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Tariffs As Electoral Weapons: The Political
Geography of the US–China Trade War
Sung Eun Kim

and Yotam Margalit

Abstract
In response to President Trump instigating conflict over trade with China,
the Chinese government countered by issuing tariffs on thousands of products worth
over USD 110 billion in US exports. We explore whether China’s tariffs reflected a strategy to apply counterpressure by hurting political support for the president’s party. We
also assess the strategy’s impact on the 2018 midterm elections and examine themechanism underlying the resulting electoral shift. We find strong evidence that
Chinese tariffs systematically targeted US goods that had production concentrated in
Republican-supporting counties, particularly when located in closely contested
Congressional districts. This apparent strategy was successful: targeted areas weremore likely to turn against Republican candidates. Using data on campaign communications, local search patterns online, and an original national survey, we find evidence that
voters residing in areas affected by the tariffs were more likely to learn about the tradewar, recognize its adverse impact, and assign the Republicans responsibility for theescalating dispute. These findings demonstrate how domestic political institutions canbe a source of vulnerability in interstate disputes.

Governments have long used economic means to apply pressure on other states whentrying to impose their will or gain concessions. From embargoes, sanctions, andthreats of default to filing complaints with the World Trade Organization (WTO),
the list of such means is long. Yet in 2018, the escalating tensions between China
and the US, the world’s two largest economies, led to the use of a different formof economic pressure on an unprecedented scale: imposition of tariffs on goods
that are primary exports of the trading partner. President Trump was the first to
strike, announcing in March of that year tariffs on washing machines and solar
panels, followed by tariffs on steel and aluminum. China promptly responded withretaliatory tariffs on 128 US products. In the following months leading to the US
midterm elections, rising hostility saw both countries imposing additional roundsof tariffs. By the elections in November, China had imposed tariffs on a total of
6,000 products worth over USD 110 billion in US exports.
A government’s decision to impose tariffs has traditionally been analyzed as a toolthat politicians use to curry favor with local interest groups or obtain financial contributions from lobbies “buying” protection.1 However, the escalating trade war
between the US and China points to a different facet of the politics of trade.

1. Grossman and Helpman 1994.
International Organization, 2021, page 1 of 38© The IO Foundation, 2021

doi:10.1017/S0020818320000612

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Rather than imposing tariffs to pander to domestic interests, China’s strategy ofplacing tariffs on specific products may have been driven by a rationale of imposing
external political costs on its trading partner.2 As vice premier of China Liu He
explained, in choosing which products to tariff, his country seeks to “strike accurately
and carefully, splitting different domestic groups in the US.”3 Indeed, China
purchased a full advertisement supplement in the De Moines Register, the largest
newspaper in the battleground state of Iowa, highlighting the impact of the trade
war on the farmers in the soybean sector and attributing the situation to “the fruitof a president’s folly.”4 Yet when President Trump accused China of meddling in
the upcoming congressional elections, China’s top diplomat, Wang Yi, rejected theallegation, saying: “We did not and will not interfere in any country’s domestic
affairs.”5
The unfolding of the crisis raises three important questions that we focus on here.
First, to what extent did China’s selection of specific products to place tariffs onreflect a political strategy that aimed to decrease domestic support for President
Trump’s party? Second, if it was politically motivated, how successful was thestrategy in exacting an electoral cost on Republican candidates in the 2018
midterm elections? Finally, why did voters respond politically to the imposition oftariffs in the way they did?
To address those questions, we begin by analyzing the potential impact of thetariffs that China chose to impose, assessing the implications of the tariffs on theworkforce in each county and congressional district. We do so by matching the listof targeted commodities to industry classifications and constructing an original
measure of the county-level share of employment targeted in each round of retaliatory
tariffs. We then examine the relationship between our measure of pain inflicted on thelocal workforce and the county’s voting in past elections.
Our analysis shows that China’s selection of products to target with retaliatory
tariffs followed a clear political logic: products produced in areas with a strong
Republican base were more likely to be targeted. This targeting was even more prevalent in heavily Republican counties located in swing congressional districts—ones
that in past elections were closely contested by the two major parties. By targeting
those specific races, China had a higher probability of affecting the election
outcome than in electorally safe Republican districts. We present a range of tests

2. This is not to say that this is the first instance of tariffs being strategically selected to externally impose
electoral costs. In 2002, the European Union famously countered American tariffs on steel with tariffs onseveral products produced in areas important for President Bush’s political support base, most famously
Florida-produced orange juice. But this was a short-term move with almost trivial economic implications.
As we show later, the extent to which the Chinese tariffs corresponded with electoral considerations is of awhole different magnitude.
3. “Minitrue: On US-China Trade Tensions,” China Digital Times, 29 June 2018.
4. “China Looks to Influence Iowa in Trade War Over Trump Tariffs,” Bloomberg, 23 September 2018.
5. Yara Bayoumy and Michelle Nichols, “Trump Accuses China of 2018 Election Meddling; Beijing
Rejects Charge,” Reuters, 26 September 2018.

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Tariffs As Electoral Weapons

3that indicate that this pattern was not the result of chance, nor can it be accounted forby other county-specific characteristics.
We then assess the actual electoral impact of these tariffs by analyzing the change
in the Republican two-party vote share between the 2016 and 2018 congressional
elections. We find that overall, support for Republican candidates decreased in relation to the share of workers in a given district who were affected by the tariffs. For
each percentage point increase in workers exposed to the retaliatory tariffs, theRepublican share of the two-party vote decreased by between 0.12 and 0.47
points, compared to the previous election (the range depending on the specificround of tariff announcement).
Different mechanisms can account for why voters punished Republican candidates
in areas more heavily targeted by China’s tariffs. It may be a case of “naive” pocketbook voting, whereby citizens knew little about the escalating trade relations with
China but turned against the incumbent party because their personal economic situation had worsened. Alternatively, voters did know about the trade war and saw it as apolicy failure of the president and his party, perhaps also as an indication of bad
things to come. Thus, if the deteriorizing trade relations were a more salient issue
in geographic areas or industries affected by the tariffs, residents in those areas orworkers in targeted sectors would be more likely to have punished the Republicans
electorally.
To test these mechanisms, we use original data from a national survey we administered and augment it with an analysis of campaign communications ahead of the elections as well as web search patterns across the country. All three data sources point
more strongly toward the latter explanation. It does not seem to be the case that theretaliatory tariffs were present long enough to exert meaningful economic pain on
voters, and hence we find little to suggest that the shift in voting reflected pocketbook
retrospection. Rather, the evidence indicates that voters who were more vulnerable tothe tariffs’ impact were likelier to learn about the escalating trade war, recognize its
adverse impact, and view President Trump and Republican Congress members as
responsible for the situation.
Our findings contribute to the debate over the role of trade as an electoral issue. A
host of recent studies provides evidence that the adverse economic effects of tradeliberalization, particularly the import shocks resulting from China’s joining theWTO, have had notable electoral consequences.6 However, these studies offer
little insight on whether voters were making a connection between the economic situation and trade policy, or whether their vote was a reaction to changing economic circumstances, without awareness of the role that international trade has had on theadverse change they experienced. Indeed, evidence indicates that ordinary citizens
possess little knowledge about, or understanding of the complex issue of trade andits distributive consequences.7 Therefore, some argue that trade hardly plays a role

6. Autor et al. 2016; Che et al. 2016; Colantone and Stanig 2018a, 2018b.
7. For instance, see Fordham and Kleinberg 2012; Rho and Tomz 2017.

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as an electoral consideration among voters.8 While this is certainly true in some elections, our study shows how trade policy can nonetheless become a meaningful electoral issue that affects voters’ preferences. Specifically, we provide evidence of how
this occurs, from opposition candidates highlighting their opponents’ culpability forthe adverse effects of the trade war, through people residing in areas harmed by thetrade war seeking more information online about the situation, to voters attributing
responsibility to politicians for the escalation in trade relations.
Our findings add to recent studies that have shown that the adverse effects of tradeliberalization can have a nontrivial impact on voting.9 The results we present showthat voters also respond to the adverse effects of the opposite shift, that is, to aforeign country acting to reduce the level of trade. These results are very much inline with the framework that Rogowski proposed, indicating that a change in exposure to trade—both an increase and a decrease—has important and theoretically predictable political repercussions.10
Finally, our findings contribute to recent research on “smart” sanctions, which areintended to harm specific actors or constituencies in the sender state while minimizing harm on society at large.11 To date, the discussion regarding smart sanctions has
focused on tools such as asset freezes, travel bans, aid reductions, and trade embargoes (e.g., in arms, oil, or diamonds). Our research indicates that a strategically
crafted tariff policy can also serve as a form of smart sanctions by imposing targetedelectoral costs on the trading partner’s government. We also show that such a strategy
can exact electoral costs even in a fairly short period of time.
A few other recent papers study related aspects of the US–China trade war. Using
different data sources and methods, they reach several conclusions that are consistent
with our own findings. Fetzer and Schwarz also find evidence that the retaliatory
tariffs China imposed on US exports were politically targeted.12 Still other studies
find that these tariffs hurt Republican candidates in the 2018 elections.13 Our study
differs in that we not only examine the rationale and impact of the Chinese tariff
strategy—we also focus on the mechanisms underlying the domestic politicalresponse to the trade war. In doing so, we provide new insights on the ways votersbecome informed about trade policy as well as on the process of assigning politicians
responsibility for the policy’s impact.

Trade Policy, Retaliation, and Anti-Incumbent Voting
A host of recent studies provide evidence that the adverse economic effects of tradeliberalization have had notable electoral consequences. In particular, the distributive

8. Guisinger 2009, 2017.
9. Autor et al. 2016; Jensen, Quinn, and Weymouth 2017.
10. Rogowski 1989.
11. Drezner 2011.
12. Fetzer and Schwarz 2019.
13. Blanchard, Bown, and Chor 2019; Chyzh and Urbatsch 2020.

