High-Skilled Immigration and the Comparative Advantage of
Foreign Born Workers across US Occupations ∗Gordon H. Hanson

Chen LiuUC San Diego and NBERUC San Diego

October 2016

Abstract
In this paper, we examine the changing presence of foreign-born college-educated workers
in the U.S. labor force and characterize the occupational specialization of these workers overtime. The presence of highly educated foreign-born workers varies markedly by occupation.
Whereas their share of employment rises modestly from 4.2% in 1960 to 11.6% in 2010-12 ineducation, law, and social-service occupations, it jumps from 6.6% to 28.1% over this same period in science, technology, engineering, and mathematics (STEM). Across occupations, there
are pronounced differences in employment patterns by immigrants according to their countryof origin. In STEM jobs, the share of U.S. workers who are from India rises from near zero in1960 to 9.3%, accounting for one-third of all foreign-born workers, in 2010-12. In health-related
occupations, it is workers from Southeast Asia whose employment shares have risen most dramatically, reaching 5.4%, or one-fifth of all foreign workers, in 2010-12 from negligible levels
five decades previously. Using an Eaton-Kortum-Roy definition of comparative advantage, wefind that occupational specialization patterns are very similar for male and female immigrants
from the same origin countries and that immigrant occupational specialization patterns persist
strongly over time. These results suggest that the factors that drive occupational specialization
among immigrants are stable across decades and common to workers in different demographic
groups from the same origin countries. Because occupational specialization patterns are common to workers born and raised in a given origin country and born in that origin country but
raised in the U.S., they do not appear to be explained by origin-country educational systems.

∗
Hanson: GPS 0519, University of California, San Diego, 9500 Gilman Drive La Jolla, CA 92093-0519 and NBER
(gohanson@ucsd.edu).

1

Introduction

The increase in the demand for more skilled labor is among the most important changes in the U.S.
economy of the last 40 years (Katz and Autor, 1999). In the narrative crafted by Goldin and Katz
(2008), technological advances and rising educational attainment are in something of a race, withthe premium for skilled labor rising during periods, as in the 1980s and 1990s, when growth in
the supply of college graduates is insufficient to meet the expanding demand for qualified labor.
High-skilled immigration changes the nature of the competition between education and technology. Whereas in 1980 the foreign-born accounted for only 7.1% of prime-age males with a college
education, by 2012 this share had reached 17.1%. Today, the United States is able to meet the needfor a more technologically sophisticated labor force either by growing its own talent through theeducation and training of native-born workers or by importing talent from abroad (Freeman, 2005).
There is growing interest in how and why high-skilled foreign-born workers enter the U.S.
labor market. One important channel of entry is U.S. higher education. Many workers who ultimately obtain U.S. permanent resident visas first enter the country as students (Rosenzweig, 2006
and 2007). The draw of U.S. universities is due in part to their global standing, especially in science,technology, engineering and mathematics (STEM). In global rankings of scholarship, U.S. institutions of higher education account for nine of the top ten programs in engineering, for eight of the
top ten programs in life and medical sciences, and for seven of the top ten programs in physicalsciences.1 The lure of studying in the United States also derives from the contact that it facilitates with potential U.S. employers (Bound, Demirci, Khanna, and Turner, 2015). A job offer froma U.S. place of business is essential to obtain a temporary work visa or an employer-sponsoredgreen card. Whether foreign students choose to stay in the United States after completing their degrees depends on immediate U.S. and foreign job-market conditions and on prospects for long-run
growth in the United States relative to their home countries (Grogger and Hanson, 2015).
In this paper, we consider the possibility that the incorporation of foreign-born workers into the
U.S. economy depends on occupational comparative advantage that is at least in part specific tothe country in which an individual is born. There is of course a long tradition of using comparativeadvantage to explain international trade in goods, with modern variants of the theory grounding
these advantages in cross-country differences in the productivity distributions from which firms
draw their industrial capabilities (Eaton and Kortum, 2002). There is also a long tradition in labor economics, dating back to Roy (1951), in which workers are posited to vary in their skills for1

See world university rankings by field at www.arwu.org.

1performing different occupational tasks. Recent work combines Eaton and Kortum (2002) withRoy (1951) to obtain models of comparative advantage in which workers are heterogeneous in
their capabilities and in which the parameters of the underlying distribution of labor productivity
differ between groups of individuals according to their demographic characteristics (Lagakos andWaugh, 2013; Hsieh, Hurst, Jones, and Klenow, 2013; Burstein, Morales, and Vogel, 2015) or theircountries of origin (Burstein, Hanson, Tian, and Vogel, 2017; Hanson and Liu, 2016).
We begin the analysis by documenting the growing presence of foreign-born workers in the
U.S. college-educated labor force. This presence varies markedly by occupation. Whereas theshare of U.S. college-educated workers who are foreign born rises modestly from 4.2% in 1960
to 11.6% in 2010-12 in education, law, and social-service occupations, it rises more impressively
from 6.6% to 28.1% over this same period in STEM occupations. Also notable is the difference inoccupational employment patterns by immigrants according to their country of origin. In STEM
jobs, the share of U.S. workers who are from India rises from near zero in 1960 to 9.3%, or onethird of all foreign workers, in 2010-12. In health-related occupations, it is workers from Southeast
Asia whose employment shares have risen most dramatically, reaching 5.4%, or one-fifth of all
foreign workers, in 2010-12 from negligible levels five decades previously. Specialization patterns
are similar for male and female immigrants from the same origin countries.
Next, we use an Eaton-Kortum-Roy definition of comparative advantage to characterize occupational specialization by nationality and over time for college-educated workers. The measureof comparative advantage we use gives the log odds of, say, an Indian immigrant working in
STEM over a manual occupation relative to the log odds of a U.S. native-born individual working in STEM over a manual job. We document three features of occupational specialization in the
U.S. labor market. First, patterns of specialization by nationality are most extreme in STEM occupations. Among prime-age male college graduates, an immigrant from India is 10.7 times more
likely than a U.S. native-born individual to work in STEM over a manual job and 54.6 times more
likely to do so than an immigrant from Mexico, Central America, and the Caribbean. Second, occupational specialization for male and female immigrant college graduates is strongly positively
correlated across origin countries, with a partial correlation of male-female comparative advantage in 2010-12 of 0.92 in STEM jobs, 0.86 in management and finance, and 0.71 in health-related
occupations. Third, immigrant occupational specialization patterns persist firmly over time. For
college-educated men, a regression of log comparative advantage in 2010 against the value in 1990
across birth countries yields very precisely estimated slope coefficients of 0.99 in STEM occupations, 1.02 in management and finance, and 1.01 in education, law, and social-service occupations.

2

We take these results to mean that the factors that drive occupational specialization among immigrants are stable across decades and common to workers in different demographic groups fromthe same origin countries.
High-skilled immigration has important consequences for U.S. economic development. In
modern growth theory, the share of workers specialized in R&D plays a role in setting the paceof long-run growth (Jones, 2002). Because high-skilled immigrants are drawn to STEM fields, they
are likely to be inputs into U.S. innovation. Recent work finds evidence consistent with highskilled immigration having contributed to advances in U.S. innovation. U.S. states and localities
that attract more high-skilled foreign labor see faster rates of growth in labor productivity (Huntand Gauthier-Loiselle, 2010; Peri, 2012). Kerr and Lincoln (2010) find that individuals with ethnic
Chinese and Indian names, a large fraction of whom appear to be foreign-born, account for rising shares of U.S. patents in computers, electronics, medical devices, and pharmaceuticals. U.S.
metropolitan areas that historically employed more H-1B workers enjoyed larger bumps in patenting when Congress temporarily expanded the program between 1999 and 2003. Further, the patentbump was concentrated among Chinese and Indian inventors, consistent with the added H-1B
visas having expanded the U.S. innovation frontier. Yet, the precise magnitude of the foreign-borncontribution to U.S. innovation and productivity growth is hard to pin down. Because the allocation of labor across regional markets responds to myriad economic shocks, establishing a causal
relationship between inflows of foreign workers and the local pace of innovation is a challenge.
High-skilled immigration may displace some U.S. workers in STEM jobs (Borjas and Doran, 2012),
possibly attenuating the net impact on U.S. innovation capabilities. How much of aggregate U.S.
productivity growth can be attributed to high-skilled labor inflows remains unknown.
When it comes to innovation, there appears to be nothing “special” about foreign-born workers, other than their proclivity for studying STEM disciplines in a university. The National Surveyof College Graduates shows that foreign-born individuals are far more likely than the native-bornto obtain a patent, and more likely still to obtain a patent that is commercialized (Hunt, 2011). It isalso the case that foreign-born students are substantially more likely to major in engineering, math,and the physical sciences, all fields strongly associated with later patenting. Once one controls forthe major field of study, the foreign-native born differential in patenting disappears. Consistent
with Hunt’s (2011) findings, the descriptive results we present suggest that highly educated immigrant workers in the United States have a strong revealed comparative advantage in STEM. The
literature has yet to explain the origin of these specialization patterns. It could be that the immigrants the U.S. attracts are better suited for careers in innovation—due to the relative quality of3foreign secondary education in STEM, selection mechanisms implicit in U.S. immigration policy,or the relative magnitude of the U.S. earnings premium for successful inventors—and therefore
choose to study the subjects that prepare them for later innovative activities. Alternatively, cultural or language barriers may complicate the path of the foreign-born to obtaining good U.S. jobs
in non-STEM fields, such as advertising, insurance, or law, pushing them into STEM careers.
To understand possible sources of occupational comparative advantage by immigrants fromdifferent origin countries, we compare our measures of occupational specialization across threegroups of individuals according to their nativity. Immigrants born and raised in an origin country(who arrived in the United States at age 18 or older) would have been exposed to foreign educational institutions, at least through secondary school. Immigrants born in the origin country
but raised in the United States (who arrived in the United States before age 18) would have been
exposed to U.S. education, at least at the university level. And individuals whose parents or grandparents were born in the origin country would have been exposed to U.S. education throughout
their lives. Occupational specialization patterns are similar across these three groups, suggestingthat the country in which one is educated is not the overriding factor that explains employment
regularities among highly educated immigrants.
In section 2, we present the data used in the analysis. In section 3, we describe the presence of
foreign-born college-educated workers in U.S. occupations. In section 4, we define and measureoccupational comparative advantage among U.S. immigrants according to their country of origin.
And in section 5, we provide a concluding discussion.

