The Effectiveness of International

Trade Boycotts

Kilian Heilmann†

First Draft: June 6, 2014

Abstract

I estimate the impact of international consumer boycotts by studying two specificboycotts that I claim are exogenous to unobserved shocks: the boycott of Danishgoods by Muslim countries following the Muhammad Comic crisis in 2005/2006 and theChinese boycott of Japanese goods in response to the Senkaku/Diaoyu Island conflictin 2012. Results from the synthetic control method suggest strong heterogeneity inthe response to boycott calls between countries with an average trade disruption ofabout 15% in the case of the Muslim countries and 4% in the case of China. In bothcases, this is only a minor share of overall exports over this period with 0.4% and 0.8%respectively. Product-level results show that the boycott is most effective for consumergoods and especially highly-branded signature export goods, and at most temporaryfor intermediates and capital goods. An event study on Japanese stock market returnssuggest that the Chinese boycott did not significantly depress stock values of explicitlyboycotted Japanese firms.

JEL classification: F14

1 Introduction

International trade boycotts as a special form of conflict between countries are not a

new phenomenon. They have been used throughout history to punish or coerce a spe-

cific behavior. Early examples of international consumer boycotts include the repeated

boycotts of Japanese goods by China throughout the 1930s in response to the Japanese

invasion (for a very early account see Lauterpacht, 1933) and the worldwide boycott move-

ment in protest of South Africa’s apartheid system in the late 1950s. The 21st century

has seen a US-American boycott of French products during the Iraq War in 2003, the boy-

cott of Danish goods by Muslim countries in reaction to the Muhammad Comic Crisis of

†Department of Economics, University of California San Diego. Email: kheilman@ucsd.edu. I wouldlike to thank Gordon Hanson, Marc Muendler, Eli Berman, Lawrence Broz, and participants of the UCSDThird Year Paper sequence for helpful comments.

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2006, and a Chinese consumer boycott against Japan in 2012 after the conflict about the

Senkaku/Diaoyu Islands.

These events share the common characteristic that they are not motivated by economic

rationale, for example by inferior product quality or containing hazardous parts, but by

trade-unrelated political events. In contrast to the more frequent boycotts against specific

firms, such as the boycott against Shell in 1995, they are directed against entire countries.

These boycotts can therefore be seen as policy means to carry out international conflicts.

They seem to become an option when other means of coercion, such as war or the severing

of diplomatic relationship appear to be infeasible. They also tend to be exercised by the

people of a country without or with only few official government support, often when the

people think that their government is unable or unwilling to address the conflict.

Yet the usefulness of this means strongly depends on whether these boycotts are effec-

tive. Their intent to punish countries can fail in many dimensions. At first, boycotts intent

to hurt the conflict partner by not importing goods from it and thus depriving it from

its export revenues and thus gains from trade. Yet if the boycotted country’s exporters

can easily redirect their sales to domestic or other foreign markets, the potential economic

loss might be relatively small. Secondly, even if disrupted exports do hurt the exporting

country significantly, boycotts are a costly tool, since the boycotting country is also giving

up its gains from trade by refusing to import. This is even more true in a world charac-

terized by increasing international integration of production, where trade is not primarily

in final goods anymore, but where the share of processing trade is rising. If production

of the boycotting country crucially depends on imports from the boycotted country, this

will raise the costs of the boycott, it might render it an incredible threat not being carried

out at all. Furthermore, trade boycotts rely on collective action that can be difficult to

organize and therefore fail to materialize.

The aim of this paper is to evaluate the effectiveness of international trade boycotts

and to quantify their impact on international trade relationships. To do so, I make use

of two instances of consumer boycotts that I claim are exogenous to unobserved trade-

related shocks and can be used to identify the effect of these boycotts on exports from the

boycotted to the boycotting country. These two events are the boycott of Danish goods

after the Muhammad Comic crisis in 2005/2006 and the Chinese boycott of Japanese goods

in the aftermath of the Senkaku/Diaoyu Island conflict in 2012. Both these conflicts were

sparked by random events carried out by private people, came along rather unexpected,

and are thus unrelated to previous trade relationships.

To measure the impact of boycotts on the exports of the boycotted country, I use the

synthetic control group method to construct counterfactual import values and compare

them to the actual imports. The results suggest that there is a strong heterogeneity in the

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response between the boycotting country, with an average one-year reduction in imports

of about 15.1% and 4.3% of total trade in the Muhammad boycott case and the Senkaku

case respectively. Product-level analysis shows that the impact is concentrated in consumer

goods with only minor effects for intermediates and capital goods, being consistent with the

notion that international trade boycotts are mainly carried out by consumers and not by

firms. While the estimated disruption in imports from the boycotted country can be large,

the fraction as total exports of the boycotted country is very low in both boycott cases

(0.4% for Denmark and 0.8% for Japan). This suggests that even though an individual

firm of the boycotted country might be hit hard, the overall effect on the export sector is

negligible, rendering the punishment effect as mostly ineffective.

The paper is organized as follows: Section II reviews the existing literature, section III

describes the event studies used and section IV presents the data. Section V then explains

the methodology while Section VI presents the empirical results. Section VII concludes.

2 Literature Review

Trade boycotts and especially consumer boycotts have received extensive treatment not

only in economics, but also in the fields of law and psychology. One important branch of this

literature is concerned with the mechanisms of consumer boycotts and the main focus of this

research is to explain the individual motivation for participating in a consumer boycott, e.g.

Friedman (1999). John and Klein (2003) study consumer boycotts as instances of collective

action that are inherently faced with the small-agent problem, i.e. the success of the boycott

depends on a mass of participants, but every individual’s impact and motivation to join

in is low. To explain that consumer boycotts do regularly happen, they propose a variety

of other reasons to participate, such as psychological motivations like guilt and self-esteem

or simply an exaggerated sense of one own’s effectiveness. This literature serves as the

theoretical foundation of any quantitative analysis of consumer boycotts.

The question whether there is a significant impact of consumer boycotts has received

more attention in the last few years. Yet thorough quantitative studies of international

consumer boycotts against an entire country have to my best knowledge been restricted to

a single boycott, the US-American boycott of French products in the aftermath of France’s

opposition to the invasion of Iraq in 2003. Michaels and Zhi (2010) estimate that trade

deteriorated by about 9% in 2002-03 when France’s favorability rating in the US fell sharply.

Pandya and Venkatesan (2013), using supermarket scanner data, find that brands that are

perceived as being French lose market shares in weeks with high media attention of the

boycott. They estimate the implied costs of this boycott to be similar to the costs of an

average product recall. For the same period, Clerides et al. (2012) find a significant but

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short-lived drop in sales of US-American soft drinks in the Middle East, but cannot find

a similar effect on other goods. Both these studies are based on local sales and do not

investigate the effect on imports. Davis and Meunier (2011) study the quarterly trade

relationships between the US and France as well as between China and Japan for the years

1990-2006, thus including the boycott of French goods. They do not find any significant

link between negative events involving these countries and the level of goods exchanged,

but find that trade as well as foreign direct investment continued to grow sharply in the

period studied.

Besides the narrow focus on explicitly announced boycotts, there is a new literature

studying the relationship between other political conflicts and international economic re-

lations. Fuchs and Klann (2013) for example study countries’ trade with China if they

officially receives the Dalai Lama. China perceives any formal relations with the Tibetan

spiritual leader as an interference into internal political affairs and threatens countries that

do so with a reduction of trade. The authors test whether China carries out its threat in

order to deter those countries from receiving the Dalai Lama. They find a significant neg-

ative short-term effect on trade volumes and confirm that, even though the effect dies out

quickly after one year, countries are willing to use trade as a tool to enforce their political

will. The main shortcoming of Fuchs and Klann’s paper, however, is the focus on annual

trade levels and the resulting short time span to test their hypothesis. Fisman et al (2014)

and Govella and Newland (2010) study the effects of Sino-Japanese conflicts on the stock

market value of Japanese firms using an event study approach. They find that stocks of

Japanese companies with a high share of sales to China lose value compared to companies

with a low exposure to China.

This paper tries to fill in a gap in the understanding of consumer boycotts by linking

the literature on boycotts with the literature studying the impact of conflicts on trade.

The novelty of this paper that distinguishes it from previous studies is the use of monthly

data allowing for a more thorough analysis of short and long-term effects of international

consumer boycotts on international trade.

3 Background

3.1 Muhammad Cartoon Crisis

On September 30, 2005 the Danish newspaper Jyllands-Posten published a series of

cartoons depicting Islamic prophet Muhammad in an arguably unfavorable manner, the

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most striking one showing him with a bomb in his turban.1 This caused a huge outcry

by Muslims in Denmark as not only the depiction of the prophet is forbidden in several

branches of Islam but also because they felt that the comics equated Muslims to terrorists.

Even though Danish Muslims protested the publication from the very beginning, it was not

until early 2006 that the controversy became an international conflict. After the comics

had been reprinted in several Arabic newspapers, violent protests sparked in many Middle

Eastern countries, leading the ambassadors of several Muslim countries to demand an

official apology by the Danish government and prosecution of the cartoon artists. The

Danish government however refused any involvement into the crisis and argued that the

private newspaper’s actions are protected by freedom of speech.

The months of January and February 2006 saw further escalation of the conflict with

Western embassies being attacked, leaving several dozen people dead. With the Danish

government still refusing any official apology, religious leaders in Saudi Arabia called for

a boycott of Danish on January 26, 2006 in order to hurt Denmark economically, and

published a boycott list of Danish firms.2 Soon other countries joined the boycott including

for example Indonesia and Syria The French supermarket chain Carrefour preemptively

removed Danish goods from its shelves and several Danish food producers such as Arla

Foods reported huge losses in the Middle East.3 At the same time, a counter-boycott

campaign called “Buy Danish” was called for, but it remains unclear whether this campaign

gained enough media attention to have any large scale effects.4

The scandal about the Muhammad cartoons eventually lost public attention and the

protests calmed down, though several incidents in later years were linked to the cartoons.

