NBER WORKING PAPER SERIESIS THE MEDITERRANEAN THE NEW RIO GRANDE? US AND EU IMMIGRATIONPRESSURES IN THE LONG RUN
Gordon Hanson
Craig McIntosh
Working Paper 22622
http://www.nber.org/papers/w22622

NATIONAL BUREAU OF ECONOMIC RESEARCH
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September 2016

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Is The Mediterranean The New Rio Grande? US And EU Immigration Pressures In The Long
Run
Gordon Hanson and Craig McIntosh
NBER Working Paper No. 22622
September 2016
JEL No. F22,J61
ABSTRACT
How will worldwide changes in population affect pressures for international migration in the
future?We contrast the past three decades, during which population pressures contributed to
substantial labor flows from neighboring countries into the United States and Europe, with thecoming three decades, which will see sharp reductions in labor-supply growth in Latin America
but not in Africa or much of the Middle East. Using a gravity-style empirical model, we examine
the contribution of changes in relative labor-supply to bilateral migration in the 2000s and thenapply this model to project future bilateral flows based on long-run UN forecasts of working-age
populations in sending and receiving countries. Because the Americas are entering an era ofuniformly low population growth, labor flows across the Rio Grande are projected to slow
markedly. Europe, in contrast, will face substantial demographically driven migration pressuresfrom across the Mediterranean for decades to come. Although these projected inflows would
triple the first-generation immigrant stocks of larger European countries, they would still absorb
only a small fraction of the 800-million-person increase in the working-age population of SubSaharan Africa that is projected to occur over the coming 40 years.

Gordon Hanson
IR/PS 0519
University of California, San Diego
9500 Gilman Drive
La Jolla, CA 92093-0519
and NBER
gohanson@ucsd.edu
Craig McIntosh
IR/PS 0519
University of California, San Diego
9500 Gilman Drive
La Jolla, CA 92093-0519
ctmcintosh@ucsd.edu

The tens of thousands of migrants streaming into Europe during late 2015 and early 2016
created an indelible image of how humanitarian crises—in this case associated with the Syrian
civil war—propel international migration. Although political instability in the Levant may havekicked migration to Europe into a higher gear, immigration flows to the continent in themedium- and long-run are likely to be sustained by sharp differentials in labor-supply growth
between regions to the north and to the south of the Mediterranean Sea. The present European
migration scene is the latest act in a long-running global drama in which cross-country
differences in population growth, abetted by disparities in aggregate labor productivity, createpressures for international migration. Periodically, economic or political crises unleash thesepressures and generate sustained flows.
During the last quarter of the 20th century, the principal actors in this global drama were
Mexico and the United States. The US baby boom came to an abrupt halt in the early 1960s,
causing growth in native-born labor supply to slow sharply two decades hence—once the babyboom generation had fully reached working age. In Mexico, birth rates declined much later.
High fertility in the 1960s—when Mexico’s fertility rate (the number of births per woman ofchildbearing age) averaged 6.8 versus 3.0 in the United States—meant that Mexico’s labor force
was expanding rapidly in the early 1980s, just as a severe financial crisis hit. This crisis, and thedecade and a half of economic instability that ensued, unleashed a great wave of Mexican
migration to the US (Hanson and McIntosh 2010). Encouraging this flow was steady US
economic growth during the “Great Moderation” period from the mid-1980s up through 2007
(Bernanke 2004). In a pattern common to migration events stretching back into human history,
early migrants eased the transition for later arrivals by offering advice on how to find jobs andhousing, opening familiar stores and restaurants, and creating enclaves in which Spanish was
spoken alongside English (Massey, Durand, Alarcon, and Gonzalez 1987; Munshi 2003).
The Mexican migration wave to the United States has now crested. Fertility rates in
Mexico, at 2.3, are only modestly above those in the United States, at 1.9 (World Development
Indicators, data for 2013). Labor-supply growth in the two countries is projected to be roughly
the same in coming decades. Although living standards in Mexico remain well below US levels,
Mexico has tamed the macroeconomic volatility it experienced during the 1980s and 1990s. Net
US immigration from Mexico plunged after the onset of the Great Recession in 2007 and has
been slightly negative every year since (Villarreal, 2014; Gonzalez Barrera, Lopez, and Rohal2015). Absent a significant new economic or political crisis in Mexico, or unexpectedly robust
US economic growth, it is unlikely that Mexico-to-US migration rates will again reach the levels
witnessed between the early 1980s and the mid-2000s.
The European immigration context today looks much like the United States did three
decades ago. In Europe, which long ago made its demographic transition to low birth rates,1

declines in fertility in the 1970s and 1980s set the stage for a situation in which the number ofworking-age residents is in absolute decline (as discussed in this journal by Lee 2003). Countries
in the North Africa and Middle East region, in contrast, have had continued high fertility,
creating bulging populations of young people looking for gainful employment in labor markets
plagued by low wages and the scarcity of steady work. Further to the south, population growth
rates in Sub-Saharan Africa, a region with still lower relative earnings, remain among thehighest in the world.
Many countries in North Africa and the Middle East are in a period of profound politicaland economic upheaval. Migrants escaping military conflict in Afghanistan, Iraq, Libya, andSyria are crossing the Straits of Gibraltar, the land and sea borders that divide Turkey and
Greece, and the narrow Mediterranean passage that separates northern Libya and southern
Italy. Further to the south, conflicts in Chad, Eritrea, Mali, and Nigeria are also generating labor
outflows. These new triggers are being tripped in a demographic environment that is ideal for
perpetuating emigration well into the future. As further motivation, established populations ofAlgerians in France, Moroccans in Spain, the Turkish in Germany, and Sub-Saharan Africans in
Italy may offer support and solace to the new arrivals as they settle in.
In this paper, we assess the contribution of differentials in population growth tointernational migration in the long run. We look backward to the significant migration episodesof the last 30 years and forward to the pressures that will encourage flows in coming decades.
Whereas in the 1970s the world was neatly divided between a high-income and low-populationgrowth North and a low-income and high-population-growth South, by the 2040s the only
high-population-growth countries likely to remain will be in Sub-Saharan Africa and parts of
the Middle East. We use past population growth to estimate a gravity model of bilateralmigration and then apply the estimated coefficients to project future migration out to midcentury, as implied by population forecasts. This exercise projects the stock of first-generation
immigrants to remain flat in the United States, as a consequence of the cessation of Mexico’s
emigration surge, and to nearly triple in the United Kingdom, Spain, and Italy.
Our exercise is not intended as a hard prediction of the future, but as a guide for how
demographic trends will recalibrate pressures for international labor movements. If Europe
tightens its borders significantly, the United States decides to relax entry restrictions, or thenations of East Asia change course to allow labor inflows at much higher levels than in the past,global migration may evolve in a very different manner from what our analysis suggests.
Future migration will undoubtedly be shaped also by crises that we cannot foresee, much as the
Arab Spring and the subsequent tumult around the Mediterranean caught the European Union
by surprise, leaving it ill-equipped for mass arrivals of refugees on its shores. Yet there remains
a certain destiny in global demography. Just as population growth in most of Latin America is
cascading downward and looks set to drop below replacement levels within a couple ofdecades, it is proceeding apace in Sub-Saharan Africa and much of North Africa and the Middle

2

East. These patterns mean that in the future the United States and Europe are likely to occupy
migration neighborhoods that differ immensely from those of the past.

