KEO Discussion Paper No. 171

Political Conflict and Angry Consumers:
Evaluating the Regional Impacts of a
Consumer Boycott on Travel Services Trade
JaeBin Ahna
Theresa M. Greaneyb
Kozo KiyotacaGraduate School of International Studies, Seoul National University;
Department of Economics, University of Hawai‘i;cKeio Economic Observatory, Keio UniversitybPolitical Conflict and Angry Consumers:
Evaluating the Regional Impacts of a
Consumer Boycott on Travel Services Trade∗
JaeBin Ahn
Seoul National University†
Theresa M. Greaney
University of Hawai‘i‡
Kozo Kiyota
Keio University, RIETI, and TCER§
June 16, 2022
Abstract
Political conflict between nations sometimes leads to consumer boycotts. We examine
the regional impacts of bilateral boycott activity by investigating the 2019 Korean consumer
boycott of travel to Japan. Employing triple- and double-differences designs, we find thatthe impact of the boycott is large and regionally heterogeneous. Japanese prefectures withhigh (i.e., 75th percentile) pre-boycott dependency on visitors from Korea suffer bilateralexport losses of 56.9 to 60.9 percent and aggregate export losses of 10.5 to 13.3 percent.
Prefectures with low (i.e., 25th percentile) Korea dependency experience bilateral losses of
47.8 to 49.7 percent and aggregate losses of 3.3 to 4.2 percent.
Key words: Political conflict; Consumer boycott; Travel services trade; Local market;
Regional impact
JEL classification codes: F14, F51, F52∗
The authors thank Jaerim Choi, Kilian Heilmann, Hiro Ishise, John McLaren, Hisahiro Naito, Hwaji Shin andKiyoyasu Tanaka for discussions and feedback on this topic. They also thank seminar participants at the Asia Pacific Institute of Research, Japanese Economic Association, Keio University, Kobe University, University of Hawai‘i,
University of North Dakota and University of Tsukuba for helpful comments. Kozo Kiyota gratefully acknowledges the financial support of the Japan Society for the Promotion of Science (JSPS) Grants-in-Aid (JP18H03637,
JP18KK0348, JP19H00598). The usual disclaimers apply. Declarations of interest: none.
†
E-mail: jaebin.ahn@snu.ac.kr; Seoul National University, Graduate School of International Studies, Gwanak-ro,
Gwanak-gu, Seoul 08826, Korea.
‡
E-mail: greaney@hawaii.edu; corresponding author; University of Hawai‘i, Economics Department, 2424
Maile Way, Saunders Hall 542, Honolulu, Hawai‘i 96822, USA.
§
E-mail: kiyota@keio.jp; Keio Economic Observatory, Keio University, 2-15-45, Mita, Minato-ku, Tokyo 1088345, Japan.

11Introduction

Political conflict between nations often impacts international trade through official trade policy. Recent examples include several rounds of tariff escalation between the US and China
in 2018 and 2019, the effects of which have been intensively investigated in the literature(e.g., Amiti, Redding, and Weinstein, 2019, Head and Mayer, 2019, and Fajgelbaum, Goldberg, Kennedy, and Khandelwal, 2020). When trade policy is used as a tool of diplomacy,planned changes in trade barriers are often announced in advance for administrative reasons(i.e., to provide time for customs officials to make necessary changes) and to create a window
for further negotiation that might result in postponement or changes in announced barriers.1
However, when political conflict leads to calls for a consumer boycott directed at a trade partner, the effects can be immediate and therefore harder to evade (e.g., by stockpiling prior toa tariff increase). In addition, the effects of a boycott may be more unpredictable than trade
policy effects since the popularity and longevity of the boycott, along with the pathway to
alleviate it, are more uncertain. These features of consumer boycotts make them unique “unofficial” outcomes of political conflict and deserving of research attention.
A consumer boycott on imported products can be interpreted as another form of import
restriction and therefore may have significantly negative effects on bilateral trade. For example, Heilmann (2016) examined the impact of several boycotts such as the boycott of Danish
goods by Muslim countries following the Muhammad Comic Crisis in 2005/2006 and the Chinese boycott of Japanese goods in response to the Senkaku/Diaoyu Island conflict in 2012. He
found an average one-year import disruption to boycotting countries of 18.8 percent in thecase involving Denmark and 2.7 percent for that involving Japan. In contrast, the reduction
in total exports of the boycotted country was small in all boycott cases (e.g., 0.4 percent for
Denmark and 0.5 percent for Japan). Similarly, Heilmann (2019) focused on the effects of thesame Muhmmad Comic Crisis on Danish service exports. He found that service exports ingeneral and especially exports of travel services from Denmark to the Muslim countries weresignificantly disrupted in the aftermath of the crisis. Yu, McManus, Yen and Li (2020) provide
further evidence of the vulnerability of travel services trade to consumer boycotts by using
seven Chinese boycott cases to estimate that Chinese visitors to boycotted countries were 36.2
percent below their expected level 12 months after the boycott event.2
While these previous studies present important findings, they implicitly assumed that the
impacts were homogenous across regions within a boycotted country or did not explicitly
consider the heterogenous impacts across regions. There is another strand of research that examined the effects of trade liberalization at the regional level. After the pioneering study by
Topalova (2007), which examined the effects of a trade shock on local labor markets in India,
1

For example, see Bowen and Kolb (2020) for a detailed timeline of President Trump’s tariff announcements
followed by announcements of country-specific exemptions (e.g., involving steel and aluminum tariffs) or changes
to product coverage (e.g., involving China-specific tariffs) in 2018 and 2019.
2
Related literature on consumer boycotts includes Ashenfelter, Ciccarella and Shatz (2007), Chavis and Leslie(2009), Davis and Meunier (2011), Clerides, Davis, and Michis (2015) and Pandya and Venkatesan (2016). The
relationships between political/cultural conflict and international economic exchange also are examined in Guiso,
Sapienza and Zingales (2009), Fuchs and Klann (2013), Fisman, Hamao and Wang (2014), Li, Jian, Tian and Zhao
(2021) and Zhou, Zhang and Zhou (2021). Heilmann (2016), Yu et al. (2020) and Zhou et al. (2021) provide excellent
literature reviews on these issues.

2

several studies focused on the effects of trade liberalization on local labor markets.3 Thesestudies found that some regions have significantly larger negative effects than other regions
due to import competition shocks. Recent studies of the United States-China trade war initiated in 2018 also suggest regional differences within the United States due to shocks transmitted through three trade war channels–import protection, import-using and foreign retaliation.4 These studies suggest that the impact of a consumer boycott also could be heterogeneous
across regions within a boycotted country.
In this connection, a recent study by Caselli, Koren, Lisicky and Tenreyro (2020) assessed
the importance of cross-country diversification, based on a quantitative trade model. One of
their findings is that international trade leads to lower income volatility because countries candiversify their sources of demand and supply across countries. This “diversification story”
suggests that higher dependency on exports to a particular country can make a county more
vulnerable to trade shocks. Applying this logic to the regional level means that the impact of aconsumer boycott could be more severe in regions with higher dependency on exports to theboycotting country.
Based on this background, this paper investigates the regional impact of consumer boycott
activity. A main contribution of this paper is to incorporate a local market perspective in analyzing the effect of a political conflict on trade.5 More specifically, we investigate the recent Korean consumer boycott activity from July 2019 in response to Japan’s restrictions on exports ofsemiconductor materials and display panels considered vital to Korea’s technology industry.6
As we will discuss in Section 2, this consumer boycott was unanticipated and plausibly exogenous to unobserved trade-related confounding effects, which helps us to identify the causal
relationship between the consumer boycott and trade. The boycott activity spread not only tothe purchase of Japanese goods but also to that of services: many Koreans stopped travelingto Japan.7 Decreases in travel to Japan mean decreases in Japan’s exports of tourism services.
This issue is important from a current policy perspective because increasing the number of
inbound tourists is one of the essential strategies for spreading growth to regional economies
under Abenomics (The Government of Japan, 2017).8
This paper focuses on the exports of accommodation services from Japan. We measure exports by the number of foreign visitors, which is defined as the number of people who residein countries other than Japan times the number of nights stayed in Japan. This means that
foreign nationals who live in Japan are excluded while Japanese nationals who live outside of
3

See, for example, Topalova (2010) for the case of India; Autor, Dorn, and Hanson (2013) and Hakobyan and
McLaren (2016) for the United States; Kovak (2013) for Brazil; and Taniguchi (2019) for Japan.
4
Fajgelbaum et al. (2020) and Caliendo and Parro (2021) develop models to simulate changes in real wages
while Waugh (2019) examines the trade war’s impacts on consumption patterns. Fajgelbaum and Khandelwal(2021) provide an excellent review of the studies on this recent trade war. Note that consumer boycott effects canbe included within the foreign retaliation channel which focuses on changes in export volumes and prices.
5
Our focus is slightly different from that of the studies on local labor markets mentioned above. While these previous studies exploited the regional variations of trade shocks on regional economic outcomes (e.g., employment),
our study exploits the regional variations of a trade shock due to political conflict on region-level trade itself.
6
For ease of exposition, “Korea” is used to refer to the Republic of Korea (i.e., South Korea) throughout the
paper.
7
This boycott activity was also widely discussed in the media. See, for example, Stangarone (2020).
8
In fact, one analyst considers the growth in inbound tourism to be “the most tangible success story of Abenomics” (Koll, 2018, p. 1).

3

Japan are included if they use accommodation services while visiting Japan. Figure 1 shows
the changes in the number of visitors from foreign countries in Japan on a monthly basis.9
Compared with visitors from China and the United States, those from Korea dropped sharply
from July 2019, exactly when the boycott started.10 Figure 1 also indicates seasonality differences across countries. For example, visitors from China and Korea tend to increase in wintermonths (e.g., January and February) whereas visitors from the United States tend to increasein spring and summer months (e.g., May and June).11

10,000
5,000

1,000
500
300

2020m1

2019m102019m72019m42019m1

2018m102018m72018m42018m1

2017m102017m72017m42017m1

2016m10

2016m7

2016m4

2016m1

2015m102015m7

100
2015m4

Number of foreign visitors in Japan (1,000)

Figure 1: Number of Foreign Visitors in Japan

Year and month
Total
China

Korea
United States

Notes: Total number of foreign visitors indicates the total number of people who reside outside of Japan times thenumber of nights stayed in Japan (unit: 1,000 person-nights).
Source: Japan Tourism Agency (2020) Overnight Travel Statistics Survey.

Note that the accommodation services market is locally segmented. Even though foreign
visitors can move across prefectures in Japan, they are not able to import (i.e., consume) accommodation services in prefecture i from another prefecture.12 An advantage of focusing on thisconflict is that the information on the number of foreign visitors is available at the prefecturemonth level in Japan. This means that we can capture the trade volume (i.e., quantity) of
9

Total visitors include visitors whose information on resident countries is not available. Section 3 provides amore detailed explanation of the data.
10
One may be concerned that this sharp drop came from a demand shock in Korea, rather than from the boycott.
For example, due to a demand shock, Korean people stopped traveling abroad, not only to Japan but also to othercountries. We examine this alternative hypothesis in Section 6.
11
Figure 1 also indicates declines in visitors from Korea from about April to June 2016. This may be due to the2016 Kumamoto earthquakes on April 14th and 16th that caused severe damage in Kumamoto and Oita prefectures.
This caused some fluctuation in arrivals from Korea to Japan (OECD, 2018, p.210).
12
Japan consists of 47 administrative prefectures. In this paper, following Taniguchi (2019), we define the local
market at the prefecture level.

4

services exports at the prefecture and month level.
Figure 2 presents each prefecture’s dependency ratio on visitors from Korea, which is defined as the average share of visitors from Korea to total visitors from foreign countries between April 2015 and June 2019. We highlight two main findings. First, the dependency ratioshows significant differences across prefectures in Japan. The ratio ranges from 1.5 percent to56.3 percent. Second, the ratios tend to be large in the Western part of Japan, which may reflect
this region’s closer proximity to Korea.13
Figure 2: Average Share of Visitors from Korea between April 2015 and June 2019, by Prefecture
in JapanNotes: A darker color means a larger Korean visitor dependency. Japanese map data are obtained from https://gadm.org/
Source: Japan Tourism Agency (2020) Overnight Travel Statistics Survey.

Using the prefecture-month foreign visitor data between April 2015 and January 2020,
we employ triple-differences (i.e., difference-in-difference-in-differences, DDD) and doubledifferences (i.e., difference-in-differences, DID) designs to estimate the impact of the boycott.
We find that the impact of the consumer boycott is heterogeneous across prefectures within
Japan, which is in line with the diversification story. For prefectures with high pre-boycott dependency on visitors from Korea, the negative impact on exports of accommodation services to
Korea is about 9 to 11 percentage points larger than it is for prefectures with low dependency,with export losses of 56.9 to 60.9 percent and 47.8 to 49.7 percent, respectively. These negative
impacts are not only disproportionate across prefectures but also too large to be offset by in13

In addition to air travel, ferry service operates between two ports in Korea (i.e., Busan and Donghae) and fiveports in Japan, of which four are in Western Japan (i.e., Hakata, Sakaiminato, Shimonoseki, and Tsushima).

5creases in exports to other countries. As a result, Japanese prefectures had net adverse effects
from the boycott, with a 10.5 to 13.3 percent decline in total exports of accommodation services
for high Korea dependency prefectures and a corresponding decline of 3.3 to 4.2 percent for
low Korea dependency prefectures. These ranges of boycott effects summarize our results inusing two estimation models and two sample periods to identify estimated bands for the boycott effects that are reassuringly narrow for a specific quartile prefecture, heterogeneous acrossprefectures and robust to the exclusion of outliers. Our main message holds even when we usean alternative measure of diversification.
To explain the boycott’s disproportionate effects on exports to Korea across prefectures, we
examine the purpose of travel for visitors from Korea to Japan’s prefectures using entry andexit survey data of foreign visitors collected by the Japan Tourism Agency. We find that prefectures with high pre-boycott dependency on visitors from Korea also tend to disproportionately
attract tourists rather than non-tourist travelers from Korea. While we cannot formally test the
hypothesis that the consumer boycott impacted leisure travel more than non-leisure travel due
to data limitations, our results are consistent with this hypothesis. These results also are consistent with the finding of stronger boycott effects for consumer goods than for intermediate
inputs or capital goods in the Muhammad Comic Crisis case (Heilmann, 2016).14 By examining
the distinction between tourists and non-tourist visitors in the boycott effects, we contributeto the trade diversity literature by adding another dimension of diversity, type of buyer (i.e.,
traveler), to the conventional dimension of diversity by countries of origin or destination.15
Our results suggest that prefectures with more diverse visitors by country of origin and by
traveler type (e.g., tourist, business traveler) may experience smaller impacts from consumer
boycotts.
The paper is organized as follows. The next section describes the background of the political conflict between Japan and Korea in 2019. Section 3 introduces the main data used in our
analysis. Sections 4 and 5 present the methodology and results of the empirical analyses at thedisaggregate and aggregate levels, respectively. Section 6 presents robustness checks and discusses the interpretations and implications of the results. Section 7 includes our conclusions.

