French Neo-Colonialism? Franco-African Trade

After Independence

Emmanuelle Lavallée∗ Julie Lochard†

Very preliminary and incomplete. Please do not quote.

July 2016

Author Keywords: Trade; Decolonization; French Empire, Sub-Saharan Africa.

JEL classification codes: F10; F54.

∗PSL, Université Paris-Dauphine, LEDa, UMR DIAL. Email: emmanuelle.lavallee@dauphine.fr.†Erudite, University of Paris-Est Créteil Val de Marne. Email: julie.lochard@u-pec.fr.

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1 Introduction

Over the long run, politics had obviously shaped the pattern of international trade (Findlay and

O’Rourke, 2007). Over the recent past, evidence are fragmented and sometimes contradictory,

especially as far as power plays in the international order is concerned.1 For instance, Berger et al.

(2013) show that increased political influence, due to CIA intervention during the Cold War, was

used to create a larger foreign market for American products. Following CIA intervention, imports

from the US increased dramatically while total exports to the US were unaffected. Mityakov,

Tang and Tsui (2013), on the other hand, show that deterioration of relations between the United

States and another country reduced U.S. imports from that country, but that the magnitude of the

effect is small and limited to petroleum and some chemical products. Beyond the United States,

the influence of other powers, like the USSR, Great Britain or France may have determined the

pattern and the evolution of foreign exchanges. France offers an interesting case in this regard. It

maintained strong connections with its former colonies in Sub-Saharan Africa (SSA). Indeed, in this

region, independence did not radically change relations between France and its former colonies.

Contrary to what happened in former colonies located in other parts of the world, France and

its former colonies in SSA kept on having strong economic and political links at least until the

beginning of the 1990s. Do this special Franco-African Relation (hereafter FAR) promote French

exports?

The French influence operated both at the political and economic level. It included not only

secret cooperation agreements, but also personal and friendly ties between French and African top-

level officials or development assistance. Beyond anecdotal evidence, singular FAR are likely to

have influenced trade. They may explain why, more than 50 years after their independence, former

French SSA colonies still trade more with their former colonizer than other African former colonies

(de Sousa and Lochard, 2012). To the best of our knowledge, the impacts of FAR on French-African

trade have never been explored at least in a comprehensive empirical manner. Little explored in

economics, a large literature deals with this issue in political sciences. Yet, Franco-African relations

are an area where objective and scientific knowledge is still relatively scarce. The aim of this paper is

twofold. First, it intends to test whether FAR distorted French exports towards SSA former French

colonies and to isolate the products that were the most affected. Second, it aims at explaining the

drivers of this potential Franco-African trade gap.

To examine the consequences on trade of FAR, we use data for bilateral aggregated and sectoral

exports of France with 188 partner countries over the period 1960-20072. Aggregate trade data are

taken from the International Monetary Fund’s Direction of Trade Statistics (DOTS) and sectoral

1As opposed to intra-state or inter-state wars which impact on trade as been studied by Martinet al., 2008 for instance)

21962 for sectoral data

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trade data, at the four-digit level, come from commodity trade statistics database of the United

Nations Statistics Division (UN Comtrade). In addition to disentangle the drivers of this FAR effect

on trade, we gathered a bunch of data on development assistance, migration, military interventions,

or on political proximity as well as original data on inter-personal ties between French and African

officials. The later is approximated by bilateral visits of top-level officials between France and

foreign countries collected from the French Ministry of Foreign Affairs.

We find that FAR distorted French exports in favor of its former SSA colonies. Indeed, our

results indicate that France exported significantly more to its former SSA colonies, as compared

to other countries, whether they be in development or other former French colonies. This extra-

trade between France and its former SSA colonies closely reflects the twists and turns of the

FAR. For example, the gap started to narrow when FAR began to fall apart at end of the 1980s.

Furthermore, this FAR effect is robust to the introduction of additional explanatory variables or the

use of other methods of estimation. Analysis at the sectoral level show that FAR impacted a large

variety of products going from manufactured goods to machinery and transport equipments through

food. Using Rauch’s classification (1999), we find that FAR affected indifferently reference-priced,

homogenous and differentiated goods. A finer analysis reveals a larger effect of FAR for French

exports of luxury goods and for goods in which France was least competitive. As regards, the

drivers of this FAR effect on trade we provide evidence that the political factors and, in particular

political proximity measured by similarity in United Nations General Assembly voting patterns,

explain most of the observed extra-trade between France and its former SSA colonies.

The paper is structured as follows. In section 2, we discuss the French influence in SSA.

Then, in section 3, we introduce the empirical model and describe our data. In section 4, we

present empirical evidence on the effect of the FAR on aggregate and sectoral trade. Finally, in

section 5, we investigate several potential economic and political drivers of the French influence.

We summarize our findings and add concluding remarks in section 6.

2 The French influence in SSA

The French influence in African former colonies after independence is the consequence of how

decolonization took place in SSA. Decolonisation in SSA was included in the scope of a of “pré-

carré”, i.e. the reinforcement of frontiers in order to better resist to external aggressions. In other

words, in the Cold War context, France wanted to keep its influence on its former SSA colonies

because they were an essential element of its international influence. Orchestrated by the the

emblematic Jacques Foccart3, decolonization in SSA aimed at setting up a network of African

3Jacques Foccart led the Franco-African policy together with the French Presidents De Gaulle,Pompidou and Chirac. He is also associated with the dark side of the Françafrique. Since 1960,

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Republics allied with France (Bat, 2012). In 1960, almost all former French colonies become

independent mostly peacefully.4

Negotiations on the terms of independence led to the signature of secret cooperation agreements

of two kinds. The first one dealt with the privileged access of France to its former colonies’ raw

materials. The second one, claimed by African Presidents themselves, concerned the military

protection of new regimes. The contract between France and its former French colonies was the

following: France supported the African Presidents “friends” of France, i.e. Presidents defending

French positions in the international arena or granting a French preference in any sector (Bat,

2012). The Franco-African policy was organised in Paris in the Office of the Executive Power

through the “Secrétariat Général des Affaires Africaines et Malgaches” led by Jacques Foccart. Its

mission was to inform the French President of the political evolutions of the African Republics,

of their reciprocal relations, as well as their relations with France. The “Secrétariat Général des

Affaires Africaines et Malgaches” organized the relations between French and African executive

powers. It not only dealt with diplomatic relations between friendly States, but it was also in

charge of the personal and friendly ties between French and African top-level officials, especially

Presidents.5 Thus, interpersonal ties were the keystone of FARs and of the French African policy.

The strength of personal bonds was at the very foundation of FARs.

