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)

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cross-sectionSame immigration

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WageConvergence

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Paper in a nutshell:Inferring convergence

from the data

Negatively selectedreturn migration

Decline in cohortquality

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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

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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

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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

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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

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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

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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

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42

62

83

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ccu

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tion

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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

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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

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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

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Relationship between first generation immigrant earnings gap

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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)