Journal of Economic Perspectives—Volume 30, Number 4—Fall 2016—Pages 1–25

Is the Mediterranean the New Rio Grande?
US and EU Immigration Pressures in theLong Run
Gordon Hanson and Craig McIntosh

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 have kicked migration to Europe intoa higher gear, immigration flows to the continent in the medium- and long-run arelikely 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, create pressures for international migration. Periodically, economic or politicalcrises unleash these pressures 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 baby-boom 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 of childbearing age) averaged
6.8, versus 3.0 in the United States—meant that Mexico’s labor force was expanding■ Gordon Hanson is the Pacific Economic Cooperation Professor in International Economic

Relations and Craig McIntosh is Professor of Economics, both at the School of Global Policy
and Strategy, University of California-San Diego, La Jolla, California. Hanson is also a
Research Associate, National Bureau of Economic Research, Cambridge, Massachusetts.
Their email addresses are gohanson@ucsd.edu and ctmcintosh@ucsd.edu.
†

For supplementary materials such as appendices, datasets, and author disclosure statements, see thearticle page at
http://dx.doi.org/10.1257/jep.30.4.1
doi=10.1257/jep.30.2.1

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Journal of Economic Perspectives

rapidly in the early 1980s, just as a severe financial crisis hit. This crisis, and the decadeand a half of economic instability that ensued, unleashed a great wave of Mexican
migration to the United States (Hanson and McIntosh 2010). Encouraging this flow
was steady US economic growth during the “Great Moderation” period from themid-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 and housing, opening familiar storesand restaurants, and creating enclaves in which Spanish was spoken alongside English(Massey, Alarcón, Durand, and Gonzalez 1987; Munshi 2003).
The Mexican migration wave to the United States has now crested. Fertility
rates in Mexico, at 2.3 births per woman of childbearing age, are only modestly
above those in the United States, at 1.9 (World Development Indicators, data for2013). Labor-supply growth in the two countries is projected to be roughly thesame 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 2015). Absent a significant new economic or politicalcrisis 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, declines in fertility in the 1970s and 1980s set the stage fora situation in which the number of working-age residents is in absolute decline (asdiscussed 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
the highest in the world.
Many countries in North Africa and the Middle East are in a period of profoundpolitical and economic upheaval. Migrants escaping military conflict in Afghanistan, Iraq, Libya, and Syria 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 arebeing tripped in a demographic environment that is ideal for perpetuating emigration well into the future. As further motivation, established populations of Algerians
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
to international migration in the long run. We look backward to the significant
migration episodes of the last 30 years and forward to the pressures that will

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3

encourage flows in coming decades. Whereas in the 1970s, the world was neatly
divided between a high-income and low-population-growth North and a low-incomeand ­high-population-growth South, by the 2040s the only high-population-growthcountries 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 bilateral migration and then apply the estimated coefficients to project future migration out to
mid-century, as implied by population forecasts. This exercise projects the stock offirst-generation immigrants to remain flat in the United States, as a consequence ofthe cessation of Mexico’s emigration surge, and the stock of first-generation immigrants 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 guidefor how demographic trends will recalibrate pressures for international labor movements. If Europe tightens its borders significantly, the United States decides torelax entry restrictions, or the nations 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 andthe 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 mostof Latin America is cascading downward and looks set to drop below replacementlevels within a couple of decades, it is proceeding apace in sub-Saharan Africa andmuch of North Africa and the Middle East. These patterns mean that the UnitedStates and Europe occupy neighborhoods whose future patterns of migration arelikely to differ immensely from those of the past.

Global Migration Patterns
In 1970, there was little indication either in the United States or in Europethat immigration would become a major political issue by century’s end. The shareof the US population that was foreign-born in 1970 was at a 100-year low, at 4.7
percent. Concerns in western Europe centered not so much on excess entry as on
restrictions that impeded those living behind the Iron Curtain from moving west
(Joppke 1999). At the time, policymakers in Bonn, Paris, and London wanted, if
anything, to facilitate immigration, not to block it. Today, of course, anxiety about
the arrival of foreign workers is running high.
We begin our discussion by summarizing the present contours of immigrationin the United States and other OECD nations. We focus on the OECD both because
it is a grouping that includes all high-income destination economies, aside fromthe oil-rich states of the Persian Gulf, and because it provides immigration databy age cohorts that are comparable across countries. We then examine past andprojected future population growth worldwide, paying particular attention to forecasted changes in growth rates in current migration hot spots.