FARs had also economic demonstrations. After attaining independence, most of the new

African states decided to keep a common exchange rate mechanism. The Franc Zone, which

dates back to the colonial period, provides an unlimited convertibility guarantee from the French

Treasury and a fixed exchange rate between the CFA franc, the Comorian Franc and the French

Franc (now the euro). Independence went hand to hand with development assistance. One of its

mechanisms, tied aid, was deemed to have long guaranteed a virtual monopoly for French firms

conducting infrastructure projects in Africa (Gounin, 2009; Dozon, 2003). Concomitantly, there

had never been so many Africans in France and French people in Africa as after independence.

France posted a large number of public servants to newly SSA independent states. Where there

were fewer than 7,000 colonial administrators in 1956, there were 8,749 people on Voluntary Service

in the newly independent states in 1963, 9,364 in 1973 and 10,292 in 1980 (Gounin, 2009, p. 23).

This phenomenon was not confined to the public sphere. A kind of “neo-colonial society” set up

in the SSA capitals of the former Empire. For instance, Dozon (2003) reports that almost 50,000

French people were living in Ivory Coast in 1970, five times more than in 1960. At the same time,

he has been in the collective psyche at the origin of political plots and intrigues in SSA formercolonies.

4Guinea attained independence in 1958. In Eastern Africa, Djibouti and Comoros gained inde-pendence respectively in 1977 and 1975.

5This peculiar mission is written in black and white in the statutes of the “Secrétariat Généraldes Affaires Africaines et Malgaches” (Bat, 2012, p. 137).

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the fifth French Republic implemented an immigration policy in favor of the nationals of former

French African colonies.

This strategy ruled the FARs up to the 1990s. After 1989, it was gradually shelved. First, the

end of the Cold War left the Franco-African policy with no geopolitical and ideological grounds.

The French security logic, its unconditional support to autocratic regimes were no longer justified.

In June 1990, François Mitterand’s speach at the 16th Franco-African summit revealed a break

in the FARs. The French President announced that the French aid (in the broad sense) would

depend of the progress of democracy in African partners. This declaration called into question

one of the fundamental pillars of the FARs. Second, the 1990s marked the end of a generation

and the crumbling of inter-personal relations. In December 1993, the death of Felix Houphouet-

Boigny (President of the Ivory Cost from 1960 to 1993) was the first of a series of funerals of

Françafrique’s protagonists.6 Last but not least, the devaluation of the CFA franc in January 1994

jeopardized FARs. The devaluation of the CFA franc against the opinion of the African Presidents

was perceived as a disengagement of France.

Therefore, historical evidence suggests that the French influence in SSA fall apart at the begin-

ning of the 1990s. We further provide empirical evidence showing that the Franco-African special

relations affect bilateral trade only on the time period preceding 1990. The following section

presents our empirical strategy and data to investigate the French influence on trade.

3 Empirical model and data

To explore the impact of the French post-colonial influence on trade, we use a gravity model. The

gravity model links bilateral trade, Xfct, 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:

Xfct =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:

lnXfct = 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

6Jacques Foccart dies in 1997, one year latter than Bokassa (President of RCA from 1966 to1966). This list ended with the death in June 2009 of Omar Bongo (President of Gabon from 1967to 2009).

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bilateral distance between trade partners, the existence of a common border, a common language

or a common currency, and a regional trade agreement (RTA).

The trade cost function can take the following form:

τfct = distµ1

fc × exp (borderfc)µ2 × exp (comlangfc)

µ3 × exp (RTAfct)µ4 , (3)

Moreover, as explained in section 2, specific FARs after independence can decrease bilateral

trade costs and reinforce trade between France and its former SSA colonies. Therefore, to test

whether former French colonies in SSA exhibit a different post-colonial trade pattern, we construct

a dummy variable (FARct) denoting whether the importing country is a former French colony

located in SSA before 1990, the golden age of the French influence in Africa (see section 2).7

We obtain the following estimated equation:

lnXfct = αt + αc + βFARct + γCct − φ ln τfct + φ(lnPft + lnPct) + εfct, (4)

where αt are year fixed effects capturing the first term in equation (2) and αc are country fixed

effects accounting for time-invariant factors affecting 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 effects, and the number of years of

independence to control for trade erosion after independence.

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 introduced through country-year fixed effects. However, in our case, country-

year fixed effects would absorb the effect of French influence when the sample is restricted to French

bilateral trade. Therefore, we adopt another solution which consists in using the method proposed

by Baier and Bergstrand (2009) where multilateral resistance (MR) indices are approximated using

a first order log-linear Taylor series expansion. This methodology allows for time variation in the

MR terms. We compute three MR terms: MRDist; MRRTA and MRComlang, 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 and MR-

Comlang are defined analogously for the RTA and common language (Comlang) 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 theory, the coefficient estimates for RTA and MRRTA

7These former colonies include Benin, Burkina Faso, Cameroon, Central African Republic,Chad, Congo, Djibouti, Gabon, Guinea, Ivory Coast, Madagascar, Mali, Mauritania, Niger, Senegaland Togo.

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are restricted to have identical but oppositely signed coefficient values.8 Numerous recent papers

have also used this method to control for MR terms (e.g. Berger et al., 2013; Lavallée and Lochard,

2015). In robustness checks, we also use country-year fixed effects to account for MR indices when

estimating our model on a world sample.

To explore the impact of FAR on trade, we estimate equation (4) using data for bilateral

trade of France with 188 partner countries over the period 1960-2007 for aggregate trade and over

the period 1962-2007 for sectoral trade. Aggregate trade data are taken from the International

Monetary Fund’s Direction of Trade Statistics (DOTS), and sectoral trade come from the United

Nations Comtrade Database (SITC rev. 1 at the 4 digits level). These databases are the two main

sources that record bilateral trade over a long period of time. All variables and sources are defined

in Table 7 in appendix. The following section presents our preliminary results.

4 The French influence in SSA and trade

This section aims at providing quantitative evidence of a special trade pattern between France and

its former colonies of SSA during the Cold War. First, we investigate the evolution of French-

African trade over time. Second, we estimate the French-African trade gap on aggregate and

sectoral trade.

4.1 The French-African trade over time

To investigate the evolution of French-AFrican trade overtime, we first break down a dummy

variable FARct, using the value 1 for flows between France and SSA former colonies, into indicator

variables for each year between 1960 and 2007. Therefore, we create 49 dummies, FAR1 to FAR49,

which take the value 1 for French exports to former French colonies in SSA for each year (1 to

49) between 1960 and 2007. Then we estimate equation (4) with our SSA dummy variables and

controlling for multilateral resistance terms using the Baier and Bergstrand (2009) method.