2

Political Conflict between Japan and Korea in 2019

Some historical context is necessary in order to understand the 2019 political conflict between
Japan and Korea. Japan annexed Korea in 1910 and ruled the country for 35 years. During
WWII, many Koreans were forced to work as slave laborers for Japanese companies and as sex
slaves for Japanese soldiers. Korea was liberated in 1945 with Japan’s defeat in the war, but
diplomatic relations between the two countries were not normalized until 1965. The normalization treaty included a declaration that the compensation matter was settled by a payment of
14

A possible interpretation is that places with a large dependence on Korean tourists could be the ones with thehighest name recognition in Korea, and subsequently suffer the most in response to the boycott. Prior studies like
Pandya and Venkatesan (2016) and Heilmann (2016) have highlighted that boycotts are concentrated in productswith high brand recognition.
15
Diversity by buyer type or buyer purpose of travel differs somewhat from Heilmann’s (2016) separation of
goods by product type. Accommodation services can be considered a “dual use” service, sold as a final consumer
service to tourists and as an intermediate input service to business travelers.

6

USD 800 million in grants and soft loans from the Japanese government to the Korean government. The lack of victims’ compensation or legal recourse in the treaty prompted mass protests
and the imposition of martial law in Korea. President Park Chung-hee used the compensation
money to fund economic development projects not to compensate victims of forced labor or
sexual slavery under Japanese rule. Relations between the two countries oscillated between
friendly and contentious over the ensuing five decades.16
In the fall of 2018, the Korean Supreme Court ruled in favor of Korean forced labor plaintiffs
seeking compensation from Japanese firms. These firms, Mitsubishi Heavy Industries, Nippon
Steel, Sumitomo Metal Corporation, and Nachi Fujikoshi, refused to pay damages citing the1965 treaty. In January, 2019, a Korean court ruled that some of Nippon Steel’s equity holdings
in a joint venture company in Korea could be seized to cover the payments due. This prompted
fears in Japan that other Japanese assets in Korea could be subject to seizure in the future asother court cases regarding forced labor compensation work their way through Korea’s court
system.
On July 4, 2019, the Japanese government dropped Korea from its “white list” of countries that receive preferential treatment for export licensing. This meant that Korea could nolonger count on receiving automatic approval of purchases of chemicals and related products(e.g., display panels) that have dual commercial and military uses. Tokyo officials stated that
this step was not retaliatory but rather due to national security concerns regarding suspicion
that the chemicals were being transshipped from (South) Korea to North Korea. Of particularconcern in Korea was continued access to three chemicals (i.e., hydrogen flouride, fluorinatedpolyimide, and resist polymers) needed to make semiconductors, Korea’s top export industry. The perceived threat to Korea’s vital industry led to consumer boycott activities in Korea
against purchases of Japanese products and services, including travel to Japan, from early July2019.
In August, 2019, President Moon Jae-in announced that he would drop Japan from Korea’s“white list” and terminate the intelligence-sharing pact between the two countries that was set
to expire in November, 2019. He also initiated a WTO case against Japan in September, 2019.
Japan took some steps to reduce bilateral tensions by issuing its first export license for one of
the restricted chemicals in August and approving of at least one shipment of each chemical
by October, 2019. In November, 2019, Korea announced that it would stay in and extend
the intelligence-sharing pact with Japan provided positive progress was being made in theirbilateral dispute. Korea also suspended the WTO case against Japan in November, 2019, butthen indicated in early June, 2020, that it would reopen the WTO case due to a lack of progressin the bilateral dispute.17
For the purposes of our study, the shock to Japan’s economy caused by Korea’s consumer
boycott of Japanese goods and services can be considered an exogenous event. Japan’s Prime
Minister Shinzo Abe sought to put pressure on Korea’s President Moon Jae-in regarding thecourt-ordered reparations so he introduced new export controls on chemicals of vital interest to
Korea’s top exporting industry, but these chemicals were a long-standing security concern for
16

See Lind (2019) for further details.
The slight easing of tensions from the Korean government’s November, 2019, announcements may help to
explain the small upturn in Korean travel to Japan at the end of our sample period as shown in Figure 1.
17

7

Japan (Obe and Kim, 2019). It seems unlikely that the Japanese government could anticipate
that the export control announcement would ignite public outrage in Korea and many angry
participants in the consumer boycott of Japanese exports. The suddenness and magnitude of
the consumer boycott shock is illustrated by showing year-on-year changes in monthly visitorsfrom Korea in Figure 3. The boycott-led drop in visitors is much stronger than those following
recent earthquake disasters in Japan, including the Great East Japan Earthquake of March 11,
2011.

Kumamoto Earthquake
April 14, 2016
M7.3

Great East Japan
Earthquake
March 11, 2011
M9.0

Hokkaido Earthquake
September 3, 2018
M6.7

Japan's export restriction
July 1, 2021

2011m1
2011m4
2011m7
2011m10
2012m1
2012m4
2012m7
2012m10
2013m1
2013m42013m7
2013m10
2014m1
2014m4
2014m7
2014m102015m1
2015m4
2015m7
2015m10
2016m1
2016m4
2016m7
2016m102017m1
2017m42017m72017m102018m1
2018m42018m72018m102019m1
2019m42019m72019m102020m1

YOY changes in the number of visitors from Korea-1000000-5000000500000Figure 3: Year-on-year Changes in Monthly Visitors from Korea

Year and month

Notes: Total number of foreign visitors indicates the total number of people who reside outside of Japan times thenumber of nights stayed in Japan (unit: 1,000 person-nights).
Source: Japan Tourism Agency (2020) Overnight Travel Statistics Survey.

3

Data

3.1

SourceIn this paper, we focus on Japan’s exports of accommodation services to visitors from foreigncountries.18 Our main outcome variable is exports from prefecture i in Japan to trading partners j at time t at monthly frequency, Yijt . We measure exports by the number of visitors fromcountry j to prefecture i in Japan at time t (year-month).
The main source of the data is the Overnight Travel Statistics Survey (Shukuhaku Ryokou Toukei
18

Trade in tourism services is a type of services trade. It is classified as mode 2 (consumption abroad) in the
General Agreement on Trade in Services.

8

Chousa in Japanese) by the Japan Tourism Agency, the Government of Japan. This survey isconducted for establishments in the accommodation services industry on a monthly basis. The
survey covers all establishments that have greater than or equal to 10 workers and for randomly sampled establishments that have less than 10 workers. The survey collects informationsuch as the location of the establishments and the number of foreign visitors, by their countryof residence and by their purpose of travel. As mentioned in Section 1, because foreign visitors
are defined as visitors who reside in countries other than Japan, foreign national visitors wholive in Japan are excluded whereas Japanese national visitors who live outside of Japan are
included if they use accommodation services while visiting Japan.
While the use of this dataset has several advantages, it also has some limitations. First,
some of the information, such as the number of visitors by country of residence, is available
only for establishments with greater than or equal to 10 workers. This in turn means that
small establishments are excluded from our analysis. Vacation rentals through services suchas AirBnB are not included if they are individually-owned small establishments. It is also
important to note that the survey focuses on accommodation establishments. Foreign visitors
who stay in the houses of their friends and/or families are not included in our analysis.
Second, the country of residence data is available only for 20 major countries as of theyear 2020. The number of major countries depends upon the period. The data are available
for 18 countries before April 2015 and for 16 countries before April 2013. We focus on the
period between April 2015 and January 2020 such that the analysis has the same 20 countries
consistently throughout the period. Third, the purpose of travel is not available by the countryof residence and prefecture visited. Therefore, we cannot explore the boycott effects on tourists
versus non-tourist travelers using this data.19 Finally, the data do not cover one-day trips sinceno accommodation services are involved. There are some Korean tourists who make one-day
trips to Tsushima, a tiny island off Nagasaki that is closely located to Korea and has duty freeshops. These cautions together imply that the survey does not cover all foreign visitors.
We exclude the period after January 2020 to exclude the effects of the travel restrictions
caused by the coronavirus pandemic. As a result, the maximum number of observations is54,520 (= 47 prefectures × 20 origin countries × 58 months).

3.2

Descriptive analysis

Before going to the econometric analysis, let us first examine the basic patterns of the data.
Table 1 presents the number of visitors from foreign countries, by country and by year. Table 1
indicates that Korea is one of the major origin countries of foreign visitors to Japan for the last
five years.20 However, the number of Korean visitors dropped by 2.24 million (person-nights)
from 2018 to 2019, while the total number of foreign visitors increased by 17.7 million (personnights). As a result, the Korean share of total visitors declined from 14.3 percent in 2018 to 9.6
percent in 2019.
19
Instead, we use visitor survey data in Section 4.3 to explore possible differences in boycott effects between
tourists and non-tourist visitors from Korea.
20
In addition to the 10 countries listed for 2019 in Table 1, the other major countries by ISO country code includedin this study are: CAN, DEU, ESP, IDN, IND, ITA, MYS, PHL, RUS, and VNM.

9

10

2019
101,306(100.0%)29,848(29.5%)
13,471(13.3%)
9,715(9.6%)
7,278(7.2%)
6,982(6.9%)
3,604(3.6%)
3,066(3.0%)
2,455(2.4%)
2,093(2.1%)
1,564(1.5%)
80,076(79.0%)

Source: Japan Tourism Agency (2020) Overnight Travel Statistics Survey.

Notes: The number of foreign visitors indicates the number of people who reside outside of Japan times the number of nights stayed in Japan (unit: 1,000 person-nights). Figuresin parenthesis indicate the share (percentage). Countries are represented by ISO country codes.

Top 10

10

9

8

7

6

5

4

3

2

Rank
1

Table 1: Number of Visitors in Japan from Foreign Countries, by Country
2015
2016
2017
2018
Total
60,50964,06772,934
83,566(100.0%)(100.0%)(100.0%)(100.0%)CHN
16,295CHN
16,867CHN
17,596CHN
22,166CHN(26.9%)(26.3%)(24.1%)(26.5%)
TWN
10,491TWN
10,529TWN
11,390TWN
12,104TWN(17.3%)(16.4%)(15.6%)(14.5%)
KOR
6,741
KOR
7,740
KOR
11,020KOR
11,955
KOR(11.1%)(12.1%)(15.1%)(14.3%)
HKG
4,809
HKG
5,209
HKG
6,259
HKG
6,214
USA
(7.9%)(8.1%)(8.6%)(7.4%)
USA
3,799
USA
4,293
USA
4,782
USA
5,576
HKG
(6.3%)(6.7%)(6.6%)(6.7%)
THA
2,396
THA
2,394
THA
2,605
THA
2,969
THA
(4.0%)(3.7%)(3.6%)(3.6%)
AUS
1,472
AUS
1,597
AUS
1,809
AUS
2,130
AUS
(2.4%)(2.5%)(2.5%)(2.5%)
SGP
1,379
SGP
1,516
SGP
1,702
SGP
1,961
SGP
(2.3%)(2.4%)(2.3%)(2.3%)
GBR
906
GBR
956
GBR
1,065
GBR
1,214
GBR
(1.5%)(1.5%)(1.5%)(1.5%)
MYS
840
MYS
934
IDN
1,001
IDN
1,184
FRA
(1.4%)(1.5%)(1.4%)(1.4%)
49,12952,03559,22867,472(81.2%)(81.2%)(81.2%)(80.7%)

Figure C1 presents the number of visitors from Korea in 2018 and 2019, by prefecture. The
prefectures are sorted by the number of visitors in 2018. This figure shows that the degreeof the decline varies across prefectures. The significant decreases are concentrated in somespecific prefectures such as Osaka, Fukuoka, Okinawa, Hokkaido and Oita. Table 1 confirmedthat the number of foreign visitors from Korea dropped by 2.24 million from 2018 to 2019. The
decline in the sum of these five regions amounted to 1.81 million. Almost 81 percent of thedecline is concentrated in these five prefectures.
Figure 4 indicates the relationship between the percentage changes in the number of visitors from Korea in 2018 and 2019 and the average share of visitors from Korea between April
2015 and June 2019. The horizontal axis corresponds to the share presented in Figure 2 whilethe vertical axis corresponds to the percentage change between 2018 and 2019 in Figure C1.
This figure shows a strong negative correlation between them (r = −0.289). This result suggests that the prefectures with high dependency on visitors from Korea are more likely to beaffected by the boycott. Note, however, that this figure presents a correlation rather than a causation. Our next section investigates this relationship, based on a more rigorous econometricframework.

Changes in the number of visitors from Korea, 2018-2019
-.6
-.4
-.2
0
.2

Figure 4: Changes in the Number of Visitors and the Share of Visitors from Korea

Kyoto
Miyagi
Chiba
Tokushima
Aichi

Shimane

Tokyo

Fukui
Iwate
Shizuoka

Niigata
Saitama
Yamagata

Kanagawa
Fukuoka
Hiroshima
IbarakiMie

Gumma
Yamanashi
Gifu

Fukushima

Ehime
KagawaKagoshima
Hokkaido

Akita
HyogoAomori

TochigiOkayama
ToyamaMiyazakiOkinawa

Kumamoto

TottoriNagasaki

Osaka

Ishikawa

Saga

NaganoNara

Oita

Yamaguchi

Wakayama
Shiga
Kochi

0

.2
.4
.6
Average share of visitors from Korea between April 2015 and June 2019

Notes: The vertical axis indicates the log difference in the number of visitors from Korea between 2018 and 2019,by prefecture. The horizontal axis indicates the share of visitors from Korea between April 2015 and June 2019. The
solid line indicates the fitted values from the linear ordinary least squares estimation and the gray areas indicate
the 95 percent confidence interval (CI).
Source: Japan Tourism Agency (2020) Overnight Travel Statistics Survey.

