18
* † * †
Diplomacy for sale? The impact of bilateral visits
on international trade
Emmanuelle Lavallée∗ Julie Lochard†
Very preliminary and incomplete. Please do not quote.
July 2016
Author Keywords: International Trade; Economic diplomacy.
JEL classi�cation codes: F10; F14; F50.
∗Université Paris-Dauphine, LEDa, UMR DIAL. Email: [email protected].†Erudite, University of Paris-Est Créteil Val de Marne. Email: [email protected].
1
1 Introduction
For a long period of time, international exchanges have been studied in isolation from their political
underpinnings. However, recent evidence points to a connection between international politics and
trade. Several papers emphasize the relationship between intra-state or inter-state wars and trade
(e.g. Martin et al., 2008). Foreign in�uence might also impact international trade. For instance,
Berger et al. (2013) show that increased political in�uence, due to CIA intervention during the
Cold War, was used to create a larger foreign market for American products. More generally,
diplomatic relations and the presence of embassies and consulates also promote trade (Rose, 2007).
Bilateral visits of state representatives are the privileged moments of bilateral exchanges. During
o�cial visits, di�erent issues might be discussed (political, environmental, etc.) but commercial and
economic issues are frequently a primary concern. This is illustrated with a very recent controversy
when the French President awarded the Legion d'Honneur to the crown prince of Saudi Arabia
during a visit in Paris for his �e�orts in the �ght against terrorism and extremism�, while at the
same time France was negotiating major arms deals. Therefore, trade might expand after a visit.
Indeed, Nitsch (2007) �nds that state and o�cial visits increase bilateral exports by 8 to 10%.
In this paper, we intend to evaluate empirically the impact of bilateral visits on trade. Our main
contributions are threefold. First, we compute an original database that gathers more than 13,000
visits of French o�cials abroad and of foreign o�cials in France over the period 1977-2007. These
data encompass bilateral visits at the level of Head of State (government), Minister, Secretary
of State, but also advisers and uno�cial visits. Our dataset is much broader than the one used
in previous studies. For instance, Nitsch (2007) considers only state visits which amount to 558
external visits on the period 1948-2003. Second, we also evaluate the impact of bilateral visits on
sectoral trade to assess which sectors are mostly a�ected by bilateral visits. We further show that
the increase in exports following a bilateral visit is greatest for complex goods. Finally, this paper
also aims at disentangling the underlying mechanisms. We argue that bilateral visits might a�ect
international trade through two di�erent channels. First, on the occasion of visits, treaties and
public contracts might be signed with governments' representatives. Second, during o�cial visits,
state representatives are often accompanied by a delegation of business managers. These private
sector managers might use the time of the visit to develop their networks, sign new contracts, etc.
Our results seem to support both channels. We also show that the e�ect of bilateral visits on trade
does not only rely on political and cultural proximity. In particular, the increase in trade following
a bilateral visit is similar, whether the country has a long history of close relationships (through a
colonial relationship) or not.
The paper is structured as follows. In section 2, we present our data on bilateral visits and
our empirical strategy. Then, in section 3, we discuss empirical evidence on the e�ect of bilateral
2
visits on aggregate and sectoral trade. We summarize our �ndings and add concluding remarks in
section 4.
2 Data and empirical strategy
2.1 Bilateral visits
We gather original data on bilateral visits between France and 187 countries over the 1977-2007
period from the French Ministry of Foreign A�airs. Our data cover bilateral visits at the level of
Head of State (government), Minister, Secretary of State, but also advisers and uno�cial visits.
These information are taken from the database �Evènements de politique internationale� of the
French Ministry of Foreign A�airs that records since 1977 quasi exhaustively all signi�cant events
in France's international relations.Thanks to these data, we know how many time a year a French
President meets its German counterpart, or how many time a French minister meets his African
counterparts. It is worth noting that these data also record private visits. For instance, we know if
on the occasion of a personal trip to France, Omar Bongo (President of Gabon from 1967 to 2009)
meets Jacques Chirac (French Prime Minister from to 1986 to 1988; and French President from
1995 to 2007). Bilateral visits are divided into visits of French o�cials abroad (external visits)
and visits of foreign o�cials in France (foreign visits). These data also allow to account for the
rank of the o�cials. We distinguish between three di�erent levels: Head of State or government
(Presidents, Prime Ministers, Chancellors, Kings or Queens), the mid-level o�cials (Ministers,
Secretaries of State, Speakers of National Assemblies, etc.) and Presidents' advisors.1
In total, we record 13,207 bilateral visits among which almost 60% were exterior to France
(i.e. were external visits). One quarter involve top-level o�cials that is to say Presidents, Prime
Ministers, Kings or Queens and so on; 72% involve mid-level o�cials (Ministers, etc.) and only 1%
President's advisors (195 bilateral visits over 13,207). Our data provide much more information
than other papers focusing on the impact of state visits. For instance, Nitsch (2007) uses a sample
of 558 o�cial visits by French Presidents on the time period 1948-2003 (along with visits by Heads
of State of Germany and the United States).
