Country Image and International Trade∗
Pao-li CHANGa and Tomoki FUJIIb†a

School of Economics, Singapore Management University,
90 Stamford Road, Singapore 178903
Phone: +65-6828-0830
Fax: +65-6828-0833
e-mail: plchang@smu.edu.sgaSchool of Economics, Singapore Management University,
90 Stamford Road, Singapore 178903
Phone: +65-6828-0279
Fax: +65-6828-0833
e-mail: tfujii@smu.edu.sg

December 7, 2012∗

Fujii received SMU Research Grant (C244/MSS10E008) for this project. We thank Doug Miller and Tove
Malmqvist at Globescan for making the detailed World Opinion Poll data available to us. Teo Wei Ting provided
research assistance.
†
Corresponding author.

1

Country Image and International Trade
Abstract
We study the impact of country image on international trade ﬂows. We ﬁnd that a
one percentage point increase in the positive response ratio—the proportion of people in
the importing country who view the exporting country positively—is associated with atleast a one percent increase in the aggregate trade ﬂow. By disaggregating trade ﬂows
by the type of goods, we also ﬁnd that both homogeneous and diﬀerentiated goods are
positively aﬀected by better country image and that the impact of country image tends
to be larger when more substitutes are available in the international market.

1

Introduction

Country image, or the way in which a country is viewed by others, varies over time. For
example, when France and Germany opposed the US-led Iraq War in early 2003, the transAtlantic relationship severely worsened. Miller (2003) reported that between February and
April in 2003 French exports to the US dropped by 17 percent and American tourism tumbled
by a quarter. In several parts of Germany, bars and restaurants refused to serve Coca-Cola,
Budweiser beer, Marlboro cigarettes, and other renowned American brands (The Economist,2003).
International politics plays an important role in the formation of country image, but otherfactors also matter. For example, a series of product scandals in China in recent years—such as
the 2007 pet food recalls and the 2008 Chinese milk scandal where pet food and infant formulawere contaminated with Melamine—have scared consumers across the world and damaged thecountry image of China. In Japan, consumers reacted particularly strongly to the 2008 incident
of insecticide-contaminated dumplings from China. In a poll conducted right after the incident,
76 percent of Japanese said that they would not use Chinese food again (Agence France Presse,2008).
These anecdotes indicate that country image may aﬀect the ﬂow of international trade. Itis plausible that consumers obtain utility [disutility] from consuming the products of a country
they view positively [negatively], which in turn may encourage [discourage] the imports from1

that country. Alternatively, country image may also aﬀect the transaction cost of marketing agood in a foreign country. An exporter may ﬁnd it more diﬃcult to engage and establish a local
distribution channel for its goods in an importing country where its country image is marred.
However, some of the goods traded internationally, e.g., oil and precious metals, are produced
in a limited set of countries. For these goods, consumers or importers may ﬁnd it diﬃcult to
avoid imports from a country they view negatively. This suggests that the impact of country
image is likely to vary across diﬀerent types of goods.
Built on Anderson and van Wincoop (2003), we derive a gravity equation disaggregated by
the type of goods. We then derive estimation equations for both aggregate and disaggregate
trade ﬂows in a manner consistent with the theory. We ﬁnd that country image signiﬁcantly
aﬀects international trade ﬂows both economically and statistically. A one percentage point
increase in the positive response ratio, or the proportion of people in the importing country
who view the exporting country positively, is associated with at least a one percent increasein the aggregate bilateral trade ﬂow. We also ﬁnd that country image positively aﬀects bothhomogeneous and diﬀerentiated goods. However, the magnitude of impact tends to be larger
in a sector where substitutes are more widely available in the international market.
This paper is organized as follows: In Section 2, we review the related literature and highlight
our contributions. In Section 3, we discuss the theoretical model and econometric speciﬁcations.
In Section 4, we describe the data. Section 5 presents the estimation results and Section 6
concludes.

2

Review of Related LiteratureThis study is related to three strands of literature. First, it is related to the boycott literature because boycott tends to take place when country image severely worsens. For example,
Michaels and Zhi (2010) argue that the worsening US-French relationship during the Iraq War
led to a reduction of bilateral trade by about 9 percent. Chavis and Leslie (2009) look at the
impact of the US boycott of French wine and ﬁnd that the boycott resulted in 26 percent lower
weekly sales at its peak and 13 percent lower sales over the six months of boycott.
Some boycott studies, however, ﬁnd no signiﬁcant eﬀects. For example, Ashenfelter et al.

2(2007) ﬁnd that the US boycott of French wine had no eﬀect once the cyclical peak at holiday
time and secular decline in the sales of French wine in the US are taken into account. Similarly,
Teoh et al. (1999) ﬁnd that the boycott of South Africa’s apartheid regime had little valuationeﬀect on the ﬁnancial sector.
Covering all industries in all the major economies, we extend the boycott literature by
studying the impact of country image in a general context. We ﬁnd that the impact is both
economically and statistically signiﬁcant. This indicates that, even in the absence of visible
events such as boycotts, country image aﬀects the real economy.
Second, this study is also related to the literature on the role of social capital and networks
in international trade, because country image is likely to be related to the social capital andnetworks among the trading partners. For example, Rauch and Trindade (2002) ﬁnd that thepresence of ethnic Chinese networks increases bilateral trade ﬂows of diﬀerentiated goods among
Southeast Asian countries by nearly 60 percent. Rauch (2001) provides a survey on the roleand importance of social networks in international trade.
Guiso et al. (2009) ﬁnd that diﬀerences in the bilateral trust level among European countries
have economically important eﬀects on bilateral trade, foreign direct investment, and foreign
portfolio investment. Their study suggests that perceptions rooted in culture are important
determinants of economic exchange. Similarly, using the share of those in each EU15 member
country who support the accession of each central and eastern European country to the EU,
Disdier and Mayer (2007) ﬁnd that bilateral aﬃnity has a positive eﬀect on trade ﬂows.
This study is similar to Guiso et al. (2009) and Disdier and Mayer (2007) in the broad sensethat we also study the eﬀects of social capital on trade. However, our study utilizes country
image, a measure not based on a speciﬁc event such as the EU accession. Instead of focusing
on Europe, we include a wide range of countries in our analysis. Our results show that theimportance of public opinion is fairly general.
In addition, this study extends Disdier and Mayer (2007) and Guiso et al. (2009) in two
aspects: In one aspect, we examine both aggregate and disaggregate trade ﬂows in a way
consistent with the theoretical model. In the other aspect, we allow for the possibility ofreverse causality. Reverse causality is potentially important because the increased level oftrade ﬂow could alter the country image and other indicators of public opinion. For example,3

if the imports from a particular country are considered useful and of good quality, the imageof that country may improve as a result of increased trade ﬂow. On the other hand, when an
increased level of trade ﬂow leads to trade frictions, the country image may worsen as a result.
These types of reverse causalities in the present context cannot be satisfactorily dealt withby time-invariant instrumental variables (IVs), as done in Guiso et al. (2009). With a panel
dataset, we address this issue by time-varying IVs. We also carry out panel Granger-causality
tests to verify the absence of reverse causality.
Third, this study is also related to the marketing literature on country of origin and foreign
products. A large number of studies have consistently found that the country of origin inﬂuences
the consumers’ perception of the product involved (See, for example, Bilkey and Nes (1982)).
Consumers may attach symbolic and emotional values to the country of origin of a product andhold social and personal norms related to the country of origin (Obermiller and Spangenberg,
1989; Verlegh and Steenkamp, 1999). As a result, consumers may avoid buying products froma country regardless of their quality evaluation.
For example, Klein et al. (1998) ﬁnd that Chinese consumers in general view Japanese
goods positively regardless of their levels of animosity towards Japan, but those with stronger
animosity towards Japan tend to avoid buying Japanese products. Similarly, Nijssen andDouglas (2004) ﬁnd that Dutch consumers with stronger animosity towards Germany tend to
be more reluctant to buy German products. These studies show that the war animosity, or
the animosity due to past military aggression, is more important than the economic animosity,
which may arise from the inﬂow of foreign products. These ﬁndings are consistent with theresults of our study: as will be shown, the impact of country image comes mainly from thecross-sectional variations, which may have reﬂected the history of war and diplomatic tensions
across countries.

4

3

Gravity Model

Overview of the Gravity ModelTo estimate the impact of country image on international trade ﬂows, we adopt the gravity
model. The use of the gravity model for international trade started in the 1960s with Tinbergen(1962) and Pöyhönen (1963). Since then, the gravity model has been routinely used in theinternational trade literature to study the eﬀects of various factors on trade ﬂows, including
the impact of international trade agreements (Rose, 2004; Chang and Lee, 2011), currency
unions (Rose, 2000; Glick and Rose, 2002), border eﬀects (McCallum, 1995; Anderson and van
Wincoop, 2003), trust (Guiso et al., 2009), and likelihood of war (Martin et al., 2008).
The development of economic theory that justiﬁes the gravity model lagged behind theearly success of its empirical applications. Anderson (1979) is the ﬁrst to provide a rigorous
microeconomic foundation for the gravity equation under the constant elasticity of substitution(CES) preferences. Various theoretical justiﬁcations have been made since then, including
Bergstrand (1985, 1989, 1990), Deardorﬀ (1998), and Anderson and van Wincoop (2003). In
this paper, we consider a straightforward extension of Anderson and van Wincoop (2003) byallowing for heterogeneous impacts across diﬀerent types of goods. In our application, we ﬁrst
categorize goods into homogeneous and diﬀerentiated goods following Rauch (1999). We thendisaggregate goods at the level of six-digit Harmonized System (HS) product codes.
When deriving an estimable equation, Anderson and van Wincoop (2003) impose a symmetric structure of trade resistance. A logical consequence of the symmetry assumption is thatthe value of bilateral exports is equal to the value of bilateral imports in each country witheach trading partner. This appears to be a problematic assumption in this study, because ourdataset includes country pairs with large bilateral trade imbalances such as the China-US andJapan-US pairings. Furthermore, the symmetry assumption is inappropriate in our application
because a worsening of the exporting country’s image in the importing country does not necessarily coincide with a worsening of the importing country’s image in the exporting country.
Therefore, we derive the empirical gravity equations without the symmetry assumption.

5

Derivation of the Gravity Equation
Assume that there are N countries and K types of goods in the economy. We denote theexporting country by i and importing country by j, where i, j ∈ {1, . . . , N }. We use thesubscript k ∈ {1, . . . , K} to denote the type of the good.
Let cijτ k be the quantity of the type-k goods exported from country i and consumed bycountry j’s consumers in year τ . We assume that consumers have Cobb-Douglas preferences
for diﬀerent types of goods exported from a given country i, which are represented by parameter∑αik (> 0) with k αik = 1 for all i.
Next, denote the producer price in the exporting country by piτ k and the corresponding
consumer price in the importing country by pcijτ k . We assume that these prices are related bypcijτ k = tijτ k piτ k , where tijτ k is the bilateral trade resistance term for the trade ﬂow of type-k
goods from country i to country j in year τ .
As with Anderson and van Wincoop (2003), we make the Armington assumption and consider a system of CES preferences with respect to the goods produced by diﬀerent countries oforigin. Thus, consumers in country j solve the following maximization problem:
σ

 σ−1
[
] σ−1
σ
∑ 1−σ ∏ αβijσcijτikkmax 

is.t. yjτ =

∑∑
i

k

pcijτ k cijτ k ,(1)kwhere βij is the taste parameter, σ(> 1) the elasticity of substitution across sources of imports,
and yjτ the national income in country j in year τ . Because our model is closed in each year, we
ﬁx the time at year τ and drop the subscript τ for the time being to simplify the presentation.
Denote the aggregate trade resistance for the trade ﬂow from country i to country j by∏Tij ≡ k [tijk ]αik and the combined taste-trade-resistance term by ωij ≡ βij Tij . Solving eq. (1),
we have:1]1−σ[αik yjωij Picijk = c ·
,
pijk
Π0j

where Pi ≡∏−1 αik][pik αik] 1[and Π0j ≡ Σi [ωij Pi ]1−σ 1−σ . (2)kPi is a producer price index for exports from country i and Π0j is the ‘inward’ multilateralresistance for importing country j, which can be considered a consumer price index for its
1

Derivation is provided in Appendix A.1.