In 2008 and 2010 attempts to kill the creator of the most controversial of the cartoons were

made, but could be prevented by police. It is therefore unclear how long the conflict and

the boycott continued to affect trade relationships between the Middle East and Denmark.

3.2 Senkaku/Diaoyu Islands Conflict

The Senkaku (in Japanese) or Diaoyu (in Chinese) Islands are a small group of islets

set in the East China Sea approximately 170 km North-East of Taiwan. None of the five

islands in the archipelago is inhabited and with a maximum size of 4.3 square kilometers the

islets are not suited for permanent settlement. In the aftermath of the First Sino-Japanese

War (1984-85) and the subsequent invasion of Taiwan, Japan began to survey the islands

and claimed it as Her territory. After the Treaty of San Francisco formally established

1For a detailed time line of the events, see Jensen (2008).2Examples of these lists can be found on http://shariahway.com/boycott/index.htm3http://www.nytimes.com/2006/01/31/international/middleeast/31danish.html? r=0.4http://www.foxnews.com/story/2006/02/16/muslim-boycotts-hurt-danish-firms/.

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peace after World War II, Japan ceded all Her claims to Taiwan and the nearby inhabited

Okinawa islands came under US control. When the Okinawa islands were returned to

Japanese control in 1972, Japan tacitly took control of the Senkaku islands as well and

remains a military presence on the islands until today.

In 1968, possible oil reserves were found in the area surrounding the Diaoyu/Senkaku

islands leading to claims of both Mainland China and the Republic of China (Taiwan) to

the islets. Japan rejected those claims denying that China had ever exerted control over

the islands and the territorial conflict remained unsolved. It was not until the 2000s when

several incidents brought the Senkaku/Diaoyu conflict back to public attention. Between

2006 and 2011 several activist groups from Mainland China, Taiwan and Hong Kong arrived

at the islands to proclaim Chinese sovereignty, just to be found to be expelled by the

Japanese navy immediately. The most severe incident happened in 2010 when a Chinese

fishing boat was destroyed by Japanese forces and its captain held captive for three days.

While these events definitely worsened Japanese-Chinese relationships, the conflict only

escalated after Japan announced to purchase the islands from its private owner in August

2012 and de facto established sovereignty over the archipelago. This led to furious anti-

Japanese protests in several Chinese cities that later turned violent. Japanese businesses

in China were attacked and protesters called for a boycott of Japanese goods. Japanese-

Chinese relations deteriorated drastically when further naval standoffs near the disputed

islands occurred, leading to worldwide fears over a military conflict of the two biggest

powers in East Asia. While the conflict has calmed down and lost media attention, the

major issue is still unresolved and remains a major problem in Japanese-Chinese relations.

A major objection against the use of the conflict as a natural experiment is that the

conflict could have been intentionally triggered by the Chinese government in order to

distort public attention from other political questions. However, this would only be a

problem if the intention was in response to economic issues that also affect trade. I argue

that the conflict evolved rather accidentally through the actions of several private activists,

but acknowledge the possibility of a political intention behind the dispute. Yet if the conflict

was planned and sparked by the Chinese government, I believe that it was only in response

to the upcoming change in the political leadership of the party and state in late 2012.

This change was planned long in advance and is a regular feature of the Chinese political

system and therefore exogenous to any economic shocks that also affect the trade relations

between Japan and China.

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4 Data

4.1 Data Sources

This paper makes use of two distinct datasets that both provide monthly trade data,

but differ significantly in certain dimensions. For the boycott of Danish goods due to the

Muhammad Crisis, I use data from the online portal of Statistics Denmark.5 This dataset

covers Danish export values in local Danish krona (DKK) to a multitude of countries

and other political entities at monthly frequency for total trade volumes as well as for

both the two-digit (66 different industries) and five-digit SITC classification (3316 different

products). The time series run from the late 1980s to currently August 2013, yet I only

use data from January 1994 onwards to abstract from effects caused by the transition of

Eastern Europe after the collapse of the Soviet Union. To avoid inconsistencies in the data

I drop observations with trading partners that are extremely small, remote or experienced

any changes in territory during the period of study.

The data for the Senkaku/Diaoyu conflict comes from the newly created Monthly Com-

trade dataset that, in addition to the standard Comtrade data, reports trade flows at

monthly frequency for all Harmonized System (HS) product codes (about 5300 different

products). The very fine disaggregation of the data is however offset by other problems as

the Monthly Comtrade database is still in development and has not passed the beta testing

phase yet. This results in very limited availability of trade data depending strongly on

the reporting country. For example, the Japanese-reported time series for monthly trade

volumes with China runs from January 2010 to January 2014 only, while the mirror data

reported by China only covers the period July 2011 to September 2012, that is it stops

exactly at the height of the Senkaku/Diaoyu Island crisis. This prohibits verification of the

Japanese export data by Chinese import data.6 Data prior to 2010 is at the moment only

available at annual frequency. In addition to export values, both datasets provide infor-

mation on net weight for a small amount of the product codes at the most disaggregated

level (SITC5 and HS6 respectively).

I employ a variety of variables to control for confounding effects on trade. These data

include measures of economic production and geographic distance which are good predictors

of trade levels in gravity regressions. For GDP levels at yearly frequency (in current US

dollars), I use data from the World Bank indicators. For data on population-weighted

5http://www.dst.dk6Import values are usually considered to be more accurate than export figures since state organizations

are thought to be more interested in accurately accounting tariff income-generating imports.

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bilateral distance between trading partners, I use the gravity dataset from CEPII.7 Data

on the Muslim population for each country is provided by the Pew Research Center.8

4.2 Descriptive Statistics

4.2.1 Denmark

At first it is to notice that exports to the Muslim world as a share of total Danish

exports is relatively small. The 34 countries with a Muslim population of 75% or more for

which trade data was available account for not more than 2.66% of Danish exports to all

trading partners in 2004. For example, Denmark exported more to Finland (with a share

of 2.95%) than to all these countries together. Even the biggest Muslim trading partner,

Saudi Arabia, accounted for less than half a percent of Danish exports in 2004.

Examining export values from Denmark to the boycotting countries shows that monthly

trade data is characterized by high volatility, seasonal patterns, and possibly changing time

trends. It is not uncommon that Danish exports to these countries increase by a multitude

over one month or that trade completely collapses even in the pre-boycott period. In

Uzbekistan for example the total trade value from March to April 2005 surged by a massive

7000% from 20,000 to 1,423,000 DKK, only to drop by 95% to 59,000 DKK the following

month. The strong month-to-month swings are more prominent for the smaller export

partners, but are still significant for the three major Danish importers. Table 1 summarizes

the descriptive statistics of the time series for the three treatment countries with the largest

imports from Denmark: Saudi Arabia, Turkey, and the United Arab Emirates.

Some of the smallest Muslim countries’ trade with Denmark is characterized by long

spells of no imports with an occasional spike. Since estimating an impact of the boycott for

these countries is both difficult to implement and economically uninteresting (for example,

even a total collapse of trade in the case of Kyrgyztan would reduce Danish exports by

only 0.004%), I omit countries from the further analysis that show zero trade flows in the

data and only include them when I use aggregates of the whole Muslim worlds.

The strong volatility might hint towards important seasonality patterns that complicate

the quantitative analysis. I test for monthly seasonality patterns by a simple F-test in a

linear regression with indicator variables for each month and find evidence for seasonal

patterns only for a handful smaller countries (Algeria, Bangladesh, Djibouti).

7www.cepii.fr/anglaisgraph/bdd/gravity.htm.8http://www.pewforum.org/2009/10/07/mapping-the-global-muslim-population/.

8

Table 1: Descriptive Statistics

Country Saudi Arabia Turkey UAE Aggregate

Mean 176,710 205,965 129,765 1,036,268Standard Deviation 31,738 87,048 37,468 188,399Std Dev as Mean 18.0% 42.3% 28.9% 18.2%Minimum 100,143 77,810 82,677 710,530Maximum 272,301 462,941 422,072 1,596,562Min % Change -33.0% -60.2% -54.4% -29.1%Max % Change 67.8% 92.6% 194.7% 46.9%F-test p-value 0.37 0.32 0.68 N/A

Statistics over the pre-boycott period October 2000 to September 2005.

Seasonality p-value is the p-value of a F-test testing for joint significance

of monthly indicator variables in a linear time series regression.

4.2.2 China-Japan

Unlike the Danish-Muslim boycott where all the boycotting countries take up only

a small share of total exports, for Japan the People’s Republic of China (from now on

referred simply as “China”) is in fact the largest export partner when considering the pre-

boycott period from January 2000 to August 2012. China alone accounts for 19.23% of

all Japanese exports. The Special Administrative Regions of Hong Kong and Macao, even

though technically part of the PRC, but with a very different political and economic system,

and Taiwan9 report separate trade statistics. Including the trade with those entities, the

total percentage of exports to the Chinese-speaking world amounts to 30.8%

For the Japanese-Chinese trade data, the month-to-month fluctuations are lower but

can still reach percentage changes of more than 30% in both directions. In general, Taiwan’s

imports from Japan are the most stable while Macao’s is the most volatile, most likely due

to its small share of Japanese exports. The time series is marked by a stark drop in March

2011, the effect of the devastating Japanese earthquake and tsunami that resulted in more

than 50,000 deaths. Seasonality might be an issue especially in the winter months in which

trade appears to slow down and the F-test testing for the joint significance of the monthly

indicator variables suggest seasonal patterns for all entities except Macao.

9For political reasons, monthly trade data for Taiwan is not officially available in the Comtrade Monthlydataset, but can be inferred from the country code 490, “Other Asia, nes”.

