export opportunities to japan and the u.s. political...

Export Opportunities to Japan and the U.S. PoliticalRealignment in the 1980s∗

Shuichiro Nishioka and Eric Olson†

January 30, 2020

Abstract

The trade deficit with Japan became political issue in the United States in the early 1980s.“Japan bashing,”in which destruction of Japanese products took place in public, was widespreadthroughout the 1980s but eventually faded in the early 1990s. While the Democratic Partyresponded to the trade deficit by supporting protectionist measures against Japanese competitorsin the early 1980s, the Reagan administration opted to use Section 301 of the Trade Act to openJapanese markets in attempts to increase U.S exports to close the trade deficit. We find thatvoting shares for Republican candidates decreased in the Midwest where they faced importcompetition from Japan but increased in the South and the farm belt where U.S. exportsto Japan created local business opportunities. Although the effect of import competition onvoting outcomes have been emphasized in the recent literature, our results suggest that exportopportunities had a substantial effect on voting shares in the 1980s.

Keywords: Trade, Japan Bashing, Section 301, U.S. Presidential Elections, Reagan RevolutionJEL Classification: F13, P26, P33, R13.

∗This research was supported by the Joint Usage and Research Center, Institute of Economic Research, Hi-totsubashi University. We thank Jonathan Chu, John T. Dalton, Kyoji Fukao, Ilyana Kuziemko, Pablo M. Pinto,David E. Weinstein, Sunny Wong and seminar participants at the Hitotsubashi Summer Institute, the Universityof Houston, and South China Normal University for useful comments and suggestions. Reiko Doi and James Deanprovided superb research assistance.†Nishioka: Department of Economics, John Chambers College of Business and Economics, West Virginia Uni-

versity, 1601 University Avenue Morgantown, WV 26506-0625, Email: [email protected]. Olson:Department of Finance, International Business, and Operations Management, Collins College of Business, The Uni-versity of Tulsa, 800 S. Tucker Dr. Tulsa, OK 74104, Email: [email protected]

1 Introduction

The Reagan Revolution substantially altered the political landscape in the United States. Ac-

cording to the National Election Studies (NES), voter identification with the Democratic Party fell

by approximately 8%, from 64.5% in 1980 to 56% in 1988, which was the largest drop in party

identification history after World War II. Meffert, Norpoth, and Ruhil (2001) argue that the po-

litical realignment that occurred during the 1980s was a shift in the partisan equilibrium in which

the Reagan Revolution cut deeply into the dominant Democratic coalition that began with Presi-

dent Franklin D. Roosevelt and the New Deal Democrats. Interestingly, the shift in the partisan

equilibrium was geographically systematic. Figure 1 displays a color coded U.S. map that illus-

trates whether a county had a high (red) or low (blue) degree of change in the voting share of the

Republican presidential candidate between 1976 and 1984. The figure shows that the Republican

Party gained support in the South, the farm belt, and the rural part of the West, whereas the

Democratic Party gained support in the Midwest, the Northeast, and the urban part of the West.

Importantly, the political realignment in the 1980s was formative because it shaped the subsequent

regional voting patterns in the United States.1

President Reagan was an advocate of fair and free trade. The Reagan administration not

only completed the most comprehensive tariff-reduction negotiations at the Uruguay Round of

the General Agreement on Tariffs and Trade (GATT) but also initiated the U.S.-Canada Free

Trade Agreement in 1988, which was later expanded to the North American Free Trade Agreement

(NAFTA). However, in the early 1980s, the rise of the Japanese economy along with the substantial

rise in the U.S. trade deficit with Japan became a political issue in the United States. Much of the

political concern regarding import competition with Japan was a result of the stagnation of the U.S.

automobile industry due to competition from Japan. In particular, the impact was significant in the

Midwest where the Big Three lost their customers to more effi cient Japanese cars after the 1973 oil

crisis. In response, the Democratic Party—supported by auto-related industries’labor unions—tried

to enact protectionist measures. A notable example was a “Made in America” bill that tried to

1Meffert, Norpoth and Ruhil (2001) argue that the political realignment that occurred during the PresidentReagan’s time in offi ce was likely a rare event in the modern history of the United States. Since gaining the major-ity in the House in 1994, the Republicans have controlled the House for 18 out of the past 22 years whereas before1994, the Democrats had controlled the House for 58 out of the previous 62 years. See also Erikson, MacKuen andStimson (1998) and Green, Palmquist and Schickler (1998).

1

impose a domestic content requirement on cars sold in the United States.2 This was an attempt

of the auto-related labor unions to restrict automobile imports and protect domestic employment.

The Northeastern and Midwestern Democrats almost uniformly voted for the bill in the House of

Representatives in 1982; however, the bill never got out of committee in the Republican-controlled

Senate (Destler, 1991). Concurrently, Japanese firms purchased several high profile U.S. companies

(e.g., Firestone and Capitol Records) and landmarks in Los Angeles (the ARCO Plaza), New York

City (Rockefeller Center), and Chicago. As a result, anti-Japanese sentiment (“Japan Bashing ”)

increased in the U.S. and manifested itself in the public destruction of Japanese products. One

high profile case of “Japan Bashing ”occurred when a group of congressman used sledgehammers

to crush a Toshiba radio on the steps of the Capitol.3

Recent academic papers have shown that international trade, particularly import competition

with China, has affected U.S. congressional elections, the 2016 presidential election (Che, Lu, Pierce,

Schott, and Tao, (2016), Autor, Dorn, Hanson and Majles, (2016)) and is partly responsible for

exacerbating political polarization (Autor, Dorn, Hanson, and Majles, (2017)). Our paper differs

in two respects from the previous literature. First, we examine the 1976-1992 time period in which

Japan, not China, was the largest Asian trading partner; importantly, our sample period captures

elections after the signing of the U.S. trade act of 1974 (but before the rapid growth of Chinese

exports) in which trade became an important political issue because fast-track authority (FTA) and

Section 301 authority was granted to the President.4 Second, we examine the net effect of trade

with Japan on U.S. elections by developing the Bartik index (1991) for export opportunities as well

as for import competition (Autor, Dorn, and Hanson, (2013)). Our trade indices show that over

the period of 1976-1984 the region that was most susceptible to Japanese imports was the Midwest

and the Northeast. Counties (similar to Howard County, Indiana and Genesee County, Michigan)

where American automobile industries located were significantly exposed to import competition

with Japan. Counties that benefited most from export opportunities to Japan was the South, the

farm belt, and the suburban areas of the West where resource-intensive industries located. This

systematic redistributional effect of trade influenced the results of the presidential elections: voting

2The bill is Fair Practices in Automotive Products Act (H.R.5133), which was first introduced to the House ofRepresentatives by Democratic Representative Richard L. Ottinger in December 1981.

3https://www.nytimes.com/1988/02/28/weekinreview/the-nation-japan-bashing-becomes-a-trade-bill-issue.html4FTA and Section 301 authority gave the President authority to negotiate trade agreements and open foreign

markets.

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shares for the Republican (Democratic) candidates decreased (increased) in the counties where they

faced strong import competition with Japan, and increased (decreased) in the counties where U.S.

exports to Japan created local business opportunities.

We also show that export opportunity had a much larger net effect on the vote share than

import competition. Our point estimates imply that, over the 1976-1984 period, conditional on

the national-level change in voting shares, the Republican (Democratic) shares of a county at the

90th percentile of export opportunity increased (decreased) by 5.7 (-6.9) percentage points more

than in a county at the 10th percentile. Over the same period, the Democratic (Republican)

shares of a county at the 90th percentile of import competition increased (decreased) by only 3.2

(-2.9) percentage points more than in a county at the 10th percentile. Interestingly, trade did

not have an impact on the Democratic share of the vote over the 1984-1992 period; however, the

Republican share continued to increase in export-oriented areas. Republican shares of a county

at the 90th percentile of export opportunity (import competition) increased (decreased) by 5.0

(-2.0) percentage points more than in a county at the 10th percentile. Thus, while the literature

has highlighted the political motives to protect domestic industries from import competition (e.g.,

Hillman (1982), Grossman and Helpman (1994), Che et al (2016), Autor et al, (2016)), promoting

exports was a politically winning strategy for Republicans during the 1980s. Our results suggest

that opening Japanese markets for U.S. exporters helped solidify the Republican Party’s base in

export-oriented rural areas.

