H Delebecque
email: henri.delebecque@supelec.fr

HDSL An e-education Hypertext Document Structuring Language

The development of a set of educational web pages is a complex task, because it involves multiple abilities: to be a good content author, a graphist, an ergonomist, following the advice of the webmaster. Moreover, this task has to be redone every time an author (or the academic site in which his pages are included) change the graphical or the navigational appearance. Last, you cannot use the advanced mechanisms of HTML (which can help in managing styles and content), since they are not fully supported by most frequently used web browsers. Our goal is to propose a language to be used by professors, helping them to structure the content of their hypertext pedagogical documents, and to free them from all the other aspects. The definition of a set of web pages is done by describing both a structural model (using our HDSL language), and a graphical model (using completely normal HTML). Using this pair of models, our HDSL engine will build automatically the XSL conversion sheets able to convert any of the documents built using the structural model into (for example) web pages using the most currently accepted HTML. HDSL also offers capabilities of inheritance between structural models, which allows a professor responsible of a whole pedagogical web site to define a look and feel for it, and derive it according to the needs of the various authors writing the hypertext supports of their courses. Moreover, the HDSL engine is able to accept presentation models in specific languages other than HTML, such as PDF, WML,…starting from the content of a single document.

Context

Developing a coherent set of web pages to produce a pedagogical document is a complex task, since the pages can usually be described using multiple approaches. One can, for example, organize them by dividing the main subject into sections (introduction, chapters, bibliography, index,…), or classifying them by kind of information (pictures, sounds, icons,…), by author. This list is not limited, and you have often to use many of these classification criteria simultaneously. This leads you to handle a great number of heavily interdependent documents, like a real software project manager. But the content of web pages has a much more dynamic structure than the source files of a software project: to be pertinent, the content of a pedagogical document has to be frequently updated.

Moreover, one has also to give a coherent look and feel to this complex set of documents. This aspect is of great importance in a pedagogical context, since it allows students not to be bothered by presentation or navigational considerations. Unfortunately, one has also to do with content authors who can either be HTML fluent, and these will take a lot of time to elaborate their own look and feel, or on the contrary they may be completely focused on the content of their document, and they will deliver them in various formats.

Another problem is the ability to describe the page content semantics for search tools, either your own, or commercial ones. The keywords HTML offers with its meta tags are also too simplistic, since they lack the ability to describe the context in which they are used, and are attached to the whole document, when we need a fine grain semantic description. A powerful and semantically-based search tool is a fundamental mechanism for helping students to browse your document, and find the definition or explanation they are looking for.

The cost of managing a huge set of HTML documents could be greatly reduced by the use of advanced tools available in HTML 4 [START_REF] Ragget | HTML 4.0.1 Specification[END_REF]. For example, CSS styles sheets can be used to insure a homogenous look, navigation toolbar and menus located in frames to standardize the interface presented to the internet reader. Unfortunately, most of these advanced tools are not universally supported by web browsers, or by other medium specific presentation languages (PDF, WML, restricted HTML).

The HDSL Project

Almost all these problems can be solved by using our Hypertext Document Structuring Language, HDSL, based on XML (Bray, 2000), that allows to describe the structure of families of hypertext documents, independently of their presentation and the medium on which they are displayed. That language allows also to add semantics to the items of the document, either on their content, or on their usage (navigation toolbar, menu,…). Separation of contents and presentation in HDSL give the ability to describe the look and feel of the pedagogical document which is different from mixing them with contents, as done with HTML.

To achieve this goal, we propose a mechanism based on pairs of documents. For every subset of the pedagogical document (course, chapter, section,…), which has its own look and feel, the content author will supply a model using HDSL, which describes its different parts (required or optional, describing the contents or the relations with other documents,…). According to this structural model, the graphist will supply a presentation model, which organizes the look and usage of theses parts. This presentation model can use various presentation languages, standard HTML, reduced HTML (for text-only browsers, low-bandwidth clients,…), WML, PDF, or others, according to the medium used to display the document. Our HDSL engine will take all these models, and generates automatically as many XSL stylesheets as needed to produce «displayable» forms of the concrete HDSL document written by content authors, for every needed medium. It allows to use a unique version of the document that defines the content, which is of a great importance to insure the coherence of the various displayable forms.

The solutions XML currently offers, such as pure XSL sheets (Clark 1999), has multiple drawbacks: you have to fully master XSL, to write a XSL sheet for every presentation medium, for every subset of your pedagogical document. Moreover, this XSL writing task must be redone every time you change the look and feel. And this way of producing web pages doesn't allow re-utilization or genericity which our tool includes, nor any possibility for handling interrelations between documents. It doesn't provide either any may to manage the versioning of the document the HDSL engine supports, and which is of great help in managing the production of many contents author.

Related Work

The WebML language (www.oasis-open.org/cover/webML.html) is a notation for specifying complex Web sites at the conceptual level. It allows the description of navigation and composition abstractions, which are assembled into pages and interconnected by links. WebML is devoted to the construction of web sites based on very dynamic information fetched from multiple databases, and linked by complex relationship, described using E/R model, ODMG object-oriented model in a structural model. The presentation model defines the appearance of pages, independently of the output devices and the rendition language, like HDSL, except for the data described in the structural model. The application domain of WebML is rather e-commerce oriented sites than educational or academic ones. The Percussion's products (Rhythmyx Content Manager) (www.percussion.com) is a commercial set of tools, devoted to the management of web pages by the content authors, without asking them to mastering HTML, or even site rules and structures. It allows the same information to be presented in multiples pages, as HDLS does, and clearly allows the separation between content and presentation. But it is a relatively cumbersome tool, more dedicated to commercial sites than educational or academics ones. HDSL, on the contrary, is a rather straightforward tool, which pushes the versatility of XML in domains such as the expression of the content semantics. Percussion also develops a XML to HTML tool, Xsplit, but which presents only some of the capabilities of HDSL


Ema Falomir

Serge Chaumette

Gilles Guerrini

A Mobility Model Based on Improved Artificial Potential Fields for Swarms of UAVs

A combination of several autonomous UAVs can be used to perform collaborative tasks. Such a combination is referred to as a swarm of drones. The use of multiple platforms can extend the system global capacities thanks to the resulting variety of embedded sensors and to information sharing. In this case, path planning and thus obstacles avoidance is still a major task. To deal with this issue, mobility models have to be implemented. Our contribution presented in this paper is a mobility model for swarms of UAVs based on the Artificial Potential Fields (APF) principle. In our model, the involved UAVs collaborate by sharing data about the obstacles that they detected. By doing so, a UAV which is not close enough to an obstacle to detect it thanks to its own sensors will still have the proper data to take this obstacle into account in its path planning. To validate our mobility strategies with realistic constraints we simulate the performances of existing sensors and transmitters, and consider real-world environment.

I. INTRODUCTION

Plenty of studies have been made on Unmanned Aerial Vehicles (UAVs) in the last few years. Fixed wings and rotor wings (or multi-rotors) are the two main families of UAVs. Fixed wings are preferred for long flights but do not offer a lot of maneuverability. In this paper, we will consider multirotors. Indeed, in order to evolve in unknown environment, possibly containing obstacles, rotor wings are often preferred because they offer low speed maneuvering and hovering.

Unmanned Vehicles (UV) can be used to perform dull, dirty, dangerous and deep (4D) missions using electronics devices instead of humans, with low manufacturing cost, high adaptability or no risk of casualties. Furthermore, several UAVs can be used in a same Area of Interest (AoI) and fulfill together a single mission with better performances than with a single one (fulfill missions quicker or in larger areas for instance). Usage of a several vehicles instead of a single UAV can allow the system resilience; i.e. the lost of one or several vehicle(s) does not cause the mission failure. We talk of "multi-platform" or "swarm". The term "swarm" has no widely admitted definition, then we will use the one proposed in one of our previous papers [START_REF] Falomir | Mobility Strategies based on Virtual Forces for Swarms of Autonomous UAVs in Constrained Environments[END_REF]: a swarm refers to a set of UAVs with similar characteristics (speed, maneuvering etc.), able to establish peer-to-peer connections and can be considered as a single entity from an operator point-ofview. If this swarm is able to fulfill a mission without the intervention of an external (artificial or human) entity, then we qualify it of autonomous. 1 Univ. Bordeaux, LaBRI, UMR5800, F-33400 Talence, France {efalomir, schaumette}@u-bordeaux.fr 2 Thales DMS France, F-33700 Mérignac, France {ema.falomir, gilles.guerrini}@fr.thalesgroup.com A main task of UAVs collaboration is path planning, including collision avoidance. The Artificial Potential Fields (APF) principle is a famous path planning method that allows smooth trajectory generation and do not require a search of a global path. Furthermore, it is highly efficient and has a computational cost low enough to be performed on embedded systems [START_REF] Mac | Improved potential field method for unknown obstacle avoidance using UAV in indoor environment[END_REF]. Our model is based on this principle, and details of APF will thus be given in section III along with alternative methods.

In this paper, a mobility model for autonomous swarms of UAVs based on APF is presented. It allows collaboration between the platforms and collision avoidance. Problems of unreachable targets and local minima have been solved and an innovative repulsive potential field allows anticipation of obstacle avoidance. The method is validated with several simulations using the network simulator OMNeT++ [START_REF] András | An overview of the OMNeT++ simulation environment[END_REF].

This paper is composed of the following sections: section II introduces possible applications of UAVs swarms and their embedded sensors. Section III presents a state of the Art of mobility strategies for UAVs especially using APF. Our model is described in section IV as well as the method hypothesis and pseudo-code. Finally, results of several simulations are analyzed in section V, before conclusions and perspectives in section VI.