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Tariffs As Electoral Weapons5effects of the import shock resulting from China joining the WTO have led to electoral shifts ranging from voting in the 2016 US elections or the Brexit referendum tothe rise of economic nationalism in Western Europe.14 Notably, these studies offer
little insight on whether voters were making a connection between the economic situation and trade policy, or whether their vote was a reaction to changing economic circumstances, without an awareness of the role that international trade has had on theadverse change they experienced. In fact, recent research suggests that the issue of
trade openness was unlikely on voters’ minds. The average citizen typically exhibits
very limited interest in trade policy and understands little about its economic
consequences.15
This may well be the case in general, but what happens in instances when voters do
become aware of the trade issue and its distributive consequences? Several recent
studies offer insight on the first question, primarily through survey experiments.16
Based on such evidence, Rho and Tomz find that informing people about thewinners and losers from trade makes it more likely that their policy preferences
will reflect their personal economic interests.17 Similarly, Schaffer and Spilker
show that people exposed to information regarding the negative impact of trade on
their industries are less likely to support free trade.18
This evidence still leaves open the question of whether such information actuallyaffects how people vote. Under what conditions do people become aware of the tradeissue and its impact—realized or potential—on their economic well being? For one,people may learn about it from the news in cases where the issue of trade becomes aprominent topic.19 Voters might also obtain information or cues from their preferredpolitician or representative of the opposition party.20 They might also learn about the
impact of trade through their employer or organized interest groups, who may seek tomobilize workers on a specific policy matter or initiative.21 In recent work we provide
evidence of this channel of influence, showing how labor unions in the US provide
information and help shape their members’ trade policy preferences.22
The threat of voter accountability helps explain politicians’ tendency to advance
trade policies that benefit and privilege their constituents. In particular, politicians
tend to support trade deals that benefit or shield specific industries concentrated inareas that are important for their electoral prospects.23 However, the inverse of thislogic is that foreign governments can potentially seek to obtain concessions fromtheir trading partner by threatening to pursue tariffs on goods produced by politically

14. Autor et al. 2016; Che et al. 2016; Colantone and Stanig 2018a, 2018b.
15. Guisinger 2009; Rho and Tomz 2017.
16. Ardanaz, Murillo, and Pinto 2013; Naoi and Kume 2015.
17. Rho and Tomz 2017.
18. Schaffer and Spilker 2019.
19. Margalit 2011.
20. Gabel and Scheve 2007; Hicks, Milner, and Tingley 2013; Kono 2006.
21. Grossman and Helpman 2002.
22. Kim and Margalit 2017.
23. McGillivray 2004.

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important industries and constituencies. Imposing foreign economic pressure on theadversary’s support base is similar to the logic underlying the use of “smart
sanctions”—a tool that targets government supporters while minimizing adverse consequences for broader society.24 Yet notably, studies of smart sanctions have to date
focused almost exclusively on traditional economic measures, such as freezing funds,
travel bans, restrictions on investment, and embargoes on goods. Little has been
written about the use of selective tariffs as a potential form of smart sanctions.
Analysts expected the costs of a protracted trade war with the US on China’s
economy to be immense. China had clearly preferred to bring the dispute with the
Trump administration to a quick resolution without succumbing to President
Trump’s demands. Our conjecture is that to resolve the dispute, the Chinesesought to impose maximum political pressure on Trump and his Republican allies
in Congress. This was done with the hope that China’s potentially inflicting electoral
harm would lead to the president seeking a quick and peaceful conclusion to thesimmering conflict. If our conjecture is correct, the key question the Chinese faced
was how to impose such electoral costs most effectively. Given that the presidential
elections were almost three years away, placing pressure on the president required
threatening his allies’ standing in Congress. With this logic in mind, we would
expect the Chinese to impose tariffs on goods produced in: (1) locales with a highshare of Trump voters; (2) Republican-heavy counties located in highly competitive
Congressional districts.
In selecting which goods to penalize with tariffs, Chinese officials must have beenaware that the tariffs would raise the cost of those goods imported from the US. Thus,
in addition to the electoral considerations we described, China may also have sought
to minimize the imposition of tariffs on items whose primary or sole exporter was the
US. By placing tariffs on only (or mostly) goods that China could import from othercountries, China would reduce the potential harm to its domestic market. It is therefore possible that the goal of placing the greatest political pressure on President
Trump with an electorally minded list of tariffs was tempered by a desire to minimize
the adverse consequences on its own economy. Alternatively, the desire to bring theconflict with the US to a swift end, combined with the fact that China is an autocracythat is presumably less concerned with public opinion, might mean that the weight the
Chinese leadership assigned to the domestic market consideration may have been
relatively low.
A number of concurrent studies also examine political aspects related to the US–
China trade war and offer evidence consistent with some of our conjectures. Fetzer
and Schwarz find that areas with traditionally higher Republican support weremore likely to be targeted by tariffs imposed by America’s major trading partners,
not just China. They report a subsequent drop in Republican support and in theapproval rating of President Trump.25 Blanchard, Bown, and Chor find that this

24. Drezner 2011.
25. Fetzer and Schwarz 2019.

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drop hurt the Republicans in the 2018 midterm elections,26 while Chyzh and
Urbatsch show that loss of Republican vote share was particularly notable insoybean-producing counties, a prominent target of China’s early tariffs.27
While these studies speak to several aspects of our study, they nonetheless differ intheir focus, the theoretical conjectures they test, and their empirical approach. Most
pertinently, our research centers on the domestic political dimension. Specifically,
we analyze how US electoral politics shaped the formation of China’s tariff strategy,
and explore Americans’ information sources regarding the trade war, their attributions of blame, and subsequent voting behavior. By contrast, Amiti, Redding, andWeinstein and Fajgelbaum and colleagues focus on estimating the economic costof the trade war to American consumers and the US economy; Chyzh and
Urbatsch center on the politics of tariffs on the soybean sector; and Fetzer and
Schwarz are primarily interested in how countries traded off between maximizing
the political targeting of tariffs and mitigating harm to their own economy.28 Thus,
our study’s focus on the behavioral mechanisms underlying voters’ response to thetrade war is largely absent from these studies. Finally, and more technically, thepapers differ in the way they measure some of the key variables, in particular
the degree of a geographical unit’s exposure to retaliatory tariffs.29 As a result of
these differences and others, the studies offer different insights into the domestic political dimension of the US–China trade war.

Data
To examine China’s tariff strategy and its electoral consequences, we develop a newmeasure of exposure to Chinese retaliatory tariffs at the county level. Combining thismeasure with election data, we examine the extent to which China’s tariff imposition
was motivated by electoral considerations, and how successful this strategy was inexacting an electoral cost on Republican candidates.
Exposure to Tariffs
We measure exposure to the retaliatory tariffs as the share of workers in each county
employed in industries targeted by China. This measure captures the geographicalvariation in the degree of exposure to the Chinese tariffs by linking commodity-

26. Blanchard, Bown, and Chor 2019.
27. Chyzh and Urbatsch 2020.
28. Amiti, Redding, and Weinstein 2019; Chyzh and Urbatsch 2020; Fajgelbaum et al. 2020; Fetzer and
Schwarz 2019.
29. We do so by measuring the share of employment in each county affected by the tariffs using finegrained data on 1,026 industries (coded at the six-digit level). Other measures of exposure rely on the
county’s total value of exports affected by the tariffs calculated across a set of 131 industries (coded at
the three-digit level). Since our chief interest is understanding the tariffs’ impacts on voters in eachcounty, focusing on the share of the workforce affected by the tariffs seems more suitable for our
purpose than the dollar figure the exports represent.

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International Organizationlevel data with county-level employment data. The employment share in the targetedindustries approximates the size of the workforce vulnerable to the retaliatory tariffs,
which captures the intended electoral costs of China’s strategy.
China issued tariffs over four rounds from April to September in 2018. In retaliation against the US administration’s decision to place tariffs on steel and aluminum,
China responded in April with the first round of retaliatory tariffs, targeting a total of
128 items. As trade relations worsened, China expanded its list to include 545, 333,
and 5,207 additional items, as announced respectively in June, August, andSeptember 2018. For each round, we collected a list of all targeted items.30 We
matched HS codes of targeted line items to NAICS sectors, both at the six-digit
level.31 Based on this matching, we examine whether or not a given NAICS sector
is targeted by retaliatory tariffs.32 Table 1 lists the five most affected industries interms of the size of employment targeted in each round of retaliatory tariffs. As
the list makes clear, the first round disproportionately targeted the agricultural
sector, while the manufacturing sector was targeted from the second roundonward. Our final measure is calculated as the percentage of workers employed inthose targeted sectors at the NAICS six-digit level.33
We calculate this measure separately for each round of tariffs as well as for all
rounds combined. Figure 1 illustrates the geographical distribution of our exposure
measure. The upper left panel illustrates the degree of exposure to the first roundof tariffs imposed in April 2018, when China targeted 128 commodities. While
only 1.1 percent of workers on average were affected by the tariffs in each county,
there were seventy-one counties with more than 10 percent of workers employedin affected industries (e.g., counties in Washington and California with large
shares of fruit- and tree-nut-farming industries). The other three panels cumulatively
add the measure of exposure to the second, third, and fourth rounds of tariffs imposed
in June, August, and September of the same year. With the expansion of retaliatory
tariffs, the average share of affected workers at the county level increased to
3.3 percent and 5.6 percent in June and August, respectively. With a sharp escalation

30. The list of targeted tariff line items in each round was officially announced by the Ministry of Financeof China. While China announced the list of HS codes at the eight-digit level, we focus our analysis on HS
codes at the six-digit level because of the HS codes’ incompatibility between the US and China at the eightdigit level.
31. We match HS codes to NAICS sectors using the concordance between ten-digit HS codes and sixdigit NAICS sectors available through the US Census. Because we focus on six-digit HS, we match it withNAICS sectors if any of the ten-digit subheadings under the six-digit HS codes is matched to a specific
NAICS sector. For instance, HS 080290 (Nuts, Others, Fresh or Dried, Whether or Not Shelled) ismatched to NAICS 111335 (Tree Nut Farming) because the ten-digit subheadings of HS 080290 (HS0802901000, HS 0802901500, HS 0802909202, HS 0802909602) are matched to NAICS 111335 according to the concordance.
32. We consider a NAICS sector as targeted if the sector is matched to HS codes targeted by the tariffs,
and otherwise we classify it as “not targeted.”
33. For sector-level employment information, we use the 2016 County Business Patterns (CBP) data andthe 2016 Quarterly Census of Employment and Wages (QCEW) data. See appendix for a description of theway we incorporate the CBP and QCEW data sets.

