economic shocks change voter prefs — Leftward (Bruner Ross
Washington ’11; Che Lu Pierce Schott Tao ’16) or rightward
(Malgouyres ’14, Dippel Gold Heblich ’15)
• Polarization effect
• Economic shocks shift support from center to extremes
• Failure of monotone likelihood ratio property: Dixit Weibull (’07),
Baliga Hanany Klibanoff (’13), Acemoglu Chernozhukov Yildiz (’15)Agenda 1 Measuring Electoral Outcomes 2 Exposure to Import Competition from China 3 Empirical Specification 4 Anti-Incumbent, Party Realignment Effects 5 Polarization Effects 6 Heterogeneity in Polarization Effects 7 1990s versus 2000s 8 Conclusions
Challenge: Mapping Political to Economic Geography Congressional districts can have extreme shapes that do not correspond to any definition of local labor market geography
An Extreme Example: District NC-12 NC-12 stretches over 100 miles and comprises parts of the Charlotte, Greensboro and Winston-Salem cities and Commuting Zones
An Extreme Example: District NC-12 The district closely follows Interstate 85, and at some points is barely wider than a highway lane
An Extreme Example: District NC-12 How should one deal with Davidson and Rowan counties, which both partly overlap with districts NC-5, NC-12, and NC-8?
Analysis at the County Level would be Problematic
The Davidson and Rowan Cty residents cast votes in three different
races; the sum of votes across these races is hard to interpret
Data Structure: County-Level Analysis
Geographic Source of Variables
Local Labor Market Demographic
Observations Shock Composition Election Outcomes Observation Weights?
1 Davidson Cty CZ Greensboro Davidson Cty NC-5?/NC-8?/NC-12? Total Votes? Population?
2 Rowan Cty CZ Charlotte Rowan Cty NC-5?/NC-8?/NC-12? Total Votes? Population?Our Analysis is at the County-District Cell Level
Incorporates the overlapping structure of economic geography
(CZ/county) and political geography (district)
Data Structure: County-District Cell Analysis
Geographic Source of Variables
Local Labor Demographic Election
Observations Market Shock Composition Outcomes Observation Weight
1 Davidson Cty x NC-5 CZ Greensboro Davidson Cty NC-5 Cell Votes/District Votes
2 Davidson Cty x NC-8 CZ Greensboro Davidson Cty NC-8 Cell Votes/District Votes
3 Davidson Cty x NC-12 CZ Greensboro Davidson Cty NC-12 Cell Votes/District Votes
4 Rowan Cty x NC-5 CZ Charlotte Rowan Cty NC-5 Cell Votes/District Votes
5 Rowan Cty x NC-8 CZ Charlotte Rowan Cty NC-8 Cell Votes/District Votes
6 Rowan Cty x NC-12 CZ Charlotte Rowan Cty NC-12 Cell Votes/District VotesEmpirical Strategy
We match local labor markets to congressional districts
• Divide US into county-by-congressional-district cells
• Attach each county to its corresponding commuting zone (CZ)
• Weight each cell by its share of congressional-district votes
• Result is a mapping of CZ shocks to district political outcomes
• Use CZ trade shocks from Acemoglu Autor Dorn Hanson Price (’16)
• Examine electoral outcomes over 2002 to 2010
• Because of redistricting, we can only examine intercensal periods
• Helpfully, these are non-presidential election years
• Our time period spans the rise of the Tea PartyData Sources
1 Voting behavior of congressional representatives
• DW-Nominate scores (Poole & Rosenthal ’85, ’91, ’97, ’01)
• Estimated for each legislator in each Congress
• Tag 2003-2005 score to winning legislator in 2002 election,
2011-2013 score to winning legislator in 2010 election
2 Vote shares by party in House elections
• Dave Leip’s Atlas of US Presidential Elections
• Vote counts for each party by county-district cellCongressional Districts Included in Sample
No. Districts % of Total
(1) (2)
Total Districts in U.S. Congress 435.0 100%
Excluded States 4.0 1%
AK 1.0
HI 2.0
VT 1.0