Global Migration Patterns
In 1970, there was little indication either in the United States or in Europe that
immigration would become a major political issue by century’s end. The share of the US
population that was foreign-born in 1970 was at a 100-year low, at 4.7 percent. Concerns inwestern Europe centered not so much on excess entry as on restrictions that impeded thoseliving behind the Iron Curtain from moving west (Joppke 1999). At the time, policymakers inBonn, Paris, and London wanted, if anything, to facilitate immigration, not to block it. Today, ofcourse, anxiety about the arrival of foreign workers is running high.
We begin our discussion by summarizing the present contours of immigration in the
United States and other OECD nations. We focus on the OECD both because it is a groupingthat includes all high-income destination economies, aside from the oil-rich states of the Persian
Gulf, and because it provides immigration data by age cohorts that are comparable acrosscountries. We then examine past and projected future population growth worldwide, paying
particular attention to forecasted changes in growth rates in current migration hot spots.
Immigrant Presence in OECD Countries
Perhaps the most fundament concept in the economics of immigration is thatinternational labor flows are driven by differences in income among countries. The seminal
work of Sjaastad (1962) framed immigration as an investment decision, in which an individual
considers incurring an up-front cost from migrating to another location—due to moving
expenses, fees for obtaining a visa, time out of the labor market, and the psychic cost of leaving
home—in return for a higher present discounted value of lifetime income. Although the
migration decision in theory is based on comparing future income streams at home versus
abroad, the vast majority of empirical work uses current income as a summary statistic forcross-country differences in labor-market conditions (for example, Clark, Hatton, andWilliamson 2007; Mayda 2008). In our first foray into the data, we follow this convention. Later,
we introduce differences in relative labor supply growth as an additional indicator of future
income streams.
Figure 1 plots the share of the population that is foreign-born in 2010 against mean PPPadjusted per capita GDP averaged over 2000 to 2010 for OECD countries. Values of per capita
GDP are from the Penn World Tables 8.0. The 2010 foreign-born population share summarizes
immigrant inflows over preceding decades, while mean income for the 2000s provides a metric3

of economic conditions in destination countries around the time during which the
preponderance of migrants would have made their location choices.
Clearly, lower-income countries, such as Bulgaria, Chile, Mexico, and Romania, lure few
immigrants, while higher-income economies are attractive destinations. The regression line isstrongly positive and statistically significant at the 1 percent level. For every 10 percent increasein average income, the foreign-born population share rises by 0.4 percent. Yet even after
controlling for income, there is also enormous heterogeneity in migrant inflows. While
Australia and the Netherlands have near identical average per capita GDP, the former’s foreignborn share (26.6 percent) is nearly two-and-a-half times that of the latter (11.1 percent). Also
dramatic is the contrast between Germany and Japan, two countries that again have similar
average living standards. Germany’s foreign-born share at 13.0 percent is almost exactly on the
regression line. At 1.7 percent, the share of immigrants in Japan’s population is the lowest
among the higher-income nations of the OECD. Other pairs with common average incomes but
sharply differing foreign-born shares include Estonia (15.9 percent) and the Slovak Republic (0.3
percent); Slovenia (11.2 percent) and Korea (2.0 percent); and Belgium (14.9 percent) and
Denmark (7.7 percent). International migration is apparently about more than current income
differences between countries.
A vast literature examines the factors that shape bilateral migration flows (for a survey,
see Hanson 2010). Similar to findings for the “gravity model” of trade (Head and Mayer 2015),
migration is strongly decreasing in geographic distance between countries and significantly
higher between country pairs that share a land border, a common language, and past colonial
linkages (Mayda 2010). These additional factors explain in part why Anglo-phone countries are
particularly popular destinations for international migrants. Four of the six highest foreign-born
population shares are found in countries that are former British subjects: Australia, Canada,
Ireland, and New Zealand. These nations have the appeal of speaking the language that the
British Empire made the lingua franca of global commerce. Also, the era of empire helped
establish migration networks—and later the creation of preferential immigration schemes suchas those among British Commonwealth nations—that resulted in reduced costs for current laborflows between former colonies of the same hegemon and between a former colony and thehegemon itself.
The additional explanatory power of gravity variables would still not account for theidiosyncratic cases of very high or very low immigration, which are evident in Figure 1. Highinflow countries include Switzerland, whose long history of neutrality has made it theheadquarters for international organizations that hire large numbers of foreign diplomats andother professionals, and Israel, whose national identity is predicated on encouraging
immigration of those of Jewish descent (Friedberg 2001). Korea and Japan discourage
immigration, ostensibly to maintain cultural homogeneity (Cornelius, Martin, and Hollifield1994), resulting in low labor inflows. These historical features, which are manifest in policies

4that would be transparent to any person attempting to emigrate to these destinations, aredifficult to measure empirically and hence end up in the regression residual.
Another shortcoming of the gravity model, at least as conventionally applied, is a focus
on current income as a predictor for bilateral migration. Because migration is a long-lived
decision, individuals are likely to evaluate the extended prospects for their livelihoods inalternative destinations, as Sjaastad (1962) proposed. Obvious additional factors that affectfuture earnings include total factor productivity growth and capital accumulation, which are
hard to forecast far into the future. Domestic labor-supply growth, on the other hand, iseminently forecastable. Current differences in birth rates between countries foretell how relativelabor supplies—holding immigration constant—will change 15 to 20 years hence, when today’s
newborns enter the labor force. Because birth rates change much less abruptly than investment
or innovation, demographers are able to forecast changes in birth rates two to three decades
into the future with considerable accuracy (O’Neil, Scherbov, and Lutz 1999; Lutz, Sanderson,
and Scherbov 2001). Thus, current population growth is informative about what domestic labor
supplies would look like 40 to 50 years from now, with no further flows of internationalmigrants. In later sections, we use this insight to estimate an augmented gravity model ofbilateral migration that accounts for both current relative incomes and expected future relative
incomes as captured by anticipated changes in domestic labor supplies.
Figure 1 illustrates the potential pitfalls of using only current income alone as anindicator of coming migration. If one were to utilize nothing other than the residuals from theregression plot, it would appear that the United States is poised for relatively high immigrationin the future. Its foreign-born share of the US population at 12.9 percent is similar to that of thelarge countries of Europe: even with Germany; slightly above France, the Netherlands, and theUnited Kingdom; and slightly below Spain. Yet, US per capita GDP exceeds that of these othernations by substantial amounts. Moreover, the relatively high labor-market rewards to moreeducated workers in the United States, in both pre-tax and post-tax terms, gives it a strong
advantage in attracting more-skilled labor (Grogger and Hanson 2011). We will see next that adifferent picture of migration pressures emerges once we examine the composition of bilateralimmigration stocks in major destination countries. These reveal that it is Europe, not the UnitedStates, in which the potential for substantial future inflows appears greatest.

Bilateral Migration Stocks in OECD Countries
To study bilateral migration, we use data on the numbers of the foreign born by countryof birth from the Database on Immigrants in OECD Countries (DIOC). 1 Following categories
See http://www.oecd.org/els/mig/databaseonimmigrantsinoecdcountriesdioc.htm. For most countries,
the source for the DIOC is a decennial census (OECD, 2008), whose large sample sizes permit the15

used in the DIOC, we focus on individuals between ages 15 and 64. This range is more
expansive than the definition of the working-age population that is conventional in US or
European labor-market analysis. Whereas many high-income nations require children to stay inschool until age 18, in most lower-income countries mandatory schooling ends between grades
six and nine. It is around age 15 that emigration rates in major sending countries pick up
noticeably, as youths leave school and begin to seek work (McKenzie and Rapoport 2007).
Table 1 summarizes the number of immigrants living in the ten largest OECD migrantreceiving countries as of 2010, which is the most recent year for which the DIOC has
comprehensive data on bilateral migration stocks. Considering absolute numbers, rather than
the foreign-born population shares in Figure 1, the United States is by far and away thedominant destination country for international migrants. The 32.8 million working-age
immigrants who resided in the United States in 2010 were 41.6 percent of all foreign-born
individuals living in an OECD country. (For perspective, the United States accounted for

one-quarter of the total OECD population in 2010.) Other destination countries are distantrunners up. Next in line are Great Britain, with 7.9 percent of the working-age immigrants that
reside in OECD countries; Spain, with 6.2 percent; and Canada, with 6.0 percent.
Equally impressive is that a single source country, Mexico, accounts for just under onethird of US working-age immigrants. The 10.2 million individuals born in Mexico and living in
the United States accounted for fully 13.1 percent of all immigrants living in an OECD country
in 2010. Remarkably for a country as large as Mexico, these immigrants were equal in number
to 13.5 percent of Mexico’s working-age population. Indeed, the Mexico-to-US migrant flow is
one of the largest international migration episodes that the world has seen. Any substantialchange in pressures for migration from Mexico to the United States could thus have a major
impact on US immigrant presence. Ten of the 20 next largest immigrant groups in the OECD
also reside in the United States. They include migrants from high-population countries (China,
India), low-income countries close to the United States (Cuba, the Dominican Republic, El
Salvador, Guatemala), and countries in which US military presence has facilitated immigration(Germany, Korea, Philippines, Vietnam). Though China’s and India’s total volumes of
migration to the United States are large, their emigration rates are low.
The United States is far from alone in having a few countries play a prominent role in its
immigration picture. Poles, Indians, and Pakistanis together are 24.2 percent of UK immigrants;
Romanians and Moroccans combine to represent 26.0 percent of immigrants in Spain; Algerians
and Moroccans account for 28.0 percent of working-age immigrants in France; and individuals

estimation of bilateral migrant stocks for the majority of sending countries. Measuring bilateral migration
is problematic in Germany, which rarely conducts a census. The German data in the DIOC are based onlabor force surveys, which suppress country of birth for immigrants from smaller origin nations. As aconsequence, we lack data on bilateral stocks that account for two-fifths of German immigration.