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Journal of Economic Perspectives

Immigrant Presence in OECD Countries
Perhaps the most fundamental concept in the economics of immigration isthat international 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 toanother location—due to moving expenses, fees for obtaining a visa, time out of thelabor 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 isbased on comparing future income streams at home versus abroad, the vast majorityof empirical work uses current income as a summary statistic for cross-country
differences in labor-market conditions (for example, Clark, Hatton, and Williamson
2007; Mayda 2010). In our first foray into the data, we follow this convention. Later,
we introduce differences in relative labor supply growth as an additional indicatorof future income streams.
Figure 1 plots the share of the population that is foreign born in 2010 against
mean purchasing-power-parity–adjusted per capita GDP averaged over 2000 to2010 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 proceeding decades, while mean income for the 2000s provides a metric ofeconomic 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 is strongly positive and statistically significant at the 1 percent level. For
every 10 percent increase in 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 foreign-born share (26.6 percent) is nearly twoand-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 incomesbut 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 isapparently about more than current income differences between countries.
A vast literature examines the factors that shape bilateral migration flows.
Similar to findings for the “gravity model” of trade (Head and Mayer 2014),
migration is strongly decreasing in geographic distance between countries andsignificantly higher between country pairs that share a land border, a common
language, and past colonial linkages (Mayda 2010). These additional factors explainin part why Anglophone countries are particularly popular destinations for international migrants. Four of the six highest foreign-born population shares are found10/12/16 3:20 PM

Is the Mediterranean the New Rio Grande? US and EU Immigration Pressures

5

Percent of population foreign born, 2010

Figure 1
Income and Immigration in OECD Destination Countries

25

Switzerland
Australia

IsraelNew Zealand

20

CanadaSpain

LatviaSlovenia

10

Portugal
GreeceCzech Republic

RussiaLithuania

50

Ireland
Austria
Belgium
SwedenEstonia15

Chile
Mexico12,000

Korea
PolandItaly

Denmark

Finland

Hungary
Bulgaria
Romania

Germany
United States
FranceUK Netherlands Norway

Japan

Slovak Republic

24,00048,000Mean per capita GDP in PPP-adjusted US dollars, 2000–2010

Source: Authors using GDP data from the Penn World Tables 8.0 and immigration data from the OECD.
Note: The figure plots the share of the population that is foreign born in 2010 against mean purchasingpower-parity–adjusted per capita GDP averaged over 2000 to 2010 for OECD countries. The fitted values
use a cubic in per capita GDP. Turkey is missing from the figure due to the absence of OECD immigration
data for the country.

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 such as those among British Commonwealth nations—that resulted in
reduced costs for current labor flows between former colonies of the same hegemon
and between a former colony and the hegemon itself.
The additional explanatory power of gravity variables would still not account for
the idiosyncratic cases of very high or very low immigration, which are evident in
Figure 1. High-inflow countries include Switzerland, whose long history of neutrality
has made it the headquarters for international organizations that hire large numbersof foreign diplomats and other 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 Hollifield 1994), resulting in low labor inflows. These
historical features, which are manifest in policies that would be transparent to any
person attempting to emigrate to these destinations, are difficult to measure systematically across countries and hence end up in the regression residual.
Another shortcoming of the gravity model, at least as conventionally applied, isa focus on current income as a predictor for bilateral migration. Because migration

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Journal of Economic Perspectives

is a long-lived decision, individuals are likely to evaluate the extended prospects fortheir livelihoods in alternative destinations, as Sjaastad (1962) proposed. Obvious
additional factors that affect future earnings include total factor productivity growthand capital accumulation, which are hard to forecast far into the future. Domestic
labor-supply growth, on the other hand, is eminently forecastable. Current differences in birth rates between countries foretell how relative labor 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 twoto 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 international migrants. In later sections, we
use this insight to estimate an augmented gravity model of bilateral migration that
accounts for both current relative incomes and expected future relative incomes ascaptured by anticipated changes in domestic labor supplies.
Figure 1 illustrates the potential pitfalls of using only current income alone
as an indicator of coming migration. If one were to utilize nothing other than theresiduals from the regression plot, it would appear that the United States is poisedfor relatively high immigration in the future. The foreign-born share of the US population at 12.9 percent is similar to that of the large countries of Europe: it is evenwith Germany, slightly above France, the Netherlands, and the United Kingdom,and slightly below Spain. Yet, US per capita GDP exceeds that of these other nations
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 a different picture of migration pressures emerges once we
examine the composition of bilateral immigration stocks in major destination countries. These comparisons reveal that it is Europe, not the United States, in which thepotential 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 bornby country of birth from the Database on Immigrants in OECD Countries (DIOC).1
Following categories used in the DIOC, we focus on individuals between ages
15 and 64. This range is more expansive than the definition of the working-age­population that is conventional in US or European labor-market analysis. Whereas
1This OECD database is at 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 the 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 on labor force surveys, which suppress country of birth for immigrants fromsmaller origin nations. As a consequence, we lack data on bilateral stocks that account for two-fifths ofGerman immigration.

