Growing social and economic inequalities, and consequently, unfulfilled life aspirations trigger the migration intentions of millions, if not billions of people around the world. Surveys by Gallup World Poll suggest that more than 750 million adults would like to migrate if they had the chance to do so (Esipova et al., 2018). Hence, globally ‘only’ one in eight adults express a desire to migrate. This is a surprisingly small fraction given the fact that a much larger but unknown number of people would have good reasons to migrate in order to realise economic, professional, political, or social opportunities elsewhere. At the same time, only small fractions of those who aspire to migrate are actually able to realise it.

However, why do people want to migrate in the first place? At specific moments in people’s lives a number of factors come together and stimulate migration intentions, which, given some achievable livelihood opportunities, may end up in temporary or permanent moves to another domestic or international destination. Factors that drive both migration intentions (and aspirations) and actual moves are manifold and multifaceted, and over the past decades, migration researchers have been identifying and describing numerous factors and contexts that shape both individual migration trajectories and broader migration processes. Researchers studying drivers of migration are hereby asking: what are relevant factors that are driving migration, and how do these drivers operate in time and space? To what extent and in what ways do they influence, i.e. trigger or hinder, migration decision-making of some people but not of others? Moreover, how do multidimensional migration drivers interact and create complex driver configurations that may affect some people more than others in aspiring and realising migration as a viable behavioural option?

A term that is often used in migration studies is migration determinants, suggesting a structural and ‘deterministic’, i.e. causal, relationship between some external factors and migration. However, this conception is rather misleading as it ignores the central role of human agency in migration processes (Bakewell, 2010; Carling & Talleraas, 2016) and the often indirect or intervening role of some contextual factors in a migration (decision-making) process. Root causes is another term widely used, particularly in policy circles, where root causes are mostly understood as ‘the social and political conditions that induce departures - especially poverty, repression, and violent conflict’ (Carling & Talleraas, 2016, p. 6). But also this concept of a migration-inducing factor is relatively narrow because it is rarely a single or specific fundamental causal factor that is setting people in motion. Rather, it is a number of factors that are mutually mediating and conjointly shaping migration decisions and broader migration dynamics and patterns. We therefore prefer the term migration drivers (rather than causes or determinants) of migration as ‘structural elements that enable and constrain the exercise of agency by social actors’ and make ‘certain decisions, routes or destinations more likely’ (Van Hear et al., 2018, p. 928).

At a higher level of aggregation, structural disparities between places, which may turn out as locations of origin and destination, create the context that make migration decisions more likely. These spatial disparities may reflect long-standing social and economic inequalities and gaps in living standards both within countries and internationally between, for instance, the global North and South—as well as cyclical or seasonal economic fluctuations. At lower levels of aggregation, i.e. at the meso- and micro-level, migration drivers facilitate or constrain migration by affecting perceptions about migration opportunities and influencing people’s capacities to realise these opportunities. Consequently, people’s perceptions about spatial opportunity gaps are necessary pre-conditions in people’s migration decision-making.

Besides structurally embedded and therefore mostly slow-changing disparities in livelihood opportunities, specific events, and sudden developments, including some rapid policy changes, may both predispose and ultimately trigger migration. The complex interplay of multiple economic, political, social, and other gradual developments and sudden events may dynamically change migration opportunities for heterogeneous groups of people. The concept of complex driver environments, that is time-space-dependent configurations of multidimensional drivers that define people’s willingness and ability to change life situations through migration, forms the theoretical underpinning of this chapter.

In the remainder of this chapter, we briefly outline some key, and by now, classical theories of migration that are often referred to in explaining migration outcomes. We then propose a taxonomy of 24 migration drivers, categorised into nine driver dimensions, and elaborate on the key features of their configurational interplay that characterises migration driver environments. Following that, we provide a meta-review of scholarly work on migration drivers and discusses the state of knowledge on person-specific, group-specific, and more macro-structural and external migration drivers.

1 Migration Drivers: The Theoretical Basis

1.1 Classical Theories of Migration: An Overview

The reasons why people migrate have been theorised and studied for decades and the scientific literature has identified a number of fundamental dimensions of migration drivers including economic, political, social, cultural, demographic, and ecological factors (for comprehensive reviews see Ghatak et al., 1996; Hagen-Zanker, 2008; King, 2012; Massey et al., 1993). We briefly outline some influential theories of migration including functional perspectives of migration being instrumental for income maximisation or historical-structural theories explaining migration as the result of class-based deprivations in (global) capitalist systems.

Neoclassical migration theory, based on Sjaastad’s (1962) cost-benefit model and Lee’s (1966) push-pull model of migration, suggests that individuals migrate due to the discrepancy in economic opportunities between those available at a destination and a lack thereof at the place of residence. The interrelated decisions of whether and where to migrate are linked to existence of substantive income or utility differentials between places. People tend to move if expected returns to migration are beneficial. Although Lee’s push-pull model, as well as the augmented gravity model, explain overall migration flows between locations relatively well, these models have also been criticised for failing to explain why the majority of people do not migrate despite severe income differentials (Bogue, 1977; Hagen-Zanker, 2008). One reason for immobility is the fact that migrants are not simple pushed and pulled between places according to wage gaps or livelihood differentials, but people’s own agency and self-determination decides whether and where to relocate (Bakewell, 2010).

Another critique of the neoclassical migration model is that it suffers from methodological individualism, i.e. it assumes that individuals are the main decision-making units. However, individuals belong to households and communities who influence or even take the decisions, or people may even move as a family. Scholars have therefore urged for a reconsideration migration decision-making, and two perspectives on the role of households have emerged (Boyd, 1989; Sell & De Jong, 1978). First, family structure and functions have both direct and indirect effects on migration decision-making but the individual remains the decision-maker, and secondly, the family is the ultimate migration decision-making unit.

Harbison (1981) argues that family structure and function are not merely additional explanatory variables. Families transmit information and shape individuals’ motivations, values, and ultimately migration norms, thereby directly and indirectly affecting migration decision-making. Further, structural and functional family characteristics affect the perceptions of costs and benefits associated with migration. However, it is unclear how potential intra-family dissent affects migration decision-making processes. Bargaining models explain family migration decisions by interrelated utility maximisations at the household and the individual level, respectively (Abraham & Nisic, 2012). However, bargaining do often not consider coordination and communication problems (Kalter, 1998).

