What Drives Youth’s Intention to Migrate Abroad? Evidence from International Survey Data

The available data suggest that most people decide to migrate when they are young. Global estimates on migrants’ net inflows show that between 2010 and 2015, the net inflows of young migrants (15–29) were five times higher than those of adults (30–64), equaling 14.8 million and reaching 18.4 million when youth aged 30–34 are taken into account (S4YE, 2017). In addition to these global estimates, the few available data on actual migrant flows by age group also suggest that young people are highly mobile. For instance, data on migration inflows for Europe show that people aged 15–29 were by far the most mobile group during the period 2013–2017, with mobility peaking at the age of 25–29 (Belmonte and McMahon, 2019). Similar findings also apply to several other countries outside Europe (Nawrotzki and Jiang, 2015). Yet, despite young people arguably account for the bulk of international migrant flows, there are still large gaps in the evidence-base concerning the factors driving young people’s international migration at the global level. This is largely due to the limited availability and inconsistency of age-disaggregated data on migrants’ flows, which impedes large cross-country analysis of the drivers of youth migration flows (Belmonte and McMahon, 2019). A relatively recent strand of research has tried to overcome this limitation by focusing on the drivers of potential migration rather than on actual migration (see Section 2). Exploring the drivers of potential migration, though relevant in its own right, may help to better understand the factors that shape youth actual migration behavior. In fact, while intentions to migrate do not necessarily translate into actual migration behavior, several studies show that intentions are often driven by the same factors triggering actual migration and can, therefore, represent a good predictor of actual emigration trends in the future (Van Dalen and Henkens, 2008 and 2013; Creighton, 2013; Ajzen, 2005; De Jong, 2000; Tjaden et al., 2019).

Tjaden et al. (2019) find that: “On average, a 1 per cent increase in emigration plans (preparations) increases a migration flow from country S to R by 0.75 per cent. Importantly, this does not mean that 10 potential migrants make 7 actual migrants. As a rule of thumb, one can divide the number of potential migrants by 10 and the number of people who have already prepared their departure by 3 to reach roughly the number of actual migrants.”

Moreover, from a policy perspective, exploring the migration incentives of those still residing in origin countries is just as important as investigating migration motives of people who already moved (Gubert and Senne, 2016; Fouarge and Ester, 2007). In fact, as data on migration intention are collected from the sending country, they are not affected by self-selection bias that allows having a broader picture of the propensity to migrate across different socioeconomic groups (Fouarge and Ester, 2007). Against this background, research on youth potential migration has grown in recent years, pointing to a wide range of demographic, socioeconomic, institutional, and contextual drivers of migration intention among young people (see Section 2). Yet, evidence remains relatively fragmented (Belmonte and McMahon, 2019; Dibeh et al., 2018), at times drawing insights from relatively small survey samples focusing on individual countries or groups of students (Williams et al., 2018). This study aims to contribute to the literature by exploring the factors that are driving intention to migrate abroad permanently among 228,802 youth aged between 15 and 34 years old surveyed across 139 countries during seven different waves of the Gallup World Poll (GWP, henceforth) conducted between 2010 and 2016.

GWP data have been already used to analyze whether migration intentions are associated with factors such as the existence of social network (Manchin and Orazbayev, 2018), gender discrimination (Ruyssen and Salomone, 2018), subjective well-being (Nikolova and Graham, 2015; Cai et al. 2014), food insecurity (Smith and Floro, 2017; Sadiddin et al., 2019), quality of local amenities (Dustmann and Okatenko, 2014), children’s well-being (Burrone et al., 2018), and other macroeconomic factors (Dao et al., 2018).

The added value of this study is threefold. First, the wealth of information contained in the GWP allows complementing existing evidence on the drivers of youth potential emigration from developing and emerging countries, which still remains rather scattered. This will also help us to examine to what extent the drivers of youth potential migration play out differently across countries at different stages of economic development. Second, this study explores not only the factors that drive young people’s desire to migrate abroad, but also those that turn desires into more concrete migration plans. Disentangling more concrete migration plans from pure migration desires may be important to understand which triggers may transform emigration aspirations into actual migration and target adequate policy responses. Finally, this study also explores whether the drivers of migration intentions among young people also play a role in steering migration propensity across the rest of the population. This comparison should allow to better understand the differences in migration incentives at different stages of the life cycle. Results from probit estimations show that individuals’ employment status and level of education are among the most important drivers of young people’s propensity to migrate abroad permanently. In particular, being unemployed, working involuntary in part-time employment, and holding post-secondary education are all factors increasing the desire of youth to migrate abroad as well as the probability that they turn this desire into more concrete migration plans over the following year. Interestingly, there appear to be little differences in how these factors influence youth migration propensity across countries at different stages of development. In addition to individual-level labor market factors, having negative expectations about the economic outlook, the number of available job opportunities, and the prospects for upward career mobility are all factors increasing the propensity to migrate abroad, both among unemployed and employed youth. Moreover, results show that, in developing and emerging countries, youth with low income, while showing higher migration desire, are less likely to transform their desires into more concrete migration plans. This reinforces earlier findings suggesting that income constraints can represent an important barrier to the realization of migration aspiration in poorer countries. Beyond socioeconomic factors, other traditional push factors, such as discontent with local amenities, weak social networks in the origin country, or lack of trust in government, do also increase the probability that youth desire to migrate abroad, although they play little or no role in determining whether those desires are turned into more concrete plans. Finally, this study shows that, while young people’s and adults’ potential migration are often driven by the same factors, youth propensity to migrate is more strongly driven by adverse labor market conditions and individuals’ level of education. The remainder of this study is structured as follows. Section 2 reviews the literature studying the drivers of youth potential migration. Section 3 describes the data used and presents the empirical estimation strategy. Section 4 presents and discusses the results emerging from the estimation of the empirical model. Section 5 offers some concluding remarks.

Literature review

Regardless of the data used, the country sample, or the time-period covered, one of the most common results emerging from the literature is that, keeping everything else constant, young(er) people consistently show stronger migration intention than the rest of population (Burrone et al., 2018; Migali and Scipioni, 2019). Against this background, the study of the determinants of potential migration among youth has sparked increasing research interest over the past decade (Belmonte and McMahon, 2019; Williams et al., 2018). Existing evidence agree that individual-level demographic and socioeconomic characteristics are all prominent drivers of youth potential migration (Kahanec and Fabo, 2013; Van Mol, 2016), although their influence may depend on country-specific context (Williams et al., 2018; Dibeh et al., 2018). On average, intentions to migrate internationally tend to be stronger among younger age cohorts (Cairns and Smyth, 2011; Kahanec and Fabo, 2013; Cairns, 2014), youth coming from urban areas (Ramos, 2019), and those who are free from partner ties and childbearing (Epstein and Gang, 2006; Williams et al., 2018). Evidence on the influence of gender remains instead rather mixed and mostly depending on the type of migration flow and the country sample considered (Van Mol, 2016; Smith and Floro, 2017). Access to information about living abroad is another factor assumed to influence the intention to migrate. In fact, young people who have past experience of living abroad or have siblings or friends who live abroad are more likely to intend to migrate (Cairns and Smyth, 2011). However, social networks in the home country can also act as barriers to potential mobility (van Dalen and Henkens, 2013) when there are strong links with family members and local communities (Cairns, 2014). Education- and labor market-related factors remain among the most studied drivers of youth potential migration, especially across European countries. Kahanec and Fabo (2013), using data from the 2009 Eurobarometer survey, show that the educational level and the employment status are not important drivers for the decision of young people to migrate inside the EU, at least when permanent migration prospects are concerned. Conversely, Van Mol (2016), using a later wave of Eurobarometer, finds that unemployed and high-educated youth are more likely to intend to emigrate, especially from countries with high youth-to-adult unemployment rate ratios. This is in line with findings from Bartolini et al. (2016) showing that education and employment prospects are important drivers of emigration of high-skilled youth from Southern European countries, where youth unemployment is high both on international standards and in comparison with adults in the same region (Pastore, 2018). Outside the European context, young people’s employment situation and education level have been presented as particularly important drivers of youth emigration from countries in the Middle East and North Africa (MENA) region (Dibeh et al., 2018; Docquier et al., 2014; Dibeh et al., 2019; Ramos, 2019), the Pacific (Gibson and McKenzie, 2011), and Latin America (Chindarkar, 2014). Dibeh et al. (2019) show that lack of job opportunities can be a particularly important driver of young people’s intention to emigrate irregularly from the MENA region. In addition to the individual-level determinants, research suggests that young people’s decision-making process to migrate can also depend on the overall macroeconomic, labor market, and institutional environments in the home country. In particular, slow economic growth, weak job creation, lack of meritocracy, and upward mobility constitute strong motives of migration, especially for high-educated youth (Dibeh et al., 2018; De Grip, et al., 2010; Bartolini et al., 2016). Likewise, perceptions of corruption in government, discontent with the political situation, and the level of public services can also increase young people’s intention to emigrate (Etling et al., 2020; Van Mol, 2016). Finally, while this study does not necessarily encompass cases of involuntary migration, research shows that significant numbers of youth migrate out of necessity due to the presence of active armed conflicts and protracted humanitarian crisis in their home country (Belloni, 2019; Crawley et al., 2017). In sum, the aforementioned survey of the literature gathers valuable information about the drivers of youth migration intentions, which will guide the specification of the empirical regression model presented in Section 3.

Empirical strategy and descriptive statistics

This article uses the individual-level survey data provided by the GWP. The GWP conducts annual interviews on a large variety of topics with 500 men and 500 women (aged 15 or older) in around 150 countries around the world, representing more than 98% of the world’s adult population.

In some large countries, such as China and Russia, sample sizes of at least 2,000 nationally representative individuals are collected.

The survey is nationally representative, covering the entire country, including rural areas. For this study, only respondents aged 15–34 are taken into account. Observations for individuals without valid information on one or more of the questions used to construct the control variables over the whole period 2010–2016 are excluded. The final sample includes 228,802 individuals across 139 countries (22 developing, 75 emerging, and 42 developed countries).

