A Nation of Immigrants:
Assimilation and Economic Outcomes in the
Age of Mass Migration
Ran Abramitzky
Stanford and
NBER
Leah Boustan Katherine Eriksson
UCLA and
UCLA
NBER
Larger project: age of mass migration (1850-1913)

We construct large panel datasets to analyze economic
decisions & outcomes of trans-Atlantic migrants


Origin (Europe : Norway): compare migrants with stayers

1.
2.

Linking migrants across population censuses
Possible with historical censuses: “72-year rule” allows to
link people by name, age, birthplace
Identifying selection of migrants using sibling-pairs
Role of childhood environment in migration
Destination (US): compare migrants and 2nd generation
migrants from 16 European countries with US natives

Today’s paper: migrants’ performance in US
Why focus on this period?
Mass migration episode: European countries lost
quarter of their population through mass migration.
In 1910, 22% of US labor force was foreign born
1.

2.
Large enough to affect labor supply and economic
development on both sides of the Atlantic
US open border policy allows us to focus on migrant
decisions, free of immigrant selection policies
Question 1: How did European immigrants perform
relative to US natives?
How did migrants perform in labor markets upon
first arrival? Did migrants converge to natives?


Convergence: a migrant starts below natives & catches up
over time

How did their children fare in the US labor market?

Economic outcome is occupation. We match
occupation to median earnings [details later]

Limitation: only capture convergence in occupations,
not within-occupation income convergence
Question 2: How were return migrants selected from
migrant pool?
Were return migrants positively or negatively selected
from the migrant pool?


Important because over 25% of migrants returned home
(Gould, 1980; Bandiera, Rasul & Viarengo, 2010)
Conceptually, nature of selection of return migrants is
ambiguous



Negatively selected: If migrants who were not successful in
US returned home
Positively selected: If migrants intended to go back home,
and more productive migrants reached “saving targets” faster
Why challenging to address these basic questions?

Because of a lack of historical panel dataset



Previous literature mostly relies on cross-sectional data
Inferring convergence from a cross section raises well-known
biases (Borjas 1985, Duleep & Dowhan 2002, Lubotsky 2007)
We construct panel of 24,000 men from 16 sending
countries in 1900-1920 using census manuscripts (in
Ancestry, then digitizing)
Paper in a nutshell:
Inferring convergence
from the data
Data
Wage
25 years
Panel
A
A
A
100
80
60
B
Repeated
cross-section
Same
immigration
cohort
Cross-section
C
D
40
5 years
1895
1900
1915
Immigrants A and B
arrived in 1895 and stayed
Year
1920
Convergence
Wage
Immigrants C and D
arrived in 1915 and stayed
Negatively selected
return migration
100
90
A
A,B
Decline in cohort
quality
Panel
A
A,B
RCS
CS
50
C,D
5
25
Years
in US
Rest of the talk

A word on historical context

Building a panel dataset, 1900-20

Results for full immigrant population

Results by country of origin

Mechanisms

Outcomes of the 2nd generation

Assimilation through marriage
“New immigrants” and assimilation

Big concerns in US at the time about migrants


Migrants have low natural intelligence
Poverty in immigrant neighborhoods and low levels of
school attendance of immigrant children

Nativist view: new arrivals would not be able to
assimilate

Progressive reformer view: immigrant behavior
could be changed

Initiated public legislation, including child labor laws and
schooling requirements to aid immigrant communities
Assimilation and temporary migration

Immigration Commission (1911) concluded that
migrants (especially from Southern/Eastern Europe)
would not be able to assimilate


Concluded that immigration was a threat to economic and
social fabric of the US
Temporary migration in part to blame
“If an immigrant intends to remain permanently in the US and become
an American citizen, he naturally begins at once… to fit himself for the
conditions of his new life…If, on the other hand, he intends his sojourn
in this country to be short… acquisition of the English language will be
of little consequence… The chief aim of a person with this intention is
to put money in his purse… not for investment here but for investment
in his home country.”
-- Jenks and Lauck, Dillingham Commission investigators (1922)