The new economics of labour migration (Stark & Bloom, 1985) put the household or family at the centre of migration decision-making, arguing that households are able to diversify income risks and control uncertainty by allocating individual household members to specific income sources, and therefore, alternative migration options (Haug, 2008; Massey et al., 1993). Family ties embody important social externalities, which affect migration decision-making (Mincer, 1978). For instance, negative externalities, represented by strong ties to the place and people at origin, decrease the likelihood of migration. At the same time, family and friends living elsewhere establish positive externalities by transmitting valuable information that reduces migration-related uncertainty (Stark & Bloom, 1985). Externalities are also the driving force behind migration when relative rather than absolute deprivation is spurring migration aspirations, as people compare their own well-being, income and living standards to others around them (Stark & Taylor, 1989, 1991). More recent migration decision-making models have been adapted as they combine individual factors with a focus on the family or household (Anam & Chiang, 2007).

Network theory claims that migration as a social outcome is based on the complex interplay of decisions taken by individual actors, family and friends, migrant organisations, and other economic and political factors (Boyd, 1989). Social networks hereby influence not only whether and how migration takes place, but also where migrants are predominantly moving to (Haug, 2008). Ritchey (1976) states that people with access to relevant social capital incentivises migration by providing information, financial assistance, and practical support. However, Heitmueller (2006) adds that network effects can go in both directions, that is information provided through networks is not necessarily only positive but can also discourage migration. More migrants at a particular place has a positive community and family effect attracting more migrants to this destination. At the same time, local labour markets may saturate and wages might in turn decrease. Therefore, there might be a point where migrants in destination countries either withhold information or send even negative messages ‘[…] to hamper further migration’ (Heitmueller, 2006, p. 706).

Network theory is also a useful perspective to understand the perpetuation of migration (Massey et al., 1993) and destination choice once migrant networks are established. However, network theory does also not explain migration when migrant networks are absent, or how migrant networks dissolve (de Haas, 2010). Epstein (2008) distinguishes between network and herd effects. Herd behaviour means discounting or disregarding private information to follow the behaviour of others. This is rational given the assumption that others base their decisions on better information (Epstein, 2002). Herd behaviour results in migrants following the flow rather than the stock (i.e. established network) of previous migrants. While herd effects cannot account for new, pioneering migration, they can explain migrant clustering in destinations when network effects are still likely to be small.

As emigration may continue over time, a “culture of migration” might emerge that changes a society’s values and perceptions associated with migration (Massey et al., 1993). The culture of migration manifests at the individual level—people who have migrated in the past are more likely to migrate in the future—and the community level when migration becomes a normative behaviour—a “rite of passage”—in the community (Kandel & Massey, 2002). When information about migration options diffuses widely in the community, it can perpetuate migration. In a culture of migration, this information can also spread to people without direct access to migrant networks. The culture of migration, in contrast to network theory, can hence explain why people may migrate even in the absence of networks (Ali, 2007).

While the theories discussed so far have mostly focused on individuals or households as the decision-making unit, more structural theories conceptualise migration as an intrinsic part of historical processes and societal developments. Zelinsky (1971) argues that demographic transitions and modernisation processes explain the development and changing patterns of human mobility in Europe over the last 200 years. Historical-structural models, based upon neo-Marxist interpretations of capitalism, stress the importance of structures and forces operating at the macro-structural level. Migration is driven by the global demand and supply of cheap and flexible workers in segmented labour markets to sustain continued economic growth and development in capitalist labour-recruiting countries (Piore, 1979; Sassen, 1991). In the same way, world systems theory holds that capitalist systems destroy traditional economic structures and livelihoods, and thereby shape domestic and international migration patterns (Wallerstein, 1974). The world capitalist system, disguised in colonialism, has hereby triggered an ‘age of migration’ in the nineteenth and early twentieth century.

Contemporarily, in the postcolonial era, world systems theory claims that postcolonial systems resemble those during colonialism due to neoliberalism and corporate capitalism, including transnational ties (international trade, foreign investment etc.) between former colonial powers and colonies, but also a shared history and culture, language, administrative links, and migration governance (Fawcett, 1989). Historical-structural models have mainly been criticised for denying migrants’ agency and regarding them “as little more than passive pawns in the play of great powers and world processes presided over by the logic of capital accumulation” (Arango, 2004, p. 27). Migration flows further do not always correspond to capital flows, as demonstrated by increasing South-South migration. These models also disregard the role of states, which political economy models of migration sought to rectify. Proponents of political economy models hold that political systems and geopolitical shifts in global economic, political, and military power drive migration processes (Castles, 2010; Czaika & de Haas, 2014).

1.2 Migration Drivers: Dimensions and Functions

Migration theories establish multiple reasons of why, when, where, and how people migrate. They attribute different forms and levels of agency to individual migrants. While some theories point to specific factors that drive migration, others remain vague about the actual factors that drive migration. The circumstances, the ways and modes, and the extent to which a set of driving factors may influence migration (decision-making) processes are dependent on the functionality of migration drivers, which is a central aspect in understanding the specific role (single or combinations of) migration drivers may play in migration. What almost all migration theories have in common is that migration, as both an individual behavioural option and a broader collective action, is highly context-dependent. Consequently, the interplay of factors and configuration of complex driver environments the effect on migration outcomes is very specific to the time and space in which migration decisions are taken.

Context-specific functionalities of migration drivers can be distinguished along some key functions (cf. Van Hear et al., 2018). Predisposing factors reflect fundamental societal structures and structural disparities and define the broadest, most fundamental layer of opportunity structures (cf. de Haas, 2010). As a basic methodological premise, we may assume that people respond to extrinsic or intrinsic predisposing stimuli when deciding about migration (Czaika & Reinprecht, 2020). Predisposing factors do not directly, nor in an ‘unfiltered’ manner, affect people’s decision-making but are mediated by drivers that facilitate, constrain, accelerate, consolidate, or diminish migration (Van Hear et al., 2018). For instance, structures of economic and social inequalities may be mediated by cultural norms (e.g. class-based, social status) or provisions of political and civil rights, which may absorb or neutralise the migration-stimulating effect of inequality structures. Similarly, drivers of immobility constrain migration and stimulate individuals to stay put (Schewel, 2019).