Figure 1 displays the estimation results for our variables of interest. It plots coefficient estimates

of FARct dummies as well as their 95% confidence intervals.9 It shows that the end of the eighties

is a turning point for French exports to former SSA colonies. Indeed, the SSA dummies are positive

and significant from 1960 to 1986 becoming non significant after 1987. It means that, in reference

8In concrete terms, we estimate the model with (RTA- MRRTA) as an additional variable. Forthe MRDIST and MRComlang variables, we do not impose a similar restriction because the initialvariables (ln(Dist), Border and Comlang) do not vary across time and are thus captured by countryfixed effects.

9Note that we take FAR2007 as the reference group. Overall estimation results are availableupon request.

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Figure 1: The SSA former French colony effect over time-.

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.51

1.5

1960 1970 1980 1990 2000 2010year

95% conf.interval SSA coeff

with 2007, in each year before 1987, France exports more to former SSA colonies than to other

countries. Moreover, the extra-trade between France and its SSA former colonies decreases more

strongly after 1990 when it becomes negative. These results provide a first evidence of a gap

between French exports to former colonies of SSA and French exports to other countries specific

to the 1960-1990 period: the golden age of FARs.

Accordingly, in the following estimations, we will evaluate the French influence using a dummy

variable (FARct) that is equal to 1 for trade between a former French SSA colony and France for

each year between 1960 and 1989 and zero thereafter. None of our results are affected if we define

instead FARct as a dummy variable for trade between a former French SSA colony and France on

the time period 1960-1986.

4.2 The French influence in SSA and aggregate trade

Table 1 reports estimation results of equation (4) according to different estimation methods and

different samples. First, we display the results of the OLS estimation without country fixed effects

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but controlling for multilateral resistance terms using the Baier and Bergstrand (BB) methodology

(column 1). Then, we use the OLS estimator with country fixed effects and without the BB multi-

lateral terms (column 2). In column 3, we add both country fixed effects and the BB multilateral

terms. Overall, our results are consistent with expectations. Countries with greater GDP tend to

import more from France (while population does not seem to exert a positive influence on trade).

The coefficient on the number of years of independence is negative and significant in columns (2)

and (3), indicating that trade flows decrease after independence. This result is consistent with the

literature (Head et al., 2010 ; Lavallée and Lochard, 2015).

Our estimations also show that France exports disproportionately more to its former SSA

colonies during the period 1960-1989, as compared to other countries. The FARct variable is

positive and significant in all three columns. Moreover, the coefficient estimate is much larger

when we do not control for fixed effects (column 1 vs. columns 2 and 3), which is in line with the

results obtained by Berger et al. (2012). This indicates that most of the variation in countries’

multilateral terms is captured through country fixed effects. Therefore, in all further regressions,

we control for both country fixed effects and the BB multilateral resistance terms. Our preferred

estimate in column (3) indicates that France exports 1.6 times more [= exp(0.47)] to a former SSA

colony than to other countries.

Recent papers advocate for the use of a Poisson quasi-maximum likelihood (PQML) estimator

when estimating a gravity model, because it incorporates the zero trade flows and it is robust to

different patterns of heteroskedasticity (Santos Silva and Tenreyro, 2006). In panel data, the fixed

effects Poisson (FEP) estimator has the same useful properties and controls for heteogeneity across

countries (e.g. Westerlund and Wilhelmsson, 2011). Estimation results obtained with the FEP

estimator (column 4) are close to the OLS estimates (column 3) and confirm our main finding,

i.e. between 1960 and 1989 France exports disproportionately more to its former SSA colonies.

In column (5), we restrict the sample to 38 low income countries.10 We still find a positive and

significant the Franco-African effect, reflecting an extra-trade gap between France and its former

SSA colonies during the period 1960-1989, as compared to trade between France and low income

countries. In column (6), we restrict the sample to 33 former French colonies. The FARct coef-

ficient is somewhat lower but still positive and significant at the 10% level.11 It indicates that

France exports 1.5 times more [= exp(0.38)] in former SSA colonies during the period 1960-1989,

as compared to other former French colonies. Thus, these results suggest that the FARct dummy

variable is not merely a former colonies’ effect. It is neither due to the more recent independence

10We consider the World Bank classification which defines low income economies as those witha GNI per capita of $1,045 or less in 2014.

11The less significant estimate is mostly explained by the increase in standard errors due to thelower number of observations.

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of SSA countries12 since, in all regressions, we control for the number of years since independence.

We further test the robustness of this SSA trade gap later in this section.

Table 1: The effect of the French influence in SSA on French exports

Partner: All partners Low income Former Col.Method: OLS OLS OLS FEP OLS OLS

(1) (2) (3) (4) (5) (6)FAR1960−89 1.92a 0.53a 0.47a 0.35a 0.44a 0.38c

(0.33) (0.09) (0.10) (0.11) (0.15) (0.21)ln(GDP) 0.61a 0.82a 0.79a 0.82a 0.58a 0.68a

(0.19) (0.09) (0.08) (0.07) (0.13) (0.12)ln(Population) 0.13 -0.15 -0.06 0.24 0.75 -0.41

(0.13) (0.16) (0.15) (0.32) (0.57) (0.49)# of years since indep. 0.01 -0.01b -0.01a -0.02b -0.04a 0.01

(0.01) (0.00) (0.00) (0.01) (0.01) (0.01)Trade cost/MR terms:

ln(Distance) -1.70a - -1.49a -0.63b 1.15 2.57(0.50) (0.33) (0.28) (1.96) (1.81)

RTA 1.19a - 0.09 0.42b 2.09 0.51c

(0.29) (0.10) (0.19) (4.64) (0.26)Common language -0.69 - -1.48a -1.65a 0.00 1.75c

(0.48) (0.53) (0.53) (1.42) (1.02)Adj. R2 0.69 0.94 0.94 - 0.88 0.94# of observations 6618 6618 6618 6764 1435 1057Country fixed effects no yes yes yes yes yesYear fixed effects yes yes yes yes yes yes

Notes: The dependent variable is the log of French exports to country c in year t.Robust standard errors clustered at the country level in parentheses. a, b and c denotesignificance at the 1%, 5% and 10% level respectively.

What are the frontiers of this Franco-African trade gap? In Table 2 we provide additional

results concerning the geographical as well as time limits of the French influence. In column (1) we

modify the definition of the FARct dummy variable by assuming that the special relationships be-

tween France and its SSA former colonies last until the CFA Franc devaluation in 1994. Therefore,

we define the dummy variable FARct to equal 1 for trade between a former French SSA colony

and France for each year between 1960 and 1993 and zero thereafter. Estimation results show

that the FARct effect is slightly smaller than in previous estimation (Table 1, column 3) but still

12Former French colonies in SSA obtained their independence between 1958 and 1977 (1961 inaverage), whereas other former French colonies obtained their independence between 1943 and 1975(1955 in average).