Provided fuel for literacy test (1917) & quotas (1924)
Building panel dataset

Panel of 24,000 native and immigrant men from 16 sending
countries


Ages 18-35 in 1900; immigrants arrived before 1900; exclude US south
We use iterative procedure to match individuals by name,
age and place of birth from 1900 to 1910/20


Note: need to be able to search complete 1910/20 censuses for procedure
(use Ancestry, then digitize)
Match rates: 19% of natives, 13% foreign-born (to both 1910 & 1920)

Illustrating our matching procedure

Is matched sample representative of population? [details]
Estimating migrant-native convergence

Estimate age-earnings profiles using cross-sections, repeated
cross-sections, panel

Outcome = occupation score. Occupation-based earnings,
expressed in 2010 dollars. 125 occupations [details]

Occupation score = f(age, Census yr, country-of-origin and…)

Years in the US indicators aggregated to 5-yr intervals

Arrival cohort indicator (before/after 1890)
j = country of origin; m = year of arrival; t = Census year; t-m = years spent in US

Regressions: Tables
Figure 2: Convergence in occupation score between immigrants and
native-born workers by years spent in the US
Years in the US
Figure 2: Convergence in occupation score between immigrants and
native-born workers by years spent in the US
Years in the US
Cohort
quality
Figure 2: Convergence in occupation score between immigrants and
native-born workers by years spent in the US
CS
RCS
Panel
Occupation-based earnings (in 2010 dollars)
800
400
Negatively selected
return migration
0
0-5 yrs
6-10 yrs
11-20 yrs
21-30 yrs
30+ yrs
Years in the US
-400
Cohort
quality
-800
-1200
-1600
Alternative specifications [details]









Concern: other sources of selective attrition [details]
Drop immigrants who arrived as children
Interact country FE * arrival cohort dummies
Match occupations to 1900 earnings [details]
Subdivide into finer arrival cohorts
Robustness to farmers’ earnings
Add state FE and state FE * urban area (endogenous, but
can shed light on mechanism)
Compare earning distributions of migrants and natives
Log(occupation-based earnings) instead of occupationbased earnings
Heterogeneity across countries
Permanent immigrants from five countries held higherpaid occupations than US natives upon first arrival


English-speaking countries: England, Scotland, Wales,
plus Russia and France

Permanent migrants from six countries held lower-paid
occupations than US natives

Permanent migrants from most sending countries appear
to experience occupational upgrading over time similar
to natives

Heterogeneity by country is important to consider…
2000
1000
0
-5000 -4000 -3000 -2000 -1000
Occupation-based earnings (in 2010 dollars)
3000
4000
Figure 3: Occupation-based earning gap, permanent
immigrants upon first arrival (0-5 years in US) vs.
natives by country of origin. Panel data
2000
1000
0
-5000 -4000 -3000 -2000 -1000
Occupation-based earnings (in 2010 dollars)
3000
4000
Figure 3: Occupation-based earning gap, permanent immigrants vs.
natives upon first arrival (0-5 years in US) and after 30+ years, by
country of origin. Panel data
0-5 years in the US
30+ years in the US
Selection of return migrants by country

We infer selection of return migrants by comparing
convergence in panel and repeated C-S



Significantly negatively selected return to five countries
(England, Italy, Norway, Russia and Switzerland)
Significantly positively selected return to one country
(Finland)
Adjust for (small) differences in return rates:
multiply each coefficient by the ratio of the average
migration rate to the country’s actual migration rate

Magnitudes do not change

Exception: even more negative selection to Russia
Figure 5: Implied selection of return migrants, by
country of origin. Difference between estimated
1000
0
-1000
-2000
-3000
-4000
-5000
-6000
Occupation-based earnings (in 2010 dollars)
2000
3000
convergence in panel and repeated cross-section data
Direct evidence on return migration to Norway

1910 Norwegian census added supplement: return migrants
were asked when they moved to US, when they returned,
and occupation held in US

We compare US occupational distribution in 1910 of
Norwegian migrants who stayed in US vs. returned

Migrants who returned had occupations paying $1,659 less
on average

Remarkably similar to indirect inference from comparing
panel and repeated cross section (-$1,757)
Explaining cross-country variation in
immigrant performance [details]

Regress country coefficients on country characteristics


Migrant countries that fared better in US:



Note: Only 16 countries and no exogenous variation, so these
relationships are merely suggestive
had higher real wages in 1880
had more similar culture, language and religious
Low correlation between countries performance in US and:


population pressure (rates of natural population increase)
health conditions (measured by infant mortality)
2nd generation migrants
How do 2nd generation migrants perform in US labor
markets?