6

born in Turkey and Poland are 27.4 percent of German immigrants. 2 The Romanians living in
Italy and Spain are equivalent to 10.0 percent of the population of working age in Romania andPoles living in Germany and the United Kingdom equal to 5.7 percent of Poland’s working-age
residents. Flows of these magnitudes mean that labor-market shocks in these sending countries
have potentially significant consequences for destination economies, and vice-versa.

Drivers of International Migration
Following the logic of the gravity model, it is no accident that many of the largest
bilateral migration stocks involve nearby countries. There is both motive and opportunity for
migration from Romania to Italy, Poland to Germany, and Morocco to Spain. The enlargementof the European Union in 2004 to countries across eastern Europe, which eliminatedimmigration restrictions on accession countries, further expanded opportunities for migrationfrom eastern to western Europe (Elsner 2013). Also consistent with the gravity framework,colonial history has eased the movement of Algerians and Moroccans to France, Ecuadorans to
Spain, and Indians and Pakistanis to the United Kingdom (Bertoli, Moraga, and Ortega 2011).
Other flows, however, involve happenstance. The Turkish migration to Germany began in the
1960s, when West Germany sought foreign guest workers to fill low-wage positions during aperiod of soaring economic growth. This first generation of immigrants created family andkinship networks that facilitated immigration in later decades (Pischke and Velling 1997).
Motive and opportunity, however, are far from sufficient conditions for migration to
occur. The United States and Mexico, which share a 2,000-mile land border, have long had
widely divergent incomes. In 1960, per capita GDP (PPP adjusted) in the United States was
triple that in Mexico, a ratio that remained essentially unchanged during the following two
decades—yet there was only a modest migration response. As a share of Mexico’s national
population, the number of the Mexicans living in the United States stood at 1.5 percent in 1960
and 3.2 percent twenty years later. In 1980, immigrants from Mexico accounted for just 1.0
percent of the US population.
What sparked substantial labor flows from Mexico to the United States was the onset of
the Mexican debt crisis of the 1980s and the “lost decade” of economic stagnation that followed
In Table 1, the foreign-born are 7.6 percent of Germany’s working-age population, which seems low
compared to 13.0 percent of Germany’s total population as seen in Figure 1. This discrepancy reflects
missing data on bilateral migration for Germany’s smaller migrant-sending countries. To arrive atestimated of the share of total German immigrants given in the text, we inflate the total number ofGerman immigrants reported in panel A of Table 1 by an adjustment factor that accounts for “missing”
bilateral migrants in the OECD data (where this conversion factor is 13.0—the percentage of the foreignborn in German’s total population in Figure 1—divided by 7.6—the percentage of the foreign-born inGerman’s working-age population in Table 1).
2

7

(Hanson and Spilimbergo 1999). Between 1982 and 2000, the ratio of US-to-Mexico per capita
GDP rose by over one-and-a-half times, from 2.3 to 3.8. 3 Financial crises are common triggers of
migration episodes (Yang 2007; Bertoli, Moraga, and Ortega 2013), as are political upheaval andmilitary conflict (Hanson and McIntosh 2012). However, temporary shocks need not induce
permanent migration. During the Mexican Revolution (1911-1920), approximately 700,000
Mexicans, or 5 percent of the population, fled to the United States. By the late 1920s, a large
fraction had returned home (Cardoso 1980; Durand, Massey, and Charvet 2000).
To sustain international migration, it helps if individuals expect that economic
conditions will diverge between origin and destination countries in coming years. In sending
nations, increases in domestic labor supply, relative to abroad, are one impetus for suchexpectations. Between 1940 and 1960, the US baby boom matched rapid population growth in
Mexico. In the ensuing two decades, birth cohorts in Mexico continued to grow in size, while
the annual number of births in the U.S fell in both relative and absolute terms. The consequence
was that between 1960 and 1980 the relative size of US-Mexican birth cohorts dropped
precipitously from 4:1 to less than 2:1, a shift which meant that Mexico’s relative labor supply
was expanding at mach speed in the 1980s just as its economy staggered.
The contribution of labor-supply growth to international migration is evident not just at
the national level but also across regions within economies. Mexico’s demographic transition
occurred unevenly across space. First affected were richer states along the US border andaround Mexico City; last affected were poorer states in central and southern Mexico. The
Mexican migrants who moved north, while roughly similar in educational attainment toworkers who stayed behind (Chiquiar and Hanson, 2005; Kaestner and Malamud, 2013), were
not drawn randomly from across Mexico. They came disproportionately from states with preexisting migration networks (Munshi 2003; McKenzie and Rapoport 2007) and with relatively
high labor-force growth. Exploiting this regional variation, Hanson and McIntosh (2010) findthat relative labor-supply growth can account for two-fifths of Mexican migration to the US
between the 1970s and the 1990s.
The Hot Spots with Population Pressures for Migration
The argument here suggests that when international borders also draw a line between
nations with significant differences in population growth, pressures for migration will result. To
illustrate this theme more concretely, we calculated national growth rates between 1970 and
If education, labor market experience, or other sources of human capital are higher in destination than insending countries, bi-national differences in per capita GDP will overstate the gain in income that aninternational migrant can expect to obtain. Holding constant observable worker characteristics, Clemens,
Montenegro, and Pritchett (2008) estimate that in 2000 a young Mexico-born male with mean years ofschooling would see his earnings rise by 2.5 times (in purchasing power parity-adjusted terms by
migrating to the United States (where the US/Mexico per GDP ratio for that year is 3.8).
38

1980 for the population of native-born individuals 0 to 14 years old. Data are from the United
Nation’s World Population Prospects (athttp://esa.un.org/unpd/wpp/Download/Standard/Population). These growth rates determine
the number of the native-born who would be coming of working age about 15 years hence—in
this example, between the years 1980 and 1995. We then did a similar calculation using
population forecasts for the years from 2040-2050. The results suggest how the populationpressures for migration will change over time. 4 Figures 2a and 2b map the outcome of thisexercise, where countries with high population growth are shown in dark grey and countries
with low population growth are shown in light grey.
In the 1970s, there was a stark North-South divide in the growth of the young
population. Most richer countries including the United States, Canada, and most nations ofwestern Europe show a decline in the relevant population from 1970 to 1980. Declines in thesecountries were the result of the end of the post-World War II baby boom and the lower birthrates that had taken hold by roughly 1960. Australia, Ireland, Japan, and Spain were the only
higher-income countries with positive growth in the number young people during the decade.
In contrast, for most poorer countries the growth rates in the 0-14 age group were positive from1970 to 1980. The change in the youth population was positive to strongly positive in Latin
America, Africa, the Middle East, and most of developing Asia. Growth in these regions was theresult of continued high fertility combined with declines in infant mortality (Lee 2003). Amongthe countries in our data, only Cambodia, which suffered depopulation under the Khmer
Rouge, Russia, which saw fertility plummet during the later years of the Soviet Union, andSuriname have negative growth in the number of 0 to 14 year olds during the 1970s.
Bilateral migration flows will tend to be high between countries that are neighbors andthat occupy different ends of this population growth continuum. Based on our analysis of the
1970s, the Mexico-US border shows up as one evident hot spot for migration. Other migration
hot spots in this time period include Europe and North Africa and the Middle East. In
particular, booming growth in the youth populations of Algeria, Libya, Morocco, and Turkey
contrasted with sagging youth populations north of the Mediterranean, which helped fuelmigration into Europe in the 1990s and 2000s. Other adjoining countries with both underlying
age 0–14 population growth differentials in the 1970s and also meaningful differences in percapita income include South Africa and its immediate neighbors, and Russia and most of thenations of Central Asia. In recent decades, there were substantial migration flows within each ofthese groups (Özden, Parsons, Schiff, and Walmsley, 2011). 5

The UN Population projections by age and sex start in 2015 and are done using the open source CRAN
R package bayesPop as documented in Raftery, et al. (2012). We use the ‘Zero Migration’ variants which
do not attempt to forecast future migration rates in projecting age cohort sizes.
5 One anomaly is the Persian Gulf. Growth in the young population in the 1970s and 1980s was rapid in
Saudi Arabia and the United Arab Emirates, yet these countries became importers of labor from South4