Inconsistently Observed Cells 14.7 3%
TX 9.3
GA 5.4
Total Districts in Sample 416.3 96%
The sample excludes Alaska and Hawaii due to complications in definining
Commuting Zones in those states, and Vermont, whose only district was represented
by a congressman without party affiliation during the sample period. It also excludes
county-district cells that are not continuously observed over time due to rezoning in
the states of Texas and Georgia. The omitted areas correspond to about 1/3 of the
districts in each of these states.Poole-Rosenthal DW-Nominate Score
Based on spatial model of voting
• PR use roll-call (recorded) votes to estimate model across time
• Each legislator is assumed to have an ideal point in the 2-D plane
• Chooses ’yea’ or ’nay’ on each bill to maximize utility (which is an
exponential function of distance plus iid stochastic term)
• Estimated parameters are the 2-D coordinates of each legislator’s
ideal point and weighting parameters on each dimension
• Since 1980s, nearly all explanatory power is in 1st dimension, which
is interpreted as a liberal-conservative scale (from −1 to 1)
• Our Nominate data comprise 1st dimension of DW-Nominate score
for each legislator in each congressional termSample Nominate Scores in the US Senate
Rankings of 717 US senators who served between 1964 & 2014
• Rand Paul (0.95): 2nd most conservative
• Ted Cruz (0.88): 4th most conservative
• Marco Rubio (0.58): 33rd most conservative
• Barrack Obama (−0.38): 596th most conservative
• Hillary Clinton (−0.40): 605th most conservative
• Bernie Sanders (−0.53): 714th most conservativeNominate Scores, Win Margins by Party in House
Parties are winning with more extreme candidates and narrower victories
-.38
.75
75
75
.7
.65
-.36
70
70
.6
.55
-.34
65
65
.5
.45
-.32
60
60
.4
1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012
Election Year Election Year
Republican vote share [%, left scale] Democrat vote share [%, left scale]
Nominate Score [right scale] Nominate Score [right scale, inverted axis]Big Story is Polarization in Nominate Scores, Not Vote Shares
1.2
.8
.4
0
-.4
-.8
1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012
Election Year
Republican mean Democrat mean
Republican min/max Democrat min/max
Republican 5th/95th percentile Democrat 5th/95th percentileAgenda 1 Measuring Electoral Outcomes 2 Exposure to Import Competition from China 3 Empirical Specification 4 Anti-Incumbent, Party Realignment Effects 5 Polarization Effects 6 Heterogeneity in Polarization Effects 7 1990s versus 2000s 8 Conclusions
Mapping Industry Import Shocks to Commuting Zones
• Observed ∆ in industry import penetration from China
cu
∆Mj,τ
∆IP j,τ =
Yj,91 + Mj,91 − Ej,91
∆Mjτ cu is 4 in China imports over ’02-’10 in US industry j,
Yj,91 + Mj,91 − Ej,91 is industry absorption in ’91 (pre-China shock)
• Exposure of commuting zone i to trade with China
X Lijt
∆IP cu
iτ =
cu
∆IPjτ
Lit
j
where Lijt /Lit is share of industry j in employment of CZ i in ’00Change in Import Penetration from China for Congressional
Districts over 2002-2010
District won District won
All Districts by R in 2002 by D in 2002
(1) (2) (3)
Mean 0.71 0.72 0.71
25th Percentile 0.40 0.42 0.40
Median 0.57 0.62 0.53
75th Percentile 0.89 0.95 0.89
P75 - P25 0.49 0.53 0.49
N=3503 district*county cells in column 1, N=2269 cells in districts that elected
Republicans in the 2002 election in column 2, N=1234 cells in districts that elected
Democrats in the 2002 election in column 3. Industry import penetration is the growth
of annual imports from China 2002-2010, divided by an industry's U.S. domestic
market volume in 1991. The Commuting Zone average of import penertration weights
each industry according to its 2000 share in total Commuting Zone employment.