Proximate drivers ‘downscale’ and localise predisposing macro-structural factors bringing them closer to the immediate ‘decision context’ of a potential migrant. Macro-structure context and developments are disaggregated and translated into situational triggering factors of migration that establish the actual reasons for migrating, including unemployment, job offer, marriage, persecution, flooding, etc. Beyond the degree of immediacy, migration driver functions can further be characterised by their temporality, selectivity, and geography. Temporality refers to the permanent or transitory character of a driver. For instance, demographic transitions or adaptations of cultural norms are usually slow-changing and therefore relatively inelastic (‘resilient’) structural drivers while natural disasters, or a coup d’état, are phenomena resulting in rapidly changing driver environments (‘shocks’). Selectivity refers to the fact that broader social, economic, or political transformations do not normally homogeneously affect all societal groups in the same way and to the same extent. Business cycles, for instance, can affect societal groups in very different ways and to an extent that depends on the age, gender, ethnicity, social status, or profession of the potential migrant. Finally, the geography refers to the locus and scope of a migration driver. The geographical scope of a macro-structural driver can be anything between local and global, while the locus of a migration driver refers to the geographical location of a migration journey where a driver may be operating (origin, transit or destination).

2 Migration Drivers: Interactions and Configurations

Migration is a decision taken in the context of personal needs, livelihood challenges and opportunities, stress, urgency and uncertainty, based on incomplete information about migration prospects and possible outcomes of alternative behavioural options. Thus, migration decisions are both situational and contextual, that is the configuration of complex driver environments is very specific to the time and place in which migration aspirations are formed and decisions taken. It is usually not a single driver but more often a complex combination of economic, political, social, and other developments and events that may dynamically influence both migration opportunities as well as the willingness and ability to migrate. The intertemporal accumulation of triggering factors leads to certain ‘tipping point’ situations, at which larger population movements are suddenly set in motion. For instance, many Syrians stayed in their hometowns years into the civil war and only fled to neighbouring countries once their economic basis of subsistence eroded and was further degrading through environmental stress to an extent where staying was no longer a viable option (Bijak & Czaika, 2020). Migration drivers may trigger, enable, mediate, or predispose an individual, a group of people, or a population to move. Migration drivers usually ‘cluster to operate as more than the sum of the single drivers that constitute them’ (Van Hear et al., 2018, p. 934). That is, migration drivers do not work in isolation but in combination with other migration drivers establishing migration driver configurations.

As we will see in the empirical part of this chapter, most studies analysing migration drivers only focus on very specific drivers of migration. Few studies explore complex configurations of drivers, including their often non-linear, interacting, and combined effects on migration processes. Interaction effects in particular are regularly neglected. They occur when the effect of one driver depends on the presence and intensity of one or more other factors. Interaction effects reflect the importance of third factors that may influence causal relationships between a driver of migration and migration outcomes.

3 Migration Drivers: Some Empirical Evidence

3.1 A Typology and Meta-review

Following the conceptualisation of migration drivers in the previous section, we now provide an overview of the existing evidence on the migration driver dimensions and factors and discuss the empirical state of knowledge. We synthesise evidence about migration processes more profoundly and hereby elaborate on key insights and findings from the scientific literature across multiple domains of migration drivers.Footnote 1 To evaluate the driving factors of why people migrate, we consulted a vast amount of the empirical academic literature on migration drivers. The total number of studies of migration drivers has increased more than eightfold since 2010 with an average annual growth rate of over 12%.Footnote 2 In an attempt to structure this knowledge accumulation on the drivers of migration, we have developed a taxonomy consisting of nine driver dimensions and 24 driving factors that may all play a direct (independent) or indirect (conjoined) role in enabling or constraining migration processes at different analytical levels (Table 3.1). The dimensionality of migration drivers refers to the nine societal areas a migration driver belongs to, each comprising a number of driving factors further specifying these broader dimensions. An extensive literature review has revealed that these 24 driving factors are not only priority areas of migration driver research but also play a key role for a more fundamental understanding of the dynamics of migration processes.

Table 3.1 Migration driver taxonomy: driver dimensions and driving factors

We identified driving factors both deductively through our and experts’ knowledge of the migration literature, and inductively using a rapid evidence assessment (REA) of almost 300 empirical studies of migration drivers in 2019. The analysis builds on a rapid evidence assessment of this vast amount of scientific literature published until 2019.Footnote 3 We have not attempted to systematically assess the quality of the studies analysed but trust that collectively these studies are authoritative for understanding the role, effects, and functions of driving factors in migration processes. Figure 3.1 displays the share and distribution of reviewed empirical and non-empirical studies by driver dimension. On average, we identified 2.5 migration drivers per empirical study, which explains why the total number of drivers exceeds the total number of studies analysed (463). Economic and socio-cultural drivers hereby outnumber the other driver dimensions while environmental drivers have received relatively little attention. While this might reflect a biased selection of the literature, we believe that our extensive literature search is broadly representative of the core body of literature on migration drivers.

Fig. 3.1
A horizontal stacked bar graph illustrates the share and distribution of reviewed empirical and non empirical studies by driver dimension. Economic and socio cultural drivers hereby outnumber the other driver dimensions while environmental drivers have received relatively little attention.

Number and distribution of empirical and non-empirical studies, by migration driver dimension (n = 463)

Figure 3.2 displays the distribution of the reviewed empirical studies according to driver dimension, method and level of analysis, locus of the migration driver, and data sources used. Almost half of all studies evaluate economic and socio-cultural drivers. Two thirds use quantitative methods and only 8% mixed methods. Micro-level studies dominate in our review, accounting for two thirds of all studies while meso-level studies merely representing a small minority of 3%. The level corresponds to the study, which is not necessarily the same level the driver operates. There is an almost equal proportion of studies evaluating migration drivers that operate at the origin, destination, or at origin and destination. Merely 1% focuses on drivers in transit contexts. Almost half of all studies use surveys with administrative records and interviews accounting for almost a quarter each. Other methods (e.g. experimental, participant observation) account for 4%.

Fig. 3.2
A horizontal stacked bar graph illustrates the distribution of the reviewed empirical studies according to driver dimension, method and level of analysis, locus of the migration driver, and data sources used. Almost half of all studies evaluate economic and socio cultural drivers. Two thirds use quantitative methods and only 8% mixed methods. There is an almost equal proportion of studies evaluating migration drivers that operate at the origin, destination, or at origin and destination. Merely 1% focuses on drivers in transit contexts. Almost half of all studies use surveys with administrative records and interviews accounting for almost a quarter each.

Overview of empirical studies (n = 296)

Figure 3.3 shows that the distribution of migration driver studies across the nine driver dimensions has remained relatively stable over time with economic drivers accounting for around a quarter of all migration drivers. The relative importance of socio-cultural and demographic drivers has decreased while that of individual and environmental drivers has increased.

Fig. 3.3
A line graph illustrates the distribution of migration driver studies across the nine driver dimensions has remained stable over time with economic drivers accounting for around a quarter of all migration drivers. The relative importance of socio cultural and demographic drivers has decreased while that of individual and environmental drivers has increased.