9

Looking forward, the geography of population growth will be very different than in thepast, which also suggests that the population pressures for migration will shift. To construct thegrowth rate of the age 0-14 population between 2040 and 2050, we used projections from theUnited Nation’s World Population Prospects. The UN Population Division constructs forecasts
both including and excluding expected immigration in the future. Because our interest is
ultimately in seeing how changes in the size of birth cohorts affects pressures for migration, we
restrict our attention to UN forecasts that leave out projected future immigration.
The decade of the 2040s is far enough in the future for present trends of declining
fertility in the Americas, Europe, and East Asia and of more stable fertility in Sub-SaharanAfrica and parts of the Middle East to produce substantial differences in population growth
between countries. By the 2040s, the North-South divide in population growth will be gone.
Instead, the differences in population growth of the 0-14 age group will be primarily regional innature. For example, essentially all countries of North and South America show declines in theage 0-14 population during the 2040s, with the exceptions of Guatemala and French Guiana,
Consequently, the US-Mexico border no longer appears as a hotspot for migration pressures incoming decades.
Most of Europe will have negative growth in the population of 0-14 year olds in the2040s, with the exceptions of Sweden and the United Kingdom, whose current high-fertility
immigrant populations will keep future fertility from declining as rapidly as on the rest of thecontinent. Morocco, Turkey, and Saudi Arabia also have a projected decline in their age 0-14
native-born population during the 2040s, reflecting projected fertility declines in the morecosmopolitan countries of the Muslim world. However, nearby are the high-fertility countries ofNorth Africa and Sub-Saharan Africa. The Mediterranean thus appears likely to continue to be a
hotspot for international migration out to mid-century. Further, new population-growth
gradients seem ready to emerge within North Africa and the Middle East, contributing topressures for intraregional migration. 6
In this scenario, a question mark hangs over Asia. East, Southeast, and South Asia allshow a decline in the age 0-14 population in the 2040s, with the exceptions of Vietnam andPapua New Guinea. The nations in this region have little history of large immigrant inflows.
They are also far from high-population-growth areas in Africa. Both factors would suggest that
Asia, Egypt, and elsewhere in the Middle East in later decades (Jain, Zacharia, and Oommen 2015). Low
rates of labor-force participation among the native-born in Gulf states may account for the unusual
combination of high growth in the native-born working-age population and high immigration.
6 Perhaps surprisingly, Russia shows up as a country with positive projected population growth. This
reflects the tendency of projection models to impose some degree of regression to the mean; for example,
China’s actual male/female gender imbalance of 1.17 in 2015 is predicted to return linearly towards theglobal average over the coming decades. Following Russia’s demographic collapse that began in the
1980s, forecasts push the country to have makeup population growth in the future.

10

migrant inflows in Asia may stay low in the future. Yet, few nations in Asia have to this point
experienced a demographic reality that entails persistent population decline. This coming drop
may create pressures for immigration policies that are more open than in the past.

Population Pressures for Migration in the Past and Future
To formalize the connection between population pressures and migration, we estimate a
simple gravity model of bilateral migration flows. We then use this model to project migrationout to mid-century, based on forecasted growth of populations coming of working age.
A Gravity Model for Migration Without the Legacy of Previous Migration
The standard framework for bilateral migration is the gravity equation, which posits
that the movement of labor from one location to another is a function of relative earnings in the
two locations, economic opportunities in alternative destinations, and bilateral migration costs(for example, Bertoli and Moraga 2013). Here, we describe one strategy for estimating a modelof this kind, and what it implies for how the population pressures for migration will evolve inthe future. In an online Appendix available with this paper at http://e-jep.org, we discussrobustness and specification checks for this approach.
We look at the migration rate in a disaggregated way: specifically, the migration rate
𝑦𝑦𝑐𝑐𝑐𝑐𝑐𝑐,𝑡𝑡 is the percentage of age-gender group c from sending-country s that has migrated todestination-country d as of year t; for example, the share of 15 to 24 year-old men born in
Mexico who are residing in the United States in 2000. The dependent variable is the change in
the age-specific net emigration rate from a sending country to an OECD destination during the
2000s: for example, the percentage emigration rate to the US for Mexico-born men who were 25
to 34 years old in 2010, 𝑦𝑦𝑐𝑐𝑐𝑐𝑐𝑐,2010 , minus that for those who were 15 to 24 years old in 2000,
𝑦𝑦𝑐𝑐𝑐𝑐𝑐𝑐,2000. Using those who were 15-54 in 2000, we have 4 10-year birth cohorts, 2 genders, and3,457 sending-destination pairs covering 175 sending countries and 25 destinations. Missing
data on GDP for early cohorts from some developing countries reduces the usable sample size
from the potential 27,656 observations to the actual of 18,297. By estimating the model in firstdifferences, we sweep out time-invariant migration barriers in a destination that are specific to asending country (for example, visa policies that favor particular origin nations).
Our starting-point regression has nine explanatory variables, plus a constant term. The
first is a demographic term based on differentials in the size of age-specific birth cohorts. The
second is an incentive term based on income differentials between countries. The next fourterms are commonly included in gravity models: they are the (the log of) distance between
countries where migration occurred, whether the two countries where migration occurred sharea contiguous border, whether the two countries where migration occurred had a colonial

11

relationship; and whether they share a common language. The final three terms are fixed effects
for the age cohort, the sending country, and the destination country, which are intended tocapture time trends in each of these dimensions. We will say a few words about each of these.
But we will then find that it is important to include one more economic factor, made up ofinteraction terms that seeks to capture whether dyads of countries have been more or lessclosely linked by migration in the past, thus laying the groundwork for higher migration by theyounger cohorts in the future.
The first regression term and the key explanatory variable for our analysis can bethought of a seeking to capture the relative abundance of labor in the sending and receiving
economies. We calculate the relative size of an age-gender cohort in the sending and destination
𝐿𝐿

countries, 𝐿𝐿 𝑐𝑐𝑐𝑐,𝑡𝑡 , and we look at how this changes over time. For example, we would look at the
𝑐𝑐𝑐𝑐,𝑡𝑡log Mexico/US birth cohort ratios for those aged 15 to 24 in 2000, minus the log birth cohort
ratio for those aged 15 to 24 in 1990. In effect, this term measures the panel changes in labor
supply over time for a given age cohort. The use of this variable is a way of exploiting the long
panel on birth cohort sizes in a context where we have only a cross-section of migration flows(2000-2010). This variable captures one way in which labor-market conditions in the two
locations differed at the time the cohort entered the labor market.
The second regression term looks at relative per capita GDP (PPP adjusted) in the𝑤𝑤sending and destination countries, 𝑐𝑐𝑐𝑐,𝑡𝑡 . Specifically, we include the log of the GDP ratios in the
𝑤𝑤𝑐𝑐𝑐𝑐,𝑡𝑡

year that a cohort turned 15. This focus on initial conditions captures the logic in Borjas (2006),
in which adjustment costs constrain migration, such that labor flows do not equilibrate wages
across locations instantaneously. 7 Rather, labor moves from lower-wage to higher-wage
locations over time in response to initial wage differentials.