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positive and highly significant.13 In column (2), we explore the geographic limits of the French

influence by considering the relations between France and not only SSA former French colonies,

but also other francophone African countries. Following Carrère and Masood (2015), we define

francophone countries as those having French as the official language (de jure dimension) and in

which more than 20% of the population speaks French (de facto dimension). Therefore, we add

8 countries in the FARct group (Burundi, Democratic Republic of the Congo, Algeria, Equatorial

Guinea, Morocco, Mauritius, Rwanda, Tunisia). We obtain a positive and significant coefficient

for the transformed FARct variable, showing that special relationships are not restricted to former

SSA colonies. Finally, in column (3) we test whether the FARct effect is limited to French exports.

We consider exports of SSA former French colonies (16 countries) to all countries in the world,

and define a dummy variable equal to one when the importer country is France on the time period

1960-1989 and zero otherwise. Estimation results on this sample including country (and year) fixed

effects reveal that the former French colonies do not export more to France than to other countries

on the time period 1960-1989. Therefore, the Franco-African trade gap seems limited to French

exports and do not hold for SSA former colonies’ exports to France.

Finally, we test the robustness of our results by exploring alternative interpretations for our

findings in Table 3. First, it may be the case that our FARct effect comes from a multilateral

resistance effect. If, for whatever reasons not related to the French influence, multilateral resistance

of SSA former French colonies increase during the 1960-1989 period, they might be more prone to

trade with France. In previous tables we control for multilateral resistance terms using the Baier

and Bergstrand methodology and country fixed effects. However, the BB methodology only gives

an approximation of MR terms and country fixed effects do not capture time variation in MR

terms. Therefore, as a further test of robustness we estimate equation (4) on a worldwide bilateral

trade sample using country-year fixed effects and a number of bilateral controls (column 1).14

Our results show that the FARct coefficient is even larger than in the restricted sample (Table 1,

column 3). It indicates that, from 1960 to 1989, on average, France and its former colonies in SSA

trade 242% [= (exp(1.23)− 1) ∗ 100] more than any other pairs of countries.

Our results might also reflect the imports’ dynamic of former French colonies specific to the

1960-1989 period but not specific to France. To test this explanation we estimate our empirical

13To save on space, we only report estimations results using the OLS estimator but we obtainsimilar results using the FEP estimator.

14In a worldwide bilateral trade sample, we can introduce country-year fixed effects because theFARct variable is not perfectly collinear with country-year fixed effects, but on the downside, thissample comprises very different trade bilateral relations. We add several bilateral controls: theexistence of a former colonial relationship (Former colony), a dummy for countries that had thesame coloniser (Common coloniser) and for countries that are in a current colonial relationship(Still colony).

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Table 2: Time and geographical limits of the French influence in SSA

Dependent variable: log French bilateral exports log SSA bilateral exports(1) (2) (3)

FAR1960−93 0.40a

(0.09)FAR1960−89 (incl. francophone) 0.39a

(0.09)FAR/France1960−89 0.29

(0.22)ln(GDP) 0.79a 0.79a 0.57a

(0.08) (0.08) (0.09)ln(Population) -0.07 -0.07 -0.33

(0.15) (0.15) (0.21)ln(GDP) exporter 0.56a

(0.10)ln(Population) exporter 0.97b

(0.50)# of years since indep. -0.02a -0.01a -

(0.00) (0.00)Trade cost/MR terms:

ln(Distance) -1.53a -1.49a 0.18(0.33) (0.34) (0.13)

RTA 0.09 0.12 1.44a

(0.10) (0.10) (0.16)Common language -1.62a -1.47a 0.40

(0.53) (0.53) (0.42)

Adj. R2 0.94 0.94 0.48# of observations 6618 6618 37459Country fixed effects yes yes yesYear fixed effects yes yes yes

Notes: In columns (1) and (2), the dependent variable is the log of French exports to country cin year t. In columns (3), the left hand side variable is the log of SSA former colonies’ exports tocountry c in year t. Robust standard errors clustered at the country level in parentheses. a, b and c

denote significance at the 1%, 5% and 10% level respectively.

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model using the world total imports of SSA former French colonies as the dependent variable

(column 2).15 The 1960-1990 dummy is not statistically different from zero. This suggests that

extra-imports from France is due to trade diversion rather than trade creation (otherwise the

dummy would be positive and significant).

Finally, it is possible that the Franco-African trade gap hides a Francophone effect. Indeed,

in previous estimates, we found that francophone African countries import more from France

than other countries during the time period 1960-1989 (see Table 2, column 2). Therefore, as a

falsification exercise, we define a Francophonect dummy variable for the 16 francophone countries

that are not SSA former French colonies (Burundi, Belgium, Canada, Switzerland, Democratic

Republic of the Congo, Comoros, Algeria, Equatorial Guinea, Haiti, Israel, Lebanon, Luxembourg,

Morocco, Mauritius, Rwanda, Tunisia) over the time period 1960-1989 (column 3). The coefficient

on this variable is not significant, showing that there is no extra-trade between France and these

francophone non SSA former French colonies specific to the period 1960-1989. In the next section,

we investigate the sectoral trade dimension of the French influence in SSA.

4.3 The French influence in SSA and sectoral trade

We now investigate which product groups are the most affected by the French influence in SSA.

Our first strategy consists in running separate regressions for groups of products usually used in the

literature. We disaggregate exports using the Standard International Trade Classification (SITC).

Table 4 reports estimation results for each SITC products group for the period 1962-2009. It

shows that the French post-colonial influence in SSA has a positive impact on French exports of

manufactured goods (SITC 5 to 8) as well on exports of ‘food and live animals’ (SITC 0) and

‘Beverages and tobacco’ (SITC 1). The extra-trade gap goes from 30% for beverages and tobacco

and 36% for machinery and transport equipment to 82% for chemicals. The large variety of product

groups characterized by an extra-trade with France over the period 1960-1989 is surprising. One

could have expected it to be limited to ‘Machinery and transport’, ie. the most politically sensitive

goods according to Fuchs and Klann (2013). Indeed, negotiations over the purchase of such products

are generally carried out during the course of high rank trade talks. The mechanisms at stake may

thus go beyond the state of political relations between countries and may also be operating through

consumer demand since a positive coefficient is found for consumption goods, namely ’Food and live

animals’. We also disaggregate exports following the classification by Rauch (1999) into reference-

priced, homogenous and differentiated goods. Indeed, one can think that the extra-trade increases

with the complexity of goods because FAR can ease the matching of international buyers and sellers

15In this specification, we compute multilateral resistance terms for total trade using the method-ology provided by Berger and al. (2012) in their Appendix.

