The Benefits of Immigration: Some Implications of Recent Findings

Ethan LewisDartmouth College

The Standard Model

• Gains to immigrants themselves are huge– Large cross-country income differences persist– The world would benefit from a much freer

immigration system (e.g., Hamilton and Whalley 1984, Kennan 2013)

• Gains to receiving country are comparatively small, and contingent on much larger distributional impacts (e.g., Borjas, 1999)– Typical estimate of net benefit: Well <1% of GDP.

The Standard Model

• Immigration produces benefits by tilting the wage structure: – A country gains the most from immigration when it admits

immigrants with “scarce” skills: those skills which are rarest in the existing population

– This lowers the wage of those scarce skill, but raises the wage of everyone else (the commoner skill) and on average natives benefit

• There are winners and losers– A skill-balanced inflow produces no long-run benefits

• Perhaps a (very) short run benefit for native “capital owners”

55.4

48.044.6

52.0

020

4060

8010

0

Non-College College

Percent College and Non-College, 1990s Immigrants and Existing Workers

Data Source: Docquier, Ozden, and Peri (2010)

United States, 2000

1990s Immigrants Existing Workers

42.0

17.819.3

28.0

16.3

30.5

13.015.7

9.58.0

010

2030

4050

Dropout HS Grad 1-3 Yrs Coll 4 yrs Coll Adv. Degree

Percent at Five Education Levels among 1990s Immigrants and Existing Workers

Data Source: 2000 Census of Population

United States, 2000

1990s Immigrants Existing Workers

15.8

81.384.2

18.7

020

4060

8010

0

Non-College College

Percent College and Non-College, 1990s Immigrants and Existing Workers, 2000

Data Source: Docquier, Ozden, and Peri (2010)

United Kingdom, 2000

1990s Immigrants Existing Workers

020

4060

8010

0

Non-College College

United States

020

4060

8010

0Non-College College

United Kingdom

020

4060

8010

0

Non-College College

Canada

020

4060

8010

0

Non-College College

Sweden

020

4060

8010

0

Non-College College

France0

2040

6080

100

Non-College College

Germany

020

4060

8010

0

Non-College College

Netherlands

020

4060

8010

0

Non-College College

Belgium

020

4060

8010

0

Non-College College

Greece

020

4060

8010

0

Non-College College

Italy0

2040

6080

100

Non-College College

Spain

020

4060

8010

0

Non-College College

Portugal

Data Source: Docquier, Ozden, and Peri (2010)

Percent College and Non-College, 1990s Immigrants and Existing Workers, 2000

The standard model: what’s missing?

Factors which make benefits larger:• Imperfect substitutability between immigrants and natives

in the same observable “skill group”– ↑s natives’ gains from immigration: adverse distributional

consequences are borne more by immigrants themselves– Related: gains from product variety

• Productivity spilloversFactors which (generally) make benefits smaller:• Long-run adjustments in production technology• Non-wage impacts: public goods, compositional amenities

Gains from Variety

• Immigrants increase variety of goods & svcs– More small firms (Olney, 2013), ethnic diversity in

restaurants (Mazzolari and Neumark, 2012) though perhaps more big-box retailers

– Hedonic value (Ottaviano and Peri, 2006)• Another mechanism: scale effects increase the

extent of the product market– Large effects from scale effects of immigration,

e.g., 5% of GDP in U.S. (di Giovanni et al., 2013)

Productivity Spillovers• High skill “H1-B” (type of US visa) immigrants may raise

productivity by e.g., generating new ideas– H1-B immigrants have high patent rates, induce more patents

from native-born: Hunt & Gauthier-Loiselle (2010), Kerr & Lincoln (2010)• Link to productivity? HGL say may have ↑d GDP ≈2% in

the 1990s (applying est’s from Furman et al. 2002)– Direct association between “H1-B induced” increase in science

workers and productivity across U.S. metro areas (Peri et al., 2013)• 1990-2000: TFP 3.8%; higher college, but not non-college

wages

Spillovers, but…

• But Paserman (2013) finds no evidence of productivity spillovers in Israel following the influx of former Soviet Union immigrants – He looks across industries and firms

• Some evidence that high-tech sectors benefitted

• Misses aggregate effects?– Israel too far from the technological frontier?