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Gordon Hanson and Craig McIntosh

T17

many high-income nations require children to stay in school 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 OECDmigrant-receiving 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 UnitedStates is by far and away the dominant 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. (Forperspective, the United States accounted for one-quarter of the total OECD population in 2010.) Other destination countries are distant runners up. Next in line areGreat 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 justunder one-third of US working-age immigrants. The 10.2 million individuals bornin Mexico and living in the United States accounted for fully 13.0 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 thelargest international migration episodes that the world has seen. Any substantial change 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
dyadic origin–destination groups in the OECD are also 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 born in Turkey and Poland
are 27.4 percent of German immigrants.2 The Romanians living in Italy and Spain
2

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 at theestimate 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. This conversion factor is 13.0 (the percentage of the foreign-bornin German’s total population in Figure 1) divided by 7.6 (the percentage of the foreign-born in German’s
working-age population in Table 1).

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8Journal of Economic Perspectives

Table 1Counting First-Generation Migrants in 2010 (Those Born between 1946 and 1995)
A: Total Migrant Counts by Destination
Destination:

Total migrants,
thousands:% of Destination
population% of All migrants
in data

USA
Great Britain
Spain
CanadaFrance
Italy
Germany
Australia
Japan
Netherlands

32,782
6,208
4,880
4,697
4,569
4,120
4,019
3,454
1,259
1,223

15.7%
15.0%
15.3%
19.8%
11.2%
10.6%
7.6%
23.1%
1.6%
11.0%

41.6%
7.9%
6.2%
6.0%
5.8%
5.2%
5.1%
4.4%
1.6%
1.6%

B: Total Migrant Counts by Origin
Origin:Total migrants,
thousands:% of Origin
population% of All migrants
in data

MexicoChina
India

10,406
3,036
2,957

13.7%
0.3%
0.4%

13.2%
3.9%
3.8%

C: Migrant Counts by Origin/Destination Dyad
Destination:

Origin:USA

MexicoIndia
Philippines
China
El Salvador
Puerto Rico
VietnamSouth Korea
Germany
GuatemalaDominican Republic
Cuba
TurkeyPolandRussiaKazakhstan
Romania
AlbanaMoroccoUSA
GuatemalaColombia
Algeria
MoroccoPortugal
Romania
MoroccoEcuadorPolandIndia
Pakistan

Germany

Italy

MexicoFranceSpain

Great Britain

Count, thousands:% of Destination
population% of Origin
population% of All migrants
in data

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

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%

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%

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%

Source: Authors’ calculations from the Database on Immigrants in OECD Countries (DIOC).

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Is the Mediterranean the New Rio Grande? US and EU Immigration Pressures9

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 inthese 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 thelargest bilateral migration stocks involve nearby countries. There is both motive andopportunity for migration from Romania to Italy, Poland to Germany, and Morocco
to Spain. The enlargement of the European Union in 2004 to countries acrosseastern Europe, which eliminated immigration restrictions on accession countries,
further expanded opportunities for migration from 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, andIndians 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 lowwage positions during a period of soaring economic growth. This first generationof immigrants created family and kinship networks that facilitated immigration inlater 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 essentiallyunchanged 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 20
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 theonset of the Mexican debt crisis of the 1980s and the “lost decade” of economic
stagnation that followed (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 2008;
Bertoli, Moraga, and Ortega 2013), as are political upheaval and military conflict(Hanson and McIntosh 2012). However, temporary shocks need not induce permanent migration. During the Mexican Revolution (1911–1920), approximately
3

If education, labor market experience, or other sources of human capital are higher in destination
than in sending countries, bi-national differences in per capita GDP will overstate the gain in incomethat an international 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 of schooling 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).

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10Journal of Economic Perspectives

700,000 Mexicans, or 5 percent of the population, fled to the United States. By thelate 1920s, a large fraction had returned home (Cardoso 1980; Durand, Massey, andCharvet 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 such expectations. Between 1940 and 1960, the US baby boom
matched rapid population growth in Mexico. In the ensuing two decades, birthcohorts in Mexico continued to grow in size, while the annual number of births
in the United States fell in both relative and absolute terms. The consequence wasthat between 1960 and 1980 the relative size of US–Mexican cohorts born each year
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
Mexican states along the US border and around Mexico City; last affected werepoorer states in central and southern Mexico. The Mexican migrants who moved
north, while roughly similar in educational attainment to workers who stayedbehind (Chiquiar and Hanson 2005; Kaestner and Malamud 2014), were not drawn
randomly from across Mexico. They came disproportionately from states with preexisting migration networks (Munshi 2003; McKenzie and Rapoport 2007) and withrelatively high labor-force growth. Exploiting this regional variation, in Hanson and
McIntosh (2010), we find that relative labor-supply growth can account for twofifths of Mexican migration to the US between the 1970s and the 1990s.

