Skilled Immigration and the
Employment Structures of US Firms
Sari Kerr
William Kerr
William Lincoln
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Disclaimer: Any opinions and conclusions expressed herein are those
of the authors and do not necessarily represent the views of the U.S.
Census Bureau. All results have been reviewed to ensure that no
con…dential information is disclosed.
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Main Objective
Hope to build a deeper view of the …rm’s role in immigration
(Was) the …rst study we know of to consider the e¤ects of
immigration using employer-employee data
Study how high skilled immigration a¤ects the employment structures
of US …rms
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Overview of Results
Total skilled employment expands with the hiring of highly skilled
immigrants
Employment expansion is larger for young skilled natives relative to
older natives
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Overview
Data
Conceptual framework
OLS Estimations
IV Estimations
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LEHD Employment Data
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LEHD Data
All private …rms and their employees
Sourced from unemployment insurance …lings
Combined with information from social security …lings
29 participating states with various start years, from 1990 to 2002,
and end year of 2008
Information for each …rm:
LEHD: establishment code, industry, total employment, payroll and
exact location
Linked to all other Census Bureau operating data
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LEHD Data
Information for each employee:
Quarterly earnings
Age, gender, and race
Citizenship status: US citizen, naturalized citizen, non-citizen
Place of birth
No information on occupation
Education is imputed
Exact location within state for …rm establishments but is imputed for
workers
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Firm Sample
Focus: major employers & patenting …rms
Sample meets one of following criteria:
Accounts for >0.05% of patents 2001-2004
Top 100 "employer name" in LBD during any year from 1990-2008
Top 100 Compustat worldwide sales or employment over full 1990-2008
period
A Fortune 200 company in 2009
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Firm Sample
Firm selection
Consider 18 states present by 1995
Drop …rms with <25% employment in LEHD states
Final group on average >50% in LEHD states
Sample: 319 …rms
Average employment is
52k workers in 18 LEHD core states
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Firm Sample
Sizeable share of activity:
Consistent with highly skewed …rm size distribution (Axtell, 2001)
34% of US patenting
10%-20% of total LEHD employment
67 million workers in total
Our baseline regressions contain 3,374 observations
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Sample Group: Employees
Skilled de…nition:
Median earnings over $50,000 in real $2008
Calculated over employment spells 1995-2008
35% of workforce earns $50k+
Aged 18-65, young-old split at 40 yrs
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Conceptual Framework
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Conceptual Framework
We are interested in looking at how changes in the employment of
skilled immigrants a¤ect changes in the employment of other groups
"Microsoft has found that for every H-1B hire we make, we add on
average four additional employees to support them in various
capacities" - Bill Gates in 2008 Congressional Testimony
We consider a simple conceptual framework that will allow us to
think about these employment patterns in a straightforward way in
terms of substitution and complementarity between di¤erent types of
workers
allow us to relate our …ndings to arguments made in the public debate
over high skilled immigration
give us guidance for empirical work
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Conceptual Framework
A …rm that makes output using two types of labor— domestic and
immigrant— with the concave production function Q = Q (LD , LI )
Positive but diminishing marginal returns to each type of labor
The concave revenue function of the …rm is R (Q, y ), with y
representing economic conditions exogenous to the …rm
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Conceptual Framework
The …rm maximizes
R (Q, y )
cD LD
c I LI
where cD is the cost for domestic workers and cI is the cost for
immigrant workers
This leads to the familiar conditions for pro…t maximization that
∂R ∂Q
∂R ∂Q
= cD and
= cI
∂Q ∂LD
∂Q ∂LI
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Conceptual Framework
Denote the change in immigrant employment by dLI , and the change
in domestic employment by dLD
Totally di¤erentiating the …rst expression above
dcD
=
∂Q
∂LD
∂R
∂Q
∂2 R ∂Q
∂Q
dLD +
dLI +
2
∂Q ∂LD
∂LI
∂2 Q
∂2 Q
dLD +
dLI +
2
∂LD ∂LI
∂LD
∂Q ∂2 R
dy
∂LD ∂Q∂y
We assume that dcD /dLI = 0 and that dy /dLI = 0, given that y is
assumed exogenous
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Conceptual Framework
We can then rearrange the remaining terms to be
dLD =
h
∂Q ∂Q ∂2 R
∂L D ∂L I ∂Q 2
∂Q
∂L D
2
+
∂2 R
∂Q 2
∂R ∂2 Q
∂Q ∂L D ∂L I
+
i
dLI
∂R ∂2 Q
∂Q ∂L 2D
Given our assumptions, the denominator is positive
The relationship between dLD and dLI will be positive only if
∂2 Q