The number of countries used in this study reduces to 139 because not all questions used in this study are asked in all the countries present in GWP. The analysis starts in 2010, and not in 2006, when data on potential migration are first available, because GWP does not provide information on plan to migrate before this year. The income groups’ classification used in this article follows the World Bank income group classification based on countries’ gross national income per capita (calculated using the Atlas method). Countries that were classified by the World Bank as upper- or lower-middle income countries are grouped together as emerging countries. The country groups used in this study are the following: Developed countries (US$12,476 or more): Australia; Austria; Belgium; Canada; Chile; Cyprus; Czechia; Denmark; Estonia; Finland; France; Germany; Greece; Hong Kong, China; Hungary; Iceland; Ireland; Israel; Italy; Japan; Republic of Korea; Latvia; Lithuania; Luxembourg; Malta; Netherlands; New Zealand; Norway; Poland; Portugal; Puerto Rico; Singapore; Slovakia; Slovenia; Spain; Sweden; Switzerland; Taiwan, China; Trinidad and Tobago; United Kingdom; United States; and Uruguay. Emerging countries (US$1,026–US$12,475): Albania; Angola; Argentina; Armenia; Azerbaijan; Bangladesh; Belarus; Belize; Bhutan; Bolivia; Bosnia and Herzegovina; Botswana; Brazil; Bulgaria; Cameroon; Colombia; Comoros; Congo; Costa Rica; Croatia; Côte d’Ivoire; Djibouti; Dominican Republic; Ecuador; Egypt; El Salvador; Gabon; Georgia; Ghana; Guatemala; Honduras; India; Indonesia; Iraq; Jamaica; Kazakhstan; Kenya; Kyrgyzstan; Lebanon; Lesotho; Macedonia, the former Yugoslav Republic of Macedonia; Malaysia; Mauritania; Mauritius; Mexico, Republic of Moldova; Mongolia; Montenegro; Morocco; Namibia; Nicaragua; Nigeria; Pakistan; Palestine; Panama; Paraguay; Peru; Philippines; Romania; Russian Federation; Senegal; Serbia; South Africa; Sri Lanka; Suriname; Swaziland; Thailand; Tunisia; Turkey; Ukraine; Bolivarian Republic of Venezuela; Vietnam; Yemen; Zambia; and Zimbabwe. Developing countries (US$1,025 or less): Afghanistan; Benin; Burkina Faso; Central African Republic; Chad; Democratic Republic of the Congo; Ethiopia; Guinea; Haiti; Liberia; Madagascar; Malawi; Mali; Mozambique; Nepal; Niger; Sierra Leone; Somalia; South Sudan; Tanzania, United Republic of Togo; and Uganda.

The effect of different factors on the probability that an individual aged 15–34 desires or plan to migrate permanently abroad is estimated using a probit regression model with the following specification:

(1)

D e s i r e / P l a n_{i t} = α + β_{1} I n d i v i d u a l_{i t} + β_{2} W e a l t h_{i t} + β_{3} N e t w o r k_{i t} + β_{4} C o n t e x t_{i t} + β_{5} L a b o u r_{i t} + z_{i} + μ_{t} + є_{i t} .

$$ Desire/Plan_{it} = α + β_1Individual_{it} + β_2Wealth_{it} + β_3Network_{it} + β_4Context_{it} + β_5Labour_{it}\\ + z_i + μ_t + є_{it}.$$

The dependent variable Desire_it is, therefore, equal to 1 if the respondent i answered in year t that he/she would like to move abroad permanently if given a chance and 0 otherwise. The variable Plan_it then considers only youth who desire to migrate and takes the value 1 if the respondent stated that he or she is making plans to migrate abroad over the next 12 months and 0 otherwise. Compared to the variable “Desire,” “Plan” should reflect more specific thoughts about how and when to act, and, as such, be able to capture more concrete intentions to migrate (Carling, 2019). Following the relevant literature (see Section 2), a large number of sociodemographic, institutional, economic, contextual, and labor market factors potentially affecting youth migration desire and plan to migrate are considered. Individual_it is a standard set of individual characteristics, including gender, age, marital status, education level, self-reported health status, and rural/ urban residence; Wealth_it denotes a group of variables measuring perceived income level, material conditions, and expected living standards

Although GWP provides information on the actual income of individuals and their relative position across the income distribution of the origin country, this study looks at the subjective perception of personal income for two reasons: (i) information on actual individual income are only available for few years, so including them in the model would considerably decrease the number of observations and (ii) A number of recent studies show that subjective well-being is a better predictor of emigration intentions than household income quintiles.

; Network_it is a vector of variables measuring social network in terms of opportunity to meet people in origin country and receive help from family members; Context_it includes three variables measuring satisfaction with the city or area of residence, satisfaction with the educational system, trust in national government, and perception of corruption in government. Moreover, given that youth continue to fare considerably worse labor market prospects than their adult counterparts (ILO, 2016; Pastore, 2018), this study devotes specific attention to the role of labor market outcomes in shaping young people’s potential migration behavior. This is done by including Labor_it, a set of variables aimed at measuring individuals’ own labor market status as well as respondent’s perception of current and future labor market conditions at both the local and national levels. Finally, z_i are country fixed-effects controlling for the country-level time-invariant factors that may affect individual migration desire, while m_t are year (survey wave) fixed effects and є_it is the error term. Table A1 in Appendix describes in detail the definition of the variables used and the underlying questions in the GWP. Since youth migration propensity is highly heterogeneous across countries at different stages of development, the model in Eq. (1) is estimated for groups of developing, emerging, and developed countries separately.

Table 1

Youth (15–34) desire/plan to migrate abroad

Country grouping:	Developing		Emerging		Developed
Dependent variable:	Desire	Plan	Desire	Plan	Desire	Plan
Female	−0.051^***	−0.003	−0.041^***	−0.017^***	−0.019^***	−0.007
	(0.005)	(0.008)	(0.003)	(0.004)	(0.006)	(0.008)
Married	−0.066^***	−0.011	−0.058^***	−0.008	−0.057^***	−0.020^*
	(0.005)	(0.010)	(0.003)	(0.006)	(0.007)	(0.011)
Age (25–34)	−0.034^***	0.009	−0.049^***	0.014^***	−0.061^***	−0.004
	(0.005)	(0.009)	(0.003)	(0.005)	(0.007)	(0.010)
Have kid(s)	0.026^***	0.003	0.012^***	−0.013^***	−0.003	−0.028^***
	(0.006)	(0.011)	(0.003)	(0.005)	(0.006)	(0.010)
Urban	0.059^***	0.033^***	0.041^***	0.027^***	0.043^***	0.010
	(0.006)	(0.009)	(0.003)	(0.005)	(0.006)	(0.009)
Poor health	−0.004	0.040^***	−0.002	0.018^***	−0.001	0.000
	(0.006)	(0.010)	(0.004)	(0.006)	(0.009)	(0.014)
Medium education	0.072^***	0.036^***	0.050^***	0.012^**	0.003	0.013
	(0.005)	(0.009)	(0.003)	(0.005)	(0.010)	(0.014)
High education	0.051^***	0.078^***	0.067^***	0.031^***	0.008	0.064^***
	(0.014)	(0.021)	(0.005)	(0.008)	(0.011)	(0.016)
Wealth
Lack of food/shelter	0.011^**	0.005	0.014^***	0.009^*	0.040^***	0.031^***
	(0.005)	(0.009)	(0.003)	(0.005)	(0.008)	(0.010)
Difficult living on present income	0.013^**	−0.016^*	−0.001	−0.029^***	0.023^***	−0.004
	(0.005)	(0.008)	(0.003)	(0.005)	(0.007)	(0.010)
No improvement in living standards	−0.007	0.005	0.011^***	0.003	0.004	0.018^*
	(0.006)	(0.010)	(0.004)	(0.005)	(0.007)	(0.010)
Network
No opportunity to meet people	−0.001	0.006	0.000	0.005	0.040^***	−0.003
	(0.005)	(0.008)	(0.003)	(0.005)	(0.007)	(0.010)
No help from friends/relatives	0.015^***	−0.002	0.019^***	−0.002	0.002	−0.010
	(0.005)	(0.009)	(0.004)	(0.006)	(0.012)	(0.015)
Context
Discontent with local amenities	0.104^***	0.008	0.093^***	0.016^***	0.140^***	0.034^***
	(0.005)	(0.008)	(0.003)	(0.005)	(0.008)	(0.010)
Discontent with education system	0.007	−0.020^**	0.014^***	0.004	0.043^***	0.011
	(0.005)	(0.008)	(0.003)	(0.005)	(0.006)	(0.009)
No trust in national government	0.045^***	−0.016^**	0.052^***	0.014^***	0.076^***	−0.020^**
	(0.005)	(0.008)	(0.003)	(0.005)	(0.006)	(0.010)
Corruption in government	0.051^***	−0.007	0.048^***	−0.003	0.047^***	−0.004
	(0.006)	(0.011)	(0.004)	(0.007)	(0.007)	(0.012)
Labor
Worsening economy	0.038^***	0.026^***	0.040^***	0.021^***	0.041^***	0.021^**
	(0.006)	(0.009)	(0.003)	(0.005)	(0.007)	(0.010)
Bad time to find a job	0.019^***	−0.019^**	0.016^***	−0.024^***	0.014^**	0.003
	(0.005)	(0.008)	(0.003)	(0.005)	(0.006)	(0.010)
No get ahead by working hard	0.048^***	0.019^*	0.036^***	0.018^***	0.073^***	0.029^***
	(0.007)	(0.011)	(0.004)	(0.006)	(0.007)	(0.009)
Unemployed	0.044^***	0.031^**	0.035^***	0.016^**	0.038^***	0.028^**
	(0.008)	(0.012)	(0.004)	(0.006)	(0.010)	(0.012)
Out of workforce	−0.010^*	−0.048^***	−0.006^*	−0.033^***	0.006	−0.032^***
	(0.005)	(0.009)	(0.003)	(0.005)	(0.007)	(0.011)

Number of countries	22	22	75	75	42	42
Observations	52,761	15,141	141,036	37,005	35,005	7,547

Note: The table reports the marginal effects from sample-weighted probit regressions. ^*, ^**, ^*** denote significance at 10, 5, 1%, respectively. Errors are clustered at the country level. All models include country dummies, year (survey wave) dummies, and a constant term. Repeated cross-sections for the years 2010–2016. The country income groups’ classification is detailed in text footnote 4. The definition of the variables is provided in Table A1 in Appendix. Note that when the dependent variable is Plan, the number of observations decreases since only youth who desire to migrate abroad are taken into account, and data are available only up to 2015.

