Sami Bensassi
email: sami.bensassi@bham.ac.uk

Joachim Jarreau

Andras Niedermayer

Price Discrimination in Bribe Payments: Evidence from Informal Cross-Border Trade in West Africa

Keywords: Informal trade, corruption, trade policy. JEL classication: O17, F14, F15

What factors explain the persistence and pervasiveness of corruption in certain parts of the world? In West Africa, many day-to-day transactions require the payment of bribes. Quantitative evidence on these bribes and their determinants is scarce. This paper sheds light on the level and the frequency of bribe payments in informal cross-border trade. It examines how bribes depend on the trade regime and on market structure. We rely on data from a survey of traders in Benin to estimate the determinants of bribe payments. We exploit variations in the trade regime across Benin's borders, as well as changes in trade restrictions over time and variations in route availability across space and time. We nd that reductions in trade barriers help to lower bribes, but do not eliminate them, with bribes remaining frequent in liberalized trade regimes. These results suggest that collusive corruption -used to circumvent regulations and taxes -coexists with coercive corruption, where ocials use their monopoly power to extract transfers from traders.

Introduction

Why is corruption so pervasive and persistent in certain parts of the world, such as Sub-Saharan Africa, for example? Despite three campaigns against corruption organized by the World Bank in 22 years, a convention on preventing and combating corruption adopted by the African Union, and numerous reports from the World Bank and the United Nations Economic Commission for Africa (UNECA), African countries remain highly corrupt according to existing rankings (OECD/WorldBank, 2012; UNECA, 2016;World Bank, 2017).

1 [START_REF] Shleifer | Corruption[END_REF] classify two types of corruption: "with theft" and "without". In cases with theft, bribe payers collude with ocials to reduce the payment of a tax or to avoid a regulation. Rents created by the transaction are shared between the private agent and the public ocial. Bribery without theft requires the bribe payer to pay the ocial an additional amount in excess of the ocial price in order to access a particular public service. This type of bribe is said to be coercive. [START_REF] Shleifer | Corruption[END_REF] suggest that "corruption with theft aligns the interests of the buyers and sellers" and consequently "it will be more persistent than corruption without theft, which pits buyers against sellers."

In this paper, we use rich data from a survey on informal cross-border trade (ICBT) in West Africa to study the frequency and level of bribes paid by traders during their journey to the border. We rst examine whether bribes are widespread.

We then study the determinants of the bribes to understand which factors depress or increase the price of bribes. We exploit cross-sectional variations in trade regimes and products, as well as changes in trade barriers over time, to identify their impact on bribes. We also exploit precise information on transport modes, on the location of traders' starting and arrival points and of border crossing points, and on the alternative routes a trader may use.

Because of the dearth of data regarding bribery, only a handful of empirical papers have tried to break down the mechanisms at play when bribes are paid.

Most existing studies have examined the trade and transport sectors, where bribery is particularly pervasive [START_REF] Lavallée | Corruption in the informal sector: Evidence from west africa[END_REF]. [START_REF] Sequeira | Corruption, trade costs, and gains from tari liberalization: Evidence from Southern Africa[END_REF] uses data from an audit study of large shipments imported by formal rms into South Africa, with direct data on bribe payments at the border. She nds that bribes paid to customs agents decreased when taris were reduced, with some evidence of a shift from collusive to coercive bribery. [START_REF] Sequeira | Corruption and rm behavior: Evidence from African ports[END_REF] link a dataset of bribes paid at ports in South Africa and Mozambique to data on formal importers.

They nd that importers adapt their transport strategy in function of corruption costs. [START_REF] Olken | The simple economics of extortion: Evidence from trucking in Aceh[END_REF] use data from a survey of truck drivers on the island of Sumatra to show that ocials apply price discrimination to bribes. The authors use changes in the number of checkpoints to observe that bribe-setting depends on market structure. Their study focuses on formal operators (truck drivers employed by formal rms). [START_REF] Foltz | Do higher salaries lower petty corruption? A policy experiment on West Africa's highways[END_REF] study cross-border trade in West Africa, also using data on formal operators (registered truck drivers in Ghana). Their focus is on the "supply side" of bribery (changes in ocials' wage rates). However, there is, to our knowledge, no quantitative study of bribes paid by informal traders in Sub-Saharan Africa, or elsewhere. Our paper's contribution is to provide evidence on the frequency and level of bribe payments made by informal traders and to identify some of the determinants of these bribes. Whether bribery is more or less frequent in informal trade, and whether the mechanisms sustaining corruption are similar to those found among formal (and generally larger) traders, are open questions that have implications for trade and development in the region.

ICBT is an important sector of economic activity in Africa. It has been shown to outstrip formal regional trade for the African countries where data are available [START_REF] Ackello-Ogutu | Unrecorded cross-border trade between Kenya and Uganda[END_REF][START_REF] Golub | National trade policies and smuggling in africa: The case of the Gambia and Senegal[END_REF]World Bank, 2011[START_REF]Cross-border trade between Nigeria and CEMAC countries estimating trade ows, describing trade relationships,and identifying barriers to cross-border trade between cameroon and nigeria[END_REF][START_REF] Bensassi | Algeria-Mali trade: the normality of informality[END_REF][START_REF] Bensassi | Regional integration and informal trade in Africa: Evidence from benin's borders[END_REF]. ICBT in Sub-Saharan Africa also provides muchneeded employment and enables deeper regional integration [START_REF] Afrika | Informal cross border trade in Africa: Implications and policy recommendations[END_REF]IMF, 2017). At the same time, the informal nature of ICBT might make this form of trade more prone to bribery.

Our results yield three groups of ndings: First, we show that bribes are highly frequent in informal cross-border trade.

Overall, about 80% of traders paid a bribe, and this rate is above 60% across all categories of traders. Bribe payments are relatively low in comparison to the value of the goods traded.

Second, we nd evidence that ocials use price discrimination when setting the level of bribes, in line with [START_REF] Olken | The simple economics of extortion: Evidence from trucking in Aceh[END_REF]. However, we also shed light on new discrimination mechanisms. Informal traders use a large variety of transport modes to cross borders, which creates variations in the price elasticity of demand for passage. Traders using lighter or slower modes of transport (such as motorbikes or pirogues) pay signicantly lower bribes than traders using trucks.

Finally, we show that trade barriers increase the level of bribes. Using changes in trade barriers over time, we show that traders of products facing an import ban pay higher expected bribe payments. Conversely, trade of local unprocessed products, for which trade impediments have been removed in the region, pay lower bribes.

These results are consistent with collusive bribery.

Overall, our paper depicts an environment of ingrained corruption in ICBT. Our results suggest that bribery may create incentives for traders to use less ecient transport modes in order to avoid paying bribes or to make lower expected payments.

Deeper regional integration may reduce bribery. This paper belongs to several strands of literature.

It contributes to the literature on the determinants of small-scale corruption. An increasing number of papers have looked at smuggling and corruption in the context of formal international trade [START_REF] Yang | Can enforcement backre? crime displacement in the context of customs reform in the philippines[END_REF][START_REF] Sequeira | Corruption and rm behavior: Evidence from African ports[END_REF][START_REF] Sequeira | Corruption, trade costs, and gains from tari liberalization: Evidence from Southern Africa[END_REF]. We establish that price discrimination is used to set bribe levels for ICBT.

Our setting is unique, in that we are able to observe the dierent routes and transport modes used by traders connecting the same markets. We can thus verify that the level of bribes depends on the transport mode used.

Our paper also complements a long line of development studies research examining informal traders' organizations, characteristics, and actions in Sub-Saharan Africa. These works have focused either on particular economic sectors, specic groups of traders, or geographical regions [START_REF] Igue | Evolution of illicit trade between Dahomey and Nigeria since the war of Biafra[END_REF][START_REF] Titeca | Regulation, Cross-Border Trade, and Practical Norms in West Nile, North-West Uganda[END_REF][START_REF] Titeca | Walking in the Dark: Informal Cross-Border Trade in the Great Lakes Region[END_REF][START_REF] Walther | Business, brokers and borders: The structure of west african trade networks[END_REF][START_REF] Grossman | Evidence from Lagos on discrimination across ethnic and class identities in informal trade[END_REF]. Some have meticulously described the bribe payment process required to trade goods across borders informally [START_REF] Bako-Arifari | La corruption au port de Cotonou : douaniers et intermédiaires[END_REF] or the power relations at play between informal traders and customs ocers [START_REF] Van Den Boogaard | Norms, networks, power and control: Understanding informal payments and brokerage in cross-border trade in Sierra Leone[END_REF], while others have focused on the most vulnerable groups of traders [START_REF] Afrika | Informal cross border trade in Africa: Implications and policy recommendations[END_REF][START_REF] Titeca | Walking in the Dark: Informal Cross-Border Trade in the Great Lakes Region[END_REF]. However, none of these works have studied bribe payments at a country-wide level where the scope is not limited to particular groups of traders.

In Section 2, we present the context of ICBT in Benin and introduce our data set. We also provide descriptive statistics on traders' characteristics, the transactions involved, and bribe payments. In Section 3 we present our empirical strategy.

Section 4 is dedicated to our results. Section 5 follows with some robustness checks. 3 In addition, a list of import prohibitions on more than 25 groups of items was maintained by the Nigerian customs authority, with regular changes to the list. In July 2011, for example, a number of products were removed from the list.

4

In our empirical analysis, we exploit this change, which occurred between the two waves of the survey, to estimate the impact of this trade barrier on the bribes paid by traders.

Informal trade

In many parts of Sub-Saharan Africa, a large share of international trade ows does not go through ocial customs border posts and is not recorded in ocial customs data: it is generally called informal cross-border trade. Such ows cover many products and sectors, and their total value exceeds, in many cases, the value of ocial trade [START_REF] Lesser | Informal cross-border trade and trade facilitation reform in sub-saharan africa[END_REF]World Bank, 2013;[START_REF] Golub | Informal cross-border trade and smuggling in Africa[END_REF][START_REF] Bensassi | Regional integration and informal trade in Africa: Evidence from benin's borders[END_REF].

This form of trade is particularly pervasive in Benin, which led the national statistics institute (INSAE) to conduct the ECENE survey (Enquête sur le commerce extérieur non enregistré) in order to better estimate trade ows and measure their magnitude and composition. Several types of trade are found in this category. First, there is trade in local or regional products: imports of products from Benin's regional neighbors (mainly Nigeria and Togo) and exports from Benin to these countries.

These ows are internal to the WAEMU or ECOWAS regional agreements. Among them, local unprocessed products benet, in principle, from total free movement.

