1 New Proposal Estimating and Determinants of Benin's Cross

New Proposal

Estimating and determinants of Benin's cross-border trade

Eudoxie H. BESSAN Laboratoire d’Economie Publique (LEP/UAC) E-mail: [email protected]

Abstract: The aim of this research proposal is to analyze cross-border trade in Benin by estimating the extent of informal flows and highlight the potential determinants of this trade. Based on the model of Mitaritonna et al., (2017) extended to socio-cultural variables and INSAE survey data carried out in 2011 and 2018 on a sample of 8,883 individuals. Using a MCG estimator, we expect our results to assess the extent of informal cross-border trade; also the economic and socio- cultural determinants of informal cross-border trade are identified. Then, these results will allow us to define new policies of commercial integration and more efficient control of the informal trade.

Keywords: cross-border trade, informal, MCG, Benin.

Keywords: cross-border trade, informality, trade networks, socio-cultural links.

Estimation et déterminants du commerce transfrontalier informel du Bénin

Résumé : L’objectif de cette proposition de recherche est d’analyser le commerce transfrontalier du Bénin en estimant l’ampleur des flux informels et en mettent en relief les potentiels déterminants de ce commerce. A partir du modèle de Mitaritonna et al.,( 2017) élargi aux variables socioculturelles et des données d’enquête de l’INSAE effectuées en 2011 et 2018 sur un échantillon de 8883 individus. A l’aide d’un estimateur MCG, nous attendons de nos résultats que l’ampleur du commerce transfrontalier informel soit évaluée ; aussi les déterminants aussi bien économiques que socio-culturels du commerce transfrontalier informel soient identifiés. Ensuite, ces résultats nous permettront de définir de nouvelles politiques d’intégration commerciale et de contrôle du commerce informel plus efficace.

Mots clés : commerce transfrontalier, informel, MCG, Bénin

1. Introduction

Cross-border trade has been the subject of several concepts in the literature, namely: "informal", "unregistered", "parallel", "smuggled" trade (Egg, Herrera, 1998). In this research, we define informal cross-border trade as all the exchanges of goods and services, which are not recorded in the official statistics of Benin and which integrate the reality of border crossing (Ayipam, 2010; Nkendah et al., 2011; Mitaritonna et al., 2017). These flows are generally out of control and are often not subject to registration. This non-registration can therefore give false signals to decision-makers when making economic policy decisions.

Also, in an environment characterized by globalization and unrestrained competition, the development impulse goes through regional integration (Nkendah and al, 2011). Regionalism has become more than a priority in trade negotiations between the various African regional groups in the context for example of the creation of the African Continental Free Trade Area (AfCFTA)1, or even the implementation of trade agreements regional, such as the Common External Tariff (CET). In this perspective, how to give concrete content to the integration of the countries of the Economic Community of West African States (ECOWAS), if the data produced and compiled by the national statistical institutes do not take into account the real extent of trade between member countries of the community? How to explain that the main part of the exchanges is done in informality especially at the borders of the countries?

Trade between African countries is generally weak2, despite significant efforts to promote it. Most regional economic community agreements (CET-WAEMU, CET-ECOWAS3, Trade Facilitation Agreement-TFA, and WTO) have had little success in increasing trade between members (Golub, 2015; De Melo and Tsikata, 2015). As a result, an assessment of regional trade in Africa cannot be complete without including informal cross-border trade (Golub, 2015), since unregistered flows of goods across borders represent a significant share of international trade on the continent (Mitaritonna et al. 2017). We, therefore, know that the low level of intra-African exchanges of official data is to some extent due to the large share of transactions that this type of data fails to record. Despite the episodes of trade liberalization for a reduction in informality; the persistence of informal trade shows that some forms of trade barriers have remained high. For evaluation and real consideration of these flows not recorded in the national statistics, it would be more than necessary to clearly identify the factors behind such a generally African trend.