6

imports.
Let the world income be yw ≡∑j yj , the income share of country j be sj ≡ yj /yw , and thevalue of type-k goods imported from country i evaluated at the consumer price be Cijk ≡ pcijk cijk .
∑ ∑Using the market-clearing condition, yi = j k Cijk , we have the following equations:2

Π0j =

[
∑
i

c
zijk
≡

[
si

ωij
Π1i

1
]1−σ ] 1−σwith Π1i ≡
[

Cijk
αik
ωij
=
·
yi yj
yw
Π0j Π1i

[
∑
j

]1−σ

[
sj

ωij
Π0j

1
]1−σ ] 1−σ

, and

,

(3)
(4)

c
where zijk
is the trade ﬂow normalized for the size of the two trading countries and Π1i is the‘outward’ multilateral resistance for exporting country i, which can be considered a consumer
price index for its exports. Eq. (4) is the gravity equation disaggregated by the type of goods.
By setting K = 1 (and thus αi1 = 1), assuming taste homogeneity (i.e., βij = βi for all j) andsymmetric bilateral trade barriers (i.e., tij1 = tji1 ), and rearranging the terms, eq. (4) reduces
to the gravity equation eq. (13) in Anderson and van Wincoop (2003).

Econometric Speciﬁcation
As noted earlier, Anderson and van Wincoop (2003) assume that the bilateral trade resistanceis symmetric (i.e., Tij = Tji for all i and j). They make this assumption in view of the factthat the trade ﬂow satisfying eq. (4) can be explained by a set of alternative trade resistanceterms T̃ij ≡ λi θj Tij and price indices P̃i ≡ Pi /λi with exporter- and importer-speciﬁc positive
scalars λi and θj .3 As a result, one cannot empirically distinguish (Tij , Pi ) and (T̃ij , P̃i ).
However, this observation does not imply that asymmetric inference is not possible. The
scaling factors can be absorbed by introducing exporter- and importer-speciﬁc ﬁxed eﬀects, andso long as there are variations in Tij after the control, we can still identify the eﬀect of factorsthat aﬀect trade ﬂows asymmetrically (See also Baier and Bergstrand (2009)).
Anderson and van Wincoop (2003) suggest two approaches to estimate eq. (4) under the2

Derivation is provided in Appendix A.2.
Correspondingly, let ω̃ij ≡ λi θj ωij and deﬁne Π̃0j and Π̃1i as Π0j and Π1i with ωij and Pi replaced by ω̃ij and
[
]1−σ
ω̃ij
αik
c
P̃i in their deﬁnitions, respectively. Then, we can rewrite eq. (4) as zijk = yw · Π̃0 Π̃1
.
3

j

7

i

symmetry assumption, which implies Π0i = Π1i for all i. One approach is to estimate this by anon-linear least squares (NLS) regression. The NLS approach requires solving the equilibrium
M R term Π0 , which in turn requires the observations of the determinants of bilateral trade
resistance for all countries. This is problematic because we do not have country image data fora large number of country pairs (i, j). Therefore, we do not adopt the NLS approach.
The other approach Anderson and van Wincoop (2003) suggest is to model the multilateralresistance Π0 (= Π1 ) as ﬁxed eﬀects. We adopt a similar approach, but because we do notassume symmetry, we distinguish between the importer- and exporter-speciﬁc ﬁxed eﬀects foreach country. This two-way ﬁxed-eﬀects estimation is attractive because we do not need theinformation about the third-party countries not included in the data.
Although a change in country image could conceptually aﬀect the taste parameter βij aswell as the bilateral trade resistance Tij , eq. (4) clearly shows that we cannot distinguish theircchanges separately in trade ﬂows, because they aﬀect zijk
jointly through the combined taste-

trade-resistance term ωij . To operationalize the estimation and simplify the exposition, we
hereafter maintain the following taste-homogeneity assumption: βij = βi for all j (see also
Anderson and van Wincoop (2003)). By making this assumption, we operationally ascribe theeﬀects of country image (and other covariates) to the change in the bilateral trade resistance.4
In the aggregate analysis, this taste-homogeneity assumption is innocuous, because we could
always reinterpret the eﬀect estimate obtained under the assumption as the combined eﬀectof tastes and trade resistance when the assumption is relaxed. In the disaggregate analysis,
however, this equivalence does not hold in some estimation equations, where the eﬀect estimate
identiﬁed can only be interpreted as the the eﬀect of covariates on trade resistance per se.
To derive an estimation equation for eq. (4), we bring back the time subscript τ into theequations. We hypothesize that the trade resistance term can be modelled in the following
manner:[tijτ k = exp −

∑

]
0
0
0 l
,
− δjτ
xijτ − ηiτ
γkl

(5)

l

where xlijτ is the l-th covariate that may aﬀect the trade resistance for the trade ﬂow with00
are speciﬁc to the exporter and the importerand δjτl ∈ {1, . . . , L}. The ﬁxed-eﬀects terms ηiτ4With the assumption βij = βi , the common factor βi in ωij and Π1i cancels out in eq. (4), leaving ωij = Tij .

80
in each year, respectively. We estimate the parameter γkl
or its transformation. In this formu-

lation, the bilateral resistance is determined by the country-speciﬁc factors in importing andexporting countries as well as the bilateral characteristics xlijτ , all of which may vary over time.
Plugging eq. (5) into the gravity equation eq. (4), taking a logarithm, and adding an error
term ϵijτ k and time subscripts, we have the following estimation equation:5cln zijτk =∑∑l

γk′ l αik′ xlijτ + aik + ηiτ + δjτ + ϵijτ k ,

(6)

k′

0
0
where aik ≡ ln αik , ηiτ ≡ − ln ywτ + [σ − 1][ηiτ+ ln Π1iτ − ln βi ], δjτ ≡ [σ − 1][δjτ+ ln Π0jτ ],0and γkl ≡ [σ − 1]γkl. It is straightforward to verify that eq. (6) remains valid if we relax thetaste-homogeneity assumption and replace the LHS of eq. (5) by βijτ tijτ k . Thus, the parameters
can be interpreted generally to incorporate both the eﬀect on taste and on trade resistance, as
argued earlier.
Eq. (6) shows that asymmetric inference is indeed possible, because any scaling factors, λiand θj , will be completely absorbed by the exporter- and importer-speciﬁc dummy variables.
As long as xlijτ varies over ij asymmetrically, the parameter γ can be identiﬁed.
To derive the aggregate estimation equation, we assume that γkl ≡ γl holds for all k. Under
this assumption, we can take a summation of eq. (4) over k to arrive at the following equation:6cln zijτ=∑γl xlijτ + ηiτ + δjτ + ϵijτ .

(7)l

We estimate eq. (7) by a two-way ﬁxed-eﬀects regression. Notice that this estimation equation
is diﬀerent from Anderson and van Wincoop (2003), because the exporter ﬁxed eﬀect η isallowed to be diﬀerent from the importer ﬁxed eﬀect δ for each country. As a result, we have a
total of 2N − 1 dummy variables in a cross-section regression (instead of N − 1 as in Anderson
and van Wincoop (2003)).7
5
To simplify the presentation, we use ϵ as the non-systematic part of the dependent variable. Therefore, ϵin each estimation equation does not necessarily have the same deﬁnition.
6
There are two other ways to derive eq. (7): First,∑simply letting K = 1 and dropping subscripts k, we haveeq. (7). Second, if we let αik = αk for all i and γl ≡ k αk γkl , we again have eq. (7).
7Because the number of importing countries diﬀers from that of exporting countries and because they varyover years in our data, we adjust the number of dummy variables appropriately in the actual estimation.

9

We also verify our aggregate estimates with the ﬁrst-order log-linear approximation to Π0jand Π1i proposed by Baier and Bergstrand (2009), which also allows for asymmetric trade
resistance. To accommodate the panel data structure, we add a year-speciﬁc ﬁxed-eﬀect term.
Therefore, the approximated estimation equation has the following form:8cln zijτ=∑γl x̃lijτ + ιτ + ϵijτ ,(8)lwhere x̃lijτ ≡ xlijτ −∑l
i′ si′ τ xi′ jτ −resistance and ιτ ≡ − ln ywτ +∑∑lj ′ sj ′ τ xij ′ τ is the l-th covariate adjusted for the multilateral

l γl

∑ ∑
i

speciﬁc ﬁxed eﬀect. The adjustment term,l0
j siτ sjτ xijτ + 2[σ − 1] ln Π1τ

∑is modelled as a year-lj ′ sj ′ τ xij ′ τ , measures the average trade resistance(in terms of xl ) for country i’s exports weighted by the income shares of all its trading partners.
∑
The adjustment term for the importing country, i′ si′ τ xli′ jτ , has an analogous interpretation.
Because we do not have observations for all countries, we use the sample average weighted by
the sample income shares.
The log-linear approximation in eq. (8) is particularly useful when the model is extended tothe context of dynamic panel estimation discussed in the Technical Appendix. This is because
the number of regressors in eq. (8) is much smaller than that in eq. (7), which in turn allows us
to avoid having too many moment conditions or arbitrarily dropping some moment conditions.
Estimating the disaggregated gravity equation eq. (6) is not as straightforward as estimating
the aggregate gravity equations (7) and (8), because eq. (6) is a non-linear equation in αik(aik ≡ ln αik ). If we treat ηiτ and δjτ as ﬁxed eﬀects, the number of parameters to be estimatedbecomes too large for estimation methods that involve numerical maximization such as nonlinear least squares and maximum likelihood estimation. Therefore, we adopt the Iterated
Linear Least Squares (ILLS) estimator proposed by Blundell and Robin (1999), which exploits
the conditional linearity of the estimation equation. In our application, when we have a “guess”
of αik , we can treat αik xlijτ as regressors and run a two-way ﬁxed-eﬀects least-squares regression.
This in turn allows us to ﬁnd a new estimate of aik , which allows us to “update” the value of
αik . The iteration continues until convergence is attained.9
8The derivation is given in Appendix A.3.
The asymptotics adopted by Blundell and Robin (1999) do not apply to our data, because the number ofregressors increases as N or T increases. However, the estimator can be justiﬁed as a ﬁrst-order approximationnear the true parameter value. We provide the derivation of the ILLS estimator based on this approach and
910A few observations are worthwhile to note regarding the estimation of eq. (6). First, notethat γ’s have to be estimated jointly across sectors, as the trade ﬂow in one sector depends on
the trade resistance of all sectors. This is in contrast with the typical practice of estimating
the gravity equation separately for each sector. Second, note that the identiﬁcation of γkl ispossible due to the variation introduced by the interaction between the covariates xijτ and theparameters αik , and not due to the variation over k within ijτ combinations, as the systematicpart of that variation would be completely absorbed by aik .
We can avoid the diﬃculty associated with the non-linear estimation of eq. (6) by directly
estimating γ 0 . This can be achieved by exploiting the diﬀerence between the normalized tradepcc
ﬂows evaluated at the producer price (zijτk ≡ zijτ k /tijτ k ) and the consumer price (zijτ k ). Taking

their log diﬀerence gives us the following estimation equation:pc(− ln tijτ k =) ln zijτk − ln zijτ k =

∑

0 l
0
0
γkl
xijτ + ηiτ+ δjτ+ ϵijτ k ,(9)lwhere the dependent variable reﬂects the gap between the producer and consumer prices andmeasures the ‘lack of trade resistance.’ It is clear that by using estimation equation (9), we canonly identify the eﬀect of covariates on bilateral trade resistance, unlike estimation equation(6), whose parameter estimates can potentially be reinterpreted to incorporate eﬀects on tastes
as well. In any case, eq. (9) holds irrespective of the taste-homogeneity assumption.
Finally, we can also derive the following estimation equation by substituting eq. (6) ineq. (9):pln zijτ
k =

∑

0 l
γkl
xijτ + aik + ψijτ + ϵijτ k ,

(10)

l

where ψijτ ≡

∑ ∑
l

0
0
l
k′ γk′ l αik′ xijτ + ηiτ + δjτ + ηiτ + δjτ .

