GORDON HANSON
University of California, San Diego

CHEN LIU
University of California, San Diego

CRAIG M c INTOSH
University of California, San Diego

The Rise and Fall of
U.S. Low-Skilled Immigration
ABSTRACT    From the 1970s to the early 2000s, the United States experienced an epochal wave of low-skilled immigration. Since the Great Recession,
however, U.S. borders have become a far less active place when it comes tothe net arrival of foreign workers. The number of undocumented immigrants
has declined in absolute terms, while the overall population of low-skilled,foreign-born workers has remained stable. We examine how the scale and composition of low-skilled immigration in the United States have evolved overtime, and how relative income growth and demographic shifts in the Western
Hemisphere have contributed to the recent immigration slowdown. Because
major source countries for U.S. immigration are now seeing and will continue
to see weak growth of the labor supply relative to the United States, future
immigration rates of young, low-skilled workers appear unlikely to rebound,whether or not U.S. immigration policies tighten further.

I

mmigration is a divisive issue in public discourse about U.S. economic
policy. At the center of the debate is how to address inflows of undocumented immigrants. During previous decades, inflows of illegal aliens
were substantial. The Pew Research Center estimates that between 1990
Conflict of Interest Disclosure: The authors received financial support for this work from theCenter on Global Transformation at the University of California, San Diego. With the exception of the aforementioned affiliations, the authors did not receive financial support from any
firm or person for this paper or from any firm or person with a financial or political interestin this paper. They are currently not officers, directors, or board members of any organization
with an interest in this paper.

83

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Brookings Papers on Economic Activity, Spring 2017

Figure 1. U.S. Foreign-Born Population, Age 18–64, 1970–2015
Millionsa
30

All

20
High school or less10
Less than high school
5

2

1980

1990
Year

2000

2010

Sources: U.S. Census Bureau, decennial census, American Community Survey; authors’ calculations.
a. Note that this axis is on a log scale.

and 2007, the U.S. population of undocumented residents, which as of2013 accounted for nearly two-thirds of the U.S. foreign-born adult population with 12 or fewer years of schooling, grew on net by an annual average of 510,000 individuals (Borjas 2016; Passel and Cohn 2016). These
inflows contributed to a sizable increase in the U.S. supply of low-skilled,foreign-born workers (figure 1). During the 1990–2007 period, the numberof working-age immigrants with 12 or fewer years of schooling more thandoubled, rising from 8.5 million to 17.8 million. Since the Great Recession,
however, U.S. borders have become a less active place when it comes to
net inflows of low-skilled labor from abroad. The undocumented population declined in absolute terms between 2007 and 2014, falling on net byan annual average of 160,000 individuals, while the overall population of
low-skilled immigrants of working age remained stable.
Viewed through the lens of the U.S. business cycle, the recent slowdown
in low-skilled immigration hardly comes as a surprise (Villarreal 2014).
Construction is the second-largest sector of employment for undocumented
labor and the third-largest among all low-skilled immigrants (Passel and
Cohn 2016). Because the collapse in the U.S. housing market helped precipitate the Great Recession (Mian and Sufi 2014), it follows logically that

GORDON HANSON, CHEN LIU, and CRAIG M c INTOSH

85

the downturn in home building after 2006 would have triggered a dropin new arrivals of low-skilled, foreign-born workers. Yet, there are goodreasons to believe that the Great Recession may have merely advanced
forward in time an inevitable reduction in low-skilled immigration. Today,
about half of low-skilled immigrants are from Mexico and another onequarter are from elsewhere in the Latin American and the Caribbean countries. Because these countries had marked declines in fertility after the late1970s, they started to see slower growth in the size of cohorts coming ofworking age in the 2000s, thereby weakening a key demographic push factor for emigration (Hanson and McIntosh 2010, 2012). Just as relatively
strong growth in U.S. GDP and Latin American labor supplies a generation
ago helped initiate the great U.S. immigration wave of the late 20th century,
the reversal of these conditions may be launching the United States into an
era of far more modest low-skilled labor inflows (Hanson and McIntosh
2009, 2016).
The policy dilemma facing the United States is thus not so much how
to arrest massive increases in the supply of foreign labor, but rather how
to prepare for a lower-immigration future. The pertinent issues for economists to address include how the scale and composition of low-skilled labor
inflows have changed over time, whether the drop in inflows is primarily a cyclical phenomenon or represents a secular decline, and how the
U.S. economy would adjust to an environment with modest numbers of
low-skilled, foreign-born workers entering the labor force each year. These
questions guide the analysis in this paper.
We begin by summarizing trends in low-skilled immigration over thelast several decades. As is well known, supplies of less-educated, foreignborn labor increased sharply after 1970, while their predominant national
origins shifted from Europe to Latin America. Perhaps less appreciated, thedemographic structure of this population has also changed, moving fromyounger, recent arrivals toward an older, more settled population. Whichtypes of individuals select into immigration also appear to have changed,a pattern we examine in detail for the case of Mexico, given its outsize
importance as a source country for U.S. immigrants. In 1990, those having
recently migrated from Mexico to the United States—as captured by the
population censuses of the two countries—were drawn more heavily fromjust above versus just below the mean of potential labor market earnings in
Mexico (Chiquiar and Hanson 2005). This mild positive selection weakened during the 1990s and the 2000s, such that by 2010 the population ofrecent Mexican immigrants was close to a random draw of working-age
individuals from Mexico, with a slight overrepresentation of individuals

86

Brookings Papers on Economic Activity, Spring 2017from the middle of the skill distribution. Although immigrant selection captured in census data may be subject to measurement error associated with
undercounts of undocumented immigrants (Fernández-Huertas Moraga2011), selection patterns in these data are similar to those in the Mexican
Family Life Survey, which appears less subject to missing information on
undocumented migrants (Kaestner and Malamud 2014). The largely neutral selection of immigrants from Mexico in terms of skill implies that any
future shock to Mexican immigration—such as a further tightening of U.S.
borders—would target middle-income earners in Mexico, while affecting
low-wage earners in the United States.
Recent changes in low-skilled immigration have occurred in a tumultuous environment for the U.S. labor market. Even before the economic turbulence that occurred after 2006, there were adverse changes in the demand
for low-skilled labor associated with automation and increased import competition from low-wage countries (Autor and Dorn 2013; Autor, Dorn, andHanson 2013; Pierce and Schott 2016; Cortes, Jaimovich, and Siu 2016).
At the higher end of the labor market, demand for young, college-educatedlabor has also weakened (Beaudry, Green, and Sand 2016). Together, thesechanges have combined to create a period low wage growth since 2000 for
all but the highest-earning U.S. workers (Valletta forthcoming).
To put recent changes in U.S. labor market conditions in a global
context, we compare the level and volatility of U.S. income with that inmajor sending countries for low-skilled immigrants. The gap between the
25th percentile of the income distribution in the United States and the50th percentile of the income distribution in Mexico—which approximatesthe expected gains in earnings for the typical Mexican migrant—was stable during the 1990s and early 2000s but shrank noticeably after 2007.
Relative volatility in income growth has also changed. The Great Moderation heralded a period of steady U.S. GDP growth from the early 1980s
to the mid-2000s (Bernanke 2004), a calm that was brought to an end by
the Great Recession. In Mexico and other migrant-sending nations of theWestern Hemisphere, the pattern is roughly the opposite. The 1980s andearly 1990s were periods of high macroeconomic volatility, whereas the
2000s were a period of steady if not spectacular economic growth. Shrinking income gaps and reduced income volatility between the United Statesand major migrant-sending nations have eased pressures for net labor flows
into the United States.
Another factor contributing to the decline in low-skilled immigration ischanges in U.S. enforcement against illegal labor inflows (Roberts, Alden,
and Whitley 2013). Between 2000 and 2010, the number of U.S. Border

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87

Patrol agents policing the U.S.–Mexico border doubled, from 8,600 to
17,500 officers, and has since remained at historically high levels. Concurrently, the U.S. government intensified immigration enforcement in the interior of the country, which led to an increase in deportations of noncriminal
aliens—many of whom are apprehended through traffic stops or otherroutine law enforcement operations—from 116,000 individuals in 2001
to an average of 226,000 a year from 2007 to 2015.1 Increases in borderenforcement, which deter potential migrants from choosing to enter theUnited States (Gathmann 2008; Angelucci 2012), and in interior enforcement, which reduces the existing population of undocumented immigrants
and may also deter future immigration, appear likely to continue under theadministration of Donald Trump (Meckler 2017; Kulish and others 2017).
Looking toward the future of U.S. low-skilled immigration, forces areat work that are likely to soften pressures for labor inflows and that willremain in place for the next several decades. By the mid-1970s, the sizeof U.S. cohorts coming of working age was growing much more slowly
than in Mexico and the rest of Latin America, creating steady pressure
for migration to the United States. However, by the mid-2000s this demographic push factor had largely disappeared. Because the United States’
neighbors to the south are today experiencing much slower growth in thelabor supply, the future immigration of young low-skilled labor looks set
to decline rapidly, whether or not more draconian policies to control U.S.
immigration are implemented.
If changes in global macroeconomic conditions and U.S. enforcement
policy have combined to weaken recent growth in the U.S. supply of lowskilled, foreign-born labor, what are the implications for U.S. labor markets? As a way of answering this question, we examine the net impactof immigration-induced changes in the labor supply on U.S. labor-market
tightness. To perform this analysis, we apply the approach of Lawrence
Katz and Kevin Murphy (1992) to data from the U.S. Current Population
Survey (CPS), which involves modeling the relative hourly earnings ofmore- and less-skilled labor as a function of their relative supplies and aflexible time trend, meant to capture the evolution of labor demand. We
estimate the model using earnings and employment data for the period
1976–2007 and then project relative earnings through 2015, using either
1. Noncitizens (including legal immigrants) convicted of an aggravated felony, a drug
crime, or multiple crimes involving moral turpitude are subject to deportation upon or
before completion of their prison sentence. Deportations of criminal aliens also increasedin the 2000s, from 73,000 in 2001 to an average of 156,000 per year over 2007 to 2015. See
Gonzalez-Barrera and Lopez (2016).

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Brookings Papers on Economic Activity, Spring 2017

actual labor supplies or labor supplies under counterfactual assumptions about low-skilled immigration. If, counterfactually, the low-skilled,foreign-born labor supply had grown at the same rate during the period
2008–15 as it did from 1994 to 2007, our simple model implies that thewage gap between more-skilled and less-skilled labor would have been
6 to 9 percentage points higher in 2015. This finding, though not a generalequilibrium assessment of the wage effects of U.S. immigration, illustrates
the magnitude of the immigration slowdown in terms of U.S. wage pressures. To the extent that slowing low-skilled immigration puts downward
pressure on the skill premium, we would expect firms to invest more inautomation and other changes in production techniques that reduce relianceon low-skilled labor (Card and Lewis 2007; Lewis 2011), effects that arelikely to register most strongly in immigrant-intensive industries such as
agriculture, construction, eating and drinking establishments, and nondurable manufacturing.
Our work complements the existing literature on immigration, muchof which takes national changes in low-skilled foreign labor supply as
given and examines its impact on the earnings of U.S. native-born workers.2 As is well known, estimates of the wage effects of immigration vary
widely across studies (Blau and Mackie 2016). Results depend on how
one defines the geographic scope of labor markets, skill groups withinthese labor markets, and the interchangeability of native- and foreign-bornworkers on the job (Borjas 2003, 2013; Card 2001, 2009; Ottaviano andPeri 2012; Dustmann, Frattini, and Preston 2013). To explain instability
in the wage effects of immigration, the literature has studied factors that
may confound empirical analysis, including offsetting migration by nativeborn workers (Borjas 2006), the location choices of immigrant workers(Cadena and Kovak 2016), firm-level changes in technology (Lewis 2011),
occupational downgrading by immigrant workers (Peri and Sparber 2009;
Dustmann, Frattini, and Preston 2013), and measurement error in labormarket earnings (Aydemir and Borjas 2011). Relative to existing work,
we offer the inverse perspective of how and why the low-skilled immigrant labor supply has changed. Given the abundance of research on how

2. Other literature on the effects of low-skilled immigration in the United States examines its consequences for local consumer prices (Cortes 2008), the labor supply of highskilled, native-born women (Cortes and Tessada 2011), local housing prices (Saiz 2007),
state GDP growth (Edwards and Ortega 2016), cultural diversity (Ottaviano and Peri 2005),
and occupational employment and wages of native-born workers in local labor markets(Burstein and others 2017).

GORDON HANSON, CHEN LIU, and CRAIG McINTOSH

89

immigration affects U.S. wages, the factors that govern the magnitude of
low-skilled immigration are understudied. Our work helps address thisgap in knowledge.

I. The Presence of Low-Skilled Immigrants
in the U.S. Labor ForceWe begin our analysis with an overview of the characteristics of low-skilled
immigrants in the United States and then examine how selection into U.S.
migration among individuals from Mexico has changed over time. For the
analysis in this section and the next, we focus on individuals of working
age, defined as those 18 to 64 years of age. We utilize data from the U.S.
population censuses, the American Community Survey (ACS), and the
CPS; and from Mexico’s population census, available from the Integrated
Public Use Microdata Series.

I.A. Characteristics of Low-Skilled Immigrants
A preliminary issue we must address is how to define low-skilled labor.
When it comes to the analysis of immigration, the literature alternatively
defines low-skilled workers as those with less than a high school education(Borjas 2003) or those with a high school education or less (Card 2001).3
This difference matters, because those completing less than 12 years ofschooling are an ever-smaller share of the U.S. native-born population but
continue to account for a majority of adults in low- and middle-income
countries. In the nations that send large numbers of low-skilled migrants
to the United States—including Colombia, Cuba, the Dominican Republic,
Ecuador, El Salvador, Guatemala, and Mexico—compulsory schooling isthrough grade 8 or 9, as opposed to being through age 16 in most U.S.
states. The median worker in many sending countries thus has well lessthan the equivalent of a U.S. high school education (Clemens, Montenegro,
and Pritchett 2008). Cross-national differences in compulsory education
are manifest in the distribution of years of schooling among less-educatedforeign- and native-born adults in the United States. In 1970, those notcompleting high school accounted for just over half of U.S. native-born
adults with a high school education or less, a share that declined to
3. We define high school education to mean completing 12 years of school, whether or
not a degree is granted, a convention we adopt because the meaning of a high school degree
varies across countries. Throughout the paper, we use high school education and 12 years ofschooling interchangeably.

90

Brookings Papers on Economic Activity, Spring 201729.4 percent in 1990 and to 16.6 percent in 2015 (table 1). Among the
U.S. foreign-born adult population with a high school education or less,the share with less than 12 years of schooling has also fallen but froma much higher base, beginning at 65.2 percent in 1970 and dropping to
55.0 percent in 1990 and to 44.7 percent in 2015. To ensure that our analysis is robust to the definition of skill, we utilize both education-based
definitions of low-skilled labor.4
When viewed over the sweep of the last half century, the U.S. lowskilled, foreign-born population has transformed not just in terms of its
size but also in its demographic structure. These evolutions are evident
in tables 1 and 2, which present summary statistics on U.S. low-skilled
foreign- and native-born individuals going back to 1970 using data fromthe U.S. census and ACS. In 1970, when the presence of the foreign born in
the U.S. population was at a historic low, low-skilled immigrants, in comparison with the native born, were relatively old and likely to be female.
This population came in its majority (52.9 percent) from Europe, was dominated by individuals who had arrived in the United States in 1960 or earlier(66.1 percent), and had a near majority (45.6 percent) with eight or fewer
years of schooling.
As the incipient immigration wave gained momentum, the composition of low-skilled immigrants became younger, more likely to have comefrom Latin America, and more educated. These changes were most dramatic between 1970 and 1990. During this period, the fraction of foreignborn people age 18–33 rose from 28.6 to 43.2 percent, the fraction ofmales rose from 41.8 to 48.8 percent, and the fraction completing 12 yearsof education rose from 34.8 to 45.0 percent. In terms of origin countries,
among immigrants with a high school education or less, the fraction born in
Mexico rose from 11.6 to 34.0 percent, the fraction born elsewhere in Latin
America (and the Caribbean) rose from 13.2 to 23.7 percent, and the fraction born in Asia rose from 5.7 to 16.2 percent.5 The 1970 to 1990 increasein the shares of immigrants coming from Mexico and the rest of Latin

4. Education is, of course, an imperfect definition of skill. Language barriers and occupational licensing present obstacles to foreign-born workers in integrating themselves into
the U.S. labor force, which may induce some immigrants to downgrade occupationally bytaking jobs for which, based on their observable skills, they would appear overqualified(Lazear 1999, 2007; Dustmann, Frattini, and Preston 2013).
5. Half of the 1970–90 increase in low-skilled immigration from Asia (55.1 percent) isfrom Southeast Asia, with much of this inflow associated with a substantial but temporaryrise in U.S. refugee admissions from the region that occurred following the end of the Vietnam War.

38.1
33.5
28.5

23.5
27.2
49.2

3.7
6.8
2.9
10.0
4.8
67.2
4.6

Age
18–33
34–49
50–64

Years of schooling
0–8
9–11
12

Industry, share of labor force
Agriculture
Construction
Eating and drinking establishments
Nondurable manufacturing
Personal services
Other industries
Unemployment rate
3.2
6.1
5.8
14.5
7.0
58.7
4.6

45.6
19.6
34.8

28.6
34.9
36.5

41.8
75.8

Foreignborn

2.9
6.8
3.8
8.9
3.2
66.6
7.8

14.3
24.5
61.2

43.4
28.1
28.5

46.6
63.9

Nativeborn

4.0
5.7
6.8
13.0
5.8
56.8
7.8

39.2
19.0
41.7

40.2
33.0
26.8

44.4
69.1

Foreignborn

1980

2.8
8.0
4.8
7.6
3.3
65.2
8.4

8.6
20.8
70.6

41.6
31.7
26.6

48.6
59.1

Nativeborn

5.7
7.8
8.7
9.5
6.9
51.6
9.8

38.2
16.8
45.0

43.2
34.5
22.3

48.8
64.1

Foreignborn

1990

2.1
9.1
5.3
5.5
2.9
67.6
7.5

4.9
15.8
79.3

36.4
37.9
25.6

50.3
56.8

Nativeborn

Sources: U.S. Census Bureau, decennial census, American Community Survey.
a. All values are expressed as percentages. The sample is restricted to individuals age 18–64 with 12 years of education or less.

46.4
71.6

Sex
Male
FemaleNativeborn

1970

5.5
10.8
9.2
7.5
6.3
52.1
8.6

33.7
16.5
49.8

42.2
38.2
19.6

51.2
63.9

Foreignborn

2000

2.3
9.2
7.6
4.3
2.9
63.9
9.7

4.0
12.6
83.4

38.2
27.9
33.8

54.3
46.2

Nativeborn

Table 1. Characteristics of Native-Born and Foreign-Born U.S. Working-Age Population with High School Education or Less, 1970–2015a

6.9
14.8
11.3
5.8
7.2
47.9
6.1

29.6
15.1
55.3

27.3
42.3
30.5

51.2
60.5

Foreignborn

2015

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Brookings Papers on Economic Activity, Spring 2017

Table 2. Summary Statistics for Foreign-Born U.S. Working-Age Population
with High School Education or Less, 1970–2015a
1970

1980

1990

2000

2005

2015

Years of residence in the United States
0–5
19.2
23.2
6–10
14.8
18.4
11+
66.1
58.4

24.2
21.6
54.2

22.8
19.1
58.1

21.8
18.4
59.9

11.9
13.0
75.1

Age of arrival in the United States
0–14
22.1
13.6
15–25
28.0
34.8
26+
44.0
51.4

14.7
42.4
43.0

19.1
47.5
33.4

18.1
47.3
34.3

20.2
45.0
34.4

Country or region of origin, less than high school education
Mexico
15.4
33.3
47.5
60.6
Central America
1.6
3.5
8.7
10.8
Caribbean
8.4
10.7
9.2
7.2
South America
2.6
3.6
3.3
3.4
Southeast Asia
1.7
4.6
6.6
5.8
Other Asia
3.3
5.4
5.6
4.7
Africa
0.4
0.5
0.4
0.7
Middle East
1.6
1.5
1.2
0.9
Europe
51.6
25.9
11.4
5.0
Other
13.5
11.0
6.2
0.9

64.0
12.4
5.7
3.2
4.7
4.0
1.0
0.8
3.5
0.7

59.3
15.9
6.0
3.0
4.8
6.0
1.6
0.9
2.0
0.5

Country or region of origin, high school education or less
Mexico
11.6
23.2
34.0
Central America
1.6
3.3
7.7
Caribbean
8.6
11.1
10.8
South America
3.0
4.4
5.3
Southeast Asia
1.7
4.9
7.5
Other Asia
4.0
7.1
8.7
Africa0.5
0.8
0.9
Middle East
1.8
1.8
1.6
Europe
52.9
30.9
16.5
Other
14.4
12.5
7.2

48.1
10.1
8.8
6.1
6.9
7.0
2.2
1.4
8.0
1.6

45.1
12.2
9.4
5.7
6.9
8.9
3.0
1.6
5.8
1.3

44.4
9.3
9.9
5.8
7.8
7.7
1.7
1.4
10.2
1.8

Sources: U.S. Census Bureau, decennial census, American Community Survey.
a. All values are expressed as percentages. The sample is restricted to individuals age 18–64 with 12 yearsof education or less.

