when is “too much” inequality not enough? the selection of israeli emigrants

When is “Too Much” Inequality Not Enough?

The Selection of Israeli Emigrants

Eric D. Gould Hebrew University

Omer Moav Royal Holloway and Hebrew University

1

(Only) Two Things Israelis Agree Upon

• There is “too much” inequality in Israel.

• Israel suffers from a “Brain Drain.”

2

“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.

3

“Too Much” Inequality in Israel

4Source: Brandolini and Smeeding (2008)

The Brain Drain from Israel

• Gould and Moav (2007): emigration rates increase with education levels.

5

.009558

.012975

.019029

.047466

0.0

1.0

2.0

3.0

4.0

5F

ract

ion

Leav

ing

Isra

el

HS Dropouts HS Graduates BA Degree MA Degree or More

All Jewish Israelis Between 30 and 40 Years OldFigure 1a: Leaving Israel By Education


• Gould and Moav (2007): emigration rates are high for doctors, engineers, scientists, profs.

6

.036621

.078261

.01596

.064854

.032197

.014073

0.0

2.0

4.0

6.0

8F

ract

ion

Leav

ing

Isra

el

Engineers Lecturers Other Physicians Scientists Teachers

All Men Between 30 and 40 Years OldFigure 2a: Leaving Israel By Occupation


• 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.

7


8

(Only) Two Things Israelis Agree Upon

• There is “too much” inequality in Israel.• Israel suffers from a “Brain Drain.”

9

• 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))

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)

10

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).

11

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.

12

Main Contributions

• Empirical: analysis of emigrant selection based on observable and unobservable skill.

• Theoretical: incorporate the notion of country-specific skills into the analysis.

13

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.

14

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

15


• Case 1: Positive Selection (β0 < β1 )

16

Host

Home

Skill

Wage

EmigrateStayS*


• Case 2: Negative Selection (β0 > β1 )

17

Host

Home

Skill

Wage

StayEmigrateS*


• 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.

18

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).

19

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.

20

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.

21


• 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.22


• 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.

23

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.24

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.”

25

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.

26

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)

27

28

MeanStandard Deviation

Mover 2004 0.016 0.126

Mover 2002 0.013 0.114

Returned 2002-2004 (for Movers 2002)

0.020 0.141

Left by end of 2000 (for Movers 2004)

0.672 0.470

Education 13.011 3.187

Observations 40713

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

Emigration increases with education

29

.006562.007169

.009029

.025915

0.0

05.0

1.0

15.0

2.0

25F

ract

ion

Lea

vin

g Is

rael


30 to 45 Year Old IsraelisFigure 1a: Native Israelis Leaving Israel By Education

Levels are higher for Non-Natives

30

.01313

.021701

.030564

.056843

0.0

2.0

4.0

6F

ract

ion

Lea

vin

g Is

rael


30 to 45 Year Old IsraelisFigure 1b: Non-Native Israelis Leaving Israel By Education

Pattern is Similar for Earlier Ages

31

.025104 .025122

.030617

0.0

1.0

2.0

3F

ract

ion

Lea

vin

g Is

rael

HS Dropouts HS Graduates HS Graduates +

22 to 29 Year Old Israeli MalesFigure 2a: Israelis Leaving Israel By Education Level

Pattern is Similar for Earlier Ages

32

.022278

.027367

.031523

.037037

0.0

1.0

2.0

3.0

4F

ract

ion

Lea

vin

g Is

rael


13 to 17 Year Old IsraelisFigure 2b: Israelis Leaving Israel By Father's Education

No Selection on Returning Israelis

33

.044444

.00641

.02 .020833

0.0

1.0

2.0

3.0

4.0

5F

ract

ion

Lea

vin

g Is

rael


All IsraelisFigure 3: Returning to Israel from 2002-2004 by Education

Emigration and Residual Wages: Inverse U-Shape

34

.011542

.014988

.021125

.01891 .018669

.019926

.017927

.013019 .012776.012039

0.0

05.0

1.0

15.0

2F

ract

ion

Lea

vin

g Is

rael

Lowest 10% 10-20% 20-30% 30-40% 40-50% 50-60% 60-70% 70-80% 80-90% Highest 10%

Controlling for Education, Age, Ethnicity, and Native StatusFigure 4: Fraction Leaving Israel by Residual Wages

35

.010806

.017199

.020874

.016704

.018423.019165

.017191

.014738

.013514

.012282

0.0

05.0

1.0

15.0

2F

ract

ion

Lea

vin

g Is

rael

Lowest 10% 10-20% 20-30% 30-40% 40-50% 50-60% 60-70% 70-80% 80-90% Highest 10%

Controlling for Industry, Education, Age, Ethnicity, and Native StatusFigure 5: Fraction Leaving Israel by Residual Wages