The taste-homogeneity assumption

again is not necessary in deriving this estimation equation, as the ﬁxed eﬀect term ψijτ will
absorb any variation in βijτ . This estimation equation can be estimated with two-way ﬁxed
eﬀects, where the ﬁrst set of ﬁxed eﬀects is determined by the combination of the exporting
country and the type of goods, and the second set by the combination of the exporting country,
importing country, and year. Similar to eq. (9), this estimation equation identiﬁes only theeﬀect of covariates on bilateral trade resistance. On the other hand, because this estimation
present the expressions for the point estimate and variance matrix in Appendix A.4.

11

relies only on z p , and because various errors are absorbed by ψijτ , we expect this speciﬁcation
to be more robust than eq. (9). The shortcoming of the speciﬁcation, however, is that, using00
the ﬁrst type as the reference type, we can only identify the relative impact γkl− γ1l
but not theabsolute impact. We discuss additional issues regarding the estimation of eqs. (6), (9), and (10)in Section 5.

4

Data

We use the United Nations Commodity Trade Statistics Database (UN COMTRADE) for
disaggregated trade ﬂows. It records trade ﬂows at the level of six-digit Harmonized System(HS) product codes. We take the data for aggregate trade ﬂows from the Direction of Trade(DOTS) statistics maintained by the International Monetary Fund. The DOTS dataset is usefulfor aggregate analysis because it ﬁlls in missing data for some country pairs and years withestimates. The trade ﬂows are all expressed in current US dollars. For yi , we use the Gross
Domestic Product also in current US dollars taken from the World Development Indicators.
Following the empirical gravity literature, we have compiled a typical list of proxies for trade
resistance. These include physical and genetic distance between trading partners, their colonial
and language ties, and their status in regional or multilateral trade agreements.
To measure country image, we use the BBC World Opinion Poll (WOP) data for years 20052011. The data contain the positive [negative] response ratio P Sijτ [N Gijτ ], which represents
the proportion of respondents in country j who said at the beginning of year τ that country i
has a mainly positive [negative] inﬂuence in the world. For most records (country-pair-years),
we also have the neutral response ratio N Uijτ and the proportion N Aijτ of respondents who
gave no answer or said “don’t know”.10 We deﬁne the valid response ratio RSijτ as the sum of
the positive, negative, and neutral response ratios.11
As shown in the Data Appendix, the set of evaluating and evaluated countries in the original
WOP data varies substantially over years. We restrict our analysis to a subsample of countries
that appear most frequently to have a stable set of countries. Having a stable set of countries
in the sample helps us to avoid attributing the eﬀects of sampling variations to the covariates
1011

Neutral responses include “neither” and “depends”.
Therefore, RSijτ ≡ P Sijτ + N Gijτ + N Uijτ = 1 − N Aijτ .

12

Table 1: The number of evaluating and evaluated countries and their GDP and population
shares in the restricted sample.
Year
2005
2006
2007
2008
2009
2010
2011

Evaluated Country
Evaluating Country# Country % GDP % Pop # Country % GDP % Pop
6
46.4
29.4
10
66.2
52.5
9
57.7
49.2
10
57.0
51.4
10
56.8
49.2
9
56.5
50.2
12
62.1
55.1
11
63.7
52.0
13
66.1
55.4
9
62.2
48.9
13
66.1
55.3
11
65.2
54.1
13
—
—
11
—
—

in the estimation of eq. (8), because the multilateral resistance terms are approximated by thecorresponding sample averages. The restricted sample consists of the following 13 countries:
Brazil, Canada, China, France, Germany, India, Iran, Israel, Japan, Pakistan, Russia, theUnited Kingdom, and the United States.12 Table 1 shows that the GDP shares for the evaluatedand evaluating countries in the Gross World Product (GWP) are reasonably stable in the
restricted sample. Because only 0.5 percent of records in the restricted sample have zero trade
ﬂows in the DOTS dataset, we ignore the complications due to zero trade ﬂows. Further detailsof the data used in this study are given in the Data Appendix.
Figure 1 provides an overview of the evaluation of countries in the restricted sample. The
left-hand-side [right-hand-side] of the ﬁgure reports the average positive response ratio P S i·τ[N Gi·τ ], where the averages are taken over the evaluating countries j without weights. Noticethat the right axis is in the reverse order to facilitate comparisons. The ﬁgure shows thatcountries like Canada and Germany have a consistently good country image (i.e., the fractionof people who view these countries positively [negatively] is larger [smaller] than other countries) over the observation period. On the other hand, some other countries, such as Iran andPakistan, have a consistently poor country image.
While the ranking of countries in terms of P S i·τ and N Gi·τ are quite stable over time, there
are two notable exceptions, which may be related to the issues discussed in the introduction.
First, the country image of the United States signiﬁcantly improved since 2007. This coincides
12

These countries appear at least 130 times as an evaluating or evaluated country. While North Korea alsosatisﬁes this criterion, we exclude it from our main analysis because we do not have reliable GDP ﬁgures andother key statistics for this country. No other country excluded from the restricted sample satisﬁes this criterion.

13

14

2005

2006

2007

2008

2009

Positive response ratio

2010

2011

PAK

IRN

ISR

RUS

CHN

USA

IND

BRA

FRA

GBR

JPN

CAN

DEU

2005

2006

2007

2008

2009

Negative response ratio
2010

2011

0.75

0.65

0.55

0.45

0.35

0.25

0.15

0.05

Figure 1: Average positive response ratio P S i·τ and average negative response ratio N Gi·τ calculated with the restricted sample.

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

with the change of administration from Bush to Obama. Second, the country image of China
hits the bottom in 2009, after which there is a sign of improvement. The turning point coincides
with a decline in the reported cases of food scandals in China.13
To see how the negative and positive responses are related, we run three-way ﬁxed eﬀect
regressions of P Sijτ and N Gijτ , where the ﬁxed eﬀects are with respect to the evaluated countryi, the evaluating country j, and the time τ . For both evaluating and evaluated countries, wetake the United States as the base country. Figure 2 gives the estimated ﬁxed eﬀects foreach evaluated and evaluating country using the whole sample. The countries that appear
on the right of the vertical axis in Figure 2(a) [Figure 2(b)] tend to receive [give] a higher
positive response ratio than the United States after controlling for the time ﬁxed eﬀect and theevaluating-country [evaluated-country] ﬁxed eﬀect. Similarly, the countries that appear above
the horizontal axis tend to receive [give] a higher negative response ratio than the United States
after controlling for the time ﬁxed eﬀect and the evaluating-country [evaluated-country] ﬁxed
eﬀect.
Figure 2(a) suggests that countries with a high positive response ratio are those with a low
negative response ratio. It also shows that, even after controlling for the evaluating-country
ﬁxed eﬀect and time ﬁxed eﬀect, Canada and Germany have a better country image thanothers, while Iran and Pakistan have a worse country image than others. Note that all the
points in Figure 2(a) are below the negative 45-degree line. This means that people in thesurveyed countries tend to have a more non-neutral (positive/negative) view about the UnitedStates than they do about other countries. This may be because the United States is the mostwell-known country in the world.
In comparison, Figure 2(b) shows that most countries are less positive/negative than the
United States towards other countries. However, some countries such as Finland, Germany,
and Turkey tend to view other countries more negatively than the United States, whereas
other countries such as Afghanistan and Senegal tend to view other countries more positively.
The presence of these ﬁxed eﬀects, however, does not pose a problem to our estimation because
the panel structure of the data allows us to control for them.
13

We have used Factiva to count the number of articles that contain the words “China” and “food scandal”
in major news and business publications. The number was over 150 between 2005 and 2008, but it dropped to
29 in 2009 and stayed below 50 until 2011.

15

16

-0.25

-0.20

-0.15

VEN

0.00
0.00

-0.30

-0.25

-0.20

IND
ZAF
-0.15

-0.10

-0.05

-0.05

KOR

RUS

-0.10

0.05

0.10

BRA

CHN

0.05

FRA

GBR

0.10

CAN

JPN

0.15

DEU

PS
-0.30

PAK

-0.25

JPN

-0.15

ZWE

IND

MEX
HUN

ARG

-0.20

TUR

FRA
0.05

0.10 IRN

KOR

RUS

POL

LKA

SAU

ZAF
PER

-0.30

-0.25

COD
-0.20

SEN

TZA
GHA

CHL
-0.15ARE

THA
ESP BRAGBR
0.00
ITA
AUS
-0.10
-0.05 CAN0.00
0.05
PRT
GRC
CHNISRPHL
IRQ -0.05
LBN
EGY
IDN
-0.10
AZE

FIN
DEU

0.10

AFG

NGA
KEN

NG

NG

0.15

PS

Figure 2: (a) Fixed eﬀects for the evaluated countries (left) and (b) the evaluating countries (right) relative to the United States. The
horizontal and vertical axes are the ﬁxed eﬀects for positive and negative response ratios, respectively.

-0.30

ISR
PRK

IRN
PAK

Evaluating Country

Evaluated CountryIn most of our estimations, we use the positive response ratio P Sijτ as a measure of country
image. This measure has the advantage that it can be readily interpreted as the approximate
percentage change in trade ﬂows in response to a one percentage point increase in P Sijτ . In
Section 5, we consider a few alternative measures of country image and verify that the main
results are robust to the choice of measure.

5

Results

Benchmark Speciﬁcation
In the benchmark speciﬁcation, we include a measure of country image (CIijτ ), the logarithmic
distance between the two trading countries (LDij ), and a constant in the set of regressors,
where CIijτ is the evaluation of country i by country j in year τ . Except for robustness checks,
we set CIijτ = P Sijτ . We favor a parsimonious model, because an additional covariate increases
the number of parameters in eq. (6) by the number of types of goods, which makes the IILS
estimation diﬃcult in the subsequent disaggregate analysis.
Table 2 presents the benchmark estimation results for aggregate trade ﬂows with the restricted sample. The upper and lower panels of the table report the estimation results for
aggregate trade ﬂows based on eqs. (7) and (8), respectively.14 The ﬁxed-eﬀects estimation reported in Column (2a) shows that an increase in positive response ratio by one percentage point
is associated with a 1.5 percent increase in trade ﬂow, which we treat as the baseline estimate.
On the other hand, a one percent increase in distance is associated with a 0.7 percent decrease
in international trade. The impact of the positive response ratio is both statistically and economically signiﬁcant. The results based on the log-linear approximation approach proposed by
Baier and Bergstrand (2009) are similar as reported in Column (2i).
In Columns (2b)-(2g) and (2j)-(2o), we report the estimations of eqs. (7) and (8) separately
for each year. Their results are generally similar across years, although the estimates based oneq. (7) tend to be less signiﬁcant, which is likely because of the large set of dummy variables
used in these regressions. The estimates without including the ﬁxed-eﬀects terms are alsosimilar as reported in Columns (2h) and (2p).
14Standard errors reported in this paper are robust standard errors unless otherwise stated.

17

18

2005-10
2005
2006
2007
2008
2009
2010
2005-10
(2a)
(2b)
(2c)
(2d)
(2e)
(2f)
(2g)
(2h)
1.53∗∗∗ 0.20
1.52∗
1.67∗
1.22
2.23∗∗∗ 2.00∗∗∗ 1.06∗∗∗(0.32)(0.50)(0.81)(0.91)(0.77)(0.83)(0.72)(0.22)
∗∗∗
∗∗∗
∗∗∗
∗∗∗
∗∗∗
∗∗∗
∗∗∗
-0.72
-0.73
-0.91
-0.87
-0.65
-0.60
-0.66
-0.60∗∗∗
LD(0.04)(0.07)(0.11)(0.10)(0.09)(0.09)(0.08)(0.04)
2
R
0.583
0.861
0.629
0.606
0.486
0.542
0.571
0.291
#Obs
577
54
81
83
120
108
131
577
ηi , δjηi , δ j
ηi , δ j
ηi , δjηi , δ j
ηi , δ j
No
Fixed Eﬀects ηiτ , δjτ
Column
(2i)
(2j)
(2k)
(2l)
(2m)
(2n)
(2o)
(2p)
∗∗∗
∗∗
∗∗
∗∗
∗∗
∗∗
∗∗∗
PfS
1.81
1.33
1.74
1.94
1.58
1.91
2.27
1.90∗∗∗(0.30)(0.55)(0.77)(0.90)(0.80)(0.77)(0.59)(0.30)
∗∗∗
∗∗∗
∗∗∗
∗∗∗
∗∗∗
∗∗∗
∗∗∗
g
LD
-0.58
-0.48
-0.65
-0.64
-0.58
-0.51
-0.57
-0.56∗∗∗(0.05)(0.13)(0.12)(0.12)(0.12)(0.11)(0.10)(0.05)
0.265
0.287
0.264
0.241
0.194
0.252
0.317
0.244
R2
#Obs
577
54
81
83
120
108
131
577
Fixed Eﬀects
ιτ
No
No
No
No
No
No
No
Note: Standard errors in parenthesis. ∗ , ∗∗ , and ∗∗∗ indicate statistical signiﬁcance at a 10%,5%, and 1% level, respectively.

