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A Nation of Immigrants:Assimilation and Economic Outcomes in the
Age of Mass Migration
Ran Abramitzky Leah Boustan Katherine Eriksson
Stanford and NBER
UCLA and NBER
UCLA
Larger project: age of mass migration (1850-1913) We construct large panel datasets to analyze economic decisions &
outcomes of trans-Atlantic migrants Linking migrants across population censuses Possible with historical censuses: “72-year rule” allows to link
people by name, age, birthplace
Origin (Europe : Norway): compare migrants with stayers1. Identifying selection of migrants using sibling-pairs
2. 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?
1. Mass migration episode: European countries lost quarter of their population through mass migration. In 1910, 22% of US labor force was foreign born
Large enough to affect labor supply and economic development on both sides of the Atlantic
2. 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)
100
1895 1915
100
60
A
C
80B
1900
A
Year
Wage Data
1920
25 years
5 years
Cross-sectionRepeated
cross-sectionSame immigration
cohort
Panel
Years in US
WageConvergence
Panel
RCS
5 25
50
CS
90
Paper in a nutshell:Inferring convergence
from the data
Negatively selectedreturn migration
Decline in cohortquality
A
40D
C,D
A,BA,B
A A
Immigrants A and Barrived in 1895 and stayed
Immigrants C and Darrived in 1915 and stayed
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
Cohort quality
Years in the US
-1600
-1200
-800
-400
0
400
800
0-5 yrs 6-10 yrs 11-20 yrs 21-30 yrs 30+ yrs
Oc
cu
pati
on-b
ased
ea
rnin
gs
(in
2
01
0 d
olla
rs)
CS RCS Panel
Figure 2: Convergence in occupation score between immigrants and native-born workers by years spent in the US
Cohort quality
Negatively selected return
migration
Years in the US
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 occupation-based
earnings
Heterogeneity across countries Permanent immigrants from five countries held higher-paid
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…
Figure 3: Occupation-based earning gap, permanent immigrants upon first arrival (0-5 years in US) vs. natives by country of origin. Panel data
-50
00
-40
00
-30
00
-20
00
-10
00
01
00
02
00
03
00
04
00
0
Occ
upat
ion-b
ased
ear
ning
s (i
n 201
0 d
olla
rs)
-50
00
-40
00
-30
00
-20
00
-10
00
01
00
02
00
03
00
04
00
0
Occ
upat
ion-b
ased
ear
ning
s (i
n 201
0 d
olla
rs)
0-5 years in the US 30+ years in the US
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
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 convergence in panel and repeated cross-section data
-600
0-5
000
-400
0-3
000
-200
0-1
000
010
0020
0030
00
Occ
upat
ion-
base
d ea
rnin
gs (
in 2
010
dolla
rs)
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 Note: Only 16 countries and no exogenous variation, so these
relationships are merely suggestive
Migrant countries that fared better in US: 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? Convergence may take more than one generation
1. 2nd generation migrants educated in US: likely fluent in English and possibly exposed to US norms and culture
2. 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
Assimilation of 2nd generation migrants1
82
02
22
42
62
83
0O
ccu
pa
tion
sco
re
20 30 40 50Age
Immigrants, 1900-1920 Sons of US born parents, 1900-1920
Separate regression for each line. Further restriction on ages between 20 and 60 in regression# obs in (immigrants 1900-20, natives 1900-20) regs are (2261,13514) respectivelyGraphs plotted for individuals aged 25 in 1900The graph for assumes immigration year 1890