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TABLE 1. Tariff-targeted industries, by the size of employment

NAICS

Industry Description

Employment

April
311611331110111219312130311421

Animal (except Poultry) Slaughtering
Iron and Steel Mills and Ferroalloy Manufacturing
Other Vegetable (except Potato) and Melon Farming
Wineries
Fruit and Vegetable Canning

147,390
93,75981,274
51,10742,826June
311615311611336111336112111219

Poultry Processing
Animal (except Poultry) Slaughtering
Automobile Manufacturing
Light Truck and Utility Vehicle Manufacturing
Other Vegetable (except Potato) and Melon Farming

231,457
147,390
85,84381,723
81,274

August211111339112331110339113336111

Crude Petroleum and Natural Gas Extraction
Surgical and Medical Instrument Manufacturing
Iron and Steel Mills and Ferroalloy Manufacturing
Surgical Appliance and Supplies Manufacturing
Automobile Manufacturing

113,404
106,760
93,75991,70485,843September
326199323111311615336411325412All Other Plastics Product Manufacturing
Commercial Printing (except Screen and Books)
Poultry Processing
Aircraft Manufacturing
Pharmaceutical Preparation Manufacturing

357,473
338,592
231,457
165,619
147,512

Notes: The table presents the list of five targeted industries with the largest number of employed workers in each of thefour rounds of Chinese retaliatory tariffs. The first and second columns list the six-digit NAICS industry code and its
description, respectively. The third column lists the number of workers employed in each industry. The employment
figures are from the 2016 County Business Patterns (CBP) and the Quarterly Census of Employment and Wages(agriculture sector only).

of the trade war, by September this figure had jumped to 16.7 percent, but the figure
shows that the degree of exposure varied greatly both across regions and withinstates.34

34. Of course, nontargeted industries may also be affected through downstream effects of tariffs on thevalue chain. Our measure of exposure does not capture these potential effects.

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0–2.5%(a) April(c) June

2.5 5%
5 7.5%
7.5 10%10 12.5%
12.5 15%
15 17.5%
17.5 20%20%(b) August(d) September

Notes: The figure illustrates county-level variation in the percentage of workers employed in the industries targeted by China’s retaliatory tariffs. Subfigures (a)–(d)illustrate the percentage of workers affected by the tariffs imposed by April, June, August, and September. For each round, we cumulatively add the percentage ofworkers affected by all previous rounds of retaliations.
FIGURE 1. County-level exposure to Chinese retaliatory tariffs

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Electoral Geography
To examine the degree of political targeting in China’s retaliatory strategy and its
electoral consequences, we combine the exposure measure we described with information on voting across districts and counties. If the Chinese aimed to reduce support
for the Republican Party in the 2018 midterm election, it is likely that they considered
America’s electoral geography in designing their tariffs. In fact, one might argue thatgiven the enormity of the stakes involved in the trade conflict, assigning a small teamof data scientists the task of identifying a tariff schedule that would hurt theRepublican’s political base—and thus affect the president’s calculus—seems analmost obvious step from the Chinese perspective. To assess whether that was
indeed the case, we focus on the results and trends in the two preceding congressional
elections. Specifically, we measure county-level support using the average
Republican two-party vote share in the 2014 and 2016 House elections.35
We define a swing district as one where the Republican two-party vote share was
between 40 and 60 percent in both of the 2014 and 2016 House elections.36 We thenconsider whether a given county is located within the boundary of a swing district.
Because counties often straddle multiple districts, we spatially merge maps of counties and districts and calculate a given county’s share of land areas that overlap withany swing district. We then consider the county as located within the swing district
when there is at least 20 percent overlap with a swing district.37
We examine the electoral impact of China’s retaliatory tariffs on the 2018 midterm
elections. Our focus is the shift in electoral support for the Republican candidate ineach district between the 2016 and 2018 House elections. By using the differencein the Republican two-party vote share between the two elections, we account for
any unobserved time-invariant county-level characteristics that are correlated withthe county’s level of support for the Republicans. We also account for a countylevel temporal trend in voting by controlling for the change in the Republican twoparty vote share in the previous election cycle (i.e., between the 2014 and 2016
House elections).

Political Targeting of Chinese Retaliatory Tariffs
To what extent did China’s selection of products for tariffs reflect an effort todecrease electoral support for Republican candidates? Before the first round of

35. The county-level election data are acquired from Dave Leip’s Atlas of the US Presidential Elections.
36. We also consider an alternative measure of a swing district following the Cook Political Report’s
competitiveness ratings. Our findings are robust to the use of the alternative classification, as reported inTables A7 and A8 in the appendix.
37. The idea being that if a sufficiently large proportion of the county lies in a given district, it is a“worthy” target. As a robustness check, we vary the threshold of 20 percent with other options of 10
percent and 30 percent. Our results remain substantively similar when using these alternative thresholds(see appendix Tables A5 and A6).

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tariffs was announced, the People’s Daily published an op-ed that called outright for
retaliatory tariffs to target key industries in Trump-supporting constituencies.38 But
that was not the official government stance. In fact, when asked by a CNN reporter
if the tariffs China placed on soybeans were aimed at hurting producers located inareas important to Republicans, Chinese Vice Minister of Finance Zhu Guangyaoemphasized only economic considerations: “We are analyzing the challenges fromthe perspective of the economy itself … Chinese farmers petitioned that the US subsidies are hurting the interests of Chinese soybean growers, and China must respect
the farmers’ demands.”39
Those denials aside, if a chief motivation guiding the Chinese strategy was to kneecap support for Republican candidates in the midterm elections, we would expect thetargeted goods to be produced in industries concentrated in areas with Republican
voters. But given that in many US districts one of the two major parties enjoys alarge, almost unassailable advantage, it is likely that the Chinese would focus theirattention on hurting the chances of Republican candidates competing in swing districts because in those cases even small shifts in voting could have a large impact
on the eventual outcome.
Figure 2 presents the geographical variation in electoral support for Republican
candidates in the 2014–16 House elections. The two-party vote share of
Republican candidates is illustrated at the county level. Swing districts are denoted
using the thick black lines. As shown in the figure, support for Republican candidates
often varies to a significant degree within the same district. Our expectation is that
Republican counties in swing districts will be more likely to be targeted by
China’s tariffs. For example, in the first round of retaliatory tariffs China included
nuts in the list of goods. It turns out that tree-nuts farming is concentrated in a few
counties in California, with Kern and Fresco counties—two Republican strongholds—accounting for more than 30 percent of employment in the entire US nutsfarming industry. The tariffs China imposed on nuts thus had a major adverse
impact on the economy of Republican-heavy counties in California’s twenty-first district, where the incumbent Republican representative was expecting a close race in2018.40
To test this expectation, we estimate the following linear regression model:
Yi ¼ α þ β1 gop vote sharei þ β2 swing districti
þ β3 gop vote sharei  swing districti þ μi þ e

ð1Þ

38. Xinyu Mei, “How to Respond to the Trade War? Trade Retaliation Should Follow the Principle ofPrecision Strike,” [our translation] People’s Daily Overseas Edition, 25 March 2018. Available at <http://m.haiwainet.cn/middle/456317/2018/0325/content_31285768_1.html>.
39. The State Council Information Office’s Press Briefing on China–US Trade held on 4 April 2018 [ourtranslation]. Available at <http://www.xinhuanet.com/talking/20180404z>.
40. The incumbent won the 2016 elections with 56.7 percent of the vote. This seat was included as one of
the competitive races in the Cook Political Report before the election.

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Tariffs As Electoral Weapons

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where the dependent variable Yi is the share of workers employed in targeted industries in county i, GOP VOTE SHARE is the average Republican two-party vote share in the2014 and 2016 House elections, and SWING DISTRICT is a binary indicator denoting
whether at least 20 percent of a given county i’s land is located within any swing district as we defined. We include an interaction term between GOP VOTE SHARE and SWING
DISTRICT as well as state fixed effects μ in all models. Observations are weighted bycounties’ total voting age population as of 2016.

<20%

20−30%

30−40%

40−50%

50−60%

60−70%

70−80%>80%

Notes: The figure shows the average Republican two-party vote share in the 2014 and 2016 House elections.
Black thick solid lines indicate swing districts where the district-level Republican two-party vote share was
between 40 percent and 60 percent in both the 2014 and 2016 House elections.

FIGURE 2. Republican share of the two-party vote for House of Representatives andthe competitive districts in the 2014 and 2016 elections

The primary variables of interest are GOP VOTE SHARE and its interaction term with
SWING DISTRICT. We expect β1, the coefficient on GOP VOTE SHARE, to be positive and stat-

istically significant. If China’s retaliatory tariffs were targeted at Republican voters, weshould observe a higher degree of exposure to the tariffs in those counties with a higher
share of Republican supporters. Also, given the expectation that China would be particularly prone to target Republican counties in competitive districts, we expect β3, thecoefficient on the interaction term, to be positive and statistically significant.
Table 2 presents the results. We separately estimate the models for each round of
retaliation. In the first two models, our dependent variable is the share of workers
employed in industries targeted by the tariffs imposed by China in the first roundof retaliation in April. The next models focus on the share of workers targeted by
the next three rounds that took place in June, August, and September. For eachround, we begin with a model with a continuous measure of the two-party GOP

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TABLE 2. Counties targeted by retaliatory tariffs

Dependent Variable: Workers Employed in Targeted Industries (%)
April

GOP VOTE SHARE (%)
SWING DISTRICT

AugustSeptember

(2)

(3)

(4)

(5)

(6)

(7)

(8)

0.018***(0.004)−0.035(0.257)0.012***(0.004)−2.520***(0.845)0.055***(0.017)3,1060.034***(0.005)−0.041(0.291)0.028***(0.005)−2.539***(0.904)0.055***(0.019)3,1060.019***(0.003)−0.238**(0.105)0.019***(0.003)0.033(0.312)−0.006(0.006)3,1060.136***(0.008)−0.351(0.427)0.134***(0.009)−1.051(1.261)
0.015(0.026)
3,106

GOP VOTE SHARE (%) * SWING DISTRICT

Observations

June

(1)

3,106

3,106

3,106

3,106

Notes: Dependent variables in models 1 to 2 are the percentage of workers employed in industries targeted by the first round of Chinese retaliation in April. The next models focus on thesecond, third, and last round of retaliation in June, August and September, respectively. All models include state fixed effects. All regressions are weighted by counties’ total voting age
population in 2016. Robust standard errors in parentheses. * p < .10; ** p < .05; *** p < .01.