11

4

Disaggregate-level Analysis

4.1

Methodology

A recent study by Caselli et al. (2020) asserted the importance of country diversification ofexports in reducing economic volatility.21 This in turn implies that the effects of the boycott
could be heterogeneous across prefectures. Specifically, the more a prefecture depends upon
exports to Korea, the larger the export decline they face as a result of the boycott. We begin
by analyzing the disaggregate impacts of the boycott, focusing on each prefecture’s exports ofaccommodation services by country. Our question is: do prefectures with higher pre-boycott
dependency on Korea suffer larger declines in exports to Korea as a result of the consumer
boycott?
To evaluate the impact of the boycott on region-level exports, this paper employs the DDD
design (Wooldridge, 2007).22 The DDD design allows us to estimate a model of exports fromprefecture i in Japan to trading partners j at time t at monthly frequency, Yijt . We hypothesize that a prefecture’s exports to Korea are more likely to be affected by the boycott if its
pre-boycott export dependency on Korea is high. Note that regional dependency on visitorsfrom Korea can be described not by a binary variable but by a continuous variable. Following Guadalupe and Wulf (2010), we treat the treatment group as a continuous variable (i.e.,
differing levels of exposure to treatment). Note that the number of foreign visitors is affectedby other factors such as prefecture-specific tourism resources and/or country-specific factors.
For example, some prefectures such as Oita attract visitors because they have nice hot springs.
Similarly, the number of visitors from China and Korea is large simply because of their proximity to Japan. To control for such prefecture- and country-specific factors, we include prefectureand country-fixed effects. Our regression equation thus is written as follows:
Yijt = α + ψi + ψj + ψt
+β1 (si × Postt ) + β2 (si × KORj ) + β3 (KORj × Postt )+γ(si × KORj × Postt ) + εijt ,(1)

where ψi , ψj , and ψt are prefecture-, country-, and time-fixed effects, respectively; si is prefecture i’s dependency on exports to Korea that is measured by the average share of visitors from
Korea to total visitors from foreign countries in prefecture i before the boycott (i.e., between
April 2015 and June 2019), which corresponds to the average shares shown in Figures 2 and4; KORj is a dummy variable taking the value one if export destination j is Korea and zerootherwise; Postt is the post-boycott dummy that takes the value one after the boycott started(i.e., from July 2019); and εjt is an error term. Note that si and KORj cannot be included bythemselves due to the collinearity with ψi and ψj , respectively. The parameter of interest is γthat captures the differential effect of the boycott on prefectures according to their dependency
21

Similarly, Kurz and Senses (2016) examined the effects of trade on employment volatility, using US firm-level
data. They found that an increase in the number of export destinations was associated with lower levels of volatility,
which is in line with the diversification story.
22
The DDD design is also called the triple difference design. For a recent application, see Muralidharan andPrakash (2017).

12

on visitors from Korea prior to the start of the boycott.23 To check the robustness of our results
to the strictest possible model specification, we also estimate our parameter of interest using
prefecture-time (ψit ), country-time (ψjt ) and prefecture-country fixed effects (ψij ).
For Yijt , we focus on exports of tourism services from prefecture i to country j in time t.
We measure exports Yijt by the number of visitors (i.e., person-accommodation-nights) fromcountry j to prefecture i at time t (year-month). For convenience in interpreting estimatedcoefficients, we use the log value of the number of foreign visitors as the dependent variable.24
In applying the DDD design to equation (1), we utilize three sets of sample data. The firstsample is a full-period sample for April 2015–January 2020. The second sample is a mediumperiod sample for July 2018–January 2020, which covers the period one year before and six
months after the boycott started (i.e., July 2019). The third sample is a short-period sample forJanuary 2019–January 2020, which covers the period six months before and after the boycott
started. The numbers of observations are 52,879, 17,573, and 12,037 for the full-, medium-, andshort-period samples, respectively, due to the observations with zero trade.
Note that one of the key assumptions behind the DDD design is a common trends assumption: in the absence of treatment (i.e., the boycott in our study), the difference between thetreatment and control groups is constant over time. One strategy to evaluate this assumption
is to check group-specific linear trends (Wing, Simon, and Bello-Gomez, 2018). This amounts
to a regression of the outcome on the treatment variable, group- and period-fixed effects, andeach group effect interacted with a linear time index. In the context of our analysis, the regression equation is written as follows:
Yijt = α + ψi + ψj + ψt + η1 (si × KORj ) + η2 (si × Trendt )+η3 (KORj × Trendt ) + λ(si × KORj × Trendt ) + εijt ,(2)

where Trendt is a time trend; and the definitions of the variables are the same as that of equation (1). Similar to equation (1), si , KORj , and Trendt cannot be included by themselves due tothe collinearity with ψi , ψj , and ψt .
The sample for equation (2) is before July 2019 when the boycott started. The numbers ofobservations for the test of the common trends assumption are 46,380, 11,074, and 5,538 forthe full-, medium-, and short-period samples, respectively. If the trend is common between
prefectures as well as between Korea and other countries, λ will be insignificant.

4.2

Estimation results

Let us first check the common trends assumption. Table 2 presents the regression results forequation (2). The standard errors are clustered by prefecture, country, and time.25 Columns (1),(2), and (3) present the results for the full-, medium-, and short-period samples, respectively.
This table indicates that the estimated coefficients are insignificant for the full- and mediumperiod samples while it is significant for the short-period sample. This result supports the23

We explore an explanation for this differential effect in Section 4.3.
Note that the use of the log of exports results in dropping observations with zero trade, which may lead tobiases in estimated coefficients. We address this issue in Section 6.
25
Multi-way clustered standard errors are computed by the stata command reghdfe developed by Correia (2017).
24

13

validity of the common trends assumption for only the full- and medium-period samples, so
we proceed with the DDD regression analysis using these two samples.26
Table 2: Common Trends Assumption: Disaggregate-level Analysis
(1)
(2)
(3)
Period2015m42018m72019m1–2019m6 –2019m6 –2019m6
si × KORj × Trendt
0.009
0.030
-0.431***[0.006][0.031][0.008]si × KORj
Yes
Yes
Yes
si × Trendt
Yes
Yes
Yes
KORj × Trendt
Yes
Yes
Yes
Fixed effectPrefecture (ψi )
Yes
Yes
Yes
Country (ψj )
Yes
Yes
Yes
Time (ψt )
Yes
Yes
Yes
N
46,38011,074
5,538
R2
0.85
0.86
0.87
Notes: Figures in brackets indicate standard errors clustered by country, prefecture, and time. *** indicates thesignificance level at 1 percent.
Source: Authors’ estimation, based on Overnight Travel Statistics Survey.

Table 3 presents the DDD regression results. Columns (1) and (2) are the estimation results for equation (1) for the full- and medium-period samples, respectively. There are two
notable findings. First, the coefficients of (KORj × Postt ) indicate significantly negative signs.
The coefficients are −0.553 and −0.529 for the full- and medium-period samples, respectively.
This result indicates that the effect of the consumer boycott on exports to Korea is between−41.1 percent and −42.5 percent, after converting the coefficients of log changes into growth
rates due to the large estimated impacts.27 Even after we control for the prefecture-, country-,and time-fixed effects, the negative effects of the boycott on exports to Korea are remarkably
large at their minimum value (i.e., for a hypothetical prefecture with zero Korea dependency
pre-boycott). Second, the coefficient of (si × KORj × Postt ), our parameter of interest, presents
significantly negative signs. This result suggests that the effects of the boycott on exports to Korea are different across prefectures in Japan based on their pre-boycott Korea dependency. The
estimation results in columns (3) and (4) of Table 3 demonstrate that our parameter of interest
results do not change much even when we use the strictest possible DDD model specification(i.e., with prefecture-time, country-time and prefecture-country fixed effects).28
One may be interested in the economic significance as well as the statistical significance.
While our study is not based on a general equilibrium framework, we can compute the eco26
It is more difficult to satisfy the common trends assumption using only six months in the short-period sample
in the presence of differences in travel seasonality across countries of origin.
27
Coefficients are converted into approximate growth rates as follows: growth rate = exp(coefficient) − 1.
28
The prefecture-time fixed effect controls for prefecture-time specific factors such as the utilization rate of accommodation services at the prefecture level. Similarly, the country-time fixed effect controls for country-time
specific factors such as country-specific seasonality and exchange rate movement. The country-prefecture fixed
effect controls for country-prefecture-specific factors such as the existence of international schools and towns (e.g.,
China town).

14

Table 3: Regression Results: Disaggregate-level Analysis
(1)
(2)
(3)
(4)
Period2015m42018m7
2015m42018m7–2020m1 –2020m1 –2020m1 –2020m1
si × Postt
0.238*
0.346***[0.131][0.108]si × KORj
8.156***
8.263***[0.340][0.441]
KORj × Postt-0.553*** -0.529***[0.032][0.028]si × KORj × Postt-2.270*** -2.373*** -2.286*** -2.380***[0.146][0.093][0.157][0.148]Fixed effectPrefecture (ψi )
Yes
Yes
No
No
Country (ψj )
Yes
Yes
No
No
Time (ψt )
Yes
Yes
No
No
Prefecture-time (ψit )
No
No
Yes
Yes
Country-time (ψjt )
No
No
Yes
Yes
Prefecture-country (ψij )
No
No
Yes
Yes
N
52,87917,57352,87917,573
R2
0.85
0.85
0.95
0.96
Notes: Figures in brackets indicate standard errors clustered by country, prefecture, and time. *** and * indicate thesignificance level at 1 and 10 percent, respectively.
Source: Authors’ estimation, based on Overnight Travel Statistics Survey.

nomic magnitude based on the estimated coefficients from our main specification and prefecture i’s dependency on exports to Korea, si , as a back-of-the-envelope calculation.29 Table
4 presents the distribution of si and the estimated economic magnitude of the boycott’s effects. The average and the median of si are 14.9 percent and 10.3 percent, respectively, while
the first and third quartiles are 5.1 percent and 17.0 percent, respectively. The results indicate
the heterogenous impacts of the boycott. For example in the case of the full-period sample,
the impact is approximately −11.6 percent (= −2.270 × 5.1) for the 25th percentile prefecture
while it is roughly −38.5 percent (= −2.270 × 17.0) for the 75th percentile prefecture. Due tothe large estimated changes in log values, these relative magnitude effects can be consideredrough estimates of the growth rates of −11.0 percent and −32.0 percent, respectively.
Note that these results are based on the comparison of exports to Korea between prefectures. In order to calculate the effect on exports relative to other countries, we need to tally
the total magnitude using the coefficients of (KORj × Postt ) and (si × KORj × Postt ), as shownin columns (4) and (5) of Table 4.30 Using the longer two sample periods, which satisfied thecommon trends assumption, a 25th percentile prefecture suffers a 47.8 to 48.8 percent loss inexports to Korea while a prefecture at the 75th percentile suffers a loss of 60.6 to 60.9 percent.31
29
A similar exercise has been done in recent studies on the effects of offshoring. See, for example, Harrison and
McMillan (2011) and Kambayashi and Kiyota (2015).
30
The values in column (4) can be interpreted as percentage point differences in average growth rates between
the treated and control groups, while the values in column (5) are approximate average treatment effects on thetreated in growth rate terms.
31
Our estimated effects of the boycott on Japan’s exports of accommodation services to Korea are admittedly

15

The implied gap between the 75th and 25th percentile prefectures is −12.1 to −12.8 percentage
points, depending on the sample period. Using the full-period sample, for prefectures withhigh dependency on Korean visitors, the negative impact on their exports of accommodation
services to Korea is 12.1 percentage points larger than that for prefectures with low dependency. The results clearly indicate that prefectures with higher dependency on visitors from
Korea are more likely to have severe declines in exports of accommodation services to Korea. We explore the explanation for this disproportionate boycott effect in Section 4.3 using analternate dataset that allows us to estimate tourists versus non-tourist travelers from Korea.
Table 4: Impact of the Boycott on Prefectures’ Exports to Korea

Percentile

(1)

(2)

Coefficientsi

2015m4–2020m1
Mean-2.27025%-2.27050%-2.27075%-2.27075-25% gap
2018m7–2020m1
Mean-2.37325%-2.37350%-2.37375%-2.37375-25% gap(3)(= (1) × (2))
Relative magnitude(log change)(4)( = (3) + KP Coeff.)
Total magnitude(log change)(5)
Total magnitude
converted(growth rate)

0.149
0.051
0.103
0.170

-0.339-0.116-0.233-0.385-0.892-0.669-0.786-0.938-0.590-0.488-0.544-0.609-0.121

0.149
0.051
0.103
0.170

-0.355-0.121-0.244-0.403-0.884-0.650-0.773-0.932-0.587-0.478-0.538-0.606-0.128Notes: Exports to Korea mean the exports of accommodation services to Korea that are defined as the number ofvisitors from Korea (the total number of visitors who reside in Korea × the number of nights stayed in Japan).
Percentile indicates the quartiles of si . Coefficients are obtained from Table 3 and KP Coeff. means (KORj × Postt )coefficient from the corresponding sample period. Rounding off of displayed numbers explains small variations in
calculated numbers shown above. Growth rate = exp(log change) − 1.
Source: Authors’ estimation, based on Overnight Travel Statistics Survey.

It is also important to note that stopping travel to Japan affects Korea as well as Japanbecause many Korean tourists utilize Korean airlines and travel agencies. In this context, weshould note that some studies such as Du, Ju, Ramirez, and Yao (2019) and Clerides et al. (2015)
argue that the impact of political conflict is short-lived. Although this is a related importantquestion, we are not able to analyze this issue because travel between countries is restrictedfrom February 2020 in many countries due to the coronavirus pandemic.32
quite large, but the magnitudes are actually smaller than those reported for bilateral beer trade. Korean imports ofJapanese beer basically stopped from August, 2019 (i.e., approximately −100 percent monthly change), leading toa −49.2 percent annual change for 2019 (Stangarone, 2020).
32
Instead, we refer the reader back to footnote 17 where we mentioned suggestive evidence that an easing ofbilateral tensions in November, 2019, may be linked to a small upturn in visitors from Korea to Japan from thatmonth through January, 2020.

