Copenhagen Economics InstituteShort Course

March 5-7 2007

Globalization in the Very Long Run

March 6

Determinants and Impact of Mass Migration

Part 1 The Determinants

North-North in the first global century

Figure 2.1 Emigration from Europe, 1846-1924

(five-year averages)

0

200

400

600

800

1000

1200

1400

1600

1846-50 1861-65 1876-80 1891-95 1906-10 1921-24

Year

Th

ou

san

ds

Total Europe

Southern and Eastern Europe

More North-North

Figure 2.2 Immigration to the Americas, 1846-1924

(five-year averages)

0

200

400

600

800

1000

1200

1400

1600

1846-50 1861-65 1876-80 1891-95 1906-10 1921-24

Year

Th

ou

san

ds

Total

United States

What about South-South?

Figure 2.4 Gross Migration of Indentured Workers by Origin 1830-1920

0

50

100

150

200

250

300

1831-1840

1841-1850

1851-1860

1861-1870

1871-1880

1881-1890

1891-1900

1901-1910

1911-1920

Decades

Thou

sand

s

Chinese

Indians

Africans

Japanese, Pacific Islanders and Others

The demise of world migration

Figure 9.2 Immigration to Chief New World Destinations, 1881-1939

0

200

400

600

800

1000

1200

1400

1600

1800

2000

1881 1886 1891 1896 1901 1906 1911 1916 1921 1926 1931 1936

Year

Th

ou

san

ds

Four-country total immigration

United States

Argentina

Canada

Brazil

The resurrection of world migrationTable 10.1

The Migrant Stock Around the World, 1965-2000

Year 1965 1975 1985 1990a 1990b 2000

Migrant Stock (Millions)

World 75.2 84.5 105.2 119.8 154.0 174.9

Africa 8.0 11.2 12.5 15.6 16.2 16.3

Asia 31.4 29.7 38.7 43.0 50.0 50.0

Latin Am. & Carib. 5.9 5.9 6.4 7.5 7.1 5.9

North America 12.7 15.0 20.5 23.9 27.6 40.8

Europe 14.7 19.5 23.0 25.1 48.4 56.1

Oceania 2.5 3.3 4.1 4.6 4.8 5.8

Percentage of World Migrant Stock

World 100.0 100.0 100.0 100.0 100.0 100.0

Africa 10.6 13.2 11.9 13.1 10.5 9.3

Asia 41.8 35.1 36.8 35.9 32.4 28.6

Latin Am. & Carib. 7.9 6.8 6.1 6.2 4.6 3.4

North America 16.9 17.8 19.5 20.0 17.9 23.3

Europe 19.6 23.1 21.8 20.9 31.4 32.1

Oceania 3.3 3.9 3.9 3.9 3.1 3.3

Migrant Stock as a Percentage of Population

World 2.3 2.1 2.2 2.3 2.9 2.9

Africa 2.5 2.7 2.3 2.5 2.6 2.1

Asia 1.7 1.3 1.4 1.4 1.6 1.4

Latin Am. & Carib. 2.4 1.8 1.6 1.7 1.6 1.1

North America 6.0 6.3 7.8 8.6 9.8 13.0

Europe 2.2 2.7 3.0 3.2 6.7 7.7

Oceania 14.4 15.6 16.9 17.8 18.0 19.1

Selection issues: who migrates?

• Immigrant mix by origin: rising ethnic diversity

• Quality: falling skill and schooling relative to native-born

• Age: high labor participation and low dependency rates relative to native-born (stable)

• Gender: bias favoring males (stable)

Rising ethnic diversity (by source) and debate about declining immigrant quality

Figure 2Emigration from Europe, 1881-1939

(five-year averages)

0

200

400

600

800

1000

1200

1400

1600

1871-1875 1881-1885 1891-1895 1901-1905 1911-1915 1921-1925 1931-1935

Year

Thou

sand

s

All Emigrants

'Old' Sources

'New' sources

… and its return in the modern era.Table 4

Source Area Composition of US Immigration, 1951-2000 (% of total)