America is even larger among those with less than a high school education,rising from 15.4 to 47.5 percent and from 12.6 to 21.2 percent, respectively.
By 1990, nearly 7 in 10 (68.7 percent) of the least-skilled U.S. immigrants
of working age came from other nations in the Western Hemisphere.
Jorge Durand, Douglas Massey, and Rene Zenteno (2001) describe thisera of U.S. immigration as one marked by a preponderance of itinerant
workers who came to the United States to take seasonal jobs, especially

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93

on farms in the Southwest, and often returned home during periods whenlabor demand was slack. During the two decades after 1970, the share of
low-skilled immigrant workers employed in agriculture did rise, from 3.2
to 5.7 percent (as compared with a decline of 3.9 to 3.0 percent among thelow-skilled, native-born workers of working age), and the fraction with
10 or fewer years of residence in the United States grew from 34.0 to
45.8 percent.6 However, throughout the sample period, farm workers
accounted for only a small share of low-skilled immigrant employment.
During the first decades of the late-20th-century immigration wave, lowskilled immigrants spread themselves across a wide range of jobs, while
concentrating more heavily, when compared with their native-born counterparts, in agriculture, construction, eating and drinking establishments, nondurable manufacturing, and personal services.
In subsequent decades, the low-skilled immigrant population has
become more mature and more settled, at least when measured in termsof length of U.S. residence. By 2015, three-quarters (75.1 percent) of lowskilled immigrants had resided in the United States for 11 or more years,
while the share of the population age 18–33 had dropped to 27.2 percent.
Since 1990, the fraction of low-skilled immigrants from Mexico and therest of Latin America has continued to rise, reaching 45.1 and 27.3 percent,respectively, in 2015. Among immigrants with less than a high school education, these shares are 59.3 and 24.9 percent, respectively, meaning thattoday, nearly 9 in 10 (85.2 percent) of the least-skilled, foreign-born workers are from Latin America and the Caribbean. It is particularly importantthat Mexico’s dominance as a source country for low-skilled immigrants
peaks in 2005, at 48.1 percent of those with a high school education orless and 64.0 percent of those with less than a high school education. The
4.7 percentage point drop in Mexico’s share of the least-skilled immigrant
population from 2005 to 2015 is largely offset by Central America’s
jump during the same period of 3.5 percentage points. As we discuss insection III, demographic push factors help account for Mexico’s recent
relative decline and Central America’s continuing relative gain as source
regions. After Latin America, Asia remains the next most important source
region of low-skilled immigration, in 2015 accounting for 15.8 percent of
all low-skilled immigrants and 10.8 percent of those with less than 12 yearsof schooling.
6. The question for length of U.S. residence in the ACS reads, “When did this person
come to live in the United States?” with the instruction, “If this person came to live in the
United States more than once, print latest year.”94

Brookings Papers on Economic Activity, Spring 2017

Over time, low-skilled immigrants have become more specialized inparticular lines of work. The share employed in immigrant-intensive sectors in 2015 reached 14.8 percent in construction (up from 7.8 percent
in 1990), 11.3 percent in eating and drinking establishments (up from
8.7 percent in 1990), 7.2 percent in personal services (up from 6.9 percent
in 1990), and 6.9 percent in agriculture (up from 5.7 percent in 1990).
The one immigrant-intensive sector registering a decline in its share of
low-skilled immigrant employment is nondurable manufacturing—which
includes apparel, footwear, furniture, and textiles—industries whose overall employment in the United States has fallen sharply in recent decades
due to technological change and competition from China and other lowwage countries (Autor, Dorn, and Hanson 2013).
The transition of the U.S. low-skilled immigrant population fromsojourners to settlers, first noted by Wayne Cornelius (1992) nearly three
decades ago, today appears to be largely complete. Part of this shift is thenatural result of a dynamic process of immigration in which early arrivals initially dominate the population, only to decline in importance as theexisting stock grows and matures (Piore 1980). However, the shift is also
the result of the pronounced slowdown in low-skilled immigration since
the mid-2000s, as seen in figure 1.
Because the immigration levels portrayed in table 2 reflect changes in
net immigration, they are uninformative about whether this slowdown
is the result of reduced inflows of new immigrants, larger outflows ofexisting immigrants returning to their home countries, or some combination of the two. We next summarize evidence on changing inflows
and outflows of immigrants over time. Figure 2 gives counts of immigrants by current age, age of arrival in the United States (inferred fromyears of U.S. residence), and census year for three source regions—
Mexico, other countries in Latin America and the Caribbean, and Southeast Asia—which together account for the large majority of low-skilled
immigration in the United States. To avoid concerns about tracking individuals who educate themselves out of the low-skilled category overtime, we include all immigrants from these source regions, regardless ofschooling.
Several patterns are apparent in the data. First, for most current-age
groups and in most census years, the largest cohorts are those arriving
between 15 and 24 years of age. That is, for a given current-age group,
if we compare bars that have the same shading (thus comparing different arrival-age cohorts in the same census year for the same current-age
group), those in the 15–24 arrival-age category are the largest in nearly

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GORDON HANSON, CHEN LIU, and CRAIG M c INTOSH

Figure 2. Number of Working-Age Immigrants by Arrival Age, Current Age, and Yearof Arrival, 1980–2010
MexicoThousands
1,500
1980
1990
2000
2010

1,000
500

5–14

15–24

5–14

15–24

15–24 25–34

5–14

25–34

15–24 25–34 35–44

35–44

Arrival age
Current age

Latin America, excluding Mexico
Thousands

600
1980
1990
2000
2010

400
200

5–14

15–24

5–14

15–24

15–24 25–34

5–14

25–34

15–24 25–34 35–44

35–44

Arrival age
Current age

Southeast Asia
Thousands

200

1980
1990
2000
2010

100

5–14

15–24

15–24

5–14

15–24 25–34

25–34

5–14

15–24 25–34 35–44

35–44

Arrival age
Current age

Sources: U.S. Census Bureau, decennial census, American Community Survey; authors’ calculations.

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all cases. Second, between 2000 and 2010, there are substantial declines
in the sizes of given arrival-age or birth-year cohorts. For individuals
from Mexico arriving in the United States between age 5 and 14, thenumber who are age 15–24 in 2000 is much larger than those who are
age 25–34 in 2010. We see similar declines in the number of Mexican immigrants who are age 25–34 in 2000 and the number who are
age 35–44 in 2010, both for the cohort arriving between age 5 and 14
and the cohort arriving between age 15 and 24. Similar patterns hold for
immigrants from other countries in Latin America and from Southeast
Asia. Declines in cohort size, as measured in the census, may result
from mortality, return migration, or changes over time in the fractionof individuals in a cohort who are enumerated in the census. Given the
youth of the cohorts considered, mortality seems unlikely to explain thisdecline. Moreover, given that we expect enumeration rates to increase
with length of residence in the United States, declines in enumeration
seem an unlikely explanation, which leaves return migration as the most
likely cause for the decline in measured immigrant cohort sizes between
2000 and 2010.
The net impact of these changes is that the size of immigrant cohorts in2010 is skewed heavily toward individuals who have more than 10 yearsof residence in the United States. For immigrants from Mexico in 2010(indicated by the darkest shaded bars), those with fewer than 10 years of
U.S. residence are the smallest cohort among all current age groups, a pattern that holds for other countries in Latin America and for Southeast Asia
as well.

I.B. The Presence of Low-Skilled Immigrants in the U.S. Labor ForceTo consider how the presence of low-skilled immigrants in the U.S.
labor force has changed in recent years, we focus on movements at annual
frequencies using data from the CPS. Because the CPS only begins asking questions about nativity in 1994, our use of these data is for that year
forward. We use two measures of the working-age population: raw data on
body counts; and these values expressed in terms of productivity-equivalent
units (PEUs), following the weighting procedure used by David Autor,
Lawrence Katz, and Melissa Kearney (2008).
Consistent with the post-1970 rise in low-skilled immigration seen in
figure 1, figure 3 shows that the presence of low-skilled, foreign-bornworkers in the U.S. working-age population rose steadily from 1994
to 2007 but has been stable since. The left panel of figure 3 plots fourmeasures of low-skilled immigration. The top line gives the share of the

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GORDON HANSON, CHEN LIU, and CRAIG M c INTOSH

Figure 3. Percentage of Low-Skilled, Foreign-Born Workers in the U.S. Working-Age
Population, 1993–2014
Percent weighted by productivityequivalent units
Percent

Raw percent
Percent
Foreign-born, highschool or less88Mexican-born, highschool or less66Foreign-born, lessthan high school
4

2

4

2

Mexican-born, lessthan high school
1995

2000

2005
Year

2010

1995

2000

2005
Year

2010

Sources: U.S. Census Bureau, Current Population Survey; authors’ calculations.

foreign born with a high school education or less among all working-age
individuals in the United States. This fraction rose from 6.5 percent in1994 to 9.1 percent in 2007, before stabilizing in subsequent years, settling at 8.8 percent in 2015. Just under half these foreign-born individuals
were born in Mexico (43.1 percent in 1994; 47.3 percent in 2015). When,alternatively, we define low-skilled immigrants more narrowly as thosewith less than 12 years of schooling, we also see a growing immigrantpresence in the U.S. working-age population, rising from 3.6 percent in1994 to 4.5 percent in 2007, and showing little change thereafter. Individuals born in Mexico account for a high fraction of the foreign-born
population with less than a high school education (61.1 percent in 1993,
64.4 percent in 2014).
Body counts of low-skilled immigrants overstate their presence in the
U.S. labor force to the extent that these individuals have low labor productivity relative to the average U.S. person of working age. To measure
the population in terms of PEUs, we apply the approach taken by Autor,
Katz, and Kearney (2008), which involves reweighting individuals by their

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projected relative earnings.7 Specifically, the weight attached to an individual is the ratio of the average weekly wage among full-time, full-year
workers in his or her race, gender, education, and labor market experiencecell to the average weekly wage for white, male, high school graduates with
8 to 12 years of potential work experience.8 Population shares expressed interms of PEUs appear in the right panel of figure 3. These shares are naturally smaller than in the left panel, owing to the fact that low-skilled immigrant workers have low earnings relative to other U.S. workers. Using the
productivity-adjusted measure, foreign-born individuals with 12 or fewer
years of schooling reach 6.5 percent of the U.S. working-age population in
2007, a share that declines slightly to 6.3 percent in 2015.9
Low-skilled immigrants tend to have high rates of labor force participation and employment when compared with similarly skilled native-bornworkers (Borjas 2016). Consequently, the population shares shown in figure 3 may give an incomplete characterization of the presence of the lowskilled, foreign-born workers in the effective U.S. labor supply. Figure 4
reports the shares of low-skilled immigrants in total hours worked, bothusing raw hours (left panel) and productivity-adjusted hours (right panel).
The share of total hours worked by immigrants with 12 or fewer years ofschooling rose from 5.2 percent in 1994 to 8.4 percent in 2007, before falling modestly to 8.0 percent in 2015. When expressed in PEUs, these sharesare 3.6, 5.8, and 5.5 percent, respectively.

7. When applying wage-based weights to the entire population, we assume that nonworking individuals would earn the same average wage as full-time workers who share theirage, gender, race, education, and nativity profile. Because employment rates increase withpotential earnings, this assumption may lead our productivity-adjusted shares of the lowskilled immigrant population to overstate the shares one would calculate based on “true”
wage weights. This problem is partially ameliorated when we examine the share of lowskilled immigrants in total hours worked, as we do in figure 4.
8. We construct these weights as follows. First, we divide workers into labor market
groups broken down by gender, two education categories (less than 12 years of education,
exactly 12 years of education), and eight experience categories (0–4, 5–9, 10–14, 15–19,
20–20, 25–29, 30–34, and 35–39 years of potential labor market experience). Then, for eachgender-education-experience group, we calculate the weight as average weekly earnings ineach year (for full-time, full-year workers, defined to be those working at least 35 hours per
week and 40 weeks a year) divided by average weekly earnings for white, male, high school
graduates with 8 to 12 years of labor market experience.
9. The number of Mexican-born workers in the United States increased by more than
350,000 per year over the 20 years from 1980 to 2000. Negative net migration of 160,000 per
year subsequent to 2007 therefore represents a drop of half a million people per year relative
to the prior trend, enough to constitute a noticeable change in the foreign-born population
when cumulated over a decade of low migration.

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GORDON HANSON, CHEN LIU, and CRAIG M c INTOSH

Figure 4. Percentage of Low-Skilled, Foreign-Born Workers in Total Hours Worked,
1993–2014
Percent weighted by productivityequivalent units
Percent

Raw percent
Percent

8Foreign-born, highschool or less
Mexican-born, highschool or less

6

4

8

Foreign-born, lessthan high school

2

4

Mexican-born, lessthan high school
1995

2000

2005
Year

6

2010

2

1995

2000

2005
Year

2010

Sources: U.S. Census Bureau, Current Population Survey; authors’ calculations.

Because undocumented immigrants account for a large share of the lowskilled immigrant population, and because the large majority of these individuals come from Mexico and Central America, low-skilled, foreign-bornlabor accounts for a relatively high fraction of employment in the statesalong the U.S. border with Mexico. Figure 5 plots the share of low-skilled
immigrants in hours worked for the four U.S. border states (Arizona,
California, New Mexico, and Texas), again in terms of both raw hours
and productivity-adjusted hours. Among these border states, the share offoreign-born workers with 12 or fewer years of education in total hours
worked rose from 11.9 percent in 1994 to 16.2 percent in 2005 and thendropped to 14.1 percent in 2015.
Given the propensity of low-skilled immigrants to concentrate in particular sectors, it is not surprising that, in selected industries, they have cometo account for a substantial fraction of total employment. As seen in table 3,
in 2015 immigrants with 12 or fewer years of schooling account for29.3 percent of total hours worked in agriculture (up from 3.9 percent in1970), 21.8 percent in personal services (up from 6.4 percent in 1970),

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Figure 5. Percentage of Low-Skilled, Foreign-Born Workers in Total Hours Worked
for the U.S. Border States with Mexico, 1993–2014a
Percent weighted by productivityequivalent units
Percent

Raw percent
Percent

15

Foreign-born, highschool or less

15

Mexican-born, highschool or less
10

5

10

Mexican-born,
less thanhigh school
1995

2000

5

Foreign-born,
less thanhigh school

2005
Year

1995

2010

2000

2005
Year

2010

Sources: U.S. Census Bureau, Current Population Survey; authors’ calculations.
a. The U.S. border states are Arizona, California, New Mexico, and Texas.

Table 3. Percentage of Foreign-Born Workers with a High School Education or Less,
by Industry, 1970–2015a
Industry

1970

1980

1990

2000

2015

Agriculture
Construction
Eating and drinking establishments
Nondurable manufacturing
Personal services
Other industries

3.9
3.9
8.3
5.9
6.4
3.0

7.0
4.4
8.5
7.1
8.9
3.3

13.5
6.9
11.4
7.9
12.5
3.7

21.4
12.0
15.6
11.2
17.7
5.1

29.3
20.3
16.8
13.5
21.8
5.8

Sources: U.S. Census Bureau, decennial census, American Community Survey.
a. All values are expressed as percentages. The sample is restricted to individuals with 12 years ofeducation or less.

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101

20.3 percent in construction (up from 3.9 percent in 1970), 16.8 percent
in eating and drinking establishments (up from 8.3 percent in 1970), and13.5 percent in nondurable manufacturing (up from 5.9 percent in 1970), as
compared with just 5.8 percent of employment in all other industries. For
these immigrant-intensive industries, future changes in low-skilled immigration matter immensely.

I.C. Who Chooses to Migrate to the United States?
Is the increase in low-skilled immigration in the United States the resultof increasing immigration from countries that are relatively abundant inlow-skilled labor or the result of low-skilled labor being relatively likely
to select into international migration? One cannot answer this question by
examining U.S. data alone. Differences in educational attainment acrosscountries would make the average worker from, say, Mexico appear to be
low skilled in the U.S. labor market, whereas at home he or she would fallin the middle of the earnings distribution.
In seminal research, George Borjas (1987) derived the conditions underwhich immigrants are negatively or positively selected in terms of skills.
Conditions favoring negative selection—meaning that immigrants are
drawn disproportionately from the bottom half of the skill distribution—
are high returns to skills in the sending country relative to the receiving
country, and migration costs that are proportional to worker productivity(for example, costs that have an iceberg form), which combine to give lessskilled workers a relatively strong incentive to migrate. Migration costs
that are fixed in nature and a marginal utility of income that is not strongly
decreasing favor positive selection of immigrants in terms of skills (Grogger
and Hanson 2011), in which case immigrants are drawn more heavily fromthe top half of the skills distribution.
Whether immigrants are negatively or positively selected in terms ofskills matters for how labor movements affect the distribution of income
in sending and receiving countries and for the ease with which immigrants
from low-income countries integrate themselves into the labor markets ofhigh-income countries. If, for example, immigrants from Mexico are negatively selected in terms of skills, shocks that contribute to a positive net
flow of labor from Mexico to the United States would tend to decrease
Mexican wage inequality—by reducing Mexico’s relative supply of lowwage workers—and to increase U.S. wage inequality—by expanding
the U.S. relative supply of low-wage workers. Further, immigrants who
are negatively selected in terms of skills may face greater challenges in102

Brookings Papers on Economic Activity, Spring 2017assimilating economically in the U.S. labor market and may be more likely
to be a net drain on public resources (Borjas 2016).
To examine the composition of low-skilled immigration in the UnitedStates from the sending country’s perspective, we focus on the case of
Mexico, which is by far the largest source country for U.S. labor inflows,
accounting for nearly half of all U.S. low-skilled immigrants and nearly
two-thirds of those with less than 12 years of schooling. We extend forward
in time the analysis of Daniel Chiquiar and Gordon Hanson (2005), which
utilizes the methodology of John DiNardo, Nicole Fortin, and Thomas
Lemieux (1996) for constructing counterfactual wage distributions.10 To
examine differences in the distribution of skills between Mexican residents(that is, nonmigrants in Mexico) and Mexican immigrants, we compare
the actual wage density in Mexico for Mexican residents with the counterfactual wage density that Mexican immigrants in the United States would
obtain if they were paid according to Mexico’s prevailing wage structure. This comparison reveals from where in Mexico’s wage distribution
migrants to the United States are drawn. Because this analysis projects U.S.
immigrants onto Mexico’s wage distribution based on workers’ observableskills, it ignores the role of unobserved characteristics in migration andearnings. And because it takes Mexico’s current wage distribution as given,
the analysis is silent about the equilibrium impact of immigration on U.S.
or Mexican wages.
Let f i(w x) be the density of wages w in country i, conditional on observed
characteristics x, h(x i = MX) be the density of observed characteristics
among workers in Mexico, and h(x i = US) be the density of observed
characteristics among Mexican immigrants in the United States. The density of wages that would prevail for Mexican immigrants in the UnitedStates if they were to be paid according to the price of skills in Mexico isgiven by(1)MX( w ) = ∫ f MX ( w x ) h ( x i = US ) dx .
gUSThis quantity corresponds to the counterfactual distribution of wages thatarises from projecting the skill distribution of Mexican immigrants in the

10. Also on the selection of immigrants from Mexico in terms of observable skill, see
Feliciano (2001), Orrenius and Zavodny (2005), Mckenzie and Rapoport (2007, 2011), andAkee (2010).