16

4.3

Tourists versus non-tourist visitors

We now turn to the task of explaining why some prefectures in Japan are disproportionately
impacted by the Korean consumer boycott at the bilateral (i.e., disaggregate) level. As statedpreviously in Section 3, our main data (i.e., visitor-night accommodations) does not provide
the purpose of travel by the country of residence and prefecture visited. This data limitation leads us to implicitly assume that travelers from Korea are a homogeneous group witha common propensity to participate in the consumer boycott of travel to Japan. However,our disaggregate-level results (i.e., finding that prefectures that are more dependent on visitors from Korea suffer larger percentage losses in visitors from Korea) are inconsistent with thisassumption.
Suppose that 50 percent of Koreans travelers chose to participate in the consumer boycottof travel to Japan.33 In that case, we would expect that prefectures with higher dependency on
visitors from Korea would suffer larger percentage losses in their exports of accommodations
services at the aggregate level (i.e., a 50 percent decline in visitors from Korea is more impactful
for a prefecture with a 40 percent dependence on such visitors than for one with only a 10
percent dependence), which we address in the next section. At the disaggregate level, we
would expect to see each prefecture lose 50 percent of its Korean visitors, which would beindicated by a negative and significant coefficient for (KORj × Postt ) but not for (si × KORj ×
Postt ). Instead, our disaggregate-level results indicate a disproportionate boycott effect based ona prefecture’s pre-boycott Korea dependency (i.e., a significant coefficient on the (si × KORj ×
Postt ) coefficient).
To explain the disproportionate boycott effect at the disaggregate level, we need to acknowledge that visitors from Korea are not homogenous but rather heterogeneous in terms of
their propensities to participate in the boycott and heterogenous in where they travel within
Japan. Leisure travelers (i.e., tourists) may be more likely to participate in a consumer boycott
since leisure travel is more discretionary in terms of destination and timing than other typesof travel (e.g., travel for business, to visit family or friends, to attend school, etc.). Leisuretravelers also favor some destinations in Japan over others. If the same prefectures that havehigh pre-boycott dependency on visitors from Korea also tend to attract Korean tourists, rather
than non-tourist Korean travelers, then that can explain our finding of disproportionate bilateral boycott effects based on Korea dependency.
To examine this hypothesis, we use information on visitors’ purpose of travel collected by
the Japan Tourism Agency’s Consumption Trend Survey for Foreigners Visiting Japan (Hounichi
Gaikokujin Shouhi Doukou Chousa in Japanese). This survey provides data on a quarterly basis
for visitors to Japan who are surveyed at their port of entry or departure. We use this data tocreate a “Korean tourist dependency” measure to see to what extent each prefecture depends
on Korean tourists relative to all Korean visitors in the pre-boycott period, and then we compare this measure to our previously defined “Korea dependency” based on the visitor-night
33

Note that this may be a conservative estimate. One online survey of Koreans collected in late August to early
September, 2019, found that 69.3 percent of respondents who had planned to visit Japan cancelled their trip orchanged their destination away from Japan as a result of Japan’s restriction on exports from July, 2019. (Korea
Culture and Tourism Institute, 2019, available online (in Korean): survey link)17

accommodations data.34
Table 5 shows this comparison by prefecture. The 47 prefectures are ranked in descending order by their level of Korea dependency (i.e., share of visitor-night accommodations forvisitors from Korea out of all foreign visitor-night accommodations) shown in Column (1).
Columns (2)–(4) reflect the survey data from the Consumption Trend Survey for Foreigners Visiting Japan. The correlation between Korea dependency and Korean tourist dependency is0.5481, indicating that prefectures with high dependency on foreign visitors from Korea also
tend to have high dependency on Korean tourists as opposed to Korean non-tourist travelers.
For the top 10 prefectures ranked by Korea dependency (i.e., Oita to Osaka in Table 5), 80.0
to 96.4 percent of their Korean visitors are tourists. For the bottom 10 prefectures (i.e., Narato Yamanashi), the range of Korean tourist dependency is much wider and lower at 29.0 to
77.7 percent, with the exception of Nara at 92.5 percent. If tourists are more likely than nontourists to participate in the consumer boycott, then the strong positive correlation between
prefectures’ Korea dependency and Korean tourist dependency helps to explain our finding ofdisproportionate impacts of the consumer boycott at the disaggregate level.35 Our results imply that prefectures that attract more diverse visitors by purpose of travel (i.e., tourism versus
non-tourism) will be less affected by a consumer boycott at the bilateral level.

5Aggregate-level Analysis

5.1

Methodology

The previous section found that prefectures with higher dependency on visitors from Korea
were more likely to have severe declines in their exports to Korea. Note, however, that theestimation results from prefecture-country-level specifications tells us the boycott effect in a“relative” sense: relative to travelers from all other foreign countries. Thus it is not necessarily
clear whether a given prefecture had a “net” adverse effect from the boycott since it is possiblethat travelers from other countries picked up the slack induced by a reduction in travel from
Korea. An aggregate prefecture-level analysis can address the prefecture-level net effect fromthe boycott.36 More specifically, this section asks the following question: do prefectures withhigher pre-boycott dependency on Korea suffer larger declines in total exports as a result of theconsumer boycott? Noting that our main outcome variable is exports from prefecture i in Japanto trading partners j at time t at monthly frequency, Yijt , we can compute each prefecture’sPtotal exports of accommodation services: Yit = j Yijt .
34

We use the regional survey data which provides the purpose of travel by nationality and by prefecture visited.
The national survey reports some data by nationality and by country of residence and indicates for 2018 that almost
all surveyed visitors to Japan who reside in Korea are Korean nationals (i.e., 99.7 percent). This high correspondence
between country of residence and nationality for visitors from Korea allows us directly to compare the visitor-night
data (based on country of residence) and visitor survey data (based on nationality).
35
Note that the “Consumption Trend Survey for Foreigners Visiting Japan” is not used in our main DDD and
DID analyses since it is only reported on a quarterly basis and we have no information as to how representative
the survey is of all foreign visitors to Japan.
36
A similar approach is employed in the recent banking and finance literature on bank’s loan supply and demand
where there are many firms that borrow from multiple banks (e.g., Jiménez, Mian, Peydró, and Saurina, 2020). In
our context, firm, bank, and loan can be interpreted as prefecture, country, and travelers (i.e., exports), respectively.

18

Table 5: Korea dependency and Korean tourist dependency
Prefecture

(1)
Korea
dependency

Total
Oita
Saga
Yamaguchi
Fukuoka
TottoriMiyazakiNagasaki
Kumamoto
Okinawa
Osaka
Kagoshima
Ehime
Hokkaido
Aomori
KagawaShimaneAkita
Hyogo
Saitama
Mie
Shiga
Yamagata
ToyamaOkayama
Kochi
NiigataTokyo
IbarakiWakayama
Tochigi
Fukushima
Iwate
Kanagawa
Kyoto
Aichi
Fukui
Tokushima
Nara
Miyagi
Hiroshima
Gumma
Gifu
NaganoChiba
Ishikawa
Shizuoka
Yamanashi
Correlation

0.56270.49650.45480.43990.40420.4006
0.32660.31220.25880.17880.17160.16980.16220.15530.15450.15150.13960.12810.12540.11680.11300.10920.10340.10280.1012
0.09670.0756
0.07150.0691
0.06840.06530.06370.05730.05220.05120.05120.04970.04830.04720.04670.04630.04370.04240.04140.04030.04000.0145(2)
All Korean
visitors
surveyed
84,658
9,694
1,218
1,646
22,520
292
266
3,212
2,439
4,152
27,002
614
134
5,377
116
586
182
72
4,802
557
233
186
110
490
270
59
201
20,554
241
186
276
88
33
2,659
13,804
2,833
47
57
2,977
336
752
169
631
458
12,293
360
808
280

(3)
Koreantourists
surveyed
67,629
9,346
1,020
1,316
20,118
255
215
2,867
2,148
3,999
24,078
492
76
5,027
86
534
163
35
4,212
224
81
64
58
399
182
36
70
10,779
59
144
89
18
17
1,179
12,890
1,459
11
39
2,755
156
381
49
490
238
8,407
212
385
144

(4)
Korean tourist
dependency(=(3)/(2))
0.79880.96410.83740.79950.89330.87330.80830.89260.88070.96320.89170.80130.56720.93490.74140.91130.8956
0.48610.8771
0.4022
0.3476
0.34410.52730.81430.67410.6102
0.34830.52440.24480.77420.32250.20450.51520.44340.93380.51500.23400.68420.92540.46430.50660.28990.77650.51970.68390.58890.47650.51430.5481

Notes: Korea dependency in column (1) defined as the share of foreign visitors from Korea between April 2015
and June 2019 using visitor-night accommodations data. Columns (2)–(4) use survey data collected from foreign
visitors to Japan at ports of entry or departure for 2015Q2 to 2019Q2. Korean tourist dependency defined as numberof surveyed Korean tourists divided by total number of surveyed Korean visitors to Japan.
Source: Authors’ estimation, based on Overnight Travel Statistics Survey (column (1)) and Consumption Trend Survey
for Foreigners Visiting Japan (columns (2)–(4)).

19The regression equation is based on a standard DID design as follows:
Yit = α + ψi + ψt + λ(si × Postt ) + εit ,(3)

where the definitions of variables are the same as those in the previous section. The si term
captures a type of “exposure to treatment”, with the consumer boycott as the “treatment” inthis standard DID design. The parameter of interest is λ that captures the differential effect ofpre-boycott dependency on Korea across prefectures. The numbers of observations are 2,726(= 47 prefectures × 58 months), 893 (= 47 prefectures × 19 months), and 611 (= 47 prefectures× 13 months) for the full-, medium-, and short-period samples, respectively. Note that si
cannot be included by itself because of the collinearity with ψi .
The corresponding regression to evaluate the common trends assumption is as follows:
Yit = α + ψi + ψt + ζ(si × Trendt ) + εit ,(4)

where Trendt is a time trend; and the other variables are the same as above. The sample forequation (4) is before July 2019 when the boycott started. The numbers of observations for thetest of the common trends assumption thus are 2,397 (= 47 prefectures × 51 months), 564 (=47 prefectures × 12 months), and 282 (= 47 prefectures × 6 months) for the full-, medium-,and short-period samples, respectively. If the trend is common between prefecture, ζ will beinsignificant.

5.2

Estimation results

We first check the common trends assumption. Table 6 presents the regression results for equation (4). Columns (1), (2), and (3) present the estimation results for the full-, medium-, andshort-period samples, respectively. The coefficient is insignificant for the full- and mediumperiod samples, but it is significant at the 10 percent level for the short-period sample. This
result supports the validity of the common trends assumption for the longer two samples.
Table 6: Common Trends Assumption: Aggregate-level Analysis
(1)
(2)
(3)
Period2015m42018m72019m1–2019m6 –2019m6 –2019m6
si × Trendt
0.003-0.032-0.157*[0.007][0.024][0.075]
Fixed effectPrefecture (ψi )
Yes
Yes
Yes
Time (ψt )
Yes
Yes
Yes
N
2,397
564
282
R2
0.97
0.98
0.98
Notes: Figures in brackets indicate standard errors clustered by prefecture and time. * indicates the significancelevel at 10 percent.
Source: Authors’ estimation, based on Overnight Travel Statistics Survey.

Table 7 presents the DID regression results. Columns (1) and (2) show the results for the20

full- and medium-period samples, respectively. Table 7 indicates significantly negative coefficients of (si × Postt ) for both types of sample. This confirms that prefectures with higher
dependency on visitors from Korea are more likely to face significantly negative impacts dueto the boycott. In the disaggregate-level analysis in Section 4, we confirmed the negative effects of the boycott on the exports of accommodation services to Korea. The results of Table 7
suggest that this negative impact is too large to be offset by any potential increases in exports
to other countries. As a result, Japanese prefectures had net adverse effects from the boycottand these adverse effects increased in prefectural dependency on Korean exports.
Table 7: Regression Results: Aggregate-level Analysis(1)(2)
Period2015m42018m7–2020m1 –2019m6
si × Postt-0.841*** -0.775***[0.183][0.152]
Fixed effectPrefecture (ψi )Yes
YesTime (ψt )
Yes
Yes
N
2,726
893
R2
0.97
0.98
Notes: Figures in brackets indicate standard errors clustered by prefecture and time. *** indicates the significancelevel at 1 percent.
Source: Authors’ estimation, based on Overnight Travel Statistics Survey.

Table 8: Impact of the Boycott on Prefectures’ Total Exports
(1)
(2)
(3)
(4)
(= (1) × (2))
Total magnitude
Percentile
Coefficientsi
Total magnitude
converted(log change)(growth rate)
2015m4–2020m1
Mean-0.841
0.149-0.126-0.11825%-0.841
0.051-0.043-0.04250%-0.8410.103-0.086-0.08375%-0.8410.170-0.143-0.13375-25% gap-0.091
2018m7–2020m1
Mean-0.7750.149-0.116-0.10925%-0.7750.051-0.040-0.03950%-0.7750.103-0.080-0.07775%-0.7750.170-0.132-0.12375-25% gap-0.084
Notes: Exports mean the exports of accommodation services that are defined as the number of foreign visitors (thetotal number of visitors who reside outside of Japan × the number of nights stayed in Japan). Percentile indicatesthe quartiles of si . Coefficients are obtained from Table 7. Rounding off of displayed numbers explains small
variations in calculated numbers shown above. Growth rate = exp(log change) − 1.
Source: Authors’ estimation, based on Overnight Travel Statistics Survey.

Table 8 computes the magnitude of these effects in log changes (i.e., percentage point differ21

ences in average growth rates between the treated and control groups) and then converted to
growth rates (i.e., approximate average treatment effects on the treated in growth rate terms)
across the distribution of si as before. Table 8 indicates that the total exports of each prefecture
declined, on average, between 11.8 percent and 10.9 percent for the full- and medium-period
samples, respectively. The results suggest that the impacts on each prefecture’s total exports
are around −11.3 percent on average. Although these export effects are significantly larger
than those of the previous studies such as Heilmann (2016), a caution may be needed because
total exports in our study mean total exports of accommodation services only, not exports ofall goods and services.37
Another notable finding is that the impacts vary across prefectures. Some regions hadlarge negative aggregate impacts on their accommodations industry from the boycott. Table
8 shows not only the mean of si but also the 1st, 2nd, and 3rd quartiles of si as 5.1, 10.3 and17.0 percent, respectively. As shown in Table 8, these values of si imply impacts of −4.2, −8.3
and −13.3 percent for the 25th, 50th and 75th percentile prefectures, respectively, for the fullperiod sample. The gap between the 75th and 25th percentile prefectures is quite large at−9.1 percentage points which exceeds the estimated loss for the 50th percentile prefecture (i.e.,−8.3 percent). The results suggest the importance of considering regional heterogeneity within
Japan in assessing the impacts of the consumer boycott.
Our results also have an important policy implication. While the negative impact of political conflict on trade could be small for a country as a whole, it could have significant effects
on trade for particular regions. Such regional heterogeneity might result in the expansion of
inequality between regions. It thus is important for policy makers to take into account suchregional impacts as a consequence of political conflict.

6

Discussion

6.1

Timing

For the decline in exports to be attributable to the boycott, prefecture i’s dependency on exports
to Korea, si , should be correlated with exports after the boycott, but not before. To determine
whether there is a relationship between a prefecture’s Korea dependency and exports in the
period before July 2019, we replace the post-boycott dummy Postt with a full set of monthyear dummies, denoted as dt .38 For the disaggregate-level analysis, regression equation (1) is
37

As mentioned above, Heilmann (2016) found that the reduction in total exports of the boycotted country was
low. He argued that “even though an individual firm of the boycotted country might be hit hard, the overall effecton the export sector is small” (Heilmann, 2016, p.180). This argument is consistent with our finding. Also note one
additional difference between our study and previous ones is that our estimates are for trade volume while otherstudies have tended to use trade values.
38
A similar approach is introduced in Pierce and Schott (2016).