2

Data

We use data from the Census Integrated Public Use Micro Samples (Ruggles et al. 2010) for theyears 1960 (5% sample), 1970 (1% sample), 1980 (5% sample), 1990 (5% sample), and 2000 (5%sample), and the American Community Survey (ACS) for 2010 to 2012. We pool ACS files for 2010
through 2012 to increase sample size and hence, measurement precision.
Throughout our analysis, we restrict the sample to individuals with positive earnings, whoare between 21 and 54 years old at the time of the survey, and who have at least a bachelor’s
degree. Our focus on college graduates follows from our interest in the high-skilled labor force.
The age restrictions we impose allow us to center on prime-wage workers who are likely to havecompleted their undergraduate studies. To measure employment, we calculate the number offull-time equivalent workers in given national-origin, gender, and occupation categories by using4

weights equal to the sampling weight for an individual times her hours of work last year, which
we take to be the product of weeks worked last year and usual hours worked per week. The U.S.
native-born population is combined of individuals who were born either in the United States orabroad to parents who are U.S. citizens. The foreign-born population is comprised of all otherindividuals.
To accommodate a perspective than spans six decades and dozens of source countries for immigrants, we aggregate occupations into six broad categories. Aggregation helps avoid having
large numbers of cells with zero entries, which is of particular concern for smaller source countries
and in earlier years. The occupation categories are:
• STEM (architects, computer programmers and software developers, engineers, life and medical scientists, physical scientists);• Management, finance, and accounting (accountants, chief executives, financial managers,
general managers, market surveyors, and economists);• Health (dentists, pharmacists, physicians, registered nurses, therapists, veterinarians);• Education, law, social work, and the arts (instructors and teachers, lawyers, social and religious workers, writers, and artists);• Technical, sales, and administrative support (administrative support staff, clerks and record
keepers, sales representatives, sales supervisors, technicians);• Less-skilled manual work (agricultural workers, construction workers, hospitality workers,
household-service workers, machine operators and production workers, mechanics and repairers, personal-service workers).
These categories divide occupations according to the level of education, the type of training, andthe range of skills that are demanded on the job. Most STEM occupations require at least a bachelor’s degree, as well as aptitude in quantitative reasoning. Because quantitative skills are groundedin mathematical logic, they may transfer from one country to another with relative ease, making
human capital acquired in STEM jobs relatively portable across borders. Although management
positions also require some familiarity with quantitative reasoning, they are intensive in the use ofcommunication and other social skills to a degree that STEM positions are not. Health, education,
law, and social work are distinguished by requiring a bachelor’s degree or higher to enter these
professions and by being subject to occupational accreditation processes that are specific to the5United States or to individual states within the country. Accreditation may limit the portability ofskills for immigrants in these professions. The final two occupational categories—manual workand technical, sales, and administrative support—typically do not require a college degree. The
first category includes jobs from which advancement to higher-level positions is usually limited.
The second encompasses jobs through which more-educated immigrants may first enter the labor
force, as they seek to establish their position in a new labor market.

3

Foreign-Born Presence in US Occupations

We begin the analysis by describing the presence of immigrants in U.S. occupations, first forcollege-educated males and then for college-educated females. We then consider the specialization of immigrants from different origin countries in particular types of jobs. For ease of presentation, we present trends for immigrants grouped according to six sending regions: China, Hong
Kong, and Taiwan; Eastern and Western Europe; East and Southeast Asia; India; Mexico, Central
America, and the Caribbean; and South America. China and India merit attention because theyaccount for a disproportionate share of the recent growth in U.S. high-skilled immigration.2 Europe, a historic but now less important source of U.S. high-skilled immigrants, offers an instructive
contrast. East and Southeast Asia include Korea, long a source of high-skilled immigrants to theUnited States, and the Philippines and Vietnam, which supply immigrants at both low- and higheducation levels. The two regions from Latin America and the Caribbean are the predominantsources of low-skilled immigrants to the United States, making them of interest in terms of theirless-studied high-skilled labor outflows. Although we leave Africa and the Middle East out of
the figures in this section, we will include these regions in the analysis presented in the following
section.

3.1

College-educated males

Figure 1 shows the share of the foreign born in total U.S. male employment of college graduates,
as measured by hours worked, for six immigrant source regions in each of the six occupationalgroups. It displays the well-known pattern of a growing presence of highly educated immigrants
in the U.S. labor force. Across all origin countries, the foreign-born share of total hours worked byprime-age male college graduates increases from 6.6% in 1960 to 28.1% in 2010-2012 in STEM occupations, from 4.1% to 14.9% in management and finance, from 10.7% to 24.7% in health-related
2

Since 1990, nearly all of the growth in U.S. immigration from China, Hong Kong, and Taiwan is due to immigrationfrom mainland China.

6occupations, from 4.2% to 11.6% in education, law, and social work, from 3.8% to 13.1% in technical, sales, and administrative support, and from 7.8% to 18.8% in manual occupations.
In 1960, Europe was by far and away the major origin region for high-skilled immigrants to theUnited States. Whereas Europe’s share of occupational employment in 1960 ranged between twoand six percent, no other region even topped one percent. Europe’s importance reflects historical
U.S. immigration policies, which between 1924 and 1965 allocated visas based on national quotasthat favored European countries (Ngai, 1999; Udansky and Espenshade, 2000).

STEM Occupations

Management & Finance

Health Occupations

.12

.12

.12

.08

.08

.08

.04

.04

.04

0

0
1960 1970 1980 1990 2000 2010
Year

Education, Law, the Arts01960 1970 1980 1990 2000 2010
Year

1960 1970 1980 1990 2000 2010
Year

Tech, Sales, Admin Support

Manual Occupations

.12

.12

.12

.08

.08

.08

.04

.04

.04

0

0

0

1960 1970 1980 1990 2000 2010
Year

1960 1970 1980 1990 2000 2010
Year

1960 1970 1980 1990 2000 2010
Year

India

China

Mexico & Cen America

South America

EuropeE & SE Asia

Figure 1: Share of immigrants in US occupational employment, males
In the decades since 1960, Europe’s role as the primary source for newly arrived high-skilled
U.S. immigrants has been supplanted by Asia. By the period 2010 to 2012, immigrants from India,
at 9.3% of college-educated U.S. employment, were the largest foreign-born group in U.S. STEM occupations, and immigrants from East and Southeast Asia, at 5.4% of employment, were the largest
foreign-born group in U.S. health-related occupations. Among the six occupational categories in
Figure 1, Europe remained the top immigrant-supply region in 2010-2012 in just two, management

7and finance, where it held a slim lead over India at 3.1% versus 2.7% of U.S. employment of thecollege educated, and education, law, social work, and the arts, where it held another slim lead in
this case over Mexico and Central America at 2.3% versus 1.8% of U.S. employment.
Asia’s rise as a source of high-skilled immigrants is the result of multiple factors. The Immigration and Nationality Act of 1965 replaced national-origin quotas with a quota-allocation schemethat favored family members of U.S. residents, and, to a lesser degree, skilled workers demanded
by U.S. employers. Over time, this change in policy allowed non-European countries to join thequeue for U.S. immigration visas. One common route through which foreign-born individuals
gain a permanent-residence or temporary-work visa is by first completing undergraduate or graduate study in the United States (Kato and Sparber, 2013; Salzman, Kuehn, and Lowell, 2013). Beinga student at a U.S. university facilitates contact with U.S. employers (Bound, Demirci, Khanna,and Turner, 2015) and creates opportunities to meet and to marry a U.S. resident (Jasso, Massey,
Rosenzweig, and Smith, 2000), either of which earns one a place in the queue for a green card. Duein part to its rapidly expanding supply of college students, Asia has become a leading source of
foreign students in U.S. universities. As of the 2013-2014 academic year, six of the top ten source
countries for foreign students in the United States were from Asia (Institute of International Education, 2015).3 These countries accounted for 57.4% of the 886,052 foreign students studying at
U.S. institutions.4 The four highest-ranking European countries on the list accounted for just 3.6%of U.S. foreign students in that year.5 Within Asia, China and India stand out as leading origin
countries for foreign students. Their shares of the U.S. foreign-student population grew from 8.7%
and 6.9%, respectively, in 1989-1990 to 31.2% and 13.6%, respectively, in 2013-2014.
In addition to geographic diversification in source regions for U.S. high-skilled immigration,
two other patterns in Figure 1 call one’s attention. One is that 1990 is an inflection point for immigrant presence in U.S. employment. It is after 1990 when India’s and China’s presence in STEM
occupations rises most dramatically and when Southeast Asia’s and India’s presence in healthrelated occupations begins to take off. One contributing factor to this growth is the H-1B program
for temporary high-skilled foreign-born workers, which Congress created as part of the Immigration Act of 1990.6 The U.S. government first allocated 65,000 H-1B visas per year, which it raised
3