Traders of transformed products need to present certicates of origin to benet from preferential treatment.

Second, there is a large sector of re-export and transit trade in Benin. Benin and Togo have long specialized in this entrepôt trade activity [START_REF] Igue | L'Etat Entrepôt au Bénin: Commerce Informel ou Réponse a la Crise? Khartala[END_REF], which involves importing products, mostly from Asia and the EU, and re-exporting them to neighboring countries (mostly Nigeria).

5

These transactions are likely to face higher restrictions than local trade, as most of this trade with Nigeria involves the smuggling of high-tax or banned products into Nigeria. For this reason, we carefully distinguish these trade ows using information in the data on the type of trade ow (imports, exports, re-exports, or transit) as well as on the country of origin and destination of the shipments.

6

Finally, illicit trade in petroleum products (gasoline) is highly developed between Nigeria and Benin due to the subsidies available in Nigeria. Although this trade is illegal, it often takes place in daylight. Our data includes many examples of this type of transaction.

To summarize, our data covers extremely diverse types of informal cross-border trade, which, taken together, make up a signicant share of regional trade [START_REF] Bensassi | Regional integration and informal trade in Africa: Evidence from benin's borders[END_REF]. Some of these trades, namely entrepôt trade and gasoline trade, would qualify as smuggling: their purpose is to avoid taxes, trade restrictions, or regulations. In our data, these types of trade coexist with trade in local products, which do not face trade barriers, but may avoid customs for other reasons (such as compliance or certication costs, delays, harassment, or bribery). In our analysis, we carefully control for the dierent types of trade and test how exposure to bribery varies across them.

Given that Benin has one of the lowest ratios of customs ocers per kilometer of land border (0.34) in the region, Benin's borders could be characterized as porous (Table 1). Nonetheless, having porous borders does not mean that this porosity is not managed. Customs agents pay individuals to serve as a supplementary force to inform them of movements of goods and to provide control outside the border posts.

This supplementary force in turn accepts bribes from traders in return for failing to inform the agents [START_REF] Blundo | État et corruption en Afrique. Une anthropologie comparative des relations entre fonctionnaires et usagers (Benin, Niger, Sénégal)[END_REF]. with the 2010 list). Figure 1 shows the coverage of the 2011 survey points.

7

The survey was designed to cover the diverse routes that connect Benin to its neighbours and that are used by traders: all-weather highways , an extensive network of bush and rural roads [START_REF] Afolayan | Trans-Border Studies : The Motivation and Integration of Immigrations in the Nigeria-Niger Border Area/ Transborder Movement and Trading. A Case Study of a Borderland in Southwestern[END_REF], as well as a network of inland waterways [START_REF] Azonsi | Atlas hydrographique du benin[END_REF].

In January 2010, the 150 points were surveyed over a 7-consecutive-day period by a team of 225 surveyors, 12 controllers, and 6 supervisors. In 2011, the operation was repeated, with the 171 points surveyed over 10 consecutive days (employing 350 surveyors, 30 controllers, and 12 supervisors).

A four-page questionnaire was completed by surveyors and traders. Information on the nature, quantity, and value of goods transported, the origin and destination of the shipments, and the transport mode was collected. In 2011, a total of 8,883 questionnaires were completed (5,173 in 2010) (INSAE, 2011). After data cleaning, 8,610 observations from 2011 were retained for analysis (5,045 in 2010). The unit of observation is a trader's crossing. Most traders were surveyed only once, although it is possible that traders crossing several times during the survey period could have been interviewed more than once.

8 Products were codied at a high level of detail (using the ten-digit codes of the Harmonized Commodity Description and Coding System, HS-10). 9

Trade ows were classied as exports, imports, re-exports, and transit. The latter two categories correspond to ows of goods originating in third countries (typically in Asia or Europe) that enter Benin (possibly legally) before crossing one of Benin's land borders to reach one of the neighboring countries. We retain all these trade regimes in our analysis, but control for entrepôt trade, which may face higher trade barriers than domestic trade, and thus higher exposure to bribery.

Previous studies on trade in the region have underlined the quantitative importance of entrepôt trade [START_REF] Igue | L'Etat Entrepôt au Bénin: Commerce Informel ou Réponse a la Crise? Khartala[END_REF][START_REF] Golub | Entrepôt trade and smuggling in West Africa: Benin, Togo and Nigeria[END_REF]. In our data, entrepôt trade and gasoline smuggling are the largest types of trade in value (see Table 4).

The value of entrepôt trade is larger than that of imports and exports (trade of domestic products with Benin's direct neighbors), which include more numerous, but lower-value transactions. , 2011). For example, the survey was conducted in cooperation with the customs administration to ensure that customs agents' control operations would not interfere with the conduct of the survey.

Concerns regarding sample selection are thus limited given that the trade activities facing the highest levels of risk and repression are frequently observed in the survey and do not appear to be signicantly under-reported. Selection issues remain possible, however, in particular because some of these trade operations are known to take place at night (and are therefore not covered by the survey). A related concern is that replies of traders engaged in these activities might be missing more often, or be less truthful. To address this concern, we conduct a number of tests to determine the sensitivity of our results to excluding these high-risk products, as presented in the empirical section of the article.

Bribes

Each trader was asked for the amount of informal tax paid during his or her travel.

This question includes all payments made during the transport of a good for which the trader received no ocial receipts. Accordingly, this variable potentially includes payments made to the military, police, or customs ocers. Unfortunately, we have no information on the recipients of these payments.

10 In this paper, we use the term bribes for informal taxes, the term used in the survey. 11

The two waves of the ECENE survey contain 13,655 observations (crossings).

Among these, the informal tax question is missing in 678 cases in 2011 and 229 in 2010 (overall rate of 6.6%). The stigma associated with paying bribes, if there is one, is thus relatively uncommon.

There is a risk that these non-responses may depend on some determinants of bribes. The data show that this issue is limited. For example, the non-response rate is 7% for traders of gasoline and 6.5% for other traders; the dierence is not signicant. To test this further, we ran probit estimates of the probability of nonresponse to the bribe question: the results are displayed in Table 3. Almost none of our variables of interest are signicantly associated with a higher or lower rate of non-response. These results conrm that the risk of non-response does not vary signicantly according to the type of trade. Paying a bribe is the norm rather than the exception among traders, as evidenced by the frequency of payment in our data.

No particular stigma appears to be associated with it. shows the average formal tax rate paid by traders.

It emerges, rst, that bribe payments are highly frequent across all trade categories: they are the norm rather than the exception. This conrms that bribery is pervasive in the countries we consider. Note that these gures also minimize the concern that respondents might be reluctant to report paying bribes. Bribes are more frequent for entrepôt trade and gasoline smuggling, which face more trade barriers than trade in domestic products. However, the average value of bribes, as a share of cargo value, appears to be lower for these categories.

The ubiquity of bribes suggests that collusion -payments made to avoid the payment of formal taxes or regulations -is not the only motive for these payments.

Panel C shows that trade at Benin's WAEMU borders, which should be more integrated, experiences a lower bribe incidence than that at Nigeria's border, although it remains above 60%. Similarly, trade in unprocessed products (panel B), which in principle benets from free movement at Benin's borders, experiences scarcely less frequent bribery than other ows.

Panel D splits the sample by transportation mode. The largest share of trade (38%) is conducted using pirogues, followed by trucks (34%). The remaining share employs cars, pedestrians, and motorcycles. Bribe payments are particularly frequent for truck and car users. A stylized fact emerges: traders using lighter modes of transport tend to pay lower bribes, and lower formal taxes (relative to traded value), than trucks.

12 These features will be further examined in the econometric analysis. Source: ECENE survey 2010 and 2011. Regional trade includes re-export and transit ows with a regional (West African) country of provenance.

Bribe payments: comparison with trade costs and with existing estimates

To put the gures on bribe payments into perspective, we compare them to the taris traders would have paid using formal trade routes and to the bribes that have been reported as being paid on formal routes in other studies.

Trade between Benin and its WAEMU partners, Burkina Faso, Niger, and Togo, is, in principle, liberalized. In practice, evidence suggests that many trade ows internal to the WAEMU area nonetheless face trade restrictions, in particular because of costly origin certication. Unprocessed products are the only items that do not require a certicate to be traded inside the WAEMU.

13 If certicates of origin are not provided, customs agents should consider the products in question as not being produced in the WAEMU and should revert to the common external tari (between 5% and 20%). In the case of trade with Nigeria, no preferential treatment applied to trade from Benin in 2011. The maximum rate on Nigeria's imports was 35%, with additional levies on some products. We compute the average most-favored nation (MFN) rate that would have been applied to shipments at the borders of Benin with WAEMU partners in cases where no certicate of origin was provided, and the rate applied to shipments at the border with Nigeria. We obtain an average MFN of 14.9%, compared to 3.57%, the sum of the informal and formal tax ratios, paid by informal traders for the WAEMU trade. In the case of Nigeria, the average MFN is 14.1%, compared to a ratio of 3.09%. These numbers provide an estimate of the dierence between the de jure tax rates applied to shipments and the de facto payments made by traders. They suggest that actual payments are considerably below the duty that would apply.

How do our gures on bribe payments compare with other studies on petty corruption?

The context examined by [START_REF] Foltz | Do higher salaries lower petty corruption? A policy experiment on West Africa's highways[END_REF] is the closest to our case: their study is based on a survey of truck drivers involved in cross-border trade between Ghana and Burkina-Faso. The incidence of bribery they report appears to be consistent with our gures: the unconditional probability of not paying a bribe at a stop is between 4.6% and 13% (based on 2,147 trucking trips surveyed). Their survey focused on trucks plying a single route. Importantly, this route is an ocial one, being the main road linking Accra to Ouagadougou; and the survey selected truck drivers with papers for the truck and cargo ... in order. Thus, the trucks in their survey qualify as being part of the formal trade sector, while our focus is on informal traders. A high frequency of bribery is clearly not a specicity of informal trade, although levels of payment may dier.

14 [START_REF] Teravaninthorn | Transport Prices and Costs in Africa : A Review of the International Corridors[END_REF] report estimates of the share of bribes in relation to variable costs for trucks operating on the main trade corridors in Africa.

Looking at the bribe to fuel costs ratio, which oers the most direct comparison, their gures are between 8% and 71% for West and Central Africa. We nd a mean value of 94%, and a median of 26%, for the same variable. This is consistent with their statement that bribe costs are higher for informal trade operations, the focus of our study, than for other type of trade actors.

