Lecture 2 The Labor Market Effects of Immigration Giovanni Peri

Lecture 2
The Labor Market Effects of Immigration
Giovanni Peri
(University of California, Davis)
Jornadas Sobre Integracion Economica,
Valencia December 2-4, 2010
1
Valencia, December 2-4, 2010
The very Debated “Distributional” Question
 What is the impact of immigrants on real wage inequality of
workers in the receiving country?
 In particular:
 Focus on the “Unskilled” = Less Educated
 Where? At the national level or at the local (city-state) level?
 The main Idea: A National-Market model, in line with the Labor
literature.
 Include the response of physical Capital, productivity and average
wage compatible with the macro analysis
 Model (production function) of reference also compatible with
labor-macro-growth
2
Valencia, December 2-4, 2010
Percentage of foreign-born in Employment
by Education group, 1960-2000
18
16.1
High School Diploma or less
16
Some College or more
14
11.9
12
Percentage
10.7
10
8.8
8
7.7
7.3
6.4
6
5.4
7.1
5.8
4
2
0
1960
1970
1980
Valencia, Decemberyear
2-4, 2010
1990
2000
3
The recent debate in the Literature
 Borjas (2003), - B 2003- Borjas and Katz (2007)-BK 2007- using
national Data and an aggregate production function approach obtain:
 Negative 3 to 4% effect in the long-run on real wage of unskilled
(=high school dropouts) 1980-2000.
 Card (2001), (2007), (2009), Cortes (2006), Kugler and Yuskel (2006)
Lewis and Card (2007). Using cross-cities or cross-states evidence
obtain:
 Between 0 and negative 1% in the long-run on real (or relative)
wages of unskilled (defined sometimes using education and some
times using education-occupation combination)
 No evidence of displacement effect on employment/population.
4
Valencia, December 2-4, 2010
Our Main Results
 Workers with no degree and workers with high school degree
turn out to be close substitutes, this dilutes much the
competition-effect of immigrants.
 Immigrants and natives with similar education and age have
a small but significant degree of imperfect substitution. This
reduces even further the competition with natives and
explains the relative deterioration of immigrant wages
relative to natives.
 The wage effects on natives are small and positive and on
less educated natives are essentially 0.
5
Valencia, December 2-4, 2010
σHL σLL σHH elasticity across schooling groups
σHL =elasticity of substitution across experience
σIMMI =elasticity of substitution US-Foreign born
Production structure:
Schooling
0-5 5-10
Experience
σEXP
Some High
School
Hkj
High School
Graduates
σLL
σEXP
γ
Fkj σIMMI
δ
Some College
δ=2
College
Graduates
6
35-40
Valencia, December 2-4, 2010
Immigrants
σHL
σHH
Physical
Capital
Role of the model
 Such a rich skill structure allows estimation of parameters with more
observations if some structure is imposed.
 It is impossible to estimate freely own and cross effects: there are 992 cross
effects and with data 1960-70-80-90-2000 and 2006 over 32 skill groups
there are 192 observations.
 First structure a CES nesting. Estimate the few elasticity in the CES. Then
use those and change in supply in each cell to produce the effect on
marginal productivity (wages).
 Adjust the average wage to account for capital response.
7
Valencia, December 2-4, 2010
8
Valencia, December 2-4, 2010
Model: Production Function
Aggregate Production combines
capital and Labor (used in Growth
since Solow)
CES of high and low educated, split at High school or less and
some college or more. Used in Labor, Katz-Murphy 1992;
Growth Caselli and Coleman 2006
Instead of assuming:
High educated nest college graduate
and some college
Less educated nest some high school
and high school diploma
Much less used- Goldin and Katz
2007
Used by B (2003) and BK (2007)
which implies the restriction:
9
Valencia, December 2-4, 2010
Model
Symmetric nest of 8 experience groups (as in
Card and Lemieux 2001, Welch 1979)
Nest between native and immigrants
First introduced by OP (2006)
Beauty of the model: assuming that immigration does not change the productivity parameters,
once we have the elasticity (σ’s) and we know the inflow of immigrants as percentage of their
initial labor supply we can calculate the effect on marginal productivity (wage) of each group.
We assume full adjustment of capital and, for now, no effect on TFP.