Year
ColumnPS

Table 2: Benchmark regression results for aggregate trade ﬂows based on eq. (7) (top) and eq. (8) (bottom). Restricted sample.

One obvious issue with a parsimonious model is that the estimates may suﬀer from omitted
variable biases. Therefore, we also consider controlling for the contiguity of the two trading
countries (CT G), whether the two countries are both island states (ILD), whether the twocountries have a regional trade agreement (RT A), whether the two countries are both in theWorld Trade Organization (W T O), whether the two countries have ever had a colonial link
(COL), whether the two countries share a common oﬃcial language (LN G), whether the twocountries share a common currency (CU R), and the genetic distance between plurality groups
in the two countries (GEN ). These covariates have been used and often found to be signiﬁcantin the empirical trade literature.
In Table 3, we add one covariate at a time to the benchmark speciﬁcation using eq. (8).
None of these variables are consistently signiﬁcant in the year-by-year regressions between 2005
and 2010, whereas P S is signiﬁcant in most years. Further, the inclusion of the additional
covariate does not aﬀect the estimated coeﬃcient on P S much. Using eq. (7) instead of eq. (8)does not alter this conclusion, either.15 Thus, our main ﬁnding that the country image is an
important determinant of trade ﬂows remains valid. Consistent with the existing empiricalliterature, the logarithmic distance is highly signiﬁcant and economically important in almost
all speciﬁcations.
Instead of adding additional covariates one by one, we have also estimated a full model withall the additional covariates. Column (4a) and (4b) in Table 4 present the estimation results
based on eqs. (7) and (8). The estimated coeﬃcient on P S is still signiﬁcantly positive and not
signiﬁcantly diﬀerent from our baseline estimate of 1.5. Thus, even if the country image in thebenchmark model may reﬂect the eﬀects of some of the additional covariates such as common
language and colonial ties, the eﬀect of country image remains economically and statisticallysigniﬁcant after controlling for these additional covariates.

Speciﬁcation Tests and Robustness Checks
The gravity equation eq. (4) implicitly imposes a unit elasticity of trade ﬂow with respect toeach trading country’s income. We relax this restriction by using ln Cijτ as the dependent
15

See Table 9 in Appendix B.

19

Table 3: Estimation of the benchmark speciﬁcation with one additional covariate based oneq. (8). Restricted sample.
Year
PfS
g
LD
]
CT
G
2
R
PfS
g
LD
]
ILD
R2
PfS
g
LD
]
RT
A
R2
PfS
g
LD
^
W
TO
R2
PfS
g
LD
]
COL
R2
PfS
g
LD
]
LN
G
2
R
PfS
g
LD
]
CU
R
R2
PfS
g
LD

2005-10 2005 2006 2007 2008 2009 2010
1.85∗∗∗ 1.23∗ 1.79∗∗ 1.95∗ 1.68∗∗ 2.05∗∗ 2.23∗∗∗-0.59∗∗∗ -0.44∗∗∗ -0.69∗∗∗ -0.65∗∗∗ -0.62∗∗∗ -0.56∗∗∗ -0.56∗∗∗-0.08
0.17 -0.17 -0.03 -0.22 -0.23
0.05
0.266 0.289 0.266 0.242 0.196 0.255 0.317
1.85∗∗∗ 1.35∗∗ 1.74∗∗ 1.95∗∗ 1.66∗∗ 1.93∗∗ 2.31∗∗∗-0.57∗∗∗ -0.47∗∗∗ -0.65∗∗∗ -0.64∗∗∗ -0.57∗∗∗ -0.50∗∗∗ -0.56∗∗∗-0.53∗∗∗ -0.61 -0.11 -0.11 -0.61 -0.69∗∗ -0.69∗∗
0.268 0.293 0.265 0.242 0.198 0.259 0.322
1.84∗∗∗ 1.38∗∗ 1.79∗∗ 2.02∗∗ 1.69∗∗ 1.89∗∗ 2.26∗∗∗-0.61∗∗∗ -0.31 -1.14∗∗∗ -0.76∗∗∗ -0.63∗∗∗ -0.49∗∗∗ -0.57∗∗∗-0.15
0.54 -1.72∗∗ -0.45 -0.24
0.10
0.01
0.266 0.293 0.298 0.248 0.196 0.253 0.317
1.85∗∗∗ 1.56∗∗∗ 1.84∗∗ 1.98∗∗ 1.59∗∗ 2.00∗∗ 2.27∗∗∗-0.59∗∗∗ -0.47∗∗∗ -0.68∗∗∗ -0.66∗∗∗ -0.59∗∗∗ -0.53∗∗∗ -0.58∗∗∗-1.11∗∗∗ 8.01 -0.79∗ -1.03 -1.23∗∗ -1.75∗∗∗ -1.50∗∗∗0.275 0.317 0.274 0.250 0.203 0.277 0.331
1.75∗∗∗ 1.35∗∗ 1.63∗∗ 1.91∗∗ 1.61∗∗ 1.79∗∗ 2.21∗∗∗-0.60∗∗∗ -0.46∗∗∗ -0.68∗∗∗ -0.68∗∗∗ -0.61∗∗∗ -0.54∗∗∗ -0.60∗∗∗
0.26∗∗ -0.06
0.21
0.26
0.43∗ 0.25
0.28
0.269 0.287 0.267 0.245 0.203 0.256 0.321
1.68∗∗∗ 1.44∗∗ 1.70∗∗ 1.80∗ 1.36
1.67∗∗ 2.16∗∗∗-0.58∗∗∗ -0.48∗∗∗ -0.65∗∗∗ -0.65∗∗∗ -0.60∗∗∗ -0.52∗∗∗ -0.58∗∗∗
0.33∗∗ -0.22
0.08
0.28
0.56∗ 0.54∗ 0.44
0.273 0.292 0.265 0.246 0.212 0.273 0.330
1.80∗∗∗ 1.26∗∗ 1.76∗∗ 1.93∗∗ 1.59∗ 1.86∗∗ 2.27∗∗∗-0.57∗∗∗ -0.46∗∗∗ -0.66∗∗∗ -0.64∗∗∗ -0.58∗∗∗ -0.50∗∗∗ -0.57∗∗∗
0.07
0.45 -0.27
0.11 -0.03
0.24
0.01
0.265 0.290 0.265 0.242 0.194 0.253 0.317

1.51∗∗∗ 1.16∗∗ 1.28∗ 1.58
1.22
1.70∗∗ 2.03∗∗∗-0.65∗∗∗ -0.55∗∗∗ -0.72∗∗∗ -0.68∗∗∗ -0.68∗∗∗ -0.57∗∗∗ -0.67∗∗∗^GEN
0.56∗∗∗ 0.77
0.63
0.40
0.60
0.37
0.63∗
R2
0.276 0.323 0.273 0.245 0.205 0.258 0.334
#Obs
577
54
81
83
120
108
131
No
No
No
No
No
No
Fixed Eﬀects ιτNote: ∗ , ∗∗ , and ∗∗∗ indicate statistical signiﬁcance at a 10%, 5%, and
1% level, respectively.

20

Table 4: Speciﬁcation tests and robustness checks. Restricted Sample.
Column
P S∗
LD∗
CT G∗
ILD∗
RT A∗
W T O∗
COL∗
LN G∗
CU R∗
GEN ∗

(4a)
0.98∗∗∗(0.37)-1.07∗∗∗(0.10)-0.74∗∗∗(0.18)
0.09(0.14)-0.03(0.16)-0.77∗∗(0.33)
0.25∗∗(0.12)
0.81∗∗∗(0.13)
0.26(0.16)
0.86∗∗∗(0.15)(4b)
1.29∗∗∗(0.35)-0.89∗∗∗(0.11)-0.43∗∗∗(0.16)-0.34(0.22)-0.20(0.18)-1.35∗∗∗(0.30)
0.32∗∗(0.14)
0.44∗∗∗(0.15)
0.35(0.22)
0.98∗∗∗(0.22)

ln yi
ln yj
Est. Eq.
R2#Obs
Fixed Eﬀects

eq. (7)0.640
577
ηiτ , δjτ

eq. (8)
0.315
577
ιτ

(4c)
1.79∗∗∗(0.30)-0.58∗∗∗(0.05)(4d)
0.63∗(0.33)-0.65∗∗∗(0.03)(4e)
1.74∗∗∗(0.37)-0.73∗∗∗(0.04)

1.05∗∗∗(0.04)
1.00∗∗∗(0.05)
eq. (8)
0.748
577
ιτ

eq. (7)0.811
295
ηiτ , δjτ

eq. (7)0.598
430
ηiτ , δjτ

(4f)
0.77∗∗∗(0.22)-0.64∗∗∗(0.03)

eq. (7)0.848
257
ηiτ , δjτ
LSPijτ
P S ∗ ijτInstrumental Variables
LSPijτ
P S ∗ ijτP Sijτ −1
P Sijτ −1
2
Robust score χ test
0.860
0.378
0.569
0.836
0.880
Robust OIR test
Note: Standard errors in parentheses. ∗ , ∗∗ , and ∗∗∗ indicate statistical signiﬁcanceat a 10%, 5%, and 1% level, respectively. x∗ = x̃ for Columns (4b) and (4c), andx∗ = x for all the other columns. P-value is reported for the robust score χ2 testof endogeneity and robust overidentiﬁcation restriction (OIR) test.

21

variable and adding ln yiτ and ln yjτ as independent variables in eq. (8).16 We ﬁnd that theestimated coeﬃcients on the logarithmic incomes are not signiﬁcantly diﬀerent from unity as
shown in Column (4c). More importantly, the estimated coeﬃcients on P S are robust to theunit-elasticity restriction.17
One potentially important issue in the estimation of eq. (7) is the endogeneity issue. We
address this issue with IV estimations and Granger-causality tests. We identify the following
three time-varying instruments for country image P Sijτ : the leadership support rate LSPijτ ,the country image from the previous year (i.e., P Sijτ −1 ) and the leave-one-out average (i.e.,∑∗
P S ijτ ≡ j ′ ̸=j P Sij ′ τ /(N − 1)).
The leadership support rate LSPijτ measures the rate of approval of country i’s leadershipby country j. The WOP data suggests that the leadership plays an important role for theformation of country image. The negative view towards Iran and Pakistan around the world,
for example, is not because most respondents have ﬁrst-hand experience in these countries but
because of their leadership. While leadership may play a pivotal role in promoting bilateral
trade ﬂows, its impact on trade ﬂow is mainly through country image, a point for which wewill provide some empirical support below. Thus, this variable would qualify as a candidate
for a valid instrument. The country image in the previous year and the leave-one-out averagecan be good instruments because the former captures the recent attitude of country j towards
country i, and the latter the current attitude towards country i in the rest of the world. Bothare likely to be correlated with country j’s current view of country i, but not directly aﬀect thecurrent bilateral trade ﬂow between the two countries.
The two-stage least-squares estimates with some or all of these instruments are reported inColumns (4d)-(4f). We cannot reject the null hypothesis that the country image is exogenousby the robust score χ2 test of endogeneity. In Columns (4e) and (4f), we cannot ﬁnd evidence
that the IV’s used are invalid based on the robust test of overidentifying restrictions. These
results are also consistent with our argument above that the impact of leadership on bilateral
trade ﬂows is mainly through the country image.
16

The estimates based on eq. (7) are not inﬂuenced by this restriction because of the exporter-year- andimporter-year-speciﬁc ﬁxed-eﬀect terms.
17This conclusion also holds when we run the regressions without time-speciﬁc ﬁxed eﬀects or separately foreach year. Details of these regressions are given in Table 11 in Appendix B.