22

rewritten as follows:
Yijt = α + ψi + ψj + ψtXX
β3t (KORj × dt )β1t (si × dt ) + β2 (si × KORj ) +
+
t

t

+

X

γt (si × KORj × dt ) + εijt .

(5)tSimilarly, for the aggregate-level analysis, regression equation (3) is rewritten as follows:
Yit = α + ψi + ψt +X

λt (si × dt ) + εit .

(6)tFigure 5 displays the 95 percent CI for the coefficients γt (light gray) and λt (dark gray)in equations (5) and (6), respectively. Standard errors are clustered by country, prefecture,and time in equation (5) and by prefecture and time in equation (6). We set July 2017 as thebase level for the coefficients.39 This figure indicates that the CI for both disaggregate- andaggregate-level results fluctuates between zero and one before July 2019. However, it shows asharp drop below zero from July 2019 when the boycott started. This pattern is consistent withthe timing of the boycott, lending further support for the baseline empirical strategy.
This figure also indicates a sharp drop around April 2016. As we argued in footnote 11,
this is possibly attributable to the 2016 Kumamoto earthquakes on April 14th and 16th thatcaused severe damage in Kumamoto and Oita prefectures. The results suggest that the impactof the consumer boycott on Japan’s accommodation services exports is comparable to that of
the 2016 Kumamoto earthquake at the aggregate level and is much larger at the disaggregate
level. This confirms the strong impact of the consumer boycott.

6.2

Alternative estimation model

One may be concerned with the use of a log-linearized specification. Several studies havepointed out the problems of log-linearization in estimating bilateral trade flows. First, the useof log values for the dependent variable will result in discarding observations of zero trade
because we cannot take the log of zero (Santos Silva and Tenreyro, 2006). Second, the loglinearized model faces severe bias when heteroskedasticity exists. Third, the sum of the fitted
values in the log-linearized model does not necessarily equal the sum of the levels (Fally, 2015).
Lastly, the log-linear specification described in Sections 4 and 5 implies an additive treatment
effect while the true treatment effect may be multiplicative (Ciani and Fisher, 2019).40
We can avoid the problems with log linearization and incorporate a multiplicative treatment effect by employing the Poisson Pseudo Maximum Likelihood (PPML) estimator (Santos
Silva and Tenreyro, 2006). Full details on and results from our use of the PPML estimatorappear in Appendix A, where we demonstrate that the main messages of the log-linearized39

We choose July 2017 because it is around the midpoint of our sample period and is the same month (i.e., July)as when the boycott started.
40
In addition, the converted growth rate from the log-linear specification (i.e., column (5) in Table 4 and column(4) in Table 8) is only approximate, while that from the multiplicative model (i.e., column (5) in Table A2 andcolumn (4) in Table A3) is in exact terms.

23

2020m1

2019m102019m72019m42019m1

2018m102018m72018m42018m1

2017m102017m72017m42017m1

2016m10

2016m7

2016m4

2016m1

2015m102015m7

2015m4

-4

-2

0

2

Estimated 95% confidence interval

4

Figure 5: Estimated 95% Confidence Intervals for Disaggregate- and Aggregate-level Analyses

Year and month
Disaggregate-level results

Aggregate-level results

Notes: This figure displays the 95 percent confidence interval (CI) for the estimated difference-in-difference-indifferences (DDD) and difference-in-differences (DID) coefficients for interactions of month-year dummies withprefecture i’s dependency on exports to Korea. The light-shaded CI represents the disaggregate-level (i.e., DDD)
results while the dark-shaded CI represents the aggregate-level (i.e., DID) results. The baseline level is set in July
2017.
Source: Authors’ estimation, based on Overnight Travel Statistics Survey.

24model hold even when we employ an alternative estimation model, which indicates the robustness of our results.41
Alternatively, we can remain neutral regarding which estimation model, log-linear or PPML,
is preferred to capture the boycott effects by using the estimated growth rates from both models to establish ranges for the boycott effects. In Table 9 we use both estimation models and two
sample periods to establish ranges for the boycott effects, which we then use as our main results. These combined results indicate that prefectures with high dependency on visitors from
Korea suffered losses in exports to Korea of 56.9 to 60.9 percent while prefectures with low
Korea dependency suffered corresponding losses of 47.8 to 49.7 percent due to the consumer
boycott. The gap in disaggregate effects of the boycott between the 75th percentile prefecture
and the 25th percentile prefecture is −9.1 to −11.1 percentage points, or about −10 percentage points. At the aggregate level, high Korea dependency prefectures experienced reductionsin their total exports of accommodation services of 10.5 to 13.3 percent while low Korea dependency prefectures faced corresponding losses of 3.3 to 4.2 percent. The gap in aggregate
boycott effects between the 75th percentile prefecture and the 25th percentile prefecture for
Korea dependency is −7.2 to −9.1 percentage points, which exceeds or approximates the estimated loss for the 50th percentile prefecture (i.e., −6.5 to −8.3 percent). These results illustrate
the importance of considering regional heterogeneity in examining boycott effects.
In addition, our main results imply that “angry consumers” can have significant economic
effects that extend beyond bilateral trade alone. As shown in Table 9, the average prefecture
suffered bilateral (i.e., Japan to Korea) export losses of 55.7 to 59.0 percent and aggregate export losses of 9.3 to 11.8 percent. These aggregate effects are relevant for estimating the net
losses to the accommodations and related traveler services industries in Japan, along with theaccompanying losses in services tax revenues.42 However, the much larger bilateral effects
are most relevant for considering the unintended victims of the consumer boycott, which are
Korean airlines and travel agencies offering Korea-Japan routes and travel packages.43

6.3

Overall exports and demand shock in Korea

Since we focus on a single political conflict, we can also aggregate exports along an alternativeP
dimension: Yjt = i Yijt , where prefecture exports of accommodation services are aggregatedto the national level. Then we can ask whether the overall accommodation services exportsof Japan to Korea relative to other countries declined as a result of the boycott. Because theboycott occurs only in Korea during this period, the exports to other countries should not beaffected. Thus, we can hypothesize that a decline in exports after the boycott is observed only
for exports to Korea. To answer this question, similar to Subsection 6.1, we run the following
regression:
Yjt = α + ψj + ψt +X

ρt (KORj × dt ) + εjt ,

(7)

t
41

We also demonstrate the robustness of our results to the use of an alternative diversification measure (Appendix B) and to the exclusion of outliers (Appendix C).
42
We estimate the boycott’s revenue effects in Section 6.4.
43
Kim (2019) quotes Gwang-ok Kim, general manager of the Korea Aviation Association, as stating that the
number of travelers on Korea-Japan routes declined by 43 percent in October, 2019, compared to the same monthin 2018. We leave a more thorough examination of the “backfire effect” of the boycott for future research.

25

Table 9: Summary of Estimated Boycott Effects Across Two Estimation Models and Two Sample Periods
Disggregate-level analysis
Aggregate-level analysis
(1)
(2)
(3)
(4)
Percentile
Max
Min
Max
Min
Mean
-0.557-0.590-0.093-0.11825%-0.478-0.497-0.033-0.04250%-0.528-0.544-0.065-0.08375%-0.569-0.609-0.105-0.13375-25% gap -0.091-0.111-0.072-0.091
Notes: Max and min values from growth rates estimated using log linear estimation (shown in Tables 4 and 8) and
PPML estimation (shown in Tables A2 and A3) across both sample periods that met the common trends assumption(i.e., the full- and medium-period samples).
Source: Authors’ estimation, based on Overnight Travel Statistics Survey.

where the variables are the same as before. We measure Yjt as the log of the number of visitorsfrom foreign country j to Japan at time t (year-month). The parameter of interest is ρt that
captures the differential effect of visitors from Korea relative to visitors from other countries.
First we test the common trends assumption using the aggregated export data. We find thatthe common trends assumption is not met for this analysis.44 Thus it is difficult to identify thecausal impact of the boycott on Japan’s aggregate bilateral exports of accommodation services.
Therefore, we present the results from equation (7) just as a reference, not as a main result.
Figure 6 plots the estimated 95 percent CI of ρt for the full sample, similar to the aggregatelevel results in Figure 5. We set July 2017 as the base level for the coefficients as in Figure 5. We
can confirm that the CI declined sharply from July 2019. Although it may not be attributable
to the consumer boycott, we can at least state that visitors to Japan from Korea relative to otherorigin countries dropped significantly from July 2019.
In this connection, one may be concerned that the sharp drop in visitors from Korea toJapan may come from an overall demand shock in Korea, rather than from the consumer boycott targeting Japan, as was pointed out in Section 1. Under this alternative hypothesis, anoverall demand shock in Korea causes Korean residents to sharply curtail their travel abroad.
If this is the case, we expect that Korean outbound travel dropped not only to Japan but also toother destinations. This in turn means that the travel from Korea to Japan relative to other destination countries will not show significant differences after the boycott. In order to investigate
this possibility, we run the following regression, using data from Korea:45
Yjt = α + ψj + ψt +X

χt (JPNj × dt ) + εjt ,(8)twhere Yjt is now (the log of) the number of departures from Korea to country j; JPNj is theJapan dummy that takes the value one if the destination country is Japan and zero otherwise;
dt is time (i.e., year-month) dummy. We focus on the period between April 2015 and January
2020. If the negative demand shock from July 2019 is specific to Japan, we expect significantly
44
45

Detailed results on the test of the common trends assumption are presented in Appendix D.
The Korean data are obtained from the Korea Tourism Organization website. The unit is number of departures.

26

2020m1

2019m102019m72019m42019m1

2018m102018m72018m42018m1

2017m102017m72017m42017m1

2016m10

2016m7

2016m4

2016m1

2015m102015m7

2015m4-2

Estimated 95% confidence interval-101Figure 6: Number of Visitors from Korea to Japan Relative to Other Origin Countries: Japanese
Data

Year and month

Notes: The CI is estimated from regression equation (7). Standard errors are clustered by country and time. The
baseline level is set in July 2017.
Source: Authors’ estimation, based on Overnight Travel Statistics Survey.

27

negative χt from July 2019.
We first test the common trends assumption for this analysis and find that it does nothold.46 Similar to the results above using the Japanese data for accommodation services exports by country, we present the results using the Korean data for departures by country as areference, not as a main result. Figure 7 plots the estimated 95 percent CI of χt , setting July
2017 as the base level for the coefficients as before. We can confirm that the CI declined sharply
from July 2019. The results suggest that the sharp drop of visitors from Korea to Japan is not
attributable to an overall demand shock in Korea.

2020m1

2019m102019m72019m42019m1

2018m102018m72018m42018m1

2017m102017m72017m42017m1

2016m10

2016m7

2016m4

2016m1

2015m102015m7

2015m4-1.5

Estimated 95% confidence interval
-1
-.5
0
.5

1

Figure 7: Number of Visitors from Korea to Japan Relative to Other Destination Countries:
Korean Data

Year and month

Notes: The CI is estimated from regression equation (8). Standard errors are clustered by country and time. The
baseline level is set in July 2017.
Source: Authors’ estimation, based on data from the Korea Tourism Organization website.

It is also interesting to note that the Korean consumer boycott behavior does not seem tocause retaliation from Japanese consumers towards Korean travel. By replacing the dependent
variable from the number of departures from Korea to that of arrivals to Korea in equation (8),we can examine the possibility of retaliation to the boycott. Again we find that the common
trends assumption is not met for this analysis, so we present the results merely as a reference
for the reader.47 Figure 8 presents the estimation results where we focus on arrivals to Korea from Japan relative to other origin countries. The CI has been significantly positive sinceNovember 2017, indicating a stronger tendency for visitors to Korea to be from Japan. We do
46
47

Detailed results on the test of the common trends assumption are presented in Appendix D.
Detailed results on the test of the common trends assumption are presented in Appendix D.

28

2020m1

2019m102019m72019m42019m1

2018m102018m72018m42018m1

2017m102017m72017m42017m1

2016m10

2016m7

2016m4

2016m1

2015m102015m7

2015m4-.5

Estimated 95% confidence interval
0.51Figure 8: Number of Visitors to Korea from Japan Relative to Other Origin Countries: Korean
Data

Year and month

Notes: The CI is estimated from regression equation (8) but using the log of the number of arrivals to Korea as thedependent variable. Standard errors are clustered by country and time. The baseline level is set in July 2017.
Source: Authors’ estimation, based on data from the Korea Tourism Organization website.

29

not find evidence that visitors from Japan to Korea decreased significantly after July 2019.

6.4

Estimated revenue effects

Our empirical strategy in Sections 4 and 5 focused on estimating the causal effects of the Korean consumer boycott on the quantity of monthly exports of accommodation services fromJapan to Korea. We turn now to a brief discussion of how our results can be utilized to estimate changes in annual export revenues by prefecture for 2019. To compute a monthly growthrate for prefectural revenues from accommodation services, we apply the following equation:d(Pit Qit )Qit dPit
Pit dQit
dPit dQit+,=+=(Pit Qit )(Pit Qit ) (Pit Qit )
Pit
Qit

(9)

where Pit and Qit are prefecture i’s price and quantity (i.e., visitor-nights) of accommodations
in month t. Our aggregate-level analysis provides us with coefficients of (si × Postt ) that can beused to calculate growth rates for quantities (i.e., dQit /Qit ), as shown in Table 8.48 However,data is not available at the prefecture-month level for average prices of accommodations. The
best available data is survey data at the annual-countrywide level that show that the average
expenditure per person-night for Korean visitors fell from 15,954 JPY to 13,461 JPY between
2018 and 2019, while the corresponding value for all foreign visitors rose from 14,533 JPY to
15,598 JPY.49 Since this annual data is insufficient for empirically testing for the existence of acausal link between these expenditure trend differentials and the consumer boycott, we adopt
a conservative approach to estimating revenue changes by assuming that the boycott caused
no changes in monthly aggregate-level prices of accommodations (i.e., assume dPit /Pit = 0).
The boycott effect is applicable for six months of 2019 (i.e., from July−December, 2019),
so the average annual effect on visitor-night quantities is roughly estimated as half of ourestimated monthly effect. By conservatively assuming no price changes due to the boycott, theaverage annual effect on quantities is then equivalent to the average annual effect on revenues,
when both are expressed in growth rate terms. Table 10 shows these annual revenue growthrate effects for each prefecture in column (2). In this table, the prefectures again are ranked
from highest to lowest in terms of Korea dependency and the annual number of visitor-nights
for 2018 is shown in column (1) to provide an indication of the relative size of each prefecture’s
accommodation services industry. With the maximum Korea dependency ratio of 0.563, Oita
prefecture suffers the largest boycott effect in terms of annual revenue growth rate with anestimated loss of 17.4 percent. At the opposite end of the spectrum, Yamanashi prefecture’s
Korea dependency ratio is only 0.015, so its estimated annual revenue loss is only 0.6 percent.
The average prefecture loses an estimated 5.4 percent of accommodations services revenue
in 2019 due to the boycott, while eight prefectures suffer losses of 10.0 percent or more. At
the opposite end of the distribution, 14 prefectures lose less than 2.0 percent of their annual
accommodations services revenues due to the boycott.
48

We use the mean of (si × Postt ) coefficients from our two estimation methods and two sample periods thatsatisfy the common trends assumption, which is −0.75924.
49
The average expenditure per person-night data is from the Japan Tourism Agency’s “Consumption Trend Survey for Foreigners Visiting Japan”, available in Japanese. Note that the average expenditure data reflects spendingon accommodations, food and drink, local transportation, etc. For convenience, we use the term “accommodation
services revenues” to refer to revenues from accommodations and related travel services.

