A Note on the Eect of Rising Trade Exposure on the 2016 Presidential Election[Appendix to Autor, Dorn, Hanson, and Majlesi Importing Political Polarization?
The Electoral Consequences of Rising Trade Exposure ]∗David AutorDavid Dorn†

Gordon Hanson‡

Kaveh Majlesi

Rev. March 2, 2017
(First version November 16, 2016)

1

Introduction

This research note examines whether the exposure of local labor markets to increased import competition from China eected voting in the U.S. presidential election in 2016. It relates the change
in the county-level Republican two-party vote share between 2000 and 2016 to the growth in local
labor markets' exposure to Chinese import penetration. We nd a robust positive eect of rising
import competition on Republican vote share gains.

The magnitude of the Republican gains is

non-trivial. A counterfactual study of closely contested states suggests that Michigan, Wisconsin,
and Pennsylvania would have elected the Democrat instead of the Republican candidate if, ceterisparibus, the growth in Chinese import penetration had been 50 percent lower than the actual growthduring the period of analysis. The Democrat candidate would also have obtained a majority in theelectoral college in this counterfactual scenario. These results add to previous research that documents negative impacts of import competition on employment and earnings in trade-exposed local
labor markets (Autor, Dorn and Hanson, 2013; Acemoglu et al. 2016) and a decreased likelihoodthat moderate politicians win congressional elections in such locations (Autor, Dorn, Hanson and
Majlesi, 2016).
∗

MIT Department of Economics and NBER. E-mail: dautor@mit.edu†

University of Zurich and CEPR. E-mail: david.dorn@econ.uzh.ch‡

UC San Diego and NBER. E-mail: gohanson@ucsd.edu

Lund University and IZA. E-mail: kaveh.majlesi@nek.lu.se

1

2

Measurement

We study the Republican two-party vote share, which corresponds to the number of Republican
votes in a county, divided by the sum of Republican and Democrat votes. The outcome period of2000 to 2016 spans the period of most rapid growth of import competition from China following
China's accession to the WTO in 2001. The 2000 and 2016 elections were both closely contested,and did not feature an incumbent president. The vote count data are based on Dave Leip's Atlasof the U.S. Presidential Elections. We do not observe the 2016 votes for some or all of the counties
in the states of Massachusetts, Maine, Mississippi and Illinois, and additionally omit Alaska and
Hawaii where it is dicult to dene local labor markets. Our nal sample comprises 2,971 counties.
We match the election data at the county level to economic conditions in commuting zones(CZs), including the exposure of these local labor markets to import competition from China. The
growth of Chinese import penetration is measured over the period 2002 to 2014. Our measure of thelocal labor market shock is the average change in Chinese import penetration in a CZ's industries,
weighted by each industry's share in initial CZ employment:∆IP cuiτ =X Lijt
Lit

j

cu
∆IPjτ
.

(1)

cu = ∆M cu /(Y + M − X ) is the growth of Chinese import penetration
j0
j0
j0
jτ

In this expression, ∆IPjτ

in the U.S. for industry j over period τ , dened to be the 2002-2014 change. It is computed as thecugrowth in U.S. imports from China during the period 2002-2014, ∆Mjτ , divided by initial absorption(U.S. industry shipments plus net imports, Yj0 + Mj0 − Xj0 ) in the base period 1991, near the startof China's export boom. The fraction Lijt /Lit is the share of industry j in CZ i 's total employment,
as measured in County Business Patterns data prior to the outcome period in the year 2000.

cu

In equation (1), the dierence in ∆IP it across commuting zones stems entirely from variation in
local industry employment structure at the start of period t. This variation arises from two sources:dierential concentration of employment in manufacturing versus non-manufacturing activities andspecialization in import-intensive industries within local manufacturing. Importantly, dierences inmanufacturing employment shares are not the primary source of variation. In a bivariate regression,
the start-of-period manufacturing employment share explains less than 40 percent of the variation

cu

in ∆IP it .
An identication challenge for the estimation is that realized U.S. imports from China in (1) may
be correlated with industry import-demand shocks. In this case, OLS estimates of the relationshipbetween increased imports from China and changes in U.S. manufacturing employment may under-2