F2

The Hot Spots with Population Pressures for Migration
The argument here suggests that when international borders also draw aline 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 1980 for the population of native-born
individuals 0 to 14 years old. Data are from the United Nations’ World Population
Prospects (at http://esa.un.org/unpd/wpp/Download/Standard/Population).
These growth rates determine the number of the native-born who would be comingof 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 population pressures for migration
will change over time.4 Figure 2 maps the outcome of this exercise, where countries

4

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.

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Gordon Hanson and Craig McIntosh

11

Figure 2
Maps of Actual and Projected Population Growth(percent change in population ages 1–14)
A: Population Growth 1970–1980

GrowthRate
rate
GrowthBelow -5%Below
−5%
-4.99% - 0%
−4.99%–0%
0% - 10%0%–10%
Over 10.01%
Over
10.01%No DataNo
data

B: Projected Population Growth 2040–2050

Growthrate
Growth
Rate
Below
−5%
Below -5%
−4.99%–0%-4.99% - 0%
0% - 10%0%–10%
Over 10.01%
Over
10.01%No DataNo
data

Source: United Nations’ World Population Prospects (http://esa.un.org/unpd/wpp/Download/
Standard/Population).

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 mostnations of western Europe show a decline in the relevant population from 1970
to 1980. Declines in these countries were the result of the end of the post–WorldWar II baby boom and the lower birth rates that had taken hold by roughly 1960.
Australia, Ireland, Japan, and Spain were the only higher-income countries withhanson_304.indd 11

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12Journal of Economic Perspectives

positive growth in the number of young people during the decade. In contrast, formost 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 positivein Latin America, Africa, the Middle East, and most of developing Asia. Growthin these regions was the result of continued high fertility combined with declines
in infant mortality (Lee 2003). Among the 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, and Suriname 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 and that occupy different ends of this population growth continuum. Basedon 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 fuel migration 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 are Russia and most of the nations of Central Asia. In recent decades,
there were substantial migration flows within each of these groups (Özden, Parsons,
Schiff, and Walmsley, 2011).5
Looking forward, the geography of population growth will be very different
than in the past, which also suggests that the population pressures for migration
will shift. To construct the growth rate of the age 0–4 population between 2040 and
2050, we used projections from the United 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 ofdeclining fertility in the Americas, Europe, and East Asia and of more stable fertility
in Sub-Saharan Africa 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 in nature. For example,
essentially all countries of North and South America show declines in the age 0–14
population during the 2040s, with the exceptions of Guatemala and French Guiana,

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 SouthAsia, Egypt, and elsewhere in the Middle East in later decades (Jain and Oommen 2016). Low rates oflabor-force participation among the native-born in Gulf states may account for the unusual combinationof high growth in the native-born working-age population and high immigration.

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Is the Mediterranean the New Rio Grande? US and EU Immigration Pressures

13

Consequently, the US–Mexico border no longer appears as a hotspot for migrationpressures in coming decades.
Most of Europe will have negative growth in the population of 0–14 year-olds inthe 2040s, 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 the continent. 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 more cosmopolitan countries of the
Muslim world. However, nearby are the high-fertility countries of North Africa andsub-Saharan Africa. The Mediterranean thus appears likely to continue to be a
hotspot for international migration out to mid-century. Further, new populationgrowth gradients seem ready to emerge within North Africa and the Middle East,
contributing to pressures for intraregional migration. 6
In this scenario, a question mark hangs over Asia. East, Southeast, and South
Asia all show a decline in the age 0–14 population in the 2040s, with the exceptionsof Vietnam and Papua New Guinea. The nations in this region have little historyof large immigrant inflows. They are also far from high-population-growth areas inAfrica. Both factors would suggest that migrant inflows in Asia may stay low in the
future. Yet few nations in Asia have to this point experienced a demographic realitythat 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 migration out 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 ofrelative 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 model of this kind and what it implies for
how the population pressures for migration will evolve in the future. In an online

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.