22

Table 5: Alternative measures of country image.
ColumnP Sijτ

(5a)

N N Gijτ

1.55***(0.31)P Sijτ /RSijτ
RSijτE
P Sijτ

(5b)

(5c)

(5d)

(5e)

(5f)

(5g)
(5h)
1.07*** 0.99***(0.33)(0.34)

1.40***(0.29)
0.27(1.26)
1.51***(0.31)P Sij2011

1.87*** 1.85***(0.33)(0.30)
0.36
0.32(0.50)(0.39)∆P Sijτ

0.91***(0.21)
1.25***(0.42)

0.35(0.34)-0.75*** -0.73*** -0.71*** -0.70*** -0.70*** -0.60*** -0.74*** -0.74***
LDij(0.05)(0.05)(0.05)(0.05)(0.04)(0.04)(0.06)(0.06)
#Obs
577
568
577
561
561
561
120
120
2
R
0.584
0.583
0.592
0.594
0.580
0.298
0.892
0.894
Fixed Eﬀects ηiτ , δjτηiτ , δjτ
ηiτ , δjτ
ηiτ , δjτ
ηi , δj , ιτιτηiτ , δjτ
ηiτ , δjτ
Note: Standard errors in parentheses. ∗ , ∗∗ , and ∗∗∗ indicate statistical signiﬁcance at a 10%, 5%,
and 1% level, respectively.
P Sjiτ

In the Technical Appendix, we derive panel Granger-causality tests and a Wald speciﬁcation
test under the assumption of possible autocorrelation in ϵ and reverse causality (i.e., Grangercausality running from the normalized trade to the positive response ratio). Using lag ordersof one, two, and three, we ﬁnd no evidence of reverse causality or misspeciﬁcation.18 In sum,
we ﬁnd no evidence that the benchmark estimates suﬀer from an endogeneity bias.
Finally, we verify our conclusions with alternative measures of country image. In Column (5a), we replace P Sijτ by the non-negative response ratio N N Gijτ ≡ 1 − N Gijτ and ﬁndthat the results are similar to the baseline estimates. It could be argued that those who give anegative response have some familiarity with the evaluated country but those who give no answer do not. Thus, it may make sense to use the conditional positive response ratio P Sijτ /RSijτ
and the response ratio RSijτ separately. However, we ﬁnd that the coeﬃcient on RSijτ is insigniﬁcant as shown in Column (5b). On the other hand, the coeﬃcient on P Sijτ /RSijτ is
18

In contrast, the positive response ratio is found to Granger-cause trade ﬂow when the lag-order is three.

23

positive and signiﬁcant.
Because the country image is measured at the beginning of the year, it may not reﬂect
important events during the year that aﬀect the subsequent trade ﬂows for that year. As arobustness check, we take the next-year positive response ratio P Sijτ +1 as a year-end measureEof country image for the current year P Sijτ(≡ P Sijτ +1 ). As Column (5c) shows, the coeﬃcientE
on P Sijτis signiﬁcant and positive. It is also quantitatively similar to the baseline estimate.

In Column (5d), we decompose the country image into the positive response ratio in year
2011, P Sij2011 , and the deviation from it, ∆P S ≡ P Sijτ − P Sij2011 . They represent the crosssectional and time-series variations in country image, respectively. We choose year 2011 as thebase year because we do not use the trade ﬂow for this year and almost all the country pairs
in our restricted sample have an observation for this year. We ﬁnd that most of the impacts
come from the cross-sectional dimension after controlling for the multilateral resistance.
The results in Column (5d) do not imply that changes in the exporting country’s overall
image have no impact on trade ﬂows, because the exporter-year ﬁxed eﬀect captures the impact
of such changes. To investigate the importance of overall country image, we depart from thetheory and replace ηiτ and δjτ with the importer- and exporter-speciﬁc ﬁxed-eﬀect terms ηiand δj , respectively, and add a year-speciﬁc ﬁxed-eﬀect term ιτ . The estimates under thisspeciﬁcation, which are reported in Column (5e), are very similar to those in Column (5d).
Only when we drop the importer- and exporter-speciﬁc eﬀects does ∆P Sijτ become signiﬁcant
as reported in Column (5f). These results suggest that “country branding” is unlikely to havea discernible impact on trade ﬂows at least in the short run unless the improvement in country
image is substantial.
Thus far, we have assumed that country image matters for the importers but not for theexporters. However, how the exporting country views the importing country may also matter,because the exporters may not wish to deal with the countries they view negatively. To verify
whether this is the case, we include P Sjiτ in the set of regressors, which is the evaluationof importing country j by exporting country i in year τ . However, the challenge is that our
restricted sample is not a balanced sample in the sense that the sets of evaluated and evaluating
countries do not match each year. To make a fair comparison between P Sijτ and P Sjiτ , wefurther restrict our sample to the ﬁve core countries in the WOP dataset—China, France,
24

Russia, UK, and USA—for which we have observations of both P Sijτ and P Sjiτ for eachcountry pair since the beginning of the survey in 2005. As the estimates based on the benchmark
speciﬁcation reported in Column (5g) show, using the sample of ﬁve core countries does notalter our main results. In Column (5h), we report the estimates with both P Sijτ and P Sjiτ
included. The former is statistically signiﬁcant but not the latter, providing no evidence ofserious misspeciﬁcation.
While we have focused on the restricted sample to avoid the confounding eﬀects due tochanges in the set of countries in the sample, our main ﬁnding remains true when the wholesample is used.19 In conclusion, the positive eﬀects of country image on trade ﬂows found in
the baseline estimates are robust and unlikely to be due to reverse causality or inappropriate
choice of the measure of country image.

Disaggregation of the Impacts
We now evaluate the eﬀects of country image allowing for heterogeneous impacts across different types of goods. We ﬁrst consider the classiﬁcation proposed by Rauch (1999). In hisclassiﬁcation, goods with and without a reference price are classiﬁed as homogeneous (HOM )and diﬀerentiated (DIF ) goods, respectively.20 We follow his “conservative” deﬁnition, which
minimizes the number of goods treated as homogeneous goods. Using a conversion table between the four-digit Standard International Trade Classiﬁcation Rev. 2 used by Rauch (1999)
and the six-digit Harmonized System (HS) product codes used in the UN COMTRADE trade
ﬂow dataset, we classify each HS product as homogeneous or diﬀerentiated. Trade ﬂows arethen aggregated by homogeneous and diﬀerentiated goods.
The estimation results with Rauch’s classiﬁcation are reported in Table 6. Columns (6a),(6b), and (6c) are based on estimation equations eqs. (6), (9) and (10), respectively. To estimatepcthese equations, we measure zijτk [zijτ k ] by the FOB [CIF] trade value reported by the exporting[importing] country where k ∈ {HOM, DIF }. Eq. (6) is estimated with the ILLS estimatorand the reported standard errors are based on Blundell and Robin (1999).21 Column (6a) gives
19

See Table 10 in Appendix B.
Rauch (1999) further divides homogeneous goods into those goods that are traded on organized exchanges
and those that are not. Because we did not ﬁnd signiﬁcant diﬀerence between these two categories, we do not
distinguish them in this study. Further discussion on Rauch’s classiﬁcation can be found in the Data Appendix.
21
See also Appendix A.4.
20

25

Table 6: Regression with disaggregation of goods by the homogeneity of goods after Rauch(1999). Restricted Sample.
Column
PS

(6a)
(6b)
(6c)
∗∗∗
∗∗∗
2.66
0.65
(0.24)(0.14)P S × DIF -2.62∗∗∗ -0.42∗∗∗0.43∗(0.31)(0.14)(0.25)-1.11∗∗∗ -0.05∗∗∗LD(0.01)(0.02)∗∗∗LD × DIF
0.82-0.06∗∗∗
0.07∗(0.02)(0.02)(0.04)
Est. eq.
eq. (6) eq. (9)eq. (10)
#Obs
1128
1048
1080
R2
0.659
0.338
0.894
Note: Standard errors in parenthesis.
∗ ∗∗, , and ∗∗∗ indicate statistical signiﬁcanceat a 10%, 5%, and 1% level, respectively.
00
an estimate of γkl (= [σ − 1]γkl) whereas Column (6b) gives an estimate of γkl. Notice also thatthe coeﬃcients based on eq. (10) reported in Column (6c) can be identiﬁed only relative to thereference type, which is the homogeneous goods in Table 6.
Before interpreting the results, it should be pointed out that there are at least ﬁve reasons
why we expect discrepancies across the columns in Table 6 even after accounting for σ − 1.
First, Columns (6a), (6c), and (6b) rely on the CIF trade value, FOB trade value, and thediﬀerence between them, respectively. This means that they are subject to diﬀerent sources ofmeasurement errors. In particular, the estimates in Column (6b) are subject to the reporting
errors in both exports and imports. Such errors could arise not only from the accounting errors
but also from the discrepancy in the timing of reporting. The timing issue may be particularly
important for the estimation of the coeﬃcient on P S because diﬀerent types of goods may havediﬀerent shipping times.
Second, we have derived the estimation equations assuming βij = βi for all j. However, thisassumption may not hold in practice. As noted earlier, it is possible to relax this assumption
when deriving eq. (6) by replacing the left-hand-side of eq. (5) with βijτ tijτ k . However, thisessentially leads to the same estimation equation. As a result, Column (6a) captures the
combined eﬀects of taste and trade resistance. On the other hand, Columns (6b) and (6c) only
capture the eﬀects on trade resistance.
26

Third, the derivation of estimation equations eqs. (6) and (9) relies on the equality of theelasticity of substitution σ across countries.22 However, the elasticity of substitution may varyacross countries in practice, in which case these estimates are biased. On the other hand, theheterogeneity in the elasticity is absorbed by ψijτ in eq. (10). Therefore, eq. (10) is robust to
the potential heterogeneity in the elasticity of substitution.
Fourth, because we do not observe trade values at domestic producer prices in the data,p
we measure zijτk by the FOB trade values. However, the domestic producer prices are likely

to be diﬀerent from the FOB prices in practice. Any discrepancy is particularly worrisome forthe estimation of eq. (9), because the impact of covariates is identiﬁed essentially through thediﬀerences between the FOB and CIF prices.
That said, we believe that eq. (9) is still worth estimating for the following reason. If
exporters ﬁnd it more costly to carry out a transaction with a hostile country (i.e., an importing
country that views the exporting country very negatively), the price diﬀerence between thedomestic producer price and CIF price reﬂects the higher transaction cost. If the FOB pricefully internalizes the transaction costs, our identiﬁcation will not work, because the diﬀerence
between the FOB and CIF prices provides no information about the transaction cost. However,
it is likely that some of the transaction costs (e.g., the cost to ﬁnd and negotiate with trading
partners) are directly borne by the importer, in which case the insurance cost may partially
reﬂect the transaction cost. This is because, in practice, the insured sum typically exceeds the
CIF value of imports to cover indirect costs such as transaction costs and a fraction of theimporter’s expected proﬁt. Thus, it is plausible that the diﬀerence in FOB and CIF prices
reﬂects some of the transaction cost, which allows us to correctly identify the signs of thecoeﬃcients of interest.23
Fifth, estimation equations eqs. (6), (9) and (10) capture in fact diﬀerent sources of variations in trade ﬂow. As mentioned above, eq. (9) captures the price variations. On the other
hand, eq. (10) relies on the quantity variations across types, because the ﬁxed-eﬀects term aik
in eq. (10) captures the variations in domestic producer prices and ψijτ captures the general
22This point can be easily veriﬁed by writing σj instead of σ in eq. (1).
κ c1−κFor example, suppose that the FOB price can be written as pF
for some κ ∈ (0, 1).
ijτ k ≈ [piτ k ] [pijτ k ]0Then, the left-hand-side of eq. (9) is approximately −κ ln t instead of − ln t. We can, therefore, identify κγkl
0
instead of γkl .
23