state the impact of the pure supply shock component of rising Chinese import competition, as both
U.S. employment and imports may rise simultaneously in the face of unobserved positive shocks to
U.S. product demand. To identify the causal eect of rising Chinese import exposure on local-level
political outcomes, we employ an instrumental-variables strategy that accounts for the potential
endogeneity of U.S. trade exposure. We exploit the fact that during our sample period, much of thegrowth in Chinese imports stems from the rising competitiveness of Chinese manufacturers, which
is a supply shock from the perspective of U.S. producers. China's lowering of trade barriers (Bai,
Krishna, and Ma, 2015), dismantling of the constraints associated with central planning (Naughton,
2007; Hsieh and Song, 2015), accession to the WTO (Pierce and Schott, 2016), and possible manipulation of its nominal exchange rate (Bayoumi, Gagnon, Londono-Yarce, Saborowski, and Sapriza,
2015; Gagnon, 2016) have contributed to a massive increase in the country's manufacturing capacity
and a concomitant rise in the country's manufacturing exports (Hsieh and Ossa, 2015).
To identify the supply-driven component of Chinese imports, we instrument for growth in Chinese
imports to the U.S. using the contemporaneous composition and growth of Chinese imports ineight other developed countries.

1 Specically, we instrument the measured import-exposure variableco∆IP cuit with a non-U.S. exposure variable ∆IP it that is constructed using data on industry-levelgrowth of Chinese exports to other high-income markets:∆IP coit =X Lijt−10jLuit−10

co
∆IPjτ
.

(2)

This expression for non-U.S. exposure to Chinese imports diers from the expression in equation(1) in two respects. In place of computing industry-level import penetration with U.S. imports bycucoindustry (∆Mjτ ), it uses realized imports from China by other high-income markets (∆Mjτ ), and it

2 As documented byreplaces all other variables with lagged values to mitigate any simultaneity bias.

Autor, Dorn and Hanson (2016), all eight comparison countries used for the instrumental variables
analysis witnessed import growth from China in at least 343 of the 397 total set of manufacturingindustries. Moreover, cross-country, cross-industry patterns of imports are strongly correlated between each of these countries and the U.S. That China made comparable gains in penetration bydetailed sector across numerous countries in the same time interval suggests that China's falling
prices, rising quality, and diminishing trade and tari costs in these surging sectors are a central

1

The eight other high-income countries are those that have comparable trade data covering the full sample period:

Australia, Denmark, Finland, Germany, Japan, New Zealand, Spain, and Switzerland.
2
The start-of-period employment shares Lijt /Lit are replaced by their 10 year lag, while initial absorption in theexpression for industry-level import penetration is replaced by its 3 year lag.

3

cause of its manufacturing export growth.
Data on international trade for 2002 to 2014 are from the UN Comtrade Database, which gives
bilateral imports for six-digit HS products.

3

To concord these data to four-digit SIC industries,

we rst apply the crosswalk in Pierce and Schott (2012), which assigns ten-digit HS products tofour-digit SIC industries (at which level each HS product maps into a single SIC industry), andaggregate up to the level of six-digit HS products and four-digit SIC industries (at which level someHS products map into multiple SIC entries).

To perform this aggregation, we use data on U.S.

import values at the ten-digit HS level, averaged over 1995 to 2005.
to dollar values in 2015 using the PCE deator.

4 All dollar amounts are inated

Data on CZ employment by industry from theCounty Business Patterns for the years 1990 and 2000 are used to compute employment shares byindustry in (1) and (2).

3Results

Our estimating equation for this analysis is0∆Yjt = γ d + β1 ∆IP cu
jt + Zjt β2 + ejt ,(3)

where the dependent variable ∆Yjt is the change in the Republican two-party vote share between
2000 and 2016 in countyj.

The main explanatory variable of interest is the contemporaneouschange in import exposure ∆IP jt in the commuting zone to which county j belongs.

Equation(3) further includes a vector of control variables Zjt measuring start-of-period economic conditions
and demographic characteristics, either at the CZ or the county level.

These include the shareof manufacturing in CZ employment, the Autor and Dorn (2013) routine-task-intensity index andoshorability index for CZ occupations, county population shares for nine age and four racial groups,
and the shares of the county population that are female, college educated, foreign born, and Hispanic,
where each of these variables is measured in 2000.
votes in the 2000 presidential election.

5 All regressions are weighted by a county's total

Following our strategy outlined above, we estimate (3)using two-stage least squares, with the import-exposure variable instrumented by contemporaneous3
4

See http://comtrade.un.org/db/default.aspx.
The crosswalk assigns HS codes to all but a small number of SIC industries. We therefore slightly aggregate thefour-digit SIC industries so that each of the resulting 397 manufacturing industries matches to at least one trade codeand none is immune by construction to trade competition.
5
The manufacturing employment share is based on County Business Pattern data. The Autor-Dorn indices are
based on occupational employment shares in the Census combined with task data from the Dictionary of Occupational
Titles. All population composition variables are based on Census short-form tabulations.