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14

Journal of Economic Perspectives

Appendix available with this paper at http://e-jep.org, we discuss robustness andspecification checks for this approach.
We look at the migration rate in a disaggregated way: specifically, the migration
rate ycsd,t is the percentage of age-gender group c from sending-country s that has
migrated to destination-country d as of year t  ; for example, the share of 15 to 24 yearold 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 United States for Mexico-born men who were 25 to 34 years old in 2010,
ycsd,2010, minus that for those who were 15 to 24 years old in 2000, ycsd,2000. Using
those who were 15–54 in 2000, we have four ten-year birth cohorts, two genders,and 3,457 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
sample size of 18,297. By estimating the model in first-differences, we sweep out timeinvariant migration barriers in a destination that are specific to a sending country(for example, visa policies that favor particular origin nations).
Our starting-point regression has nine explanatory variables, plus a constantterm. The first is a demographic term based on differentials in the size of age-specificbirth cohorts. The second is an incentive term based on income differentials between
countries. The next four terms are commonly included in gravity models: they are
the (the log of) distance between countries where migration occurred, whether thetwo countries where migration occurred share a contiguous border, whether the twocountries where migration occurred had a colonial relationship; and whether theyshare a common language. The final three terms are fixed effects for the age cohort,the sending country, and the destination country, which are intended to capture timetrends 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
of interaction terms that seeks to capture whether dyads of countries have been moreor less closely linked by migration in the past, thus laying the groundwork for higher
migration by the younger cohorts in the future.
The first regression term and the key explanatory variable for our analysis canbe thought of as seeking to capture the relative abundance of labor in the sending
and receiving economies. We calculate the relative size of an age-gender cohort inLcs,tthe sending and destination countries, ​​ ___​​, and we look at how this changes overLcd,ttime. For example, we would look at the log 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 conditionsin 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)
wcs,tin the sending and destination countries, ​​ ___
wcd,t ​​. Specifically, we include the log of

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Gordon Hanson and Craig McIntosh

T2

15

the GDP ratios in the 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 thecountries of a dyad share a land border, a common language, and past colonial
linkages. These variables can be thought of as controls for bilateral migration costs.
By virtue of the first-difference specification, we can use fixed effects forage/gender cohort, sending-country, and destination-country, δc  , δs  , and δd tocapture differential time trends along these dimensions, perhaps representing
differential exposure to economic shocks, over the 2000 to 2010 period. Morespecifically, sending-country fixed effects help control for changes in the alternativelabor-market opportunities of prospective migrants, while destination-country fixed
effects absorb changes in immigration barriers in the destinations that are common
across origins.8
The first column of Table 2 presents the results from this regression. The firstcolumn shows (contrary to our prior work in the Americas in Hanson and M­ cIntosh2012) that global migration is not effectively absorbing population in rapidly
growing countries: the coefficient on 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 (thatis, 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 aweighted sample mean flow migration rate of .02 percent between 2000 and 2010,
with standard deviation of 0.23).
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 thislumpiness is due to gravity factors. Because Spain is the closest high-income country
to Morocco, it is a favored destination for Moroccan emigrants. Clustering may
7

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 wemeasure relative per capita GDP around the year that an age group would have entered the labor force,
which we take to be age 15.
8
For those who would like to see their regressions written out in algebraic form, rather thanthe description in the text, it is: ycsd,t+1 – ycsd,t = δc + δs + δd + α​​[ln ​ ___​– ln ​ ____​]​​ + β​​[ln ​ ___
wcd,t ​]​ ​+ γ τsd +
L
L
Lcs,t

Lcs,t−1

wcs,t

Lcs,t
Lcs,t−1iρ​​(​[ln ​ ___​– ln ​ ____​ ​  * τsd)​​  + εcsd, where ​​τ​  sd​​​  is the value for gravity variable i (i.e., distance, language,
Lcd,t
Lcd,t−1 ]cd,tcd,t−1

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

hanson_304.indd 15

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hanson_304.indd 16

0.108(0.088)−0.0288**(0.014)0.0743***(0.009)−0.00722(0.007)0.000553(0.001)0.721**(0.303)
18,2970.081

Contiguous

Colonial relationshipCommon languagelog (origin/dest) GDP ratio in year
cohort turned 15(log (origin/dest) GDP ratio in year
cohort turned 15) squared
Constant

Observations
R2
F-test that (interaction+birth ratio) = 0
Prob > F

18,297
0.082
0.809
0.368

0.000841(0.001)0.768**(0.303)−0.00591(0.007)0.0747***(0.009)−0.0265*(0.014)0.111(0.088)−0.0184**(0.008)−0.0350***(0.005)