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Tariffs As Electoral Weapons

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VOTE SHARE and a binary indicator for SWING DISTRICT along with state fixed effects. We
then add the interaction term between GOP VOTE SHARE and SWING DISTRICT.
The results are consistent with our predictions. First, the coefficient on GOP VOTESHARE is positive and statistically significant throughout the models. The sizable coefficient suggests that counties with more Republican supporters were more heavily hit
by the retaliatory tariffs. Second, targeting of Republican counties appears to be concentrated in swing districts in the first two rounds of retaliation, as suggested by the
positive and statistically significant coefficient on the interaction term in models 2
and 4. According to model 2, a ten-percentage-point increase (approximately halfof one standard deviation) in the two-party vote share of the Republican party is associated with a 0.12 percentage point increase in the share of the targeted workforce innonswing districts. This represents a 10.8 percent increase above the mean. Yet inswing districts, the corresponding figure is substantially larger: the targeted shareof the workforce is higher by 0.55 points, accounting for a 48.4 percent increase
above the mean. The substantive effects are similar in model 4 which examines thesecond round of retaliation.
The results also suggest that the political targeting of tariffs was less precise in thelater rounds. While China had to counter America’s sharp escalation of the trade war
by placing tariffs on a similar volume of trade, media reports speculated that China
had already exhausted its options in the early rounds and therefore had to broaden
its tariffs to cover goods that were not produced in areas with similar politically sensitivity.41 Our results are consistent with this observation. While the coefficient on
GOP VOTE SHARE appears positive and statistically significant throughout all four
rounds, the interaction term is significant only in the first two rounds, as models 1
and 3 show.42
An alternative explanation for these findings may be that industries that produce
the bulk of exports just happen to be located in Republican-leaning counties, inwhich case the correspondence between the Chinese tariffs and the electoral variables
is not evidence of an electorally motivated retaliatory strategy. While it is unlikelythat exporting industries would just happen to be concentrated in Republicanleaning counties in competitive congressional districts, we further test this possibility
by analyzing the Chinese targeting strategy using the product (rather than industry) as
the unit of analysis. Specifically, we ask whether the likelihood of being targeted by
Chinese tariffs is higher when the relevant industry’s production is concentrated in
Republican voting districts. To this end, we generate a new measure, REPUBLICANCONCENTRATION, which captures the degree of concentration of the relevant industry’s

41. Eduardo Porter and Karl Russell, “Firing Back at Trump in the Trade War with Tariffs Aimed at His
Base,” The New York Times, 3 October 2018.
42. The results are consistent with previous findings reported in Fajgelbaum et al. 2020 and Fetzer and
Schwarz 2019 who report that the retaliatory tariffs targeted Republican-leaning counties. However, ourresults highlight that swing districts were the main targets of the retaliatory tariffs.

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employment in Republican-voting areas.43 Using this measure, we estimate thefollowing model:
Probit(Yi ) ¼ α þ β1 Reublican Concentrationi
þ β2 Log(Exports to China)i þ Import Dependence on the USi þ ei ,ð2Þwhere Yi denotes whether product i was targeted in each round of Chinese retaliation.
In addition to REPUBLICAN CONCENTRATIONi , we control for product i’s export volumeof China in 2016.44 We also control for the the US share of Chinese imports tomeasure each product’s import dependence on the US.
TABLE 3. Product-level analysis of Chinese tariffs

Dependent variable: Products in the Retaliation List

REPUBLICAN CONCENTRATION
LOG(CHINA IMPORTS VOLUME FROM THE US)
IMPORTS DEPENDENCE ON THE US

Observations

April
(1)

June
(2)

August(3)

September(4)0.163***(0.032)0.001(0.000)0.027***(0.006)

0.276***(0.059)−0.001*(0.001)
0.058***(0.012)0.167***(0.047)0.001(0.001)0.019(0.012)−0.173(0.124)0.007***(0.001)−0.038*(0.019)

4,231

4,169

4,003

3,896

Notes: Dependent variables in models 1 to 4 are the binary indicators for being included in the Chinese retaliation tariff
list announced in April, June, August, and September, respectively. We include all six-digit HS products model 1, but
subsequently exclude the products targeted in the earlier rounds from model 2. REPUBLICAN CONCENTRATION measures therelevant industry’s degree of concentration in Republican districts. Marginal effect in the main entries. Robust standard
errors in parentheses. * p < .10; ** p < .05; *** p < .01.

The results, reported in Table 3, show that a relevant industry’s concentration in
Republican-leaning districts is systematically associated with a higher probabilityof being targeted in early rounds of retaliation, even when controlling for theindustry’s export volume to China and the Chinese import dependence on the US.
Consistent with the pattern we reported, we again see that in the later rounds,
when a huge number of additional tariffs were introduced, REPUBLICAN

43. We first compute REPUBLICAN CONCENTRATIONk for each industry k at the six-digit NAICS level, whichis calculated as Ln (Ek j /Ek)2 × REPUBLICAN VOTE SHARE j where Ek j and Ek denote industry k’s number of
employees for district j and for the entire nation, respectively, and REPUBLICAN VOTE SHARE j is the average
Republican two-party vote share in the 2014 and 2016 House elections in district j. This measure of industry concentration follows Lee 2017 and McGillivray 1997. Because our unit of analysis is product, we thencalculate REPUBLICAN CONCENTRATIONi for each six-digit HS product i by mapping each product i to eachindustry k. When a single product is mapped to multiple industries, we calculate the weighted average
weighting by each industry k’s size of employment.
44. The export volume data comes from Schott 2007, which is updated to provide US import and export
data from 1989 to 2017.

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Tariffs As Electoral Weapons

17

CONCENTRATION is no longer a statistically significant predictor. We further show thatthe findings are not a result of chance by simulating a set of alternative tariffschedules that are randomly selected to cover a similar volume of goods to the one
actually pursued, following a similar approach to the one introduced by Fetzer and
Schwarz.45 As we discuss in section A2.2 in the online appendix, these simulations
demonstrate that the tariff schedule that China selected was targeted at Republicanheavy districts in a way that was clearly not the result of chance. In fact, 99
percent of the randomly simulated tariff schedules were less targeted at Republican
voters.
We also find that there existed a wide range of alternative tariffs schedules onbaskets of goods that were less dependent on imports from the US, further indicatingthat the chief concern of the Chinese was to inflict maximum political pressure on the
Republicans, rather than minimizing potential disruption of imports to the Chinese
market. In the appendix we also present results of a similar analysis to the one
shown in Table 2, this time controlling for each county’s total export value andagricultural export value. The findings remain substantively similar, as reported inappendix Tables A9 and A10, further demonstrating that our results are not driven
by the concentration of exports in targeted areas.
Taken together, these results provide compelling evidence that the Chinese tariff
strategy was designed to hamper the electoral prospects of the Republican Party.
The strong empirical relationship we observe between the political geography of production and a county’s exposure to the newly imposed tariffs clearly indicates thatelectoral considerations played an important role in guiding China’s selection ofretaliatory tariffs.

Electoral Impact of China’s Retaliatory Tariffs
How successful was China’s strategy in exacting an electoral cost on Republican
candidates? We assess the electoral consequences of China’s retaliatory tariffs byexamining the county-level shift in the Republican vote share between the 2016
and 2018 House elections.46 With the dependent variable Yi denoting the change in
the Republican share of the two-party vote for House representatives in eachcounty i, our regression model is specified as follows:
Yi ¼ α þ β1 Targeted by Chinese Tariff t þ β2 Protected by US Tariffs þ Xi γ
ð3Þþ μi þ e

45. Fetzer and Schwarz 2019.
46. Because one’s county of residence may differ from county of employment, we estimate the samemodels with commuting zone as the unit of analysis. The results reported in appendix Table A15
remain largely unchanged.

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where TARGETED BY CHINESE TARIFF is the county-level exposure, measured as thepercentage of workers targeted by retaliatory tariffs.47 Because the US concurrently
imposed tariffs on Chinese imports, we control for PROTECTED BY US TARIFFS, whichmeasures the percentage of workers employed in industries protected by theAmerican tariffs.48 All models include a vector of demographic controls, Xi, toaccount for their likely effect on voting.49 State-fixed effects μ are also included in
all models. To assign more weight to larger counties that are more influential in determining the electoral outcome, we weight the observations by voting-age population
as of 2016.50 We also control for the average over-the-year percentage change inweekly wage between 2017 and 2018.51 Finally, some specifications control also
for the voting trend in the previous election cycle (Δ REPUBLICAN VOTE (%), 16–14).52
Table 4 presents the results. Since tariffs were imposed at different times, theexpected electoral effects also differ because of the difference in temporal proximity
to the midterm election. We thus estimate the same models varying the measure of
TARGETED BY CHINESE TARIFF, depending on the timing of tariff imposition. In thefirst two models, we estimate the effects of the county-level exposure to retaliatory
tariffs imposed by April. We then estimate the effects of the exposure to tariffs accumulated by June (i.e., adding the exposure in June to the first round of tariffs in April)
and through August (i.e., the first three rounds of tariffs) in models 3 to 4 and 5 to 6,
respectively. The last two models estimate the effects of overall exposure to tariffs
imposed at any time, including the tariffs that came into effect on 23 September.53
The results indicate that the Chinese strategy was quite effective in exacting electoral costs on the Republican Party. In all models, the county-level exposure to theChinese tariffs is negatively associated with the shift in the Republican share of

47. When counties are divided by the district border, the county-level measure of election outcome captures votes cast for candidates from different districts. When any district within the county boundary is
uncontested with only one major party candidate running, we include the election results from uncontested
seats in calculating the county-vote share. We report results that exclude uncontested districts in calculating
the county-level two-party vote share in Table A11 in the appendix.
48. We calculated the share of workers employed in the industries affected by the US tariffs on China in
the exact same way we did for the measure of exposure to Chinese tariffs. Our list of tariff line items
included in each round of tariff imposition is from the official announcement by the United States Trade
Representatives.
49. We control for the share of females, the share of four racial groups (White, Black, Asian, andAmerican Indian and Alaska Native), the share of the Hispanic population, and the composition of different
age groups. We use the statistics for 2016.
50. In the appendix we also present results without the weights, and when observations are weighted by
the total votes cast in each county in the 2016 House election. See Tables A12 and A13.
51. The data are from the QCEW. We use the over-the-year change in wage focusing on the third quarterof 2017 and 2018.
52. In the appendix, we estimate the same models but with an additional control for swing district indicator. This helps ensure that our results are not simply driven by a general decline of support for theRepublican Party in competitive districts (Table A14).
53. We include the measure of PROTECTED BY US TARIFFS from model 3 because the list was finalized inJune after going through a public notice and comment process after its announcement in April. We control
for the effects of the US-initiated tariffs imposed by June in models 3–4, by August in models 5–6, and by
September in models 7–8.