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Table 2: Descriptive Statistics

Country China Taiwan Hong Kong Macao Aggregate

Mean 12,800,000 4,175,000 3,502,000 19,964 20,496,964Standard Deviation 1,378,000 363,400 362,700 3,568 1,949,993Std Dev as Mean 10.8% 8.7% 10.4% 17.9% 9.5%Minimum 9,626,000 3,090,000 2,674,000 13,871 15,570,000Maximum 15,420,000 4,770,000 4,149,000 29,157 24,350,000Min % Change -30.4% -25.2% -29.4% -32.9% -28.2%Max % Change 32.6% 22.9% 39.2% 56.8% 29.0%Seasonality (p-value) 0.01 0.00 0.00 0.28 N/A

Statistics over the pre-boycott period January 2010 to August 2012.

Seasonality p-value is the p-value of a F-test testing for joint significance

of monthly indicator variables in a linear time series regression.

5 Methodology

5.1 Synthetic Control Group

To identify the effect of a trade boycott it is necessary to construct counterfactual export

levels that one can compare the actual export figures to. Simply comparing the trade levels

of the boycott period to the pre-boycott months will not account for any idiosyncratic

shocks to exports that would have been present even without the boycott. Then we might

attribute shocks to the boycott which have nothing to do with it. Using a difference-in-

difference approach to compare boycotting countries to non-boycotting countries requires

choosing an appropriate control group. In my dataset with few boycotting countries and

a large pool of non-boycotting countries, choosing the “correct” one is not a trivial task.

The results may strongly depend on the choice of the control group and may not be robust

to alternative choices.

Trade theory gives us little guidance in determining the control group. Even though the

gravity equation shows that bilateral distance and partner GDP are strong predictors for

overall trade volumes, this relationship describes long-time averages and is less applicable

to monthly trade data. In the short-term, there may be many more confounding factors

that are unobserved. To construct counterfactual trade values for the treated countries, I

therefore follow the synthetic control group method first used in Abadie and Gardeazabal

(2003) and later further developed in Abadie et al. (2010, 2014).

The synthetic control group method follows a pragmatic data-driven approach to choose

the right control group by creating an artificial control unit using a weighted average of

all the available control units. The weights are chosen such that the synthetic control

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group resembles the actual treatment unit in both the outcome variable as well as well

in as any known explanatory characteristics in the pre-treatment period. An estimate for

the treatment effect can then be calculated by the difference in the actual outcome of the

treatment unit and the synthetic control unit in the post-treatment period. The following

exposition follows closely the one in Abadie et al. (2014)

Suppose that there are J + 1 units in a balanced dataset with T observations which

consists of one treatment unit and J potential control units. Denote the number of pre-

treatment periods as T0 and the first period with the treatment in place as T0+1. Without

loss of generality, we can define the first unit to be the treated unit and specify the units

2 . . . J + 1 to be the control units. If there are more treated units one can simply remove

them from the data and repeat the same procedure for these units.

Assume that log export values Yj,t of the boycotted country to its trading partners j

are given by the following factor model

log Yj,t = δt + θtXj + λtµj + βt boycottj,t + εj,t (1)

where δt is a time trend common to all export partners, θt and λt are (1 × r) and (1 ×F ) vectors of common factors, Xj and µj are and (r × 1) and (F × 1) vectors of factor

loadings, and εj,t is an iid error term with mean zero that captures idiosyncratic shocks.

The parameters of interest are {βt}Tt=T1 that measure the dynamic impact of a boycott that

is captured by the dummy variable boycottj,t. The difference between Xj and µj is that

the former one is known to the econometrician and includes observable trade determinants

like GDP and bilateral distance while the latter factors are unobserved and might include

variables like industry composition and consumer preferences.

The ideal experiment to estimate the impact of the boycott would be to compare the

outcome of the boycotting country to the outcome of a non-boycotting country that has the

same factor loadings Xj and µj . However, this is infeasible for two reasons: Firstly, most

likely no such unit exists and secondly, µj is unobserved. While the former problem could be

resolved by a regression-based approach, the unknown factor loadings cause the regression

to be necessarily misspecified. Instead, the synthetic control group method constructs the

counter-factual as a weighted average of the availabl control countries.

Denote as Y Ij,t the counterfactual value of the exports in case the boycott had not

happened. Let T0 the period of the intervention. For all the countries in the control group,

I assume that Y Ij,t = Yj,t for j = 2 . . . J and all periods t = 1 . . . T . This implicitly assumes

that there are no substitutions of exports from boycott to non-boycott countries and is not

realistic. If these substitutions happen, the control group will be affected positively and

the treatment effect will be upward biased. However, the estimated treatment effect can

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still be interpreted as an upper bound of the true causal effect of the boycott on exports.

The goal is to construct the counterfactual export levels for the boycott country so that

we can obtain the estimator β̂t = Y1,t − Y I1,t for the treatment effect at time t.

A synthetic control group is defined by a set of J weights wj for j = 2 . . . J + 1 that

determine a weighted average of the control units. The ideal synthetic control group would

match both the factors X and µ of the treatment unit, yet this is impossible since µ is

unobserved. However, Abadie et al. (2010) show that under mild regularity conditions, the

synthetic control group can only match µ if it also matches a long period of pre-treatment

outcome variables Yj,t. This motivates to choose the weights to minimize both the deviation

in the known characteristics X as well as the pre-treatment oucomes Yj,t.

Assume that there exist weights w∗j with∑J+1

j=2 w∗j = 1 and 0 < w∗j < 1 ∀j = 2 . . . J + 1

such that

J+1∑j=2

w∗jXj = X1 (2)

J+1∑j=2

w∗jYj,t = Y1,t ∀ t = 1 . . . T0, (3)

that is the synthetic control group resembles both the pre-treatment outcomes as well as

the known explanatory variables of the treatment unit perfectly. The restrictions on w∗jensure that no extrapolation outside the support of the data takes place.10

The model in (1) implies that for the synthetic control group it holds that

J+1∑j=2

w∗jYj,t = δt + θt

J+1∑j=2

w∗jXj + λt

J+1∑j=2

w∗jµj +

J+1∑j=2

w∗j εj,t

and that the difference between the actual treatment unit and the synthetic control

group in the pre-treatment period t = 1 . . . T0 is

Y1,t −J+1∑j=2

w∗jYj,t︸ ︷︷ ︸= 0

= θt

X1 −J+1∑j=2

w∗jXj

︸ ︷︷ ︸

= 0

+λt

µ1 − J+1∑j=2

w∗jµj

+

J+1∑j=2

w∗j (ε1,t − εj,t)

10This is the crucial difference to a regression-based construction of the counterfactual. Abadie et al.(2014) show that a regression-based counterfactual can be interpreted as a weighted average of the controlswith weights that also sum up to one, but allow for negative values or values larger than one.

12

Rearranging, summing over all time periods, and dividing by T0 yields

µ1 − J+1∑j=2

w∗jµj

1

T0

T0∑t=1

λt = −J+1∑j=2

w∗j1

T0

T0∑t=1

(ε1,t − εj,t)

Note that as the number of pre-treatment periods T0 becomes large, the right hand side

of the equation goes to zero. As long as 1T

∑Tt=1 λt 6= 0 (that is the average effect of the

unobserved factors is not equal to zero over time), this implies that the difference in the

unobserved characteristics goes to zero as well.

This suggests to use∑J+1

j=2 w∗jYj,t as the counter-factual and subsequently calculate the

treatment effect as

β̂t = Y1,t −J+1∑j=2

w∗jYj,t ∀t > T0

In practice however, one will not be able to find weights such that equations (2) and (3)

hold exactly. This is the case if the characteristics of the treatment country are not in the

convex hull of the characteristics of the control countries and thus cannot be replicated with

the restrictions on wj . In this case the weights are chosen such that the equations hold

approximately. Formally, define Zj = (Yj,1, Yj,2 . . . Yj,T0 , X′j)′ as the column vector that

stacks all export values for the pre-treatment period 1 . . . T0 and the known characteristics

of country j. Similarly, define the matrix ZC = [Z2 Z3 . . . ZJ+1] that collects these column

vectors for all the potential control countries.

The (J × 1) vector W ∗ is then the solution to the following minimization problem

W ∗ = arg minW

‖Z1 − ZCW‖ = arg minW

√(Z1 − ZCW )′ V (Z1 − ZCW ) (4)

for a given weighting matrix V . The choice of V allows to assign different importance to the

explanatory variables or specific pre-treatment outcomes. In order to reduce the deviation

between treatment and synthetic control group the factors with the largest predictive power

should be given the highest relative weights.

If the number of pre-treatment periods T0 is small and the number of potential controls

J is large, the characteristics of the treatment unit will be mechanically replicated by a

combination of the control units. To circumvent this problem of overfitting, Abadie et

al. (2014) suggest restricting the pool of potential controls to countries that have similar

characteristics as the treatment country to break the spurious fit. Restricting the controls

13

to units that are “close” in terms of the characteristics also helps to reduce potential

interpolation bias.

5.2 An Idea for Inference

In practice, the fit between the treatment group and the synthetic control group in the

pre-boycott period will not be perfect, but subject to the idiosyncratic shocks captured in

the error term εj,t. This will bring randomness into the estimate of the treatment effect

βt. The exact distribution of the estimate depends on the unobserved parameter vector λt

and can therefore not be computed.

A pragmatic approach to evaluate the significance in the parameter estimates is to

compare them to the prediction error in the pre-boycott period. The intuition behind

this is the notion that if the synthetic control group fits the actual treatment unit poorly

before the boycott happened, this would undermine the confidence in the estimate of the

treatment effect. If the fit between the actual treatment country and its synthetic control

however is close in the pre-boycott period, we can be more confident in assuming that any

divergence after the treatment is actually caused by the boycott and not due to unrelated

shocks.

Assume that the divergence νt between the treatment and the control group follows an

iid distribution with mean zero and constant variance σ2ν

νt = Y1,t −J+1∑j=2

w∗jYj,t ∼ i.i.d. (0, σ2ν)

The variance σ2ν can be estimated by the sample variance on the pre-treatment differ-

ences between treatment and synthetic control group. Taking the square root, this is the

root mean squared prediction error for the pre-treatment period and can be interpreted as

the expected deviation between the actual treatment group and the synthetic control group

due to country-specific shocks. I assume that this error is not affected by the boycott, so

that νt is also present in the estimate of the treatment effect as well:

β̂t = Y1,t −J+1∑j=2

w∗jYj,t + νt = βt + νt ∀t > T0

The estimate then follows the distribution β̂t ∼ (βt, σ2) which allows me to compute

asymptotic standard errors for the estimate. These standard errors should be interpreted

14

as a lower bound on the true standard errors as they ignore potential serial correlation in

the true deviations, but they can still provide an idea of the reliability of the estimates.