Bilateral visits are not a rare phenomenon. There are in average 2.3 visits per country and per
year and in 60% of the cases (country-year) there is at least one visit of a French o�cial abroad
or of a foreign o�cial in France. The total number of visits increases overtime and particularly
during the 1990s and the early 2000s (see Figure 1). This sharp increase is mainly due to bilateral
1These Presidents' advisors have a special role in particular in former French colonies locatedin Sub-Saharan Africa, where they were in charge of diplomatic relations, but also of the personaland friendly ties between French and African top-level o�cials, especially Presidents (see Lavalléeand Lochard, 2016). Indeed, most of these visits concern African countries.
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visits with other EU countries related to the European integration process and the creation of the
euro in 1999.
Figure 1: Total number of visits per year
020
040
060
080
0
1975 1980 1985 1990 1995 2000 2005 2010year
All visits External visits
Indeed, the share of bilateral visits with EU members has risen dramatically. It has increased
from 9% in average over the period 1977-1986 to 26% over the period 1997-2007 (see Figure 2).2
Germany is by far the main diplomatic partner with more than 20.5 visits per year in average,
followed by the United States (13 visits) and Italy (10 visits). At the same time, the share of ex-
colonies sharply decreases overtime. Until the end of the 1980s, former French colonies represented
a substantial share of these bilateral visits (31% in average over 1977-1986) which makes this group
of countries the �rst `diplomatic' partner of France. After the end of the 1980s the share of former
colonies in France's bilateral visits drops by almost 40% (representing 19% of all bilateral visits on
the time period 1997-2007).
2This trend could re�ect the EU enlargement. This is not entirely the case. The share of bilateralvisits with EU members also increases even if we compute this share for a constant number of EUmembers, 15 for instance (14% during 1977-1986 to 22% during 1997-2007).
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Figure 2: Share of countries' groups in the total number of visits (%)
2.2 Empirical model
To explore the impact of bilateral visits on trade, we use a gravity model. The gravity model
relates bilateral trade, Tfct, e.g. exports of country f (France) to country c at time t, to their
economic sizes (Yft and Yct), bilateral trade costs (τfct) and multilateral trade resistances (Pft and
Pct) (see Anderson and van Wincoop, 2003). The gravity equation can be written as:
Tfct =YftYctYwt
(τfctPftPct
)1−σ, (1)
where Ywt is the nominal world income and σ > 1 the elasticity of substitution between goods.
Rearranging and taking natural logs, we obtain the following equation:
lnTfct = lnYftYwt
+ lnYct − (σ − 1) ln τfct + (σ − 1)(lnPft + lnPct), (2)
Trade costs (τfct) are generally modeled as a function of some observable factors, including
bilateral distance between trade partners, the existence of a common border, a common language
or a common currency, and regional trade agreements (RTA).
The trade cost function can take the following form:
τfct = distµ1fc × exp (borderfc)
µ2 × exp (comlangfc)µ3 × exp (RTAfct)µ4 , (3)
Moreover, as discussed in section 1, bilateral visits can decrease bilateral trade costs and
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reinforce trade between France and its trade partners. Therefore, to test whether trade increases
after a visit, we introduce a dummy variable (V isitct) denoting whether there is at least one
bilateral visit (of French o�cials in country c or of country c 's o�cials in France) at time t. We
introduce alternatively the number of visits (#V isitct).
We obtain the following estimated equation:
lnTfct = αt + αc + βV isitct + γCct − φ ln τfct + φ(lnPft + lnPct) + �fct, (4)
where αt are year �xed e�ects capturing the �rst term in equation (2) and αc are country �xed
e�ects accounting for time-invariant factors a�ecting trade, such as bilateral distance, common
language or common border. We also control for a vector of time-varying country variables (Cct),
such as countries' GDP and population to account for size e�ects.
