Jasmin Baier, Johannes Haushofer, Hannah Lea Shaw

with input from Toman Barsbai and Merve Demirel

1. Introduction

Two thirds of the world’s population live on less than $10 per day (Our World in Data), and 60% of the global variability in income is explained by where one is born (Milanovich, 2015). Perhaps as a result, many people want to permanently migrate to another country: in low-income countries, the share ranges from 20–59%; among Africans under 25, it is 33% (Gallup World Poll, own calculations; Figure 1). 

At the same time, many high-income countries experience significant gaps in their labor force, both in terms of skills and raw numbers, resulting from low birth rates and aging populations. The OECD estimates that this workforce gap will amount to 450 million people by 2050. 

These figures suggest that increased international migration would have benefits both for migrants themselves, and for destination countries. Indeed, the economic benefits of removing international migration barriers amount to large fractions of world GDP — one or two orders of magnitude larger than those of trade barriers (Clemens, 2011). 

Recognizing this, many high-income countries are actively working to reduce migration barriers. This is especially true for educational migration: many countries are working towards internationalizing their higher education systems, simplifying student visa and work requirements, and reducing tuition costs (e.g., in the case of Germany, keeping university completely free, even for third-country nationals). For the migrants, international educational migration provides access to the labor market in the destination country, and additionally combines the returns to migration with those to education. 

The goal of this investigation is therefore to explore international educational migration as a possible mechanism to increase migration opportunities and alleviate poverty. 
 

2. Potential Impact

2.1 Direct Economic Impact on Migrants

Cross-country estimates of the returns to migration suggest potential impacts on income around 1,500% (Figure 2). Studies that permit causal claims have shown that international migration generates income increases of several hundreds of percent. For instance, labor migrants from Tonga to New Zealand increase their income by 263% one year after migration (McKenzie, Stillman, and Gibson 2010). Similarly, Mobarak et al. (2020) estimate returns of around 200% in terms of income from low-skilled migration between Bangladesh and Malaysia. 

Because these studies estimate individual economic gains from labor migration, often low-skill, we view these estimates as lower bounds for the impact of educational migration, which combines the benefits of migration with those of education. Specifically, education itself has large returns, on the order of 10% per year (Peet, Fink, and Fawzi 2015). Importantly, these returns are unlikely to decay over time. (This stands in sharp contrast to e.g. cash transfer programs, whose effects have been shown to dissipate over time; e.g. Blattman, Fiala, and Martinez 2020).

Unfortunately, we are not aware of causal evidence on the returns to international educational migration. However, to provide a concrete example for the likely returns, we briefly summarize estimates for the impact of the educational migration program Malengo, with which we are associated. Malengo enables Ugandan secondary school graduates to apply for and complete an English-taught Bachelor’s program in Germany. 

The cost of the program per student net of administration costs is EUR 11,971, which includes a living expense stipend and semester fees for the first year; application expenses (visas, tests); and travel. After the first year, students have to support themselves through part-time jobs (as is the case for three quarters of German university students). Administration and staff costs are not included in this figure to provide a scale-independent cost estimate. 

Using relatively conservative assumptions about the share of students who graduate, remain in Germany vs. return to Uganda, have children, leave the workforce, and so on, we estimate that the average yearly income across the entire pool of students in the first year after graduation is EUR 24,980. Compared to the outside option of taking a job in Uganda with the average yearly income of those with a high school diploma or some college, EUR 1,598, this corresponds to a treatment effect on income of 1,563%.

This compares favorably to the treatment effects of unconditional cash transfers on income, which are typically between 10 and 30%. It is important to note, however, that cash transfers are usually much smaller in magnitude. To provide a better comparison, we can calculate the internal rate of return of the initial program investment of EUR 11,971. During the first 10 years after program entry, the assumptions listed above imply a cumulative return of 1,717%, which corresponds to a yearly internal rate of return of 20.87%. In comparison, unconditional cash transfers generate an internal rate of return around 1.68% over this period.