4changes in Chinese imports to other non-U.S. high-income countries as in (2).
In Table 1, we report estimates for equation (3), in which the dependent variable is the change
in the Republican two-party vote share between 2000 and 2016 in a county. The rst two columns
contain initial OLS and 2SLS estimates, respectively, with no additional control variables includedin the estimation. In column (1), the OLS coecient is positive but imprecisely estimated. When
import penetration is instrumented in column (2) using our strategy outlined above, the estimate
increases in magnitude and becomes statistically signicant. The fact that the 2SLS point estimate
exceeds its OLS counterpart is consistent with ndings in Autor, Dorn, and Hanson (2013) showingthat the exogenous component of rising China import penetration generates more negative local
labor market impacts than the endogenous trade measure, because the latter also comprises domestic
demand shocks that can lead to both greater imports and greater demand for local outputs. The

th percentile of thecolumn (2) point estimate of 2.23 indicates that for two counties, one at the 25

increase in trade exposure (a 2002-2014 increase of 0.58 percentage points in import penetration)th percentile of the increase in trade exposure (a 2002-2014 increase of 1.35

and another at the 75

percentage points in import penetration), the Republican two-party vote share between 2000 and2016 is predicted to increase by an additional 1.7 (2.23 × [1.35 − 0.58]) percentage points in the more
exposed county. Relative to the mean change in the Republican two-party vote share between 2000
and 2016 of -0.6 percentage points, this magnitude is non-trivial.
We include as additional regressors in column (3) industry and occupation controls that aremeasured at the CZ level and comprise the share of manufacturing in total employment (from the2000 County Business Patterns data), and the routine employment share and oshorability amongoccupations (based on Autor and Dorn (2013) and derived from 2000 Census data).

We control

for the start-of-period manufacturing share within CZs so as to focus on variation in exposure to
trade stemming from dierences in industry mix within local manufacturing sectors. Additionally,in column (4), we add demographic controls that comprise the percentage of a county's populationin nine age and four racial groups, as well as the population shares that are female, college-educated,
foreign-born, and Hispanic. Finally, adding Census division dummies in column (5) allows for diering trends in the Republican two-party vote share across the nine geographical Census divisions. In
this case, our identication comes from within-division, cross-county variation in trade exposure andvoting outcomes. The addition of these control variables only modestly aects the point estimatesfor import penetration. In alternative regression models where the outcome is the level of the 2016
Republican two-party vote share (with the 2000 vote share included as a control), we obtain similar
results.

5

Table 1: Exposure to Chinese Import Competition and Presidential Elections, 2016 vs.
2000. Dependent Variable: Change in Percentage of Two-Party Vote Obtained by the
Republican Candidate, 2016 (Trump) vs. 2000 (Bush)

(1)

(2)

Δ CZ Import Penetration

1.09(0.82)

2.23 ~ 3.04 ** 1.86 ** 1.67 *(1.15)(1.14)(0.69)(0.70)

Estimation

OLS

2SLS

Control Variables
2000 Ind/Occ Controls
2000 Demography Controls
Census Division Dummies

(3)

(4)

(5)

2SLS

2SLS

2SLS

yes

yes
yes

yes
yes
yes

Notes: N=3,107 counties. The mean Republican two-party vote percentage in our sample in 2016is 48.91 (s.d. 18.10), and its mean change between 2000 and 2016 is -0.74 (s.d. 9.95). CZ importpenetration increased by an average of 1.18 (s.d. 0.78) from 2000 to 2014. The county-level voting
data excludes all or parts of the states of AK and HI. The republican two-party vote sharecorresponds to republican votes divided by the sum of republican and democrat votes. The growthof Chinese import penetration is measured over the period 2000 to 2014. The 2SLS models incolumns 2-5 instrument for the change in Chinese import penetration in the US using the change
in other developed countries' imports from China. Industry and occupation controls in column 3
are measured at the CZ level and comprise the share of manufacturing in total employment (fromthe 2000 County Business Patterns data), as well as routine share and offshorability amongoccupations (based on Autor and Dorn (2013) and derived from 2000 Census data). Demographic
controls in column 4 comprise the percentage of a county's population in 9 age and 4 racial
groups, as well as the population shares that are female, college-educated, foreign-born, andHispanic. Census division dummies in column 5 allow for different time trends across the 9geographical Census divisions. Observations are weighted by a counties' total votes in the 2000
presidential election. ~ p ≤ 0.10, * p ≤ 0.05, ** p ≤ 0.01.