• In AADHP ’16 “Great Sag,” estimate that each 1pt of import
penetration reduces working age adult emp/pop by 1.89pts
• 90/10 district-country ∆ in import exposure is 1.28ptsIsolating the Supply Shock Component of China Imports:
Instrumental Variables Approach
Problem
• US import demand ∆0 s may contaminate estimation
Instrumental variables approach
• IV for US imports from China using other DCs (Austria, Denmark,
Finland, Germany, Japan, New Zealand, Spain, Switzerland)
• Assumption: Common component of ∆ in rich country imports
from China is China export supply shock
X Lijt−10
∆IP co
it =
co
∆IPjτ
Luit−10
j
where ∆IP co co
it = ∆Mjτ / (Yj,88 + Mj,88 − Ej,88 ) is based on change
in imports from China in other high-income countriesImports from China in the US and Other Developed
Economies 1991 – 2007
Imports from China in the U.S. and Other Developed Economies 1991 - 2007 (in Billions of 2007$),
and their Correlations with U.S.-China Imports
United States Japan Germany Spain Australia
∆ Chinese Imports (Bil$) 303.8 108.1 64.3 23.2 21.5
No. Industries with Import Growth 385 368 371 377 378
Correlation w/ U.S.-China Imports 1.00 0.86 0.91 0.68 0.96
8 Non-US New
Countries Finland Denmark Zealand Switzerland
∆ Chinese Imports (Bil$) 234.7 5.7 4.7 3.8 3.3
No. Industries with Import Growth 383 356 362 379 343
Correlation w/ U.S.-China Imports 0.92 0.58 0.62 0.92 0.55
Correlations of imports across 397 4-digit industries are weighted using 1991 industry employment from the NBER Manufacturing
database.Agenda 1 Measuring Electoral Outcomes 2 Exposure to Import Competition from China 3 Empirical Specification 4 Anti-Incumbent, Party Realignment Effects 5 Polarization Effects 6 Heterogeneity in Polarization Effects 7 1990s versus 2000s 8 Conclusions
Empirical Specification
We examine the impact of trade shocks on political outcomes
• Party orientation of a congressional district
• Change in party, change in party vote shares
• Ideological positioning of elected representatives
• Changes in nominal, absolute Nominate scores of legislators
• Changes in likelihood liberal, moderate, or conservative is elected
• Heterogeneity of trade impacts across districts
• By initial party in power in 2002
• By vote shares in Bush–Gore election in 2000
• By racial composition (white majority, non-white majority)Empirical Specification
Primary specification
0 0
∆Yjkt = γ d + β1 ∆IP cu
jt + Xjkt β3 + Zjt β2 + ejkt
• ∆Yjkt is ’02-’10 change in electoral outcome for county j, district k
• ∆IP jt is ∆ in import exposure in CZ for county j (IV using ∆IP coit )
• Xjkt is vector of control variables, γ d is census division dummy
• Pol. conditions in ’02 for county-district jk (winning party, winner’s
vote share, whether winner unopposed, winner’s Nominate
score—interacted w/ GOP dummy)
• Econ. conditions in ’00 for CZ containing county j (manuf. emp.
share, routine-task intensity, offshorability index)
• Demogr. composition in ’00 in county j (pop. shares by age,
gender, education, race, ethnicity, nativity groups)
• Weight by jk vote share in district k, cluster by CZ and by districtAgenda 1 Measuring Electoral Outcomes 2 Exposure to Import Competition from China 3 Empirical Specification 4 Anti-Incumbent, Party Realignment Effects 5 Polarization Effects 6 Heterogeneity in Polarization Effects 7 1990s versus 2000s 8 Conclusions
Effect of Trade Exposure on Pr[Change in Party in Power]
Note: Mean of Dependent Variable 12.5%
Trade exposure is weakly related to changes in party in power
Import Exposure and Congressional Election Outcomes 2002-2010.