The rest of the paper proceeds as follows. Section 2 provides a brief summary of the political

issues on trade in the 1980s as well as a summary of U.S. trade with Japan. Section 3 describes our

data, trade indices, and empirical strategy. Section 4 displays our baseline results from presidential

elections, various robustness, and results from congressional and senate elections, and section 5

concludes.

2 Trade Politics

Article I Section 8 of the Constitution grants Congress the constitutional authority to set tariffs.

In spite of the Constitutional definition of power, the authority on tariffs shifted to the President

after the U.S. trade act of 1974 (H.R.10710) was enacted in the 93rd Congress. The trade act of

1974 created (1) the fast-track authority (FTA) for Republican President Gerald Ford to negotiate

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trade deals, particularly the Tokyo Round of GATT, for a specific length of time and (2) Section 301

for the President to eliminate the alleged practices of foreign firms and governments that are unfair

or restrict U.S. commerce. The FTA authority is subject to renewal and only permits Congress to

approve or disapprove the trade deal but does not allow Congress to amend it. Conconi et al (2012)

is the first paper to develop a theoretical model to describe how congressional members may vote

on FTA legislation. The FTA was extended for Democratic President Jimmy Carter in July 1979,

before the 1980 presidential election, for eight years by a Democratic controlled House and Senate.

As a candidate, Republican President Ronald Reagan ran on a platform of free trade and a

type of North American globalization. In fact, at the announcement of his candidacy in November

1979, he announced his support for a “North American accord” in which the U.S., Canada, and

Mexico would allow labor and capital to move freely across borders. The early 1980s, however,

marked the first U.S. trade policy crisis after the President was substantially granted authority for

trade negotiations. Prior to the 1980s, the United States’current account was roughly balanced.

However, beginning in the early 1980s, the U.S. began to run consistent trade deficits, particularly

with Japan. Figure 2 shows that the bilateral U.S. trade deficit with Japan increased from $3

billion in 1976 to $37 billion in 1984 primarily due to an increase in Japan’s auto exports (see the

first panel of Table 1). Although Japan’s imports from the United States were relatively small,

areas where resource-intensive industries were located greatly benefited from export opportunities

to Japan (see the second panel of Table 1).

Given the rising competitive pressure from the Japanese auto sector in 1980, Ford and the United

Automobile Workers (UAW) submitted escape clause petitions to slow Japanese auto exports. The

U.S. International Trade Commission (USITC), however, ruled against Ford and the UAW because

the stagnation in the U.S. auto industry was due to the recession in the United States and shifts

in consumer preferences towards fuel effi cient cars. As a result of the USITC decision, in February

1981, Republican Senator John Danforth introduced a bill (S.396) to impose quotas on automobiles

from Japan. The bill was introduced primarily to serve as leverage against the free-traders within

the Reagan cabinet that did not think that the trade deficit was an issue. This point was further

reinforced when Republican Senate Majority Leader Bob Dole informed the Reagan administration

that he had enough votes in the Senate to override a presidential veto of the Danforth Bill. In

response, the Reagan administration pushed Japan for a voluntary export restraint (VER), and

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the Ministry of International Trade and Industry (MITI) of Japan announced the plan to reduce car

exports to the United States in May 1981. In spite of the VER, the share of Japanese automobiles

in U.S. markets continued to increase.5 In response, the UAW pressed for a bill (H.R.5133) that

imposed a domestic content requirement on manufacturers with greater than 100,000 motor vehicles

sold in the United States. This was the bill aimed to put “Made in America” tags on cars sold

in the United States and intended to restrict automobile imports from Japan. To meet the bill’s

requirements, a Congressional Budget Offi ce report (1982) stated that Toyota and Nissan would

be forced to move a significant part of their production to the United States. The Big Three,

as well as Volkswagen, could easily escape the penalties even though some components and parts

of their cars were imported.6 Importantly, the domestic content bill had a clear partisan divide.

The Northeastern and Midwestern Democrats almost uniformly voted for the bill in the House of

Representatives and passed the bill by a 215-188 margin in 1982; however, the bill never got out of

committee in the Republican-controlled Senate due to Republican opposition (Destler (1991)).

Because the deficit was occurring during a Republican President, and the Congress had ex-

tended the President’s FTA authority for eight years in 1979, the Democrats blamed President

Reagan and the Republican Party for the trade deficit. The Democrats viewed trade as a viable

campaign issue that would help it regain the Senate during the 1986 midterm elections. In July

1985, Representative Dan Rostenkowski (Chairman of the House Ways and Means Committee) and

Senator Lloyd Bentsen (the ranking Democrat on the Senate Finance Committee) introduced a bill

(H.R.3035) that imposed a 25% import surcharge on countries that ran large trade deficits with

the United States. The Republicans, not wanting to look weak on trade, responded with Senator

Bob Packwood’s bill (S.1404) which required retaliation against Japan for unfair trade practices.

Due to mounting political pressure regarding the trade deficit, the Reagan administration sought

to expand exports of American products rather than block imports to reduce the trade deficit. In

particular, the Reagan administration responded to the increased anti-trade sentiment by opting

to use Section 301 of the trade act of 1974. Previously, Section 301 was invoked only after a private

company filed a complaint accusing a foreign firm or government of unfair trade practices. The

5Japan’s Voluntary Export Restraint (VER) continued offi cially until 1994 when the world trade treaty underthe GATT reached agreement for phasing out VERs.

6See also Washington Post article “Domestic Content-Bill ”Takes Aim at Japan" (December 9, 1982). TheCBO reports estimated that Volkswagen would escape the penalties in the bill because its U.S. sales were less than170,000 and its Rabbits were produced in New Stanton, PA with high degree of U.S. domestic content.

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Reagan administration argued that the U.S. government should initiate Section 301 cases against

foreign trade barriers as a tool to open foreign markets for American exporters. On September 21,

1985, President Reagan gave a “Fair Trade”speech stating his administration’s intent to use Section

301 to open foreign markets. Concurrently, Secretary of the Treasury James Baker negotiated the

Plaza Accord in which the United States, Japan, West Germany, France, and the United Kingdom

agreed to depreciate the U.S. dollar. After the Plaza Accord was announced on September 22,

1985, the U.S. dollar subsequently depreciated against the Japanese yen by 46.9% from 1985 to

1992. As the Reagan administration had hoped, U.S. exports increased substantially across a broad

range of products including automobiles, aircraft, and electronics (the third panel of Table 1), and

trade deficits against Japan as well as West Germany, France, and the United Kingdom declined

(Figure 2). Use of Section 301, as well as its strengthened variants adopted by the Congress in

1988 (i.e., Super 301, the Omnibus Foreign Trade and Competitiveness Act (H.R.4848)) was the

blueprint behind virtually all of the successful trade agreements to open Japan’s markets ranging

from supercomputers and satellites to wood products (Grier (1992)).

3 Trade and U.S. Elections

3.1 Data

We obtained the share of votes won in each county by the Democratic or Republican candidates

from the Inter-University Consortium on Political and Social Research (ICPSR) database in presi-

dential, house, and senate elections for 1976, 1980, 1984 and 1988, and from Dave Leip’s Atlas of

U.S. Elections for 1992. We use the data on presidential elections after 1974 because the President

was granted greater authority on trade after the passage of the trade act of 1974. Data on U.S. and

Japanese trade at the three-digit 1987 SIC industry level were developed from the UN comtrade

database (4-digit and 5-digit categories of SITC Rev.1) for 1972, 1976, 1980, 1984, 1988, and 1992.

