NBER WORKING PAPER SERIESHIGH-SKILLED IMMIGRATION AND THE RISE OF STEM OCCUPATIONS IN
U.S. EMPLOYMENT
Gordon H. Hanson
Matthew J. Slaughter
Working Paper 22623
http://www.nber.org/papers/w22623

NATIONAL BUREAU OF ECONOMIC RESEARCH
1050 Massachusetts Avenue
Cambridge, MA 02138
September 2016

We thank John Bound, Charles Hulten, and Valerie Ramey for valuable comments and Chen Lui
for excellent research assistance. The views expressed herein are those of the authors and do notnecessarily reflect the views of the National Bureau of Economic Research.
NBER working papers are circulated for discussion and comment purposes. They have not been
peer-reviewed or been subject to the review by the NBER Board of Directors that accompanies
official NBER publications.
© 2016 by Gordon H. Hanson and Matthew J. Slaughter. All rights reserved. Short sections oftext, not to exceed two paragraphs, may be quoted without explicit permission provided that fullcredit, including © notice, is given to the source.

High-Skilled Immigration and the Rise of STEM Occupations in U.S. Employment
Gordon H. Hanson and Matthew J. Slaughter
NBER Working Paper No. 22623
September 2016
JEL No. F22,J61
ABSTRACTIn this paper, we document the importance of high-skilled immigration for U.S. employment in
STEM fields. To begin, we review patterns of U.S. employment in STEM occupations among
workers with at least a college degree. These patterns mirror the cycle of boom and bust in the
U.S. technology industry. Among younger workers, the share of hours worked in STEM jobs
peaked around the year 2000, at the height of the dot-com bubble. STEM employment shares are
just now approaching these previous highs. Next, we consider the importance of immigrant labor
to STEM employment. Immigrants account for a disproportionate share of jobs in STEM
occupations, in particular among younger workers and among workers with a master's degree or
PhD. Foreign-born presence is most pronounced in computer-related occupations, such assoftware programming. The majority of foreign-born workers in STEM jobs arrived in the U.S. atage 21 or older. Although we do not know the visa history of these individuals, their age at arrival
is consistent with the H-1B visa being an important mode of entry for highly trained STEM
workers into the U.S. Finally, we examine wage differences between native and foreign-bornlabor. Whereas foreign-born workers earn substantially less than native-born workers in nonSTEM occupations, the native-foreign born earnings difference in STEM jobs is much smaller.
Further, foreign-born workers in STEM fields reach earnings parity with native workers much
more quickly than they do in non-STEM fields. In non-STEM jobs, foreign-born workers require
20 years or more in the U.S. to reach earnings parity with natives; in STEM fields, they achieveparity in less than a decade.

Gordon H. Hanson
IR/PS 0519
University of California, San Diego
9500 Gilman Drive
La Jolla, CA 92093-0519
and NBER
gohanson@ucsd.edu
Matthew J. Slaughter
Tuck School of Business
Dartmouth College
100 Tuck Hall
Hanover, NH 03755
and NBER
matthew.j.slaughter@dartmouth.edu

1 Introduction
U.S. business has long dominated the global technology sector.

Among the top ten technology

companies in terms of revenues worldwide, six are headquartered in the United States and employ
most of their workers in U.S. facilities.

1 U.S. preeminence in advanced industries is perhaps surprisingin light of the perceived weakness of U.S. students in science, technology, engineering and math
(STEM). When it comes to STEM disciplines, U.S. secondary-school students tend to underperformtheir peers in other high-income nations. In the 2012 PISA exam, for instance, U.S. 15-year olds

2

ranked 36th in math and 28th in science, out of 65 participating countries.

Middling test scores notwithstanding, the U.S. economy has found ways to cope with the labormarket demands of the digital age. The country makes up for any shortcomings in growing its own
STEM talent by importing talent from abroad. Foreign-born workers account for a large fractionof hires in STEM occupations, especially among those with advanced training.

Not surprisingly,

the tech sector is unied in its support for expanding the number of U.S. visas made available tohigh-skilled foreign job seekers.

3 Helping maintain U.S. leadership in technology is the country's

strength in tertiary education in STEM disciplines, which attracts ambitious foreign students, andfaculty, to U.S. universities. In global rankings of scholarship, U.S. institutions of higher educationaccount for nine of the top ten programs in engineering, for eight of the top ten programs in life and4

medical sciences, and for seven of the top ten programs in physical sciences.

The U.S. succeeds in attracting highly trained workers from around the world even though
the country's immigration system provides only modest ostensible reward for skill.

Family-based

immigration absorbs the lion share of U.S. permanent residence visas. Immediate family members of
U.S. citizens, who are eligible for green cards without restriction, accounted for 44.4% of admissionsof legal permanent residents in 2013 (Oce of Immigration Statistics, 2014).
members of U.S. citizens and legal residents accounted for another 21.2%.

Additional family

Employer-sponsoredvisas made up only 16.3% of the total. These outcomes are consistent with long-standing priorities
of U.S. immigration policy. The Immigration Act of 1990, which moderately reformed the landmark
Immigration and Nationality Act of 1965, allocated 480,000 visas to family-sponsored categories but
just 140,000 visas to employer-sponsored ones.

1

These companies (from communications equipment, computers, electronics, internet services, semiconductors,

and software and programming) are: Apple (U.S.), Samsung (Korea), Hon Hai Precision (Taiwan), Hewlett-Packard(U.S.), IBM (U.S.), Microsoft (U.S.), Hitachi (Japan), Amazon (U.S.), Sony (Japan), and Google (U.S.). See Erin
Grith, The World's Largest Tech Companies: Apple Beats Samsung, Microsoft, Google.

2
3
4

See www.oecd.org/pisa.
Miriam Jordan, U.S. Firms, Workers Try to Beat H-1B Visa Lottery System,
See world university rankings by eld at www.awru.org.

1

Forbes, May 11, 2015.

Wall Street Journal, June 2, 2015.

Despite the pro-family-reunication orientation of U.S. immigration legislation, high-skilled workers nd their way into the country and into STEM jobs. U.S. immigration standards turn out to be
more exible in practice than they appear on paper. A foreign student who succeeds in gaining admission to a U.S. university is likely to garner a student visa. Studying in the United States creates
opportunities to make contacts with U.S. employers (Bound, Demirci, Khanna, and Turner, 2015)
and to meet and to marry a U.S. resident (Jasso, Massey, Rosenzweig, and Smith, 2000), either ofwhich outcome opens a path to obtaining a green card. Although the hurdles involved in securing
legal permanent residence can take many years to clear, a foreign citizen with sucient training
and a U.S. job oer is eligible for an H-1B visa, which has come to function as a de facto queue
for a green card, at least among those with sought-after skills. These visas, which go primarily tohighly educated workers in the tech sector, last for three years and are renewable once. The U.S.
awards 65,000 H-1B visas annually on a rst-come, rst-served basis, and another 20,000 visas to

5 Other temporary work visas

individuals with a master's or higher degree from a U.S. institution.

are available to employees of foreign subsidiaries of U.S. multinational companies and to companies
headquartered in countries with which the U.S. has a free trade agreement.
In this paper, we document the importance of high-skilled immigration for U.S. employment
in STEM elds.

To begin, we review patterns of U.S. employment in STEM occupations among

workers with at least a college degree.