Table 2

Employed youth desire/plan to migrate abroad

Country grouping:	Developing		Emerging		Developed
Dependent variable:	Desire	Plan	Desire	Plan	Desire	Plan
Female	−0.058^***	−0.011	−0.031^***	−0.022^***	−0.030^***	−0.011
	(0.006)	(0.011)	(0.004)	(0.007)	(0.007)	(0.012)
Married	−0.062^***	−0.034^***	−0.049^***	−0.017^**	−0.044^***	−0.039^***
	(0.007)	(0.012)	(0.004)	(0.008)	(0.008)	(0.014)
Age (25–34)	−0.032^***	0.008	−0.040^***	−0.005	−0.055^***	−0.023^*
	(0.007)	(0.012)	(0.004)	(0.007)	(0.008)	(0.013)
Have kid(s)	0.013	0.016	0.004	−0.013^*	−0.004	−0.018
	(0.008)	(0.014)	(0.004)	(0.007)	(0.008)	(0.013)
Urban	0.061^***	0.032^**	0.045^***	0.039^***	0.049^***	0.017
	(0.008)	(0.014)	(0.004)	(0.007)	(0.007)	(0.012)
Poor health	0.007	0.037^***	0.005	0.022^**	−0.002	0.001
	(0.008)	(0.013)	(0.006)	(0.009)	(0.012)	(0.019)
Medium education	0.053^***	0.051^***	0.045^***	0.015^*	−0.015	0.017
	(0.007)	(0.012)	(0.005)	(0.008)	(0.015)	(0.022)
High education	0.021	0.097^***	0.054^***	0.039^***	0.002	0.067^***
	(0.018)	(0.027)	(0.007)	(0.012)	(0.016)	(0.023)
Wealth
Lack of food/shelter	0.020^***	0.012	0.022^***	0.018^**	0.053^***	0.030^**
	(0.007)	(0.012)	(0.004)	(0.007)	(0.010)	(0.014)
Difficult living on present income	0.009	−0.015	0.009^**	−0.023^***	0.035^***	−0.003
	(0.007)	(0.012)	(0.004)	(0.007)	(0.009)	(0.014)
No improvement in living standards	0.002	−0.003	0.016^***	0.007	−0.005	0.011
	(0.008)	(0.013)	(0.005)	(0.008)	(0.009)	(0.014)
Network
No opportunity to meet people	−0.002	−0.013	0.006	0.007	0.041^***	−0.010
	(0.007)	(0.011)	(0.005)	(0.007)	(0.009)	(0.013)
No help from friends/relatives	0.016^**	0.011	0.011^**	−0.002	0.009	−0.023
	(0.007)	(0.012)	(0.005)	(0.009)	(0.015)	(0.020)
Context
Discontent with local amenities	0.099^***	0.016	0.095^***	0.004	0.132^***	0.012
	(0.006)	(0.011)	(0.005)	(0.007)	(0.010)	(0.013)
Discontent with education system	0.008	−0.022^*	0.010^**	0.004	0.042^***	0.015
	(0.006)	(0.011)	(0.004)	(0.007)	(0.008)	(0.012)
No trust in national government	0.056^***	−0.016	0.059^***	0.016^**	0.068^***	−0.012
	(0.006)	(0.011)	(0.004)	(0.007)	(0.008)	(0.014)
Corruption in government	0.044^***	−0.016	0.046^***	−0.002	0.057^***	0.014
	(0.009)	(0.015)	(0.006)	(0.010)	(0.009)	(0.016)
Labor
Worsening economy	0.028^***	0.030^**	0.043^***	0.021^***	0.043^***	0.027^**
	(0.007)	(0.012)	(0.005)	(0.007)	(0.008)	(0.013)
Bad time to find a job	0.023^***	−0.011	0.018^***	−0.029^***	0.012	−0.001
	(0.006)	(0.012)	(0.004)	(0.007)	(0.008)	(0.013)
No get ahead by working hard	0.059^***	0.040^***	0.047^***	0.017^*	0.078^***	0.035^***
	(0.009)	(0.015)	(0.006)	(0.009)	(0.008)	(0.012)
Involuntary part-time	0.037^***	0.030^**	0.035^***	0.025^***	0.042^***	0.015
	(0.007)	(0.012)	(0.005)	(0.008)	(0.011)	(0.016)
Self-employed	−0.023^***	−0.008	−0.009^**	0.007	0.008	0.017
	(0.007)	(0.013)	(0.004)	(0.007)	(0.009)	(0.015)

Number of countries	22	22	75	75	42	42
Observations	30,833	8,419	70,786	17,747	21,987	4,271

Note: ^*, ^**, ^*** denote significance at 10, 5, 1%, respectively. See the note of Table 1 for other details.

Table 3

Sensitivity tests across different subsamples, selected results

Panel A: Sample restricted to the period 2010–2012
Country grouping	Developing		Emerging		Developed
Dependent variable	Desire	Plan	Desire	Plan	Desire	Plan
High education	0.012	0.094^***	0.053^***	0.018	0.006	0.052^**
	(0.026)	(0.029)	(0.008)	(0.011)	(0.017)	(0.024)
Worsening economy	0.030^***	0.012	0.035^***	0.031^***	0.042^***	0.034^**
	(0.009)	(0.013)	(0.005)	(0.007)	(0.010)	(0.014)
Bad time to find a job	0.014^*	−0.006	0.009^*	−0.034^***	0.003	−0.011
	(0.008)	(0.012)	(0.005)	(0.007)	(0.010)	(0.016)
No get ahead by working hard	0.040^***	0.012	0.029^***	0.003	0.059^***	0.021
	(0.011)	(0.016)	(0.006)	(0.008)	(0.010)	(0.013)
Unemployed	0.048^***	0.027	0.027^***	0.019^**	0.035^**	0.020
	(0.012)	(0.017)	(0.006)	(0.009)	(0.015)	(0.019)

Number of countries	22	22	75	75	42	42
Observations	21,828	7,272	63,594	18,224	15,290	3,413

Panel B: Sample restricted to the period 2013−2016
Country grouping	Developing		Emerging		Developed
Dependent variable	Desire	Plan	Desire	Plan	Desire	Plan
High education	0.065^***	0.075^***	0.078^***	0.046^***	0.004	0.080^***
	(0.017)	(0.028)	(0.007)	(0.011)	(0.015)	(0.021)
Worsening economy	0.040^***	0.034^***	0.044^***	0.013^*	0.038^***	0.007
	(0.007)	(0.013)	(0.004)	(0.007)	(0.009)	(0.012)
Bad time to find a job	0.020^***	−0.029^**	0.023^***	−0.011	0.021^**	0.014
	(0.006)	(0.012)	(0.004)	(0.007)	(0.008)	(0.013)
No get ahead by working hard	0.052^***	0.025	0.041^***	0.032^***	0.082^***	0.035^***
	(0.009)	(0.015)	(0.005)	(0.009)	(0.009)	(0.012)
Unemployed	0.038^***	0.033^*	0.042^***	0.013	0.040^***	0.036^**
	(0.010)	(0.017)	(0.006)	(0.009)	(0.013)	(0.016)

Number of countries	22	22	75	75	42	42
Observations	30,933	7,869	77,442	18,759	19,715	4,134

Panel C: Including/excluding Top 15 refugee−sending countries
	Excluding Top 15 refugee- sending countries				Top 15 refugee-sending countries
Country grouping	Developing		Emerging
Dependent variable	Desire	Plan	Desire	Plan	Desire	Plan
Poor health	−0.001	0.036^***	−0.004	0.019^***	−0.002	0.033^**
	(0.007)	(0.011)	(0.004)	(0.006)	(0.008)	(0.014)
High education	0.055^***	0.072^***	0.061^***	0.028^***	0.095^***	0.073^***
	(0.019)	(0.025)	(0.006)	(0.009)	(0.013)	(0.021)
Lack of food/shelter	0.009	0.004	0.013^***	0.010^*	0.020^***	0.010
	(0.006)	(0.010)	(0.003)	(0.005)	(0.007)	(0.011)
Difficult living on present income	0.010^*	−0.015	−0.006^*	−0.031^***	0.031^***	−0.016
	(0.006)	(0.010)	(0.003)	(0.005)	(0.007)	(0.011)
Discontent with local amenities	0.102^***	0.009	0.091^***	0.015^***	0.111^***	0.012
	(0.006)	(0.009)	(0.003)	(0.005)	(0.007)	(0.011)
No trust in national government	0.048^***	−0.027^***	0.053^***	0.015^***	0.041^***	0.012
	(0.006)	(0.009)	(0.003)	(0.005)	(0.007)	(0.011)
Worsening economy	0.038^***	0.024^**	0.039^***	0.020^***	0.043^***	0.031^***
	(0.006)	(0.010)	(0.004)	(0.005)	(0.007)	(0.012)
No get ahead by working hard	0.048^***	0.016	0.037^***	0.022^***	0.036^***	0.007
	(0.008)	(0.013)	(0.004)	(0.006)	(0.009)	(0.014)
Unemployed	0.044^***	0.035^**	0.036^***	0.019^***	0.037^***	0.000
	(0.009)	(0.014)	(0.005)	(0.007)	(0.010)	(0.016)

Number of countries	15	15	67	67	15	15
Observations	38,510	11,541	124,312	32,530	30,975	8,075

Note: ^*, ^**, ^*** denote significance at 10, 5, 1%, respectively. The list of top 15 refugee-sending countries is provided in text footnote 5. Full regression results and related detailed notes are presented in Table A3 (for Panel A), Table A4 (for Panel B), and Table A5 (for Panel C) in Appendix.

Table 4

Adults desire/plan to migrate abroad, selected results

Panel A: Total adults (aged 35–64)
Country grouping:	Developing		Emerging		Developed
Dependent variable:	Desire	Plan	Desire	Plan	Desire	Plan
Female	−0.043^***	−0.014	−0.017^***	−0.030^***	−0.037^***	−0.031^***
	(0.005)	(0.014)	(0.003)	(0.006)	(0.003)	(0.006)
Married	−0.022^***	−0.048^***	−0.022^***	−0.029^***	−0.018^***	−0.019^***
	(0.006)	(0.016)	(0.003)	(0.006)	(0.004)	(0.006)
Have kid(s)	0.015^**	0.014	0.006^**	−0.000	0.008^**	−0.006
	(0.008)	(0.019)	(0.003)	(0.006)	(0.004)	(0.007)
High education	0.025^*	0.054^*	0.053^***	0.039^***	0.038^***	0.019^*
	(0.013)	(0.029)	(0.004)	(0.009)	(0.006)	(0.011)
Difficult living on present income	0.014^**	−0.040^***	0.006^**	−0.015^**	0.019^***	0.021^***
	(0.006)	(0.015)	(0.003)	(0.006)	(0.004)	(0.007)
Discontent with local amenities	0.073^***	0.003	0.068^***	0.019^***	0.104^***	0.018^***
	(0.006)	(0.014)	(0.003)	(0.006)	(0.004)	(0.007)
No trust in national government	0.033^***	−0.004	0.046^***	0.004	0.057^***	−0.007
	(0.005)	(0.015)	(0.003)	(0.006)	(0.004)	(0.008)
No get ahead by working hard	0.032^***	−0.025	0.033^***	0.010	0.051^***	0.026^***
	(0.007)	(0.019)	(0.003)	(0.007)	(0.004)	(0.007)
Unemployed	0.020^*	0.030	0.036^***	0.027^***	0.034^***	0.049^***
	(0.010)	(0.024)	(0.005)	(0.009)	(0.007)	(0.009)
Out of workforce	−0.021^***	−0.014	−0.025^***	−0.006	−0.013^***	0.019^**
	(0.007)	(0.018)	(0.003)	(0.007)	(0.004)	(0.008)

