in country _i_ during month _m_ of year _t_ as follows: where _Rilep,gi_ is the Rile index associated to party _p_ in government _gi_ of country _i_ during month _m_ of year _t_ , and _seatp,gi_ is the number of seats that party _p_ obtained in the Parliament of country _i_ in the last elections.<sup>24</sup> For the same set of countries, we also register changes of cabinet. Specifically, we construct the dummy variable _Change of cabineti,m,t_ which takes value one if the government of country _i_ is replaced in month _m_ of year _t_ , and zero otherwise.

# **5 Empirical strategy**

Our main regression model is: where _Yi,m,t_ is the share of migrants in the FMP _i_ in month _m_ in year _t_ who are long stayers, i.e., report being in that location for more than 6 months. Long stayers are the large majority of migrants in Libya (IOM, 2019) and in all FMPs in our sample (see Figure A3). We use this variable to capture the change in the pace of the movements of migrants across Libya and in their migration journey.<sup>25</sup> _Tone News Destinationsi,m−_ 1 _,t_ is the average tone of all migration-related article news published in month _m−_ 1 in year _t_ in the top destination countries for migrants in FMP _i_ in month _m_ . _Nightlightsi,m−_ 1 _,t_ is the intensity of night lights within a 5 km radius of FMP _i_ in month _m −_ 1 and year _t_ and _Conflicti,m−_ 1 _,t_ is the number of conflict events within a 5 km radius of FMP _i_ in month _m −_ 1 and year _t_ . We control both for the possibility that migrants are not planning to leave Libya using _Remaini,m−_ 1 _,t_ , a dummy taking value one if Libya is one of the top destination countries for migrants in FMP _i_ in month _m_ , and for the possibility that migrants are planning to return home using _Returni,m−_ 1 _,t_ , a dummy taking value one if one of the countries of origin is also one of the top destination countries for migrants in FMP _i_ in month _m_ . _θi_ is the FMP fixed effects and _φm,t_ is the month-year fixed effects. _γo_ is the countries of origin fixed effect and _μd_ is the destination countries fixed effect. By including country-of-origin fixed effects, we compare - for a given FMP, the effect of the tone of the news on the migrants’ decision to move between two months of the same year when the FMP’s composition of migrants is the same. By including country-of-destination fixed effects instead, we compare - for a given FMP, the effect of the tone of the news on the migrants’ decision to move during two months of the same year when the FMP’s preferred country-of-destination is the same. As a result, by adding both sets of fixed effects, we sort out from our estimates the

> 24The list of parties part of each government coalition and the number of seats obtained by them in each election is retrieved from the ParlGov project ( `www.parlgov.org` ). Data from the Manifesto and the ParlGov project are matched using information from PartyFacts ( `https://partyfacts.herokuapp.com/` ).

> 25Note that the IOM-DTM data does not distinguish departing migrants from a FMP by their length of stay at the FMP. It follows that using the number of departing migrants from the FMP as an outcome would not be informative of the effect of changes in the tone of the news on the pace of the migrants’ journey.

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