Spillovers, but…• A concern: “spillover” model may be symmetrically used

to argue that unskilled immigration harms productivity:

(e.g., Moretti, 2004)But: – Results sensitive, especially to who is “unskilled”

• Acemoglu and Angrist (2000), Sand(2007), Iranzo&Peri (2009)– Don’t forget: unskilled immigration is the source of benefits in

standard model (in the US)– Indeed: in the U.S., immigration is associated with faster wage

growth, despite reducing college share: e.g., Peri(2012)…

-.2-.1

0.1

.2

-.02 0 .02 .04 .06 .08Change in Share Foreign-Born, 2000-2010

Adjusted Wage Growth, Native-Born*

-.05

0.0

5.1

.15

-.02 0 .02 .04 .06 .08Change in Share Foreign-Born, 2000-2010

Change in College ShareUnited States Metropolitan Areas, 2000-2010

Data Sources: 2000 Census of Population and 2009-2011 American Community Surveys.*Change in Mean ln(hourly wages) of native born regression adjusted for experience, education, race and gender separately in 2000 and 2010.

Immigration Lowers College Share but Raises Wages

more

Other factors not in standard model

• Entrepreneurship: immigrants have high rates– May also have productivity benefits

• Long run adjustments in production techn.• Public goods

Conclusions

• Standard model: immigration has benefits, but come with larger distributional consequences– Benefits largest from immigrants with skill that are

scarce in the native population (low-skill in US)• Standard model leaves out many things, some

of which may dwarf benefits in stand. model– Although more research supporting this claim

would be helpful

Bonus Slides

Imperfect Substitutability - details

Imperfect Substitutability

• Imms & natives are imperfect substitutes = wage gap between them responds to their relative #s*– In U.S. appears tied to English language skills:

• Immigrants w/strong English much closer substitutes for natives (Lewis, 2011)

– Immigrants and natives specialize in different jobs • Natives specialize in jobs which require communication (Peri &

Sparber, 2009) • …and benefit from lower cost of low-skill svcs (Cortes, 2008)

– This increases natives’ net gains from immigration, as adverse distributional consequences are borne more by the immigrants themselves

* e.g., Ottaviano and Peri, 2012.

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-1-.5

0.5

1Im

mig

rant

-Nat

ive

ln W

age

Gap

-4 -3 -2 -1 0 1Ln(Immigrant/Native) Total Hours

Immigrants Fluent in English

-1-.5

0.5

1

-4 -3 -2 -1 0 1Ln(Immigrant/Native) Total Hours

Immigrants with no English

ln(Wage Gap) between similar Immigrants and Natives, by Immigrants' English Skills

Data sources: 2000 Census of Population and 2007-9 American Community Surveys. Each dot represents a metropolitan area x broad education (college/non-college) x year cell. Wage gaps computed w/in narrow experience x education cells. Regression adjusted for education x year dummies.

Imperfect Substitutability

Back to what’s missingBack to language skills

Aggregate Trends in Israel, Before and after FSU Immigration

-.2-.1

0.1

.2.3

ln(G

DP

per

Cap

ita) r

elat

ive

to 1

988

1980 1985 1990 1995 2000 2005

IsraelSynthethic Control**

Data souce: World Development Indicators. *In constant U.S. dollars, deviations from 1988 value.**Combination of OECD countries matched on GDP/cap in 1978-1989, education, and openness.Largest weights on Belgium (37%), New Zealand (35%), Finland(13%), and US(8%).

ln GDP per capita*: Israel and Synthetic Control**,before and after fall of Soviet Union

back

-12.

8-1

2.6

-12.

4-1

2.2

-12

-11.

8ln

(Pat

ents

Per

Wor

king

Age

Pop

ulat

ion)

1980 1985 1990 1995 2000 2005

IsraelSynthethic Control**

Data souce: World Development Indicators. *ln(Patents/Working Age Population) **Combination of OECD countries matched on patents/Capita 1978,84,89; GDP/capita in 1989;and education. Largest weights are: US(41%), UK(32%), Korea(15%), New Zealand(7%).

Ln(Patents/Capita)*: Israel and Synthetic Control**,before and after fall of Soviet Union

1820

2224

2628

Inve

stm

ent/G

DP

1980 1985 1990 1995 2000 2005

IsraelSynthethic Control*

Data souce: Penn World Tables and World Development Indicators. *Combination of OECD countries matched on investment/GDP 1980-89, GDP/cap in 1989,openness, and real interest rate. Largest weights are: Ireland(39%), US(39%), Chile(21%).

Investment/GDP: Israel and Synthetic Control*,before and after fall of Soviet Union

back

-.2-.1

0.1

.2.3

ln(G

DP

per

Cap

ita) r

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ive

to 1

988

1980 1985 1990 1995 2000 2005year

IsraelOECD

Data souce: World Development Indicators. *In constant U.S. dollars, deviations from 1988 value

ln GDP per capita*: Israel and OECD, before and after fall of Soviet Union

back

More Native Wage Growth Results

-.2-.1

0.1

.2

0 .05 .1 .15 .2

Adjusted Wage Growth, Native-Born*

-.05

0.0

5.1

.15

0 .05 .1 .15 .2

Change in College ShareUnited States Metropolitan Areas, 2000-2010, Versus Ethnic Enclave Instrument

Ethnic Enclave Instrument: Change in Predicted Share Foreign-Born, 2000-2010Data Sources: 2000 Census of Population and 2009-2011 American Community Surveys.*Change in Mean ln(hourly wages) of native born regression adjusted for experience, education, race and gender separately in 2000 and 2010.