10
Valencia, December 2-4, 2010
Capital and average wages
 Average wage:
Capital-labor ratio
In any model (Solow, Ramsey, Ramsey in open economy) and for the US data the capital
labor ratio grows at a constant rate in the long run at a rate depending only on TFP growth
and the real return to capital r is constant
Average wage and capital-labor ratio in the long run do not
depend on labor supply
Capital adjustment in the long run ensures that the immigration effects on average wages is
null.
11
Valencia, December 2-4, 2010
Using the Model
 We derive estimating equations for each of the elasticity σΗΗ σHL σHH σEXP
σΙΜΜΙ and using immigration as a supply shock, where possible, we estimate
them on data from 1960-70-80-90-2000 Census and 2006 ACS.
 Where limitation of data does not allow a credible estimate
 we use CPS annual data to estimate elasticity.
 We use best existing estimates as reference
 We then calculate (simulate) the long-run effects of immigration 1990-2006
on wages of each group and show the differences in our estimates with
Borjas 2003.
12
Valencia, December 2-4, 2010
Data
 IPUMS samples: Census 1% (1960-70), Census 5% (1980-90-2000), ACS
1% (2006).
 Variables:
 Wages: weekly real wages (CPI adjusted). Average by cell calculated either on




13
full-time workers or weighting individual wages by PERWT times hours worked.
Hours worked: sum in each cell over all workers with positive weeks and hours
worked.
Education: Some high school, High school degree, some college, College degree
Experience: 1 to 40 divided in groups of 5 years.
Immigrants: non-citizens or naturalized citizens.
Valencia, December 2-4, 2010
Estimates of σIMMI
From the Model
Implemented
The education-experience-year specific demand factors are eliminated by the ratio.
192 observations.
14
Valencia, December 2-4, 2010
Other Evidence on Imperfect substitution Native-Immigrants
 Card (2009) Estimates 1/ σΙΜΜΙ around 0.05
 Manacorda et al. (2007) on UK estimate the parameter around 0.15.
 D’Amuri et al (2009), Felbermayr et al (2008) on Germany estimate the
parameter around 0.06
 Borjas et al (2008) estimate the inverse elasticity around 0.05 but not
significant.
 There is previous extensive evidence that the impact of immigrants for a skill
group is larger on previous immigrants than on natives (Card 2001, literature
review by Longhi Nykamp and Poot 2007).
15
Valencia, December 2-4, 2010
Ln(Wage immigrants/Wage natives)
-.3
-.2
-.1
0
.1
Figure 5
Correlation between relative Immigrant-Native wages and hours worked.
Slope 0.05
Std. error: 0.007
-5
-4
-3
-2
-1
Ln(Hours immigrants/Hours natives)
Education-Experience-Year Group
WLS regression Line
16
Valencia, December 2-4, 2010
0
Ln(Immigrant Wages/Native Wages)
-.2
-.1
0
.1
.2
Figure 6
Partial correlation between relative Immigrant-Native wages and hours worked
Slope 0.06
Std. error: 0.008
-2
-1
0
1
Ln(Immigrant hours/Native hours)
Education_Experience-Year Groups
WLS (with fixed effects) regression line
17
Valencia, December 2-4, 2010
2
18
Valencia, December 2-4, 2010
Reasonable estimates of 1/ σΙΜΜΙ
 All in all there seem to be evidence compatible with small but significant
imperfect substitution, with 1/ σΙΜΜΙ =0.05 and σΙΜΜΙ =20.
 Possibly for less educated 1/ σΙΜΜΙ =0.10
 Does this make a difference relative to perfect substitution? Mainly for
less educated natives and old immigrants.
19
Valencia, December 2-4, 2010
Other elasticity parameters
 We estimate all the other elasticity parameters (between
schooling and age groups) using estimating equations derived
from the model and we compare them with the literature.
 We obtain reasonable values, compatible with previous
estimates.
20
Valencia, December 2-4, 2010
Capital adjustment in the short-run
 Immigrants entered the country in flows always less than 0.5% of the
employment in each year.
 Investment adjusted continuously.