Note that these calculations do not include spillover effects to family members in Uganda (see below).

Impact in Log Utility Terms

We can produce a similar calculation in utility terms. We assume that during their studies, Malengo students consume EUR 28 per day (this corresponds to their living cost stipend). After they finish studying, they consume EUR 68 per day, reflecting their average starting salary described above. Without the program, students would consume EUR 1598/365 = EUR 4.38 per day. Malengo therefore has a treatment effect in log terms of log(28) – log(4.38) = 1.87 while studying, and log(68) – log(4.38) = 2.75 afterwards. Assuming a 2% yearly discount rate, and 4% yearly income growth, this corresponds to a treatment effect of 33.24 log points for the Malengo program over the first 12 years after program entry.

Cash transfers are again a useful benchmark. Egger et al. (2021) find a 13% increase in consumption after USD 1,000 (EUR 888) cash transfers to low-income families in Kenya. We know less about how this treatment effect evolves over time, but existing studies suggest that it decays relatively quickly. We therefore assume that it remains unchanged for 5 years after the transfer, then goes to 50% of that magnitude for 2 years, and then to zero. Using the same 2% yearly discount rate, and extrapolating the effect of the USD 1,000 cash transfer linearly to a transfer of EUR 11,971, the total treatment effect over the first 12 years after program entry is 9.83 log points for unconditional cash transfers of the same magnitude as the cost of Malengo.

Note that this calculation is somewhat generous to cash transfers: these don’t have to be repaid, while Malengo students are asked to make contributions to future generations of students via an income share agreement (more on this below). This will decrease their own welfare gains from the program, but increase those of others who have lower income and therefore higher marginal utility.

Again these calculations do not include spillover effects to family members in Uganda (see below). 

2.2 Impact on Health and Psychological Well-being

Migration is likely to have effects on health and psychological well-being. To estimate effects on life expectancy, we use the following crude calculation: we first compute the life expectancy (in terms of healthy life expectancy, HALE) of a person who spends their entire life in a country with a low Sustainable Development Index (SDI) using IHME Global Burden of Disease figures. We then estimate the life expectancy of a person who spends the first 20 years of their life in a low-SDI country and then moves to a high-SDI country, with an 80% likelihood of remaining there for the rest of their life, and a 20% likelihood of returning to the home country within 5 years. The adjusted expected life expectancy of the person who migrates is 65.1 years, while that of the person who does not migrate is 56.8 years. 

Evidence on the psychological well-being effects of international migration is scarce and ambiguous. For example, McKenzie et al (2013) find that migration from Tonga to New Zealand led to improvements in mental health, but a decline in happiness. It is likely that competing forces are at play — increased incomes may improve psychological well-being and mental health, while other factors such as loneliness and experienced discrimination may decrease it. 

More broadly, it is possible that migration has effects on attitudes; for example, Gaikwad et al. (2022) show that migration from India to the Persian Gulf increased the migrants’ tolerance and internationalism. It is unclear how to value these effects. 

2.3 Spillover effects

A common argument against international migration is the possibility of negative economic externalities for the home country, often referred to as “brain drain”. Our reading of the evidence is that in fact economic benefits of emigration outweigh the costs: a “brain gain” effect. Several channels are responsible: remittances; incentives for human capital investment; foreign direct investment; and information and aspirations. 

Remittances

International migrants from developing countries sent home $548 billion in remittances in 2019, an amount as large as all foreign direct investment, and more than three times larger than foreign aid flows to developing countries (World Bank, 2021). These resource flows have substantial positive effects on education and enterprise investments in migrant-origin households (Yang, 2008; Gibson & McKenzie, 2011; Clemens and Tiongson, 2017), and on overall economic development in migrant-origin areas (Dinkelman & Mariotti, 2016; Khanna et al., 2022). 