These patterns mirror the cycle of boom and bust in the

U.S. technology industry (Bound, Braga, Golden, and Khanna, 2015). Among young workers with acollege education, the share of hours worked in STEM jobs peaked around the year 2000, at the heightof the dot-com bubble. STEM employment shares are just now approaching these previous highs.
Next, we consider the importance of immigrant labor to STEM employment. Immigrants accountfor a disproportionate share of jobs in STEM occupations, in particular among younger workers
and among workers with a master's degree or PhD. Foreign-born presence is most pronounced incomputer-related occupations, such as software programming. The majority of foreign-born workers
in STEM jobs arrived in the U.S. at age 21 or older. Although we do not know the visa historyof these individuals, their age at arrival is consistent with the H-1B visa being an important modeof entry for highly trained STEM workers into the U.S. labor market.

Finally, we examine wage

dierences between native and foreign-born workers. Opposition to high-skilled immigration, and to
H-1B visas in particular, is based in part on the notion that foreign-born workers accept lower wages
than the native born, thereby depressing earnings in STEM occupations.

5
6

6 Whereas foreign-born

Employees of U.S. universities and non-prot or public research entities are excluded from the H-1B visa cap.
See, e.g., the justication provided by Senator Chuck Grassley (R-Iowa) for reforming the H-1B visa program(http://www.grassley.senate.gov/issues-legislation/issues/immigration).

2

workers earn substantially less than native-born workers in non-STEM jobs, the native-foreign born
earnings dierence in STEM is much smaller. Foreign-born workers in STEM elds reach earnings
parity with native workers much more quickly than they do in non-STEM elds. In non-STEM jobs,
foreign-born workers require 20 years or more in the U.S. to reach earnings parity with natives; in
STEM elds, they achieve parity in less than a decade.
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 pace of long-run
growth (Jones, 2003). Because high-skilled immigrants are drawn to STEM elds, they are likely
to be inputs into U.S. innovation.

Recent work nds evidence consistent with high-skilled immi-

gration 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 (Hunt and GauthierLoiselle, 2010; Peri, 2012). Kerr and Lincoln (2010) nd that individuals with ethnic Chinese andIndian names, a large fraction of which appear to be foreign born, account for rising shares of U.S.
patents in computers, electronics, medical devices, and pharmaceuticals. U.S. metropolitan areasthat historically employed more H-1B workers enjoyed larger bumps in patenting when Congress
temporarily expanded the program between 1999 and 2003. Further, the patent bump 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-born contribution to U.S.

innovation and productivity growth is hard to pin down.

Because the allocation of labor acrossregional markets responds to myriad economic shocks, establishing a causal relationship betweeninows 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 thenet impact on U.S. innovation capabilities. How much of aggregate U.S. productivity growth can be
attributed to high-skilled labor inows 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 university.

The National Survey ofCollege Graduates shows that foreign-born individuals are far more likely than the native-born toobtain 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 elds strongly associated with later patenting. Once one controls forthe major eld of study, the foreign-native born dierential in patenting disappears. Consistent with
Hunt's (2011) ndings, the descriptive results we present suggest that highly educated immigrant
workers in the United States have a strong revealed comparative advantage in STEM. The literature3

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 of 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 activity. Alternatively, cultural or language
barriers may complicate the path of the foreign-born to obtaining good U.S. jobs in non-STEM elds,such as advertising, insurance, or law, pushing them into STEM careers.
In the political debate surrounding H1B visas, the foreign-born are criticized for putting U.S.
workers out of jobs due to their willingness to work for low wages (Hira, 2010). Critics of the H-1B
program portray it as allowing Indian rms in business services, such as Wipro and Infosys, to set
up low-wage programming shops in the United States (Matlo, 2013). Our results do not support
such characterizations. After controlling for observable characteristics, there is little discernible difference in the average earnings of native and foreign-born workers in STEM occupations. Moreover,the pattern of assimilation among foreign-born STEM workers suggests that immigrants end up in
higher-wage and not lower-wage positions. Unknown is how the selection of workers into occupationsor the selective return migration of the foreign-bornaect these observed native-immigrantwage dierences. If native-born workers with high earnings potential move out of STEM jobs more
rapidly over time (into, say, management positions) or if, within STEM occupations, lower-wage
immigrants are more likely to return to their home countries, our results may overstate the relative
wage trajectory of immigrant workers in STEM jobs.
Section 2 presents data used in the analysis; section 3 documents the role of STEM in overall U.S.
employment; section 4 describes the presence of foreign-born workers in STEM occupations; section
5 examines earnings dierences between native and foreign-born workers; and section 6 concludes.

2 DataThe data for the analysis come the Ipums 5% samples of the 1980, 1990 and 2000 U.S. population
censuses and 1% combined samples of the 2010-2012 American Community Surveys (ACS). We
also use data from the Ipums sample of the March Current Population Survey.

We dene total

employment to be total hours worked for individuals in the civilian population not living in groupquarters. Because we focus on individuals with a college or advanced degree and who are oriented
toward STEM occupations, in much of the analysis we limit the sample to those 25 to 54 years ofage. Excluding those younger than 25 drops individuals still in school or still making their schooling4decisions. In early sample years, dropping those older than 54 excludes the generation of workers
who would have made schooling decisions well before the computer revolution. In the Census and
ACS, hours worked is calculated as weeks worked last year times usual hours worked per week,
weighted by sampling weights. Earnings are calculated, alternatively, as average annual earnings,
average weekly earnings, or average earnings per usual hours worked.
Our denition of STEM occupations follows that of the Department of Commerce (Langdon etal. 2011), except that we drop the relatively low-skill categories of technicians, computer supportsta, and drafters. These excluded categories have a relatively high fraction of workers who havecompleted no more than a high school degree. The resulting occupations classied as STEM are:• Computer-related elds (computer scientists, computer software developers, computer systems
analysts, programmers of numerically controlled machine tools);• Engineers (aerospace, chemical, civil, electrical, geological and petroleum, industrial, materials
and metallurgical, mechanical);• Life and medical scientists (agricultural and food scientists, biological scientists, conservationand forestry scientists, medical scientists),• Physical scientists (astronomers and physicists, atmospheric and space scientists, chemists,
geologists, mathematicians, statisticians); and• Other STEM occupations (surveyors, cartographers, and mapping scientists).
Occupational denitions used by the U.S. Bureau of the Census have expanded over time as aconsequence of technological progress (Lin, 2011). In order to compare employment patterns fromthe 1980s to the present, we are obligated to use the 1990 Ipums occupational categories.

This

categorization does not include elds that became common only in the later phases of the digital revolution (e.g., information security analysts, web developers, computer network architects).
However, these new categories fall almost entirely within the old categories of software developers,
computer scientists, and computer systems analysts.

Because we work with STEM occupations

either as an aggregate or for the broad category of computer-related elds, the proliferation ofoccupations within information technology does not pose a problem.

5

3 Employment in STEM Occupations
3.1

Rising Employment in STEM Fields

To set the stage for discussing the role of foreign-born workers in U.S. employment in science andtechnology, it is helpful to consider rst how national employment in these lines of work has evolved
over time.

Figure 1 uses the March CPS to show the fraction of total work hours in STEM oc-

cupations for 25-54 year olds across all education categories. This share rises steadily during the
1990s, plateaus after the 2001 dot-com bust, and then rises again in the mid and late 2000s. Whenlooking at workers in all education categories, STEM jobs still account for a small fraction of total
employment, breaking 6 percent only briey during the sample period.

Figure 1: Share of Total Hours Worked in STEM Occupations

Share of total employment in STEM jobs
.06

.05

.04
1993

1997

2001

Year

2005

2009

2013

(Source: CPS, 1994 to 2014.)

To put the employment shares in Figure 1 in context, in Table 1 we show the total number offull-time equivalent workers in STEM occupations over 2000-2012 and the fractions of these workers
with a BA degree and with a BA degree in a STEM discipline.