Distribution of migration drivers in empirical studies, 2000–2018, 5-year rolling averages (n = 271)

As an indication of the relevance of complex migration driver configurations, Table 3.2 shows the existing coverage of empirical studies in the migration driver literature that address more than one specific driver. Note that the percentages in rows and columns do not add up to 100, as on average one study elaborates on 2.5 driver dimensions. For instance, the first row indicates that 69% of all empirical studies of demographic drivers also evaluate economic drivers. Demographic studies are almost equally likely to be examined conjointly with socio-cultural drivers (66%) but a lot less likely with security (9%) and have never been examined conjointly with environmental drivers in our sample. The column on the far right indicates that 9% of all studies that examine demographic drivers do so without reference to any of the other eight driver dimensions.

Table 3.2 Analytical coverage of migration driver dimensions in (n = 296 studies).

Table 3.2 demonstrates that the dominance of economic drivers stems not from the fact that many studies only examine economic drivers but that they are analysed conjointly with many other migration drivers, for instance in 51% of the studies with individual-level drivers or in 80% of the studies with factors representing human development (column ‘Econ.’). Economic drivers are predominantly studied conjointly with socio-cultural drivers, reflecting a considerable number of quantitative studies that examine the importance of economic push and pull factors in combination with, for instance, migrant networks. Environmental factors are often investigated in conjunction with economic drivers, reflecting the link between economic opportunities and climate change and natural disasters. However, almost a third of all studies focusing on environmental drivers examine these in isolation. Human development drivers are overwhelmingly examined conjointly with economic drivers, reflecting the interplay between employment, education, and training. Individual drivers are often studied together with socio-cultural drivers, as personal migration experience is often linked to migrant networks and cultural ties. The miniscule overlap with supranational drivers (3%) highlights the fact that studies generally evaluate micro- and macro- levels separately but very rarely together. A quarter of studies have evaluated individual aspirations, attitudes, and resources without recourse to other driver dimensions. Politico-institutional drivers are rarely studied in combination with environmental drivers but rather with security-related drivers.

The following sections explore these driver dimensions in detail. We emphasise driver dimensions where certain drivers have been studied disproportionately for certain migration forms or geographical regions. We group migration-driving factors into individual-specific, group-specific, and macro-structural drivers. We hereby provide a succinct and comprehensive overview of the current state of knowledge of all relevant migration drivers. However, due to their relevance in both academia and public discourse, we put some more elaboration on four driver dimensions, namely economic, political (public and migration policies), security-related, and environmental factors.

3.2 Individual-Specific Drivers: Aspirations and Capability

Material and non-material personal and household resources, or lack thereof, can facilitate or constrain migration. Such resources include financial assets and property (Kley, 2011; Zijlstra & van Liempt, 2017) but also information and access to information and communication technologies (Dekker et al., 2016; Farré & Fasani, 2013; Muto, 2012). These resources not only affect whether individuals migrate and which channel they choose but also are particularly important during the migration journey. The lack of financial resources constrains the poorest who might not be able to afford relocation costs (De Jong et al., 2005), paying for visas, or if necessary, to hire smuggling services (Düvell, 2018). At the same time, wealth can also be associated with smaller emigration rates from more developed countries compared to less developed middle-income countries (Clemens, 2014, Dustmann & Okatenko, 2014). Thus, the nexus between economic resources and migration propensity follows a non-linear, often inverse U-shaped relationship. For instance, small but growing landholdings increase migration inclinations but only until they are large enough to sustain a regular income from farming (Oda, 2007).

Aside economic resources, non-tangible resources such as migration experience of either oneself or other family members are an important (psychological) factor, which has widely been found to be formative in developing migration aspirations and in the decision-making process itself (Richter & Taylor, 2008; Tsegai, 2007). Aspirations such as the immanent “desire for a better life” (Özden et al., 2018) and for the fulfilment of individual or collective needs (Cai et al., 2014) are an important personal resource and important prerequisite for considering and realising a migration project. To better life circumstances, people require a “capacity to aspire” (Appadurai, 2004), which refers to the capacity to imagine a better life better and it is nurtured by a person’s economic, social, emotional, and cognitive resources (Sell & De Jong, 1978). However, aspirations are not static but individuals who migrate to fulfil their (life) aspirations might actually see their aspirations increase rather than decrease after migration, as they become exposed to new opportunities and lifestyles (Czaika & Vothknecht, 2014). However, migration can also decrease aspirations, as migrants are unable to realise their aspired lives at the new destination (Boccagni 2017).

Attitudes, views, and perceptions about one’s own country and the desire to live in another country influence whether and where individuals migrate (Schapendonk, 2012). Some specific individual characteristics and personality traits may reason this inner drive for migration (Canache et al., 2013; Frieze et al., 2006; Jokela, 2009). Open mindedness, longing for personal experience, and an adventurous personality are consistently found to drive migration intentions and behaviour. Emotions and feelings, often in conjunction with more tangible drivers, also affect migration (Boccagni & Baldassar, 2015). Overall, however, non-tangible attitudes and perceptions seem to be rather subordinate to more tangible socio-economic resources or demographic factors, including a person’s age or marital status.

3.3 Group-Specific and Internal Migration Drivers

As already indicated in the theoretical part, and in accordance with the new economics of labour migration (Stark & Levhari, 1982), several studies find evidence for the effect of household size and family structure on migration patterns as well as the influential role the family can play in migration decision-making (Meyer, 2018). Household size is found to affect (internal) migration of family members to other rural or urban locations, aiming to work in different economic sectors than other family members to diversify risk and smooth household income (Gubhaju & De Jong, 2009; VanWey, 2003). International migration is often driven by similar goals (Constant & Massey, 2002), but often with a gender-specific effect on the migration propensity. The presence of children or elderly dependents generally increases male migration but decreases female migration, highlighting the gendered division of the work-care nexus (De Jong, 2000). In contrast, the presence of elderly non-dependent family members increases female migration, as they are enabled to participate in the labour market (Danzer & Dietz, 2014). Life course events, such as retirement and one’s children leaving the house spur migration, as spouses are not constrained by employment or educational responsibilities (Stockdale, 2014). In contrast, being in marriage and in a dual breadwinning household decreases the likelihood of migration (Etling et al., 2018).