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TABLE 4. Retaliatory tariffs and midterm election

Dependent Variable: Δ GOP Vote Share (%), 18–16
(1)

(2)

(3)

by April−0.470***(0.150)−0.358***(0.113)0.091(0.090)0.147**(0.073)−0.470***(0.045)

State FE
Demographic Controls

Yes
Yes

Observations

3,098% TARGETED BY CHINESE TARIFFS

Δ GOP VOTE (%), 16–14

(5)

(6)

(7)

by August(8)
by September−0.239***(0.083)−0.104(0.084)0.075(0.090)−0.179***(0.066)−0.089(0.067)0.134*(0.073)−0.471***(0.045)−0.152**(0.069)−0.030(0.075)0.078(0.090)−0.116**(0.056)0.005(0.061)0.139*(0.073)−0.472***(0.045)−0.132(0.120)0.070(0.122)0.083(0.090)−0.188*(0.102)0.181*(0.103)0.146**(0.072)−0.473***(0.045)

Yes
Yes

Yes
Yes

Yes
Yes

Yes
Yes

Yes
Yes

Yes
Yes

Yes
Yes

3,096

3,098

3,096

3,098

3,096

3,098

3,096

% PROTECTED BY US TARIFFS
Δ WEEKLY WAGE, 18–17(4)
by JuneNotes: Dependent variables are the continuous measures of the change in the percentage of two-party Republican vote share between 2016 and 2018. The primary independent variable,
TARGETED BY CHINESE TARIFFS, measures the percentage of workers targeted by the retaliatory tariffs imposed by April in models 1–2, by June in models 3–4, by August in models 5–6, and by
September in models 7–8. All models include state fixed effects and demographic controls. All regressions are weighted by counties’ total voting age population in 2016. Robust standard
errors in parentheses. * p < .10; ** p < .05; *** p < .01.

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the two-party vote in the House elections, while the US-initiated tariffs seem to have
had only marginally positive effects. Substantively, for every percentage point
increase in the share of workers exposed to China’s retaliatory tariffs, theRepublican share of the vote decreased by 0.12 to 0.47 percentage points comparedto the previous election, depending on the timing of when the tariffs were imposed.
Note that these models account for the over-the-year change in weekly wages. Thissuggests that the negative effect of the tariffs on the Republican Party’s support was
driven not just by the realized economic changes that voters experienced as a result of
the trade war. Rather, these voting patterns probably reflected voters’ expectations
about the future impact of the trade war and were a rebuke of Republican candidates
backing the president’s trade policy.
Another notable finding is that the size of the coefficient consistently shrinks as we
include the later rounds of tariffs. While the county-level exposure to tariffs is associated with a decrease in Republican vote share of 0.47 percentage points when we
look at only the tariffs imposed by April (model 1), the corresponding figure drops
to 0.13 points when we examine the exposure to all rounds of tariffs announced by
September (model 7). This weakening effect may be explained by several factors.
First, voters were probably less informed about the specific industries and goods targeted in the later round of tariffs because it included over 5,000 products. Second, in
the September round there was a considerable overlap between Chinese retaliatory
tariffs and the US-initiated tariffs because both countries targeted thousands of line
items. This might have muddled voters’ assessments of the potential effects of the
trade war.54 Finally, the September rounds were less likely to have had an economic
impact on voters because they were announced only a short time before the elections
took place.
To assess the implications of the shift in vote induced by the tariffs, we conduct a
counterfactual analysis in which we examine how the election results would have differed had there been no retaliatory tariffs imposed by China while keeping the US
tariffs. We also consider a more realistic scenario whereby neither the US-initiatednor the Chinese retaliatory tariffs had been imposed. While our results differ depending
on our model selections, our estimates suggest that the Republican Party would havegained three to eight more seats under different hypothetical scenarios (see appendix
section A5 for more discussions on the counterfactual analysis).55 These results indicatethat the tariff war had a modest, albeit non-negligible impact on the House elections,particularly when one considers the gerrymandered and highly polarized nature of US
electoral geography, where only a small number of seats is truly competitive.
Finally, we examine possible heterogeneous effects of Chinese tariffs imposed on
agricultural versus non-agricultural sectors. Given China’s importance as an export

54. Because the two measures of TARGETED BY CHINESE TARIFFS and PROTECTED BY US TARIFFS are highly
correlated in models 7 and 8, the results should be interpreted with caution. In contrast, the targetedbaskets of goods were very different in the earlier rounds.
55. Blanchard, Bown, and Chor 2019’s estimation also suggests that the trade war accounts for five of
Republicans’ lost seats in the House.

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Tariffs As Electoral Weapons

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market for American agricultural products, the retaliatory tariffs were particularly
painful for the US agricultural sector. To assuage farmers’ discontent, the Trump administration promised to provide USD 12 billion in subsidies, though a sum of only USD
838 million was paid by the time of the election.56 Our analysis shows that the electoral
effects are largely driven by the agricultural sector (appendix Table A18).57 China’s
retaliatory tariffs are negatively associated with support for Republicans in both agricultural and non-agricultural sectors, but the effects are a good deal larger and statisticallysignificant in areas that produce targeted agricultural goods. The findings also provide
suggestive evidence that the votes against the Republican Party might have been
largely driven by exporters’ mobilization against the party, given that the targeted agricultural sector was mainly concerned about the export markets.58
The increase we find in the Democratic vote share in areas targeted by China’s
tariffs can arise from former Republican voters switching their vote to the
Democratic candidates. Yet it can also arise from the tariffs mobilizing new votersto turn out to vote. Table 5 offers insight on this by examining the percentagechange in total vote counts in the two last elections, that is, before (2016) and after(2018) the trade war began. We do not find any consistent evidence that turnout incounties more affected by the tariff was different than in less-affected counties. Ifanything, turnout decreased slightly in areas that were protected early by US-initiatedtariffs, as models 3 and 4 report. It therefore seems that an increase in turnout does notaccount for the electoral shift we observe following China’s retaliatory tariffs.

Was China’s Tariff Strategy Unique?
The escalating trade war with China was by far the most salient, but not the only frontof America’s trade disputes. Since the election of President Trump, the US has beenin conflict over trade policy with the European Union and with Mexico and Canada,
its trading partners in the North American Free Trade Agreement (NAFTA). As partof the dispute, those trading partners also countered US measures by placing tariffs on
American goods. How similar was their strategy of product selection compared tothat of China, and did those tariffs have a comparable electoral impact?

56. Alan Rappeport, “A $12 Billion Program to Help Farmers Stung by Trump’s Trade War Has Aided
Few,” New York Times, 19 November 2018.
57. Blanchard, Bown, and Chor 2019 also report that the Republican Party’s electoral losses were largely
driven by the agricultural sector. Chyzh and Urbatsch 2020 similarly find that soybean-producing counties
were more likely to turn against the Republican Party.
58. For instance, Minnesota Farm Bureau Federation President Kevin Paap testified before the US House
Ways and Means Subcommittee on Trade that “The current tariffs, continuing back-and-forth retaliatory
actions and trade uncertainties are hitting American agriculture from all sides and are causing us to lose
our markets. Once you lose a market, it is really tough to get it back.” Retrieved from <https://fbmn.org/
blog/2018/07/18/minnesota-farm-bureau-president-testifies-on-the-importance-of-trade>. Similarly, Texas
Farm Bureau President Russell Boening stated, “Forty-six percent of cotton exported to China comesfrom Texas. Any potential loss of this important market would be very difficult for our cotton farmers.”
Retrieved from <https://www.fb.org/files/RBoening-AFBF-HouseWM18.07.16.pdf>.

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TABLE 5. Retaliatory tariffs and voter turnout

Dependent Variable: Percentage Change in Total Votes
(1)

(2)

(3)

by April
0.082(0.069)−0.034(0.071)−0.048(0.096)0.059(0.098)−0.175***(0.024)

State FE
Demographic Controls

Yes
Yes

Observations

3,108% TARGETED BY CHINESE TARIFFS% CHANGE IN TOTAL VOTES, 16–14

(5)

(6)

(7)

by August(8)
by September

0.109**(0.053)−0.108***(0.037)−0.053(0.096)0.028(0.053)−0.094***(0.036)0.053(0.099)−0.175***(0.024)−0.063(0.043)−0.031(0.060)−0.050(0.096)−0.102**(0.041)−0.024(0.054)0.055(0.099)−0.176***(0.024)0.076(0.119)−0.154(0.123)−0.054(0.096)0.038(0.115)−0.124(0.120)0.052(0.100)−0.175***(0.024)

Yes
Yes

Yes
Yes

Yes
Yes

Yes
Yes

Yes
Yes

Yes
Yes

Yes
Yes

3,106

3,108

3,106

3,108

3,106

3,108

3,106

% PROTECTED BY US TARIFFS
Δ WEEKLY WAGE, 18–17(4)
by JuneNotes: Dependent variables are the percentage change of total number of votes cast for the House elections between 2016 and 2018. The primary independent variable, TARGETED BY CHINESETARIFFS, measures the percentage of workers targeted by the retaliatory tariffs imposed by April in models 1 and 2, by June in models 3 and 4, by August in models 5 and 6, and by September

in models 7 and 8. All models include state fixed effects and demographic controls. All regressions are weighted by counties’ total voting age population in 2016. Robust standard errors inparentheses. * p < .10; ** p < .05; *** p < .01.