Even though these income gains have already been counted in the impact calculation above, such remittances will accrue to lower-income individuals (e.g., family members staying behind) and thus have a larger treatment effect in log utility terms.

To estimate the magnitude of remittances in the specific case of Uganda-Germany educational migration, a useful statistic is that the average Ugandan migrant in the UK sends USD 4,000 per year back to Uganda (Cooper et al., 2018). While we don’t have data for the remittances sent by the 2,600 Ugandans living in Germany, it is likely that the magnitude is similar due to the relatively similar household incomes in Germany and the UK. (The per capita GDP of Germany is 14% higher than that of the UK.) Note that this amount is four times as large as a typical unconditional cash transfer; and that these remittances flow every year, rather than being one-off payments (as is often true of cash transfers). Thus, it is likely that Malengo students will contribute significantly to the economic well-being of their families and home communities in Uganda. Indeed, already while studying, the seven Malengo students of the pilot cohort of 2021 send an average of EUR 165 per month to their families in Uganda. 

Human capital investment

The existence of migration opportunities, and exposure to family members and others who migrate, also creates incentives amongst siblings and other young people in the home community to invest in their own education. As long as enough of those who invest in human capital remain in the country, the opportunity of skilled migration could lead to a net increase in the stock of human capital (Stark et al., 1997; Beine et al., 2001, 2008). Indeed, several studies suggest strong human capital responses to migrant exposure. Bedasso et al. (2020) find an effect of migrant exposure on completing secondary education of 14–17%, and on own future migration of 22%. Abarcar and Theoharides (2021) show that for each nurse who migrates from the Philippines to the USA, nine additional nurses are licensed in the Philippines. Dinkelman and Mariotti (2016) show that migration opportunities to South African mines increased human capital by 5–7% in Malawi. Batista et al. (2011) estimate an elasticity of secondary school completion likelihood with respect to migration likelihood of 0.4 in Cape Verde. Thus, educational migration opportunities are likely to have significant positive human capital externalities in the communities and countries of origin. 

Information, aspirations, and trade

In addition to sending remittances and increasing human capital beyond one’s own educational achievement, migrants build bridges between their old and new homes. Through contact with the student abroad, family members and friends back home are exposed to new economic opportunities, different economic systems, and political institutions. These links can give rise to important diaspora externalities (Rapoport, 2019), fostering trade and foreign direct investment (Burchardi et al., 2019), increasing aspirations in terms of educational attainment and life goals, increasing demand for human capital, better institutions and governance, or changing views towards more gender equality and tolerance of minorities (Barsbai et al., 2017). Ultimately, these factors may contribute to improving the set of economic opportunities available to family members who stay behind, and migrants’ home communities more generally. 

3. Potential for scale

International educational migration has significant potential for scale. 

On the demand side, UNESCO data indicate that there are 76.1m students who graduate from secondary school in low-income countries each year. Combining this figure with country-specific data on the share of people under 25 who want to permanently migrate to another country from the Gallup World Poll, we estimate that 22.2m secondary school graduates from low-income countries may want to study abroad each year.

On the supply side, we estimate the number of potentially available spots for foreign students in 14 European countries, where higher education is subsidized. In each of these countries, we obtained the number of Bachelor programs taught in English or French. Based on informal conversations with universities, we conservatively estimate that each program has 30 spots for foreign students per year. Results are shown in Table 1. At current levels, we estimate that there are 1.16 million spots for students from low-income countries each year across these countries. 

Table 1. Potential for scale of international educational migration in 14 European countries

To estimate affordability, we calculate the total tuition fees for an undergraduate degree in each country, and add to it the first-year living expenses (on the assumption that students can finance themselves through part-time jobs after the first year). We then compare these costs to the estimated average income in the first year after graduation (using OECD data). We find that students earn back the cost of the degree within between 2.5 months and 1.5 years of working in the destination country. 