Sample statistics

Table 5 displays descriptive statistics for our estimation sample. The distribution of the size of shipments is skewed, with many small-scale shipments and a few large ones. This reects the high diversity of our data in terms of trader types and transport modes.

Distance measures the road distance between a shipment's market of origin and the border crossing point where the survey took place. We used data from the Google maps API on driving and walking itineraries between each pair of locations.

The walking itinerary reports the shortest route on secondary/bush roads available in the application. It is therefore relevant for both pedestrians and motorcycles, and is shorter than the driving route in most cases. 15 We applied the driving distance to traders using cars and trucks. We thus measure the actual distance traveled by each trader as accurately as possible.

16 Note that the route is generally located in Benin, in the case of exports, re-exports, and transit; or in one of the neighboring countries, in the case of imports.

Distance is missing in cases where the market of origin could not be located on the map. We thus ran our estimations on both the subsample with the distance variable, and on the whole sample, omitting distance, in order to verify that our results hold in both cases.

Distance to ocial post measures the distance between the survey's border crossing and the nearest ocial customs post. A list of 45 customs points with their location was built using Beninese customs' codes (code des douanes), as well as additional sources for secondary points.

About one third of traders were operating within the WAEMU area (i.e. the goods' countries of origin and destination were both WAEMU members). Traders of local unprocessed products made up 30% of observations. Gasoline smugglers made up another 30%. About 9% of shipments contained products facing an import ban in Nigeria.

Nigeria's ocial tari schedule, in 2010-2011, included ve bands from 0% to 35% ad-valorem rates. In addition, the country also applied excise duties and levies to specic products, such as rice, palm oil, and automobile parts (WTO, 2011). We included these additional taxes in the computation of our tari variable (in the case of multiproduct shipments, a sum of taris weighted by traded value). Nigeria's trade policy also includes a list of products banned for import, which was coded using the ocial list from Nigeria's customs authority. In the case of import ows into Benin, the WAEMU's external tari applies, which has four bands ranging from 0% to 20%.

It is possible that the tari and ban variables do not capture the entirety of Nigeria's complex trade restrictions. In our empirical analysis, we rely on models with product xed eects (at SH4 product code level) to control for unobserved trade barriers.

Beyond cargo size, we used two proxies for the size of the trader's rm: the total number of co-workers as reported by the trader (including temporary and permanent workers) and a dummy for traders who reported having wholesalers as main clients or suppliers (Wholesalers). The majority of traders operate alone. Frequency is the number of months before the survey in which the trader reported having made at least one passage (the survey asked for detailed activity month by month). Most traders operate regularly, throughout the year.

69% of traders were men, based on the 2011 survey data. This information was not available in the 2010 data. 17

The last line of the table reports the number of distinct border crossings used by traders who connect the same markets (same origin and destination markets). This is a de facto measure of the number of alternative routes that traders can use.

This number of alternatives varies by transport mode. We computed the number of alternative routes connecting two markets used by traders with a given transport mode. Figure 2 shows the distribution of this variable for each mode of transport.

For trucks, cars, and pedestrians, the number of routes is most often between one and three. By contrast, this number is most often higher for pirogues and motorcycles, and can reach up to ten. Traders on pirogues or motorcycles have access to a greater number of alternative routes, and this could lower their willingness to pay at a given Our theoretical background is a model of a public ocial who sets the level of bribes in order to maximize revenue, similar to the approach of [START_REF] Olken | The simple economics of extortion: Evidence from trucking in Aceh[END_REF]. The ocial may be a customs ocer (or a supplementary person reporting to customs), a policeman, or another ocial who stands on a road and has some power to control passage. Abundant evidence exists on the numerous checkpoints that are found on roads in the region.

The ocial can take into account the trader's observable characteristics, such as the nature, size, and value of cargo, the mode of transport, and the type of trader, and price discriminate accordingly. A trader's willingness to pay for passage will generally not be perfectly observed by the ocial, who can nonetheless use third degree discrimination. In cases where a tari or trade restriction applies to the product, the ocial may also demand a payment in exchange for not applying the duty, ne, or sanction (i.e. a collusive bribe).

The traders in our data may have paid one or several bribes; however, we only observe the total payment as reported by the trader. We model the expected payment in exponential form, controlling for distance traveled. The distance variable is transport-mode specic, and is measured as described in the previous section. We expect total expected payment to increase with distance (as the expected number of stops increases), and study the impact of trader and cargo characteristics on the expected bribe, conditional on distance.

18

We estimate our model of expected payment using Poisson quasi-maximum likelihood estimation. The conditional expected bribe payment is assumed to take the form:

E[Inf T ax ikt |D i , Q i , U V i , X ikt ] = λ t .D α i Q β 1 i U V β 2 i .exp(γX ikt ) (1)
where Inf T ax ikt is the total bribe paid by a trader i of good k, surveyed in year t. As explained in Section 2, this variable sums all the payments without receipts made by the trader during the passage.

Q i and U V i are the quantity and unit value of the trader's cargo. Q i * U V i is the total cargo value. (In the case of multiproduct cargo, the unit value is computed as the weighted average of the prices of the goods.) D i is the road distance between the origin market and the border crossing point where the survey took place. X ic is a set of trader and cargo characteristics. They include measures of the trade regime (taris and bans at product level; indicator variables for within-WAEMU trade, for entrepôt trade, for trade of unprocessed local products -to which free movement applies -, and for gasoline products -which are illegal); transport mode (truck, motorcycle, pirogue, car, or pedestrian); and the trader's nationality (Benin or other), sex, and self-declared frequency of passage.

We also include measures of the number of alternative roads available to traders.

We rst built an interaction term stream * non water transport, a dummy equal to one if the respondent does not use a pirogue despite there being traders who started from the same origin market using pirogues. Second, we computed the number of crossings in the vicinity of each given crossing point.

19 These measures are intended to test the eect of traders' existing alternatives on bribes paid.

As shown in [START_REF] Wooldridge | Quasi-Likelihood Methods for Count Data[END_REF], the Poisson QML estimator is consistent under the assumption that the conditional mean of the dependent variable is correctly specied in Equation 1. In particular, it is not necessary for the dependent variable to actually follow a Poisson distribution; there are no additional requirements regarding the variance of the dependent variable.

An alternative to the Poisson QML estimator is the negative binomial model, estimated by full maximum likelihood. This model is intended to model a variable that exhibits overdispersion -i.e. where the variance is superior to the mean.

20

Note, however, that the Poisson QML estimator is also ecient in the case of overdispersion. In the case of over-dispersion, the actual form of the variance determines which model is more ecient -the Poisson QML or the negative binomial model.

21

In our case, we rely primarily on the Poisson QML estimator. This model has the additional advantage of not suering from the incidental parameters issue, which allows us to estimate models with sets of xed eects without risk of bias in the coecients. As a robustness check, we ran our estimations with the negative binomial model to check that our results were not dramatically dierent with this alternative model. 22 Coecients are to be interpreted as elasticities. In columns 4 and 5, we use xed eects for the trader's market of origin, in order to control for unobserved, time-constant market characteristics (such as market size). In column 5, we add sector (4-digit HS code) xed eects, which control for unobserved characteristics of the products traded that may aect bribes.

Results

Expected payment

Our results show that the average bribe payment is positively correlated with cargo value (weight and unit value). The two coecients are smaller than one, meaning that amounts paid increase less than proportionately with cargo value. Payments are also positively correlated with the road distance traveled by the trader, as are exposure to bribery and the expected number of payments. Distance measures road distance taking into account the trader's transport mode. In the case of pirogues, we add an interaction term with distance in order to control for the specicity of this transport mode.

The distance from the border crossing to the nearest ocial customs post is negatively associated with bribes, signicantly in columns 4 and 5. This may reect the fact that controls are more likely in the vicinity of customs posts.

Bribes vary by trade regime. Traders of local unprocessed products, which benet from complete liberalization, pay lower bribes. Traders operating within the WAEMU area, where trade barriers are lower, also tend to pay lower bribes, although the eect is not signicant. Gasoline smugglers pay higher average amounts, although the impact is, perhaps surprisingly, not so high: these traders pay an amount about 50% higher, on average, than the amount paid by traders of other products with otherwise similar values and characteristics. These results are consistent with collusive bribery, which serves in part to avoid a restriction, regulation, or the payment of tax.

Contrary to this result, however, the coecient for the tari variable (applied by the importing country) and entrepôt trade are negative. One possibility is that traders of products facing high protection invest more resources in order to avoid the payment of bribes. We further investigate the impact of taris and non-tari barriers in the next set of results, by using product xed-eect estimates and the impact of changes in trade barriers between 2010 and 2011.

Bribes also vary in function of a trader's transport mode and characteristics.

Traders operating on motorcycles pay signicantly lower amounts, for otherwise similar cargo. This is also the case for pedestrians. The average bribe paid by motorcycles is about 65% (based on column 5) lower than that paid by trucks.

These eects are consistent with price discrimination being used to determine bribe amounts. The demand for passage of a trader on a motorcycle is likely to be more price elastic than other transport modes, as the trader has access to greater numbers of alternative routes than traders using other transport modes. Anecdotal evidence suggests that motorcycles are used precisely for this reason: to avoid controls and payments. On the other hand, pedestrians may be less exible in their choice of route but have a lower willingness to pay for passage. The stream * non water transport variable is equal to one for traders who do not use pirogues, despite operating in an area (dened by the origin market) where transport on pirogues exists. This variable is used to test whether the possibility of water transport inuences the level of bribes paid by other transport modes, through a competition eect between alternative crossing points. Results indicate that it does. The eect is also negative, and signicant, when controlling for origin market xed eects (column 4): in that model, the coecient measures the eect of changes in streams between the two survey periods.

23

We also use controls for trader characteristics -nationality, frequency of passage, and two proxies for the size of the trader's rm: the reported number of co-workers and whether the trader reported having wholesalers as main clients or suppliers.

Beyond cargo size, the trader's operating size may be partially observable to a bribetaking ocial, who could use this information to price discriminate. Results indicate that size is positively associated with bribes.

Impact of trade policy variables The taris and bans applied by the importing country vary at product (HS-6) level. Our estimates, thus far, capture both the eect of dierences in trade barriers over time and across products in a given sector. The cross-sectional variation in protection could be correlated with unobserved variables: such as traders' investment and eorts to avoid controls. Non-tari barriers, which we do not observe perfectly, could also be correlated with taris.