To complement the analysis, I also perform placebo tests that traditionally have been

used in the context of synthetic control groups. There are two dimensions where a placebo

test can detect wrongful inference: Within a single time series, a random assignment of a

treatment time should not break the close fit between actual and synthetic control group

and should not produce large estimates of the treatment effect. If both series deviate even

though there is no boycott, then this should warn us that the synthetic control group is

merely picking up unrelated idiosyncratic effects. Furthermore, we can estimate the same

treatment effect for the control countries. If these countries are indeed unaffected by the

boycott, the synthetic control group method should not find large treatment effects. If

however the control countries seem to be negatively affected by the boycott, this would

hint to mis-specification in the model and would greatly undermine our confidence in the

method.

6 Empirical Implementation

6.1 Muslim Boycott of Danish Goods

6.1.1 Country-level Results

Since the publication of the Muhammad Comics was offensive to all Muslims, there are

many potential countries that could be induced to boycott Danish goods. I could find evi-

dence of a boycott call in many countries, but even if a country did not announce a boycott,

it could still have participated. I therefore allow for “silent” boycotts and use the share of

Muslim population in a country as a guidance to choose potential treatment countries. I

assign all countries that have a share of Muslim population of the total population of more

than 75% into the treatment group. Conversely, I assign countries for which this share is

less than 10% into the control group. I drop all other countries to avoid a contamination

of the control group. This leaves me with 34 countries in the treatment group and 100

countries in the control group. (See Table 15 and Figure 10 in the Appendix.)

Since the comics were published on the last day of September 2005 and a same-day

effect is unlikely, I define October 2005 to be the first treatment period in the sample.

This is considerably earlier than the official announcement of the consumer boycott in

January 2006, but allows for undeclared boycotts as an immediate reaction to the insult. I

separately fit the pre-treatment trend for each potential boycott country by minimizing the

15

prediction error for all monthly export levels five years prior to October 2005 as well as for

the averages of the GDP level for this time period and the population weighted distance.

Since the number of potential control units is large, I restrict the potential control

countries to countries that are ‘close’ in both distance and GDP in the month prior to the

boycott to avoid overfitting. In specific, I allow the GDP to differ by 75% in both directions

and distance to deviate by 5,000 km. This avoids that the relatively small economies of

the Middle East are replicated by large and distant countries like Japan and the US. I

experiment with the time dimension and change the pre-treatment period to three years

as well as seven years respectively and the results are generally robust.

The goodness of fit between the different boycott countries and their synthetic control

in general varies between 0.6 and 0.7. Table 3 summarizes the pre-treatment congruence

by employing two different measures of fit, the simple correlation coefficient and the root

mean squared prediction error.

Table 3: Pre-Treatment Fit

Three Years Five Years Seven Years

Country Corr. RMSPE Corr. RMSPE Corr. RMSPE

Saudi Arabia 0.872 14322 0.793 17005 0.701 21677Turkey 0.651 25010 0.674 29909 0.674 30385United Arab Emirates 0.416 15290 0.473 16495 0.684 14975

Average 0.726 6793 0.654 7707 0.619 7846

Corr: Correlation Coefficient

RMSPE: Root Mean Squared Prediction Error (in thousands of DKK)

To calculate the value of the foregone trade, I simply add up the treatment effects of

all treatment countries for each month and calculate the percentage loss as a share of total

trade levels. Table 4 shows the estimated percentage reduction for all countries for a period

a three, twelve, and 24 months and the associated prediction errors. The results suggest a

strong heterogeneity between the different Muslim countries. Some larger export partners

like Egypt, Libya, and Saudi Arabia see a strong and persistent negative effect, while some

even show a positive reaction, which is likely due to the very small and irregular trade flows.

Most notably, the second and third largest Danish trading partners Turkey and UAE show

no reaction to the boycott. Figure 1 shows the geographical distribution of the estimated

treatment effect.

Adding up the estimated treatment effects for all countries, I can calculate the total

disruption of trade due to the boycott. I estimate the total costs to be about 0.51 billion

DKK after three months, 2.86 billion DKK after twelve months, and 4.28 billion DKK after

two years. This corresponds to a 14.8%, 20.9%, and 15.1% reduction in overall trade to the

Muslim countries. The US-Dollar equivalents after taking into account fluctuations of the

16

Table 4: Estimated Percentage Reduction in Trade by Country

Country 3m 12m 24m Country 3m 12m 24m

Albania -32.7% -29.3% -18.6% Maldives 325.1% 158.3% 144.8%(39.4%) (21.0%) (15.6%) (177.6%) (105.4%) (71.0%)

Algeria -44.2% -17.7% -6.8% Mali -52.9% -70.1% -50.0%(23.7%) (12.4%) (9.3%) (33.8%) (14.9%) (13.4%)

Azerbaijan -49.8% -54.4% -39.1% Morocco -49.1% -19.8% -12.3%(25.5%) (15.2%) (11.7%) (42.8%) (22.2%) (14.7%)

Bahrain -16.9% -0.2% 10.6% Mauritania -12.4% 274.0% 63.2%(13.0%) (6.6%) (4.9%) (145.1%) (72.6%) (43.0%)

Bangladesh 25.5% 34.4% 32.2% Oman 3.5% -29.7% -9.3%(33.5%) (16.4%) (11.1%) (17.8%) (8.5%) (5.9%)

Djibouti -42.0% -34.2% -46.8% Pakistan 131.2% 48.2% 40.5%(97.5%) (55.3%) (32.4%) (22.8%) (12.4%) (9.3%)

Egypt -43.6% -29.3% -20.5% Qatar -2.2% 2.1% 49.0%(17.4%) (10.2%) (6.3%) (37.4%) (19.8%) (14.2%)

UAE -1.3% -7.3% -1.4% Saudi Arabia -31.6% -41.9% -34.5%(7.7%) (3.7%) (2.6%) (5.4%) (2.7%) (1.8%)

Gambia 131.3% 85.2% 114.5% Senegal -61.5% 83.2% 45.3%(76.3%) (46.1%) (33.1%) (70.8%) (42.7%) (27.1%)

Guinea -7.9% 34.1% 67.4% Syria -14.8% -29.0% -14.5%(54.0%) (33.7%) (27.6%) (16.5%) (8.4%) (6.0%)

Indonesia 31.6% 5.1% -1.2% Tunisia -20.2% -11.4% 24.3%(11.5%) (5.3%) (3.6%) (30.3%) (15.0%) (10.4%)

Iran -6.6% -34.5% -31.2% Turkey -10.4% -3.6% 0.0%(20.2%) (10.0%) (7.2%) (8.8%) (4.4%) (2.9%)

Jordan -32.1% -38.4% -37.4% Uzbekistan 2.8% 15.8% 1.8%(16.9%) (8.5%) (5.5%) (151.8%) (87.7%) (49.8%)

Kuwait -20.2% -46.4% -48.9% Yemen -27.4% -40.7% -36.4%(14.4%) (6.9%) (4.6%) (16.4%) (8.5%) (5.3%)

Libya -61.7% -80.3% -56.1% Total -14.8% -20.9% -15.1%(25.6%) (12.8%) (9.3%) (3.6%) (1.8%) (1.3%)

Prediction errors in parentheses.

exchange rate are, 198 million USD after three months, 444 million USD after one year,

and 758 million USD after two years.

Figure 2 plots the estimated cumulative trade loss for all treatment countries combined.

The graph reveals that the boycott constantly impacts Danish imports at about the same

rate and dies out after about 16 months when the curve becomes flatter. After that time,

the realized imports increase at the same rate as the implied counterfactuals, so that we

can conclude that the boycott is a one time reduction in trade that does not change the

long-term growth rates of trade. It is however permanent in the sense that it appears that

there is no catch-up effect. The dynamic response is robust to changes in the pre-treatment

period.

While the percentage loss for all the Muslim countries combined is sizable, this loss is

marginal when compared to the total exports of Denmark. Over the period from October

2005 to September 2007, Danish exports with all its trading partners summed to 1.08

17

Figure 1: Spatial Distribution of Treatment Effect

Legend

Estimated Treatment Effect

less

than -55%

-40 to -55%

-20 to -40%

-15 to -20%

-5 to -15%

-5 to 5

%

5 to 1

0%

10 to 3

0%

30-5

0%

50-9

9%

Controls

Exc

luded

No D

ata

>100%

trillion DKK (185 billion USD). The implied overall disruption of trade caused by the

boycott is then only 0.4% of all Danish exports during this period. While the boycott

might have hit individual Danish companies hard, the effect on the total Danish export

sector is negligible.

6.1.2 Robustness Checks and Placebo Tests

To check whether the results depend strongly on the parametrization of the synthetic

control group, I deploy a variety of robustness checks. Since most countries in the analysis

have such a small trade volume with Denmark that even a total boycott would barely have

an effect, I restrict the robustness checks to the three largest export partners Saudi Arabia,

Turkey, and the United Arab Emirates.