One possible worry is that any program that facilitates international educational migration for students from low-income countries may simply crowd out other home or international students. We do not view this as a significant concern, for three reasons. First, many university programs in high-income countries are “open admission”, i.e. they are not selective, and anyone who fulfills the admission criteria is admitted. In fact, universities often have incentives to admit as many students as they can (while maintaining quality), as public funding is linked to the number of students.

Second, most current international students in high-income countries are from middle-income rather than low-income countries (DAAD, Statistisches Bundesamt 2019). Thus, any displacement would likely affect higher-income students. 

Perhaps most importantly, it is likely that the number of available slots will grow substantially over time, both in response to demand from international students, and in response to demand for highly skilled labor in high-income countries. In this context, we note that an estimated 26.1m students start a Bachelor's program in high- and upper-middle income countries each year. Recall from above that 22.2m secondary school graduates from low-income countries may want to study abroad each year. Thus, existing capacity in high- and upper-middle income countries would have to increase by a relatively modest 85% to accommodate all potentially interested students from low-income countries. (About 9% of students in high-income countries are from abroad.)

4. Neglectedness

Funding for, and research on, low-income to high-income country educational migration is limited, especially for large-scale, non-elite programs. Some European governments (notably Germany) have large budgets to attract university students, but this happens largely at the Master’s and PhD level, which is hard to attain for low-income students who often cannot afford the undergraduate education required for entry.

Most of the prominent educational foundations also focus on graduate studies, and often on high-performing students, including Fulbright (budget: 397 million USD in FY 2018), Ford Foundation ($9,3 million for education and scholarships in 2022), and Chevening (56 million USD in 2016).

A number of non-governmental organizations provide scholarships for undergraduate educational migration. However, these programs (e.g. KenSAP, Mastercard Foundation) often prioritize prestige and opportunities for a small number of students; for example, KenSAP has supported 239 students since 2004, many of whom went to Ivy League universities. The potential for scale of these programs is therefore limited. 

A small number of organizations use financial engineering to enable educational migration. Prodigy Finance, Credenc, and 8B Education Investments offer for-profit loans for international education. Our own organization, Malengo, provides income-share agreements for students from low-income countries, in addition to mentoring. These organizations are young and still have significant funding gaps to reach the desired scale. 

The NGO ConsiliumBots provides information about educational opportunities through technology provision; the NGO Refugee and Migrant Education Network engages in advocacy on behalf of migrants, with a specific view towards education. Given that these organizations work on scaleable programs, but remain relatively small, it is likely that they are currently constrained by funding.

There are a few funding agencies which display interest in funding this space without implementing programs themselves. The Agency Fund recently funded ConsiliumBots and is currently accepting applications in the areas of Education and Mobility & Migration. However, actual disbursement of funds in this particular area seems to be a minor part of the agency fund’s investments, as besides ConsiliumBots all featured projects focus on other types of projects. This may be the case either because there currently aren’t many organizations worth funding (see above), or because educational migration is not a funding priority. 

The second funder in this space, Mastercard Foundation, works primarily with higher education institutions in low-income countries.

5. Tractability

5.1 Possible interventions

Scholarships, Loans, Income Share Agreements (ISAs)

Scholarships, loans,  and income share agreements (ISAs) are possible tools to relax the capital shortfalls that often constrain educational migration. Scholarships are attractive in that they do not require repayments from the student, thus enabling the largest individual-level welfare gains. However, international educational migration is capital-intensive, and the scalability of scholarships is therefore limited.