The four standard gravity equation variables are the log geographic distance between
the sending and destination countries and dummies for whether the countries of a dyad share aland border, a common language, and past colonial linkages. These variables can be thought ofas controls for bilateral migration costs.
By virtue of the first-difference specification, we can use fixed effects for age/gendercohort, sending-country, and destination-country, 𝛿𝛿𝑐𝑐 , 𝛿𝛿𝑠𝑠 , and 𝛿𝛿𝑑𝑑 to capture differential timetrends along these dimensions, perhaps representing differential exposure to economic shocks,
To avoid reverse causality from migration to labor-market conditions, we measure
relative labor supply for an age group in the birth year (or as close to the birth year as decennial
censuses allow), and we measure relative per capita GDP around the year that an age groupwould have entered the labor force, which we take to be age 15.
712

over the 2000 to 2010 period. More specifically, sending-country fixed effects help control forchanges in the alternative labor-market opportunities of prospective migrants, while
destination-country fixed effects absorb changes in immigration barriers in the destination thatare common across origins. 8
The first column of Table 2 presents the results from this regression. The first column
shows contrary to our prior work in the Americas (Hanson and McIntosh 2012), global
migration is not effectively absorbing population in rapidly-growing countries: the coefficienton labor supply growth is negative and significant at the 10 percent level. However, consistent
with previous literature, the migration rate is higher for age groups confronting larger
differences in per capita GDP in the destination versus the sending country. For birth cohorts
ten years apart (that is, the same age group observed in successive decades), the one facing a 10
percent higher destination-sending country income ratio in the year of labor-market entry
would have a 0.001 percentage-point lower bilateral migration rate (relative to a weighted
sample mean flow migration rate of .02 percent between 2000 and 2010, with standard deviationof .23).
A Model Including Interactions with Earlier Migration
Bilateral migration is lumpy, as was apparent earlier in Table 1. Migrants fromparticular sending countries tend to cluster in particular destinations. Some of this lumpiness isdue to gravity factors. Because Spain is the closest high-income country to Morocco, it is afavored destination for Moroccan emigrants. Clustering may also embody enclave effects, inwhich earlier generations of migrants ease assimilation for later generations, or built-in
persistence created by migration policy, such as family-reunification provisions that give thekin of existing residents preferential access to visas. The presence of migration networks meansthat initial labor-market conditions may affect bilateral migration. We wish to take these dyadspecific migration networks into account (Munshi 2003; McKenzie and Rapoport 2007; Beine,
Docquier, and Özden 2011). Thus, we seek to interact initial labor-market conditions with thekernels around which networks may form.

8

For those who would like to see their regressions written out in algebraic form, rather
𝐿𝐿

𝐿𝐿

than the description in the text, it is: 𝑦𝑦𝑐𝑐𝑐𝑐𝑐𝑐,𝑡𝑡+1 − 𝑦𝑦𝑐𝑐𝑐𝑐𝑐𝑐,𝑡𝑡 = 𝛿𝛿𝑐𝑐 + 𝛿𝛿𝑠𝑠 + 𝛿𝛿𝑑𝑑 + 𝛼𝛼 �ln 𝐿𝐿 𝑐𝑐𝑐𝑐,𝑡𝑡 − ln 𝐿𝐿 𝑐𝑐𝑐𝑐,𝑡𝑡−1 � +𝛽𝛽1 �ln𝑤𝑤𝑐𝑐𝑐𝑐,𝑡𝑡

𝑤𝑤𝑐𝑐𝑐𝑐,𝑡𝑡

𝑤𝑤

𝑐𝑐𝑐𝑐,𝑡𝑡2

𝑐𝑐𝑐𝑐,𝑡𝑡−1

𝑖𝑖
is the value for gravity variable i (i.e.,� + 𝛽𝛽2 �ln 𝑤𝑤 𝑐𝑐𝑐𝑐,𝑡𝑡 � + + + 𝜀𝜀𝑐𝑐𝑐𝑐𝑐𝑐 , where 𝜏𝜏𝑠𝑠𝑠𝑠
𝑐𝑐𝑐𝑐,𝑡𝑡

distance, language, common border, or colonial linkages) corresponding to sending-country s
and destination-country d.

13

In a first approach to capture this interaction, we calculate the stock of sending-country
migrants over age 50 in the destination as of 2000. Since most international migration occurs
when individuals are young, over-age-50 migrants in 2000 represent inflows that occurred in
the 1960s, 1970s and 1980s, when many networks were first being filled out. We use thismeasure because many dyads do not have available and age-specific bilateral migration data
years before 2000, so we lack the ability to measure immigration networks comprehensively inearlier years. More specifically, we measure these migration networks, using dummy variables
that indicate whether the over-age-50 bilateral migration rate for a dyad was in the top 50
percent, top 20 percent, or top 5 percent across all dyads in 2000.
Our primary interest here is in the relationship between relative labor supply in thesending and destination country and bilateral migration. As noted earlier, the uninteracted
effect of bilateral labor supply is negative, indicating that in countries without strong migrant
networks, relative labor supply has little effect (indeed, a counterintuitively negative effect) on
migration. However, the impact of relative labor supply on bilateral migration becomesstrongly positive once interacted with indicators for migration networks. In columns 2, 3, and 4,
we add the dummy variable for previous over-50 migration in the dyad, as well as aninteraction term between this and the change in the age-specific birth cohort ratio. For birthcohorts ten years apart, the one in which the sending-destination population ratio change was
10 percent larger would have a 0.006 percentage-point (column 2), a 0.02 percentage-point(column 3), or a 0.08 percentage-point (column 4) higher bilateral migration rate, depending onwhether initial migration networks were above the median (over-age-50 bilateral migration rate
in 2000 in the top 50 percent), strong (over-age-50 bilateral migration in the top 20 percent), orvery strong (over-age-50 bilateral migration in the top 5 percent). All of these interactions are
statistically significant at conventional levels.
When the existence of earlier migration networks between two countries is taken into
account by using the size of the earlier migrant group, the relative supply of labor does have aneffect on migration patterns. Another way to take the earlier migration networks into account is
to use the interactions with each of the four gravity variables and the 1990-2000 age-specificbirth cohort ratio. The relationship between bilateral migration and sending-destination
country population ratios for an age group is stronger for dyads that are geographically more
proximate (column 5), contiguous (column 6), or share a colonial language or common language(columns 7 and 8), where all of these interactions are precisely estimated. F-tests at the bottomof the table show that the effect of labor supply differentials are significantly positive withinmost of these well-networked dyad relationships.

14

Population Growth and Migration in the Future
Our next step is to utilize these results for understanding migration pressures in the
future. We run a new regression (reported in the online Appendix) in which we add to thespecification in column (1) all seven of the covariates used across columns (2) to (8) at the sametime, as well as the interactions between these covariates and the relative labor supply effect. 9This regression thus includes in a single specification all of the interactions that are shown
individually in Table 2, and provides a way of predicting panel changes in bilateral migration
over the coming decades. 10 We then combine our regression coefficient estimates with laborsupply projections derived from UN population counts and GDP per capita projections derived
from IMF forecasts to predict the dyadic decadal migration flows that will occur for each 10year age cohort over 2010-2050. 11 We add these decadal inflows to any pre-existing migrant
stocks to calculate future stocks of migrants from each origin in each destination in each decade.
Where we observe stocks of migrants at the beginning of the period, we add the forecasted
flows to these stocks. These predicted stocks can then be summed across gender, age, origin, or
destination to calculate totals of foreign-born migrants aged 15-54 for each decade, as shown in
Figure 3.
Several details of the forecasting exercise merit further discussion. By restricting thesample to be individuals aged 15-54 in the initial year of a decade and dividing these
individuals into 10-year age groups, we forecast a total of 16 decadal migration rates for eachsending-destination dyad. 12 Because there is an assumed a 15-year lag between when an
individual is born and when he enters the labor force, our projected changes in labor supply
combine cohorts already born and cohorts yet to be born. This time lag also means that only twoof the seven birth cohorts represented in the analysis were born after 2015. Thus, most of thechanges in labor supply that we incorporate in the forecasts are based on population growththat has already occurred. We allow initial relative per capita GDP to change over time, basedon IMF forecasts of annual GDP growth for 2018-2020(http://www.imf.org/external/datamapper/index.php) which we assume are sustained out to
We also add to the specification the square of relative GDP per capita at age 15, which helps account for
non-linearities in how income differentials affect migration. Whether or not we include this squared
income term has little impact on the forecasts. An online Appendix provides further discussion.
10
In an online Appendix, we discuss additional forecasts based on alternative assumptions regarding
future GDP growth that we performed to check the robustness of our findings. These alternative forecasts
yield predicted migration flows that are very highly correlated with those shown in Figures 3 and 4.
11 These time periods are 2010-2020, 2020-2030, 2030-2040, and 2040-2050.
12 The birth cohorts (and the number of times they are used in the analysis) are those born in 1965-1974(which are used once over the 2010-2020 period), 1975-1984 (used twice over 2010-2030), 1985-1994 (usedthree times over 2010-2040), 1995-2004 (used four times over 2010-2050), 2005-2014 (used three times over2020-2050), 2015-2024 (used twice over 2030-2050), and 2025-2034 (used once over 2040-2050).
9152040. 13 In the online Appendix we show that alternate GDP forecasts for low-income sending
regions do not materially change our forecasts of future migration.
By holding all other regression variables constant in constructing migration forecasts,
we are assuming that the impacts of distance, contiguity, common language, colonial linkages,
and existing migration networks change at the same rate over future decades as they did
between 2000 and 2010. These projections also incorporate our estimated age-and-gender group,
sending-country, and destination-country fixed effects, which imposes the further assumptionthat time trends for migration along these dimensions are the same in future decades as they
were during the 2000s. 14 These are, of course, very strong assumptions. No decade is exactly likeany other. Just as rapid productivity growth in developing countries could attenuate migrationpressures, civil wars in Sub-Saharan Africa, climate-change-induced coastal flooding inSoutheast Asia, or a return to macroeconomic instability in Latin America could exacerbate
them. Further, new countries outside of the OECD could emerge as major destinations forinternational migrants, possibly attenuating flows into current high-income nations. In holding
sending-country fixed effects constant, we implicitly assume that destination options for thesecountries are unchanged as well. Thus, we emphasize that our intent is not to produce
projections of future immigration levels around which policymakers should make decisions.
Rather, we simply wish to see how the changing population-growth patterns over the next few
decades will affect pressures for international migration.
Figure 3 shows the projected first-generation migrant totals (aged 15-64) for the major
destinations through 2050. Destinations are heterogeneous in the population growth to which
their current migrant stocks expose them, via the interaction between future changes in relativelabor supply and the migrant-network indicators. The United States sees relatively steadycounts of immigrants for the next three decades, which means that arriving young immigrants
replace those who age out of the working-age population with little net change in the total
stock. Three European destinations are heavily exposed to rapidly-growing sending countries
and hence see tremendous future growth in immigration pressure: the United Kingdom, Spain,
and Italy. Germany, exposed in the past primarily to relatively wealthy Turkey and Eastern
Europe, sees a rapid decline in immigrant stocks over the coming 30 years in these calculations.
However, it should be noted that our analysis precedes the recent burst of asylum-driven