∂L D ∂L I > 0 and is su¢ ciently large to o¤set the magnitude of the
(negative) …rst term in the summation of the numerator
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Conceptual Framework
We can then rearrange the remaining terms to be
dLD =
h
∂Q ∂Q ∂2 R
∂L D ∂L I ∂Q 2
∂Q
∂L D
2
+
∂2 R
∂Q 2
∂R ∂2 Q
∂Q ∂L D ∂L I
+
i
dLI
∂R ∂2 Q
∂Q ∂L 2D
This makes sense intuitively— if domestic and immigrant worker
employment are complementary and su¢ ciently strong to overcome
the concavity of the revenue function, then we should see a positive
relationship between growth in domestic employment and growth in
immigrant employment in the data
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OLS Estimations
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Following the results from the conceptual framework we consider the
following speci…cation
∆Yf ,t = β ∆ ln(EmpfYSI
,t ) + δ ∆Xf ,t + η i ,t + εf ,t ,
Firm f, sector i, year t
ln(EmpfYSI
,t ) is the log number of young skilled immigrants employed
in year t by …rm f
Yf ,t is the outcome variable of interest
Xf ,t is a vector of …rm-year controls
η i ,t are sector-year …xed e¤ects
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Firm-Year Controls Xf ,t
Local area controls— calculate …rm’s initial employment across counties
and then weight county trends by these shares: LEHD employment,
immigrant share, and share of workers over 40 (Card)
"Supply-Push" controls— Calculate each …rm’s initial skilled immigrant
distribution across 12 geographic groups (Europe, Latin America, etc.).
Then interact this with the growth of skilled immigrants at the national
level, weighting by the initial distribution. Do the same for low skilled
workers. (Card)
Age-education controls— calculate …rm’s initial employment
distribution across 6 age-education cells (young, old; HS or less, some
college, college or more) and interact this with national growth in
skilled immigration in these categories (Borjas)
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Table: OLS Estimations
∆ Log employment of skilled worker group:
Older natives Young natives Older immigrants
∆ Log employment of
young skilled immigrants
0.564
(0.021)
0.656
(0.020)
0.709
(0.045)
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Table: OLS Estimations
∆ Log employment of
young skilled immigrants
∆ Log total
skilled emp.
∆ Older skilled
worker share
∆ Older native skilled
worker share
0.626
(0.020)
-0.031
(0.003)
-0.019
(0.003)
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IV Estimations
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Instrumental Variable Estimations
While the OLS estimations account for …xed e¤ects and a wide
variety of additional controls, there still may be omitted factors
driving the results
We now turn to an IV approach that uses large changes in national
high skilled immigration policy
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Instrumental Variable Estimations
Speci…cally, we take advantage of changes in the limit on H-1B visas
H-1B is a non-immigrant visa
Category governing high-skilled immigration
Employment in "specialty occupations"
Employer is responsible for visa application
Three-year visa, renewable once
Prevailing wage requirement
Cap on visa issuances since 1990
Computer-related and SE occupations ( 60%)
Large percentage coming from India ( 40%) or China ( 10%)
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H-1B National Trends
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Instrumental Variable Estimations
We instrument for ∆ ln(EmpfYSI
,t ) with
Depf ,t0 ∆ ln (H
1BPopt )
where Depf ,t0 is a measure of how likely they are to …nd and hire
H-1B visa holders (or the …rm’s "dependency" on high-skilled
immigrants)
The results we consider here measure the variable Depf ,t0 with the
…rm’s initial share of skilled immigrant workers that were born in India
and China.
This is similar to Card’s (2001) approach except the dependency is at
the …rm rather than city level.
It takes advantage of the fact that high skilled immigrants from these
countries are likely to go to …rms where there are already high skilled
immigrants from their own countries
The …rst stage F statistic is 32
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Table: IV Estimations Using the Chinese and Indian Worker Dependency
∆ Log employment of skilled worker group:
Older natives Young natives Older immigrants
∆ Log employment of
young skilled immigrants
0.449
(0.115)
0.740
(0.083)
0.597
(0.104)
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Table: IV Estimations Using the Chinese and Indian Worker Dependency
∆ Log employment of
young skilled immigrants
∆ Log total
skilled emp.
∆ Older skilled
worker share
∆ Older native skilled
worker share
0.632
(0.081)
-0.110
(0.022)
-0.090
(0.022)
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Instrumental Variable Estimations
We also considered two alternative instruments, interacting the
change in the log national H-1B population with
The log ratio of the …rm’s LCAs (H-1B applications) to its skilled
employment in 2001
Share of the …rm’s workforce in STEM occupations
We come to similar conclusions with these instruments
We also consider similar IV estimations controlling for changes in
medium-skilled employment. This approach is somewhat more robust
and yields similar magnitudes.
