metadata: authors: 0: author: Di Maio,Michele
1: author: Elmallakh,Nelly Youssef Louis William
2: author: Leone Sciabolazza,Valerio
count: Libya
docna: 0: docna: News Sentiment in Destination Countries and
            Migration Choices : Evidence from Libya
docty: Policy Research Working Paper
abstracts: cdata!: Changes in the sentiment of
            migration-related news published in destination countries
            affect the timing of migrants&#x2019; journeys to these
            countries. Using geo-localized data on migrants in Libya and
            the complete record of news articles in their country of
            destination, this paper shows that a worsening news
            sentiment leads to migrants staying longer in Libya, slowing
            down their journeys to their final destinations. The paper
            validates these results by showing that the effect is
            concentrated in locations with internet connections. The
            results indicate that changes in news sentiment have a
            significant impact only for some groups of migrants and
            under specific conditions, suggesting a limited effect on
            overall migrant movements. Finally, the paper provides
            suggestive evidence that a worsening news sentiment in the
            preferred destination induces substitution across
            destination countries, yet it does not make migrants return
            to their country of origin.
display_title: News Sentiment in Destination
            Countries and Migration Choices : Evidence from Libya
listing_relative_url: /research/2024/04/34303056/news-sentiment-destination-countries-migration-choices-evidence-libya
topicv3: Mass Transit m1327372 1726,Policy Making and Policy Coordination
            m1503539 2906,Mass movement m1503624 3159,Emigration m1504169 2844
publishtoextweb_dt: 2024-04-17T00:00:00Z
dois: 10.1596/1813-9450-10754
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
no_of_pages: 45
author_keys: 0: author_key: 1426679
1: author_key: 1686135
2: author_key: 1720519
model_extractions: Policy Research Working Paper 10754 News Sentiment in Destination Countries and Migration Choices

# Evidence from Libya

_Michele Di Maio Nelly Elmallakh Valerio Leone Sciabolazza_ Middle East and North Africa Region Office of the Chief Economist April 2024
Policy Research Working Paper 10754

# **Abstract**

Changes in the sentiment of migration-related news published in destination countries affect the timing of migrants’ journeys to these countries. Using geo-localized data on migrants in Libya and the complete record of news articles in their country of destination, this paper shows that a worsening news sentiment leads to migrants staying longer in Libya, slowing down their journeys to their final destinations. The paper validates these results by showing that the effect is concentrated in locations with internet connections.

The results indicate that changes in news sentiment have a significant impact only for some groups of migrants and under specific conditions, suggesting a limited effect on overall migrant movements. Finally, the paper provides suggestive evidence that a worsening news sentiment in the preferred destination induces substitution across destination countries, yet it does not make migrants return to their country of origin.

This paper is a product of the Office of the Chief Economist, Middle East and North Africa Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at michele.dimaio@uniroma1.it, nelmallakh@worldbank.org, and valerio.leonesciabolazza@uniroma1.it.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
News Sentiment in Destination Countries and Migration Choices: Evidence from Libya<sup>∗</sup> Michele Di Maio<sup>†</sup> Nelly Elmallakh<sup>‡</sup> Valerio Leone Sciabolazza<sup>§</sup> University and IZA World Bank Sapienza University Sapienza University and IZA

**Keywords:** Migration, news sentiment, GDELT, Libya, Europe **JEL codes:** F22, J61, L82.

> ∗We thank the participants to the 16 _th_ AFD-World Bank Conference on Migration and Development (Boston University), the Conflicts and the Economy Workshop (Universit`a Cattolica of Milan), and the EBRD Research Seminar (London) for their comments and suggestions. We also thank Margherita Bove, Francesca Calamunci, and Cecilia Nardi for excellent research assistance. The authors gratefully acknowledge the financial support from the Office of the Chief Economist for the Middle East and North Africa (MNACE) under the regional Labor and Gender Research Programs. The views expressed in this paper are those of the authors and do not necessarily reflect the position of the World Bank. All errors are our own.

> †michele.dimaio@uniroma1.it, Sapienza University of Rome (Italy) and IZA

> ‡nelmallakh@worldbank.org, World Bank: _Corresponding Author_

> §valerio.leonesciabolazza@uniroma1.it, Sapienza University of Rome (Italy)
# **1 Introduction**

Migration is one of the most politically polarizing issues in advanced countries. This is reflected in the way migration and its effects are described in the news. While some studies look at how migration-related news influences public opinion and votes in destination countries,<sup>1</sup> little is known about the effect of changes in the sentiment of migration-related news in destination countries on _en route_ migrants and their migration choices. Investigating this matter is important to better understand the impact of the increasing use of an aggressive tone by populist leaders trying to discourage migrants from seeking entry into Europe or the US.<sup>2</sup> This paper studies how changes in the sentiment of migration-related news published in migrants’ preferred destination countries affect migrants’ movements within Libya and the timing of their journey to these countries. Libya is the country with the largest number of international migrants in all of North Africa and the major gateway from Africa to Europe.<sup>3</sup> By showing to what extent changes in the news sentiment in destination countries impact the choices of migrants in Libya, we contribute to a better understanding of the determinants of the movements along the most important irregular migration route to Europe.

Our analysis combines two main data sources. First, we use data on migrants located in Libya during the period 2017-2020 collected by the International Organization for Migration (IOM). Even though some migrants plan to remain in the country, for most of them Libya is a transit country towards their final destination. While in Libya, migrants often stay for long periods in the same location, live in rented houses, and have an (informal) job (IOM, 2018a). During their stay in the country, migrants often move across locations several times, using different internal routes according to their nationality (Di Maio et al., 2023). To study migrants’ movements within Libya, we use geo-localized monthly data collected by the IOM in 167 Flow Monitoring Points (FMPs) in Libya. FMPs are located at important points of permanence and transit for migrants. For each FMP, we have information on the country of origin of migrants located in the area, their destination countries, and their length of stay at the FMP. Second, we use data from the Global Database of Events, Language, and Tone project (GDELT)<sup>4</sup> to identify all the news articles related to migration published in any of the destination countries of the migrants located in all the FMPs in Libya. Importantly, GDELT provides a variable measuring the sentiment of each news article, allowing us to construct, for each FMP in each month, a measure of the average tone of all migration-related news articles published in the destination countries of migrants located near the FMP.

Our main result shows that a reduction in the measure of the sentiment of migration-related news articles published in destination countries increases the share of migrants remaining in their current location in Libya for a long period. The characteristics of migration flows in Libya

> 1See for instance, Benesch et al. (2019); Meltzer et al. (2021); Couttenier et al. (2023); Djourelova (2023).

> 2This is the case of the message broadcasted by Italian PM Giorgia Meloni on September 16 2023, `https: //youtu.be/gFrSJyF7xfU?feature=shared` or that by Florida Gov. Ron DeSantis `https://www.foxnews.com/ politics/desantis-warns-migrants-bused-texas-dc-florida-do-not-come`

> 3One important reason why Libya is the preferred transit country to Europe is the lack of border and internal controls, which has characterized the country since the beginning of the Civil War in 2011 (UNHCR, 2019b). 4 GDELT represents a rich source of information to study news coverage of migration events. The database registers events worldwide by collecting all online news released every 15 minutes, and categorizing each news item by location, subject, and sentiment, among other things.

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allow us to interpret this result as also informative about how a change in the tone of the news influences the timing of migrants’ movements toward their final destinations. Migration in Libya is in fact largely unidirectional: most migrants move from South to North-West, i.e. toward the coastal areas where there are more employment opportunities, and from where - those directed to Europe - then attempt to cross the Mediterranean Sea.<sup>5</sup> It follows that our result, i.e. a more negative tone in the migration-related news at destination makes more migrants stay longer in Libya, can be read as indicating that a worsening news sentiment slows down the migrants’ journey by delaying the next step of their travel to destination countries.

Our interpretation is corroborated by results showing that the effect of a change in the tone of migration-related news on migrant’s choices is driven by articles related to _discrimination_ . This finding is consistent with the idea that the tone of news articles on discrimination episodes predicts the attitude of the local population in destination countries towards migration and the level of difficulty that migrants may encounter in terms of job opportunities, access to housing, and social integration in these countries (World Bank, 2023; Aksoy et al., 2023). A more negative news sentiment in articles related to these events indicates that conditions at destination are worsening, reducing the incentive for the individual to make the next step of the migration journey towards the final destination.

To validate our findings, we provide suggestive evidence that the effect of changes in the tone of the news is significant only for migrants located in areas where it is more likely that they have access to the Internet and that our results are not driven by reverse causality. We also show that our findings are robust to several checks, including: an alternative way of measuring the tone of the news; the use of different samples; an alternative estimator; increasingly demanding model regression specifications; the inclusion of additional controls for the political orientation of the governments in destination countries, and the tightness of the Mediterranean Sea patrolling activity; and the use of spatial regression models to account for spatial autocorrelation in migrants’ movement choices.

Next, we characterize the conditions under which changes in the tone of the news are more likely to have an effect. Our results show that a worsening news sentiment at destination slows down migration movements in Libya _only_ for migrants coming from West African countries (who are those usually making a step-by-step journey to Europe) or located in the Western region of Libya (where there are more economic opportunities), and when migration-related news articles are more numerous and mostly positive, or economic conditions in destination countries are not favorable. While these results corroborate our main findings by showing that the effect of changes in the tone of the news is significant when, where, and for whom we expect it to be so, they also indicate an overall limited impact on migrants’ movements within Libya and towards destination countries.

Finally, we test for the possibility that changes in the news sentiment generate spillover effects across countries. Results indicate that when the tone of migration-related news becomes more negative in a country, migrants tend to switch to other countries as preferred destinations. Yet, we find that this substitution does not occur between European and non-European

> 5This characteristic of the migration flows in Libya (i.e., they mainly move in the same direction) is similar to that of migration flows from Central America to the U.S., suggesting that our approach can also be used in other contexts if similar data are available.

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countries. We interpret these findings as suggesting that - while there is substitution across destinations that are similar - a worsening news sentiment in European countries does not induce migrants to return to their origin country.

Our findings have important policy implications. Attempts to discourage migrants from reaching their preferred destination countries based on the use of negative, aggressive, and discriminatory tone in the public discourse on migration are unlikely to be successful. While a more negative tone of the news makes some migrants stay longer in Libya, delaying their journey to their final destination, this happens _only_ for migrants located in specific areas of the country or traveling using a specific route, and only when there are specific conditions in destination countries. Moreover, the strategy employed by populist governments to reduce migration to their country by creating an unwelcoming environment for migrants is politically unsustainable due to spillover effect of the tone of the news across destination countries. Given that a more negative tone of the news in one country increases migration flows to similar destinations (but not the willingness of migrants in Libya to return to their country of origin), the use of this populist strategy undermines the basis for the cooperation between European governments in the management of migration flows from Africa.

The paper is organized as follows. Section 2 discusses the related literature. Section 3 provides background information on migration in Libya. Section 4 describes the data. Section 5 presents the empirical strategy, and Section 6 discusses the results. Section 7 concludes.

# **2 Literature**

Our paper is related to the small - but rapidly growing - literature looking at the effects of media exposure on migration choices. A few studies document a positive association between Internet access and migration intentions and aspirations (Pesando et al., 2021; Grubanov-Boskovic et al., 2021). B ̈ohme et al. (2020) show how geo-referenced online search data on migration-related terms can be used to measure migration intentions in origin countries and to predict bilateral migration flows. Adema et al. (2022) uses individual-level data from 112 countries to provide causal evidence that an increase in mobile internet access increases the desire to migrate and actual migration by lowering the cost of acquiring information on potential destinations. Other studies look at the effect of TV and newspaper articles on migration. For instance, Farr ́e and Fasani (2013) find that exposure to TV reduces internal migration in Indonesia. Wilson (2021) shows that access to newspaper articles and TV news providing information about potential labor market opportunities increases migration to areas mentioned in the news.<sup>6</sup> More generally, our paper is related to the literature on the effect of information on migrants’ decisions. Shrestha (2019) shows that information on migrants’ mortality rates decreases migration flows from Nepal to Malaysia and to the Gulf countries. Baseler (2023) documents that providing information on urban earnings in Kenya increases migration to the capital. Some papers use randomized controlled trials to understand the role of risk information and perceptions in the decision-making process of migrants. Bah et al. (2023) show that providing information

> 6Another important source of information is migration networks at destination. These provide prospective migrants with information about the migration process and economic opportunities, helping them to shape their migration decision (McKenzie and Rapoport, 2010; Beine et al., 2015; Giulietti et al., 2018).