Estimating properly equation (4) faces the challenge of accounting for the importer and ex-
porter multilateral resistance terms. In panel empirical analysis, these multilateral resistance in-
dices are generally taken into account by country-year �xed e�ects. However, in our case, country-
year �xed e�ects would absorb the e�ect of visits. Therefore, we adopt another solution which
consists in using the method proposed by Baier and Bergstrand (2009) where multilateral resis-
tance (MR) indices are approximated using a �rst order log-linear Taylor series expansion. This
methodology allows for time variation in the MR terms. We compute four MR terms: MRDist;
MRRTA; MRBorder; MRComlang and MRComcur, and include them as additional explanatory
variables. MRDist is the sum of a time-varying GDP-weighted average of c's log bilateral distance
to all other countries and a time-varying GDP-weighted average of f's log bilateral distance to all
other countries minus a third world resistance term. MRRTA; MRBorder, MRComlang and MR-
Comcur are de�ned analogously for the RTA, adjacency (Border), common language (Comlang)
and Common currency (Comcur) variables. Note that these MR variables have been computed on
a world sample including all countries as reporting and partner countries. In keeping with the the-
ory, the coe�cient estimates for RTA and MRRTA are restricted to have identical but oppositely
signed coe�cient values3. Numerous recent papers have also used this method to control for MR
terms (e.g. Berger et al., 2013; Lavallée and Lochard, 2015).
Therefore, to explore the in�uence of bilateral visits on trade, we estimate equation (4) using
data for bilateral trade of France with 187 partner countries over the period 1977-2007. The main
database recording bilateral trade for a long period of time is the International Monetary Fund's
Direction of Trade Statistics (DOTS). All variables and sources are de�ned in Table 6 in appendix.
3In concrete terms, we estimate the model with (RTA- MRRTA) as an additional variable. Wefollow the same strategy for the Comcur variable. For the MRDIST, MRBorder and MRComlangvariables, we do not impose a similar restriction because the initial variables (ln(Dist), Border andComlang) do not vary across time and are thus captured by country �xed e�ects.
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The following section presents our preliminary results.
3 Evidence on the e�ects of bilateral visits on trade
3.1 The e�ects of bilateral visits on aggregate trade
Table 3.1 reports estimation results of equation (4). In all regressions, we add both country
�xed e�ects and Baier and Bergstrand multilateral terms. Overall, our results are consistent with
expectations. Countries with greater GDP tend to import more from France (while population
seems to exert a negative in�uence on trade). The coe�cient on the dummy variable Visit is
positive but not signi�cant (column 1). However, when we break down visits into external visits
(visits of French o�cials abroad) and visits of foreign o�cials in France, we �nd a positive and
signi�cant e�ect of external visits on French exports. This estimate indicate that bilateral visits in
a foreign country c are in average associated with an increase in French exports in that country by
8% (= exp(0.08)− 1). This order of magnitude is close to the estimate obtained by Nitsch (2007).
In column (3), we introduce the number of visits (external visits and visits in France) instead of
a dummy variable. The number of visits, per se, does not seem to a�ect French exports. The
coe�cients on the number of visits (#External Visits and #Visits in France) are close to zero and
not signi�cant. We also introduce a dummy variable equal to one if there are three visits or more
in a country and for one year (representing 12% of cases) and zero otherwise. This dummy is not
signi�cant, reinforcing the conclusion that it is not the number of visits but rather the occurrence
of visits that matter for trade.
In Figure 3, we report estimates of the external visit dummy variable varying across continents.4
These results reveal that external visits that matter for trade are bilateral visits in African, Asian
and South American countries. However, external visits in Europe, North America and Oceania
do not lead to an increase in French exports. This may be due to the fact that these visits are
more related to diplomatic issues and less used as a vector to promote exports.
In Figure 4 we display the e�ect of visits overtime. To do so, we add several dummy variables
capturing the e�ect of visits from 4 years before the visit to 10 years after.5 The coe�cient is
not signi�cant before the visit takes place and becomes signi�cant the year of the visit and in the
eight years after the visit (note that the coe�cient start to decrease six years after the visit). This
pattern suggests that the e�ect of visits is not anticipated,6 and that visits have medium-term
4We estimate equation (4) with six dummy variables for external visits according to the desti-nation of these visits (Africa, Asia, Europe, North America, Oceania, South America).