GORDON HANSON, CHEN LIU, and CRAIG M c INTOSH

103

United States onto the current wage structure of Mexico. Although this
distribution is unobserved, we can rewrite it as( 2)MX( w ) = ∫ θ f MX ( w x ) h ( x i = MX ) dx ,g USwhere Mexico’s conditional wage distribution f MX(w x) and the skill distribution of its resident population h(x i = MX) are observed, and where
( 3)

θ=

h ( x i = US ).
h ( x i = MX )Hence, we can obtain the counterfactual wage density that we desire inequation 1 simply by applying the appropriate weight q to the existing dis­tribution of wages in Mexico. To compute this weight, we use Bayes’s
theorem to write(4)h( x ) =h ( x i = US ) Pr ( i = US )Pr ( i = US x )

and
( 5)

h( x ) =h ( x i = MX ) Pr ( i = MX ).
Pr ( i = MX x )Combining equations 4 and 5, we obtain an expression for q that is theratio of the conditional probability that a Mexican-born worker resides in
the United States, Pr(i = US x)/Pr(i = MX x), to the unconditional probability that a Mexican-born worker resides in the United States, Pr(i = US)/Pr(i = MX). We estimate these probabilities via a logit model, use the estimates to calculate q, and apply the q weights to estimate the counterfactual
wage density in equation 2.11

11. This method for constructing weights ignores differences in labor force participation
rates in the two countries. Whereas labor force participation among male residents of Mexicoand male Mexican immigrants in the United States are similar, labor force participation ishigher among immigrant Mexican women than among nonmigrant Mexican women. Not
accounting for these differences would tend to overstate negative selection among immigrants. See Chiquiar and Hanson (2005) for details and for methods to account for crossnational differences in labor force participation.

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We construct actual and counterfactual wage densities for males andfemales, separately, in the years 1990, 2000, and 2010. Earnings are annuallabor income for individuals age 18–64. We estimate the logit regressions
used to predict whether an individual born in Mexico resides in the UnitedStates separately for men and women as a function of education (seven categories, based on years of schooling: 0–4, 5–8, 9, 10–11, 12, 13–15, 16+)and age (46 categories, one for each year in the range 18–64). The population is all working-age individuals in Mexico and Mexican immigrants in
the United States who have resided in the country for 10 or fewer years.
Results are similar when we expand the analysis to include immigrants
with 20 or fewer years of U.S. residence, who constitute the large majorityof working-age Mexican immigrants in the United States. Wage densities
are plotted relative to mean log earnings for workers in Mexico of a given
gender in a given year, such that actual wage densities are centered on zero.
Figure 6 presents the results, where in each plot the dashed line is the actual
wage density for Mexico and the solid line is the counterfactual wage density in Mexico for current Mexican immigrants.
For the case of males, shown in the left-side panels of figure 6, we seethat in each year, the actual and counterfactual densities are very similar
to each other, suggesting that the observable skills of Mexican immigrants
match closely those of individuals who have not migrated abroad. In 1990,
the counterfactual wage density lies slightly to the right of the actual wage
density, indicating that Mexican immigrants are mildly positively selected
in terms of observable skills. This difference is more defined in figure 7,
which plots the difference between counterfactual and actual wage densities. In 1990, this difference, as seen in the top-left panel, has a negative mass just below zero and a positive mass just above zero, indicatingthat male immigrants are underrepresented among those who would earnslightly less than mean earnings in Mexico and overrepresented among
those who would earn slightly more than mean earnings in Mexico. The
slight rightward shift in the counterfactual relative to the actual wage density is also present in 2000 and 2010. However, the difference between
actual and counterfactual densities becomes less pronounced over time,such that in the top-left panel of figure 7, the negative hump below zero andthe positive hump above zero are smaller in 2000 than in 1990 and smaller
still in 2010 relative to 1990. These changes are also seen in the top-rightpanel of figure 7, which reports the double difference in densities: counterfactual relative to actual wage densities in 2010 relative to this differencein either 1990 or 2000. The double difference using 2010 and 1990 is larger
than that for 2010 and 2000, indicating a lessening of positive selection

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GORDON HANSON, CHEN LIU, and CRAIG M c INTOSH

Figure 6. Actual Wage Density in Mexico and Counterfactual Wage Density
for Mexican Immigrants in the United States
Males, 1990

Females, 1990

Density
0.5
0.4

Density

Mexicanresidents
in Mexico

Mexicanimmigrants
in theUnited States

0.5
0.4

0.3

0.3

0.2

0.2

0.1

0.1
–0.4

–0.2

0
0.2
Log wage

0.4

–0.4

Males, 2000
Density

0.5

0.5

0.4

0.4

0.3

0.3

0.2

0.2

0.1

0.1
–0.2

0
0.2
Log wage

0
0.2
Log wage

0.4

Females, 2000

Density

–0.4

–0.2

0.4

–0.4

Males, 2010

–0.2

0
0.2
Log wage

0.4

Females, 2010

DensityDensity

0.6

0.5

0.5

0.4

0.4

0.3

0.3
0.2

0.2

0.1

0.1
–0.4

–0.2

0
0.2
Log wage

0.4

–0.4

–0.2

0
0.2
Log wage

0.4

Sources: U.S. Census Bureau, decennial census, American Community Survey; Instituto Nacional de Estadísticay Geografía, Mexican decennial census; authors’ calculations.

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Figure 7. Selection of Immigrants from Mexico in Terms of Observable Skills
Migration selection evaluatedat 1990 wage, males
Density

Double difference, males
Density

0.1

0.1

2000

0.05

2010 minus 1990

0.05

2010

0

0

–0.05

–0.05

1990

–0.1
–4

–2

0
2
Log wage4–4Migration selection evaluatedat 1990 wage, females
Density

0.1

0.1

0.05

0.05

0

0

–0.05

–0.05

–0.1

–0.1
–2

0
2
Log wage

4

–2

0
2
Log wage

4

Double difference, females

Density–42010 minus 2000

–0.1

–4

–2

0
2
Log wage

4

Sources: U.S. Census Bureau, decennial census, American Community Survey; Instituto Nacional de Estadísticay Geografía, Mexican decennial census; authors’ calculations.

over time. By the time that we arrive in 2010, working-age Mexican immigrants who reside in the United States appear to be close to a random draw
on the population of working-age individuals in Mexico.
The three right-side panels of figure 6 repeat the analysis for women.
Among women, we see evidence of stronger positive selection in 1990
and 2000 when compared with men. In each year, the rightward shift ofthe counterfactual wage density relative to the actual wage density is more
pronounced than the corresponding density difference for males. As withmales, the strength of positive selection diminishes over time, such thatby 2010 the counterfactual and actual wage densities for women are very

GORDON HANSON, CHEN LIU, and CRAIG M c INTOSH

107

similar. We conclude that by 2010, the selection of immigrants from Mexico is close to neutral in terms of observable skills. As mentioned above,these results are silent about the pattern of immigrant selection in terms ofunobservables.
One concern about the results shown in figures 6 and 7 is that we use
census data to evaluate immigrant selection. Any undercount of the Mexicoborn population in either Mexico or the United States that depends systematically on an individual’s age or education could result in biased estimateseither of the wage density for Mexico or of the counterfactual wage densitythat we construct for Mexican immigrants in the United States. There isa long-standing belief among demographers that the U.S. census undercounts undocumented immigrants in the United States (Warren and Passel
1987). To address this undercount issue, some studies evaluate immigrant
selection using data exclusively from Mexico (Ibarraran and Lubotsky2007). In noteworthy work, Jesús Fernández-Huertas Moraga (2011) uses
data from Mexico’s national employment survey (Encuesta Nacional de
Empleo, ENE), which follows households for five consecutive quarters andincludes in the survey questions about whether household members havemigrated to the United States during the period since the last survey was
conducted. Distinct from Chiquiar and Hanson (2005), Fernández-Huertas
Moraga (2011) finds that Mexican immigrants are negatively selected
in terms of skills, as captured by residuals from Mincerian wage regressions. The ENE, however, has measurement problems of its own. It suffers from high rates of attrition by households from the sample within thefive-quarter survey window, which a recent study by the National Academiesof Science, Engineering, and Medicine concludes makes it problematic
as a data source for evaluating Mexican migration to the United States(Carriquiry and Majmundar 2013).
Fortunately, there is a source that provides longitudinal data on households in Mexico and that tracks information on individuals who migrate
to the United States. The Mexican Family Life Survey (MxFLS) has been
conducted in three waves—2002, 2006, and 2009—with a recontact rate of
respondents between each wave of 90 percent. Of particular importance,
the survey follows household members who migrate to the United States
between waves. Robert Kaestner and Ofer Malamud (2014) use data fromthe first two MxFLS waves to analyze the selection of immigrants according to various measures of skills. Similar to what one sees in census data,
migrants to the United States in the MxFLS are more likely to be young. In
terms of education, both male and female migrants are more likely to havemiddle levels of schooling (4–9 years for men, 4–12 years for women) than

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to have low levels of schooling (0–3 years). For men, but not for women,
migrants are less likely to have very high levels of schooling (more than
12 years) than to have very low levels (0–3 years). The MxFLS also provides a measure of cognitive ability in the form of a Raven’s Progressive
Matrices test score (Raven, Raven, and Court 2000). Although cognitive
ability is a frequently discussed source of skill in the analysis of earnings(Heckman and Vytlacil 2001), few data sources provide evidence of how
cognitive skills relate to migration decisions. Among both men and women,
Kaestner and Malamud (2014) report no difference between migrants andnonmigrants in terms of their Raven scores, suggesting that the two populations have a similar distribution of observable cognitive abilities. Following Fernández-Huertas Moraga (2011), Kaestner and Malamud (2014)
also examine migrant selection in terms of observable and unobservablecharacteristics using Mincerian wage regressions. Their analysis showsthat workers with the highest predicted earnings or the highest residual
earnings in the first MxFLS wave—meaning those among the top quintile
of predicted or residual wage earners—are less likely to migrate but that
there is no pattern of selection among lower-wage individuals.
Despite problems with possible undercounts of undocumented migrants
in census data, they provide a characterization of immigration selectionthat is comparable to that based on high-quality longitudinal micro data.
Immigrants from Mexico to the United States are overrepresented amongindividuals whose skills place them in the middle of Mexico’s wage distribution and mildly underrepresented among individuals who would bevery low-wage or very high-wage earners in their home country. When we
examine U.S. immigration from other source countries, evidence of positive selection in terms of observable skills such as education is even more
pronounced (Grogger and Hanson 2011). In nearly all source countries for
U.S. labor inflows, immigrants are relatively likely to come from among
the more educated.12

I.D. Summary
The U.S. population of low-skilled immigrants has gone through anepochal half century of growth, transforming from a small cadre of older
immigrants from Europe to a large population of immigrants from Latin
America and Asia who are nearing middle age and who have now lived in
12. One exception to this pattern is Puerto Rico, which as an unincorporated territory of
the United States is not subject to the same barriers to U.S. immigration as foreign nations(Borjas 2008).

GORDON HANSON, CHEN LIU, and CRAIG M c INTOSH

109

the United States for an extended period of time. Immigrants from Mexico,
who account for one-half to three-fifths of the low-skilled foreign born
population, depending on the definition of skill, are preponderantly individuals who would be middle-income earners in their birth country. As
the United States looks forward to an era of weakened incentives for lowskilled immigration due to changing labor demand and labor supply conditions at home and abroad, it will be shocks to middle-wage workers inmigrant-sending countries that matter disproportionately for who migrates.
Efforts to reduce the existing population of low-skilled immigrants,
such as through increased deportations of undocumented immigrants,
would target a population that appears to have a long tenure of residence
in the United States.

II. Labor Demand, Labor Supply, and Low-Skilled Immigration
In this section and the next, we examine factors affecting the net flow of
low-skilled immigrants into the United States. We begin in this section bydescribing recent changes in conditions surrounding low-skilled immigration, including income differences between the United States and major
migrant-sending countries, U.S. immigration policy, and relative labor
supply growth in the United States and major sending countries. We thenanalyze for the case of Mexico the contribution of labor demand and labor
supply shocks to migration to the United States.

II.A. Income Differences between Countries
Perhaps the simplest manner in which to evaluate the incentive for immigration is to compare income between countries. Beginning with Larry
Sjaastad (1962), economists have modeled immigration as an investment
decision, in which the upfront cost of migration yields an income flow
over time equal to the difference in earnings between the home and foreign
economies. There may be considerable heterogeneity in the time horizon
over which individuals consider migration (Dustmann 2003). Seasonal
workers may focus on income differences between countries no morethan a few months in advance, other individuals may be uncertain about
their desire to relocate permanently and so put weight on the income differences they expect to be sustained over the next several years, and stillothers may treat migration as a long-term decision and therefore evaluate
the expected discounted difference in income streams over their full working lives. To examine high-frequency changes in the incentive for immigration, we abstract away from such heterogeneity and consider point-in-time

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income differences between the United States and migrant-sending countries, an approach taken in the large body of literature that uses the gravity
model to analyze bilateral migration flows (Karemera, Oguledo, and Davis
2000; Clark, Hatton, and Williamson 2007; Bertoli and Fernández-Huertas
Moraga 2013).
Even in making point-in-time income comparisons, one faces many
choices for how to measure income. One approach is to evaluate earnings
for individuals with similar observable skills who were born in the same
country and now live in different countries. Using data from the U.S. andMexican population censuses, Hanson (2006) reports that in 2000 the average hourly wage for a 28- to 32-year-old male with 9 to 11 years of education is $2.40 in Mexico and $8.70 among recent Mexican immigrants in the
United States (these income values, like those we report below, are adjusted
for purchasing power parity in terms of 2000 dollars). At a labor supply of
35 hours per week and 48 weeks per year, this would yield an annual income
gain of $10,600. Combining data from Mexico’s national survey of income
and expenditures with data from the U.S. census, Michael Clemens, Claudio
Montenegro, and Lant Pritchett (2008) obtain similar results, estimatingthat in 2000 the annual income gain to migration for a 35-year-old Mexican
male with 9 to 12 years of education is $9,200.
Comparing migrants with nonmigrants is problematic if there areunobserved characteristics that affect both the migration decision and an
individual’s income-earning ability. An alternative approach is to use longitudinal data for the same individual, which allows comparisons of income
before and after migration. Mark Rosenzweig (2007) uses data from the
New Immigrant Survey to estimate the change in income for new U.S.
permanent legal immigrants in 2003. He checks their current U.S. earnings
against earnings in the last job they held in their country of origin. For alegal immigrant from Mexico with 9 to 12 years of education, the averagegain in income is $15,900 (at 35 hours a week and 48 weeks a year). Comparing the same individual in two countries corrects for selection into themigration associated with unobserved, time-invariant individual characteristics but may introduce other complications. If, in preparing to migrate,individuals reduce their labor supply in a manner that diminishes income(or if negative shocks to income precipitate migration), this approach may
overstate the income gains to migration.13
13. Since Rosenzweig (2007) examines legal immigrants, his figures are not directlycomparable to those of Hanson (2006) or Clemens, Montenegro, and Pritchett (2008), whose
samples include all immigrants.

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Evaluating how the incentive to migrate to the United States has changed
across countries and over time is complicated by the fact that few countries
produce annual household survey data, and census data are amassed infrequently. Our approach is to construct income differences between countries by combining annual data on average income from national accounts
with data on the variance in income as inferred from summary statistics
on income inequality. Although statistics on income inequality, such as theGini coefficient, are often constructed at less than an annual frequency,
they tend to change slowly from one year to the next (Solt 2016), which
permits interpolation of their values to create an annual series. Under theassumption that income is log-normally distributed across households,
which is approximately consistent with data for many countries (Pinkovskiy
and Sala-i-Martin 2009), one can use the Gini coefficient to calculate thevariance of income across individuals and then combine this value withaverage income to construct income at different percentiles of the distribution (Grogger and Hanson 2011).14 Given the neutral selection of immigrants from Mexico in terms of observable skills, the 50th percentile (equal
to $8,800 in 2000) is a natural choice for the reference income of a prospective Mexican migrant. To select the reference income in the United States
for a typical immigrant from Mexico, we choose the percentile of the U.S.
income distribution that yields an income gain to migration in 2000 that is
approximately equal to the average income gain for migrants as described by
Hanson (2006); Clemens, Montenegro, and Pritchett (2008); and Rosenzweig(2007). The 25th percentile of the U.S. income distribution ($20,100 in 2000)
serves this purpose.
In the left panel of figure 8, we report the ratio of the 50th percentileof the Mexican income distribution to the 25th percentile of the U.S.
income distribution, where we construct these values using Gini coefficients from UNU-WIDER’s World Income Inequality Database, and per
capita GDP, adjusted for purchasing power parity, from the World Bank’s
World Development Indicators.15 This ratio is stable in the 1990s andearly 2000s, averaging 0.44 between 1990 and 2007. During this period,2
14. Suppose log income is normally distributed with mean µ and variance– s . Given an
estimate of the Gini coefficient G, the standard deviation of log income is s = √2F-1([G + 1]/2).
The value of log income at the a quantile is then µe(sza-s2/2), where za is the ath percentile ofN (0,1).
15. Because Gini coefficients are not available in all years, we interpolate values formissing years. The series on Gini coefficients ends in 2012 in some countries and in 2013 inothers. We assume that Gini coefficients in later years equal those in the last year for whichdata are available.

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Figure 8. The Ratio of the 50th Percentile of Income in the Sending Country
to the 25th Percentile of Income in the United States, 1990–2015
MexicoLatin America, excluding Mexicoa

0.55

0.35

0.5

0.3
0.25

0.45

0.2
1995

2000 2005
Year

2010

1995

2000 2005
Year

2010

Sources: UNU-WIDER, World Income Inequality Database; World Bank, World Development Indicators;
authors’ calculations.
a. The Latin American countries considered are Colombia, the Dominican Republic, Ecuador, El Salvador,
Guatemala, Honduras, and Jamaica.

a middle-income earner in Mexico who chooses to become a low-income
earner in the United States would see his or her real earnings increase bya factor of 2.3. After the Great Recession, the U.S.–Mexico income difference compresses, with the ratio of the 50th percentile of Mexican income
to the 25th percentile of U.S. income rising to an average of 0.53 between
2008 and 2015 and to 0.58 during the later period of 2011 to 2015. In theright panel of figure 8, we report the corresponding ratio of the 50th percentile of the sending country’s income to 25th percentile of U.S. income
for a composite of other countries in Latin America and the Caribbean.
We choose the next-largest sending countries for which data on Gini coefficients are available—Colombia, the Dominican Republic, Ecuador, El
Salvador, Guatemala, Honduras, and Jamaica—and we weight each country’s income by its relative share of working-age, low-skilled immigrants
in the United States in 2000.16 The time path of relative income is similar
16. In 2000, the share of U.S. low-skilled, working-age immigrants accounted for bythese countries is 15.0 percent (4.1 percent for El Salvador, 2.8 percent for the Dominican
Republic, 2.3 percent for Guatemala, 1.7 percent for Jamaica, 1.6 percent for Colombia,
1.3 percent for Honduras, and 1.1 percent for Ecuador). Gini coefficients are unavailable
for Cuba and Haiti (2.5 and 1.5 percent of U.S. low-skilled immigrants in 2000, respectively), leading us to leave them out of figures 8 and 9. Significant sending nations for U.S.
low-skilled immigrants outside the Western Hemisphere (and their shares of this populationin 2000) include Vietnam (4.1 percent), the Philippines (2.2 percent), China (2.1 percent),
South Korea (1.5 percent), Germany (1.4 percent), Italy (1.2 percent), Canada (1.2 percent),
India (1.2 percent), and Poland (1.1).