Region of Origin 1951-60 1961-70 1971-80 1981-90 1991-2000

Europe

52.7

33.8

17.8

10.3

14.9

West

47.1

30.2

14.5

7.2 5.6

East

5.6

3.6

3.3

3.1 9.4

Asia

6.1

12.9

35.3

37.3

30.7

Americas

39.6

51.7

44.1

49.3

49.3

Canada

15.0

12.4

3.8

2.1

2.1

Mexico

11.9

13.7

14.2

22.6

24.7

Caribbean

4.9

14.2

16.5

11.9

10.8

Central America

1.8

3.1

3.0

6.4

5.8

South America

3.6

7.8

6.6

6.3

5.9

Africa

0.6

0.9

1.8

2.4

3.9

Oceania

0.5

0.8

0.9

0.6

0.6

Total (000's)

2,515

3,322

4,493

7,338

9,095

Notes: National origin based on country of last residence. Totals include 2.7 million former illegal aliens receiving permanent resident status under the Immigration Reform and Control Act, 1986. Of these, 1.3 million fall in the decade 1981-1990 period and 1.4 million in the decade 1991-2000.

Four Stylized Quality Facts

• Native-born ‘quality’ (= human capital per person) rose across the late 19th century

• Foreign-born ‘quality’ fell across the late 19th century

• Native-born ‘quality’ rose across the late 20th century

• Foreign-born ‘quality’ rose far less across the late 20th century. Ergo, relative quality fell.

US native-born ‘quality’ rose 1870-1930

Quality Proxies for the US Population 1870-1930

____________________________________________ Enrollment Attendance % 17 Year olds Illiteracy

Rates Per 100 Rates per Graduating Rate Population Student High School

_________________________________________________________________________

1870 48.4 78.4 2.0 20.0 1880 57.8 81.1 2.5 17.0 1890 54.3 86.3 3.5 13.3 1900 50.5 99.0 6.3 10.7 1910 59.2 113.0 8.6 7.7 1920 64.3 121.2 16.3 6.0 1930 69.9 151.7 28.8 4.3

__________________________________________________________________________

The ‘quality’ of US immigrants fell 1820-1898

The Occupations of US Immigrants

Occupation 1820-

1831 1832-1846

1847-1854

1855-1864

1865-1873

1873-1880

1881-1893

1894-1898

Skilled 61 40 24 36 31 30 24 30 Farmers 23 33 33 23 18 18 14 12 Unskilled 16 26 43 41 51 48 60 55 Miscellaneous -- -- -- 0 1 5 3 3 Percent male 70 62 59 58 62 63 61 57

The ‘quality’ of the sending country pool of potential emigrants c1900

Literacy in Europe and the Brain Drain

France Britain Italy Spain Portugal

Literacy rate of adult immigrants to the US, 1899-1909

94.6 99.0 47.0 85.4 31.8

Literacy rate of adult population, 1901 83 97 52 44 22

Literacy loss (outflow of literates as a percentage of a literate adults)

0.4 1.6 8.6 0.6 2.0

School enrolment as a percentage of literate adults in 1901

25.9 23.4 24.2 31.3 29.5

Relative wage and relative education: US immigrants vs native-born 1960-1990

1960 1970 1980 1990

Percentage earnings differential relative to the native-born

All Immigrants

Earnings unadjusted 4.1 0.1 9.7 16.3

Earnings adjusted 1.3 1.7 7.1 10.0

Recent immigrants

Wage unadjusted 13.9 18.8 32.8 38.0

Wage adjusted 16.2 19.8 24.1 26.9

Percentage point difference in educational attainment relative to native-born

All immigrants

Education > 16 years 3.5 2.4 0.0

Education < 12 years 3.2 14.3 22.1

Recent immigrants

Education > 16 years 12.9 7.5 4.9

Education < 12 years 5.6 13.1 20.4

Selection bias by age.