In order to focus on the impact of changes in protection over time, we turn to estimates of a model including product-origin-destination xed eects (at the HS-6 level). This specication fully controls for all constant trade barriers. It also controls for the non-time-variable component of demand and supply for trade at the product level (such as product demand in the destination country). This model estimates the impact of changes in protection over time. In July 2011, the list of products prohibited for import in Nigeria was shortened.

24

This model thus yields a dierence-in-dierence estimate of the impact of import bans on bribes. Products that remained on the prohibition list serve as the control group, to which we compare the changes in bribes for products that were removed from the list. Note that the survey took place shortly (two months) after the change in the list, so we focus on the short-term impact of the change. The informal trade sector's adjustments to the change may not have occurred at the time of the survey.

Regarding taris, only a few products in our data experienced a change in tari protection between the two surveys (this concerns three product-level tari changes in the WAEMU's external tari, and three changes in Nigeria's MFN rate). This limits our ability to obtain a realistic estimate of the eect of tari changes in this setting.

Table 7 displays the results of estimates of the product-origin-destination xed effects model. In columns 1 and 2, we restrict the sample to single-product shipments.

In columns 3 and 4, we include multi-product shipments (in order to attribute a xed eect to the shipment, the main product is taken to be the one with the highest total value in the shipment).

Our results indicate that import bans are positively correlated with bribe amounts paid. Traders of products on the prohibition list are expected to pay bribes about twice as high (exp(1.09) -1 = 1.97) as traders of other products. The removal of some products from the list appears to have reduced the bribes paid by traders of these products, possibly because the threat of conscation or penalty cannot be used by the ocial to extract bribes. This is consistent with collusive bribery. However, we cannot entirely exclude the possibility that other mechanisms are at work. (For instance, the removal of a ban could lower a product's price in Nigeria, reducing traders' margins and, hence, their willingness to pay for passage.) Regarding taris, coecients are negative but not signicant.

Number of crossing points

In the next set of results, we further test the impact of alternative routes on the level of bribes by including the number of near crossings in the model. This variable can, however, be endogenous. It is possible, for instance, that traders facing higher bribe prices will invest more eort and resources in order to nd, and use, alternative routes. To address this endogeneity, we use an instrument based on the local environment: the terrain ruggedness index, which measures the average local slope in an area.

25

We expect ruggedness to be associated with higher road building costs, and to thus constrain the number of alternative routes that can be used to link two points.

For the instrument to be valid, ruggedness must not aect bribe levels through any other channel (apart from the number of routes). A threat to this assumption is that ruggedness could directly aect the transport costs incurred by traders, e.g. by lengthening the road distance connecting two points. In turn, transport costs might inuence a trader's capacity to pay. We control for transport costs with the distance and transport mode variables. Our distance variable is specic to the transport mode and measures the actual road distance traveled. Conditional on transport costs, ruggedness should not inuence bribes other than through its eect on alternative routes.

Table 9 presents the results of our estimates of the inuence of alternative routes.

Number of near crossings is the number of recorded crossings within an 8 km distance of the surveyed point (which is twice the median value of distance to the nearest crossing point).

In and from the equation for number of crossings (IV Poisson).

These results show that ruggedness negatively predicts the number of crossings.

The instrument is moderately strong. The Kleibergen-Paap statistic is about 7.

The number of near crossings is associated with lower bribes in the Poisson model. However, when instrumenting the model, we cannot conrm that this link is causal, since the coecient is non signicant.

Testing for sex discrimination There is a concern that women suer specically from harassment and violence from ocials. For example, [START_REF] Brenton | Risky 44 business: Poor women cross-border traders in the great lakes region of Africa[END_REF] argue that small-scale women traders face gender-specic constraints. These include household commitments and a higher risk of violence and vulnerability.

26

To the best of our knowledge, however, there are no systematic quantitative data studies of dierences in treatment between male and female traders with respect to bribe payments.

Our data allow us to test whether bribes paid by female traders dier systematically from those paid by men with otherwise similar observable characteristicsgoods and quantities traded, transport modes, distance traveled. To do this, however, we have to rely on the 2011 survey data, as information on individuals' sex is only available in that dataset. Our results are shown in Table 10.

Traders' sex is not associated with any signicant dierence for the sample as a whole (columns 1-3). When focusing on pedestrians only, it appears that women do pay signicantly more than men (col. 4-6). This eect is not signicant for the other transport modes in the data. The eect is large and signicant when controlling for sector xed-eects. However, the sample size is much reduced, and results need to be treated with caution.

27

These results appear consistent with the hypothesis that female traders are more vulnerable than male traders, and that this translates into higher bribes. As pointed out earlier, our results seem to indicate that bribes are, at least in part, coercive.

Qualitative evidence, for example as reported in [START_REF] Brenton | Risky 44 business: Poor women cross-border traders in the great lakes region of Africa[END_REF], shows that for traders operating on foot, extraction of bribes can take the form of pure extortion.

Importantly, this dierence is conditional on transport modes. For example, no such eect appears among traders using pirogues, which has the highest share of female traders in our data (45%). Model includes xed eects for product (HS-6 code), country of origin, and country of destination groups. In columns 1 and 2, multi-product shipments are excluded. As explained in Section 2.3, a rst attempt to address these issues involves verifying the consistency of the data with other sources of information on the composition of informal trade. Products known to be important in smuggling with Nigeria appear frequently in the data and show up among the top products by trade value recorded in the survey. This conrms that no major product appears to be missing from the data. Many of these products -such as rice, cars, and gasoline -are also the products that face the most stringent controls on smuggling with Nigeria. The survey thus managed to measure a signicant number of these trade activities, despite their high degree of illegality and risk. However, these qualitative assessments do not rule out the possibility of sample selection. To further probe the inuence that such selection may have on our results, we estimate our model excluding products with a high degree of illegality and risk.

A related concern is that of measurement error in the data. Traders' responses to the survey may not always be truthful, which is problematic if the probability of truthful responses varies with certain trader or cargo characteristics.

There is also evidence that some traders may pay a portion of the bribes in advance, through arrangements made by large smugglers, meaning that bribes paid at the time of the crossing may understate the actual bribes paid. This is a second source of potential measurement error.

To check the robustness of our results, we ran a series of estimations of our models, excluding products with a high risk of measurement error.

We started by excluding all products classied as entrepôt trade. This type of trade, involving the re-exportation of products, mostly to Nigeria, may imply a higher level of measurement error risk than other products. High protability may go hand-in-hand with larger-scale activities, as well as a specic organization of operations.

Next, we tried excluding products under an import ban in Nigeria at the time of the survey.

28

Finally, we excluded the products most associated with smuggling: cars, rice, palm oil, poultry meat, textiles, and gasoline. Some of these products, such as rice,

were not banned in 2010-2011, but were heavily smuggled into Nigeria as a result of high trade barriers in that country. Smuggling at the Benin-Nigeria border is known to be concentrated on these products (see e.g. Golub ( 2012)), and they appeared frequently in our data. Trade of these products is likely to be high-risk, protable, and well organized, which could imply either undermeasurement in our data or a measurement error in the bribes reported.

Table 11 reports the results of these estimations. Most results of the baseline Poisson model are stable when excluding either of these three groups of products.

Estimates without the distance variable

The results presented so far were obtained on the subsample of data for which the distance variable (the distance between the goods' market of origin and the survey point) could be computed. This excludes observations for which the origin market could not be localized on the map. We relied primarily on these estimates, as the distance variable is one important determinant of the amount of the bribes paid by traders (as it measures the sum of payments made during the journey). However, there is a risk that the selection of this subsample could aect some of our results.

29

To test whether our results were potentially aected by this selection process, we ran our estimations on the whole sample, excluding the distance variable from the model. The results are presented in Tables 12,13, 14 and 15.

Overall, the results are similar to those obtained on the selected sub-sample including the distance variable. Coecients on most variables are similar in magnitude to those presented above. Coecients on transport mode variables are the most aected: this makes sense, as the average distance traveled by traders (and thus the exposure to the risk of bribery) diers by transport mode. The coecient for pedestrians is thus more negative in the no-distance estimates: this coecient captures in part the eect of shorter distance for these traders.

For this reason, we view our estimates on the model including distance as more reliable. But it is reassuring that our eects of interest do not change dramatically when using the no distance alternative. The impact of import bans, when measured on the panel of products (Table 13), is again positive and signicant. IV estimates also tend to conrm the sign of competition eects found previously: the number of crossing points available to traders is negatively associated with the level of bribes paid.

Negative binomial model

Poisson quasi-maximum likelihood estimates are consistent even if the dependent variable does not really follow a Poisson distribution, for example if the conditional variance exceeds the mean (overdispersion). However, the use of the negative binomial model is sometimes advocated as an alternative to Poisson QMLE when there is overdispersion. The negative binomial model entails a more general specication of variance, which encompasses Poisson as a special case. The Poisson QML, or the negative binomial estimation, may be more ecient, depending on the actual functional form of the variance (see [START_REF] Wooldridge | Econometric Analysis of Cross Section and Panel Data[END_REF]). An additional advantage of the Poisson QML over the negative binomial is that the Poisson QML does not suer from the incidental parameters problem, so that xed eects can be included in the specication without introducing a risk of bias.

As a robustness check, we ran our estimations using the negative binomial model.

We verify that these results are essentially similar to those obtained by Poisson QML estimation. Results are available on request. Model includes xed eects for product (HS-6 code), country of origin, and country of destination groups.

In columns 1 and 2, multi-product shipments are excluded.

Conclusion

In many developing countries, corruption is ubiquitous in the form of the small bribes that citizens are required to pay for day-to-day access to public services and for conducting business. These payments distort prices and may induce an inecient allocation of resources. They may allow agents to circumvent taxes and regulations, thus leading to private gain for the bribe taker and payer, at the expense of the rest of society. They may also lead agents to use resources to avoid the payment of bribes. There is, therefore, a need to better understand the mechanisms of bribery and the reasons for its persistence.

In this paper, we focus on a region, West Africa, and a sector, cross-border trade, where bribes are particularly common. We rely on a unique source of information on bribe payments, an extensive survey of informal traders conducted in Benin in 2010 and 2011, which covered a wide range of cross-border transactions. We use variation in trade regimes, transport modes, and products to identify some of the determinants of the bribes paid by traders. Our results conrm that bribes are particularly pervasive in the trade sector: overall, about 80% of traders have paid a bribe. This incidence remains high across trader types and products.