5More precisely, we estimate equation (4) replacing the Visit dummy variable by 15 new dummyvariables (Visit t−4; Visit t−3; ...; Visit t;...; Visit t+10)).
6It is also somehow reassuring as to the simultaneity between visits and trade. We further
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Table 1: The e�ects of bilateral visits on trade
(1) (2) (3) (4)
ln(GDP) 0.70a 0.69a 0.70a 0.69a
(0.11) ( 0.11) ( 0.11) (0.11)
ln(Population) -0.99a -0.98a -0.97a -0.98a
(0.26) (0.26) (0.26) (0.26)
Visit 0.05
(0.03)
External Visit 0.08a 0.07a
(0.03) ( 0.02)
Visit in France -0.03
(0.03)
#External Visits 0.01
(0.01)
#Visits in France -0.01
(0.01)
More than 3 external visits 0.03
( 0.03)
Trade cost/MR terms:
ln(Distance) -1.21b -1.24b -1.24b -1.22b
(0.51) (0.51) (0.52) (0.51)
RTA 0.30b 0.29b 0.30b 0.29b
(0.12) (0.12) (0.12) (0.12)
Common border 5.00 4.97 5.02 5.05
(4.65) (4.66) (4.68) (4.66)
Common language -1.04 -1.02 -1.05 -1.04
(1.17) (1.18) (1.18) (1.18)
Adj. R2 0.94 0.94 0.94 0.94
#Observations 4896 4896 4896 4896
Country �xed e�ects yes yes yes yes
Year �xed e�ects yes yes yes yes
Notes: The dependent variable is the log of French exports tocountry c in year t. Robust standard errors clustered at thecountry level in parentheses. a, b and c denote signi�cance atthe 1%, 5% and 10% level respectively.
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Figure 3: The e�ect of external visits (dummy) on French exports by continent-.
3-.
2-.
10
.1.2
Afric
aAs
ia
Europ
e
North
Ame
rica
Ocea
nia
South
Ame
rica
95% conf. interval External Visit Coeff
e�ects. This may suggest that diplomatic relationships help to develop lasting trade relationships
and not just a one shot increase in trade. It is also possible that the contracts signed during the
visit involve major deals that promote exports during several years.
Next, we estimate the e�ect of bilateral visits by focusing on the level of the visit. In Table 2,
we �rst estimate the e�ect of external visits by breaking down the Visit dummy into three vari-
ables de�ned according to the level of the bilateral visit. We introduce a �rst dummy (External
Visit_high level) when the visit concerns Head of State or government (Presidents, Prime Minis-
ters, Chancellors, Kings or Queens), a second dummy for mid-level o�cials (External Visit_mid
level) (Ministers, Secretaries of State, Speakers of National Assemblies, etc.) and a third dummy
for Presidents' advisors (External Visit_cons level). As highlighted in section 2.1, the majority
of visits involve mid-level o�cials. Estimation results are reported in column (1). It seems that
mid-level visits as well as visits involving Presidents' advisors favor French exports, whereas visits
involving top-level o�cials do not seem to matter for trade. We further investigate the e�ect of
correct for the endogeneity problem using the instrumental variables approach.
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Figure 4: The e�ect of external visits (dummy) on French exports overtime-.
10
.1.2
-4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10year
95% conf.interval External Visit Coeff
bilateral visits by looking at several sub-periods. We break down the visit dummy variable into �ve
dummy variables for each presidency.7 In the �rst row (columns 2 to 6) we estimate the overall ef-
fect of external visits, without breaking down visits according to the level of the visit. Interestingly,
estimation results show that external visits promote French exports only under the presidency of
Valéry Giscard d'Estaing and the �rst period of François Mitterrand's presidency. When we break
down the e�ect of visits (bottom rows of Table 2), we �nd that visits at the top level o�cials never
a�ect trade, while visits at the mid-level promote French exports during the �rst two presidencies.
It is worth noting that visits involving the President's advisor strongly a�ect trade during the �rst
period of François Mitterrand's presidency. This may be related to the crucial role of these advisors
at that period in displomatic and economic relations with former colonies in Sub-Saharan Africa.
As emphasized in the introduction, external visits may increase trade through public contracts
discussed or signed at the occasion of o�cial visit. It is very di�cult to obtain data on public
7Over our sample period, there are three di�erents French Presidents: Valéry Giscard D'Estaing(1974-1981); François Mitterrand (1981-1988 and 1988-1995); Jacques Chirac (1995-2002 and 2002-2007).