1.
2.

Convergence may take more than one generation
2nd generation migrants educated in US: likely fluent in
English and possibly exposed to US norms and culture
Differences can persist over generations: if lived in
migrant enclaves or inherited occupational skills from
parents
We find persistence over generations: if 1st generation
out- (under-)performs natives, so does 2nd generation
Occupation score comparisons
for immigrants from England
nd
18
20
22
24
26
28
30
Assimilation of 2 generation
migrants
All areas
20
30
40
50
Age
Immigrants, 1900-1920
Sepa rate regressi on for e ach li ne. Further restricti on on ages between 20 and 60 i n regressio n
# obs i n (i mm i gra nts 19 00-20, natives 1900 -20 ) reg s are (2261,1351 4) re specti vel y
Gra phs pl otted for in dividu als ag ed 25 i n 1900
T he graph for assum es im m ig ratio n year 189 0
Sons of US born parents, 1900-1920
Occupation score comparisons
for immigrants from England
nd
18
20
22
24
26
28
30
Assimilation of 2 generation
migrants
All areas
20
30
40
50
Age
Immigrants, 1900-1920
Sons of US born parents, 1900-1920
Sons of immigrants, 1920-1950
Sons of US born parents, 1920-1950
Sepa rate regressi on for e ach li ne. Further restricti on on ages between 20 and 60 i n regressio n
# obs i n (i mm i gra nts 19 00-20, natives 1900 -20 , im m igrants' sons 1920-50, na ti ves 19 20-5 0) re gs are (2261 ,135 14,4 957,33542 ) respecti vel y
Gra phs pl otted for fi rst-ge n an d secon d-ge n i ndi vi dual s aged 25 i n 19 00 a nd 1920 re specti vel y
Gra phs pl otted for natives in the sam e ages as the fi rst- or second -gen erati on im m igrants
2nd gen erati on im m igrants are sons to m other a nd father bo rn Engl and
T he graph for the first-g enerati on im m i gran ts assum e im m i grati on year 18 90
Occupation score comparisons
for immigrants from Norway
nd
18
20
22
24
26
28
30
Assimilation of 2 generation
migrants
All areas
20
30
40
50
Age
Immigrants, 1900-1920
Sepa rate regressi on for e ach li ne. Further restricti on on ages between 20 and 60 i n regressio n
# obs i n (i mm i gra nts 19 00-20, natives 1900 -20 ) reg s are (1435,1351 4) re specti vel y
Gra phs pl otted for in dividu als ag ed 25 i n 1900
T he graph for assum es im m ig ratio n year 189 0
Sons of US born parents, 1900-1920
Occupation score comparisons
for immigrants from Norway
nd
18
20
22
24
26
28
30
Assimilation of 2 generation
migrants
All areas
20
30
40
50
Age
Immigrants, 1900-1920
Sons of US born parents, 1900-1920
Sons of immigrants, 1920-1950
Sons of US born parents, 1920-1950
Sepa rate regressi on for e ach li ne. Further restricti on on ages between 20 and 60 i n regressio n
# obs i n (i mm i gra nts 19 00-20, natives 1900 -20 , im m igrants' sons 1920-50, na ti ves 19 20-5 0) re gs are (1435 ,135 14,3 976,33542 ) respecti vel y
Gra phs pl otted for fi rst-ge n an d secon d-ge n i ndi vi dual s aged 25 i n 19 00 a nd 1920 re specti vel y
Gra phs pl otted for natives in the sam e ages as the fi rst- or second -gen erati on im m igrants
2nd gen erati on im m igrants are sons to m other a nd father bo rn Norway
T he graph for the first-g enerati on im m i gran ts assum e im m i grati on year 18 90
Difference in predicted occupational score between migrants
(1st and 2nd generation) and natives
First generation
Second generation
8
6
4
2
SS
R/
Ru
ss
ia
ot
la
nd
Sc
la
nd
En
g
ce
Fr
an
lan
d
Ire
ale
s
W
a
Ge
rm
an
y
st
ri
Au
Ita
ly
en
Sw
ed
m
Be
lgi
u
De
nm
ar
k
er
lan
d
wa
y
la
nd
Fin
Sw
izt
Ot
he
rU
-2
No
r
Po
rtu
ga
l
0
-4
Predicted values are for males aged 35 in 1910 and who immigrated in
1890 (for 1st generation)
Assimilation through marriage [details]