Traders of local products operating within the WAEMU customs union, where most trade barriers have, in principle, been removed, face lower bribe levels. However, even these traders have to pay bribes most of the time. Our results indicate that trade barriers and restrictions are associated with higher bribes, consistent with collusive bribery. We also nd evidence that bribe levels are set according to traders' elasticity of demand. Those with lighter transport modes tend to pay lower bribes.

These results are consistent with a view of bribe-taking ocials as prot maximizers, who exploit market power to extract bribes from traders, and use third degree price discrimination.

Overall, our results suggest that reductions in regional trade barriers may help to reduce traders' exposure to bribery, but are probably not sucient to eliminate it. These measures could also help to formalize a large share of regional trade and make regional trade more ecient. 2 These include "mineral products extracted from [member countries'] soil or de- posited on the shore of the maritime coasts, live animals born within the community, harvested vegetable products, shery and hunting products, products extracted from the sea by boats registered in a member state, products from live animals that are reared in a member state" (WAEMU treaty).

3 It is possible that other trade restrictions were applied in Nigeria without being reported to the WTO. To control for this possibility, we rely on product (SH4) xed eects in our empirical specications.

4 See https://www.customs.gov.ng/Publications/news_results.php?NewsID= 132 5 Some transit trade is legal and takes place with landlocked countries for which Cotonou is the main sea access point. This trade is conducted within the framework of an agreement with Benin. However, most (87%) of the transit trade ows in our data go to Nigeria. The role of Benin as a trade platform is related to its geographical situation [START_REF] Golub | Informal cross-border trade and smuggling in Africa[END_REF]. Cotonou has a deep water port and is located only 125 km from Lagos.

15 In about 10% of cases, it is longer than the driving route, which uses highways.

In these cases we applied the driving distance to motorcycles.

16 For pirogues, we used the walking itinerary, which is usually close to the water- way. In the empirical model, we interact the distance variable with an indicator for pirogues, in order to better control for the specicity of measuring distance in this case.

17 The sex of traders was coded using traders' names, which are reported in the 2011 survey data. This variable is missing for about 20% of the cases because the name is missing or uninformative (last name only).

18

The distance variable also serves to control for transport costs, which may in- uence the level of bribes.

19 We counted the number of crossings at a distance less than a set threshold. The threshold value used was twice the median of the distance to the closest point. We then experimented with multiples of this value.

20 Specically, the variance of dependent variable y i , conditional on regressors x i , is assumed to take the form V ar(y i |x i ) = exp(βx i ) + η 2 exp(βx i ) 2 , where η is the variance of an individual heterogeneity term. 21 The Poisson QML estimator is more ecient under the assumption that V ar(y i |x i ) = σ 2 exp(βx i ) for some σ. See [START_REF] Wooldridge | Econometric Analysis of Cross Section and Panel Data[END_REF].

22 We experimented with dierent levels of clustering and found that standard errors do not change much if the clustering is at a higher level, such as the arrondissement or the commune. We therefore retain the border crossing (point de passage) in order to keep a high number of cluster groups (following e.g. Cameron and Miller ( 2013)).

23 The number of arrondissements in which water transport was recorded increased from 28 in the 2010 survey to 39 in 2011 (out of a total of 84 arrondissements) due to the fact that the rst survey was conducted in January 2010 and the second in

Figure 1 :

 1 Figure 1: Map of surveyed border crossings (2011 survey). Source: INSAE.

  on bribery. In panel A, we split the sample by type of trade. In terms of value traded, the largest categories are entrepôt trade and the smuggling of gasoline. Columns 3 and 4 display the incidence of informal and formal taxes, i.e. the frequency of positive payments. Column 5 shows the average bribe payment made by traders during their journey to the border, as a percentage of total cargo value. Column 6

Figure 2 :

 2 Figure 2: Number of crossing points: cumulative distribution by mode of transport

Notes1

  In 17 of the 23 years during which the Transparency International Corruption Perception Index has been published, a Sub-Saharan African country was considered the most corrupt. The number of countries ranked was quite limited until 2007. Since then, the Transparency International Corruption Perception Index has included around 180 countries. As a region, Sub-Saharan Africa has been considered the most corrupt for the last six years. Source: https://www.transparency.org/research/cpi.

Table 1 :

 1 Number of customs ocers per kilometer of land border

		Benin	Burkina Faso	Niger	Nigeria	Togo
	Number of customs ocers	723	1795	1502	17570	748
	Length of land border (km)	2123	3611	5834	4477	1880
	Ratio	0.34	0.49	0.25	3.92	0.39
	Source: World Customs Organization and CIA 2016

Table 2

 2

	indicates the nature, the main origin, and the

Table 3 :

 3 Informal tax: determinants of non-response (probit)

		(1)	(2)
	Weight	-0.00	0.02
		(0.03)	(0.03)
	Unit value	0.02	0.03
		(0.03)	(0.03)
	Multiproduct	-0.15	-0.13
		(0.10)	(0.09)
	Entrepôt trade	-0.10	-0.18
		(0.13)	(0.14)
	Petroleum products	0.15	0.10
		(0.15)	(0.14)
	Local unprocessed	0.18**	0.17**
	products	(0.09)	(0.08)
	WAEMU	0.09	0.10
		(0.25)	(0.24)
	Tari	-0.27	-0.12
		(1.08)	(1.00)
	Import ban	0.11	0.04
		(0.22)	(0.22)
	Transport mode:
	Bicycle/pedestrian		0.40*
			(0.24)
	Pirogues		0.15
			(0.22)
	Motorcycles		0.19
			(0.20)
	Cars		0.16
			(0.17)
	Nb. of workers		-0.01
			(0.08)
	Wholesalers		0.10
			(0.09)
	Frequency		-0.04
			(0.08)
	Beninese		-0.03
			(0.11)
	Year 2011	0.30*	0.32*
		(0.15)	(0.17)
	Observations	13387	13285
	Pseudo R 2	0.018	0.025
	Standard errors in parentheses clustered by crossing point.

* p < 0.10, ** p < 0.05,*** p < 0.01 13 2.4.1 Bribe frequency and levels

Table 4

 4 provides statistics on the structure of informal trade at Benin's borders and

Table 4 :

 4 Statistics: payment frequency and value

		(1)	(2)	(3)	(4)	(5)	(6)
		Total value traded	Number	Bribe	Formal tax	Bribe	Formal tax
		('000 USD)	of obs.	incidence (%)	incidence (%)	ratio (%)	ratio(%)
				Panel A: trade category
	Entrepôt trade	6237	1599	85.1	25.9	1.24	1.23
	Gasoline	9755	3706	89.4	37.2	2.16	0.63
	Exports	3260	2480	71.0	35.7	2.25	1.04
	Imports (excl. gasoline)	2579	4399	70.1	35.3	2.76	1.22
	Regional trade	2395	289	79.6	38.1	2.28	0.72
				Panel B: product type
	Unprocessed pdts.	5894	3721	71.6	39.2	2.33	0.94
	Other Products	8577	5046	74.8	29.8	2.31	1.31
				Panel C: border
	Nigeria	21845	7806	86	31.8	2.13	0.96
	WAEMU Countries	2381	4667	65.1	39.9	2.51	1.06
				Panel D: transport mode
	Trucks	7964	1175	92.3	59.2	3.08	1.89
	Bicycle/pedestrians	2154	2255	69.9	26.2	2.17	0.68
	Pirogues	8922	3135	74.8	35.5	1.94	1.17
	Motorcycles	645	3541	79	28.7	2.06	0.68
	Cars	3661	2283	82.1	39.6	2.78	1.1

Table 5 :

 5 Statistics on estimation sample

		Mean	Median	Min	Max	Std. dev.	Nb. Obs
	Cargo value (USD)	1942.34	182.91	0.2	384279.5	9589.61
	Weight (kg.)	2479.75	250	0.68	500000	10990.12
	Multiproduct	0.13	0	0	1	0.34
	Distance (km.)	79.32	31.9	.002	923.05	129.10	7521
	Distance to
	ocial post (km.)	13.56	9.74	.02	69.50	13.37
	Trade regime
	WAEMU	0.34	0	0	1	0.47
	Entrepôt trade	0.13	0	0	1	0.34
	Gasoline	0.30	0	0	1	0.46
	Local unprocessed	0.30	0	0	1	0.46
	products
	Tari (%)	10.4	7.8	0	45	10.6
	Import ban	0.09	0	0	1	0.29
	Trader characteristics
	Nb. of workers	2.64	1	1	143	5.07
	Wholesalers	0.36	0	0	1	0.48
	Beninese	0.72	1	0	1	0.45
	Frequency of passage	9.81	12	1	12	3.33
	Male (2011 only)	0.69	1	0	1	0.46	6023
	Nb. of crossing points	3.02	2	1	23	3.94
	per market pair

Source:

INSAE, ECENE survey 2010 and 2011, authors' computations.

point. We test whether bribes are set accordingly lower for these categories of trader.

Table 6

 6 reports the results of our estimation of model 1 by Poisson quasi-maximum likelihood. The dependent variable is a trader's expected bribe payment. Standard errors are clustered at the level of border crossing points.

  column 1, we use Poisson QML estimates. In columns 2 and 3, we rely on a linear model and use 2SLS estimates, restricting the sample to observations with positive values for the bribe payment. In column 4, we use an IV Poisson model estimated by the control function approach. This estimate is not consistent when including xed eects in the model.The lower part of the table reports results from the rst stage estimation (2SLS)

Table 6 :