30

Table 10: Estimated Revenue Losses in 2019 Due to the Korean Consumer Boycott
Korea
dependency
Prefecture

Oita
Saga
Yamaguchi
Fukuoka
TottoriMiyazakiNagasaki
Kumamoto
Okinawa
Osaka
Kagoshima
Ehime
Hokkaido
Aomori
KagawaShimaneAkita
Hyogo
Saitama
Mie
Shiga
Yamagata
ToyamaOkayama
Kochi
NiigataTokyo
IbarakiWakayama
Tochigi
Fukushima
Iwate
Kanagawa
Kyoto
Aichi
Fukui
Tokushima
Nara
Miyagi
Hiroshima
Gumma
Gifu
NaganoChiba
Ishikawa
Shizuoka
Yamanashi
Mean
Total

Share for2015m42019m6
0.56270.49650.45480.43990.40420.4006
0.32660.31220.25880.17880.17160.16980.16220.15530.15450.15150.13960.12810.12540.11680.11300.10920.10340.10280.1012
0.09670.0756
0.07150.0691
0.06840.06530.06370.05730.05220.05120.05120.04970.04830.04720.04670.04630.04370.04240.04140.04030.04000.01450.1494Aggregate-level analysis
(1)
(2)
(3)
Foreign
Foreign Accommodations
visitor-nights visitor-nights
revenue& revenue
Quantity inEst. annual
Est. ch. For
2018growth rate
2019 in JPY
in 2019
1,057.7-0.1738-2,672,322372.5-0.1570-849,973
90.7-0.1460-192,424
3,026.9-0.1420-6,245,227138.7-0.1321-266,344
297.4-0.1311-566,760
512.1-0.1098-817,244860.5-0.1055-1,319,576
4,554.7-0.0892-5,904,172
11,637.6-0.0635-10,734,710
658.6-0.0611-584,621
190.9-0.0605-167,795
7,164.2-0.0579-6,031,948
283.4-0.0556-229,007
377.1-0.0553-303,30554.9-0.0543-43,33999.9-0.0503-72,996
1,074.5-0.0463-723,591
140.8-0.0454-92,891290.3-0.0424-179,006
380.3-0.0411-227,185117.8-0.0398-68,096235.3-0.0378-129,101405.9-0.0375-221,441
61.0-0.0370-32,766260.3-0.0354-133,884
19,258.8-0.0279-7,806,436
162.8-0.0264-62,504
359.4-0.0256-133,474
225.6-0.0253-82,953121.1-0.0242-42,546236.7-0.0236-81,192
2,130.2-0.0213-658,984
4,506.3-0.0194-1,272,398
2,439.4-0.0191-675,853
57.7-0.0191-15,98677.7-0.0185-20,908311.5-0.0180-81,513311.3-0.0176-79,639731.0-0.0174-185,044
255.1-0.0173-64,038
1,063.1-0.0163-252,112
1,043.1-0.0158-240,119
3,652.3-0.0155-821,224725.8-0.0151-158,917
1,460.1-0.0150-317,365
1,506.6-0.0055-119,861
1,595.3-0.0536-1,243,154
74,979.3-51,982,790

Notes: Foreign visitor-nights reflect total for 20 countries used in estimation (unit: 1,000 person-nights). The unit
for revenue is 1,000 JPY. Est. ch. = estimated change. Revenue change in (3) = (1) × (14,533 JPY) × (2), with small
discrepancies due to rounding off of displayed values.
Source: Authors’ estimation, based on Overnight Travel Statistics Survey.

31Column (3) of Table 10 uses the growth rates in column (2) and the prefectures’ total accommodations revenues from 2018 to estimate the nominal revenues lost in 2019 due to theboycott.50 The top three prefectures for boycott-induced accommodation revenue losses are
Osaka, Tokyo and Fukuoka with estimated losses of 10.7 billion JPY, 7.8 billion JPY and 6.2
billion JPY, respectively. Across all prefectures, our conservative estimate of accommodation
revenue losses caused by the boycott is 52.0 billion JPY (or approximately 476.9 million USD).51
If the boycott-induced travel cancellations by Koreans caused hotels in Japan to discount theirrooms and/or caused a shift in Korean visitors away from higher-spending travelers and towards lower-spending travelers, then our estimates of revenue changes based on quantitychanges alone is a lower bound for the real effect on accommodation revenues.52
While acknowledging that the export revenue losses are roughly estimated, the large range
in estimated loss ratios across prefectures, from less than one percent to over 17 percent,demonstrates again the importance of considering regional heterogeneity in examining theimpacts of a consumer boycott. It is also important to note that our focus on the regional impacts of the Korean consumer boycott on accommodation services in Japan does not include
the negative direct impacts on Korean airlines and travel agencies, nor the indirect effects onother businesses in Korea that benefit from Korean travel to Japan.53

7

Concluding Remarks

Political conflict impacts international trade not only through trade policy but also throughconsumer boycotts. In light of increasing concerns regarding political conflict and regional
inequality, we present the first study of the heterogeneous impacts of a boycott across regions
within a boycotted country.54 We investigate the recent Korean consumer boycott activity from
July 2019 in response to Japan’s restrictions on exports of semiconductor materials and display
panels considered vital to Korea’s technology industry. Using prefecture-month foreign visitor
data in Japan between April 2015 and January 2020, we employ difference-in-difference-indifferences (DDD) and difference-in-differences (DID) designs.
Estimation results indicate that the impact of the Korean consumer boycott is heterogeneous across prefectures within Japan, which is consistent with the diversification story andwith the hypothesis that Korean tourists were the most likely participants in the consumer
50

The average expenditure per person-night for all foreign tourists in 2018 (i.e., 14,533 JPY) is used along withvisitor-night quantities to calculate total revenues per prefecture for 2018.
51
An average exchange rate for 2019 of 109 JPY/USD is used.
52
Note that Inada, Irie and Shimoda (2019) use input-output analysis to estimate that Osaka prefecture lost 27
billion JPY in terms of value-added due to the Korean travel boycott. Our finding that Osaka prefecture loses 10.7
billion JPY is similar in magnitude but not directly comparable to their estimate because: 1) we focus on revenues
rather than value-added, 2) their estimate is based on a traditional input-output analysis, and 3) they take into
account inter-industry linkages while we do not with our conservative estimates.
53
Japan Tourism Agency (2019) reports that travel to Japan (either personally or by family member or acquaintance) motivated about 50 percent of those purchasing Japanese food and beverage products overseas. The otheroptions for motivations were “information and articles on travel to Japan”, “TV programs and special articles on
travel to Japan”, and “other”. The overseas questionnaire survey was conducted in February, 2019.
54
Note that regional heterogeneity in the impacts of political conflict is addressed by Che, Du, Lu and Tao (2015),
but their research does not involve a consumer boycott. They examine the heterogeneous long-run impacts ofJapan’s invasion of China from 1937 to 1945 and find that Chinese regions that suffered larger civilian casualties
during the invasion have lower trade with and investment from Japan in 2001.

32

boycott. For prefectures with high (i.e., 75th percentile) pre-boycott dependency on Korean
visitors, the negative impact on exports of accommodation services to Korea is about 9.1 to
11.1 percentage points larger than that for prefectures with low (i.e., 25th percentile) dependency, with export losses of 56.9 to 60.9 percent and 47.8 to 49.7 percent, respectively. These
negative impacts on prefectural exports to Korea are too large to be offset by increases in exports to other countries. As a result, Japanese prefectures had net adverse effects from theboycott, with a 10.5 to 13.3 percent decline in total exports of accommodation service for highKorea dependency prefectures and a corresponding decline of 3.3 to 4.2 percent for low Korea
dependency prefectures. These ranges of boycott effects summarize our results in using two
estimation models and two sample periods to identify estimated bands for the boycott effects
that are reassuringly narrow for a specific quartile prefecture, heterogeneous across prefectures and robust to the exclusion of outliers. Our main message holds even when we use analternative measure of diversification.
Our results have important policy implications. While the Japan–Korea political conflict
was sparked by actions taken by the countries’ national leaders and the ensuing Korean consumer boycott targeted Japanese services (and products) nationwide, the impacts of the boycott are not spread equally throughout Japan. We conservatively estimate that the average
prefecture loses 5.4 percent of its annual revenue from accommodations exports in 2019 due tothe boycott but that single estimate obscures prefectural heterogeneity in outcomes. Eight prefectures suffer annual accommodations export revenue losses of more than 10 percent while
14 prefectures suffer losses of less than 2 percent. These disparate outcomes may contribute toincreased inequality between regions. Therefore it is important for policy makers to take into
account such regional impacts as a consequence of political conflict at the national level. To
make regions less vulnerable to a foreign consumer boycott, travel promotion policies should
target visitors with more diverse travel purposes and from more diverse countries of origin.
Before closing this study, we point out several pathways for future research. First, extending the analysis to a general equilibrium framework is an important avenue for future research.
For example, our analysis did not take into account the potential substitution between Korean
and domestic (i.e., Japanese) visitors. A general equilibrium analysis will allow us to quantify
the full impact of the boycott, including welfare effects in Japan and Korea, more precisely.
Second, it is also important to ask whether the impact of the consumer boycott is short- or
long-lived. Due to the coronavirus pandemic, our analysis was not able to address this issue
but we do find significant “angry consumer” effects that persisted at least for seven months inthis case. Analysis of another boycott may enable us to pursue the issue of boycott longevity.
Finally, it is also interesting to investigate the effect of the boycott, or more generally politicalconflict, on regional economic outcomes. Although regional outcomes such as GDP and employment are not available on a prefecture-month basis in Japan as of today, the construction of
more detailed regional outcome data will help us to calculate the overall impact of the tourism
decline on the regional and/or national economy by incorporating the share of tourism as partof the overall GDP. This would allow us to address an interesting political economy question
as the policies that lead to international conflict are made at the national level but have very
diverse regional impacts. We include these issues in our future research agenda.

33

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37

Appendices for Online Publication OnlyA

Alternative Estimation Method: PPML Details

PPML estimation specifies the regression equation as the cross product between the exponential of the set of independent variables and the error term. For example, for the case of thedisaggregate-level analysis (i.e., equation (1)), the regression equation is written as follows:
Yijt = exp [α + ψi + ψj + ψt
+β1 (si × Postt ) + β2 (si × KORj ) + β3 (KORj × Postt )+γ(si × KORj × Postt )] × εijt ,(A1)

where the variables are the same as before except for Yijt which is the actual (not log) value.
Thus, this specification includes observations of zero exports. The aggregate-level analysis
in equation (3) is rewritten in a similar manner so that both analyses can be estimated using
PPML.
Table A1 presents the estimation results.55 Columns (1)–(2) correspond with the disaggregate analysis of equation (1) while columns (3)–(4) apply the strictest possible model specification for our disaggregate analysis. Columns (5)–(6) correspond with the aggregate analysis
of equation (3). There are two notable findings. First, the number of observations is the sameas that of the log-linearized specification for columns (5)–(6) while it is different for columns(1)–(4). Thus there are no observations with zero trade for the aggregate-level analysis whereas
there are some observations with zero trade for the disaggregated-level analysis. The sharesof observations with zero trade are very small: 3.0 and 1.6 percent for the full- and mediumperiod samples, respectively. Second, although the coefficients are slightly different, the signs
and significance levels of the coefficients are quite similar to those of the log-linearized specification.56
Table A2 uses the coefficients from PPML estimation for our disaggregate-level analysis,shown in columns (1)–(2) of Table A1, to calculate total magnitude effects of the boycott. These
effects measure the losses in prefectural exports to Korea due to the boycott. The estimatedexport losses at the mean and at each quartile of the si distribution are shown in Table A2 andare used, along with those shown in Table 4, to create summary Tables A4 and 9.
Table A3 uses the coefficients from PPML estimation for our aggregate-level analysis, shownin columns (5)–(6) of Table A1, to calculate total magnitude effects of the boycott. These effects
measure the losses in prefectural total exports due to the boycott. The estimated export losses
at the mean and at each quartile of the si distribution are shown in Table A3 and are used,along with those shown in Table 8, to create summary Tables A4 and 9.
To facilitate a comparison between our main (log linear) and alternative (PPML) estimation methods, we present side-by-side comparisons of estimated boycott impacts in Table A4.
This table merely summarizes the growth rate estimates from Tables 4, 8, A2, and A3. The
disaggregate-level growth rates are within a tight range of −1.9 to 4.0 percentage points fromour main specification (log linear) results and the aggregate-level growth rates are within aneven tighter range of 0.4 to 1.8 percentage points from our main specification (log linear) results, as shown in Table A4. Thus, our results are robust to an alternative estimation method.
55
Following Santos Silva, Tenreyro, and Windmeijer (2015), we compute R2 as the square of the correlation
between the dependent variable and the estimated conditional mean. Multi-way clustered standard errors arecomputed by the stata command ppmlhdfe developed by Correia, Guimarães, and Zylkin (2020).
56
Note that our coefficient of (si × KORj × Postt ) is even larger in (absolute value) magnitude in our robustness
check using prefecture-time, country-time and prefecture-country fixed effects, but we use the coefficient values
from our main specification to compute growth rates because we need a coefficeint for (KORj × Postt ) for thiscomputation.