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Table 2: External Visit e�ect breakdown
Period 1977-2007 Giscard Mitterrand82−88 Mitterrand89−95 Chirac96−02 Chirac03−07
(1) (2) (3) (4) (5) (6)
External Visit 0.08a 0.14b 0.20a 0.01 0.01 0.07
(0.03) (0.06) (0.07) (0.06) (0.06) (0.06)
External Visit_high level -0.02 0.07 0.02 -0.02 -0.05 -0.05
(0.02) (0.06) (0.04) (0.05) (0.04) (0.06)
External Visit_mid level 0.07a 0.13b 0.15b 0.01 0.04 0.10
(0.02) (0.06) (0.06) (0.06) (0.06) (0.07)
External Visit_cons level 0.12b -0.13c 0.16b 0.03 0.06 0.08
(0.02) (0.06) (0.06) (0.08) (0.07) (0.24)
Notes: The dependent variable is the log of French exports to country c in year t. Robust standard errors clusteredat the country level in parentheses. a, b and c denote signi�cance at the 1%, 5% and 10% level respectively. Othervariables (ln(GDPP); ln(Pop); MRdistance, MRRTA, MRCommon border, MRCommon language) and countryand year �xed e�ects included but not reported
contracts. However, one indirect way to test for this channel is to identify countries where the
share of government expenditures in GDP is larger. In column (1) of Table (3) we include a
dummy variable (High Gvt) for countries where is share is larger than 16.9% (the median in
our sample) and we interact this variable with our dummy for external visits (Ext. Visit×High
Gvt). Estimation results show that in countries that have a government share above the median,
external visits increase French exports since the coe�cient on the interaction variable is positive
and signi�cant. However, for a country with a government share below the median, the impact of
visits is null (the coe�cient on the External Visit dummy variable is not signi�cant).8 These results
suggest that visits seem to favor public contracts.9 In columns (2) and (3) we introduce additional
control variables for potential determinants of bilateral visits. We �rst add a dummy variable for
leader turnover and a variable accounting for current leader tenure as in Berger et al. (2013). We
�nd no evidence that leader turnover or leader tenure a�ect French exports.10 In column (3), we
control for the occurence of wars. We add a dummy variable (War) equal to one if there is a war
in country c at time t.11 The war dummy variable does not seem to a�ect systematically French
8The coe�cient on the External Visit variable provides the estimated impact of visits for acountry with zero government expenditure.
9In unreported regressions, we also added the level of government expenditures (intead of adummy) and the interaction with the Exteral Visit dummy variable. However, none of thesevariables turned signi�cant, showing that the impact of external visits may depend on the level ofgovernment control but not linearly.
10Note that due to data unavailability, the sample is restricted to 1977-2001 in column (2).Data on leader tenure and turnover come from Berger et al. (2013) and Bueno de Mesquita et al.(2003).
11Data on Militarized Interstate Dispute (MID) come from the Correlates of War (COW). We
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exports and does not a�ect the estimated coe�cient on the Visit dummy variable. In column (4),
we control for political proximity by adding a dyadic voting similarity index ranging from 0 to
1 (1 denoting most similar interests). It is equal to the total number of votes where both states
agree divided by the total number of joint votes (see Voeten, 2013). Estimation results reveal that
voting similarity has no systematic impact on French exports and that external visits promote
French exports even when we control for political proximity. Finally, in column (5) we interact the
bilateral visit variable with a dummy for former French colonies to test whether the impact of visit
only rely on countries with which France has strong historical ties. We show that external visits
have the same impact on former French colonies and on other countries, since the coe�cient on
the interacted variable (Ext. Visit×Ex-colonies) is not statistically signi�cant. This reinforces the
conclusion that the e�ect of bilateral visits on trade is not (only) due to political or historical ties.