These countries in descending rank order are China, India, Korea, Taiwan, Japan, and Vietnam.
This total includes undergraduate students, graduate students, non-degree students and students in Optional Practical Training. Together, undergraduate and graduate students accounted for an average of 88.3% of foreign students in
the United States in the 1990s and 2000s.
5These countries in descending rank order are the United Kingdom, Germany, France and Spain.
6
The H-1B program is the largest and most well-known source of temporary work visas for high-skilled U.S. workers
but it is far from the only such program. Other programs that supply high-skilled immigrants with temporary work
visas include the L-1 visa (for intra-company transferees, which allows foreign workers of U.S. multinational companies
48to 115,000 per year in 1999 and to 195,000 in 2001, before settling at 85,000 per year in 2006 (General Accounting Office, 2011).7 Since these visas are for a period of three years and are renewable
once, a single visa expands the supply of high-skilled U.S. immigrants by up to six person years.
If all visa recipients stay for a full three-year term, in steady state a supply of 85,000 temporary
visas would accommodate a rotating stock of 255,000 immigrants. If these recipients in turn eachrenew their visas and stay for a full additional three-year term, the initial visa allocation would
accommodate a rotating stock of 510,000 visaholders.
Of course, far from all H-1B visa recipients extend their visas or even stay for their complete
initial three-year terms.8 Nevertheless, given that the total stock of U.S. immigrants in 2010-2012
aged 21 to 54 years old with a college education was 5.8 million, a temporary visa program of themagnitude of the H-1B is capable of bringing about a sizable increase in immigrant labor supply.
In practice, the H-1B visa appears to operate as a queue for a green card (Lowell, 2000). Congress
allocates 140,000 employer-sponsored green cards each year. It is common for employers to firstseek H-1B visas for foreign employees, and later, depending on their performance and desire tostay in the United States, to apply for a green card on their behalf. The two largest recipient
countries for H-1B visas are India and China. Over the 2000-2009 period, they accounted for 46.9%
and 8.9%, respectively, of approved H-1B workers (General Accounting Office, 2011).
A second pattern evident in Figure 1 is variation in occupational specialization patterns by immigrants from different origin regions. To see these details more clearly, in Figure 2 we plot theshare of total labor hours worked by male college graduates from each of the six immigrant origin
regions in each of the six occupational categories. India’s and China’s specialization in STEM isstrongly apparent in Figure 2, with occupations in this group in 2010-2012 accounting for 51.0%
of Indian immigrant employment and 43.5% of Chinese immigrant employment. Although STEM
occupations are also the top employment category for immigrants from East and Southeast Asia,
the sector’s dominance is much less pronounced for this region than for India and China. For immigrants from Europe and South America, management and finance is the top occupation for malecollege graduates, whereas for immigrants from Mexico and Central America, the top category ishealth-related professions. These patterns are a first indication of differences in occupational comto work in the United States), the O visa (for individuals of extraordinary ability or achievement), the P visa (for artists,
athletes, and entertainers), and the TN visa (for professional workers from NAFTA countries). To give a sense of therelative scale of these programs, in 2008 the United States issued 129,000 H-1B visas (the sum of new visas and visa
extensions) and 84,000 L-1 visas.
7
The current level of 85,000 H-1B visas includes 65,000 visas for temporary immigrant workers in specialty occupations and 20,000 visas for foreign-born individuals who have earned an advanced degree from a U.S. institution ofhigher education.
8
Clemens (2010) finds that for an Indian software company in the mid 2000s just 44.8% of H-1B visa recipients werein the United States two years after obtaining a visa.

9parative advantage for immigrants from difference source countries. In the following section, we
examine occupational specialization by immigrants in more detail.

Europe

India

China

.5

.5

.5

.4

.4

.4

.3

.3

.3

.2

.2

.2

.1

.1

.1

0

0

0

1960 1970 1980 1990 2000 2010
Year

1960 1970 1980 1990 2000 2010
Year

1960 1970 1980 1990 2000 2010
Year

E & SE Asia

Mexico & Cen America

South America

.5

.5

.5

.4

.4

.4

.3

.3

.3

.2

.2

.2

.1

.1

.1

0

0
1960 1970 1980 1990 2000 2010Year

01960 1970 1980 1990 2000 2010
Year

1960 1970 1980 1990 2000 2010
Year

STEM Occupations

Management & Finance

Health Occupations

Education, Law, the Arts

Tech, Sales, Admin Support

Manual Occupations

Figure 2: Share of occupation in immigrant employment by national origin, males

3.2

College-educated females

We next examine high-skilled immigration among women and occupational specialization by female immigrants according to their region of birth. Analogous to Figure 1 for males, Figure 3
shows the share of the foreign born in total U.S. female employment of college graduates for six
immigrant source regions in each of the six occupational groups. Similar to patterns for men, immigrant presence in high-skilled female employment has increased substantially over time. Across all
origin countries, the foreign-born share of total hours worked by prime-age female college graduates increases from 9.2% in 1960 to 31.1% in 2010-2012 in STEM occupations, from 4.6% to 14.5% inmanagement and finance, from 8.5% to 17.9% in health-related occupations, from 2.3% to 8.7% in
education, law, social work, and the arts, from 6.8% to 13.9% in technical, sales, and administrative10

support, and from 17.3% to 21.4% in manual occupations.

STEM Occupations

Management & Finance

Health Occupations

.12

.12

.12

.08

.08

.08

.04

.04

.04

0

0

0

1960 1970 1980 1990 2000 2010
Year

1960 1970 1980 1990 2000 2010
Year

1960 1970 1980 1990 2000 2010
Year

Education, Law, the Arts

Tech, Sales, Admin Support

Manual Occupations

.12

.12

.12

.08

.08

.08

.04

.04

.04

0

0
1960 1970 1980 1990 2000 2010Year

01960 1970 1980 1990 2000 2010
Year

1960 1970 1980 1990 2000 2010
Year

India

China

Mexico & Cen America

South America

EuropeE & SE Asia

Figure 3: Share of immigrants in US occupational employment, females
As with men, in 1960 Europe begins as the dominant source region for college-educated immigrant women and by the 2000s is replaced by another region in all six occupational categories.
India and China become the largest immigrant-origin regions in STEM occupations, Southeast
Asia becomes the largest immigrant-origin region in health-related occupations, and Mexico andCentral America become the largest immigrant-origin region in manual occupations. In 2010-2012,
female immigrants from India and China represent 9.1% and 7.3% of U.S. female STEM employment, compared to 9.3% and 4.2% for these regions, respectively, among men. Immigrant womenfrom East and Southeast Asia account for 6.1% of female employment in health-related occupations, compared to 5.4% for this region among men. And immigrant women from Mexico andCentral America account for 5.8% of female employment in manual occupations, compared to
6.2% for this region among men. These findings are broadly suggestive that occupational specialization patterns are more country-of-origin specific than gender specific.

11

Europe

India

China

.5

.5

.5

.4

.4

.4

.3

.3

.3

.2

.2

.2

.1

.1

.1

0

0
1960 1970 1980 1990 2000 2010Year

01960 1970 1980 1990 2000 2010
Year

E & SE Asia

1960 1970 1980 1990 2000 2010
Year

Mexico & Cen America

South America

.5

.5

.5

.4

.4

.4

.3

.3

.3

.2

.2

.2

.1

.1

.1

0

0

0

1960 1970 1980 1990 2000 2010
Year

1960 1970 1980 1990 2000 2010
Year

1960 1970 1980 1990 2000 2010
Year

STEM Occupations

Management & Finance

Health Occupations

Education, Law, the Arts

Tech, Sales, Admin Support

Manual Occupations

Figure 4: Share of occupation in immigrant employment by national origin, females
To explore occupational specialization in more detail, Figure 4, similar to Figure 2, plots theshare of total labor hours worked by female college graduates from each of the six immigrant
origin regions in each of the six occupational categories. Although occupational specialization
among female immigrants is less extreme than among men, male and female immigrants fromsome origin regions tend to specialize in similar lines of work. For immigrants from Europe, thetop category for both men and women is management and finance, and for immigrants from India
and China, it is STEM occupations. For Southeast Asia and Latin America, however, the less-skill
intensive activities of technical, sales, and administrative support and manual occupations are thelargest categories of female employment, distinct for patterns for men from these regions.
Entering the United States on a student visa and later transitioning to a green card appears to
be a common path for settlement in the United States among high-skilled immigrant women, as
it is for high-skilled immigrant men. Yet, the large majority of H-1B visas appear to go to men,suggesting that the student-visa-to-H-1B-to-green-card transition path is primarily open to male12

workers (and in particular those in the technology sector). The literature contains little information
about differences by gender in how immigrants enter and remain in the United States.

4

Comparative Advantage of Foreign-Born Workers

The previous section reveals that immigrant presence in the U.S. high-skilled labor force has grownover time, that immigrant presence has risen much more strongly in some occupations (STEM,
management, and finance) than in others (education, law, social work, and the arts), and that thepropensity to specialize in particular occupations varies by region of birth. In this section, we
define, measure and evaluate comparative advantage across broad occupations for high-skilled
immigrants, where we allow advantage to vary both over time and by origin country.