Full-time equivalent workers arecalculated as the sum (weighted by survey weights) of usual hours worked per week times weeks
worked last year divided by 2000. STEM workers are, not surprisingly, a relatively highly educatedgroup. Whereas only 34.5% of 25 to 54 year-old full-time workers in non-STEM occupations have6

Table 1: Characteristics of STEM workers, 2010-2012

a BA degree, college education predominates in STEM jobs, ranging from 58.9% among network
administrators to 81.6% among engineers and to 91.9% among life and physical scientists. In STEM
occupations, the majority of those with a BA degree have earned that degree in a STEM eld (as seen
by taking the ratio of column 3 to column 2 in Table 1). Consistent with much previous evidence,
STEM jobs tend to pay substantially more than non-STEM jobs. Considering just those workers
with at least bachelor's degree, average annual earnings in 2010-2012 for full-time college-educatedworkers in non-STEM occupations was $78,635, compared with $92.095 for software programmers
and $94,297 for engineers.

Only earnings for life and physical scientists lag those in non-STEM

positions.
Given that STEM jobs tend to require a college education, the upward trend in STEM employment in Figure 1 may be in part a byproduct of the rising educational attainment of the U.S. labor
force. We next examine how employment patterns have changed among workers with at least a BA
degree. Figure 2 uses the March CPS to show the fraction of total work hours by 25-54 year olds
accounted for by STEM occupations in each of three education categories: workers whose highest
attainment is a bachelor's degree, workers whose highest attainment is a master's or professional
degree, and workers with a PhD. Once we condition on having a college education, employment in
the broad science and technology sector has been relatively at since the late 1990s, ranging from
10-12% for college graduates, 9-12% for master's and professional degrees, and 14-22% for PhDs.
(Employment shares among PhDs appear more variable in Figure 2 due in part to relatively small
sample sizes for this subcategory.)

7

Figure 2: Employment of College-Educated Males in STEM Occupations

Share of employment in STEM jobs
.22
employment share

.2
.18
.16
.14
.12
.1
.08
1993

1997

2001

Year

2005

2009

2013

workers with BA degree
workers with MA, professional degree
workers with PhD(Source: CPS, 1994 to 2014.)

In select lines of work, STEM employment has exploded. Creating software, programming computer systems, and managing computer networks were minor occupations in 1980.
are ubiquitous.

Today, theyComputer science is among the most popular majors on many college campuses.

The lives of programmers appear in popular culture, inspiring major motion pictures (The Social
Network, Steve Jobs: The Man in the Machine), TV series (Silicon Valley), and even contemporary music (Big Data).

Figure 3 shows the share of hours worked in STEM occupations bycomputer systems analysts and computer scientists, developers of computer software, and programmers of numerically controlled machine tools, where the rst two subgroups account for the vast
majority of employment in this category. Among bachelor degree holders, the share of employment
in computer-related jobs rises sharply from 22.0% in 1980 to 31.7% in 1990 before jumping steeply
again to 52.5% in 2000 and then stabilizing at 55.8% for 2010-2012. STEM employment shares incomputer occupations among advanced degree holders (master's degree, professional degree, PhD)
show a similar temporal pattern of evolution but are about 10 percentage points lower.

8

Figure 3: Employment of College-Educated Males in STEM Occupations

Share of STEM workers in software, programming
1980
1990

BA degree
2000
2012

1980
1990

Adv degree
2000
2012
.1

.2
.3
.4
employment share.5

Source: Ipums Census, ACS.
3.2

Revealed Comparative Advantage in STEM Occupations

Who gets STEM jobs? Because the rise of information technology is a recent phenomenon, younger
workers are those most likely to have chosen a path of study that gives them entry into the STEM
labor force. In part because men are more likely to study STEM disciplines in universityespeciallyin computer science and engineeringthey are in turn more likely to be employed in STEM occupations once they enter the labor force.

To examine occupational sorting by age and gender, we

calculate employment shares for ve-year age cohorts, separately for men and women. For college
graduates, we consider 25-29 year olds to be the entry cohorti.e., the age at which individualsrst have stable, full-time workwhich allows for the possibility that it may take individuals several years after obtaining their BA to nd their professional bearings. Similarly, for those with anadvanced degree we discuss results nominally treating 30-34 year olds as the entry cohort.

9Figure 4: Employment of College-Educated Males in STEM Occupations

Employment in STEM jobs, males with BA degree

Share of hours worked

.25

.2

.15

.1

.05

0
25-29

30-34
1980

35-39
40-44
Age cohort
1990

45-49
2000

50-54
2012

Employment in STEM jobs, males with advanced degree

Share of hours worked

.25

.2

.15

.1

.05

0
25-29

30-34
1980

35-39
40-44
Age cohort
1990

45-49
2000

50-54
2012

Source: Ipums census, ACS.
Figure 4 shows the share of hours worked in STEM occupations for malesboth native andforeign-bornwith at least a college education. Consider rst the upper panel, which shows maleswith a bachelor's degree. Between 1980 and 1990, the share of 25-29 year-olds in STEM jobs climbs
from 11.1% to 17.5%. During the 1980s, which saw the introduction of the Apple Macintosh personal

10

computer, the Microsoft MS-DOS operating system, and the Intel 80386 microprocessor, STEM jobs
drew in relatively large numbers of young workers.

The STEM employment share for 25-29 year

olds rises again to 19.0% in 2000, as the dot-com wave crests, and then declines somewhat to 17.1%
for the 2010-2012 period, following the Great Recession and the ensuing slow recovery. The shift
toward employment in STEM is much lower among individuals who were in their 30s in the 1980s
and non-existent among those 40 years old and older in the 1980s.
Turning to hours worked for those with an advanced degree, shown in the lower panel of Figure
4, the lure of STEM employment in the 1980s and 1990s is even more pronounced. Among 30-34
year olds, the share working in STEM rises from 11.6% in 1980 to 15.1% in 1990 and to 21.0% in
2000, before falling to 19.5% in 2010-2012. The higher incidence of STEM employment among themost educated workers may reect the need for advanced training in order to perform the job tasks
demanded in science and technology.

Alternatively, the disproportionate share of STEM workers

with graduate degrees may reect an arms race, in which workers compete via education to improve their chances of obtaining the high-paying jobs available in information-technology industries.
Anticipating the patterns that we shall see in section 4, the arms-race motivation may be particularly strong among immigrant workers. Those born abroad may lack access to informal networks
through which native-born workers obtain information about employment opportunities. Earning
an advanced degree provides foreign-born workers with a mechanism for signaling their capabilities,
perhaps helping compensate for any lack of informal signaling options.
Silicon Valley is frequently cited in the business press for the lack of professional opportunities
that it oers women. The reputation of the tech sector as being male-dominated appears to be wellfounded. Figure 5 shows STEM employment shares for females with a bachelor's degree (upper panel)and an advanced degree (lower panel). Among workers with no more than a bachelor's degree, theshare of female employment in STEM occupations is markedly lower than that for males. Among
25-29 year-old women, STEM occupations accounted for only 4.6% of employment in 2010-2012(compared to 17.1% for men), a gure that was lower than both 2000 at 6.2% (19.0% in that year
for men) and 1990 at 6.7% (17.5% in that year for men). For women with an advanced degree (lower
panel of Figure 5), specialization in STEM is modestly higher. Among 30-34 year olds, the share offemales in STEM jobs is 7.2% in 2010-2012 (19.5% in that year for men), down from 8.7% in 2000
(21.0% in that year for men) and up from 5.8% in 1990 (15.1% in that year for men). As with men,
STEM employment shares are higher among all age cohorts for women with an advanced degreecompared to women with no more than a bachelor's degree.