All areas
Occupation score comparisons for immigrants from England
Assimilation of 2nd generation migrants1
82
02
22
42
62
83
0O
ccu
pa
tion
sco
re
20 30 40 50Age
Immigrants, 1900-1920 Sons of US born parents, 1900-1920
Sons of immigrants, 1920-1950 Sons of US born parents, 1920-1950
Separate regression for each line. Further restriction on ages between 20 and 60 in regression# obs in (immigrants 1900-20, natives 1900-20, immigrants' sons 1920-50, natives 1920-50) regs are (2261,13514,4957,33542) respectivelyGraphs plotted for first-gen and second-gen individuals aged 25 in 1900 and 1920 respectivelyGraphs plotted for natives in the same ages as the first- or second-generation immigrants2nd generation immigrants are sons to mother and father born EnglandThe graph for the first-generation immigrants assume immigration year 1890
All areas
Occupation score comparisons for immigrants from England
Assimilation of 2nd generation migrants1
82
02
22
42
62
83
0O
ccu
pa
tion
sco
re
20 30 40 50Age
Immigrants, 1900-1920 Sons of US born parents, 1900-1920
Separate regression for each line. Further restriction on ages between 20 and 60 in regression# obs in (immigrants 1900-20, natives 1900-20) regs are (1435,13514) respectivelyGraphs plotted for individuals aged 25 in 1900The graph for assumes immigration year 1890
All areas
Occupation score comparisons for immigrants from Norway
Assimilation of 2nd generation migrants1
82
02
22
42
62
83
0O
ccu
pa
tion
sco
re
20 30 40 50Age
Immigrants, 1900-1920 Sons of US born parents, 1900-1920
Sons of immigrants, 1920-1950 Sons of US born parents, 1920-1950
Separate regression for each line. Further restriction on ages between 20 and 60 in regression# obs in (immigrants 1900-20, natives 1900-20, immigrants' sons 1920-50, natives 1920-50) regs are (1435,13514,3976,33542) respectivelyGraphs plotted for first-gen and second-gen individuals aged 25 in 1900 and 1920 respectivelyGraphs plotted for natives in the same ages as the first- or second-generation immigrants2nd generation immigrants are sons to mother and father born NorwayThe graph for the first-generation immigrants assume immigration year 1890
All areas
Occupation score comparisons for immigrants from Norway
Difference in predicted occupational score between migrants (1st and 2nd generation) and natives
-4
-2
0
2
4
6
8
Portuga
l
Norway
Finlan
d
Swizt
erlan
d
Denmark
Belgium
Sweden Ita
ly
Austria
German
yW
ales
Irelan
dFra
nce
Engla
nd
Scotla
nd
Other USS
R/Russi
a
First generation Second generation
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: didn’t hold lower-paid occupations than US natives experienced similar occupational upgrading over time
Apparent convergence in CS data between immigrants and natives driven by: 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 procedureSTEP 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”
2. If find multiple matches for same birth year, observation is thrown out
3. If do not find a match, we try matching first within a one-year band (older and younger) and then with a two-year band around the reported birth year; only accepts unique matches
If these attempts do not produce a match, observation is thrown out
Table 1: Match rates by country [back]
Country 1900 # in universe
Number matched
Match rate, total
1900 # unique
Match rate, unique