4.1

Defining comparative advantage

We consider the possibility that specialization patterns arise from occupation-specific differences inworker productivity across source countries. As a result of cross-country variation in the quality ofhigher education, traditions of excellence in particular academic disciplines, or other institutions
through which individuals acquire occupation-specific skills, workers from particular countries
may be relatively likely to develop aptitudes that are highly valued in particular occupations.
Russia’s long tradition of excellence in mathematics, for instance, may result in college graduatesfrom Russia being relatively likely to pursue careers in engineering, mathematics, or physics.
Consider a Roy model of occupational sorting, as in Lagakos and Waugh (2013), Hsieh, Hurst,
Jones and Klenow (2013), or Burstein, Morales and Vogel (2015). Suppose that college-educatedworkers from origin-country and gender groups, indexed by λ, choose the country in which to reside, indexed by κ, and an occupation in which to work, indexed by σ. Suppose also that productivity for an individual from origin-country λ (e.g., India) working in occupation σ (e.g., software
programming) in destination κ (e.g, the United States) is determined by a random draw from a
Fréchet distribution, with location parameter Tλ,κ,σ . We allow productivity to be λ − κ specific, as
may result from variation across origin-countries λ in the portability of human capital to destination country κ, λ − σ specific, as may result from variation across origin countries λ in the aptitude
for occupation σ (e.g., the excellence of Russian mathematicians), and κ − σ specific, as may result
from variation across destination countries κ in the productivity of workers in occupation σ (e.g.,
the success of the United States in software services). We can then derive a simple expression forcomparative advantage (see, e.g., Hanson and Liu, 2016), in which the productivity for a worker

13

from origin-country λ in occupation σ relative to some base occupation σ 0 (e.g., software programming versus manual work) is compared to relative productivity in the same two occupations fora worker from a base country (λus = United States), where these productivities are evaluated in acommon destination market (κus = United States). This expression is given by,
Tλ,κus ,σTλ,κus ,σ0Tλus ,κus σ
Φλ,κus ,σ=0
Tλus ,κus ,σΦλ,κus ,σ0Φλus ,κus ,σ.
Φλus ,κus ,σ0(1)where Φλ,κus ,σ denotes the share of workers from origin-country gender group λ (India, males)
working in the United States (κus ) in occupation σ (software programming). Equation (1) statesthat the employment shares of Indian immigrants relative to U.S. native-born workers in software
programming relative to manual jobs reveals the comparative advantage of Indian immigrants
in programming. By comparing employment shares for workers from a common origin-country
gender group (India, males) in two distinct occupations (software programming vs. manual work)in the United States, we neutralize the average productivity loss incurred by immigrants from
India when working in the U.S. Similarly, by comparing employment shares for workers from two
distinct origin countries (India vs. the U.S.) in the same occupation, we neutralize productivity
effects specific to the occupation in the destination market.
We evaluate the revealed comparative advantage of immigrants from different origin countries
working in the United States using the log of the expression on the right of (1). Throughout theanalysis, we treat U.S.-born workers as the base demographic group and manual work support as
the base occupational category. Although (1) suppresses time subscripts, we will allow comparative advantage to evolve freely over time. Because of the double differencing in (1), the evolution ofcomparative advantage will be free of the effects of changes in the average productivity of Indian
immigrants or in average labor productivity in the U.S. software programming.

4.2

Comparative advantage of foreign-born relative to native-born workers
ΦΦ

In Figure 5, we plot log Φ λ,κus ,σ0 − log Φ λus ,κus ,σ0 , log comparative advantage for foreign-born workλ,κus ,σλus ,κus ,σers relative to native-born workers in an occupation using manual jobs as the base category, foreach non-base occupation in the six origin regions over time, where the sample is male primeworking-age college graduates. Given the double log difference form, a positive value of log comparative advantage for an origin group in an occupation indicates comparative advantage relative
to U.S. workers and a negative value indicates comparative disadvantage relative to U.S. workers.
It is in STEM occupations that variation in comparative advantage across origin regions is most
14

STEM Occupations

Management & Finance

Health Occupations

4

4

4

2

2

2

0

0

0

-2

-2

-2

-4

-4
1960 1970 1980 1990 2000 2010
Year

Education, Law, the Arts

-4
1960 1970 1980 1990 2000 2010
Year

1960 1970 1980 1990 2000 2010
Year

Tech, Sales, Admin Support

Manual Occupations

4

4

4

2

2

2

0

0

0

-2

-2

-2

-4

-4

-4

1960 1970 1980 1990 2000 2010
Year

1960 1970 1980 1990 2000 2010
Year

1960 1970 1980 1990 2000 2010
Year

India

China

Mexico & Cen America

South America

EuropeE & SE Asia

Figure 5: Log comparative advantage by occupation, males relative to US counterpartspronounced. Male college-educated immigrants from India and China exhibit a strong revealed
comparative advantage in STEM jobs, whereas immigrants from Europe and Southeast Asia display a modest advantage in the sector and immigrants from Latin America possess a clear disadvantage in STEM. The direct implication is that U.S. college-educated men have a disadvantage in
STEM relative to manual occupations when compared to immigrants from India and China andan advantage when compared to immigrants from Latin America. In 2010-2012, Figure 5 showsthat the log difference in the likelihood of Indian immigrants working in STEM over manual occupations when compared to U.S. native-born men is 2.37, for European immigrants it is 0.59, forSoutheast Asian immigrants it is 0.31, for immigrants from South American it is -0.41, and for Mexican and Central American immigrants it is -1.23. This quantity is the relative log odds of working
in an occupation for immigrants from a particular origin region. It is worth pausing for a moment
to appreciate the magnitude of the differences in occupational specialization patterns that these log
odds imply. Male immigrants from India are 10.7 times (exp {2.37}) more likely to work in STEM

15

than in manual jobs, when compared to U.S. native-born men, and 7.9 times (exp {2.37 − 0.31})
more likely to do so, when compared to male immigrants from Southeast Asia.
In other occupations, comparative advantage of immigrant men relative to U.S. native-born
men is compressed when evaluated against STEM. Relative to U.S. native-born men, the log odds
of working in management and finance (over manual jobs) range from 0.96 for Indian immigrants
to -1.35 for Mexican and Central American immigrants, the relative log odds of working in healthrelated occupations range from 1.43 for Indian immigrants to -1.02 for immigrants from Mexico
and Central America, the relative log odds of working in education and law range from 0.65 for
Chinese immigrants to -1.33 for Mexican and Central American immigrants, and the relative log
odds of working in technical, sales, and administrative support range from 0.23 for Chinese immigrants to -1.12 for immigrants from Mexico and Central America. The pervasive negative log odds
for immigrants from Mexico and Central reveal that in the U.S. economy, their revealed comparative advantage (when compared to college graduates from other origin regions) lies in manual
occupations.
In Figure 6, we show the analogous log comparative advantage plots for women. The patterns
are broadly similar to those for men. In STEM, college-educated immigrants from India and China
display a strong comparative advantage, whereas immigrants from Mexico and Central America
display a comparative disadvantage. STEM is again the sector with the widest variation in comparative advantage. In 2010-2012, the log difference in the likelihood of Indian immigrant women
working in STEM over a manual job when compared to U.S. native-born women is 2.13 and for
Mexican and Central American immigrants it is -1.31. Relative to U.S. native-born women, the log
odds of working in management and finance (over manual jobs) range from 0.54 for Chinese immigrants to -1.21 for Mexican and Central American immigrants, the relative log odds of working
in health-related occupations range from 0.48 for Indian immigrants to -1.02 for immigrants from
Mexico and Central America, the relative log odds of working in education and law range from
-0.44 for Chinese immigrants to -1.62 for Southeast Asian immigrants, and the relative log odds ofworking in technical, sales, and administrative support range from 0.07 for Chinese immigrants to-0.88 for immigrants from Mexico and Central America.
What explains differences in occupational specialization across U.S. workers according to theircountry of birth? One possibility is that the quality or availability of education in science varies
across countries (Hanushek and Kimko, 2000), with differences in math and science perhaps being
most important (Hanson and Liu, 2016). To obtain a job in STEM generally requires a college oradvanced degree in a STEM discipline. U.S. students and students from Latin America may begin

16

STEM Occupations

Management & Finance

Health Occupations

4

4

4

2

2

2

0

0

0

-2

-2

-2

-4

-4
1960 1970 1980 1990 2000 2010
Year

Education, Law, the Arts

-4
1960 1970 1980 1990 2000 2010
Year

1960 1970 1980 1990 2000 2010
Year

Tech, Sales, Admin Support

Manual Occupations

4

4

4

2

2

2

0

0

0

-2

-2

-2

-4

-4

-4

1960 1970 1980 1990 2000 2010
Year

1960 1970 1980 1990 2000 2010
Year

1960 1970 1980 1990 2000 2010
Year

India

China

Mexico & Cen America

South America

EuropeE & SE Asia

Figure 6: Log comparative advantage by occupation, females relative to US counterparts
their undergraduate studies with relatively poor math and science skills, which leaves them illequipped to complete an engineering or science degree. When it comes to STEM disciplines, U.S.
secondary-school students do tend to underperform their peers from other high-income nations.
In the 2012 PISA exam, U.S. 15-year-olds ranked 36th in math and 28th in science, out of 65 participating countries.9 Students from Latin America also underperform on PISA exams relative tocountries at similar income levels. Among the eight Latin American countries that participated in
the 2012 exam, the highest ranking country was Chile at 51st in math and 46th in science.
A second possible explanation for immigrant success in obtaining STEM jobs is that these arejobs in which workers educated or trained abroad can signal their skills to employers at relatively
low cost. In some non-STEM professional fields, such as insurance and marketing, the foreign born
may have an absolute disadvantage because they lack a nuanced understanding of American culture or because subtleties in face-to-face communication are an important feature of interactions in
9

See www.oecd.org/pisa.

