Unveiling Inequality in Israeli Emigration: A Study in Brain Drain

1. When is “Too Much” Inequality Not Enough? The Selection of Israeli Emigrants Eric D. Gould Hebrew University Omer Moav Royal Holloway and Hebrew University

2. (Only) Two Things Israelis Agree Upon • There is “too much” inequality in Israel. • Israel suffers from a “Brain Drain.”

3. “Too Much” Inequality in Israel • Israel Social Security Agency • Every 6 months: “poverty report” • Brandolini and Smeeding (2008) • Among 24 high income countries, only the US has a higher 90-10 ratio in disposable personal income.

4. “Too Much” Inequality in Israel Source: Brandolini and Smeeding (2008)

5. The Brain Drain from Israel • Gould and Moav (2007): emigration rates increase with education levels.

6. The Brain Drain from Israel • Gould and Moav (2007): emigration rates are high for doctors, engineers, scientists, profs.

7. The Brain Drain from Israel • Dan Ben-David (2008) looks at academics. • The number of Israelis in the top 40 American departments in physics, chemistry, philosophy, computer science and economics, as a percentage of their remaining colleagues in Israel, is over twice the overall academic emigration rates from European countries.

8. The Brain Drain from Israel

9. (Only) Two Things Israelis Agree Upon • There is “too much” inequality in Israel. • Israel suffers from a “Brain Drain.” • Our paper: solving one of these problems, may make the other one worse. • Main idea: A “Brain Drain” may be indicative of “too little” inequality. (Borjas (1987), Roy (1951))

10. Goals of the Paper • Examine the effect of inequality on the incentives to emigrate according to skill levels. • Theoretically and empirically. • For Two types of skills: observable (education) and unobservable (residual wages)

11. Unique Data • 1995 Israeli Census • Matched with info on who leaves the country during the next 9 years. • Unique: wages of those who stay and leave. • Existing Literature: rare to have wage info on emigrants before they leave (the home country).

12. Unique Data • Existing Literature: rare to have wage info on emigrants before they leave (the home country). • Without wages: cannot assess selection based on wages, unobservable skill, etc. • Existing Literature: examines mostly education • But, education explains little variation in earnings.

13. Main Contributions • Empirical: analysis of emigrant selection based on observable and unobservable skill. • Theoretical: incorporate the notion of country-specific skills into the analysis.

14. Outline of the Talk • Present the Borjas model and discuss the evidence. • Present the basic patterns of the data. • Show that the basic predictions work for observable skills but not for unobservable skills. • Present a model which explains why this is so. • Empirical Work.

15. Borjas (1987) Model of Emigration • Based on Roy (1951) model. • A person maximizes wages. • Wage in “Home” country: w0 = α0+β0skill • Wage in “Host” country: w1 = α1+β1skill • A person decides to emigrate if: w1 > w0

16. Borjas (1987) Model of Emigration • Case 1: Positive Selection (β0 < β1 ) Host Wage Home S* Skill Stay Emigrate

17. Borjas (1987) Model of Emigration • Case 2: Negative Selection (β0 > β1 ) Home Wage Host S* Skill Emigrate Stay

18. Borjas (1987) Model of Emigration • Inequality affects the selection of immigrants. • Low inequality (β0 < β1 ) induces a Brain Drain. • This is true even if β0 is considered “high.” • Relative Inequality is what matters.

19. Evidence on the Borjas (1987) Model • Some evidence using immigrant wages from different countries in the US. • (Borjas (1987), Cobb-Clark (1993)) • Selection by education in US or OECD: very mixed • (Feliciano (2005), Grogger and Hanson (2008), Belot and Hatton (2008)). • Possible explanation: comparisons across countries may be confounded by other differences across countries (different moving costs, language, etc).

20. Evidence on the Borjas (1987) Model • Large Literature on the selection of Mexican immigrants in the US according to education. • Borjas model predicts negative selection – since the returns to education are higher in Mexico. • Chiquiar and Hanson (JPE, 2005) find “intermediate selection,” not negative selection.

21. Chiquiar and Hanson (JPE, 2005) • Find “intermediate”, not negative selection. • They add “moving costs” to the model which decline with education levels. • Chiswick (1999) and McKenzie and Rapoport (2007) also argue that migration costs decline with education.

22. Chiquiar and Hanson (JPE, 2005) • Find “intermediate”, not negative selection. • Low education → low emigration due to high moving costs. • High education → low emigration due to high return to education in Mexico. • Mid-level education → highest rate of emigration.

23. Chiquiar and Hanson (JPE, 2005) • They look only at selection in terms of education. • We also find “intermediate selection” for wages. • Their explanation cannot be used to explain this. • Since returns to skill are higher in US versus Israel. • Therefore, we add “country-specific” skills to model.