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and testimonials about the risks of backway migration from The Gambia to Europe reduces migration intentions in the five years after the information treatment. Similarly, Tjaden and Dunsch (2021) find that peer-to-peer information transmission on the dangers and potential risks associated with irregular migration raised risk awareness and reduced irregular migration intentions in Senegal. Importantly, Bertoli et al. (2020) emphasize that acquiring information about destinations can be costly for migrants. As a consequence, migration flows from countries in which collecting information is more costly or people have stronger priors are less responsive to variations in economic conditions in destination countries.<sup>7</sup> Our paper differs from these previous studies in two main aspects. First, this paper is the first to empirically assess the impact of migration-related news articles in destination countries on the movement decision of migrants located in a (transit) developing country.<sup>8</sup> Second, in our analysis, we study the effect of changes in the _content_ of migration-related news (i.e., their sentiment) on migrants’ behavior, thus improving on previous studies using metrics for media access and exposure. We are also the first to use the GDELT dataset to study migration issues.<sup>9</sup> Finally, our paper contributes to the understanding of the evolution of the Libyan economy (Irhiam et al., 2023; Del Prete et al., 2023; Rahman and Di Maio, 2020; Di Maio et al., 2023) and of the migration phenomenon in the country after the fall of the Gaddafi regime (Di Maio et al., 2023). Moreover, by providing novel evidence on the determinants and the timing of migrants’ movements within Libya, our analysis is also related to studies looking at the dynamics of migration flows to Europe (Battiston, 2022; Deiana et al., 2024) and of international migration flows in Northern and Sub-Saharan African countries (Friebel et al., 2023; Di Maio et al., 2023).

# **3 Migration in Libya**

Libya is the country with the largest number of migrants in North Africa. In 2020, Libya hosted approximately 571,400 migrants, which corresponds to 12% of its total population (UN-DESA, 2020). Migrants in Libya are very heterogeneous in terms of nationality, background, motivation for migration, and preferred final destination (IOM, 2018b; UNHCR, 2019b).

Libya has been an important destination for migrants since the 1970s. For decades, people from Sub-Saharan Africa, the Middle East and North Africa, and Asia have moved to Libya to work (UNHCR, 2019b). During the Gaddafi regime, economic migrants accounted for more than 50% of the total Libyan labor force (World Bank, 2015).

Since 2011, the security situation in Libya has been critical. The country has experienced a prolonged period of conflict which started with the fall of the Gaddafi regime and the First Libyan Civil War, and continued with the Second Libyan Civil War in 2014.<sup>10</sup> The conflict

> 7There is also some evidence that migrants’ knowledge of the political orientation at destination influences their location choices (see e.g., Bove et al., 2023). Bracco et al. (2018) show that the election of an anti-immigration mayor resulted in a decline in the number of immigrants entering the same town.

> 8For a discussion of the characteristics of transit migration, see Djaji ́c (2017) and Artuc and Ozden (2018).

> 9Until recently, GDELT has been mainly used to study macroeconomic or financial aspects (Bayer et al., 2020; Consoli et al., 2021). An exception is Deiana et al. (2023), who use the Pope’s message about the refugees’ situation to analyze how persuasion modifies beliefs.

> 10The Second Libyan Civil War is one of the geopolitically most relevant conflicts of our times (Fitzgerald and Toaldo, 2016). The conflict - which is still ongoing - is characterized by two rival governments claiming authority over Libya, one (based in Tripoli) controlling the Western part of the country and the other (based in Tobruk)

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transformed the characteristics of migration in Libya, deeply affecting flows to, within, and from the country (Cummings et al., 2015). While Libya is still an important destination country for foreign workers, Libya is now also a transit country for migrants heading to Europe. Due to the dissolution of the Libyan state and the lack of a government able to control the territory, the migration routes through the country remained unguarded and Libya has become the main gateway to Europe for irregular migrants (Friebel et al., 2023; UNHCR, 2019b).

Libya is the arrival point of two important irregular migrant routes, the Western and the Eastern route (see Figure A1). The Western route to enter Libya is used by migrants from West and Central Africa traveling from Niger and reaching the southern district of Sebha. The Eastern route is instead used predominantly by East Africans who usually cross the Sudanese border to enter the district of Kufra in the southeast (UNHCR, 2019b).

Once migrants arrive in Libya, most head north to the coastline. This is the part of the country where more employment opportunities exist (Wittenberg, 2017) and also from where it is easier to attempt reaching the other side of the Mediterranean Sea (Mixed Migration Hub, 2015; IOM, 2015; UNHCR, 2017a). The trip to Europe is expensive. This is why - once in Libya - many migrants stay for long periods in hubs along their route, working to afford the next leg of their trip (Adesina, 2021). Information on and analysis of the internal movements of migrants within Libya is very scarce. One exception is Di Maio et al. (2023), which documents the large migrants movements within the country (see Figure A2). They identify the different migrants’ routes in Libya and their evolution across time, showing that migrants tend to move north, and that they sort into these routes according to their nationality.

Migrants in Libya travel in stages, turning to smugglers for the difficult legs of the journey.<sup>11</sup> Ethnographic research documents that the relationship between migrants and smugglers in Libya is more complex than usually thought (Sanchez, 2020). Identifying smugglers as criminals and migrants as victims oversimplify a complex and often symbiotic relationship (World Bank, 2023).<sup>12</sup> In the Libyan context, smugglers are often ordinary people, living in border areas along migration pathways, and in coastal towns and cities, who facilitate the movement of migrants through the country (Sanchez, 2020). Migrants survey data indicates that smugglers are in most of the case considered “travel agents” (59% of respondents) and only in few cases “criminals” (11%) (Murphy-Teixidor et al., 2020). Other studies emphasize that smugglers and migrants have a common interest that their interaction is successful. Moreover, the various roles of migrants (who sometimes contribute to the organization of the journey) blurs the boundary between smugglers and their clients (Achilli, 2021). Taken together, the evidence from these studies suggests that migrants do not perform a passive role in their journeys: they have agency and are essential actors of their mobility (Sanchez, 2020). Based on these arguments, migrants in Libya should be thought of as (most often) being able to choose when and where to move in controlling the Eastern part of the country.

> 11In a survey of 5,159 migrants conducted in Libya in 2019, 32% reported not using any smuggler, while 37% used one smuggler, and 31% used several smugglers along their journey (Murphy-Teixidor et al., 2020).

> 12Smuggling and human trafficking are two very distinct phenomena (Adesina, 2021). Smuggling refers to a voluntary movement of migrants (within or between countries) facilitated by an agent (smuggler) who receives payment to take them to a destination. Instead, human trafficking refers to a movement of migrants into another country that includes an element of extortion, exploitation, or coercion. In the case of Libya, only rarely smuggling overlap with human trafficking (Achilli, 2021).

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their step-by-step journey through Libya.

There are differences between the migration journey through Libya along the Western and the Eastern routes. Migration through the Western route almost always takes place in stages (Wittenberg, 2017). Migrants use smugglers to avoid the large number of checkpoints that have to be crossed, though there are few reports of detention and deportation along the routes heading north. Anecdotal evidence indicates that migrants from West African countries often stop in a locality until they save enough money (often by being employed informally) to be able to continue their journey (UNHCR, 2017b). The journey through the Eastern route is instead non-stop, characterized by strict cooperation between smugglers and human traffickers, and often involves cases of violence. In recent years, this journey has become very short, between ten days and three weeks (Micallef, 2017).

# **4 Data**

## **4.1 Migration data**

Data on migrants and their movements are retrieved from the IOM’s Libya Displacement Tracking Matrix (DTM). The IOM-DTM is the only source providing reliable data on migration flows in Libya. The DTM tracks the movements of migrants in Libya using the data collected at the IOM Flow Monitoring Points (FMPs) in the Libyan territory.<sup>13</sup> FMPs are located at important points of transit and permanence of migrants such as border crossings, bus stations, harbors, markets, public squares, areas where migrants look for work, etc. (IOM, 2016).

Migrants included in the DTM dataset live close by a FMP and are able to move across the country. These migrants are not victims of human trafficking or imprisoned in detention centers.<sup>14</sup> Most of them often remain for long periods in a given location, live in rented houses, and have a job (often informal) (IOM, 2018b, 2019).

In our empirical analysis, we use DTM data for the period June 2017 to February 2020.<sup>15</sup> The DTM collects information on migrants located in, arriving to, and departing from the area around each FMP. For each FMP, it reports the three most common countries of origin, the three most common countries of destination, and the share of migrants by length-of-stay category (i.e., less than 2 weeks, less than 3 months, between 3 and 6 months, more than 6 months). Data are collected daily and reported weekly. In our analysis, we aggregate them at the monthly level.

The primary method of data collection at the Flow Monitoring Point (FMP) is through Key

> 13The IOM definition of migrant does not distinguish between legal and illegal migration. Yet, as discussed in Di Maio et al. (2023), migration flows recorded by the DTM are likely to be mostly made of irregular migrants.

> 14According to the Libyan immigration law, individuals entering the country without a permit are at risk of detention at any time. The Directorate to Combat Illegal Migration (DCIM) (established in 2012 to tackle irregular migration flows into the country) is in charge of arresting and organizing the deportations of irregular migrants, and managing the detention centers. While immigration law is rarely enforced, migrants arrested and detained often face severe abuses by security forces and militias, including torture, rape, extortion, forced labor, and extra-judicial execution (Mixed Migration Centre, 2019). The living conditions in the detention centers remain an issue of grave concern with serious humanitarian consequences for the migrants detained (UNSMIL, 2018). In 2019, 4,700 migrants (around 1% of the total migrant population) were in detention centers.

> 15The period of analysis begins with the first wave of the DTM data for Libya (2017) and ends with the beginning of the COVID-19 pandemic (March 2020).

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Informants (KIs) interviews (IOM, 2016).<sup>16</sup> While information is not directly collected from migrants, DTM data have been collected repeatedly and cross-checked since 2017 and have been used in several official reports. These data are considered the best source of information—in terms of accuracy and coverage—available at the moment on migration flows in Libya.

Figure 1 shows the physical map of Libya and the location of the 167 FMPs used in our analysis. The FMPs are located across all of Libya. Importantly, there are FMPs in both the areas controlled by the (Tripoli-based) Government of National Accord and by the (Tobrukbased) House of Representatives (see footnote 10). There are FMPs at all the entry points to Libya, at all the important crossroads, close to the largest cities (e.g., Tripoli, Misurata, Banghazi), and in both the desert areas (South) and the coastal areas (North).

Most migrants located in the FMPs in Libya come from sub-Saharan African countries. During the period under analysis, the most common origin countries are: Niger, the Arab Republic of Egypt, Chad, Sudan, and Nigeria. The destination countries for migrants in Libya are mostly European ones. Yet, some migrants want to remain in Libya or to return home. In our data, the list of most common destination countries includes: Italy, France, Germany, Libya, and Niger.

# **4.2 News articles data**

Data on news articles are retrieved from the Global Database of Events, Language, and Tone (GDELT) ( `https://www.gdeltproject.org/` ). GDELT is an open-source news database built combining advanced natural language and data mining techniques to monitor news at the global level in real time. The project gathers meta-data from online news sources globally (every 15 minutes) and translates it into English from more than 65 languages In our analysis, we use GDELT 2.0 Global Knowledge Graph (GKG), the most comprehensive version of the GDELT dataset. GDELT identifies for each news article its source, its nationality, and its main topics.<sup>17</sup> We define a news article to be migration-related if one of its top 3 topics is one of the following: migration, refugees, borders, displacement. GDELT thus allows us to identify all the news articles related to migration for the period 2017-2020 for any of the destination countries of the migrants located in all the FMPs in Libya.

An important feature of GDELT is that it provides a numerical variable measuring the tone of each news article. The tone is calculated as the positive score minus the negative score, where the positive (negative) score is the share of all words in the article found to have a positive (negative) connotation. In practice, a news article has a positive (negative) tone score if the share of words with a positive connotation is larger (smaller) than that with a negative connotation. This tone variable has been shown to perform similarly to human-coded sentiment

> 16A Key Informant (KI) is a “person within the community who, due to his or her position, has access to specialized knowledge about the situation and context of the populations residing within. KIs can be local officials, religious, community, or tribal leaders, government administrators, local humanitarian workers, representatives of displaced groups, or others.” As an example, during Round 18 (February-March 2018), 1360 KIs interviews were conducted, in all 100 Baladiyas (i.e., districts) of Libya (IOM, 2018a).