17

the marketplace. Others of these fields, such as the law or real estate, may involve an occupational
accreditation process that imposes relatively high entry costs on those educated or trained abroad.
A related explanation is that there are network effects in job search, which result in a tendency for
immigrants from particular origin countries to cluster in specific occupations (Card, 2001).
A third possible explanation is that U.S. immigration policy has implicit screens that favor
more-educated immigrants in STEM fields over those in non-STEM fields. Although H-1B visas
do go in disproportionate numbers to workers in STEM occupations (Kerr and Lincoln, 2010),
there is nothing preordained about this outcome in terms of U.S. immigration policy. H-1B visas
are designated for “specialty occupations”, which are not limited to jobs in the technology sector.10
That most H-1B visas are captured by STEM workers may simply be the consequences of strong
relative demand for foreign STEM labor by U.S. companies.

4.3

Persistence in comparative advantage

In Figures 5 and 6, there is only modest variation in occupational comparative advantage overtime, especially in the second half of the sample period from 1990 forward. This suggests thatoccupational comparative advantage for college-educated immigrants is persistent at the level ofsending countries. To characterize the degree of this persistence, in Figure 7 we plot log immigrantcomparative advantage in STEM occupations (relative to manual occupations) in 2010 versus 1990.
The 45-degree schedule is shown as a solid blue line and the regression plot as a dashed red line.
We expand the sample to include all origin regions for U.S. immigrants. We present data for the 30
largest sending countries for immigrants and for remaining countries aggregated into 10 regionalgroups.11 To make within-country comparisons as precise as possible, we control for differencesin the age composition of immigrants by restricting the sample to be individuals 21 to 37 years old(as compared to the full sample of individuals 21 to 54 years old used in previous sections).
In Figure 7a, which displays results for college-educated males, we see evidence of strong persistence over time in comparative advantage in STEM occupations for immigrants by country of
10Specialty occupations are ones in which (1) a bachelor’s or higher degree or its equivalent is normally the minimum entry requirement for the position; (2) the degree requirement is common to the industry in parallel positions
among similar organizations; (3) the employer normally requires a degree or its equivalent for the position; or (4) thenature of the specific duties is so specialized and complex that the knowledge required to perform the duties is usuallyassociated with attainment of a bachelor’s or higher degree. See http://www.uscis.gov/eir/visa-guide/h-1b-specialtyoccupation/understanding-h-1b-requirements.
11
The 30 largest sending countries for college-educated immigrants are Bangladesh, Brazil, Canada, China, Colombia,
Cuba, Dominican Republic, Egypt, France, Germany, Great Britain, Haiti, Hong Kong, India, Iran, Japan, Jamaica,
Korea, Mexico, Nigeria, Peru, the Philippines, Pakistan, Poland, Russia, Spain, Taiwan, Venezuela, and Vietnam. The 10
regional groups are Central America, South America, Western Europe, Southern Europe, Eastern Europe, South Asia,
Southeast Asia, Middle East and North Africa, Sub-Saharan Africa, and Oceania

18

origin. The regression of log comparative advantage in STEM for 2010 against the 1990 value yields
a slope coefficient estimate of 0.99 (t-value 7.5) and an R2 of 0.60 (N=40). Further evidence reveals
that this persistence is not a new phenomenon in the U.S. labor market. In unreported results, aregression of 2010 log comparative advantage in STEM jobs against the 1970 value yields a slope
coefficient of 0.53 (t-value 2.7) and an R2 of 0.42. Whatever factors drive immigrants from particular origin countries and regions to specialize in particular occupations in the United States, they
appear to change slowly across decades.
A second pattern evident in Figure 7a is positive drift. Most countries lie above the 45-degreeline, indicating that log comparative advantage in STEM relative to U.S. native-born workers wasstronger in 2010 than in 1990. As seen in Figure 5, this positive drift is a new phenomenon. In
unreported results, we plot comparative advantage in STEM for 2010 against 1970 and find a moreeven distribution of countries above and below the 45-degree line, indicating that over a full 40year time span, countries are mixed in terms of whether their comparative advantage in STEM
is strengthening or weakening relative to the United States. A third pattern evident in Figure 7a
relates to the exceptionalism of India, a country frequently singled out for having benefited fromaccess to H-1B visas. Although India is the top country in terms of comparative advantage in
STEM occupations for 2010, other countries also display high levels of specialization in the field.
Immigrants from France, China, and Hong Kong also have relative log odds of working in STEM
in 2010 of over two (as compared to 2.86 for India).
Does persistence in immigrant comparative advantage apply as strongly for women as it does
for men? In Figure 7b, we plot log comparative advantage in STEM for female immigrants. Patterns are very similar to those for men, displaying both strong persistence and positive drift overtime. As suggested by Figures 5 and 6, male and female immigrants from the same origin country
tend to have a comparative advantage in similar occupations. In 2010, the correlation in log comparative advantage for male and female immigrants is 0.92 in the STEM field. This commonality is
not unique to STEM. For other occupations in that year, the correlation in log comparative advantage for males and females is 0.86 in management and finance, 0.71 in health-related occupations,0.67 in education and law, and 0.59 in administrative support. This similarly in occupational specialization by male and female immigrants from the same origin country is present in earlier years,
as well.12
Next, we examine the persistence of comparative advantage for male college-educated im12
In 1990, the correlation in log comparative advantage across origin countries for male and female immigrants is 0.82
in STEM, 0.73 in management and finance, 0.62 in health-related occupations, 0.66 in education and law, and 0.75 inadministrative support.

19

migrants in the other four occupations, shown in Figure 8. Similar to STEM, these occupations
display strong persistence over time in comparative advantage by immigrant origin country anda tendency for positive drift in comparative relative to U.S. native-born workers, as indicated by
the mass of points lying above the 45-degree line. The slope coefficient (t-value) for a regressionof log comparative advantage in 2010 on 1990 values is 1.02 (6.8) in management and finance, 0.43(2.9) in health-related occupations, 1.01 (7.7) in education and law, and 0.81 (6.4) in administrative
support. Persistence in comparative advantage appears to be weakest in health-related occupations. In unreported results, we find patterns in comparative advantage in these occupations for
women that are similar to those for men, though for women, persistence in comparative advantage
appears to be somewhat weaker.13
What explains the persistence in occupational comparative advantage for immigrants acrosstime? One possibility is long-standing differences between countries in the quality of educational
institutions or occupational training. Russia’s preeminence in mathematics dates back to the 18th
century, which may have helped create a long-lived tendency for Russian migrants abroad to pursue occupations that are intensive in the use of quantitative reasoning. If differences in educational
quality are a root cause of comparative advantage, we should observe differences in occupational
choice between immigrants from Russia who arrive in the United States as adults, thus having
completed their education in the origin country, and immigrants who arrive in the United States
as children, who complete their education in U.S. schools. In Figure 9, we plot comparative advantage for two groups of male immigrants 21 to 37 years old: one group that arrived in the UnitedStates at age 18 or older, whose comparative advantage is given by values on the vertical axis, anda second group that arrived in the United States before age 18, whose comparative advantage is
given by values on the horizontal axis. For all occupations, the slope coefficient is near one. Occupational comparative advantage for immigrants who arrive as children is nearly identical to thatfor immigrants who arrive as adults. We find similarly strong positive correlations in comparativeadvantage between immigrants who arrive as adults and immigrants who arrive as young children(age 12 or younger). These results suggest that the origin of immigrant comparative advantage by
occupation is not the country in which one completes tertiary education or even the country inwhich one completes secondary education. The transmission of occupational skills to a nation’s
workers (or at least to the workers who migrate abroad) appears to operate through mechanisms
other than direct learning in school.
13
For the sample of female workers, the slope coefficient (t-value) for a regression of log comparative advantage in2010 on the 1990 value is .62 (4.3) in STEM occupations, 0.48 (3.24) in management and finance, 0.60 (4.22) in healthrelated occupations, 0.49 (4.42) in education and law, and 0.26 (1.84) in administrative support.

20

A second explanation for persistence in immigrant occupational comparative advantage is thepresence of job networks that are specific to individuals from the same origin country. A preponderance of immigrants from India working in the software industry, for instance, may lower
search costs in the sector for recently arrived Indian workers, making them relatively likely totake up software jobs (Card, 2001). As in the Ellison and Glaeser (1997) analysis of U.S. industry
agglomeration, occupational specialization of immigrants due to comparative advantage (arising,e.g., from origin-country educational institutions) is observationally equivalent to occupational
specialization due to origin-country-specific external economies (resulting, e.g., from knowledge
spillovers between immigrant workers with common ancestry). We acknowledge that externalities
in occupational choice across workers from the same origin country may exist, but we lack empirical leverage to distinguish this source of occupational specialization from traditional comparativeadvantage.
A third explanation for occupational specialization patterns by origin country is family reunification provisions of U.S. immigration policy. By favoring new immigrants who have kinshipconnections to existing U.S. residents, U.S. immigration policy may select immigrants who aredisproportionately likely to learn job and other skills from earlier arrivals from the same origin
country. The occupational skills picked up by immigrant arrivals from China in the 1980s may
then transmit to immigrant arrivals from China in the 1990s and 2000s due in part to the earlier
arrivals consisting of many of their relatives.