 6 Bribe payments (Poisson QML)

		(1)	(2)	(3)	(4)	(5)
	Weight	0.73*** 0.62*** 0.56*** 0.56***	0.56***
		(0.05)	(0.04)	(0.04)	(0.04)	(0.04)
	Unit value	0.45*** 0.44*** 0.42*** 0.43***	0.39***
		(0.05)	(0.05)	(0.05)	(0.04)	(0.05)
	Distance	0.22*** 0.24*** 0.23*** 0.42***	0.36***
		(0.07)	(0.07)	(0.07)	(0.08)	(0.08)
	Distance *	0.00	0.13	0.08	-0.11	-0.11
	pirogue	(0.03)	(0.19)	(0.17)	(0.20)	(0.20)
	Distance to	0.06	-0.03	-0.05	-0.13***	-0.16***
	ocial point	(0.05)	(0.05)	(0.05)	(0.05)	(0.04)
	Multiproduct	-0.02	-0.05	-0.08	-0.08	-0.08
		(0.15)	(0.15)	(0.15)	(0.14)	(0.14)
	Entrepôt trade	-0.47* -0.36	-0.30	-0.01	-0.15
		(0.25)	(0.26)	(0.25)	(0.23)	(0.30)
	Petroleum products	0.46*	0.56** 0.47**
		(0.24)	(0.23)	(0.21)
	Local products	-0.49***-0.50***-0.50***
		(0.15)	(0.14)	(0.14)
	WAEMU	-0.15	-0.06	-0.07	-0.42	-0.08
		(0.33)	(0.32)	(0.28)	(0.26)	(0.29)
	Tari	-0.89	-0.17	0.12	-2.34***	0.85
		(1.26)	(1.22)	(1.10)	(0.80)	(1.48)
	Import ban	-0.54** -0.66** -0.56** 0.43**	-0.07
		(0.26)	(0.27)	(0.25)	(0.21)	(0.40)
	Transport mode:
	Bicycle/pedestrian		-0.78***-0.72***-0.57***	-0.59***
			(0.24)	(0.25)	(0.21)	(0.21)
	Pirogues		-2.00	-1.55	0.77	0.96
			(2.08)	(1.84)	(2.42)	(2.34)
	Motorcycles		-1.25***-1.18***-0.97***	-1.07***
			(0.29)	(0.25)	(0.24)	(0.23)
	Cars		-0.28	-0.26	-0.31**	-0.25
			(0.18)	(0.17)	(0.15)	(0.16)
	Stream * non-water transport		-0.51***-0.52***-0.87**	-0.51
			(0.14)	(0.15)	(0.36)	(0.34)
	Trader characteristics:
	Frequency			0.00	0.03	0.10
				(0.13)	(0.10)	(0.10)
	Beninese nationality			0.02	0.09	0.00
				(0.15)	(0.15)	(0.12)
	Nb. of workers			0.29*** 0.23***	0.22***
				(0.07)	(0.05)	(0.05)
	Wholesalers			0.41*** 0.38***	0.47***
				(0.12)	(0.10)	(0.09)
	Year 2011	-0.45** -0.32* -0.27	-0.06	-0.05
		(0.18)	(0.19)	(0.19)	(0.16)	(0.16)
	Origin market f.e.				X	X
	Sector (SH4) f.e.					X
	Observations	7519	7519	7519	7519	7519
	Pseudo R 2	0.573	0.596	0.624	0.721	0.754

Standard errors in parentheses, clustered by border crossings. * p < 0.10, ** p < 0.05,*** p < 0.01 Dependent variable: expected bribe payment. Local products: 1 for unprocessed products from the ECOWAS region, which benet from free movement within the WAEMU and ECOWAS areas. Transport mode: Trucks are the omitted category. Stream * non-water transport: equals 1 if the transport mode is not a pirogue, despite trade by pirogue existing between the same origin and destination markets. Frequency: nb. of passages reported by the trader in the 12 months before the survey. Wholesalers: 1 if traders declare that they have wholesalers as their main clients or suppliers.

Table 7 :

 7 Bribe payments (Poisson QML), product-origin-destination xed eects

		(1)	(2)	(3)	(4)
		Single-product only
	Weight	0.76***	0.56***	0.77***	0.56***
		(0.06)	(0.05)	(0.06)	(0.05)
	Unit value	0.60***	0.44***	0.58***	0.42***
		(0.10)	(0.08)	(0.09)	(0.07)
	Distance	0.22***	0.19***	0.23***	0.19***
		(0.08)	(0.06)	(0.07)	(0.06)
	Distance *	0.01	0.13	-0.00	0.10
	pirogue	(0.03)	(0.21)	(0.03)	(0.19)
	Distance to	0.06	-0.05	0.06	-0.04
	ocial post	(0.06)	(0.05)	(0.06)	(0.05)
	Multiproduct			0.08	0.04
				(0.21)	(0.18)
	Tari	-5.43	-6.70	-5.47	-6.90
		(27.85)	(23.65)	(5.31)	(4.64)
	Import ban	1.99***	1.43**	1.07	1.09**
		(0.61)	(0.58)	(0.85)	(0.55)
	Bicycle/pedestrian		-0.92***		-0.91***
			(0.22)		(0.21)
	Pirogues		-1.89		-1.75
			(2.26)		(2.07)
	Motorcycles		-1.16***		-1.24***
			(0.25)		(0.24)
	Cars		-0.19		-0.24
			(0.18)		(0.17)
	Stream * non-water transport		-0.40***		-0.38***
			(0.14)		(0.14)
	Frequency		0.05		0.08
			(0.14)		(0.13)
	Beninese		0.04		0.11
			(0.17)		(0.15)
	Nb. of workers		0.35***		0.34***
			(0.07)		(0.07)
	Wholesalers		0.49***		0.54***
			(0.11)		(0.11)
	Year 2011	-0.42**	-0.29	-0.33	-0.20
		(0.20)	(0.20)	(0.21)	(0.20)
	Observations	6373	6373	7519	7519
	Pseudo R 2	0.661	0.712	0.660	0.713
	Standard errors in parentheses, clustered by border crossing.
	* p < 0.10, ** p < 0.05,*** p < 0.01

Table 8 :

 8 Bribe payments: number of crossings Nb. near crossings: number of border crossings at distance less than 8 km from surveyed point. Models include controls: taris, import bans, WAEMU dummy, year xed eect. Weak id. Wald F statistic is the Kleibergen-Paap rank Wald F statistic. Underidentication test is based on the Kleibergen-Paap rk LM statistic. Column 4 estimates IV-Poisson model using a control function approach. See next table for rst stage results.

		(1)	(2)	(3)	(4)
		Poisson	2SLS	IV Poisson
	Nb. near crossings	-0.08*** -0.03	-0.02	0.03
		(0.03)	(0.06)	(0.06)	(0.11)
	Weight	0.63*** 0.55*** 0.55***	0.67***
		(0.05)	(0.03)	(0.03)	(0.04)
	Unit value	0.44*** 0.46*** 0.45***	0.58***
		(0.05)	(0.04)	(0.04)	(0.04)
	Distance	0.21*** 0.08*** 0.08***	0.08***
		(0.07)	(0.03)	(0.02)	(0.03)
	Distance * pirogue	0.10	-0.01	-0.01	-0.01
		(0.21)	(0.08)	(0.08)	(0.09)
	Distance to	-0.12*	0.10	0.09	0.10
	ocial point	(0.07)	(0.10)	(0.09)	(0.18)
	Multiproduct	-0.07	-0.13	-0.14*	-0.20*
		(0.15)	(0.08)	(0.08)	(0.11)
	Entrepôt trade	-0.29	-0.23* -0.16	-0.01
		(0.26)	(0.12)	(0.14)	(0.17)
	Petroleum products	0.57**	0.29** 0.64	0.17
		(0.22)	(0.12)	(0.50)	(0.15)
	Local products	-0.47*** -0.20** 0.46	-0.29***
		(0.14)	(0.08)	(0.35)	(0.07)
	Bicycle/pedestrian	-0.64*** -0.66***-0.71***	-1.01***
		(0.24)	(0.20)	(0.20)	(0.26)
	Pirogues	-1.27	-0.28	-0.24	-0.79
		(2.29)	(1.01)	(0.93)	(1.23)
	Motorcycles	-1.09*** -0.83***-0.80***	-0.98***
		(0.29)	(0.15)	(0.16)	(0.20)
	Cars	-0.22	-0.28** -0.21	-0.34*
		(0.18)	(0.13)	(0.13)	(0.18)
	Stream * non-water transport	-0.42*** -0.19	-0.18	-0.20
		(0.14)	(0.13)	(0.13)	(0.15)
	Sector (SH4) xed eects			X
	Observations	7503	5728	5728	7503
	R 2		0.567	0.598
	Weak id. Wald F		7.20	7.31
	Underid. Chi p-value		0.02	0.01
	Standard errors in parentheses
	* p < 0.10, ** p < 0.05,*** p < 0.01

Table 9 :

 9 Table 8 continued: number of crossings equation

	(1)	(2)	(3)	(4)
		2SLS	IV Poisson
			Nb. near crossings
	Ruggedness	-0.21***-0.22***	-0.18***
		(0.08)	(0.08)	(0.06)
	Weight	-0.23***-0.20**	-0.16**
		(0.08)	(0.08)	(0.07)
	Unit value	-0.35***-0.36***	-0.24***
		(0.10)	(0.11)	(0.09)
	Distance	-0.13	-0.13	-0.08
		(0.09)	(0.09)	(0.07)
	Distance * pirogue	-0.30	-0.23	-0.32
		(0.49)	(0.47)	(0.37)
	Distance to	-1.26***-1.17***	-1.41***
	ocial point	(0.30)	(0.29)	(0.28)
	Multiproduct	-0.27	-0.34	-0.28
		(0.28)	(0.25)	(0.22)
	Entrepôt trade	-0.57	-0.80**	-0.36
		(0.47)	(0.38)	(0.45)
	Petroleum products	0.41	1.04	0.42
		(0.59)	(1.35)	(0.55)
	Local products	0.07	0.44	-0.03
		(0.23)	(1.00)	(0.20)
	Bicycle/pedestrian	2.25*** 2.47***	1.82***
		(0.72)	(0.73)	(0.57)
	Pirogues	7.68	7.10	7.72**
		(5.22)	(4.91)	(3.93)
	Motorcycles	1.08	1.15*	0.99*
		(0.67)	(0.64)	(0.54)
	Cars	1.06** 1.15***	1.02***
		(0.44)	(0.41)	(0.37)
	Stream * non-water transport	1.20*** 1.18***	0.89***
		(0.36)	(0.35)	(0.33)
	Residual			-0.09
				(0.11)
	Observations	5728	5728	7503
	R 2	0.567	0.598
	Weak id. Wald F	7.20	7.31
	Underid. Chi p-value	0.02	0.01
	Standard errors in parentheses, clustered at market-pair level.
	* p < 0.10, ** p < 0.05,*** p < 0.01
	Nb. near crossings: number of border crossings at distance less than 8 kilometers from
	surveyed point. Wald F statistic is the Kleibergen-Paap rank Wald F statistic. Underi-
	dentication test is based on the Kleibergen-Paap rk LM statistic. Column 4: IV-Poisson
	model using control function approach.

Residual reports coecient on the residual variable included to control for endogeneity. Controls: WAEMU, tari, import ban, year.

Table 10 :

 10 Bribe payments, Poisson estimates: 2011 data only The objective of the ECENE survey was to obtain a representative sample of informal trade at Benin's borders. However, the nature of informal trade raises concerns in terms of both sample selection and potential measurement errors. First, the sample might suer from selection, if traders of certain products operate at night. There is evidence that some of the smuggling activity across the Benin-Nigeria border takes place at night. Such trade would not be captured in the survey. If this type of trade is quantitatively important, it could threaten the representativeness of the survey data.