11

Figure 5: Employment of College-Educated Females in STEM Occupations

Employment in STEM jobs, females with BA degree

Share of hours worked

.25

.2

.15

.1

.05

0
25-29

30-34
1980

35-39
40-44
Age cohort
1990

45-49
2000

50-54
2012

Employment in STEM jobs, females with advanced degree

Share of hours worked

.25

.2

.15

.1

.05

0
25-29

30-34
1980

35-39
40-44
Age cohort
1990

45-49
2000

50-54
2012

Source: Ipums census, ACS.
Putting Figure 5 together with Figure 4 reveals that the under-representation of women in STEM

12

has not improved over time. To see this, we measure occupational specialization using the revealed
comparative advantage of males in STEM, given by:[Share of male employment in STEM jobs/Share of male employment in non-STEM jobs]/[Share of female employment in STEM jobs/Share of female employment in non-STEM jobs]

Among 25-29 year olds with a bachelor's degree, revealed comparative advantage for men in STEM
rises from 3.0 (.175/(1 − .175))/(.067/(1 − .067)) in 1990 to 4.4 (.171/(1 − .171))/(.045/(1 − .045))
in 2010-2012. Stated dierently, the log odds of a college educated male being employed in STEM
relative to a college-educated female being employed in STEM rises from 1.10 in 1990 to 1.48 in2010-2012. Among 30-34 year olds with an advanced degree, revealed comparative advantage formen in STEM rises less sharply from 2.9(.152/(1 − .152))/(.058/(1 − .058)) to 3.1 (.195/(1 −.195))/(.072/(1 − .072)), for an increase in the log odds of 1.07 to 1.13. Among the foreign born,
more-educated women are also under-represented in STEM jobs when compared to immigrant men.
When we turn next to comparing employment patterns for native and foreign-born workers, will we
examine employment for men and women summed together.

4 Foreign-Born Workers in STEM Occupations
4.1

Immigrant Workers in the U.S. Economy

To provide context for the analysis of specialization patterns by native and foreign-born workers in
STEM occupations, we rst examine the share of the foreign-born across all occupations. The upper
panel of Figure 6 shows the fraction of hours worked accounted for by the foreign-born among 25 to
54 year-old workers (males and females combined) with a bachelor's degree, master's or professional
degree, and a PhD. As the literature has documented, the immigrant share of U.S. employment
for the more educated is rising steadily over time.

Among workers whose highest attainment isa bachelor's degree, the foreign-born employment share reaches 15.2% in 2013, up from 10.1% in
1993. As is also well-known, for workers with at least a college degree immigrant employment sharesrise monotonically by education level. In 2013, the foreign born account for 18.1% of hours worked
among master's and professional degrees and 28.9% among PhDs.

For comparison, in 2013 theshare of the foreign-born in the total U.S. civilian labor force was 16.5%, up from 9.2% in 1990.
Immigrants are, then, mildly under-represented among college graduates, slightly over-represented
among those with master's degrees, and strongly over-represented among PhDs.

13

Figure 6: Share of Foreign-Born Workers in Non-STEM Occupations

Foreign born share of US employment.7

employment share

.6
.5
.4
.3
.2
.1
1993

1997

2001

Year

2005

2009

2013

workers with BA degree
workers with MA, professional degree
workers with PhD(Source: CPS, 1994 to 2014.)

Foreign born share of employment, STEM jobs
.7
.6
.5
.4
.3
.2
.1
1993

1997

2001

Year

2005

2009

2013

workers in STEM occupations with BA degree
workers in STEM occupations with MA, professional degree
workers in STEM occupations with PhD(Source: CPS, 1994 to 2014.)

Source: Ipums census, ACS.
Relative to employment across all occupations, the presence of the foreign-born in STEM em-

14

ployment is higher for all education groups, as seen in the lower panel of Figure 6 which shows
foreign-born employment shares for the same categories as the upper panel but now for jobs in
STEM. In 2013, the foreign-born share of STEM employment is 19.2% among bachelor degrees,
higher at 40.7% among master's degrees, and higher still at 54.5% among PhDs.

Since the mid

2000s, immigrants have accounted for the majority of U.S. workers in STEM with doctoral degrees.
The majority of advanced degree holders who are foreign born obtained their degrees in the UnitedStates (Bound, Turner, and Walsh, 2009). Thus, there is a sense in which the U.S. is growing its own
STEM talent. U.S. universities have become a pipeline for advanced degree recipients born abroad
to enter the U.S. labor force. These institutions attract foreign students and train them in STEM
disciplines, before sending them to work in U.S. employers. The large majority of those completing
their PhDs in the U.S., in particular those from lower and middle income countries, intend to stayin the United States after graduation (Grogger and Hanson, 2015).
Also apparent in Figure 6 are dierences in the cyclicality of foreign-born employment in STEM
by education level. Whereas among college graduates the foreign-born share peaks in 2000 and has
been stable since, among master's degree holders the foreign-born share rises by over 10 percentage
points in the 2000s and among PhDs the foreign-born share rises by a full 25 percentage points
between 2001 and 2007, before dipping during the Great Recession.

4.2

Revealed Comparative Advantage of Foreign-Born Workers

We have already seen that among the college educated young workers are relatively likely to selectinto STEM employment. Since a disproportionate share of the foreign born are workers in their 20s
and 30s, it is conceivable that the rising presence of immigrants in U.S. STEM careers is simply
a byproduct of diering demographic patterns among natives and immigrants.

Evidence on thispossibility is seen in the upper panel of Figure 7, which shows the share of workers in STEM
occupations that are foreign born by ve-year age cohorts for those with bachelor's degrees. The
foreign-born share among 25-29 year olds in STEM jobs rises from 5.8% in 1980 to 9.1% in 1990
and then peaks at 21.1% in 2000 before declining to 17.0% in 2010-12. The corresponding shares of
non-STEM jobs going to immigrants (for 25-29 year olds with a bachelor's degree), as shown in thelower panel of Figure 7, are 4.2% in 1980, 6.5% in 1990, 9.5% in 2000, and 9.2% in 2010-2012. Even
controlling for age, the foreign-born are strongly over-represented in STEM employment.

15

Figure 7: Share of Workers Who Are Foreign-Born, Bachelor's Degree Holders

STEM occupations, workers with BA degree
Foreign born share of hours worked

.6
.5
.4
.3
.2
.1
0
25-29

30-34
1980

35-39
40-44
Age cohort
1990

45-49
2000

50-54
2012

Non-STEM occupations, workers with BA degree
Foreign born share of hours worked

.6
.5
.4
.3
.2
.1
0
25-29

30-34
1980

35-39
40-44
Age cohort
1990

45-49
2000

50-54
2012

Source: Ipums census, ACS.
The already substantial presence of immigrants in STEM jobs for a birth cohort at labor-market

16

entry becomes even larger as the cohort ages. Consider the cohort born between 1971 and 1975,
which is the heart of Generation X. The upper panel of Figure 7 shows that by 2010-12, the shareof immigrants among Gen-X 35-39 year olds with BA degrees employed in STEM reaches 25.6%, up
4.5 percentage points over the level for 25-29 year olds in 2000. This increase is accounted for by a
combination of immigrants in this birth cohort who arrived during the 2000s being disproportionately
selected into STEM jobs and immigrants in this birth cohort already in the country as of 2000 being
relatively unlikely to exit STEM employment. Similar patterns of rising shares of STEM employment
going to immigrant workers exist for other birth cohorts, as well.
The relatively strong specialization of immigrant workers in STEM occupations is even more
pronounced among for those with advanced degrees, as seen in Figure 8. For the period 2010-12,
the share of STEM jobs going to the foreign born relative to the share of non-STEM jobs going to
the foreign born is 39.4% versus 13.6% among 25-29 year olds, 47.7% versus 15.9% among 30-34
year olds, and 50.0% versus 18.2% among 35-39 year olds.