II. APPLICATIONS OF UAVS SWARMS AND EMBEDDED SENSORS

In this paper we focus on UAVs of the multi-rotors type, with a width of approximately 50 centimeters. This section presents some applications of swarms of UAVs, and the sensors that they can carry.

A. Applications of Multi-UAV Systems

Swarms of UAVs have many civil applications. For instance, they are used to help firemen in the case of large forest fires [START_REF] Maza | Firemen monitoring with multiple UAVs for search and rescue missions[END_REF], to spray pesticides on crops [START_REF] Bruno | Fine-Tuning of UAV Control Rules for Spraying Pesticides on Crop Fields[END_REF], or to survey points of interest [START_REF] Laplace | Applications et services DTN pour flotte collaborative de drones[END_REF] or AoIs [START_REF] Jaimes | An approach to surveillance an area using swarm of fixed wing and quad-rotor unmanned aerial vehicles UAV(s)[END_REF].

In frame of military activities, some studies [START_REF] Boskovic | Collaborative Mission Planning & Autonomous Control Technology (CoMPACT) System Employing Swarms of UAVs[END_REF] have been done about UAVs swarms in contexts of collaborative mission planning for Intelligence, Surveillance and Reconnaissance (ISR) missions. Path planning for multi-platform have also been studied [START_REF] Redding | Heterogeneous Cooperative Control of Multiple UAVs with Collaborative Assignment and Reactive Motion Planning[END_REF], including embedded sensors capacities, in environments containing obstacles.

B. Radio Module

To fulfill collaborative tasks, the involved UAVs should share information, for example concerning their environment. To do that autonomously, they have to carry a radio module.

XBee platforms are communication modules that suit well the applications of UAVs because they are small, have a light weight, require little energy and are easily customizable and programmable [START_REF]Digi XBee Ecosystem -Everything you need to explore and create wireless connectivity[END_REF]. They are often used on multi-rotors, see for example [START_REF] Laplace | Applications et services DTN pour flotte collaborative de drones[END_REF], [START_REF] Zhih | Development of a simple, low-cost autopilot system for multi-rotor UAVs[END_REF].

In our application, we simulate the characteristics of a XBee-PRO module [START_REF] Xbee | RF Module Family for OEMs[END_REF]. In our study the two key characteristics are the data rate (250 Kbps) and the communication range in urban environment (90 meters).

C. Obstacle Detection

There are three major technologies to deal with obstacle detection for the kind of small scale drones that we are addressing: SONAR (SOund Navigation And Ranging), RADAR (RAdio Detection And Ranging) and LI DAR (LIght Detection And Ranging). In the following, we discuss the characteristics of each of them in the context of our scenario, and select the most adapted.

SONAR technology relies on the uptake of the reflection of a sound wave by a distant object in a given angular area. SONARs are cheap, light and low-power. However this technology is not appropriate for long range aerial detection with a maximum range of 10 meters, which is far from enough for our scenario.

RADARs use radio waves instead of sound waves, and so allow long-distance obstacles detection. Nevertheless, only small ones can be carried by multi-rotors and they have a level of resolution too low for our application.

The LIDAR technology relies on the use of a laser beam focused to a tight spot, thus providing a high level of precision. Compact portable LIDARs can be used to detect objects located at several tens of meters. As LIDARs capture one measure per spot, they generates a lot of measurements. They thus need more computational resources to deal with.

We have decided to use a LIDAR because of the precision and of the distance to potential obstacles in the scenario that we address. We have selected the LIDAR-Lite v3 by Garmin which has a range of 40m, a resolution of 1cm and a correct accuracy (when considering obstacle detection). Its update rate is high (up to 500Hz), so we can mount it on a servomotor in order to get information on wide angles.

D. Network Simulator: OMNeT++

In order to simulate the characteristics of the sensor and transmitter presented above, we chose the network simulator OMNeT++ [START_REF] András | An overview of the OMNeT++ simulation environment[END_REF]. It allows numerous degrees of realism in the simulations, from ideal environment and performances of each element, to the modeling of energy consumption, antennas parameters or signal propagation. The main communication parameters that we chose are given in appendix.

III. RELATED WORK

Many path planning methods for UVs exist. In this section we will first focus on trajectory planning for UAVs, and then on planning methods based on APF and finally on methods designed for multi-platform systems.

A. Path Planning for Autonomous UAVs

A task of major importance for autonomous UVs is path planning. This task is of increasing complexity if the environment in which the drones operate are not known prior to the mission (and contain obstacles) and thus need to be calculated on board, in real-time. Some methods have been extensively studied to solve this problem: APF methods introduced by Khatib [START_REF] Khatib | Real-time obstacle avoidance for manipulators and mobile robots[END_REF], that we detail in the following section; virtual forces [START_REF] Falomir | Mobility Strategies based on Virtual Forces for Swarms of Autonomous UAVs in Constrained Environments[END_REF], [START_REF] Gobel | A model of autonomous motion in ad hoc networks to maximise area coverage[END_REF]; Genetic Algorithms [START_REF] Ioannis | Evolutionary algorithm based offline/online path planner for UAV navigation[END_REF]; chaotic processes [START_REF] Rosalie | From random process to chaotic behavior in swarms of UAVs[END_REF]; fuzzy logic [START_REF] Du | A common obstacle avoidance module based on fuzzy algorithm for Unmanned Aerial Vehicle[END_REF]; Particule Swarm Optimization (PSO) or Reinforcement Learning [START_REF] Di | A comparison of PSO and reinforcement learning for multi-robot obstacle avoidance[END_REF].

B. Path planning for UVs based on APFs

The APF method introduced by Khatib [START_REF] Khatib | Real-time obstacle avoidance for manipulators and mobile robots[END_REF] has numerous successors today. Indeed, this methods has some drawbacks as "Goal Non Reachable with Obstacle Nearby" (GNRON), local minima, oscillations close to obstacles (jitter problem) or the impossibility to go between two close obstacles [START_REF] Sun | Collision Avoidance for Cooperative UAVs With Optimized Artificial Potential Field Algorithm[END_REF].

Sun et al. [START_REF] Sun | Collision Avoidance for Cooperative UAVs With Optimized Artificial Potential Field Algorithm[END_REF] propose a collision avoidance method based on APFs for cooperative UAVs, with a local adjustment to overcome the jitter problem, and potential fields definition removing the other problems (GNRON, local minima and impossibility to go between two close obstacles). The collaboration process is not explained in this paper.

Additionally to the issues cited above, we can quote the consideration of obstacles as points, which can be used only for the small ones. Mac et al. [START_REF] Mac | Improved potential field method for unknown obstacle avoidance using UAV in indoor environment[END_REF] overcame this problem by considering large obstacles as a sum of punctual ones. In this paper, they also proposed a solution to the GNRON problem. This approach seems to be efficient for a single UAV, but an important part of our work consists in using collaboration between drones to improve the the model performances. The model defined by Mac et al. is not designed for swarming and consequently we cannot use it as is.

The local minima problem can be solved by creating a temporary and virtual waypoint, as presented by Liu and Zhao [START_REF] Liu | A virtual-waypoint based artificial potential field method for UAV path planning[END_REF]. In order to reduce the number of local minima, they consider close obstacles as one single larger obstacle. There is no simulation showing what happens if the goal is inside the envelope of several close obstacles. Furthermore, some of the other problems listed above are not mentioned.

Thanks to the several ways to solve the traditional APF method problems, it is possible to use this kind of algorithms for trajectory planning adapted to single and multi-platform, as shown by Galvez et al. [START_REF] Galvez | Obstacle avoidance algorithm for swarm of quadrotor unmanned aerial vehicle using artificial potential fields[END_REF]. In their paper, they use APF to maintain several quadrotors in formation on their way (possibly containing obstacles) towards a target point. Note that our objectives do not include formation control, then their model is not adapted to our scenario.

Finally, in all the improved APF that we found in the literature, the repulsive field linked to obstacles is symmetrical. We believe that it should depend on the UV and target point locations. Indeed, if a UV is close to its target, it is useless to consider a repulsive potential field due to an obstacle located at the back of the vehicle. Similarly, if an obstacle is not on the path towards the target point of a UV, it should not have any influence on the vehicle path planning. In our model, we reduce the repulsive field to the safety distance if the obstacle is not on the UAV path; if it is, then it has a great influence, as detailed in section IV-F.1.

C. Path Planning for Multi-Platform Systems

Some path planning methods are particularly adapted for multi-platform systems.

For example, we can quote Ant Colony Optimization methods (introduced in [START_REF] Colorni | Distributed Optimization by Ant Colonies[END_REF], widely studied since then [START_REF] Cekmez | Multi colony ant optimization for UAV path planning with obstacle avoidance[END_REF]) which are especially efficient for area coverage missionsbut this is not our main target scenario.

Models based on Boids are also widely studied. They allow stabilization of compact formations, even in environments containing obstacles [START_REF] Saska | Swarms of micro aerial vehicles stabilized under a visual relative localization[END_REF]. Rosalie et al. [START_REF] Rosalie | Coverage Optimization with Connectivity Preservation for UAV Swarms Applying Chaotic Dynamics[END_REF] recently proposed a model combining Boids principle, Ant Colony Optimization and chaotic dynamics to fulfill a mission of area coverage while maximizing connectivity within the swarm.