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Interpretation
If high skilled immigrants are unique inputs (especially at the high
end), then being able to hire more could expand …rm market share
and lead to greater use of citizen workers (relation to trade literature,
innovation).
It could be that immigrants and citizen workers are substitutes within
occupation categories but are complements across categories.
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Conclusions
Total skilled employment expands with the hiring of highly skilled
immigrants
Employment expansion is larger for young skilled natives relative to
older natives
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Thank You
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Appendix
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Di¢ culties in Constructing Firms
The primary basis in the LEHD for identifying employer-employee
linkages is the state employer identi…cation number (SEIN) that
identi…es individual establishments.
The BRB includes for each SEIN the associated federal EIN and
Census Bureau …rm identi…er by year.
From the BRB, we collect the SEINs that are associated with our
…rms at any point in time.
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Di¢ culties in Constructing Firms
This collection of complete SEIN records is important as …rms
occasionally change SEINs for reasons unrelated to our interests, and
these legal adjustments could otherwise be confused with actual
changes in the company’s employment dynamics.
With the collected SEINs, we then prepare the employment records
for our …rm sample.
We need each SEIN to be uniquely associated with a …rm, and
therefore we research any overlapping identi…ers and assign them to
the appropriate company.
As many of our …rms are multi-establishment companies, on average
our composite …rms contain roughly 200 SEINs.
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Firm Sample
Sample: 319 …rms
Older natives are 50% of skilled group
Younger natives are 31% of skilled group
Immigrants are 19% of skilled group
Hiring and departing rate of 13-14% per year
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Firm Sample
Sector distribution within LEHD:
Manufacturing: 30%
Wholesale and retail trade:
FIRE and services: 30%
Other sectors: 15%
25%
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OLS Robustness
Similar results when
Controlling for changes in medium skilled immigration
When considering the subsample of just top patenting …rms
Considering di¤erent weighting strategies
Using a …rm-state approach using all 29 states
Raising the threshold to 66% employment in LEHD states
Splitting the sample by the long-term growth rates of the …rms
Setting minimum employment thresholds for companies
Using alternative de…nitions of skilled workers
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IV Robustness
Similar results when
When considering the subsample of just top patenting …rms
Using a balanced panel
Dropping major M&A …rms
Dropping …rms that lobby about immigration
Splitting the sample across industries
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STEM Match
CPS collects employment data from a random group of workers in the
US every year
A bridge between the 1986-1997 CPS and LEHD has been established
Ascertain the occupations of over 25k workers in our …rm sample at
the time of their inclusion in the CPS survey
Share of the …rm’s workforce in STEM occupations measured in the
…rst three years where matched employees are observed, which may
be later than the typical initial period. Winsorize these shares at the
5% and 95% values.
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Table: IV Estimations Using STEM Occupation Share Dependency
∆ Log employment of skilled worker group:
Older natives Young natives Older immigrants
∆ Log employment of
young skilled immigrants
0.330
(0.261)
0.630
(0.170)
0.360
(0.297)
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Table: IV Estimations Using STEM Occupation Share Dependency
∆ Log employment of
young skilled immigrants
∆ Log total
skilled emp.
∆ Older skilled
worker share
∆ Older native skilled
worker share
0.583
(0.167)
-0.140
(0.057)
-0.104
(0.049)
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Table: IV Estimations Using the Chinese and Indian Worker Dependency with
Medium Skilled Workforce Control
∆ Log employment of skilled worker group:
Older natives Young natives Older immigrants
∆ Log employment of
young skilled immigrants
0.442
(0.098)
0.736
(0.077)
0.591
(0.098)
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Table: IV Estimations Using the Chinese and Indian Worker Dependency with
Medium Skilled Workforce Control
∆ Log employment of
young skilled immigrants
∆ Log total
skilled emp.
∆ Older skilled
worker share
∆ Older native skilled
worker share
0.627
(0.071)
-0.112
(0.018)
-0.092
(0.020)
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Table: IV Estimations Using the Chinese and Indian Worker Dependency with
Medium-Skilled Workforce Control and H-1B Cap Summations
∆ Log employment of skilled worker group:
Older natives Young natives Older immigrants
∆ Log employment of
young skilled immigrants
0.423
(0.109)
0.785
(0.091)
0.619
(0.135)
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Table: IV Estimations Using the Chinese and Indian Worker Dependency with
Medium Skilled Workforce Control and H-1B Cap Summations
∆ Log employment of
young skilled immigrants
∆ Log total
skilled emp.
∆ Older skilled
worker share
∆ Older native skilled
worker share
0.654
(0.078)
-0.130
(0.024)
-0.116
(0.026)
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