I assign placebo treatment times and estimate the trade disruption for these false boy-

cott instances. I estimate the cumulative treatment effect over six months for the 30 months

18

Figure 2: Cumulative Treatment Effect over Time to Muslim Countries (Aggregate)

0 3 6 9 12 15 18 21 24−5

−4.5

−4

−3.5

−3

−2.5

−2

−1.5

−1

−0.5

0x 10

6

Months since Boycott

in b

illio

n D

KK

Boycott officially announced

Estimated Cumulative Treatment95% Confidence Interval

preceding the publication of the comics. 25 of these placebo treatment times are not re-

lated to the boycott, but the five 6-month estimates prior to the actual treatment month

will contain at least one of the actual treatment months respectively. Figure 11 shows

the distribution of estimated treatment effects. Some of the placebo treatments do create

negative treatment effects, but in general are of smaller magnitude and not as persistent

as the estimated trade disruption of the actual treatment. For Saudi Arabia and UAE,

the countries that showed significant trade disruption, all six-month estimates including

the actual treatment month are negative and large. For Turkey, the estimate of the actual

treatment is still negative, but at a much smaller scale especially compared to previous

large negative and positive effects. These random fluctuations are in line with Turkey’s

estimated, non-significant effect of almost 0%.

In addition to the synthetic control group methodology, I also estimate a difference-

in-difference model of imports from Denmark Yj,t by country j at time t. I use the share

of Muslim population as a continuous treatment and control for GDP as well as bilateral

distance, the usual gravity model explanatory variables, and also include a time trend The

regression equation is given by

log Yj,t = α+β1muslimj+β2postt+β3muslimj×postt+β4GDPt+β5distj+β6t+εj,t. (5)

19

In addition, I include country-fixed effects to control for heterogeneity in the industry

composition. This specification does not allow to identify the effect of distance or the share

of muslim population separately, but the two time-invariant coefficients will be merged

into the fixed effect. The results in Table 5 indicate that the treatment effect is negative,

but it only becomes statistically significant if country-fixed effects are included. GDP and

distance have the expected positive and negative sign respectively. The large negative

coefficient on the share of Muslim population indicates that the treatment countries in

general import less from Denmark. My preferred fixed effects estimate suggest that the

average decline in exports due to the boycott is 12.2%, which is in line with the estimates

of the synthetic control group.

Table 5: Fixed Effects Results

Log Imports from Denmark(1) (2)

GDP 0.000∗∗∗ 0.000∗∗∗

(0.000) (0.000)

Distance -0.000310∗∗∗

(0.00000364)

Muslim -1.268∗∗∗

(0.0412)

Post 0.115∗∗ 0.279∗∗∗

(0.0357) (0.0138)

Post× Muslim -0.00365 -0.122∗∗∗

(0.0774) (0.0291)

Constant 11.04∗∗∗ 8.145∗∗∗

(0.0521) (0.0839)

Fixed Effects no yesTime Trend yes yes

N 30500 30500adj. R2 0.319 0.908

Standard errors in parentheses.∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001

6.1.3 Industry-level Results

To analyze the potentially heterogeneous effect on different product groups, I break up

the analysis into three main product types: Consumer goods, intermediate goods, capital

20

goods.11 This allows us to gain some insight about who is the main driver behind the

boycott. If the boycott is mainly consumer-driven, we should expect a higher trade dis-

ruption in consumer goods as compared to non-consumer goods. If the boycott however is

state-organized, we would expect a similar disruption in all goods.

I repeat the methodology for each product type by classifying all SITC-5 product code

into either consumer, intermediate, capital goods and others. Where available, I use the

Broad Economic Categories (BEC) classification developed by the UN Statistics Depart-

ment.12 SITC-5 codes that are not available in the BEC were coded by my own judgment

in close concordance with the logic of the BEC classification. The conversion table can be

found in the online appendix.

Figure 3: Realized and Counterfactuals by Class

−60 −40 −20 0 20 4018.5

19

19.5

20

Log

Con

sum

er E

xpor

ts

Comics

BoycottRealized ConsumerSynthetic Consumer

−60 −40 −20 0 20 4019

19.5

20

20.5

Log

Inte

rmed

iate

Exp

orts

Comics

BoycottRealized IntermediateSynthetic Intermediate

−60 −40 −20 0 20 4018

18.5

19

19.5

20

20.5

Months since Boycott

Log

Cap

ital E

xpor

ts

Comics

BoycottRealized CapitalSynthetic Capital

−60 −40 −20 0 20 4014

14.5

15

15.5

16

16.5

17

17.5

18

Months since Boycott

Log

Oth

er E

xpor

ts

Comics

BoycottRealized OthersSynthetic Others

11The remaining goods are classified in a category called “Others” which only makes up a tiny share ofoverall trade and contains unclassified goods such as coin and charitable donations.

12http://unstats.un.org/unsd/trade/BEC%20Classification.htm.

21

Table 6: Treatment Effect by Product Type

Period Consumer Intermediate Capital Others

3 Month 1.5% -9.0% -13.4% -36.1%(8.7%) (4.5%) (11.5%) (11.7%)

12 Month -27.5% -10.9% -12.0% 52.8%(4.3%) (2.3%) (6.3%) (4.4%)

24 Month -24.8% -1.7% -1.7% 43.0%(3.0%) (1.6%) (4.4%) (3.1%)

Prediction Error in parentheses.

I first combine the Danish exports to the treatment countries and then separate them

by product type. Table 3 shows the realized and counterfactual log Danish exports to of

each classification. Consistent with the boycott being consumer-driven, I see the largest

relative decline in consumer goods imports with long-term reductions in this category of

27.5% and 24.8% after one and two years respectively. This suggests that the publication

of the comics itself did not cause a major consumer reaction, but only after the official

boycott announcement did imports from Denmark decline.

For non-consumer goods, the reaction is less strong and in many cases not statistically

significant. Danish capital goods exports to the Muslim world seems to decline marginally

in the short and medium run, but the large prediction errors render this result statistically

insignificant. Over two years, this decline is reduced to less than 2%, so that any eventual

boycott effect in the short run is leveled off by a catch-up effect.

For intermediate goods, we do see a significant reduction in imports from Denmark of

about 9.0% and 10.9% after 3 and 12 months respectively that is ,similar to the capital

goods case, reduced to 1.7% after two years. This is inconsistent with the idea of a pure

consumer boycott and could be explained by nationalistic sentiment of business owners or

official trade restrictions such as complicating the processing of imports at custom offices.

6.2 Chinese Boycott of Japanese Goods

6.2.1 Country-Level Results

For the Senkaku Island Crisis case, I identify four political entities that are potentially

affected by the boycott announcement: The People’s Republic of China, its Special Admin-

istrative Regions (SAR) Hong Kong and Macao, as well as the Republic of China (Taiwan).

All these entities are quintessentially Chinese and I found evidence for sovereignty claims

to the Diaoyu Islands for all of them except Macao.

22

Table 7: Top Japanese Export Destinations

Rank Trade Partner Value Share

1 China 409,581 19.23%2 United States of America 342,378 16.08%3 Republic of Korea 169,204 7.95%4 Taiwan 133,596 6.27%5 Hong Kong SAR 112,078 5.26%6 Thailand 100,137 4.70%7 Singapore 68,629 3.22%8 Germany 57,839 2.72%80 Macao SAR 638 0.03%

Sum of China 655,895 30.80%

Sum of Exports from Japan to its trade partners for the period

1/2010 to 8/2012 in million USD.

The nature of Japanese trade with Mainland China creates problems with the synthetic

control group method. As discussed above, Mainland China is not only Japan’s largest

export partner over the pre-treatment period but it is also geographically close. It is thus

at the end of the distribution of both outcome as well as explaining factors and it will be

impossible to replicate its imports from Japan with a weighted average given the strong

restrictions on the weights given in equations (2) and (3). The other treatment units,

Taiwan and Hong Kong, have a similar level of imports from Japan with shares of 6.2%

and 5.2% respectively. Although these shares are much smaller than the share of Mainland

China, there are still only two control countries that have more imports (USA and Korea).

I therefore relax the conditions of the weights to be in the unit interval and instead allow

for arbitrary weights.

To avoid overfitting, I restrict the number of control units to countries that have a

similar GDP.13 In general, a small number of countries is able to replicate the Chinese

trade patterns rather well. Table 8 shows the composition of the control group and reports

statistics of the closeness of fit.

Figure 4 shows the realized and counterfactual exports from Japan to China on a log

scale. The strong decline in realized exports after the boycott is announced is easily visible

and trade levels even fell below those after the devastating earthquake in 2011. Yet one can

also see that Chinese imports from Japan were on a downward trend and that only a portion

of the decline can be attributed to the boycott, as the counterfactual trade figures implied

by the synthetic control group decline as well. The negative effect of the boycott compared

13In specific, the replicating country’s GDP should have at least 20% of GDP of the treatment countryand it should not exceed it by the factor 1.8. While arguably arbitrary, this creates control pools of around10 control countries.

23

Figure 4: Realized and Counterfactual Trade Levels (Mainland China)

−30 −25 −20 −15 −10 −5 0 3 6 9 12

22.9

23

23.1

23.2

23.3

23.4

23.5

Months since Boycott

Log

Japa

nese

Exp

orts

Boycott

Realized ChinaSynthetic China

to the prediction error is low in the first two months and only starts to be realized in the

trade data in the third boycott month, resulting in a 5% reduction of Japanese imports.

The boycott effect then accelerates and reaches its maximum after five months so that

trade is reduced by 9.5% after half a year. After that the boycott effect seems to fade

out and imports from Japan might even be catching up as the realized imports are above

the counterfactual values. Given the high prediction errors however, this catch-up effect is

highly speculative. The total reduction in Japanese exports within one year of the boycott

amounts to 4.3% and is equivalent to 5.82 billion USD. This estimated trade disruption

amounts to a share of 0.8% of total Japanese exports over the same time period. As in

the case of the Muhammad Comic boycott, this is a rather small percentage of the total

Japanese export economy.