13

Table 3: Robustness and falsification exercise

Sample: Worldwide database Countries world imports French bilat. exports(1) (2) (3)

France/SSA_FAR1960−89 1.23a

(0.16)SSA_FAR1960−1989 0.03

(0.09)France/Francophone non SSA1960−1989 0.12

(0.11)ln(GDP) 0.58a 0.81a

(0.08) (0.08)ln(Population) 0.08 -0.13

(0.11) (0.16)# of years since indep. -0.01a -0.02a

(0.00) (0.00)ln(Distance) -1.29a -0.93b -1.57a

(0.02) (0.44) (0.34)RTA 0.68a -0.66b 0.09

(0.04) (0.29) (0.11)Common language 0.45a 1.52 -1.78a

(0.03) (1.40) (0.54)Former colony 1.35a

(0.08)Still colony 0.87b

(0.39)Common coloniser 0.79a

(0.04)

Adj. R2 0.71 0.98 0.94# of observations 650,010 6075 6618Country fixed effects no yes yesYear fixed effects no yes yesCountry-year fixed effects yes no no

Notes: In column (1), the dependent variable is the log exports of country i to country c in yeart. Regressions include country-year fixed effects. In column (2), the left hand side variable is worldimports of country c at time t. In column (3), the dependent variable is French bilateral exports tocountry c at time t. Robust standard errors clustered at the country level in parentheses. a, b andc denote significance at the 1%, 5% and 10% level respectively.

14

of differentiated products. Table 4 shows the results of the estimation of separate regressions for

each type of goods. The FAR dummy is positive and highly significant in each regression, meaning

that the extra-trade concern both French exports of differentiated, homogenous or reference priced

goods.

To analyse further the French influence in SSA, we undertake a second strategy that consists

in interacting the FAR variable with dummy variables for broad categories of products. Our

intention is to test whether the effects of the FAR are stronger for such products. We focus on three

categories. The first one is industrial products. Indeed, our previous regressions tend to indicate

that manufactured goods are more strongly affected by the French influence. We compute a dummy

variable equals to one if the product traded is an industrial product according to the International

Standard Industrial Classification of All Economic Activities (ISIC). The second category concerns

arms since the Franco-African relation is often associated with military interventions and 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 classification that

gathers all firearms of war and ammunition from armoured fighting vehicles to artillery weapons,

machine guns and so on. The third category is luxury goods. After independence most of the

former French colonies of SSA had been ruled by autocrats known to confuse their personal wealth

with their country wealth.16 As France is a major player in the luxury good world market, we

expect the FAR to go hand in hand with greater French exports of high-end varieties. We construct

an indicator variable for high end product following the strategy developed by Fontagné and Hatte

(2014). In a first step, we identify product groups exported by the firms belonging to the French

association of luxury goods (Comité Colbert). Then, after having excluded outliers,17 we define

high-end product trade flows as observations in the upper decile of the distribution of unit values

for each product and year.

Our estimation results show no specific effect of the French influence in SSA on French exports

of industrial products and arms, but they reveal a larger effect for French exports of luxury goods.

For these products, the SSA trade gap amounts to 63%.

As a third strategy, we test a thesis developed by Marseille (1984) according to which France

used its colonies as an outlet for its declining industries. We approximate French competitiveness

across industries and years using the Balassa measure of comparative advantage (RCA). This index

16The involvement of some of them or of their relatives in money laundering scandals, as well asin the in cases of ill-gotten gains and stolen assets actually filed in France illustrate the corruptionof SSA elites.

17The upper and lower extreme unit values were flagged computing the difference between theunit value of each flow and the mean of the unit value of each product group exported calculatedby decade. We retain only the observations between the 5th and the 95th percentiles

15

can be written as:

RCAcit =xcit∑c xcit

/

∑i xcit∑

i

∑c xcit

, (5)

where xcit is the exports of country c in a 4-digit SITC industry i in year t. A RCA index above

one denotes that the country has a comparative advantage in producing in industry i. On the

contrary, if the ratio is less than unity, the country is said to have a comparative disadvantage in

industry i, since its share of world exports in industry i is lower than the average. We use the RCA

index in its continuous form as well as a dummy variable indicating whether or not country c has

a comparative advantage in industry i in year t. We introduce the RCA index in Equation (4) and

interact it with the FAR dummy. This specification allows the effect of the FAR to differ across

industries depending on whether France has a comparative advantage in industry i. Bottom lines

of Table 4 report our estimation results. The coefficients of the RCA variables are positive and

statistically significant, meaning that France exports more in industries where it has a comparative

advantage. The FAR coefficient is positive and statistically significant, but the FAR∗RCA is

negative and significant indicating that the French influence enhances French exports in industries

in which France has a comparative disadvantage rather than a comparative advantage. In other

words, the average effect of FAR on French exports in every industry is positive, but it is larger in

industries in which France was least competitive. Overall, these results provide evidence that over

1962-1990, former French SSA colonies were an outlet of ailing French industries.

5 Exploring the channels of the Franco-African trade

gap

As discussed is section 2, several mechanisms could explain the Franco-African trade gap. Some are

political and are linked with interpersonal ties or international politics. Others are economic-related

and deal, for instance, with development assistance, or business networks.

5.1 Political drivers

We investigate the political dimensions of the FAR, that is to say the close interpersonal ties

maintained between French and African governments after independence, as well as the political

proximity between France and its former colonies of SSA during the Cold War. As explained in

section 2, the latter took the form of a kind of contract: French support and military protection

against defense of French positions in the international arena. Several international databases allow

to measure both elements. To quantify French support we use the data on military interventions

collected by Kisangani and Pickering (2007). We compute a dummy variable equals to one if France

16

Table 4: The effect of the French influence in SSA on French exports: a sectoraloverview

Regressions by products group FARCoefficient Standard errors Observations

Standard International Trade Classification0 - Food and live animals 0.36a (0.05) 1824711 - Beverages and tobacco 0.27c (0.15) 268862 - Crude materials, inedible, except fuels 0.06 (0.07) 1066493 - Mineral fuels, lubricants and related materials 0.17 (0.17) 197584 - Animal and vegetable oils and fats 0.12 (0.14) 255325 - Chemicals 0.60a (0.06) 2019626 - Manufactured goods classified chiefly by material 0.45a (0.03) 5003187 - Machinery and transport equipments 0.31a (0.04) 2909418 - Miscellaneous manufactured articles 0.34a (0.04) 2240559 - Commod. and transacts. not classified 0.01 (0.17) 11955Rauch’s liberal classificationDifferentiated products 0.39a (0.03) 913810Homogenous goods 0.16a (0.06) 157954Reference priced 0.43a (0.03) 401604

Regression with interactionsIndustrial productsFAR 0.40a (0.03) 1590527FAR*Industrial products 0.03 0.03)ArmsFAR 0.39a (0.02) 1590527FAR*Arms 0.01 (0.24)High End productsFAR 0.39a (0.03) 1590527FAR*High End 0.10a (0.04)RCAFAR 0.49a (0.02) 1137849RCA index 0.24a (0.01)FAR*RCA index 0.12a (0.03)FAR 0.43a (0.03) 1137849RCA dummy 0.30a (0.01)FAR*RCA dummy 0.16a (0.03)

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 fixed effects, year fixed effects, Baier and Bergstrandmultilateral resistance terms, the logarithm of GDP, the logarithm of population, as well as the number of yearssince independence. Robust standard errors clustered at the country-4-digit SITC industry level in parentheses.a, b and c denote significance at the 1%, 5% and 10% level respectively.