> 17The GDELT algorithm extracts from the content of the news the major topics discussed, categorize them into topics using the taxonomy provided by the World Bank Topical Ontology, and then rank topics in order of importance, so to make it possible to identify the primary focus of each news article. For more details, see `https://blog.gdeltproject.org/mapping-the-media-a-geographic-lookup-of-gdelts-sources/`

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scores and other sentiment indicators (Ribeiro et al., 2016).

Using GDELT data, we construct the variable _Tone News Destinationsi,m−_ 1 _,t_ , which measures the average tone of all migration-related news articles published in month _m-1_ in year _t_ in the destination countries _j_ of migrants in FMP _i_ . This is done with the formula: where _Nji_ is the number of top destination countries indicated by migrants in FMP _i_ , _xz,ji,m−_ 1 _,t_ is the tone (i.e., the sentiment) of migration-related news article _z_ published in country _j_ in month _m-1_ in year _t_ , and _Nz,ji_ is the number of migration-related news article published in destination country _j_ in that period of time.<sup>18</sup> The variable _Tone News Destinations i,m−_ 1 _,t_ has mean -2.39 and standard deviation 0.59.

We construct the same index also for different subsets of topics in migration-related news published in destination countries. To this end, we calculate the following index: where _xztopic,ji,m−_ 1 _,t_ is the sentiment of a migration-related news article discussing a specific topic published in destination country _j_ of migrants in FMP _i_ in month _m-1_ in year _t_ , while the other terms are the same as in the previous formula. In our analysis, we consider the following topics: 1) discrimination (at destination); 2) sea incidents; 3) economic conditions (at destination); 4) crime (at destination); 5) government announcements (related to migration). Finally, we create a variable measuring the tone of the migration-related news articles referring to any other topic not included in this list.<sup>19</sup> Summary statistics for these variables are shown in Table A1.

# **4.3 Other data**

**Economic activity in Libya** To compute a proxy for economic activity around the FMP, we use the intensity of night lights in the 5 km radius from the FMP’s location in the corresponding month. Night lights data are often used to measure economic activity when data to construct economic indicators are missing or badly measured, as in our case.<sup>20</sup> In our analysis, we use 18The tone of a given news article reflects the attitude towards migration of the media outlet on which it is published, e.g., its political orientation. As long as the number of outlets does not vary systematically across periods, our measure captures - given the average political orientation of all the outlets in a given country - the variation in the tone of migration-related news across time. 19 For more details on the construction of these variables, see Appendix C. The correlation between the variable _Tone News Destinations_ and a variable given by the sum of the tone of news articles by topic (i.e., _Tone News Destinations Discrimination_ + _Tone News Destinations Crime_ + _Tone News Destinations Economic conditions_ + _Tone News Destinations Sea incidents_ + _Tone News Destinations Government announcements_ + _Tone News Destinations Other_ ) is 64%. This depends on two factors: i) some news could be double counted (e.g., they refer to both discrimination and crime) and used in the construction of more than one variable; ii) the average tone of the news in a destination country is averaged with the average tone of the news in other destination countries indicated in that FMP.

> 20Data on the Libyan economy are extremely limited. Official data on economic activities were collected only until 2011. After that, statistics on the Libyan economy have been largely unreliable due to the limited capacity

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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.

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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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possible confounding effects due to the time-invariant characteristics of the FMP, the common characteristics of all FMPs in Libya in a given month, and the time-invariant characteristics of both migrants’ nationality and their destination country. As robustness check, we will also include country of destination-specific time trends and country of origin-specific time trends. Finally, _εi,m,t_ is the error term. Standard errors are clustered at the FMP level. Descriptive statistics for all variables used in the analysis are reported in Tables A1 and A2.

# **6 Results**

## **6.1 Main results: Tone of the news and migrants’ movements**

Table 1 reports the estimation results from our regression model 1. The outcome variable is the share of migrants in the FMP who are long-term stayers, i.e., reporting that they are in that location for more than 6 months. Column 1 shows the results of the baseline specification in which we control only for the full set of fixed effects. Results indicate that an increase (decrease) in the variable measuring the tone of migration-related news articles in the destination countries decreases (increases) the share of migrants who stay in a FMP in Libya for a long period.<sup>26</sup> This result is also confirmed when we control for the local-level of economic activity and of conflict intensity in the neighborhood of the FMP (column 2),<sup>27</sup> and for the presence of migrants who plan to remain in Libya or to return to their origin country (column 3).<sup>28</sup> We interpret our results on the effect of the tone of the news on the timing of migrants’ movements across FMPs within Libya as also informative of its impact on the timing of their migration journey. Migration in Libya is largely unidirectional: most migrants tend to move from South to North-West, i.e. toward the coastal areas where there are more employment opportunities and from where - those directed to Europe - then attempt to cross the Mediterranean Sea (see Section 3). It follows that our result, i.e., a worsening tone of the news at destination makes more migrants stay longer in their FMP, can be interpreted as indicating that a more negative sentiment in the migration-related news delays the migrants’ journey toward their destination countries.<sup>29</sup> Finally, in Table 1 column 4, we explore the possible mechanisms explaining the effect

> 26Table A3 shows that the tone of the news has no effect on the movements of short-stayer migrants at the FMP, i.e., migrants who have been at the FMPs for less than 2 weeks, between 2 weeks and 3 months, and between 3 and 6 months.

> 27While all these controls are not significant at conventional levels, the signs are in line with our expectations: a higher local economic activity and lower conflict intensity induce migrants to stay longer in the FMP.

> 28A more negative tone of the news may also have an impact on the choice of migrants to remain in Libya as the final destination, i.e., the variable “FMP with migrants planning to remain in Libya” may be endogenous with respect to the main explanatory variable, _Tone News Destinationsi,m−_ 1 _,t_ . Reassuringly, when the latter variable is regressed against the former, controlling for both FMP and month-year fixed effects, its estimated effect is small (0.010) and not statistically significant (p-value = 0.622), suggesting that the choice to remain in Libya is not determined by a change in the tone of the news in destination countries.

> 29A possible concern with our interpretation of these findings is related to the role of smugglers in determining migrants’ movement decisions in Libya. Our data in fact does not allow us to distinguish between voluntary or forced choices in the timing (and direction) of the migration journey. However, as discussed in Section 3, two elements support our interpretation of these findings as being the result of migrants choosing when and where to move within Libya. First, our sample of migrants does not include migrants in detention centers or victims of human trafficking. Second, the ethnographic literature on smuggling documents that migrants in Libya (most often) choose when and where to do the next step of their migration journey toward these countries.

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of changes in the sentiment news in destination countries on migrants’ movement choices in Libya. To this end, we modify our baseline specification by replacing our main explanatory variable with a set of five variables, each measuring the tone of (migration-related) news articles referring to one specific topic, and one variable measuring the tone of the news of all the other (migration-related) news articles not covering these five topics. The five specific topics are: 1) discrimination; 2) sea incidents; 3) economic conditions; 4) crime; 5) (migration-related) government announcements. Results indicate that among all these topics, the only one with a statistically significant effect is _discrimination_ : a more negative tone in migration-related news articles on discrimination episodes induces migrants to postpone the next step in their migration journey and stay longer in their current location. Changes in the tone of news articles focusing on all the other topics, including sea incidents or (migration-related) government announcements, do not seem to have an effect. One possible interpretation of this finding is that the sentiment of news articles on discrimination episodes predicts the attitude of the local population in the destination country towards migration and the level of difficulty that migrants may encounter in terms of job opportunities, access to housing, and social integration (World Bank, 2023; Aksoy et al., 2023).<sup>30</sup> A more negative tone of the news indicates that these conditions at destination may be getting worse and that integration is becoming more difficult, reducing the incentive for the individual to make the next step in the migration journey.<sup>31</sup>

**Validation** To corroborate these results, we conduct three validation exercises. First, we show that the effect of tone of the news is significant only in FMPs located in areas in which it is more likely that migrants have access to the Internet. Second, we conduct two different placebo tests showing that our results are not spurious. Finally, we provide suggestive evidence that our results are unlikely to be driven by reverse causality.

**Internet access** Our finding that migrants’ movement choices in Libya are influenced by the tone of news published in their preferred destination countries rests on the idea that migrants are able to access and read the news. While we do not have information on mobile ownership nor on individual-level Internet usage, Table 2 provides suggestive evidence that the tone of the news affects only migrants who are more likely to be able to access them. Column 1 shows that the effect of the tone of the news in the destination countries increases with the level of economic activity around the FMP (as proxied by night lights). As long as economic activity positively correlates with Internet access, these results indicate that the effect is stronger in areas where migrants are more likely to read the news and to use it to take their decisions. Columns 2 and 3 offer additional support to this interpretation by using information on internet coverage in Libya. Results show that the effect of the tone of the news is significant only where there is Internet coverage and there is a high-speed connection. The fact that the changes in the tone of the news have an effect only in the locations where migrants are more likely to actually access

> 30Aksoy et al. (2023) show that attitudes towards migrants (proxied by a “sentiment index” built using Twitter data) are as important as local unemployment rates in shaping refugees’ integration outcomes in Germany.

> 31Note that for our argument to be valid, migrants do not need to be able to read the news articles (e.g., to know the language of the destination country) nor to actually read them. It is enough to assume that someone in contact with the migrants can read the news articles published in the destination country and then communicate to them the sentiment of the news articles.

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and read the news articles corroborates our interpretation of the main findings.

**Placebo tests** Figure A4 shows the results of two placebo tests. In the first one (displayed in panel a), we randomly assign to migrants in a given FMP the tone of the news published in the preferred destination countries of the migrants in _another_ FMP. If migrants’ movements are influenced by the tone of the news in _their_ destination countries, we should expect a null effect of the tone of the news in _other_ countries. In the second test (displayed in panel b), we assign to migrants in a given FMP the tone of the news in their destination countries but published in a random month. If migrants’ movements are influenced by the tone of the news in destination countries in the month before, we should expect a null effect for the news articles published in any other period. Figure A4 shows that, in both cases, the average placebo effect is almost zero and is considerably smaller with respect to the observed effect of the tone of the news.<sup>32</sup>

**Reverse causality** One possible concern with our results is that of reverse causality, namely that migration movements within Libya influence the tone of the news in destination countries. Two possible mechanisms may lead to this. The first is that the tone of the news in destination countries is determined by the number of migrants potentially moving to Europe. A reduction in the share of long-stayers - which we interpret as an increase in the number of migrants moving North, possibly leading to more attempts to reach Europe - is thus expected to worsen the tone of the news in destination countries. In this case, the two variables would be _positively_ correlated. In fact, our analysis shows the opposite result, thus excluding the existence of this potential source of reverse causality. Another possibility is that the tone of the news is influenced by the size of migration in Libya. The more migrants are in Libya, the larger the number of those that – at some point – will attempt to reach Europe. In this case, the tone of the news would be influenced by the overall number of migrants in the country, in particular, the larger the number of migrants the more negative the tone of the news. To test for this, we look at the correlation between the number of migrants in all FMPs in a given month and the tone of the news in the following month in the most important destination countries (Italy, France, and Germany). We also do the same with only the FMPs located close to the northeastern coastal border, i.e., the area from where migrants most often attempt to reach Europe. As shown in Figures A5 and A6, the correlation between these variables in both cases is practically zero.

# **6.2 Robustness**

Our results are robust to a number of checks. We show them in Table 3.

**Alternative measure of the tone of the news** Column 1 shows that the magnitude of the effect is unchanged if we use as explanatory variable the median value of the tone (i.e., the most common tone) rather than the average tone of migration-related news articles.

> 32For Panel (a), 5 observations out of 10k are lower than the observed effect: i.e., probability of error is 0.0005%. For Panel (b), 2 observations are lower than the observed effect: i.e., probability of error is 0.0002%.