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Figure 9. Volatility of GDP Growth, 1993:Q1–2016:Q1a
MexicoStandard deviations of GDP growth

Latin America, excluding Mexicob
Standard deviations of GDP growth

12

5

9
6

MexicoUnitedStates3

Latin America

4
3
2
1

1995 2000 2005 2010 2015
Year

United States
2004

2008

2012

2016

Year

Sources: World Bank, World Development Indicators; authors’ calculations.
a. GDP growth volatility is shown for rolling eight-quarter windows. Tick marks on the horizontal axes
indicate quarter 1.
b. The Latin American countries considered are Colombia, the Dominican Republic, Ecuador, El Salvador,
Guatemala, Honduras, and Jamaica.

to that for the U.S.–Mexico comparison, though the absolute income gap
is larger. The income ratio is stable from 1990 to 2007, averaging 0.22,
and then rises after the onset of the Great Recession, averaging 0.30 from2008 to 2015. Since 2007, relatively slow U.S. income growth and rapid
growth in neighboring countries have compressed the income gap between
the United States and migrant-sending nations, presumably weakening
incentives for immigration.
In forming expectations about future income differences between countries, prospective migrants are likely to consider not just the level of income
but also its variance. Over short time horizons, higher perceived variance inincome in the sending country relative to the receiving country may add tothe incentive for migration. At a monthly frequency, changes in attempted
undocumented migration from Mexico to the United States, as captured byapprehensions at the U.S.–Mexico border, are strongly sensitive to changes
in the dollar–peso real exchange rate, with attempted entry surging during periods following currency crises in Mexico (Hanson and Spilimbergo
1999; Monras 2015). When expanding data to include countries throughout
the Western Hemisphere, emigration rates to the United States are larger
for cohorts subject to a higher incidence of financial crises in their homecountry (Hanson and McIntosh 2012).
To characterize changes in U.S. income volatility relative to that in
migrant-sending economies, the left panel of figure 9 reports the standard

114

Brookings Papers on Economic Activity, Spring 2017deviation in quarterly real GDP growth in Mexico and the United States
for rolling eight-quarter windows covering the period 1991:Q1–2016:Q1.
Throughout this time span, volatility in GDP growth is higher in Mexico(average eight-quarter standard deviation of 3.7 percent) than in the UnitedStates (average eight-quarter standard deviation of 2.0 percent). However,there are evident changes in relative volatility over time. After the 1995
peso crisis, volatility spiked in Mexico while remaining low in the UnitedStates. During the ensuing 10 years, volatility remained uniformly higher
in Mexico, though well below the elevated levels of crisis periods. With
the onset of the global financial crisis of 2008–10, volatility jumps in both
economies, declining thereafter to roughly equal levels. Reduced differences between Mexico and the United States in income volatility reflect the
improved execution of monetary and fiscal policies in Mexico—which, as inmuch of Latin America, has helped lower inflation, reduce government debt,
and stabilize GDP growth (Edwards 2009). In the right panel of figure 9,
we compare volatility in quarter-to-quarter GDP growth in the United Stateswith the same migrant-sending countries examined with regard to relative
GDP levels in figure 8. Here again, we see that volatility in GDP growth inmigrant-sending nations has decreased relative to the United States, whichhas presumably dampened pressures for cross-border labor flows.

II.B. U.S. Immigration Policy
Low-skilled immigrants enter the United States through three channels:
with permanent legal residence visas (green cards), with temporary work
visas, and as undocumented entrants. During the 2000s, the U.S. government engaged in a massive buildup in border enforcement efforts, withmost newly committed resources allocated to the U.S. border with Mexico.
To understand how changes in immigration policy may have affected
incentives for low-skilled immigration, we review recent adjustments in
U.S. policy mechanisms.
LEGAL IMMIGRATION The vast majority of low-skilled immigrants who
obtain green cards do so through family sponsorship, for which visa eligibility derives from having a relative who is a U.S. citizen or legal resident,
or as refugees or asylees (Rosenzweig 2007). The number of U.S. green
cards and the policies governing their allocation have been stable since1990. In that year, the Immigration Act set the annual number of familysponsored visas at 480,000, the annual number of employer-sponsored visas(which go primarily to skilled workers) at 140,000, and the annual numberof diversity visas (allocated via lottery to countries that have historically
low migration to the United States) at 55,000. Visas available to immediate

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115

relatives of U.S. citizens are uncapped, though applications for these visas
may be subject to processing delays. The number of green cards given to
refugees and asylees, though having no set cap, shows no trend over time,
having fallen from 114,000 per year in the 1990s to 83,000 per year in the
2000s, before rising to 109,000 per year during the 2010–15 period.17 Any
increase in low-skilled immigration via permanent legal visas thus cannot
have occurred through expanded quotas for green cards. It must instead
have occurred through increases in the number of low-skilled immigrants
qualifying for, applying for, and receiving visas from the annual allocationof visas.18
Qualifying for a green card under family sponsorship requires having an
immediate relative who is a U.S. citizen—which gives one access to visas
that are not subject to numerical limit—or a more distant relative who is
a U.S. citizen or legal resident—which allows one to apply for the fixedannual allocation of green cards. Because the number of new applications
exceeds the annual cap on green cards, and has for many years, there isoften a substantial lag between the time of application and the time of visa
receipt, with waiting times of several years in length being common. The
waiting time depends in part on one’s visa preference category, which is afunction of how closely related one is to a U.S. resident, and in part on thenumber of green card applicants from an individual’s country of birth whoare higher up in the visa queue.
Family sponsorship for green cards makes immigration a self-reinforcing
process. As the number of permanent legal immigrants from a sending
country increases, so too does the number of residents of that country whoare eligible for a green card. For example, permanent visas awarded toresidents of Mexico rose from 64,000 per year in the 1970s to 166,000
per year in the 1980s and to 225,000 per year in the 1990s, before dropping to 169,000 per year since 2000.19 Visa growth from the 1970s to the
17. A refugee is a foreign resident who is unable or unwilling to remain in his or her
country of nationality because of fear of persecution based on race, religion, social group, or
political opinion; an asylee is a foreign national who meets the conditions of a refugee andis already in the United States. A refugee is eligible to apply for a green card after one yearof U.S. residence. At the beginning of each fiscal year, the president, in consultation with
Congress, sets a worldwide ceiling on refugee admissions.
18. All data on legal immigration are from the U.S. Department of Homeland Security’s
Yearbook of Immigration Statistics (https://www.dhs.gov/immigration-statistics).
19. Regarding admission of permanent legal residents from nations in the rest of Latin
America and the Caribbean, green cards issued have risen from 50,000 per year in the 1970s,
to 180,000 per year in the 1980s, to 205,000 per year in the 1990s, and to 250,000 per year
since 2000.

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2000s partly reflects the growing population of Mexican residents who
have family members who are legal U.S. residents, which has expanded
the pool of eligible green card applicants. However, idiosyncratic changes
in immigration policy are also at work. The 1990s blip in green cards
awarded to residents of Mexico was partly a result of the legalization of
undocumented immigrants under the Immigration Reform and Control
Act of 1986, which delivered green cards to undocumented residents who
met eligibility requirements on a one-time basis. The recent slowdown in
low-skilled immigration is evident in green card allocations. Green cards
awarded to Mexican residents declined from 175,000 per year during the
2001–05 period to 140,000 per year during the 2011–15 period. Because
more residents of Mexico were eligible for green cards in 2010 than in
2000, the slowdown in green cards issued must be due to a decrease indemand for U.S. visas, which may be due to improved economic conditionsin Mexico relative to the United States.
Another form of legal immigration available to low-skilled, foreignborn workers is a temporary work visa. These visas permit a non-U.S. resident to work in the United States for a period of less than one year. The
H-2A program provides work permits to agricultural workers, while the
H-2B program gives work permits to nonagricultural workers, often forseasonal jobs in construction or tourism. The number of H-2 visas has risenover time—H-2A visas from 46,000 in 2006 to 284,000 in 2015, and H-2B
visas from 97,000 in 2006 to 120,000 in 2015. However, because thesevisas permit stays of less than one year and are nonrenewable, they accountfor no more than a small share (less than 3 percent) of the over 17 million
low-skilled, working-age immigrants who resided in the United States asof the mid-2010s.20
UNDOCUMENTED IMMIGRATION The most significant recent changes in the
United States’ policy governing low-skilled immigration pertain to how thecountry monitors and enforces its borders and ports of entry. Undocumented
immigrants gain entry to the United States either by overstaying their legal
immigration visas or by crossing a U.S. border or entry point illegally.21
The United States has substantially expanded the resources it devotes to
20. That is, if a current H-2 visa holder desires to return on an H-2 visa in the following
year, he or she must return to his or her country of residence and seek admission out of thefollowing year’s visa allocation.
21. As of the mid-2000s, approximately 45 percent of undocumented immigrants in the
United States appeared to be visa overstayers (many of whom do not remain in the UnitedStates in the longer term). See Pew Research Center (2006) and U.S. Department of Homeland Security (2016b) for estimates of annual overstay rates by country.

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Figure 10. Number of U.S. Border Patrol Agents, 1992–2016

Nationwide
20,000Southwestern states

15,00010,000

5,000

1996

2000

2004
Year

2008

2012

Source: U.S. Department of Homeland Security.

preventing undocumented labor inflows (Roberts, Alden, and Whitley 2013).
Figure 10 plots the number of U.S. Border Patrol agents stationed at
the U.S.–Mexico border and other entry points. In 2016, 85.9 percent
of agents were stationed in the Southwest, a share similar to that in 1992.
The expansion in personnel at the border—which increased by a factor of
4.8 by 2016—encompasses only part of the buildup. There have also been
substantial investments in infrastructure at the border and changes in how
those caught attempting undocumented entry are treated.
To comprehend the dimensions of these changes, consider how theenvironment along the San Diego–Tijuana segment of the U.S.–Mexico
border today compares with that in 1992, before the modern enforcement buildup began. In 1992, there were 1,009 Border Patrol agentsassigned to the San Diego region, which stretches from the Pacific Ocean
for about 60 miles east, among the 3,555 agents stationed along theentire U.S.–Mexico border. Barriers at the border itself were insubstantial, consisting in many areas, including those adjacent to the heart ofurban Tijuana, of no more than a chain-link fence, in which large holes
were frequently cut. In 1992, the Border Patrol apprehended 545,000
individuals in the San Diego sector, representing 542 apprehensions
per agent. Across the entire U.S.–Mexico border, there were 1,134,000

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apprehensions, representing 319 apprehensions per agent. Agents spentmuch of their time chasing down migrants as they attempted to run into
the United States and find cover in San Diego neighborhoods. More than95 percent of those apprehended were Mexican nationals, and nearly
all were subject to “voluntary removal,” under which they face no legal
sanction for being apprehended. After capture, most were bused acrossthe nearest border crossing, leaving them free to attempt entry again soon
thereafter (Hanson 2007). Thus, as of the early 1990s, the U.S.–Mexico
border was porous, the enforcement presence was unsophisticated andlightly resourced, and sanctions against migrants attempting illegal entry
were weak.
Today, the San Diego–Tijuana border, as with much of the U.S.–Mexico
border, is a much different place. The number of Border Patrol officers
in San Diego has grown to 2,325, among the 17,026 stationed along theentire border. San Diego and Tijuana are now separated by multiple layers of border barriers, which include rows of closely spaced, verticallymounted steel beams that reach 18 feet in height. These barriers constitutepart of the 650 miles of fencing along the U.S.–Mexico border, 600 milesof which were constructed between 2006 and 2010 (Roberts, Alden, andWhitley 2013), which cover nearly all the U.S–Mexico border that does
not coincide with the Rio Grande, a river that spans the near entirety ofTexas’s border with Mexico. The San Diego–Tijuana border is patrolled
by Border Patrol agents in SUVs, who traverse groomed roads constructed
between each layer of border fencing, with manned and unmanned aircraft surveilling from above. Night-vision-capable video cameras posted
every few hundred yards provide a continuous feed to Border Patrol
stations nearby. In 2015, apprehensions in the San Diego sector were
down to 26,000 (11 apprehensions per agent) and down to 337,000 for the
U.S.–Mexico border as a whole (29 apprehensions per agent). Whereas,in the past, the Border Patrol spent much of its time physically apprehending migrants, today its job is to serve as a deterrent force against those
who would consider illegal entry. In fiscal year 2017, the U.S. Department of Homeland Security spent an estimated $7 billion on salaries andbenefits for Border Patrol agents and Customs and Border Protection
officers (whose employment numbers are roughly equal); $3.6 billion on
Coast Guard efforts to maintain the security of U.S. ports, waterways,
and coastal areas; $2.9 billion on the detention and removal of deportable
aliens; and $410 million to maintain infrastructure and purchase communications equipment related to border security (U.S. Department of Homeland Security 2016a).

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Sanctions against undocumented immigration have also changed. The
era of voluntary removal is over, replaced by a Consequence Delivery System (Argueta 2016). The disposition of those apprehended is conditionalon their previous crossing activity and other circumstances. Since 2000,
nearly all those apprehended at the border (meaning within 100 milesof a border and 14 days of entering the United States) have been fingerprinted and recorded in a digital database. Consequences depend onwhether the apprehension is the first ever or a repeat event. Since the early2010s, the large majority of those apprehended (more than 85 percent)have been subject at a minimum to “expedited removal” (or “reinstatement of removal,” if they have been removed before), which is a formal
and immediate removal order that carries the considerable penalty of making the individual ineligible for legal U.S. immigration during the subsequent 10 years (enforceable via an individual’s fingerprint record). Thosewith multiple prior apprehensions may be subject to a “warrant of arrest”
and misdemeanor prosecution. Roughly one-third of those deported are
now repatriated to a port of entry far from their attempted crossing point,
which disrupts smuggling operations in which individuals pay smugglers
for multiple attempts to cross the border (as a hedge against the risk ofapprehension).22 Since the enactment of the Consequence Delivery System, recidivism rates have dropped. During the 2005–07 period, 25 to
30 percent of those apprehended were caught within the same year. Recidivism began to decline in 2009, when the consequence program was rolled
out, and in 2015 it stood at 15 percent.
The intensity of immigration enforcement has also increased in the U.S.
interior.23 U.S. Immigration and Customs Enforcement (ICE) is the government agency tasked with locating and removing “deportable aliens”
in the U.S. interior, meaning all immigrants whose criminal activities—
whether related to immigration or nonimmigration infractions—warrant
deportation. By working more closely with local law enforcement agencies, ICE agents have expanded the deportations of individuals accusedof minor infractions, including those driving without a license or driving

22. The Alien Transfer Exit Program repatriates Mexican nationals through geographic
areas different from their attempted point of entry (Argueta 2016).
23. Changes in interior enforcement are important, in light of the fact that about twofifths of undocumented immigrants may have entered the country on legal visas, which they
subsequently overstayed (Passel and Cohn 2016). By increasing border and interior enforcement simultaneously, the Department of Homeland Security may reduce incentives for border crossers to become visa overstayers.

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under the influence of alcohol or other substances (Thompson and Cohen2014). These changes in part account for the increase in the deportations of noncriminal aliens (that is, those whose nonimmigration crimes
alone do not warrant deportation) from 112,000 per year during the first
three years of the George W. Bush administration (2001–03) to 223,000
per year during the first three years of the Barack Obama administration(2009–11). Deportations of criminal aliens—those whose nonimmigration crimes do warrant deportation—has also increased, from 77,000 per
year in 2001–03 to 164,000 per year in 2009–11, which may reflect a
combination of an expanding population of criminal aliens and increased
efforts by ICE to locate and remove these individuals when they finish
their prison terms. The Department of Homeland Security’s ICE budget
in fiscal year 2017 was $6.2 billion (U.S. Department of Homeland Security 2016a).
What have these changes in policy meant for undocumented immigration? One indication of the impact of border enforcement is movementsin the price for smuggling services. Most of those attempting to cross the
U.S.–Mexico border hire a smuggler, known as a coyote, to serve as a guidethrough the desert and mountain regions of Arizona and Texas, where mostundocumented immigrants now attempt to cross the border. Measures ofcoyote prices are available from the Border Patrol, which asks a subset of
those apprehended whether they hired a coyote and the price paid; from the
Mexican Migration Project (MMP), which surveys individuals in Mexico
about their previous border-crossing experiences; and from the Survey ofMigration on the Northern Border of Mexico (Encuesta sobre Migracíon en
la Frontera Norte de México, EMIF Norte), which surveys migrants returning from the United States at bus stations and other transportation points in
Mexico.24 None of these sources are free of measurement problems. Border Patrol data are only available from those apprehended and questioned
about their behavior, while the MMP and EMIF Norte are based on theselected sample of migrants who have returned to Mexico. Although
average coyote prices differ across these sources, their time trends are
similar (Roberts and others 2010). Border Patrol data show smuggler
prices rising from $1,000 in 1999 to $1,600 in 2008 (in 2007 dollars).
Using data from the MMP, Christina Gathmann (2008) estimates that a
24. For the methodology of the Mexican Migration Project, see http://mmp.opr.princeton.
edu/research/design-en.aspx; and for EMIF Norte, see http://www.colef.mx/emif/eng/bases_
metodologicas.php.

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10 percent borderwide increase in enforcement (measured in man-hours)
increases the average smuggler price by 4.9 percent. Using this elasticity, the increase in Border Patrol manpower on the U.S.–Mexico borderfrom 2007 to 2015 of 27.6 percent would have increased smuggler prices
by 13.6 percent.
The cumulative effect of the U.S. enforcement buildup is hundreds ofmiles of new fencing, the rollout of technologically sophisticated bordersurveillance, a near quintupling of Border Patrol agents since the early
1990s, and the criminalization of illegal border crossings since the late
2000s. These changes combine with the recent compression in income differences between the United States and major sending nations to weaken
incentives for low-skilled labor inflows.

II.C. Demographic Pressures for U.S. Immigration
In the 1970s, 1980s, and 1990s, macroeconomic shocks and relatively
low incomes in Mexico and the rest of Latin America helped trigger laborflows to the United States. What sustained these flows over time was
rapid growth in the relative labor supplies of these countries (Hanson and
McIntosh 2012). Whereas the U.S. baby boom came to a halt in the early
1960s, Latin America’s baby boom did not abate until two decades later.
Differences in the timing of the U.S. and Latin American demographictransitions mean that though the sizes of U.S. cohorts coming of working
age began to slow in the early 1980s, they kept growing in Latin America
until the 2000s, fueling pressures for emigration.
The relationship between labor supply growth and changes in U.S.
migration appears in figure 11, which charts, for countries in Latin
America and the Caribbean, the percent change in migration rates to theUnited States from 1980 to 2015 against the percent change in national
birth cohort sizes over this interval. A strong positive relationship is evident, with the R2 on the population-weighted linear fit equal to 0.45. As
noted, the most important origin countries in terms of absolute number ofcurrent migrants residing in the United States are Mexico, El Salvador,
Guatemala, the Dominican Republic, and Honduras. Mexico stands out
in this group, with roughly 10 times the number of U.S. immigrants in
the 15–40 age group as the next largest origin country in Latin America(figure 12). Mexico also stands out in terms of having the largest dropin migration between 2010 and 2015. Although most countries in Latin
America see some decrease in the number of immigrants in this age groupbetween 2010 and 2015, the number of Mexican-born individuals age

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Figure 11. U.S. Labor Supply Growth versus U.S. Migration Changes
from Latin America and the Caribbean, 1980–2015
Percent change in migration rate
Honduras
1,250
Guatemala1,000
El Salvador
750
500

Venezuela
Dominican
Antigua andPeru
MexicoTrinidad and Republic
BarbudaHaiti
TobagoNicaragua
250 Grenada
BoliviaParaguay
Ecuador
GuyanaBahamasJamaica Colombia
Belize
Costa
Rica
Chile
0 Barbados
PanamaUruguay
–25

0

25
50
75
100
Percent change in birth cohort size

125

150

Sources: U.S. Census Bureau, American Community Survey; United Nations, World Population Prospects.

Figure 12. Migration Rates and Migration Counts for Age 15–40, 1980–2015
Migration rates

Migration counts

Percent

MillionEl Salvador

65

15
Mexico10

43Guatemala5
Honduras
1990

2000
Year

Dominican
Republic

2
1

2010

Source: U.S. Census Bureau, American Community Survey.