Thus, among prime-age workers withan advanced degree, the foreign born now account for one-half of total hours worked in STEM
occupations.

This fraction is up from one-quarter in the 1990s and from one-fth in the 1980s.

Many of the highly educated workers employed in engineering, science, and technology are at theforefront of U.S. innovation. Foreign-born professionals would seem to have become a vital of the U.S.
R&D labor force. These workers enter STEM employment in their youth and remain in technicaloccupations after decades of potential labor-market experience.
Putting together the top and bottom panels of Figure 7, and similarly for Figure 8, the employment of foreign-born workers is consistent with their having a strong revealed comparative advantage
in STEM occupations. Among 25-29 year olds with a bachelor's degree, revealed comparative advantage of foreign-born workers in STEM, which is dened as,[Share of foreign-born employment in STEM/Share of foreign-born employment in non-STEM]/[Share of native-born employment in STEM/Share of native-born employment in non-STEM]

17

Figure 8: Share of Workers Who Are Foreign-Born, Advanced Degree Holders

STEM occupations, workers with advanced degree
Foreign born share of hours worked

.6
.5
.4
.3
.2
.1
0
25-29

30-34
1980

35-39
40-44
Age cohort
1990

45-49
2000

50-54
2012

Non-STEM occupations, workers with advanced degree
Foreign born share of hours worked

.6
.5
.4
.3
.2
.1
0
25-29

30-34
1980

35-39
40-44
Age cohort
1990

45-49
2000

50-54
2012

Source: Ipums census, ACS.
rises from 1.4 (.058/(1 − .058))/(.042/(1 − .042)) in 1980 to 2.0 (.17/(1 − .17))/(.094/(1 − .094)) in18

2010-12. The log odds of a young foreign-born college graduate being employed in STEM relative
to a young native-born college graduate being employed in STEM increases from 0.34 to 0.69 overthis period.

Similar increases are evident among older college-educated workers.

The revealed

comparative advantage of the foreign born in STEM appears to be even stronger among individuals
with advanced degrees. Among 30-34 year olds with a master's degree, professional degree or PhD,
the revealed comparative advantage of the foreign born rises from 2.5 (.174/(1 − .174))/(.077/(1 −.077)) in 1980 to 4.8 (.477/(1 − .477))/(.159/(1 − .159)) in 2010-12, for a substantial increase in
the log odds of STEM employment for the foreign-born relative to the native-born of 0.9 to 1.6.
Among holders of an advanced degree, the revealed comparative advantage of foreign over nativeborn workers in STEM is much larger than that even of male over females workers.
Software development is among the most rapidly growing areas for STEM jobs and among themost hotly contested occupations regarding the allocation of H-1B visas. The revealed comparativeadvantage of the foreign-born in computer-related occupations is manifestly stronger than theircomparative advantage in STEM positions overall, as seen in Figure 9. In this subcategory, 23.0% ofhours worked among 25-29 year olds with bachelor's degrees were foreign born in 2010-2012, up from
10.5% in 1990; and 60.0% of hours worked among of 30-34 year olds with advanced degrees were by
the foreign born in 2010-2012, up from 32.3% in 1990. Given that occupational sorting tends to bestable over time for individual birth cohorts, the foreign-born would appear to be set to account fora high fraction of U.S. workers who are employed in computer-related jobs for many years to come(unless, for some reason, foreign-born workers currently on H-1B visas fail to gain legal permanent
residence at the rates they have in the past).

19

Figure 9: Share of Foreign-Born in Computer Occupations

Computer occupations, workers with BA degree
Foreign born share of hours worked

.6
.5
.4
.3
.2
.1
0
25-29

30-34
1980

35-39
40-44
Age cohort
1990

45-49
2000

50-54
2012

Computer occupations, workers with advanced degree
Foreign born share of hours worked

.6
.5
.4
.3
.2
.1
0
25-29

30-34
1980

35-39
40-44
Age cohort
1990

Source: Ipums census, ACS.

20

45-49
2000

50-54
2012

4.3

Age of U.S. Entry by Foreign-Born Workers in STEM Jobs

How do foreign-born STEM workers enter the United States? Although the ACS does not report thetypes of visas through which an individual rst gained entry to the U.S. or rst secured a U.S. job,
it does report the age at which an individual rst arrived in the United States. STEM occupations
that employ foreign-born workers primarily hire those who arrived in the U.S. at age 21 or older.
In Figure 10, we see that among bachelor's-degree holders, those arriving in the U.S. at age 21
plus account for 60.5% of immigrant workers with STEM jobs (across all age cohorts in that year),compared to 51.9% of of immigrant workers in non-STEM jobs. This pattern is even stronger among
advanced-degree holders. Those arriving in the U.S. at age 21 or older are 82.7% of foreign born
workers in STEM with a master's degree, professional degree or PhD, compared to 63.6% of similarly
educated immigrants in non-STEM jobs. Although we cannot determine the type of visa throughwhich these individuals entered the U.S., the pattern of post-age 21 entry is consistent with work
visas, including the H-1B, being an important admissions channel for STEM-oriented immigrants.

Figure 10: Share of Foreign-Born Workers Arriving in US at Age 21 or Older

Share of foreign born workers arriving age 21+, 2012
Share of hours worked

1

.8

.6

.4

.2
25-29

30-34

35-39
Age cohort

STEM workers with advanced degree
STEM workers with BA degree
Non-STEM workers with avanced degree
Non-STEM wokers with BA degree

Source: Ipums census, ACS.

21

40-44

4.4

Explanations for Foreign-Born Comparative Advantage in STEM

The preceding results, while consistent with immigrant workers having a comparative advantage in
STEM, are silent on the factors behind this outcome. One explanation is that K-12 education inother countries oers stronger training in math and science than is available in the U.S.. The inferior
performance of U.S. 15 year olds in PISA exams is consistent with this possibility. Yet, U.S. students
also perform relatively poorly in reading, ranking 24th in this dimension in the 2012 test. Although
the ranking for reading is superior to U.S. scores in science (28th) and math (36th), it would notseem to indicate an overriding comparative disadvantage among U.S. high school students in technical elds. Relative to most other high-income countries, U.S. 15-year-olds may have an absolutedisadvantage in all disciplines and a mild comparative disadvantage in math and science. However,it could be unwise to read too much into the consequences of relatively poor U.S. exam scores, as
little is known about the cross-country variation in how individual performance on standardized
tests translates into professional success.
A second explanation for immigrant success in STEM is that these jobs are the only positions
available to more-educated immigrants and that advanced degrees are how one demonstrates competence in technical disciplines. Non-STEM professions in which more-educated workers predominate
include arts, the media, nance, management, insurance, marketing, medicine, law, and other business services (architecture, consulting, real estate).

Some of these elds, such as insurance andmarketing, are ones in which the foreign born or non-native English speakers may have an absolutedisadvantage because they lack a nuanced understanding of American culture or because subtleties
in face-to-face communication are an important feature of interactions in the marketplace. Othersof these elds, such as the law or real estate, may involve an occupational accreditation process that
imposes relatively high entry costs on those born abroad.
A third explanation is that U.S. immigration policy has implicit screens that favor more-educatedimmigrants in STEM elds over those non-STEM elds.

H-1B visas do go in disproportionate

numbers to workers in STEM occupations (Kerr and Lincoln, 2012).

However, there is nothing

preordained about this outcome in terms of U.S. immigration policy. H-1B visas are designated forspecialty occupations which are dened as those 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 iscommon to the industry in parallel positions among similar organizations; (3) the employer normallyrequires a degree or its equivalent for the position; or (4) the nature of the specic duties is so
specialized and complex that the knowledge required to perform the duties is usually associated with22

7 H-1B visas are thus available to the more-educated in

attainment of a bachelor's or higher degree.

non-STEM lines of work, too. That most H-1B visas are captured by STEM workers may simply
be the consequences of strong relative labor demand for STEM labor by U.S. companies.
Are immigrant workers displacing native-born workers in STEM jobs?