Figure 5.1Age distribution of emigrants; Denmark, 1868-1900 and Ireland,

1871-1910

0

5

10

15

20

25

30

35

40

0-4 5--9 10--14 15-19 20-24 25-29 30-34 35-39 40-49 50-59 60+

Age

Perc

en

t o

f em

igra

nts

Denmark

Ireland

… and by gender.

Figure 5.2Return Migration and Sex Composition, US 1910-14

0

10

20

30

40

50

60

70

80

50 55 60 65 70 75 80 85 90 95 100

Percent of inflow male

Re

turn

ra

te

To be explained in the modern era: changing FB shares in high-wage countries

Table 3 Shares of Foreign-Born in Populations, 1870/1-2000/1

1870/1 1890/1 1910/11 2000/1

Europe

Germany 0.5 0.9 1.9 8.9

France 2.0 3.0 3.0 10.0

United Kingdom 0.5 0.7 0.9 4.3

Denmark 3.0 3.3 3.1 5.8

Norway 1.6 2.4 2.3 6.3

Sweden 0.3 0.5 0.9 11.3

New World

Australia 46.5 31.8 17.1 23.6

New Zealand 63.5 41.5 30.3 19.5

Canada 16.5 13.3 22.0 17.4

United States 14.4 14.7 14.7 11.1

Argentina 12.1 25.5 29.9 5.0

Brazil 3.9 2.5 7.3 Notes: All entries in percent

The Most Parsimonious Migration Model(in terms of stocks of FB) a la Robert Lucas

020

40

60

80

Fore

ign B

orn

Share

%

0 10 20 30 40GDP per Capita (Thousands USD 2000)

Figure 1 Foreign-Born Share versus GDP per Capita 2000

Table 1 Determinants of the Foreign-Born Share: The Parsimonious Model

Weighted

Coefficient on GDP per

Coefficient on land area

Region FB Mean capita (size) Western Europe/North America 10.07 0.518 0.438 0.96 0.75 Eastern Europe/Central Asia 8.24 0.112 0.218 0.26 0.45 Middle East/North Africa 4.98 0.195 -7.165 0.23 -1.16 Sub-Saharan Africa 3.66 0.2 -2.094 0.78 -1.14 East Asia 1.05 0.532 0.938 2.21 1.57 Latin America/Caribbean 1.01 0.621 -0.764 3.06 -1.47 South Asia 0.92 -0.26 -0.44 -0.03 -0.78 World 2.98 0.294 0.114 3.84 0.41 Notes: The weighted regional foreign-born means are population weighted. The figures in italics are t-statistics.

However, …

We clearly need a better model!

And let’s try finding one that explains the First

Global Century with unrestricted migration

before turning to the Second Global Century

where migration is so tightly restricted.

The (panel) emigration rates to be explainedMigration Rates by Decade (per 1000 mean population)

Country 1851-60 1861-70 1871-80 1881-90 1891-00 1901-10

European Emigration Rates

Austria-Hungary 2.9 10.6 16.1 47.6

Belgium 8.6 3.5 6.1

British Isles 58.0 51.8 50.4 70.2 43.8 65.3

Denmark 20.6 39.4 22.3 28.2

Finland 13.2 23.2 54.5

France 1.1 1.2 1.5 3.1 1.3 1.4

Germany 14.7 28.7 10.1 4.5

Ireland 66.1 141.7 88.5 69.8

Italy 10.5 33.6 50.2 107.7

Netherlands 5.0 5.9 4.6 12.3 5.0 5.1

Norway 24.2 57.6 47.3 95.2 44.9 83.3

Portugal 19.0 28.9 38.0 50.8 56.9

Spain 36.2 43.8 56.6

Sweden 4.6 30.5 23.5 70.1 41.2 42.0

Switzerland 13.0 32.0 14.1 13.9

The incentives and investment sourcesInternationally Comparable Wage Rates and Wage Ratios