Gender affects migration at the macro-level (differential labour demand, e.g. domestic work vs. construction), meso-level (work-care nexus), and micro-level (family roles) (Lutz, 2010). Gender roles and norms, such as caregiving and breadwinning, affect men and women’s propensities to migrate (Danzer & Dietz, 2014) as well as the types of migration networks and channels that are available and migrants’ use thereof (Heering et al., 2004; Hoang, 2011). People may both migrate to conform to gendered cultural norms (Hernández-Carretero & Carling, 2012; Kandel & Massey, 2002), but also to escape these norms (Rutten & Verstappen, 2014). Gender-based discrimination might hence be both an incentive and obstacle to migrate (Ruyssen & Salomone, 2018). In this context, marriage is an important factor in explaining migration patterns. While it has mostly been confined to women to look for or join their spouses (Czaika, 2012), in some contexts it is increasingly also men who migrate to urban areas or abroad to look for wives due to distorted sex ratios. Marriage is often used to circumvent other barriers to migration, such as poverty (Rao & Finnoff, 2015) or migration policies, and of the only opportunity to realise international migration (Böcker, 1994). At the same time, in families that are more egalitarian it is also the employment opportunities status of wives that drives migration decisions (Cooke, 2008).

Moreover, migrant networks and transnational communities have long been recognised as important drivers of migration, as they facilitate and sustain migration by providing information and hands-on assistance (Boyd, 1989). Migrant networks are often measured as the number (or, stock) of previous migrants from the same family, town, region, or country at the destination. The importance of networks and social ties has repeatedly been empirically confirmed for explaining alternative migration forms and patterns (Bertoli & Ruyssen, 2018; Düvell, 2018; Haug, 2008; Havinga & Böcker, 1999). However, people also migrate in the absence of networks, highlighting the importance of other migration drivers (Gilbert & Koser, 2006; Sue et al., 2018). The importance of networks increases with restrictive migration policies, as settled migrants may act as gatekeepers and bridgeheads (Carling, 2004). However, networks may be irrelevant if migration is deemed too difficult (Collyer, 2005). As already mentioned, networks do not necessarily increase migration, as new migrants may compete for jobs and other resources with already established migrants (Heitmueller, 2006). The relation between migration flows and stocks might hence follow an inverse U-shape (Bauer et al., 2009). Flows of migrants also affect subsequent migration, as potential migrants get inspired to follow prior migrant though a ‘herd effect’ (Epstein & Gang, 2006). Networks also affect the gender and skill composition of migration flows (Hoang, 2011; McKenzie & Rapoport, 2010).

With growing numbers of migrants worldwide, migrant networks also grow in prevalence. Migration often becomes self-perpetuating and a cultural norm (Alpes, 2012; Castle & Diarra, 2003), in particular when migration becomes ingrained in the local culture and a rite of passage (Massey et al., 1993). Emigrants are social role models and individuals migrate due to the inability to fill a social role (Hernández-Carretero & Carling, 2012). Because of social norms and pressure, people may migrate even if they would have greater economic opportunities at the place origin (Ali, 2007). However, those who stay put are often seen as lazy, losers, failed, undesirable as potential mates, and experience feelings of shame and embarrassment (Heering et al., 2004; Kandel & Massey, 2002). Men are disproportionately affected by this cultural shame, as migration is often linked to masculinity (Maroufof & Kouki, 2017).

3.4 Macro-structural and External Migration Drivers

The Economy

Historically, economic hardship and downturns in rural and semi-urban areas have led to internal migration to urban areas but has also resulted in international migration, for instance from Europe to the North America in the nineteenth century (Massey, 1988). Deteriorating economic conditions tend to push people to migrate (Kunuroglu et al., 2018). Short to medium-term changes and fluctuations in macroeconomic conditions, particularly growth of gross domestic product (GDP) and a country’s business cycle, are robust drivers of migration (Beine et al., 2019; Docquier et al., 2014). By trend, GDP growth in sending countries decreases migration while GDP growth in receiving countries increases migration. For instance, the 2008 financial crisis and its aftermath caused considerable but diverse macroeconomic changes and fluctuations in European Union countries. Its impact on migration varied with both a potential migrant’s main reason for migration and employment status (Beets, 2009). For instance, students preferred to study in countries less affected by the crisis and the wish to emigrate was stronger in countries that suffered more (Van Mol & Timmerman, 2014). However, the effect of negative economic shocks on migration is not necessarily the inverse of positive shocks. Their magnitudes might differ considerably, in line with a “migration prospect theory” by which potential migrants value losses more than equal-sized gains and respond accordingly in their migration propensity (Czaika, 2015).

Neoclassical migration theory suggests that individuals migrate due to economic opportunities at the destination and/or lack thereof at the origin in order to maximise expected income (or utility). Macro-level quantitative studies consistently find that bilateral migration flows respond to unemployment rates and differentials (Migali et al., 2018; Geis et al., 2013), job opportunities (Baizán & González-Ferrer, 2016), and wages (Beine et al., 2014; Grogger & Hanson, 2011). The magnitudes differ across individuals and countries. For instance, high-skilled migrants respond more strongly to wage differentials than low-skilled ones (Grossa & Schmitt, 2012) and higher origin wages decrease emigration from developed countries but not from developing countries (Ruyssen et al., 2014). Micro-level studies add that individual unemployment, employment satisfaction, and anticipated career opportunities drive migration (Hoppe & Fujishiro, 2015; Zaiceva & Zimmermann, 2008). However, when the effect on income dominates the spatial substitution effect, unemployment may also decrease emigration due to poverty constraints (DeWaard et al., 2012). A number of qualitative studies confirm the explanatory power of the economic factors in migration processes a variety of contexts (Afifi, 2011; Bal, 2014). While employment opportunities are per se primary drivers for economic migrants, they have also been found to affect migration decisions of other migrant groups including asylum seekers, refugees, and irregular migrants (Dimitriadi, 2017; Van Hear et al., 2018). For these groups, however, economic factors are often of secondary importance compared to other factors.

The migration-development nexus has been widely studied with a majority of studies concluding that development will not stop migration, at least not in the short term (Castles, 2009; de Haas, 2007). In fact, development - generally proxied by GDP per capita—might initially increase internal migration from rural to urban areas or across international borders, as immobile potential migrants overcome poverty constraints (Czaika & de Haas, 2012, Clemens, 2014). According to this so-called migration hump –an inverse U-shaped relation between migration and development—rising income levels lead an increase in emigration from developing countries, that is in particularly from Asia and Africa, while the opposite is true from more developed countries (Czaika & de Haas, 2014; Hatton & Williamson, 2005; Sanderson & Kentor, 2009, Migali et al., 2018). Wage differentials and higher income levels at destination attract migrants (Ortega & Peri, 2013; Palmer & Pytliková, 2015). This effect seems to be particularly strong for migrants from developing countries (Ruyssen et al., 2014), but the same logic applies to internal migration (Guriev & Vakulenko, 2015). Discrepancies in living costs and conditions, housing standards, as well as broader in the quality of life and lifestyles drive migration in a variety of forms, including both internal and international migrants, regular and irregular migration, of low-skilled as well as highly skilled migrants (Baizán & González-Ferrer, 2016; de Haas & Fokkema, 2011; Péridy, 2006).