3.2 The e�ects of bilateral visits on sectoral trade
In a second step, we estimate the impact of visits on sectoral trade. As a preliminary exploration
we run two sets of regressions for each categories of the Standard International Trade Classi�cation
(SITC). One uses the indicator variables for external and domestic visit (columns (1) and (2) of
table 4), the other the number of domestic and external visits (columns (3) and (4)). These
regressions con�rm our previous �ndings on several points. They show that it is the occurrence
of visits rather the number of visits that matters for France's exports. For each categories of
merchandises, the coe�cients on the number of visits (#External Visits and #Visits in France)
are close to zero and not signi�cant. Here again, we �nd that external visits have a greater impact on
exports than domestic visits. External visits have a positive and signi�cant e�ect on French exports
on 5 (out of 9) categories of goods, whereas the impact of domestic visits is positive and signi�cant
for only three of them. It is worth noting that except for `Food and live animals' and `Miscellaneous
manufactured articles', the impacts of domestic visits and external visit di�er drastically. External
visits enhance French exports of industrial/manufactured products by meaningful extents, ranging
from 6.2%(= exp(0.06)− 1) for `Machinery and transport equipment' to 3% (= exp(0.03)− 1) for
`chemicals' through 4.1% (= exp(0.04)− 1) for manufactured goods. The larger impact of external
visits on exports of machinery and transport equipements might indicate that visits also increase
trade through the promotion of private contracts. Firms in this sector are generally large and
business managers, such as the manager of Renault, may accompany French o�cials and use the
time of the visit to develop their networks, sign new contracts, etc. While limited to `Beverages
and tobacco', the e�ect of domestic visits is rather important (6.2%(= exp(0.06)− 1)) and can be
consider that there is a war when the hostility level reaches �Display of force�, �Use of force� and�War�.
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Table 3: Channels and robustness
Gvt Expenditures Leader War Political proxim. Ex-colonies
(1) (2) (3) (4) (5)
ln(GDP) 0.70a 0.53a 0.70a 0.70a 0.69a
(0.11) (0.14) (0.11) (0.10) (0.11)
ln(Population) -0.98a -1.09a -0.98a -0.95a -1.01a
(0.26) (0.26) (0.26) (0.27) (0.27)
External Visit 0.02 0.08a 0.08a 0.08a 0.07a
(0.03) (0.03) (0.03) (0.03) (0.03)
Ext. Visit×High Gvt 0.11b
(0.04)
High Gvt -0.06
(0.06)
New leader -0.00
(0.03)
Leader tenure 0.01
(0.00)
War -0.01
(0.04)
Vote Similarity 0.20
(0.28)
Ext. Visit×Ex-colonies 0.04(0.07)
Trade cost/MR terms:
ln(Distance) -1.27b -1.71a -1.22b -1.04b -1.25b
(0.52) (0.58) (0.51) (0.49) (0.52)
RTA 0.29b 0.41a 0.29b 0.29b 0.29b
(0.12) (0.15) (0.12) (0.12) (0.12)
Common border 4.85 -0.47 5.00 5.18 4.80
(4.57) (4.36) (4.65) (5.35) (4.74)
Common language -1.03 -1.69 -1.04 -1.45 -1.02
(1.18) (1.21) (1.18) (1.20) (1.18)
Adj. R2 0.94 0.95 0.94 0.95 0.94
#Observations 4896 3454 4896 4513 4854
Country �xed e�ects yes yes yes yes yes
Year �xed e�ects yes yes yes yes yes
Notes: The dependent variable is the log of French exports to country c in year t. Robust standarderrors clustered at the country level in parentheses. a, b and c denote signi�cance at the 1%, 5% and10% level respectively.
13
explained by France's central position in the wine world market.
Table 4: E�ect of bilateral visits by products group
Variables of interest Ext. visit Visit in F. #Ext. visits #Visits in F.
(1) (2) (3) (4) #Obs.