Number of countries	22	22	75	75	45	45
Observations	31,678	5,351	127,749	19,605	73,970	9,113

Panel B: Employed adults (aged 35–64)
	Developing		Emerging		Developed
Dependent variable:	Desire	Plan	Desire	Plan	Desire	Plan
High education	0.004	0.041	0.047^***	0.045^***	0.030^***	0.014
	(0.015)	(0.032)	(0.005)	(0.011)	(0.008)	(0.013)
Difficult living on present income	0.017^**	−0.028	0.009^***	−0.014^*	0.019^***	0.017^**
	(0.007)	(0.018)	(0.003)	(0.008)	(0.005)	(0.007)
Involuntary part-time	0.041^***	0.028	0.038^***	0.008	0.017^**	-0.002
	(0.007)	(0.019)	(0.005)	(0.009)	(0.007)	(0.011)
Self-employed	−0.032^***	−0.011	−0.009^**	0.016^**	−0.006	0.024^***
	(0.008)	(0.019)	(0.003)	(0.008)	(0.005)	(0.008)

Number of countries	22	22	75	75	45	45
Observations	23,225	3,961	81,917	12,937	53,546	6,449

Note: ^*, ^**, ^*** denote significance at 10, 5, 1%, respectively. Full regression results and related detailed notes are presented in Table A6 (for Panel A) and Table A7 (for Panel B) in Appendix.

Table A1

Description of GWP questions underlying main explanatory variables

Variable name	Questions asked
Desire (=1 if respondents answered “Like to move to another country,” =0 if respondents answered “Like to continue living in this country”)	Ideally, if you had the opportunity, would you like to move PERMANENTLY to another country, or would you prefer to continue living in this country?
Plan (=1 if respondents answered “Yes, will move in the next 12 months,” =0 if respondents answered “No”)	Are you planning to move permanently to another country in the next 12 months, or not? (asked only of those that would like to move to another country)
Lack of food/shelter (=1 if respondents answered “Difficult” or “Very difficult” to at least one of the two questions, =0 otherwise)	Have there been times in the past 12 months when you did not have enough money to buy food that you or your family needed? (WP40) Have there been times in the past 12 months when you did not have enough money to provide adequate shelter or housing for you and your family?
Difficult living on present income (=1 if respondents stated “Difficult” or “Very difficult,” =0 otherwise)	Which one of these phrases comes closest to your own feelings about your household’s income these days: living comfortably on present income, getting by on present income, finding it difficult on present income, or finding it very difficult on present income?
No opportunity to meet people (=1 if respondents stated “not satisfied,” =0 otherwise)	In the city or area where you live, are you satisfied or dissatisfied with the opportunities to meet people and make friends?
Have help from friends/relatives (=1 if respondents stated “yes,” =0 otherwise)	If you were in trouble, do you have relatives or friends you can count on to help you whenever you need them, or not?
No improvement in living standards (=1 if respondents stated “worse,” =0 otherwise)	Right now, do you feel your standard of living is getting better or getting worse?
Discontent with local amenities (=1 if respondents stated “worse,” =0 otherwise)	Are you satisfied or dissatisfied with the city or area where you live?
Discontent with education system (=1 if respondents stated “worse,” =0 otherwise)	In the city or area where you live, are you satisfied or dissatisfied with the educational system or the schools?
No confidence in government (=1 if respondents stated “worse,” =0 otherwise)	Do you have trust in the national government?
Corruption in government (=1 if respondents stated “Yes,” =0 otherwise)	Is corruption widespread throughout the government in (this country), or not?
Bad time to find a job (=1 if respondents stated “worse,” =0 otherwise)	Thinking about the job situation in the city or area where you live today, would you say that it is now a good time or a bad time to find a job? (WP89)
Worsening economic situation (=1 if respondents stated “Worse,” =0 otherwise)	Right now, do you think that economic conditions in the city or area where you live, as a whole, are getting better or getting worse?
No get ahead by working hard (=1 if respondents answered “No,” =0 otherwise)	Can people in this country get ahead by working hard, or not?
Poor self-reported health (=1 if respondent answered “Yes,” =0 otherwise)	Do you have any health problems that prevent you from doing any of the things people your age normally can do?
Involuntary part-time (=1 if respondent answered “Yes,” =0 otherwise)	Respondents are “underemployed” if they are employed part time but want to work full time
Medium education (=1 if respondent completed secondary education, =0 otherwise)	What is your highest completed level of education?
High education (=1 if respondent completed tertiary education, =0 otherwise)	What is your highest completed level of education?
Age (25–34) (=1 if the age of the respondent is between 25 and 34, =0 otherwise)	Please tell me your age
Have kid(s) (=1 if the respondent has at least one children living in the household)	How many children under 15 years of age are now living in your household?

Before turning to a more elaborate empirical analysis of the drivers of youth potential migration, what follows provides an overview of the share of youth who desire (plan) to migrate abroad, and how this fraction varies across country groupings, time, and with respect to the rest of the working-age population. Data show that, on average, over 32% of youth (aged 15–34) surveyed in the GWP between 2010 and 2016 would like to migrate permanently abroad if given a chance. Among these, close to 17% is planning to do so in the next 12 months (see Table A2 in Appendix). Between 2010 and 2016, the average share of young people desiring to migrate abroad permanently is the highest across developing countries, at 36%, followed by emerging countries (31.6%) and developed ones (30.2%) (Figure 1). In each of these country groupings, youth are significantly more likely to desire to migrate permanently abroad than adults (aged 35–64). In developing countries, the average share of adults surveyed between 2010 and 2016 who desire to migrate abroad permanently is around 21%, 15% points lower than that of youth. This gap is only slightly smaller across emerging and developed countries, where the average share of adults desiring to migrate was at 18.7 and 17.1%, respectively. Differences between youth and adults are smaller when the share of those planning to migrate in the next 12 months is considered (Figure 1). Overall, youth account for around 62% of the total working-age population desiring to migrate abroad and for over 68% of those who are planning to migrate over the next 12 months.

Average migration intention among youth (15–34) and adults (35–64).
Note: Figure shows the average percentage of youth (15–34) and adults (35–64) who desire to move permanently abroad between 2010 and 2016 (Desire) across groups of developing, emerging, and developed countries (see text footnote 4 for details on the country groupings). The figure also shows the average percentage of those who desire to migrate and are planning to do so over the next 12 months (Plan). Data for the variable Plan are only available for the period 2010–2015.
Source: The author’s elaboration from GWP data.

Table A2

Summary statistics

	Total(N = 228,802)		Stayers(N = 154,649)		Desire(N = 74,153)		Plan(N = 10,151)
	Mean	SD	Mean	SD	Mean	SD	Mean	SD
Desire	0.324	0.468	–	–	–	–	–	–
Plan	–	–	–	–	0.169	0.375	–	–
Female	0.521	0.5	0.539	0.498	0.482	0.5	0.442	0.497
Married	0.408	0.491	0.449	0.497	0.322	0.467	0.311	0.463
Age (25–34)	0.514	0.5	0.541	0.498	0.459	0.498	0.478	0.5
Have kid(s)	0.649	0.477	0.655	0.475	0.637	0.481	0.662	0.473
Urban	0.371	0.483	0.355	0.479	0.405	0.491	0.435	0.496
Poor health	0.149	0.356	0.149	0.356	0.149	0.356	0.174	0.379
Medium education	0.569	0.495	0.552	0.497	0.605	0.489	0.583	0.493
High education	0.126	0.332	0.127	0.333	0.123	0.328	0.127	0.333
Lack of food/shelter	0.437	0.496	0.41	0.492	0.492	0.5	0.542	0.498
Difficult living on present income	0.428	0.495	0.407	0.491	0.472	0.499	0.49	0.5
No improvement in living standards	0.218	0.413	0.194	0.396	0.267	0.443	0.291	0.454
No opportunity to meet people	0.24	0.427	0.222	0.416	0.278	0.448	0.299	0.458
No help from friends/relatives	0.818	0.386	0.817	0.387	0.821	0.383	0.793	0.405
Discontent with local amenities	0.283	0.45	0.227	0.419	0.399	0.49	0.453	0.498
Discontent with education system	0.38	0.485	0.345	0.476	0.451	0.498	0.492	0.5
No trust in national government	0.51	0.5	0.461	0.498	0.613	0.487	0.615	0.487
Corruption in government	0.805	0.396	0.782	0.413	0.853	0.354	0.86	0.347
Worsening economy	0.337	0.473	0.291	0.454	0.434	0.496	0.47	0.499
Bad time to find a job	0.608	0.488	0.577	0.494	0.673	0.469	0.664	0.472
No get ahead by working hard	0.158	0.365	0.139	0.345	0.199	0.399	0.21	0.407
Unemployed	0.11	0.313	0.097	0.296	0.137	0.344	0.171	0.376
Out of workforce	0.35	0.477	0.352	0.478	0.345	0.475	0.295	0.456

Note: Summary statistics refer to the observations for individuals aged 15–34 with valid information for all the questions throughout the whole period 2010–2016. Data for the dependent variable Plan are only available for the period 2010–2015.

Source: The author’s elaboration on GWP data.

The share of youth desiring to move permanently abroad remained broadly stable between 2010 and 2016, albeit declining slightly over the period 2010–2012 in both emerging and developing countries and subsequently recovering. Conversely, the share of youth planning to migrate shows much larger variability, both over-time and across country groupings. This share increased the most in developing countries, from 17.3% in 2010 to almost 29% in 2015—the last year with available data. Over the same period, this share reached 18.6% in 2015 (up from 12.1% in 2010) in emerging countries, whereas it remained rather stable around 10% in developed countries.

Percentage of youth (15–34) who desire (plan) to move abroad from 2010 to 2016.
Note: Figure shows the evolution of percentage of youth (15–34) who desire to move permanently abroad between 2010 and 2016 across groups of developing, emerging, and developed countries (see text footnote 4 for details on the country groupings). The figure also shows the average percentage of those who desire to migrate and are actually planning to do so over the next 12 months (Plan). Data for the variable Plan are only available for the period 2010–2015.
Source: The author’s elaboration from GWP data.