27

trade resistance between countries i and j in year τ . In contrast, eq. (6) captures both priceand quantity variations across the combination of i, j, and τ .
With these points in mind, let us return to Table 6. While we are not aware of a theory
directly linking country image and the homogeneity of goods, two competing eﬀects can be
expected. On one hand, if we believe that country image reﬂects similarities in tastes or thestrength of informal ties between the two trading countries, country image would give a higher
impact on diﬀerentiated goods than on homogeneous goods. This is because taste similarities
may make the marketing of goods easier, and the informational gaps between two trading
countries may be mitigated by the presence of complementary networks and social capital.
On the other hand, the presence of a reference price may indicate that the buyers of the
product can easily ﬁnd substitutes in the international market. If close substitutes produced
in another country are widely available, the cost of favoring or boycotting the products of aparticular country would be small. In this view, the (positive) impact of P S on diﬀerentiatedgoods is expected to be smaller than on homogeneous goods. Therefore, we can test these twocompeting hypotheses with the data.
In Columns (6a) and (6b), we see that the impact of country image on trade ﬂows is
positive and the impact of distance is negative for homogeneous goods. On the other hand, theimpact of country image on diﬀerentiated goods is small and not signiﬁcant. The trade ﬂow ofhomogeneous goods increases by more than 2 percent when the positive response ratio increases
by one percentage point. In contrast to Columns (6a) and (6b), the impacts of country image
on diﬀerentiated goods are bigger than for homogeneous goods in Column (6c), although thecoeﬃcient on P S × DIF is only marginally signiﬁcant.
Although instructive, the previous analysis may mask a high level of heterogeneity in the
degree of substitutability within each of the two types of goods. Thus, we conduct the analysis
at the six-digit HS level to look further into the relative importance of tastes/networks vis-à-vis
substitutability. We measure the availability of substitutes in the international market for eachsix-digit HS product by the logarithmic number of countries exporting that product, LN Xτ k .
We let LN X interact with P S and LD and use the interaction terms as regressors.
Because we have too many diﬀerent combinations of exporting countries and types at thesix-digit HS level, the estimation of eq. (6) (with aik ) is not feasible. For the same reason, we
28

Table 7: Regression with disaggregation of goods at the six-digit HS level.
ColumnPS(7a)0.58∗∗∗(0.03)
0.03(0.03)

(7b)
(7c)
(7d)
-0.32
(0.23)P S × DIF
0.14
0.53∗∗∗
0.42∗∗∗(0.30)(0.14)(0.13)∗∗∗P S × LN X
0.21
-0.02
(0.05)(0.28)P S × DIF × LN X
-0.04
1.56∗∗∗(0.07)(0.24)
∗∗∗
LD
-0.07
0.03
(0.00)(0.02)LD × DIF-0.09∗∗∗ -0.09∗∗∗
0.00-0.89∗∗∗(0.00)(0.03)(0.02)(0.05)∗∗∗LD × LN X-0.03
0.23∗∗∗(0.01)(0.02)LD × DIF × LN X
0.00
0.41∗∗∗(0.01)(0.03)
Est. eq.
eq. (9) eq. (9) eq. (10)eq. (10)966792 966792 1147775
1147775
#Obs
R2
0.070
0.070
0.110
0.116
∗ ∗∗
∗∗∗
Note: Standard errors in parenthesis. , , andindicate
statistical signiﬁcance at a 10%, 5%, and 1% level, respectively.
slightly modify eq. (10) to enable the estimation. We ﬁrst subtract the mean over j and τ from∑ ∑both dependent and independent variables (e.g., xijτ k − j τ xijτ k [N T ]−1 instead of xijτ k ).
We then regress the mean-subtracted dependent variable on the mean-subtracted independent
variables with error terms clustered by the combination of j and τ .
Table 7 reports the estimation results with goods disaggregated at the six-digit HS level.
Columns (7a) and (7b) based on eq. (9) measure the price eﬀects of P S whereas Columns (7c)and (7d) based on eq. (10) capture the quantity eﬀects of P S. As shown in Columns (7a), thecoeﬃcient on P S is positive and signiﬁcant as with the corresponding aggregate estimate in
Column (6b) in Table 6. However, the estimated coeﬃcient on P S × DIF is not statisticallysigniﬁcant, suggesting that the eﬀect of P S on bilateral trade resistance—as measured by
the diﬀerence between the FOB and CIF prices—does not diﬀer between homogeneous anddiﬀerentiated goods. The distance eﬀects in Column (7a) are similar to those reported inColumn (6b) in Table 6.
In Column (7b), we incorporate the measure of substitutability, LN X, to allow diﬀerent29degrees of substitutability within each broad category of HOM and DIF goods. Note thatthe point estimate (not reported) of the marginal eﬀect of P S, including the main eﬀect andthe interaction eﬀects, is signiﬁcantly positive for an overwhelming majority of observations.
In addition, the estimated coeﬃcient on P S × LN X is positive and signiﬁcant. Hence, whenthe market for a good is served by a larger number of exporters, the price eﬀect of PS on
that good tends to be bigger. Further, the coeﬃcient on P S × DIF × LN X is insigniﬁcant.
This implies that the magnitude of the impact of country image through the price channel isprimarily determined by the availability of substitutes and not by the presence of a reference
price.
In Columns (7c) and (7d), we report the estimation results based on the modiﬁed eq. (10)described above. These columns show that the quantity of diﬀerentiated goods tends to respond
more strongly to country image than homogeneous goods, suggesting the importance of theeﬀects of tastes and network. The availability of substitutes is also an important determinantof the marginal impact of country image on the trade ﬂow for diﬀerentiated goods as theestimated coeﬃcient on P S × DIF × LN X indicates. However, the same does not hold forhomogeneous goods.
In conclusion, the availability of substitutes intensiﬁes the positive eﬀect of country image
on trade. Its eﬀect on diﬀerentiated goods via the quantity channel appears to be larger than on
homogeneous goods, although its price eﬀects are similar regardless of product diﬀerentiation.

6

Conclusion

In this paper, we have investigated the impact of country image on trade ﬂows. The impact
is signiﬁcant both economically and statistically, and this ﬁnding is robust. In the majorityof our estimates for aggregate trade ﬂows, a one percentage point increase in the positive
response ratio in the importing country is associated with a more than one percent increasein the bilateral trade ﬂow. This is at least as large as the impact of a one percent change
in the bilateral distance in most estimates. The impact of country image is mainly due tocross-sectional variations rather than time-series variations.
While our contribution is primarily empirical, we extend the gravity model and disaggregate30

the trade ﬂow by the type of goods and derive three diﬀerent estimation equations consistent
with the theory. Analysis conducted at the six-digit HS level suggests that the substitutabilityof the good in the international market is an important determinant of the marginal impact ofcountry image on trade ﬂow. These ﬁndings are of interest not only to academic researchers
but to country leaders and policy-makers who intend to promote trade ﬂows by improving theircountry image.

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Data Appendix
Country Image
To measure country image, we use the seven rounds of the World Opinion Poll survey data
between 2005 and 2011. The survey was conducted for BBC by GlobeScan and the Programon International Policy Attitudes of the Center for International and Security Studies at the34

University of Maryland. In each country surveyed, about one thousand respondents or moreare interviewed. The respondents are asked to answer whether they think the country to beevaluated is having a mainly positive or mainly negative inﬂuence in the world. Other than“mainly positive” and “mainly negative”, the recorded responses include “depends”, “neither,neutral”, “DK/NA (don’t know or no answer)”, even though these choices are not volunteeredby the interviewer. We treat both “depends” and “neither, neutral” answers as “neutral”
because these two answers are not distinguished from each other for some countries and years.
The exact timing of the survey slightly varies from year to year and from country to country,
but the survey is conducted in less than two weeks in January of the reference year (i.e., yearτ ) or December of the previous year (i.e., year τ − 1) in most cases. In a few cases, the survey
was conducted slightly earlier or later. Therefore, the country image variables in our data referto the country image around the beginning of the year. We exclude non-countries such as theEuropean Union and Central America from the data. The set of countries included in the wholesample is as follows:
Evaluated countries Brazil (2008-2011); Canada (2005-2007, 2009-2011); China (2005-2011);
France (2005-2011); Germany (2008-2011); India (2006-2011); Iran (2006-2011); Israel(2007-2011); Japan (2006-2011); North Korea (2007-2011); Pakistan (2007-2011); Russia(2005-2011); South Africa (2009-2011); South Korea (2010-2011); United Kingdom (20052011); United States (2005-2011); Venezuela (2007).
Evaluating countries Afghanistan (2006); Argentina (2005-2008); Australia (2005-2011);
Azerbaijan (2010); Brazil (2005-2011); Canada (2005-2011); Chile (2005-2011); China(2005-2011); Congo (2006); Egypt (2006-2011); Finland (2006); France (2005-2011);
Germany (2005-2011); Ghana (2006, 2008-2011); Greece (2007); Hungary (2007); India (2005-2011); Indonesia (2005-2011); Iran (2006); Iraq (2006); Israel (2008); Italy(2005-2011); Japan (2005, 2008-2011); Kenya (2006-2011); Lebanon (2005, 2007-2008);
Mexico (2005-2011); Nigeria (2006-2011); Pakistan (2010-2011); Peru (2011); Philippines(2005-2011); Poland (2005-2007); Portugal (2007-2011); Russia (2005-2011); Saudi Arabia(2006); Senegal (2006); South Africa (2005-2006, 2011); South Korea (2005-2008, 20102011); Spain (2005-2006, 2008-2011); Sri Lanka (2006); Tanzania (2006); Thailand (2010);
35

Table 8: The number of evaluating and evaluated countries and their GDP and population
shares in the whole sample.
Year
2005
2006
2007
2008
2009
2010
2011

Evaluated Country
Evaluating Country# Country % GDP % Pop # Country % GDP % Pop
6
46.4
29.4
23
81.7
64.9
9
57.7
49.2
35
74.6
70.7
12
57.2
50.0
27
71.5
65.7
13
62.1
55.5
28
79.8
67.7
15
66.6
56.5
22
75.2
63.1
16
68.3
57.1
27
78.6
70.3
16
—
—
27
—
—

Turkey (2005-2011); United Arab Emirates (2007-2008); United Kingdom (2005-2011);
United States (2005-2011); Zimbabwe (2006).
Table 8 is the same table as Table 1, except that the statistics are calculated for the wholesample. As the table shows, the number of evaluated countries has increased from 6 in 2005 to
16 in 2011. These countries account for about half or more of the Gross World Product (GWP).
The number of evaluating countries is between 22 and 35, and their economies account for atleast 70 percent of the GWP.
We have compiled data from BBC, PIPA and Globescan websites, which include the proportion of “mainly positive” and “mainly negative” answers. Globescan kindly made moredetailed data with “neutral” and “DK/NA” answers available to us. For a small proportion ofrecords, the data compiled from the websites and the data we received from Globescan were
not consistent. In such cases, we used the data from Globescan following its suggestion.

Trade Flow
For disaggregate trade ﬂows at the level of six-digit HS product code, we use the UN COMTRADE data compiled by the United Nations Statistical Division. We downloaded the datafrom the World Integrated Trade Solution (WITS) website24 under the H0 nomenclature (HS1988/1992 version) for years 2005-2010 (See also, Amjadi et al. (2011)). For aggregate tradeﬂows, we use the DOTS data maintained by the International Monetary Fund for 2005-2010.
All the trade statistics are reported in current US dollars.
24

http://wits.worldbank.org.

36

Gross Domestic Product and Other Country-Speciﬁc Variables
The Gross Domestic Product ﬁgures expressed in current US dollars are taken from the World
Development Indicators (WDI) except for Iran for 2010, for which WDI data are not available
and thus the estimate published in the CIA Factbook is used instead. GDP ﬁgures in the WDI
and CIA Factbook are generally close.

Classiﬁcation of Goods
We follow Rauch (1999) for the “conservative” deﬁnitions of homogeneous and diﬀerentiatedgoods.25 We downloaded the goods classiﬁcation data from Jon Haveman’s website.26 Because
the downloaded data gives the categorization of goods for four-digit Standard International
Trade Classiﬁcation Rev. 2 (SITC2) codes, we link the six-digit HS product codes to the fourdigit SITC2 codes. We have dropped from our analysis those records for which we could not
ﬁnd Rauch’s categorization. They account for about 13 percent of the total trade ﬂow in the
restricted sample.