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**Measurement error in the tone of the news** The variable _Tone News Destinationsi,m−_ 1 _,t_ is constructed considering the news article published in the destination countries of the migrants located at the FMP. Because the majority of migrants are long stayers (see Fig. A3), our analysis makes the plausible assumption that these destinations reflect the preferences of this group of migrants. However, this may not always be the case. In order to deal with this issue, we restrict the analysis to those cases in which the top preferred destinations in the FMP are most likely to represent those of long stayers: i.e., when the percentage of long stayers in the FMP is very high at month _m_ (equal to or more than 75%), and the number of arrivals at month _m+1_ is equal to zero. Column 2 shows that our results are qualitatively unchanged.

**Change in the number of migrants in the FMP** One possible concern with our results is that the reduction in the share of long-stayer migrants in the FMP may be mechanically driven by arrivals. Column 3 shows that this is not the case: our results still hold when we restrict the analysis only to FMPs in which there are no arrivals at month _m_ .

**Alternative estimator** When the dependent variable is a share, OLS estimates can be potentially biased. In these situations, one alternative is to use a fractional probit estimator. Results in column 4 show that our findings are confirmed also when using this estimator.

**Time trends in origin and destination countries** Our results are robust to augmenting our model specification with country-of-origin specific time-trends (see column 5). These fixed effects control for all the (linearly) time-varying characteristics in the origin country that may influence the movement decision of any national group of migrants. Our results also hold when we add to our baseline specification country-of-destination specific time-trends accounting for all the characteristics which (linearly) change across time in the destination country that may influence the movement decision of any national group of migrants (see column 6).

While Libya is still an important destination country for foreign workers, for a large number of migrants it has become a transit country toward Europe. Most of these migrants reach the coastal Northern border of the country to then attempt the crossing of the Mediterranean Sea (see Section 3). One possible concern with our results is that changes in the tone of the news in fact capture changes in migration policies or in the intensity of patrolling of the Mediterranean Sea rather than in the news sentiment in destination countries. Results in Table 1 column 4 already show that news related to _Sea incidents_ and _Government announcements_ is not what is driving our results. In the following, we provide additional evidence that this is not the case.

**Political orientation of the government in destination countries** One possible confounder of the effect of tone of the news is a change in the political orientation towards migration of the government of the destination country. To test for this, we include, as additional controls in our baseline specification, a variable measuring the Rile Index of the parties in the government coalition and a dummy to capture changes in the cabinet compositions for each of the five most important destination countries. Table 3, column 7 shows that the effect of

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changes in the tone of the news is significant above and beyond the changes in the political orientation towards migration of the governments in destination countries.

**Sea patrolling intensity and Mediterranean Sea crossing** A possible concern with our results is that changes in the tone of the news could be mirroring the tightness of the patrolling activities in the Mediterranean Sea having the objective of stopping migrants’ attempts to reach Europe. In other words, the slowing down of migrants’ movements in Libya could be due to the increase in the difficulty and riskiness in crossing, reflected in a more negative tone of the news articles on migration. During our period of analysis, there have been several changes in the extent and intensity of _both_ sea patrolling activities and Search and Rescue Operations (SARs) in the Mediterranean Sea conducted by _both_ the Libyan and the European authorities, and the NGOs. The (net) effects of these different operations are unclear, being highly dependent on the data used and the period considered. Among these various phases, the 2017 Minniti Compact represents a turning point in the Italian government’s attempt to reduce the arrivals on its coasts (Marchesi, 2021; Deiana et al., 2023). Given the large discontinuity in the modality of the patrolling activity due to this policy change, it is difficult to compare the dynamics of the crossing of the Mediterranean Sea pre and post the Minniti Compact. For this reason, in our analysis of the patrolling activity as a mediator of the effect of tone of the news on migrants’ choices, we exclude the implementation period of the Minniti Compact and we focus on the period 2018-2020. For our test, we define months with high or low-intensity of patrolling using the actual flows of migrants arriving in Italy in this period (see Section 4.3). Column 8 shows that the effect of the tone of the news on movements in Libya is significant in both periods and the magnitude of the effect is not stronger in months with high intensity patrolling.

**Spatial analysis** Our results are robust to the use of alternative regression models to account for the possible presence of spatial autocorrelation in the migrants’ movement choices across FMPs. To this end, we re-estimate equation 1 using a Spatial Error Model (SEM) and a Spatial Lag Mode (SLM). Results reported in Table A5 show that for both models the effect is statistically significant (and higher than in our baseline model), confirming that changes in the tone of the news affect the movement decisions of migrants (for more details, see Section D).

# **6.3 Heterogeneity**

In the following, we discuss a set of heterogeneity results that help us characterize the conditions under which changes in the tone of the news affect migrants’ movement choices.

## **6.3.1 Country of origin and FMP location**

Table 4 looks at the differential effect of the tone of the news depending on the migrants’ country of origin and their current location in Libya. As discussed in Section 3, migrants from West African countries travel through Libya differently with respect to migrants from East African countries: the former group most often follows a multi-step journey towards the North, while the latter usually makes only a few stops before reaching the coastal areas (see UNHCR, 2017b; Di Maio et al., 2023). Results reported in Table 4, column 1, are in line with this anecdotal 15
evidence: the effect of the tone of the news is significant only for migrants coming from West African countries. For the others, the sentiment toward migrants in destination countries, no matter how negative it is, does not influence their journey through Libya. This is also consistent with the fact that migrants coming from East African countries are often escaping violence and conflict, heading directly to Europe to apply for a refugee status (UNHCR, 2019a). Next, we consider the differential effects of the tone of the news on the movements of migrants located in different areas of Libya. Results in Table 4 column 2 show that a more negative tone of the news slows down the movements of migrants only in FMPs located in the Western part of the country. This differential effect is explained by two elements. First, the main migration route to Europe passes through the Western region of Libya (while migration is low from the Eastern one, see Section 3). Second, this is the part of the country where economic activity is higher (Del Prete et al., 2023). The availability of job opportunities allows migrants to (temporarily) interrupt their journey by choosing to work and live there for some time. It follows that these locations are where we expect changes in news sentiment to influence migrants’ movement.

# **6.3.2 Prevailing sentiment and numerosity of migration-related news**

Table 5 column 1 looks at the differential effect of changes in the tone of the news depending on the prevailing sentiment (positive vs negative) of migration-related news in that period. The results indicate that migration choices are only influenced by changes in the tone of the news in months during which migration-related news are mostly positive. Instead, in months in which news are mostly negative or there is an equal number of positive and negative news, the change in the tone of the news has no effect on migrants’ decisions. Column 2 further explores this heterogeneity by checking if the effect of the tone of the news varies depending on how relevant migration is in the public discourse in that period. Results show that the effect is significant only in months in which there are many news items on migration. Finally, column 3, which combines these two aspects, shows that migrants’ journey plans are influenced by changes in the tone of the news only in periods during which there are many news items on migration and they are mostly positive. Taken together, these results speak to the effectiveness of the strategy - pursued by several right-wing political parties in Europe - to incite a negative sentiment toward migrants in destination countries as a mean of discouraging them from heading to Europe. Our results indicate that this strategy is unlikely to have a lasting effect: the exacerbation of an already negative sentiment toward migration does not affect migrants’ plans, possibly because they may have already discounted the existence of a negative sentiment against them when making their initial migration decision.

# **6.3.3 Economic conditions in destination countries**

Economic conditions in destination countries are an important driver of migration flows and thus may influence how the tone of the news impacts migrants’ choices. To account for this, in Table 6 column 1 we include GDP per-capita and employment rate in the destination countries as additional controls. As expected, better economic conditions in destination countries tend to reduce the duration of migrants’ stay in their current location in Libya, i.e., suggesting that these factors tend to make migrants move faster toward their final destination. Importantly, 16
the effects of the tone of the news is still significant (at 10%) and its magnitude is only slightly reduced with respect to the baseline specification. Next, columns 2 and 3 show that a worsening tone of the news induces migrants to delay their next migration movement only when the economic conditions in the destination country are unfavorable (low employment rate or low GDP per capita). Conversely, during periods of favorable economic conditions, a worsening tone of the news has no effect. We interpret these results as indicating that economic conditions in destination countries are a strong pull factor: good economic opportunities can offset the (expected) high discrimination associated with a negative sentiment towards migrants, the latter becoming more costly and binding when the economic conditions are instead unfavorable.

**Summary of the results** Taken together, these heterogeneity results characterize the conditions under which changes in news sentiment affect migrants’ choices. A worsening tone of the news slows down the migrants’ movements in Libya - delaying their journey towards the preferred destination countries - only for migrants coming from West African countries (who are those following a step-by-step journey) or located in the Western part of Libya (where there are more economic opportunities), and when the migration-related news articles are numerous and mostly with a positive tone, or economic conditions in destination countries are not favorable.

# **6.4 Additional results: The tone of the news and spillover effects across destination countries**

Our main analysis looks at the effect of changes in tone of the news in the destination country on migrants’ movements within Libya. At the same time, one may expect that changes in the tone of the news in the destination country could also have spillover effects on other countries, e.g., potentially making them more attractive for migrants. For instance, migrants may substitute across destination countries by excluding those in which the news sentiment toward them is more negative. To test for this possibility, we estimate the following regression model: _Preferredd,m,t_ = _β_ 0 + _β_ 1 _Tone News Destination Highd,m−_ 1 _,t_ + _φm,t_ + _μd_ + _εd,m,t_ (2) where _Preferredd,m,t_ is the share of FMPs for which country _d_ in month _m_ in year _t_ is a preferred destination. _Tone News Destinations Highd,m−_ 1 _,t_ is a dummy variable that takes value one if the tone of the news of country _d_ in month _m −_ 1 is higher than the median value of the distribution of the tone of the news in close-by destination countries, and 0 otherwise.<sup>33</sup> _φm,t_ is the month-year fixed effects; _μd_ is the destination country fixed effects, and _εd,m,t_ is the error term. To account for the possibility that the set of the preferred destination countries varies only because of the change in the composition of the migrants present in the FMP, we restrict the analysis only to FMPs for which the number of migrants does not change across two periods.

Results in Table 7 suggest that changes in the tone of the news in a destination country have spillover effects on other countries. Column 1 shows the OLS results: a more negative tone of the news in a country (with respect to _other_ countries) reduces the share of FMPs for

> 33We define close-by countries as those in the same continent, with the idea that a meaningful comparison group is that of countries which are similar and not too much geographically distant from each other.

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which that country is a preferred destination among migrants in Libya. Column 2 shows that this result is confirmed also when we use a Spatial Lag Model (SLM) to account for the fact that a lower preference for country _d_ may be mechanically correlated with a higher preference for _d_ ’s close-by countries.<sup>34</sup> Finally, column 3 shows that the results still hold when controlling for economic conditions in the destination country.

A more negative tone of the news in the preferred destination induces migrants to switch to another country. One important related question is whether this substitution occurs between European and non-European destination countries. This seems not to be the case. To begin with, Figure A7 shows no correlation between the tone of the news in European destination countries and the number of FMPs registering an increase in non-European countries as preferred destinations in the following period. This is also confirmed by results in Table A4 showing that there is no correlation between the change (or the difference) between month _m_ and _m_ + 1 in the number of returnee countries which are the preferred destinations of migrants in FMP _i_ and our main explanatory variable, i.e., the tone of the news in the destination countries of migrants located in FMP _i_ in the previous period. Taken together, these results show that a more negative tone of the news in European destination countries is not associated with an increase in returnee countries among the most preferred destinations for migrants in the next period. We interpret these findings as suggesting that a worsening tone of the news - while it induces a substitution across European destinations - does not make migrants return to their country of origin.

# **7 Concluding remarks**

This paper provides evidence that migrants’ movements in Libya and the timing of their journey towards their destination countries are influenced by changes in the news sentiment of migration-related articles in these same countries. Importantly, we document that these effects are significant only for some groups of migrants and under specific conditions, indicating an overall limited effect of a more negative tone of the news on migrants’ movements towards destination countries.

Relying on IOM data providing information on migrants located in 167 FMP in Libya for the period 2017-2020, we show that a more negative tone of migration-related news articles published in migrants’ destination countries increases their length of stay in Libya, slowing down their migration journey. To validate our findings, we provide evidence that the effect is significant only for migrants located in areas where it is more likely that they have internet access and that our results are not due to reverse causality. Our heterogeneity analysis shows that the effect is significant only for migrants coming from West African countries and located in the Western part of the country. Moreover, a worsening tone of the news has no effect in periods during which most of migration-related news articles have a negative tone or the economic conditions in destination countries are very good. Taken together, these results indicate that the effect of changes in news sentiment on migrants’ decisions is significant when, where, and for whom we expect it to be so. Yet, they also suggest a limited impact on overall migration movements.