A. 1900 source: IPUMS Austria 4,722 397 0.084 -- -- England 7,296 916 0.126 France 11,615 728 0.063 Germany 19,855 2,891 0.146 Ireland 9,737 1,115 0.115 Italy 6,649 1,076 0.162 Norway 3,541 575 0.162 Russia 5,641 771 0.136 Sweden 6,164 633 0.102 US natives 10,000 1,891 0.190 -- -- B. 1900 source: Ancestry.com Belgium 6,060 545 0.090 5,962 0.091 Denmark 34,594 1,980 0.058 17,425 0.114 Finland 23,843 828 0.035 22,197 0.037 Portugal 12,585 584 0.046 8,362 0.070 Scotland 53,091 4,349 0.082 15,529 0.280 Switzerland 22,276 3,311 0.149 20,588 0.161 Wales 17,767 1,342 0.076 9,876 0.135
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?1. Assign individuals median income in their reported
occupation from 1950 income distribution (“OCCSCORE” variable)
2. Other ways: social class, education required, etc
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]
Natives Foreign-born Occupation Freq. Percent Occupation Freq. Percent 1. Farmer 352 24.82 Farmer 3,301 18.09 2. Manager 129 9.10 Manager 1,999 10.95 3. Laborer 117 8.25 Laborer 1,791 9.81 4. Salesman 75 5.28 Operative 1,102 6.04 5. Operative 71 5.00 Foreman 603 3.30 6. Clerical 45 3.17 Mine operative 596 3.27 7. Carpenter 45 3.17 Machinist 578 3.17 8. Machinist 45 3.17 Carpenter 529 2.90 9. Farm laborer 39 2.75 Salesman 495 2.71 10. Foreman 27 1.90 Clerical 326 1.79 Total 945 66.61 11,320 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 socio-economic status
Table 3: Comparing matched samples with the population, 1920 [back]
Mean, Panel sample
Difference, Panel sample - population
Levels Logs Native born $23,187 52.92 0.010 (301.546) (0.013) Foreign born $24,215 368.75 0.024 (127.42) (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 -1.553 -1.106 -1.697 -1.330 -2.019 (0.196) (0.254) (0.101) (0.148) (0.295) (0.313) 6-10 yrs in US -0.104 -0.399 -0.500 -1.022 -1.168 -2.375 (0.164) (0.260) (0.127) (0.167) (0.126) (0.161) 11-20 yrs in US 0.258 0.153 0.472 0.027 -0.045 -1.081 (0.114) (0.253) (0.122) (0.171) (0.101) (0.140) 21-30 yrs in US 0.485 0.428 0.411 0.172 0.707 -0.189 (0.181) (0.296) (0.122) (0.187) (0.155) (0.191) 30+ yrs in US 0.591 0.401 0.077 -0.245 0.695 -0.117 (0.215) (0.325) (0.211) (0.260) (0.159) (0.215) Age 0.383 0.384 0.359 0.361 0.337 0.337 (0.009) (0.009) (0.008) (0.008) (0.008) (0.008) Age > 35 14.263 14.317 13.345 13.249 12.443 12.504 (0.420) (0.419) (0.358) (0.358) (0.345) (0.345) Age * Age > 35 -0.441 -0.443 -0.409 -0.407 -0.385 -0.386 (0.011) (0.011) (0.010) (0.010) (0.009) (0.009) Constant 12.153 12.118 13.697 13.665 15.317 15.265 (0.228) (0.228) (0.198) (0.198) (0.200) (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]
[back]Table 5: OLS estimates, Age-earnings profile for natives and foreign-born, 1900-1920,
Occupation-based earnings in $2010 dollars (1) Cross-section
(2) Pooled cross-section and panel
RHS variable (a) Cross-section coefficients
(b) Panel coefficients
0-5 yrs in US -1184.27 -302.72 279.67 (223.14) (193.96) (287.57) 6-10 yrs US -673.57 66.16 447.92 (200.01) (176.39) (254.85) 11-20 yrs US -378.28 139.52 396.15 (171.53) (135.57) (171.07) 21-30 yrs US -273.55 136.59 222.87 (179.52) (139.29) (170.96) 30 yrs in US -18.00 98.79 91.17 (217.551) (182.72) (216.03) Arrive 1891+ --- -756.38 -360.47 (110.07) (188.92) Native born --- --- -118.68 (167.99)
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)
A. Without country FE B. 4 arrival cohorts C. Country x cohort FE RCS Panel RCS Panel RCS Panel 0-5 years in US -123.99 1236.04 73.60 644.31 17.72 521.42 (178.15) (277.75) (229.34) (334.01) (257.03) (330.27) 6-10 yrs in US 372.09 1473.64 113.55 298.21 347.48 611.45 (151.04) (240.27) (204.25) (293.17) (235.99) (298.15) 11-20 yrs in US 484.47 1360.54 254.71 418.23 450.95 606.50 (95.872) (144.08) (161.86) (203.48) (210.00) (233.08) 21-30 yrs in US 441.59 1187.24 222.50 225.95 426.23 430.14 (101.17) (143.25) (162.18) (201.39) (211.82) (233.30) 30+ yrs in US 290.43 1003.94 194.68 113.79 410.13 324.87 (153.28) (191.62) (194.15) (231.63) (244.42) (271.49) N 262,248 262,248 262,248