Results and discussion

4.1

Main results

While a preliminary examination of summary statistics already provides some suggestive evidence of the socioeconomic characteristics and subjective experiences of young potential migrants vis-à-vis those of young “stayers” (see Table A2 in Appendix), a more elaborated econometric analysis is necessary to better isolate the contribution of each of these factors in shaping youth desire, and more concrete plan, to migrate abroad permanently. Table 1 presents the average marginal effects obtained from probit estimations of Eq. (1) for groups of developing, emerging, and developed countries separately. Since all the independent variables included in the estimation model are coded as binary variables, the coefficients presented in the following tables reflect the average change in the probability that a young individual desire (plan) to migrate when one of the explanatory variables changes from 0 to 1, keeping everything else constant. In line with most of the findings in the literature (see Section 2), results confirm that youth who desire to migrate permanently abroad are more likely to be below the age of 24, single, male, and coming from urban areas. These results largely hold across each of the three country-income groupings. However, only a few of these personal characteristics appear to influence the chances that youth turn migration desires into actual plans. In particular, only young men in emerging countries and youth from urban areas are found to be more likely to plan to migrate. Interestingly, although women are less likely to desire to migrate across all country groupings, there is little evidence that the probability to plan to migrate significantly depends on gender, especially in developing and developed countries. This can be explained by the fact that the actual planning of migration is, regardless of gender, mainly guided by traditional drivers, such as income and education, whereas the mere desire to migrate is more influenced by gender-related factors (Ruyssen and Salomone, 2018). Moreover, among those who desire to emigrate, youth reporting poor health conditions are more likely to plan to do so in the next 12 months from both emerging and developing countries. This result can be explained by the fact that individual health is highly correlated with life satisfaction (Graham, 2008), which, in turn, is a strong driver of migration intention (Chindarkar, 2014). Youth with post-secondary education are more likely to desire to emigrate from both developing and emerging countries. Moreover, in all the three-country groupings considered, high-educated youth are more likely to turn their migration desire into more concrete plans. In particular, youth with post-secondary education are almost 8% points more likely to follow-up on their migration desire in developing countries, and over 6% points more likely to do so in developed ones. Possible explanations for this result are that more educated youth may have more knowledge about immigration policy and opportunities in destination countries, while also having more confidence in finding a job in these countries (Docquier et al., 2014). Results also show that material deprivation may represent a significant push factor of youth migration. Youth having experienced lack of food or shelter in the recent past or who report difficulties in getting by with present income are in fact more likely to desire to migrate. However, youth with low income are found to be less likely to transform their migration desire into plans in both emerging and developing countries. As already highlighted by studies on the migration intention of the whole population (Smith and Floro, 2017; Sadiddin et al., 2019), this result reflects the fact that budgetary constraints can represent an important barrier to materializing migration desires in lower-income economies. Concerning network effects, youth who have relatives or friends whom they can count on for help are slightly more likely to desire to migrate from developing and emerging countries, while those who are not satisfied by the opportunities to meet people have higher probability to desire to emigrate only from developed countries. Consistent with the previous studies (Manchin and Orazbayev, 2018), these results demonstrate the importance of social and family networks in shaping migration intention, albeit social networks do not appear to influence the probability that desires are turned into actual migration plans. In accordance with previous evidence (Etling et al., 2020; Van Mol, 2016), findings show that young people’s desire, and plan, to migrate is also positively associated with contextual and institutional factors. In particular, youth dissatisfied with local amenities, having no trust in national government and believe that the incidence of corruption in the government is widespread, are more likely to desire to migrate abroad across all the three-country groupings. However, only dissatisfaction with local amenities appears to make youth more likely to follow up on their migration aspiration.

Moreover, as already shown in previous studies (Van Mol, 2016; Dibeh et al., 2019, among others), results in Table 1 indicate that labor market-related factors play a key role in shaping youth desire to migrate abroad and the subsequent plan to do so in the next 12 months. Being unemployed is found to increase the probability that youth desire to migrate by around 4% points in virtually all the three-country groupings. At the same time, unemployed youth are also more likely to plan to migrate, and more so in developed and developing countries than in emerging ones. Youth who are outside the labor force are instead found to be less likely to both desire and plan to migrate abroad. Although in the GWP there is no information on whether individuals are currently enrolled in formal education or training, this result likely reflects the fact that inactive youth are more likely to be students and, as such, less prone to have imminent plans of permanent migration at this stage of their life. In addition to the individual labor market status, young people’s subjective perceptions of economic, labor market, and career prospects also matter in shaping youth propensity to migrate, with little differences across country groupings. It is found that youth who believe it is a bad time to find a job in their area of residence are more likely to desire (and plan) to migrate, especially in developing and emerging countries. The same holds for youth expecting worsening economic conditions in their city or area of residence. In developed countries, youth who believe it is not possible to get ahead by working hard in their home country are particularly more likely to desire (and plan) to migrate, which reinforce previous findings on the role of career prospect as a driver of youth migration (Bartolini et al., 2016).

In addition to being unemployed, holding a poor-quality job can also represent an important incentive to migrate. Table 2 reports results obtained from estimating the model in Eq. (1) on a subsample of employed youth. Results show that, in each of the country groupings, youth in involuntary part-time employment are around 4% points more likely to desire to migrate abroad permanently than their counterparts with full-time jobs or taking up part-time employment voluntary. Being in involuntary part-time employment also acts as a strong incentive to transform migration aspiration into more concrete plans in developing and emerging countries. This result is consistent with evidence showing that involuntary part-time accounts for an increasing share of youth employment and it is often associated with low pay and poor working conditions (ILO, 2016). In contrast, self-employed youth appear to be less likely to have migration desire than youth in wage and salaried employment in developing countries, and to a lesser extent in emerging countries. Although the relationship between international migration and self-employment remains theoretically unclear (Giambra and David, 2019), this result may reflect the fact that, in poor countries, self-employment often provides employment of last resort for poor and low educated youth (ILO, 2016), who are typically less likely to migrate. Results on the other individual and socioeconomic drivers of migration among employed youth do not change significantly from those reported in Table 1 for the whole youth population. However, it is worth noting that employed youth with post-secondary education are 9% points more likely to transform their desires into migration plans in developing countries, and almost 7% points more likely to do in developed ones. Again, this result may be explained by the fact high-educated individuals tend to be more likely to materialize their migration desires, thanks to a combination of higher financial resources, greater personal aspirations, and better knowledge of migration barriers in destination countries (Browne, 2017).

4.2

Sensitivity tests

This section presents a number of sensitivity tests conducted by estimating the main specification presented in Table 1 on different subsamples to check whether and how estimates change when: (a) the sample is split into two subperiods, 2010–2012 and 2013–2016, respectively; (b) the top 15 refugee sending countries (7 developing and 8 emerging) with available data are excluded from the sample; and (c) only the top 15 refugee-sending countries are considered.

The top 15 refugee-sending countries are identified among the top 20 refugee-sending countries with available data in the GWP and ranked according to the United Nations High Commissioner for Refugees (UNHCR), Statistics Database. These include: Afghanistan, Central African Republic, Colombia, Democratic Republic of the Congo, Ethiopia, Iraq, Mali, Nigeria, Pakistan, Somalia, South Sudan, Sri Lanka, Ukraine, Viet Nam, West Bank, and Gaza.

Results show that the magnitude and statistical significance of the coefficients on most of the socioeconomic and demographic factors do not change significantly when the sample is split into two subperiods (see full results in Tables A3 and A4 in Appendix). However, the influence of education and labor market-related factors on the probability that youth desire (plan) to migrate tends to be stronger in the years after 2013. In particular, unemployed youth are significantly more likely to plan to emigrate from developed countries over the period 2013–2016 (Panel B, Table 3) than in previous years (Panel A, Table 3). A similar conclusion applies to youth holding post-secondary education across emerging and developed countries as well as to youth who believe it is not possible to get ahead by working hard in their home country. Taken together, these results suggest that migrating abroad become an increasingly desirable option for many youth, and especially for the highly educated and unemployed ones in developed countries where the effects on the youth labor market of the economic downturn of the late 2000s and early 2010s were persistent and comparatively stronger than in other countries (ILO, 2016; Bartolini et al., 2016). Moreover, results presented above in Table 1 for developing and emerging countries do not change significantly when the top 15 refugee-sending countries are excluded from the sample (Panel C, Table 3). This is not surprising insofar the influence of key factors, such as education, unemployment, and income level on youth intention to emigrate from top refugee-sending countries is similar to that found across other developing and emerging countries. Of particular interest is the result showing that high-educated youth is almost 10% points more likely to plan to migrate over the next 12 months from top refugee-sending countries, which complements the scant evidence on the role of education in driving emigration from fragile and conflict-affected countries (Browne, 2016). However, results also show that being unemployed and having little prospects of career advancement have no effect on the likelihood that youth turn desires into actual plans to emigrate from these countries. This is in line with the argument that the primary reasons for emigrating from refugee-sending countries are more related to the desire for better livelihood, physical safety and security, with economic factors playing a secondary role (Browne, 2016).

Table A3

Youth (15–34) desire/plan to migrate abroad, 2010–2012, full results corresponding to Panel A of Table 3