		(1)	(2)	(3)	(4)	(5)	(6)
					Pedestrians only
	Weight	0.63*** 0.60*** 0.63*** 0.27*** 0.30*** 0.41**
		(0.06)	(0.05)	(0.05)	(0.09)	(0.10)	(0.21)
	Unit value	0.35*** 0.34*** 0.27*** 0.80*** 0.82*** 1.03***
		(0.06)	(0.06)	(0.05)	(0.13)	(0.13)	(0.37)
	Distance	0.12*	0.13*	0.13** -0.12** -0.14***-0.20***
		(0.07)	(0.07)	(0.06)	(0.06)	(0.05)	(0.05)
	Distance to	0.01	0.02	0.03	0.75*** 0.77*** 0.71***
	ocial post	(0.12)	(0.11)	(0.10)	(0.12)	(0.13)	(0.13)
	Multiproduct	0.28	0.20	0.27	0.31	0.35	-0.67
		(0.29)	(0.25)	(0.27)	(0.28)	(0.36)	(0.47)
	Entrepot trade	0.08	0.09	0.29	-0.17	-0.20	-0.24
		(0.24)	(0.24)	(0.25)	(0.45)	(0.51)	(0.55)
	Petroleum products	0.50	0.40		3.63*** 3.92***
		(0.34)	(0.32)		(0.62)	(0.76)
	Bicycle/pedestrian	-0.65* -0.53* -0.46*
		(0.33)	(0.31)	(0.28)
	Pirogues	-0.97***-1.03***-1.21***
		(0.36)	(0.37)	(0.35)
	Motorcycles	-1.26***-1.12***-1.17***
		(0.38)	(0.35)	(0.35)
	Cars	-0.16	-0.10	-0.22
		(0.28)	(0.26)	(0.26)
	Stream x non-water transport	-0.39** -0.51** -0.51*** 0.80** 0.70*	0.54
		(0.19)	(0.20)	(0.19)	(0.38)	(0.40)	(0.36)
	Local products	-0.61***-0.56***	1.44*** 1.45***
		(0.15)	(0.14)		(0.45)	(0.45)
	WAEMU	-0.09	0.01	0.44	-0.45	-0.65	-0.25
		(0.42)	(0.42)	(0.41)	(0.40)	(0.41)	(0.46)
	Tari	-1.06	-0.81	1.75	8.57*** 8.20***12.54***
		(1.64)	(1.60)	(1.79)	(2.29)	(2.43)	(4.52)
	Import ban	-0.88***-0.78** -1.58*** 0.85*** 0.90** 1.24
		(0.33)	(0.32)	(0.40)	(0.33)	(0.37)	(0.76)
	Male trader	-0.03	0.01	-0.08	-0.50***-0.65** -0.94***
		(0.15)	(0.13)	(0.13)	(0.19)	(0.28)	(0.32)
	Frequency		0.22** 0.22**		0.28	0.39*
			(0.09)	(0.09)		(0.20)	(0.21)
	Wholesalers		0.36** 0.51***	-0.27	-0.59*
			(0.17)	(0.14)		(0.40)	(0.34)
	Beninese		0.10	0.24		-0.17	-0.45*
			(0.21)	(0.18)		(0.28)	(0.24)
	Nb. of workers		0.12*	0.08		-0.21	-0.34**
			(0.07)	(0.07)		(0.20)	(0.16)
	Product (SH4) f.e.			X			X
	Observations	4129	4129	4129	498	498	498
	Standard errors in parentheses, clustered by border crossing.

* p < 0.10, ** p < 0.05,*** p < 0.01

Table 11 :

 11 Robustness check: excluding sensitive products

		(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
		All	No entrepôt	No banned	No smuggling
	Weight	0.56*** 0.56*** 0.59*** 0.56*** 0.57*** 0.57*** 0.56***	0.58***
		(0.04)	(0.04)	(0.04)	(0.05)	(0.04)	(0.05)	(0.04)	(0.05)
	Unit value	0.42*** 0.39*** 0.48*** 0.39*** 0.45*** 0.42*** 0.40***	0.41***
		(0.05)	(0.05)	(0.05)	(0.06)	(0.05)	(0.06)	(0.05)	(0.06)
	Distance	0.23*** 0.36*** 0.22*** 0.30*** 0.21*** 0.30*** 0.12**	0.25***
		(0.07)	(0.08)	(0.07)	(0.08)	(0.06)	(0.08)	(0.05)	(0.07)
	Distance *	0.08	-0.11	0.09	-0.08	0.07	-0.13	-0.07	0.03
	pirogue	(0.17)	(0.20)	(0.18)	(0.21)	(0.18)	(0.18)	(0.12)	(0.20)
	Distance to	-0.05	-0.16***-0.04	-0.14***-0.06	-0.16*** 0.03	-0.08
	ocial point	(0.05)	(0.04)	(0.05)	(0.05)	(0.06)	(0.05)	(0.05)	(0.05)
	Multiproduct	-0.08	-0.08	-0.08	-0.07	-0.06	-0.07	-0.45***	-0.43***
		(0.15)	(0.14)	(0.16)	(0.16)	(0.16)	(0.15)	(0.14)	(0.11)
	Entrepôt trade	-0.30	-0.15			-0.07	0.08	0.39	0.42**
		(0.25)	(0.30)			(0.24)	(0.20)	(0.25)	(0.18)
	Petroleum products	0.47**		0.54**		0.41*
		(0.21)		(0.22)		(0.22)
	Local products	-0.50***	-0.29**		-0.51***	-0.38***
		(0.14)		(0.13)		(0.14)		(0.13)
	WAEMU	-0.07	-0.08	-0.04	-0.06	-0.18	0.32	-0.45**	-0.36
		(0.28)	(0.29)	(0.28)	(0.29)	(0.28)	(0.30)	(0.22)	(0.25)
	Tari	0.12	0.85	0.09	0.82	-0.84	2.86*	-1.56	-0.54
		(1.10)	(1.48)	(1.09)	(1.16)	(1.28)	(1.67)	(1.05)	(0.96)
	Import ban	-0.56** -0.07	-0.29	0.48			-0.83**	-0.60**
		(0.25)	(0.40)	(0.26)	(0.45)			(0.33)	(0.30)
	Bicycle/pedestrian	-0.72***-0.59***-0.70***-0.71***-0.78***-0.60***-0.60***	-0.55**
		(0.25)	(0.21)	(0.22)	(0.21)	(0.28)	(0.22)	(0.23)	(0.23)
	Pirogues	-1.55	0.96	-1.60	0.73	-1.41	1.31	0.30	-1.04
		(1.84)	(2.34)	(2.00)	(2.45)	(1.99)	(2.14)	(1.26)	(2.28)
	Motorcycles	-1.18***-1.07***-1.08***-1.26***-1.21***-1.06***-0.86***	-0.94***
		(0.25)	(0.23)	(0.24)	(0.23)	(0.25)	(0.24)	(0.22)	(0.27)
	Cars	-0.26	-0.25	-0.30* -0.36** -0.34** -0.26* -0.25	-0.21
		(0.17)	(0.16)	(0.16)	(0.15)	(0.16)	(0.15)	(0.17)	(0.18)
	Stream *	-0.52***-0.51	-0.60***-0.49	-0.62***-0.58* -0.26	-0.51**
	non-water transport	(0.15)	(0.34)	(0.17)	(0.33)	(0.17)	(0.31)	(0.20)	(0.24)
	Frequency	0.00	0.10	-0.02	0.07	-0.04	0.16** -0.05	-0.01
		(0.13)	(0.10)	(0.12)	(0.07)	(0.11)	(0.08)	(0.14)	(0.10)
	Beninese	0.02	0.00	-0.01	0.06	0.15	0.12	0.12	0.04
		(0.15)	(0.12)	(0.16)	(0.13)	(0.17)	(0.13)	(0.16)	(0.16)
	Nb. of workers	0.29*** 0.22*** 0.30*** 0.21*** 0.30*** 0.20*** 0.14*	0.10
		(0.07)	(0.05)	(0.07)	(0.05)	(0.07)	(0.05)	(0.07)	(0.07)
	Wholesalers	0.41*** 0.47*** 0.48*** 0.50*** 0.43*** 0.51*** 0.23	0.32***
		(0.12)	(0.09)	(0.12)	(0.08)	(0.12)	(0.08)	(0.14)	(0.09)
	Year 2011	-0.27	-0.05	-0.31	0.00	-0.20	-0.00	-0.39*	-0.47***
		(0.19)	(0.16)	(0.20)	(0.15)	(0.19)	(0.15)	(0.20)	(0.18)
	Origin market f.e.		X		X		X		X
	Sector (SH4) f.e.		X		X		X		X
	Observations	7519	7519	6428	6428	6695	6695	4422	4422
	Pseudo R 2	0.624	0.754	0.649	0.782	0.640	0.772	0.532	0.695

* p < 0.10, ** p < 0.05,*** p < 0.01 Poisson QML estimates. entrepôt trade is excluded in col. 3-4. All products under an import ban in Nigeria are excluded in col. 5-6. The main products in large-scale smuggling into Nigeria -rice, palm oil, textiles, automobiles, and poultry meat -as well as gasoline, are excluded in col. 7-8.