38

Table A1: Regression Results: Alternative Estimation Model

Periodsi × Postt
si × KORj
KORj × Postt
si × KORj × Postt
Fixed effectPrefecture (ψi )Country (ψj )Time (ψt )Prefecture-time (ψit )Country-time (ψjt )Prefecture-country (ψij )N
R2

Disggregate-level analysis
(1)
(2)
(3)
(4)
2015m42018m7
2015m42018m7–2020m1 –2020m1 –2020m1 –2020m1
0.159***
0.302***[0.045][0.022]
6.547***
6.516***[0.857][0.842]-0.613*** -0.618***[0.097][0.072]-1.346*** -1.368*** -1.725*** -1.618***[0.139][0.116][0.418][0.395]
Yes
Yes
Yes
No
No
No
54,520
0.245

Yes
Yes
Yes
No
No
No
17,860
0.234

No
No
No
Yes
Yes
Yes
54,520
0.485

No
No
No
Yes
Yes
Yes
17,8600.475

Aggregate-level analysis(5)(6)
2015m42018m7–2020m1–2020m1-0.766***-0.655***[0.232][0.212]

Yes
NA
Yes
NA
NA
NA
2,726
0.542

Yes
NA
Yes
NA
NA
NA
893
0.555

Notes: The PPML is employed for the estimation. Figures in brackets indicate standard errors clustered by prefecture, country, and time for columns (1)–(4) and by prefecture and time for columns (5)–(6). *** indicates thesignificance level at 1 percent. NA stands for not applicable.
Source: Authors’ estimation, based on Overnight Travel Statistics Survey.

39

Table A2: Impact of the Boycott on Prefectures’ Exports to Korea: PPML Estimates

Percentile

(1)

(2)

Coefficientsi

2015m4–2020m1
Mean-1.34625%-1.34650%-1.34675%-1.34675-25% gap
2018m7–2020m1
Mean-1.36825%-1.36850%-1.36875%-1.36875-25% gap(3)(= (1) × (2))
Relative magnitude(log change)(4)( = (3) + KP Coeff.)
Total magnitude(log change)(5)
Total magnitude
converted(growth rate)

0.149
0.051
0.103
0.170

-0.201-0.069-0.138-0.229-0.814-0.682-0.751-0.842-0.557-0.494-0.528-0.569-0.075

0.149
0.051
0.103
0.170

-0.204-0.070-0.141-0.232-0.822-0.688-0.759-0.850-0.561-0.497-0.532-0.573-0.075

Notes: Exports to Korea mean the exports of accommodation services to Korea that are defined as the numberof visitors from Korea (the total number of visitors who reside in Korea × the number of nights stayed in Japan).
Percentile indicates the quartiles of si . Coefficients are obtained from Table A1 and KP Coeff. means (KORj ×Postt )coefficient from the corresponding sample period. Rounding off of displayed numbers explains small variations in
calculated numbers shown above. Growth rate = exp(log change) − 1.
Source: Authors’ estimation, based on Overnight Travel Statistics Survey.

Table A3: Impact of the Boycott on Prefectures’ Total Exports: PPML Estimates
(1)
(2)
(3)
(4)
(= (1) × (2))
Total magnitude
Percentile
Coefficientsi
Total magnitude
converted(log change)(growth rate)
2015m4–2020m1
Mean-0.7660.149-0.114-0.10825%-0.7660.051-0.039-0.03850%-0.7660.103-0.079-0.07675%-0.7660.170-0.130-0.12275-25% gap-0.0842018m7–2020m1
Mean-0.6550.149-0.098-0.09325%-0.6550.051-0.034-0.03350%-0.6550.103-0.067-0.06575%-0.6550.170-0.111-0.10575-25% gap-0.072
Notes: Exports mean the exports of accommodation services that are defined as the number of foreign visitors (thetotal number of visitors who reside outside Japan × the number of nights stayed in Japan). Percentile indicatesthe quartiles of si . Coefficients are obtained from Table A1. Rounding off of displayed numbers explains small
variations in calculated numbers shown above. Growth rate = exp(log change) − 1.
Source: Authors’ estimation, based on Overnight Travel Statistics Survey.

40

Table A4: Comparison of Log Linear and PPML Estimates of Boycott Impacts
Disggregate-level analysis
Aggregate-level analysis
(1)
(2)
(3)
(4)
(5)
(6)
(= (2) − (1))(= (5) − (4))
Log-linear PPML
Difference
Log-linear PPML
Difference
2015m4–2020m1
Mean-0.590-0.557
0.033-0.118-0.1080.010
25%-0.488-0.494-0.006-0.042-0.0380.004
50%-0.544-0.5280.016-0.083-0.0760.007
75%-0.609-0.5690.040-0.133-0.1220.011
2018m7–2020m1
Mean-0.587-0.561
0.026-0.109-0.0930.016
25%-0.478-0.497-0.019-0.039-0.0330.006
50%-0.538-0.5320.006-0.077-0.0650.012
75%-0.606-0.5730.033-0.123-0.105
0.018
Max
0.040
0.018
Min
-0.0190.004
Notes: Growth rates from log linear (PPML) estimation are obtained from Table 4 (A2) for the disaggregate-level
analysis and from Table 8 (A3) for the aggregate-level analysis. Differences measure the percentage point differencein growth rates across the two estimation methods. Rounding off of displayed numbers explains small variations
in calculated numbers shown above.
Source: Authors’ estimation, based on Overnight Travel Statistics Survey.

41

B

Alternative measure of diversification

One may suggest that we use a different measure of diversification because each prefecture’s
dependency ratio on Korean visitors, si , focuses on the concentration of exports to Korea alone.
Therefore, the dependency ratio does not take into account the export diversification to othercountries. One of the most frequently used measures of the diversification of exports is the
Herfindahl index. Following Cadot, Carrère, and Strauss-Kahn (2011), we measure export
diversification using the Herfindahl index hi as follows:
P 2
j sij − 1/n
hi =
,
(B1)
1 − 1/nwhere sij is the average share of visitors from country j to total visitors from foreign countries
in prefecture i before the boycott; n is the number of countries, which consists of 20 countries
and the rest of the world (i.e., n = 21). The Herfindahl index takes a value between 0 and 1,
where 0 would indicate the most diverse export destination profile while 1 would indicate theleast diverse profile. We estimate our regression equations, replacing si with hi in equations(1)–(4).
Let us first check the common trends assumption. Table B1 presents the regression results.
Columns (1)–(3) and (4)–(6) are the results for disaggregate- and aggregate-level analyses, respectively. Columns (1) and (4) are the results for the full-period sample, Columns (2) and (5)are the results for the medium-period sample, and columns (3) and (6) are the results for the
short-period sample. Except for the full-sample in the disaggregate-level analysis in column(1), all of the coefficients are insignificant. The results generally support the validity of thecommon trends assumption. As for the disaggregate-level analysis, we present the results forthe full-period sample as a reference.
Table B1: Common Trends Assumption: Alternative Diversification Measure
Disggregate-level analysis
Aggregate-level analysis
(1)
(2)
(3)
(4)
(5)
(6)
Period2015m42018m72019m1
2015m42018m72019m1–2019m6 –2019m6 –2019m6–2019m6 –2019m6 –2019m6
hi × Trendt
Yes
Yes
Yes
-0.005-0.001-0.089[0.015][0.025][0.191]
hi × KORj × Trendt 0.025***
0.042-0.325[0.005][0.087][0.177]
Fixed effectPrefecture (ψi )
Yes
Yes
Yes
Yes
Yes
Yes
Country (ψj )
Yes
Yes
Yes
NA
NA
NA
Time (ψt )
Yes
Yes
Yes
Yes
Yes
Yes
hi × KORj
Yes
Yes
Yes
NA
NA
NA
KORj × Trendt
Yes
Yes
Yes
NA
NA
NA
N
46,38011,074
5,538
2,397
564
282
R2
0.84
0.85
0.85
0.97
0.98
0.98
Notes: Figures in brackets indicate standard errors clustered by prefecture, country, and time for columns (1)–(3)and by prefecture and time for columns (4)–(6). *** indicates the significance level at 1 percent. NA stands for not
applicable.
Source: Authors’ estimation, based on Overnight Travel Statistics Survey.

Table B2 presents the DDD and DID estimation results. Columns (1)–(3) and (4)–(6) are
the estimation results for disaggregate- and aggregate-level analyses, respectively. There are42

two notable findings in this table. First, for the disaggregate-level analysis, the coefficientsof (hi × KORj × Postt ) are significantly negative for all three sample periods.57 The results
imply that the impact of the boycott on exports to Korea is heterogeneous across prefectureseven when we measure export diversification by the Herfindahl index. Prefectures with moreconcentrated (i.e., less diverse) export portfolios suffer larger losses in exports to Korea due tothe boycott. This result is consistent with the diversification story.
Table B2: Regression Results: Alternative Diversification Measure
Disggregate-level analysis
Aggregate-level analysis
(1)
(2)
(3)
(4)
(5)
(6)
Period2015m42018m72019m1
2015m42018m72019m1–2020m1 –2020m1 –2020m1–2020m1 –2020m1 –2020m1
hi × Postt
0.003
0.134
0.187
-0.561-0.485-0.545[0.333][0.229][0.179][0.483][0.460][0.448]hi × KORj
5.827***
5.949***
6.222***[1.294][1.360][1.382]
KORj × Postt-0.705*** -0.678*** -0.633***[0.070][0.048][0.049]hi × KORj × Postt -1.176** -1.294*** -1.565***[0.485][0.332][0.235]
Fixed effectPrefecture (ψi )
Yes
Yes
Yes
Yes
Yes
Yes
Country (ψj )
Yes
Yes
Yes
No
No
No
Time (ψt )
Yes
Yes
Yes
Yes
Yes
Yes
N
52,87917,573
12,037
2,726
893
611
2
R
0.840
0.850
0.850
0.970
0.970
0.970
Notes: Figures in brackets indicate standard errors clustered by prefecture, country, and time for columns (1)–(3) and by prefecture and time for columns (4)–(6). *** and ** indicate the significance level at 1 and 5 percent,respectively.
Source: Authors’ estimation, based on Overnight Travel Statistics Survey.

Second, for the aggregate-level analysis, the coefficient of (hi × Postt ) is insignificant for
all three sample periods. At first sight, this seems to suggest that the aggregate impact iscommon across regions. However, this does not necessarily contradict the diversification story.
The difference in the results between the Herfindahl index hi and the Korea dependency ratio
si comes from the fact that some prefectures heavily depend upon exports to Korea whileother prefectures depend upon exports to other countries.58 For example, suppose that some
prefectures export only to Korea while other prefectures export only to China. Despite the factthat the export destinations are equally concentrated (i.e., less diversified) for both types ofprefectures, the impact of the boycott appears only on the former prefectures, not the latter,if the boycott occurs only in Korea. Although the Herfindahl index is a useful measure ofexport diversification, a careful interpretation may be needed for the use of the Herfindahl
index in analyzing the impact of a boycott, or that of a political conflict between two countries
in general.
57

The DDD estimation results using prefecture-time, country-time and prefecture-country fixed effects are notshown in Table B2 due to space constraints. This specification produces coefficients of (hi × KORj × Postt ) of
−1.195, −1.308, and −1.597 for the full-, medium- and short-period samples, respectively, and all are significant at
the 1 percent level. These values are very similar to those shown in Table B2, which supports the robustness of ourresults to the strictest possible specification.
58
Note that the correlation between hi and si is 0.368, indicating that prefectures that are more export concentrated (i.e., less export diverse) also tend to be more Korea dependent, but the correlation is far from perfect.

43

C

Checking for Outlier Effects

C.1

Excluding top 5 prefectures for receiving Korean visitors in 2018

Based on the skewed distribution of Korean visitors across Japanese provinces implied by Figure C1, one might ask whether outliers are driving our results. To test this hypothesis, wedrop the top five prefectures in receiving Korean visitors in 2018 (i.e., Osaka, Tokyo, Fukuoka,
Hokkaido and Okinawa) and repeat our disaggregate-level and aggregate-level analyses using our main specification. First we check the common trends assumption. Table C1 presents
these results, with columns (1)–(3) showing the disaggregate-level results and columns (4)–(6)showing the aggregate-level results. The common trends assumption does not hold for either
level of analysis for the short-period sample, but holds for the full- and medium-period samples. Therefore we report the regression results only for the full- and medium-period samples,
similar to our reporting strategy in the main text.

Fukui
Tokushima
Fukushima
Kochi
Yamagata
Akita
ShimaneMiyagi
Iwate
IbarakiGumma
Nara
Tochigi
Niigata
SaitamaToyamaMie
Ishikawa
Yamanashi
Wakayama
Aomori
Ehime
Hiroshima
Yamaguchi
Shiga
Gifu
Tottori
Okayama
NaganoKagawaShizuoka
Miyazaki
Hyogo
Kanagawa
Aichi
Kagoshima
Chiba
Saga
Nagasaki
Kyoto
Kumamoto
Oita
Okinawa
Hokkaido
Fukuoka
Tokyo
Osaka

0

500

1000

1500

2000

2500

Number of Visitors from Korea, by Prefecture (1,000)

Figure C1: Number of Visitors from Korea in 2018 and 2019, by Prefecture

Prefecture
2018

2019

Notes: Total number of foreign visitors indicates the total number of visitors who reside outside of Japan times thenumber of nights stayed in Japan (unit: 1,000 person-nights).
Source: Japan Tourism Agency (2020) Overnight Travel Statistics Survey.

Table C2 reports our log linear estimation results, excluding the top five prefectures. Columns(1)–(2) correspond with the disaggregate analysis of equation (1) while columns (3)–(4) correspond with the aggregate analysis of equation (3). The size, sign and significance level of ourvariables of interest are similar to those reported earlier in Tables 3 and 7.
To more easily compare the regression results without the top five prefectures to our main
results, we convert the estimated coefficients from Table C2 into growth rates in Tables C3and C4 for the disaggregate-level and aggregate-level analyses, respectively. Note that the
distribution of si changes slightly due to our exclusion of the top five prefectures. The average
44

Table C1: Common Trends Assumption: Excluding Top 5 Prefectures
Disggregate-level analysis
Aggregate-level analysis
(1)
(2)
(3)
(4)
(5)
(6)
Period2015m42018m72019m1
2015m42018m72019m1–2019m6 –2019m6 –2019m6–2019m6 –2019m6 –2019m6
si × Trendt
Yes
Yes
Yes
0.004
-0.04
-0.177**[0.007][0.027][0.069]si × KORj × Trendt
0.008
0.041
-0.441***[0.007][0.025][0.017]
Fixed effectPrefecture (ψi )
Yes
Yes
Yes
Yes
Yes
Yes
Country (ψj )
Yes
Yes
Yes
NA
NA
NA
Time (ψt )
Yes
Yes
Yes
Yes
Yes
Yes
si × KORj
Yes
Yes
Yes
NA
NA
NA
KORj × Trendt
Yes
Yes
Yes
NA
NA
NA
N
41,283
9,874
4,938
2,142
504
252
2
R
0.82
0.83
0.84
0.95
0.96
0.96
Notes: Figures in brackets indicate standard errors clustered by prefecture, country, and time for columns (1)–(3) and by prefecture and time for columns (4)–(6). *** and ** indicate the significance level at 1 and 5 percent,respectively.
Source: Authors’ estimation, based on Overnight Travel Statistics Survey.