(A = Real Wage, Great Britain 1905 = 100; B = Real Wage Ratio, Home to Receiving Countries)

Country 1850–9 1860–9 1870–9 1880–9 1890–9 1900–13

Belgium A 45.5 52.8 64.2 73.9 85.6 86.9 B – 118.2 110.7 109.0 115.9 109.9 Denmark A – – 41.0 52.6 70.6 94.2 B – – 34.6 40.1 47.9 56.8 France A – 46.2 52.0 60.4 65.1 71.2 B – – 45.6 45.4 38.3 42.9 Germany A 52.5 55.4 62.3 68.5 78.1 85.9 B – – 54.1 53.4 53.9 52.7 Great Britain A 59.4 59.0 70.3 83.5 99.4 98.2 B – - 59.6 63.0 66.0 59.4 Ireland A 44.4 43.6 51.7 64.5 87.3 90.9 B – - 45.4 50.0 60.2 56.2 Italy A – – 26.2 34.2 37.4 46.4 B – – 37.8 42.6 40.7 45.5 Netherlands A 45.7 48.9 62.8 79.9 88.1 77.8 B – 52.5 53.0 60.9 59.8 46.9 Norway A 27.2 30.7 40.1 45.8 67.5 83.8 B 26.0 32.9 25.0 34.9 45.8 50.5 Portugal A 18.8 19.6 20.1 27.4 23.3 24.6 B – 36.2 33.7 36.1 25.1 23.9 Spain A 30.4 28.0 27.6 25.5 26.8 30.4 B – 56.3 52.1 36.6 30.9 31.7 Sweden A 24.2 34.6 39.0 51.1 70.7 92.2 B – - 36.7 43.2 52.3 59.9

The Stylized ELC Fact

Figure 2A Country’s Emigrant Life Cycle

EmigrationRate

e1

e2

e0 = 0w0 w1 w2

A

B

C

D

Modeling the ELC emigration responses

Figure 3 Stylized Emigration Responses

Home Wage w2

w1

w0

e0 = 0 e2 e1’ Emigration rate

D

B’

A

EM EM’

C

B

e1

Estimation

Table 4.3 Regression Estimate for European Emigration, 1860-1913 MigRate = 20.74 8.19 LnWRatio + 0.37 LagBirth + 0.96 MigStock (2.2) (4.2) (3.6) (3.0) + 3.19 LnRWage 0.18 MigStock*LnRWage + 5.64 Dum (1.6) (2.3) (4.6) R2 = 0.72 Note: t statistics in parentheses. Variable definitions: MigRate = gross emigration rate per thousand population per decade to all foreign destinations; LnWRatio = log of the ratio of purchasing power parity adjusted wage rates, source country to a weighted average of destination countries; LnRWage = log of source country purchasing power parity adjusted real wage; LagBirth = source country birth rate lagged 20 years; Migstock = stock of previous immigrants in destination countries at beginning of decade per thousand of source country population; Dum = dummy for Belgium, Italy, Portugal and Spain. Method: Pooled OLS regression on 48 country/decade-average observations. Source: Variant of Hatton and Williamson (1998: Table 3.3, column 4, p. 39).

Decomposition of the ELC

Figure 4.3Factors in Trend Emigration

-2.5

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

2.5

0 1 2 3 4 5 6 7 8

"Emigration time" (in decades)

Con

trib

utio

n to

tren

d (p

er 1

000

popu

latio

n)

<--Home real wage

Natural increase-->Emigrant stock-->

Real wage ratio-->

Does the same model work today?

Yes.

It works for African emigration.

It works for US immigration.

And why not? Even though immigration policy is far more restrictive today, the fundamentals driving mass migration are also far bigger.