Dependent Variable: 100 x Dummy for Change in Party
Change in Party, Election 2010 vs 2002
(1) (2) (3) (4) (5) (6) (7) (8)
Δ CZ Import Penetration 4.64 8.48 8.42 8.73 7.54 ~ 8.40 8.73 7.71
(2.89) (5.40) (5.38) (5.38) (4.00) (9.09) (8.37) (8.26)
Estimation: OLS 2SLS 2SLS 2SLS 2SLS 2SLS 2SLS 2SLS
F-statistic first stage 33.47 ** 33.45 ** 34.93 ** 35.04 ** 13.76 ** 13.67 ** 11.87 **
Control Variables:
2002 Elected Party yes yes yes yes yes yes
2002 Election Controls yes yes yes yes yes
2002 Nominate Controls yes yes yes yes
2000 Ind/Occ Controls yes yes yes
2000 Demography Controls yes yes
Census Division Dummies yes
N=3503 County*District cells. Observations are weighted by a cell's fraction in total votes of its district in 2002, so that each district has
an equal weight in the regression, and standard errors are two-way clustered on CZs and Congressional Districts. ~ p ≤ 0.10, * p ≤ 0.05,
** p ≤ 0.01.Effect of Trade Exposure on Change in Party Vote Shares
Trade exposure reduces vote shares for party in power but
doesn’t realign vote shares in favor of any one party
% Vote for Change in Voting Outcomes 2002-2010
Party that Repub- Demo- Pr(Winner
Won in lican Vote cratic Vote Other Vote gets >75% Pr(Winner is
2002 Share Share Share of Vote) Unopposed)
(1) (2) (3) (4) (5) (6)
A. Point Estimates
Δ CZ Import -6.98 * 1.97 0.31 -2.28 -13.49 -13.04 ~
Penetration (2.75) (2.71) (2.88) (1.79) (12.23) (6.68)
B. Summary Statistics
Δ 2002 - 2010 -8.48 1.20 -1.28 0.08 -11.81 -12.03
Level in 2010 62.06 50.16 46.69 3.15 14.71 6.15
N=3503 County*District cells. All regression include the full set of control variables from Table 1. Observations are weighted by a cell's fraction
in total votes of its district in 2002, so that each district has an equal weight in the regression, and standard errors are two-way clustered on CZs
and Congressional Districts. ~ p ≤ 0.10, * p ≤ 0.05, ** p ≤ 0.01.Agenda 1 Measuring Electoral Outcomes 2 Exposure to Import Competition from China 3 Empirical Specification 4 Anti-Incumbent, Party Realignment Effects 5 Polarization Effects 6 Heterogeneity in Polarization Effects 7 1990s versus 2000s 8 Conclusions
Effect of Trade Exposure on Change in Nominate Scores
Note: Level in 2002 = 13.9, Level in 2010 = 21.3
Trade exposure induces shift away from center and net shift to
right in legislator voting—due to turnover not within-person ∆0 s
2002-2010 Change in Political Decomposition of Change in
Position Absolute Nominate Score
Absolute Shift Shift
Nominate Nominate to to
Score Score Right Left
(1) (2) (3) (4)
A. Between and Within Person Change of Nominate Score
Δ CZ Import Penetration 18.41 * 14.15 * 10.69 * 3.46
(7.93) (6.09) (5.30) (2.32)
B. Between Person Change of Nominate Score Only
Δ CZ Import Penetration 20.13 ** 15.61 ** 12.14 * 3.47
(7.86) (5.95) (5.15) (2.33)
N=3503 County*District cells. Panel B replaces the Nominate scores of the 2010 election winners with their Nominate
score from the 108th (2003-2005) congress or the first subsequent congress to which they were elected. This eliminates a
within-person change in the Nominate score for districts that elected the same representative in 2002 and 2010.