Immigration Lowers College Share but Raises Wages

Back

-.02

0.0

2.0

4.0

6.0

8

0 .05 .1 .15 .2Ethnic Enclave Instrument: Change in Predicted Share Foreign-Born, 2000-2010

United States Metropolitan Areas, 2000-2010

Data Sources: 2000 Census of Population and 2009-2011 American Community Surveys.

First Stage: Change in Immigrant Share vs. Ethnic Enclave Instrument

-.2-.1

0.1

.2

0 .05 .1 .15 .2

Native-Born College-Educated

-.2-.1

0.1

.2

0 .05 .1 .15 .2

Native-Born Non-College-EducatedUnited States Metropolitan Areas, 2000-2010, Versus Ethnic Enclave Instrument

Ethnic Enclave Instrument: Change in Predicted Share Foreign-Born, 2000-2010Data Sources: 2000 Census of Population and 2009-2011 American Community Surveys.*Change in Mean ln(hourly wages) of native born regression adjusted for experience, education, race and gender separately in 2000 and 2010.

Adjusted Wage Growth, Native-Born, by College/Non-College*

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Immigrant Entrepreneurship

Immigrant Entrepreneurship

• Entrepreneurial ability – or willingness to take risks – may be another “scarce” skill that immigrants bring, with productivity benefits– Imms more likely to start businesses (eg, Hunt 2011)

• Hunt studied college educated imms, but imms have high entrepreneurship rates at all education levels

– Raises productivity? Immigrant-owned businesses have 12% more revenue/worker than native-owned businesses (Garcia-Perez, 2008)

• Also, business turnover accounts for a large fraction of productivity growth (e.g., Haltiwanger 2009)

6.22

5.17

7.62

6.09

7.51

6.72

9.86

8.87

6.69 6.68

02

46

810

Dropout HS Grad 1-3 Yrs Coll 4 yrs Coll Adv. Degree

United States, 2010-12

Data Source: 2010-12 March Current Population Surveys.NOTE: Lawyers, doctors, taxi drivers, and construcion workers not counted as self-employed in this figure.

Self-Employment Rates by Nativity and Education

ImmigrantsNative-born

back

Details: Characteristics of Immigrant-Owned Businesses

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2030

$0-$4

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$5,00

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$200

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249,9

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$250

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$500

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Data Source: US Department of Commerce, Bureau of the Census. Characteristics of Business Owners.CBO92-1, Washington, DC: US Government Printing Office. September 1997. Table 6b.

Percent in Each Revenue Categoryby Nativity of Business Owner, 1992

Foreign-Born OwnerNative-Born Owner

Foreign-Born: 82.4% Non-EmployerNative-Born : 84.7% Non-Employer

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Data Source: US Department of Commerce, Bureau of the Census. Characteristics of Business Owners.CBO92-1, Washington, DC: US Government Printing Office. September 1997. Table 6c.

Percent in Each Employment Category at EmployerBusinesses, by Nativity of Business Owner, 1992

Foreign-Born OwnerNative-Born Owner

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Other Factors Not in Standard Model

The Long Run

• In the long-run, adjustments in production tech may diminish adverse distributional impacts of immigration (Lewis, 2013)– In response to unskilled imm, shift to (Beaudry &

Green 2003, 2005) or develop (Acemoglu 1998, 2002) more unskilled production technology

• Other mechanisms: capital adjustments under capital-skill complementarity; adjustments in product mix

– Also implies that benefits will diminish over time

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Public goods

• Likely animates much of the opposition to immigration, rather than labor market impacts– Natives reveal a strong distaste for living in

neighborhoods (Saez & Wachter, 2011), sending children to school w/imms (Cascio & Lewis, 2012)

– Also, some benefits (social security solvency) – Needs more research

• Natives may have an exaggeratedly negative view of immigrants’ impact on public goods

back

Sources CitedMany of the ideas in this article derive fromLewis E. 2013. “Immigration and Production Technology.” Annual Review of Economics 5.

The standard model is well described in:Borjas, GJ. 1999. “The Economic Analysis of Immigration.” In Handbook of Labor Economics Volume 3A, ed. O. Ashenfelter and D. Card, pp. 1697-1760. Amsterdam: Elsevier.