Current IMF GDP growth projections do not extend beyond 2020. As growth rates for most countries
are constant after 2018, we assume by this time they have achieved the expected long-run rate of growth.
14 For cohorts that have a 2010 migrant count observed, we add the predicted flows on to the realized
levels to simulate migrant stocks into the future. For younger cohorts in which 2010 stocks are notobserved, we begin to cumulate the predicted changes from 0 and total the predicted flows acrossdecades to arrive at a predicted stock of migration. Because we use a linear model, in some dyads we
predict negative migrant stocks (obviously not possible in reality). We retain these negative values inorder to avoid biasing upward our totals when we sum predicted stocks across origins for a destination.
13

16

migration into Germany, which establishes a base of migrants that is likely to attract future
migrants from those countries for years to come.
Figure 4 drills down into migration inflows for six key destination countries in moredetail. Because of the interactive effect between relative labor supply and the gravity andmigrant-network indicators, forecasted dyadic migrant inflows in major destination countries
differ markedly across major sending regions. In the left-hand column, we see the three rapidly
growing destinations discussed above. The pattern in all three of these countries is relatively
similar: future migration growth coming in equal measure after 2020 from a diverse set ofdestinations as well as from India and Sub-Saharan Africa. Spain and Italy, bordering theMediterranean, are most exposed to migration from Africa. The United Kingdom, given its
colonial linkages, is exposed to India and Sub-Saharan Africa in equal measure. Origins thatplay a large role in each of these destinations are Ethiopia, Philippines, and Nigeria (Italy);
Nigeria, Morocco, and Brazil (Spain); and Philippines, Kenya, and Pakistan (United Kingdom).
In the right-hand panels, we see three destinations that are less exposed to these rapidlygrowing origins. The United States sees Mexican immigrant counts drop from over 10 million in2010 to a little over 2 million in 2050, alongside substantial drops in migrants from all otherdestinations except India, China, and Sub-Saharan Africa. France has relatively constantmigrant stocks over time, resulting from a balance of declining migration from MENA andIndia and a small increase from sub-Saharan Africa. Germany displays the most pronounced
decreases in future migration. These arise from a combination of origins with rapidly falling
fertility—Turkey, Eastern Europe—and the implication of the positive interaction effects onvariables such as colonial linkages and common language, of which Germany has few.
Consequently, the top origins our model predicts for Germany in 2050 are very distinct from theother destinations: France, Romania, Morocco, Somalia, Iraq, Poland, and Russia. Japan is notshown separately in Figure 4, but given its tradition of demographic isolationism and its
distance from population growth centers, it remains a low-migration destination. By 2050, only
China is projected by our model to have more than a few hundred thousand migrants in Japan.

Discussion
Given the prominent role of economic shocks, political conflict, and natural disasters indriving international migration, predicting long-term migration flows is a hazardous
proposition. The demographic component of migration can be useful in such an exercise,because medium-run changes in labor supply are determined by population growth that has
already occurred and longer-term differentials in population growth can be predicted withreasonably high accuracy. While changes in relative labor supply may not independently

17trigger large-scale migration, they set the stage for other types of shocks to set off the movementof large numbers of people across national borders.
This demographic way of thinking about immigration presents some stark implications.
The era in which immigration levels are rising in a way that can feel out-of-control appears to
be coming to an end in the United States, while it is be just beginning in the European Union,
despite current political debates over immigration in the two domains that are quite similar. In
the decades to come, America will find itself to be an island of slow population growth,
insulated by the Atlantic Ocean from the motor of future population change in the Middle Eastand North Africa region and in Sub-Saharan Africa. These weakening immigration pressures
follow a decade-long buildup in US immigration enforcement, which has raised annual
government spending by tens of billions of dollars (Roberts, Alden, and Whitley, 2013). The
completion of the demographic transition in most of the Western Hemisphere leaves one towonder whether the benefits of continued US enforcement spending will justify its costs.
Europe, by contrast, will be surrounded by an eastern region comprised of Belarus,
Ukraine, Russia, and Central Asia that is showing faster population growth, and it is separated
only by the Mediterranean from the still-growing countries of North Africa and the booming
populations of Sub-Saharan Africa. Thus, the conditions exist for western Europe to face strong
population pressures for immigration for decades to come. Though the European Union allows
the free movement of labor among member states, enforcement of immigration from outside the
EU largely falls to individual countries, creating a free-rider problem. Spending by Italy on
patrolling its coast line, for instance, ultimately affects the number of immigrant arrivals innorthern Europe (Labanca, 2016). Given the potential for substantially greater immigrationpressures in the future, one would expect that there would be positive gains to greater
coordination of immigration policy within the EU.
The sending countries will have an alternative perspective on demographically driven
international migration. The possibility of emigration served as a safety valve for theburgeoning populations of Southern Europe and Ireland as they went through their own
demographic transformations in the late 19th and early 20th centuries (Hatton and Williamson,2005). Our previous work shows that emigration to the United States, Canada, the United
Kingdom, and Spain was strongly driven by demographic growth in Latin America and the
Caribbean during the latter half of the 20th century (Hanson and McIntosh, 2015). Strikingly,
this pattern does not hold at a global level. Countries with rapidly growing populations thatlack the benefits of proximity or large established migrant populations already in OECDdestinations see lower emigration rates than more slowly-growing countries. This suggests thatthe migration safety valve may not operate for the next century’s population growth in the waythat it did in the past century. As an example, we predict the number of African-born firstgeneration migrants aged 15 to 64 outside of Sub-Saharan Africa to grow from 4.6 million to18

13.4 million between 2010 and 2050. During this same period, the number of working-age
adults born in the region will expand from under half a billion to more than 1.3 billion, meaningthat international migration would only absorb 1 percent of the overall population growth.
Given an African continent expected to contain almost 4 billion people by 2100, the presence orabsence of a migration safety valve would have profound implications. The coming half century
will see absolute population growth in Sub-Saharan Africa five times as large as Latin
America’s growth over the past half century. Even with our predictions for expanded
population pressures to certain countries of Europe, which are likely to be perceived as very
high levels of immigration by those countries, Europe on track to absorb only a small share of
Africa’s population increase.