What about cultural assimilation of immigrants? Look at
inter-marriage between immigrants and US natives

Endogamy could reflect preferences or constraints

We find strong endogamy among 1st generation
immigrants; less endogamy among 2nd generation

Strong cross-country persistence of in-group marriage
rates across generations

Migrants from countries with better-paid occupations
somewhat less likely to marry within same country
Conclusions

Contrary to conventional wisdom, in early 20th
century, long term migrants:



Apparent convergence in CS data between
immigrants and natives driven by:




didn’t hold lower-paid occupations than US natives
experienced similar occupational upgrading over time
lower occupational quality of later immigrant cohorts
lower occupational quality of temporary/return migrants
Substantial variation by country
Persistence in labor & marriage patterns over
generations
Other sources of selective attrition

Any form of selective attrition of migrants vs. natives
could drive assimilation-pattern differences between
panel and repeated CS:
1.
Selective mortality:



Quantitatively less important than return migration
For natives, repeated cross sections are similar to panel,
implying selective mortality is non-issue for them
Direct data on mortality by country of origin and by
occupation (from death registries)
Other sources of selective attrition [back]
2.
Selective name changes:

Name changes that occurred upon entry to US (before we
first observe migrants) are non-issue

Men who changed name between censuses would not be in
panel but stay in repeated CS before & after name change

Foreign-born men in panel have slightly more “foreign”
names than their foreign-born counterparts in the CS


An indication they may have changed name
Difference in the “foreignness” index is associated with
only a $60 difference in occupation-based earnings
Other sources of selective attrition [back]

The “foreignness” index: first calculating probability
of being foreign born conditional on having a given
first name (and, separately, a given last name) in the
1900-20 IPUMS samples

The “foreignness” index is then the sum of the two
probabilities; the index varies between zero and two.
Foreign-born men in the cross-section (panel sample)
have an index value of 1.13 (1.23)
Matching procedure

Potential 1900 population to be matched:

Men aged 18-35

Small sending countries: find all migrants who moved
to US between 1880-1900

Big sending countries and natives: start with all
migrants in 5% Integrated Public Use Microdata Series
(IPUMS) sample
Matching procedure
STEP 1: Standardize first and last names of men in
1900 sample to address orthographic differences
between phonetically equivalent names


using the NYSIIS algorithm (Atack & Bateman,1992)
Men who are unique by first and last name, birth
year, and place of birth (state or country) in 1900
become candidates for our matching procedure
Matching procedure
STEP 2: Identify potential matches in 1910 and 1920

by searching for all men in our 1900 sample in the 1910
and 1920 Census manuscripts

For small sending countries, we compile complete
populations of men with relevant sample characteristics in
1910 and 1920

For large sending countries and native born, we use the
(expansive) Ancestry.com algorithm to search for candidate
matches in 1910 and 1920; this search returns many potential
matches for each case, which we cull using the iterative match
procedure described in the next step
STEP 3. Iterative matching procedure

We start by looking for a match by first name, last name,
place of birth (state or country) and reported birth year

Three possibilities:
1.
If find a unique match, stop and consider the observation
“matched”
If find multiple matches for same birth year, observation
is thrown out
If do not find a match, we try matching first within a oneyear band (older and younger) and then with a two-year
band around the reported birth year; only accepts unique
matches
2.
3.