Finally, we can quote Virtual Forces methods, that make it possible to maintain formations in constrained environments or to fulfill coverage missions [START_REF] Falomir | Mobility Strategies based on Virtual Forces for Swarms of Autonomous UAVs in Constrained Environments[END_REF]. Virtual Forces are also adapted for systems that rely on leaders and followers [START_REF] Gobel | A model of autonomous motion in ad hoc networks to maximise area coverage[END_REF]. Still, methods based on virtual forces have problems similar to those of APF-based methods.

IV. OUR MOBILITY MODEL

A. Hypothesis

At the beginning of the mission (or simulation), all UAVs are inside the AoI. They have to reach a common target point, which is also inside the AoI. The UAVs always instantly know with sufficient precision their own location, the location of the target and the limits of the AoI. The UAVs evolve in a discrete environment. At each iteration, they can either move to the center of one of the 8 adjacent square-cell, or stay at the same location. No mobile obstacle is considered in this study. Each UAV carries a sensor allowing obstacles detection (fix obstacles or other UAVs) which are located in a given range (see section II-C) at 360 • . In our model, the cells width must be greater than this sensor range. The UAVs can communicate if the distance between them is smaller than the radio range (see section II-B).

B. Notations

The environment in which the UAVs move is meshed. All the cells are squares of equal size. Hence, we consider a matrix of l max lines and c max columns, both finite numbers. We note c i,j the cell in line i and column j where, (i, j) ∈ 1, l max × 1, c max . We note t i,j the cell in line t i and column t j containing the target. Likewise, uav i,j represents the cell containing a UAV in line uav i and column uav j . Three matrices are considered for this model, each of size l max × c max . The first one represents the potential field and is noted pot. The second stores the locations of known fix obstacles; it is noted obs. The third is used to anticipate the fix obstacle avoidance (see below) and for the UAV avoidance, and is noted avoid.

C. Principle

At each iteration, each UAV moves to the adjacent cell with the smallest potential.

If an obstacle is (even partially) in a cell, the potential of this cell is increased by a fixed positive value called obsInc.

D. Matrices Meanings and Initializations

The matrix obs is the only one exchanged by the UAVs. Each cell of this matrix has a value representing if:

• there is no information about the cell; • it does not contains an obstacle and is safe; • it does not contains an obstacle but is not safe or has been revisited; • it contains an obstacle;

• it is on a path already used by a UAV to reach the target. The matrix noted pot contains the APF related to the target point, the APF related to the obstacles which have been detected and the potential related to cells which have already been visited.

The matrix noted avoid stocks the potential related to the anticipation of fix obstacles avoidance and the temporary potential linked to neighbors. It is the only one that is reinitialized at each iteration.

The potential related to the target is initialized with the distance between the cell and the target (in cell t i,j ), using the infinity norm, as given in the following equation:

pot i,j = max (|t i -i|, |t j -j|) . (1)
At the beginning of the mission, the UAVs have no information on the AoI (except its limits). Then, there is no potential linked to obstacles, all the cells of the obs matrix represent "no information" and the avoid matrix is null.

E. Collision Avoidance 1) Collision Avoidance UAV-fix obstacle:

We consider a collision if a UAV is in a cell containing an obstacle. Hence, when an obstacle is detected, the potential of its cell is increased by a fixed value noted obsInc and all the adjacent cells are increased by obsInc/2.

2) Collision Avoidance UAV-UAV: At each iteration, the UAVs detect their neighbors in their sensor range. UAVs consider each other as a temporary obstacle, and apply the same increases as with fix obstacles: increase by obsInc on the neighbor cell and obsInc/2 on the adjacent cells. These potential increases are recorded in the avoid matrix, which is re-initialized at each iteration, because the neighbors location can change at each iteration.

F. Algorithm 1) Obstacle Avoidance Anticipation:

In the case of the traditional APF, the influence of an obstacle is symmetrical. In our mobility strategy, an obstacle has an influence on a UAV while it is on its way towards the target point. In other words, if a UAV has information about an obstacle located between itself and its target point, it will increase the potential of all the cells between itself and the obstacle. Fig. 1 shows the variations of potential in this case. for j ← min(uav j , o jM in ) to max(uav j , o jM ax ) do if a Min < angle(c i,j , uav i,j , g i,j ) < a Max and dist(c i,j , o i,j ) < dist(uav i,j , o i,j ) then avoid i,j = ceil obsInc dist((ci,j ,oi,j )+1 ; end end end ALGORITHM 1. Obstacle Avoidance Anticipation (OAA) * o i,j is the considered cell containing an obstacle on the way toward the target Data: uav i,j , pot matrix; avoid ← 0 lmax,cmax ; if uav i,j has already been visited then pot i,j ← pot i,j + 1 * ; else Add uav i,j to visited cells; end Detect fix obstacles; foreach obstacle stored in avoid do OAA(uav i,j , g i,j , o i,j ); end Detect other UAVs and increase potential around them; Calculate the minimum potential of the adjacent cells * * ; Store the cell with minimum potential in m minPot ; if m minPot = uav i,j then pot i,j = mean of adjacent cells; end Next cell ← m minPot ; ALGORITHM 2. Move decision by each UAV. * The increase is of 1 because it is the difference between the potential of two adjacent cells when there is no influence of obstacles. Then, it is enough to favor the exploration of other cells, and is small enough not to interfere with obstacles (anticipation) avoidance. * * If several of these cells have the smallest potential, the one closest to the target is recorded. This anticipation of obstacle avoidance is particularly efficient if the UAVs can detect obstacles at a long distance, or if they get information from other sources (other UAVs in our case). UAVs have information on obstacle presence cell by cell. Then, if one cell containing an obstacle is on their way towards the target, they verify the presence in all the cells at a distance 2 (calculated with the infinity norm) to deduce the whole obstacle location. We consider a safety distance around obstacle of one cell, then two obstacles separated by 2 cells are considered as a single one, as shows Fig. 1. Algorithm 1 is the pseudo-code describing this process.

2) Mobility Strategy: The motion decision of a UAV at each iteration depends on the location of its target point, of its close neighbors and on the discovered obstacles, as detailed in algorithm 2.

When a UAV reaches the target point, it sets the cells of the avoid matrix corresponding the ones crossed to "on a way already used to reach the target". When the other UAVs receive this information, they decrease the potential of theses cells by a fix value. Then, if a UAV is on a cell on a way to the target, it will follow this path which ensures the arrival.

V. SIMULATIONS A. Simulated Environment

One application of our model is the identification of an intruder in restricted areas. Then, we used the open source French land register [START_REF] Géoportail | [END_REF] to reproduce the buildings of the Blayais nuclear power station (France). We focused on a part of this area of 500m large and 500m long, as shows Fig. 2.

B. Parameters

As the considered UAVs are 0.5m large, we state a 1.5m safety distance. Then, the minimal cell size of the mesh is 2m. Considering the 500m times 500m area, the obstacles matrix has 250 lines and 250 columns. Each cell of this matrix contains one of the 5 different states described in section IV-D, represented by integers. We used integers coded on 8bits, then the data in the matrix is coded on 500kbit. The XBee data rate is up to 250kbit/s and the communication is half-duplex. Thus a single UAV can broadcast its updated matrix every two seconds. Even though collaboration allows the UAVs to reach the target quicker, the low communication rate is not a safety issue as UAVs do not require collaboration to evolve safely towards the target. The simulated sensor for obstacle detection is a LIDAR-Lite (see section II-C), with a detection range up to 40m. We suppose that it is mounted on a platform allowing detection all around the UAV. So as to test the model in different conditions, we performed the simulations using multiple cell widths (2, 4, 6, 8 and 10m), several swarms sizes (3, 5, 7 and 9 UAVs), and with three different targets.

C. Collision Avoidance

For all the simulations performed, the distance between a UAV and another object (building or UAV) was greater than half of the cell width, so there were no any collision.

D. UAVs Trajectories

Fig. 2 and3 represent the trajectories of the 3 UAVs composing a swarm moving toward the target point T2 using different cell widths. The part of the trajectory circled in the case of a 8m-large cell illustrates well the collaboration between the UAVs: a first one detects an obstacle, avoids it and transmit the information to the others that anticipate the best path toward the goal. It has to be noted that our mobility strategy allows the UAVs to reach all these targets, including surrounded by obstacles (case of target T3). In other words, our model solves the "Goal Non Reachable with Obstacle Nearby" (GNRON) problem. Furthermore, our model also allows the UAV to go between two close obstacles (Fig 3 and4).

E. Influence of the Cell Width

Cell width is an important parameter in our model. First, we consider that a cell is either empty or filled with an obstacle since a building has at least one square meter (precision of the environment model) within it. The larger the cells, the larger the forbidden areas. This leads in one hand to longer path to avoid these areas (see Fig. 3 and5) and on the other hand to non reachable goals as in the case of T1 and T3 for cells larger than 6 meters. Fig. 5 represents the travel distance of UAVs from their launch point to the target T2 with swarms from 3 to 9 UAVs. It shows that the smaller the cell, the shortest the path to the goal.

Furthermore, fewer iterations are required to travel the same distance with large cells than with small ones and so the process is less calculation intensive. Also, the larger the cell, the smaller the matrices. The messages exchanged between the UAVs are thus smaller with large cells.

Finally, the UAVs travel from a cell to an adjacent one at each iteration. Then, the larger the cell, the longer the distance at each iteration, and so the quicker at the target.

VI. CONCLUSION AND FUTURE WORK

This paper presents a new mobility strategy for autonomous swarms of collaborative UAVs. Our method uses the principle of APF but differs from the traditional one: in our solution, the influence of obstacles depends on the locations of the UAVs and the location of their target. This allows anticipation of obstacles avoidance and solve the "Goal Non Reachable with Obstacle Nearby" problem. We validated the efficiency of our mobility strategy with simulations under OMNeT++, in a realistic environment and with the simulation of existing sensors performances.