19

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22

Table 1: Counting First-Generation Migrants in 2010 (those born between 1946 and 1995).
Panel A. Total Migrant Counts by Destination.
Destination:
USA
Great Britain
Spain
CanadaFrance
Italy
Germany
Australia
Japan
Netherlands

Total Migrants,'000s:
32,782
6,208
4,880
4,697
4,569
4,120
4,019
3,454
1,259
1,223

% of Destination
Population
15.7%
15.0%
15.3%
19.8%
11.2%
10.6%
7.6%
23.1%
1.6%
11.0%

% of All Migrants
in Data
41.6%
7.9%
6.2%
6.0%
5.8%
5.2%
5.1%
4.4%
1.6%
1.6%

Panel B. Total Migrant Counts by Origin.
Origin:MexicoChina
India

Total Migrants,'000s:
10,406
3,036
2,957% of Origin
Population
13.7%
0.3%
0.4%

% of All Migrants
in Data
13.2%
3.9%
3.8%

Panel C. Migrant Counts by Origin/Destination Dyad:
Destination:
USA

Germany

Italy

MexicoFranceSpain

Great Britain

Origin:MexicoIndia
Philippines
China
El Salvador
Puerto Rico
VietnamSouth Korea
Germany
GuatemalaDominican Republic
Cuba
TurkeyPolandRussiaKazakhstan
Romania
AlbanaMoroccoUSA
GuatemalaColombia
Algeria
MoroccoPortugal
Romania
MoroccoEcuadorPolandIndia
PakistanCount, '000s:
10,242
1,431
1,296
1,089
1,022
993
914
867
792
717
667
603
1,021
870
780
611
728
342
315
707
29
12
699
579
379
663
636
392
613
492
399

% of Destination
Population
4.9%
0.7%
0.6%
0.5%
0.5%
0.5%
0.4%
0.4%
0.4%
0.3%
0.3%
0.3%
1.9%
1.6%
1.5%
1.2%
1.9%
0.9%
0.8%
0.9%
0.0%
0.0%
1.7%
1.4%
0.9%
2.1%
2.0%
1.2%
1.5%
1.2%
1.0%

23

% of
Origin Population
13.5%
0.2%
2.2%
0.1%
27.2%
40.2%
1.5%
2.4%
1.5%
8.6%
10.8%
7.6%
2.1%
3.2%
0.8%
5.4%
5.3%
17.5%
1.5%
0.3%
0.3%
0.0%
2.9%
2.7%
5.4%
4.8%
3.0%
4.2%
2.2%
0.1%
0.4%

% of All Migrants
in Data
13.0%
1.8%
1.6%
1.4%
1.3%
1.3%
1.2%
1.1%
1.0%
0.9%
0.8%
0.8%
1.3%
1.1%
1.0%
0.8%
0.9%
0.4%
0.4%
0.9%
0.0%
0.0%
0.9%
0.7%
0.5%
0.8%
0.8%
0.5%
0.8%
0.6%
0.5%

Table 2: The Heterogeneity of Labor Supply Growth by Dyad Characteristics.

Columns show interaction between 1990-2000 change in log birth cohort ratios and the following covariate:
Dependent variable is change in % ofcohort migrated within dyad, 2000-2010

No
Interactions:

Dummy variable indicating that 2000 dyad-specificmigration rates among those over 50 are:in top 50%(1)
Interaction effect:
1990-2000 change in age-specificln(origin/dest) birth cohort ratio
Interacted variable from column title:-0.0200*(0.011)

Log Distance-0.0329***(0.005)0.108(0.088)-0.0289**(0.014)0.0745***(0.009)-0.00997*(0.005)0.719**(0.303)

Contiguous
Colonial RelationshipCommon Language
Log (origin/dest) GDP ratio in year
cohort turned 15
Constant

Observations
R-squared
F-test that (interaction+birth ratio) = 0
Prob > F

18,2970.081

in top 20%

in top 5%

Log Distancefrom Origin to
Destination

Origin and
Destination are
Continguous

Colonial
RelationshipCommonLanguage

(2)

(3)

(4)

(5)

(6)

(7)

(8)

0.0613***(0.023)-0.0418***(0.011)-0.0179**(0.008)-0.0349***(0.005)0.111(0.088)-0.0267*(0.014)0.0750***(0.009)-0.0101*(0.005)0.764**(0.303)0.186***(0.052)-0.0416***(0.010)-0.00101(0.014)-0.0305***(0.004)0.114(0.091)-0.0451***(0.014)0.0684***(0.009)-0.00959*(0.005)0.712**(0.307)0.846***(0.287)-0.0298***(0.009)0.153(0.095)-0.0269***(0.005)0.0952(0.078)-0.0542**(0.022)0.0699***(0.008)-0.0103*(0.005)0.660**(0.305)-0.115***(0.035)1.001***(0.309)-0.0188***(0.007)

1.122***(0.429)-0.0327***(0.011)0.101(0.084)-0.0333***(0.005)0.131(0.094)-0.0343**(0.015)0.0776***(0.010)-0.00889(0.005)0.592*(0.310)0.0762*(0.043)-0.0220**(0.011)-0.0418**(0.017)-0.0325***(0.005)0.109(0.088)-0.0381**(0.017)0.0798***(0.010)-0.0105*(0.005)0.716**(0.304)0.141**(0.056)-0.0364***(0.011)
0.0532***(0.008)-0.0331***(0.004)0.119(0.091)-0.0336**(0.015)0.0740***(0.009)-0.00980*(0.005)0.717**(0.303)-0.0102*(0.005)0.749**(0.304)

18,297
0.082
0.805
0.370

18,297
0.085
7.527
0.006

18,297
0.101
7.997
0.005

18,297
0.084
10.4
0.001

18,297
0.095
6.543
0.011

18,297
0.081
1.531
0.216

18,297
0.082
3.871
0.049

Analysis includes fixed effects for 10-year birth cohorts, origin, destination, and gender. Weighted by birth cohort population size to make results representative for all individuals born in the origin
countries. Standard errors clustered at the dyad/gender/cohort level. *** p<0.01, ** p<0.05, * p<0.1.

Foreign-born share of population, 2010 (%)

Figure 1: Income and Immigration in OECD Destination Countries

Australia
Switzerland

25

IsraelNew Zealand

20

Canada15

Spain

LatviaSlovenia

10
Portugal
GreeceCzech Republic

RussiaLithuania

50

Ireland
Austria
Belgium
SwedenEstonia

Chile
Mexico

12000

Korea
PolandItaly
Denmark
Finland

Hungary
Bulgaria
Romania

Germany
United States
FranceUK Netherlands Norway

Japan

Slovak Republic

24000

48000

Mean per capita GDP, 2000-2010
Fitted Values

Raw Data

Note: Turkey is missing in Figure 1 due to the absence of OECD immigration data for thecountry.

Figure 2a: Population Growth 1970-1980

Figure 2b: Projected Population Growth 2040-2050

26

Figure 3. Predicted Migration by Destination

Predicted Counts of 1st Generation Immigrants Age 1564, millions
35

30

25
Germany
20

Spain
FranceGreat Britain

15

Italy
Japan
USA

10

5

0
2000

2010

2020

2030

-5

27

2040

2050

Figure 4. Migration Projections by Origin and Destination

Migration to ITA, by Origin Region

0

0

1

5

Millions
10

Millions
2
3

15

4

5

20

Migration to USA, by Origin Region

2000

2010

2020

2030

2040

2050

2000

year
India
Eastern Europe
MENA

2010

2020

2030

2040

2050

year

China
Sub-Saharan Africa
All OtherIndia
MexicoMENA

ChinaSub-Saharan Africa
All Other

Migration to FRA, by Origin Region

0

-.5

0

2

Millions

Millions
1
.5

4

1.5

2

6

Migration to ESP, by Origin Region

2000

2010

2020

2030

2040

2050

2000

year
India
Eastern Europe
MENA

2010

2020

2030

2040

2050

year
China
Sub-Saharan Africa
All Other

India
Eastern Europe
MENA

China
Sub-Saharan Africa
All Other

Migration to DEU, by Origin Region

0

-1

1

0

Millions
2
3

Millions

1

4

5

2

Migration to GBR, by Origin Region

2000

2010

2020

2030

2040

2050

2000

2010

2020

India
Eastern Europe
MENA

India
Eastern Europe
MENA

China
Sub-Saharan Africa
All Other

28

2030

2040

year

year

China
Sub-Saharan Africa
All Other

2050

Online Appendix
A1. Estimation of the forecasting model.
We estimate the forecasting model using 2000-2010 migration flows, as in Table 2, except thatnow we include all seven of the dyad-level gravity variables simultaneously in a single
regression. The variables used in the analysis are: 𝑦𝑦𝑐𝑐𝑐𝑐𝑐𝑐,𝑡𝑡 , the percentage of age-gender group c
𝐿𝐿from sending-country s that has migrated to destination-country d as of year t; 𝐿𝐿 𝑐𝑐𝑐𝑐,𝑡𝑡 , the relative𝑤𝑤