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In Table 6 we examine the strategy of tariff imposition by the EU (models 1–2),
Canada (models 3–4), and Mexico (models 5–6). As the results indicate, all three
have targeted counties with strong GOP support. However, unlike the Chinese
approach, we find no clear differentiation between swing versus nonswing districts.
In other words, the composition of China’s tariffs seems to have been uniquely suited
to inflict electoral costs on the Republicans in a way that the retaliation strategy
pursued by other trading partners was not.
Following a simulation approach introduced by Fetzer and Schwarz,59 we again
compare the actual baskets of targeted items with randomly simulated hypotheticalbaskets. While the approaches differ slightly, the findings are largely consistent. As
we explain and show in the online appendix (section A2.2), the degree of political targeting in the basket of items actually selected for tariffs by Mexico, Canada, and the EU
was not systematically different from other randomly selected baskets. By contrast,
China’s selection showed a distinctive pattern of heavy concentration in Republicansupporting areas. Another notable difference is the extent to which the domestic economic costs were considered in the selection of retaliatory tariffs of each country or
bloc. While it is beyond the scope of our study to estimate these domestic costs of retaliatory tariffs, when using a measure of import dependence on the US as a proxy, we findthat China was the least likely to consider the potential costs of the tariffs on its domestic economy. In contrast, the EU seems to have selected a basket of items that would
minimize the potential costs on the region’s economy.
To compare the electoral impact of retaliatory approaches taken by the US’s
trading partners, we analyze the empirical relationship between the tariffs they
selected and support for Republican candidates in the 2018 midterm elections. In
Table 7 we present the association between the tariffs’ coverage and theRepublican two-party vote share, analyzing the effect separately for the EU,
Canada, and Mexico (models 1–6) and combining the exposure to tariffs imposedby all three counterparts (models 7–8). The coefficient on TARGETED BYRETALIATORY TARIFFS is consistently negative except for the case of Canada’s tariffs,
but statistically indistinguishable from zero in all models.60

Mechanisms: Why Were Republican Candidates Punished?
The results so far reveal a strong association between geographic exposure to China’s
retaliatory tariffs and a drop in support for Republican candidates in the 2018 elections. This pattern raises several questions. Were people simply voting against theincumbent party because of worsening economic conditions, that is, without

59. Fetzer and Schwarz 2019.
60. In Table A3 in the appendix, we also estimate the effects of overall exposure to the retaliatory tariffs
imposed by all counterparts (China, the EU, Canada, and Mexico). The effects are similar to when we
examine the effects of tariffs by China only, suggesting that our main findings are largely driven by the
Chinese tariffs.

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TABLE 6. Political targeting of retaliatory tariffs by the EU, Canada, and Mexico

Dependent Variable: Workers Employed in Targeted Industries (%)
(1)

(2)

(3)

The EUGOP VOTE SHARE (%)
SWING DISTRICT

0.013***(0.002)0.010(0.100)GOP VOTE SHARE(%) * Swing District

(4)

(5)

Canada0.013***(0.002)−0.104(0.362)0.003(0.007)0.039***(0.004)−0.201(0.186)(6)
Mexico0.039***(0.004)−0.141(0.691)−0.001(0.014)0.029***(0.003)−0.052(0.124)0.028***(0.003)−0.439(0.397)0.009(0.009)State FE

Yes

Yes

Yes

Yes

Yes

Yes

Observations

3,106

3,106

3,106

3,106

3,106

3,106

Notes: Dependent variables are percentage of workers employed in industries targeted by the retaliatory tariffs of the EU in models 1 and 2, Canada’s in models 3 and 4, and Mexico’s inmodels 5 and 6. All models include state fixed effects. All regressions are weighted by counties’ total voting age population in 2016. Robust standard errors in parentheses. * p < .10;** p < .05; *** p < .01.

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TABLE 7. Retaliatory tariffs and midterm election, by trading partner

Dependent Variable: ΔGOP Vote Share (%), 18–16
(1)

(2)

(3)

The EU(4)(5)

Canada(6)(7)

Mexico(8)All−0.033(0.191)−0.057(0.042)0.080(0.090)−0.033(0.146)−0.001(0.033)0.141*(0.073)−0.473***(0.045)0.039(0.083)−0.074(0.050)0.078(0.090)0.023(0.063)−0.013(0.038)0.140*(0.073)−0.473***(0.045)−0.057(0.096)−0.048(0.046)0.081(0.090)−0.114(0.071)
0.021(0.036)0.143**(0.073)−0.474***(0.045)−0.008(0.084)−0.057(0.052)0.080(0.090)−0.045(0.064)0.014(0.038)0.142*(0.072)−0.473***(0.045)

State FE
Demographic Controls

Yes
Yes

Yes
Yes

Yes
Yes

Yes
Yes

Yes
Yes

Yes
Yes

Yes
Yes

Yes
Yes

Observations

3,098

3,096

3,098

3,096

3,098

3,096

3,098

3,096

TARGETED BY RETALIATORY TARIFFS, %PROTECTED BY US TARIFFS, %Δ WEEKLY WAGE, 18–17
Δ GOP VOTE SHARE (%), 16–14

Notes: Dependent variables are the continuous measures of the change in the percentage of two-party Republican vote share between 2016 and 2018. The primary independent variable,
TARGETED BY CHINESE TARIFFS, measures the percentage of workers targeted by the retaliatory tariffs imposed by the EU in models 1 and 2, by Canada in models 3 and 4, by Mexico in models 5
and 6, and by all three actors in models 7 and 8. All models include state fixed effects and demographic controls. All regressions are weighted by counties’ total voting age population in 2016.
Robust standard errors in parentheses. * p < .10; ** p < .05; *** p < .01.

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making a connection to the trade war with China? Or did the drop in Republican
support stem from citizens’ conscious response to the US–China trade war, assigning
blame to those they perceived as responsible for the escalating situation? If it was a
conscious response, how did voters learn about the trade war? Finally, were votersreacting to a worsening of their own employment situation or to indications of economic decline in their area of residence? To get some handle on these questions, we
examine campaign communications by congressional candidates, data on online
search patterns, and original survey data of American voters.
Evidence from Campaign Communications
To understand how voters learned about the trade war, we begin with the immediatesuspects: representatives of the challenger party, in this case the Democrats, because
they had a clear incentive to inform voters about the adverse effects of the trade war
and blame their Republican rivals for this development.
We find a host of examples that illustrate how Democratic candidates used thetrade war as a key campaign issue. For instance, TJ Cox, a Democrat from
California’s twenty-firstst district, repeatedly criticized her rival Representative
David Valadao (R), then third-term incumbent, for being culpable: “As Trump’s
trade war with China escalates, we face dire consequences here at home. By
voting with Trump 99 percent of the time, @dgvaladao shows he is unwilling tostand up for our farms and communities here in the Central Valley.”61 Democrats
attacked their Republican rivals not only for active support of Trump’s trade
policy but also for not standing up against it. Angie Craig of Minnesota’s second district, for example, issued a press release stating: “our nation’s trade war with China is
having a real and very negative impact on farmers … Politicians in Washington—
including US Rep. Jason Lewis—are only making things harder for Minnesota’s
farmers … Lewis is silent on this administration’s trade policies. That says alot.”62 (See appendix section A1.3 for additional examples of press releases and official tweets by members dealing with the trade war in the run-up to the elections).
We expect that such campaign communications on the trade war would be mostcommon in the areas most targeted by China, that is, in places where the issue ismore likely to resonate with voters. Furthermore, it is likely that opposition candidates would be playing up the issue since they ostensibly have more to gain frommaking the costs of the president’s policy salient. We test these expectations bydrawing on a data set we assembled of all candidate communications in official

61. TJ Cox, Twitter post, 22 June 2018. Retrieved from <https://twitter.com/TJCoxCongress/status/
1009872679996780544>.
62. Angie Craig, “Farmers in Minnesota’s Second District Deserve a Representative Who Will Fight for
Them,” Southwest News Media, 31 August 2018. Retrieved from <https://www.swnewsmedia.com/jordan_independent/news/opinion/guest_columns/paid-letter-farmers-in-minnesota-s-nd-district-deserve-a/article_a763ad0f-e0c9-5829-90c2-7c3c9d1a9187.html>.

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Tariffs As Electoral Weapons

27

press releases and social media platforms in the 2018 House elections.63 We constructed the measure of the percentage of campaign publications that include thephrase “trade war” by Democratic and Republican candidates in the House election.
With this measure as the dependent variable (ranging from 0 to 9.6), we examine
whether the degree of a district’s exposure to the tariffs is associated with an increasein candidate attention to the trade war, especially among Democratic candidates.64
A simple comparison of means is revealing. About 35.8 percent of Democratic
candidates addressed the trade war in their campaign communications with voters,
while only 8.5 percent of Republican candidates did so. Examining whether these patterns also reflect the districts’ degree of exposure to the tariffs, we see the expected
results in Table 8. Not only were Democratic candidates more likely to discuss thetrade war in their communications with voters but they also discussed it even more
frequently when their districts were exposed to China’s retaliatory tariffs. This
result is indicated by the positive and statistically significant coefficient on the interaction term. Using model 6 as an example, a one standard deviation in the exposure
measure of the district is associated with an increase in the share of Democrats’ campaign communications related to the trade war by 0.11, representing a 56.1 percent
increase above the baseline. By contrast, the effects for Republican candidates arealmost negligible.65
Evidence from Web Search Patterns
To further assess the link between voters’ exposure to the trade war and their awareness of it, we examine data on people’s online searches. Specifically, we wish toassess whether people residing in areas at a higher risk of being harmed by thetrade war were more likely to actively seek information about the conflict. To thisend, we obtained data from Google Trends on the search frequency of the terms“trade war” and “Chinese (China) AND tariffs.” We take the index of the searchvolume for each key word at the level of metropolitan areas during 2018. The

63. We counted the total number of official press releases and social media posts (YouTube, Facebook,and Twitter) published by both Democratic and Republican candidates for the 2018 House elections during
the campaign period and the number of such publications that included the phrase “trade war.” We acquired
relevant information via Voxgov <www.voxgov.com> which provides access to documents, publications,and social media posts from all elected officials and political candidates. As a robustness check, we
examine campaign communications that include trade and China or tariff and China as presented in
section A3.8 in the appendix.
64. We recalculate the exposure measure by aggregating county-level data to the level of congressional
districts. When a single county straddles multiple districts, we weigh the exposure measure according to thecounty’s share of land areas that overlap with a given district.
65. In the cases where Republicans did discuss the trade war, the tone was very different, often more
optimistic about the trade dispute. For instance, Rep. Dusty Johnson of South Dakota tweeted on his confidence in Trump: “I’m a free trade guy. I didn’t like how the trade war started. But I have to admit that
@realDonaldTrump did a great job with the USMCA and what it will do for our economy. We still
have work to do for soy beans with China but I am confident he can negotiate a deal. #SDElection18.”
Dusty Johnson, Twitter post, 19 October 2018. Retrieved from <https://twitter.com/DustyJohnson/status/
1053100220421234691>.