In future work for the optimization of our method we will consider moving obstacles and simulate the 3 dimensions. Furthermore, we plan to combine these potential fields with a global path planning algorithm (such as A*), in order to shorten the UAVs trajectories.

APPENDIX

Fig. 1 .

 1 Fig. 1. Illustration of the potential field in the case of obstacle avoidance anticipation. UAV is represented by a dark empty square, the obstacle is represented in dark blue. The cells colored in green are not submitted to any increase. The cells submitted to a potential increase are represented with gradation from brown (smallest) to red (highest). Data: uav i,j , t i,j , o i,j * Calculate min and max line and column of the whole obstacle: o iM in , o iM ax , o jM in , o jM ax ; Calculate min and max oriented angle obstacle-UAV-target: a Min , a Max ; for i ← min(uav i , o iM in ) to max(uav i , o iM ax ) dofor j ← min(uav j , o jM in ) to max(uav j , o jM ax ) do if a Min < angle(c i,j , uav i,j , g i,j ) < a Max and dist(c i,j , o i,j ) < dist(uav i,j , o i,j ) then

Fig. 2 .

 2 Fig. 2. Environment of simulations. Obstacles are represented in red. Trajectories of the 3 UAVs composing the swarm are plotted from their initial location (circles) toward their target point called T2 (represented by a green pentagon). The target point T1 is represented by a red square and T3 by a blue triangle.

Fig. 3 .

 3 Fig. 3. The trajectories of the 3 UAVs composing the swarm from their initial location toward their target point T2.

Fig. 4 .

 4 Fig. 4. Example of UAVs trajectories with targets T1 and T3, with various cell width.

Fig. 5 .

 5 Fig. 5. Superposition for a given cell width (2, 4, 6, 8 and 10) of the distances traveled towards the target T2 by each individual UAV in swarms of 3, 5, 7 and 9 vehicles,.

  Main Communication Parameters used is OMNeT++:

	• Wireless Local Area Network (WLAN)
	-type name: "IdealWirelessNic"
	-communication range: 90m
	-full duplex: false
	• Routing
	-destination address: 10.0.255.255
	-forwarding: false
	-optimize routes: false
	• UDP application
	-type name: own model based on "UDPBasicApp"
	-send interval: 500ms
	-message length: 62500B (see section V-B)
	-bitrate: 250kbits/s
	• Radio
	-radio type: "APSKScalarRadio"
	-radio medium type: "APSKScalarRadioMedium"
	-carrier frequency: 2.4Ghz
	-bandwith: 2MHz
	-background noise power: -90dBm
	-transmitter power: 63mW
	-receiver sensitivity: -102dBm
	-receiver snir threshold: 4dB
	-receiver ignore interference: false
	-transmitter header bit length: 192b

• Environment ground type: "FlatGround" ground elevation: 0m path loss type: "TwoRayGroundReflection"


Killian Foubert

Rémi Bazillier

Daniel Mirza

Terrorism and Emigration: Does it Depend on the Measure and Sample Used?

Keywords: Migration, Terrorism, Psychological costs, Freedom of Movement JEL: C23, I39, O15

The relationship between terrorism and migration is currently an international issue.

This paper studies the impact of the total number of victims and the total number of attacks on migration flows. Moreover, I estimate the impact of terrorism on migration flows according to countries' level of development. The main hypothesis is that terrorism can decrease freedom of movement in the poorest countries, and increase migration costs due to higher adaptation costs. At the world level, I find that the number of victims has a positive and significant effect on international emigration, while this is not true for the number of attacks. Interestingly, South-South migration is not affected by terrorism, while the South-North corridor is influenced by both the number of victims and the number of attacks. However, when the development gap between origin and destination increases, these effects tend to shade off.

INTRODUCTION

Terrorism is one of the central concern in today's society. After the 09/11 attacks, George W. Bush, President of the United States, declared open the "War on terror". Fifteen years later, our days are still marked by terrorism, both in developed and developing countries. Moreover, in the last two years, Europe has endured a migrant crisis. According to the United Nations High Commissioner for Refugees, the top three nationalities of migrants of the over one million Mediterranean Sea arrivals between January 2015 and March 2016 were Syrian (46.7%), Afghan (20.9%) and Iraqi (9.4%). Precisely three countries directly threatened by the Islamic State (I.S.I.S.). The combination of both the migrant crisis and international terrorism leads to increased xenophobia. Indeed, developed societies tend to follow populist policies, increasing social tensions and rising nationalism. In Europe and the United States, migrants are sometimes seen as a potential source of terrorism. Donald Trump, for instance, recently tried to sign an executive order to temporarily curtail immigration and the rights of refugees in attempt to keep out "radical Islamic terrorists". Concerns about terrorism, migration and the relationship between these two phenomena are more than topical issues: they are at the core of political debates and will determine the future of societies and international peace.

The United Nations' 1566 Security Council Resolution defined terrorism as "criminal acts, including against civilians, committed with the intent to cause death or serious bodily injury, or taking of hostages, with the purpose to provoke a state of terror in the general public or in a group of persons or particular persons, intimidate a population or compel a government or an international organization to do or to abstain from doing any act[...]". Terrorism has economic, social, and political costs. From an economic perspective, terrorism decreases the return on investment, tourism and trade (e.g. [START_REF] Abadie | Terrorism and the world economy[END_REF] ; [START_REF] Nitsch | Terrorism and international trade: an empirical investigation[END_REF] ; [START_REF] Enders | An econometric analysis of the impact of terrorism on tourism[END_REF]). [START_REF] Frey | Calculating tragedy: Assessing the costs of terrorism[END_REF] have shown that the psychological impact of political violence (i.e. terrorism) on the deterioration of life condition might actually be greater than the effect of economic costs. [START_REF] Klein | The shock doctrine: The rise of disaster capitalism[END_REF] theorised what she called the shock strategy, namely that psychological weakening due to a shock reinforces political instability. This theory can be applied to human violence, as demonstrated by [START_REF] Frey | Calculating tragedy: Assessing the costs of terrorism[END_REF] as well as [START_REF] Campos | International terrorism, domestic political instability, and the escalation effect[END_REF].

The combination of the economic, social and political costs in addition to the risk of death or injury, can drive individuals to migrate. Every civilian seeks security as well as political and economic stability. Theoretically, this decision to migrate will depend on the impact of terrorism on their utility, namely on the detrimental effects on utility at home compared to the expected utility after migration. The latter includes the migration costs and the expected utility for migrants in the destination country. [START_REF] Dreher | Hit and (they will) run: The impact of terrorism on migration[END_REF] focused on the impact of terrorism on emigration for different skill levels. I propose another approach. I test two hypothesis in this paper. Following [START_REF] Frey | Calculating tragedy: Assessing the costs of terrorism[END_REF], I expect a greater impact on migration of the psychological costs induced by terrorism on emigration than the number of attacks solely. To test this, I create an index of "human costrelated terrorism". It is defined as the total number of victims due to terrorism over the total population in the country impacted. The second measure is the total number of attacks. Then, I look at the impact of these two variables on seven migration corridors. I make the assumption that migration costs depend on the characteristics of countries of origin and destination, and the development gap between them. This paper asks whether a greater development gap reinforces migration costs due to terrorism. This would be the consequence of the differences in culture, leading to an increase of xenophobia in the destination country. Notably, people wanting to avoid the importation of violent political values. I call this a "counter-pull" effect, with an increase in the adaptation costs for migrants. For example, when individuals migrate to richer countries, terrorism at origin might reinforce difficulties to integrate in destination's society, to find a job or a dwelling. I also suppose that terrorism can drive individuals in a state of shock, following [START_REF] Klein | The shock doctrine: The rise of disaster capitalism[END_REF]'s concept. People become more docile after a terrorist shock, and bad governance can be reinforced in the origin country, ultimately leading to a possible decrease in freedom to move.

Consequently, this paper contributes to the literature in several ways. Previous studies have differentiated individuals' reaction to terrorism according to their level of education. I differentiate migrants' decision to move according to the measure of terrorism used and the development gap between countries. This paper aims at providing insights for future public policies and help policymakers to understand how migration flows are affected by terror.

The empirical analysis makes use of the United Nations database on migrant stocks between 1960 and 2000. This dataset provides information on 226 origin and destination countries. I construct migration flows by taking first differences of these decennial migrant stocks. This methodology mitigates concerns of possible contemporaneous reverse causality, since migration flows cannot influence terrorism in origin countries before the year of migration.

For the construction of the two terrorism measures, I use the Global Terrorism Database, providing information for the 1970-2014 period. Due to data constraints and the methodology used, I can only compute the emigration rate for 1980, 1990 and 2000. Considering the large number of zero in migrant flows, I estimate the empirical model using a Poisson estimator. This technique allows me to keep the zero flows in my regression (which would for instance be eliminated if a log transformation was used with an OLS estimation). In order to control for potential omitted variable bias, I use high dimensional fixed effects. Specifically, these consist in origin country fixed effects to control for time invariant origin specific push factors as well as time invariant origin costs. This mostly captures geographic position, since, for instance, coming from an island might increase migration costs due to transport limitations. Furthermore, an important issue in the gravity literature is the multilateral resistance bias, namely the influence of other countries on bilateral migration flows. Therefore, I include destination-year fixed effects,1 which allow me to control for any changes in immigration policies in destination countries. However, I do not include a dyadic fixed effect in order to avoid collinearity in my regressors and to keep the largest number of observations in my dataset. The database is then split between North and South countries, defined first on the basis of the median of GDP per capita, and subsequently based on percentiles of wealthiness as robustness checks. I still include GDP per capita in the origin countries, in order to control for possible heterogeneity in the GDP per capita distribution among groups of countries. The possible heterogeneity bias at destination is captured by the destination-year fixed effect.