1990

2000
Year

2010

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15–40 residing in the United States fell by more than 1.1 million overthese five years.25
The durability of the decrease in migration from Mexico to the UnitedStates depends strongly on the reason it occurred. If the slowdown is due
primarily to earlier shifts in population growth (Hatton and Williamson
2011), then given projected near constant U.S.–Mexico labor supply ratios,
we may expect large-scale Mexican emigration to be a thing of the past. If
the recent border enforcement buildup plays a significant role in the immigration slowdown (Gathmann 2008; Amuedo-Dorantes, Puttitanun, andMartinez-Donate 2013), then the pace of immigration from Mexico may
be effectively in the control of the U.S. government.26 Alternatively, if labordemand shocks are primarily responsible for the immigration slowdown(Villarreal 2014), the hiatus in high levels of immigration may end once theU.S. economy recuperates more fully.
To understand the causes of the decline in Mexican migration to theUnited States, we turn to data from the Mexican population census. We
exploit variation in labor supply and per capita GDP across Mexican states
to explain emigration. This analysis updates the work of Hanson andCraig McIntosh (2010) to the 2000–10 period.27 We count the base size of
Mexican state-age-gender birth cohorts when they are first seen in the data,
and use successive censuses to count the number of individuals remaining
in Mexico each year. Because more than 95 percent of emigrants from
Mexico go to the United States (Passel and Cohn 2009), and because westudy young cohorts in which mortality is low, these numbers provide a

25. Our data indicate that the total number of Mexican-born individuals in the UnitedStates of all ages fell by 272,000 from 2010 to 2015, roughly in line with the estimate from
Gonzalez-Barrera (2015) using data from the Mexican National Survey of Demographic
Dynamics (Encuesta Nacional de la Dinámica Demográfica, ENADID) that the United States
lost 141,000 Mexican-born individuals from 2009 to 2014. This muted change relative to the
population age 15–40 indicates both that the young are more sensitive to changes in conditions than the old, and foreshadows the results later in this section that the population of older
Mexicans will continue to grow even once the number of younger individuals starts to fall
(Giorguli-Saucedo, García-Guerrero, and Masferrer 2016). Due to how mortality increases
with age, our measure of net migration (differences between migrant numbers and birthcohort size) becomes less reliable as cohorts become older.
26. Other factors driving migration from Mexico to the United States in recent decades
include Mexican policy reforms in the 1990s that privatized land rights, which allowed rural
residents to sell their land, or to leave for urban areas without fear of relinquishing their claim
to communal land (de Janvry and others 2015).
27. Although the 2015 Mexican micro census Conteo de Población y Vivienda should
permit a similar exercise to be conducted, we found the resulting population estimates to be
too noisy to use. Hence, the analysis uses data only through 2010.

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Brookings Papers on Economic Activity, Spring 2017usable estimate of net emigration to the United States from each Mexican
state. To form our dependent variable, we aggregate individuals into threeyear birth cohorts, and then calculate decadal changes in the percentageof the cohort that has emigrated, as measured by the change in its size. To
investigate the role of labor supply in emigration, we include as a regressor
the log ratio of the Mexican state birth cohort size to the U.S. birth cohort
size. We restrict the analysis to individuals age 15–40, which is the age
range during which most migration occurs. The regression specification is

(6)

N 
 GDPic16 
dmicgt = β 0 + γ 1 log  icgt  + γ 2 log 
16 
 GDPUSc NUSgt  GDPitC + γ 3 log + α i + µ g + ηc + ρt + ε icgt , GDPUStC 

where dmicgt is the change in the emigration rate for Mexican state i, birthcohort c, gender g, and census year t. To focus on low-skilled immigration,
we take the log of the ratio of the Mexican state birth cohort size Nicgt tothe current U.S. native-born population with less than a high school education NUSgt. Because we may be concerned that education decisions among
U.S. natives are endogenous to Mexican immigration rates, we instrument
for this ratio using the log ratio of the Mexican birth cohort to the entire
respective U.S. birth cohort. Labor demand shocks are captured by the log
ratio of GDP per capita in a Mexican state to the United States in the yearthat a cohort was age 16, GDP16ic /GDP16
USc, as well as the log ratio of contemporary GDP per capita in a Mexican state to the United States, GDPitC /GDPCUSt. We select age 16 because it is a common year for entry into thelabor market in Mexico; relative income in this year indicates prevailing
economic conditions at a time when individuals first make choices aboutlabor supply. To express labor demand factors in terms of deviations fromtrend changes in economic activity, we use residuals from a regression of
the log ratio of Mexican state GDP per capita to U.S. GDP per capita on
state-specific intercept and slope terms. To control for confounding shocks
on migration, we include fixed effects for the Mexican state (ai), gender(µg), birth cohort (hc), and census wave (rt).
The regression specification in equation 6 is motivated by the migration model of Borjas (2006), in which differences in relative labor supply combine with shocks to labor demand to create the incentive forthe movement of labor between economies. Adjustment costs prevent

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Table 4. Analysis of Net Migration at the Mexican State Levela
Decadal change in net migrationb

Independent variablec
Labor supply with less thana high school educationd
Innovations to GDP per
capita at age 16
Innovations to GDP per
capita in census year
No. of observations
R2

(1)

(2)

(3)

(4)

PooledExcluding
2010

Men

Women

0.1441***(0.010)-0.0197*(0.010)0.022(0.020)
3,328
0.122

0.1643***(0.011)-0.0735***(0.016)-0.0472(0.039)
2,432
0.175

0.1733***(0.015)-0.0262(0.017)0.0234(0.034)
1,664
0.108

0.1190***(0.013)-0.0134(0.012)0.0187(0.024)
1,664
0.189

Sources: U.S. Census Bureau, decennial census; Instituto Nacional de Estadística y Geografía, Mexican
decennial census; authors’ calculations.
a. The units of analysis are three-year birth cohorts. The log of the ratio for Mexican state birth cohort
size to the U.S. birth cohort size is used as an instrument for the log of the ratio for Mexican state birthcohort size to the U.S. labor supply with less than a high school education. Robust standard errors inparentheses are clustered by birth cohort and weighted by birth cohort size. Statistical significance isindicated at the ***1 percent, **5 percent, and *10 percent levels.
b. The mean decadal change in net migration for the pooled sample is 0.0619, holding the Mexican
state cohort size fixed at its initial value.
c. Each independent variable is the log of the ratio for the Mexican state to the United States.
d. The native U.S. labor supply with less than a high school education is measured contemporaneously.

migration from quickly equilibrating wages between regions, meaning that
initial differences in labor supply—in particular, at the time a cohort enters
the labor market—contribute to continuing pressure for migration in subsequent periods as a cohort ages. Hence, the migration rate is a functionof initial labor supply and labor demand conditions, as well as subsequentinnovations to labor demand.
The results of this analysis, presented in table 4, confirm a prominentrole for labor supply in driving migration from Mexico to the UnitedStates. Column 1 shows the pooled results using all available census waves
including 2010, column 2 excludes 2010, column 3 shows the results formales only, and column 4 shows the results for females only. In all cases,
the log Mexico–U.S. labor supply ratio is positive and strongly statistically significant. The coefficient in the first column implies that a 10 percent increase in relative labor supply would translate into a 1.4 percentage
point increase the decadal flow of net migration. The relationship is slightly
weaker among women, with a coefficient approximately 70 percent aslarge as for men, but still very precisely estimated (t value of 9.1). The
coefficient of 0.1441 in column 1 of table 4 combined with the doubling of

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the labor supply ratio between Mexico and the United States from 1970 to2000 can more than explain the rise in the decadal net migration rate from
2.5 to 8.3 percent during this period. Now that U.S. cohorts are growing
more rapidly than their Mexican counterparts, our results suggest that thedrop in the average log labor supply ratio for a Mexican state to the UnitedStates, from -3.82 in 2000 to -3.73 in 2010, is responsible for more thanfour-fifths of the observed decrease in the decadal average net migration
rate to the United States during that time interval (from 8.3 to 6.6 percent).
The effect of labor demand, as measured by the log ratio of GDP per
capita for a Mexican state to the United States, is weaker and less stable.
Contemporaneous GDP ratios are never significant and alternate in sign
across specifications, while the GDP ratio at age 16 is consistently negative
but significant only in the specifications that pool men and women. Surprisingly, it appears that this relationship becomes less pronounced during
the Great Recession; when we exclude 2010, the fit becomes significant
at the 1 percent level and the coefficient is almost four times as large inabsolute value.28 The relatively large swings in Mexican GDP during thisperiod, as well as the fact that the recession occurred close to the end of thedecade, may have dampened the sensitivity of migration to shocks during
this interval. Nevertheless, positive income shocks to Mexican states (ornegative income shocks to the United States) clearly have the overall effectof slowing migration.
How much of the migration slowdown can be attributed to the GreatRecession? In trying to understand the labor demand effects of the GreatRecession shock on migration, we conduct a simple simulation exercise. We
use the marginal effect from the model estimated in column 2 of table 4—
for the period preceding the Great Recession—to ask the out-of-sample
question as to what would have happened to Mexico–U.S. migration if
the United States had not experienced the Great Recession. We simulate thecounterfactual log GDP ratios that would have occurred if the United States
had remained on its long-term trend of GDP per capita. Because it is the
GDP-at-age-16 variable through which income changes primarily affectmigration, GDP shocks operate by altering the initial labor supply choicesof individuals when they first enter the labor force: to seek work in Mexico
or to move to the United States. The left panel of figure 13 shows the time
28. Villarreal (2014) also finds declining trend migration and a weak discontinuous
effect of Mexican and U.S. GDP on migration during the Great Recession. His analysis suggests that migration tracks U.S. employment rates quite closely during this time period, but
due to endogeneity concerns we do not pursue this control for economic conditions.

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Figure 13. Actual and Simulated GDP per Capita in the Absence of the GreatRecession, 1960–2015a
Log GDP per capita ratio,
Mexico to United States

GDP per capita
Dollars
60,000

Ratio
Quadratic fit

50,00020,000

Simulated ratio–1.1

40,00030,000

–1.05

–1.15
U.S. GDPper capita

Actual ratio

–1.2
–1.25

10,000–1.3
1970 1980 1990 2000 2010
Year

2002 2004 2006 2008
Year

Sources: U.S. Census Bureau, American Community Survey; United Nations, World Population Prospects;
International Monetary Fund; authors’ calculations.
a. Data for the years preceding the Great Recession are actual; data for the Great Recession years are simulated
by projecting the long-term trend.

series projection of U.S. GDP in the absence of the recession, and theright panel shows the actual and counterfactual log labor demand ratiosthat would have resulted. The right panel shows that the difference in log
GDP ratios from actual versus predicted in 2010 opens up to about 0.06 =1.25 - 1.19 log points, or 6 percentage points. This indicates that the total
predicted effect on the migration rate for cohorts turning 16 after 2007 is
0.0042 = 0.06 × 0.07 log points, or roughly a half percentage point decreasein the decadal migration rate. Using the 2010 age cohort sizes and migration rates, there were 22 million Mexican-born individuals between the ageof 15 and 25, and we would have expected 1.9 million of them to migrate
to the United States. Adjusting the decadal migration rate downward by
0.5 percent for the decade that transpired between the Great Recession andthe 2015 ACS, we would have expected a decrease in the total stock ofmigrants of 109,000 arising from the labor demand shock to those exposedto the Great Recession shock when 16 or younger. Given the results in theprevious section illustrating that the stock of migrants in this age groupfell by more than 1.1 million between 2010 and 2015, it would appear thatlabor demand shocks as captured by GDP per capita can explain only amodest portion of the reversal in migration flows.

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To what extent are other factors, such as the ramp-up in enforcement
at the border, responsible for the decreases in Mexico–U.S. migration
between 2007 and 2015? We attempted to investigate the role of borderenforcement using an instrumentation approach that first calculated theshare of migrants from each Mexican state apprehended in each Border
Patrol sector in the earliest available year, 1999. We then multiplied this
sector- or state-specific enforcement incidence by an index of overall Border Patrol effort, the number of Border Patrol “linewatch” hours per year.
The resulting instrument proved to be strongly positively correlated with
Mexican state emigration rates, indicating that despite our effort to exogenize enforcement, it may be so strongly endogenous to migration that
one cannot estimate a credible long-term impact of border enforcement on
successful crossings. Our compromise, reduced-form approach describedabove omits direct measures of enforcement, whose effect on migration
may therefore be absorbed by other covariates. How may the exclusionof enforcement affect our results? One possibility is that that enforcement
responds endogenously to relative GDP ratios, in which case it is capturedby the reduced-form relationship between GDP per capita and migration(Hanson and Spilimbergo 2001). A second possibility is that enforcement
is orthogonal to our core explanatory variables, and hence remains in theresidual. A third possibility is that omitted enforcement variation is incidentally correlated with changes in labor supply, in which case its effects
load onto this variable. In any case, it appears clear that the push factors
driving migration from Mexico to the United States have abated sharply
during the past decade, and hence the marginal effectiveness of borderenforcement spending in terms of prevented crossings is falling.
Taking our results at face value, the analysis suggests that labor supply
shocks play a major role in driving low-skilled immigration flows in the
United States. This fact, combined with the relatively predictable nature of
future population growth, provides an opening for predictive analysis. We
therefore turn next to a forecasting exercise using data from all the Latin
American sending countries to assess U.S. immigration pressures decades
into the future.

II.D. Summary
From the early 1980s to the mid-2000s, there were robust pressuresfor low-skilled immigration in the United States. U.S. incomes for lowskilled workers far exceeded those in migrant-sending nations; the U.S.
macroeconomy was considerably more stable than Latin America’s; andenforcement against illegal entry, though not entirely lax, permitted large

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129

inflows to occur. These conditions changed abruptly after the Great Recession. Gaps in the level and volatility of income between the United Statesand migrant-sending nations have compressed, while there has been an
extensive buildup in U.S. immigration enforcement. Despite these recentchanges in the pattern of relative income growth, it appears that changes inrelative labor supply growth have mattered the most for current trends in
U.S. immigration.

III. Low-Skilled Immigration in the Long Run
It may be tempting to view the period since the Great Recession as a temporary pause in the U.S. immigration wave that began in the 1970s. After all,
U.S. incomes for low-wage labor are still roughly twice those in Mexico,
and are even larger when compared with those of other sending nations in
Latin America. Should not high levels of immigration resume once the U.S.
economy returns to a period of normal growth? Such a perspective downplays both the recent increase in U.S. enforcement and the demographic
determinant of recent U.S. labor inflows from the Western Hemisphere.
Looking forward, demographic pressures for U.S. immigration are set to
weaken significantly. In this section, we construct a model of long-run
changes in U.S. immigration, which we use to project U.S. labor inflows incoming decades. We then characterize how changes in low-skilled immigration may affect labor market conditions in the United States.

III.A. A Predictive Analysis of Future Migration from Latin America
to the United States
To evaluate the effects of future demographic change on U.S. immigration, we turn to the national level and incorporate the long-term population growth forecasts for countries in the Western Hemisphere provided
by the United Nations’ World Population Prospects.29 We examine how
immigration from Latin American countries may have changing effects
on U.S. labor inflows in coming decades. Recognizing that future migration episodes will also be driven by unanticipated economic and politicalshocks, differential demographic growth estimates for the next 15 years
provide one of the clearest lenses on the future that is available. Labor
29. To avoid circularity in studying how future population growth will drive migrationfrom countries whose population will in turn be determined by migration, we use the UN’s
“no migration” population forecast, which ignores as-yet-unobserved future migration in its
projected population estimates.

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Figure 14. Relative Labor Supply Ratios by Origin Country for Age 15–40, 1980–2050
Change in labor supply ratio, origin country to United States, 1980 = 1
3ActualProjected

Guatemala2.5
Honduras2
Mexico1.5
El Salvador

1990

2000

2010

2020

2030

2040

Year
Sources: United Nations, World Population Prospects; author’s calculations.

supply provides a uniquely forecastable component of migration pressures:
The cohorts that will enter the labor force in the next 16 to 20 years havealready been born, and changes in cohort sizes for the next two decades canbe predicted relatively accurately using current trends in fertility.30
A simple visual perspective on the issue is given in figure 14, whichplots observed and projected population relative to the United States for
major sending countries from 1980 to 2050, where we normalize 1980 values to 1. The most striking demographic transition is in El Salvador; having
reached a peak in relative labor supply in 2010, its population relative to
the United States is projected to decline rapidly in the future, reaching 1980
levels again by 2050.31 Mexico follows a similar temporal pattern, but witha slower future decline. Guatemala and Honduras have seen similarly steep
increases in relative labor supply, reaching roughly 250 percent of their
30. Many migration episodes, such as the recent surge of Syrians into Europe andthe arrival of Vietnamese immigrants in the United States in the late 1970s, were driven
by shocks other than labor supply. Some shifts in labor supply, such as the rapid fertility
decreases in Catholic Southern Europe and Latin America, were not forecasted. Nonetheless, given our lack of ability to anticipate future income shocks, demographic differentials
remain an attractive way of predicting medium-term migration trends.
31. Because the U.S. population is growing, declines in these relative population size
ratios do not imply absolute declines in the populations of origin countries.

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131

1980 values by 2015. The future experiences of these two Central American countries diverge strongly, with Guatemala’s relative labor supply continuing to grow almost linearly for another 25 years, whereas Honduras’s
supply begins to decline immediately. Continuing robust labor supply growthin Guatemala may allow it to partially replace diminished U.S. migrant
inflows from other Latin American countries. However, given Guatemala’s
small size—its 2015 population was 16 million, compared with Mexico’s
population of 127 million—its migrant-sending capacity is limited.
To use population forecasts as the basis for a predictive model of future
U.S. immigration, we first calculate five-year birth cohort ratios in thehistorical (census) and future (United Nations) data in the same manner,and project future GDP based on growth forecasts from the International
Monetary Fund so as to be able to take log GDP per capita ratios. Using
data for the 25 available countries in Latin America and the Caribbean,
we estimate the model using observed migration rates from 1980 to 2015,
and use the resulting parameters to project age-gender-country–specificmigration rates through 2040. In order to capture the dramatic shift inimmigration enforcement that occurred between 1990 and 2000, we estimate a trend-break model that fits piecewise linear time trends to the “lowenforcement” era preceding 2000 and the “high enforcement” era startingin 2000.32 Because we want to estimate effects for the full age distributionof immigrants in the United States, we use all available age cohorts and notjust those of young workers. The estimating equation is
( 7)

 N 
 GDPit 
micgt = β 0 + γ 1 log  icg  + γ 2 log + α i + µ g + ηc
 GDPUSt  NUScg + ∑ α i δ 2 t + ∑ ϕ1t α i t1 + ∑ ϕ 2 i α i t2 + ε icgt .
iiiHere, micgt is the net migration rate to the United States for source country i, age cohort c, and gender g, at the time of census wave t. Regressors
include the log ratio of the origin-country birth cohort to the U.S. birth cohort(Nicg/NUScg) and the contemporaneous log GDP ratio of the origin-country’s
GDP per capita to U.S. GDP per capita (GDPit /GDPUSt). We include fixed

32. These trend breaks (which we allow to be country-specific) are consistent with theinclusion of time period fixed effects in the first-differenced model in equation 6. We movefrom first differences in equation 6 to levels in equation 7 to accommodate forecasting immigrant stocks.

