# _5.2.3 Clustered destinations within Libya for same-country-of-origin migrants_

To identify the possible presence within Libya of clustered destinations for migrants from the same country of origin, we analyzed the movements of same-country-of-origin migrants across Libya and over time.<sup>15</sup> The results are presented in figure 9. Our analysis identifies clusters of country-of-origin migrants in five different cases. The first is Egyptians arriving in the area near Tripoli and Tobruk in January 2017.<sup>16</sup> The second is Ethiopians in Tobruk in July 2017. The third is migrants from Benin arriving in the area of Sebha in August 2017. The fourth, also in August 2017, is two clusters of Eritreans in Alkufra and Tobruk. The fifth is migrants from Bangladesh in the area of Tripoli in January 2018. These results support the idea that same-country-of-origin migrants, moving in the same direction, sort themselves into close routes following similar paths.

# _5.2.4 International migration flows to and from Libya_

Next, we move to the analysis of the direction and composition of international migration flows to identify the backbone of migration in Libya.<sup>17</sup> Figure 10 reconstructs the international migration routes passing through Libya. We create a network in which origin country _i_ and destination country _j_ are connected when country _i_ is found as the major source of migrants at one of the FMPs of a province at least once in a year, and country _j_ is registered as the preferred destination for the majority migrants in one of said FMPs at least once in a year.<sup>18</sup> In figure 10, the circle segments represent origin and destination countries of international migration routes through Libya. The color of a segment is uniquely associated with a country, and its size indicates the total number of incoming and outgoing migrants in that country. The direction of the flow is encoded

> 15 To conduct this analysis, we followed four steps. First, we reconstructed the network of internal movements at the monthly level (similar to what was done in figure 8). Because we are interested in short-term dynamics, we used monthly data on migrants registered at the FMPs. Second, we identified significant spatial concentrations of migrants at FMPs located near each other. Specifically, we tested whether same-nationality migrant clusters occurred more frequently than would be expected if FMPs were distributed in a spatially random way. To this end, we used the join count test for k-colored factors (Cliff and Ori, 1981; Upton and Fingleton, 1985). We defined close FMPs as those located within a radius of 250 km, which is the minimum radius required to make sure that each FMP has at least one neighboring FMP, a condition that is necessary for implementation of the join count. Because only 5% of the FMPs are located more than 250 km from all the others, we dropped them from the sample. Third, for each month, _t_ , we identified the nationalities for which the test indicated significant spatial concentration. Fourth, for each of these nationalities, we plotted the location of the FMPs within 250 km of each other where the majority of the arrivals corresponded to nationality _i_ at time _t_ .

> 16 The distance between these two cities is more than 1,000 km, more than four times the radius used to detect migrant agglomerations, which means that the test evaluated the movements in these provinces separately and indicates that the two effects are independent.

> 17 This exercise requires assuming that migrants found in an FMP are all moving along the same route and are all trying to reach the most-preferred country of arrival recorded at the FMP. The assumption is instrumental to identifying the migration network passing through Libya, which is discussed below in Section 5.2.5.

> 18 This analysis is conducted under the assumption that migrants found at an FMP are all moving along the same route and are equally interested in reaching the top 1 preferred country of arrival recorded at that FMP.

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