Demographic Arithmetic

1955 1965 1975 1985 1995

Percent growth in previous five years of population aged 20-29

Europe 3.1 3.2 14.0 1.9 3.7

Latin Am. + Carib. 11.1 12.3 19.9 15.5 8.5

Asia 10.6 4.8 21.4 13.1 9.0

Africa 11.6 10.4 14.4 17.0 14.3

Economic Arithmetic

Sending Regions Relative GDP per capita (%) (in 1990 International Geary-Khamis dollars) 1950 1975 2000 Relative to US Eastern Europe 22 33 20 Former USSR 30 38 14 Latin America 27 29 21 Asia 7 11 13 Africa 9 8 5 Relative to Western Europe (WE) Eastern Europe 46 46 30 Former USSR 62 53 22 Latin America 56 40 32 Asia 16 15 20 Africa 19 12 8 WE/US 48 71 66

Part 2 Immigrant Impact

World Wage Convergence, Host Country Crowding Out, and Inequality

The Welfare State, Fiscal Effects and Accommodating Adjustment in Other Markets

Culture, Language, Disease and Violence

Road Map for World Wage Convergence, Host Country Crowding Out, and Inequality

The total impact of mass migration on world wages has four component parts (cet. par.):[1] the wage gap between countries falls (e.g.

convergence between countries)[2] host country wages fall (crowding out and

supply glut → more host country inequality)[3] sending country wages rise (crowding in and

rising supply scarcity → less sending country inequality)

[4] The migrants gain the most by far (→ less world inequality)

Remember, ceteris paribus!

Magnitudes will depend on the:

Size of the migration

Size of the sending and receiving countries

Other (domestic) determinants of wages, independent of the migration

Other (domestic) determinants of wages,

dependent on and responding to the migration

Searching for the causes of convergence

Allocating Labor Supplies between New and Old World

New World wage

w1n

w2n

N

O

O’

Old World wage

w3o

w2o

w1o

l1 l2

Measuring the impact

Table 6.2 The Cumulative Impact of Mass Migration 1870-1910

Persons Labor Force Net Migration Cumulative Net Migration Cumulative Impact on Real Impact on GDP Impact on GDP Rate 1870-1910 1910 Impact Rate 1870-1910 1910 Impact Wage 1910 per capita 1910 per worker 1910 Argentina 11.74 60% 15.50 86% -21.5% -8.2% -21.0% Australia 6.61 30 8.73 42 -14.6 -6.8 -14.4 Brazil 0.74 3 0.98 4 -2.3 -0.5 -1.5 Canada 6.92 32 9.14 44 -15.6 -7.6 -15.5 United States 4.03 17 5.31 24 -8.1 -3.3 -8.1 New World 6.01 29 7.93 40 -12.4 -5.3 -12.1 Belgium 1.67 7 2.20 9 -4.4 -3.1 -5.1 Denmark -2.78 -11 -3.67 -14 7.6 3.7 7.4 France -0.10 0 -0.13 -1 1.4 0.2 0.3 Germany -0.73 -3 -0.96 -4 2.4 1.3 2.2 Great Britain -2.25 -9 -2.97 -11 5.6 2.8 5.8 Ireland -11.24 -36 -14.84 -45 31.9 NA NA Italy -9.25 -31 -12.21 -39 28.2 14.2 28.6 Netherlands -0.59 -2 -0.78 -3 2.7 1.1 1.9 Norway -5.25 -19 -6.93 -24 9.7 3.1 10.4 Portugal -1.06 -4 -1.40 -5 4.3 0.0 0.0 Spain -1.16 -5 -1.53 -6 5.9 0.0 0.0 Sweden -4.20 -15 -5.55 -20 7.5 2.5 8.2 Old World -3.08 -11 -4.06 -13 8.6 2.3 5.4

Qualifying the convergence bottom line

• Let capital chase labor so that r=f(k) is constant. What then? (Remember: the pre-1913 and post-1970 years are ones of global capital markets.)