0-Food and live animals 0.05a 0.04a 0.01b 0.01a 145291
(0.01) (0.01) (0.00) (0.00)
1-Beverages and tobacco 0.02 0.06a 0.01 0.01 20824
(0.03) (0.02) (0.01) (0.01)
2-Crude materials, inedible, except fuels 0.01 -0.04b -0.00 -0.01b 82284
(0.02) (0.01) (0.00) (0.00)
3-Mineral fuels, lubricants and related mat. 0.01 -0.03 0.00 -0.01 14752
(0.04) (0.04) (0.01) (0.01)
4-Animal and vegetable oils and fats -0.06b -0.00 0.01 0.00 20017
(0.03) (0.03) (0.01) (0.01)
5-Chemicals 0.03b -0.00 0.01a -0.01a 151383
(0.01) (0.01) (0.00) (0.00)
6-Manufactured goods classi�ed chie�y by mat. 0.04a 0.00 0.01a 0.00 374209
(0.01) (0.01) (0.00) (0.00)
7-Machinery and transport equipments 0.06a 0.01 0.01a 0.00 224190
(0.01) (0.01) (0.00) (0.00)
8-Miscellaneous manufactured articles 0.04a 0.02a 0.01a 0.00 171506
(0.01) (0.01) (0.00) (0.00)
9-Commod. and transacts. not classi�ed 0.03 -0.07 0.02 -0.01 9440
(0.05) (0.05) (0.01) (0.01)
Notes: The dependent variable is the log of French exports to country c in year t in a 4-digit SITC industryi. Regressions include country- 4-digit SITC industry �xed e�ects, year �xed e�ects, Baier and Bergstrandmultilateral resistance terms, the logarithm of GDP, as well as the logarithm of population. Robust standarderrors clustered at the country-4-digit SITC industry level in parentheses. a, b and c denote signi�cance at the1%, 5% and 10% level respectively.
To analyse further the in�uence of external visits on exports, we interact the visit_ext dummy
with variables characterizing the products at stake. Our intention is to test whether the e�ects
of the external visits vary according to these products speci�cities. We focus on two variables.
The �rst one is di�erentiated product according to Rauch's classi�cation (1999). Indeed, one can
think that external visits favor exports of complex of goods by easing the matching of international
buyers and sellers. The second variable is an indicator of arms trade since France is historically
an important world arms producer and exporter. The binary variable Arms takes the value one if
the product traded belongs to the division 95 of SITC rev 1 classi�cation that gathers all �rearms
of war and ammunition from armoured �ghting vehicles to artillery weapons, machine guns and so
14
on. Table 5 reports our estimation results. They show no speci�c e�ect of the external visits on
French exports of arms. The interaction term Visit_ext* Arms is not signi�catively di�erent from
zero. However, they reveal that the impact of external visit is stronger for di�erentiated products.
For these products, the e�ect of external visits amounts to 6.2% against 2% for other goods. Such
a result suggests that bilateral visits promote French exports of complex products.
Table 5: Regressions with interactions
Coe�cient Standard errors # Observations
Di�erentiated products
Visit_ext 0.02a 0.01 1123304
visit_ext* di�erentiated 0.04a 0.01
Arms
Visit_ext 0.04a 0.00 1213896
Visit_ext* Arms 0.08 0.09
Notes: The dependent variable is the log of French exports to country c in yeart in a 4-digit SITC industry i. Regressions include country- 4-digit SITC indus-try �xed e�ects, year �xed e�ects, Baier and Bergstrand multilateral resistanceterms, the logarithm of GDP, as well as the logarithm of population. Robuststandard errors clustered at the country-4-digit SITC industry level in paren-theses. a, b and c denote signi�cance at the 1%, 5% and 10% level respectively.
4 Conclusion
In this paper, we investigate the impact of bilateral visits on trade using an original database
that gathers more than 13,000 visits of French o�cials abroad and of foreign o�cials in France
over the period 1977-2007. Our preliiminary results indicate that visits of French o�cials abroad
(particularly in developing and emerging countries) tend to promote French exports. This increase
in exports lasts typically for several years after a visit. We also estimate the impact of visits on
sectoral trade and show that this impact is particularly important for di�erentiated goods.
These estimation results might be a�ected by endogeneity in the relationship between visits
and trade. First, French o�cials may visit disproportionately countries with which they have
strong trade relations (simultaneity bias). The relationship between visits and trade may also be
a�ected by omitted variables that are related to visits but also determinants of trade. Finally, our
estimation results may be a�ected by mismeasurement in the visit variable.
We intend to use a two stage least squares method to control for endogeneity. As an instrument
we will use the number of visits in neighbouring countries by year. O�cials often visit several close
countries at the same time. Therefore the number of visits in neighbouring countries may determine
15
the occurence of visits in a country for one speci�c year. Furthermore, the number of visits in other
countries should not be directly related to bilateral trade with one speci�c country.
16
Appendix
Table 6: Data description and sources
Xfct At the aggregated level trade data (exports of France to all countries, i.e. imports ofall countries from France) come from the IMF (DOTS database). We use mirror datato improve the coverage of these data. When import data are missing or recorded aszero, we replace these data with the reverse �ow (exports), where available. As inHead et al. (2010), we add 10% to the export �ow to adjust for the fact that exportsare reported FOB and imports are recorded CIF. Sectoral trade data at the four-digitlevel come from commodity trade statistics database of the United Nations StatisticsDivision (UN Comtrade).