> 34The negative and significant _ρ_ indicates that this is the case: an increase in the likelihood that _d_ is a preferred country corresponds to a reduction in the preference for _another_ country (for details, see Appendix D).

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Given that most migrants in Libya have reached the country after a risky and difficult journey, it is not surprising that a more negative news sentiment makes only a few of them change their initial plan and decide not to reach their final destination anymore. Finally, we document that a worsening news sentiment, while it induces a substitution across destinations, does not lead migrants to decide to return to their country of origin.

This paper provides the first assessment of how changes in the sentiment of migration-related news in destination countries may affect migrants’ choices in developing countries. More specifically, our findings contribute to the understanding of the drivers of migration in Libya and to the policy debate on how to control migration flows to Europe. Our results also provide a contribution to the public debate on migration in European countries. In particular, our results suggest that a strategy to reduce migration based on the deterrence effects of adopting a very negative attitude towards migrants is - at best - short-sighted and likely to have a marginal effect. To begin with, the overall effect of worsening the tone of the news on migrants’ choices is limited, being significant only for some groups of migrants and under some specific conditions. For instance, a worsening tone of the news has no effect when most of the migration-related news articles already have a negative tone, suggesting that the deterrence effect of using an increasingly negative tone is small and its marginal effect tends rapidly to zero. Thus a political campaign based on an aggressive anti-migration discourse is unlikely to have a lasting effect in terms of reducing migration flows: the exacerbation of an already negative sentiment toward migration does not affect migrants’ plans, possibly because they may have already discounted the existence of a negative sentiment against them when making their choice of the destination country. Similarly, a more negative news sentiment has no effect when the economic conditions at destination are favorable: good economic opportunities offset the (expected) high discrimination associated with a negative sentiment towards migrants. Again, under these conditions, an aggressive anti-migration discourse is unlikely to have any effect. Finally, the fact that changes in the news sentiment generate spillover effects across countries indicates that such anti-migration strategy is also not politically sustainable: a worsening tone of the news in one destination increases migration flows to _other_ European countries while it does not induce migrants in Libya to return to their country of origin. This implies that an aggressive and negative attitude towards migrants will not reduce the overall migration pressure on advanced countries. It may however undermine the possibility of cooperation between destination countries to regulate migration flows from developing countries.

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23
# **Tables**

Table 1: **Tone of migration-related news at destination and migrants’ movements**

||Sh|are of migran|ts in the FM|P|
|---|---|---|---|---|
|||who are lo|ng-stayers||
||(1)|(2)|(3)|(4)|
|Tone of the news at destination|-0.0314**<br>(0.0172)|-0.0312**<br>(0.0177)|-0.0312**<br>(0.0177)||
|Tone of the news at destination (related to)|||||
|- Discrimination||||-0.0290*|
|||||(0.0165)|
|- Sea incidents||||-0.0180|
|||||(0.0200)|
|- Economic conditions||||-0.0109<br>|
|||||(0.0260)|
|- Crime||||0.0089|
|||||(0.0227)|
|- Government announcements||||0.0128|
|||||(0.0203)|
|- Other||||-0.0073|
|||||(0.0222)|
|Intensity of night lights within 5 km||0.0097|0.0099|0.0030|
|||(0.0593)|(0.0592)|(0.0643)|
|Number of conficts within 5 km||-0.0101|-0.0102|-0.0166|
|||(0.0179)|(0.0179)|(0.0182)|
|FMP with migrants planning to|||||
|remain in Libya (1 = Yes)|||0.0019|0.0129|
||||(0.0270)|(0.0280)|
|return home (1 = Yes)|||0.0045|0.0132|
||||(0.0369)|(0.0411)|
|FMP FEs|Yes|Yes|Yes|Yes|
|Year-month FEs|Yes|Yes|Yes|Yes|
|Country of destination FEs|Yes|Yes|Yes|Yes|
|Country of origin FEs|Yes|Yes|Yes|Yes|
|Number of Observations|1,568|1,568|1,568|1,568|

_Notes:_ OLS regression results. Standardized OLS estimated coefficients are reported. Standardization of coefficients is obtained using the formula<sup>_sd_</sup> _sd_<sup><u>(</u></sup> (<sup>_x_</sup> _y_ )<sup><u>)</u></sup><sup>_βx_, where</sup><sup>_βx_is the point estimate associated to control variable</sup><sup>_x_, while</sup> _sd_ ( _x_ ) and _sd_ ( _y_ ) indicate the standard deviation or respectively control variable _x_ and dependent variable _y_ . Standard errors clustered at the FMP level in brackets. The dependent variable _Share of migrants in the FMP who are long-stayers_ is the share of migrants in the FMP _i_ in month _m_ in year _t_ who report being in that location for more than 6 months. The variable _Tone of the news at destination_ ( _Tone News Destinationsi,m−_ 1 _,t_ ) is the average tone of migration-related news in the top destination countries for migrants in FMP _i_ in month _m−_ 1 in year _t_ . For the definitions of the variables: _Tone News Destinations Discrimination_ ; _Tone News Destinations Crime_ ; _Tone News Destinations Economic conditions_ ; _Tone News Destinations Sea incidents_ ; _Tone News Destinations Government announcements_ , and _Tone News Destinations Other_ see Section C. All other variables are defined in Tables A1 and A2 and additional details are provided in Section 4. *, **, *** indicate statistical significance at the 10%, 5%, and 1%, respectively.

24
Table 2: **Validation: Internet access**

||Share of m<br>who a|igrants in t<br>re long-sta|he FMP<br>yers|
|---|---|---|---|
||(1)|(2)|(3)|
|Tone of the news at destination in||||
|_FMPs where night lights have_||||
|Low intensity|0.0342|||
||(0.0599)|||
|Medium intensity|-0.0464|||
||(0.0456)|||
|High intensity|-0.1234***|||
||(0.0496)|||
|_FMPs with network coverage_||||
|Not present||-0.0787|-0.0708|
|||(0.1077)|(0.1061)|
|Present (with)||-0.0309*<br>(0.0172)||
|High-speed connection|||-0.0437*|
||||(0.0228)|
|Low-speed connection|||0.0223|
||||(0.0185)|
|Other controls|Yes|Yes|Yes|
|FMP FEs|Yes|Yes|Yes|
|Year-month FEs|Yes|Yes|Yes|
|Country of destination FEs|Yes|Yes|Yes|
|Country of origin FEs|Yes|Yes|Yes|
|Number of Observations|1,568|1,568|1,568|

_Notes:_ OLS regression results. Standardized OLS estimated coefficients are reported. Standardization of coefficients is obtained using the formula<sup>_sd_</sup> _sd_<sup><u>(</u></sup> (<sup>_x_</sup> _y_ )<sup><u>)</u></sup><sup>_βx_,where</sup><sup>_βx_isthepointestimateassociatedtocontrolvariable</sup><sup>_x_,while</sup><sup>_sd_(</sup><sup>_x_)and</sup><sup>_sd_(</sup><sup>_y_)</sup> indicate the standard deviation or respectively control variable _x_ and dependent variable _y_ . Standard errors clustered at the FMP level in brackets. The dependent variable _Share of migrants in the FMP who are long-stayers_ is the share of migrants in the FMP _i_ in month _m_ in year _t_ who report being in that location for more than 6 months. The variable _Tone of the news at destination_ ( _Tone News Destinationsi,m−_ 1 _,t_ ) is the average tone of migration-related news articles published in the top destination countries for migrants in FMP _i_ in month _m −_ 1 in year _t_ .“Other controls” include: _Intensity of night lights within 5 km_ ; _Number of conflicts within 5 km_ ; _FMP with migrants planning to remain in Libya (1 = Yes)_ ; _FMP with migrants planning to return home (1 = Yes)_ . All other variables are defined in Tables A1 and A2 and additional details are provided in Section 4. *, **, *** indicate statistical significance at the 10%, 5%, and 1%, respectively.

25
Table 3: **Tone of migration-related news at destination and migrants’ movement: Robustness checks**

||||Share of mig|rants in FMP|who are lo|ng-stayers|||
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Tone of the news at destination (median)|-0.0312*<br>(0.0177)||||||||
|Tone of the news at destination||-0.4025***|-0.1052*|-0.1582***|-0.0331*|-0.0255*|-0.0300*||
|||(0.1302)|(0.0564)|(0.0503)|(0.0170)|(0.0163)|(0.0172)||
|Tone of the news at destination when|||||||||
|High intensity patrolling<br>Low intensity patrolling||||||||-0.13921*<br>(0.07281)<br>-0.1020*<br>(0.0550)|
|Other controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|FMP FEs|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Year-month fxed FEs|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Country of destination FEs|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Country of origin FEs|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Country of destination * time trend|No|No|No|No|Yes|No|No|No|
|Country of origin * time trend|No|No|No|No|No|Yes|No|No|
|Government political orientation controls|No|No|No|No|No|No|Yes|No|
|Wald Test [p.value]||||||||0.24 [0.62]|
|Estimator|OLS|OLS|OLS|Fractional<br>Probit|OLS|OLS|OLS|OLS|
|Sample|Full|Resticted|No Arrivals|Full|Full|Full|Full|After 2017|
|Number of Observations|1,568|201|239|1,568|1,568|1,568|1,568|1,024|

_Notes:_ Results from OLS in columns 1 to 3, and 5 to 8. Results from Fractional Probit in column 4. For columns 1-3, and 5-8, standardized estimated coefficients are reported. Standardization of coefficients is obtained using the formula<sup>_sd_</sup> _sd_<sup><u>(</u></sup> (<sup>_x_</sup> _y_ )<sup><u>)</u></sup><sup>_βx_,where</sup><sup>_βx_isthepointestimateassociatedtocontrolvariable</sup><sup>_x_,while</sup><sup>_sd_(</sup><sup>_x_)and</sup><sup>_sd_(</sup><sup>_y_)indicate</sup> the standard deviation or respectively control variable _x_ and dependent variable _y_ . Standard errors clustered at the FMP level in brackets. The dependent variable _Share of migrants in the FMP who are long-stayers_ is the share of migrants in the FMP _i_ in month _m_ in year _t_ who report being in that location for more than 6 months. The variable _Tone of the news at destination_ ( _Tone News Destinationsi,m−_ 1 _,t_ ) is the average tone of migration-related news articles published in the top destination countries for migrants in FMP _i_ in month _m −_ 1 in year _t_ . _Restricted_ sample includes only FMPs for which the percentage of long stayers in the FMP at month _m_ is equal or more than 75%, and the number of arrivals at month _m+1_ is equal to zero. _No Arrivals_ sample includes only FMPs in which no migrants arrived at month _m_ . The Wald _χ_<sup>2</sup> test in column (7) evaluates the statistical difference of point estimates between variables _Tone of the news at destination when High Intensity Patrolling_ and _Tone of the news at destination when Low Intensity Patrolling_ . The variable “Tone of the news at destination (median)” is equal to the average value of the median tone of migration-related news published in the destination countries for migrants in FMP _i_ in month _m-1_ . “Other controls” include: _Intensity of night lights within 5 km_ ; _Number of conflicts within 5 km_ ; _FMP with migrants planning to remain in Libya (1 = Yes)_ ; _FMP with migrants planning to return home (1 = Yes)_ . “Government orientation controls“ include: the _Rile Index Coalitioni,m,t_ for each of the main destination countries (i.e., Italy, France, Germany, Spain, and UK) and the dummy variable _Change of cabineti,m,t_ . All other variables are defined in Tables A1 and A2 and additional details are provided in Section 4. *, **, *** indicate statistical significance at the 10%, 5%, and 1%, respectively.
Table 4: **Heterogeneity: Migrants’ country of origin and FMP location**

||Share of mig<br>who are l|rants in FMP<br>ong-stayers|
|---|---|---|
||(1)|(2)|
|Tone of the news at destination in|||
|_FMPs with a major group of migrants from_|||
|West Africa|-0.0710***<br>(0.0273)||
|East Africa|0.0664<br>(0.0455)||
|_FMPs located in_|||
|West Libya||-0.1570**<br>(0.0698)|
|East Libya||-0.0348<br>(0.0486)|
|Other controls|Yes|Yes|
|FMP FEs|Yes|Yes|
|Year-month FEs|Yes|Yes|
|Country of destination FEs|Yes|Yes|
|Country of origin FEs|Yes|Yes|
|Number of Observations|1,568|1,568|

_Notes:_ OLS regression results. Standardized OLS estimated coefficients are reported. Standardization of coefficients is obtained using the formula<sup>_sd_</sup> _sd_<sup><u>(</u></sup> (<sup>_x_</sup> _y_ )<sup><u>)</u></sup><sup>_βx_,where</sup><sup>_βx_isthepointestimateassociatedtocontrolvariable</sup><sup>_x_,while</sup><sup>_sd_(</sup><sup>_x_)and</sup><sup>_sd_(</sup><sup>_y_)</sup> indicate the standard deviation or respectively control variable _x_ and dependent variable _y_ . Standard errors clustered at the FMP level in brackets. “Other controls” include: _Intensity of night lights within 5 km_ ; _Number of conflicts within 5 km_ ; _FMP with migrants planning to remain in Libya (1 = Yes)_ ; _FMP with migrants planning to return home (1 = Yes)_ . All variables are defined in Tables A1 and A2 and additional details are provided in Section 4. *, **, *** indicate statistical significance at the 10%, 5%, and 1%, respectively.