24. Data • 1995 Israeli Census • contains demographic, labor force, information • Merged with an indicator for being a “mover” as of 2002 and 2004. • if he is a “mover,” we also have the year he moved. • “Mover” = out of Israel more than a year.

25. Weaknesses in the Data • No info on where he “moved.” (most are in US) • No info on whether he intends to come back. • All papers on emigration suffer from this. • The individual probably does not know this. • Our strategy: check robustness of results to different ways of defining a “mover.”

26. Strengths in the Data • Info on everyone before they decide to move. • Wages, education, occupation, industry, etc. • We can see where they are in the distribution of observable skill (education) and unobservable skill (wages) before they leave.

27. Our Sample • A strong attachment to the labor force. • at least 30 hrs a week, 6 months in previous year • not self-employed. • Males • ≥ 30 years old as of 1995 (finished schooling) • Young enough so that the moving decision is likely to be career related. (30-45 years old in 1995)

28. Table 1: Descriptive Statistics for Male Workers from the 1995 Israel Census

29. Emigration increases with education

30. Levels are higher for Non-Natives

31. Pattern is Similar for Earlier Ages

32. Pattern is Similar for Earlier Ages

33. No Selection on Returning Israelis

34. Emigration and Residual Wages: Inverse U-Shape

35. Emigration and Residual Wages: Inverse U-Shape

36. Emigration and Residual Wages: Inverse U-Shape

37. Table 2: Descriptive OLS Regressions for Male Workers in Israel and the US

38. Table 2: Descriptive OLS Regressions for Male Workers in Israel and the US

39. Overall Patterns in the Data • Selection in terms of education: Positive • consistent with the Borjas Model • ROR to education is much higher in the US. • Selection on unobservables: Inverse U-shape • NOT consistent with the Borjas Model • ROR to unobservable ability is higher in the US.

40. Overall Patterns in the Data • Selection on unobservables: Inverse U-shape • Chiquiar and Hanson cannot explain this either. • We need to explain why the high end moves less. • They add moving costs which decline with skill, and this will only make them move more. • Our explanation: country-specific skills

41. A Model of Emigration with Country-Specific Skills • A person maximizes wages. • Wage in “Home” country: w0 = α0 + educ + g + s • Normalize the ROR to educ at home = 1 • “Residual wage” ũ = g + s

42. A Model of Emigration with Country-Specific Skills • Wage at “Home”: w0 = α0 + educ + g + s • g = “general” unobservable skill (ability, etc) • s = “country-specific” unobservable skills • personal connections, language skills, cultural barriers, knowledge about business practices, laws, consumer tastes, regulations, etc. • firm specific skills • “luck” (being at the right place at the right time)

43. A Model of Emigration with Country-Specific Skills • Wage at “Home”: w0 = α0 + educ + g + s • g and s are uniformly distributed [0,1], independent • Wage at “Host”: w1 = α1 + β1educ + γ1g - f • s is lost if he moves to the “host” country. • f is the fixed-cost of moving • Assume:β1>1 γ1>1 (Israel versus U.S.)

44. A Model of Emigration with Country-Specific Skills • Wage at “Home”: w0 = α0 + educ + g + s • Wage at “Host”: w1 = α1 + β1educ + γ1g – f • A person decides to emigrate if: w1 > w0 β∙educ + γ∙g > a + s • where β= β1-1 γ= γ1-1 a= α0- α1+f

45. A Model of Emigration with Country-Specific Skills • A person decides to emigrate if: w1 > w0 β∙educ + γ∙g > a + s • where β= β1-1 γ= γ1-1 a= α0- α1+f Benefits of Emigration Costs of Emigration

46. A Model of Emigration with Country-Specific Skills • Wage at “Home”: w0 = α0 + educ + g + s • Wage at “Host”: w1 = α1 + β1educ + γ1g • Restrict our attention to the cases where: β1>1 andγ1>1 → Returns to skill are higher in host country β1andγ1 are not “too high” → most people do NOT move.

47. A Model of Emigration with Country-Specific Skills Results: Selection in terms of Education • Emigrants are positively selected. • The curve is convex (like Figures 1 and 2). • The positive selection intensifies as β1increases.

48. A Model of Emigration with Country-Specific Skills Probability to Emigrate ↑β1 Education

49. Positive and Convex Selection

50. A Model of Emigration with Country-Specific Skills Results: Selection in terms of Residual Wage = g + s • Inverse U-shaped function (like Figures 4-6) • The positive selection intensifies as γ1increases. • The curves shifts right, but u-shape remains intact.