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Table 5. Results of the Prediction Regressiona
Independent variable
Log birth cohort ratio for origin country to United States
Log birth cohort ratio × under 40 years old
Log GDP ratio for origin county to United States
Log GDP ratio × under 40 years old
FemaleFemale × under 40 years old
Covariatesb
No. of observations
R2

Net migration rate
0.7716(0.760)3.9292***(0.734)
1.9581***(0.518)-1.4201*(0.798)0.3558***(0.116)-0.7988***(0.116)
Yes
3,310
0.842

Sources: U.S. Census Bureau, American Community Survey; United Nations, World Population Prospects; International Monetary Fund; authors’ calculations.
a. The units of analysis are five-year birth cohorts. The analysis is conducted on 25 Latin Americancountries’ net migration rates to the United States over the years 1980–2015. Robust standard errors inparentheses are clustered by birth cohort and weighted by birth cohort size. Statistical significance isindicated at the ***1 percent, **5 percent, and *10 percent levels.
b. Covariates included but not reported are: (i) five-year age cohort dummies; (ii) country fixed effects;(iii) a country dummy for the year 2000 and later; (iv) a country-specific linear time trend for the lowenforcement era of 1980–90; (v) a country-specific time trend for the high-enforcement era of 2000–15;
and (vi) the interactions between all these covariates and being under 40 years old.

effects for the origin country (ai), the age cohort (hc), and gender (µg).
We let time trends (and trend breaks) be specific to the origin country byallowing each country to have separate intercept and time trend terms
within the low-enforcement and high-enforcement eras. First, we interact
the country fixed effect with a dummy variable indicating the year 2000
and later (d2t), then we interact the fixed effect with a 1980–90 trend (t1),and finally with a second term picking up the high-enforcement 2000–15
time trend (t2). We predict over the interval 2020–40, meaning that we
forecast for the same number of periods as are used to estimate the postenforcement trend. In using a broad set of age cohorts (age 2–67), we
partition the regression to estimate separate coefficients for the young(age 2–37) and the old (age 42–67), given the divergent migration trajectories of these two groups.
The parameter estimates for the central parameters in the cross-country
regression for Latin America and the Caribbean are given in table 5,
where we suppress the large number of country-level intercept and slope

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Figure 15. Migration Rates by Origin Country for Age 15–40, 1980–2040
Percent
ActualProjected
El Salvador

15

10

Honduras
Guatemala5

Mexico

1990

2000

2010
Year

2020

2030

Sources: U.S. Census Bureau, American Community Survey; United Nations, World Population Prospects;
International Monetary Fund; authors’ calculations.

interactions. The regression results contain several noteworthy features.
First, the strong push factor of large cohort size is experienced entirely
before age 40, after which there is no significant relationship between thelog birth cohort ratio and net migration rates. Second, the effect of GDP
ratios flips across ages; for the young cohorts, high income in the origin
country has no effect on migration, whereas for the older cohorts, highorigin-country income accelerates migration. This latter result is consistent with gravity model estimates of bilateral migration (Clark, Hatton, andWilliamson 2007). It could be explained by migration costs being a greater
constraint to migration across Latin America as a whole than in Mexico,with positive income shocks enabling migration by credit-constrained individuals (Mckenzie and Rapoport 2007).
Having estimated the model on decadal changes in the migration rate,
we then predict the net migration rate for future decades, using the International Monetary Fund’s GDP forecasts and UN population growth forecasts
for future decades. We maintain the time trends estimated on data for 2000
and later, such that we presume the strong immigration enforcement regime
stays in effect. The results are shown in figure 15. With this predicted future
migration rate in hand, we multiply these by the UN-projected future birthcohort sizes to calculate predicted migrant counts for each cohort. These134

Brookings Papers on Economic Activity, Spring 2017

Figure 16. Stock of Foreign-Born Migrants by Origin Country for Age 15–40, 1980–2040
Millions
6
5
4

Mexico3

ActualProjected

2
1Guatemala

El Salvador
1990

2000

2010
Year

2020

Honduras
2030

Sources: U.S. Census Bureau, American Community Survey; United Nations, World Population Prospects;
International Monetary Fund; authors’ calculations.

values are then summed up to obtain migration totals for each country ineach year.
As seen in figure 15, this empirical structure predicts declining net
migration rates from Mexico over the coming decades, with other major
Latin American destinations seeing roughly constant rates. Figure 16 showsthat Mexican-born migrant stocks in the key age 15–40 group are predicted
to drop to less than half their current level by 2040. Rather than showingthat the Great Recession has caused a temporary pause in an ongoing waveof immigration from Mexico, these long-term trends suggest that the 1990–
2007 housing boom may have caused a temporary surge in migration,
arresting a demographically driven long-run slowing.33 Given the strongrole that demographic factors play in our estimation model, convergence infertility rates across the Americas removes a powerful factor pushing workers across borders. We find little support for the idea that Latin American
immigration will surge again as the U.S. economy recovers.
Our focus on the declining population of migration-age individuals overlooks an important role that Mexican-born individuals will play
33. Despite using a much longer panel and a different estimation structure than that usedby Hanson and McIntosh (2016), these results confirm these previous predictions that new
inflows of working-age Mexicans will drop substantially by the middle of the 21st century.

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GORDON HANSON, CHEN LIU, and CRAIG M c INTOSH

Figure 17. Age Frequencies of Mexican-Born Immigrants in the United States, by Year
Thousands per five-year age cohort
2040, projected
2,000
1,500

2015

1,000
500

1980

10

20

30
40
50
Midpoint of five-year age cohort

60

Sources: U.S. Census Bureau, American Community Survey; United Nations, World Population Prospects;
International Monetary Fund; authors’ calculations.

in American demographics. In a manner even more pronounced than for
Mexico itself—which has recently undergone a rapid demographic transition (Tuiran and others 2002)—the U.S. Mexican-born population will age
very quickly. We can draw frequencies of Mexican-born individuals in ourdata, starting with the observed numbers in 1980 and 2015, and then plot
the predicted values in 2040, as shown in figure 17. Whereas the modal
Mexican-born resident in the United States was 20 years old in 1980 and
40 years old in 2015, he or she will be almost 60 years old by 2040. Rapid
aging arises from the confluence of declining fertility in Mexico and thedemographic amplifier of emigration, which pushes a larger share of larger
cohorts into the United States and therefore accentuates the implications of
Mexico’s demographic transition for the age structure of the Mexico-born
population on the U.S. side of the border. A large elderly population of
undocumented immigrants is a policy challenge that the United States hashitherto not faced.
Stepping back to examine the age distribution of all Latin American immigrants, we see broad evidence of an aging population in the
United States. Table 6 illustrates that the total Latin American–born population under 40 in the United States by 2040 will be only 56 percent of
its current size (4.9 million versus 8.8 million), while the foreign-born

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Table 6. Thousands of Foreign-Born Residents in the United States
1980

2015

2040 projecteda

Country

Youngerthan 40

Olderthan 40

Youngerthan 40

Olderthan 40

Youngerthan 40

Olderthan 40

Antigua and Barbuda
BahamasBarbados
BelizeBolivia
Chile
Colombia
Costa Rica
Dominican Republic
EcuadorEl Salvador
GrenadaGuatemala
GuyanaHaiti
Honduras
Jamaica
MexicoNicaragua
PanamaParaguay
Peru
Trinidad and Tobago
Uruguay
Total

2.8
7.8
16.5
10.0
9.6
24.5
101.5
101.5
115.1
61.3
74.5
4.6
49.3
37.0
63.0
25.3
123.3
1,574.7
28.6
39.4
2.2
38.8
46.9
8.8
2,485.5

0.8
4.0
8.3
3.6
3.5
10.3
40.2
40.2
43.8
24.0
17.8
2.2
12.6
11.6
25.2
10.1
59.9
452.3
12.1
16.6
0.7
15.6
17.1
4.7
805.1

4.4
13.7
8.9
15.3
30.3
32.2
253.0
37.3
439.0
186.7
650.2
5.5
557.1
81.1
256.0
369.0
227.0
5,299.0
95.0
25.5
7.0
157.4
73.8
13.7
8,838.1

10.9
18.5
33.1
25.4
36.6
54.4
374.6
40.0
536.9
222.3
655.7
19.1
346.1
164.7
349.9
211.7
391.8
5,663.8
138.0
60.2
7.0
248.5
131.6
23.3
9,764.1

6.8
4.5
1.1
3.7
-61.4
-29.4
-83.7
-38.0
402.1
22.8
594.1
0.2
813.5
55.9
205.0
488.0
151.0
2,707.0-69.7-35.4-167.8
-81.0
22.8
-4.5
4,907.7

8.9
47.1
28.1
33.5
86.7
73.4
782.5
50.3
1,104.4
281.0
1,601.7
19.5
1,102.3
186.2
967.7
698.1
539.8
14,146.0
336.2
50.3
45.3
536.7
150.1
54.3
22,930.1

Sources: U.S. Census Bureau, American Community Survey; United Nations, World Population Prospects; International Monetary Fund; authors’ calculations.
a. Negative values are an artifact of the linear model used to forecast future flows, and are not possible
in practice. See the text for interpretation, specifically note 34.

population over 40 will be 235 percent of its current size (22.9 million versus
9.8 million). The model predicts a negative net migration rate for somecounties of origin in 2040 (negative values are not possible in practice; but
in our linear predictive model, they indicate a phenomenon that could beinterpreted as net migration pressure out of the United States).34 The main

34. Negative net migration rates are an artifact of the linear model used to forecast future
flows. Our model predicts only 14 percent of the dyads to have negative net migration in
2020, but future decreases in population growth in sending countries drive the share of predicted negative dyads to 24 percent by 2040.

GORDON HANSON, CHEN LIU, and CRAIG M c INTOSH

137

policy question posed by first-generation immigrants from Latin America
and the Caribbean thus appears likely to shift from one of the labor market
effects of large-scale labor inflows to one of the cost of social programs
and health care for an elderly immigrant population with relatively low
incomes (and relatively low rates of naturalization when compared withhigh-skilled immigrants).

III.B. Changes in Low-Skilled Immigrationand U.S. Labor Market Tightness
We have seen that incentives for low-skilled immigration in the UnitedStates have changed markedly since the early 2000s, and have already manifested demographic pressures that are likely to compress migration inflows
in coming decades. How will these developments affect the U.S. economy? A reduction in the relative supply of low-skilled labor, by putting upward pressure on wages for these workers, may operate directly
by causing changes in the U.S. wage structure (Borjas 2003). Alternatively, wage pressures may induce firms to alter their production techniques in a manner that mitigates the wage effects of shocks to therelative labor supply by generating endogenous changes in labor demand(Lewis 2011). Whichever form labor market adjustments take, the magnitudes of these adjustments are likely to be determined by the implicit
pressure of changes in immigration inflows on U.S. wages, which weanalyze next.
As a final exercise, we consider how the United States’ demand for andsupply of labor have evolved over time and how the supply of low-skilled,foreign-born workers meshes with these changes. Our approach employs
the methodology of Katz and Murphy (1992), as applied by Autor, Katz,
and Kearney (2008), to examine the relative earnings of more- and lessskilled workers. The exercise we perform allows us to translate the recent
slowdown in low-skilled immigration into implied pressures on the wage
premium enjoyed by skilled workers.
Consider a production function with constant elasticity of substitutionthat takes as its arguments the employment of low-skilled and high-skilledworkers, where within each skill group we treat native-born and foreignborn workers as perfect substitutes, as is consistent with recent evidence(Borjas, Grogger, and Hanson 2012).35 From the first-order conditions for

35. The fact that the CPS does not include measures of nativity until 1994 makes thisassumption a necessity if we are to estimate equation 6 based on time series variation.

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firm profit maximization, we obtain an expression for the relative wages ofhigh-skilled and low-skilled labor,(8 )log ( wht ) − log ( wlt ) = γ 0 + γ 1 [ log ( N ht ) − log ( N lt )] + γ 2 X t + ε t ,where log(wht) - log(wlt) is the log U.S. wage for high-skilled workers
relative to the log U.S. wage for low-skilled workers, log(Nht) - log(Nlt)is the log U.S. supply of high-skilled workers relative to the log U.S.
supply of low-skilled workers, and Xt is a vector of controls that capturelabor demand shocks.36 Each skill group is made up of a combination ofnative-born and foreign-born labor. By taking the difference in earnings
between skill groups in equation 8, we remove from the specification labordemand shocks that are common to high-skilled and low-skilled workers(for example, aggregate changes in labor demand associated with recessions, and growth in total factor productivity). Following Autor, Katz,
and Kearney (2008), within each skill group we measure wages as average
weekly earnings—holding constant the age, gender, and racial composition of workers—and we measure employment in terms of labor supplies
expressed in PEUs. High-skilled workers are those with at least a college education, whereas low-skilled workers are those with a high school
education or less. We estimate equation 6 with annual data from the CPS
for the period 1963–2007, and we use the results to predict relative earnings for the period 1963–2015, which includes the out-of-sample range
2008–15.
In our baseline specification for equation 8, which includes a time trend
as the only additional covariate, the coefficient estimate for g1 is -0.42(with a t value of 9.9) when we define the low-skilled group to be workers with a high school education or less; and it is -0.18 (with a t valueof 8.0) when we define the low-skilled group to be workers with strictly
less than a high school education. Consistent with the theory underlying
equation 8, increases in the relative supply of skilled labor drive down thewage premium for skill. These estimates change little when we expand theperiod from 1963 to 2015 or include the following additional covariates:
a quadratic time trend, the aggregate unemployment rate, and the log real

36. This estimation approach makes the strong assumption that labor is freely mobile
across occupations. See Burstein and others (2017) for an analysis that uses a Roy (1951)
model in the analysis of how immigration affects labor market outcomes at the occupationlevel.

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139

Figure 18. Skill Premium for All Industries, 1963–2015
College and above versus
less than high school
Percent
1.1
0.9

Percent
Counterfactual
predicted wage gap

Katz and Murphy (1992)
predicted wage gap

1.1
0.9
0.7

0.7
0.5

College and above versus
high school or lessObserved
wage gap

0.5
20071970 1980 1990 2000 2010
Year

1970 1980 1990 2000 2010
Year

Sources: U.S. Census Bureau, Current Population Survey; authors’ calculations.

federal minimum wage.37 The first coefficient—for the impact of relativelabor supply on the wage gap between college- and high school–educatedworkers—compares in value with the estimates given by Autor, Katz, andKearney (2008) of -0.40 to -0.62, depending on the covariates included,
for the period 1963–2005, suggesting that our coefficient estimates are at
the low end of those obtained in previous empirical work.
Figure 18, which presents the results, shows three series for relative
earnings: the actual skill premium for the period 1963–2015; the projectedskill premium for 1963–2015 based on equation 6, using coefficients estimated on data for 1963–2007; and a counterfactual projection of the skill
premium, where we again use the estimation results for equation 8, but now
replace the relative labor supplies we feed into the projection with a counterfactual series in which we assume that the number of low-skilled immigrant workers grows at the same rate from 2008 to 2015 as it does for 1994
to 2007. For the out-of-sample period 2008–15, the first projection is basedon observed changes in the relative supply of skilled labor (which embody
37. The time trend carries a positive coefficient, indicating a positive trend in the relative
demand for skilled labor; the unemployment rate carries a negative sign but is imprecisely
estimated; and the minimum wage enters negatively, indicating that a higher minimum wage
compresses the skill premium. The assumption that the time trend for relative labor demandis linear over the course of several decades is of course quite strong. Nevertheless, allowing
for a quadratic time trend has minimal impact on the estimate of g1.

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actual changes in both high-skilled and low-skilled immigration), whereas
the second projection is based on the observed labor supply through 2007
and the counterfactual supply thereafter (which suppresses the slowdown
in low-skilled immigration).
In constructing the counterfactual projection, it is worth noting that
inflows of both low-skilled and high-skilled immigrants slowed after 2007.
Our counterfactual labor supply series, by imposing continued growth for
low-skilled but not for high-skilled immigration, thus understates the post2007 growth in the relative supply of skilled labor. The resulting counterfactual projection of the skill premium therefore corresponds to an artificial
setting, which we view as useful for describing the magnitude of the lowskilled immigration slowdown in terms of wage pressures but not for evaluating the impact of immigration on earnings during this period.38
For low-skilled workers defined to be those with less than a high school
education (the left panel of figure 18), the actual skill premium is flat fromthe early 1960s to the late 1970s, rises steadily from the late 1970s to themid-2000s, and is flat again thereafter. During the within-sample period
1963–2007, the predicted skill premium rises more slowly than the actual
skill premium in the mid-1970s, suggesting that the relative demand forskill rises more slowly than the linear trend would indicate, and rises more
rapidly than the actual skill premium from the late 1970s to the late 1990s,
suggesting growth in the demand for skill that exceeds the linear trend during this interval.
Turning to the out-of-sample period 2008–15, the post-2007 slowdown
in immigration tempers growth in the supply of low-skilled labor, causing
the predicted skill premium to rise more slowly after 2007 than before.
Replacing actual relative labor supplies with the counterfactual series thatassumes sustained growth in low-skilled immigration (that is, a 2007–15
annual growth rate equal to the 1994–2007 annual growth rate), the projected path of the skill premium naturally lies above the projected pathbased on observed data. If low-skilled immigration had not slowed after
2007, the relative supply of skilled labor would have grown more slowly,
which in turn would have mandated a larger increase in relative earnings
for skilled labor. The difference between the two projected wage series in2015 is 8.6 log points, which indicates that the magnitude of the slowdown
in low-skilled immigration—holding all else equal, including high-skilled

38. For analyses of the impact of immigration on earnings, see Card (2001); Borjas(2003); Ottaviano and Peri (2012); and Dustmann, Frattini, and Preston (2013).

GORDON HANSON, CHEN LIU, and CRAIG M c INTOSH

141

immigration—is consistent with a decrease in the skill premium of 1.1 percent a year during the 2007–15 period. To put this magnitude in context, theobserved increase in the wage premium for college-educated workers versus workers with less than a high school education during the 1980–2007
period of rapidly rising wage inequality is 1.6 percent a year. Again, we donot take this value to be the true change in wages due to the immigration
slowdown (because we are not addressing changes in high-skilled immigration), but rather as an indication of the magnitude of the immigrationinduced change in the labor supply expressed in terms of wage pressures.
When we instead define low-skilled workers to be those with a highschool education or less (right panel of figure 18), the broad patterns forthe actual skill premium are similar, though the flattening in the premium
during the 2000s is less pronounced and the absolute premium is smaller.
For the out-of-sample period 2008–15, it is again the case that the projectedskill premium based on actual labor supplies lies above the observed skill
premium, indicating an increase in the demand for skill that is less thanthe linear trend. Comparing this projected skill premium with that whichobtains when using counterfactual labor supplies (involving no post-2007
slowdown in immigration), the latter exceeds the former by 6.1 log points
in 2015, or a difference of 0.8 percent a year during the period 2007–15. To
put this magnitude in context, the observed increase in the wage premiumfor college-educated workers versus workers with a high school educationor less for 1980–2007 is 1.1 percent a year. Because low-skilled immigrants are a smaller share of the skill group with a high school education orless than of the skill group with strictly less than a high school education,
the implied wage pressures of the immigration slowdown are weaker when
we move to this more expansive definition of being low-skilled. Similar
patterns are observed when we restrict the sample from all industries(figure 18) to low-skill industries (figure 19).

III.C. Summary
The U.S. immigration wave of the late 20th century was enabled to a
substantial extent by the rapid growth of the labor supply in Latin America and the Caribbean relative to the United States. Because labor supply
growth in migrant-sending nations is slowing and will continue to slow, thedemographic push for U.S. immigration is abating. Abetting these demographic forces is the substantial increase in U.S. immigration enforcement,
which thus far has been maintained. Absent economic or political crisesin the Western Hemisphere that reignite international migration, standard
migration models predict that migration rates from major U.S.-sending

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Figure 19. Skill Premium for Low-Skill Industries, 1968–2015
College and above versus
less than high school

College and above versus
high school or less

Percent

Percent
Counterfactual
predicted wage gap

1.1

1.1

Observed
wage gap

0.9

0.9
0.7

0.7
0.5

Katz and Murphy (1992)
predicted wage gap
2007
1970

1980

1990
Year

2000

0.5
2010

1970

1980

1990
Year

2000

2010

Sources: U.S. Census Bureau, Current Population Survey; authors’ calculations.

nations will drop sharply in coming decades. Indeed, the weakening ofthese migration pressures began in the early 2000s, and may have beenmasked by the temporary labor demand boost provided by the U.S. housing
boom. The resulting post-2007 slowdown in low-skilled immigration is of amagnitude consistent with a decrease in the wage gap between high-skilled
and low-skilled U.S. labor of 6 to 9 percentage points. If, as predicted bydemographic forces, low-skilled immigration continues to decline in future
decades, U.S. firms—especially those located in U.S.–Mexico border statesand in the immigrant-intensive industries of agriculture, construction, eating and drinking establishments, and nondurable manufacturing—arelikely to face pressure to alter their production techniques in a manner that
replaces low-skilled labor with other factors of production.