18,297
0.085
7.333
0.007

0.000659(0.001)0.714**(0.307)−0.00632(0.007)0.0681***(0.010)−0.0452***(0.014)0.114(0.091)−0.000599(0.014)−0.0305***(0.004)−0.0424***(0.009)0.186***(0.052)(3)

in top20%

18,297
0.101
7.911
0.005

0.00135**(0.001)
0.663**(0.306)−0.00359(0.006)

0.0694***(0.008)−0.0543**(0.022)

0.0947(0.078)0.155(0.095)−0.0270***(0.005)−0.0313***(0.009)0.843***(0.287)(4)in top5%

18,297
0.084
10.9
0.001

0.00116**(0.001)0.593*(0.310)−0.00311(0.006)0.0772***(0.010)−0.0343**(0.015)0.131(0.094)−0.0186***(0.007)1.016***(0.307)−0.117***(0.035)(5)

Log distancefrom origin todestination

18,297
0.096
6.712
0.010

0.00195***(0.001)0.721**(0.304)−0.000824(0.007)0.0792***(0.010)−0.0380**(0.017)0.1(0.084)−0.0335***(0.004)−0.0352***(0.011)1.139***(0.429)(6)

Origin anddestination arecontinguous

18,297
0.081
1.504
0.220

0.000562(0.001)0.718**(0.303)−0.00701(0.007)0.0738***(0.009)0.109(0.088)−0.0418**(0.017)−0.0326***(0.005)−0.0226**(0.011)0.0764*(0.043)(7)

Colonial
relationship

18,297
0.082
3.846
0.050

0.000686(0.001)0.751**(0.305)−0.00682(0.007)−0.0335**(0.015)0.119(0.091)

0.0528***(0.008)−0.0331***(0.004)−0.0373***(0.011)0.142**(0.055)(8)

Commonlanguage

Source: Authors.
Note: Analysis includes fixed effects for 10-year birth cohorts, origin, destination, and gender. All regressions are weighted by birth cohort population size to make results representative forall individuals born in the origin countries. Standard errors are clustered at the dyad/gender/cohort level. ***p < 0.01, **p < 0.05, *p < 0.1.

−0.0330***(0.005)log distanceInteracted variable from column title:−0.0434***(0.010)−0.0207*(0.011)1990­–2000 change in age-specificln(origin/dest) birth cohort ratio(2)
0.0629***(0.022)(1)

Interaction effect:

Dependent variable is change in % ofcohort migrated within dyad, 2000–2010

in top50%

Nointeractions:

Dummy variable indicating that 2000
dyad-specific migration rates among those over 50 are:Columns show interaction between 1990–2000 change in log birth cohort ratios and the following covariate:

Table 2
Interaction Effects of Birth Cohort Ratio Growth on Migration Flows

16
Journal of Economic Perspectives

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Is the Mediterranean the New Rio Grande? US and EU Immigration Pressures

17

also embody enclave effects, in which earlier generations of migrants ease assimilation for later generations, or built-in persistence created by migration policy,
such as family-reunification provisions that give preferential access to visas to thekin of existing residents. The presence of migration networks means that initiallabor-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 labormarket conditions with the kernels around which networks may form.
In a first approach to capture this interaction, we calculate the stock of sendingcountry migrants over age 50 in the destination as of 2000. Since most internationalmigration occurs when individuals are young, over-age-50 migrants in 2000 represent inflows that occurred in the 1960s, 1970s, and 1980s, when many networks werefirst being filled out. We use this measure because many dyads do not have available
and age-specific bilateral migration data years before 2000, so we lack the ability tomeasure immigration networks comprehensively in earlier 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 the sending and destination country and bilateral migration. As noted earlier, theuninteracted effect of relative labor supply is negative, indicating that in countries
without strong migrant networks, relative labor supply has little effect (indeed, acounterintuitively negative effect) on migration. However, the impact of relativelabor supply on bilateral migration becomes strongly positive once interacted withindicators for migration networks. In columns 2, 3, and 4, we add the dummy variable for previous over-50 migration in dyad, as well as an interaction term between
this and the change in the age-specific birth cohort ratio. For birth cohorts 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 percentagepoint (column 3), or a 0.08 percentage-point (column 4) higher bilateral migration
rate, depending on whether initial migration networks were above the median (overage-50 bilateral migration rate in 2000 in the top 50 percent), strong (over-age-50
bilateral migration in the top 20 percent), or very strong (over-age-50 bilateralmigration 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 istaken into account by using the size of the earlier migrant group, the relative supplyof labor does have an effect 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-specific birth cohort ratio. The relationshipbetween bilateral migration and sending-destination country population ratios foran 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

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18

Journal of Economic Perspectives

the bottom of the table show that the effect of labor supply differentials are significantly positive within most of these well-networked dyad relationships.