Rising immigration of

more-educated workers has not led to an overall expansion in the share of total U.S. employment in
STEM occupations. The expansion of labor supply for workers with expertise in technical elds may
shift the mix of output toward industries intensive in the use of these skills. Under directed technicalchange, expanded incentives for innovation emanating from the labor supply shock could provide afurther boost to U.S. output in high-tech sectors (Acemoglu, 2002). Yet, expanded immigration ofhighly educated individuals has occurred along with an unchanged share of aggregate employment
in STEM occupations, consistent with foreign-born workers having displaced native-born ones in thecompetition for positions in STEM elds. Of course, many other events occurred in the U.S. labormarket in the 2000s, most notably the bursting of the dot-com bubble and the Great Recession. The
magnitude of these shocks makes it dicult to know how employment of U.S. native-born workers
in STEM occupations would have fared absent high-skilled immigration.
Evidence on native displacement eects from immigration is mixed. Lewis (2011) and Gandal,
Hanson and Slaughter (2004) nd no evidence that immigration inows shifts the output mix inregional or national economies toward industries intensive in the use of immigrant labor.

Borjasand Doran (2012) nd that the arrival of Russian mathematicians in the U.S. induced the exit ofincumbent scholars in the sub-elds of the discipline in which Russia had historically been dominant.
Kerr, Kerr, and Lincoln (2015) do not detect evidence of displacement eects of skilled immigrants
on native workers, at least inside rms.

Within U.S. manufacturing establishments, the arrivalof young, high-skilled foreign-born workers is associated with increases and not decreases in theemployment of young, high-skilled native-born workers.

5 Wage Dierences between Native and Foreign-Born Workers
It is well-known that across all occupations, immigrants earn less than natives, even once one controls
for age, education, gender and race. Do similar earnings dierences between the native and foreignborn materialize when we examine more-educated workers and in particular those employed in
STEM occupations? This issue is of central concern in the public debate about U.S. immigration
policy.

7

Concerns have been expressed about foreign-born STEM workers being willing to accept

See http://www.uscis.gov/eir/visa-guide/h-1b-specialty-occupation/understanding-h-1b-requirements.

23

8 We aim to provide fresh evidence on the subject.

lower earnings that U.S. native-born workers.

To begin we compare earnings for native-born and foreign-born workers in STEM occupations.
Figure 11 shows annual earnings for full-time, full-year male workers 25-44 years old who have atleast a bachelor's degree.

We show earnings by foreign-born status, whether workers have just abachelor's or an advanced degree, and by year. In 1990, average annual earnings for natives exceed
those for immigrants; in 2000 the picture is mixed, with native-born earnings exceeding those for
immigrants among those with an advanced degree but not among those with just a bachelor's
degree; and by 2012, the earnings of the foreign-born exceed those of the native-born in both degree
categories. Similar patterns obtain when we examine average weekly wages or average hourly wages.
Although the comparison in Figure 11 is for workers who have selected into STEM jobs, there may
be important sources of unobserved heterogeneity between workers. In particular, the foreign-born
may be relatively likely to work in high-paying occupations. We next perform wage comparisons,while exibly controlling for individual characteristics.

Figure 11: Earnings comparisons, males aged 25-44

Annual earnings, male full-time STEM workers
1990

2000

2012

BA
MA+
BA
MA+
BA
MA+
0

20,00040,00060,00080,000average annual earnings in thousands (2012 USD)

100000Foreign-born
Native-born(Source: Census 1990, 2000; ACS 2010-2012.)

Pooling data from the 1990 and 2000 population censuses and the 2010-2012 American Communities Surveys, we limit the sample to 25-54 year olds who are full-time (at least 35 usual hours

8

See,

e.g.,

EduardoPorter,Immigration

and

the

Labor

Market,

New

York

Times,

http://economix.blogs.nytimes.com/2013/06/25/immigration-and-the-labor-market/?_r=0.

24

June

25,

2013,

Table 2: Earnings regressions for native-born and foreign-born

VariableLog hourly earnings

Log weekly earnings

Log annual earnings

(1)

(2)

(3)

(4)

(5)

(6)

STEM = 1

0.191(0.001)

0.112(0.001)0.154(0.001)

0.069(0.001)0.164(0.001)

0.073(0.001)

Foreign born = 1-0.101(0.001)-0.124(0.001)-0.120(0.001)-0.146(0.001)-0.119(0.001)-0.149(0.001)

STEM x Foreign born

0.094
(0.003)0.095(0.002)0.084(0.003)0.086(0.002)0.079(0.003)0.082(0.002)

No

Yes

No

Yes

No

Yes

0.285

0.327

0.297

0.341

0.296

0.345

Industry dummies
2

R

Note: N=2,550,537. Sample is full-time, full-year workers 25-54 years old with at least a BA degree.
Additional regressors include dummy variables for gender, race, year, Census geographic region,and five-year age category interacted with educational degree (BA, MA or professional degree,
PhD). Data are from the 1990 and 2000 Census and 2010-2012 ACS. Regressions are weighted bysampling weights.

worked per week) and full-year (at least 40 weeks worked last year) workers with at least a bachelor's degree. We use three measures of earnings: log annual earnings, log weekly earnings (annual
earnings divided by weeks worked last year), and log hourly earnings (annual earnings divided by
weeks worked last year times usual hours worked per week). All regressions are weighted by annual
hours worked (multiplied by the Census sampling weight) and include as controls indicators for gender, race, the Census geographic region, the year, and a full set of interactions between indicatorsfor education (bachelor's degree, master's degree, professional degree, PhD) and age (ve-year age
groupings). Later regressions include indicators for the industry of employment.
The regression shown in column (1a) of Table 2 reveals that STEM workers receive hourly
earnings that are on average 19.1 log points higher than those of non-STEM workers who have
similar demographic characteristics, education, and region of residence.

For weekly and annual

earnings, shown in columns (2a) and (3a), the STEM earnings premium is broadly similar at 15.4
log points and 16.4 log points, respectively. Column (1b) adds controls for ten one-digit industries,
which compresses the STEM hourly earnings premium to 11.2 log points; declines are similar for
weekly and annual earnings, shown in columns (2b) and (3b). Although these ndings may seem25

to suggest that STEM positions are good jobs that pay high wages, we should caution that theseresults are purely descriptive and say nothing about the origin of the STEM earnings dierential.
This dierential may reect higher ability workers being disproportionately selected into STEM
occupations, such that the coecient on the STEM earnings dummy picks up the average dierencein unobserved ability between STEM and non-STEM positions. Alternatively, the STEM earnings
bump may reect a compensating dierential for the higher cost of obtaining the training needed towork in a STEM eld (e.g., the extra hours of study required for a computer science or engineering
degree).

A yet further alternative is that employers that hire relatively large numbers of STEM

workers (e.g., Apple, Google, Microsoft) earn rents and share these rents with their employees.
Across all more-educated workers, the foreign-born in non-STEM occupations earn less than thenative-born, as shown by the negative and signicant coecient on the indicator for a worker being
an immigrant. For hourly earnings, the immigrant wage discount is -10.1 log points (column 1a);for weekly and annual earnings it is comparable at -12.0 log points (column 2a) and -11.9 log points(column 3a), respectively. Immigrant earnings discounts increase modestly when indicators for onedigit industries are added (columns 1b, 2b, 3b). These estimated immigrant earnings dierentials arealso descriptive. They may represent an unobserved-ability dierential between similarly educatednative and foreign-born workers or they may capture the limited portability of human capital between
countries, such that a degree from, say, China is worth less in the U.S. labor market than is U.S.
degree. Earnings dierences from either of these sources would be unlikely to diminish over time.
A source of temporary earnings dierences between immigrants and natives is adjustment costs insettling into a new labor market.