17

intervenes in country c at time t. Votes at United Nations General Assembly give a good proxy of

States’ political proximity (see Mityakov, Tang and Tsui, 2013). We use a dyadic voting similarity

index ranging from 0 to 1 (1 denoting most similar interests) taken from Voeten, Strezhnev and

Bailey (2009). It is equal to the total number of votes where both states agree divided by the total

number of joint votes. Interpersonal ties, on the other hand, are by far the most difficult to grasp

in an objective way. This paper uses data on diplomatic visits to capture this proximity between

the French and the SSA top level officials. We gather original data on bilateral visits between

France and foreign countries over the 1977-2007 period from the French Ministry of Foreign Affairs.

Our data cover bilateral visits at the level of Head of State (government), Minister, Secretary of

State, but also advisers and unofficial visits. These information are taken from the database

“Evènements de politique internationale” of the French Ministry of Foreign Affairs that records

since 1977 quasi exhaustively all significant events in France’s international relations.These data

offer several advantages. Above all, they provide an objective and time-varying measure of the

strength of the relations between France and the African States and administrations. Thanks to

these data, we know how many time a year a French President meets a Malian or a Malagasy one,

or how many time a French minister meets his African counterparts. We acknowledge that these

data only allow us to observe the tip of the iceberg. They do not encompass all the elements of the

French inter-actions with its former colonies of SSA, notably the ones of the French Intelligence

Services. However, Françafrique is not only a question of “barbouzes”.18 We believe our data tell us

enough on the proximity of the top level officials.19 Furthermore, these data allow for international

comparisons. We are thus able to compare the number of visits between French and SSA top level

officials and the number of equivalent visits between French and other countries officials. Lastly,

our data cover a time span long enough (1977-2007) to take into account the general evolution of

foreign relations and the stop and goes in the French African relations. In all, we recorded 13,770

bilateral visits among which almost 60% were exterior to France (i.e. were external visits) and one

quarter involved the Head of State (government) that is to say Presidents, Prime Ministers, Kings

or Queens and so on. 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 official visits by French

Presidents on the time period 1948-2003 (along with visits by Heads of State of Germany and the

United States). We use the number of bilateral visits between France and country c at time t− 1

as a proxy of interpersonal ties.

18Pejorative, the term “barbouzes” marks out intelligence services agents or officers as well asadvisers and special correspondents appointed by Paris to support a President “friend of France”(Bat, 2015).

19It is worth noting that these data also record private visits. For instance, we know if on theoccasion of a personal trip to France, Omar Bongo (President of Gabon from 1967 to 2009) meetsJacques Chirac (French Prime Minister from to 1986 to 1988; and French President from 1995 to2007).

18

To test for these political channels, we introduce these additional explanatory variables to

our model and interact them with our FAR dummy. Table 5 presents our estimation results. We

estimate our model on various samples: the whole sample (column 1), a sample restricted to luxury

goods (column 2) and samples for each SITC products groups affected significatively (at the 5%

level) by the French influence (columns 3 to 7). Panel A reports results for voting similarity, Panel

B for military interventions and Panel C for bilateral visits. In almost all cases, the coefficient

of political variables is positive and significant. In other words, France exports more to countries

where it intervenes militarily, to countries that share similar positions in the international arena and

to countries with which it has maintained closed relations. The sign and the statistical significance

of interaction terms depend on the political variable at stake. It is positive and significant at

the 5% level when using the voting similarity index (except for food, columns 3), denoting that

the effect of the FAR increases with proximity in the international politics. As regards, military

interventions and bilateral visits, the coefficient of the interaction variables is, in almost all cases,

negative and significant. Such a result means that the SSA trade gap decreases with the number

of bilateral visits or during French military interventions. It is worth noting that when using the

voting similarity index, the coefficient of the FAR variable turns non significant. This coefficient

provides the estimated impact of the FAR for an hypothetical country that never voted as France

at the United Nations General Assembly. A coefficient not statistically different from zero suggests

that international politics explain nearly all the effect of the FAR on French exports. This results

holds at the aggregate as well as at the disaggregated level, with the exception of ‘Machinery and

transport equipments’. For this specific product group the coefficient of the FAR variable is now

negative and significant at the 5% level indicating that once we control for international politics,

France exports less machinery and transport equipments to its former SSA colonies during the

1962-1990 period than to any comparable countries. Such a result shows that much of the SSA

trade gap is explained by the political proximity of France and its former colony in the international

arena.

5.2 Economic and other potential drivers

We now turn to the potential alternative explanations for the SSA trade gap. As explained in

section 2, the FAR had also economic demonstrations such as development assistance, migration

and currency arrangements. All of these channels could potentially explain the extra French exports

to SSA former colonies before 1990. Indeed, foreign aid can be exports-enhancing for the donor

country (France in our case). In an intertemporal model of trade, Djajić, Lahiri and Raimondos-

Moller (2004) find that in the presence of habit-formation effects, aid may shift preference of the

recipient in the favour of the donor’s exports goods in future periods. Using a multi-donor gravity

19

Table 5: Exploring the political channels of the Franco-African trade gap

Sample: World H-E SITC 0 SITC 5 SITC 6 SITC 7 SITC 8Food Chem. Manuf. Mach. Msc. manuf.