Loans and income share agreements both rely on repayments from students, and are attractive because they could in principle be market-driven and thus highly scalable. ISAs are particularly promising because, in contrast to loans, they protect the students from having to make repayments they cannot afford: In an ISA, like with a loan, students receive a sum of money to finance their university studies, and later make repayments. In contrast to a loan, however, these repayments are income-contingent: students repay a share of their income (rather than a share of the principal plus interest) for a fixed period of time. Importantly, ISAs typically have a minimum income threshold, below which no repayments are required; students are thus protected in case they are unemployed or have low-income jobs (e.g. after moving back to their home country). ISAs also often have a maximum repayment amount (e.g. 2.5 times the principal) to prevent students from repaying much more than they originally received. ISAs have the potential to be commercially viable if the returns are high enough to attract investors. In our view, ISAs are therefore a particularly promising tool because they are both attractive to students and potential investors. A concern is that they may be subject to adverse selection (Herbst & Hendren, 2021); however, this is only a problem if students have strong and accurate beliefs about their future earnings prospects (unlikely in the case of international educational migration), and if alternatives such as student loans exist (which is rarely the case for international educational migration). Nevertheless, if a market for loans emerges, ISAs may become problematic. 

Information and aspirations

A number of studies have shown that providing information about the returns to education, in some cases combined with light-touch administrative support, can increase school attendance, graduation rates, and college application and enrollment rates (Hoxby and Turner, 2013; Jensen, 2010;  Nguyen, 2008; Bettinger et. al,2019). 

Similarly, interventions that raise aspirations have recently been shown to affect economic outcomes, including investment in human capital. For example, Carlana et al. (2022) used psychological exercises to raise aspirations of students with immigrant backgrounds in Italy, and found increased enrollment in technical high schools that give access to university studies, instead of vocational high schools that lead to low-skilled jobs. Bernard et al. (2014) find that a video documentary was successful in raising aspirations and increasing human capital investment in rural Ethiopia, with effects lasting up to 5 years. Dinkelman and Martínez (2014) examine the effect of providing low-income Chilean adolescents with information about financing higher education through role models, finding that the program raised college preparatory high school enrollment, as well as primary school attendance.

Thus, information and aspiration interventions show some promise. In our view, however, the capital constraints to international educational migration are likely so significant that these interventions by themselves may not be sufficient, and loans or ISAs will be required to make migration feasible. 

Lobbying and Advocacy

Many governments appear motivated to maintain and expand the number of higher education slots available for foreign students. Lobbying could enhance and accelerate these efforts. In cases where the political climate does not permit further increases in the number of available slots, other dimensions could be targeted. For example, many countries have central authorities that regulate which secondary school degrees qualify for university entry. These authorities could be lobbied to expand the list of countries whose degrees qualify. Anecdotal information about the authority that creates the German version of this list, “Anabin”, suggests that it is open to input and new information.

Once educational migration from low-income countries reaches very high levels, public-facing advocacy may be necessary to minimize potential backlash in public opinion. Such efforts could also be beneficial in origin countries to prevent restrictions on emigration. 

Effective lobbying and advocacy require solid causal evidence. In particular, in our view more evidence is needed on the “brain drain vs. brain gain” effect of international educational migration; and on potential negative effects of migration, e.g. on psychological well-being or experiences of discrimination. 

5.2 Reasons why interventions may fail

There is a risk that governments may reduce their tuition subsidy for international students. However, the combination of demographics, strained pension systems, and widening skills gaps in many high-income countries gives governments strong economic incentives in favor of attracting international students. We therefore believe this risk to be small. 

However, if public sentiment about migration worsens, governments might nevertheless be forced to place limitations on foreign students, their economic benefits notwithstanding. International migration has increasingly become politically weaponized, undermining evidence-based policies in this domain. If international educational migration grows to a large scale, this may therefore lead to backlash. As a result, supporting this cause area is potentially most viable when combining direct interventions with advocacy, as described above.