Following Autor et al (2013), we derive the local employment data at the county level from the

County Business Patterns (CBP) data from 1974 to 1993. To improve the quality of the data, we

develop time series data over the years from 1973 to 1993 and eliminate outliers and interpolate

missing observations. As a robustness check, we compare our total manufacturing employment

data to the NBER production database. Our results are comparable, but slightly higher, to the

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corresponding number in the NBER production database.7

3.2 Bartik Indices of Trade

In order to examine trade effects on U.S. elections, we assume business opportunity is an im-

portant factor in determining how voters are likely to vote in presidential elections. Our empirical

trade variable is from Autor et al (2013) who study the general equilibrium effect of rising Chi-

nese import competition on U.S. local labor market outcomes. We are different from them for the

following two points. First, we focus on business opportunities in general and we do not focus on

the general equilibirum impacts of trade on local labor market outcomes because we use relatively

short election intervals (i.e. four-year or eight-year). Second, whereas Autor et al (2013) define

commuting zones developed by Tolbert and Sizer (1996) that cluster areas with strong economic

ties as local labor markets, in our baseline analysis, we use county-level data because some com-

muting zones cross state lines (i.e. St. Louis). This is particularly problematic when we use the

congressional (both the House of Representatives and Senate) elections data.

In our model, Japan affects each U.S. county through two channels. First, similar to Autor et

al (2013), we use the import exposure index as in Bartik (1991), which is the per worker change in

industry-level Japanese imports weighted by county i’s employment share in industry j at starting

year t− 1:

∆IJit =∑j

Lij,t−1Li,t−1

∆IMUJjt

Lj,t−1(1)

where Lij,t−1 is county i’s employment in industry j in year t − 1, Li,t−1 =∑j Lij,t−1, Lj,t−1 =∑

i Lij,t−1, and ∆IMUJjt = IMUJjt − IMUJj,t−1, which is a nominal change ($1,000) of U.S.

industry-level imports from Japan over eight-year periods between t− 1 and t (i.e., t− 1 = 1976 to

t = 1984 or t− 1 = 1984 to t = 1992). Intuitively, equation (1) captures the change in per worker

imports weighted by the composition of workers across industries within a county. Given that

7Since we want to extend the CBP data back to 1968 in order to obtain the appropriate instrumental variables,we were forced to use the 1967 SIC data for years from 1967 to 1973. To be precise, the data over the period of1967-1969 are available only for the counties in the Northeast. For the missing observations, we use the averagegrowth rates over the period of 1970-1993 and extend with the predicted values. Over the revision from the 1967to the 1972 version of the SIC system, the significant number of new 4-digit categories were added (Peterson etal, 1972). Therefore, we aggregated four-digit industries into the three-digit industries and we use the two-digitaggregates for agriculture, forestry, fishery, and coal mining, and merge some three-digit industries to one three-digit industry. For example, 226-229 are merged into 225; 253-259 are merged into 252; 263 is merged with 262;and 279 is merged with 278.

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∆IMUJjt/Lj,t−1 is industry-specific, ∆IJit will differ across counties simply due to the composition

of industries within that specific county. For example, as shown in the first panel of Table 1, U.S.

car imports from Japan increased substantially over the 1976-1984 period. As such, ∆IJit would

be higher for counties that employ more workers in the automovtive industry. Thus, the import

competition index for a county (∆IJit) increases as the county specializes in producing products

that Japan exports.

Second, similar to Feenstra et al (2017), we use the following export opportunity index, which

captures the change in Japan’s import expenditure in each industry j:

∆EJit =∑j

Lij,t−1Li,t−1

∆EXUJjtLj,t−1

(2)

where ∆EXUJjt = EXUJjt−EXUJj,t−1 is a nominal change ($1,000) of U.S. industry-level exports

to Japan.

Data on U.S. imports from Japan and U.S. exports to Japan for years 1976, 1984, and 1992 at

the 3-digit SIC (Revision 1987) industry level were derived from the United Nation (UN) comtrade

database. We use 160 industries in agriculture, forestry, fishery, mining, and the manufacturing

sectors. Following Autor et al (2013), we derive the local employment structure at the county

level from the County Business Patterns (CBP) data. After combining these sources, we have data

for the trade indices for approximately 3,100 U.S. counties in election years 1976, 1984, and 1992.

However, for robustness, we also develop the trade indices over four-year periods (1976, 1980, 1984,

1988, and 1992) and those at the commuting zone level as in Autor et al (2013). The first panel

in Table 2 reports descriptive statistics of ∆IJit and ∆EJit over the period from t − 1 = 1976 to

t = 1984. In the median county, the eight-year growth of U.S. imports from Japan was $200 per

worker, whereas that of U.S. exports to Japan was $100 per worker. We find that the area that was

most susceptible to Japanese imports was, without a doubt, the industrial Midwest. The second

panel of the table lists the ten counties that had the highest exposure to Japanese imports. For

example, Howard County, Indiana where the city of Kokomo was located was a pioneer of U.S.

automobile manufacturing. The Haynes Apperson Automobile Company that achieved the first

mass production of commercial autos in 1898 in Kokomo led to other auto related companies such

as Kokomo Casting (Chrysler), Kokomo Transmission (Chrysler) and Delco Electronics and Safety

(GM) locating nearby as well. We also find that the area that benefited the most from export

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opportunities was the South, the farm belt, and the suburban area of the West. The counties in

the list of the table are primarily areas that specialized in resource-intensive industries such as

agriculture, fishing, forestry, and mining.

3.3 Trade and Voting Patterns

We focus on the changes in voting shares of a candidate from a party p (Spit) over the years

between t− 1 and t:8

∆Spit = Spit − Spi,t−1.

In our baseline specification, we use the eight-year changes in voting shares of presidential

candidates over the two stacked periods of 1976-1984 and 1984-1992 for two reasons. First, the

second Reagan administration (1985-1989) began using Section 301 as a tool to open Japanese

markets. Second, our samples allow us to examine how the Plaza Accord in 1985 affected voter

preferences for Republican and Democratic candidates. In particular, the U.S.-Japan trade conflict

intensified during the 1976-1984 period due to import competition, whereas after the Plaza Accord

Japanese consumers began consuming more American goods throughout the mid and late 1980s.

We assume that social issues, demographic characteristics of U.S. counties (e.g., the persistent

impact of slavery in Acharya et al (2016) and Kuziemko and Washington (2018)), and right-to-work

laws in Feigenbaum et al (2018) do not change substantially over our eight-year spans. Nonetheless,

we address possible changes of these variables in the robustness check section. By using the change

over the two elections, we are able to focus on the medium-run factors that contributed to the

changes in voting patterns. We use the following baseline specification:

∆Spit = γ1∆IJit + γ2∆EJit + εit. (3)

In our baseline specification, we use county-level data on the U.S. presidential elections. While

Autor et al (2013) use commuting-zones, we opt to use counties to examine the counterfactual

on past presidential elections. That is, given that some commuting zone cross state lines (i.e.,

St. Louis) and presidential votes are allocated in a winner-take-all format, using commuting zones

8See Appendix I for the determinants of voting shares similar to Acharya et al (2016).

9

would not allow us to make counterfactual statements about historical elections. There are several

potential endogeneity and simultaneity problems in estimating equation (3). As discussed in Autor

et al (2013), the primary concern is that imports (exports) from (to) Japan may be correlated with

industry specific U.S. domestic demand shocks. If that were the case, the OLS estimate would not

reflect the true change in the United States that resulted from the change in trade with Japan.

To address these problems, we follow Autor et al (2013) and prepare the following instrumental

variable (IV). We define the following instrumental variable for ∆IJit:

∆IIVJit =∑j

Lij,t−2Li,t−2

∆EXJOjtLj,t−2

(4)

where EXJOjt is Japan’s exports to all the trade-partner countries other than the United States,

Canada, Mexico, France, West Germany, and the United Kingdom.