 K/L is trend stationary and K/Y stationary
 Our international analysis shows no sign of changes in K/L even in the
short-run
 We will consider the long-run effect
21
Valencia, December 2-4, 2010
Figure 1
U.S. Capital-Output Ratio 1960-2006
3.500
3.000
2.500
2.000
k/y
capital/output ratio in the U.S.
average
1.500
1.000
0.500
22
Valencia, December 2-4, 2010
year
20
06
20
04
20
02
20
00
19
98
19
96
19
94
19
92
19
90
19
88
19
86
19
84
19
82
19
80
19
78
19
76
19
74
19
72
19
70
19
68
19
66
19
64
19
62
19
60
0.000
Figure 2
Log Capital-Labor Ratio and Trend 1960-2006
0.4
0.35
0.3
Ln(Capital/Labor)
0.25
0.2
0.15
0.1
Ln(Capital/Labor)
Trend
0.05
-0.05
Years
23
Valencia, December 2-4, 2010
20
05
20
03
20
01
19
99
19
97
19
95
19
93
19
91
19
89
19
87
19
85
19
83
19
81
19
79
19
77
19
75
19
73
19
71
19
69
19
67
19
65
19
63
Ye
ar
19
61
0
Comparison with previous effects
(Borjas and Katz 2007)
 Simulate the effects of immigration 1990-2006 on wages.
 Use estimated parameters.
 BK assume perfect substitutability native-immigrants
 BK do not differentiate in the elasticity across education
groups.
 We reconcile the national estimates (close to 0 for unskilled
and skilled) with the local ones by card 2009
24
Valencia, December 2-4, 2010
25
Valencia, December 2-4, 2010
26
Valencia, December 2-4, 2010
Our preferred calculations say
 Immigrants 1990-2006, had a small positive effect on real wages of
native workers with no high school of around 0.6%, not significantly
different from 0 .
 The distributional effect of immigrants was similarly very small as
highly educated only gained 0.3%.
 The overall average wage of US workers had a 0.6% real gain in the
long-run also not significantly different from 0. Long-term immigrants,
however suffered a wage was of 6% due to immigration 1990-2006.
27
Valencia, December 2-4, 2010
Why are Native-Immigrants, especially less
educated imperfect substitutes?
 Key Point: Immigrants and native-born workers differ from each other
in skills and performed productive tasks. Even among workers with low
education:
 Immigrants specialize in manual/physical tasks.
 US natives specialize in language/communication intensive tasks.
 This limit their wage competition and increases their productive
complementarities. These specialization effects will differ across states
as states received very different amounts of immigrants
 Use cross-state variation in the US
28
Valencia, December 2-4, 2010
Percentage of foreign-born in Employment
US States, Highest and Lowest 1960-2004
Percentage of foreign-born in Employment: 1960-2005
35.00%
31.98%
California
33.17%
U.S.A.
30.00%
West Virginia
24.59%
25.00%
20.00%
Percentage
16.06%
15.00%
10.00%
13.00%
9.40%
10.00%
5.90%
5.00%
0.00%
29
0.30%
1960
Valencia, December
2-4, 2010
5.10%
0.20%
1970
14.40%
8.90%
6.40%
0.50%
1980
0.80%
1990
Year
1.00%
2000
1.10%
2004
A simple model of Demand and Supply of Tasks
 US states: open economies, each produce the same perfectly
tradable final good.
 YL. produced by less educated, L and YH produced by more
educated workers, H. They are combined in a final Consumption
Good.
 Each type of workers perform production tasks to produce the
intermediate good
 Less educated: Manual and Communication
 More educated: Analytical/Managerial
30
Valencia, December 2-4, 2010
Relative demand of tasks for the aggregate economy
 Positively depending on relative task productivity
 Negatively depending on relative task compensation
31
Valencia, December 2-4, 2010
Supply of tasks among less educated
Less educated workers are heterogeneous: Domestic (D) or ForeignBorn (F)
Each of them splits one unit of labor endowment in Communication and
Manual tasks. Calling lj the share of labor supplied in manual tasks, by
individual j The effective units of Communication and Manual tasks
supplied are
Where m and z are the effectiveness in performing manual and
communication services and δ<1
Comparative advantages:
Labor income:
32
Valencia, December 2-4, 2010
Supply of tasks among less educated
 By maximizing wage income with respect to the choice of lj we
obtain the relative supply of each type j:
 Each allocation corresponds to an occupation in a continuum. The
choice of occupation fully reveals relative productivity. We
measure effective supply of skill by occupation.