F3

Population Growth and Migration in the Future
Our next step is to utilize these results for understanding migration pressuresin the future. We run a new regression (reported in the online Appendix) in which
we add to the specification in column 1 all seven of the covariates used acrosscolumns 2 to 8 at the same time, as well as the interactions between these covariates
and the relative labor supply effect.9 This regression thus includes in a single specification all of the interactions that are shown individually in Table 2, and provides away of predicting panel changes in bilateral migration over the coming decades.10
We then combine our regression coefficient estimates with labor-supply 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 thefour 10-year age cohorts from 2010–2020, 2020–2030, 2030–2040, and 2040–2050.
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 weobserve 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
the sample to 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 each sending-destination dyad.11 Because there is an assumed 15-year lag
between when an individual is born and when that person 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 two of the seven birth cohorts
represented in the analysis were born after 2015. Thus, most of the changes in labor
supply that we incorporate in the forecasts are based on population growth that has
already occurred. We also allow initial relative per capita GDP to change over time9

We also add to the specification the square of relative GDP per capita at age 15, which helps accountfor nonlinearities 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
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(used three times over 2010–2040), 1995–2004 (used four times over 2010–2050), 2005–2014 (usedthree times over 2020–2050), 2015–2024 (used twice over 2030–2050), and 2025–2034 (used once over
2040–2050).

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Gordon Hanson and Craig McIntosh

19

Figure 3
Predicted Migration by Destination(count of 1st generation immigrants age 15–64; millions)
35
30
25

United States

20
15

Spain
United Kingdom

10

Italy

5

0

France
2000

2010

2020

2030

2040

2050 Japan
Germany−5Note: Because we use a linear model, in some dyads we predict negative migrant stocks (obviously notpossible in reality). We retain these negative values in order to avoid biasing upward our totals when wesum predicted stocks across origins for a destination.

based on IMF forecasts of annual GDP growth for 2018–2020 (http://www.imf.org/
external/datamapper/index.php), which we assume are sustained out to 2040.12
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 overfuture 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 assumption that time trends for migration
12
In the online Appendix, we show that alternate GDP forecasts for low-income sending regions do notmaterially change our forecasts of future migration. 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 haveachieved the expected long-run rate of growth.

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20

F4

Journal of Economic Perspectives

along these dimensions are the same in future decades as they were during the
2000s.13 These are, of course, very strong assumptions. No decade is exactly likeany other. Just as rapid productivity growth in developing countries could attenuate migration pressures, civil wars in Sub-Saharan Africa, climate-change-induced
coastal flooding in Southeast 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 for international migrants, possibly attenuating flows
into current high-income nations. In holding sending-country fixed effects constant,
we implicitly assume that destination options for these countries are unchanged aswell. 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) forthe major destinations through 2050. Destinations are heterogeneous in the population growth to which they are exposed through their current migrant stocks andthe interaction between future changes in relative labor supply and the various
migrant-network factors. The United States sees little net change in the numberof immigrants between 2000 and 2050, meaning 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 inimmigrant stocks over the coming 30 years in these calculations. However, it should
be noted that our analysis precedes the recent burst of asylum-driven 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 more detail. Because of the interactive effect between relative labor supply andthe gravity and migrant-network indicators, forecasted dyadic migrant inflows inmajor destination countries differ markedly across major sending regions. In theleft-hand panels, we see the three rapidly growing destinations discussed above.
The pattern in all three of these countries is relatively similar: future migration
growth after 2020 coming mainly from sub-Saharan Africa and India, with asimilar, slightly larger, amount coming from a diverse set of other countries. Spainand Italy, bordering the Mediterranean, are most exposed to migration fromAfrica. The United Kingdom, given its colonial linkages, is exposed to India and13

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
not observed, 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.

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Is the Mediterranean the New Rio Grande? US and EU Immigration Pressures

21

Figure 4
Predicted Migration by Destination(count of 1st generation immigrants age 15–64; millions)