If these attempts do not produce a match, observation is
thrown out
Table 1: Match rates by country [back]
Country
1900 # in
universe
A. 1900 source: IPUMS
Number
matched
Match rate,
total
1900 #
unique
Match rate,
unique
Austria
England
France
Germany
Ireland
Italy
Norway
Russia
Sweden
4,722
7,296
11,615
19,855
9,737
6,649
3,541
5,641
6,164
397
916
728
2,891
1,115
1,076
575
771
633
0.084
0.126
0.063
0.146
0.115
0.162
0.162
0.136
0.102
--
--
US natives
10,000
1,891
0.190
--
--
545
1,980
828
584
4,349
3,311
1,342
0.090
0.058
0.035
0.046
0.082
0.149
0.076
5,962
17,425
22,197
8,362
15,529
20,588
9,876
0.091
0.114
0.037
0.070
0.280
0.161
0.135
B. 1900 source: Ancestry.com
Belgium
6,060
Denmark
34,594
Finland
23,843
Portugal
12,585
Scotland
53,091
Switzerland
22,276
Wales
17,767
Occupation-based earnings

No individual information about wages or income in
1900-20 Census; only occupation is observed

We collect occupation string by hand from the
historical manuscripts on Ancestry.com

How to use occupations meaningfully?
Assign individuals median income in their reported
occupation from 1950 income distribution
(“OCCSCORE” variable)
Other ways: social class, education required, etc
1.
2.
Occupation-based earnings [back]

Reliance on occupation-based earnings in 1950 is a concern.
The decades of the 1940s and 1950s were a period of wage
compression (Goldin and Margo, 1992)

For example, if immigrants were clustered in low-paying
occupations, the occupation score variable may understate
both their initial earnings penalty and the convergence implied
by moving up the occupational ladder

To address this concern, we match our occupations to the 1901
Cost of Living survey (which has several disadvantages). We
get larger initial penalty, but otherwise similar results
Table 2: Common occupations for natives and foreign-born
in matched samples, 1920 [back]
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
Total
Occupation
Farmer
Manager
Laborer
Salesman
Operative
Clerical
Carpenter
Machinist
Farm laborer
Foreman
Natives
Freq.
352
129
117
75
71
45
45
45
39
27
945
Percent
24.82
9.10
8.25
5.28
5.00
3.17
3.17
3.17
2.75
1.90
66.61
Foreign-born
Occupation
Freq.
Farmer
3,301
Manager
1,999
Laborer
1,791
Operative
1,102
Foreman
603
Mine operative
596
Machinist
578
Carpenter
529
Salesman
495
Clerical
326
11,320
Percent
18.09
10.95
9.81
6.04
3.30
3.27
3.17
2.90
2.71
1.79
62.03
Is matched sample representative of population?

Men in both panel and repeated CS must have survived
and remained in US until 1920

By 1920, up to sampling error: any difference between
cross-section and panel (given age 38-55; arrive by 1900)
due to imperfect matching