Observations are weighted by a cell's fraction in total votes of its district in 2002, so that each district has an equal weight in
the regression, and standard errors are two-way clustered on CZs and Congressional Districts. ~ p ≤ 0.10, * p ≤ 0.05, ** p
≤ 0.01.Interpreting Magnitudes
Consider two congressional districts that are at the 25th and 75th
percentile of change in trade exposure, respectively
• More trade-exposed district would have:
• change in Nominate score that is 0.18 (18.41 × (0.89 − 0.40)/49)
standard deviations higher
• change in distance from political center that is 0.36
(14.15 × (0.89 − 0.40)/19) standard deviations greaterDecomposing Changes in Nominate Scores and Republican
Percentage of Two-Party Vote Share
Big changes are between legislator (both D to R and R to R)
I. Party Change II. Representative Change III. No Change
Democrat Republican Republican Democrat
to to to to Republican Democrat
Republican Democrat Republican Democrat Persists Persists
(1) (2) (3) (4) (5) (6)
A. Number of Districts
30 22 104 42 95 123
B. Average Change in 100*Nominate Score by Type of District
94.75 -72.51 14.89 -2.94 6.00 -1.50
C. Contribution to Overall Change in Average Nominate Score
6.83 -3.80 3.73 -0.30 1.37 -0.44
D. Change in Republican Percentage of Two-Party Vote by Type of District
29.61 -18.28 -10.31 10.88 -1.18 6.22
E. Contribution to Overall Change in Pct Republican Two-Party Vote
2.14 -0.96 -2.58 1.10 -0.27 1.84
N=3503 County*District cells. Observations are weighted by a cell's fraction in total votes of its district in 2002. Values
in Panel C sum to the average change in Nominate score of 7.39 for the whole sample. Values in Panel E sum to the
average change in Republican two-party vote percentage of 1.27 for the whole sample.Effect of Trade Exposure Ideological Position of Winners
Trade exposure hurts moderates, helps conservative Republicans
and Tea Party
Change in Probability 2002-2010 that Winner has Given Political Orientation
Moderate Conserv-
Liberal Moderate Repub- ative Tea Party
Moderate Democrat Democrat lican Republican Member
(1) (2) (3) (4) (5) (6)
A. Point Estimates
Δ CZ Import -37.66 ** 0.27 -23.69 ** -13.97 37.38 ** 24.44 ~
Penetration (13.95) (7.11) (8.72) (9.58) (14.04) (12.77)
B. Means
Δ 2002 - -19.64 2.64 -4.61 -15.03 17.00 11.74
2010 in 2002
Level 56.78 19.92 27.01 29.77 23.31 6.15
Level in 2010 37.13 22.56 22.40 14.74 40.31 17.89
N=3503 County*District cells."Liberal Democrats", "Moderates" and "Conservative Republicans" are defined as politicians whose
Nominate scores would respectively put them into the bottom quintile, middle three quintiles, or top quintile of the Nominate score in the
107th (2001-2003) congress that preceeds the outcome period. A Tea Party Member is defined as a representative who was a member of
the Tea Party or Liberty Caucus during the 112th (2011-2013) Congress. ~ p ≤ 0.10, * p ≤ 0.05, ** p ≤ 0.01.Note on Alternative Specifications
How we define the change in the ideological position of winners
• Previous table examines change in outcome (eg, whether moderate
elected in 2010 minus whether moderate elected in 2002)
• Given initial values on are RHS, we could have used the ’10 level on