Alternative specifications (2/4)
D. ln(occupation score) E. Raise farmer income F. 1900 income RCS Panel RCS Panel RCS Panel 0-5 years in US 0.052 0.097 -656.50 -14.28 -3229.19 -2684.36 (0.010) (0.013) (189.66) (281.89) (153.61) (243.84) 6-10 yrs in US 0.066 0.088 -328.66 138.39 -2694.53 -1905.97 (0.008) (0.012) (172.13) (248.69) (146.11) (211.46) 11-20 yrs in US 0.064 0.076 -237.93 54.00 -2262.48 -1902.43 (0.006) (0.008) (132.72) (167.11) (116.32) (143.81) 21-30 yrs in US 0.053 0.065 -219.24 -112.75 -2059.95 -1933.76 (0.006) (0.007) (136.21) (166.18) (117.98) (145.53) 30+ yrs in US 0.042 0.052 -225.82 -206.18 -1823.73 -1833.61 (0.008) (0.009) (176.29) (207.30) (141.41) (170.24) N 262,248 262,248 264,338
Alternative specifications (3/4)
G. Drop child migrants H. State FE I. State * urban FE RCS Panel RCS Panel RCS Panel 0-5 years in US -393.57 233.82 -1668.94 -1304.36 -2234.38 -1734.13 (199.28) (296.34) (199.36) (443.40) (198.16) (443.61) 6-10 yrs in US -69.63 312.48 -1296.98 -831.86 -2022.29 -1304.84 (183.52) (266.25) (191.95) (342.69) (190.84) (345.44) 11-20 yrs in US -23.83 190.52 -1204.18 -730.71 -1869.18 -962.62 (148.76) (191.50) (158.29) (257.41) (157.95) (252.69) 21-30 yrs in US 145.43 118.38 -1084.33 -1267.70 -1668.38 -1229.44 (152.17) (195.11) (164.63) (229.37) (164.32) (231.92) 30+ yrs in US 130.07 139.59 -1018.07 -677.59 -1547.67 -550.95 (208.89) (256.89) (196.78) (231.55) (194.03) (232.55) N 246,365 228,793 227,930
Alternative specifications (4/4) [back]Occupation-based earnings distribution, 1900-20
Cross-section Panel Immigrants Natives Immigrants Natives 10th $9,900 $8,100 $12,550 $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
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
First generation Second generation
Relationship between first generation immigrant earnings gap
and second generation endogamy rates [back]
0
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0.7
-5000 -4000 -3000 -2000 -1000 0 1000 2000 3000 4000
Shar
e of
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ond-
gene
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n m
en m
arrie
d to
firs
t or
sec
ond
gene
ratio
n w
oman
Initial earnings gap with natives, immigrants in the US 0-5 years
Russia
Norway
Portugal
Finland
Italy
Switzerland
Denmark
BelgiumAustria
Sweden
Germany
Ireland
FranceScotland
Wales
England
Explaining cross-country variation in immigrant performance [back]
Characteristic of sending country (RHS variable)
Mean/standard deviation of
RHS variable
Univariate regression*
Multivariate regression:
Add economic variable**
Multivariate regression:
Add cultural variables***
Share in agriculture 0.466 -6526.86 -7476.85 3546.71 (0.172) (3113.67) (3619.31) (4309.10) Real wage 57.726 43.93 23.70 12.79 (25.636) (24.67) (23.77) (17.28) Natural increase 10.406 -7.62 -85.76 -206.82 (3.635) (169.49) (156.14) (105.02) Infant mortality rate 174.933 10.02 16.09 7.35 (54.934) (10.15) (9.48) (8.19) Linguistic distance 0.526 -3419.61 -2229.88 1090.03 (0.344) (1534.52) (2540.56) (1860.67) Cultural distance 1.053 -2999.37 -2610.20 -1848.62 (0.588) (677.38) (961.15) (920.47) Religious similarity 0.852 39,433.23 39,140.04 22,244.94 (0.045) (8484.51) (11,222.16) (12,943.87)