27
Table 5: **Heterogeneity: Prevailing news sentiment and numerosity of migrationrelated news**

||Share <br>who|of migrants i<br> are long-sta|n FMP<br>yers|
|---|---|---|---|
||(1)|(2)|(3)|
|Tone of the news at destination (during months when)||||
|_Prevailing news sentiment is_||||
|Mostly negative|0.0070||0.0072|
||(0.0242)||(0.0243)|
|Same number of negative and positive news|-0.0254||-0.0240|
||(0.0233)||(0.0235)|
|Mostly positive (and)|-0.0573*|||
||(0.0324)|||
|Many news about migration|||-0.0777**<br>(0.0383)|
|Few news about migration|||-0.0070|
||||(0.0493)|
|_Among published news articles_||||
|Many news about migration||-0.0607**<br>(0.0246)||
|Few news about migration||-0.0048||
|||(0.0198)||
|Other controls|Yes|Yes|Yes|
|FMP FEs|Yes|Yes|Yes|
|Year-month FEs|Yes|Yes|Yes|
|Country of destination FEs|Yes|Yes|Yes|
|Country of origin FEs|Yes|Yes|Yes|
|Number of Observations|1,568|1,568|1,568|

_Notes:_ OLS regression results. Standardized OLS estimated coefficients are reported. Standardization of coefficients is obtained using the formula<sup>_sd_</sup> _sd_<sup><u>(</u></sup> (<sup>_x_</sup> _y_ )<sup><u>)</u></sup><sup>_βx_,where</sup><sup>_βx_isthepointestimateassociatedtocontrolvariable</sup><sup>_x_,while</sup><sup>_sd_(</sup><sup>_x_)and</sup><sup>_sd_(</sup><sup>_y_)</sup> indicate the standard deviation or respectively control variable _x_ and dependent variable _y_ . Standard errors clustered at the FMP level in brackets. The dependent variable _Share of migrants in the FMP who are long-stayers_ is the share of migrants in the FMP _i_ in month _m_ in year _t_ who report being in that location for more than 6 months. The variable _Tone of the news at destination_ ( _Tone News Destinationsi,m−_ 1 _,t_ ) is the average tone of the migration-related news articles published in the top destination countries for migrants in FMP _i_ in month _m −_ 1 in year _t_ .“Other controls” include: _Intensity of night lights within 5 km_ ; _Number of conflicts within 5 km_ ; _FMP with migrants planning to remain in Libya (1 = Yes)_ ; _FMP with migrants planning to return home (1 = Yes)_ . All variables are defined in Tables A1 and A2 and additional details are provided in Section 4. *, **, *** indicate statistical significance at the 10%, 5%, and 1%, respectively.

28
Table 6: **Heterogeneity: Economic conditions in destination countries**

|||Share <br>who<br>|of migrants in<br> are long-sta<br>|FMP<br>yers<br>|
|---|---|---|---|---|
|||(1)|(2)|(3)|
|Tone of the news at destination||-0.0300*<br>(0.0185)|||
|Tone of the news at destination (during mont|hs when)||||
|Employment rate at de|stination||||
||High||-0.0179<br>(0.0176)||
||Low||-0.0292*<br>(0.0175)||
|GDP per capita at de|stination||||
||High|||-0.0146<br>(0.0170)|
||Low|||-0.0268*<br>(0.0164)|
|Employment rate in destination countries||-0.0310|-0.0296|-0.0282|
|||(0.0663)|(0.0667)|(0.0668)|
|GDP per-capita in destination countries||-0.2813***<br>(0.0981)|-0.2823***<br>(0.0976)|-0.2825***<br>(0.0979)|
|Other controls||Yes|Yes|Yes|
|FMP FEs||Yes|Yes|Yes|
|Year-month FEs||Yes|Yes|Yes|
|Country of destination FEs||Yes|Yes|Yes|
|Country of origin FEs||Yes|Yes|Yes|
|Number of Observations||1,568|1,568|1,568|

_Notes:_ OLS regression results. Standardized OLS estimated coefficients are reported. Standardization of coefficients is obtained using the formula _sdsd_ <u>((</u> _xy_ ))<sup>_βx_,where</sup><sup>_βx_isthepointestimateassociatedtocontrolvariable</sup><sup>_x_,</sup> while _sd_ ( _x_ ) and _sd_ ( _y_ ) indicate the standard deviation or respectively control variable _x_ and dependent variable _y_ . Standard errors are clustered at the FMP level in brackets. The dependent variable _Share of migrants in the FMP who are long-stayers_ is the share of migrants in the FMP _i_ in month _m_ in year _t_ who report being in that location for more than 6 months. The variable _Tone of the news at destination_ ( _Tone News Destinationsi,m−_ 1 _,t_ ) is the average tone of migration-related news articles published in the top destination countries for migrants in FMP _i_ in month _m −_ 1 in year _t_ .“Other controls” include: _Intensity of night lights within 5 km_ ; _Number of conflicts within 5 km_ ; _FMP with migrants planning to remain in Libya (1 = Yes)_ ; _FMP with migrants planning to return home (1 = Yes)_ . All other variables are defined in Tables A1 and A2 and additional details are provided in Section 4. *, **, *** indicate statistical significance at the 10%, 5%, and 1%.

29
Table 7: **Tone of the news and changes in preferred destination countries**

||cou|Share of FMPs for<br>ntry _d_ is a preferred|which<br> destination|
|---|---|---|---|
||(1)|(2)|(3)|
|Tone of the news at destination _d_ higher than in _other_ countries|0.7368**<br>(0.3524)|0.7531**<br>(0.3322)|0.7668**<br>(0.3447)|
|Economic conditions in country _i_|No|No|Yes|
|Country of destination fxed efects|Yes|Yes|Yes|
|Year-month fxed efects|Yes|Yes|Yes|
|Estimator|OLS|SLM|SLM|
|_ρ_ [p.value]|-|0.0373*** [0.0000]|-0.2649*** [0.0079]|
|Sample|F|MPs with constant|population|
|Number of Observations|863|863|846|

_Notes:_ Results from OLS in columns 1, results from Spatial Lag Model (SLM) in columns 2 to 3. The dependent variable _Tone of the news at destination d higher than in other countries_ is a dummy variable that takes value one if the average tone of the news in _d_ at month _m_ in year _t_ is higher than the median value of the average tone of the news published in all countries located in the same continent of _d_ during the same period and zero otherwise. The variable _Tone of the news at destination d higher than in other countries_ is constructed as follows. For country _i_ in month _m_ , we compute the distribution of the tone of the news at _m_ in all planned destinations (including _i_ ) belonging to the continent where _i_ is situated. We then create a dummy variable which takes value one if the tone of the news of country _i_ is higher than the median value of the distribution, and zero otherwise. In columns 2-3, spatial autocorrelation is modelled using matrix _W_ , where the generic _ij_<sup>_t_</sup> _h_ is a dummy variable which takes value one if countries _i_ and _j_ are located in the same continent, and zero otherwise. The sample includes only _FMPs with constant population_ . The sample is constructed as follows. For each FMP, we only select observations taken in two subsequent months, if there were no migrants arriving at or departing from the FMPs from month _m −_ 1 to month _m_ . The parameter _ρ_ is the degree of spatial autocorrelation between outcomes of countries within the same continent. Economic conditions in country _i_ include _Employment Rate_ and _GDP per capita_ are monthly and computed as described in the Note to Table 6. All other variables are defined in Tables A1 and A2. *, **, *** indicate statistical significance at the 10%, 5%, and 1%, respectively.

30
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# **A Appendix: Additional tables**

Table A1: Data Descriptives

||**Variable Defnition**|**Mean**|**St. Dev.**|
|---|---|---|---|
||**Dependent Variables**|||
|_Share of migrants who stay in FMP..._||||
|more than 6 months|Share of migrants who have been recorded to be located for more than 6 months in FMP _i_ during month _m_.|57.10|29.45|
|less than two weeks|Share of migrants who have been recorded to be located for less than 2 weeks in FMP _i_ during month _m_.|14.62|10.63|
|less than 3 months|Share of migrants who have been recorded to be located for less than 3 months in FMP _i_ during month _m_.|13.93|11.86|
|between 3 and 6 months|Share of migrants who have been recorded to be located between 3 and 6 months in FMP _i_ during month _m_.|18.45|12.42|
|Share of FMPs for which country _d_ is a<br>preferred destination|Share of FMPs indicating country _d_ as a preferred destination at month _m_ in year _t_. Countries considered:<br>all those nominated at least once among the set of preferred destinations by a FMP. FMPs considered: for<br>each FMP, we only select observations taken in two subsequent months, if there were no migrants arriving at<br>or departing from the FMPs from month _m −_1 to month _m_ (i.e., _FMPs with constant population_).|2.03|7.76|
||**Migrant-related news variables**|||
|Tone of the news at destination|Average tone of migration-related news published in the destination countries for migrants in FMP _i_ in month<br>_m-1_. The tone of a news article is obtained from the diference between the positive and the negative score<br>assigned to that article. A higher value indicates a more positive tone.|-2.39|0.53|
|- Discrimination|Average tone of migration-related news discussing discrimination events published in the destination countries<br>for migrants in FMP _i_ in month _m-1_. The tone of a news article is obtained from the diference between the<br>positive and the negative score assigned to that article.|-0.32|0.03|
|- Sea incidents|Average tone of migration-related news discussing sea incidents published in the destination countries for<br>migrants in FMP _i_ in month _m-1_. The tone of a news article is obtained from the diference between the<br>positive and the negative score assigned to that article.|-0.01|0.01|
|- Economic conditions|Average tone of migration-related news discussing economics facts published in the destination countries for<br>migrants in FMP _i_ in month _m-1_. The tone of a news article is obtained from the diference between the<br>positive and the negative score assigned to that article.|-0.01|0.01|
|- Government announcements|Average tone of migration-related news referring to government authorities published in the destination coun-<br>tries for migrants in FMP _i_ in month _m-1_. The tone of a news article is obtained from the diference between<br>the positive and the negative score assigned to that article.|-0.02|0.02|
|- Economic conditions<br>_Pl_|Average tone of migration-related news not referring to any of the previous topics (Discrimination, Sea inci-<br>dents, Economic conditions, Crime, and Government announcements) published in the destination countries<br>for migrants in FMP _i_ in month _m-1_. The tone of a news article is obtained from the diference between the<br>positive and the negative score assigned to that article.|-0.07|0.05|
|_revaiing news sentiment is..._<br>Mostly negative|Dummy variable.<br>It takes value one if the positive score of the news in the top destination countries for<br>migrants in FMP is below its annual median, while the negative score is above its annual median. It takes<br>zero otherwise.|0.23|0.42|
|Same number of negative and positive news|Dummy variable.<br>It takes value one if both the positive and the negative scores of the news in the top<br>destination countries for migrants in FMP are above (or below) their annual median. It takes zero otherwise.|0.55|0.50|
|Mostly positive|Dummy variable.<br>It takes value one if the positive score of the news in the top destination countries for<br>migrants in FMP is above its annual median, while the negative score is below its annual median. It takes<br>zero otherwise.|0.22|0.41|
|_Among published news articles..._||||
|Many news about migration|Dummy variable. It takes value one if the monthly number of news in the top destination countries for migrants<br>in FMP is above its annual median. It takes zero otherwise.|0.32|0.47|
|Few news about migration|Dummy variable. It takes value one if the monthly number of news in the top destination countries for migrants<br>in FMP is below its annual median. It takes zero otherwise.|0.67|0.47|
|_Tone of the news at destination when..._||||
|High Intensity Patrolling|Dummy variable. It takes value one if the number of arrivals from Libya to Italy in a given month is below<br>the median value observed from 2018 to 2020 (1150 individuals). It takes zero otherwise.|0.47|0.50|
|Low Intensity Patrolling|Dummy variable. It takes value one if the number of arrivals from Libya to Italy in a given month is above<br>the median value observed from 2018 to 2020 (1150 individuals). It takes zero otherwise.|0.54|0.50|
|Tone of the news at destination _d_ higher<br>than in _other_ countries|Dummy variable. It takes value one if the average tone of the news in _d_ at month _m_ in year _t_ is higher than<br>the median value of the average tone of the news published in all countries located in the same continent of|0.50|0.50|
||_d_ during the same period. It takes zero otherwise. Note: only countries registered at least once among the<br>preferred destinations indicated by FMPs are considered.|||
|Number of Observations||1|,568|