Table 12 :

 12 Bribe payments (Poisson QML): model without distance

		(1)	(2)	(3)	(4)	(5)
	Weight	0.70*** 0.62*** 0.57*** 0.59*** 0.59***
		(0.04)	(0.04)	(0.04)	(0.04)	(0.05)
	Unit value	0.54*** 0.54*** 0.53*** 0.56*** 0.48***
		(0.07)	(0.07)	(0.07)	(0.07)	(0.06)
	Distance to	0.09	0.03	0.00	-0.03	-0.11*
	ocial post	(0.06)	(0.06)	(0.07)	(0.07)	(0.06)
	Multiproduct	-0.14	-0.13	-0.13	-0.10	-0.08
		(0.14)	(0.14)	(0.14)	(0.14)	(0.12)
	Entrepôt trade	-0.57* -0.51	-0.51	-0.26	-0.54*
		(0.29)	(0.31)	(0.33)	(0.24)	(0.32)
	Petroleum products	0.05	0.24	0.24
		(0.19)	(0.19)	(0.17)
	Local products	-0.43***-0.42***-0.45***
		(0.17)	(0.16)	(0.16)
	WAEMU	-0.07	0.04	0.02	-0.48	-0.16
		(0.29)	(0.28)	(0.26)	(0.32)	(0.31)
	Tari	-0.92	-0.37	-0.27	-2.55*** 0.58
		(1.09)	(1.08)	(0.99)	(0.69)	(1.43)
	Import ban	-0.25	-0.30	-0.33	0.64*	0.06
		(0.32)	(0.33)	(0.37)	(0.37)	(0.36)
	Bicycle/pedestrian		-1.00***-0.97***-1.11***-1.03***
			(0.20)	(0.19)	(0.25)	(0.20)
	Pirogues		-0.66** -0.76***-1.79***-1.24***
			(0.31)	(0.29)	(0.35)	(0.22)
	Motorcycles		-1.36***-1.37***-1.37***-1.42***
			(0.24)	(0.24)	(0.17)	(0.20)
	Cars		-0.38** -0.36** -0.34* -0.37***
			(0.16)	(0.16)	(0.18)	(0.14)
	Stream * non-water transport		-0.16	-0.13	-1.43***-0.87***
			(0.17)	(0.17)	(0.24)	(0.17)
	Frequency			-0.20	-0.23	-0.24
				(0.16)	(0.21)	(0.23)
	Beninese			-0.09	-0.11	-0.08
				(0.13)	(0.15)	(0.10)
	Nb. of workers			0.26*** 0.26*** 0.27***
				(0.06)	(0.05)	(0.05)
	Wholesalers			0.18	0.06	0.20*
				(0.15)	(0.15)	(0.12)
	Year 2011	-0.31	-0.26	-0.26	-0.07	-0.07
		(0.21)	(0.19)	(0.19)	(0.18)	(0.18)
	Origin market f.e.				X	X
	Sector (SH4) f.e.					X
	Observations	12473	12389	12389	12131	12131
	Standard errors in parentheses, clustered by border crossing.

* p < 0.10, ** p < 0.05,*** p < 0.01

Table 14 :

 14 Bribes payments: number of crossings (no distance model)

		(1)	(2)	(3)		(4)
		Poisson	2SLS	IV Poisson
	Nb. near crossings	-0.10*** -0.02	0.01		-0.02
		(0.03)	(0.07)	(0.07)		(0.10)
	Weight	0.61*** 0.60*** 0.61***	0.70***
		(0.04)	(0.03)	(0.04)		(0.04)
	Unit value	0.51*** 0.50*** 0.50***	0.60***
		(0.07)	(0.03)	(0.04)		(0.04)
	Distance to	-0.12	0.13	0.13		0.02
	ocial post	(0.08)	(0.10)	(0.10)		(0.16)
	Multiproduct	-0.15	-0.12	-0.12		-0.12
		(0.14)	(0.07)	(0.07)		(0.10)
	Entrepôt trade	-0.43	-0.27** -0.23		-0.21
		(0.31)	(0.13)	(0.14)		(0.20)
	Petroleum products	0.30*	0.12	0.60		0.11
		(0.18)	(0.11)	(0.40)		(0.13)
	Local products	-0.42*** -0.23*** 0.37		-0.23***
		(0.15)	(0.07)	(0.31)		(0.08)
	Bicycle/pedestrian	-0.86*** -0.66***-0.73***	-0.90***
		(0.20)	(0.17)	(0.17)		(0.20)
	Pirogues	-0.20	-0.41	-0.55		-0.66
		(0.32)	(0.39)	(0.39)		(0.56)
	Motorcycles	-1.25*** -0.66***-0.66***	-0.86***
		(0.24)	(0.16)	(0.16)		(0.18)
	Cars	-0.36** -0.25** -0.23**		-0.36***
		(0.16)	(0.11)	(0.11)		(0.14)
	Stream * non-water transport	-0.06	-0.13	-0.16		-0.13
		(0.16)	(0.12)	(0.12)		(0.12)
	WAEMU	-0.29	-0.09	0.13		-0.30
		(0.30)	(0.26)	(0.28)		(0.34)
	Tari	-0.57	-1.01*	0.15		-0.35
		(1.08)	(0.55)	(0.70)		(0.79)
	Import ban	-0.39	-0.43***-0.35**		-0.55***
		(0.34)	(0.13)	(0.15)		(0.19)
	Year 2011	-0.27	-0.50***-0.50***	-0.43***
		(0.18)	(0.12)	(0.12)		(0.13)
	Observations	9538	9538	12221
	R 2	0.57	0.59
	Weak id. Wald F		6.91	7.27
	Underid. Chi p-value		0.02	0.01
	Standard errors in parentheses, clustered at border crossing level.
	* p < 0.10, ** p < 0.05,*** p < 0.01

Table 15 :

 15 Table 14, continued:number of crossings equation

		(2)	(3)		(4)
		2SLS	IV Poisson
		Nb. near crossings
	Ruggedness	-0.19***-0.19***	-0.16***
		(0.07)	(0.07)		(0.06)
	Weight	-0.31***-0.33***	-0.20**
		(0.09)	(0.10)		(0.08)
	Unit value	-0.32***-0.38***	-0.23**
		(0.12)	(0.14)		(0.10)
	Distance to	-1.32***-1.25***	-1.43***
	ocial post	(0.27)	(0.26)		(0.24)
	Multiproduct	-0.38	-0.47**		-0.41**
		(0.23)	(0.22)		(0.18)
	Entrepôt trade	-0.93** -1.22***	-0.67
		(0.44)	(0.46)		(0.41)
	Petroleum products	0.58	0.06		0.51
		(0.45)	(1.13)		(0.41)
	Local products	0.34	-0.10		0.17
		(0.24)	(0.85)		(0.19)
	Bicycle/pedestrian	1.37** 1.43**		1.20**
		(0.61)	(0.63)		(0.49)
	Pirogues	4.57*** 4.54***	4.50***
		(0.77)	(0.76)		(0.70)
	Motorcycles	0.92	0.90		0.95*
		(0.65)	(0.65)		(0.55)
	Cars	0.52	0.53		0.59*
		(0.39)	(0.39)		(0.35)
	Stream * non-water transport	0.87** 0.84**		0.77**
		(0.37)	(0.38)		(0.31)
	WAEMU	-3.17***-3.27***	-2.99***
		(0.66)	(0.81)		(0.57)
	Tari	3.76*	1.95		2.57
		(2.27)	(2.76)		(2.01)
	Import ban	0.34	0.50		0.49
		(0.56)	(0.47)		(0.52)
	Year 2011	0.11	0.14		0.12
		(0.43)	(0.44)		(0.35)
	Residual				-0.03
					(0.11)
	Observations	9538	9538		12221
	Weak id. Wald F		6.91		7.27
	Underid. Chi p-value		0.02		0.01

Standard errors in parentheses, clustered at border crossing level * p < 0.10, ** p < 0.05,*** p < 0.01

For example, extra-WAEMU origin imports are classied as entrepôt. Since they enter Benin through a land border, it is very likely that they are intended to be re-exported to Nigeria, rather than being intended for consumption in Benin.

the African school of economics and DIAL seminars for useful comments and discussions, as well as INSAE for their cooperation with the data. This work was supported by the International Food Policy Research Institute (IFPRI), through the African Growth and Development Policy (AGRODEP) Modeling consortium [Gaps in Research grant number 2015X115]. This work is licensed under the CC-BY-NC-ND 4.0 license. http://creativecommons.org/licenses/by-nc-nd/4.0/ 1

7 Note: some crossing points are not close to a border but are located on water streams or lakes, such as around lake Nokoue in the center of Benin's south coast. This lake communicates with Nigeria through the lagoon river Yewa and is for this reason a hotspot of trade with Nigeria.

8 We used data on traders' characteristics to verify that at least 83% of observa- tions related to traders observed only once in the data. 9 The number of incorrect product codes is low; the within-product dispersion in unit values is coherent. The most frequently observed products and their direction of trade are as expected based on existing studies on informal trade in the area. 10 Foltz and Opoku-Agyemang (2015) use data on payments made by truck drivers at each roadblock, in a context close to ours. They report that most payments were made to police and customs agents. We expect the same to be true in our case.

11 The next question in the survey asked traders for the amount paid in formal taxes, dened as all payments made with a receipt. The distinction between the two types of payment was therefore made clear in the questionnaire.

12 Anecdotal evidence in [START_REF] Igue | Evolution of illicit trade between Dahomey and Nigeria since the war of Biafra[END_REF] and World Bank (2013) suggests that, because they can be driven on narrow bush paths, bicycles and motorbikes are used to avoid encounters with law enforcement ocers. 13 See, for example, the report by the International Trade Centre (ITC, 2017) based on a survey of rms in Benin: In principle, regulations within WAEMU guarantee free movement of products of origin, with total exemption of customs taxes and duties. Yet, the survey reveals that customs taxes are applied de facto by member countries on imported products. Firms also complain about the lack of transparency surrounding these taxes, and of the delays, cost, and complexity of the procedure for obtaining certicates of origin.

14 [START_REF] Foltz | Do higher salaries lower petty corruption? A policy experiment on West Africa's highways[END_REF] report bribes per kilometer of between $0.03 and $0.17. In our data, bribes per (straight line) kilometer have a mean of $0.19 and a mean of $1.1 for trucks not transporting oil.

September 2011, at the end of the rainy season.

24 The following products were removed from the prohibition list in July 2011 (see Nigeria's customs website, https://www.customs.gov.ng/Publications/news_results. php?NewsID=132): raw or processed Cassava, motor vehicle seats, seats other than garden seats or camping, equipment convertible into beds, all kinds of tooth picks, lace fabrics and made-up garments like shirts, trousers, skirts, blouses, etc.

25 We calculated the TRI using elevation data from the US geological survey (GMTED dataset). Elevation is measured at a 30' resolution. The TRI was computed as the mean square dierence of altitude between each point and its eight neighbors. We then averaged the TRI by arrondissement.

26 Note, however, that the majority of small-scale traders in the context studied in [START_REF] Brenton | Risky 44 business: Poor women cross-border traders in the great lakes region of Africa[END_REF] are women, contrary to our data in which women are in the minority, including among pedestrians.

27 The sign of the distance variable is inverted when focusing on this sub-sample.

This could be due to the fact that the origin market as reported by traders does not reect actual the actual distance traveled by the pedestrian.

28 We excluded all shipments, including some products under ban. 29 We measured distance for observations where the origin market could be found on geographic search engines, and thus, geolocalized. Smaller or less well-known origin markets were therefore more likely to be dropped at this point.