4Counterfactual Exercise

In this section, we conduct a counterfactual exercise to explore how the composition of votes in a
number of closely contested states would have diered had Chinese import penetration grown lessthan it actually did. The computation of the counterfactual is based on the coecient estimate incolumn (5) of Table 1, which indicates that Republican gain in the two-party vote share of 1.67
percentage point for a one percentage point greater growth of average Chinese import penetration
in a CZ. For each county, we compute the fraction of two-party votes that would have been obtainedby the Democrat instead of the Republican candidate if the trade shock had been X% smaller ascu

the product of 1.67 × X%× ∆IPjt , that is, the point estimate of the causal eect of the trade shockon the vote share, the size of each county's measured trade shock, and the scaling factor X%. We
next multiply this product with the number of two-party votes in a county in order to obtain the6number of additional votes that the Democrat candidate would have obtained in the counterfactual
scenario. We nally aggregate these county-level votes to state totals.
Table 2 presents the results of this counterfactual analysis. Column (1) shows the actual votemargin in favor of the Republican candidate in the 2016 election for a set of closely contested states.
The three subsequent columns show counterfactual outcomes had the growth in Chinese importpenetration in the U.S. had been 10%, 20%, or 50% smaller.

Table 2:

Counterfactual Outcomes in Closely Contested States

Vote Margin in Favor of Republican Candidate
Chinese Import Growth
Actual Outcome
10% Smaller
25% Smaller
Georgia
Arizona
North Carolina
FloridaPennsylvania
Wisconsin
Michigan
New Hampshire
Minnesota

215,380 (5.28%)
84,904 (4.12%)
177,009 (3.78%)
119,489 (1.27%)
73,224 (1.24%)
24,081 (0.81%)
13,107 (0.27%)-2,687 (-0.37%)-43,783 (-1.49%)

Electoral Votes Trump
Electoral Votes Clinton

202,810 (4.97%)
77,860 (3.78%)
141,689 (3.03%)
100,727 (1.07%)
52,630 (0.89%)
11,067 (0.37%)-3,979 (-0.08%)-6,830 (-0.94%)-54,009 (-1.84%)

306
232

290
248

183,956
67,29588,70872,584
21,739-8,455-29,608-13,045-69,347(4.51%)(3.27%)(1.89%)(0.77%)(0.37%)(-0.28%)(-0.61%)(-1.80%)(-2.36%)280
258

50% Smaller
152,531
49,685407
25,679-29,746-40,991-72,324-23,404-94,911(3.74%)(2.41%)(0.01%)(0.27%)(-0.50%)(-1.38%)(-1.49%)(-3.22%)(-3.23%)260
278

Notes: The computation of the counterfactual is based on the estimate that a growth of import penetration by one percentage
point reduces the Republican share of the two-party vote by 1.67 percentage points. Numbers in parentheses indicate the votemargin in favor of the Republican candidate as a percentage of the total vote.

Since we nd that local labor market exposure to import competition from China increased
the vote share for the Republican candidate, the counterfactual analyses for lower import growthcorrespondingly indicates more favorable results for the Democrat presidential candidate. The resultsin Table 2 show that the Democrat candidate would have won the states of Michigan and Wisconsinin counterfactual scenario with a 25% smaller trade shock, and additionally the state of Pennsylvania
had the trade shock been 50% smaller than observed. In the latter scenario, the Democrat candidate
would also have won the electoral college.
We stress that this exercise corresponds to a ceteris paribus scenario where the China shockaects the U.S. presidential general election exclusively through its eect on the Republican twoparty vote share. This counterfactual is of course extremely restrictive. As documented by Autor,
Dorn, Hanson and Majlesi (2016), the China shock directly contributed to the rapidly shifting7

ideological composition of the House of Representatives in the decade leading up to the Presidential
election, and it is likely that those representatives' legislative and campaign activities subsequently
contributed to the general election outcome in 2016.

Moreover, it is conceivable that a dierent

candidate would have been the victor of the 2016 Republican presidential primary election were
it not for the China shock, as argued by reporters for the New York Times (Schwartz and Bui,
2016) and the Wall Street Journal (Davis and Hilsenrath, 2016).

6 While these observations makeclear that our counterfactual exercise does not correspond to a realistic alternative scenario, they
simultaneously oer little reason to believe that this exercise overstates the impact of the China
shock on the outcome of the 2016 U.S. Presidential election.

5

Concluding Remarks

This note relates the change in the county-level Republican two-party vote share between 2000
and 2016 presidential elections to the growth in Chinese import penetration. We nd that rising
import competition was a robust positive contributor to Republican vote gains. A counterfactual
exercise indicates that the Democrat candidate would have won the states of Michigan, Wisconsin,
and Pennsylvaniaresulting in a majority of votes in the electoral collegeif the growth of importcompetition from China had only been half as large as actually observed.

6

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9