Scale, Selection, and Sorting in International Migration: Lectures 1 and 2

1. Scale, Selection, and Sorting in International Migration:Lectures 1 and 2 Gordon H. Hanson UC San Diego and NBER

2. Questions confronting current migration research • What explains the scale of international migration? • Flows are small (despite large wage differences) • Which individuals select themselves into migration? • In most source countries, migrants are positively selected by skill • How do migrants sort themselves across destinations? • There is positive sorting of migrants across destinations

3. Scale of international migration UN Migration Report, 2005

4. Gains to international migration Clemens, Montenego and Pritchett (2008)

5. Income gain to US legal immigrants (Rosenzweig, 2007)

6. Positive selection of emigrants is nearly universal

7. Positive sorting of emigrants across OECD destinations

8. Literature • Migrant scale & selection • Borjas; Chiquiar & Hanson; McKenzie & Rapoport; Mayda • Rosenzweig; Grogger & Hanson; Belot & Hatton; Brücker & Defoort • Brain drain • Adams; Ozden & Schiff; Beine, Docquier & Rapoport • Docquier, Lohest & Marfouk; Desai, McHale & Kapur • Sorting of migrants • Borjas, Bronars & Trejo; Dahl; Grogger & Hanson

9. (I) Scale

10. Model • Wage is fn. of education (primary (j=1), secondary (j=2), tertiary (j=3)), for person i from source s in destination h • Migration costs (fixed and skill-specific components) • Utility (with α > 0 and an iid extreme value RV)

11. Scale equation • Log odds of migrating from source s to destination h • Scale of migration should rise as rises (the level difference in destination-source wages) is the population share of education group j in s that migrates to h is the population share of education group j in s that remains in s

12. (II) Selection

13. Selection equation • Difference scale equation between high skill (j=3) and low skill (j=1) groups to obtain selection equation: • On left-hand side • Difference in log odds of emigrating between high-skill and low-skill groups (positive value indicates positive selection) • On right-hand side: • (1) difference in skill-related wage differences between destin. and source countries, (2) difference in migration costs for high and low-skilled migrants, (3) common migration costs (fsh) disappear

14. Estimating equations • Scale equation (assume fsh and gjsh are function of xsh) • Selection equation (assume gjsh is function of xsh) • Coefficient on wages in scale and selection equations is the same

15. Data on migrant stocks • Emigration: Beine, Docquier, and Rapoport (2006) • Counts of emigrants in 15 OECD destination countries from 192 source countries by education level for 2000 • Population: Age 25 and older • Immigrants: those born outside country of current residence • Education groups: Primary (0-8 years of schooling), Secondary (9-12 years of schooling), Tertiary (13+ years of schooling)

16. Earnings data • Measuring skill related wage differences in 1990s • Sources • Luxembourg Income Survey, WDI combined with WIDER • Measure difference in wages between high-skilled and low-skilled as difference in earnings at 80th and 20th percentiles • For WDI/WIDER, assume lny ~ N(μ,σ), such that E(y)=exp(μ+σ2/2) • Given gini, G, variance in log income is: • and α quantile of y is (for Zα, the α quantile of N(0,1)):

17. Controls for migration costs • Language • Anglophone destination, common language in source and destin. • Geography • Log distance, contiguity, longitude difference • History • Colonial relationships • Immigration policy • Share of asylees and refugees in destination country immigration • Visa waivers, Schengen signatories by source-destination

18. Legal status of US immigrants, 2005 Legal Permanent Resident Aliens 10.5 million (28%) Unauthorized Migrants 11.1 million (30%) Temporary Legal Residents 1.3 million (3%) Naturalized Citizens 11.5 million (31%) Refugee Arrivals 2.6 million (7%)

19. US legal immigrants by entry status

20. Foreign student flows (Rosenzweig, 2006)

21. US legal immigration (Rosenzweig, 2006)

22. Estimating equations • Scale equation (assume fsh and gjsh are function of xsh) • Selection equation (assume gjsh is function of xsh) • Coefficient on wages in scale and selection equations is the same

23. Results for scale and selection regressions (clustered standard errors in parentheses, other regressors not shown) In theory, coefficient estimates should be identical In the selection regression, fixed migration costs are differenced away, while in the scale regression they are not