An Anglo-American example

Counterfactual General Equilibrium Effects of Migration on Real Wages Effect on 1890 economy of no

migration from 1870 to 1890 Effect on 1910 economy of no migration from 1870 to 1910

Capital Immobile

Capital Mobile Capital Immobile

Capital Mobile

United States +14.4 +3.7 +34.0 +9.2 Great Britain –8.8 –4.7 –12.2 –6.6

Was capital mobile?

Figure 1Emigration and net Capital Flows, 1891-1910

-8

-6

-4

-2

0

2

4

6

-20 -15 -10 -5 0 5 10

Net Emigration (per 1000 Population)

Cap

ital O

utflo

w (p

erce

nt o

f GD

P)

Arg

Aus

Can

USA

Nld Fin

Nor

DenSwe

SpaFra Ger

Ita

UK

Qualifying the convergence bottom line

• By letting capital chase labor so that r=f(k) is constant, the amount of convergence explained by mass migration drops to 70%, with 30% left for other forces. Mass migration still played a huge role, …

if only in the Atlantic economy (= North).

• That is, world labor markets between the South and the North were segmented.

Give me two reasons why you would NOT expect the same result today.

• Remember ceteris paribus!• Remember we are talking about raw or

unskilled labor![1] The relative size of the migrations are

less today (especially sending regions). After all, immigration is restricted everywhere.

[2] The other (independent) influences are much bigger today.

Host Country Crowding Out?The Economic Effects of Immigration

Wage

a

b

c

S1 S2

D1

X

Y Z

Labor force

W1

W2

D2

What matters?

• Suppose bigger skill (S) and/or capital (K) scarcity causes an elastic K and/or S response? Bigger or smaller crowding out?

Host Country Crowding Out?The Economic Effects of Immigration

Wage

a

b

c

S1 S2

D1

X

Y Z

Labor force

W1

W2

D2

What matters?

• Suppose bigger skill (S) and/or capital (K) scarcity causes an elastic K and/or S response? Bigger or smaller crowding out?

• Suppose labor demand is more elastic? Bigger or smaller crowding out?

Host Country Crowding Out?The Economic Effects of Immigration

Wage

a

b

c

S1 S2

D1

X

Y Z

Labor force

W1

W2

D2

What matters?

• Suppose bigger skill (S) and/or capital (K) scarcity causes an elastic K and/or S response? Bigger or smaller crowding out?

• Suppose labor demand is elastic? Bigger or smaller crowding out?

• Suppose immigration is endogenous? Bigger or smaller crowding out?

Host Country Crowding Out?The Economic Effects of Immigration

Wage

a

b

c

S1 S2

D1

X

Y Z

Labor force

W1

W2

D2

What matters?

• Suppose bigger skill (S) and/or capital (K) scarcity causes an elastic K and/or S response? Bigger or smaller crowding out?

• Suppose labor demand is elastic? Bigger or smaller crowding out?

• Suppose immigration is endogenous? Bigger or smaller crowding out?

• Suppose native-born labor supply is endogenous? Bigger or smaller crowding out?

Host Country Crowding Out?The Economic Effects of Immigration

Wage

a

b

c

S1 S2

D1

X

Y Z

Labor force

W1

W2

D2

Williamson’s favorite example where immigrants crowded-out native-born

World War I and US immigrant restrictions lowered foreign immigration big time, and crowded in black Americans from the US south. The resulting “Great Migration” south to north had a spectacular impact on 20th century US social history.

An example from the presentImmigration and Internal Migration: UK Regions by Year, 1982-2000

NetMigRt = 0.43 NetImRt-1 + 0.44 LogVacst – 0.13 LogUnRt + 2.78 LogEarnt-1 (2.1) (2.3) (0.3) (2.4) – 0.83 LogHsePt + 0.94 LogHsePt; Adj R2 = 0.92 (2.2) (1.7) Note: ‘t’ statistics in parentheses. Sample: Balanced panel of region/years. The regions are: Greater London, Rest of the Southeast, East Anglia, East Midlands, West Midlands and South West. Variable definitions: NetMigR = net migration rate into the region from elsewhere in the UK, per 1000 of the region’s population; NetImR = net immigration from abroad of foreign citizens per 1000 of the region’s population; LogVacs = log of the region’s vacancy inflow rate; LogUnR = Log of the region’s unemployment rate; LogEarn = Log average earnings of full time equivalent workers in the region; LogHseP = log of average house price in the region; LogHseP = change in log house price. Method: OLS regression; fixed region effects and year dummies included but not reported. Note that, because year dummies are included, this is equivalent to defining the logs of vacancies, unemployment, lagged earnings and house prices as log ratios to the UK mean.

