The Benefits of Immigration: Some Implications of Recent Findings Ethan Lewis Dartmouth 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” United States, 2000 100 Percent College and Non-College, 1990s Immigrants and Existing Workers Existing Workers 60 80 1990s Immigrants 55.4 52.0 44.6 0 20 40 48.0 Non-College Data Source: Docquier, Ozden, and Peri (2010) College United States, 2000 50 Percent at Five Education Levels among 1990s Immigrants and Existing Workers 1990s Immigrants Existing Workers 40 42.0 20 30 30.5 28.0 17.8 19.3 16.3 15.7 13.0 8.0 0 10 9.5 Dropout HS Grad Data Source: 2000 Census of Population 1-3 Yrs Coll 4 yrs Coll Adv. Degree United Kingdom, 2000 100 Percent College and Non-College, 1990s Immigrants and Existing Workers, 2000 1990s Immigrants 84.2 18.7 15.8 0 20 40 60 80 81.3 Existing Workers Non-College Data Source: Docquier, Ozden, and Peri (2010) College Percent College and Non-College, 1990s Immigrants and Existing Workers, 2000 College 100 80 0 Non-College 100 Belgium 60 40 0 20 0 College Non-College College 100 40 60 80 100 60 40 0 0 20 0 College College Portugal 80 100 60 40 Data Source: Docquier, Ozden, and Peri (2010) Non-College Spain 20 Non-College College 80 100 60 40 20 0 Non-College 80 100 80 60 40 20 0 College Netherlands Italy College 40 Non-College 80 100 80 20 40 60 80 60 40 20 0 College Greece Non-College 20 0 College Germany 100 France Non-College 60 80 60 40 20 Non-College 20 Non-College Sweden 100 Canada 0 20 40 60 80 100 United Kingdom 0 20 40 60 80 100 United States Non-College College Non-College College 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 spillovers Factors 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 noncollege 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: 𝑠𝑘𝑖𝑙𝑙𝑒𝑑 ln 𝑝𝑟𝑜𝑑𝑢𝑐𝑡𝑖𝑣𝑖𝑡𝑦 = 𝛼 + 𝛽 𝑠𝑘𝑖𝑙𝑙𝑒𝑑 + 𝑢𝑛𝑠𝑘𝑖𝑙𝑙𝑒𝑑 (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)… Immigration Lowers College Share but Raises Wages United States Metropolitan Areas, 2000-2010 Change in College Share .1 -.2 -.05 0 -.1 0 .05 .1 .2 .15 Adjusted Wage Growth, Native-Born* more -.02 0 .02 .04 .06 .08 Change in Share Foreign-Born, 2000-2010 -.02 0 .02 .04 .06 .08 Change in Share Foreign-Born, 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. 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. Back Imperfect Substitutability ln(Wage Gap) between similar Immigrants and Natives, by Immigrants' English Skills .5 0 -.5 -1 -1 -.5 0 .5 1 Immigrants with no English 1 Immigrants Fluent in English -4 -3 -2 -1 0 Ln(Immigrant/Native) Total Hours 1 -4 -3 -2 -1 0 Ln(Immigrant/Native) Total Hours 1 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. Back to language skills Back to what’s missing Aggregate Trends in Israel, Before and after FSU Immigration .3 ln GDP per capita*: Israel and Synthetic Control**, before and after fall of Soviet Union -.2 -.1 0 .1 .2 Israel Synthethic Control** 1980 1985 1990 1995 2000 2005 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%). back -11.8 Ln(Patents/Capita)*: Israel and Synthetic Control**, before and after fall of Soviet Union -12.8 -12.6 -12.4 -12.2 -12 Israel Synthethic Control** 1980 1985 1990 1995 2000 2005 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%). Investment/GDP: Israel and Synthetic Control*, before and after fall of Soviet Union 18 20 22 24 26 28 Israel Synthethic Control* 1980 back 1985 1990 1995 2000 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%). 2005 .3 ln GDP per capita*: Israel and OECD, before and after fall of Soviet Union -.2 -.1 0 .1 .2 Israel OECD 1980 1985 1990 year 1995 2000 2005 Data souce: World Development Indicators. *In constant U.S. dollars, deviations from 1988 value back More Native Wage Growth Results Immigration Lowers College Share but Raises Wages United States Metropolitan Areas, 2000-2010, Versus Ethnic Enclave Instrument Change in College Share .1 -.2 -.05 0 -.1 0 .05 .1 .2 .15 Adjusted Wage Growth, Native-Born* 0 .05 .1 .15 .2 0 .05 .1 Ethnic Enclave Instrument: Change in Predicted Share Foreign-Born, 2000-2010 .15 .2 Back 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. First Stage: Change in Immigrant Share vs. Ethnic Enclave Instrument -.02 0 .02 .04 .06 .08 United States Metropolitan Areas, 2000-2010 0 .05 .1 .15 .2 Ethnic Enclave Instrument: Change in Predicted Share Foreign-Born, 2000-2010 Data Sources: 2000 Census of Population and 2009-2011 American Community Surveys. Adjusted Wage Growth, Native-Born, by College/Non-College* United States Metropolitan Areas, 2000-2010, Versus Ethnic Enclave Instrument .1 0 -.1 -.2 -.2 -.1 0 .1 .2 Native-Born Non-College-Educated .2 Native-Born College-Educated 0 .05 .1 .15 .2 0 .05 .1 .15 Ethnic Enclave Instrument: Change in Predicted Share Foreign-Born, 2000-2010 Back 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. .2 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) Self-Employment Rates by Nativity and Education United States, 2010-12 10 9.86 Immigrants Native-born 8.87 8 7.62 7.51 6.72 6.22 6.69 6.68 6 6.09 0 2 4 5.17 Dropout HS Grad 1-3 Yrs Coll 4 yrs Coll Data Source: 2010-12 March Current Population Surveys. NOTE: Lawyers, doctors, taxi drivers, and construcion workers not counted as self-employed in this figure. back Adv. Degree Details: Characteristics of Immigrant-Owned Businesses 30 Percent in Each Revenue Category by Nativity of Business Owner, 1992 $0 - $4 ,9 99 $5 ,0 00 -$ $1 9, 99 0, 00 9 0$2 $2 4, 99 5, 00 9 0$4 $5 9, 0, 99 00 9 0 -$ $1 99 00 ,9 ,0 99 00 -$ $2 19 00 9, 99 ,0 00 9 $2 $2 49 50 ,9 ,0 99 00 -$ $5 49 00 9, 99 ,0 00 9 -$ 99 9, 99 >$ 9 1, 00 0, 00 0 0 10 20 Foreign-Born Owner Native-Born Owner 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. 80 Percent in Each Employment Category at Employer Businesses, by Nativity of Business Owner, 1992 60 Foreign-Born Owner Native-Born Owner >1 00 Em p p 50 - 99 Em p 49 20 - 19 10 - Em p Em p Em 59 14 Em p 0 20 40 Foreign-Born: 82.4% Non-Employer Native-Born : 84.7% Non-Employer 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. back 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 capitalskill complementarity; adjustments in product mix – Also implies that benefits will diminish over time back 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 Cited Many of the ideas in this article derive from Lewis 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. 113:1055–89 Acemoglu 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. 116:381–422 di 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 Park Haltiwanger, 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. Press Hamilton 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. 29:417–57 Hunt J, Gauthier-Loiselle M. 2010. “How much does immigration boost innovation?” Am. Econ. J.Macroecon. 2:31–56 Iranzo 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 crosssectional 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.