 At any relative wage level immigrant supply relatively less c/m
(i.e they are in occupations with low c/m)
33
Valencia, December 2-4, 2010
Aggregate Task supply
 Relative task supply
34
Valencia, December 2-4, 2010
Figure 1
Relative Communication/Manual Task Supply and Demand
Aggregate
Relative
Demand
ln(wC/wM)
Relative Supply,
Foreign-Born
Aggregate Relative
Supply
Relative Supply,
Native-Born
Decrease in
(CF/MF)
E1
*
*
ln(w C/w M)1
D1
E0
ln(w*C/w*M)0
F0
D0
Increase in f
or Drop in
(CF/MF)
35
Valencia, December 2-4, 2010
New
ln(C*/M*)1
Initial
ln(C*/M*)0
ln(CD/MD)0 ln(CD/MD)1
ln(C/M)
Testable Equilibrium Implications of immigration on native
workers
 Foreign-born workers supply relatively more manual versus
communication tasks than domestic workers.
 A higher share of foreign-born workers induces higher supply of
communication relative to manual tasks by native workers
 A higher share of foreign-born induces higher compensation paid to
communication relative to manual tasks ,
36
Valencia, December 2-4, 2010
Log linearizing the equilibrium conditions and the
relative demand:
γ>0
θL >0
37
The parameters an be estimated if changes in the share of less educated
immigrants are exogenous
Valencia, December 2-4, 2010
Data
 On individual characteristics, wage, schooling, race, gender, occupation
they are from Census IPUMS 1960-2000.
 Measures of skills: US Department of Labor's O*NET abilities survey. This
dataset assigns numerical values to describe the importance of 52 distinct
employee abilities (which we refer to as "tasks" or "skills") within each
occupation. Then we attach them to individual over time using
homogenized occupation codes
 We use all the variables in the O*NET abilities dataset – standardize each of
them between 0 and 1 using its percentile value in 2000. Then we aggregate
them in different ways to construct a summary measure for manual and
communication skills.
.
38
Valencia, December 2-4, 2010
Skill Variables and Datasets
Type of Skill
Manual (or
Physical) Skills
Definition
Basic Definition:
Movement and
Strength
Extended
definition: Includes
sensory-perceptions
skills
Communication
(or Language)
Skills
Basic Definition:
Oral and Written
Extended
Definition:
Includes CognitiveAnalytical-Vocal
Skill Sub-Type
Limb-Hand-Fingers
Dexterity
O*NET Variables
Arm-Hand Steadiness; Manual Dexterity; Finger Dexterity;
Control Precision; Multilimb Coordination; Response
Orientation; Rate Control; Reaction Time; Wrist-Finger
Speed; Speed of Limb Movement
Body CoordinationFlexibility
Strength
Extent Flexibility; Dynamic Flexibility; Gross Body
Coordination ; Gross Body Equilibrium.
Static Strength; Explosive Strength; Dynamic Strength;
Trunk Strength; Stamina.
Perceptual Speed; Spatial Orientation; Visualization;
Selective Attention; Time Sharing.
Near Vision; Far Vision; Visual Color Discrimination; Night
Vision; Peripheral Vision; Depth Perception; Glare
Sensitivity.
Hearing Sensitivity; Auditory Attention; Sound Localization.
Oral Comprehension; Oral Expression
Written Comprehension; Written Expression
Fluency of Ideas; Originality; Problem Sensitivity; Category
Flexibility; Mathematical Reasoning; Number Facility;
Deductive Reasoning; Inductive Reasoning; Information
Ordering; Memorization; Speed of Closure; Flexibility of
Closure
Speech Recognition; Speech Clarity
General Perception
Visual Perception
Hearing Perception
Oral
Written
Cognitive-Analytical
Vocal
39
Valencia, December 2-4, 2010
Table 1
Occupations, Relative Task Intensity, and
Changes in the Foreign-Born Share of Less-Educated Employment
Change in ForeignBorn Share of LessC/M
Educated
Communication
Manual
Percentil
Occupation
Employment 1970Intensity Index Intensity Index
e
2000
(Percentage Points)
Four Occupations with Highest Communication/Manual Values
Financial managers
0.83
0.23
0.999
+5.7
Managers of properties and real estate
0.74
0.21
0.997
+1.8
Editors and reporters
0.87
0.27
0.991
+12.2
Operations and systems researchers and
analysts
0.64
0.20
0.990
+4.1
Five Occupations with Average Communication/Manual Values
Cashiers
0.38
0.73
0.562
+12.0
Cooks, variously defined
0.32
0.67
0.530
+19.9
Hairdressers and cosmetologists
0.30
0.62
0.498
+17.0
Repairers of industrial electrical equipment
0.36
0.77
0.490
+9.5
Kitchen workers
0.28
0.62
0.489
+2.8
Four Occupations with Lowest Communication/Manual Values
Vehicle washers and equipment cleaners
0.04
0.72
0.021
+20.6
Furniture and wood finishers
0.01
0.72
0.021
+13.4
Roofers and slaters
0.01
0.64
0.020
+26.4
Valencia, December
Drywall installers
0.00 2-4, 2010 0.72
0.006
+24.2 40
ln(C/M) native workers, 2000
-1.1
-1
-.9
-.8
-.7
Figure 3
Share of Immigrants and the Relative C/M Supply of Natives, Less Educated
-1.2
California
0
West Virginia
.1
.2
.3
.4
S h a r e im m ig r a n ts a m o ng l es s ed uc a te d , 2 0 0 0
.5
F itte d v a lu e s
U .S . s ta te s p lu s D .C .