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TABLE 8. Retaliatory tariffs and campaign communications on the trade war

Dependent Variable: Campaign Communications on the Trade War (%)
(1)

(2)

(3)

by April
DEMOCRATIC CANDIDATE% TARGETED BY TARIFFS

0.249***(0.045)0.044**(0.021)% TARGETED BY TARIFFS * DEMOCRATIC
Observations

840

(4)

(5)

by June0.201***(0.044)0.000(0.006)0.078*(0.042)840

0.252***(0.045)0.049***(0.015)

840

(6)

(7)

by August0.149***(0.047)0.009(0.009)0.077**(0.030)840

0.252***(0.045)0.059***(0.019)840

0.140***(0.052)0.021*(0.013)0.073*(0.039)840(8)by September

0.258***(0.045)0.027***(0.006)840−0.124(0.080)0.005**(0.003)0.042***(0.011)
840

Notes: Dependent variables are the percentage of campaign communications on the trade war. DEMOCRATIC CANDIDATE denotes the partisanship of House candidate (coded 1 for the Democratic
Party and 0 for the Republican Party). TARGETED BY TARIFF measures the percentage of workers targeted by Chinese retaliatory tariffs imposed by April in models 1 and 2, by June in models 3
and 4, by August in models 5 and 6, and by September in models 7 and 8. Robust standard errors in parentheses * p < .10; ** p < .05; ** p < .01.

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Tariffs As Electoral Weapons

29

index ranges from 0 to 100 and captures the relative degree of search intensity acrossareas.66
We examine whether the variation in the search index is correlated with the degreeof exposure to China’s retaliatory tariffs. Because the exposure measure is constructed at the county-level, we aggregate county-level data to the level of metropolitan areas and recalculate the exposure measure.67 Because the search index is anannual measure, we use the share of employment targeted by the retaliatory tariffs
imposed at any of the rounds. And because engagement with news and search intensity are likely to be correlated with education, we control for the share of individuals
with at least a bachelor’s degree or a professional degree.
Table 9 presents the results, which provide strong support for the expectations welaid out. We show the results for “Chinese tariffs” in models 1 and 2 and for “tradewar” in models 3 and 4.68 The results show that individuals searched more for“Chinese tariffs” on the web the greater the exposure to retaliatory tariffs in agiven metropolitan area. With one standard deviation increase in the share of targetedindividuals, the search index increases by approximately 2.7, representing about 20.5
percent increase above the mean. The results are comparable for searches related to“trade war.” Taken together, these findings indicate that awareness of escalating traderelations with China, as well as interest in learning new information about the situation, were significantly higher in the areas more affected by the trade war. Thesefindings suggest that the drop in support for the Republicans was more than a“generic” response to worsening economic conditions and instead reflected an
informed response of voters conscious of the trade war itself.
Evidence from Individual-level Data
We have demonstrated that people residing in areas targeted by China’s tariffs weremore likely to receive communications about the trade war from their Congressional
candidates and were also more likely to actively seek information online about thesituation. Next, we examine how people assessed the effects of the trade war andhow they evaluated the role of relevant political actors in leading to the escalation.

66. Google Trends assigns the value 100 to the metropolitan area with the highest search intensity of eachterm, and all other areas are assigned a figure proportionate to the top search rate. Google does not provide
information on the intensity of online searches in a given metropolitan area when there is an insufficient
search volume. We code such cases as 0 in our analysis.
67. The geographical unit is a core-based statistical area (CBSA) which is defined as a geographical areathat consists of one or more counties anchored by an urban center of at least 10,000 people plus adjacentcounties that are tied to the urban center by commuting. Our analysis thus does not include rural areas nottied to the urban center. Among 929 CBSAs (388 metropolitan statistical areas and 541 “micropolitan” statistical areas), Google provides information on the search pattern of areas that can be mapped to 262 CBSAs.
To recalculate the exposure measure at the CBSA level, we used the concordance between core-based statistical areas and counties available at <https://www.nber.org/data/cbsa-fips-county-crosswalk.html>.
68. We take the mean index for searches of “China AND tariffs” and “Chinese tariffs” for the first two,and the mean search index for “trade war” as keyword and as topic in the latter two.

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To this end, we fielded a national survey among the US population in February 2019,using the services of online polling company Lucid.69
TABLE 9. Retaliatory tariffs and web searches on trade war

Dependent Variable: Web-Search Index (0–100)
(1)

(2)

(3)

Chinese Tariffs
0.439***(0.129)1.530***(0.425)0.867(0.532)0.446***(0.163)1.504***(0.440)0.838(0.535)

State FE

No

Observations

262% TARGETED BY CHINESE TARIFFS% BACHELOR’S DEGREE% GRADUATE OR PROFESSIONAL DEGREE(4)
Trade War0.695***(0.192)
0.954**(0.373)1.503***(0.437)0.684***(0.215)0.907**(0.423)
1.588***(0.492)

Yes

No

Yes

262

262

262

Notes: Dependent variables are the yearly Google search frequency for “Chinese tariffs” in models 1 and 2 and “tradewar” in models 3 and 4 in metropolitan areas. Robust standard errors in parentheses. * p < .10; ** p < .05; *** p < .01.

Our key outcomes of interest pertained to: (1) the perceived impact of China’s tariffs;and (2) the assignment of responsibility for the trade war. With respect to the former,
we asked respondents: “As you may know, the US imposed tariffs on some Chinese
goods this past year, and China responded by imposing tariffs on some US goods.
Please tell us what you think the impact of these tariffs has been on the US
economy as a whole, on people in your area, and on you personally.” Respondents
then rated the impact on “the US economy,” “people in my area,” and “on me andmy family.” The ratings were on a five-point scale ranging from “very bad” (1) to“very good” (5). We dichotomize the dependent variable to equal 1 if the respondent
described the effect as either “bad” or “very bad,” and 0 otherwise.70
The second question read as follows: “People have different views of who is
responsible for the tariffs the US and China have been placing on each other in thepast year. From what you have read or heard, what role have each of the following
played in these tariffs coming about?” Items were randomized in order of appearance
and included: Democrats in Congress, Republicans in Congress, President Trump.71
The response options were “no role,” “small role,” “big role,” or “don’t know.”

69. Lucid is an online panel provider that uses quota sampling to target a nationally representative
sample. It has a very large pool of subjects drawn from various online sources. Respondents weredrawn by quota sampling to match the US population in terms of gender, age, region, ethnicity, andlevel of education.
70. We do so given that our interest lies in differentiating those who view the effect negatively rather thananalyzing any shift along the scale.
71. We asked about two additional options: Chuck Schumer and Paul Ryan. Respondents were also
given the option of choosing “Other.” Analysis of those three options does not add additional insight,
so we focus the discussion on the first three options.

Tariffs As Electoral Weapons

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TABLE 10. Views on impact of the trade war and assignment of responsibility

Dependent Variable:
(1)
(2)
(3)
Tariff imposition has had negative
impact on:
US
Area
Self/Family
INDUSTRY WORKERS TARGETEDCOUNTY WORKERS TARGETEDCOUNTY PROTECTED BY US TARIFFS

Observations

(4)
(5)
(6)
Has responsibility for tariff
escalation:
Trump
GOP
Dems

0.006
(0.070)0.265(1.245)−0.416(1.241)0.127**(0.059)0.610(1.353)−0.680(1.310)0.079(0.062)0.214(0.945)−0.330(0.932)0.076(0.058)0.375(1.349)−0.380(1.364)0.116**(0.056)−0.105(1.026)0.035(1.008)−0.121*(0.061)−0.506(1.005)0.419(1.009)

1,492

1,492

1,492

1,492

1,492

1,492

Notes: Dependent variables are binary indicators for whether a respondent considers the imposition of tariffs by China tohave had an adverse impact on: the US; one’s area of residence; one’s family, in columns 1 to 3 respectively. Columns 4
to 6 denote whether the respondent views President Trump, Republicans in Congress, or Democrats in Congress as
responsible for the tariff escalation, respectively. The independent variable INDUSTRY WORKERS TARGETED is measured as theshare of tariffs-affected workers in one’s industry of employment; COUNTY WORKERS TARGETED denotes the share of workers
in the county employed in sectors targeted the China’s retaliatory tariffs; PROTECTED BY US TARIFFS is the share of county
workers employed in sectors protected by the tariffs the US imposed. All models include state fixed effects and control for
education, gender, race, immigrant status, employment status, age, and an indicator variable denoting whether theindustry NAICS classification is missing. Standard errors clustered by states presented in parentheses. * p < .10;** p < .05; *** p < .01.

We begin by analyzing the association between the level of exposure to theChinese tariffs and perceptions of the trade war. To assess whether respondents’ attitudes reflect the adverse impact on their area of residence or on the industry they workin, the model includes two measures of exposure: (1) the workforce in one’s county ofresidence employed in sectors targeted by China’s tariffs (i.e., the same measure weuse in all previous analyses); (2) the exposure level of respondents’ own industry of
employment.72 In addition, we control for PROTECTED BY US TARIFFS, the measure usedearlier, denoting the share of county workers employed in sectors protected by thetariffs the US imposed on China, as well as basic demographics (age, gender, education, race, and employment status).
As Table 10 shows, we find no association between the level of exposure of theindustry of employment and perceptions of the US as a whole being hurt by thetrade war with China. However, we find a strong empirical relationship between
industry exposure and respondents perceiving the trade war as adversely affecting

72. The survey includes information on respondents’ industry of employment at the NAICS two-digit
level. We calculate the degree of exposure at the NAICS two-digit level by computing the share of workforce employed in affected industries of NAICS six-digit subheadings within the two-digit heading. We
take into account all four rounds of retaliatory tariffs. We also included an indicator variable forworkers without affiliation to any NAICS category (e.g., homemakers, unemployed). The overwhelming
share of respondents whose industry was affected by the tariffs were either employed in manufacturing
or agriculture.