LHS instead of the ’02-’10 change
• Results are robust to:
• Using ’10 levels, rather than ’02-’10 changes, on LHS
• Controlling for quadratic in or bin sizes of ’02 Nominate scores
• Defining liberals and conservatives cardinally as outside [−0.5, 0.5]Specification Checks: Using 2002-2010 First-Difference
Liberal Moderate Moderate Conservative
Democrat Democrat Republican Republican
(1) (2) (3) (4)
A. No Nominate 2002 Control 0.54 -24.06 * -14.53 38.05 **
(6.78) (11.57) (10.59) (14.61)
B. Linear Nominate 0.54 -24.08 * -14.50 38.03 *
(6.79) (9.58) (10.07) (15.68)
C. Quadratic Nominate 0.70 -24.28 ** -14.78 38.36 **
(7.03) (8.84) (9.55) (14.31)
D. Linear Nominate x Party 0.27 -23.69 ** -13.97 37.38 **
(Primary Spec) (7.11) (8.72) (9.58) (14.04)
E. Quadratic Nominate x Party 1.48 -23.54 ** -14.56 36.62 **
(6.73) (8.58) (9.69) (13.22)
F. 4 Nominate Categories 4.63 -29.50 ** -8.39 33.26 *
(6.31) (8.95) (7.59) (13.55)
G. Linear x Party + 4 Categories 7.45 -29.47 ** -7.93 29.95 *
(5.14) (8.87) (7.49) (11.73)
N=3503 County*District cells. Classifications of candodate ideology is as in Table 4. Observations are weighted by a cell's
fraction in total votes of its district in 2002. Standard errors are two-way clustered on CZs and Congressional Districts. ~ p
≤ 0.10, * p ≤ 0.05, ** p ≤ 0.01. ~ p ≤ 0.10, * p ≤ 0.05, ** p ≤ 0.01.Specification Checks: Using 2010 Outcome (Level)
Liberal Moderate Moderate Conservative
Democrat Democrat Republican Republican
(1) (2) (3) (4)
A. No Nominate 2002 Controls 9.03 -32.55 ** -5.81 29.33 *
(9.61) (9.44) (7.55) (14.06)
B. Linear Nominate 9.01 -32.54 ** -5.82 29.35 *
(7.90) (9.63) (7.50) (11.59)
C. Quadratic Nominate 8.60 -32.19 ** -5.68 29.26 *
(5.81) (9.95) (7.32) (11.87)
D. Linear Nominate x Party 9.78 ~ -33.20 ** -6.12 29.53 *
(Primary Spec) (5.63) (10.17) (7.35) (11.94)
E. Quadratic Nominate x Party 9.36 ~ -31.43 ** -6.69 28.75 **
(5.66) (9.04) (7.46) (10.83)
F. 4 Nominate Categories 4.63 -29.50 ** -8.39 33.26 *
(6.31) (8.95) (7.59) (13.55)
G. Linear x Party + 4 Categories 7.45 -29.47 ** -7.93 29.95 *
(5.14) (8.87) (7.49) (11.73)
N=3503 County*District cells. Classifications of candodate ideology is as in Table 4. Observations are weighted by a cell's
fraction in total votes of its district in 2002. Standard errors are two-way clustered on CZs and Congressional Districts. ~ p
≤ 0.10, * p ≤ 0.05, ** p ≤ 0.01. ~ p ≤ 0.10, * p ≤ 0.05, ** p ≤ 0.01.Agenda 1 Measuring Electoral Outcomes 2 Exposure to Import Competition from China 3 Empirical Specification 4 Anti-Incumbent, Party Realignment Effects 5 Polarization Effects 6 Heterogeneity in Polarization Effects 7 1990s versus 2000s 8 Conclusions
Heterogeneity in Effects
Trade exposure raises likelihood of within-party transitions in
legislators for the GOP
No Change in Party
Change in Party Different Rep Same Rep
(1) (2) (3)
A. All Districts
Δ CZ Import Penetration 7.71 15.94 -23.65 *
(8.26) (11.45) (10.63)
B. Initially Democratic District
Δ CZ Import Penetration 29.88 ~ -5.21 -24.67
(17.82) (18.05) (18.37)
C. Initially Republican District
Δ CZ Import Penetration -13.95 ~ 41.12 * -27.17 ~
(7.69) (16.31) (13.91)