21

(a) Males

Log comparative advantage, 2010

STEM Occupations
3

India

France2

China

Hongkong

Germany

CanadaPakistanUK
OceaniaJapan
IranRussiaW Europe

1

SE Asia
S Europe

Venezuela

TaiwanVietnam

S AsiaNigeria0-1Bangladesh
Jamaica
E Europe
BrazilUS
Philippines
Egypt
Poland SS Africa
S America
ColombiaPeru

M East-N Africa

Dominican Republic
Cen America
Cuba

Haiti

Mexico

-2
-3
-3

-2

-1
0
1
Log comparative advantage, 1990
log CA

45 degree line2

3

regression fit(b) Females

STEM Occupations
Log comparative advantage, 2010

3

India

China

2

Hongkong

TaiwanPakistan
FranceS Europe
Germany Bangladesh
UK
Japan

1

Oceania

W Europe
Iran
M East-N Africa
CanadaVenezuela
SE Asia

S AsiaVietnam
RussiaE Europe0

PolandSS Africa
USJamaica
Colombia
S America
Peru
Philippines

BrazilNigeria
Mexico-1

Egypt
Cen America
Cuba

Haiti

-2

Dominican Republic

-3
-3

-2

-1
0
1
Log comparative advantage, 1990
log CA

45 degree line2

3

regression fit

Figure 7: Log comparative advantage for immigrants in STEM, 2010 vs. 1990

22

Management & Finance

Health Occupations3

Log comparative advantage, 2010

Log comparative advantage, 2010

3

France2

UK
CanadaIndia
Hongkong
Germany W Europe
TaiwanJapan

1

Pakistan
China

Iran
S Europe
Russia Venezuela

SE Asia
Jamaica0

Colombia
VietnamBrazilNigeria
S AsiaBangladesh
Peru
PolandPhilippines
Egypt
Cen America
DominicanHaiti
Republic
Mexico-1

Oceania

M East-N
AfricaUS

S America
E Europe
SS Africa

Cuba

-2
-3

2

Iran

UK

1

Vietnam
China
Germany

FranceTaiwanPhilippines
Japan Venezuela
Egypt

CanadaPakistan
India

Hongkong
M East-N Africa

RussiaNigeria S Asia

SE Asia0

Bangladesh

Colombia

BrazilJamaica
PolandS Europe
SS Africa
HaitiW Europe
US

OceaniaCuba

E Europe
S America
Peru-1

Cen America
Dominican Republic
Mexico

-2
-3

-3

-2

-1
0
1
Log comparative advantage, 1990
log CA

45 degree line

2

3

-3

-2

regression fit-10
1
Log comparative advantage, 1990
log CA

Education, Law, the Arts

45 degree line2

3

regression fit

Tech, Sales, Admin Support

3

Log comparative advantage, 2010

Log comparative advantage, 2010

3

2
France1

IranGermany

CanadaChina
UK
TaiwanOceania

Hongkong
S Europe
W Europe0

Jamaica
Colombia

Japan
RussiaIndia
Venezuela US
M East-N Africa

S Asia Brazil
S America
Bangladesh
Peru
E Europe
Nigeria SE Asia
Vietnam

Egypt-1

Pakistan
Haiti
Philippines
PolandDominican Republic
Cen America
MexicoSS Africa

Cuba

-2
-3

2
France1

CanadaUK
Hongkong
TaiwanJapan
Pakistan Germany
OceaniaChina
W Europe
Iran
India Venezuela
RussiaS Europe
Jamaica
US
SE
Asia
Bangladesh
Colombia
Vietnam
S
AmericaM
East-N
AfricaEgypt
S AsiaDominican Haiti
Republic Philippines
Peru
BrazilSS Africa
NigeriaE Europe

0Poland-1

Cen America

Cuba

Mexico

-2
-3

-3

-2

-1
0
1
Log comparative advantage, 1990
log CA

45 degree line

2

3

-3

regression fit

-2

-1
0
1
Log comparative advantage, 1990
log CA

45 degree line

Figure 8: Log comparative advantage for male immigrants, 2010 vs. 1990

23

2
regression fit

3Management & Finance

India

2

China
CanadaUK
Japan
Germany

TaiwanHongkong

Oceania
FranceW Europe
Iran
RussiaVietnam
Pakistan
S Europe
S AsiaSE Asia
Venezuela
East-N Africa
E Europe MUkraine

0

Bangladesh
BrazilNigeria
Jamaica
PolandSS Africa
SColombia
AmericaPhilippines
Egypt
Cen America
Haiti
MexicoDominican Republic

-2

Peru
Cuba

-4
-4

-2
0
2
Log comparative advantage, arrive before 18
log CA

45 degree line4

Log comparative advantage, arrive after 18

Log comparative advantage, arrive after 18

STEM Occupations
4

2
UK
CanadaJapanHongkong
GermanyIndia

0Pakistan
RussiaColombia
M East-N Africa
BrazilAmerica
Jamaica
SE SAsia
Nigeria
SS Africa
E Europe
S AsiaBangladesh
UkrainePolandVietnamEgypt
Philippines
Cen America
Dominican
Haiti
Mexico Republic Peru

-2

Cuba

-4

4

-4

-2
0
2
Log comparative advantage, arrive before 18

regression fit

log CA

Iran
India
Pakistan

Hongkong
Nigeria
Egypt
Philippines
FranceVenezuela
China
Germany Japan
M East-N Africa
W Europe

0

S America
Peru
Cen America

SSS
EuropeUK
RussiaAfricaVietnam
Oceania
Bangladesh
Colombia
PolandHaitiE Europe
SE Asia
UkraineBrazilCuba

TaiwanS AsiaDominican Republic-2

Mexico

-4
-4

-2
0
2
Log comparative advantage, arrive before 18
log CA

45 degree line

regression fit

Education, Law, the Arts

CanadaJamaica

45 degree line44

Log comparative advantage, arrive after 18

Log comparative advantage, arrive after 18

Health Occupations
4

2

FranceW Europe
Oceania

TaiwanChina
S Europe Iran
Venezuela

2
CanadaChina

UK
Germany
Hongkong
JapanIranS Europe

0

TaiwanOceania

FranceW Europe

RussiaIndia
Venezuela
Colombia M East-N Africa
Nigeria
S America
Brazil Jamaica
S AsiaEEgyptEuropeSS
AfricaBangladesh SE Asia
PolandVietnam Haiti
Dominican
Republic
Pakistan
Cen
AmericaUkrainePeru
Philippines Cuba
Mexico

-2

-4

4

-4

-2
0
2
Log comparative advantage, arrive before 18

regression fit

log CA

45 degree line

4

regression fit

Log comparative advantage, arrive after 18

Tech, Sales, Admin Support
4

2
CanadaUK
HongkongJapan

W Europe
TaiwanFranceGermany
India
Pakistan
Oceania
Iran
China Venezuela0

M East-N Africa
SBangladesh
Asia
S America
S Europe
Philippines
Egypt
RussiaJamaica
NigeriaVietnam
Colombia
SESS
Asia
AfricaDominican Republic
HaitiPeru
E Europe
PolandBrazilCuba
Cen America
UkraineMexico

-2

-4
-4

-2
0
2
Log comparative advantage, arrive before 18
log CA

45 degree line

4

regression fit

Figure 9: Log comparative advantage for male immigrants in 2010-12, US arrivals at age 0-17 vs.
US arrivals at age 18+

24

4.4

Ancestry Analysis

A further way to identify the types of mechanisms that may transmit occupational skills across individuals with a given nationality is to compare job choice by immigrants from a particular origin
country with native-born workers who have ancestral ties to that origin nation. If transmission
mechanisms (e.g., job-search networks) operate on the basis of country of birth, then we would expect to see immigrants from a particular origin country (e.g., India) choosing common occupationsin the United States, regardless of whether they arrived in the country as children or as adults. But
there is no reason occupational choice among Indian immigrants, say, will overlap with that for
native-born U.S. residents of Indian heritage unless these job-search networks are spread broadly
throughout the Indian community in the United States.
Our final exercise is to examine comparative advantage for three groups of workers: immigrants from a given origin country who arrive in the United States at or after age 18, immigrants
from a given origin country who arrive in the United States before age 18, and individuals born in
the United States (or abroad to U.S. citizens) who claim ancestry from a given origin country. We
define ancestry according to the first country of ancestry an individual selects in Census or ACS
surveys. It is important to note that these surveys do not distinguish ancestry according to thenumber of generations from which an individual is removed from immigration. Sharing a common ancestry thus may combine those whose parents were born abroad with those who families
have resided in the United States for many generations. We again define origin countries (andnow ancestral countries) using the 40 country/region groups defined in the previous section. The
sample is college-educated males between 21 to 44 years old.
Figure 10 plots log comparative advantage in STEM occupations for immigrants who arrived
in the United States as adults (y-axis of the left panel) and for immigrants who arrived as children(y-axis of the right panel) against that for U.S. native-born individuals with the same ancestry(x-axis). In both panels, there is a strong positive correlation between immigrant comparativeadvantage in STEM and comparative advantage in STEM for U.S. native-born individuals with
common ancestry. In the left panel (immigrants who arrived as adults), the slope coefficient is 0.92
(t-value 3.43) with an R2 of 0.25, while in the right panel (immigrants who arrived as children) theslope coefficient is 0.68 (t-value 4.23) with an R2 of 0.32. The persistence in comparative advantage
in STEM thus applies across generations: current generations of immigrants show a tendency to
specialize in STEM jobs that is strongly related to the tendency of current descendants of earlier
immigrants.
Next, in Figure 11a and 11b we display the analogous comparative advantage plots for im25