Numerous studies have attempted to determine the drivers of informal Benin’s cross-border trade with Nigeria (Golub and Mbaye, 2009; Ayadi et al., 2013; Golub, 2012, Bensassi et al, 2016; Mitaritonna et al. 2017). It was pointed out that, in general, trade policies such as import tariffs, trade restrictions, or bans on certain consumer goods are the main determinants of informal trade. The restrictions encourage informal trade, seen primarily as a way to avoid paying taxes, which leads to an underestimation of cross-border trade. In addition, when neighboring countries have different trade policies coupled with different exchange systems; this can lead to the development of informal trade (Azam and Daubrée, 2007). Also, it is more and more recognized even if this recognition is of the socio-anthropological source but generally taken up by economists (Golub, 2012; Benjamin et al, 2012) that, the exchanges take place informally due to strong socio-cultural proximity between these countries based on ethnic, religious and kinship ties detected between neighboring countries can be a solid base for informal exchanges.

1 The African Continental Free Trade Area (AfCFTA), the world's largest free trade area since the creation of the World Trade Organization (WTO), will expand into a market of 1.2 billion people, representing gross domestic product (GDP) of $ 2.5 trillion in all 55 African Union member states (CAPC, 2017). 2 The share of internal trade reached 40% in North America and 63% in Western Europe in the mid-2000s, it was estimated at only 10 to 12% in Africa (UNECA, 2015). 3 For example for CET-ECOWAS, out of the 15 member countries of the community, only 9 have implemented the tariff agreement.

Several arguments are developed to denounce the artificial and arbitrary nature of African borders (Alésina et al, 2011; Salifou, 2015). While it is true that the populations of sub-Saharan Africa have not been associated with the delimitation of their national territories (Young 1994; Herbst, 2000), it is undeniable that they can be used for purposes economic. In Africa, cross-border dynamics have long been neglected in the implementation of public development policies. This is explained by the fact that development policies based on classical conceptions of economic activity do not allow the economic logics that structure trade to be identified (Walther, 2007). However, these cross-border areas are the site of a strong proliferation of trade, economic, and migratory activities. However, despite its informal nature, this trade is very structured around organizations and networks that can operate on large scales where official rules are systematically evaded. Informal networks facilitate the dissemination of market information, the execution of contracts, and allow the provision of credit and money transfers quickly and at a low cost. In addition, because of the social and religious ties that unite the peoples of neighboring countries, fleeing official regulations is considered legitimate (Benjamin et al, 2015).

According to data from the Unregistered Foreign Trade Survey (ECENE) carried out in 2011, the value of imports in just 10 days could be estimated at 3,734 million CFA francs with a volume of 14,878 tonnes. Exports (including re-export) are valued at 2,167 million CFA francs with a volume of 4,578 tonnes. As for products in transit, they are valued at 1,481 million CFA francs for a volume of 1,026 tonnes during the same reference period. What characterizes trade between Benin and its neighbors is informality. In fact, 90% of this trade takes place in the informal sector. It must be recognized here that the narrowness of Benin's borders can play an important role in the dynamics of this trade. The total length of the Benin border is 1,989 km shared with Nigeria (773 km), Togo (644 km), Niger (266 km) and Burkina-Faso (306 km). Thus, most of Benin’s informal cross-border trade takes place with Nigeria. It is recognized that unregistered trade between Benin and Nigeria is likely to be substantial and vital for both countries. Nigeria is Benin's main trading partner with almost 86% of the value of informal imports, 95% of informal exports and 92% of informal re-exports across all informal trade (INSAE, 2012); while he occupies 9th place in the formal. In addition, the arbitrariness in the border demarcations between Benin and its neighbors (as for many countries in sub-Saharan Africa) with long-standing ethnic and religious ties leads to an absence of clear geographic or social separators (Young 1994) which are sources informal traffic.

The importance of the informal trade is not any more to demonstrate, however it is important to analyze its dynamics considering the extent which it takes these days and the difficulty of the governments to control it however source of tax losses. Our research proposal therefore seeks to answer the question: what are the characteristics of the dynamics of the development of informal trade at borders? The interest of our research is to broaden the study of the factors of cross- border trade to socio-cultural factors. This study is important in the sense that it will first allow a better assessment of unregistered trade and secondly a definition of more inclusive regional integration policies. It can also make it possible to rethink the fight against informal trade, which will not only be focused on the restructuring or reorientation of trade policies, but also on the definition of policies based on socio-cultural links between countries. The study is the first of its kind to consider economic and socio-cultural factors simultaneously empirically. It is based on a very recent database on the monitoring of transactions at the Benin-Nigerian borders carried out in November 2018, by INSAE4.