	2010–2012
Country grouping	Developing		Emerging		Developed
Dependent variable	Desire	Plan	Desire	Plan	Desire	Plan
Female	−0.052^***	−0.015	−0.043^***	−0.014^**	−0.018^**	−0.015
	(0.007)	(0.011)	(0.004)	(0.006)	(0.008)	(0.013)
Married	−0.073^***	0.010	−0.056^***	−0.001	−0.048^***	−0.028
	(0.008)	(0.013)	(0.005)	(0.008)	(0.011)	(0.017)
Age (25–34)	−0.020^**	0.003	−0.050^***	0.019^***	−0.058^***	−0.005
	(0.008)	(0.012)	(0.005)	(0.007)	(0.010)	(0.015)
Have kid(s)	0.031^***	−0.003	0.013^***	−0.013^*	−0.001	−0.024
	(0.010)	(0.014)	(0.005)	(0.007)	(0.010)	(0.015)
Urban	0.075^***	0.033^**	0.044^***	0.036^***	0.037^***	0.014
	(0.010)	(0.013)	(0.004)	(0.007)	(0.009)	(0.013)
Poor health	−0.008	0.024^*	0.008	0.015^*	−0.022	−0.005
	(0.009)	(0.013)	(0.005)	(0.008)	(0.014)	(0.020)
Medium education	0.069^***	0.035^***	0.044^***	0.009	0.004	−0.005
	(0.008)	(0.012)	(0.005)	(0.007)	(0.014)	(0.020)
High education	0.012	0.094^***	0.053^***	0.018	0.006	0.052^**
	(0.026)	(0.029)	(0.008)	(0.011)	(0.017)	(0.024)
Lack of food/shelter	0.014^*	0.004	0.013^***	0.004	0.024^*	0.050^***
	(0.008)	(0.012)	(0.004)	(0.006)	(0.012)	(0.016)
Difficult living on present income	0.012	0.001	−0.005	−0.026^***	0.020^*	0.001
	(0.008)	(0.012)	(0.004)	(0.006)	(0.012)	(0.016)
No improvement in living standards	−0.013	0.007	0.009	0.002	0.017	0.005
	(0.010)	(0.014)	(0.006)	(0.008)	(0.012)	(0.014)
No opportunity to meet people	−0.006	0.003	0.003	0.002	0.034^***	−0.004
	(0.008)	(0.011)	(0.005)	(0.007)	(0.011)	(0.014)
No help from friends/relatives	0.021^**	−0.007	0.023^***	0.009	−0.005	−0.021
	(0.009)	(0.013)	(0.006)	(0.008)	(0.020)	(0.026)
Discontent with local amenities	0.130^***	0.005	0.089^***	0.006	0.143^***	0.037^**
	(0.008)	(0.011)	(0.005)	(0.007)	(0.012)	(0.015)
Discontent with education system	0.016^**	−0.014	0.013^***	0.003	0.045^***	0.004
	(0.008)	(0.011)	(0.004)	(0.006)	(0.010)	(0.014)
No trust in national government	0.046^***	−0.031^***	0.048^***	0.014^**	0.080^***	−0.037^**
	(0.008)	(0.011)	(0.004)	(0.007)	(0.010)	(0.015)
Corruption in government	0.051^***	0.024	0.042^***	−0.008	0.043^***	−0.002
	(0.010)	(0.016)	(0.006)	(0.009)	(0.012)	(0.018)
Worsening economy	0.030^***	0.012	0.035^***	0.031^***	0.042^***	0.034^**
	(0.009)	(0.013)	(0.005)	(0.007)	(0.010)	(0.014)
Bad time to find a job	0.014^*	−0.006	0.009^*	−0.034^***	0.003	−0.011
	(0.008)	(0.012)	(0.005)	(0.007)	(0.010)	(0.016)
No get ahead by working hard	0.040^***	0.012	0.029^***	0.003	0.059^***	0.021
	(0.011)	(0.016)	(0.006)	(0.008)	(0.010)	(0.013)
Unemployed	0.048^***	0.027	0.027^***	0.019^**	0.035^**	0.020
	(0.012)	(0.017)	(0.006)	(0.009)	(0.015)	(0.019)
Out of workforce	0.007	−0.059^***	−0.005	−0.032^***	−0.002	−0.035^**
	(0.008)	(0.012)	(0.005)	(0.007)	(0.010)	(0.016)
Observations	21,828	7,272	63,594	18,224	15,290	3,413

Table A4

Youth (15–34) desire/plan to migrate abroad, 2013–2016, full results corresponding to Panel B of Table 3

	2013–2016^*
Country grouping:	Developing		Emerging		Developed
Dependent variable:	Desire	Plan	Desire	Plan	Desire	Plan
Female	−0.050^***	0.009	−0.039^***	−0.020^***	−0.017^**	−0.001
	(0.006)	(0.012)	(0.004)	(0.006)	(0.007)	(0.011)
Married	−0.063^***	−0.038^***	−0.059^***	−0.013^*	−0.062^***	−0.014
	(0.007)	(0.013)	(0.004)	(0.008)	(0.009)	(0.014)
Age (25–34)	−0.042^***	0.018	−0.048^***	0.009	−0.063^***	0.001
	(0.007)	(0.013)	(0.004)	(0.007)	(0.009)	(0.013)
Have kid(s)	0.017^**	0.001	0.011^***	−0.012^*	−0.009	−0.033^***
	(0.008)	(0.015)	(0.004)	(0.007)	(0.008)	(0.013)
Urban	0.049^***	0.027^**	0.039^***	0.019^***	0.049^***	0.006
	(0.008)	(0.013)	(0.004)	(0.007)	(0.008)	(0.012)
Poor health	−0.003	0.059^***	−0.015^***	0.021^**	0.020	0.003
	(0.008)	(0.014)	(0.006)	(0.010)	(0.013)	(0.019)
Medium education	0.071^***	0.037^***	0.053^***	0.017^**	−0.002	0.034^*
	(0.007)	(0.012)	(0.005)	(0.008)	(0.013)	(0.019)
High education	0.065^***	0.075^***	0.078^***	0.046^***	0.004	0.080^***
	(0.017)	(0.028)	(0.007)	(0.011)	(0.015)	(0.021)
Lack of food/shelter	0.009	0.007	0.016^***	0.015^**	0.049^***	0.014
	(0.007)	(0.012)	(0.004)	(0.007)	(0.010)	(0.013)
Difficult living on present income	0.010	−0.036^***	0.005	−0.029^***	0.027^***	−0.005
	(0.007)	(0.012)	(0.004)	(0.007)	(0.010)	(0.013)
No improvement in living standards	−0.006	0.005	0.012^**	0.003	−0.004	0.029^**
	(0.008)	(0.013)	(0.005)	(0.008)	(0.010)	(0.013)
No opportunity to meet people	0.001	0.008	−0.002	0.004	0.043^***	0.000
	(0.007)	(0.012)	(0.005)	(0.007)	(0.010)	(0.013)
No help from friends/relatives	0.013^*	0.004	0.014^***	−0.012	0.006	−0.005
	(0.007)	(0.013)	(0.005)	(0.009)	(0.016)	(0.018)
Discontent with local amenities	0.090^***	0.018	0.095^***	0.023^***	0.137^***	0.033^***
	(0.007)	(0.012)	(0.004)	(0.007)	(0.010)	(0.013)
Discontent with education system	0.003	−0.031^**	0.014^***	0.006	0.040^***	0.015
	(0.006)	(0.012)	(0.004)	(0.007)	(0.008)	(0.012)
No trust in national government	0.043^***	−0.005	0.054^***	0.017^**	0.076^***	−0.006
	(0.006)	(0.012)	(0.004)	(0.007)	(0.009)	(0.014)
Corruption in government	0.049^***	−0.028^*	0.053^***	0.006	0.047^***	−0.009
	(0.008)	(0.016)	(0.005)	(0.009)	(0.010)	(0.015)
Worsening economy	0.040^***	0.034^***	0.044^***	0.013^*	0.038^***	0.007
	(0.007)	(0.013)	(0.004)	(0.007)	(0.009)	(0.012)
Bad time to find a job	0.020^***	−0.029^**	0.023^***	−0.011	0.021^**	0.014
	(0.006)	(0.012)	(0.004)	(0.007)	(0.008)	(0.013)
No get ahead by working hard	0.052^***	0.025	0.041^***	0.032^***	0.082^***	0.035^***
	(0.009)	(0.015)	(0.005)	(0.009)	(0.009)	(0.012)
Unemployed	0.038^***	0.033^*	0.042^***	0.013	0.040^***	0.036^**
	(0.010)	(0.017)	(0.006)	(0.009)	(0.013)	(0.016)
Out of workforce	−0.021^***	−0.033^**	−0.006	−0.033^***	0.015^*	−0.028^**
	(0.007)	(0.013)	(0.004)	(0.007)	(0.009)	(0.014)
Observations	30,933	7,869	77,442	18,759	19,715	4,134

Note: The table reports the marginal effects from sample-weighted probit regressions. ^*, ^**, ^*** denote significance at 10, 5, 1%, respectively. Errors are clustered at the country level. All models include country dummies, year (survey wave) dummies, and a constant term. Repeated cross-sections for the years 2013–2016. The country income groups’ classification is detailed in text footnote 4. The definition of the variables is provided in Table A1 in Appendix. Note that, when the dependent variable is Plan, the number of observations decreases since only youth who desire to migrate abroad are taken into account, and data are only available for the period 2013–2015.

4.3

Comparing migration drivers between youth and adults

As mentioned in Section 3, youth are considerably more likely to desire, and a more concrete plan, to migrate than adults. Migration literature suggests that such a higher propensity to migrate among youth is due to a combination of socioeconomic, cultural, and psychological factors (Fouarge and Ester 2007; Van Mol, 2016). However, to the best of our knowledge, there are virtually no studies that test whether the same factors driving migration intention among youth also matter in shaping potential migration across the rest of the population. Therefore, after having explored the main factors behind youth potential migration, one important remaining question is: Can the results on the drivers of youth potential migration be extended to adults?

Table A5

Youth (15–34) desire/plan to migrate abroad in country samples including and excluding Top 15 refugee-sending countries, full results corresponding to Panel C of Table 3

	Excluding Top 15 refugee-sending countries
Country grouping	Developing		Emerging		Top 15 refugeesending countries
Dependent variable	Desire	Plan	Desire	Plan	Desire	Plan
Female	−0.049^***	−0.002	−0.037^***	−0.015^***	−0.066^***	−0.019^*
	(0.005)	(0.009)	(0.003)	(0.005)	(0.006)	(0.011)
Married	−0.066^***	−0.015	−0.061^***	−0.007	−0.050^***	−0.006
	(0.006)	(0.011)	(0.004)	(0.006)	(0.007)	(0.012)
Age (25–34)	−0.027^***	0.017^*	−0.050^***	0.011^**	−0.048^***	0.013
	(0.006)	(0.010)	(0.003)	(0.005)	(0.007)	(0.012)
Have kid(s)	0.028^***	0.012	0.016^***	−0.014^***	−0.008	−0.014
	(0.007)	(0.011)	(0.003)	(0.005)	(0.008)	(0.013)
Urban	0.052^***	0.033^***	0.040^***	0.026^***	0.055^***	0.036^***
	(0.007)	(0.010)	(0.003)	(0.005)	(0.007)	(0.012)
Poor health	−0.001	0.036^***	−0.004	0.019^***	−0.002	0.033^**
	(0.007)	(0.011)	(0.004)	(0.006)	(0.008)	(0.014)
Medium education	0.071^***	0.037^***	0.045^***	0.008	0.081^***	0.043^***
	(0.006)	(0.009)	(0.004)	(0.006)	(0.008)	(0.013)
High education	0.055^***	0.072^***	0.061^***	0.028^***	0.095^***	0.073^***
	(0.019)	(0.025)	(0.006)	(0.009)	(0.013)	(0.021)
Lack of food/shelter	0.009	0.004	0.013^***	0.010^*	0.020^***	0.010
	(0.006)	(0.010)	(0.003)	(0.005)	(0.007)	(0.011)
Difficult living on present income	0.010^*	−0.015	−0.006^*	−0.031^***	0.031^***	−0.016
	(0.006)	(0.010)	(0.003)	(0.005)	(0.007)	(0.011)
No improvement in living standards	−0.004	0.004	0.013^***	0.002	−0.009	0.008
	(0.007)	(0.011)	(0.004)	(0.006)	(0.008)	(0.013)
No opportunity to meet people	−0.006	0.013	0.001	0.008	0.004	−0.018
	(0.006)	(0.009)	(0.004)	(0.005)	(0.007)	(0.011)
No help from friends/relatives	0.012^*	−0.008	0.021^***	0.001	0.015^**	0.001
	(0.006)	(0.010)	(0.004)	(0.006)	(0.008)	(0.013)
Discontent with local amenities	0.102^***	0.009	0.091^***	0.015^***	0.111^***	0.012
	(0.006)	(0.009)	(0.003)	(0.005)	(0.007)	(0.011)
Discontent with education system	0.011^**	−0.017^*	0.015^***	0.003	−0.002	−0.006
	(0.006)	(0.009)	(0.003)	(0.005)	(0.007)	(0.011)
No trust in national government	0.048^***	−0.027^***	0.053^***	0.015^***	0.041^***	0.012
	(0.006)	(0.009)	(0.003)	(0.005)	(0.007)	(0.011)
Corruption in government	0.042^***	0.009	0.048^***	−0.001	0.062^***	−0.033^**
	(0.008)	(0.013)	(0.004)	(0.007)	(0.009)	(0.015)
Worsening economy	0.038^***	0.024^**	0.039^***	0.020^***	0.043^***	0.031^***
	(0.006)	(0.010)	(0.004)	(0.005)	(0.007)	(0.012)
Bad time to find a job	0.017^***	−0.006	0.017^***	−0.027^***	0.019^***	−0.031^***
	(0.006)	(0.009)	(0.003)	(0.005)	(0.007)	(0.012)
No get ahead by working hard	0.048^***	0.016	0.037^***	0.022^***	0.036^***	0.007
	(0.008)	(0.013)	(0.004)	(0.006)	(0.009)	(0.014)
Unemployed	0.044^***	0.035^**	0.036^***	0.019^***	0.037^***	0.000
	(0.009)	(0.014)	(0.005)	(0.007)	(0.010)	(0.016)
Out of workforce	−0.007	−0.042^***	−0.006^*	−0.032^***	−0.005	−0.052^***
	(0.006)	(0.010)	(0.003)	(0.005)	(0.007)	(0.012)