For the other Chinese entities, there is no negative effect. While trade does decline

over the months following the boycott call, this is a general trend affecting the control

countries as well. In the case of Hong Kong and Taiwan, one can see positive effects at

longer horizons. This might be a substitution of exports from Mainland China towards the

other Chinese entities, significantly reducing the overall negative impact of the Mainland

boycott. One can conclude that the boycott was effective only in Mainland China and that

24

Figure 5: Realized and Counterfactual Trade Levels (Taiwan, Hong Kong, Macao)

−30 −25 −20 −15 −10 −5 0 5 10 15 2021.5

22

22.5

Months since Boycott

Log Exports

Boycott

Realized TaiwanSynthetic Taiwan

−30 −25 −20 −15 −10 −5 0 5 10 15 2021.5

22

22.5

Months since Boycott

Log Exports

Boycott

Realized Hong KongSynthetic Hong Kong

−30 −25 −20 −15 −10 −5 0 5 10 15 2016

16.5

17

17.5

Months since Boycott

Log Exports

Boycott

Realized MacaoSynthetic Macao

25

Figure 6: Estimated Cumulative Loss in Trade over Time (Mainland China)

0 2 4 6 8 10 12−12

−10

−8

−6

−4

−2

0

2x 10

9

Months after the Boycott

Loss

in T

rade

(in

bn

US

D)

Boycott announced

Estimated Cumulative Treatment95% Confidence Interval

the movement was unable to encourage Chinese people in Taiwan, Hong Kong, and Macao

to participate in the boycott.

6.2.2 Robustness Checks and Placebo Tests

The short pre-boycott period does not allow for a sensible placebo assignment of the

treatment time. I instead estimate the treatment effect for the control countries that should

not be affected by the boycott. I calculate the percentage losses of Japanese imports for

the countries of France, Germany, Russia, India, Thailand, the UK, and the US which

are all major trading partners of Japan. Table 10 summarized the estimates for these

countries. The results show that for the majority of the controls, the boycott did not

have a significant effect on imports from Japan. France and Russia are exceptions as they

show negative impact over a 6-month period, this effect however disappears at the one-year

window. The US shows a positive reaction to the Chinese boycott which is statistically

significant at both the 6 and 12 month window, suggesting that the Japanese exporters

substituted their goods towards the US.

26

Table 8: Synthetic Control Groups and Associated Weights

China Taiwan Hong Kong Macao

Australia 0 Bangladesh 0.08 Azerbaijan 0 Brunei 0.45Canada 0.03 Indonesia -0.40 Bangladesh 0 Cambodia 0.41France -0.08 Kazakhstan -0.04 Sri Lanka 0.06 Sri Lanka 0.12Germany 0.16 Malaysia 0.85 Finland 0.02 Estonia 0.19Indonesia 0.09 Pakistan 0.05 Kazakhstan -0.12 Georgia -0.06Italy 0.02 Philippines 0.48 Malaysia 0.64 Laos -0.11Korea 0.38 Singapore 0.14 Oman 0.12 Mongolia -0.13Mexico 0.11 Vietnam -0.18 Pakistan 0.02 Nepal 0.08Netherl. -0.24 Thailand 0.07 Philippines 0.17 New Guinea 0.09Russia 0.07 Singapore -0.08 Turkmenist. 0.05India 0.15 Vietnam 0.19 Uzbekistan -0.07Spain 0.15 Thailand 0.03Turkey -0.04UK 0.02

Corr. .92 Corr. .84 Corr. .92 Corr. .49RMSPE .002 RMSPE .002 RMSPE .002 RMSPE .011

Unrestricted country weights for synthetic control group.

Corr. is the correlation coefficient between treatment and synthetic control group in

the pre-treatment period.

RMPSE is the root mean squared prediction error between treatment and synthetic

control group in the pre-treatment period standardized by the mean of the time series.

In addition, I estimate a difference-in-difference model of Japanese exports given by

log Yj,t = α+β1Chinesej+β2postt+β3Chinesej×postt+β4GDPt+β5distj+β6t+εj,t. (6)

where Chinesej is an indicator variable that takes the value of one if the country is

either China, Taiwan, Hong Kong, or Macao. Since the synthetic control group method

suggested that besides Mainland China, the boycott did not have an effect, I experiment

with a setup where only China is assigned the treatment. The results in Table 11 show that

treatment effect is negative in all specifications, but that the estimates fluctuate widely. As

expected, the inclusion of the Taiwan, Hong Kong, Macao render the results insignificant as

these entities do not seem to truly belong to the treatment group. My preferred estimate in

column (4) indicates a 20.4% reduction in Chinese imports from Japan which is significantly

larger than the result obtained by the synthetic control group of 4.3%. This suggests

that even though both methods detect a significant reduction in trade due to the boycott

announcement, the choice of the control group can influence the result drastically.

27

Table 9: Estimated Trade Disruption

Country 3 Months 6 Months 12 Months

Mainland China -5.01% -9.50% -4.32%(2.49%) (1.84%) (1.35%)

Taiwan 3.30% 4.11% 10.22%(3.16%) (2.30%) (1.79%)

Hong Kong SAR 2.71% 4.18% 5.94%(2.33%) (1.70%) (1.31%)

Macao SAR 9.31% -2.40% 1.78%(11.09%) (7.44%) (5.29%)

Prediction errors in parentheses.

Table 10: Placebo Treatment Effect for Control Countries

Country Correlation 3 months 6 months 12 months

China 0.848 -5.01% -9.50% -4.32%(2.49%) (1.84%) (1.35%)

France 0.856 -7.10% -6.60% -3.58%(4.63%) (3.33%) (2.43%)

Germany 0.864 3.47% 3.32% 1.06%(3.81%) (2.74%) (2.02%)

Russia 0.852 -5.97% -14.03% -3.58%(6.55%) (4.63%) (3.62%)

India 0.868 4.57% 7.03% 2.16%(5.39%) (3.93%) (2.82%)

Thailand 0.712 -4.46% 3.36% 1.92%(3.31%) (2.55%) (1.93%)

UK 0.962 -6.14% -2.17% -4.05%(9.72%) (6.92%) (5.19%)

USA 0.913 -0.11% 3.84% 6.60%(1.92%) (1.41%) (1.06%)

Correlation refers to the pre-treatment correlation coefficient

between treatment and synthetic control unit.

Prediction errors in parentheses.

28

Table 11: Diff-in-Diff Japan

Log Imports from Japan

Full Treatment Mainland China only(1) (2) (3) (4)

GDP 8.15e-13∗∗∗ 7.70e-14∗∗ 8.74e-13∗∗∗ 8.82e-14∗∗

(4.63e-14) (2.80e-14) (5.78e-14) (3.06e-14)

Distance -0.000158∗∗∗ -0.000158∗∗∗

(0.00000954) (0.00000950)

Post -0.208 -0.191∗∗∗ -0.208 -0.191∗∗∗

(0.135) (0.0391) (0.135) (0.0392)

Treat 2.312∗∗∗ -0.277(0.330) (0.424)

Post × Treat -0.517 -0.0817 -1.172∗∗∗ -0.204∗∗∗

(1.026) (0.0541) (0.195) (0.0585)

Constant 16.50∗∗∗ 12.96∗∗∗ 16.56∗∗∗ 12.96∗∗∗

(2.282) (0.653) (2.283) (0.658)

Fixed Effects no yes no yesTime Trend yes yes yes yes

N 6034 6034 5998 5998adj. R2 0.245 0.944 0.236 0.943

Robust standard errors in parentheses.∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001

6.2.3 Identifying Consumer Industries

Instead of dividing trade into consumer, intermediate, and capital goods I look at more

disaggregated product-level data. While the Broad Economic Categories classification is

available for HS codes, the individual product type series will suffer from the same problem

as the total trade values of China, since they will be the largest and cannot be reproduced

without non-negative weights. This problem is less likely to appear for product-level HS6

codes, as China might not be the biggest export market for all of them. I make use of

publications of the Chinese boycott movement itself to identify consumer goods that are

most prone to the boycott. These publications are two flyers that were circulated on the

internet at the height of the conflict and contain pictures of Japanese brands that Chinese

consumers should avoid (see Figure 7). I collect these brand names and their industry in

Table (17). Most of these firms are concentrated in a few industries, namely automotive,

consumer electronics, foods, clothing, and cosmetics, while the remaining companies engage

in industries as diverse as toys, cigarettes, and airline services.

29

Figure 7: Internet Flyers Calling for Boycott

I searched through the companies’ internet representations and identify the brands’

major brands and products. I then classify these products into the corresponding HS codes

using the official description and the commercial website http://hs.e-to-china.com/ that

allows to search for keywords and outputs the relevant HS code. These signature products

can be subsumed into seven product codes which show significant amount of trade between

Japan and China. Table 12 summarizes the trade codes and their description. These codes

contain highly branded goods such as passenger cars, make-up and beauty articles, foods,

and a variety of consumer electronics such as cameras and video recording devices.

Table 12: List of Main HS Codes

HS Code Description

1902 Pasta, prepared or not, couscous, prepared or not22 Beverages, spirits & vinegar3304 Beauty, make-up & skin-care prep, manicure etc8508 Electromechanical tools, working in hand, parts8521 Video recording or reproducing apparatus8703 Motor cars & vehicles for transporting persons9006 Photographic still cameras, flash apparatus etc

I estimate the impact of the boycott on these consumer goods and Table 13 summarizes

the results. The category that sees the most drastic decline in trade is unsurprisingly 8703

which includes passenger cars. Figures 8 shows the realized and counterfactual trade levels

for Mainland China. Clearly visible is the massive drop in car imports and although they

catch up to the control group after about nine months, Japanese car exports to China drop

by a 32.3% within a single year.

While the effect of the boycott is very clear for vehicles, evidence for other product codes

is not obvious. The estimated percentage disruption in trade in highly-branded goods like

30

Table 13: Estimated One Year Trade Disruption by HS Code

HS Code in USD in % HS Code in USD in %

22 -12.885 -26.5% 8521 5.678 32.1%(5.330) (10.9%) (4.996) (28.2%)

1902 0.550 124.6% 8703 -1,972.778 -32.3%(0.143) (32.5%) (229.351) (3.8%)

3304 -14.597 -7.7% 9006 -4.630 -26.1%(6.754) (3.6%) (1.783) (10.1%)

8508 1.066 40.4%(0.786) (29.8%)

In million USD. Prediction errors in parentheses.

beverages, beauty products, and cameras is large, but in absolute values the estimates of a

mere 12, 14 and 4 million USD for these categories are dwarfed by the huge trade disruption

of almost two billion for passenger cars. The other signature product categories seem not

to be negatively affected by the boycott and the effect is imprecisely estimated due to the

low import values in the first place.