IV. Looking Forward
From the early 1970s to the early 2000s, the United States experienced anepochal wave of low-skilled immigration, which was the combined result ofrelatively high U.S. incomes, relatively stable U.S. GDP growth, relatively
slow U.S. labor supply growth, and moderately permissive immigration
enforcement. Since the mid-2000s, each of these drivers has attenuated.
The U.S. macroeconomy is no longer so stable relative to migrant-sending
countries; U.S. labor supply growth is now similar to that in much of the

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143

Western Hemisphere; and the U.S. borders, having been heavily fortified,
are much harder to cross without a visa.
The future of low-skilled immigration thus appears to be less about
streaming inflows of young workers from lower-income nations and more
about the needs of an aging population of lower-income adults that is settled in the United States. Those within this group who are undocumented—
somewhere between one-half and three-fifths—do not qualify for mostfederally funded welfare benefits, including Medicare and Medicaid. In
recent decades, the primary fiscal effects of low-skilled immigration werethe cost of primary and secondary education for the children of immigrants,
and, to a lesser extent, for publicly funded health care for the subpopulationof this group that was born in the United States (Blau and Mackie 2016).
Judith Treas and Zoya Gubernskaya (2015) document that 51 percent of
the foreign-born population is covered by some form of public insurance,
as compared with 35 percent of the native born, suggesting that the costsof caring for the foreign born are likely to fall disproportionately on publicly funded programs. Given our estimates of an increase of 13 million(134 percent) in the population of foreign-born immigrants over the ageof 40 by the year 2040, there may be considerable growth in the demand forsafety net programs as a result of past and future immigration. Under existing financing rules, U.S. states and localities would be the entities primarily
responsible for shouldering these costs.
In light of the changing demographics of migrant-sending nations, andthe apparent effects of the existing immigration enforcement surge, the current emphasis of the U.S. government on further intensifying immigration
enforcement is puzzling. One interpretation of the planned enforcementbuildup is that it is driven by politics. Having lived through the great immigration wave of the last 35 years, some native-born voters may be upset
by the laxity of past enforcement and willing to reward politicians whoare seen as atoning for these transgressions. Supporting stronger enforcement may be a way for politicians to signal their disapproval of earlier
policy choices. Such signaling would come at a substantial cost, however, given that the U.S. immigration enforcement budget now exceeds$20 billion a year. Another interpretation is that intensifying enforcement
is an effort to forestall future claims on public resources. The aging of the
low-skilled, foreign-born population means that by increasing deportations
today—when many low-skilled immigrants are approaching middle age—
the United States may avoid demand for social spending in the future. If
U.S. voters oppose providing public benefits to low-skilled immigrants—
and if the U.S. government cannot credibly commit to deny benefits to144

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low-income, elderly, foreign-born residents down the road—then expanding current deportations may reduce the expected drain on U.S. public
coffers in later decades.39 The cost of these extra deportations—beyond
the incremental spending on enforcement—includes reducing the supply
of workers who are in their prime earning years, who have accumulated
substantial U.S. labor market experience, and who are well established intheir communities.
Changes in U.S. immigration policy affect not just the U.S. economy but
also the economies of migrant-sending nations. To the extent that immigration enforcement has played a dominant role in the recent slowdown inmigration, improvements in the welfare of U.S. workers come in part at thecost of potential migrants who forgo higher wages, and their nonmigrantcompatriots, who now face more crowded labor markets at home. Because
the modal immigrant from Mexico would be a middle-income earner athome, expanding deportations and tightening border security would tend to
expand labor supplies and depress earnings in the middle of Mexico’s wage
distribution (and from higher quantiles of the wage distributions in othersending countries for migrants). From the perspective of those born insending countries, wages take a hit from immigration restrictions whether
or not workers decide to migrate.
Mexico, by virtue of its status as a transit country for undocumented
immigrants, is doubly exposed to changes in U.S. immigration enforcement. Many Central Americans planning to enter the United States traverse
Mexico illegally on their way north. Stronger U.S. enforcement may havethe indirect consequence of increasing the supply of undocumented Central
Americans seeking to live and work in Mexico. Given that Guatemala isthe one major U.S. migrant-sending nation in the Western Hemisphere thatwill continue to experience high rates of labor supply growth in coming
decades, Mexico faces the real possibility that continued tightness in U.S.
immigration policy would increase its supply of low-skilled, foreign-born
residents.
Taking immigration controls as a means to improve the plight of lowskilled U.S.-born workers, how would we expect the incidence of benefits
to stack up against alternative policies? An increase in minimum wages
may benefit those with jobs that pay equilibrium wages above a higher wage
39. This characterization of political support for immigration enforcement is roughly
consistent with the framework used by Alesina, Baqir, and Easterly (1999), in which
enthusiasm for public spending is diminished by increased ethnic and racial diversity in ajurisdiction.

GORDON HANSON, CHEN LIU, and CRAIG M c INTOSH

145

floor, at a cost to consumers who purchase labor-intensive goods and services and to workers excluded from the labor market. Investments in skill
development and retraining may target native-born workers more finely,
but the effectiveness of such programs is in question (LaLonde 1995). The
attractiveness of immigration restrictions relative to these alternative policies also depends on the extent to which we take the welfare of the foreignborn into account. Immigration controls therefore appear to be a prolabor
instrument that comes at a high cost to consumers and foreign-born workers relative to alternative potential policies.
It may be premature to declare that the most recent episode of high U.S.
immigration is over. Many factors could cause the population of lowskilled immigrants in the United States to begin growing again. Primeamong these is increased economic or political instability in the Western
Hemisphere. Indeed, heightened insecurity in Central America, due in largepart to violence associated with organized crime, appears to have increased
recent labor outflows from the region. Mexico, for its part, has not had afinancial crisis since 1995. The reform of the country’s electoral laws in
1997 created a political process that is competitive and free of the vote rigging that marred the Institutional Revolutionary Party’s 70-year rule during
the 20th century. However, the openness of Mexico’s economy leaves itvulnerable to external shocks, in particular in the United States, which isthe destination for more than 80 percent of its exports. At least in the short
to medium runs, the U.S. government itself seems to be in a position todetermine—whether through its trade or its immigration enforcement
policies—the potential supply of low-skilled immigrants.

ACKNOWLEDGMENTS    We thank Janice Eberly, Adriana Kugler, Edward
Lazear, James Stock, and the participants in the Spring 2017 Brookings Panelon Economic Activity for helpful comments on a draft of this paper; Daniel
Leff for excellent research assistance; and the Center on Global Transformation at the University of California, San Diego, for financial support.

146

Brookings Papers on Economic Activity, Spring 2017

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Comments and Discussion
COMMENT BYADRIANA KUGLER   This paper by Gordon Hanson, Chen Liu, and Craig
McIntosh provides a timely analysis of an important issue. The paper first
documents the decline in low-skilled immigration over the past decade.
It shows that the share in low-skilled immigration was stable during the
1990s and until the early 2000s, but had fallen since 2007, largely due toa decline in immigration from Mexico and the rest of Latin America. This
paper, thus, seeks to clarify and correct preconceptions about the magnitude and direction of low-skilled immigration during recent decades.
It is particularly important that the rest of the paper tackles the seldomstudied question of what factors drive low-skilled immigration. In particular,
the paper focuses on trying to explain how much of the decline in low-skilled
immigration was due to (i) temporary factors related to the Great Recession;(ii) declines in labor supply in Latin America, because of a demographictransition in these countries; or (iii) increased enforcement at U.S. borders. The paper documents the role of these three causes in explaining thedecline in low-skilled immigration to the United States. However, as I discuss below, the three causes are not considered in a single analysis with asingle data set, and the paper does not quantify their relative importance;
moreover, the paper does not attempt to examine the causal effects of thesefactors on the fall in immigration.
DOCUMENTING THE FALL IN IMMIGRATION The paper documents a change
in the characteristics of low-skilled immigrants toward younger, moreeducated, and more Latin American immigrants. The paper then uses datafrom the Current Population Survey (CPS) to document raw counts andraw hours of low-skilled immigrants (defined in two ways: those with lessthan a high school education, and those with a high school education orless). This shows a steady increase in the shares of low-skilled immigrants
152

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153

from 1994 to 2007 using both definitions, although the effect is greaterusing the broader definition of high school or less. Starting in 2007, theshare using counts stabilizes. Because immigrant men from Latin America
work much longer hours than native men, they also examine the share interms of total hours worked. Not surprisingly, there is a decline starting in2007 using this measure.
Because a year of education and experience in Mexico is not necessarily as valuable as a year of education and experience in the United States
for workers in the United States, body counts of immigrants may overstate their presence in the United States. Reweighting the data to generate
productivity-equivalent units (PEUs) shows a decline in the share of lowskilled immigrants after 2007 using both definitions of low skill. The effectis even bigger using PEUs for hours.
Although the changes in shares are useful for understanding recent
trends, it would also be interesting to report the changes in inflows and outflows of immigrants from different skill groups and different source countries using census data (Card 2001). These changes could provide evidence
to help disentangle the different causes of the decline in low-skilled immigrants to the United States. As it is, the paper only shows stocks of immigrants in different skill groups and some evidence of changes in inflows
and outflows for different cohorts of immigrants from all skill groups.
The paper also documents the positive selection of Mexican immigrants,
who make up between half and three-fifths of low-skilled immigration,
depending on the definition used. This means that Mexico is sending someof its more qualified workers to the United States and not those at thevery bottom of the distribution, as is sometimes portrayed. Others have also
documented this positive selection (Feliciano 2001; Akee 2010). In addition, the paper shows greater positive selection for women, but decreased
positive selection for both men and women from 1990 to 2010. Although
the paper does not delve in detail into the potential reasons behind thedecline in positive selection, there are two potential explanations. First, thisdecline could well be due to the important role of networks, as pointed out
by David McKenzie and Hillel Rapoport (2010). Second, there have beenwell-documented changes in the distribution of income in Mexico and therest of Latin America (Esquivel 2011; López-Calva and Lustig 2010) thatwould make it more attractive for both middle- and lower-income workers
to stay in their home countries rather than migrate. This decline in income
inequality in Mexico and the rest of Latin America is also another potential explanation for the decline in immigration from this region to theUnited States.

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Another way to understand what is driving the decline in low-skilledmigration to the United States, and also the decline in positive selectivity to
the United States, would be to examine what is happening to the immigration trends in other countries receiving immigrants from Latin America. If
the driving factors are mainly the improving conditions in Latin America
and a decline in labor supply, then very similar trends should be observedin other countries receiving Latin American immigrants. If, conversely,
enforcement is a key force, then one should not expect to find similar
trends in other receiving countries that have not increased stringency inimmigration enforcement during the past few decades.
DRIVERS OF CHANGES IN LOW-SKILLED IMMIGRATION The paper puts forththree main causes for the decline in low-skilled immigration: (i) differencesin economic conditions between the two countries, (ii) a decline in labor
supply in Latin America, and (iii) stricter enforcement in the United States.
The paper first presents a descriptive analysis of the three causes behindthe observed declines in low-skilled migration since 2007, and then turns
to regression analysis. The paper explores changes in labor income for lowskilled workers in Mexico and the United States, and gives an interesting
description of changes in the types of visas and in enforcement in recent
decades. This description is very engaging, but neither the timing nor stringency across various states offers a way to quantify the relative importanceof these factors in reducing low-skilled immigration to the United Statessince 2007.
The paper does include two separate analyses to quantify the importance of relative demand and relative supply. The first analysis uses the
Mexican population census from 2000 to 2010 to estimate a regressionof the change in emigration from each Mexican state on the ratio of GDP
in the state to U.S. GDP, and on the ratio of the birth cohort size in eachstate to the native U.S. population without a high school degree. This speci­
fication does not include any information on enforcement in the UnitedStates affecting migrants from different states differentially. The paper
could exploit variation in enforcement over time and exploit geographic
variation in border enforcement for Mexican states closer to or farther
from various U.S. states. However, the authors explain that including
enforcement in the regressions leads to positive correlations, probably
because of reverse causality, but that they were unable to find a credible
instrument. For this reason, they instead leave enforcement as an omitted
variable, and this would lead to an upward bias in the coefficient of relative GDP and to a downward bias in the coefficient of relative supplies.
In addition, the analysis leaves out relative inequality in Mexico and theCOMMENTS and DISCUSSION

155

United States, another relevant measure of the relative returns a low-skilledworker is likely to earn.
The results of this analysis are interesting and show a negative coefficient
in relative detrended GDP at age 16, though not in concurrent detrended
GDP. The results also show a positive coefficient on relative supplies. Surprisingly, the effects on detrended GDP become bigger when 2010 is dropped(which is meant to capture the effect of the Great Recession—though,officially, the Great Recession started in 2007:Q4 and ended in 2009:Q2).
Finally, it is not clear whether these can be taken as causal estimates ofthe effects of labor demand conditions and labor supply on migration.
The analysis uses the ratio of the Mexican state birth cohort size to the
U.S. birth cohort size as an instrument for the relative labor supply shock.
However, the first stage is never reported, and U.S. birth cohort size could
itself be affected by previous migration from Latin America.
The paper, then, provides a similar regression to the one described above,
but using data from 25 Latin American countries from 1980 to 2015. Thisis a regression of emigration on relative supplies of low-skilled migrants
in the major sending countries relative to the United States, and GDP in
sending countries relative to U.S. GDP. The regression shows a negative
coefficient on the GDP ratio and a positive coefficient on the birth cohort
ratio, but only for those less than 40 years of age. The regression can explaina large part of the net migration rate and can explain the relative demandand supply story well for young people. The authors use the regression to
do simulations showing what emigration would have been from these countries if GDP ratios had grown as predicted by the International Monetary
Fund’s GDP forecasts and if the birth cohort sizes had grown as predictedby the UN population growth forecasts for future decades. The results show
a continued decline in migration rates from Latin America, net negative
migration from Mexico during the coming decades, and a drop in Mexican
immigrants to less than half by 2040. In addition, future net migration fromthe other major Latin American countries is likely to remain roughly constant during the next few decades. Even though the regression is incomplete,
it does provide some evidence that Latin American immigration is likely toincrease again after the full recovery of the economy.
EFFECTS OF THE FALL IN LOW-SKILLED IMMIGRATION ON THE WAGE GAP The
final section of the paper turns to an analysis à la Lawrence Katz and Kevin
Murphy (1992) to try to quantify the effects of the fall in the demand forlow-skilled workers on the wage gap between skilled and unskilled workers.
This analysis regresses skilled to unskilled wage differentials on a trend, andrelative supply is captured by the relative supply of skilled and unskilled

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workers using CPS data for the United States from 1963 to 2007. The
analysis is not altogether satisfactory, for two reasons. The time trend ismeant to capture growth in labor demand, but this would imply that labordemand is growing at a constant rate during this period or within each
subperiod, and this is clearly not the case. The analysis also tries specifications that include a quadratic time trend and the aggregate unemployment rate, but it does not include the relative unemployment rate for skilled
and unskilled workers or other measures more clearly associated with labordemand for skilled relative to unskilled workers. Also, the relative labor
supply does not distinguish immigrants from natives, although it does use
David Autor, Katz, and Melissa Kearney’s (2008) conversion of labor
supplies to efficiency units, using age, gender, and racial composition.
Given the interest in immigrants, it would be important to make this distinction; to use efficiency units for immigrants, and to allow for differential
elasticities of substitution for immigrant and native workers of different
skill levels. Rather than trying to estimate this impact with a regression thatcannot capture the causal impact of low-skilled immigration on the wage
gap between skilled and unskilled workers, the paper could refer here toother credible analyses of the causal impact of low-skilled immigration on
U.S. wage differentials (Card 2001; Kugler and Yuksel 2011; Ottaviano
and Peri 2012). Finally, these counterfactuals about what would have happened to relative wages do not take into account the fact that high-skilled
immigration has also slowed down since 2007, so a slowing down of theskill premium is unlikely to actually materialize.
CONCLUSION This paper tackles a policy issue of great importance, given
the present reality. The Trump administration is taking measures to addressa trend that has already been reversed by proposing to spend billions morein further building a wall along the Mexico border, creating travel bans for
immigrants from several countries, and intensifying internal enforcement
across the country.
This paper, indeed, shows that low-skilled immigration has been declining for decades. This means that fears of low-skilled immigration continuing to spiral up over the next decades are unfounded. Also, the paper showsthat immigrants tend to come from the middle or upper part of the earnings
distribution in their home countries. Thus, contrary to recent portrayals,
the large majority of immigrants are not criminals or people who add little
value to society in their home countries.
The paper is also valuable for determining the reasons why low-skilled
immigrants come to the United States. The paper finds that the most important cause of the slowdown is the recent demographic transition experienced

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157

by Latin America and, as a result, the smaller cohorts of young migrants
willing to go north. The least convincing part of the paper is the section
documenting that this slowdown will reduce the skill premium in the
United States. Just as previous evidence showed little effect of immigration on the rise in the skill premium, a further slowing down of migrationis also unlikely to help reduce the skill premium experienced over thelast few decades. Instead, one of the most effective policies to increase
the earnings of low-skilled workers is to invest in skill development andretraining programs. There is rigorous evidence that well-designed sectoral
training programs can help increase employment, earnings, and working
conditions in new jobs (Maguire and others 2010).

REFERENCES FOR THE KUGLER COMMENT

Akee, Randall. 2010. “Who Leaves? Deciphering Immigrant Self-Selection from a
Developing Country.” Economic Development and Cultural Change 58, no. 2:
323–44.
Autor, David H., Lawrence F. Katz, and Melissa S. Kearney. 2008. “Trends in
U.S. Wage Inequality: Revising the Revisionists.” Review of Economics andStatistics 90, no. 2: 300–23.
Card, David. 2001. “Immigrant Inflows, Native Outflows, and the Local Labor
Market Impacts of Higher Immigration.” Journal of Labor Economics 19, no. 1:
22–64.
Esquivel, Gerardo. 2011. “The Dynamics of Income Inequality in Mexico since
NAFTA.” Economía 12, no. 1: 155–79.
Feliciano, Zadia M. 2001. “The Skill and Economic Performance of Mexican
Immigrants from 1910 to 1990.” Explorations in Economic History 38, no. 3:
386–409.
Katz, Lawrence F., and Kevin M. Murphy. 1992. “Changes in Relative Wages,
1963–1987: Supply and Demand Factors.” Quarterly Journal of Economics 107,
no. 1: 35–78.
Kugler, Adriana, and Mutlu Yuksel. 2011. “Do Recent Latino Immigrants Compete
for Jobs with Native Hispanics and Earlier Latino Immigrants?” In Latinos andthe Economy: Integration and Impact in Schools, Labor Markets, and Beyond,edited by David L. Leal and Stephen J. Trejo. New York: Springer.
López-Calva, Luis F., and Nora Lustig. 2010. Declining Inequality in Latin America: A Decade of Progress? Washington: Brookings Institution Press.
Maguire, Sheila, Joshua Freely, Carol Clymer, Maureen Conway, and Deena
Schwartz. 2010. “Tuning In to Local Labor Markets: Findings from the Sectoral
Employment Impact Study.” New York: Public/Private Ventures.
McKenzie, David, and Hillel Rapoport. 2010. “Self-Selection Patterns in Mexico–
U.S. Migration: The Role of Migration Networks.” Review of Economics andStatistics 92, no. 4: 811–21.

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Ottaviano, Gianmarco I. P., and Giovanni Peri. 2012. “Rethinking the Effect of
Immigration on Wages.” Journal of the European Economic Association 10,
no. 1: 152–97.