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Table A2: Data Descriptives

||**Variable Defnition**<br>**Eiibl**|**Mean**|**St. Dev.**|
|---|---|---|---|
|Employment rate in destination countries|**conomc varaes**<br>It is calculated in three steps.<br>First, we retrieve the annual employment rate of preferred destination<br>country _j_ indicated in FMP _i_ during month _m_ in year _y_. Second, we impute the monthly value of the<br>employment rate of _j_ at month _m_ with the formula _xy ∗_(1 + (#_m −_1)_∗_<sup>_xy_+1</sup><sup>_−xy_</sup><br>11<br>), where _xy_ and _xy_+1<br>indicate the value of _Employment Rate (annual)_ in the year when the observation was recorded and the<br>following one, and #_m_ indicates the number of the month in which the observation was recorded. We<br>repeat this operation for all preferred destination countries indicated in FMP _i_, during month _m_ in year _y_.<br>Third, we compute the average value of the metrics obtained in step 2, so to obtain the average monthly<br>employment rate of preferred destination countries FMP _i_ during month _m_ in year _y_.<br>Data Source:<br>International Labour Organization (ILO).|11.94|4.31|
|Employment rate at destination High|For each FMP_i_, we take the distribution of the variable_Employment Rate (monthly)_in year_y_, and register<br>its median value, call it ̄_xi,y_. Then, we create a dummy variable which takes value one if _xi,my >_ ̄_xi,y_,<br>where _xi,my_ is the value the variable _Employment Rate (monthly)_ associated to _i_ at month _m_ in year _y_,<br>anderootherise|0.45|0.50|
|Employment rate at destination Low|z w.<br>For each FMP_i_, we take the distribution of the variable_Employment Rate (monthly)_in year_y_, and register<br>its median value, call it ̄_xi,y_. Then, we create a dummy variable which takes value one if _xi,my <_= ̄_xi,y_,<br>where _xi,my_ is the value the variable _Employment Rate (monthly)_ associated to _i_ at month _m_ in year _y_,<br>andzerootherwise.|0.55|0.50|
|GDP per capita in destination countries|<br>It is calculated in three steps.<br>First, we retrieve the annual GDP per capita of preferred destination<br>country _j_ indicated in FMP _i_ during month _m_ in year _y_. Second, we impute the monthly value of the<br>GDP per capita of _j_ at month _m_ with the formula _xy ∗_(1 + (#_m −_1)_∗_<sup>_xy_+1</sup><sup>_−xy_</sup><br>11<br>), where _xy_ and _xy_+1<br>indicate the value of _GDP per capita (annual)_ in the year when the observation was recorded and the<br>following one, and #_m_ indicates the number of the month in which the observation was recorded. We<br>repeat this operation for all preferred destination countries indicated in FMP _i_, during month _m_ in year _y_.<br>Third, we compute the average value of the metrics obtained in step 2, so to obtain the average monthly<br>GDP per capita of preferred destination countries FMP _i_ during month _m_ in year _y_. Data Source: The<br>WorldBank|18934.49|10418.57|
|GDP per capita at destination High|.<br>For each FMP _i_, we take the distribution of the variable _GDP per capita (monthly)_ in year _y_, and register<br>its median value, call it ̄_xi,y_. Then, we create a dummy variable which takes value one if _xi,my >_ ̄_xi,y_,<br>where _xi,my_ is the value the variable _GDP per capita (monthly)_ associated to _i_ at month _m_ in year _y_, and<br>zero otherwise.|0.46|0.50|
|GDP per capita at destination Low|For each FMP _i_, we take the distribution of the variable _GDP per capita (monthly)_ in year _y_, and register<br>its median value, call it ̄_xi,y_. Then, we create a dummy variable which takes value one if _xi,my <_= ̄_xi,y_,<br>where _xi,my_ is the value the variable _GDP per capita (monthly)_ associated to _i_ at month _m_ in year _y_, and<br>zero otherwise.|0.54|0.50|
||**Controlvariables**|||
|Intensity of night lights within 5 km|<br>Intensity of night lights in the 5 km radius around the location of FMP _i_ at month _m-1_.|12.75|15.99|
|Number of conficts within 5 km|Number of conficts in the 5 km radius around the location of FMP _i_ at month _m-1_.|2.46|8.94|
|_Some migrants in FMP plan to..._<br>Remain in Libya (1 = Yes)|Dummy variable. It takes value one if one of the top destination countries for migrants in FMP _i_ in month<br>|0.45|0.50|
|Return home (1 = Yes)|_m-1_ is Libya, and zero otherwise.<br>Dummy variable. It takes value one if at least one country is both a top planned destination and a top<br>origin country for migrants in FMP _i_ in month _m-1_, and zero otherwise.|0.37|0.48|
|_FMPs where night lights have..._<br>Low Intensity|Dummy variable. In each year, we calculate the distribution of the intensity of night lights in the 5 km<br>radius around FMPs. The variable assigns value one to FMPs falling in the lowest 33rd percentile of the<br>distribution.Ittakeszerootherwise.|0.33|0.47|
|Middle Intensity|<br>Dummy variable. In each year, we calculate the distribution of the intensity of night lights in the 5 km<br>radius around FMPs.<br>The variable assigns value one to FMPs falling between the 34th and the 66th<br>ercentileofthedistributionIttakeszerootherwise|0.33|0.47|
|High Intensity|p . .<br>Dummy variable. In each year, we calculate the distribution of the intensity of night lights in the 5 km<br>radius around FMPs. The variable assigns value one to FMPs falling in the highest 33rd percentile of the|0.33|0.47|
|_FMPswithnetworkcoverae_|distribution. It takes zero otherwise.|||
|_g..._<br>Not present|Dummy variable. It takes value one if the FMP is not within a 5 km radius from a cell tower, and zero<br>otherwise|0.92|0.26|
|Present|.<br>Dummy variable.<br>It takes value one if the FMP is within a 5 km radius from a cell tower, and zero<br>|0.08|0.26|
|High-speed connection|otherwise.<br>Dummy variable. It takes value one if the FMP is within a 5 km radius from a 4G cell tower, and zero<br>otherwise|0.41|0.49|
|Low-speed connection|.<br>Dummy variable which takes value one if the FMP is within 5 km radius from a 3G or 2G cell tower, and<br>zero otherwise.|0.59|0.49|
|_FMPslocatedin..._||||
|<br>West Libya|Dummy variable. It takes value one if the FMP is located in the area of the country west to the Libyan<br>|0.44|0.49|
|East Libya|centroid. It takes zero otherwise.<br>Dummy variable. It takes value one if the FMP is located in the area of the country east to the Libyan<br>centroid. It takes zero otherwise.|0.56|0.49|
|_FMPswithamajorgroupofmigrantsfrom_||||
|_.._<br>West Africa|Dummy variable. It takes value one if one of the major group of migrants located in the FMP is from a<br>WestAfricancountrIttakeszerootherwise|0.92|0.27|
|East Africa|y. .<br>Dummy variable. It takes value one if one of the major group of migrants located in the FMP is from a<br>East African country. It takes zero otherwise.|0.04|0.19|
|Number of Observations||1|,568|

33
Table A3: **News sentiment and migrants’ movement: Other groups of migrants**

||Sha<br>|re of migrants who sta<br>|y in FMP<br>|
|---|---|---|---|
||Less than 2 weeks|Less than 3 months|Between 3 and 6 months|
|Tone of the news at destination|0.0099<br>(0.0119)|0.0262<br>(0.0216)|0.0312<br>(0.0237)|
|Other controls|Yes|Yes|Yes|
|FMP FEs|Yes|Yes|Yes|
|Year-month FEs|Yes|Yes|Yes|
|Country of destination FEs|Yes|Yes|Yes|
|Num. Obs.|1,568|1,568|1,568|

_Notes:_ OLS regression results. Standard errors clustered at the FMP level in brackets. Variables are defined in Tables A1 and A2. _Other Controls_ includes thew same controls as in Table A11 column 3, namely: _Intensity of night lights within 5 km_ ; _Number of conflicts within 5 km_ ; _FMP with migrants planning to remain in Libya (1 = Yes)_ ; _FMP with migrants planning to return home (1 = Yes)_ .. *, **, *** indicate statistical significance at the 10%, 5%, and 1%, respectively.

Table A4: **News in Europe and preference for returning home**

||Returnee cou<br>|ntries among preferred destinations<br>between m and m+1<br><br>|
|---|---|---|
||Change (1 =|Yes)<br>Diference|
|Tone of the news at destination|0.0641<br>(0.0639)|0.2285<br>(0.1485)|
|Country of destination fxed efects|Yes|Yes|
|Year-month fxed efects|Yes|Yes|
|Number of Observations|1,165|1,165|

_Notes:_ OLS regressions results. Standard errors clustered at the FMP level in brackets. In column 1, the dependent variable is a dummy which takes value one if there is a difference in the number of returnee countries indicated among the preferred destinations in FMP _i_ between month _m_ and _m −_ 1, and zero otherwise. In column 2, the dependent variable is equal to the difference in the number of returnee countries indicated among the preferred destinations in FMP _i_ between month _m_ and _m −_ 1.The variable _Tone of the news at destination_ ( _Tone News Destinationsi,m−_ 1 _,t_ ) is the average tone of migration-related news articles published in the top destination countries for migrants in FMP _i_ in month _m −_ 1 in year _t_ . *, **, *** indicate statistical significance at the 10%, 5%, and 1%, respectively.

34
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<!-- Start of picture text --> iio weg np<br>Ee aN ieee<br>eeOe<br>yy DS<br>y= "\)<br>——<br><!-- End of picture text -->
Figure A3: **Share of migrants located at Libyan FMPs by length of stay** <!-- Start of picture text --> % Less than 2 weeks % Less than 3 months<br>100%<br>75%<br>50%<br>25%<br>0%<br>% Less than 6 months % More than 6 months<br>100%<br>75%<br>50%<br>25%<br>0%<br>2017 2018 2019 2020 2017 2018 2019 2020<br>Percentage of migrants in FMPs<br><!-- End of picture text --> Note: The figure shows the boxplot distribution of the share of migrants located at Libyan FMPs by length of stay during our period of analysis.

Figure A4: **Spatial and time placebo tests** <!-- Start of picture text --> 200<br>150<br>100<br>50<br>0<br>(a) Effect of the news in the top destination countries<br>for other FMPs<br>−0.0347−0.0317 0.0069 0.0515<br><!-- End of picture text --> <!-- Start of picture text --> 200<br>150<br>100<br>50<br>0<br>−0.0432 −0.0317 −0.0045 0.0268<br><!-- End of picture text --> (b) Effect of the news in the FMP’s top destination countries but in _another_ time Notes: The dotted line indicates the estimated coefficient for the explanatory variable in the main regression specification (see Table 1 column 2). Placebo results obtained with 10,000 replications.

