What drives youth's intention to migrate abroad? Evidence from international survey data

Milasi, Santo Article What drives youth's intention to migrate abroad? Evidence from international survey data IZA Journal of Development and Migration Provided in Cooperation with: IZA – Institute of Labor Economics Suggested Citation: Milasi, Santo (2020) : What drives youth's intention to migrate abroad? Evidence from international survey data, IZA Journal of Development and Migration, ISSN 2520-1786, Sciendo, Warsaw, Vol. 11, Iss. 1, pp. 1-30, https://doi.org/10.2478/izajodm-2020-0012 This Version is available at: https://hdl.handle.net/10419/298683 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes. You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Santo Milasi* What Drives Youth’s Intention to Migrate Abroad? Evidence from International Survey Data Abstract Despite the bulk of international migrants being youth, little is known about the factors driving young people’s migration behavior at the global level. Using the individual-level survey data from Gallup World Poll across 139 countries over the period 2010–2016, this study contributes to the literature by exploring a wide range of factors potentially shaping young people’s (aged 15–34) desire, and a more concrete plan, to migrate abroad permanently. Results show that factors, such as holding post-secondary education, being unemployed, and working part-time involuntary, are increasing the desire of youth to migrate abroad as well as the probability that they turn this aspiration into a more concrete plan over the following year. Similarly, having negative expectations about the economic outlook, the number of available job opportunities, and the prospects for upward career mobility are found to increase the propensity to migrate abroad, both among unemployed and employed youth. Results also show that material deprivation may represent a significant push factor behind youth migration, although budgetary constraints may prevent youth from transforming their migration desires into actual plans in low-income countries. Moreover, findings suggest that contextual factors, such as discontent with local amenities and national governments, increase the desire of youth to migrate abroad, but they have little or no influence on the probability that these dreams are turned into more concrete plans. Finally, this study shows that while youth’s and adults’ migration propensities are often driven by the same motives, the influence of education and labor market-related factors on migration intentions is considerably stronger among youth than adults. Disclaimer The content of this paper does not reflect the official opinion of the European Commission. Responsibility for the information and views expressed in the paper lies entirely with the author. Current version: April 19, 2020 Keywords: migration; intention to migrate; youth; labor market; brain drain JEL codes: F22, J13, O15, R23 Corresponding author: Santo Milasi santo[email protected]opa.eu Milasi. IZA Journal of Development and Migration (2020) 11:12 © The Author(s). 2020. Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. Cite as: Milasi. IZA Journal of Development and Migration (2020) 11:12. https://doi.org/10.2478/izajodm-2020-0012 European Commission, Joint Research Centre, Rue champs de mars 21, 1050, Bruxelles, Belgium. Page 2 of 30 Milasi. IZA Journal of Development and Migration (2020) 11:12 1 Introduction 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).1 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 2016.2 The added value of this study is threefold. First, 1 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.” 2 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). Page 3 of 30 Milasi. IZA Journal of Development and Migration (2020) 11:12 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. 2 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 Page 4 of 30 Milasi. IZA Journal of Development and Migration (2020) 11:12 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. Page 5 of 30 Milasi. IZA Journal of Development and Migration (2020) 11:12 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.3 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).4 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: Desire/Planit = α + β1Individualit + β2Wealthit + β3Networkit + β4Contextit + β5Labourit + zi + μt + єit. (1) The dependent variable Desireit 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 Planit 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. Individualit is a standard set of individual characteristics, including gender, age, marital status, education level, self-reported health status, and rural/ urban residence; Wealthit denotes a group of variables measuring perceived income level, material 3 In some large countries, such as China and Russia, sample sizes of at least 2,000 nationally representative individuals are collected. 4 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. Page 6 of 30 Milasi. IZA Journal of Development and Migration (2020) 11:12 conditions, and expected living standards5; Networkit is a vector of variables measuring social network in terms of opportunity to meet people in origin country and receive help from family members; Contextit 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 Laborit, 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, zi are country fixed-effects controlling for the country-level time-invariant factors that may affect individual migration desire, while mt are year (survey wave) fixed effects and єit 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. 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. 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. 5 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. Page 7 of 30 Milasi. IZA Journal of Development and Migration (2020) 11:12 4 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 pres- Figure 2 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. Figure 1 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. Page 8 of 30 Milasi. IZA Journal of Development and Migration (2020) 11:12 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) (Continued) Page 15 of 30 Milasi. IZA Journal of Development and Migration (2020) 11:12 Panel C: Including/excluding Top 15 refugee−sending countries Excluding Top 15 refugeesending countries Top 15 refugeesending countries Country grouping Developing Emerging Dependent variable Desire Plan Desire Plan Desire Plan 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 3 Continued 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? 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, Page 16 of 30 Milasi. IZA Journal of Development and Migration (2020) 11:12 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. Page 17 of 30 Milasi. IZA Journal of Development and Migration (2020) 11:12 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. 5 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 Page 18 of 30 Milasi. IZA Journal of Development and Migration (2020) 11:12 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 higheducated 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. Availability of data and material Restrictions apply to the availability of the data used under license from Gallup for this study. Data are available from the authors upon reasonable request and with permission of Gallup only. Competing interests The author declares that he has no competing interests. Funding Not applicable. Page 19 of 30 Milasi. IZA Journal of Development and Migration (2020) 11:12 Authors’ contributions Not applicable. 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M.; Calvin Jephcote; Hania Janta; Gang Li (2018): The Migration Intentions of Young Adults in Europe: A Comparative, Multilevel Analysis. Population, Space and Place 24(1), e2123. Page 21 of 30 Milasi. IZA Journal of Development and Migration (2020) 11:12 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 (Continued) Appendix Page 22 of 30 Milasi. IZA Journal of Development and Migration (2020) 11:12 Variable name Questions asked 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? Table A1 Continued 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. Page 23 of 30 Milasi. IZA Journal of Development and Migration (2020) 11:12 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) (Continued) Page 24 of 30 Milasi. IZA Journal of Development and Migration (2020) 11:12 2010–2012 Country grouping Developing Emerging Developed Dependent variable Desire Plan Desire Plan Desire Plan 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 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 2010–2012. The country income groups classification is detailed in text footnote 4. The definition of the variables is provided in Table A1 in Appendix. Table A3 Continued 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) (Continued)