𝑐𝑐𝑐𝑐,𝑡𝑡

size of age cohort c for sending-country s and destination-country d at time t; 𝑤𝑤 𝑐𝑐𝑐𝑐,𝑡𝑡 , relative per
𝑐𝑐𝑐𝑐,𝑡𝑡

capita GDP for age cohort c corresponding to dyad sd at time t; and 𝛿𝛿𝑐𝑐 , 𝛿𝛿𝑠𝑠 , and 𝛿𝛿𝑑𝑑 , fixed effects
for the cohort, sending country, and destination country.
The estimating equation is,
𝑦𝑦𝑐𝑐𝑐𝑐𝑐𝑐,𝑡𝑡+1 − 𝑦𝑦𝑐𝑐𝑐𝑐𝑐𝑐,𝑡𝑡

2

𝐿𝐿𝑐𝑐𝑐𝑐,𝑡𝑡
𝐿𝐿𝑐𝑐𝑐𝑐,𝑡𝑡−1
𝑤𝑤𝑐𝑐𝑐𝑐,𝑡𝑡
𝑤𝑤𝑐𝑐𝑐𝑐,𝑡𝑡= 𝛿𝛿𝑐𝑐 + 𝛿𝛿𝑠𝑠 + 𝛿𝛿𝑑𝑑 + 𝛼𝛼 �ln− ln
� + 𝛽𝛽1 �ln
� + 𝛽𝛽2 �ln
�𝐿𝐿𝑐𝑐𝑐𝑐,𝑡𝑡
𝐿𝐿𝑐𝑐𝑐𝑐,𝑡𝑡−1
𝑤𝑤𝑐𝑐𝑐𝑐,𝑡𝑡
𝑤𝑤𝑐𝑐𝑐𝑐,𝑡𝑡
𝐿𝐿𝑐𝑐𝑐𝑐,𝑡𝑡
𝐿𝐿𝑐𝑐𝑐𝑐,𝑡𝑡−1𝑖𝑖𝑖𝑖+ � 𝜌𝜌𝑖𝑖 ��ln
− ln
� ∗ 𝜏𝜏𝑠𝑠𝑠𝑠+ � 𝛾𝛾 𝑖𝑖 𝜏𝜏𝑠𝑠𝑠𝑠
� + 𝜀𝜀𝑐𝑐𝑐𝑐𝑐𝑐
𝐿𝐿𝑐𝑐𝑐𝑐,𝑡𝑡
𝐿𝐿𝑐𝑐𝑐𝑐,𝑡𝑡−1
𝑖𝑖

𝑖𝑖

𝑖𝑖
where the gravity variables 𝜏𝜏𝑠𝑠𝑠𝑠
i=1,…,7, are log distance between the sending and destination
country, indicators for the sending and destination country having a common border, common
language, and shared colonial history, and indicators for whether the migration rate of overage-50 migrants from the sending to the destination country in 2000 was in the top 50 percent,top 20 percent, and top 5 percent across all sending-destination dyads.

Using our labor-supply projections derived from UN data and GDP forecasts derived from IMF
data (as described in the text), we predict the dyadic decadal migration flows that will occur for
the four 10-year age cohorts (15-24, 25-34, 35-44, and 45-54) over four decades (2010-2020, 20202030, 2030-2040, and 2040-2050). The labor-supply values that we feed into the forecasts include
a mix of cohorts already born and cohorts yet to be born, as of 2015. The oldest already-borncohort is aged 45-54 in 2010 and thus is used in the forecast only once for the change inmigration rate over 2010-2020; the youngest already-born cohort is aged 5-14 in 2010 and which
is used to forecast migration flows over 2020-2030, 2030-2040, and 2040-2050; and the youngest
yet-to-be born cohort is aged 15-24 in 2040 and used to forecast migration flows over 2040-2050.
Of the 7 birth cohorts represented in the analysis, two are born after 2015; of the 16 age cohorts
used in the forecasts (2 birth cohorts appear in the analysis once, 2 appear 2 times, 2 appear 3
times and 1 appears 4 times), 3 correspond to individuals born after 2015. See note 13 in themain text for a listing of each birth cohort and how many times it appears in the data.

29

To construct forecasts of migrant stocks, we add these forecasted decadal migration inflows to
any pre-existing migrant stocks and proceed to calculate future stocks of migrants from eachorigin in each destination in each decade. Where we observe stocks of migrants at the beginningof the period, we add the forecasted flows to these stocks. Where we predict flows for newcohorts with no observed migration, we assume that the initial stock of migrants is zero. These
predicted stocks can then be summed across gender, age, origin, or destination to calculate
totals of foreign-born migrants aged 15-64 for each decade. With zero predicted future
migration flows, or predicted decadal flows after 2010 that are smaller than average decadal
flows before 2010, forecasted migrant stocks in destination countries decline over time, as larger
cohorts of older migrants age out of the 15-64 age group and smaller cohorts of new migrants
are incorporated into the stock. With predicted decadal flows after 2010 that are larger than
average decadal flows before 2010, forecasted migrant stocks rise over time.

30

Appendix Table A1: Regression used to fit the prediction model.
Dependent variable is change in % of cohort migrated
within dyad, 2000-2010

Coefficient(Standard Error)

1990-2000 change in age-specificln(origin/dest) birth cohort ratio
Log (origin/dest) GDP ratio in year
cohort turned 15(Log (origin/dest) GDP ratio in year
cohort turned 15) squared
top 50% 2000 migration rate over 50

0.295**(0.133)0.00272(0.006)0.00265***(0.001)0.0111**(0.006)-0.0389***(0.014)0.0111(0.012)0.121***(0.032)0.159*(0.096)
0.675***(0.248)-0.0213***(0.005)-0.0381**(0.015)0.0972(0.079)0.917**(0.366)-0.0504**(0.023)-0.178**(0.077)0.0622***(0.008)0.0547(0.035)0.555*(0.310)

top 50% * birth cohort ratio
top 20% 2000 migration rate over 50
top 20% * birth cohort ratio
top 5% 2000 migration rate over 50
top 5% * birth cohort ratio
Log Distance from Origin to Destination
Log Distance * birth cohort ratio
Contiguous Origin & Destination
Contiguous * birth cohort ratio
Colonial RelationshipColonial * birth cohort ratio
Common Language
Common Language & birth cohort ratio
Constant

Observations
R-squared

18,297
0.114

Analysis includes fixed effects for 10-year birth cohorts, origin, destination, andgender. Weighted by birth cohort population size to make results representative
for all individuals born in the origin countries. Standard errors clustered at the
dyad/gender/cohort level. *** p<0.01, ** p<0.05, * p<0.1.

31

A2. Varying GDP growth in the forecasts.
We are almost certainly able to forecast population more accurately than GDP. This raises thequestion of the sensitivity of our forecasts to potential future swings in GDP. For example,
imagine that Africa were to enter a high-growth period, how much would the resulting increasein labor demand hold back pressures for emigration that our model otherwise suggests willtake place? To answer this question, we feed alternate scenarios for GDP growth into our
prediction model, examining outcomes if African GDP grew 10%, 25%, and 50% faster than the
IMF forecast currently predicts.
Examination of Table A1 already suggests that the effect may be modest, but positive; meaningthat higher income ratios would encourage higher emigration. The coefficients on GDP ratios
are small, and indicate a convex relationship meaning that the effect will be smallest for
countries such as those in SSA who would begin a change in income ratios from a low base.
Indeed, when we examine the migration predictions produced by the different GDP runs, wefind a trivially small difference between them. Table A2 shows that while we do get a noticeable
difference in prediction values when we move from a model without GDP to one with the linear
and quadratic function of the GDP ratios, varying the economic growth rate of countries in SubSaharan Africa by wide margins has little effect on migration rates. This is largely because GDP
ratios have only a very weakly positive relationship with migration when the income gap
between origin and destination is large.
Appendix Table A2: Predicted Migration when Shifting GDP Forecasts.
Correlation between forecasts of migrant stocks for SSA from 2020 on:
GDP ratio linear African GDP up
No GDPby 10%& quadratic
No GDP

African GDP up
by 25%

African GDP up
by 50%

1

GDP ratio linear &0.9741
1
quadratic
African GDP up by
0.97351
1
10%African GDP up by
0.97260.99991
1
25%
African GDP up by
0.97130.99970.99980.999950%
Note: 'GDP ratio linear & quadratic' is the specification used in the paper's main tables.

32

1