4
India

TaiwanImmigrants arriving age 18+

China

2

Japan
Oceania UK
CanadaFranceW Europe
Germany
S Europe Russia

0

US

Iran
SEPakistan
Asia

M East-N
AfricaVietnam
E Europe
Ukraine

S AsiaVenezuela

PolandBrazil Bangladesh
Jamaica Philippines
Nigeria Egypt
Colombia
S America

SS Africa

Peru

Haiti

Cen America
Cuba
MexicoDominican Republic

-2

-4
-4

-2
0
2
Native-born workers with common ancestry
log CA

45 degree line

4

regression fit

Immigrants arriving age 0-17

4

Spain

2

FranceIran
China
Bangladesh
UkraineRussiaVietnam Pakistan

TaiwanIndia S Asia

UK
Germany Korea
Japan Africa
SE Asia
M East-N
CanadaPhilippines
Nigeria Egypt
SS AfricaColombia
W Europe
E Europe
S Europe
Oceania
Peru
US
PolandS AmericaBrazil
Jamaica
Dominican
Republic

0

Venezuela

Cen America
Cuba
Haiti
Mexico

-2

-4
-4

-2
0
2
Native-born workers with common ancestry
log CA

45 degree line

4

regression fit

Figure 10: Log comparative advantage in STEM for immigrants versus native-born workers with
common ancestry, males 2010-12
26

Table 1: Dispersion of Log comparative advantages by occupations and groupsGroup
Immigrants arrival after 18
Immigrants arrival before 17
Native, same ancestry
Immigrants arrival after 18
Immigrants arrival before 17
Native, same ancestry
Immigrants arrival after 18
Immigrants arrival before 17
Native, same ancestry
Immigrants arrival after 18
Immigrants arrival before 17
Native, same ancestry
Immigrants arrival after 18
Immigrants arrival before 17
Native, same ancestry

Occupation
STEM
STEM
STEM
Management & FinanceManagement & FinanceManagement & Finance
Health occupations
Health occupations
Health occupations
Education, Law, the Arts
Education, Law, the Arts
Education, Law, the Arts
Tech, Sales, Admin Support
Tech, Sales, Admin Support
Tech, Sales, Admin Support

Standard Deviation
1.34
0.85
0.68
1.15
0.75
0.59
1.15
1.09
0.97
1.13
0.81
0.55
0.76
0.63
0.48

migrants and common-ancestry native-born workers in other occupations. These plots also reveal
positive correlations in comparative advantages for immigrants and common-ancestry natives, butthese correlations are weaker than for STEM. Slope coefficients in the left panels (for immigrants
who arrived in the United States as adults) are 0.53 (t-value 1.80) in management and finance, 0.60
(t-value 3.76) in health occupations, 0.41 (t-value 1.28) in education, law, and the arts, and 0.31
(t-value 1.31) in technical, sales, and administrative support. Corresponding slope coefficients for
immigrants who arrived as children are slightly smaller in all cases.
One pattern that is evident in Figure 10, 11a, and 11b is that dispersion in comparative advantage tends to be higher among immigrant workers than among common-ancestry native-bornworkers. In Table 1, we summarize dispersion in comparative advantage across origin countries
for each nativity group (immigrants who arrived as adults, immigrants who arrived as children,common-ancestry native-born workers) in each of the five occupation groups. For each occupation, dispersion in comparative advantage decreases with time in the United States: it is highest
among immigrants who arrived in the United States as adults, second highest among immigrants
who arrived in the United States as children, and lowest among common-ancestry native-born individuals. Accumulated time in the United States thus seems to be associated with attenuation in
the impact of origin-country factors that create occupational comparative advantage.

27

Management & Finance42

Immigrants arriving age 0-17

Immigrants arriving age 18+

4

TaiwanJapan
UK
W Europe
Oceania
FranceCanadaGermany

India

China
S Europe0

Iran
Venezuela

US

SE Asia Pakistan
M East-N Africa
Jamaica
Colombia
SS Africa
S America
Brazil Russia
E Europe
UkraineNigeria
Poland Egypt
Philippines
Bangladesh
Vietnam Peru
Dominican Republic

S AsiaCen
America
Mexico
Cuba
Haiti

-2

-4

2

Spain
FranceIran
S AsiaTaiwanUkrainePakistan
UK
Canada
India
China
Korea
Bangladesh
Germany
M East-N Africa
RussiaS Europe
SSOceania
AfricaW Europe
NigeriaJapan PeruUSE Europe
Philippines
Egypt
Jamaica
Colombia
SE Asia
BrazilVietnamVenezuela
S PolandAmerica

0

Cuba
Haiti
Dominican
Republic
Cen America

Mexico

-2

-4
-4
-2
0
2
4
Native-born workers with common ancestry

log CA-4-20
2
4Native-born workers with common ancestry

45 degree line

regression fit

Health Occupations
4

4

2

IranTaiwan
India

CanadaPakistanJapan Philippines
China

Germany

M East-N Africa
Egypt

Nigeria
SUK
EuropeSS AfricaW Europe
Russia
USColombia
Oceania
Vietnam
E Europe

0

S AsiaJamaica

S America
Asia
Poland SE
Ukraine
Cuba

BrazilBangladesh

PeruFranceCen America

-2

Haiti

Dominican Republic

Immigrants arriving age 0-17

Immigrants arriving age 18+

Spain

Mexico-4

Iran

Taiwan2

UkraineUK

M East-N Africa

Korea
W Europe
VietnamNigeria Philippines
GermanyJapan
CanadaSS Africa
China

0BrazilEgypt

E Europe
S Europe Russia
Peru
Haiti
Oceania
US
Cuba SE Asia
PolandColombia

India
Pakistan

S AsiaBangladesh

S America
Jamaica
Cen America
Dominican Republic
FranceMexico

-2

-4
-4
-2
0
2
4
Native-born workers with common ancestry

log CA

45 degree line

-4
-2
0
2
4
Native-born workers with common ancestry

regression fit

Figure 11a: Log comparative advantage in other occupations for immigrants versus native-bornworkers with common ancestry, males 2010-12
28

Education, Law, the Arts
4

42

Immigrants arriving age 0-17

Immigrants arriving age 18+

Spain

TaiwanChina
CanadaOceaniaJapan
UK
Germany
W Europe
SFrance
Europe

0

Iran

India

US

M East-N Africa
Venezuela
RussiaS AsiaJamaica
BrazilS
America
Nigeria
Colombia
E AsiaEuropeSE
SS Africa
Haiti
Egypt

-2

VietnamBangladesh
PakistanPeru
UkraineCen America
Dominican
Republic
Philippines
PolandMexico2

FranceIran

UK
TaiwanGermany
S AsiaKorea
WJapanEuropeCanadaNigeria
Oceania
SPeru
EuropeUS
UkraineM East-N Africa
PakistanSE
Asia
Jamaica
RussiaEgypt
India
Colombia
Philippines
Cuba
China
BrazilE HaitiEuropeSS
Africa Republic
Dominican
Cen America
S America
VietnamBangladesh
Venezuela
Mexico0

Poland

-2

Cuba

-4

-4
-4
-2
0
2
4
Native-born workers with common ancestry

log CA-4-20
2
4Native-born workers with common ancestry

45 degree line

regression fit

Tech, Sales, Admin Support
4

4

2

2

Immigrants arriving age 0-17

Immigrants arriving age 18+

FranceTaiwan
Japan
UK

0

Iran
India

W Europe
CanadaChina
FranceOceania
PakistanUS
Germany
M East-N Africa
Jamaica
Venezuela
SE Asia
Bangladesh
SS Africa
Egypt
Philippines
Vietnam
S America
S Europe
Colombia
Nigeria
BrazilDominican
Republic
Cuba Russia
Haiti
Peru
E Europe

S AsiaPolandMexicoUkraineCen
America

-2

-4

Spain
Iran

Taiwan0

UK M East-N Africa
Korea
UkraineNigeria
Egypt
India
W Europe
China
Pakistan
S Europe
Colombia
SE Asia
Philippines
CanadaS AsiaPeru
SSUS
AfricaGermany
Bangladesh
Venezuela
Vietnam
Dominican
RepublicJapan
Jamaica
PolandCuba
E Europe
Oceania
Brazil Russia
Haiti
S America
Cen America
Mexico

-2

-4
-4
-2
0
2
4
Native-born workers with common ancestry

log CA

45 degree line

-4
-2
0
2
4
Native-born workers with common ancestry

regression fit

Figure 11b: Log comparative advantage in other occupations for immigrants versus native-bornworkers with common ancestry, males 2010-1229

5Discussion

The United States has built its strength in high technology in part through its businesses having
access to exceptional talent in science, engineering, and mathematics. Although U.S. universities
continue to dominate STEM disciplines globally, it is individuals born abroad who increasingly
make up the U.S. STEM labor force. The success of Amazon, Facebook, Google, Microsoft, andother technology standouts seems to hinge, at least in part, on the ability of the U.S economy toimport talent from abroad. U.S. continued success in STEM fields thus may depend on which
immigrants the country chooses to admit.
We document strong differences across origin countries in occupational specialization patterns
by foreign-born workers in the U.S. economy. Immigrants from China, India, and some other countries in Asia are much more likely to specialize in STEM occupations than are native-born workers
or immigrants from other origin regions. These specialization patterns are persistent across time,common to males and females from the same origin countries, common to immigrants from an origin country regardless of their age of arrival in the United States, and even common to immigrants
and native-born workers who share a common ancestry. Persistence in occupational specialization patterns across age cohorts, arrival cohorts, and nativity cohorts suggests that factors otherthan the country in which one completes secondary or tertiary schooling play a role in occupational sorting. These additional factors may include job-search networks that are specific to ethnic
groups and cultural norms that vary across origin countries and ethnicities in the prestige assigned
to particular occupations.