GDPct; Popct Current GDP and population come from the World Bank (World Development Indi-cators, WDI).
Distancefc;Commonlanguagefc
Bilateral distance and common language dummies come from the cepii database. Seewww.cepii.fr/francgraph/bdd/distances.htm
RTAfct The Regional Trade Agreement dummy is computed using information from the WTO(see de Sousa, 2012).
Visitct Bilateral visits at the level of Head of State (government), Minister, Secretary ofState, but also advisers and uno�cial visits between France and foreign countriesover the 1977-2007 period. Data come from the French Ministry of Foreign A�airs(Evenements de politique internationale).
High Gvtct Dummy variable for countries where the share of government expenditures in GDPis larger than the median (16.9%). Data on government expenditures come from thePenn World Tables 6.3.
Leader tenurect;New leaderct
Leader tenure (in years) and dummy variable equal to one if there is a new leader incountry c in year t. Data come from Berger et al. (2013) for 1977-1990 and Buenode Mesquita et al. (2003).
Warct Data on Militarized Interstate Dispute (MID) from the Correlates of War (COW)project. We consider that there is a war when the hostility level reaches �Display offorce�, �Use of force� and �War�..
Votesimilarityfct
Voting similarity index (between 0 and 1) equal to the total number of votes whereboth states agree divided by the total number of joint votes. Data come from Voeten(2013).
Di�erentiatedic Dummy variable equals to 1 if the product traded i is a di�erentiated product ac-cording to the Rauch's classi�cation (1999).
Armsic The binary variable takes the value one if the product traded belongs to the division95 of SITC rev 1 classi�cation that gathers all �rearms of war and ammunition fromarmoured �ghting vehicles to artillery weapons, machine guns and so on.
17
References
Anderson, J. and van Wincoop, E. (2003). �Gravity with Gravitas: A Solution to the Border
Puzzle�. American Economic Review, 93(1): 170-192.
Baier, S. and J. Bergstrand (2009). �Bonus Vetus OLS: A Simple Method for Approximating
International Trade-Cost E�ects using the Gravity Equation�. Journal of International Eco-
nomics 77, pp. 77-85.
Berger, D., Easterly, W., Nunn, N. and Satyanath, S. (2013). �Commercial Imperialism? Political
In�uence and Trade During the Cold War�, American Economic Review, 103, 863-896.
Bueno de Mesquita, B., A. Smith, R. Siverson, and J.D. Morrow. (2003). The Logic of Political
Survival, Cambridge: The MIT Press.
de Sousa, J. (2012). �The Currency Union E�ect on Trade is Decreasing over time�, Economics
Letters 117, 917-920.
Fuchs, A. and N-H. Klann (2013). �Paying a Visit: The Dalai Lama E�ect on International
Trade�. Journal of International Economics 91(1), pp. 164-177.
Head, K., Mayer, T. and J. Ries (2010). �The Erosion of Colonial Trade Linkages after Indepen-
dence�. Journal of International Economics 81(1), pp. 1-14.
Lavallée, E., Lochard, J. (2015). �The Comparative E�ects of Independence on Trade�. Journal
of Comparative Economics, 43, pp. 613-632.
Lavallée, E., Lochard, J. (2016). �French Neo-Colonialism? Franco-African Trade After Indepen-
dence�. Working Paper.
Martin, P., Mayer, T. and M. Thoenig (2008). �Make Trade not War?�. Review of Economic
Studies, 75, pp. 865-900.
Nitsch, V. (2007). �State Visits and International Trade�. The World Economy, 30(12), pp.1797-
1816.
Rauch, J. E. (1999). �Networks Versus Markets in International Trade.� Journal of International
Economics, 48 (1), pp. 7-35.
Rose, A. K. (2007). �The Foreign Service and Foreign Trade: Embassies as Export Promotion�.
The World Economy, 30(1), pp. 22-38.
Voeten, E. (2013). Data and Analyses of Voting in the UN General Assembly, Routledge Hand-
book of International Organization.
18
IntroductionData and empirical strategyBilateral visitsEmpirical model
Evidence on the effects of bilateral visits on tradeThe effects of bilateral visits on aggregate tradeThe effects of bilateral visits on sectoral trade
Conclusion