Bilateral Resistance Data
The WTO membership data was compiled from the WTO website.27 We assume that thecountry is a WTO member in year τ if the country joined the WTO in or before June of year τ .
For the currency union and regional trade agreement data, we downloaded data compiled by
de Sousa (2012) from his website.28 The downloaded currency union data primarily comefrom Appendix B of Glick and Rose (2002) but are updated to include the Euro currency.
The regional trade agreement data are derived from Table 3 of Baier and Bergstrand (2007),Frankel (1997), and the Regional Trade Agreements Information System (RTA-IS) of the World
Trade Organization.29 Because the original downloaded data reﬂect RTAs until 2008, we haveupdated the data using the current RTA-IS. The resulting data we use for our analysis containcurrency union and RTA information up to year 2010.
25

Using “liberal” deﬁnitions does not change our results much.
http://www.macalester.edu/research/economics/PAGE/HAVEMAN/Trade.Resources/TradeData.html.
27
http://www.wto.org/english/thewto_e/whatis_e/tif_e/org6_e.htm.
28
http://jdesousa.univ.free.fr/data.htm.
29
http://rtais.wto.org/UI/PublicAllRTAList.aspx

26

37

A number of bilateral-resistance variables were obtained from the website of CEPII,30 aFrench research center in international economics. The downloaded data include contiguity,
distance, language, and colonial link. The distance is based on the great circle formula, whichuses latitudes and longitudes of the most important cities in the two countries. The genetic
distance data after Spolaore and Wacziarg (2009) are downloaded from Romain Wacziarg’s
website.31 We use FST distance between the plurality ethnic groups in each of the two countries.
We normalize it by dividing it by one thousand.

Support of Leadership
The GALLUP Worldview data includes the data on the approval of leadership in major
economies including China, France, Germany, Japan, Russia, United Kingdom, and UnitedStates for years 2006-2010. Each respondent is asked whether he approves or disapproves of
the job performance of the leadership of each of these seven evaluated countries. The GALLUP
Worldview covers a large number of evaluating countries and about one thousand people areinterviewed in most countries.

Technical Appendix
In Section 3, we have implicitly assumed that the covariates x and the error term ϵ are uncorrelated. However, this assumption is violated when reverse causation and autocorrelation in
the error term are simultaneously present. Hence, we now allow for the possibility of reverse
causation and autocorrelation, and develop Granger-causality tests and a Wald speciﬁcation
test. Because we have a short panel dataset, we keep the model structure as simple as possible.
We hereafter assume that the ﬁrst covariate is a measure of country image CIijτ ≡ x1ijτ .
Because the country image is likely to be persistent and to depend on the characteristics speciﬁcto the country pair and possibly on the year-speciﬁc ﬁxed eﬀect, we adopt the following modelthat allows for reverse causation:

c
c
c
1
c
CIijτ = Rz1 ln zijτ
−1 + Rc CIijτ −1 + υij + ϕτ + νijτ ,30
31

http://www.cepii.fr/anglaisgraph/bdd/distances.htm
http://www.anderson.ucla.edu/faculty_pages/romain.wacziarg/papersum.html

38

(11)

where Rz1 and Rc1 are parameters, υijc is a country-pair-speciﬁc ﬁxed eﬀect, ϕcτ is a year-speciﬁcc
ﬁxed eﬀect, and νijτ
is an independently and identically distributed error term. We assumezthat ϵijτ follows an AR(1) process such that we have ϵijτ = ρϵijτ −1 + νijτ
. We further assume

that ν and ϵ are uncorrelated with each other regardless of their subscripts and superscriptszz
except that E[ϵijτ νijτ
] = V [νijτ] ̸= 0. Under these simple but plausible assumptions, we have

E[CIijτ · ϵijτ ] = Rz1 ρ. Therefore, when both reverse causality (i.e., Rz1 ̸= 0) and autocorrelation(i.e., ρ ̸= 0) are present, we will have a biased estimate of γ.
Suppose now that xlijτ for l ≥ 2 is time-invariant. Then, the following form of augmented
dynamic panel equations can be obtained from the AR(1) process of ϵijτ and eqs. (8) and (11):cln zijτ=H∑crzh ln zijτ
−h +

H
∑

h=1

CIijτ =

H∑h=0c
Rzh ln zijτ−h +zf ijτ −h + υijz + ϕzτ + νijτ
rch CI

h=0
H∑c
Rch CIijτ −h + υijc + ϕcτ + νijτ
.

(12)

(13)

h=1

We estimate these equations piecewise by the one-step dynamic-panel-data system estimator(DPD-SYS) derived by Blundell and Bond (1998). This estimator builds on the dynamic-paneldata diﬀerence (DPD-DIF) estimator proposed by Arellano and Bond (1991) and extends theidea to use lagged diﬀerences as instruments for the level equation originally proposed by
Arellano and Bover (1995). We adopt the DPD-SYS estimator in this paper because the DPDDIF estimator tends to perform poorly when the panel ﬁxed-eﬀect term υij has a much larger
variance than the idiosyncratic error term νijτ .
Eqs. (12) and (13) suggest panel Granger-causality tests, in which the rejection of the null
hypothesis H0 : Rzh = 0 [rch = 0] for all h ∈ {0, . . . , H} is taken as the evidence for the presenceof Granger causality running from the normalized trade [the country image] to the country
image [the normalized trade]. In addition, the following equation holds under the assumptions
stated above:rc0 rz1 + rc1 = 0
We use this equation for the Wald speciﬁcation test.32

32

The derivation of eqs. (12), (13), and (14) are provided in Appendix A.5.

39

(14)

A

Derivation of Equations

A.1

Derivation of eq. (2)Let Vij ≡∏αikk cijk and Wj ≡

∑

1−σ

σ−1

σ
σ
. We can form the Lagrangian Lj for eq. (1) asi βij Vij

follows:[σ
σ−1

Lj ≡ Wj+ µ yj −∑∑i]pik tijk cijk ,

(15)

k

where µ is the Lagrange multiplier. Taking the ﬁrst order conditions, we have:
1
1−σ
σ−1
∂Lj
= Wjσ−1 βijσ Vij σ αik /cijk − µpik tijk = 0.
∂cijk

(16)
σ

Multiplying eq. (16) by cijk , summing over i and k, and solving for µ, we have µ = Wjσ−1 /yj .
Plugging this in eq. (16) and solving for cijk , we have:
1−σ

σ−1

αik yj βijσ Vij σcijk =.
Wj pik tijk

(17)

Therefore, substituting this in the deﬁnition of Vij and solving for Vij , we have:yjσ βij1−σ
Vij = σ σ σ .
Wj Tij Pi(18)

Substituting this in the deﬁnition of Wj , we have:

Wj = y

σ−1
σ

[
∑

]1/σ
[βij Tij Pi ]1−σ

(19)

i

Plugging eqs. (18) and (19) into eq. (16), we have:cijk =

αik yj[βij Tij Pi ]1−σ.
·∑
1−σpik tijk
i′ [βi′ j Ti′ j Pi′ ]Applying the deﬁnition of Π0j , we have eq. (2). 

40

(20)

A.2

Derivation of eqs. (3) and (4)Because the derivation is similar to Anderson and van Wincoop (2003), we only describe the∑main steps. Using k αik = 1, eq. (2), and the deﬁnitions of pcijk , Cijk , and Π1i , we have:

yi =

∑∑
j

Cijk =∑∑jk[
αik yjk

ωij PiΠ0j]1−σ= Pi1−σ

∑
j

[
yj

ωij
Π0j

]1−σ

[ ]1−σ= Pi1−σ yw Π1i. (21)

Solving this for Pi1−σ and plugging in the deﬁnition of Π0j and eq. (2), we have eqs. (3)and (4), respectively. 

A.3

Derivation of eq. (8)By the Maclaurin approximation of eq. (3) with respect to ln ωijτ and ln Π1iτ , we have:ln Π0jτ =

1
ln
1−σ

[
∑

]
∑ [
[
]
]
siτ exp (1 − σ) ln ωijτ − (1 − σ) ln Π1iτ ≃siτ ln ωijτ − ln Π1iτ .

i

i

(22)
Similarly, we have ln Π1iτ ≃∑0j sjτ [ln ωijτ − ln Πjτ ].

It is straightforward to verify that thefollowing expressions give a solution to these equations:ln Π0jτ =∑siτ [ln ωijτ − ln ωi1τ ] + ln Π01τ for j ≥ 2,iln Π1iτ =∑jsjτ [ln ωijτ + ln ωj1τ ] −∑∑i′si′ τ sjτ ln ωi′ jτ + ln Π01τ for i ≥ 2,

(23)
(24)

j

where ln Π01τ is arbitrarily chosen as the numeraire and ln Π11τ follows its approximate expressions above. Because the dataset used by Baier and Bergstrand (2009) does not have a time
dimension, they adopt the following normalization: ln Π01 = 0. However, this normalization isinappropriate in our case because the prices may vary over time. Plugging eqs. (23) and (24)into eq. (7) and using eq. (5), we have eq. (8). Eq. (8) shows that the variation of ln Π01τ overtime can be modelled as a year-speciﬁc ﬁxed eﬀect. 

41

A.4

Derivation of the ILLS estimator

We provide here an alternative derivation of the ILLS estimator originally proposed by Blundell
and Robin (1999). This is necessary because the number of regressors increases as N and T
increase and thus their asymptotics do not apply to our problem. We assume that the regressors
are ﬁxed and examine the properties of the ﬁrst-order approximation of the estimator.
It is convenient to express eq. (6) in a matrix form. To this end, we introduce some additional
notations. First, let us denote an N (K − 1)-vector of aik by a ≡ [a11 , . . . , aN (K−1) ]T , whereaiK is omitted for all i. Similarly, we denote the parameter vectors for γkl , ηiτ , and δjτ byγ ≡ [γ11 , . . . , γKL ]T , η ≡ [η11 , . . . , ηN T ]T , and δ ≡ [δ11 , . . . , δ(N −1)T ]T , respectively. Let α be an
N ×K-matrix whose (i, k)-element is αik and X be an N 2 T ×L-matrix whose (iN T +jT +τ, l)element is xlijτ . Because there is a bijective relationship between a and α, we can writeα(a). We let the vector forms of the logarithmic normalized trade ﬂow and error terms be
Z ≡ [ln z1111 , . . . , ln zN N T K ]T . ϵ ≡ [ϵ1111 , . . . , ϵN N T K ]T , respectively.
We denote the n × n identity matrix by I n , and n-column vectors of zeros and ones by
0n and 1n , respectively. We use ◦ and ⊗ to denote the elementwise and Kronecker products,
respectively. Using these notations, we deﬁne the following matrices:








 I K−1  I N −1 
G1 ≡ I N ⊗1N T ⊗  , G3 ≡ I N ⊗1N K ⊗I T , and G4 ≡ 1N ⊗  ⊗1K ⊗I T , (25)
T
T
0K−1
0N −1
where G1 is an N 2 T K × N (K − 1)-matrix of the exporter-type-speciﬁc dummy variables, G3
an N 2 T K × N T -matrix of the exporter-year-speciﬁc dummy variables, and G4 an N 2 T K ×(N −1)T -matrix of the importer-year-speciﬁc variables. Further let G2 (a) ≡ [α(a)⊗1N T K×L ]◦[1TK ⊗ X ⊗ 1K ] be an N 2 T K × KL-matrix of regressors for γ. Then, the parameter vectorθ ≡ [aT , γ T , η T , δ T ]T has a dimension of D ≡ N (2T +K −1)−T +KL. Letting the N 2 T K ×D
design matrix be G(θ) ≡ [G1 G2 (a) G3 G4 ], we can rewrite eq. (4) as follows:

Z = G(θ)θ + ϵ.