The empirical analysis provides evidence of a significant impact of terrorism on international migration. Moreover, my findings confirm the greater impact of the human cost associated to terrorism relative to the total number of attacks. This is in line with [START_REF] Frey | Calculating tragedy: Assessing the costs of terrorism[END_REF]'s hypothesis on the importance of psychological factors, and thus with my paper's assumption.

Splitting up the sample, the impact of terrorism appears insignificant for North-North migration. For migration originating from the South, I find different results depending on the countries of destination. First, there is no statistical effect of terrorism on South-South migration. This result suggests that people in the South not value migration to another country potentially threatened by the same kind of violence as a solution to escape from terrorism. Admittedly, it would be interesting to see whether I would obtain the same result for temporary or internal migration, but the construction of my dataset prevents me from further exploring this.

Nonetheless, I find evidence for a significant push effect of terrorism on international migration in South-North corridors. People seek to maximize their utility by avoiding violence, leading to an increase of emigration to the North. When the gap between origin and destination countries increases, the impact of terrorism on emigration decreases. This result might signal a larger sensitivity to migration costs depending on the development gap between countries of origin and destination. The greater the gap between these countries, the greater the impact of terrorism on migration costs. I suggest that this might be due to a distrust effect in richer countries towards people coming from poor countries hit by terrorism. Also, terrorism might decrease freedom of movement at origin for the most affected countries, due to a reinforcing mechanism between bad governance and terrorism.

The remainder of this paper is organized as follows. Section 2 discusses the related literature. In section 3, I will present the theoretical background of this paper. In section 4, the data are detailed, and in section 5, the results and robustness checks are analyzed. Finally, section 6 will conclude this study.

RELATED LITERATURE

There is a large literature on the impact of shocks and violence on individuals, economic life and emigration. I will first review the papers focusing on the impact of terrorism on emigration, trying to capture a possible push effect of terrorism on the individual decision to migrate.

Then, studies about the importance of psychological trauma will be highlighted. I will describe the theoretical foundations of the gravity models, discussed in well published papers.

THE EFFECT OF TERRORISM ON EMIGRATION

The relationship between violence and migration is increasingly studied in the recent literature. [START_REF] Dreher | Hit and (they will) run: The impact of terrorism on migration[END_REF] show that terrorism specifically increases skilled emigration. They use a dataset with only six of the OECD countries as possible destinations. The authors suggest that this type of violence decreases the return to education and the expected utility of skilled workers in their home country. However, on average, they find a non-significant effect of terrorism on emigration. They conclude that the impact of terrorism on emigration depends on the level of education. This selection effect will not be an issue in this paper, since I explicitly look at the average emigration rate. I purposely do not differentiate between education levels.

In the same way, Bohra-Mishra and Massey [2011] studied how armed violence during a period of civil conflict in Nepal influenced the likelihood to migrate. Interestingly, they find that civil conflicts affect migration differently depending on levels of violence. From low to moderate levels of violence, this reduces the odds of migration, while from a certain level of violence, emigration increases.

Following the same idea, [START_REF] Alvarado | Search of peace: Structural adjustment, violence, and international migration[END_REF] nuanced the impact of violence on emigration. They argue that modest levels of violence can eventually have a negative effect on the emigration rate because people respond to risk by limiting outside mobility. Their study assesses the impact of violence (homicide) in Mexico, Guatemala, Nicaragua and Costa Rica on migration to the United States. They show that people from different countries respond differently, and thus "violence does not appear to have uniform effects on patterns of international migration but depends on broader social and political conditions within particular countries". Even if this paper focuses on homicides, the results suggest that a non-linear impact of violence on migration might exist, depending on the level of development in the origin countries.

Another paper on terrorism also focuses on a possible non linear effect on migration. [START_REF] Morrison | Violence or economics: what drives internal migration in guatemala? Economic Development and Cultural Change[END_REF] studies the impact of terrorism on emigration from South America during the period 1976-1981. He shows that violence is a key determinant of migration in a country suffering from endemic political violence. Particularly, an increasing and convex relationship is suggested between violence and migration, with an intensification of this effect when violence escalates. It means that the impact of terrorism on migration might be greater in countries where conflicts are more frequent, namely developing countries.

Edwards [2008] uses a more theoretical approach. The author explains that emigration is positively correlated with the level of violence of civil conflicts, because people rationally try to maximize their utility by reducing the risk of being killed or injured. This study reinforces the need to take into account the intensity of terrorism, and not only the onset of attacks.

2.2 THE EFFECT OF TERRORISM ON PSYCHOLOGICAL TRAUMA [START_REF] Frey | The life satisfaction approach to valuing public goods: The case of terrorism[END_REF] study how terrorism can be measured to assess its impact on well-being. This paper first details the methodology to measure terrorism. They also present microeconomic techniques to measure the psychological impact of terrorism. They compare the effect of the psychological measure and economic one on life satisfaction. Adapting their methodology to a macroeconomic study, I create two indexes of terrorism: one reflecting the terrorism violence by including the number of victims (psychological-human costs), and the other being solely the number of attacks, without integrating the number of victims. In another paper, [START_REF] Frey | Calculating tragedy: Assessing the costs of terrorism[END_REF] show that the psychological costs have a more detrimental effect on life satisfaction than the total number of attacks. Following their results, I expect a greater impact of the psychological costs on emigration rate.

In a well known book called The Shock Doctrine, [START_REF] Klein | The shock doctrine: The rise of disaster capitalism[END_REF] argues that a collective trauma such as a war, an economic crisis, a natural disaster, or a terrorist attack, plunges individuals in a state of shock. She claims that after a shock, people turn more docile, and more incline to follow politic leaders. I suggest two consequences. First, as argued by [START_REF] Campos | International terrorism, domestic political instability, and the escalation effect[END_REF], terrorism weaken states, and weakened states increase terrorism. I make the hypothesis that this is a consequence of the "docility" effect. Fragile states are more threatened by terrorism, and terrorism induces political instability because of the trauma effect, which reinforces the fragility of these states. This mechanism may decrease freedom of movement, and increase migration costs. Second, I suggest that this psychological shock can be exported. If people are afraid by the terrorist threat, they can turn more suspicious towards immigrants from affected countries. This can lead to an increase in the adaptation costs for immigrants in destination countries. Özden et al. [2011] provide a complete database on bilateral stocks of migrants for 232 origin countries and 231 destination countries between 1960 and 2000 for each decade, now adopted by the United Nations. [START_REF] Beine | Climatic factors as determinants of international migration[END_REF] used this database, and looked at the impact of climate shocks on international migration. Particularly, they worked on the impact of unexpected short-run climate shocks. Their paper uses a gravity framework, with a Poisson estimator. High frequency migration data are only produced for a very limited number of countries globally. Therefore, they faced a trade-off between geographic coverage and the frequency of observations. Making strong assumptions about international flows and internal migrations, they provide results on migration enhanced by climatic shocks. They construct their proxy for migration flows by differencing data for contiguous census rounds, as explained before. I use the same technique to compute the migration flows.

THEORETICAL APPROACH ON MIGRATION AND DATASET ISSUE

Finally, Beine et al. [2015] published a theoretical paper providing complete information on gravity models and a meta-analysis on studies using bilateral data in a gravity framework. The authors decompose the Random Utility Model (RUM), and explain how external variables can influence the willingness to migrate, depending on the effect of these variables on expected utility in the country of origin and in the possible destination countries. In the same way, [START_REF] Ramos | Gravity models: a tool for migration analysis[END_REF] wrote another study on the gravity framework, with a more practical approach and therefore more applied advices. These two papers are used for the theoretical section of this paper.

AN INCOME MAXIMIZATION APPROACH

The empirical framework used in this paper relies on an income maximization approach. One of the main advantages of this approach is that it allows to consider the choice of moving to a particular country j among all potential destinations. Indeed, the influence exerted by other destinations on bilateral flows is important and should be controlled for. 2 This issue has received a lot of attention in the literature, and different methods have been theorized by [START_REF] Eaton | Technology, geography, and trade[END_REF] as well as [START_REF] Anderson | Gravity with gravitas: a solution to the border puzzle[END_REF]. These methods were gathered by [START_REF] Ramos | Gravity models: a tool for migration analysis[END_REF], who suggests that one possible solution is to include origin-year dummies or destination-year dummies. Terrorism variables are time varying at origin, therefore I will not include origin-year dummies. Nonetheless, including a destination-year dummy α j ,t allows me to control for both migration policies at destination and multilateral resistance to migration. Time-invariant origin specific push factors A i and time-invariant origin costs will be captured by origin country dummies α i . 3 2 [START_REF] Bertoli | Multilateral resistance to migration[END_REF] show that an increase of the attractiveness of a destination country k perceived as a close substitute to j will decrease the migration flow between i and j more than the natives staying in i . Thus we definitely need to control for this issue, which they refer to as "multilateral resistance to migration".