(1) (2) (3) (4) (5) (6) (7)Panel A: voting similaritySSA_FormerCol -0.03 -0.47 0.25 -0.07 -0.21b 0.08 -0.08

(0.05) (0.46) (0.16) (0.14) (0.08) (0.11) (0.11)Voting similarity index 0.33a 1.68a 1.06a -0.17 0.27b 0.33b 0.65a

(0.05) (0.25) (0.17) (0.16) (0.09) (0.11) (0.12)SSA_FormerCol*Voting similarity index 0.71a 1.55b 0.17 1.18a 1.10a 0.35a 0.69a

(0.08) (0.8) (0.25) (0.22) (0.13) (0.18) (0.18)# of observations 1456438 42078 166025 185894 458219 267493 205049

Panel B: military interventionsSSA_FormerCol 0.40a 0.41a 0.36a 0.62a 0.45a 0.32a 0.33a

(0.02) (0.07) (0.05) (0.05) (0.03) (0.04) (0.04)Military intervention 0.08a (0.02 0.11b 0.16a 0.04c 0.10a 0.03

(0.01) (0.08) (0.04 ) 0.04 (0.03) (0.03) (0.03)SSA_FormerCol* Military int. -0.05b 0.18 -0.01 -0.24a -0.03 -0.17b 0.18b

(0.03) (0.17) (0.08) (0.07) (0.05) (0.06) (0.07)# of observations 1590527 45207 182471 201962 500318 290941 224055

Panel C: bilateral visitsSSA_FormerCol 0.43a 0.43a 0.38a 0.74a 0.46a 0.36a 0.39a

(0.02) (0.09) (0.06) (0.06) (0.03) (0.04) (0.04)Log of # of bilateral visits 0.04a 0.01 0.06a 0.02b 0.04a 0.04a 0.04a

(0.00) (0.01) (0.01) (0.01) (0.00) (0.00) (0.00)SSA_FormerCol*Log of # of bil. visits -0.04a 0.00 0.03 -0.08a -0.07a -0.05a -0.04a

(0.00) (0.05) (0.02) (0.03) (0.01) (0.01) (0.02)# of observations 1213749 41512 145275 151369 374171 224155 171483

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 fixed effects, year fixed effects, Baier and Bergstrandmultilateral resistance terms, the logarithm of GDP, the logarithm of population, as well as the number of yearssince independence. Robust standard errors clustered at the country-4-digit SITC industry level in parentheses.a, b and c denote significance at the 1%, 5% and 10% level respectively.

20

model of trade Martínez-Zarzoso et al. (2014) show that every aid dollar spent leads to an increase

in donor’s exports ranging from $0.50 in the short run to $1.8 in the long run. The large number

of public servants posted to newly SSA independent states had probably favoured French exports

through the effect of social and business networks,20 but also through the preferences of French

expatriates for the varieties produced in France, thereby directly enhancing the volume of bilateral

trade. Lastly monetary arrangements, such as fixed exchange rate regime, may ease trade.

To test for these alternative channels, we introduce additional explanatory variables to our

model and interact them with our FAR dummy. We test for foreign aid using the total net Overseas

Development Assistance (hereafter ODA) disbursements in current US$21 from 1962 to 2007 taken

from the OECD Development Database on Aid from the DAC Members. We use the total number

of French people living in country c at time t to test for the migration channel. Lastly, to control

for the effect of monetary arrangements, we compute a dummy variable denoting whether trade

partners were members of the same monetary union or whether they had a fixed exchange rate

regime between them at time t.

Table 6 depicts our estimation results for the whole sample (column 1), a sample restricted

to luxury goods (column 2) and samples for each SITC product groups affected significatively (at

the 5% level) by the French influence (columns 3 to 7). Panel A reports results for ODA. In

line with our expectations, our results indicate that aid has a positive effect on French exports

at the aggregated as well as at the sectoral level (except for ‘Food and live animals’ products).

They also show that development assistance reduces the Franco African trade gap on aggregated

exports (column 1) and French exports of luxury goods (column 2) and of manufactured products

(columns 6 and 8). The marginal effect of the French influence for a SSA former French colony

with a value of Log of ODA equal to the sample average is still positive and significant.

In Panel B, we test for the migration explanation. Our estimation show that migration en-

hances French exports at the aggregated as well as at the sectoral level. The coefficient of the

interaction term is positive and significant at the 1% for regression computed on the global sam-

ple (column 1) and a sample restricted to chemicals products (column 4). Such results indicate

the SSA trade gap increases with the number of French people living in former SSA colonies

during the 1960-1990 period. It is worth noting that for several products, namely ‘Chemicals’

(column 4), ‘Manufactured goods classified chiefly by material’ (column 5) and ‘Machinery and

transport equipment’ (column 6) the introduction of the Migration variable and of the interaction

term FAR∗Migration changes drastically our findings. The FAR coefficient is no longer statisti-

cally significant. This suggests that, for such products, the number of French expatriates explain

20See Rauch (2001) and Rauch and Trindade (2002), among others, for a discussion a discussionof the trade-enhancing effect of networks.

21The total net ODA disbursements are the sum of grants, capital subscriptions, total net loansand other long term capital.

21

nearly all the extra-trade between France and SSA former French colonies. Unfortunately, we are

not able to disentangle the business from consumer preferences effect.

Panel C reports our estimation results for the currency arrangements explanation. Our esti-

mation results are quite heterogenous. They show that currency arrangements have no effect on

global French exports (column 1), on French exports of chemicals (column 4). However, they have

a positive and statistically significant effect on French exports of ‘Food and live animals’ (column 3)

and ‘Miscellaneous manufactured articles’ (column 7), and a negative effect on exports of luxury

goods (column 2), ‘Manufactured goods classified chiefly by material’ (column 5) and ‘Machinery

and transport equipments’ (column 6). When statistically different from zero, the coefficient of the

interaction term FAR∗Currency_ar is positive and significant indicating that currency arrange-

ments increase the Franco African trade gap. Nevertheless, our findings suggest that currency

arrangements alone do not explain this exports gap, except for luxury goods (column 2). Indeed,

for this category of products, the addition of the Currency_ar variable and of the interaction

term FAR∗Currency_ar leads the FAR variable to turn non significant. This results suggests

that currency arrangements between France and its former colonies of SSA explain most of the

trade gap observed for High End products. The over-evaluation of the CFA Franc until 1994 may

have favored the purchasing power of the African elite.

In unreported regressions, we also test for a price channel. Indeed, it is often argued that

African countries and especially former French colonies of SSA used to pay more for their imports

(see Yeats, 1990). In such conditions, the Franco-African trade gap may just hide a price effect.

We use unit value as a proxy of price, and compute a ratio between the price paid by country c

for good i (4-digit SITC industry) in year t and the world (or developing countries) average price

observed for good i (4-digit SITC industry) in year t. Our results indicate that the Franco African

trade gap is not driven by difference in prices.

6 Conclusion

This paper investigates the consequences of the FAR on the pattern of French exports. Until the

beginning of the 1990s, France and its former SSA colonies had close and enduring political and

economical relations. Studying French exports after 1960, this paper aims at assessing whether

France exported disproportionately to its former SSA colonies as suggested by the FAR nexus. It

also seeks to explain the sources of this peculiar trade pattern. To the best of our knowledge, it is

the first paper to address systematically the effects of the French influence on trade and to shed

light on its drivers.

Our study yields a number of sobering results. Our estimations reveal peculiar trade patterns

between France and its SSA former colonies as compared to other comparable countries. During the

22

Table 6: Exploring the economic channels of the Franco-African trade gap

Sample: World H-E SITC 0 SITC 5 SITC 6 SITC 7 SITC 8Food Chem. Manuf. Mach. Msc. manuf.