While there is rather mixed evidence on the exact relation between migration and poverty (Black et al., 2006; Skeldon, 2002), consensus emerged regarding the fact that it is generally not the poorest who migrate. Similar to the migration-development nexus, poverty and migration might follow an inverse U-shape (Du et al., 2005). Material and non-material relocation costs are the reason why the poor are constrained to migrate, even in times of severe crisis (Danzer & Dietz, 2014). However, not just absolute deprivation and poverty shape migration patterns, but also relative deprivation and the feeling of being deprived in comparison to an internal or international peer or reference group (Czaika, 2013; Stark & Taylor, 1989). The relation between internal relative deprivation, or within-country inequality, and migration is ambiguous with studies suggesting that it is positive (Stark et al., 2009), negative (Czaika & de Haas, 2012), or, mirroring the migration-development nexus, following an inverse U-shape (Péridy, 2006). Relative inequality between sending and receiving countries has been found to affect the self-selection of migrants according to Borjas’ (1989) theoretical propositions (Mayda, 2010). Higher inequality in receiving countries may hereby attract migrants, as it signals social mobility (Czaika & de Haas, 2012) but might deter those who favour social justice and are averse to the risk of income losses. While neoclassical theory predicts that people migrate to places where returns to skills and education is highest, empirical evidence often finds that people migrate to places and countries with lower expected returns, suggesting that other drivers are also at play (Belot & Hatton, 2012; Brücker & Defoort, 2009).

Public Policies

The claim that the welfare state affects the scale and composition of migration flows (Borjas, 1999) has been at the heart of many political and academic debates. The ‘welfare magnet effect’ assumes to attract low-skilled migrants—due to generous social benefits of a well-established welfare state—but to deter high-skilled migrants—due to high income and wealth taxation for funding public spending. There is some evidence for such a selection and attraction effect (Belot & Hatton, 2012; De Jong et al., 2005; Fafchamps & Shilpi, 2013). However, while higher welfare spending at both origin and destination increases migration from developing to developed countries, as they relax financial constraints and provide a safety net, they rather decrease migration between affluent countries due to high tax rates (Palmer & Pytliková, 2015; Ruyssen et al., 2014; Yoo & Koo, 2014). Other studies rather question the centrality of the welfare state and highlight the importance of other economic and socio-cultural drivers (Giulietti, 2014; Nannestad, 2007). Labour market protection and social insurance systems may for instance rather deter immigration as they create insiders and outsiders (Geis et al., 2013). Overall, the effects of welfare systems differ across countries as it depends much on the type and structure of those systems.

The effect of public infrastructure on migration is also rather ambiguous. A well-developed and functioning public infrastructure might increase migration (and mobility) by decreasing the cost of transportation but may also decrease migration propensities by providing more and better economic opportunities (Gachassin, 2013). Contentment with local public services has been found to explain variations in migration intentions in developing countries (Dustmann & Okatenko, 2014).

The educational infrastructure, in particular the quality of higher education, is a primary but not the only driver of student mobility. Students migrate internally or internationally due to the quality and reputation of universities but also due to available scholarship and costs of living (Beine et al., 2014; Findlay et al., 2011). Cities and regions with good universities attract students and retain graduates, potentially due to available jobs and employer-university interactions (Ciriaci, 2014). Educational opportunities for oneself or one’s children in receiving countries, and lack thereof in sending countries, drive international migration of students (Timmerman et al., 2016), unaccompanied migrant minors (Vervliet et al., 2015), and asylum seekers, refugees, and irregular migrants (Day & White, 2001; McAuliffe, 2017). However, study opportunities are often secondary to other factors, including security or labour market considerations, or prospects for residency and citizenship (Dimitriadi, 2017). Professional training and professional education to advance one’s career are the main driving factors for high-skilled migrants, such as health professionals (Awases et al., 2004), academics (Czaika & Toma, 2017) and consistently rank among the top reasons mentioned for emigration (Bartolini et al., 2017).

We have not identified a study that mentions the healthcare system as the sole or main driver of migration. However, there is ample evidence that the situation of the healthcare system acts as a push factor for healthcare professionals from developing countries plagued by HIV/AIDS (Aiken et al., 2004). Health risks, such as malaria and dengue, drive emigration from developing countries (Marchiori et al., 2012). Health considerations, often associated with a better climate and the availability of quality healthcare, are central to retirement migration for residents of developed countries, such as North-South migration in Europe or retirement in Mexico for US Americans (Rodriguez et al., 2004; Sunil et al., 2007). Well-developed healthcare systems can act as attracting factors for different forms of migration (Narayan & Smyth, 2006).

Migration Policies and Human Rights

Migration scholars often argue that social transformations, globalisation, and transnationalism, as well as political regime transitions drive migration processes and are therefore rather sceptical about the ability of more specific migration policies to affect the volume and composition of migration flows (Castles, 2004a, 2004b). Empirical studies find rather mixed evidence in favour or against this proposition, partly due to difficulties measuring migration policies (Migali et al., 2018; De Haas & Czaika, 2013). Restrictive migration policy is generally associated with fewer asylum applications (Hatton & Moloney, 2017; Thielemann, 2006) and less international migrants (Fitzgerald et al., 2014). Migration policies may also deflect some migrants to alternative destinations (Barthel & Neumayer, 2015; Crawley & Hagen-Zanker, 2019) or merely change their migration route while they are not important for other migrants (Gilbert & Koser, 2006). Migration restrictions seem to reduce emigration and circular migration and promote permanent settlement of migrants, which may even result in the unintended consequence of even higher net migration (Czaika & de Haas, 2017). The deterrence effect of migration policies varies with specific policy instruments: visa restrictions, for instance, may simultaneously deter regular entries while increasing attempts for irregular entry (Czaika & Hobolth, 2016).