There are two important points to note on the creation of the instrumental variable. First,

we use the lagged value of employment. Second, the validity of the instrumental variable depends

on the following assumptions: (1) countries—all the trading partners of Japan except the U.S.,

Canada, Mexico, France, West Germany, and the U.K.—faced import competition with Japan; and

(2) import demand shocks of these countries were not correlated with those of the United States. As

such, we exclude the NAFTA countries from our IV (i.e., Canada, Mexico and the United States)

because these countries were likely affected by import demand shocks in the United States due

to geographical proximity. We also exclude members of the Group 5 (G5) countries (i.e., France,

West Germany, Japan, the United Kingdom and the United States) because these countries were

simultaneously affected by the systematic changes in exchange rates after the Plaza Accord. We

also need to account for Japan’s demand shocks to U.S. local labor markets. Similarly, we prepare

the following instrumental variable for ∆EJit:

∆EIVJit =∑j

Lij,t−2Li,t−2

∆IMJOjt

Lj,t−2(5)

where IMJOjt is Japan’s imports from the countries other than the United States, Canada, Mexico,

France, West Germany, and the United Kingdom.

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4 Results

4.1 Presidential Election Results

We believe that trade with Japan had systematic positive or negative economic impacts on U.S.

counties which in turn affected voting patterns. In Appendix II, consistent with Autor et al (2013),

we report results showing that counties that faced severe import competition with Japan reduced

county-level employment and increased the share of social security recipients whereas counties that

created export opportunities to Japan increased wages. When we estimate equation (3), we use

the weights of total votes at the start of the period because the dependent variables (∆Spit) are

developed from total votes (see Solon, Haider, and Wooldridge, 2013). Throughout the rest of the

paper, standard errors are clustered at the state level to account for spatial correlations across

counties.

The main results for voting shares of the Democratic and Republican presidential candidates

with two-stage least squares (TSLS) are reported in Table 3.9 The first and second columns in

Table 3 display the results from estimating the effect of import competition and export opportunities

separately, whereas the third column estimates the combined effect.10 The fourth and fifth columns

report the results when we estimate both effects for the first (1976-1984) versus the second (1984-

1992) eight-year interval only with cross-sectional variation. Our results suggest that over the

1976-1984 time period, voting shares for the Democratic presidential candidates increased in the

counties where U.S. firms faced strong import competition with Japan and decreased in the counties

where U.S. products were exported to Japan. Interestingly, the impact of trade with Japan on

voting shares for the Democratic presidential candidates is not statistically significant over the

1984-1992 period. We believe that this is partly because of Japanese automakers’shift in strategy

from exporting to foreign direct investment (FDI). Moreover, Japan began importing a wide range

of American goods including computers, aircraft, cars, and auto parts in the late 1980s due to

the use of Section 301 and the steep depreciation of the U.S. dollar after the Plaza Accord. Note

that the results of the Republican shares are quite different from those of the Democratic shares.

The voting share of the Republican presidential candidates decreased in the counties where they

9See Appendix Table A5 for OLS results.10When we use the stacked and panel data, we include the year dummy variables to account for the election spe-

cific factors such as national-level popularity (or unpopularity) of candidates.

11

faced strong import competition with Japan and increased in the counties where their products

were exported to Japan. Importantly, the effect of expanding export opportunities is statistically

significant throughout the period (both the 1976-1984 and 1984-1992 periods) and across different

specifications.

Our point estimates in the fourth column of Table 3 imply that, over the period of 1976-

1984, the Republican shares of a county at the 90th percentile of export opportunity increased

(decreased) by 5.7 (-6.9) percentage points more than in a county at the 10th percentile. Over

the same period, the Democratic shares of a county at the 90th percentile of import competition

increased (decreased) by only 3.2 (-2.9) percentage points more than in a county at the 10th

percentile. Given that states allocate their electoral college votes in a winner-take-all manner (with

the exception of Maine and Nebraska), the net effect from trade is substantial. For example, in

the 1976 election Jimmy Carter (D) won Ohio by 0.27% (11,000 votes) and won Wisconsin by

1.67% (35,000 votes), if those states would have been won by Gerald Ford (R), Ford would have

won the 1976 presidential election. As such, our results suggest that the efforts by the Reagan

administration to open Japanese markets helped form a foundation for the Republican Party in

subsequent elections in export-oriented regions.

4.2 Robustness Checks

4.2.1 Alternative Specifications

We report various robustness checks in Table 4 using the Republican share of the vote.11 In

the first column of the table, we report the results from the stacked eight-year sample without

any analytical weights of total votes (Solon et al. (2013)); note, that the results do not change

with or without the weights. In the second column of the table, we report the results from the

stacked eight-year sample with control variables (i.e., the start of the period values of the population

shares of white, male, and college graduates); our results do not change given the inclusion of the

demographic control variables. To examine the period-specific effects of trade with Japan, we

examined the correlations between past changes in voting shares and future changes in our trade

indices. If the effect is specific to the period of the 1980s, we should not have statistically significant

11We only reported the results for Republican shares to conserve space. Robustness checks using the Democraticshares are consistent with those in Table 3 and may be obtained upon request.

12

relationships between voting shares and trade indices. As such, we regress the changes in voting

shares over the 1964-1972 and 1972-1980 periods with the trade indices for the periods of 1976-

1984 and 1984-1992. As reported in the third column of the table, we do not find false evidence for

either export opportunity or the import exposure index which does not suggest reverse causality

in our analysis. The fourth column reports the results without the presidential candidates’home

states. That is, we exclude Georgia (Jimmy Carter), Michigan (Gerald Ford), Minnesota (Walter

Mondale), California (Ronald Reagan), Texas (George H. W. Bush), and Arkansas (Bill Clinton).

Again, note that our results are robust to the exclusion of the candidates’home states. The fifth

and sixth columns report the results when we use the commuting zone- and state-level stacked

eight-year first differences data. Somewhat surprising, at the state-level aggregation, we cannot

find the statistically significant association between import competition and vote share but we do

again confirm the statistically significant relationship between export opportunities and vote share.

To control for social issues and demographic characteristics, we perform a couple of robustness

checks that are reported in columns (7) and (8) in Table 4. Voters’ racial attitudes are critical

factors in voting patterns. Acharya et al (2016) and Kuziemko andWashington (2018) show that the

history of slavery and its subsequent political events have persistent impacts on the contemporary

voting patters in the United States. Kuziemko and Washington (2018) show that the Democratic

Civil Rights platforms that began in the 1960s caused a large majority of the Southern Democrats

to leave the party. Anecdotally, the success of Alabama’s Governor George Wallace in the 1968

presidential election provides support for that interpretation given that the Civil Rights legislation

was passed in 1963 and 1964. Wallace had been a Democrat but ran as an independent because the

Democratic Party had rejected pro-segregationist ideas. Wallace ran on a segregationist platform

and won 13% of the vote and carried Arkansas, Louisiana, Mississippi, Alabama, and Georgia and

won their 45 electoral college votes. Kuziemko and Washington (2018) provide strong quantitative

evidence that racial attitudes explain the sharp decline in support for the Democratic Party during

the 1960s. Therefore, we obtained the slave population share in 1860 from Acharya et al (2016)

and develop a dummy variable (Dsi ) that is equal to one if the county’s slave share is greater than

28% (75th percentile value across all the counties) and zero otherwise. We included year-specific

slave dummy variables as a proxy for the density of racial attitudes (Kuziemko and Washington,

13

2018).12 We also report results using county-level four-year first differences data including the

1976, 1980, 1984, 1988, and 1992 elections. By using shorter intervals, slow changes in social issues

and demographic characteristics are likely differenced out. As can be seen in columns (7) and (8),

although the coeffi cients on import competition and export opportunity become slightly smaller,

they are statistically significant at the 1% confidence level. The results are robust using simple

state fixed effects as well.