Note: The fitted lines are from a weighted least square regression (weights equal
to employment of less educated in the state): slope=0.68 standard error=0.10
Valencia, December 2-4, 2010
41
First Test: A higher foreign-born share (s) of less-educated workers in an
economy induces higher provision of communication relative to manual
tasks among less-educated native workers
γ>0
γC >0
γM <0
42
Valencia, December 2-4, 2010
Table 2
Foreign-Born Workers and the Native Supply of Tasks
Workers with a High School Degree or Less
Explanatory Variable: Foreign-Born Share of Workers with a High School Degree or Less
(1)
(2)
(3)
(4)
(5)
Communication Definition:
Basic
Basic
Basic
Manual Definition:
Basic
Extended
Basic
Method of Estimation
WLS
Additional Controls:
State and Year Fixed
Effects
Dependent Variables:
Ln(CD/MD)
γ
Basic
Extended
2SLS using
Imputed Mexican Share,
Geographic Variables as
Instruments
State and Year Fixed Effects
(6)
Basic
Basic
Basic
Extended
2SLS using
Imputed Mexican Share, Geographic
Variables as Instruments
State and Year Fixed Effects, Computer
Use, Sector-Driven C/M
0.34**
(0.05)
0.31**
(0.04)
0.37**
(0.05)
0.33**
(0.04)
0.51**
(0.04)
0.44**
(0.04)
Ln(cD)
γC
0.31**
(0.03)
0.31**
(0.04)
0.33**
(0.05)
0.33**
(0.04)
0.43**
(0.04)
0.43**
(0.04)
Ln(mD)
γΜ
-0.03
(0.02)
0.00
(0.02)
-0.04**
(0.02)
0.00
(0.02)
-0.08**
(0.03)
-0.01
(0.04)
First Stage
43
Joint F-Test of the
Instruments (p-value)
NA
NA
18.9
(0.00)
18.9
(0.00)
6.90
(0.00)
6.90
(0.00)
Test of Over-Identifying
Restrictions
NA
NA
12.5
13.2
10.2
10.4
Probability (χ2 > test)
under the Null of
Instrument Exogeneity
NA
NA
0.14
0.11
0.25
0.24
Valencia,
December 2-4, 2010
Number
of Observations
255
255
255
255
255
255
Instruments: Mexican Immigrants’ Supply Shock
 We Instrument the share of less educated immigrants with the imputed
share of Mexican (based on 1960 distribution by state and national
trends).
 Mexican were located unevenly across states in 1960
 Immigration of less educated from Mexico boomed 1960-2000
 States with large initial share of Mexican experienced larger supplydriven increase in less educated workers (enclave preference).
 Alternatively: use proximity to the Mexican Border (distance, distance
squared and border dummy) interacted with decade dummies, as factor
affecting inflow of immigrants but not demand.
44
Valencia, December 2-4, 2010
Test 3: A higher foreign-born share (s) of less-educated workers induces
high compensation paid to communication relative to manual tasks
 How complementary are the two types of tasks?
Compensation for each task in each labor market is estimated from
average occupational wages in occupation j and state s. As follows:
45
We evaluate state-year specific returns to skills, wM and wC by running
occupation-state wages on occupation-specific skill intensity allowing different
coefficients
by 2-4,
state.
