night light data distributed by the Visible Infrared Imaging Radiometer Suite (VIIRS).

**Conflict intensity exposure** For each FMP, we construct a time-varying measure of conflict intensity exposure as the number of conflict events that occurred in the 5 km radius of the FMP’s location during the corresponding month. Data on conflict events are from the PRIO/Uppsala Armed Conflict and Location Event (ACLED) dataset which covers conflict events worldwide, providing the geo-localization, the date, and the type of event (Raleigh et al., 2010).

**Internet coverage** Data on the geo-localization and the speed connection of cell towers in Libya are collected by OpenCellid ( `https://opencellid.org` ), the world’s largest open database of cell towers, and distributed by GSMA ( `https://www.gsma.com` ), a non-profit industry organization which provides source of data and analysis for the mobile industry worldwide. Data from Libya cover all the network operators present in the country: Almadar Ajadeed, Libyana, LibyaPhone Mobile. Data is available only for a single year: 2021 for the operators Almadar Ajadeed and LibyaPhone Mobile, and 2019 for Libyana.

**Sea patrolling activities and Mediterranean Sea crossings** Data on monthly patrolling activities in the Mediterranean Sea are not available. Yet, data exist on the number of arrivals from Libya to Italy. We retrieve these data from the Operational Data Portal of the United Nations High Commissioner for Refugees (UNHCR).<sup>21</sup> We use these data to construct a dummy variable which takes value one when patrolling activities are intense, i.e., when arrivals during a month are below the median in the period 2018-2020 (1150 individuals), and zero otherwise.<sup>22</sup>

**Economic data for destination countries** Data on country-level employment rate and percapita income are sourced from the International Labour Organization (ILO) and the World Bank (WB), respectively. Values for these variables are provided at the annual level. From them, we impute the variables _Employment Rate (monthly)_ and _GDP per capita (monthly)_ . For each FMP _i_ , we use this data to calculate the average employment rate and GDP per-capita for the top destination countries of migrants located at FMP _i_ during month _m_ in year _t_ .

**Political data for destination countries** We measure the political orientation of the government coalitions in the main European destination countries (Italy, France, Germany, Spain, and United Kingdom) using the Rile index. The Rile index locates a party on a right-left scale by measuring how much the party mentions left or right issues in its electoral program.<sup>23</sup> Using this measure, we construct the variable _Rile Index Coalitioni,m,t_ for the government coalition

> of government services (Rahman and Di Maio, 2020).

> 21 Data are available at: `https://data2.unhcr.org/en/situations/mediterranean/location/5205` .

> 22To build this variable, we consider data after 2017. The reason is that the introduction of the Minniti Compact in 2017 caused the crossing to decrease by more than 80% the next year, from 117,153 to 23,037. Instead, in the following years, the number of crossings remained fairly similar, 11,4171 in 2019 and 34,154 in 2020. For more details on the effects of the Minniti Compact see Marchesi (2021) and Deiana et al. (2024).

> 23 Data are from the Manifesto project: `https://manifesto-project.wzb.eu/` . The Rile index is a continuous variable obtained with the formula _R−L_ , where _R_ and _L_ are the shares of issues in an electoral program commonly associated to a right-wing and a left-wing political platform, respectively.

9