Number of countries	15	15	67	67	15	15
Observations	38,510	11,541	124,312	32,530	30,975	8,075

Note: The table reports the marginal effects from sample−weighted probit regressions. ^*, ^**, ^*** denote significance at 10, 5, 1%, respectively. Errors are clustered at the country level. All models include country dummies, year (survey wave) dummies, and a constant term. Repeated cross-sections for the years 2010–2016. The country income groups’ classification is detailed in text footnote 4. The list of top 15 refugee-sending countries is provided in text footnote 5. The definition of the variables is provided in Table A1 in Appendix. Note that, when the dependent variable is Plan, the number of observations decreases since only youth who desire to migrate abroad are taken into account, and data are only available for the period 2010–2015.

Table A6

Adults (35–64) desire/plan to migrate abroad, full regression results corresponding to Panel A of Table 4

Country grouping	Developing		Emerging		Developed
Dependent variable	Desire	Plan	Desire	Plan	Desire	Plan
Female	−0.043^***	−0.014	−0.017^***	−0.030^***	−0.037^***	−0.031^***
	(0.005)	(0.014)	(0.003)	(0.006)	(0.003)	(0.006)
Married	−0.022^***	−0.048^***	−0.022^***	−0.029^***	−0.018^***	−0.019^***
	(0.006)	(0.016)	(0.003)	(0.006)	(0.004)	(0.006)
Age (35–39)	0.111^***	−0.039	0.108^***	0.030^**	0.100^***	0.023^*
	(0.011)	(0.031)	(0.005)	(0.013)	(0.007)	(0.014)
Age (40–44)	0.072^***	−0.035	0.092^***	0.018	0.087^***	0.024^*
	(0.011)	(0.032)	(0.005)	(0.013)	(0.006)	(0.014)
Age (45–49)	0.065^***	−0.050	0.070^***	0.011	0.072^***	0.006
	(0.012)	(0.033)	(0.005)	(0.013)	(0.006)	(0.014)
Age (50–54)	0.030^**	−0.036	0.050^***	0.010	0.053^***	−0.002
	(0.012)	(0.035)	(0.005)	(0.014)	(0.006)	(0.014)
Age (55–59)	0.020	0.006	0.027^***	−0.004	0.029^***	0.010
	(0.013)	(0.039)	(0.006)	(0.015)	(0.006)	(0.013)
Have kid(s)	0.015^**	0.014	0.006^**	−0.000	0.008^**	−0.006
	(0.008)	(0.019)	(0.003)	(0.006)	(0.004)	(0.007)
Urban	0.054^***	0.041^***	0.034^***	0.027^***	0.022^***	0.009
	(0.007)	(0.016)	(0.003)	(0.006)	(0.003)	(0.006)
Poor health	0.008	0.007	0.004	0.002	0.011^***	−0.006
	(0.005)	(0.015)	(0.003)	(0.006)	(0.004)	(0.008)
Medium education	0.052^***	0.029^*	0.038^***	0.027^***	0.023^***	0.005
	(0.006)	(0.015)	(0.003)	(0.007)	(0.006)	(0.010)
High education	0.025^*	0.054^*	0.053^***	0.039^***	0.038^***	0.019^*
	(0.013)	(0.029)	(0.004)	(0.009)	(0.006)	(0.011)
Lack of food/shelter	0.015^***	0.009	0.026^***	0.019^***	0.038^***	0.019^***
	(0.006)	(0.015)	(0.003)	(0.006)	(0.004)	(0.007)
Difficult living on present income	0.014^**	−0.040^***	0.006^**	−0.015^**	0.019^***	0.021^***
	(0.006)	(0.015)	(0.003)	(0.006)	(0.004)	(0.007)
No improvement in living standards	0.003	0.009	0.025^***	−0.003	0.028^***	0.003
	(0.006)	(0.016)	(0.003)	(0.006)	(0.004)	(0.007)
No opportunity to meet people	−0.008	0.026^*	0.002	0.001	0.034^***	0.001
	(0.006)	(0.014)	(0.003)	(0.006)	(0.004)	(0.007)
No help from friends/relatives	−0.007	0.002	−0.012^***	0.002	−0.026^***	−0.004
	(0.005)	(0.014)	(0.003)	(0.006)	(0.005)	(0.008)
Discontent with local amenities	0.073^***	0.003	0.068^***	0.019^***	0.104^***	0.018^***
	(0.006)	(0.014)	(0.003)	(0.006)	(0.004)	(0.007)
Discontent with education system	0.006	0.015	0.019^***	−0.000	0.028^***	0.010
	(0.005)	(0.014)	(0.003)	(0.006)	(0.004)	(0.006)
No trust in national government	0.033^***	−0.004	0.046^***	0.004	0.057^***	−0.007
	(0.005)	(0.015)	(0.003)	(0.006)	(0.004)	(0.008)
Corruption in government	0.037^***	0.010	0.039^***	−0.010	0.050^***	0.003
	(0.007)	(0.020)	(0.004)	(0.009)	(0.004)	(0.008)
Worsening economy	0.034^***	0.026	0.032^***	0.023^***	0.016^***	−0.006
	(0.006)	(0.016)	(0.003)	(0.006)	(0.004)	(0.007)
Bad time to find a job	0.008	−0.031^**	0.006^**	−0.027^***	0.011^***	0.003
	(0.005)	(0.015)	(0.003)	(0.007)	(0.004)	(0.009)
No get ahead by working hard	0.032^***	−0.025	0.033^***	0.010	0.051^***	0.026^***
	(0.007)	(0.019)	(0.003)	(0.007)	(0.004)	(0.007)
Unemployed	0.020^*	0.030	0.036^***	0.027^***	0.034^***	0.049^***
	(0.010)	(0.024)	(0.005)	(0.009)	(0.007)	(0.009)
Out of workforce	−0.021^***	−0.014	−0.025^***	−0.006	−0.013^***	0.019^**
	(0.007)	(0.018)	(0.003)	(0.007)	(0.004)	(0.008)

Number of countries	22	22	75	75	45	45
Observations	31,678	5,351	127,749	19,605	73,970	9,113

Note: The table reports the marginal effects from sample-weighted probit regressions. ^*, ^**, ^*** denote significance at 10, 5, 1%, respectively. Errors are clustered at the country level. All models include country dummies, year (survey wave) dummies, and a constant term. Repeated cross-sections for the years 2010–2016. The country income groups’ classification is detailed in text footnote 4. The definition of the variables is provided in Table A1 in Appendix. Note that, when the dependent variable is Plan, the number of observations decreases since only youth who desire to migrate abroad are taken into account, and data are only available for the period 2010–2015. With respect to Table 1, a set of dummy variables for each 5 years age group has been included (e.g., age 35–39 = 1 if the respondent is between 35 and 39 years’ old and equal 0 otherwise).

Table A7

Employed adults (35–64) desire/plan to migrate abroad, full regression results corresponding to Panel B of Table 4