5.3 Major sources of uncertainty

• The causal evidence that exists on international migration in general is scarce, and focuses on low-skill labor migration (Stillman et al., 2009; Mobarak et al., 2020; Gaikwad et al., 2022).
• We are not aware of causal evidence on the effects of international educational migration, both with respect to direct effects and spillovers (e.g. on families and siblings), and with respect to positive (e.g. income) and negative effects (e.g. psychological well-being).
• Relatedly, many calculations in this document are based on estimates of the expected impact of migrating from Uganda to do a Bachelor’s degree in Germany. The assumptions that go into these estimates are simply guesses in some cases due to a lack of data. 
• Little is known about possible general equilibrium effects of international educational migration, both the origin and destination countries. 
• We are very uncertain of the potential impact of increased educational migration on public opinion in both origin and destination countries. It would be valuable to spend time looking further into theories and evidence of how such exposure might impact opinions, and in which contexts the effect can be positive or negative.

References

Abarcar, Paolo, and Caroline Theoharides. 2021. “Medical Worker Migration and Origin-Country Human Capital: Evidence from U.S. Visa Policy.” The Review of Economics and Statistics, October, 1–46. 

Barsbai, Toman, Hillel Rapoport, Andreas Steinmayr, and Christoph Trebesch. 2017. “The Effect of Labor Migration on the Diffusion of Democracy: Evidence from a Former Soviet Republic.” American Economic Journal: Applied Economics 9 (3): 36–69. 

Batista, Catia, Aitor Lacuesta, and Pedro C. Vicente. 2012. “Testing the ‘Brain Gain’ Hypothesis: Micro Evidence from Cape Verde.” Journal of Development Economics 97 (1): 32–45. 

Bedasso, Biniam, Ermias Gebru Weldesenbet, and Nonso Obikili. 2020. “Emigration and Education: The Schooling of the Left behind in Nigeria.” Migration and Development: 1–17. 

Beine, Michel, Frédéric Docquier, and Hillel Rapoport. 2001. “Brain Drain and Economic Growth: Theory and Evidence.” Journal of Development Economics 64 (1): 275–89.

Beine, Michel, Fréderic Docquier, and Hillel Rapoport. 2008. “Brain Drain and Human Capital Formation in Developing Countries: Winners and Losers.” The Economic Journal 118 (528): 631–52.

Bernard, Tanguy, Stefan Dercon, Kate Orkin, and Alemayehu Seyoum Taffesse. 2014. “The Future in Mind: Aspirations and Forward-Looking Behaviour in Rural Ethiopia.” CSAE Working Paper Series, University of Oxford. https://ideas.repec.org/p/csa/wpaper/2014-16.html.

Bettinger, Eric, Oded Gurantz, Laura Kawano, Bruce Sacerdote, and Michael Stevens. 2019. "The Long-Run Impacts of Financial Aid: Evidence from California's Cal Grant." American Economic Journal: Economic Policy, 11 (1): 64-94. 

Blattman, Christopher, Nathan Fiala, and Sebastian Martinez. 2020. “The Long Term Impacts of Grants on Poverty: 9-Year Evidence from Uganda’s Youth Opportunities Program.” American Economic Review: Insights 2 (3): 287–304.

Burchardi, Konrad B, Thomas Chaney, and Tarek A Hassan. 2019. “Migrants, Ancestors, and Foreign Investments.” The Review of Economic Studies 86 (4): 1448–86.

Carlana, Michela, Eliana La Ferrara, and Paolo Pinotti. 2022. “Goals and Gaps: Educational Careers of Immigrant Children.” Econometrica 90 (1): 1–29.

Clemens, Michael A. 2011. “Economics and Emigration: Trillion-Dollar Bills on the Sidewalk?” Journal of Economic Perspectives 25 (3): 83–106.

Clemens, Michael A., and Erwin R. Tiongson. 2017. “Split Decisions: Household Finance When a Policy Discontinuity Allocates Overseas Work.” The Review of Economics and Statistics 99 (3): 531–43.

Cooper, Barry, Antonia Esser, Rose Tuyeni Peter, and Shazeaa Lal Mohamod. 2018. Remittances in Uganda. Centre for Financial Regulation & Inclusion.