Finally, Japanese automakers did shift their strategy into American markets from exporting

autos from Japan to producing them in the U.S. through FDI. Our data suggest that the number of

Japanese FDI establishments located in the United States was 182 in 1976 and increased to 1,192 in

1992.13 While the shift was driven mainly to avoid trade frictions (e.g., Markusen (1984), Helpman,

Melitz, and Yeaple (2004)), the location choice of Japanese FDI could have been influenced by

regional political climates (e.g., Blonigen and Figlio (1998)).14 As such, it is possible that import

competition could be correlated with Japanese FDI patterns. Without controlling for the potential

FDI effect, our trade results could be biased. To account for the FDI effect on import competition,

we included log of the number of Japanese FDI establishments at the county level as an additional

instrumental variable for both import competition and export opportunity variables. The last

column of Table 4 confirms that our main results from Table 3 are robust to the inclusion of the

FDI variable.

4.2.2 Alternative Dependent Variables

Our results do not necessarily suggest that voters switch parties. The increase in the Republican

share of the vote resulting from increased export opportunities may simply be a result of increased

Republican turnout. In the first column of Table 5, we report the results using turnout rates of the

U.S. presidential elections.15 Our results suggest that voters in regions where export opportunities

were created were more likely to turnout to vote. Similarly, the second and third columns of the

table report the results when we use the differences in log of the number of votes for the Democratic

12During the period of 1976-1992, RTW law was new only in Idaho (1985) during our sample.13See Appendix III for additional details.14Blonigen and Figlio (1998) examine the correlation between the voting pattern of U.S. Senators from 1985 to

1994 and the flows of FDI into a legislator’s state. They find a diverging effect of FDI on senators’voting patternssuch that legislators who already supported free trade would further soften their stance for protectionist measuresbut senators who opposed free trade hardened their stance for protectionism.15We obtain a proxy for voter turnout from total votes divided by total population.

14

and Republican candidates. Note that an increase in import competition with Japan did not reduce

the number of votes for Democratic candidates; however, an increase in export opportunities did

reduce the number of votes for Democratic candidates. The Republican results are consistent with

our baseline results: voters in export oriented regions turn out to vote. Lastly, columns (4) and (5)

in Table 5 report the results excluding the voting shares of independent candidates. Essentially,

columns (4) and (5) exclude the vote share won by Ross Perot in 1992. While the point estimates

are smaller, the results are consistent with our main results.

4.3 Congressional and Senate Elections

As noted in Conconi et al (2012), a district’s trade exposure is positively related to how congres-

sional members may vote regarding presidential FTA. Congressman will consider (1) the similarity

of their views to the President, (2) would the Congress or the President be able to negotiate a better

deal? and (3) will FTA improve the likelihood that a trade deal is completed in deciding whether

to give up their trade negotiating authority to the President. As such, a more protectionist set of

legislators are much less likely to grant FTA authority to a president who does not share similar

views. Although we emphasized that trade authority was substantially shifted to the President

after the trade act of 1974, the historical events in the House and Senate in which “Japan Bash-

ing” took place suggest that the trade war with Japan played an important role in trade related

bills developed in Congress. While Conconi et al (2012) examine how congressional members vote

for FTA authority, we compliment their analysis by examining voters’(rather than congressman)

response to trade exposure. As such, we use the voting shares from the House of Representatives

elections for years 1976, 1984, and 1992 and those from Senate elections for years 1976, 1984, and

1992. Because a senator serves a six-year term, approximately one-third of the seats are up for

election every other year. We use the votes from the 1974, 1976, and 1978 elections to create the

nationwide votes of the Senate for year 1976. When we do not have the candidate from one of the

two main parties for the senate elections, we drop the votes from these states. For example, there

was no Republican candidate in the U.S. Senate Virginia election in 1976 when Independent Harry

Flood Byrd Jr. won the election. Therefore, the voting shares at the county level for Virginia

are derived from the 1978 elections. We report the results from the House and Senate elections

in Table 6. Note in Table 6 that export opportunities to Japan increase the share of Republican

15

votes in the House and the Senate from 1976-1984 and import competition decrease the share of

the Republican vote but the effect dissipates after 1984. Likewise, import competition increases

the share of the Democratic vote from 1976-1984 and export opportunities decrease the share of

the Democratic vote but not after 1984.

5 Conclusion

Trade with Japan was a major political issue during the 1980s. This paper argues that the

redistributional effect of international trade across U.S. counties played an important role in the

political realignment that occurred in the 1980s. While previous literature argued that the political

realignment in the 1980s was primarily caused by the drop in support for the redistribution policies

of the Democrats (i.e., Brewer and Stonecash (2001) and Shafer and Johnston (2009)), our results

suggest that the effect of trade on the political realignment could have been an integral part of the

political realignment. In particular, we find that expanding export opportunities was a significant

factor in the gains Republicans made in Presidential, Senate, and House elections. Voting shares for

the Republican candidates decreased in the industrial Midwest where they faced import competition

from Japan and increased in the South, the farm belt, and the suburban area of the West where

U.S. exports expanded by opening Japanese markets. Although the political impact of import

competition have recently been emphasized in the literature, our results suggest that efforts to

expand exports has a larger net effect on the changes in the vote share than does import competition.

While the literature has highlighted the political motives to protect domestic industries from import

competition (e.g., Hillman (1982), Grossman and Helpman (1994), Che et al (2016) and Autor et

al (2016)), we provide evidence that promoting exports was a politically winning strategy for

Republicans during the 1980s and helped solidify the Republican Party’s base in export-oriented

areas.

Appendix

I. Voting Patterns and Social Characteristics

While this paper studies the impact of trade on the presidential votes, voters’racial attitudes

and their union memberships are also critical factors (e.g., Acharya, Blackwell and Sen, 2016;

16

Kuziemko and Washington, 2018; Feigenbaum et al, 2018). Acharya et al (2016) and Kuziemko

and Washington (2018) show that the history of slavery and its subsequent political events have

persistent impacts on the contemporary voting patters in the United States. Right to work (RTW)

laws also have a significant positive effect on the local labor markets as well as the business cli-

mate. Labor unions, however, oppose the RTW legislation because the laws typically curtail union

membership which reduces the bargaining power of the unions and their political clout. Given

the importance of unions in the U.S. presidential elections (Feigenbaum et al, 2018), we develop

a state-level dummy variable (Dri ) that is one if the county belongs to the RTW state and zero

otherwise.16

To better understand the determinants of voting shares, we estimate the following equation

with and without various fixed effects:

Spit =∑

βcXcit + βsD

si + βrD

ri + εit

where Spit is the county-level voting share of a candidate from party p where p is the Democratic

candidate (D) or the Republican candidate (R); Xcit includes the share of white population, college

graduates, and male population.

Table A1 displays the summary statistics of voting shares for selected years. And, Table A2

reports the results from estimating equation above with ordinary least squares (OLS) from the 1976,

1980, 1984, 1988, and 1992 U.S. presidential elections; the first two columns display the results with

the Democratic share of the vote as the dependent variable, the next two columns display the results

with the Republican share of the vote, and the last two columns use voter turnout as the dependent

variable. As shown in the table, populations that are predominantly whites as well as those with

high shares of males tend to vote for the Republican candidates. Consistent with Acharya et al

(2016) and Kuziemko and Washington (2018), the southern counties that had high shares of slaves

in 1860 are more likely to vote for the Republican candidates. Our results also support Feigenbaum

et al (2018) that the Democrat share of the vote are lower in the RTW states. Finally, high

population shares of college graduates and whites are significant determinants for turnout rates.

16During the period of 1976-1992, RTW law was newly enactmented only in Idaho (1985).