3 Admittedly, a dyadic fixed effect α i j would also allow to control for time-invariant characteristics of country pairs such as the distance between them or the presence common language. The latter will, however, not be Following the methodology from [START_REF] Beine | Climatic factors as determinants of international migration[END_REF], Beine et al. [2015] and [START_REF] Anderson | The gravity model[END_REF], the empirical analysis relies on a classical random utility maximisation model of migration. Purposely, agents are assumed homogeneous, i.e. not categorised by education level or age. 4 In this model, each individual decides first whether or not to migrate. Subsequently comes the choice of destination, where individuals are assumed to choose the destination that delivers the highest utility. In this framework, the utility function is log-linear in income, and depends upon both country of origin and possible countries of destination characteristics, as well as on the migration costs. The extension that this paper proposes is the inclusion of terrorism as an additional country of origin characteristic, in order to introduce the effect of violence stemming from terrorist attacks on the utility at home as well as on migration costs.

First, we denote m i , j ,t the migration flow from i to j at time t . It is defined as:

m i , j ,t = p i , j ,t × s i ,t (3.1)
where p i , j ,t is the share of individuals residing in i who move to j at time t , and s i ,t the stock of individuals living in i at time t . The aim of the random utility maximization model is to derive p i , j ,t . First the utility from staying in the origin country can be written as:

u i ,i ,t = ln(w i ,t ) + A i ,t + i ,t (3.2)
with w i ,t representing the wage in country i at time t and A denoting country i 's characteristics (political, demographic, and terrorism intensity). represents the random term.

Then, the following expression represents the utility that an individual living in i at time t -1 would get by migrating to j at time t :

u i , j ,t = ln(w j ,t ) + A j ,t -C i , j ,t + j ,t (3.3)
where C i , j ,t denotes the costs of migrating from i to j at time t . It has to be highlighted that the wage at destination w j ,t does not integrate the origin country information due to the assumption of homogeneous agents, while [START_REF] Borjas | Self-selection and the earnings of immigrants[END_REF] has shown that the expected earnincluded in order to preserve the largest possible sample size. Thus I will explicitly control for distance and common language in the regression. 4 See also [START_REF] Sjaastad | The costs and returns of human migration[END_REF], and [START_REF] Ortega | The causes and effects of international migrations: Evidence from oecd countries 1980-2005[END_REF] for the introduction of heterogeneity among individuals.

ings of migrants in the destination country is heterogeneous with respect to the conditions of their home countries. Therefore, deriving the expected share of individuals coming from i and moving to j among a full set k of destinations at time t can be written as:

E(p i , j ,t ) = P [u i , j ,t = max k u i , j ,k ] = N i , j ,t N i ,t = e ln(w j ,t )+A j ,t -C i , j ,t k e ln(w k,t )+A k,t -C i ,k,t (3.4)
Focusing only on country of origin i , and only on one country of destination j , we get the following equilibrium bilateral migration rate:

N i , j ,t N i ,i ,t = e ln(w j ,t )+A j ,t -C i , j ,t
e ln(w i ,t )+A i ,t (3.5)

where N i , j ,t represents the migrant flow between i and j at t , and N i ,i ,t is the number of natives in country i at time t . We then add logs to obtain a log-linear model, which gives:

ln( N i , j ,t N i ,i ,t ) = ln( w j ,t w i ,t ) + A j ,t -A i ,t -C i , j ,t (3.6)
Terrorism intensity will be integrated as one of the origin country's political characteristics A i ,t . Formally we get:

A i ,t = A(C on f l i c t s i ,t , Ter r or i ,t ) (3.7)
where Conflicts is a dummy equal to 1 when there is at least one conflict with more than 1,000 battle related deaths in the origin country during the ten years preceding t . Including this factor as a determinant of political characteristics has two advantages. First, [START_REF] Findley | Terrorism and civil war: A spatial and temporal approach to a conceptual problem[END_REF] show that based on the PRIO database, the percentage of war-related terrorism amounted to 56% between 1970 and 2004. 5 Even if both phenomena are not entirely the same, more than half of terrorist attacks can be related to civil conflicts. This justifies to control for this variable in order to avoid endogeneity through an omitted variable bias. Second, it allows to reflect political stability in a country. If we focus on political regimes, by including the Polity IV index for instance, the theoretical impact of democracy index on emigration is a priori unknown. Indeed, repressive political regimes might increase residents' desire to leave, but also the costs of migration since these regimes might, for example, be less willing to deliver visa to those who want to emigrate. Democratic regimes, on the other hand, can be associated with lower desires to leave, and hence, lower migration.

Terror is the main characteristic of interest, and is composed of two variables. One is representing the psychological-human cost, inspired by the index developed by Dreher et al.

[2011], and the study by [START_REF] Frey | Calculating tragedy: Assessing the costs of terrorism[END_REF]. 6 It takes the following form:

Ter r or i ,t = d eat hs i ,t + wound ed i ,t popul at i on i ,t (3.8)

The second terrorism measure is the total number of attacks the ten years before t , without any distinction across the types of violence.

Destination countries' characteristics A j ,t are captured by the destination-year fixed effect. The migration cost function can then be written as follows: It also depends on time-varying factors in destination countries (x j ,t ) such as binding constraints in immigration policies. 8 See [START_REF] Mayda | International migration: A panel data analysis of the determinants of bilateral flows[END_REF] and [START_REF] Anderson | Gravity with gravitas: a solution to the border puzzle[END_REF] for further explanations on the role of policy constraints, and how they may weaken both pull and push factors for migration.

C i , j ,t = C (M i , j ,t , d i , j , l i , j , x j ,t , x j , x i ) ( 3
The final form of the empirical model is then defined as: 9 Which has been adopted by the United Nations. 10 Between 1960 and 2000, South-South migration: largest fraction (from 54 to 44% of total) ; North-North migration: decreasing component (from 24 to 18%) ; South-North migration: growing component (from 10 to 33%).

ln( N i , j ,t N i ,i ,t ) = ln(w j ,t ) -ln(w i ,t ) -A(Pol i ,t ) -A(Ter r or i ,t ) -C (M i , j ,t ) -C (d i , j ) -C (l i , j ) -C (x j ,t ) -C (x j ) -C (x i ) + j ,t (
11 See Table 4.4. Then I derive the numerator. As said before, the database only provides data on stocks of migrants for each decade between 1960 and 2000. To derive the migrant flows between i and j at t (with t corresponding to 1970, 1980, 1990 and 2000), I subtract the stock of migrants from i to j at t -10 from the stock of bilateral migrants in this corridor at t . This approximation gives by construction a high proportion of zero. It also gives negatives values when bilateral migrants stocks between two countries decline over time. This can be due to return migration, new migration to a third country, or migrants death. I will thus not consider the decreases of migrant stocks in this paper, that is the negative computed migrant flows. They are mostly driven by Serbia during the fall of USSR, therefore due in a large proportion to political border changes, and not return migrations.

TERRORISM INDEX CONSTRUCTION

The Global Terrorism Database is the most complete database on terrorism, ranging from 1970 to 2014. I extract from this resource the exact location of each attack, as well as the total number of death and wounded due to terrorism. I then derive the total number of victims (deaths and wounded) in a country i during the last ten years before t with the size of the population at t . I compute the psychological human costs of terrorism by dividing the total number of victims for each decade by countries' population where violence took place. The distribution of victims between South and North is unequal. Overall, the accumulated number of deaths and wounded for each decade is around 396, which gives an index of violence on average equal to 0.00004. I split the sample between countries below the GDP per capita median (South countries) and countries above (North ones). When we look only at North countries, the average index of terror is around 0.00002. In the South however the index of violence is three times higher, with on average 5.4 times more death each decade than in North countries. These statistics confirm that South countries are more threatened by terrorism. This reinforces the need to use the most complete migration dataset, in order to include the poorest countries. The total number of attacks is also higher in poorer countries.

It is important to recall that due to data constraints, I can only look at the 1980-2000 period. Figure 1, taken from [START_REF] Frey | Calculating tragedy: Assessing the costs of terrorism[END_REF], presents the evolution of the violence per attack between 1968 and 2004. We can see that the number of fatalities per attack increase since 2000, reinforcing the necessity to differentiate human costs from the number of attacks. We can imagine that the current rise of the Islamic threat and its international activities have reinforced this rise of violence, and thus, the total human costs of terrorism. It may have deepened the distortions between North and South countries in terms of attacks and victims, since it was born in the Middle East (South), and progressively expands to the North.

OTHER CONTROLS

The remaining variables come from several sources. I calculate the GDP per capita with the The network of country i in country j at time t is considered as the bilateral migrant stock in the beginning year to which a flow corresponds. Therefore, the data come from the same database than my dependent variable, namely the United Nations database on bilateral migrant stocks. The remaining geographical and linguistic dyadic factors come from the CEPII database. 12 The language dummy takes the value one when at least 9% of both populations in a country-pair speak the same language.

Once all the data are merged, I have 231 origin and destination countries in my database.

RESULTS AND ROBUSTNESS CHECKS

In this section, I present the results and use them to discuss my hypothesis. First, I include the different measures of terrorism. I want to test if psychological related costs do matter more than the number of attacks to explain emigration. In other words, if this hypothesis is confirmed, the number of victims would increase the emigration rate more than solely the number of attacks. Second, I assume that individuals will respond differently to terror according to the level of development of the home and destination countries. I establish this hypothesis using a utility maximisation framework, arguing that the greater the development gap between two countries, the greater the increase of migration costs. I suggest two explanations.

On the one hand, this can be due to a psychological state of shock after terrorist attacks. 13 Following this kind of shock, people are more incline to follow politic leaders. In the least developed countries, precisely the most attacked countries, people can thus be easily manipulated, and then suffer from bad governance. In today's Nigeria for instance, people are so poor and so threatened by daily political violence that most of them "passively" endure bad governance (corruption and political instability mostly). This bad governance will increase terrorism, and the least developed countries are then stuck in a trap. This trap may conduct to higher migration costs due to a potential impact on freedom of movement. To test this hypothesis, I should include an interaction term between Terrorism and a bad governance index (reflecting bad institutions or corrupt policies for example). However, once again, due to data limitations and the objective to integrate the greatest number of South countries in my study, I cannot test it. Indeed, this would drop most of the countries in the 10% percentiles that I want to study. Yet I strongly believe that this interaction term would be interesting to include in future papers.