Concern: men with uncommon names and consistent age
reporting are more likely to be successfully linked
between Censuses. Both may be correlated with socioeconomic status
Table 3: Comparing matched samples with the population, 1920 [back]
Mean,
Panel sample
Native born
$23,187
Foreign born
$24,215
Difference,
Panel sample - population
Levels
Logs
52.92
0.010
(301.546)
(0.013)
368.75
(127.42)
0.024
(0.006)
Table 4: Age-earnings profile for natives and the foreign-born, Cross-sections by year
1900
1910
1920
0-5 yrs in US
-1.208
(0.196)
-1.553
(0.254)
-1.106
(0.101)
-1.697
(0.148)
-1.330
(0.295)
-2.019
(0.313)
6-10 yrs in US
-0.104
(0.164)
-0.399
(0.260)
-0.500
(0.127)
-1.022
(0.167)
-1.168
(0.126)
-2.375
(0.161)
11-20 yrs in US
0.258
(0.114)
0.153
(0.253)
0.472
(0.122)
0.027
(0.171)
-0.045
(0.101)
-1.081
(0.140)
21-30 yrs in US
0.485
(0.181)
0.428
(0.296)
0.411
(0.122)
0.172
(0.187)
0.707
(0.155)
-0.189
(0.191)
30+ yrs in US
0.591
(0.215)
0.401
(0.325)
0.077
(0.211)
-0.245
(0.260)
0.695
(0.159)
-0.117
(0.215)
Age
0.383
(0.009)
0.384
(0.009)
0.359
(0.008)
0.361
(0.008)
0.337
(0.008)
0.337
(0.008)
Age > 35
14.263
(0.420)
14.317
(0.419)
13.345
(0.358)
13.249
(0.358)
12.443
(0.345)
12.504
(0.345)
Age * Age > 35
-0.441
(0.011)
-0.443
(0.011)
-0.409
(0.010)
-0.407
(0.010)
-0.385
(0.009)
-0.386
(0.009)
Constant
12.153
(0.228)
12.118
(0.228)
13.697
(0.198)
13.665
(0.198)
15.317
(0.200)
15.265
(0.199)
Country FE?
N
Y
N
Y
N
Y
N
119,538
159,092
169,296
119,538
159,092
169,296
Notes: IPUMS data, men aged 18-55 in labor force. Contains same set of countries as in matched
sample. “Implied Convergence” = 30+ yrs in US – 0-5 years in the US. For columns 2, 4 and 6,
omitted country = Italy.
[back]
Table 5: OLS estimates, Age-earnings profile for natives and foreign-born, 1900-1920,
Occupation-based earnings in $2010 dollars
(1) Cross-section
RHS variable
(2) Pooled cross-section and panel
0-5 yrs in US
-1184.27
(223.14)
(a) Cross-section
coefficients
-302.72
(193.96)
(b) Panel
coefficients
279.67
(287.57)
6-10 yrs US
-673.57
(200.01)
66.16
(176.39)
447.92
(254.85)
11-20 yrs US
-378.28
(171.53)
139.52
(135.57)
396.15
(171.07)
21-30 yrs US
-273.55
(179.52)
136.59
(139.29)
222.87
(170.96)
30 yrs in US
-18.00
(217.551)
98.79
(182.72)
91.17
(216.03)
Arrive 1891+
---
-756.38
(110.07)
-360.47
(188.92)
Native born
---
---
-118.68
(167.99)
[back]
Alexander James in 1900
Alexander James in 1910
Alexander James in 1920 [back]
Mass migration from Europe 1850-1913 [back]
Alternative specifications (page 1/4)
0-5 years in US
A. Without country FE
Panel
RCS
1236.04
-123.99
(277.75)
(178.15)
B. 4 arrival cohorts
Panel
RCS
644.31
73.60
(334.01)
(229.34)
C. Country x cohort FE
Panel
RCS
521.42
17.72
(330.27)
(257.03)
6-10 yrs in US
372.09
(151.04)
1473.64
(240.27)
113.55
(204.25)
298.21
(293.17)
347.48
(235.99)
611.45
(298.15)
11-20 yrs in US
484.47
(95.872)
1360.54
(144.08)
254.71
(161.86)
418.23
(203.48)
450.95
(210.00)
606.50
(233.08)
21-30 yrs in US
441.59
(101.17)
1187.24
(143.25)
222.50
(162.18)
225.95
(201.39)
426.23
(211.82)
430.14
(233.30)
30+ yrs in US
290.43
(153.28)
1003.94
(191.62)
194.68
(194.15)
113.79
(231.63)
410.13
(244.42)
324.87
(271.49)
N
262,248
262,248
262,248
Alternative specifications (2/4)