The rest of the document consists of four parts: the first presents the research objectives and hypotheses, the second analyzes the literature review, the third provides information on the methodology and the last part presents the expected results.

FIGURE 1 - West African languages spoken by more than one million people.

4 National Institute of Statistics and Economic Analysis of the Ministry of Development and Plan of Benin 3

______Source: Atlas of Regional Integration in West Africa, CSAO, 2009.

2. Research objectives and hypotheses 2.1. Main objective

The main purpose of our study is to analyze the characteristics of informal cross-border trade in Benin.

2.2. Specific objectives

Specifically it will be:

- Estimate the extent of informal cross-border trade broken down by product and flow;

- identify the economic and socio-cultural determinants characterizing informal cross-border trade in Benin.

2.3. Research hypotheses

- Informal trade reveals a strong dynamic of trade between African countries through agricultural products;

- The obstacles to the development of formal cross-border trade are: the premium of the parallel exchange market, the implementation of the TEC as well as the differential in trade policies;

- Socio-cultural factors and commercial networks serve as channels for facilitating informal cross-border exchanges.

3. Some descriptive statistics of the nature of trade between Benin and its neighbors

Based on ECENE data from INSAE (2011), Nigeria is Benin's main trading partner, around 70% among the countries bordering Benin. Exchanges are generally carried out in the informal sector because at almost 90%, the actors do not make customs declarations and it is the import flows which are affected (graph.1) at more than 60%. The main reason for these exchanges is first of the entire relatively low attractive price charged in Nigeria (59%) immediately followed by the proximity of the borders between the countries at almost 20% (grah.2). This last observation indicates the role of borders in informal exchanges.

Table 1 : Border countries and trade dynamics

Valid Cumulative Frequency Percentage Percentage Percentage Nigeria 5480 61,7 61,7 61,7 Togo 3274 36,9 36,9 98,5 Niger 29 ,3 ,3 98,9 Niger et Nigeria 48 ,5 ,5 99,4 Burkina-Faso et Togo 52 ,6 ,6 100,0 Total 8883 100,0 100,0

Graph 1: Nature of trade at the Beninese borders

Customs declaration Flows nature

Yes No Don't know Import Export Transit

8% 15%

33%

63% 77%

Graph 2: Main reason for exchanges

Price is more interesting Quality of the products is appreciated Price-performance ratio is better Country does not manufacture the equivalent Proximity to the border Other specify

18%

10% 59% 6%

4. Literature review 4.1. Economic factors of informal cross-border trade

In the literature, it is recognized that illegal transactions in informal cross-border trade can only take place when there are certain forms of restrictions or distorting factors that generally lead to divergences between domestic and foreign prices. These divergences cannot be justified by the transport costs of a market. Distorting factors are generally cited customs taxes, differences in trade policy, barriers to trade facilitation, and the premium of the parallel exchange market.

Customs tariffs: The first cause of the development of informal trade, undoubtedly one of the oldest, is customs taxes. The incentive to avoid paying taxes in response to increased tax pressure has been recognized since the work of Bhagwati (1964, 1967). The level of the tax rate is linked to the security of the tax base, so the higher the taxes, and the greater the risk of avoidance for the base. Thus, the increase in the customs duty rate reflects, not only the price differences between cross-border countries but also induce a significant level of smuggling in the economy (Nkendah et al, 2011; Bensassi et al, 2016 and Mitaritonna et al, 2017). Van Dunem and Arndt (2009) using the mirror statistics method for the case of Mozambique find that for an increase of 1 percentage point in tax rates, tax evasion increases by 1.4%. This elasticity found represents about half of that found in the case of eastern China from 1992-2007; Javorcik and Narciso (2008) support the hypothesis that the higher tariff level stimulates higher levels of customs fraud with estimated elasticities which tend to be lower than those found by Fisman and Wei (2004). Javorcik and Narciso (2008) also show that the relationship between the ratios of deviations to customs duties is stronger for differentiated products. In this way, they show the great facility to hide the real value of goods when they are differentiated, as suggested by Bhagwati (1967).