Table C2: Regression Results: Excluding Top 5 Prefectures
Disggregate-level analysis
Aggregate-level analysis
(1)
(2)
(3)
(4)
Period2015m42018m7
2015m42018m7–2020m1–2020m1–2020m1–2020m1
si × Postt
0.177
0.296**-0.897***-0.839***[0.135][0.129][0.181][0.137]si × KORj
8.052***
8.174***[0.309][0.450]
KORj × Postt-0.530***-0.508***[0.034][0.024]si × KORj × Postt -2.372***-2.490***[0.122][0.127]
Fixed effectPrefecture (ψi )
Yes
Yes
Yes
Yes
Country (ψj )
Yes
Yes
NA
NA
Time (ψt )
Yes
Yes
Yes
Yes
N
47,082
15,673
2,436
798
R2
0.82
0.82
0.95
0.96
Notes: Figures in brackets indicate standard errors clustered by prefecture, country, and time for columns (1)–(2) and by prefecture and time for columns (3)–(4). *** and ** indicate the significance level at 1 and 5 percent,respectively.
Source: Authors’ estimation, based on Overnight Travel Statistics Survey.

45

and the median of si are 14.1 percent and 9.9 percent, respectively, while the first and third
quartiles are 5.0 percent and 15.4 percent, respectively. Using the longer two sample periods,
which satisfied the common trends assumption, we see that prefectures with high (i.e., 75th
percentile) dependency on visitors from Korea lost about 59.6 percent of their exports to Korea
while prefectures with low (i.e., 25th percentile) Korea dependency lost about 47.9 percent.
These estimates are within the 56.9 to 60.9 percent and 47.8 to 49.7 percent export loss ranges
established in the main text.
The net effects on prefectural exports shown in Table C4 also are right in line with previous results. We find that prefectures with high Korea dependency lost about 12.5 percent of
their total accommodations services exports while prefectures with low Korea dependency lost
about 4.3 percent. These exports losses are within or only slightly above the ranges establishedin the main text (i.e., −10.5 to −13.3 percent and −3.3 to −4.2 percent, respectively). The export
loss gap between the 75th percentile and 25th percentile prefecture for Korea dependency is−8.0 to −8.5 percentage points, which is as large as the export loss experienced by the median
prefecture (i.e., −8.0 to −8.5), similar to our previous finding. This demonstrates that our main
results are robust to the exclusion of the top five prefectures receiving visitors from Korea in2018 (i.e., pre-boycott).
Table C3: Impact of the Boycott on Prefectures’ Exports to Korea: Excluding Top 5 Prefectures

Percentile

(1)

(2)

Coefficientsi

2015m4–2020m1
Mean-2.37225%-2.37250%-2.37275%-2.37275-25% gap
2018m7–2020m1
Mean-2.49025%-2.49050%-2.49075%-2.49075-25% gap(3)(= (1) × (2))
Relative magnitude(log change)(4)( = (3) + KP Coeff.)
Total magnitude(log change)(5)
Total magnitude
converted(growth rate)

0.141
0.050
0.099
0.154

-0.334-0.118-0.235-0.366-0.864-0.648-0.765-0.896-0.578-0.477-0.535-0.592-0.115

0.141
0.050
0.099
0.154

-0.350-0.124-0.246-0.385-0.880-0.654-0.776-0.915-0.585-0.480-0.540-0.599-0.119Notes: Exports to Korea mean the exports of accommodation services to Korea that are defined as the numberof visitors from Korea (the total number of visitors who reside in Korea × the number of nights stayed in Japan).
Percentile indicates the quartiles of si . Coefficients are obtained from Table C2 and KP Coeff. means (KORj ×Postt )coefficient from the corresponding sample period. Rounding off of displayed numbers explains small variations in
calculated numbers shown above. Growth rate = exp(log change) − 1.
Source: Authors’ estimation, based on Overnight Travel Statistics Survey.

C.2

Excluding top 4 prefectures for Korea dependency

As an additional robustness check, we also consider whether our results are driven by the disproportionate boycott effects on the prefectures that are most dependent on Korean visitors. As
seen in Figure 4, Oita, Saga, and Yamaguchi prefectures all have very high Korea dependency
and they suffered strong declines in visitors from Korea between 2018 and 2019. We check therobustness of our results by re-estimating the boycott effects after excluding these three prefec46

Table C4: Impact of the Boycott on Prefectures’ Total Exports: Excluding Top 5 Prefectures
(1)
(2)
(3)
(4)
(= (1) × (2))
Total magnitude
Percentile
Coefficientsi
Total magnitude
converted(log change)(growth rate)
2015m4–2020m1
Mean-0.8970.141-0.126-0.11925%-0.8970.050-0.045-0.04450%-0.8970.099-0.089-0.08575%-0.8970.154-0.138-0.12975-25% gap-0.0852018m7–2020m1
Mean-0.8390.141-0.118-0.11125%-0.8390.050-0.042-0.04150%-0.8390.099-0.083-0.08075%-0.8390.154-0.129-0.12175-25% gap-0.080Notes: Exports mean the exports of accommodation services that are defined as the number of foreign visitors (thetotal number of visitors who reside outside of Japan × the number of nights stayed in Japan). Percentile indicatesthe quartiles of si . Coefficients are obtained from Table C2. Rounding off of displayed numbers explains small
variations in calculated numbers shown above. Growth rate = exp(log change) − 1.
Source: Authors’ estimation, based on Overnight Travel Statistics Survey.

tures, along with Fukuoka which reports a Korea dependency level close to Yamaguchi’s level(i.e., 0.4399 versus 0.4548, as shown in Table 5).
The regression results for both the disaggregate and aggregate levels after excluding the
top four prefectures for Korea dependency are shown in Table C5. We report results for the
full- and medium-period samples for comparative purposes with the results reported in themain text. The size, sign and significance level of our variables of interest are similar to thosereported earlier in Tables 3 and 7.
To facilitate a comparison of the results without the top four prefectures for Korea dependency with our main results, we convert the estimated coefficients in Table C5 into growth
rates in Tables C6 and C7 for the disaggregate-level and aggregate-level analyses, respectively.
Again, note that the distribution of si changes slightly due to our exclusion of the top four
prefectures for Korea dependency. The average of si declines to 11.8 percent, while the first,
second and third quartiles drop to 5.0, 9.7 and 15.4 percent, respectively.
Across the longer two sample periods, we estimate that prefectures with low (i.e., 25th percentile) Korea dependency lost about 46.6 percent of their exports to Korea, as shown in Table
C6. This estimated loss is only slightly below the 47.8 to 49.7 percent export loss range reported
in the main text. Prefectures with high (i.e., 75th percentile) Korea dependency lost about 61.0
percent of their exports to Korea, which is slightly above the 56.9 to 60.9 percent range established in the main text. Similarly, the net effects on prefectural exports reported in Table C7 arewithin or only slightly below the ranges reported in the main text and shown in Table 9. For
example, after dropping the top four prefectures for Korea dependency, we estimate an about
9.7 percent loss in total exports for high (i.e., 75th percentile) Korea dependency prefectures,
which is slightly below the 10.5 to 13.3 percent range established in the main text. Dropping
the top four prefectures for Korea dependency changes our point estimates marginally, but not
in a substantive way. We conclude that our main message is robust to excluding these outlier
prefectures for Korea dependency.

47

Table C5: Regression Results: Excluding Top 4 Prefectures for Korea Dependency
Disggregate-level analysis
Aggregate-level analysis
(1)
(2)
(3)
(4)
Period2015m42018m7
2015m42018m7–2020m1–2020m1–2020m1–2020m1
si × Postt
0.587**
0.642***-0.671**-0.645***[0.227][0.134][0.301][0.182]si × KORj
10.131***10.372***[0.564][0.653]
KORj × Postt-0.496***-0.459***[0.021][0.027]si × KORj × Postt -2.883***-3.126***[0.333][0.279]Fixed effectPrefecture (ψi )
Yes
Yes
Yes
Yes
Country (ψj )
Yes
Yes
NA
NA
Time (ψt )
Yes
Yes
Yes
Yes
N
48,427
16,094
2,494
817
R2
0.85
0.85
0.97
0.97
Notes: Figures in brackets indicate standard errors clustered by prefecture, country, and time for columns (1)–(2) and by prefecture and time for columns (3)–(4). *** and ** indicate the significance level at 1 and 5 percent,respectively.
Source: Authors’ estimation, based on Overnight Travel Statistics Survey.

Table C6: Impact of the Boycott on Prefectures’ Exports to Korea: Excluding Top 4 Prefectures
for Korea Dependency

Percentile

(1)

(2)

Coefficientsi

2015m4–2020m1
Mean-2.88325%-2.88350%-2.88375%-2.88375-25% gap
2018m7–2020m1
Mean-3.12625%-3.12650%-3.12675%-3.12675-25% gap(3)(= (1) × (2))
Relative magnitude(log change)(4)( = (3) + KP Coeff.)
Total magnitude(log change)(5)
Total magnitude
converted(growth rate)

0.118
0.050
0.097
0.154

-0.340-0.143-0.279-0.445-0.836-0.639-0.775-0.941-0.566-0.472-0.539-0.610-0.137

0.118
0.050
0.097
0.154

-0.368-0.156-0.302-0.483-0.827-0.615-0.761-0.942-0.563-0.459-0.533-0.610-0.151

Notes: Exports to Korea mean the exports of accommodation services to Korea that are defined as the numberof visitors from Korea (the total number of visitors who reside in Korea × the number of nights stayed in Japan).
Percentile indicates the quartiles of si . Coefficients are obtained from Table C5 and KP Coeff. means (KORj ×Postt )coefficient from the corresponding sample period. Rounding off of displayed numbers explains small variations in
calculated numbers shown above. Growth rate = exp(log change) − 1.
Source: Authors’ estimation, based on Overnight Travel Statistics Survey.

48

Table C7: Impact of the Boycott on Prefectures’ Total Exports: Excluding Top 4 Prefectures for
Korea Dependency
(1)
(2)
(3)
(4)
(= (1) × (2))
Total magnitude
Percentile
Coefficientsi
Total magnitude
converted(log change)(growth rate)
2015m4–2020m1
Mean-0.6710.118-0.079-0.07625%-0.6710.050-0.033-0.03350%-0.671
0.097-0.065-0.06375%-0.671
0.154-0.104-0.09875-25% gap-0.0662018m7–2020m1
Mean-0.6450.118-0.076-0.07325%-0.6450.050-0.032-0.03250%-0.6450.097-0.062-0.06075%-0.6450.154-0.100-0.09575-25% gap-0.063
Notes: Exports mean the exports of accommodation services that are defined as the number of foreign visitors (thetotal number of visitors who reside outside of Japan × the number of nights stayed in Japan). Percentile indicatesthe quartiles of si . Coefficients are obtained from Table C5. Rounding off of displayed numbers explains small
variations in calculated numbers shown above. Growth rate = exp(log change) − 1.
Source: Authors’ estimation, based on Overnight Travel Statistics Survey.

49

D

Common Trends Assumption for Overall Trade of Accommodation Services

To evaluate the common trends assumption for Japan’s overall exports of accommodation services in equation (7), we run the following regression:
Yjt = α + ψj + ψt + τ (KORj × Trendt ) + εjt .

(D1)

As in equation (2), Trendt cannot be included by itself due to the collinearity with ψt . The
numbers of observations for the test of the common trends assumption thus are 1,020 (= 20
origin countries × 51 months), 240 (= 20 origin countries × 12 months), and 120 (= 20 origin
countries × 6 months) for the full-, medium-, and short-period samples, respectively. If thetrend is common between Korea and other countries, τ will be insignificant.
Columns (1)–(3) in Table D1 present the regression results for equation (D1). The results
indicate significant coefficients for the full-, medium-, and short-period samples. The results
suggest that the common trends assumption does not hold for this analysis, which makes it
difficult for us to apply the DID design to regression equation (7).
Table D1: Common Trends Assumption: Alternative Aggregation
(1)
(2)
(3)
(4)
Period2015m42018m72019m1
2015m4–2019m6 –2019m6 –2019m6 –2019m6
KORj × Trendt
0.005*** -0.025*** -0.157*** 0.004***[0.001][0.007][0.027][0.001]
Fixed effect
Country (ψj )
Yes
Yes
Yes
No
Time (ψt )
Yes
Yes
Yes
Yes
Country-month (ψjm )
No
No
No
Yes
N
1,020
240
120
1,020
R2
0.94
0.94
0.96
0.99
Notes: Figures in brackets indicate standard errors clustered by country and time. *** indicates the significancelevel at 1 percent.
Source: Authors’ estimation, based on Overnight Travel Statistics Survey.

One may be concerned that the number of foreign visitors is not only affected by countryspecific factors but also affected by country-specific seasonality as we confirmed in Figure 1.
To address this concern, we include country-month-fixed effects ψjm , instead of country-fixed
effect ψj for the full-period sample.59 Column (4) presents the results, indicating significant
coefficients. Once again, the results do not support the common trends assumption even if wecontrol for unobserved country-month specific effects.60 These results together suggest thatthe common trends assumption does not hold for the Japanese accommodations data for totalexports to Korea relative to other countries. Applying the DID design to equation (7) thus is
not appropriate with our dataset.
We also examined whether the common trends assumption holds for equation (8) using
the Korean data for the full sample (i.e., 2015m4–2019m6). As shown in Table D2, we foundthat the common trends assumption did not hold for Korean outbound and inbound data.
59
It is impossible to include country-month-fixed effect for the middle- and short-period samples because they
cover only 12 and 6 months, respectively.
60
To control for seasonality, one can also compute the dependent variable as the ratio or difference from the samemonth in the previous year (e.g., Yjt /Yj,t−12 ). However, such analysis leads to comparisons of growth rates orchanges, rather than levels.

50

Therefore, we present the results using the Korean data just as a reference, not as a main result.
Table D2: Common Trends Assumption: Korean data
(1)
(2)
Outbound InboundPeriod2015m4
2015m4–2019m6 –2019m6
KORj × Trendt
0.010***
0.012***[0.001][0.001]
Fixed effect
Country (ψj )Yes
YesTime (ψt )
Yes
Yes
N
1,477
9,870
R2
0.97
0.97
Notes: Figures in brackets indicate standard errors clustered by country and time. *** indicates the significancelevel at 1 percent.
Source: Authors’ estimation, based on data from the Korea Tourism Organization website.

51