17

II. Trade and Labor Market Outcomes

Since Autor et al (2013) examine the impact of China’s exports on local labor market outcomes,

it is important to associate our measures of Japan’s trade indices with labor market outcomes such

as the share of social security recipients, wage, and employment. Over the 1960-2010 period, the

number of the U.S. manufacturing workers did not decline substantially until 2000 when import

competition with China caused a significant impact on U.S. manufacturing workers (Pierce and

Schott, 2016). Although the number of total workers had been stable in the 1980s, production

workers in U.S. manufacturing declined, whereas non-production workers increased. The increasing

reliance on global outsourcing (e.g., Feenstra and Hanson, 1996) and skill-biased technological

change (e.g., Krusell, Ohanian, Ríos-Rull, and Violante, 2000) created relatively more demand for

skilled workers over the 1980s.

We use the three variables: (1) the percentage point change in the share of social security recip-

ients (Autor et al, 2014); (2) the change in log wage; and (3) the change in log total employment.

These variables are obtained from the U.S. Census (see the summary statistics in Table A3). In

our baseline specification, we use the eight-year changes at the county level over the two stacked

periods of 1976-1984 and 1984-1992 for one of the three dependent variables (∆Xit):

∆Xit = α1∆IJit + α2∆EJit + εit (6)

where we include the control variables of the start of the period population shares of whites, college

graduates, and males when the dependent variable is log wage.

The first, fourth, and seventh columns in Table A4 report the results from estimating the effect

of import competition and export opportunities using the county level data. Similar to Autor et al

(2013), we confirm that U.S. trade with Japan had systematic impacts on U.S. local labor markets.

The counties that faced severe import competition with Japan reduced county-level employment

and increased the share of social security recipients, and those that created export opportunities to

Japan increased the wage. Thus, consistent with the arguments in Autor et al (2013), trade had

systematic impacts on U.S. local labor markets.

The results from the commuting zone level data are reported in the second, fourth, and seventh

columns, and those from the four-year interval county-level data over the 1976-1992 period are

18

reported in the third, sixth, and ninth columns.

III. Japanese FDI Data

We develop a unique data set on Japanese establishments in the United States for years 1972,

1976, 1980, 1984, 1988, and 1992 from the Toyo Keizai’s Overseas Japanese Companies yearbooks.

The data are ideal for our study because we are able to use the information on zip codes and

addresses of the Japanese subsidiaries and affi liates and allocate them into U.S. counties.17 Note

also that we are able to distinguish between establishments that were founded as sales representative

offi ces (i.e., import their products from Japan and work for customer services) and those that

invested or acquired production facilities (i.e., produce or assemble their products in the United

States). In our empirical analysis, FDI refers only to the production facilities; we exclude any sales

representative offi ces or service related establishments. Our Japanese FDI data suggest that the

number of Japanese FDI establishments located in the United States was 182 in 1976 and increased

to 1,192 in 1992. Although our FDI employment data is not as complete as the establishment data,

American workers employed by Japanese affi liates increased from 29,265 in 1976 to 311,239 in 1992.

Japanese firms invested not only in the big cities (San Francisco, Los Angeles, and New York City)

but also to the South and the Midwest. In particular, a large number of Japanese firms invested in

California, suggesting the importance of proximity to Japan and information externalities related

to location choice (e.g., Head at al, 1995).

17The offi cial data from Japanese government do not report county-level location information. In particular,the Survey on Overseas Business Activities by Ministry of Economy, Trade, and Industry reports the locations ofJapanese establishments at the state level.

19

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23

Figures and Tables

Notes: (1) We use the U.S. presidential elections: Jimmy Carter (D) versus Gerald Ford (R) in 1976 and Walter

Mondale (D) versus Ronald Reagan (R) in 1984. (2) The data is based on the 1990 counties.

24

Table 1. U.S. imports and exports by products (1976-1984)

Notes: (1) We use the SIC 1987 2-digit categories. (2) “change” in Japan’s shares is a percentage point change.

1.1. U.S. imports from Japan (1976-1984)

SIC 2-digit sectors Imports (billion $US) Japan's shares (%)

Rank Code Name 1976 1984 Change 1976 1984 Δ% point

1 37 Transportation equipment 3.6 18.2 14.7 27.3% 39.6% 12.3%

2 36 Electronic equipment 3.1 13.8 10.6 39.8% 37.4% -2.4%

3 35 Industrial machinery 1.6 8.5 7.0 27.9% 29.9% 2.0%

4 38 Measuring, medical and optical goods 0.9 4.1 3.2 29.6% 33.5% 3.9%

5 33 Primary metal products 1.2 3.9 2.8 19.2% 19.2% 0.0%

6 28 Chemicals 0.2 1.3 1.1 5.8% 9.1% 3.4%

7 30 Rubber and plastic products 0.2 1.3 1.1 9.7% 14.8% 5.1%

8 34 Fabricated metal products 0.6 1.6 1.0 36.8% 24.2% -12.6%

9 39 Other manufacturing products 0.3 1.2 0.8 11.1% 10.9% -0.3%

10 23 Apparel products 0.2 0.5 0.3 6.4% 4.2% -2.2%

1.2. U.S. exports to Japan (1976-1984)

SIC 2-digit sectors Exports (billion $US) Japan's shares (%)


1 7 Agricultural goods 2.8 4.5 1.8 17.0% 22.8% 5.8%

2 28 Chemicals 0.6 2.3 1.6 9.4% 12.0% 2.7%


4 20 Food products 0.8 1.7 0.9 12.0% 14.7% 2.7%


6 36 Electronic equipment 0.4 1.2 0.8 5.0% 6.8% 1.8%

7 13 Oil and gas extraction 0.0 0.5 0.5 5.8% 15.2% 9.4%

8 33 Primary metal products 0.2 0.6 0.5 4.1% 13.7% 9.6%

9 26 Paper products 0.2 0.6 0.4 8.1% 15.3% 7.2%

10 9 Fishing 0.0 0.4 0.4 17.1% 62.5% 45.4%

1.3. U.S. exports to Japan (1984-1992)

SIC 2-digit sectors Exports (billion $US) Japan's shares (%)


1 37 Transportation equipment 0.6 4.7 4.1 3.7% 7.6% 3.9%


3 20 Food products 1.7 4.7 3.0 14.7% 20.6% 5.9%

4 24 Lumber and wood products 0.4 2.7 2.3 25.4% 39.7% 14.3%

5 28 Chemicals 2.3 4.4 2.2 12.0% 10.8% -1.3%

6 36 Electronic equipment 1.2 3.2 2.0 6.8% 7.2% 0.3%


8 9 Fishing 0.4 1.9 1.5 62.5% 65.7% 3.2%

9 21 Tobacco products 0.1 1.3 1.2 8.1% 28.7% 20.6%

10 33 Primary metal products 0.6 1.4 0.7 13.7% 11.4% -2.3%

25

Table 2. Summary statistics of trade indices

Notes: In the second panel, we limit the counties with total employment greater than 1,000.

2.1. Summary statistics

Import penetration (1976-1984) Export opportunity (1976-1984)Statistics Index Statistics Index

10 percentile 0.01 10 percentile -0.09

25 percentile 0.05 25 percentile 0.03




mean 0.48 mean 0.16

standard deviation 0.89 standard deviation 0.52

observations 3,106 observations 3,106

2.2. Ranking

Import penetration (1976-1984) Export opportunity (1976-1984)County Index County Index

1 Howard, IN 15.48 Clearwater, ID 6.742 Frederick, VA 11.03 Twiggs, GA 6.373 Winnebago, IA 11.00 Perry, MS 6.314 Greene, TN 9.35 Pike, OH 5.975 Monroe, IN 8.67 Greenlee, AZ 5.806 Edgar, IL 8.37 Pacific, WA 5.327 Edwards, IL 7.73 Taylor, FL 5.308 Genesee, MI 7.45 Reynolds, MO 5.299 Boone, IL 7.10 Wilkinson, GA 5.19

10 Genesee, NY 6.06 Hidalgo, NM 4.80

26

Table 3. Changes in voting shares of Democratic and Republican candidates

Notes: (1) Standard errors that are clustered at the state level are in parentheses. (2) ***, ** and * indicate significance at the 1%, 5% and 10% confidence levels, respectively. (3) We use the changes over 8 years from 1976 to 1984 and/or from 1984 to 1992 at the county level. (4) In the first three columns of the table, we do not report the coefficients on the 1992 dummy variable. (5) Our regression results are weighted by the start of the period total votes. (6) The first-stage results are available upon request. We report the Kleibergen and Paap (2006) LM test for weak or underidentification of instrumental variables.