Dinkelman, Taryn, and Claudia Martínez A. 2014. “Investing in Schooling In Chile: The Role of Information about Financial Aid for Higher Education.” The Review of Economics and Statistics 96 (2): 244–57. 

Dinkelman, Taryn, and Martine Mariotti. 2016. “The Long-Run Effects of Labor Migration on Human Capital Formation in Communities of Origin.” American Economic Journal: Applied Economics 8 (4): 1–35. 

Egger, Dennis, Johannes Haushofer, Edward Miguel, Paul Niehaus, and Michael Walker (in press). General Equilibrium Effects of Unconditional Cash Transfers: Experimental Evidence from Kenya. Econometrica.

Gaikwad, Nikhar, Kolby Hanson, and Aliz Tóth. 2022. “Bridging the Gulf: Experimental Evidence on Migration’s Impact on Tolerance and Internationalism.” Working Paper.

Gibson, John, and David McKenzie. 2011. “Eight Questions about Brain Drain.” Journal of Economic Perspectives 25 (3): 107–28.

Herbst, Daniel, and Nathaniel Hendren. In press. Missing Markets for Financing Human Capital. American Economic Review.

Hoxby, C., & Turner, S. (2013). Expanding college opportunities for high-achieving, low income students. Stanford Institute for Economic Policy Research Discussion Paper, 12(014).

Jensen, Robert. 2010. “The (Perceived) Returns to Education and the Demand for Schooling.” The Quarterly Journal of Economics 125 (2): 515–48. 

Khanna, Gaurav, Emir Murathanoglu, Caroline Theoharides, and Dean Yang. 2022. “Abundance from Abroad: Migrant Income and Long-Run Economic Development.” SSRN Scholarly Paper. Rochester, NY. https://papers.ssrn.com/abstract=4068032.

Milanovic, Branko. 2015. “Global Inequality of Opportunity: How Much of Our Income Is Determined by Where We Live?” The Review of Economics and Statistics 97 (2): 452–60. 

McKenzie, David, Steven Stillman, and John Gibson. 2010. “How Important Is Selection? Experimental vs. Non-Experimental Measures of the Income Gains from Migration.” Journal of the European Economic Association 8 (4): 913–45.

Mobarak, Mushfiq, Iffath Sharif, and Maheshwor Shrestha. 2020. “Returns to Low-Skilled International Migration: Evidence from the Bangladesh-Malaysia Migration Lottery Program.” Working Paper. Washington, DC: World Bank. 

Nguyen, Trang. 2008. Information, Role Models and Perceived Returns to Education: Experimental Evidence from Madagascar. Working paper. Retrieved at: https://www.povertyactionlab.org/sites/default/files/documents/Nguyen%202008.pdf

Peet, Evan D., Günther Fink, and Wafaie Fawzi. 2015. “Returns to Education in Developing Countries: Evidence from the Living Standards and Measurement Study Surveys.” Economics of Education Review 49 (December): 69–90. 

Rapoport, Hillel. 2019. “Diaspora Externalities.” IZA Journal of Development and Migration 10 (2).

Shrestha, S. A. (2017). “No man left behind: Effects of emigration prospects on educational and labour outcomes of non‐migrants.” The Economic Journal 127(600): 495-521. 

Stark, Oded, Christian Helmenstein, and Alexia Prskawetz. 1997. “A Brain Gain with a Brain Drain.” Economics Letters 55 (2): 227–34. 

Stark, Oded, and Yong Wang. 2002. “Inducing Human Capital Formation: Migration as a Substitute for Subsidies.” Journal of Public Economics 86 (1): 29–46.

Stillman, Steven, David McKenzie, and John Gibson. 2009. “Migration and Mental Health: Evidence from a Natural Experiment.” Journal of Health Economics 28 (3): 677–87.

Yang, Dean. 2008. “International Migration, Remittances and Household Investment: Evidence from Philippine Migrants’ Exchange Rate Shocks.” The Economic Journal 118 (528): 591–630.