It may take foreign-born workers a while after arriving in the

United States to nd employment that is well matched to their particular skills. Assimilation into
the U.S. labor market, which we examine in more detail below, may attenuate or even reverse
native-immigrant earnings dierences.
The earnings discount for foreign-born workers falls considerably when we compare native andforeign-born individuals employed in STEM occupations.

This result is seen in the positive andstatistically signicant interaction between the STEM indicator and the foreign-born indicator. For
hourly earnings in column (1a), the immigrant wage discount falls to −0.7 (−10.1 + 9.4) log points;
for weekly and annual earnings the immigrant discount falls to −3.6 (−12.0+8.4) log points (column2a) and −4.0 (−11.9+7.9) log points (column 3a), respectively. Although all of these dierentials are
statistically signicant, they are far smaller than the earnings dierences observed between nativeand immigrant workers in non-STEM occupations.
Moreover, once we limit the sample to STEM workerswhich implicitly allows the returns

26

to education and labor-market experience to vary between STEM and non-STEM categoriestheimmigrant-native earnings dierence becomes of indeterminate sign. Unreported results for regressions similar to Table 2 in which we restrict the sample to workers employed in STEM occupations
show that the immigrant earnings dierential is positive and signicant for hourly earnings (at 1.7
log points without industry controls and 2.6 log points with industry controls), while negative andweakly signicant for weekly earnings (-0.3 log points without industry controls and -1.4 log points
with industry controls) and negative and strongly signicant for annual earnings (-0.7 log points
without industry controls and -1.8 log points with industry controls).
Could the immigrant-earnings discount be a consequence of adjustment costs that are erased bylabor-market assimilation? Borjas (2013) nds suggestive evidence that the process of assimilationin immigrant wageswhich was evident in earlier decadeshas broken down. That is, across all
education groups immigrants earnings appear to be catching up to native earnings more slowly thanthey did in the past.

We examine patterns of assimilation for more-educated immigrants to see

if his ndings are replicated among more-skilled workers. Because one cannot separately identify
wage eects for the birth cohort, the year of immigration, and years since immigration (Borjas,
1987), we are unable to decompose the immigrant-native earnings dierence into separate eects
for the birth cohort (which may reect time variation in the quality of education), the immigration
entry cohort (which may reect time-varying conditions that shape the pattern of selection into
international migration), and years since immigration (which may pick up assimilation eects).
Still, it is instructive to examine how earnings for immigrant entry cohorts evolve over time. Tables
3 and 4 show earnings regressions run separately by year and that include indicators for gender, race,and education-age interactions. The regressions also include indicators for the immigration entry
cohort measured as the years a foreign-born individual has resided in the United States (0-5 years,
6-10 years, 11-15 years, 16-20 years, 20+ years) as of a particular year (1990, 2000, 2010-2012),
following the structure in Borjas (2013). Table 3 shows results for workers employed in non-STEM
occupations; Table 4 shows results for workers employed in STEM occupations.
Looking down column (1) in Table 3, we see how the immigrant-native earnings dierence forrecently arrived immigrants (5 or fewer years in the U.S.) compares with that for immigrants who
have longer tenure in the country (6-10 years, 11-15 years, 16-20 years, 21 plus years).

For non-

STEM immigrant workers in 2010-2012 (column 3), the wage discount relative to natives is -24.6
log points among those with 5 or fewer years in the U.S., -19.4 log points for those with 6-10 years
in the U.S., -9.6 log points for those with 11-15 years in the U.S., and -5.0 log points for those with
16-20 years in the U.S. Only for the foreign-born with 21 or more years in the United States does27

Table 3: Year-by-year earnings regressions, non-STEM

Workers in non-STEM occupations
1990
2000
(2)
(1)

2010-12(3)Foreign-born
0-5 years in US-0.289(0.007)-0.244(0.006)-0.246(0.007)

Foreign-born
6-10 years in US-0.222(0.006)-0.222(0.005)-0.194(0.006)

Foreign-born
11-15 years in US-0.104(0.006)-0.172(0.005)-0.096(0.006)

Foreign-born
16-20 years in US-0.034(0.006)-0.086(0.005)-0.050(0.006)

Foreign-born
20+ years in US

0.018(0.004)0.012(0.004)0.003(0.004)

0.165

0.135

0.181

692,417

897,896654,200

2

R
N

Note: Robust standard errors are in parethenses. Sample is full-time, fullyear workers 25-54 years old with at least a BA. Additional regressors:
dummy variables for gender, race, Census region, and five-year age
category interacted with ed. degree (BA, MA or prof. degree, PhD). Data:
1990, 2000 Census; 2010-2012 ACS. Regressions use sampling weights.

28

the wage discount relative to the native-born disappear. This pattern could be the consequence ofassimilation, as immigrants shed their earnings disadvantages relative to the native-born over time.
It could also be due to selective out-migration of immigrants, if say within any entry cohort thosewith lower earnings potential in the U.S. are those most likely to return to their home countries. Or
it could be due to decreases over time in the average ability of later immigrant cohorts relative toearlier immigrant cohorts.
Whatever the origin of the entry cohort eect on earnings, it is far dierent for workers in STEM
occupations, as seen in Table 4. In 2010-2012 (column 3), recently arrived STEM workers earn 5.7
log points less than their native-born counterparts. This dierential becomes positive for those with
6 or more years in the country, indicating that in less than a decade immigrant STEM workers begin
earning more native-born STEM workers. Again, we cannot say whether or not this pattern reects
assimilation. It could be that lower-wage immigrant workers in STEM are those most likely to beon temporary work visas that either don't get renewed or don't get converted into green cards. Or
it could be that native STEM workers are disproportionately likely to get promoted out of STEM
jobs into management positions, which may convert them into non-STEM lines of work.
Comparing across columns in Tables 3 and 4, we obtain a sense of how the earnings discountfor a particular entry cohort fairs over time.

In columns (1) and (2) of Table 3 for non-STEM

workers, we see that the -28.9 log point earnings discount earned by the cohort that entered the U.S.
between 1985 and 1990 (and so had 0-5 years in the U.S. in 1990, column 1) had fallen to 17.2 log
points in 2000 (by which point this entry cohort had 11-16 years in the U.S.). The corresponding
fall in the wage discount for the 1995-2000 entry cohortfrom 24.4 log points in 2000 (column
2) to 9.6 log points in 2010-2012 (column 3)is even larger. Thus, in contrast to Borjas (2013),
we do not see evidence consistent with the assimilation of more-educated non-STEM immigrant
workers into the U.S. labor market becoming weaker over time. Indeed, if anything assimilation of
more-educated non-STEM immigrant workers appears to be accelerating. There is not evidence ofa similar acceleration of assimilation for immigrant workers in STEM occupations.
Overall, we observe that the average immigrant earnings discount relative to native-born workers
is far smaller in STEM occupations than in non-STEM occupations, that immigrant workers in
STEM with 6 or more years in the United States have earnings parity with natives, and that theprocess of earnings assimilation for immigration entry cohorts is uneven across time.