Differences in trade policies: The difference between trade policies lies in the prohibition or even the outright ban of certain consumer products (Bhagwati, 1967; Ackello-Ogutu, 1997; Benjamin and Mbaye, 2012). Many authors, such as Egg and Herrera (1998), Golub (2012b), therefore widely recognize the differences in national trade policies as a determining factor in encouraging informal trade. Golub and Mbaye (2009), and Oyejide et al. (2008) go further by stating that the large and varied differences in retail prices of goods between border countries confirm the existence of incentives for smuggling. Basing their analysis on the socio-anthropological relations which link Benin and Nigeria and; Gambia and Senegal, these authors show the consequences of price differentiation. Likewise, Ayaldi et al. (2013) find for the case of Tunisia with its neighbors (Libya and Algeria) that the main reasons for the large scale of the smuggling trade exist in the differences in the subsidy levels of each side of the border as well as in varying tax regimes. For example, the price of fuel is around a tenth in Algeria than in Tunisia and they estimate that around 25% of the fuel consumed in Tunisia is in the form of informal imports from Algeria.

Factors facilitating trade: Similarly, trade barriers can naturally explain a good part of the differences observed in wholesale prices between neighboring countries, but not all. According to Golub (2012b), the impact of other factors, including trade facilitation, the enforcement of rules governing border crossing points, the business climate, and the reduction of customs hassles can be taken into account. Indeed, the speed of customs clearance of goods, speed in customs procedures, port efficiency, and security noted in the ports of Cotonou and Banjul unlike the ports of Dakar and Lagos are factors as important as rights customs in the incentive factors for smuggling (Benjamin et al, 2012). The importance of facilitating factors in the dynamism of intra-regional trade and the reduction of informal exchanges has prompted the implementation of several regional projects5. However, the disparity in customs tariffs and the obstacles to trade facilitation is not the only determining factors in the development of informal cross-border trade.

Monetary factors, the parity argument: To these factors, we must add the parallel exchange rate which is the price of one currency for another on the black market. The existence of two prices at the same time for the same currency (the official exchange rate and parallel exchange rate) also constitutes a factor of distortion that can lead to and/or intensify smuggling (Amoussouga, 1984). The factor in which conditions this development is also monetary (Igué, 1983, Igué and Soulé 1992, Azam, 1991). All countries with non-convertible currencies are generally at a disadvantage compared to those whose currencies have exchange value outside their territorial space. Indeed, Barnett (2003) based on a model in which smuggling and the parallel market emerge because of government restrictions that prevent agents from also holding

5 Abidjan-Lagos Corridor Trade and Transport Facilitation Project-Abidjan-Lagos Corridor Organization (PFCTCAL-OCAL), the many activities of the Bordeless Alliance and the WTO Trade Facilitation Agreements. 6 foreign exchange. The author attributes movements in parallel rates to non-fundamental uncertainty and finds some interesting results. First, Barnett's model creates balances with positive and negative parallel premiums and correlations between illegal trade and the premium. Second, he suggests in an unprecedented way that currency speculation is the source of contraband and that this affects real economic activity in all sectors of the economy. The Black Market Premium is therefore an important determinant of the volume of informal trade.

These factors do not generally define the operating mechanism of illegal transactions due to the ambiguous role played by them in the coexistence of the legal market and the illegal market for goods. For example, many authors consider the parallel exchange market as the cause of illegal transactions (Amoussouga, 1984; Boismery, 1996, Dzaka, 2003, Thai, 2015, Onour, 2017); others, however, consider it only an element of facilitation of illegal trade, or an instrument for financing illegal trade (Amoussouga, 1994, Bahmani-Oskooee and Goswami, 2003, Golub, 2012, Hong and Pak, 2017; Ogoun, 2017). However, despite its importance in the context of Benin's exchanges with Nigeria, recent studies (Bensassi et al, 2016 and Mitaritonna et al, 2017) have not included it, which constitutes a great limit to the results found, however very elaborate. The importance of these factors in the development of informal cross-border trade cannot be questioned, but its development, which has not been mastered in recent years, has prompted several authors to look for other less economic factors than the former to explain the persistence and the resistance of informal cross-border trade to control policies. Sociological and anthropological factors such as socio-cultural links are today pointed out to explain this phenomenon.