Valencia, December
2010
Figure 5
Share of Less Immigrants and the Compensation of C relative to M Skills, less educated
log of relative compensation w_C/w_M
-.4
-.2
0
.2
.4
.6
U.S. states in year 2000
0
.1
.2
.3
.4
Share of Immigrants among less educated workers, 2000
U.S. state plus D.C 2000
Fitted values
46
Valencia, December 2-4, 2010
.5
Table 3
Foreign-Born Workers, Aggregate Supply of Tasks and Communication-Manual Wage Elasticity
Workers with a High School Degree or Less
(1)
(2)
(3)
(4)
Communication Definition:
Basic
Basic
Basic
Basic
Manual Definition:
Basic
Extended
Basic
Extended
2SLS using Imputed Mexican Share
Method of Estimation:
WLS
and, Geographic Variables as
Instruments
Additional Controls State and Year Fixed Effects
State and Year Fixed Effects
Panel B:
Explanatory Variable: Ln(C/M)
Dependent Variable: Ln(wc/wM)
Estimated relative wage elasticity:
-1/θL
-0.75*
(0.37)
Implied Elasticity of Substitution
Joint F-Test of the
Instruments (p-values)
Number of Observations
-0.70
(0.39)
-1.58**
(0.26)
-1.36**
(0.32)
1.33
0.63
0.73
255
11.4
(0.00)
255
11.4
(0.00)
255
1.42
First Stage
NA
NA
255
Use variables predicting share of immigrants to instrument for relative C/M
Elasticity of Substitution implied: between 0.63 and 1.42
47
Valencia, December 2-4, 2010
Effects on real wages of natives of 1990-2000 immigration
 Using the estimated response in task supply (Table 2) and the average
estimated elasticity of substitution (Table 3) between tasks plus wages wM
and wC we can simulate the effect of immigration on wages of less educated
US workers in each state
Effect on compensation, weighted at
domestic worker average
48
Valencia, December 2-4, 2010
Shift in task supply, weighted at
task compensation
Table 12
The Simulated Effects of Immigration on Native Wages and Task Compensation, 1990-2000
(1)
%Δ HighlyEducated due to
Immigration
(2)
(3)
%Δ Less%Δ in
Educated Wage Paid
due to
to HighlyImmigration Educated
Workers
Selected States
8%
29%
Arizona
12%
24%
California
6%
10%
DC
14%
14%
Florida
7%
8%
Hawaii
7%
12%
Illinois
16%
34%
Nevada
13%
10%
New Jersey
10%
13%
New York
8%
22%
Texas
United States
6% 2-4, 20109%
Valencia, December
49
3.2%
1.5%
0.5%
-0.1%
0.1%
0.8%
3.5%
-0.6%
0.3%
2.1%
0.6%
(4)
%Δ in
Manual
Task
Return
-14.2%
-8.4%
-3.3%
-2.3%
-3.4%
-3.5%
-12.0%
-0.4%
-2.5%
-8.8%
-2.8%
(5)
(6)
%Δ in
%Δ in Wage
Communication
of LessTask Return
Educated,
Assuming
Perfect
NativeImmigrant
Substitution
-1.3%
0.3%
-0.4%
2.8%
4.9%
0.7%
1.4%
3.7%
1.1%
0.0%
1.2%
-8.2%
-4.5%
-2.0%
0.2%
-0.3%
-1.8%
-5.8%
1.6%
-0.7%
-4.8%
-1.2%
(7)
(8)
%Δ in
%Δ
Wage of
Change in
LessWage Paid
Educated
to Lessdue to Task Educated
Complemen Natives
tarities and
Specializati
on
2.5%
-5.7%
2.3%
-2.2%
1.9%
-0.1%
1.2%
1.4%
0.9%
0.6%
1.3%
-0.5%
2.2%
-3.6%
1.3%
2.9%
1.6%
0.9%
1.8%
-3.0%
0.9%
-0.3%
What About Other Countries?
 Using the similar structure we can evaluate the wage effects
of immigration 1990-2000 in European Countries (Docquier,
Ozden and Peri 2010).
 Add also the effects of emigration.
 Add the possibility of a human capital externality, hence a
productivity effect of highly skilled
50
Valencia, December 2-4, 2010
Immigration as % of nationals
51
U.S.
Canada
Australia
U.K.
Belgium
France
Germany
Greece
Italy
Netherlands
Portugal
Spain
Sweden
Czech R.