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Figure A5: **Tone of the news in major destination countries vs number of migrants in Libyan FMPs** <!-- Start of picture text --> −2.0 Regress. Coefficient: 0.00002 (0.0001). P.value:0.8200<br>−2.5<br>−3.0<br>−3.5<br>0 500 1000 1500<br>Total number of migrants in Libyan FMPs at month m<br> and Italy in month t+1<br>Avg. tone of the news in France, Germany,<br><!-- End of picture text --> Note: For each month, we indicate the average tone of the news in France, Germany, and Italy in a given month, recorded on the y-axis, associated to the total number of migrants in Libyan FMPs during the same month, registered in the x-axis. The black line describes a regression line obtained with model _ymy_ = _α_ + _βxmy_ + _γy_ + _εmy_ , where _α_ is the intercept, _ymy_ indicates the value recorded on the y-axis in a given month, _xmy_ indicates the value recorded on the x-axis in the same month, _γy_ indicates year fixed effects, and _εmy_ is the error term.

Figure A6: **Tone of the news in major destination countries vs number of migrants in Libyan FMPs located in the North West** <!-- Start of picture text --> −2.0 Regress. Coefficient: 0.000008 (0.00006). P.value:0.9900<br>−2.5<br>−3.0<br>−3.5<br>0 100 200 300<br>Total number of migrants in Libyan FMPs at month m<br> and Italy in month t+1<br>Avg. tone of the news in France, Germany,<br><!-- End of picture text --> Note: For each month, we indicate the average tone of the news in France, Germany, and Italy in a given month, recorded on the y-axis, associated to the total number of migrants found in FMPs located in the Nort-West area of Libya during the same month, registered in the x-axis. The black line describes a regression line obtained with model _ymy_ = _α_ + _βxmy_ + _γy_ + _εmy_ , where _α_ is the intercept, _ymy_ indicates the value recorded on the y-axis in a given month, _xmy_ indicates the value recorded on the x-axis in the same month, _γy_ indicates year fixed effects, and _εmy_ is the error term.

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Figure A7: **News in Europe and preference for returning home** <!-- Start of picture text --> 200<br>100<br>0<br>−100<br>−3.2 −3.0 −2.8 −2.6<br>Avg. Tone of European News at m−1<br>(lower is worse)<br> between m−1 and m<br> among preferred destinations<br>Variation in the number of retunee countries<br><!-- End of picture text --> Note: The horizontal axis shows the average _Tone of the news_ across all European countries that are a preferred destination for any FMP at month _m_ . The vertical axis reports the variation between month _m_ and _m_ + 1 in the total number (i.e., summed over all FMPs) of preferred destinations that are the country of origin of migrants. In other words, we are plotting the news sentiment in European countries that are preferred destinations for the migrants at a given moment on their preference for returning to the origin country in the following period. The black line describes a regression line obtained with model _ymy_ = _α_ + _βxmy_ + _γy_ + _εmy_ , where _α_ is the intercept, _ymy_ indicates the value recorded on the y-axis in a given month, _xmy_ indicates the value recorded on the x-axis in the same month, _γy_ indicates year fixed effects, and _εmy_ is the error term. Returnee countries considered: Algeria, Bangladesh, Benin, Burkina Faso, Cameroon, Chad, Democratic Republic of Congo, Egypt, Eritrea, Ethiopia, Gambia, Ghana, Guinea, Guinea Bissau, Ivory Coast, Jordan, Liberia, Malaysia, Mali, Mauritania, Morocco, Niger, Nigeria, Pakistan, Palestine, Senegal, Sierra Leone, Somalia, Sudan, Syria, Togo, Tunisia.

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# **C Appendix: Measuring the tone of migration-related news articles by specific topics**

In the following, we describe how we categorize migration-related news articles by topic. First, we identify the top 150 GDELT keywords/topics in migration-related news during our period of analysis and for our sample of countries. Second, among these, we select the keywords that are related to the following topics: 1) discrimination (at destination); 2) sea incidents; 3) economic conditions (at destination); 4) crime (at destination); 5) governments announcements.

We define a migration-related news article to be about _Discrimination_ if at least one of its top 3 GDELT keywords is among the following: _discrimination_ , _migration fear_ , _xenophobia_ , _hate_ , _racial_ , _racism_ , _racist_ , _stereotype_ . On average, 7.95% of all migration-related news articles include at least one of these keywords (min. 7.12, max 35.00).

We define a migration article news to about _Sea incidents_ if at least one of its top 3 topics is related to one of the following GDELT keywords: _maritime incident_ , _sea_ , _oceans_ . On average, 3.79% of all migration-related news articles include these keywords (min. 3.38, max 7.29).

We define a migration article news to be about _Economic conditions_ if at least one of its top 3 GDELT keywords is among the following: _economy_ , _macroeconomic vulnerability and debt_ , _growth_ , _jobs_ , _labor_ , _labor markets_ , _employment_ , _unemployment_ , _jobs strategies_ , _job quality_ , _tax workers_ . On average, 6.78% of all migration-related news articles include at least one of these keywords (min. 5.91, max 18.12).

We define a migration article news to be about _Crime_ if at least one of its top 3 GDELT keywords is among the following: _crime_ , _common robbery_ , _organized crime_ , _general crime_ , _crime violence_ , _kill_ . On average, 8.25% of all migration-related news articles include at least one of these keywords (min. 1.11, max 35.00).

We define a migration article news to be about _Government announcements_ if at least one of its top 3 topics is related to one of the following GDELT keywords: _gov repatriation_ , _policy reform_ , _gov reform_ , _general government_ , _leader_ , _tax candidate_ , _tax chancellor_ , _tax judge_ , _tax king_ , _tax leader_ , _tax minister_ , _tax representative_ , _public sector management_ . On average, 8.32% of all migration-related news articles include at least one of hese keywords (min. 6.55, max 51.61).

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# **D Appendix: Robustness - Spatial Analysis**

The presence of spatial autocorrelation in the migrants’ movement choices across FMPs is a possible threat to our model identification.

First, spatial autocorrelation may be present when migrants living in FMPs geographically located in the same area are exposed to common shocks (e.g., an increase in conflict intensity, a drop in economic activity, etc.), and thus they make similar movement choices. In the case of Libya, defining what is to be considered “the same area” is not trivial: for instance, the common choice of using the boundaries of the administrative areas is unlikely to be informative in a context of a conflict-affected country that lacks a ruling government by more than ten years. To circumvent this problem, we assume that exposure to common shocks is a function of the distance of the FMPs from one another. Thus we re-estimate equation 1 using a Spatial Error Model (SEM), with which we test two different hypotheses on the role that common exposure to shocks may play: i.e., 1) all FMPs are exposed to the same shock but its impact depends on FMPs’ distance from it; 2) only FMPs close to one another are exposed to common shocks). To test the first hypothesis, we modify equation 1 so that the error term _εi,m,t_ is now equal to _λWui,m,t_ , where _W_ is an adjacency matrix with the generic _ij_<sup>_th_</sup> entry measuring the distance between FMPs _i_ and _j_ , and _λ_ is an estimated parameter used to endogenously assess the degree of spatial autocorrelation between observations. In this way, SEM allows us to model unobserved common exposure to shocks among FMPs as a function of the distance between them, without making any other assumption on the form of spatial autocorrelation potentially at work. Table A5 column 1 reports the estimates obtained with this model. Results indicate the presence of some form of spatial autocorrelation in the decisions of relocation of migrants residing in different areas. This is indicated by the parameter _λ_ having a negative and statistically significant impact on the presence of migrants in the FMPs. At the same time, our main result is qualitatively unchanged: i.e., the tone of the news affects migrants’ movement decisions. Notably, the statistical significance of the effect is now even stronger than in our baseline model 1. Next, we test the second hypothesis, i.e., only FMPs close to one another are exposed to common shocks. This is done by changing matrix _W_ in the SEM. This time, the generic _ij_<sup>_th_</sup> cell takes value one if FMPs _i_ and _j_ are located close to one another, and zero otherwise. Closeness between two FMPs is defined as a distance of 230 km, that is the minimum distance allowing each FMP to be exposed to a common shock at least with another FMP.<sup>35</sup> The estimation of the SEM using the new version of _W_ is reported in Table A5 column 2. Reassuringly, all our results are confirmed.

Another situation in which the outcome of one FMP is spatially autocorrelated with the outcome of a different FMP is when the migrants’ movement choices affect the composition of both their FMP and that of other FMPs in the same way.<sup>36</sup> We model this form of spatial

> 35 Choosing a smaller distance to define closeness is not possible, because SEM can only be estimated when _W_ is invertible. A necessary condition for this to hold is that the row sums of _W_ must be higher than zero: i.e., all FMPs must be correlated at least with another FMP.

> 36For instance, this is the case when migrants who have been staying for a long time in one FMP decide to leave it (reducing the share of migrants who stay there for more than six months) and move to another FMP (also reducing the share of migrants who stay there for more than six months). Another case is the one in which migrants in different FMPs are in contact with one another, and the choice to postpone the journey to Europe by those in one FMP (thus increasing the share of migrants who stay there for more than six months) influence

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autocorrelation using the Spatial Lag Model, again testing two different hypotheses: i.e., 1) each FMP influences all other FMPs, but its impact depends on its distance from the other FMPs’; 2) each FMP affects only close-by FMPs). In practice, with this model we augment equation 1 with the term _ρWy_ , where _y_ is our dependent variable (i.e., the share of migrants who stay in one FMP for more than six months), _W_ is the same adjacency matrix used when estimating the SEM, and _ρ_ is the estimated impact of the relocation choices of migrants in one FMP on the relocation choices of migrants in other FMPs. We test this model adopting both versions of _W_ employed in the estimation of the SEM: i.e., that considering the potential influence of one FMP over all others, and that constraining the influence of one FMP only to closer FMPs. The results are reported in Table A5, respectively in columns 3, and 4. Also in this case, we find no evidence that spatial autocorrelation do affect our main finding: also with this model we find that a more negative tone of the news in destination countries makes migrants stay longer in their current location - postponing their next step in their migration journey - and the statistical significance of the effect is now even stronger than in our baseline model.<sup>37</sup> Table A5: **News sentiment and migrants’ movement Robustness: Spatial Analysis**

|||Share of migr<br>who are lo|ants in FMP<br>ng-stayers||
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Tone of the news at destination|-0.0355***<br>(0.0127)|-0.0293***<br>(0.0127)|-0.0314***<br>(0.0125)|-0.0312***<br>(0.0125)|
|Estimator|SEM|SEM|SLM|SLM|
|Spatial autocorrelation exists between|All FMPs|Closest FMPs|All FMPs|Closest FMPs|
|_λ_[p.value]|-0.9425[0.0000]|0.1364[0.0065]|-|-|
|_ρ_[p.value]|-|-|0.3571[0.0004]|-0.0012[0.9655]|
|Other controls|Yes|Yes|Yes|Yes|
|FMP FEs|Yes|Yes|Yes|Yes|
|Year-month FEs|Yes|Yes|Yes|Yes|
|Country of destination FEs|Yes|Yes|Yes|Yes|
|Country of origin FEs|Yes|Yes|Yes|Yes|
|Number of Observations.|1,568|1,568|1,568|1,568|

_Notes:_ Results from Spatial Error Model (SEM) in columns 1 and 2, Results from Spatial Lag Model (SLM) in columns 3 and 4. In columns 1 and 3, spatial autocorrelation is modelled as a function of FMPs’ geographic distance between each other. In columns 2 and 4, spatial autocorrelation is assumed to exist only between FMPs closer than 230km: i.e., the minimum distance allowing each FMP to be influenced at least by another FMP. The parameter _λ_ is the degree of spatial autocorrelation between errors estimated by the SEM model. The parameter _ρ_ is the degree of spatial autocorrelation between outcomes estimated by the SLM model. All other variables are defined in Tables A1 and A2 and additional details are provided in Section 4. “Other controls” include: _Intensity of night lights within 5 km_ ; _Number of conflicts within 5 km_ ; _Some migrants in FMP plan to remain in Libya (1 = Yes)_ ; _Some migrants in FMP plan to return home (1 = Yes)_ . *, **, *** indicate statistical significance at the 10%, 5%, and 1%, respectively.

migrants in other FMPs to do the same (increasing as well the share of migrants who stay there for more than six months).

> 37The magnitude of the coefficients obtained using the SLM and the OLS cannot be compared because - as in the case of SEM - the quantification of the coefficients is different with the two methods.

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