(26)

As with Blundell and Robin (1999), we assume that E[ϵϵT ] = I N 2 T ⊗ Ω, where Ω is a K ×

42

K variance matrix for the vector εijk ≡ [ϵijτ 1 , . . . , ϵijτ K ]T of idiosyncratic terms for a given
combination of i, j, and τ .
Eq. (26) is linear in θ when G(θ) is taken as given. Therefore, if we have an estimate θ̂(q)of θ in the qth round of iteration, we can update it by the ordinary least squares such that
θ̂

(q+1)

(q)

(q)

(q)

(q)

(q)

← [GT (θ̂ )G(θ̂ )]−1 GT (θ̂ )Z, assuming [GT (θ̂ )G(θ̂ )] is a non-singular matrix.

When the iteration converges, the ILLS estimator θ̂ satisﬁes θ̂ = [GT (θ̂)G(θ̂)]−1 GT (θ̂)Z.
Now, let us deﬁne the estimate of residual by ϵ̂ ≡ Z − G(θ̂)θ̂. Then, premultiplying thisequation by −GT (θ̂)/N 2 T K, we have:−GT (θ̂)ϵ̂−[GT (θ̂)G(θ̂)][GT (θ̂)G(θ̂)]−1 GT (θ̂)Z + [GT (θ̂)G(θ̂)]θ̂
F (θ̂) ≡== 0D .
N 2T KN 2T K(27)

We let ed be a D-vector whose dth element is one and all the other elements are zero. Then,
we can write the derivative of F (θ̂) as follows:∂F (θ̂)
∂ θ̂

T
θ̂=θ

[
] ]
D [
T
∑
∂G(θ)
∂G
(θ)
1
· GT (θ)G(θ) +GT (θ)
θ−ϵ ed=
N 2T K
∂θ d
∂θ d
d=1
[
] ]
D [
∑
1
∂G(θ)
≃
· GT (θ)G(θ) +GT (θ)
θ ed ≡ B(θ),
N 2T K
∂θ
d
d=1

(28)

where the approximation in the last line is justiﬁed by the Law of Large Numbers provided
N 2 T K is suﬃciently large and some regularity conditions are satisﬁed. Using this and theﬁrst-order approximation of F (θ̂) around the true parameter value θ in eq. (27), we have:
F (θ) + B(θ)(θ̂ − θ) ≃ 0D , or θ̂ ≃ θ − B −1 (θ)F (θ) = θ + B −1 (θ)GT (θ)ϵ.

(29)

Taking an expectation and variance, we have:E[θ̂] ≃ θ(30)

V [θ̂] ≃ B −1 (θ)GT (θ)[I N 2 T ⊗ Ω]G(θ)B −T (θ)(31)

We estimate Ω by Ω̂ = [N 2 T ]−1

∑ ∑ ∑
i

j

T
τ ε̂ijτ ε̂ijτ , where ε̂ijτ is constructed from the regression

residuals. Then, V [θ̂] can be estimated by V̂ [θ̂] ≡ B −1 (θ̂)GT (θ̂)[I N 2 T ⊗ Ω̂]G(θ̂)B −T (θ̂). 

43

A.5

Derivation of eqs. (12), (13), and (14)c
Let us ﬁrst consider the case where H = 1. Then, by adding Rz0 ln zijτto eq. (11), we haveeq. (13).
∑
Now, let ξ˜ij ≡ l≥2 γl x̃ij . Notice that we do not need the subscript τ here because of theassumption that xlijτ is time-invariant for l ≥ 2. Using this and AR(1) process of ϵ, we cantransform eq. (8) as follows:czf ijτ + ξ˜ij + ιτ + ρϵijτ −1 + νijτ
ln zijτ= γ1 CIz˜
f ijτ + ξ˜ij + ιτ + ρ[ln z cf= γ1 CIijτ −1 − γ1 CI ijτ −1 − ξij − ιτ −1 ] + νijτ

(32)
(33)

By letting rz1 ≡ ρ, rc0 ≡ γ1 , rc1 ≡ −ργ1 , υijz ≡ (1 − ρ)ξ˜ij , and ϕzτ ≡ ιτ − ριτ −1 , we have eq. (12).
The Wald speciﬁcation test equation eq. (14) directly follows from these deﬁnitions. WhenH ≥ 2, we simply need to add higher-order lag terms. 

BAdditional Tables

Table 9 shows the benchmark estimation results with one additional covariate based on eq. (7).
Table 10 is the whole-sample version of Table 2. We only present the results based on eq. (7),because the estimation of eq. (8) is vulnerable to changes in the set of countries in the sample.
Table 11 provides additional regression results without the restriction of unit elasticity of trade
ﬂow with respect to each trading country’s income.

44

Table 9: Estimation of the benchmark speciﬁcation with one additional covariate based oneq. (7). Restricted sample.
Year
2005-10 2005 2006 2007 2008 2009 2010PS
1.72∗∗∗ 0.10
1.66∗ 1.85∗ 1.41∗ 2.63∗∗∗ 2.16∗∗∗
∗∗∗
∗∗∗
LD
-0.80 -0.70 -1.01∗∗∗ -0.97∗∗∗ -0.72∗∗∗ -0.72∗∗∗ -0.71∗∗∗
CT G-0.35∗∗ 0.15 -0.50 -0.46 -0.41 -0.58 -0.23
R2
0.588 0.862 0.637 0.613 0.491 0.555 0.573
PS
1.55∗∗∗ 0.21
1.51∗ 1.66∗ 1.29
2.25∗∗∗ 2.02∗∗∗LD-0.72∗∗∗ -0.72∗∗∗ -0.91∗∗∗ -0.87∗∗∗ -0.63∗∗∗ -0.59∗∗∗ -0.65∗∗∗
ILD-0.30∗∗ -0.35
0.11
0.02 -0.47 -0.40 -0.47∗∗
R2
0.584 0.863 0.629 0.606 0.488 0.544 0.574
PS
1.53∗∗∗ 0.14
1.69∗ 1.73∗ 1.15
2.20∗∗ 1.98∗∗∗
LD-0.72∗∗∗ -0.87∗∗∗ -1.44∗∗∗ -0.94∗∗∗ -0.62∗∗∗ -0.56∗∗∗ -0.65∗∗∗
0.00 -0.43 -1.84∗∗ -0.25
0.13
0.18
0.06
RT A
2
R
0.583 0.864 0.658 0.608 0.486 0.543 0.572
PS
1.53∗∗∗ 0.20
1.53∗ 1.66∗ 1.21
2.26∗∗∗ 1.98∗∗∗-0.73∗∗∗ -0.73∗∗∗ -0.91∗∗∗ -0.87∗∗∗ -0.65∗∗∗ -0.61∗∗∗ -0.67∗∗∗
LD
WTO-0.35 -0.64∗∗∗ -0.14
0.09 -0.28 -0.87 -0.90
2R
0.584 0.861 0.629 0.606 0.486 0.547 0.576
PS
1.42∗∗∗ 0.09
1.30∗ 1.62∗ 1.28
2.08∗∗ 1.91∗∗∗-0.76∗∗∗ -0.77∗∗∗ -0.95∗∗∗ -0.91∗∗∗ -0.69∗∗∗ -0.63∗∗∗ -0.69∗∗∗LD
COL
0.33∗∗∗ 0.20
0.38
0.35
0.53∗∗ 0.28
0.27
2R
0.589 0.864 0.635 0.612 0.498 0.546 0.575
PS
1.16∗∗∗ -0.03
1.14
1.26
0.82
1.72∗∗ 1.77∗∗
∗∗∗
∗∗∗
∗∗∗
∗∗∗
∗∗∗
LD
-0.74 -0.74 -0.92 -0.89 -0.67 -0.62∗∗∗ -0.67∗∗∗
LN G
0.74∗∗∗ 0.40
0.60∗ 0.67∗ 1.03∗∗∗ 0.80∗∗∗ 0.71∗∗∗
R2
0.613 0.873 0.644 0.628 0.542 0.581 0.602
PS
1.55∗∗∗ 0.20
1.61∗ 1.71∗ 1.20
2.22∗∗ 2.00∗∗
∗∗∗
∗∗∗
∗∗∗
∗∗∗
∗∗∗
LD
-0.73 -0.73 -0.93 -0.88 -0.64 -0.60∗∗∗ -0.66∗∗∗
CU R
-0.11
0.00 -0.64 -0.26
0.07
0.06 -0.02
2R
0.583 0.861 0.631 0.607 0.486 0.542 0.571
PS
1.32∗∗∗ 0.18
1.32
1.52
0.98
2.07∗∗ 1.84∗∗
∗∗∗
∗∗∗
∗∗∗
∗∗∗
∗∗∗
LD
-0.79 -0.79 -0.95 -0.90 -0.71 -0.66∗∗∗ -0.75∗∗∗GEN
0.41∗∗∗ 0.45∗∗ 0.31
0.18
0.33
0.30
0.50∗
R2
0.588 0.870 0.630 0.607 0.488 0.545 0.579
#Obs
577
54
81
83
120
108
131
Fixed Eﬀects ηiτ , δjτ ηi , δj ηi , δj ηi , δj ηi , δj ηi , δjηi , δj
Note: ∗ , ∗∗ , and ∗∗∗ indicate statistical signiﬁcance at a 10%, 5%, and
1% level, respectively.

45

46

2005-10
2005
2006
2007
2008
2009
2010
2005-10
(10a)
(10b)
(10c)
(10d)
(10e)
(10f)
(10g)
(10h)
∗∗∗
∗∗
∗∗∗
0.71
0.35
0.58
0.31
0.64
0.95
1.30
1.21∗∗∗(0.21)(0.67)(0.49)(0.54)(0.46)(0.44)(0.48)(0.14)
∗∗∗
∗∗∗
∗∗∗
∗∗∗
∗∗∗
∗∗∗
∗∗∗
-0.86
-0.87
-1.03
-1.00
-0.79
-0.77
-0.75
-0.69∗∗∗
LD(0.04)(0.10)(0.10)(0.14)(0.09)(0.10)(0.08)(0.03)
R2
0.505
0.661
0.453
0.516
0.504
0.491
0.506
0.215
1694
129
285
287
321
297
375
1694
# Obs
Fixed Eﬀects ηiτ , δjτηi , δ j
ηi , δ j
ηi , δjηi , δjηi , δjηi , δjNo∗ ∗∗∗∗∗Note: Standard errors in parenthesis. , , andindicate statistical signiﬁcance at a 10%,5%, and 1% level, respectively.

Year
ColumnPS

Table 10: Benchmark regression results for aggregate trade ﬂows based on eq. (7). Whole sample.

47

2005
2006
2007
2008
2009
2010
2005-10
(11a)
(11b)
(11c)
(11d)
(11e)
(11f)
(11g)
∗∗∗
∗∗∗
∗∗∗
∗∗∗
∗∗∗
∗∗∗
∗∗∗
0.71
1.06
0.99
1.11
1.08
1.06
1.06
ln yiτ
(0.11)(0.16)(0.11)(0.08)(0.08)(0.07)(0.04)
∗∗∗
∗∗∗
∗∗∗
∗∗∗
∗∗∗
∗∗∗
1.18
1.20
1.13
0.86
0.92
0.97
0.99∗∗∗
ln yjτ
(0.13)(0.15)(0.20)(0.09)(0.15)(0.08)(0.05)
∗∗∗
∗∗
∗∗
∗∗
∗∗
∗∗∗
1.60
2.09
2.05
1.64
1.86
2.24
1.86∗∗∗
PfS(0.56)(0.83)(0.88)(0.79)(0.73)(0.58)(0.30)
∗∗∗
∗∗∗
∗∗∗
∗∗∗
∗∗∗
∗∗∗
-0.49
-0.66
-0.64
-0.58
-0.52
-0.57
-0.56∗∗∗
gLD(0.13)(0.12)(0.13)(0.11)(0.11)(0.10)(0.05)
R2
0.756
0.716
0.715
0.756
0.766
0.794
0.741
#Obs
54
81
83
120
108
131
577
∗∗
0.291
0.813
0.126
0.554
0.655
0.376
P (ln yiτ = ln yjτ = 1) 0.020
Fixed Eﬀects
No
No
No
No
No
No
No
∗ ∗∗
∗∗∗
Note: , , andindicate statistical signiﬁcance at a 10%, 5%, and 1% level, respectively.

Year
ColumnTable 11: Benchmark regression results without the unit-income-elasticity constraints based on eq. (8). Restricted Sample.