Other sources include:Acemoglu D. 1998. “Why do new technologies complement skills? Directed technical change and wage inequality.” Q. J. Econ. 113:1055–89Acemoglu D. 2002. “Technical change, inequality and the labor market.” J. Econ. Lit. 40:7–72

Acemoglu D, Angrist J. 2000. “How large are human capital externalities? Evidence from compulsory schooling laws.” In NBER Macroeconomics Annual, ed. BS Bernanke, K Rogoff, pp. 9–74. Cambridge, MA: MIT Press

Beaudry P, Green DA. 2003. “Wages and employment in the United States and Germany: What explains the differences?” Am. Econ. Rev. 93:573–602

Beaudry P, Green DA. 2005. “Changes in U.S. wages, 1976–2000: ongoing skill bias or major technological change?” J. Labor Econ. 23:609–48

Cascio, EU and Lewis, EG. 2012. “Cracks In the Melting Pot: Immigration, School Choice, and Segregation.” Am. Econ J. Econ. Pol. 4: 91-117

Sources Cited (2)Cortes P. 2008. “The effect of low-skilled immigration on US prices: evidence from CPI data.” J. Polit. Econ. 116:381–422di Giovanni J, Levchenko A, Ortega F. 2013. “A Global View of Cross-border Migration.” Unpublished Manuscript, International Monetary Fund Dustmann C, Frattini T, and Preston I. 2013. The Effect of Immigration along the Distribution of Wages.” Rev. Econ. Stud., forthcoming.Eaton J, Kortum S. 1996. “Trade in ideas: patenting and productivity in the OECD.” J. Int. Econ. 40:251–78 Furman JL, Porter ME, Stern S. 2002. “The determinants of national innovative capacity.” Res. Policy 31:899–933 Garcia-Perez M. 2008. “Does it matter who I work for and who I work with? The impact of owners and coworkers on hiring and wages.” Unpublished manuscript, Univ. Maryland, College ParkHaltiwanger, J. 2009. “Entrepreneurship and Job Growth.” In Entrepreneurship, Growth and Public Policy, ed. ZJ Acs, DB Audretsch, RJ Strom, pp. 119-145. Cambridge: Cambridge Univ. PressHamilton B, Whalley J. 1984. “Efficiency and Distributional Implications of Global Restrictions on Labour Mobility: Calculations and Policy Implications.” J. Dev. Econ. 14: 61-75.Hunt J. 2011. “Which immigrants are most innovative and entrepreneurial? Distinctions by entry visa.” J. Labor Econ. 29:417–57Hunt J, Gauthier-Loiselle M. 2010. “How much does immigration boost innovation?” Am. Econ. J.Macroecon. 2:31–56Iranzo S, Peri G. 2009. “Schooling externalities, technology, and productivity: theory and evidence from U.S. states.” Rev. Econ. Stat. 91:420–31 Kennan, J. 2013. “Open Borders.” Rev. Econ. Dyn., forthcoming.

Sources Cited (3)Lewis E. 2011. “Immigrant-native substitutability: the role of language ability.” NBER Work. Pap. 17609

Mazzolari F, Neumark D. 2012. “Immigration and Product Diversity.” J. Pop. Econ. 25: 1107-1137.

Moretti E. 2004. “Estimating the social return to higher education: evidence from longitudinal and repeated cross-sectional data.” J. Econom. 121:175–212

Olney W. 2013. “Immigration and Firm Expansion.” J. Reg. Science 53: 142-157

Ottaviano G, Peri G. 2006. “The Economic Value of Cultural Diversity: Evidence from U.S. Cities.” J. Econ. Geography 6: 9-44.

Ottaviano G, Peri G. 2012. “Rethinking the Effects of Immigration on Wages.” J. Eur. Econ. Assoc. 10: 152-197

Paserman DM. 2013. “Do high-skill immigrants raise productivity? Evidence from Israeli manufacturing firms, 1990–1999.” Unpublished manuscript, Boston Univ.

Peri G. 2012. “The effect of immigration on productivity: evidence from U.S. states.” Rev. Econ. Stat. 94:348–58

Peri G, Shih K, Sparber CS. 2013. “STEM Workers, H1B Visas, and Productivity in US Cities.” Unpublished manuscript, University of California Davis

Peri G, Sparber CS. 2009. “Task specialization, immigration, and wages.” Am. Econ. J. Appl. Econ. 1:135–69

Saez A, Wachter S. 2011. “Immigration and the Neighborhood.” Am Econ. J. Econ. Pol. 3:169-188

Sand B. 2007. “Has there been a structural change in the labor market? Evidence from U.S. cities.” Unpublished manuscript, Univ. British Columbia, Vancouver

Sources Cited (4)US Department of Commerce, Bureau of the Census. 1997. Characteristics of Business Owners. CBO92-1. Washington, DC: United States Government Printing Office.