Notable is the lack of a negative reaction in the area of consumer electronics, even

though 34 Japanese companies in this sector were mentioned on the flyers. One explanation

could be the outsourcing of Japanese firms’ production to China. As it is well known,

China is the “world’s workshop” and produces consumer electronics under foreign brands

(Feenstra and Wei, 2009). If this is true, even though sales of Japanese-branded products

in China might decline, this will not show up in the trade data as these products are

manufactured in China already.

6.2.4 Event Study

To solve the problem of detecting a boycott of Japanese-branded goods that are pro-

duced in China, I use an event study approach on Japanese stock prices. Even though a

boycott of these goods does not appear in the trade data, it should affect the profits of

the companies and, given a well-functioning stock market, therefore be reflected in their

stock prices. I follow the standard procedures in the event study literature as outlined

in MacKinley (1997) and use the market model that explains individual stock returns by

an affine function of the market return with the prediction error being the unexplained

abnormal returns.

To implement the event study, I collect daily market prices of the 23 boycotted firms in

Table 17 that are listed on the Tokyo Stock Exchange from Thompson Reuters Datastream.

In addition, I collect data for 12 Japanese control firms with a low export exposure to China

to control for confounding factors that are unrelated to the Chinese boycott. These control

31

Figure 8: Realized and Counterfactual Trade Levels (8703 Passenger Cars)

−40 −30 −20 −10 0 3 6 9 12 152.9

2.95

3

3.05

Time since Boycott

Log

Car

Impo

rts

Chi

na

Boycott

Realized Mainland ChinaSynthetic Mainland China

Japanese Earthquake

countries are taken from “Japan’s Best Domestic Brands 2012”, published by Interbrand

(2012), and have less than 10% foreign sales. Most of these firms are engaged in non-traded

services like broadband telecommunication, construction, and real estate. Both treatment

and control firms are listed in Table 18. In addition, I use data for the Nikkei225 market

index to approximate the overall market return.

With daily stock market data, it is important to carefully choose the treatment time

and window. Following Govella and Newland (2010), I carry out the event study for several

incidents with different event window lengths. The events include the announcement of the

nationalization of the islands by the Japanese government on 11 September 2012 that most

likely caused the boycott. I also use the onset of the actual protests on September 17 as

the beginning of the event period. In addition, I analyze the effect of the protests in front

of the Japanese embassy in Beijing on 15 August and a protest call by Chinese internet

users on 20 August. I experiment with event windows of one, three, and seven trading

days. For each incident and trading window I report the t-statistics of a two-sided test

testing whether the cumulative abnormal returns over the period are statistically different

from zero. Negative t-stats indicate abnormal negative returns while positive indicate a

positive abnormal return.

32

Table 14: Event Study Results

Event Event Window Obs Treat Control

Embassy Protest Aug 15 1 -2.76 -0.68Aug 15 - 17 3 1.20 -2.35

Netizen Protest Call Aug 20 1 -0.16 -1.69Aug 20 - 22 3 -0.06 1.52Aug 20 - Aug 28 7 -0.16 0.69

Nationalization of Islands Sep 11 1 -0.08 -1.76Sep 11 - 13 3 -1.04 -2.50

Protest and Boycott Sep 17 1 0.07 -0.14Sep 17 - 19 3 1.11 -1.92Sep 17 - Sep 25 7 -0.35 2.03

t-statistics for the test that the cumulative return is significantly different from zero.

Estimation period from 8/16/2010 onwards.

The event study results indicate that most of the events mentioned above did not have

a statistically significant effect on the Japanese firms who were mentioned in the boycott

calls. The embassy protest of August 15 seems to have caused abnormal returns for the

same day it is happening, but this result is not robust to extending the event window and

the effect disappears within three days. Surprisingly, the control group sees statistically

significant negative returns even though the control companies have little foreign sales.

Neither the netizen protest on August 20th nor the nationalization of the Diaoyu Islands,

which is believed to be the main cause of the boycott, do affect stock market prices. Even

though the estimates are all negative, the deviations from the market model are all in the

range of the usual fluctuations around the expected return.

The China-wide protests that eventually caused the boycott announcement also seem

not to influence stock prices of the treatment countries with high China exposure and again

we see a statistically positive estimate for the control countries. This surprising results is

most likely due to the fact that the treatment countries make up a big share of the market

index. To check for this, I plot the cumulative abnormal returns in Figure 9 for both

the treatment and control country after the embassy protest of August 2012 and add the

market return. Neither the treatment nor the control group’s abnormal returns deviate

much from zero until about seven trading days after the boycott announcement when both

groups start to diverge. This coincides with negative returns of the market index which

track the treatment group pretty well. The cumulative abnormal returns however start

to converge again after 20 trading days and eventually are close to zero. This suggests

that the boycott did have a lagged effect not only on the stock prices of Japanese firms

mentioned in the boycott flyers but also on the whole market index. This effect however

is temporary and dies out in about one month. The Japanese stock market being only

33

Figure 9: Average Cumulative Returns

0 10 20 30 40 50 60−0.05

−0.04

−0.03

−0.02

−0.01

0

0.01

0.02

0.03

0.04

Time

Cum

ulat

ive

Abn

orm

al R

etur

ns

Embassy Protest

Netizen Protest Call

Nationalization

Boycott

TreatmentControlsMarket Return

slightly affected by the conflict might be explained by either the Chinese market being

relatively unimportant for Japanese firms or by political tensions already being priced into

the stocks, which is plausible given the long history of conflict between the two countries.

Doing the analysis on a firm level shows minor heterogeneity between firms. Figure

12 depicts the cumulative abnormal returns over time for each company and the market

return. While some firms see negative and some see positive returns, none of them seem

to be major outliers driving the result as the negative impact never exceeds 20%. On the

other hand, one control company’s stock price (KDDI) suffers heavily over the period and

this might explain the negative estimates for the control group in the event study.

7 Conclusion

The analysis has shown that boycotts can have a significant effect on trade relations,

and that political conflict has spillovers to international trade. There is no such thing as a

34

typical reaction to a boycott, but the impact on trade is very heterogeneous. Estimates of

trade reductions in the range of 30% and 40% show that some countries are very willing

to carry out boycotts, however there are also many countries that do not seem to boycott

or even increase their trade with the boycotted country. Comparing the results of the

Danish-Muslim and the Japanese-China boycotts, the similarities are that they both cause

one-time reductions in the trade between boycotting and boycotted countries, with trade

reverting to previous levels after about 10-16 months. They are also impacting consumer

goods much more than non-consumer goods, which is in line with the theory of boycotts

being mainly consumer-driven with no or very moderate official government assistance.

This suggests that agents do take losses of trade into account when choosing their conflict

strategy, as boycotting consumer goods is plausibly less costly than stopping the import of

intermediate and capital goods.

The boycotted countries in this analysis, Denmark and Japan, happen to be well-

diversified export economies. In the case of Denmark, the Muslim countries that are prone

to boycotting made up only a fraction of 2.6% of total exports. This a priori limits the

total impact of boycotts on the boycotted country’s export sector, so that even though

the trade disruption was large for some boycotting countries, overall exports declined by

only 0.4% in the case of Denmark. The opposite is true for Japan, where the boycotting

country China makes up almost one fifth of total exports. The rather low estimate for

the boycott effect however causes the total trade disruption to be only 0.8%, twice the

estimate of the Danish case. One explanation for this rather small estimate is that trade

from Japan to China is still dominated by intermediate and capital goods. This suggests

that for exporting countries that have a diverse range of destinations and export goods, the

intended punishment effect of a boycott is likely not to be very effective. The inability of

the empirical approach to account for substitution effects towards non-boycotting countries

is likely further strengthening this argument.

This conclusion is not necessarily true for firms within the boycotted country. While

the overall disruption of exports for both Denmark and Japan is low, some firms might have

suffered heavily. The event study approach on individual firms’ stock market prices however

shows that this is not true for the Japanese companies that are listed on the Tokyo Stock

Exchange. Stock prices of explicitly boycotted Japanese firms did not decline abnormally

during the boycott, which suggests that either these firms were able to redirect their sales

to other markets or that the risk due to Japanese-Chinese conflicts was already priced into

the stocks. It is likely that these large companies serve widely diversified markets with

different products, so that a boycott of a single country is not overly damaging.

35

8 References

Abadie, Alberto, Alexis Diamond, and Jens Hainmueller (2010): Synthetic Control

Methods for Comparative Case Studies. Estimating the Effect of California’s Tobacco

Control Program, Journal of the American Statistical Association 105 (490), 493-505.

Abadie, Alberto, Alexis Diamond, and Jens Hainmueller (2014): Comparative Politics

and the Synthetic Control Method, American Journal of Political Science, Forthcoming.

Abadie, Alberto and Javier Gardeazabal (2003): The Economic Costs of Conflict. A

Case Study of the Basque Country, American Economic Review 93 (1), 113-132.

Clerides, Sofronis, Peter Davis, and Antonis Michis (2012): The Impact of the Iraq War

on U.S. Consumer Goods Sales in Arab Countries, Rimini Centre for Economic Analysis

Working Paper Series.

Davis, Christina L. and Sophie Meunier (2011): Business as Usual? Economic Re-

sponses to Political Tensions, American Journal of Political Science 55 (3), 628-646.

Fearon, James D. (1995): Rationalist Explanations for War, International Organization

49 (3), 379-414.

Feenstra, Robert C. and Shang-Jin Wei (2009): Introduction to China’s Growing Role

in World Trade, NBER Working Paper 14716.

Fisman, Raymond, Yasushi Hamao, and Yongxiang Wang (2014): Nationalism and

Economic Exchange: Evidence from Shocks to Sino-Japanese Relations, NBER Working

Paper 20089.