24. What happens if migration costs are proportional to wages, as in Borjas (1987)? • Let wages, costs be as before but assume log utility • Further, assume migration costs are proportional to wages, such that

25. Log utility and proportional migration costs • Scale and selection equations are Scale Selection where λ>0 and δ3h = ln W3h / W1h (Mincerian return to tertiary education)

26. lnW Mx Wage US Wage s* S Theory: The Borjas View of Negative Selection

27. Linear utility versus log utility (clustered standard errors in parentheses, other regressors not shown) In theory, all wage coefficients should be positive

28. Log utility model predicts negative selection neg. selection of mig. to US neg. selection of mig. to Ger.

29. While linear utility model predicts negative selection pos. selection of mig. to US pos. selection of mig. to Ger.

30. Data strongly support positive selection of emigrants

31. Why negative selection fails: productivity differences • International wage differences • Suppose in Nigeria • Tertiary educated earn $5,000 a year, while primary educated earn $1,000 (meaning return to extra year of education is 20%) • while in the US • Tertiary educated earn $40,000 a year, while primary educated earn $20,000 (meaning return to extra year of education is 8%) • Predicted pattern of migrant selection • Proportional-cost model predicts negative selection: δN – δUS > 0 • Fixed-cost model predicts positive selection: WTUS- WTN > WPUS- WPN • Because of large differences in US-Nigerian raw labor productivity, gain to migration is higher for tertiary educated

32. Positive selection versus negative selection • Condition for migrants to be positively selected by skill • Ignoring migration costs, condition becomes Ratio of labor productivity in destination relative to source (>1) Ratio of return to skill in source relative to destination (>1)

33. Estimating fixed migration costs • Scale equation with source-destination fixed effects is an alternative way to write the selection equation • Estimate equation with a full set of source-destination dummies • Divide dummy coefficients by –α to estimate fixed costs fsh • To capture skill specific migration costs, include controls for costs (xsh) interacted with dummy for skill group

34. Estimated fixed migration costs, selected countries (000s of 2000 USD, relative to US-Mexico migration costs)

35. (III) Sorting

36. Sorting equation • Collect terms with source country subscripts in selection equation (ie, add source fixed effects), which yields: • Only requires data on wages in destination • Common coefficient on wages in scale, selection, sorting eqs.

37. Estimating equations • Scale equation (assume fsh and gjsh are function of xsh) • Selection equation (assume gjsh is function of xsh) • Sorting equation (assume gjsh is function of xsh)

38. Earnings and Taxes • Earnings data from household surveys in rich countries • Luxembourg Income Survey • Adjusting for tax treatment across OECD destinations • Low-wage tax rate (67% of average production worker wage) • High-wage tax rate (167% of average production worker wage) • Tax rate includes income taxes net of benefits (as tallied by OECD) plus both sides of the payroll tax • Rates are averaged over 1996-2000

39. LIS data on wage levels and differences (000s USD)

40. Regression results (other regressors suppressed) similarity of coefficients pre vs. post tax

41. Additional regressors

42. Decomposing the immigrant skill gap • Why do some destinations get more skilled migrants? • Redefine key variables to write sorting regression as: • By properties of OLS, we have • Differencing between US, destination h yields immigrant skill gap decomposition

43. Skill gaps among destination countries

44. Explaining the immigrant skill gap

45. Concluding remarks • Dominant features of international migration are small scale, positive selection and positive sorting • A simple model of income maximization can go a long way in accounting for these outcomes • Wage differences contribute to positive selection and why educated migrants choose Anglophone countries over continental Europe • Methodological issues • Bilateral migration costs (broadly defined) appear to be large • Not controlling for unobserved bilateral migration costs yields inconsistent estimates of how wages affect migration • Models with log utility and proportional costs fail to explain scale or skill composition of emigration • Seemingly crude measures of absolute skill-based wage differences perform surprisingly well

46. Extra slides

47. Results for proportional migration costs (Borjas)

48. Robustness checks • Measure wages adjusting for PPP • Use Freeman Oosterndorp measure of wages • Drop destination countries one by one to test IIA • Control for university quality, lagged migrant stock • Redefine high skilled as secondary or tertiary educated