3.1. Democrat share

Dependent variable: Δ Democrat share

Stacked (76-84-92) 76-84 84-92

(1) (2) (3) (4) (5)

0.002 0.012*** 0.026*** -0.002

(0.004) (0.004) (0.007) (0.005)

-0.075*** -0.074*** -0.130*** -0.013

(0.015) (0.015) (0.029) (0.011)

Kleibergen-Paap test

F-statistics 9.9 8.1 7.9 3.9 14.2

P-values 0.002 0.004 0.005 0.047 0.000

Observations 6,174 6,174 6,174 3,067 3,107

3.2. Republican share

Dependent variable: Δ Republican share

Stacked (76-84-92) 76-84 84-92

(1) (2) (3) (4) (5)

-0.011*** -0.021*** -0.024*** -0.016**

(0.003) (0.005) (0.005) (0.006)

0.074*** 0.073*** 0.106*** 0.044***

(0.016) (0.015) (0.024) (0.014)


F-statistics 9.9 8.1 7.9 3.9 14.2

P-values 0.002 0.004 0.005 0.047 0.000

Observations 6,174 6,174 6,174 3,067 3,107

Δ U.S. imports from Japan

Δ U.S. exports to Japan



27

Table 4. Various specifications for robustness checks

Notes: (1) Standard errors that are clustered at the state level are in parentheses. (2) ***, ** and * indicate significance at the 1%, 5% and 10% confidence levels, respectively. (3) “Lagged” uses the 12-year lagged changes in voting shares. (4) Our regression results are weighted by the start of the period total votes. (5) The first-stage results are available upon request. We report the Kleibergen and Paap (2006) LM test for weak or underidentification of instrumental variables.

Dependent variable: Δ Republican share

no weight controls lagged home states cz-level state-level 4-year panel slave control FDI

(76-84-92) (76-84-92) (64-72-80) (76-84-92) (76-84-92) (76-84-92) (76-92) (76-84-92) (76-84-92)

(1) (2) (3) (4) (5) (6) (7) (8) (9)

-0.011** -0.015*** 0.005 -0.023*** -0.024** -0.003 -0.040*** -0.017*** -0.020***

(0.005) (0.005) (0.008) (0.007) (0.010) (0.018) (0.013) (0.005) (0.005)

0.048*** 0.058*** 0.040 0.083*** 0.118*** 0.301*** 0.166*** 0.059*** 0.063***

(0.010) (0.016) (0.028) (0.017) (0.032) (0.104) (0.058) (0.012) (0.016)

Control variables No Yes No No No No No No No

Slave county × year dummy No No No No No No No Yes No


F-statistics 2.9 7.8 7.9 6.4 10.0 5.4 14.9 8.2 10.8

P-values 0.089 0.005 0.005 0.011 0.002 0.020 0.000 0.004 0.005

Observations 6,174 6,174 6,053 5,250 1,444 96 12,386 6,174 6,174



28

Table 5. Various dependent variables

Notes: (1) Standard errors that are clustered at the state level are in parentheses. (2) ***, ** and * indicate significance at the 1%, 5% and 10% confidence levels, respectively. (3) In column (1), we use the change in turnout rate (total votes divided by population). In columns (2) and (3), we use the vote shares computed from the Democrat and Republican votes without third party votes. In columns (4) and (5), we report the log difference in the number of votes for each party. (4) Our regression results are weighted by the start of the period total votes. (5) The first-stage results are available upon request. We report the Kleibergen and Paap (2006) LM test for weak or underidentification of instrumental variables.

Δln(number of votes) without independent candidates

Democrat Republican Democrat Republican

(76-84-92) (76-84-92) (76-84-92) (76-84-92) (76-84-92)

(1) (2) (3) (4) (5)

-0.006 0.006 -0.065*** 0.017*** -0.017***

(0.005) (0.015) (0.013) (0.004) (0.004)

0.054*** -0.152*** 0.160*** -0.065*** 0.065***

(0.010) (0.046) (0.038) (0.014) (0.014)


F-statistics 7.2 7.9 7.9 7.9 7.9

P-values 0.007 0.005 0.005 0.005 0.005

Observations 6,121 6,174 6,174 6,174 6,174

Dependent variable: Δ turnout



29

Table 6. The results from the House and Senate elections

Notes: (1) Standard errors that are clustered at the state level are in parentheses. (2) ***, ** and * indicate significance at the 1%, 5% and 10% confidence levels, respectively. (3) Our regression results are weighted by the start of the period total votes. (4) The first-stage results are available upon request. We only report the Kleibergen and Paap (2006) LM test for weak or underidentification of instrumental variables. (5) U.S. House of the Representatives elections are based on the elections in 1976, 1984, and 1992. (6) Because Senators’ terms are six years, and there are elections for every other year, the voting shares of year around 1976 are derived from the 1974, 1976, and 1978 elections, those of year around 1984 are derived from the 1982, 1984, and 1986 elections, and those of year around 1992 are derived from the 1990, 1992, and 1994 elections.

6.1. Dependent variable: Δ Democrat shareHouse of Representatives Senate

76-84-92 76-84 84-92 76-84-92 76-84 84-92(1) (2) (3) (4) (5) (6)

0.016 0.022* 0.008 0.006 0.019*** -0.006(0.010) (0.013) (0.014) (0.007) (0.007) (0.011)-0.051* -0.096** -0.011 -0.026 -0.081** 0.022(0.030) (0.047) (0.035) (0.022) (0.037) (0.037)

Kleibergen-Paap test F-statistics 7.5 3.4 14.7 7.5 3.2 14.5 P-values 0.006 0.065 0.000 0.006 0.076 0.000Observations 5,686 2,847 2,839 6,011 2,940 3,071

6.2. Dependent variable: Δ Republican shareHouse of Representatives Senate

76-84-92 76-84 84-92 76-84-92 76-84 84-92(1) (2) (3) (4) (5) (6)

-0.011 -0.035*** 0.006 -0.006 -0.020*** 0.005(0.009) (0.013) (0.014) (0.007) (0.006) (0.010)0.045* 0.077** 0.001 0.025 0.067* -0.015(0.024) (0.039) (0.026) (0.023) (0.035) (0.040)

Kleibergen-Paap test F-statistics 10.7 4.6 16.7 7.5 3.2 14.5 P-values 0.001 0.033 0.000 0.006 0.076 0.000Observations 4,910 2,374 2,536 6,011 2,940 3,071





30

Appendix

Table A1. Summary statistics of changes in voting shares

Notes: (1) See Figure 1 for the voting shares of the Republican candidates. (2) In the second and fourth panels, we

limit the counties with total employment greater than 1,000.