Hungary
Poland
Turkey
Mexico
Low Education
5.8
0.8
-0.6
0.4
1.7
0.1
2.2
0.2
0.9
1.3
1.3
2.7
1.5
-0.1
-0.2
-1.1
0.3
0.0
Valencia, December 2-4, 2010
High education
4.4
8.0
10.6
8.5
4.4
2.8
3.1
0.2
0.8
5.1
1.9
3.8
5.1
3.9
0.1
-0.7
3.1
0.6
Emigration as % of nationals
Low Education
0.0
-1.0
0.3
-0.7
-0.2
0.3
-0.1
-0.3
-0.5
0.0
2.1
-0.2
0.3
0.6
0.0
-0.3
1.8
7.8
High education
0.2
1.2
1.3
5.0
2.5
1.4
1.2
3.5
1.3
2.3
8.9
2.1
1.8
1.2
0.3
5.6
2.7
11.2
Simple aggregate representation of
Production
• Total Factor Productivity
• Aggregate of effective labor
• Stock of Physical Capital
• Long run. We assume that returns to capital are equalized (open
economy) or that they depend on savings or discount rates we have:
52
Valencia, December 2-4, 2010
…continued
 Substituting and solving out the capital stock, output is linear in
the effective labor composite
Modified TFP, increasing function of TFP and return
to capital
53
Valencia, December 2-4, 2010
Labor Aggregate:
 Qh and Qh are the aggregate employment of highly educated
(College graduates) and less educated (High School graduates
and less). σq is the high-low educated elasticity.
 The specification above is consistent with the papers above,
simply eliminate for simplicity the age dimension.
54
Valencia, December 2-4, 2010
Native and Immigrant Labor
 Ns, Is are natives and immigrants (of schooling s) and σI is
their elasticity of substitution.
 Consistent with the recent immigration literature: Ottaviano
and Peri (forthcoming), Manacorda, Manning and Wadsworth
(forthcoming), Borjas and Katz (2007).
55
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Wages
 Considering wages as equal to the marginal productivity of
labor, we can calculate the wages of non-migrant nationals.
56
New immigrants affect the wages through the aggregates Q,
new emigrants through Q and N
Valencia, December 2-4, 2010
Experiment and Counterfactual
 To evaluate the effect of immigration:
 Calculate the wages of native non movers in 2000 and the
counterfactual wage keeping stock of immigrants at levels of
1990. Take the difference and express it as percentage of wage
value.
To evaluate the effect of emigration:
Calculate the wages of native non-movers in 2000 and the
counter-factual wages including among them those who
emigrated between 1990 and 2000. Take the difference and
express it as percentage of 1990 value.
57
Valencia, December 2-4, 2010
Externality of schooling
 Following Moretti 2004a-2004b, Acemoglu and Angrist 2001,
Peri and Iranzo 2009 we consider that the share of college
graduates may have a positive productive externality. λ is the
elasticity of productivity to the share of college graduates.
 Learning, adoption of better technologies, improvement of firm-
worker matching, better institutions, embodied ideas, are the
channels of these externalities.
58
Valencia, December 2-4, 2010
Is the model Appropriate to capture
relative wage effects?
 1) Assume full employment. If employment rate has a natural
long-run level (different across countries) we correct for that
 2) Is consistent with all the international literature on wage
premium, appropriate technology (Acemoglu 2002).
 3) Is simple and can be easily extended to the short-run using
estimates of the speed of adjustment of capital and if we have
measure of net migration by skill yearly.
 4) Alternative to the regression analysis (National level) that has
the equally thorny issue of endogeneity of immigrant flows.
59
Valencia, December 2-4, 2010
Parameterization
 Key parameters:
 σq: Elasticity of Substitution between highly and less educated.
 σI: Elasticity of Substitution between immigrants and natives.
 λ: Elasticity of productivity to the share of college graduates.
60
Valencia, December 2-4, 2010
Range from the Literature
Parameter Estimates
(source of estimates)
61
Low value
Intermediate Value
High value
σq
(source)
1.3
(Borjas 2003)
1.5
(Katz and Murphy
1992)
2.0
(Angrist 1995)
σI
(source)
6.0
(Manacorda et al.
forthcoming)
λ
(source)
0.0
(Acemoglu and Angrist
2000)
Valencia, December 2-4, 2010
20.0
Infinity
(Ottaviano and Peri (Borjas et al. 2008)
forthcoming,
Card 2009)
0.44
0.75
(Moretti 2004a,
(Iranzo and Peri 2009)
2004b)
Data
 Statistics on labor force per education level
 Labor force proxied by population aged 25-65
 Skill composition taken from different data sources
 Census data on labor mobility per education level
 Docquier and Marfouk (2005): collection of immigration data in 30
OECD destinations
 Here: collection of data in 46 (2000)/31 (1990) additional destinations.
