Migration drivers

Definition

There is no generally accepted definition of migration drivers. Van Hear et al. (2018, p.927) describe migration drivers as “forces leading to the inception of migration and the perpetuation of movement” that “shape the broader context within which aspirations and desires to migrate are formed and in which people make their migration decisions – whether to move or not” (Van Hear et al., 2018, p.930). 

IOM defines drivers of migration as follows:

 

“Complex set of interlinking factors that influence an individual, family or population group’s decisions relating to migration, including displacement. [..] The concept of “drivers of migration” is dynamic, reflecting an interaction of personal, social, structural, environmental and circumstantial factors working in tandem with local, national, regional and global level incentives and constraints.  Drivers influence the decisions to migrate, whether the migration is internal or international, regular or irregular, and/or temporary or permanent; and they operate along a spectrum between voluntary and involuntary movement.” (IOM Glossary, 2019)



Related but distinct concepts to migration drivers are “root causes” and “migration determinants”. Root causes are “the social and political conditions that induce departures - especially poverty, repression, and violent conflict” (Carling and Talleraas, 2016, p.6). Determinants allude to “quantitative modelling and the search for data that might explain and predict migration patterns” (ibid.). Migration drivers is a more inclusive term that encompasses factors that eventually lead to migration.



In general, multidimensional disparities between places (of actual and potential residence) create a driver environment in which migration becomes a possible option for people. These “driver complexes” may incorporate both long-standing economic and non-economic inequalities – such as between the global North and South – as well as cyclical or seasonal fluctuations or ad hoc changes in life circumstances. In addition, driver discrepancies between places as well as connectivity between places shape migration processes and their dynamic. At the household or community level, specific – often observable and identifiable – events or developments may trigger the decision to migrate or stay put.



Drivers of migration have been 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 (Massey et al. 1993, Migali et al. 2018). The circumstances, the ways and modes, and the extent to which sets of driving factors may influence individual migration decision-making and larger migration processes are dependent on the functionality of migration drivers, which is a central aspect in understanding the specific role of single or combinations of multiple drivers may play at different stages of a migration (decision-making) process. Migration as a behavioural option is highly context-dependent and, as such, the configuration of complex driver environments is very specific to the time and place in which migration aspirations are formed and decisions taken. However, context-specific functionalities of specific migration drivers can be generalised and categorised along some key functions (cf. Van Hear et al., 2018). Predisposing drivers, for instance, define fundamental societal structures and structural disparities. As the basic methodological premise, we may assume that (potential) migrants respond to extrinsic or intrinsic stimuli when deciding about migration (Czaika and Reinprecht, 2020). From this perspective, predisposing factors define the broadest, most fundamental layer of opportunity structures (de Haas, 2010).



More proximate drivers downscale and localise broader structural dispositions, and are closer to the immediate ‘perception and decision spheres’ of potential migrants, while the ultimately triggering factors of migration function as the actual reasons why people decide to migrate. This can be joblessness or job offers, marriage, exposure to threat or persecution, loss of assets, or similar (Bijak and Czaika, 2020).

Table 1: Key migration driver dimensions and factors

Demographic: Demographic migration drivers include population dynamics that become manifest at the aggregate level by changes in population size and composition, fertility and mortality transitions, but also by socio-demographic processes including changes in family size, models and structures.

Economic: Economic drivers include both structural and long-running macroeconomic differences between sending and receiving regions or countries, such as development and income levels (GDP), poverty, and inequality and short and medium-term fluctuations, such as the business cycle, economic crises, and recessions. They further include labour market conditions, such as unemployment and employment, wages, and employment opportunities. Lastly, they also incorporate housing and lifestyle-related reasons for migration, such as moving to more pleasant places promising higher quality of life.

Environmental: Climate change and natural disasters are fundamental environmental drivers predisposing internal and international migration. The effects of gradual climate change affects migration mostly indirectly through multiple transmission channels including its impact on economic factors, such as incomes, livelihood opportunities, or food security. Natural disasters and sudden environmental shocks, such as floods, storms, droughts, earthquakes, and human-made disasters and accidents trigger often immediate and large-scale, but mostly temporary population displacements.

Human development: Human development drivers go beyond economic drivers including factors defining broader standards of living (e.g. as they are captured by UNDP’s human development index) ranging from availability and access to education and training opportunities to the provision and quality of the healthcare system and services.

Individual: Individual drivers include pecuniary and non-pecuniary resources, such as financial resources, migration experience, life aspirations and an associated capacity to aspire migration, or lack thereof. They shape the migration decision-making process at the personal or household level. Attitudes, views, norms and perceptions towards one’s own country and other countries influence whether and where individuals desire to migrate.

Politico-institutional: Politico-institutional drivers include immigration and emigration regulations and policies as well as some public sector policies aimed at and relevant to migrants, such as labour market access and citizenship that constrain or facilitate migration and/or integration. They further include general public services, public infrastructure, and public goods, not particularly aimed at migrants. Migration governance and infrastructures, including state and non-state actors (such as recruitment agencies) shape migration processes. Provision of civil and political rights at destination may attract migrants while the absence thereof at origins might drive some to emigrate.

Security: Security-related drivers, such as conflict, war, and violence, can trigger migration and make large-scale migration more likely, in particular, when the security situation for a broader population deteriorates. Instable political situations, political repression, and regime transitions may spark motivation to leave a country but might at the same time also constrain emigration opportunities.