4.2. Socio-cultural factors and networks of informal cross-border trade

Theory of commercial networks: In general, research on the impact of networks on trade concerns co-ethnic networks and business groups with publicly registered members, such as the Japanese keiretsu (Rauch, 2001). Co-ethnic networks are communities of individuals or businesses that share a demographic attribute such as ethnicity or religion. In some contexts, the key characteristic of the networks studied is that their members are engaged in repeated exchanges that help maintain cooperation. In other contexts, the essential feature is that network members have in-depth knowledge of the characteristics of others, with each other, or refer to outside business opportunities. Rauch (2001) reminds us that these key characteristics correspond roughly to two definitions of economic networks used in the sociological literature. The first, based on Podolny and Page (1998), defines an economic network as a group of agents who pursue repeated and lasting exchange relations with each other. The second, weaker definition is based on the work of Granovetter (1973, 1995): a set of actors who know the relevant characteristics of each other or who can learn them by reference. These two definitions complement each other.

Importance of ethnic and/or religious trade networks: Egg and Errera (1998) note that in Africa, cross-border and long-distance trade takes place in an environment marked by information asymmetries, numerous risks, and incomplete markets, in particular by the difficulties of access to capital. To reduce the resulting uncertainty, synonymous with high transaction costs, the players have adopted a set of organizations and rules, including the trading networks that David and Moustier (1998) characterize by a series of connections between actors (family, cultural, territorial ties) coupled with hierarchical relationships, obligations, and dependence. Ethnic networks have weight in the structuring of cross-border trade (Igue and Soulé, 1992). Indeed, this trade is sometimes, on both sides of the border, controlled by actors of the same ethnic origin, sharing the same culture and de facto prohibiting other actors from entering the market.

From an economic point of view, this ethnic control of the market a priori prevents the free entry of new players, resulting in situations of imperfect competition. For Dzaka-Kikouta (2003), cross-border trade networks allow African entrepreneurs, often confronted, and more seriously than their counterparts in other developing countries (DCs), to conditions of contractual insecurity exposing them to the opportunism of partners, to reduce the risk of breach of contracts by reducing transaction costs as much as possible. To this end, ethnic networks facilitate exchanges within borders and between countries, and these networks play a particularly important role in States where the rule of law is weak and where full information and the formal application of contracts by third parties are insufficient. Belonging to a group makes it possible to establish relationships of trust which make it possible to outsource specific activities to specialized service providers who are active along the trade route, such as carriers, financiers, customs agents. Ethnic networks also facilitate trade by reducing risks and uncertainties along the trade corridor and improving access to information, in particular with better access to mobile phones (Rauch, 2001). Ethnic networks also facilitate information sharing and reduce information 7 asymmetry. Not being part of a network is so difficult that the vast majority of merchants who are not part of an association or cooperative in a network are keen to become members, according to the study by Titeca and Kimanuka (2012) on small informal traders in the big lakes.

Actors of ethnic and religious trade networks: There are different forms of the social construction of the illegality that is developing at the borders of states in the context of globalization (Nugent, 2012). This shows their importance in cross- border trade. The grey border6 situation facilitates contact, creates social relationships, and encourages all actors to maintain the relationship once it develops (Dobler, 2016). This combination transforms the grey border into an ideal social space for the emergence of commercial networks, administrative and political actors (Raeymaekers 2009; Titeca, 2012; Meagher 2014; Titeca and Flynn 2014). These stakeholder groups are not isolated individuals. They often include different levels of institutional networks or patronage: truck drivers and transport contractors, warehouse managers and warehouse owners, customs officials and supervisors, politicians at different levels. If the situation is stable enough, interactions at all levels can interconnect and networks between different groups of actors can interact with networks of internal groups. Different forms of power relations are integrated into a relatively stable system that can transform into an apparatus of domination7. Basically, the networks in their evolution partially reach across the border to the other country and can, therefore, acquire scope, flexibility, and endowment making them difficult for single state institutions to control (Little 2010; Walther 2014).