 Here: estimate of bilateral missing migration stocks
 Final database: comprehensive migration matrices for 195 countries,
1990 and 2000, stock of college graduates and less educated by country
of residence and origin. Allows to measure total emigration flows!
62
Valencia, December 2-4, 2010
This paper focuses on
 10 large Western European countries
 3 non-EU English-speaking countries (US, Canada, Australia)
 3 Large Eastern European countries (Poland, Hungary and Czech
republic)
 Large countries of emigration (Turkey and Mexico) and NONOECD countries of immigration
 Measure of recent migration flows = migration stock in 2000-
migration stock in 1990
 Net (of remigration) values
 Includes all immigrants, including those with visas and sometimes
irregular
 Has a break-down by schooling
63
Valencia, December 2-4, 2010
Extensions
 What about illegal immigrants? We will use some estimates
 What about downgrading of skills and lower quality of
schooling? We will consider a correction based on relative
test scores (from Canada) and one from relative wages (in
the US)
 What about employment rates? We adjust for that.
64
Valencia, December 2-4, 2010
Basic results: median parameter
values
1a. Impact on average wages of non-movers
Western Europe
65
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1c. Impact on wages of less educated non-movers
Western Europe
66
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1b. Impact on wages of highly educated non movers
Western Europe
67
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Robustness: parameter σq
2a. Impact on average wages of
non-movers
68
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2c Impact on wages of less educated
non movers
Robustness: parameter σI
3a. Impact on average Wages of
non-movers
69
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3c. Impact on Wages of less
educated non-movers
Robustness: parameter λ
4a. Impact on average
Wages of non-movers
70
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4c Impact on Wages of less
educated non-movers
71
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Best-case
Worst case
South Af.
Singapore
Mexico
Turkey
Argentina
Poland
Hungary
Czech R.
EU15
Sweden
Spain
Portugal
Netherl.
Italy
Greece
Germany
France
Belgium
U.K.
Australia
Canada
U.S.
Best Case and Worst-Case scenario on
Average wages: Immigration
6
4
2
0
-2
72
Valencia, December 2-4, 2010
Best-case
Worst-case
South Af.
Singapore
Mexico
Turkey
Argentina
Poland
Hungary
Czech R.
EU15
Sweden
Spain
Portugal
Netherl.
Italy
Greece
Germany
France
Belgium
U.K.
Australia
Canada
U.S.
Best Case and Worst-Case scenario on
Average wages: Emigration
1
0
-1
-2
-3
Robustness
 The results hold also if
 We include estimates of undocumented
 Adjust for schooling quality and skill downgrading
 Adjust for congestion effects
 Adjust for employment rates
73
Valencia, December 2-4, 2010
Conclusions and Thoughts
 Labor market popular view: intl. migration hurts the EU
economy on two counts
 Immigration hurts national wages (by crowding, diluting skills)
 It mostly hurts less educated ones (competition)
 The most likely results supported by this paper:
 At the level and types of immigration of the 1990’s there were
wage gains for natives, especially low skilled. Gains were not
large but losses are very unlikely.
 Emigration from some European countries (mostly high skilled)
was costly for non-movers, especially low educated
74
Valencia, December 2-4, 2010
Further Questions
 How did this change in the 2000’s? Certainly for some
countries (Spain) immigration increased much, but in other
it decreased much (Germany). The skill composition of
immigrants relative to natives is not clear.
 Why do countries of origin not care at all about their
emigrants?
 Can we compare the wage gains with possibly unemployment
and welfare costs (if immigrants have higher unemployment
rates).
75
Valencia, December 2-4, 2010
Germany, Gross Flows 1990-2007
1800000
total gross immigration
1600000
1400000
total gross immigration from
rich countries
1200000
1000000
800000
600000
400000
200000
0
1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007
Spain, Gross Flows 1990-2007
1000000
900000
800000
700000
600000
500000
total gross immigration
total gross immigration from
rich countries
400000
300000
200000
100000
0
1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007
76
Valencia, December 2-4, 2010
Overall Conclusions
 The task complementarity between immigrants and natives is
likely to create positive wage and productivity effects for
natives
 This implies little competition and positive wage effects on
average. Old immigrants feel the competition
 This gains requires adjustment in occupation and capital, may
take time to accrue.
 In the long run Europe seem to have the same benefit from
measured net immigration by skill in the 1990’s. European
wages were hurt by emigration in the 1990’s.
77
Valencia, December 2-4, 2010