15

India
Mexico
MENA

1.5
1
0.5
0

China
Sub-Saharan Africa
All other

India
Eastern Europe
MENA

20
50

20
40

20
30

2
Millions

5
4
3
2
1
0

1
0

India
Eastern Europe
MENA

China
Sub-Saharan Africa
All other

India
Eastern Europe
MENA

20
50

20
40

20
30

20
20

20
10

20
00

20
50

20
40

20
30

20
20

−1
20
10

Millions

China
Sub-Saharan Africa
All other

F: Migration to Germany, by Origin Region

E: Migration to United Kingdom, by Origin Region

20
00

20
20

20
50

20
40

20
30

20
20

20
10

India
Eastern Europe
MENA

20
10

−0.5

0
20
00

2
Millions

2

China
Sub-Saharan Africa
All other

D: Migration to France, by Origin Region

C: Migration to Spain, by Origin Region

4

20
50

20
00

20
50

China
Sub-Saharan Africa
All other

20
00

6

20
40

0
20
30

0
20
20

5

20
10

1

20
40

10

20
30

2

20
20

3

20
10

Millions

4

India
Eastern Europe
MENA

Millions

B: Migration to USA, by Origin Region
20

20
00

Millions

A: Migration to Italy, by Origin Region
5

China
Sub-Saharan Africa
All other

Note: Because we use a linear model, in some dyads we predict negative migrant stocks (obviously notpossible in reality). We retain these negative values in order to avoid biasing upward our totals when wesum predicted stocks across origins for a destination. MENA = Middle East and North Africa.
Source: Authors’ calculations based on data from the 2015 United Nations World Population Prospects
and from the Database on Immigrants in OECD Countries.

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22Journal of Economic Perspectives

sub-Saharan Africa in equal measure. Origins that play a large role in these various
destinations are: for Italy—Ethiopia, Philippines, and Nigeria; for Spain—Nigeria,
Morocco, and Brazil; and for the United Kingdom—the Philippines, Kenya,
and Pakistan.
In the right-hand panels, we see three destinations that are less exposed tothese rapidly growing origins. The United States sees Mexican immigrant counts
drop from over 10 million in 2010 to a little over 2 million in 2050, alongsidesubstantial drops in migrants from all other destinations except India, China, andsub-Saharan Africa. France has relatively constant migrant stocks over time, resulting
from a balance of declining migration from China and MENA and India and asmall increase from sub-Saharan Africa. Germany displays the most pronounced
decreases in future migration. This outcome arises from a combination of origins
with rapidly falling fertility—Turkey, Eastern Europe—and the fact that Germany
lacks the colonial and linguistic connections that are important in driving migration. Consequently, the top origins our model predicts for Germany in 2050 arevery distinct from the other destinations: France, Romania, Morocco, Somalia, Iraq,
Poland, and Russia. Japan is not shown separately in Figure 4, but given its traditionof demographic isolationism and its distance from population growth centers, itremains a low-migration destination. By 2050, only China is projected by our modelto have more than a few hundred thousand migrants in Japan.

Discussion
Given the prominent role of economic shocks, political conflict, and natural
disasters in driving international migration, predicting long-term migration flows is
a hazardous proposition. The demographic component of migration can be usefulin such an exercise, because medium-run changes in labor supply are determinedby population growth that has already occurred, and longer-term differentials in
population growth can be predicted with reasonably high accuracy. While changes
in relative labor supply may not independently trigger large-scale migration, they
set the stage for other types of shocks to set off the movement of large numbers ofpeople 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 seems
to be just beginning in the European Union, despite current political debates overimmigration 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 immigrationpressures 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 thehanson_304.indd 22

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Gordon Hanson and Craig McIntosh

23

Western Hemisphere leaves one to wonder 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 ofNorth 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 oflabor among member states, enforcement of immigration from outside the European Union largely falls to individual countries, creating a free-rider problem.
Spending by Italy on patrolling its coast line, for instance, ultimately affects thenumber of immigrant arrivals in northern Europe (Labanca 2016). Given thepotential for substantially greater immigration pressures in the future, one would
expect that there would be positive gains to greater coordination of immigration
policy within the European Union.
The sending countries will have an alternative perspective on demographically driven international migration. The possibility of emigration served as a safetyvalve for the burgeoning 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 bydemographic growth in Latin America and the Caribbean during the latter half ofthe 20th century (Hanson and McIntosh 2012). Strikingly, this pattern does not holdat a global level. Countries with rapidly growing populations that lack the benefitsof proximity or large established migrant populations already in OECD destinations
see lower emigration rates than more slowly growing countries. This suggests thatthe migration safety valve may not operate for the next century’s population growthin the way that it did in the past century. As an example, we predict the number ofAfrican-born first-generation migrants aged 15 to 64 outside of sub-Saharan Africa
to grow from 4.6 million to 13.4 million between 2010 and 2050. During this same
period, the number of working-age adults born in the region will expand fromunder half a billion to more than 1.3 billion, meaning that international migration
would only absorb 1 percent of the overall population growth. Given an African
continent expected to contain almost four 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 aslarge 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 would
be absorbing only a small share of Africa’s projected population increase.

hanson_304.indd 23

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24

Journal of Economic Perspectives

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