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their area of residence. Substantively, an increase of a standard deviation in exposure
represents a 9 percent increase above the baseline rate in the share of people holding
this view. A shift from an industry that was not affected at all by the tariffs (e.g., rental
and leasing, educational services) to one that was most highly affected (manufacturing) is associated with an increase of 44.5 percent in the probability that respondents
will report that people in their area of residence have been hurt by the trade war. In
line with this finding, the table also shows a positive association between industry of
employment and respondents’ report that their family was hurt by the tariffs. It’s
interesting that this effect is below statistical significance.
We also find a positive association between exposure to the tariffs and respondents
attributing responsibility to the Republican members of Congress. By contrast, wefind a negative association with assigning responsibility to the Democrats inCongress. The positive estimate of attribution to President Trump is below statisticalsignificance, a seemingly surprising result that is probably caused by the small variation in responses to this question. Most respondents attributed responsibility to thepresident regardless of whether or not they were exposed to the tariffs. A standard
deviation increase in industry exposure is associated with a 4.5 percent increase
above (10% decrease below) the baseline rate in attributing responsibility to the
Republicans (the Democrats). A shift from non-exposure to the highest exposure isassociated with a 22.4 percent increase above (49.9% decrease below) the likelihoodof viewing the Republicans (the Democrats) as responsible for the trade war.
We find no clear relationship between the share of workers in the county employedin targeted sectors and respondents’ attitudes on any of the six outcomes we examine.
This suggests that respondents’ views about the trade war were shaped much more by
its impact on their employer and (possibly) their job prospects than on their area ofresidence. Whether it is because information was primarily conveyed through industry sources (e.g., employers, union representatives) or because people care more
about the trade conflict when it directly affects their own livelihood is a questionthat our data cannot resolve conclusively.
That said, we do find clear indications that the personal toll of the trade war was
important in shaping people’s views. Specifically, we examine whether those whoreported that their families were harmed by China’s tariffs were more likely toassign blame to one side or the other. Because such blame attributions can be expost rationalizations among partisans, we also control for respondents’ party affiliation. Nonetheless, as Table 11 shows, people whose families were hurt by
China’s response—as captured by this self-reported measure—were more likely toview the trade war as harmful to the US and their area of residence, as well as toblame Trump and the Republicans for the malaise. Importantly, this effect holds
even when taking account of respondents’ political preferences, indicating that theassignment of responsibility among those directly hurt by the tariffs goes aboveand beyond the effects associated with partisanship.
Returning to the questions that we posed at the outset, this analysis offers several
insights of note. First, it suggests that the shift in the vote against the Republicans in2018 was at least partially a conscious response to the escalation of the trade war, andTariffs As Electoral Weapons

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TABLE 11. Views on the trade war and assignment of responsibility, by perceived

impact of Chinese tariffs on family
Dependent Variable:(1)(2)
Tariff imposition has had
negative impact on:
USArea
FAMILY NEGATIVELY AFFECTED
DEMOCRATINDEPENDENT

Observations

(3)
(4)
(5)
Has responsibility for tariff escalation:
Trump

GOP

Dems

0.530***(0.024)0.208***(0.022)0.069***(0.026)0.715***(0.024)0.094***(0.020)0.021(0.021)

0.098***(0.030)0.093***(0.027)−0.045(0.037)0.119***(0.038)0.151***(0.031)
0.011(0.030)−0.048*(0.024)−0.082***(0.024)−0.046**(0.021)

1,492

1,492

1,492

1,492

1,492

Notes: Dependent variables are binary indicators for whether a respondent considers the imposition of tariffs by China tohave had an adverse impact on: the US and on their area of residence in columns 1 and 2 respectively. Columns 3 to 5
refer to whether a respondent views President Trump, Republicans in Congress, or Democrats in Congress as responsible
for the tariff escalation, respectively. The binary variable FAMILY NEGATIVELY AFFECTED denotes a respondent who perceives
the Chinese tariffs as having had an adverse impact on their family. All models include state fixed effects and control for
education, gender, race, immigrant status, employment status, and age. Standard errors clustered by states presented inparentheses. * p < .10; ** p < .05; *** p < .01.

not simply a vote against the incumbent for a worsening economic situation. People
in affected areas were both more likely to hear from their politicians about the conflictwith China and to actively seek out information about it. Second, people’s views
about the trade war were closely tied to its (realized or expected) economic impact
on them. Finally, these views corresponded more strongly with the impact ofChina’s tariffs on individuals’ industry of employment than on their area of residence,suggesting that employment concerns or communications within the workplace wereconsequential for the way people saw the conflict and attributed responsibility to thepoliticians.

Conclusion
Trade policy is often described as an electoral issue that is mostly dormant.73 Yet
empirical evaluation of this depiction is limited by the fact that candidate positions
on trade may change (and even converge) to align closer to voters’ preferences on
the issue.74 Thus, analysis of vote choice on candidates’ positions on trade might
not reveal a significant effect, even if the issue is important to voters. The US–
China trade war provides a special case in this respect because China’s tariffs strategy
was bound to generate a significant wedge between the stance of the two parties’

73. Guisinger 2009; Nguyen 2018; Taylor 2015.
74. Feigenbaum and Hall 2015.

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candidates on the trade war. Therefore, the endogeneity problem that typically constrains analysts’ ability to properly detect the electoral impact of trade is less present
in this case.
Our findings show that voters were indeed sensitive to the issue of trade policy, andin this case, not only to their own government’s policy but also to that of the trading
partner. As our evidence indicates, politicians competing in areas affected by thetariffs were more likely to speak about the trade war in the campaign, and residents
in those areas were more likely to seek more information about the issue and to attribute responsibility for the escalating trade war to members of the ruling party. Thesefindings indicate that voters were not merely responding to a deterioration in the economic conditions, but were knowingly reacting to the trade war itself.
In light of these findings, an important question is how unique the US–China trade
war of 2018 was in a historical sense, and whether it can provide lessons about thepolitics of trade that extend beyond the specific episode. In this context, it isperhaps useful to return to the Smoot-Hawley Tariffs Act of 1930, which massively
raised tariffs on more than 900 products, and instigated the largest trade conflict the
US was last embroiled in. Recall that while the Smoot-Hawley tariffs were describedand debated at the time as a domestic issue, they had significant international repercussions. Not only did American tariffs reduce imports and hurt foreign trading partners, research suggests they also had political consequences that resemble the effectof China’s tariffs on the US. For example, McDonald, O’Brien, and Callahan present
evidence that the Smoot-Hawley tariffs hurt crucial swing voters in Quebec and theprairie provinces of Canada.75 This contributed to voters’ abandonment of the rulingparty and to the victory of the Conservatives in the subsequent elections. Moreover,studies indicate that America’s trading partners sought to retaliate against the SmootHawley legislation by applying counterpressure. Several European countries—
including Spain, Italy, Switzerland, and Portugal—retaliated directly by singlingout the US for new tariffs.76 Other countries did not officially declare they were
doing so, but instead selectively increased duties on products that were predominantly imported from the US.77
Despite those similarities, the tariffs imposed by America’s trading partners did notappear to target specific politically important constituencies. Clearer evidence of sucha strategy’s use is available only much later. This may partly be because effective political targeting of tariffs requires quality data that were not available until relatively
recently. A cruder version of such a targeting approach occurred during the 1992
Uruguay round of the General Agreement on Tariffs and Trade (GATT), after the
French government resisted American demands to reduce agricultural subsidies
and price supports. In retaliation, the US placed import restrictions on French
white wine, produced in districts that were politically important for the ruling

75. McDonald, O’Brien, and Callahan 1997.
76. Irwin 2017, 167–68.
77. Ibid., 164

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Tariffs As Electoral Weapons

35

Socialist Party. The move appeared to be effective because the French soon
relented.78 Clearly, the “surgical” approach China has pursued in its targeting oftariffs seems unique both in comparison to the approach taken by other countries
in the 2018 trade war and its sheer scale. Investigating how politically minded targeting of tariffs came to be used is beyond the scope of this study, but is surely a fruitful
topic for future research.
Our findings point to several additional promising avenues for study. One iswhether certain political systems make the government more vulnerable to tariffs
imposed by a trading partner. Earlier studies have shown that regime type is systematically associated with differences in levels of openness to trade,79 and with the typeof trade protections governments use.80 As we have shown, the retaliatory tariff strategy that China advanced was distinctly more combative than those pursued by
Canada, Mexico, and the EU. Specifically, China’s tariffs appeared to assign
greater weight to hurting US political interests and lesser weight to the tariff’s potential impact on the home economy. The fact that governments of democracies are moreaccountable to the public makes them, in theory, more susceptible to foreign economic pressure than leaderships of autocracies. Whether leaders of nondemocracies
are indeed more willing to enter high-stakes trade conflicts is a question that merits
further investigation.
The evidence we presented indicates that voters punished the incumbent party even
without, or before, experiencing the full brunt of the tariffs but a question remains
about how voters would respond if and when the actual costs are fully realized. On
the one hand, higher costs may strengthen the perception among voters of a policy
failure, leading them to turn against the incumbent even more than we have seen.
On the other hand, if things turn sufficiently bad, voters may rally in support ofthe leadership and shift the blame to the foreign adversary. These long-term effects
may also vary across different groups of partisans as a function of their prior attitude
about the government. The electoral fortunes of the incumbent party may very muchdepend on the balance between those two opposite responses.
Finally, a question of growing importance is how people learn about the implications of trade openness. Our analysis put the spotlight on the information provided bypoliticians, as well as on people’s own efforts to learn about the situation. Earlier
research indicates that labor unions also serve as information providers about trade
policy to their members.81 But that is surely only a partial list. How influential isreporting in the national media, as opposed to local news or social media? As key
aspects of globalization are politicized into contentious electoral issues, it is increasingly important to understand the sources of information that people rely upon andhow that information shapes their views. As the unfolding of the US–China conflict

78. McGillivray 2004, 163.
79. Mansfield, Milner, and Rosendorff 2000, 2002.
80. Kono 2006.
81. Kim and Margalit 2017.

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indicates, the implications for both incumbent politicians and for the trade policy they
pursue is likely to be of great consequence.

Data Availability Statement
Replication files for this article may be found at <https://doi.org/10.7910/DVN/
1GGBBK>.

Supplementary Material
Supplementary material for this article is available at <https://doi.org/10.1017/
S0020818320000612>.

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Authors
Sung Eun Kim is Assistant Professor of Political Science and International Relations at Korea University.
She can be reached at sung_kim@korea.edu.

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Yotam Margalit is Professor of Political Science at Tel Aviv University. He can be reached at ymargalit@
tau.ac.il.

Acknowledgments
We thank Taegyun Lim, Lanxin Ju, and Shir Raviv for excellent research assistance. For valuable feedback
and suggestions, we thank Su-Hyun Lee, Dennis Quinn, Joonseok Yang, and participants at the 2019
World Congress for Korean Politics and Society, the 2019 International Studies Association AsiaPacific Conference, the 2019 Annual Meeting of the American Political Science Association, the 2019
International Political Economy Society, the political economy seminar at Konstanz University, as wellas the editors and reviewers of this journal.

Funding
This work was supported by the Ministry of Education of the Republic of Korea and the National Research
Foundation of Korea (Grant No. NRF-2019S1A5A8034621).

Key Words
Trade war; election; retaliatory tariffs
Date received: March 16, 2020; Date accepted: August 14, 2020