29

Table 4: Year-by-year earnings regressions, STEM

Workers in non-STEM occupations
1990
2000
(2)
(1)

2010-12(3)Foreign-born
0-5 years in US-0.289(0.007)-0.244(0.006)-0.246(0.007)

Foreign-born
6-10 years in US-0.222(0.006)-0.222(0.005)-0.194(0.006)

Foreign-born
11-15 years in US-0.104(0.006)-0.172(0.005)-0.096(0.006)

Foreign-born
16-20 years in US-0.034(0.006)-0.086(0.005)-0.050(0.006)

Foreign-born
20+ years in US

0.018(0.004)0.012(0.004)0.003(0.004)

0.165

0.135

0.181

692,417

897,896654,200

2

R
N

Note: Robust standard errors are in parethenses. Sample is full-time, fullyear workers 25-54 years old with at least a BA. Additional regressors:
dummy variables for gender, race, Census region, and five-year age
category interacted with ed. degree (BA, MA or prof. degree, PhD). Data:
1990, 2000 Census; 2010-2012 ACS. Regressions use sampling weights.

30

6 Discussion
The United States has built its strength in high technology in part through its businesses having
access to exceptional talent in science and engineering.

Although U.S. universities continue todominate STEM disciplines globally, it is individuals born abroad who increasingly make up the
U.S. STEM labor force, particularly among those with advanced degrees. In software development
and programming, and other computer-related occupations, the foreign born make up the majorityof U.S. workers in STEM jobs with a master's degree or higher. The success of Amazon, Facebook,
Google, Microsoft, and other technology standouts thus seems to depend, at least partially, on theability of the U.S economy to import talent from abroad. In the press, it is entry-level programmers
from abroad admitted under H-1B visas won by foreign outsourcing shops who draw much of theattention. In the data, what catches the eye is the strong and rising presence of foreign-born master's
and doctorate-degree holders in STEM elds, whose training, occupational status, and earnings put
them in the highest rungs of the U.S. skill and wage distributions.
It is little wonder why high-skilled workers from lower-wage countries desire to move to the UnitedStates to make their careers. Earnings for technology workers from India rise by a factor of six when
individuals succeed in obtaining a U.S. work visa (Clemens, 2010).

Grogger and Hanson (2011)

show that the absolute reward for skill in the U.S. labor-market is substantially higher than in otherhigh-income countries (either in pre-tax or post-tax terms).

Although foreign-born workers earn

less than their native-born counterparts with similar demographic characteristics and educational
attainment, the wage discount for immigrants in STEM jobs is substantially smaller than in nonSTEM jobs. Immigrants in STEM occupations with ten or more years of experience in the UnitedStates earn equal to or more than native-born workers doing similar tasks. The data thus provide
little support for the claim made by critics of U.S. immigration policy that foreign-born workers in
STEM jobs accept persistently lower wages than their native-born counterparts.
Our understanding of immigration and its impacts on the U.S. economy is limited by the scarcity
of data at the individual level regarding how workers gain entry into the U.S. labor market. We arelargely unable to distinguish among workers who arrive on family-based visas, employer-sponsoredvisas, student visas, or H-1B visas or how these individuals may transition from temporary visa
status into permanent residence.

These shortcomings in the data impede analysis of how shocks

to foreign economies or changes in U.S. immigration policy aect the supply of high-skilled foreignlabor in the United States. Relaxing these data constraints is essential for the informed study of how
high-skilled immigration aects U.S. economic outcomes, including the pace of productivity growth,31the earnings premium commanded by highly skilled labor, and dierential wage and employment
growth across local labor markets in the United States.

References
Acemoglu, Daron. 2002. Directed Technical Change. Review of Economic Studies, 69(4): 781-809.
Beaudry, Paul, David A. Green, and Benjamin M. Sand.

2013.

The Great Reversal in the

Demand for Skill and Cognitive Tasks. NBER Working Paper No. 18901.
Blanchard, Emily, John Bound, and Sarah Turner.
Country-Specic Shocks in U.S. Doctorate Education.

2009.

Opening (and Closing) Doors:

University of Michigan Population Stud-

ies 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
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.
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.
Freeman, Richard. 2005. Does Globalization of the Scientic/Engineering Workforce Threaten
U.S. Economic Leadership? NBER Working Paper No. 11457.
Goldin, Claudia and Lawrence F. Katz.

2008.

The Race Between Education and Technology.

Cambridge, MA: Harvard University Press.
Gandal, Neil, Gordon Hanson, and Matthew Slaughter. 2004. "Technology, trade, and adjustment to immigration in Israel." European Economic Review, 48(2): 403-428.
Grogger, Jerey, and Gordon H. Hanson. 2011. Income Maximization and the Selection andSorting of International Migrants, Journal of Development Economics, 95(1): 42-57.
Grogger, Jerey, and Gordon H. Hanson. 2013. Attracting Talent: Location Choices of ForeignBorn PhDs in the US. NBER Working Paper No. 18780.
Hanson, Gordon H. 2009.

The Economic Consequences of International Migration, Annual

Review of Economics , 1: 179-208.

Hira, Ron. 2010. The H-1B and L-1 Visa Programs: Out of Control. Economic Policy InstituteBrieng Paper No. 280.
Hunt, Jennifer, and Marjolaine Gauthier-Loiselle. 2010. How Much Does Immigration Boost
Innovation? American Economic Journal: Macroeconomics, 2(2): 31-56.

32

Hunt, Jennifer. 2011. Which Immigrants Are Most Innovative and Entrepreneurial? Distinctions by Entry Visa. Journal of Labor Economics, 29(3): 417-457.
Jaeger, David A., and Marianne E. Page. 1996. Degrees Matter: New Evidence on SheepskinEects in the Returns to Education. Review of Economics and Statistics, 78 (4): 733-740.
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.

Kerr, Sair Pekkala, William R. Kerr, and William F. Lincoln. 2015. "Skilled Immigration andthe 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.
Lan, Xiaohuan.

2011.

The Prevalence of Low-Paid Postdoctoral Positions:

The Impact ofForeign PhDs on the Wage of Natives. Manuscript, April.
Langdon, David, George McKittrick, David Beede, Beethika Kahn, and Mark Doms.

2011.

STEM: Good Jobs for Now and for the Future. U.S. Department of Commerce, No. 03-11.
Lewis, Ethan. 2011. Immigration, Skill Mix, and Capital-Skill Complementarity.

Quarterly

Journal of Economics 126(2): 1029-1069.

Lin, Jerey. 2011. Technological Adaption, Cities, and New Work. Review of Economics andStatistics, 93(2): 554-574.

Matlo, Norman. 2013. Are Foreign Students the `Best and the Brightest' ? Data and Implications for Immigration Policy. Economic Policy Institute Brieng Paper No. 356.
Moser, Petra, Alessandra Voena, and Fabian Waldinger. 2012. German Jewish Émigrés andU.S. Invention. Manuscript.
National Academy of Sciences, National Academy of Engineering, and Institute of Medicine.
2010. Rising Above the Gathering Storm, Revisited: Rapidly Approaching Category 5. Washington,
DC: The National Academies Press.
National Governors Association. 2011. Building a Science, Technology, Engineering, and Math
Education Agenda. Washington, DC: NGA Center for Best Practices.

National Research Council of the National Academies.

2012.

Rising to the Challenge:

U.S.

Innovation Policy for the Global Economy. Washington, DC: The National Academies Press.

National Science Board. 2012. Science and Engineering Indicators 2012. Arlington, VA: National Science Foundation (NSB 12-01).
Oce of Immigration Statistics. 2014. 2013 Yearbook of Immigratoin Statistics. Washington,
DC: U.S. Department of Homeland Security
Park, Jin Heum. 1994. Estimation of Sheepskin Eects and Returns to Schooling Using the
Old and New CPS Measures of Educational Attainment. Princeton University Industrial Relations
Section Working Paper No. 338.
Peri, Giovanni. 2012. The Eect of Immigration on Productivity: Evidence from U.S. States.
Review of Economics and Statistics, 94(1): 348-358.

Stuen, Eric T., Ahmed Mushq 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.

33