Country grouping	Developing		Emerging		Developed
Dependent variable	Desire	Plan	Desire	Plan	Desire	Plan
Female	−0.046^***	−0.014	−0.015^***	−0.024^***	−0.040^***	−0.025^***
	(0.006)	(0.017)	(0.003)	(0.007)	(0.004)	(0.007)
Married	−0.022^***	−0.046^**	−0.019^***	−0.030^***	−0.025^***	−0.018^**
	(0.008)	(0.019)	(0.004)	(0.008)	(0.004)	(0.007)
Age (35–39)	0.102^***	−0.037	0.105^***	0.038^**	0.089^***	0.029^*
	(0.014)	(0.038)	(0.007)	(0.018)	(0.009)	(0.017)
Age (40–44)	0.060^***	−0.041	0.093^***	0.031^*	0.073^***	0.028
	(0.014)	(0.039)	(0.007)	(0.018)	(0.008)	(0.017)
Age (45–49)	0.054^***	−0.047	0.065^***	0.018	0.063^***	0.010
	(0.014)	(0.040)	(0.007)	(0.019)	(0.008)	(0.017)
Age (50–54)	0.018	−0.066	0.048^***	0.009	0.039^***	0.005
	(0.015)	(0.043)	(0.007)	(0.019)	(0.008)	(0.017)
Age (55–59)	0.009	−0.007	0.022^***	0.005	0.020^**	0.005
	(0.016)	(0.048)	(0.008)	(0.021)	(0.008)	(0.018)
Have kid(s)	0.019^**	0.027	0.002	−0.007	0.004	−0.010
	(0.009)	(0.023)	(0.003)	(0.008)	(0.004)	(0.007)
Urban	0.045^***	0.033^*	0.034^***	0.031^***	0.023^***	0.003
	(0.008)	(0.019)	(0.003)	(0.007)	(0.004)	(0.007)
Poor health	0.014^**	−0.003	0.005	0.010	0.010^*	0.004
	(0.006)	(0.018)	(0.004)	(0.008)	(0.005)	(0.009)
Medium education	0.048^***	0.020	0.033^***	0.032^***	0.017^**	0.004
	(0.007)	(0.018)	(0.004)	(0.008)	(0.007)	(0.012)
High education	0.004	0.041	0.047^***	0.045^***	0.030^***	0.014
	(0.015)	(0.032)	(0.005)	(0.011)	(0.008)	(0.013)
Lack of food/shelter	0.013^*	0.001	0.026^***	0.026^***	0.036^***	0.020^**
	(0.007)	(0.017)	(0.003)	(0.008)	(0.005)	(0.008)
Difficult living on present income	0.017^**	−0.028	0.009^***	−0.014^*	0.019^***	0.017^**
	(0.007)	(0.018)	(0.003)	(0.008)	(0.005)	(0.007)
No improvement in living standards	0.006	0.001	0.027^***	−0.010	0.030^***	0.011
	(0.007)	(0.018)	(0.004)	(0.008)	(0.004)	(0.008)
No opportunity to meet people	−0.006	0.027	0.004	−0.006	0.040^***	0.003
	(0.006)	(0.017)	(0.004)	(0.008)	(0.005)	(0.007)
No help from friends/relatives	−0.011	0.001	−0.015^***	−0.004	−0.030^***	−0.003
	(0.007)	(0.017)	(0.004)	(0.008)	(0.006)	(0.009)
Discontent with local amenities	0.078^***	−0.005	0.070^***	0.019^***	0.106^***	0.014^*
	(0.006)	(0.017)	(0.004)	(0.007)	(0.005)	(0.008)
Discontent with education system	0.008	0.003	0.026^***	−0.002	0.026^***	0.009
	(0.006)	(0.016)	(0.003)	(0.007)	(0.004)	(0.007)
No trust in national government	0.031^***	0.006	0.047^***	−0.001	0.053^***	0.002
	(0.006)	(0.017)	(0.003)	(0.008)	(0.004)	(0.009)
Corruption in government	0.041^***	0.003	0.049^***	−0.012	0.052^***	−0.002
	(0.008)	(0.023)	(0.005)	(0.011)	(0.005)	(0.009)
Worsening economy	0.033^***	0.031^*	0.032^***	0.027^***	0.017^***	−0.011
	(0.007)	(0.018)	(0.004)	(0.008)	(0.004)	(0.008)
Bad time to find a job	0.009	−0.026	0.006	−0.027^***	0.013^***	0.001
	(0.006)	(0.017)	(0.004)	(0.008)	(0.005)	(0.009)
No get ahead by working hard	0.034^***	−0.016	0.040^***	0.019^**	0.060^***	0.025^***
	(0.009)	(0.022)	(0.004)	(0.009)	(0.004)	(0.007)
Involuntary part-time	0.041^***	0.028	0.038^***	0.008	0.017^**	-0.002
	(0.007)	(0.019)	(0.005)	(0.009)	(0.007)	(0.011)
Self-employed	−0.032^***	−0.011	−0.009^**	0.016^**	−0.006	0.024^***
	(0.008)	(0.019)	(0.003)	(0.008)	(0.005)	(0.008)

Number of countries	22	22	75	75	45	45
Observations	23,225	3,961	81,917	12,937	53,546	6,449

Note: The table reports the marginal effects from sample-weighted probit regressions. ^*, ^**, ^*** denote significance at 10, 5, 1%, respectively. Errors are clustered at the country level. All models include country dummies, year (survey wave) dummies, and a constant term. Repeated cross-sections for the years 2010–2016. The country income groups’ classification is detailed in text footnote 4. The definition of the variables is provided in Table A1 in Appendix. Note that, when the dependent variable is Plan, the number of observations decreases since only youth who desire to migrate abroad are taken into account, and data are only available for the period 2010–2015. With respect to Table 2, a set of dummy variables for each 5-years age group has been included (e.g., age 35–39 = 1 if the respondent is between 35 and 39 years’ old and equal 0 otherwise).

To answer this question, the main regression models presented in Tables 1 and 2 for the youth sample are estimated on a sample of individuals aged between 35 and 64 years old (adults, henceforth). Not surprisingly, results show that many of the factors driving potential migration among youth also matter in shaping migration propensity across adults (Table 4). Yet, some significant differences in the relative importance of these factors emerge. First, holding a post-secondary level of education increases the probability of desire (plan) to migrate significantly more among youth than adults. This is particularly the case when employed youth (Table 2) are compared to working adults (Panel B, Table 4). This result is consistent with the human-capital theory of migration suggesting that highly educated young people are more willing to migrate than their adult counterparts since they have a longer time horizon to capitalize on their education (Fouarge and Ester 2007). Relatedly, results show that the influence of labor market-related motives on adults’ migration intentions, while often significant, is on average not as strong as the one found among youth. In particular, unlike the case of youth, being in involuntary part-time and lacking opportunities for career progresses only modestly increases the desire to migrate among adults, with little or no effects on the probability to follow up on this desire. Overall, this is consistent with the evidence that youth continue to fare worse labor market conditions than adults globally (ILO, 2016). However, only in developing countries, being unemployed seems to increase the likelihood of desiring (planning) to migrate more among youth than adults. In addition, results show that the effects of sociodemographic characteristics on adults’ migration desire (plan) are quite similar to those found among youth, albeit with some differences between the two age groups. First, while no gender differences in the probability of materializing migration desires was found among youth in developed countries (Tables 1 and 2), adult women in these country group are instead significantly less likely to plan to migrate abroad. This is consistent with evidence showing that in much of the developed world, gender differences in migration are closing among younger age cohorts (Abel, 2018). Second, being married tends to discourage more adults than the youth from turning their desire into an actual plan, while the opposite holds for having dependants. This supports the argument that family-related factors may have different effects on migration propensity at different stages of life.

Overall, results in this section give some important indications of how the influence of main socioeconomic drivers of migration may differ (or not) between age groups. However, while this study heavily focuses on socioeconomic drivers, many other factors are likely to explain differences in migration intentions at different stages of the life cycle. For instance, the literature suggests that, beyond the classical socioeconomic motives, youth, more than adults, relate their decision to migrate to noneconomic and sociocultural factors (Van Mol, 2016). In fact, while a deeper analysis of these factors is outside the scope of this study, results show that, with respect to youth, adults’ desire (plan) to migrate are less influenced by factors such as satisfaction with local amenities and trust in national government.

Conclusions

Growing numbers of people are on the move worldwide (IOM, 2019), while many more, especially youth, would like to migrate or are already planning to do so in the near future (Migali and Scipioni, 2019; Burrone et al., 2018). Hence, understanding why they decide to migrate need to be a major goal for researchers and policymakers. This study aimed to contribute to this objective by providing an assessment of the main factors driving young people desire and more concrete intention to migrate abroad permanently. Of course, it is important to bear in mind that only a small fraction of those intending to migrate will ultimately migrate (Tjaden et al., 2019), and therefore, the results in this study can only provide an approximation of the drivers of actual youth migration in the future. Moreover, this study’s results only concerns the drivers of young people’s intentions to migrate abroad permanently, and therefore they do not necessarily explain why youth would like to migrate internally or temporarily. In addition, this study heavily focuses on the socioeconomic and labor market-related drivers of youth migration, providing no insights on the many other cultural, personal, and psychological factors driving youth potential migration. Nevertheless, taken together, findings in this study have important policy implications. First, as unemployment, lack of job opportunities, and career advancement are all found to be among the main drivers of youth potential migration, combining supply- and demand-side interventions to support youth in the labor market can play a key role in shaping migration propensity among youth. For instance, Kluve et al. (2017) show that entrepreneurship promotion (e.g., startup grants, technical support, and access to capital) and skills training (e.g., for job-specific technical skills, business or soft skills) are among the most promising programs to increase the employment and earnings potentials of those youth who participate in them. Target such programs at youth with high propensities to migrate, such as the high-educated and those living in urban areas, is likely to have the greatest impact on youth international migration, especially in emerging and developing countries. Enhancing employment opportunities for high-educated youth is particularly relevant for developing countries where, according to this study’s results, the significant brain drain observed over the past decades (Docquier, 2014) is likely to continue in the next future. Moreover, this study shows that youth having negative expectations about the economic and labor market outlook are more likely to desire to migrate. Therefore, improving the timeliness, relevance, and accessibility of labor market information through, for instance, job placement activities, career fares, and employer workshops can reduce information asymmetries and job search costs and help youth evaluate the most suitable education or career path in their home country (S4YE, 2017). Within this context, creating more capillary and systematic linkages between education institutions and employers remains key to ensure smoother school-to-work transitions (Pastore, 2017). At the same time, social protection policies can also play an important role in affecting migration propensity. On the one hand, social assistance programs by alleviating poverty could reduce willingness to migrate among youth (Dibeh et al., 2019). However, as shown by Adhikari and Gentilini (2018), if not properly designed, unrestricted cash transfers to poor people can have the opposite effect and, by alleviating income constraints, can actually increase emigration from low income countries. This is consistent with this study’s finding showing that low income is one of the main barrier preventing youth from turning migration desire into more concrete plans. More generally, most of developing and emerging countries are undergoing significant transformations, including greater integration in global value chains, increasing employment in services, intensifying urbanization, and growing enrolment rates in post-secondary education. Managing these processes, focusing on the equitable generation and distribution of education and decent job opportunities for youth in both rural and urban areas, may ultimately have important implications on international migration flows (Adhikari and Gentilini, 2018). Finally, findings in this study suggest that, beyond socioeconomic factors, the quality of local amenities, such as local infrastructure, public services, and education system, as well as the level of confidence in national government also contribute to shaping young people’s migration intention. Therefore, enhancing young people’s engagement in civic processes and their satisfaction with local amenities could also be a powerful tool to shape youth migration behavior.

eISSN:: 2520-1786
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Zeitrahmen der Veröffentlichung:: Volume Open
Fachgebiete der Zeitschrift:: Wirtschaftswissenschaften, Volkswirtschaft, Mikroökonomie, Makroökonomie, Wirtschaftspolitik, Mathematik und Statistik für Ökonomen, Ökonometrie

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What Drives Youth’s Intention to Migrate Abroad? Evidence from International Survey Data

Online veröffentlicht: 16. Juli 2020

Seitenbereich: -

DOI: https://doi.org/10.2478/izajodm-2020-0012

Schlüsselwörtermigration, intention to migrate, youth, labor market, brain drain

© 2020 Santo Milasi, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Figure 1

Figure 2

Schlüsselwörter
migration, intention to migrate, youth, labor market, brain drain