Demand curves ARE downward sloping to the right

• If crowding out is so obvious in history, how come modern economists like David Card don’t believe it?

• The first to challenge the Mariel boat lift results (Altonji and Card 1991; Card 1991, 2001) were Borjas, Freeman and Katz (1997).

• Now we know that a 10% labor supply increase (decrease) lowers (raises) wages by 3-4% in Canada, the US and Mexico!

Part 2 Immigrant Impact

World Wage Convergence, Host Country Crowding Out, and Inequality

The Welfare State, Fiscal Effects and Accommodating Adjustment in Other Markets

Anti-immigrant attitudes in the 1990s

Attitudes Toward Immigration and Trade, 1995/6 Country Anti-

Immigration opinion

Anti-Imports opinion

Correlation coefficient

No of observations

Australia 3.768 3.999 0.271 2318 Austria 3.808 3.907 0.267 923 Canada 3.311 3.292 0.284 1310 Germany 4.270 3.283 0.370 1630 Great Britain 4.060 3.772 0.325 955 Ireland 3.073 3.664 0.178 919 Italy 4.148 3.599 0.243 1020 Japan 3.373 2.939 0.219 1000 Netherlands 3.822 2.930 0.272 1864 New Zealand 3.737 3.401 0.310 950 Norway 3.845 3.146 0.240 1333 Spain 3.385 3.889 0.180 1014 Sweden 3.970 3.254 0.253 1132 USA 3.880 3.765 0.249 1090 All countries 3.770 3.480 0.237 17458 Note: Based on data from the 1995 ISSP module on national identity. Figures are average attitudes on a five point scale where respondents were asked whether immigrants or imports should be increased a lot (1), increased a little (2), kept the same (3), reduced a little (4), or reduced a lot (5).

And it rose 1960s-2001!

Government Immigration Policies, 1976-2001 (Percent of governments aiming to restrict immigration more)

Year 1976 1986 1996 2001 All Countries 7 20 40 40 More Developed Countries 18 38 60 44 Less Developed Countries 3 15 34 39

Source: United Nations (2002), p. 18.

Why has immigrant restriction risen since the 1960s? Was it just rising globalization?

The Response of Public Opinion to Immigrants and Imports

NegativePublic Opinion

Immigration

Imports

Immigration or Imports

What about the rise of the welfare state and fiscal effects?

Figure 4 Welfare State Spending 1880-1980

0

5

10

15

20

25

30

1880 1890 1900 1910 1920 1930 1960 1970 1980

Year

Per

cent

of G

DP

US Canada France

Germany UK Sweden

Who gets the goodies?

Welfare Dependency and Personal Characteristics in the EU 1994-6 (differences between immigrants and EU nationals)

Country Percentage point difference between immigrants and EU nationals in receipt of

Difference in characteristics between immigrants and EU nationals

Unemp. Benefit

Family Benefit

Pensions Low educated

High educated

Age (years)

No. of children

Germany 1.6 -- -- 21.2 5.5 8.6 0.54

Denmark 24.5 5.3 17.9 14.7 0.6 7.8 0.47

Netherlands 7.0 7.9 14.9 22.7 5.3 7.7 0.65

Belgium 6.7 1.1 6.1 10.6 14.1 2.5 0.12

France 4.9 16.7 12.8 22.5 7.2 3.6 1.10

UK 0.6 0.6 23.4 15.4 21.2 8.7 0.85

Austria 8.9 8.1 18.0 7.8 12.2 10.6 0.35

Finland 31.7 0.2 12.7 12.3 17.5 7.4 0.04

Do voters think so?