N=3,503 County*District cells in Panel A, N=1,234 in Panel B, N=2,269 in Panel C. All regression include the full set
of control variables from Table 1. Observations are weighted by a cell's fraction in total votes of its district in 2002, so
that each district has an equal weight in the regression, and standard errors are two-way clustered on CZs and
Congressional Districts. ~ p ≤ 0.10, * p ≤ 0.05, ** p ≤ 0.01.Heterogeneity in Effects: Initial Party in Power
Losses of centrists compensated by gains on the left and right
(initially Dem districts), or right only (initially GOP)
Change in Probability 2002-2010 that Winner has Given Political Orientation
Conserv-
Nominate Liberal Moderate Moderate ative Tea Party
Score Moderate Dem Dem Repub Repub Member
(1) (2) (3) (4) (5) (6) (7)
A. Initially Democratic District
Δ CZ Import 17.13 -45.67 * 15.61 -45.48 * -0.19 30.07 31.46
Penetration (15.06) (21.04) (18.85) (18.96) (6.61) (19.14) (23.35)
Mean in 2002 57.56 42.44 57.56 0.00 0.00 0.00
Δ 2002 - 2010 12.99 -17.42 5.63 -21.00 3.58 11.79 5.39
B. Initially Republican District
Δ CZ Import 12.19 ~ -34.64 * 0.00 -13.95 ~ -20.69 34.64 * 16.53
Penetration (7.11) (17.54) . (7.69) (14.62) (17.54) (15.67)
Mean in 2002 56.09 0.00 0.00 56.09 43.91 11.58
Δ 2002 - 2010 1.71 -21.61 0.00 9.88 -31.49 21.61 17.36
N=1,234 County*District cells in Panel A, 2,269 County*District cells in Panel B. All regression include the full set of control variables from
Table 1. Observations are weighted by a cell's fraction in total votes of its district in 2002, so that each district has an equal weight in the
regression, and standard errors are two-way clustered on CZs and Congressional Districts. ~ p ≤ 0.10, * p ≤ 0.05, ** p ≤ 0.01.Heterogeneity in Effects: Racial Composition
Trade exposure helps conservative GOPers in white-majority
districts, liberal Dems in non-white-majority districts
Change in Probability 2002-2010 that Winner has Given Political
Orientation
Conserv-
Nominate Liberal Moderate Moderate ative Tea Party
Score Dem Dem Repub Repub Member
(1) (2) (3) (4) (5) (6)
A. Counties >1/2 of Voting Age Pop is Non-Hispanic White
Δ CZ Import 20.98 * -0.01 -27.22 ** -15.64 42.87 ** 25.28
Penetration (8.69) (7.94) (9.88) (11.61) (16.17) (15.47)
Mean in 2002 16.09 24.74 33.60 25.57 6.32
Δ 2002 - 2010 8.44 2.15 -4.14 -17.57 19.57 13.31
B. Counties ≤ of Voting Age Pop is Non-Hispanic White
Δ CZ Import -7.03 26.88 * -25.33 * 11.36 -12.91 1.58
Penetration (8.60) (12.87) (12.02) (7.17) (10.20) (8.10)
Mean in 2002 40.02 38.97 9.59 11.42 5.25
Δ 2002 - 2010 1.89 5.24 -7.08 -1.67 3.51 3.52
N=3241 County*District cells covering 349.8 weighted districts in Panel A, N=262 County*District cells covering 66.5 districts
in Panel B. All regression include the full set of control variables from Table 1. Observations are weighted by a cell's fraction inAgenda 1 Measuring Electoral Outcomes 2 Exposure to Import Competition from China 3 Empirical Specification 4 Anti-Incumbent, Party Realignment Effects 5 Polarization Effects 6 Heterogeneity in Polarization Effects 7 1990s versus 2000s 8 Conclusions
Polarization of House Ongoing Since Late 1970s
Do We Find Same Trade Exposure Effects in the 1990s?