On the other hand, the fear of terrorism has no frontier. People might react more adversely to immigration in richer countries after terrorist attacks, to avoid contagion of potentially dangerous political ideas. This can lead to an increase of xenophobia in destination countries. This moral cost induced by distrust for immigrants can decrease their capacity of integration in destination's society. This can also have an adverse effect on the probability to find a job, or to find facilities to learn a new language for example. This xenophobia effect can be considered as a "counter-pull" factor, and therefore decrease migration flows.

RESULTS

In Table 1, I separate South and North countries according to their level of development. To do so I use the median of GDP per capita each year.14 South countries are those below the median, while North countries are those above.

Columns (1) and ( 2) stand for the whole sample, columns (3) and ( 4) for the North-North corridor, columns ( 5) and ( 6) focus on South-North migrations, and columns ( 7) and ( 8) provide results for South-South flows.

The main coefficients of interest (Terrorism and Attacks) are in levels, while emigration rate is a logged variable. Therefore, if the independent variable increases by one unit (which is an enormous increase, since it would mean a number of victims equal to the whole country population) then the dependent variable increases by β * 100 percent. The logged variables are elasticities, such as a 1% change corresponds to an equivalent percentage change of the dependent variable equal to the size of the coefficient. For the dummies, it can also be interpreted as a percentage change when the dummy switches from 0 to 1. Overall, we can notice that most of the control variables' coefficients are very stable across corridors. First, a larger diaspora always leads to a greater emigration rate. This is in accordance with the literature on migration. 15 In the same way, the GDP per capita in origin countries is never significant. [START_REF] Mayda | International migration: A panel data analysis of the determinants of bilateral flows[END_REF] explains this result as a lack of financial push factor effect, while GDP per capita at destination does play a significant pull role in her estimation (already controlled for through destination-year fixed effect in my regressions). The two dyadic time invariant variables are also consistently significant. The language dummy is always significant across sub-samples as well, and has a positive impact on emigration. It decreases learning costs, and therefore migration costs.

I first focus on columns (1) and (2). At the world level, the presence of conflict seems to deteriorate enough the expected utility at home to have a positive and significant effect on the emigration rate. By comparing the two terrorism variables, we can observe that the human cost has overall a positive and significant effect while the number of attacks is not significant.

The results are in line with one of the paper's assumption: the violence of attacks seems to play a push effect on individuals, while solely the number of attacks does not. This can be explained by the greater detrimental effect of the psychological costs on well-being. This result tends to confirm my first assumption.

For the North-North corridor, results are different. Columns (3) and ( 4) only keep origin and destination countries with a GDP per capita above the median. I find that terrorism does not play any role on the emigration rate in this corridor. I suspect that this kind of violence does not have a sufficient influence on individuals' expected utility in the richest countries.

Accordingly, good conditions of life and binding costs of migration can explain these results.

Columns ( 5) and ( 6) show a positive impact of terrorism on the emigration rate, with no effect of conflicts. This result might be explained by the global destruction and costs involved by wars. Wars induce an important deterioration of utility at origin and an increase in the migration costs, since countries at war can reduce the freedom of movement by trying to maintain an internal security (curfew is a good example). I suggest that it is not the case of terrorism, or in a lower proportion. The results show that the terrorism impact is very different. Terrorism is an unpredictable political violence aiming at spreading fear among societies.

I suggest that terrorism and conflicts decrease utility at home and increase migration costs differently. More precisely, due to the high number of fatalities to consider conflicts, and following the hypothesis that human costs have a great impact on well-being, this violence might have a greater negative effect on expected utility at home than terrorism. In the same way, migration costs might be increased in a less sensitive way in the case of terrorism than for conflicts, for the security reasons cited above (restrictions during wars). Note that the number of terrorist attacks has a positive and significant effect on emigration as well. This suggests that, when we look at relatively close levels of development, an increase in the number of attacks, without distinction about the number of victims, is sufficient to increase emigration. In other words, in that case, the number of attacks decreases enough the expected utility at home and has a low impact on migration costs, such as the expected utility at destination turns greater.

Looking at columns ( 7) and ( 8), we can observe that the South-South corridor is not affected by terrorism. I suggest that crossing borders from South to South is easier due to more porous separations, and therefore lead to more temporary migrations that I cannot capture.

The variable Conflicts is not significant anymore. [START_REF] Alvarado | Search of peace: Structural adjustment, violence, and international migration[END_REF] suggest that violence has a non linear impact on emigration. Indeed, they explain that people might respond differently according to the level of risk. The poorest countries being victims of the most violent wars, a non-linear effect of violence on migration could explain the results for this variable.

Columns (1) and ( 2) confirm the previous results. Indeed, when looking at the richest countries at origin and destination, terrorism does not distort enough the expected utility between home and destination to increase emigration rate. This confirms that terrorism has a greater negative effect on poorer countries. The theory about a positive relationship between the development gap and the increase in migration costs due to terrorism also holds. Following this idea, origin and destination countries are homogeneous in this sample, thus terrorism has a smaller effect on migration costs. Note that GDP per capita at origin is positive and significant. This reflects a non-linear effect of GDP per capita at origin according to the corridor that we look at.

When we look specifically at the poorest countries in columns ( 3) and ( 4), we can observe that the South-South corridor is still not affected by terrorism. I assume that this is due to the absence of temporary and internal migrations in my dataset, as previously explained for regressions ( 7) and (8) in the Table 1. Theoretically, the impact of terrorism is integrated in origin countries individuals' expected utility and migration costs. But both origin and destination countries, in this corridor, are potentially threatened by terrorism. Individuals have therefore no incentive to migrate in the long run. They will thus maximize their utility by staying at home. They can also temporary migrate, or move in their own country.

Columns ( 7) and ( 8) provide very interesting results. When we look at migration flows between the poorest and the wealthiest countries, the impact of terrorism on emigration rate is weakened compared to regressions ( 5) and ( 6) in Table 1. Note first that there are only few observations due to the GDP per capita thresholds used. Therefore, we have to be careful on the interpretations of the coefficients. Still, the terror index magnitude is divided by two, and the number of attacks turns non-significant. First, these results confirm that psychological costs have a greater impact on international emigration than the total number of attacks. Also, they confirm that the greater is the development gap between origin and destination countries, the greater will be the impact of terrorism on the increase of migration costs (and the lower the magnitude of the terror index). A large gap in development levels between origin and desti-nation countries induces large differences in culture, and may have a multiplier effect on the costs of adaptation for migrants coming from countries intensively hit by terrorism. Immigrants will not be accepted, will be suspected, and perceived as unpredictable and potential rings for international terrorism in the most developed societies. In addition, the shock theory may be more pronounced in the least developed countries. Lack of education and lack of wealth can increase individuals' docility after a terrorist shock. As explained, this can ultimately lead to a trap effect with bad governance, and might have a negative repercussion on freedom of movement.

The results show that the impact of terrorism on emigration does not uniquely depend on the education level of migrants, but also on the definition of terrorism and the specific origin and destination countries that we look at. Taking a larger set of countries in my database has allowed me to provide possible insights for public policies to manage migrations flows induced by terrorism. This paper proposes to study the impact of terrorism on emigration with a different approach than the previous literature. First, I wanted to test if psychological costs of terrorism had a greater impact on migration flows than the number of attacks. Second, I assumed that individuals react differently to terror according to the level of development of their home and destination countries. I established this hypothesis using a utility maximisation framework, arguing that the greater the development gap between two countries, the greater the impact of terror on migration costs. I also supposed a negative effect of terrorism on freedom of movement in the least developed countries. Theoretically, the literature demonstrated that fragile states are more hit by terrorism. I add that terrorism may further weaken the poorest countries, and lead to a trap effect potentially harmful for freedom of movement.

I found that, at the world level, human costs have an effect on emigration rate, while the total number of attacks does not. Terrorism has no impact on migrations neither on North-North corridors nor on South-South ones. I suggest that South-South international migrations would be a temporary solution. Since the South is more threatened by terrorism, long term migration to another South country might not protect from the danger, and thus not maximize migrants utility. However, in those countries, the effect of terrorism on temporary and internal migrations can be important. I cannot capture this effect with the database used. Therefore, studying the impact of terrorism on internal and temporary migrations in the South may be a promising path for future research.

The impact of terrorism on South-North migrations is a negative function of the gap between origin and destination countries. I found a positive effect of both the number of victims and the total number of attacks on emigration from South to North when taking the median definition. Nevertheless, I find a reduction in the significance and magnitude of those coefficients when applying a more important gap between origin and destination countries.

The total number of attacks even turns non significant, and the human costs' magnitude is divided by two. The main hypothesis of this study are consistent with these results.

This paper provides some insights for policymakers. Terrorism does increase migration flows from developing to developed countries. I made the hypothesis that it can lead to a "counter-pull" effect in the richest countries, notably through an increase of xenophobia. Consequently, managing migration flows coming from countries threatened by terrorism has to be a priority for developed countries' public policy, in order to avoid the rise of populist politics.

