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Importing Political Polarization? The Electoral Consequences of Rising Trade Exposure - May 2016 - Pubdocs.worldbank.org.
Importing Political Polarization?
The Electoral Consequences of Rising Trade Exposure

             David Autor    David Dorn

            Gordon Hanson   Kaveh Majlesi

                      May 2016
Importing Political Polarization? The Electoral Consequences of Rising Trade Exposure - May 2016 - Pubdocs.worldbank.org.
Trade and Politics
Importing Political Polarization? The Electoral Consequences of Rising Trade Exposure - May 2016 - Pubdocs.worldbank.org.
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
Importing Political Polarization? The Electoral Consequences of Rising Trade Exposure - May 2016 - Pubdocs.worldbank.org.
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
Importing Political Polarization? The Electoral Consequences of Rising Trade Exposure - May 2016 - Pubdocs.worldbank.org.
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
Importing Political Polarization? The Electoral Consequences of Rising Trade Exposure - May 2016 - Pubdocs.worldbank.org.
...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)
Importing Political Polarization? The Electoral Consequences of Rising Trade Exposure - May 2016 - Pubdocs.worldbank.org.
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
Importing Political Polarization? The Electoral Consequences of Rising Trade Exposure - May 2016 - Pubdocs.worldbank.org.
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)
Importing Political Polarization? The Electoral Consequences of Rising Trade Exposure - May 2016 - Pubdocs.worldbank.org.
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
Importing Political Polarization? The Electoral Consequences of Rising Trade Exposure - May 2016 - Pubdocs.worldbank.org.
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-running
Many 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 Votes
Empirical 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 Party
Data 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 cell
Congressional 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 term
Sample 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 conservative
Nominate 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 percentile
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
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 ’00
Change 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 1.28pts
Isolating 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 countries
Imports 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 district
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 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 greater
Decomposing 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 in
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
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 diverging
Counterfactual 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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