Besides policies that aim to deter unwanted immigration, many countries have implemented policies that target certain types of migrants including skilled workers or students (Czaika & Parsons, 2017). Despite the continuous proliferation of such skill-selective migration policies, the degree to which such policies are effective remains contested (Bhagwati & Hanson, 2009). Czaika and Parsons (2017) find that supply-driven systems (points-based systems) increase both the absolute numbers of high-skill migrants and the skill composition of international labour flows. Conversely, demand-driven systems—usually based on the principle of job contingency—are shown to have a rather small, even negative effect. Doomernik et al. (2009) suggest that the potential of attracting high-skilled migrants largely depends on broader socio-economic and professional factors rather than immigration policies per se. More generally, a generous and welcoming treatment of migrants and lenient migration policies attracts migrants (McAuliffe & Jayasuriya, 2016). Other migration-facilitating policies such as free movement areas (Beine et al., 2019), or prospects for (dual) citizenship (Fitzgerald et al., 2014) affect migration propensities positively, even though such policies may disproportionately affect low-skilled migrants (Grossa & Schmitt, 2012). In addition, emigration policies may affect migration propensity, but seem equally secondary to more fundamental economic and socio-cultural drivers (de Haas & Vezzoli, 2011).

Entitlements for civil and political rights or lack thereof, in origin, transit, and destination countries play a significant role in migration and destination choices. Concerns with their legal status drive asylum seekers and refugees from host and transit countries in expectation of (easier) access to refugee status or citizenship (Crawley & Hagen-Zanker, 2019; Düvell, 2018). Discrimination and a lack of civil and political rights in origin countries increases the number of individuals who seek asylum in developed countries (Erdal & Oeppen, 2018; Hatton & Moloney, 2017). Migrants’ rights in receiving countries also encourage labour migration (Ruhs, 2013) while gender-based discrimination, as we have discussed before, can both constrain and encourage migration (Ruyssen & Salomone, 2018). Racism, anti-immigrant attitudes, discrimination and other integration obstacles in potential destination countries deter migrants, and in particular, highly skilled migrants who are often privileged by having alternative migration opportunities (Duch et al., 2019; Gorinas & Pytliková, 2017).

The so-called ‘migration industry’—agents that mediate migration processes—is closely connected to but separate from migration policies and migrant networks. Such agents facilitate migration and smugglers frequently decide both route and destination (Crawley, 2010; Hugo et al.,2017; Koser, 1997). While there is little empirical evidence on the effect of smugglers on migration (Sanchez, 2017), states have increasingly tried to deter the use of smugglers to curb irregular migration (Watkins, 2017). Recruitment agencies are other actors that facilitate migration. Historically, they attracted guest workers post-WWII and nowadays they focus on both high-skilled individuals, such as nurses, and low-skilled ones, such as domestic workers and seasonal agricultural workers, mostly from developing countries or poorer countries within the European Union (Massey, 1988; Labonté et al., 2015). Multinational and transnational corporations have been recognised as other actors that drive migration, particularly of high-skilled labour migrants (Beaverstock, 1994).

Conflict and Security

Civil, ethnic, and religious conflict, war, torture, and human rights violations are drivers of migration, particularly of asylum seekers, refugees, irregular migrants, unaccompanied migrant minors, and internal displacement. Safety and security concerns might initially decrease migration, as it is unsafe to prepare for exit and individuals often hope for an improving security situation. However, migration might increase once insecurity or crime levels exceed a certain threshold (Bohra-Mishra & Massey, 2011). Individuals migrate both due to personal experience of threat and violence but also as a consequence of broader feeling of insecurity (Lundquist & Massey, 2005) and there is ample evidence for the link between insecurity in sending countries and large-scale emigration (Castles et al., 2003; Davenport et al., 2003; Migali et al., 2018; Moore & Shellman, 2007). However, war and conflict also drive migration indirectly through its effect on infrastructure, economic opportunities, and ultimately livelihoods (Khavarian-Garmsir et al., 2019). While conflict might trigger migration, environmental or political drivers might cause conflict itself (Moore & Shellman, 2004; Naudé, 2010). Most studies in this area have focused on sending countries in Africa and the Middle East, particularly Afghanistan, Iraq, and Syria, with a substantial number also examining European destinations.

Political factors in sending and receiving countries that drive migration include repression, persecution, political terror, and political freedom (Hatton & Moloney, 2017; Narayan & Smyth, 2006), military conscription (Mallett et al., 2017), political instability (Naudé, 2010), democracy differentials and satisfaction with democracy (Moore & Shellman, 2004), corruption (Lapshyna, 2014), and regime transitions, political protests, and dissidents (Davenport et al., 2003). Individuals migrate because they are directly affected (e.g. through conscription), fear that they might be affected in the future, or due to the insecurity created by the general political situation. Factors more closely linked with conflict and with implications for physical security, such as political terror, and forced military conscription seem to better explain migration intentions and behaviour than lack of democratic rights. The latter have been linked to non-conflict induced migration and might be particularly relevant for the young and high-skilled migrants who face fewer barriers to emigration (Etling et al., 2018). Political factors in receiving countries that have been looked at are right-wing populism and electoral outcomes, which result in fewer asylum applications (Neumayer, 2004) and smaller bilateral migration flows (Fitzgerald et al., 2014).

Environmental Change

Climate change and environmental degradation as a fundamental predisposing driver of internal and international migration has been studied extensively at the macro- and micro-levels by qualitative and quantitative research, although almost exclusively for countries in the global South (Migali et al., 2018). The majority of quantitative studies find that slow-onset changes in temperatures and precipitation are associated with emigration, particularly from more agricultural countries and rural areas (Berlemann & Steinhardt, 2017; Neumann & Hermans, 2017). However, if climatic factors are evaluated alongside economic factors, the latter affect migration in a stronger and more direct way (Joseph & Wodon, 2013). Those most adversely affected by environmental degradation are also those most financially constrained and therefore unable to move (Veronis & McLeman, 2014). That is, migration as an adaptation strategy is not available to this deprived and therefore trapped group (Cattaneo et al., 2019). Some studies conclude that climatic factors do not directly explain migration intentions and behaviour (Beine & Parsons, 2015; Mortreux & Barnett, 2009). Climate change affects migration mostly indirectly through multiple transmission channels including its impact on economic factors, such as incomes, livelihood opportunities, and food security (Black et al., 2014; Khavarian-Garmsir et al., 2019), health-related risks, such as malaria and dengue (Marchiori et al., 2012), or conflict (Abel et al., 2019). While climatic factors are often analysed merely as detrimental factor potentially uprooting larger populations, but a favourable climate in certain countries or regions is often attracting immigrants, and in particular, retirees (Gottlieb & Joseph, 2006; Sunil et al., 2007; Van der Geest, 2011).