Therefore, it is increasingly recommended to revisit the determinants of informal trade under the dual aspect of economic and socio-cultural factors to explain the phenomenon (Nkendah et al, 2011; Salifou, 2015; Dobler, 2016). Thus, in their founding work on the economic geography of Benin, Igué and Soulé (1992) stipulate that the rise of informal trade would be the product of a cross between socio-anthropological factors and the opportunities offered by disparities in economic and monetary policies of the States. Similarly, for Burgess and Stern (1993), fraud is based on cultural factors as much as on economic development, incentives to fraud, and the weaknesses of tax collection agencies. It then becomes relevant to analyze, using economic tools, the determinants of informal cross-border trade with an emphasis on socio-cultural ties.

5. Research methodology 5.1. Data source

In order to study the determinants of informal trade in Benin, we use a rich and very recent data source: the Survey of Unregistered Foreign Trade (ECENE) carried out from September 19 to 28, 2011, at 171 crossing points fraudulent spread over the entire national territory. The main objective of the survey is to assess foreign trade not registered with the customs cordon so that it can be taken into account in the preparation of economic statistics. At the end of the ten days of collection, 10,749 articles of all flows combined were recorded and 8,883 trade players interviewed (traders, transporters, smugglers, etc.). However, the results analyzed relate to 10,415 of these articles, i.e. a completion rate of 97% due to the estimation methodology which starts from the availability of the value of the products observed. ECENE is a four- quarterly survey whose scope mainly covers all border municipalities within the territory of the Republic of Benin. However, certain non-border municipalities such as Cotonou in the Littoral, Parakou in the Borgou and Natitingou in the Atacora are also concerned with regard to certain constraints of practicability and accessibility of the routes. The last date is December 2018. We also use this database.

5.2. Theoretical model and specification

We start from the theoretical model of Mitaritonna et al, (2017) to install a theoretical framework of the determinants of informal cross-border trade. Their work is the first quantitative study on cross-border trade. Indeed, due to the lack of data, most of the existing studies on informal cross-border trade in Africa are qualitative and based on fieldwork or case studies based on indirect inference and accounting.

The authors specify a fractional response model. The model can be identified under the assumption:

6 Dobler analyzes in this article the economic and social relevance of borders in Africa. To do this, it presents a typology of borders from the colors green, grey and blue which respectively designate the paths, the bushes and the villages; roads, railways and border towns and corridors, airports and ports. 7 See Mühlmann and Llaroya (1968) Popitz (1992) for theoretical analyzes of this process. 8

풊풏풇 푿풇 푬 | | = 푮(휷ퟏ푿풊 + 휷ퟐ풁풊풄) (1) 푿풇

풊풏풇 푿풇 With the share of informal trade in the total trade of product i 휷ퟏ푿풊 + 휷ퟐ풁풊풄 is the empirical counterpart of 푿풇

흀풊(푺푻푭 − 푺푻푰) + 푻풊 − 품(푻풊). 풁풊풄 is a vector of trade policy variable and 푿풊 a vector of the characteristics of product i.

We follow the same logic to establish a specification that relates to the determinants of the choice to trade in the informal sector. As the authors show, very few products appear in the formal and informal trade data, which reduces the relevance of the variable of interest. Therefore our dependent variable is the volume of goods traded (CTI) by each actor. In addition, the interest of this study is the analysis of the characteristics of the actors (traders, transporters) of the informal trade and the reasons supporting this activity. Unlike Mitaritonna et al, (2017) we include in the analysis of the determinants of cross-border trade, the characteristics of the players, while integrating the characteristics of the products traded. We extend the model to socio-cultural and socio-demographic characteristics as follows:

푪푻푰풊 = 푮(휷ퟏ푿풊 + 휷ퟐ풁풊) (2)

With

푿풊 a vector of economic variables and trade policies; 풁풊 a vector of socio-cultural variables; 푪푻푰풊 the volume of goods traded monthly.

Since our variable to be explained is quantitative and the explanatory variables are both quantitative and qualitative, using OLS would lead to biased estimators. The econometric model adopted here is a generalized linear model (GLM) which can be estimated with a quasi-maximum likelihood estimator, as in Papke and Wooldridge (1996).

5.3. Definitions of variables

Most of the informal trade takes place through illegal crossing points. As a result, unlike a survey carried out at customs crossings, the survey "monitoring informal cross-border trade in Benin" only takes into account informal actors who generally prioritize illegal crossings. Thus, the question of the probability of the occurrence of informal trade is no longer relevant. What would be relevant is the volume of goods traded informally. Our dependent variable is therefore measured by the quantity traded monthly (CTI). Informality in cross-border trade would depend on three categories of factors: (i) economic factors, and (ii) socio-cultural factors.