References
Acemoglu, Daron. 2002. “Directed Technical Change.” Review of Economic Studies, 69(4): 781809.
Blanchard, Emily, John Bound, and Sarah Turner. 2009. Opening (and Closing) Doors: CountrySpecific Shocks in U.S. Doctorate Education. University of Michigan Population Studies Center
Report 09-674.
Borjas, George. 2003. “The Labor Demand Curve Is Downward Sloping: The Impact of Immigration on the Labor Market.” Quarterly Journal of Economics, 118(4): 1335-1374.
Borjas, George J. 2006. “Immigration in High-Skill Labor Markets: The Impact of Foreign Students on the Earnings of Doctorates.” NBER Working Paper No. 12085.
Borjas, George J. 2014. Immigration Economics.Cambridge, MA: Harvard University Press.
Borjas, George, and Kirk Doran. 2012. “The Collapse of the Soviet Union and the Productivityof American Mathematicians.” Quarterly Journal of Economics, 127(3): 1143-1203.
Bound, John, Murat Demirci, Gaurav Khanna, and Sarah Turner. 2015. “Finishing Degrees andFinding Jobs: U.S. Higher Education and the Flow of Foreign IT Workers.” In William Kerr, Josh
30

Lerne, Scott Stern, eds., Innovation Policy and the Economy, Volume 15.
Bound, John, Sarah Turner, and Patrick Walsh. 2009. “Internationalization of U.S. Doctorate
Education.” NBER Working Paper No. 14792.
Bound, John, Breno Braga, Joseph Golden, and Gaurav Khanna. 2015. "Recruitment of Foreigners in the Market for Computer Scientists in the United States." Journal of Labor Economics, 33(S1):S187-S223.
Burstein, A., Morales, E. and Vogel, J. (2015), Accounting for changes in between-group inequality. National Bureau of Economic Research
Burstein, A., Hanson, GH., Tian, L and Vogel, J. 2017. Tradability and the Labor Market Impactof Immigration: Theory and Evidence for the U.S. working paper
Card, David, 2001. "Immigrant Inflows, Native Outflows, and the Local Labor Market Impactsof Higher Immigration," Journal of Labor Economics, 19(1): 22-64.
Card, David. 2005. “Is the New Immigration Really So Bad?” Economic Journal, 115: F300-F323.
Clemens, Michael. 2010. “The Roots of Global Wage Gaps: Evidence from Randomized Processing of U.S. Visas.” Center for Global Development Working Paper 212.
Eaton, Jonathan, and Samuel Kortum. "Technology, geography, and trade." Econometrica 70,
no. 5 (2002): 1741-1779.
Ellison, Glenn, and Edward L. Glaeser. "Geographic concentration in US manufacturing industries: a dartboard approach." Journal of political economy 105.5 (1997): 889-927.
Freeman, Richard. 2005. “Does Globalization of the Scientific/Engineering Workforce Threaten
U.S. Economic Leadership?” NBER Working Paper No. 11457.
General Accounting Office. 2011. “H-1B Visa Program: Reforms are Needed to Minimize theRisks and Costs of Current Program.” GAO Report 11-26.
Goldin, Claudia and Lawrence F. Katz. 2008. The Race Between Education and Technology. Cambridge, MA: Harvard University Press.
Grogger, Jeffrey, and Gordon H. Hanson. 2011. “Income Maximization and the Selection andSorting of International Migrants,” Journal of Development Economics, 95(1): 42-57.
Grogger, Jeffrey, and Gordon H. Hanson 2015. "Attracting talent: location choices of foreignborn PhDs in the United States." Journal of Labor Economics 33, no. S1 (2015): S5-S38.
Hanson, Gordon H. 2009. “The Economic Consequences of International Migration,” Annual
Review of Economics , 1: 179-208.
Hanushek, Eric A., and Dennis D. Kimko. 2000. "Schooling, Labor-Force Quality, and theGrowth of Nations." American Economic Review 90(5): 1184-1208.
Hsieh, C.-T., Hurst, E., Jones, C. I. and Klenow, P. J. 2013, The allocation of talent and us economic growth, NBER Working Paper (w18693).
Hunt, Jennifer, and Marjolaine Gauthier-Loiselle. 2010. “How Much Does Immigration Boost
Innovation?” American Economic Journal: Macroeconomics, 2(2): 31-56.
Hunt, Jennifer. 2011. “Which Immigrants Are Most Innovative and Entrepreneurial? Distinctions by Entry Visa.” Journal of Labor Economics, 29(3): 417-457.
Institute of International Education. (2015). "Top 25 Places of Origin of International Students,
2013/14-2014/15." Open Doors Report on International Educational Exchange. Retrieved fromhttp://www.iie.org/opendoors.
Jasso, Guillermina, Douglas S. Massey, Mark R. Rosenzweig and James P. Smith. 2000. “Assortative Mating among Married New Legal Immigrants to the United States: Evidence from the
New Immigrant Survey Pilot.” International Migration Review, 34(2): 443-459.
Jones, Charles I. 2002. “Sources of U.S. Economic Growth in a World of Ideas.” American Economic Review, 92(1): 220-239.

31

Kato, Takao, and Chad Sparber. 2013. “Quotas and Quality: The Effect of H-1B Visa Restrictions on the Pool of Prospective Undergraduate Students from Abroad.” Review of Economics andStatistics, 95(1): 109-126.
Katz, L.F. and Autor D.H., 1999. Changes in the wage structure and earnings inequality. Handbook of Labor Economics, 3, pp.1463-1555.
Kerr, Sair Pekkala, William R. Kerr, and William F. Lincoln. 2015. "Skilled Immigration and the
Employment Structures of U.S. Firms." Journal of Labor Economics, forthcoming.
Kerr, William R., and William F. Lincoln. 2010. “The Supply Side of Innovation: H-1B Visa
Reforms and U.S. Ethnic Invention.” Journal of Labor Economics, 28 (3): 473-508.
Lagakos, David, and Michael E. Waugh 2013. "Selection, agriculture, and cross-country productivity differences." The American Economic Review 103, no. 2 (2013): 948-980.
Lewis, Ethan. 2011. “Immigration, Skill Mix, and Capital-Skill Complementarity.” Quarterly
Journal of Economics 126(2): 1029-1069.
Lin, Jeffrey. 2011. “Technological Adaption, Cities, and New Work.” Review of Economics andStatistics, 93(2): 554-574.
Lowell, B. Lindsay. 2000. “H-1B Temporary Workers: Estimating the Population.” Center for
Comparative Immigration Studies Working Paper No. 12.
Ngai, Mae M. 1999. “The Architecture of Race in American Immigration Law: A Reexaminationof the Immigration Act of 1924.” Journal of American History, 86(1): 67-92.
Office of Immigration Statistics. 2014. 2013 Yearbook of Immigration Statistics. Washington, DC:
U.S. Department of Homeland Security
Peri, Giovanni. 2012. “The Effect of Immigration on Productivity: Evidence from U.S. States.”
Review of Economics and Statistics, 94(1): 348-358.
Rosenzweig, Mark, 2006. Global Wage Differences and International Student Flows. Brookings
Trade Forum. Rosenzweig, Mark, 2007. Education and Migration: A Global Perspective. Mimeo,
Yale.
Roy, Andrew Donald. "Some thoughts on the distribution of earnings." Oxford economic papers 3, no. 2 (1951): 135-146.
Ruggles, Steven, Matthew Sobek, Trent Alexander, Catherine A. Fitch, Ronald Goeken, Patricia
Kelly Hall, Miriam King, and Chad Ronnander. 2010. Integrated Public Use Microdata Series:
Version 3.0. Minneapolis, MN: Minnesota Population Center.
Salzman, Hal, Daniel Kuehn, and B. Lindsay Lowell. 2013. “Guestworkers in the High-Skill
U.S. Labor Market.” EPI Briefing Paper No. 359.
Stuen, Eric T., Ahmed Mushfiq Mobarak, and Keith E. Maskus. 2012. “Skilled Immigration andInnovation: Evidence from Enrollment Fluctuations in U.S. Doctoral Programmes.” The Economic
Journal, 122(565): 1143-1176.
Udansky, Margaret L. and Thomas J. Espenshade. 2000. “The H-1B Debate in Historical Perspective: The Evolution of U.S. Policy Toward Foreign-Born Workers.” Center for Comparative
Immigration Studies Working Paper No. 11.

32