Socio-cultural: Socio-cultural drivers include transnational migrant communities and networks that might enable and facilitate migration by providing information and hands-on support and assistance. Cultural norms around migration might increase the salience of migration even in the absence of migrant networks. In addition, (changing) gender norms, which are often interlinked with socio-cultural norms and practices, affect migration decisions in complex ways.

Supranational: Supranational drivers include manifestations and features of the global, post-colonial economic system and globalisation processes as established by international exchange in goods, services and capital, transnational linguistic, cultural, geographic, and religious ties, and the effects of regional integration processes and geopolitical shifts and transformations.

Data sources

The quantitative data sources to analyse the drivers of migration are diverse. They include individual, household or community surveys, administrative data (census, population register), but more recently also social media and other big data. The general benefits and drawbacks of these data sources are discussed here.

To quantitatively assess the importance of different migration drivers, studies often link the migration flows or stocks to structural indicators that operationalise certain migration driver dimensions. For instance, economic drivers are often measured by GDP per capita, GDP growth, or unemployment rates, which are generally retrieved from administrative data. For environmental drivers, on the other hand, indicators include precipitation or frequency of natural disasters. Micro-level surveys focus on individual and household decision-making processes identifying relevant reasons for migration.

In addition, interviews and focus groups are frequently used qualitative data collection methods to assess why people migrate or do not migrate. The focus hereby is on the reasons people give when asked why they would like to migrate, have migrated, or have not migrated, and how migration journeys have been developed and experienced.

For quantitative (large-N) analyses some of the following databases on contextual migration drivers are often used by researchers:

The Penn World Table (version 9.1) is a database with information on relative levels of income, output, input and productivity, covering 182 countries between 1950 and 2017. 

The Luxembourg Income Study Database (LIS) is the largest available income database of harmonised microdata on income, wealth, employment, and demographic data from many high- and middle-income countries, harmonised cross-nationally for about 50 high- and middle-income countries. 

The World Development Indicators is a compilation of internationally comparable statistics about global development containing 1,400 country-level indicators for more than 200 countries with data for many indicators going back 1960s. 

The Armed Conflict Dataset provided by the Centre for the Study of Civil War (CSCW) at the International Peace Research Institute, Oslo (PRIO) and Uppsala Conflict Data Program (UCDP) at the Department of Peace and Conflict. Dataset of armed conflicts, both internal and external, in the period 1946 to the present. The variables in the dataset describe different aspects of armed conflict, such as: year of observation, sides, issues, location, type and start and end dates.

Cross-National Time-Series (CNTS) Data Archive. The CNTS offers a listing of international and national country data facts. The dataset contains statistical information on a range of countries, with data entries ranging from 1815 to the present. The dataset contains 199 variables, which are organised under 22 categories.

The Comparative Political Data Set is a collection of political and institutional annual country-level data for 36 democratic countries for the period of 1960 to 2018 or since their transition to democracy. The core of the dataset is composed of variables related to the political system and electoral rules. In addition, the dataset contains some socio-economic variables. 



The Database of Political Institutions presents institutional and electoral results data such as measures of checks and balances, tenure and stability of the government, identification of party affiliation and ideology, and fragmentation of opposition and government parties in the legislature, among others. The current version of the database expands its coverage to about 180 countries for 40 years, 1975-2017.



The Historical Index of Ethnic Fractionalization dataset contains an ethnic fractionalization index for 165 countries across all continents and covers annually the period 1945-2013. The ethnic fractionalization index corresponds to the probability that two randomly drawn individuals within a country are not from the same ethnic group. The dataset allows its users to compare developments in ethnic fractionalization over time.



The Freedom of the World survey is to provide an annual evaluation of the state of global freedom as experienced by individuals. The survey measures freedom according to two broad categories: political rights and civil liberties. It is composed of numerical ratings and supporting descriptive texts for 195 countries and 15 territories. Freedom in the World has been published since 1973, allowing tracking global trends in freedom over more than 40 years.

CEPII’s Geographical Distance datasets incorporate country-specific geographical variables for 225 countries in the world, including the geographical coordinates of their capital cities, the languages spoken in the country under different definitions, a variable indicating whether the country is landlocked, and their colonial links. A second dataset is dyadic including different measures of bilateral distances (in kilometres) available for most country pairs across the world.

Strengths & limits of the data

Aggregate macro-level data usually have a broad coverage of countries (often global) and often have a longitudinal dimension, which is useful for identifying trends and causal relationships between certain drivers and migration outcomes. The key advantages are coverage, the level of harmonisation, and the often-available longitudinal dimension. However, aggregate country-level data does not allow the investigation of actual reasons or factors influencing the migration decision-making of individuals.

Aggregate micro-level data focus on migration desires, plans, reasons, and sometimes behaviour, of individuals. This is usually captured by large-scale surveys, as provided, for instance, by the Mexican Migration project (MMP) or the Migration from Africa to Europe (MAFE) project. Even though these datasets also include a longitudinal dimension, the limited geographical scope often do not allow broader generalisations. Nationally representative surveys such as the National Sample Survey (NSS) for India or the Indonesian Family Life Survey overcome this shortcoming.

This thematic page was authored by M. Czaika (Danube University Krems) and C. Reinprecht (University of Oxford) as an outcome of the H2020 Cross-Migration Project (Grant agreement Ares(2017) 5627812-77012), https://migrationresearch.com/).