Natural disasters and environmental shocks, such as floods, storms, droughts, or earthquakes, but also human-made disasters and accidents, trigger mostly immediate and often large-scale population displacements. Natural disasters lead to an increase in internal, particularly rural-to-urban, and also international emigration (Beine & Parsons, 2015). At the same time, incidence of natural disasters discourages immigration (Ruyssen & Rayp, 2014). However, again, these factors may be secondary to more fundamental economic drivers, such as employment prospects in cities (Warner et al., 2010). Some studies do not find a significant effect of disasters on internal migration (Bohra-Mishra et al., 2014) or on the number of asylum seekers (Neumayer, 2005). However, natural disasters might lead to temporary displacement and indirectly affect migration through increasing the likelihood of conflict (Naudé, 2010). Whether disasters really trigger migration depends on a number of factors, such as adaptability and the presence or absence of broader socio-economic opportunities elsewhere. As for climate change, the overwhelming majority of studies examining the effects of natural disasters focus on developing countries.

International Connections and Relations

Transnational connections between countries and societies are often rooted in a common history and expressed by linguistic, cultural, geographic, and religious ties that transcend national or societal boundaries connecting two or more countries. A common or similar official language increases bilateral migration flows (Kim & Cohen, 2010). Other studies find evidence of English as the main spoken language in migrants’ destination choice (Adserà & Pytliková, 2015), while other studies rather discount the importance of language (Ruyssen & Rayp, 2014). Geographical distance generally decreases migration flows, as with distance, monetary and non-monetary migration costs increase, while proximity, proxied by a shared border, does rather increase migration (Wang et al., 2016). Distance is also associated with ‘positive’ skill selection, indicating that high-skilled migrants are able to travel farther than low-skilled migrants, asylum seekers, or refugees, who mostly migrate to neighbouring or near-by countries (Belot & Hatton, 2012; Grogger & Hanson, 2011; Yoo & Koo, 2014, Özden et al., 2018). Cultural factors often have more explanatory power than traditional economic factors when it comes to migration between developed countries (Belot & Ederveen, 2012). However, the effect of culture does change over time as moving towards greater cultural proximity is usually associated with larger migration flows (Lanati & Venturini, 2018).

Major geopolitical shifts have historically affected the direction and magnitude of migration flows (Czaika & de Haas, 2014). Such events include, for instance, the end of World War II, the Cold War, the dissolution of the USSR, the end of communism and the fall of the Berlin wall, or the breakup of Yugoslavia, to name a few. Geopolitical shocks and shifts have been widely acknowledged as central to major changes in international migration patterns. At the same time, it is argued that international relations drive migration rather indirectly if deteriorating inter-state relations might result in war or the breakup of colonial empires or multi-ethnic states that may end up with larger displacements (Weiner, 1996). International relations may also affect migration through its influence on immigration and emigration policymaking (Massey, 1999). For instance, the lifting of the Iron Curtain removed emigration restrictions, which resulted in increased emigration (Salt & Clarke, 2000). International relations also affect aid, trade, and investment policies, all of which are shown above to drive migration (Berthélemy et al., 2009; Parsons & Winters, 2014). Economic integration and postcolonial ties are linkages between countries that make migration more likely and migration flows are up to three times higher between former colonies (Fitzgerald et al., 2014; Kim & Cohen, 2010; Robinson & Carey, 2000). Emigrants from former colonised to former colonial powers are often less skilled compared to the origin population, potentially reflecting the ease of migration and less stringent migration policies (Grogger & Hanson, 2011). The effects of globalisation on migration manifest themselves via trade, foreign direct investment (FDI), exchange rates, and aid or official development assistance. Against neoclassical propositions, trade and migration are rather complements than substitutes as increasing trade volumes seem to be associated with larger migration (Campaniello, 2014). This is mostly due to the dominance of the income effect and pre-existing cultural linkages between countries. Evidence on the effect of foreign investment on emigration is rather mixed and can have a negative effect, in particular in the secondary sector (e.g. manufacturing), whereas FDI in the primary sector (e.g. mining, farming) is rather accelerating emigration (Sanderson & Kentor, 2009). Aid, through its effects on incomes and transnational ties, increases found to increase rather than to deter emigration (Berthélemy et al., 2009). However, aid targeted at rural development, the health sector or educational services might actually decrease emigration, as improvements in public services may outweigh the migration-inducing income effect (Gamso & Yuldashev, 2018; Lanati & Thiele, 2018a, 2018b).

4 Conclusion

This chapter has set out to give an overview of the conceptual understanding of migration drivers as well as the state of knowledge in the empirical assessment of migration drivers based on a comprehensive evidence assessment of the drivers of migration. We propose a migration typology based on 9 driver dimensions and 24 driving factors of migration and emphasise the fundamental importance of studying migration driver environments as complex configurations of drivers. Scholarship on complex driver configurations is still in its infancy as most studies—although, as we have seen, they assess on average 2.5 drivers per study—hardly consider complex interaction, nor cascading and feedback effects between multiple drivers. Thematically, besides rising research output on environmental drivers, research exploring individual-level factors such as migration aspirations, experience, and decision-making have gained increasing prominence in the literature. However, economic and social-cultural drivers are still the dominant focus in a large part of the literature. Some more specific areas are still relatively understudied, such as the role of family ties in migration, or constraining and facilitating effects of various technologies. The meta-analysis has revealed current research trends and several research gaps in the migration drivers literature. The focus on drivers at the origin and destinations and the relative neglect of those operating in transit, i.e. on the migration journey, as well as their shifting significance over time and space has recently received more attention but remains an understudied area (Crawley & Skleparis, 2018).

Different migration drivers affect distinct societal groups in different ways. To advance our understanding of the relative importance of different migration drivers in specific contexts, future research on migration drivers should further disaggregate and specify driver analyses along more complex intersections of age, gender, geography, sector of employment, and socio-economic status. The migration driver literature is also relatively silent with regard to the transitory character of drivers during migration. Migration drivers are not static but may change dynamically; while some drivers rapidly change (‘shocks’), other drivers may change only gradually over time. However, even when drivers are slow changing, they may still be perceived very differently during a migration journey or a life cycle. In addition, individual perceptions of migration drivers have hardly been explored so far. Future research on ‘migration driver complexes’ should further refine conceptualisation and empirical validation regarding the changing nature of migration drivers as predisposing, mediating, enabling, and triggering factors that may change dynamically over time and over the course of a migration journey.