Friedman, Monroe (1999): Consumer Boycotts. Effecting Change Through the Mar-

ketplace and the Media. New York, N.Y.: Routledge Publisher.

Fuchs, Andreas and Nils-Hendrik Klann (2013): Paying a Visit. The Dalai Lama Effect

on International Trade, Journal of International Economics 91 (1), 164-177.

Govella, Kristi and Sara Newland (2010): Hot Economics, Cold Politics? Reexamining

Economic Linkages and Political Tensions in Sino-Japanese Relations, APSA 2010 Annual

Meeting Paper.

Interbrand (2012): Japan’s Best Global Brands,

http://www.interbrandjapan-seminar.info, retrieved 2 February 2014

36

Imbens, Guido W. and Jeffrey M. Wooldridge (2007): What’s New in Econometrics?,

Lecture Notes 10, unpublished notes, NBER.

http://www.nber.org/WNE/lect 10 diffindiffs.pdf, retrieved 5 June 2014.

Jensen, Hans Rask (2008): The Mohammed Cartoons Controversy and the Boycott of

Danish Products in the Middle East, European Business Review 20 (3), 275-289.

John, Andrew and Jill Klein (2003): The Boycott Puzzle. Consumer Motivations for

Purchase Sacrifice, Management Science 49 (9), 1196-1209.

Lauterpacht, Hersch (1933): Boycott in International Relations, British Yearbook of

International Law 14, 125–140.

MacKinley, A. Craig (1997): Event Studies in Economics and Finance, Journal of

Economic Literature 35, 13-39.

Pandya, Sonal and Rajkumar Venkatean (2013): French Roast. Consumer Responses to

International Conflict - Evidence from Supermarket Scanner Data, SSRN Working Paper.

Pew Research Center (2010): Muslim Population by Country. The Future of the

Global Muslim Population. http://features.pewforum.org/muslim-population/, retrieved

7 November 2013.

United Nations Statistics Division, BEC Classification

http://unstats.un.org/unsd/trade/ BEC%20Classification.htm, retrieved 19 January 2014.

Zeile, William J. (1997): U.S. Intrafirm Trade in Goods, Survey of Current Business 77

(2), 23-38.

37

Appendix

Figure 10: Distribution of Muslim Population

LegendMuslim Populationin Percent of Total Population

Less than 5%5-20%20-50%50-75%More than 75% Data Source: PEW Center

38

Figure 11: Treatment Effects of Placebo Treatment Times

Cumulative estimated trade disruption in thousand DKK over 6 months for placebo treatment assignmentsfor the 30 months prior to the actual treatment (October 2006, normalized to 0). Estimates for periodsthat include at least one actual treatment period are in red.

39

Table 15: Treatment and Control Countries (Muhammad Boycott)

Country and Share of Muslim Population

Treatment Countries N = 34

Afghanistan 99.8% Jordan 98.8% Saudi Arabia 97.1%Albania 82.1% Kuwait 86.4% Senegal 95.9%Algeria 98.2% Kyrgyzstan 88.8% Syria 92.8%Azerbaijan 98.4% Libya 96.6% Tajikistan 99.0%Bahrain 81.2% Maldives 98.4% Tunesia 99.8%Bangladesh 90.4% Mali 92.4% Turkey 98.6%Djibouti 97.0% Mauritania 99.2% Turkmenistan 93.3%Egypt 94.7% Morocco 99.9% United Arab Emirates 76.0%Gambia 95.3% Niger 98.3% Uzbekistan 96.5%Guinea 84.2% Oman 87.7% Yemen 99.0%Indonesia 88.1% Pakistan 96.4%Iran 99.6% Qatar 77.5%

Control Countries N = 100

Angola 1.0% France 7.5% Norway 3.0%Antigua & Barbuda 0.6% Gabon 9.7% Palau 0.0%Argentina 2.5% Germany 5.0% Panama 0.7%Armenia 0.0% Greece 4.7% P. New Guinea 0.0%Australia 1.9% Grenada 0.3% Paraguay 0.0%Austria 5.7% Guatemala 0.0% Peru 0.0%Barbados 0.9% Guyana 7.2% Philippines 5.1%Belarus 0.2% Haiti 0.0% Poland 0.1%Belgium 6.0% Honduras 0.1% Portugal 0.6%Belize 0.1% Hong Kong 1.3% Rwanda 1.8%Bhutan 1.0% Hungary 0.3% Samoa 0.0%Bolivia 0.0% Iceland 0.1% Slovakia 0.1%Botswana 0.4% Ireland 0.9% Slovenia 2.4%Brazil 0.1% Italy 2.6% South Africa 1.5%Burundi 2.2% Jamaica 0.0% South Korea 0.2%Cambodia 1.6% Japan 0.1% Spain 2.3%Canada 2.8% Kenya 7.0% Sri Lanka 8.5%Cape Verde 0.1% Laos 0.0% Swaziland 0.2%Central African Rep. 8.9% Latvia 0.1% Sweden 4.9%Chile 0.0% Lesotho 0.0% Switzerland 5.7%China 1.8% Lithuania 0.1% Thailand 5.8%Colombia 0.0% Luxembourg 2.3% Tonga 0.0%Congo, Republic 1.4% Macao 0.0% Trinidad & Tobago 5.8%Costa Rica 0.0% Madagascar 1.1% USA 0.8%Croatia 1.3% Malta 0.3% Ukraine 0.9%Czech Republic 0.0% Marshall Islands 0.0% United Kingdom 4.6%Dominica 0.2% Mexico 0.1% Uruguay 0.0%Dominican Republic 0.0% Moldova 0.4% Vanuatu 0.0%Ecuador 0.0% Mongolia 4.4% Venezuela 0.3%El Salvador 0.0% Namibia 0.4% Vietnam 0.2%Equatorial Guinea 4.1% Nepal 4.2% Zambia 0.4%Estonia 0.1% Netherlands 5.5% Zimbabwe 0.9%Fiji 6.3% New Zealand 0.9%Finland 0.8% Nicaragua 0.0%

Muslim Population as Percent of Total Population. Source: PEW Center

40

Table 16: Top Danish Export Partners

Rank Country Share of Exports in 2004

1 Germany 18.197%2 Sweden 13.207%3 United Kingdom 8.996%4 USA 5.998%5 Norway 5.745%

26 Saudi Arabia 0.487%31 Turkey 0.426%33 United Arab Emirates 0.325%35 Iran 0.290%45 Egypt 0.164%48 Kuwait 0.121%51 Indonesia 0.106%54 Algeria 0.101%57 Pakistan 0.074%58 Morocco 0.071%61 Libya 0.059%62 Jordan 0.059%63 Oman 0.056%66 Yemen 0.052%68 Bangladesh 0.042%69 Syria 0.038%74 Qatar 0.029%75 Bahrain 0.029%76 Afghanistan 0.028%77 Tunisia 0.027%86 Senegal 0.012%87 Azerbaijan 0.011%93 Albania 0.008%96 Uzbekistan 0.007%98 Maldives 0.007%

104 Gambia 0.005%114 Guinea 0.004%115 Kyrgyzstan 0.004%116 Mali 0.004%118 Djibouti 0.003%120 Turkmenistan 0.003%123 Niger 0.003%131 Mauritania 0.002%133 Tajikistan 0.002%

Subtotal Treatment Countries 2.660%

41

Table 17: Japanese Brands mentioned on Boycott Flyers

AutomotiveAcura Bridgestone Daihatsu Denso Hino HondaInfiniti Isuzu Kawasaki Kubota Lexus MazdaMitsubishi Nissan Subaru Suzuki Toyota Yokohama

Electronicsaudio technica Brother Canon Capcom Casio EpsonFujifilm Fujitsu Hitachi JVC Kenwood KonamiKonica Korg Kyocera Mitsumi NEC NikonNintendo OKI Olympus Panasonic Pentax PioneerRicoh Sansui Sanyo Sega Sharp SonyTDK Toshiba Vaio Yamaha

FoodAsahi Biore BOSS Glico Kewpie KikkomanKirin Meiji Nissin Pocari Sweat Suntory UCC CoffeeUHA Yakult Yoshinoya

CosmeticsDHC Dongyangzhihua Kanebo Kao Kose ShiseidoShu Uemura SK-II Sofina

ClothingAsics Kenzo Uniqlo

OtherANA Bandai Butterfly Citizen Daikin INAXKato Komatsu Mild Seven Mitsukoshi Mizuno MujiNippon Noritz Omron Orient Rinnai RolandSeiko Sogo Sumitomo Tadano Toray TotoYasaka Yonex Zojirushi

42

Table 18: List of Companies in the Event Study

Treatment Control

Bridgestone Benesse HoldingsBrother Industries Daiwa House IndustryCanon KDDIFujitsu Mitsubishi EstateHonda MOTOR Mitsubishi UFJ Financial GroupKao Mizuho Financial GroupKawasaki Heavy Industry NTT DataKikkoman NTT Docomo IncKirin Holdings RakutenKyocera SecomNikon SoftbankNissan Motor Sumitomo Real Estate & Dev.PanasonicPioneerSeiko HoldingsSharpShiseidoSonySuzuki MotorToshibaToyota MotorYakult HonshaYokohama Rubber

Treatment countries are countries that are mentioned on at least one of

the two flyers in Figure 7 and are listed at the Tokyo Stock Exchange.

Control countries are countries with less than 10% foreign sales.

Source: Interbrand, Japan’s Best Global Brands, 2012.

43

Figure 12: Average Cumulative Returns by Company

Honda

Toyota

Nissan

Yokohama

Bridgestone

Kawasaki

Suzuki

Seiko

Brother

Canon

Fujitsu

Kyocera

Nikon

Panasonic

Pioneer

Sharp

Sony

Toshiba

Kikkoman

Kirin

Yakult

Kao

Shiseido

Market Index

Controls

0 10 20 30 40 50 60−0.4

−0.3

−0.2

−0.1

0

0.1

0.2

Time

Cumulative Abnormal Returns

EmbassyProtest

Netizen Protest Call

Nationalization

Boycott

44