Economic factors: customs tariffs measured by customs duties (CET WAEMU or ECOWAS) and/or export taxes (TD).The black market premium measured by the ratio between the black market exchange rate and the official Naïra exchange rate (BMP). The policy of facilitation of trade at the borders of the two countries measured by customs harassment (TD) and the type of goods traded (PAGRI).

Socio-cultural factors: membership of a commercial network with ethnic and / or religious overtones (personal commercial network, tontine group) (RsoC); Religion (REL), ethnicity (ETH).

In order to have an overview of these factors and to facilitate the monitoring of their evolution, we are building a composite index of socio-cultural factors. Several approaches make it possible to aggregate the different dimensions of the factors considered. These approaches include, among others, the entropy approach and the inertia approach (Abdelkhalek and Ejjanoui, 2010). The construction of a factor indicator will be based on our work on the inertia approach through multidimensional analyzes. The choice of this approach is mainly explained by the fact that it allows eliminating as much as possible the arbitrary in the calculation of the composite indicator while avoiding redundancy in the choice of the relevant dimensions of the risk. The factor analysis technique which is most suitable in our case is that of Multiple Correspondence Analysis (MCA) because the indicators of socio-cultural factors are measured at the level of individuals in qualitative form and can be coded under a binary form. We complete the analysis with a few socio-

9 demographic factors of individuals such as education (EDUC), age (AGE) number of dependents (NPC), gender (WOMEN)

Table 1: Definitions of expected variables and signs

Variables Definitions Expected signs Dependant variable CTI Volume of goods traded by players in cross-border markets (in tonnes)

Economic factors TD-CET (+) They are entered in the form of log log (1 + t) with t the ad- Javorcik et Narciso (2008) Golub valorem customs duty for each category of product i (2012) ; Bensassi and al, (2016) ; Mitaritonna et al, (2017) Takes the value 1 if the product is agricultural 2 otherwise BMP BMP measured by the ratio between the black market exchange (+) rate and the official FCFA / Naïra exchange rate Azam and Daubrée (1991) ; Amoussouga Gero (1994) Barnett (2003), FACom Customs harassment (+)

PAGRI Takes the value 1 if the product is agricultural 2 otherwise (+) Ackello-Ogutu and Echessah (1997) Mitaritonna et al, (2017) Socio-cultural factors RsoC Takes the value 1 if the individual says he belongs to a (+) commercial network based on kinship, ethnicity or religion Egg and Herrera (1998), Golub and helping him in his activity Hansen-Lewis (2012) ETH Takes the value 1 if the actor is Yoruba, 2 if he is a goun, 3 if he (+) is an Ibô Igué and Soulé (1992), Alésina and al, (2011) REL Takes the value 1 if the actor is a Muslim, 2 if he is a Christian, 3 (+) if he is an animist Igué and Soulé (1992) ; Alésina and al, (2011) ICF Composite index of socio-cultural factors for individual i obtained from the multiple correspondence analysis (MCA) Socio-demographic factors FEMME Takes the value 1 if the actor is a woman and 2 if not (+) Ackello-Ogutu et Echessah (1997), Titeca and Kimunaka (2012) EDUC Takes the value 1 if the actor has no level, 2 if primary level, 3 if (−), first cycle secondary level, 4 if second cycle secondary level, 5 if we hypothesize that from a certain higher level level of study, the actor is more attracted to formal trade knowing and aware of the interests linked to the formalization of his activity AGE Takes the value 1 if the actor belongs to the age group of [15- (+/− ), 24┤ [; 2 if [25-34┤ [; 3 if [35-44┤ [; 4 if [45-54┤ [; 5 if [55-and Atta and al, (2016). more┤ [ We could include AGE2 to observe a threshold effect. NPC Number of people in charge (+/−) Source: Author

6. Expected results 10

At the end of this research work, we expect our results to assess the extent of informal cross-border trade; also the economic and socio-cultural determinants of informal cross-border trade are identified. This result will allow us to define new policies for trade integration and the fight against the informal sector based on these links.

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