A1.1. Summary statistics (1976-1984)

Democrats shares (1976-1984) Republican shares (1976-1984)Statistics % change Statistics % change

10 percentile -27.4% 10 percentile 6.1%





mean -15.0% mean 16.1%

standard deviation 9.1% standard deviation 8.7%


A1.2. Ranking (1976-1984)

Democrats shares (1976-1984) Republican shares (1976-1984)County % change County % change

1 Thurston, NE 36.4% Forsyth, GA 51.5%2 San Francisco, CA 15.3% Paulding, GA 48.9%3 Cheyenne, NE 14.6% Habersham, GA 48.2%4 Tompkins, NY 9.2% Murray, GA 47.9%5 Lake, MN 8.4% Cherokee, GA 47.6%6 Marin, CA 6.7% Banks, GA 47.2%7 Benton, OR 6.3% Baker, FL 46.4%8 San Juan, WA 6.1% Gordon, GA 46.2%9 Beaver, PA 6.0% White, GA 45.8%

10 Buchanan, VA 6.0% Somervell, TX 45.1%


Democrats shares (1984-1992) Republican shares (1984-1992)Statistics % change Statistics % change

10 percentile -3.1% 10 percentile -30.9%

25 percentile 0.1% 25 percentile -27.2%




mean 2.8% mean -22.6%

standard deviation 4.6% standard deviation 6.4%


A1.4. Ranking (1984-1992)Democrats shares (1984-1992) Republican shares (1984-1992)

County % change County % change1 Ashley, AR 24.3% Madison, MS 0.8%2 Prairie, AR 22.7% Macon, AL -3.3%3 Clay, AR 22.3% Edgefield, SC -4.6%4 Poinsett, AR 21.6% District of Columbia -4.6%5 Hempstead, AR 21.1% Lowndes, AL -5.2%6 Lawrence, AR 21.0% Clarke, MS -5.9%7 Mississippi, AR 20.5% Bullock, AL -6.0%8 Arkansas, AR 19.6% Haywood, TN -6.6%9 Howard, AR 19.4% Lauderdale, MS -6.7%

10 Woodruff, AR 19.4% Copiah, MS -7.1%

31

Table A2. Determinants of voting shares

Notes: (1) Standard errors that are clustered at the state level are in parentheses. (2) ***, ** and * indicate

significance at the 1%, 5% and 10% confidence levels, respectively. (3) We use the commuting-zone data for 1976,

1980, 1984, 1988, and 1992. (4) Slave dummy variable is developed from the 1860 census data (Acharya et al,

2016). (5) Our regression results are weighted by total votes.

Dependent variable: Democrat share Republican share Turnout rate

(1) (2) (3) (4) (5) (6)

-0.372*** -0.504*** 0.304*** 0.442*** 0.345*** 0.253***

(0.043) (0.027) (0.040) (0.019) (0.056) (0.065)

-0.113* -0.105 0.061 0.096 0.210*** 0.216***

(0.061) (0.073) (0.057) (0.067) (0.040) (0.036)

-0.946*** -0.300*** 0.830*** 0.318** -0.983*** -0.981***

(0.233) (0.111) (0.230) (0.125) (0.190) (0.107)

-0.028** 0.039*** -0.023

(0.014) (0.012) (0.014)

-0.030*** -0.026*** 0.034*** 0.026*** 0.013 0.016*

(0.011) (0.007) (0.010) (0.006) (0.010) (0.009)

Year fixed effects Yes Yes Yes Yes Yes Yes

State fixed effects No Yes No Yes No Yes

Observations 15,493 15,493 15,493 15,493 15,483 15,483

R-squared 0.394 0.546 0.542 0.66 0.366 0.611

Right-to-work states

White share

College degree share

Slave counties

Male share

32

Table A3. Summary statistics for the changes in labor market outcome variables

Notes: In the second panel, we limit the counties with total employment greater than 1,000.


Social security share Per worker income EmploymentStatistics Δ% Statistics Δ% Statistics Δ%

10 percentile -1.0% 10 percentile 59% 10 percentile -10%

25 percentile -0.2% 25 percentile 65% 25 percentile 2%

50 percentile 0.6% 50 percentile 71% 50 percentile 14%



mean 0.6% mean 71% mean 16%

standard deviation 1.4% standard deviation 11% standard deviation 28%

observations 3,105 observations 3,075 observations 3,101

A3.2. Ranking (1976-1984)

Social security share Per worker income EmploymentStatistics Δ% Statistics Δ% Statistics Δ%

1 Falls Church, VA 22.9% Woodford, KY 119% Stafford, VA 135%2 Covington, VA 7.8% Houston, TX 114% Flagler, FL 132%3 Lexington, VA 7.3% Spink, SD 110% Douglas, CO 130%4 Hernando, FL 7.1% La Paz, AZ 107% James City, VA 125%5 Gila, AZ 6.7% Bath, VA 101% Gwinnett, GA 124%6 Salem, VA 6.6% Summit, CO 101% Somervell, TX 122%7 Curry, OR 5.8% Sargent, ND 99% Uinta, WY 114%8 Indian River, FL 5.2% Day, SD 99% Grant, LA 113%9 Mohave, AZ 4.9% Mercer, ND 99% Fayette, GA 106%

10 Polk, NC 4.8% Redwood, MN 97% Summit, CO 105%

33

Table A4. Changes in labor market outcome variables

Notes: (1) Standard errors that are clustered at the state level are in parentheses. (2) ***, ** and * indicate significance at the 1%, 5% and 10% confidence levels,

respectively. (3) In columns (1), (4), and (7), we use the changes over 8 years from 1976 to 1984 and from 1984 to 1992 at the county level. In columns (2), (5),

and (8), we use the changes over 8 years from 1976 to 1984 and from 1984 to 1992 at the commuting zone level. Finally, columns (3), (6), and (9) use the

changes over 4 years from 1976 to 1992 at the county level. (4) Our regression results are weighted by total population.

Δ share of social security recipients Δ ln(wage) Δ ln(employment)

(76-84-92) (76-84-92) (76-92) (76-84-92) (76-84-92) (76-92) (76-84-92) (76-84-92) (76-92)

County CZ County County CZ County County CZ County

(1) (2) (3) (4) (5) (6) (7) (8) (9)

0.004*** 0.005*** 0.005*** -0.025*** -0.025** -0.045 -0.044*** -0.066*** -0.058***

(0.001) (0.001) (0.001) (0.008) (0.013) (0.031) (0.010) (0.020) (0.020)

-0.003 -0.006* -0.012** 0.112*** 0.169*** 0.256* -0.011 0.071 0.153

(0.002) (0.003) (0.005) (0.025) (0.046) (0.132) (0.044) (0.081) (0.106)

Control variables No No No Yes Yes Yes No No No


F-statistics 7.2 8.6 13.3 12.6 18.2 6.9 7.2 8.6 13.3

P-values 0.007 0.003 0.000 0.000 0.000 0.009 0.007 0.003 0.000

Observations 6,152 1,444 12,304 3,076 722 9,228 6,150 1,444 12,301



34

Table A5. Changes in voting shares (OLS)

Notes: (1) Standard errors that are clustered at the state level are in parentheses. (2) ***, ** and * indicate

significance at the 1%, 5% and 10% confidence levels, respectively.

A5.1. Dependent variable: Δ Democrat share

Stacked (76-84-92) 76-84 84-92

M1 M2 M3 M3 M3

0.001 0.001 0.017** -0.001

(0.002) (0.002) (0.006) (0.002)

-0.003*** -0.003*** -0.003*** -0.001

(0.001) (0.001) (0.001) (0.001)

Observations 6,174 6,174 6,174 3,067 3,107

R-squared 0.481 0.488 0.488 0.039 0.005

A5.2. Dependent variable: Δ Republican share

Stacked (76-84-92) 76-84 84-92

M1 M2 M3 M3 M3

-0.005*** -0.005*** -0.016** -0.003**

(0.001) (0.001) (0.006) (0.002)

0.003*** 0.003*** 0.003*** 0.002***

(0.000) (0.000) (0.001) (0.001)

Observations 6,174 6,174 6,174 3,067 3,107

R-squared 0.859 0.86 0.861 0.035 0.014

Δ Japan's exports to ROW

Δ Japan's imports from ROW

Δ Japan's exports to ROW

Δ Japan's imports from ROW

export opportunities to japan and the u.s. political...

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