COMMENT BYEDWARD P. LAZEAR   Gordon Hanson, Chen Liu, and Craig McIntosh
clearly document two important facts. First, the supply of unskilled immigrants has declined over time, and, left to its own devices, is likely todecline further in the future. Second, immigrants from Mexico, which is
the major supplier of immigrants to the United States, are low-skilled byAmerican standards but are primarily middle-income earners in Mexico.
This comment focuses on the authors’ conclusion and warning that
there will be a shortage of unskilled workers in the future. The frame ofreference for this conclusion is a combination of the historical trend andforecasts, as well as conceptual reasoning based on supply considerations,most clearly articulated in the context of immigration in the early work ofGeorge Borjas (1987). The analysis is based on the model of Arthur Roy(1951), which is used to discuss the choice of one occupation or industry
over another. Selection into industry, occupation, or country is determinedby supply on the basis of comparative advantage. The Roy model in itspurest form assumes that there are no barriers to entry that affect agentsdifferentially.
Although this model has been used to describe immigration, it may not
be completely appropriate because there is a large queue for immigration
slots (green cards), and policy filters are as likely to determine who ends up
being in the United States as does relative supply from various countries.
The following example illustrates the point.
Algeria, Israel, and Japan supply migrants to the United States. Consider
the following thought experiment: Rank the countries’ immigrants, highest
to lowest, in terms of educational attainment. The ranking is Algeria, Israel,
Japan.1 This ranking deviates substantially from average educational attainment in the donor countries, but U.S. immigrants are not selected randomly
from those populations, nor do they necessarily follow the predictions of a
Roy model. More important is the additional fact that Algerians make up
0.0004 percent of U.S. immigrants, Israelis account for 0.003 percent, and

1. These rankings are based on the responses of roughly 1.5 million individuals in theAmerican Community Survey from 2011 through 2015.

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Figure 1. Number of Immigrant Visas Granted by the United States and the Annual
Waiting List, 2009–14
Millions

4
3
2
1
2009

2010

2011

2012

2013

2014

Year
Immigrant visas granted

People still on waiting list

Sources: U.S. Department of State, Bureau of Consular Affairs (2015); U.S. Department of Homeland Security,
Office of Immigration Statistics (2016).

about 1 percent of immigrants are from Japan. As discussed by Hanson,
Liu, and McIntosh, the largest source country of immigrants is Mexico,
making up 27 percent of the immigrant pool. Mexican immigrants rank
134th out of 136 in educational attainment, as compared with Algerian
immigrants, who rank 25th. However, average educational attainment in
Mexico is 8.5 years, whereas in Algeria, it is only 7.6 years.
My figure 1 illustrates the excess supply of would-be immigrants
to the United States. About four times as many apply for green cards
each year as actually receive them, and many more likely would apply ifthey thought the chances of obtaining a green card were high. Similarly,
my figure 2 shows the results of a survey of Europeans, asking them inwhich country they would like to work. The United States is the countryof choice.
If, as is likely, most countries have a sufficient number of educated citizens
who would be willing to consider moving to the United States, the distribution of those here is more likely to reflect whom we let in, not who wants
to come. Indeed, the fact that Algerians in the United States have high levels of education, even higher than Israelis and Japanese, undoubtedly is a
result of the very high bar that is set for immigration from Algeria. The factthat Mexicans have a higher level of education in general than Algerians
does not show up in their relative standing in the United States, because the160

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Figure 2. Answers to the Eurobarometer Question “In Which Country Would You
Prefer to Work?”aUnited States
United Kingdom
Australia
Spain
Germany
FranceCanada
Italy
Netherlands
New ZealandSwedenSwitzerland
Austria
NorwayBelgium
IrelandDenmarkSouth Africa
BrazilOther
Don't know
5

10

15

20

Percent
Source: European Commission (2010).
a. The survey question “In which country(ies) would you prefer to work?” was asked of a sample of 3,486
Europeans who previously answered that they envisioned working abroad. Respondents were allowed to give
multiple answers.

policy that most affects Mexicans—namely, family reunification—has less
impact on Algerians.
My table 1 shows that the number of immigrants in the United Statesfrom a given country is an important negative determinant of the average educational attainment of those immigrants. The same result holds true
when the dependent variable is wage or income. Most important for the
purposes here is the number of immigrants from a country in explaining
the attainment of those immigrants in the United States. The fact that the

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Table 1. Regression of the Average Educational Attainment of Immigrants
to the United Statesa
Average years of schoolingamong immigrants fromthe donor country livingin the United States
Number of immigrants from a donor country
Population of donor country
Average level of education in the donor country
Constant
No. of observations
Adjusted R2-0.0000109***(0.000000694)
2.530***(0.309)0.324***(0.0532)
10.19***(0.496)129
0.720

Source: Lazear (2017).
a. The regression is weighted by the number of observations per country. Statistical significance isindicated at the ***1 percent level.

effect is negative suggests that immigration policy, implicit or explicit, is
an important factor in determining who is here. Supply models do not havethis as an obvious implication.
The relation of these results to the findings of Hanson, Liu, and McIntosh
is that if policy is an important determinant of who is in the United States,
the concern about the decline of unskilled immigrants in the future ismisplaced or at least overstated. To the extent that the United States wants
more unskilled immigrants, policy—again, explicit or implicit—can makethis happen. There are literally billions of unskilled workers in the world.
A sufficiently large fraction of them are almost certainly willing to moveto the United States if permitted, given the wage differences. When I waschair of the President’s Council of Economic Advisers (2006–09), we estimated the premium that the typical Mexican worker received from moving to the United States was about $10,000. This is close to the number
reported by Hanson, Liu, and McIntosh. The difference between Bangladesh and the United States is surely even greater. As a first approximation,
the supply of unskilled migrants to the United States is perfectly elastic.2
As such, the authors’ concern is not one that I share.
2. One point made by Caroline Hoxby in the general discussion of this paper is that those
who come to the United States illegally may be closer to a supply-based Roy model. Policy
is less likely to affect these workers, although border enforcement and deportation are alsoa choice, to some extent.

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Before concluding, it is useful to pose somewhat more philosophicalquestions. Even if low-skilled immigrants declined in number, how would
we judge whether this is a good or bad event? To do this, we must determine
the optimal number of immigrants and their distribution by skill level. Thisis a daunting task, but one that we do implicitly by specifying immigration
policy. Specifically, it is important to determine whether green cards should
be issued on the basis of skills, family reunification, the speed with which
immigrants assimilate, or some other factors.
To make this determination, it is best to take a step back and determine
what we are trying to maximize. This choice leads to very different policy
implications. For example, do we care about the well-being of the native
population? If so, we are searching for those who are most complementary to
existing labor and who will pay the most for land in the United States, which
is owned by current residents. Alternatively, we might want our immigrants
to be successful when here. That would push in the direction of choosing
those with successful labor market outcomes who can assimilate quickly,
even if they are substitutes for existing labor. Under those circumstances,
returns go to capital in the form of lower wages paid. Another alternative
is to maximize the welfare of all potential migrants, recognizing that thecurrent U.S. system favors certain countries over others. Algerians have amuch tougher time obtaining a green card than do Mexicans, and that may
not be viewed as an equitable policy. Finally, we might want to maximize
world welfare, taking into account how brain drain and other migratory
events affect donor countries.
Without specifying the maximization problem, it is difficult to articulate
an optimal immigration policy. Consequently, the authors’ concern about
the declining supply of unskilled migrants to the United States, even if asignificant empirical reality in coming decades, may be misplaced.

REFERENCES FOR THE LAZEAR COMMENT

Borjas, George J. 1987. “Self-Selection and the Earnings of Immigrants.” American Economic Review 77, no. 4: 531–53.
European Commission. 2010. “Geographical and Labour Market Mobility.” Special Eurobarometer 337, wave 72.5. Brussels: TNS Opinion & Social. http://
ec.europa.eu/public_opinion/archives/ebs/ebs_337_en.pdf
Lazear, Edward P. 2017. “Why Are Some Immigrant Groups More SuccessfulThan Others?” Working Paper no. 23548. Cambridge, Mass.: National Bureau
of Economic Research.
Roy, Arthur D. 1951. “Some Thoughts on the Distribution of Earnings.” Oxford
Economic Papers 3, no. 2: 135–46.

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U.S. Department of Homeland Security, Office of Immigration Statistics. 2016. 2014
Yearbook of Immigration Statistics. Washington. https://www.dhs.gov/sites/
default/files/publications/ois_yb_2014.pdf
U.S. Department of State, Bureau of Consular Affairs. 2015. “Annual Immigrant Visa
Waiting List Report as of November 1, 2015.” Washington. https://travel.state.
gov/content/dam/visas/Statistics/Immigrant-Statistics/WaitingListItem.pdf

GENERAL DISCUSSION   John Abowd suggested the authors consider
supplementing their analysis with estimates of the undocumented immigrant population. He referred them to his recent work that tabulates thereuse of Social Security numbers, which might plausibly be associatedwith the activity of undocumented immigrants.1 Abowd and his coauthors
find that, each year, between 10 million and 15 million Social Security
numbers are used in employment by what the authors deem “immigrantcandidates,” situations in which valid Social Security numbers are used bymultiple people or the age of the person issued the Social Security number
is inconsistent with labor market activity.
Steven Davis wondered if the authors could shed light, first, on how
episodes of major civil and political unrest in Latin America have historically affected immigration to the United States, particularly undocumented
immigration; and second, if the authors could combine this historical evidence with assessments of political risk to give some sense for the likelihood of large future immigration waves and the likely selectivity ofimmigration.
Robert Gordon noted that because of the decline in illegal immigration,
there has been a diminished flow into the United States of workers who
have traditionally worked in agriculture, construction, retailing, and nondurable manufacturing. There is a suggestion that with a continuing decline
in illegal immigration, there would be upward pressure on wages and ashortage of labor supply in those sectors. He wondered what the state of theliterature was on the question of the hollowing-out of middle-class jobs.
The theory is that good middle-class jobs have hollowed out, and that many
people who used to have middle-class jobs have been pushed into unskilled
work, so there has been an increase in the supply of domestic native workers
in unskilled occupations. Are the 5 million involuntary part-time workers a
1. John M. Abowd, Kevin L. McKinney, and Nellie L. Zhao, “Earnings Inequality andMobility Trends in the United States: Nationally Representative Estimates from Longitudinally Linked Employer–Employee Data,” Journal of Labor Economics (forthcoming,
January 2018).

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potential labor supply for these jobs, and are some of the labor force dropouts discussed in the paper by John Fernald, Robert Hall, James Stock, andMark Watson candidates for these jobs? In short, he wondered how elastic
low-skilled labor supply is in the United States.
Discussant Edward Lazear suggested that many millions of people in
the rest of the world want to come to the United States, so all we need to
do is raise the legal immigration quotas. But Gordon noted that all the talkin the political realm is about reducing them. The Republicans want to cut
down on family-connected immigration without increasing employmentrelated immigration. To put this in perspective, as a share of its population
Canada lets in three times as many legal immigrants as the United States. If
Americans were thinking like Canadians, they would be letting in 3 million
legal immigrants a year, and the United States would be well on the roadto easing some of its old-age Medicare and Social Security fiscal burdens,
he concluded.
David Romer had a narrow comment about the specifications of someof the regressions underlying the authors’ forecasts. In an earlier versionof the paper, the authors regress the change in the immigration rate from a
Mexican state on the level of the birth cohort relative to the U.S. birth cohort,which appears not to control for the lagged immigration rate. Mechanically,
this implies that a permanent change in the size of the birth cohort is goingto cause a permanent change in the growth rate of immigration, implyingthat immigration will tend toward zero or infinity. Romer thought this might
be driving the authors’ predictions that, for example, Mexican immigration
to the United States is going to reach zero in 50 years. If the regression
is otherwise sound, he thought that it would be beneficial to include thelagged immigration rate on the right-hand side. The identification behind
the regression is not completely airtight, he stated, and another possibility
is that the very large coefficient observed for the birth cohort variable issymptomatic of deeper problems with identification. Nonetheless, getting
away from this notion of a permanent change in levels causing a permanent change in growth rates might be a simple but important change to theauthors’ specification.
Caroline Hoxby agreed strongly with discussant Adriana Kugler’s suggestion that the authors examine inflows and outflows separately, becausesome hypotheses that the authors were testing have different predictions for
immigrant inflows than they have for outflows. For instance, formal U.S.
immigration policies (“filters” in Lazear’s language) affect inflows, but not
outflows. She agreed with Lazear’s point that the authors were focusing
too much on the supply of immigrants from Central and South America.

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While the supply from such countries may be waning, there is in essencea limitless supply of unskilled people in the world who would be eager toaccess American wages and living conditions. In the future, non-Americancountries may supply most immigrants to the United States.
Hoxby also doubted whether the supply of Central and South American
potential immigrants was really waning. Reductions in the flow of such
immigrants after 2007 were more likely due to waning labor demand in
the United States. If U.S. demand is the dominant force in immigration,
especially illegal immigration, there might be little empirical evidence that
immigrants reduce the wages of low-skilled, native-born Americans, yetthose natives might benefit much less from economic booms than they
otherwise would. During booms, wages in low-skilled jobs may simply failto rise, owing to the inflows of immigrants.
Martin Baily recalled a report in which he participated for the McKinsey
Global Institute that deemed the Mexican economy a “two-speed” economy.2 In the modern, largely urbanized part of Mexico, productivity andreal wages are growing. But in the informal part of the Mexican economy,
wages and productivity are declining while the share of employment is
actually increasing. Given this situation, Baily was surprised by the authors’
depiction of Mexico with respect to inflows of illegal immigrants. His
understanding was that the flow of illegal immigration was coming becauseof the pressure from surplus labor in the informal sector; however lousy
the U.S. jobs taken by immigrants were, they were still better than thejobs available in Mexico. The authors seem to paint a somewhat different
picture with a convergence of wages, and therefore a slowing down of
future immigration.
Robert Hall contended that the central issue related to source countries
for immigrants is that almost all of them are poorly governed relative to
the United States. He thought this was a much more important factor thanbrain drain.
Andrew Levin stated that the central question on immigration is how
strong the labor force is in the United States relative to Mexico. To backup his claim, he read a quotation from Abowd’s recent paper (see note 1):
Our results . . . suggest that, unlike previous recessions, substantial numbers of
persons employed prior to the Great Recession did not return to employment
even five or more years after the start of that recession. While previous research
2. Eduardo Bolio, Jaana Remes, Tomás Lajous, James Manyika, Morten Rossé, andEugenia Ramirez, “A Tale of Two Mexicos: Growth and Prosperity in a Two-Speed Economy” (San Francisco: McKinsey Global Institute, 2014).

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focused primarily on employed persons or persons in the labor force, the large
and persistent decrease in the employment-to-population ratio . . . during andafter the Great Recession argues strongly for an expansion of inequality measures to include at least some inactive but eligible workers. . . . [Their exclusion]understates the degradation at the bottom of the distribution. (pp. 43–44)

In contrast to Hall’s suggestion and in confirmation of Gordon’s remarks,
Levin thought that researchers needed to take very seriously the extent towhich labor force participation declined steeply during the Great Recession
due to weakened demand and still has not completely recovered.
Valerie Ramey thanked the authors for highlighting in their paper whatmany people in San Diego already know—that increased border enforcement, walls, and similar things have been going on for a long time. She wasintrigued by the idea that the stock of undocumented immigrants is aging.
She wondered how many of them can actually get access to Medicare andMedicaid—and to the extent that they cannot, she thought the United Statesmight actually see an outflow of older undocumented immigrants. For
example, Ramey’s family gardener is a legal U.S. resident, but he returns
to Mexico regularly because medical care is much cheaper there for a manin his 50s. To the extent that undocumented immigrants do not have accessto Medicare or Medicaid, many will go back to Mexico because, typically,
they still have strong family ties and places to live there. So there might
actually be an increase in immigrant outflows.
Karen Dynan observed that the authors’ regressions try to explain theflows of immigrants using relative GDP per capita as an explanatory variable. As Kugler showed in her remarks, this can produce strange coefficients. Dynan wondered if the authors had grappled with the issue of how
meaningful GDP per capita really is when there are big changes over time inincome distribution, and whether that could somehow be diluting what theauthors were trying to capture in those variables. Also, the authors posit thatstable income growth in the United States relative to other countries helped
draw immigrants into the country during the Great Moderation. But the stability was only found in macro data—a body of literature using micro datashows that while aggregate growth was quite moderate in the United States
during the Great Moderation, there was actually a rise in income volatility
at the household level.
Benjamin Friedman observed that some aspects of the discussion seemed
to contemplate that the United States should worry about having a shortageof low-skilled workers. To the contrary, Friedman thought having a shortage of low-skilled workers would be a good thing, because it would drive
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Who is going to pay for that? To the extent that low-skilled work consistsof services that are consumed by other low-skilled workers, the transfer
would be neither here nor there for welfare purposes. But to the extent that
low-skilled services are consumed by people at the upper end of the distribution, having a shortage of low-skilled workers would constitute a
redistribution—not through the usual mechanism of raising tax rates andincreasing transfers but through a market mechanism. Moreover, to theextent that the image or prestige of jobs normally goes along with the compensation level, then over time there would be a change in the image of
low-skilled work to make it more attractive and reduce status inequalities
as well.
Gordon Hanson responded to several issues that were raised. First,
the tenor of many comments encouraged the authors to make normative
statements about immigration policy, which he insisted the present paper
is not about. Instead, it is about trying to understand the economic consequences of a continued slowdown in low-skilled immigration. Regarding
the paper’s exercise on the impact of immigration on wages, Hanson andhis coauthors did not want to stake out a position on the wage consequencesof the immigration slowdown, but instead to use standard models of labordemand to evaluate the implied wage pressures of the observed changes.
Although we can see a change in relative labor supply growth coming,
there are many reasons we might not expect these changes to have alarge impact on wages. Firms may make accommodating adjustments
in production techniques and product mix, which is what much of Ethan
Lewis’s research has found: Firms are less likely to upgrade to new technology when there are larger supplies of low-skilled immigrant workers.3
The present paper essentially runs this exercise in reverse, he explained.
On the issue of immigration enforcement, the authors searched for evidence of negative effects of enforcement on migration rates, but could not
find any. There is a good economic reason why such effects are hard to
establish empirically. Research using micro data at the U.S. Departmentof Homeland Security taught Hanson that enforcement levels today are areflection of perceived illegal immigration 14 to 18 months prior, which
is how long it typically takes for the U.S. Border Patrol to recognize achange in immigration patterns, pass the information along to senior staff
at the department, and obtain additional resources from Congress.
3. See, for example, Ethan Lewis, “Immigration and Production Technology,” Annual
Review of Economics 5 (2013): 165–91; and Ethan Lewis, “Immigration, Skill Mix, and Capital Skill Complementarity,” Quarterly Journal of Economics 126, no. 2 (2011): 1029–69.

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Thus, Hanson noted, enforcement responds almost perfectly in the longrun to increased immigration, which yields a robust, positive correlationin the two series. Hanson contrasted the United States’ varied immigration
regimes since the 1920s: Before 1924, immigration was open; between 1924
and 1965, it was relatively closed; and since then, the immigration regime has
been quasi-laissez-faire, whereby demand for low-skilled labor has influenced the supply of workers allowed to move across the border. Although
enforcement has slowly ratcheted up in response to political pressure,market forces have largely determined how many people are allowed tocross the border. This regularity is why the focus on Latin America, andMexico in particular, has been so intense, Hanson explained. The willingness to be an undocumented immigrant has been an important filter applied
to immigration during the last 35 years. The United States is now at a political juncture, with growing unhappiness about this filter. The country is
also at an economic juncture, because demands from U.S. employers for
immigrants will not generate comparable flows as in the past, given thatlabor supply and other economic conditions in immigrant-sending countries have changed.
The United States is now likely to see new patterns emerge in how immigration responds to changes in U.S. labor demand. Could the United States
open its doors to low-skilled immigrants from other parts of the world inorder to replace the migrants who will not be arriving from the Western
Hemisphere? In the current political environment, such an outcome seems
unlikely. More likely, the United States will face a period of transition similar to the 1960s, when the country began to institute changes in immigration policy, and employers responded by acquiring a substantial say in theintensity of enforcement, creating the filters that were applied in deciding
how immigrants were admitted.
Such a change in the immigration regime may take a decade to establish
itself, Hanson predicted. If past experience serves, and if one considers thepolitical economy of immigration policy, the coming transition will playout over a much longer time frame than between now and when Congress
is to authorize funding for President Donald Trump’s southern border wall.