(1) (2) (3) (4) (5) (6) (7)Panel A: ODAFAR 0.49a 0.94a 0.35a 0.59a 0.56a 0.34a 0.49a

(0.03) (0.46) (0.10) (0.09) (0.05) (0.07) (0.08)Log of ODA 0.03a 0.03c -0.00 0.03a 0.04a 0.08a 0.05a

(0.00) (0.01) (0.01) (0.01) (0.01) (0.01) (0.01)FAR*log of ODA -0.03a -0.14a 0.01 0.00 -0.03a -0.01 -0.04b

(0.01) (0.04) (0.02) (0.01) (0.01) (0.01) (0.01)

Panel B: MigrationFAR 0.13c 1.06a 0.58b -0.25 0.21 0.07 0.39b

(0.08) (0.38) (0.23) (0.21 (0.14) (0.17) (0.18)Migration 0.07a 0.07a 0.12a 0.09a 0.06a 0.06a 0.06a

(0.00) (0.02) (0.01) (0.01 (0.01) (0.01) (0.01)FAR*Migration 0.03a -0.08c -0.03 0.10a 0.03 0.03 -0.01

(0.01) (0.05) (0.03) (0.03 (0.02) (0.02) (0.02)

Panel C: Currency ArrangementsFAR 0.34a 0.13 0.44a 0.58a 0.33a 0.26a 0.29a

(0.02) (0.14) (0.07) (0.06) (0.04) (0.05) (0.06)Currency_ar. 0.00 -0.23a 0.29a -0.04 -0.07b -0.09b 0.08b

(0.02) (0.08) (0.04) (0.05) (0.03) (0.03) (0.04)FAR*Currency_ar. 0.06a 0.34b -0.10 0.03 0.14a 0.06 0.06

(0.02) (0.14) (0.07) (0.06) (0.03) (0.04) (0.05)

# of observations 1590527 45207 182471 201962 500318 290941 224055

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 fixed effects, year fixed effects, Baier and Bergstrandmultilateral resistance terms, the logarithm of GDP, the logarithm of population, as well as the number of yearssince independence. Robust standard errors clustered at the country-4-digit SITC industry level in parentheses.a, b and c denote significance at the 1%, 5% and 10% level respectively.

23

period 1960-1989, the golden age of FAR, France exported significantly more to these countries than

to other countries. It suggests that France used these special FARs to serve its own interests and

kept its former colonies as a private ground. Suggestion indirectly confirmed by the fact that the

FAR effect was stronger in industries in which France suffered from a comparative disadvantage.

Furthermore, we show that political drivers and, in particular political proximity, are the main

channels explaining the extra-trade between France and SSA former colonies during the time

period 1960-1989. Indeed, whereas migration explains the FAR trade gap for some specific products

(chemical, manufactured goods, machinery and transport equipments), State’s political proximity

is the only channel accounting for the whole trade gap.

This paper contributes to several bodies of literature. It complements the literature on the

the general question of whether power is an important determinant of international trade in the

recent past. It extents it to another power than United States,France, and to another context

: post-colonial relations. It contributes also to the debate on Sub-Saharan Africa’s isolation in

international trade, and add another explanation, foreign influence.

24

Appendix

25

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Table 7: 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 flow (exports), where available. As inHead et al. (2010), we add 10% to the export flow 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

Former colonyfc Dummy variable equals to 1 if country c was a colony of country f in 1945 taken fromthe cepii database. See www.cepii.fr/francgraph/bdd/distances.htm

Commoncoloniserfc

Dummy variable equals to 1 if country f and c were part of the same colonial Empiretaken from the cepii database. See www.cepii.fr/francgraph/bdd/distances.htm

Still colonyfct Dummy variable equals to 1 if country c is a colony of country f at time t taken fromthe cepii database. See www.cepii.fr/francgraph/bdd/distances.htm

RTAfct The Regional Trade Agreement dummy is computed using information from the WTO(see de Sousa, 2012).

Francafrct Dummy variable equals to 1 if country c is a former French colony in Sub-SaharanAfrica during the time period 1960-1989 and 0 otherwise.

CurrencyUnionfct

Dummy variable equals to 1 if countries f (France) and c have a common currencyat time t. We computed this variable on the basis of the classification proposed byde Sousa (2012).

#Years of Indepct Number of years since independence for country c at time t. This number is equal to0 before independence.

Visitct Bilateral visits at the level of Head of State (government), Minister, Secretary ofState, but also advisers and unofficial visits between France and foreign countriesover the 1977-2000 period. Data come from the French Ministry of Foreign Affairs(Evenements de politique internationale).

Migrationfct Bilateral migrant stocks come from the World Bank Global Bilateral Migrationdatabase (see Ozden et al., 2011). These data are available for each decade overthe period 1960-2000. For the years in between each decade, we replicate the valueof the previous decade.

Currency_arfct Dummy variable equals to 1 if countries f (France) and c have currency arrangement(a fixed exchange rate regime, a peg) at time t. We computed this variable on thebasis of the classification used in Shambaugh (2004) for fixed exchange rate regimes.

ODAfct Net flows of Official development assistance (ODA)received by country c from coun-try f at time t. Data come from OECD (2016), Net ODA (indicator). doi:10.1787/33346549-en (Accessed on 20 July 2016).

Militaryinterventionfct

Dummy variable equals to 1 if country f intervenes militarily in country c at time t.Data are taken from Kisangani and Pickering (2007).

Voting similarityindexfct

Dyadic voting similarity index ranging from 0 to 1 (1 denoting most similar interests)taken from Voeten, Strezhnev and Bailey (2009). It is equal to the total number ofvotes where both states agree divided by the total number of joint votes.

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Table 8: Data description and sources continued

Industrialic Dummy variable equals to 1 if the product traded i is an industrial product accord-ing to the International Standard Industrial Classification of All Economic Activities(ISIC).

Armsic The binary variable takes the value one if the product traded belongs to the division95 of SITC rev 1 classification that gathers all firearms of war and ammunition fromarmoured fighting vehicles to artillery weapons, machine guns and so on.

HEict Indicator variable for high end product computed following the strategy developed byFontagné and Hatte (2014).

RCAfit Balassa measure of comparative advantage (RCA) computed for France on a world-wide database over the period 1962-2007. A RCA index above one denotes that Francehas a comparative advantage in producing in industry i at time t. On the contrary,if the ratio is less than unity, France is said to have a comparative disadvantage inindustry i, since its share of world exports in industry i is lower than the average.

RCA dummyfit Binary variable denoting whether or not France has a comparative advantage in pro-ducing in industry i at time t.

30