that are central to the network of migration flows and that “broker” the movement of migrants along different human corridors.

Table 4 presents the model specification that produced the best results in terms of goodness of fit (Hunter et al., 2008).<sup>11</sup> The results of STERGM are to be interpreted in a way similar to logit estimates: they assess the likelihood of observing the formation or dissolution of a link between two provinces with respect to a situation in which migrants move at random.<sup>12</sup> Results indicate that the only significant driver in the formation of a migration link between two provinces is conflict; the probability of observing a migration flow toward a province with fewer conflicts is 18% higher with respect to a situation in which migrants move at random. By contrast, the statistically significant (counter) drivers of dissolution of migration flows are the variables intransitivity and differences in economic activity. The former indicates a lower probability to observe the dissolution of hub structures in the network than by chance: i.e. over time, migrants depart from several provinces and reach different destinations, but their routes tend to overlap in some provinces that are used as a bridge in the network.<sup>13</sup> The latter shows that it is less likely to observe a flow to dissolve when this is directed to a wealthier province. Based on these results, we argue that migration in Libya should be characterized as forced migration because conflict intensity is the main determinant of relocation decision across provinces.

# _5.2.1 Hotspot provinces and clusters of provinces in Libya_

Next, we use spatial statistics to compare the numbers of migrants in neighboring provinces. In practice, we test for the presence of spatial autocorrelation in migrant distribution across the Libyan provinces (whether the number of migrants was similar in provinces that are near each other) by identifying hotspot provinces and clusters of provinces (Anselin 1995; Barthel et al. 2015).<sup>14</sup> The results of the identification of hotspots are presented in figure 7. Two provinces can be characterized as hotspots in 2017 and 2018 (Almarj and Wadi Ashshati). In 2018, we observe the emergence of four new hotspots (Ejadabia, Ghat, Murzuq, and Tobruk). The small number of migrants

> 11 Goodness of fit assesses the extent to which the STERGM specification incorporates all the fundamental drivers of connectivity in the network and correctly replicates the structural features of the observed network, resulting in unbiased coefficient estimates. Specifically, as is standard in the literature, we examined the extent to which our model specification correctly replicated the distributions of degree (number of network connections of each province), the number of edgewise shared partners (number of partners that a linked dyad of provinces shares), and minimum geodesic distance (minimum number of links between two provinces).

> 12 �<sup>��</sup> ERGM estimates are in log odds, so the probability of a connection is obtained as ���<sup>��</sup> ~~,~~ where _x_ is the estimated coefficient of a given covariate. For example, consider the coefficients associated with the variable night lights (1.77 and 1.05): the probability of observing the formation and dissolution of a link between a province with low night light intensity and one with �<sup>��.��</sup> �<sup>��.��</sup> high night light intensity is ���<sup>��.��</sup> �0.14 and ���<sup>��.��</sup> �0.74, respectively.

> 13 To be more specific, the variable assesses the extent to which two provinces tend to have _x_ shared connections, regardless of whether the two provinces themselves are tied, controlling for marginally decreasing returns.

> 14 A hotspot province is a province with significantly more migrants than in neighboring provinces. A cluster of provinces is a group of provinces hosting many (or few) migrants.

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