Today, the world is facing numerous challenges, among which mass migration and Islamic terrorist attacks are of utmost concern. Given the cross-border nature of these shocks, supra-national collaboration (in the form of bilateral, regional and global policy responses) to deal with these concerns is more needed than ever. These global challenges can induce countries' withdrawal from cooperative politics, or, on the contrary, increase regional integration.

This study could therefore be extended by looking at countries' reaction in terms of international cooperation to these global challenges.

  .9)[START_REF] Beine | Diasporas[END_REF] as well as[START_REF] Mckenzie | Self-selection patterns in mexico-us migration: the role of migration networks[END_REF] have shown that migration costs are a decreasing function of migrant networks (M i , j ,t ) which are hence expected to exert a positive influence on migration flows. 7 Migration costs also depend upon dyadic timeinvariant factors, such as the distance between the origin and destination countries (d i , j ) as well as their linguistic proximity (l i , j ). I do not include contiguity here because of missing values for the poorest countries and/or countries with borders changes, such as West Bank and Gaza, Serbia, etc. I need to include these countries in my regression, since they are part of the countries most threatened by political violence. Migration costs also depend upon country's specific factors which do not vary over time (x i and x j ) such as their geographic position.

  definitions of migrants. The determinants of an international migrant might be based on country of birth, country of citizenship, purpose of visit or visa type, and place of last permanence residence or duration of stay.Bilateral migration data imply a trade-off between data on a complete range of countries and frequency of censuses. Stock of migrants are available with theÖzden et al. [2011] database. 9 It details bilateral migration stocks between 226 origin and destination countries, but is limited to five census rounds each decade between 1960 and 2000. This database considers as a migrant every individual living in another country than their country of birth. The OECD dataset on migration flows delivers yearly data between 1975 and 2014 but only with the 35 OECD countries as possible destinations. There are other databases on migration but with limited data as well. As shown byÖzden et al. [2011], South-South migration represents the largest fraction of bilateral stocks compared to others corridors. 10 South countries are also more threatened by terrorist attacks. 11 In addition, the purpose of my paper is to assess the impact of terrorism on a large set of countries. Therefore, I will rely on the United Nations dataset constructed byÖzden et al. [2011]. It is important to highlight that due to the construction of my database, I cannot capture temporary migrations. I can only measure the average emigration rate, which can lead to an underestimation of the actual flows (particularly South-South ones since there are more porous borders and therefore potentially more temporary migration flows).

World

  Development Indicators constructed by the World Bank. It is important to notice that, due to missing data on the poorest countries, I use the average GDP per capita of the region where they belong as proxy for the latter.The Conflicts dummy takes the value 1 if there is at least one conflict in the origin country involving 1,000 related battle deaths for one year during the ten-year period to which a particular flow corresponds. Thus if there is at least one conflict with that number of deaths during one year between 1970 and 1979, the dummy takes the value 1 for the year 1980. This information is extracted from the UCDP/PRIO database. This dataset considers extra-systemic conflicts, interstate wars, intrastate wars and internationalized intrastate wars. I use UCDP/PRIO instead of Correlates of War or State failure because of the detailed information on the motive, the type of conflicts and the threshold considered. It allows me to attribute each war to the country(ies) where the conflict took place.

  gravity model with Poisson estimations to assess the impact of terrorism on the average emigration rate. My first contribution is to differentiate two measures of terrorism: the human costs induced by terrorist attacks, and the total number of attacks. Usually, this distinction is made for microeconomic study. I adapt it to a macroeconomic paper. I proxy the human costs by dividing the total number of victims (deaths and wounded) by the population at origin, and compute the total number of attacks, using the Global Terrorism Database. Then, I measure their respective impact on the emigration rate computed with the United Nations database on migrant stocks. My second contribution is to consider heterogeneity of reaction according to countries' level of development, and not individual education. I split my sample to obtain results for seven different corridors.

  8 6 Note, however, that I create an index only composed of victims of terrorist attacks. The number of attacks is for my second variable. 7 Beine et al. [2011] McKenzie and Rapoport [2010], among others, have found that a larger network leads to lower cost of migration through better information on border crossing, or housing services. It relaxes credit constraints, and has a substitution effect with education.

Table 4

 4

	.1: Summary Statistics for Migration between 1970 and 2000
		mean	sd	min	max
	Stock of migrants from i to j	2,813	57,327	0	9,367,910
	Migrants flows from i to j	402	27011	0	4,705,677
	Population in country i	24,800,000 102,000,000 7,488 1,260,000,000
	Total immigrants in country i	619,196	2,141,607	0	34,800,000
	Natives in country i	24,100,000 101,000,000 4,310 1,260,000,000
	Emigration rate of i	0.00005	0.034	0	1.45
	Observations	159,390
	To construct the dependent variable, I first start by deriving the number of natives in

countries of origin at time t . To do so, I use the data on population of origin countries at t from the World Development Indicators, and subtract from it the total number of immigrants in countries of origin in the same year. It gives an approximation of the total number of natives in country i at time t , which is the denominator of my dependant variable.

  My second vari-able is the total number of attacks for each decade. As I only got data from 1970 onwards, I can only start to compute the total number of attacks and victims from 1970 to 1979, to create observations for 1980.

	Table 4.2: Summary Statistics for Terrorism per decade between 1970 and 2000 -Whole sample
		mean	sd	min	max
	Death	201	966	0	10,930
	Wounded	195	754	0	7,661
	Attacks	100	369	0	4,223
	Terrorism index 0.00004 0.0002	0	0.003
	Observations	159,390
	Table 4.3: Summary Statistics for Terrorism per decade between 1970 and 2000 -North sample
	(median definition)
		mean	sd	min	max
	Death	67	376	0	5,501
	Wounded	117	496	0	6,858
	Attacks	81	282	0	2,833
	Terrorism index 0.00002 0.00006	0	0.0005
	Observations	86,940

Table 4

 4

	.4: Summary Statistics for Terrorism per decade between 1970 and 2000 -South sample
	(median definition)
		mean	sd	min	max
	Death	363	1,355	0	10,930
	Wounded	288	969	0	7,661
	Attacks	122	451	0	4,223
	Terrorism	0.00006 0.0003	0	0.003
	Observations 72,450

Table 1 :

 1 Impact of terrorism on emigration rate: median level

		(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
	VARIABLES	W-W	W-W	N-N median N-N median S-N median S-N median S-S median S-S median
	Log GDP origin	-0.04	-0.03	-0.26	-0.26	-0.06	-0.0006	-0.21	-0.21
		(0.09)	(0.09)	(0.21)	(0.21)	(0.16)	(0.16)	(0.16)	(0.16)
	Log Network	0.34***	0.34***	0.21***	0.21***	0.44***	0.44***	0.40***	0.40***
		(0.02)	(0.02)	(0.03)	(0.03)	(0.05)	(0.05)	(0.05)	(0.05)
	Conflicts	0.40***	0.41***	0.56**	0.57**	0.13	0.19	-0.11	-0.08
		(0.15)	(0.16)	(0.28)	(0.27)	(0.18)	(0.19)	(0.29)	(0.29)
	Terrorism	206.30**		131.90		626.40***		-217.80
		(101.40)		(814.20)		(117.70)		(359.70)
	Log Distance	-0.90***	-0.90***	-0.91***	-0.91***	-1.01***	-1.20***	-1.07***	-1.07***
		(0.07)	(0.07)	(0.08)	(0.08)	(0.09)	(0.15)	(0.10)	(0.10)
	Common Language	1.22***	1.22***	2.05***	2.05***	0.98***	1.40***	0.57**	0.57**
		(0.18)	(0.18)	(0.25)	(0.25)	(0.17)	(0.47)	(0.23)	(0.23)
	Attacks		0.00015		0.0002		0.0004***		-0.0005
			(0.00012)		(0.0004)		(0.0001)		(0.0004)
	Origin FE	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
	Destination-Year FE	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
	Observations	103,031	103,031	27,164	27,164	26,364	26,364	23,781	23,781
				Robust standard errors in parentheses
				*** p<0.01, ** p<0.05, * p<0.1

See Feenstra [2015].

Meaning before, after, or during a conflict.

See Mayer and Zignago [2011].

As explained before, see[START_REF] Klein | The shock doctrine: The rise of disaster capitalism[END_REF].

Median: 2023Median: .385 U.S. dollars in 1980Median: , 2237Median: .955 in 1990Median: and 2126Median: .832 in 2000. .

See for example[START_REF] Mckenzie | Self-selection patterns in mexico-us migration: the role of migration networks[END_REF],[START_REF] Parsons | Migrant networks and trade: The vietnamese boat people as a natural experiment[END_REF],[START_REF] Hatton | What fundamentals drive world migration? In Poverty, international migration and asylum[END_REF] and[START_REF] Pedersen | Selection and network effects -migration flows into oecd countries 1990-2000[END_REF].

90% percentiles: 11,176 U.S. dollars in 1980, 18,986 in 1990 and 24,124 in 2000.

10% percentiles: 354 U.S. dollars in 1980, 320 in 1990 and 281 in 2000.

South countries are more affected by terrorism. This supposes that, in this corridor, terrorism has bad influences on expected utility at home, on migration cost and on expected utility at destination. When individuals rationally maximize their utility, long term displacement to another South country may not be attractive enough since the destination country might also be under this kind of violence in the long run. It is also necessary to recall that due to the construction of my dependent variable, I cannot capture a possible increase in temporary migrations. I can thus only conclude on the average effect of terrorism on international emigration rate. To test the robustness of my results, I look at the impact of terror on emigration for the poorest and the richest countries. The table 2 presents the results for the countries in the 90% percentiles of GDP per capita, 16 and for those in the 10% percentiles. 17

ROBUSTNESS CHECKS