0-5 years in US
D. ln(occupation score)
RCS
Panel
0.052
0.097
(0.010)
(0.013)
E. Raise farmer income
RCS
Panel
-656.50
-14.28
(189.66)
(281.89)
F. 1900 income
RCS
Panel
-3229.19
-2684.36
(153.61)
(243.84)
6-10 yrs in US
0.066
(0.008)
0.088
(0.012)
-328.66
(172.13)
138.39
(248.69)
-2694.53
(146.11)
-1905.97
(211.46)
11-20 yrs in US
0.064
(0.006)
0.076
(0.008)
-237.93
(132.72)
54.00
(167.11)
-2262.48
(116.32)
-1902.43
(143.81)
21-30 yrs in US
0.053
(0.006)
0.065
(0.007)
-219.24
(136.21)
-112.75
(166.18)
-2059.95
(117.98)
-1933.76
(145.53)
30+ yrs in US
0.042
(0.008)
0.052
(0.009)
-225.82
(176.29)
-206.18
(207.30)
-1823.73
(141.41)
-1833.61
(170.24)
N
262,248
262,248
264,338
Alternative specifications (3/4)
0-5 years in US
G. Drop child migrants
Panel
RCS
233.82
-393.57
(296.34)
(199.28)
H. State FE
Panel
RCS
-1304.36
-1668.94
(443.40)
(199.36)
I. State * urban FE
Panel
RCS
-1734.13
-2234.38
(443.61)
(198.16)
6-10 yrs in US
-69.63
(183.52)
312.48
(266.25)
-1296.98
(191.95)
-831.86
(342.69)
-2022.29
(190.84)
-1304.84
(345.44)
11-20 yrs in US
-23.83
(148.76)
190.52
(191.50)
-1204.18
(158.29)
-730.71
(257.41)
-1869.18
(157.95)
-962.62
(252.69)
21-30 yrs in US
145.43
(152.17)
118.38
(195.11)
-1084.33
(164.63)
-1267.70
(229.37)
-1668.38
(164.32)
-1229.44
(231.92)
30+ yrs in US
130.07
(208.89)
139.59
(256.89)
-1018.07
(196.78)
-677.59
(231.55)
-1547.67
(194.03)
-550.95
(232.55)
N
246,365
228,793
227,930
Alternative specifications (4/4) [back]
Occupation-based earnings distribution, 1900-20
th
10
Cross-section
Immigrants
Natives
$9,900
$8,100
Panel
Immigrants
$12,550
Natives
$8,100
25th
$18,000
$12,550
$18,000
$12,550
50th
$20,700
$20,700
$20,700
$20,700
75th
$22,500
$25,200
$23,400
$25,200
90th
$28,800
$34,200
$30,600
$34,200
99th
$37,800
$55,800
$37,800
$56,700
Assimilation through inter-marriage
First generation
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
Second generation
Relationship between first generation immigrant earnings gap and
second generation endogamy rates
[back]
Share of second-generation men married to first or second generation woman
0.7
Finland
0.6
Russia
Italy
Portugal
0.5
0.4
Norway
Ireland
Belgium
0.3
Austria
Germany
Sweden
0.2
Wales
Denmark
England
0.1
Switzerland
France
Scotland
0
-5000
-4000
-3000
-2000
-1000
0
1000
Initial earnings gap with natives, immigrants in the US 0-5 years
2000
3000
4000
Explaining cross-country variation in
immigrant performance [back]
Mean/standard
deviation of
RHS variable
Univariate
regression*
-6526.86
(3113.67)
Multivariate
regression:
Add economic
variable**
-7476.85
(3619.31)
Multivariate
regression:
Add cultural
variables***
3546.71
(4309.10)
0.466
(0.172)
Real wage
57.726
(25.636)
43.93
(24.67)
23.70
(23.77)
12.79
(17.28)
Natural increase
10.406
(3.635)
-7.62
(169.49)
-85.76
(156.14)
-206.82
(105.02)
Infant mortality rate
174.933
(54.934)
10.02
(10.15)
16.09
(9.48)
7.35
(8.19)
Linguistic distance
0.526
(0.344)
-3419.61
(1534.52)
-2229.88
(2540.56)
1090.03
(1860.67)
Cultural distance
1.053
(0.588)
-2999.37
(677.38)
-2610.20
(961.15)
-1848.62
(920.47)
Religious similarity
0.852
(0.045)
39,433.23
(8484.51)
39,140.04
(11,222.16)
22,244.94
(12,943.87)
Characteristic of sending
country (RHS variable)
Share in agriculture