The Determinants of Anti-Imports and Anti-Immigration Attitudes

Explanatory Variable

(1) Anti-Immigration Opinion

(2) Anti-Imports Opinion

Individual-level variables ‘Patriotism’ 0.055 (1.81) 0.201 (7.39) ‘Chauvinism’ 0.374 (8.23) 0.397 (13.7) Foreign-born -0.035 (0.32) -0.130 (1.99) 2nd Generation Immigrant -0.283 (6.21) 0.085 (2.11) Female 0.035 (1.13) 0.304 (11.3) Age/100 0.009 (0.07) -0.001 (1.08) Married 0.038 (1.77) 0.029 (1.40) Highly Educated -0.219 (7.13) -0.280 (7.32) Employed -0.008 (0.51) -0.032 (1.07) Country-level variables Log GDP Per Capita 0.692 (2.58) -0.294 (0.57)

Inequality 1.850 (2.26) 4.043 (2.23) Log Population 0.077 (1.51) -0.072 (0.64) Welfare Expenditure /GDP 0.047 (7.26) Share of Popn Foreign 0.044 (3.13) Imports/GDP 0.006 (0.28) OECD Trade/GDP -0.009 (0.93) R2 0.207 0.219 No of obs 14820 14820

Part 2 Immigrant Impact

World Wage Convergence, Host Country Crowding Out, and Inequality

The Welfare State, Fiscal Effects and Accommodating Adjustment in Other Markets

Culture, Language, Disease and Violence

Road Map

• Violence: Has (young male adult) migration influenced levels of violence in Mexican municipalities?

• Language: Does migration destroy languages?

• Disease: How have the 15th c. Colombian migration connection and the 20th c. African AIDS migration connection impacted the host countries?

• Culture: Does immigration destroy mainstream host country culture?

Violence: Migration, Sex Ratios and Crime

• Two facts of life: Testosterone-driven young men are much more violent than young women, and the scarcity of young women (and glut of young men) makes matters even worse as competition for partners intensifies.

• A fact of migration: Migration self selects young males.

• Hypothesis: Recent Mexican migration should have reduced violent crime in high-exit Mexican municipalities and increased it in high-entrance US municipalities.

• Did it?

Source: Jesús Viejo, “Migration, Sex Ratios and Crime: Evidence from Mexico’s Municipalities,” BU Economics

(October 2006).

Migration:Language Loss or Gain?

• Source: David Clingingsmith, “Bilingualism, Language Shift, and Industrialization in India 1931-1961,” Economics Department, Harvard (November 2006).

• 1500: About 10,000 languages spoken.• 2000: About 6,700 languages spoken.• 2100: Estimated 3,350 languages spoken (with

the vast majority speaking English, Spanish, Chinese or Hindi).

Why the loss of language?

Individual investment: Costs and benefits of acquiring bilingualism to migrating parents; costs and benefits of transferring bilingualism to kids

Market determinants of benefits: – Manufacturing (urban) vs farming (rural)– More generally, trade and GDP growth foster

specialization by location– Both foster migration and language loss– This explains most of the language loss in India 1931-

1961 (where 180 languages are still spoken).

Disease and Death: The Columbian Connection

(from Livi-Bacci 2006)• 1492-1574: In 1574, Hispanic American

population was 8-10 million (80 years after Columbus; 50 years after Cortés destroyed the Aztec capital; 40 years after the Incan emperor was assassinated by Pizarro). In 1492, “new” population estimates are 50.1-90.4 million (80-91% loss).

• 1568-1595: Central Mexican population had fallen from 2.7 to 1.4 million (48% loss).

• 1570-1600: Peruvian population fell from1.3 to 0.9 million (31% loss).

Tak!