Comparable effects on vote shares, turnover to 2000s. But no
effect on Nominate scores
A. Change in Voting Outcomes
% Vote for Pr(Winner Pr(Winner
Party that Republican Democrat gets >75% Unopposed
Won in 2002 Vote Share Vote Share of Vote) )
(1) (2) (3) (4) (5)
Δ CZ Import -10.33 * -3.79 5.24 -14.81 ~ -4.62
Penetration (4.68) (4.09) (4.38) (8.30) (5.18)
C. Change in
B. Change of Party and Incumbent Nominate Index
Same Party, Absolute
Change in Different Same Party, Nominate Nominate
Party Rep Same Rep Score Score
(1) (2) (3) (4) (5)
Δ CZ Import 7.52 16.18 -23.70 ~ -0.12 0.47
Penetration (11.12) (12.70) (14.06) (9.51) (6.90)
N=3523 County*District cells covering 421.7 districts (all except districts omitted states AK and HI, two districts in VT
and VA that elected independents, and 8.3 districts with cells that are not continuously observable to rezoning, primarily
located in LA, GA, NC, VA).Do We Find Same Trade Exposure Effects in the 1990s?
No significant effect on ideological orientation of those elected
Change in Probability 1992 - 2000 that
Winner has Given Political Orientation
Conserv-
Liberal Moderate Moderate ative
Moderate Democrat Democrat Republican Republican
(1) (2) (3) (4) (5)
Δ CZ Import -8.23 0.57 -1.78 -6.45 7.66
Penetration (12.80) (6.24) (11.20) (7.71) (12.36)
N=3523 County*District cells covering 421.7 districts (all except districts omitted states AK and HI, two districts in VT
and VA that elected independents, and 8.3 districts with cells that are not continuously observable to rezoning, primarily
located in LA, GA, NC, VA). Observations are weighted by a cell's fraction in total votes of its district in 1992, so that
each district has an equal weight in the regression, and standard errors are two-way clustered on CZs and Congressional
Districts. ~ p ≤ 0.10, * p ≤ 0.05, ** p ≤ 0.01.Agenda 1 Measuring Electoral Outcomes 2 Exposure to Import Competition from China 3 Empirical Specification 4 Anti-Incumbent, Party Realignment Effects 5 Polarization Effects 6 Heterogeneity in Polarization Effects 7 1990s versus 2000s 8 Conclusions
Discussion
Rising political polarization is striking but not well understood
• Coincidence with widening income inequality leads naturally to
conjecture that economic shocks are behind greater partisanship
2016 U.S. prez primary doesn’t appear anomalous in retrospect
• US manufacturing decline—which voters see as due in part to
globalization—likely to have political consequences
• Was affecting 2002-2010 House elections (we now know)
Why would trade shocks contribute to political polarization?
• Divergent responses to common shocks based on differences in prior
beliefs (Dixit Weibull ’07)
• Voter attitudes are stable in aggregate, but Dem and GOP beliefs
about policy, ideology, partisanship are sharply divergingCounterfactual Calcs: Dialing Back the Trade Shock by 50%
Change in Number of House Seats by Category in 416-District Sample
Conserv-
Total Liberal Moderate Moderate ative Tea Party
Total Dem Repub Dem Dem Repub Repub Member
(1) (2) (3) (4) (5) (6) (7)
I. Actual Change 2002-2010
-8 8 11 -19 -63 71 49
II. Counterfactual Change 2002-2010: 50% Lower Import Growth
4 -4 7 -3 -56 52 36
[-3,11] [-11,3] [2,12] [-10,4] [-63,-50] [42,63] [25,46]
III. Actual minus Counterfactual (Net Effect of 50% Shock)
-12 12 4 -16 -6 18 13
[ -5, -19 ] [ 19, 5 ] [ 9, -1 ] [ -9, -23 ] [ 0, -13 ] [ 29, 8 ] [ 24, 3 ]
The counterfactuals are constructed using the coefficient estimates in Panel B1 and C1 of Table 6, and columns 3-7 of
Table 7. They subtract from the actual seat changes the product of 50% x the mean growth of import penetration
(Appendix Table 2) x the r2 of the 2SLS first stage (0.38) x the start-of-period number of seats in the indicated category x
the regression coefficient divided by 100. The numbers in brackets in Panel II indicate a confidence interval for the
counterfactul, which is obtained by computing the counterfactul using the point estimates from Tables 6 and 7 plus/minus
one standard error.New York Times Graphic (4/26/16)
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