36

.01203

.015733

.019406

.017436

.020388 .020147

.017927

.013261.014008

.010565

0.0

05.0

1.0

15.0

2F

ract

ion

Lea

vin

g Is

rael

Lowest 10% 10-20% 20-30% 30-40% 40-50% 50-60% 60-70% 70-80% 80-90% Highest 10%

Controlling for Occupation, Education, Age, Ethnicity, and Native StatusFigure 6: Fraction Leaving Israel by Residual Wages


37

Log Wage Mover 2004

Returned(to Israel) 2002-2004

US (CPS Data)

Israel(Census)

Education 0.002*** -0.002(0.000) (0.002)

Native 0.005*** -0.016(0.002) (0.026)

Age Arrived in 0.001*** -0.001(0.000) (0.001)

Log Wage -0.001 0.003(0.003) (0.014)

Root MSEObservations 40,713 538

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

38

Log Wage Mover 2004

Returned(to Israel) 2002-2004

US (CPS Data)

Israel(Census)

Education 0.100*** 0.071*** 0.002*** -0.002(0.001) (0.001) (0.000) (0.002)

Native -0.099*** 0.005*** -0.016(0.008) (0.002) (0.026)

Age Arrived in -0.019*** 0.001*** -0.001(0.000) (0.000) (0.001)

Log Wage -0.001 0.003(0.003) (0.014)

Root MSE 0.523 0.498Observations 33,302 40,713 40,713 538

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

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.

39

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 skills40

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 41


• 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)

42



– 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.)43



• Wage at “Host”: w1 = α1 + β1educ + γ1g – f


β∙educ + γ∙g > a + s

• where β= β1-1 γ= γ1-1 a= α0- α1+f44



β∙educ + γ∙g > a + s

• where β= β1-1 γ= γ1-1 a= α0- α1+f45

Benefits of Emigration

Costs of Emigration



• Wage at “Host”: w1 = α1 + β1educ + γ1g

• Restrict our attention to the cases where:

β1>1 and γ1>1 → Returns to skill are higher in host country

β1 and γ1 are not “too high” → most people do NOT move.

46


Results: Selection in terms of Education

• Emigrants are positively selected.

• The curve is convex (like Figures 1 and 2).

• The positive selection intensifies as β1 increases.

47


48

Probability to Emigrate

Education

↑β1

Positive and Convex Selection

49

.006562.007169

.009029

.025915

0.0

05.0

1.0

15.0

2.0

25F

ract

ion

Lea

vin

g Is

rael


Figure 1: Native Israelis Leaving Israel By Education


Results: Selection in terms of Residual Wage = g + s

• Inverse U-shaped function (like Figures 4-6)

• The positive selection intensifies as γ1 increases.

– The curves shifts right, but u-shape remains intact.

50


51

Probability to Emigrate

Residual Wage (g+s)

↑γ1


52

.011542

.014988

.021125

.01891 .018669

.019926

.017927

.013019 .012776.012039

0.0

05.0

1.0

15.0

2F

ract

ion

Lea

vin

g Is

rael

Lowest 10% 10-20% 20-30% 30-40% 40-50% 50-60% 60-70% 70-80% 80-90% Highest 10%

Controlling for Education, Age, Ethnicity, and Native StatusFigure 4: Fraction Leaving Israel by Residual Wages


• Intuition: Inverse U-shaped function

• A person emigrates if: β∙educ + γ∙g > a + s

• Person’s Residual = g + s

• g increases the probability of emigrating• s decreases the probability of emigrating

• Therefore, a higher g/s increases the chances to emigrate.53

Benefits of Emigration

Costs of Emigration


• Who is more likely to have a high g/s ratio?

• High residual wage → g and s are high, so g/s ≈ 1

• Low residual wage → g and s are low, so g/s ≈ 1

• Mid-level residuals → variation in g and s, g/s varies

– If g/s is high, more likely that you are in the middle of the residual wage distribution than in the tails.

54

Summary of Our Model’s Results

• Positive selection in terms of education.

• Inverse U-shaped curve in terms of residuals.

• For both types of skill: positive selection intensifies if the return increases abroad.

– Shifts the curve, but keeps the shape intact.

55

Empirical Analysis of Selection on Education

• Strategy: exploit differences between Israel and the US in the returns to education across sectors.

– Sectors are defined by industries or occupations

• Israeli and US Data: run regressions within each sector.

– Estimate the ROR to educ in each sector (both countries).

56

57

NMean

Mover 2004ROR to Educ

in IsraelROR to Educ

in USResidual SD

in IsraelResidual SD

in US

Agriculture, Forestry, Fishing 663 0.015 0.039 0.070 0.488 0.525

Mfg 13493 0.017 0.078 0.113 0.451 0.500

Electric, Water 1038 0.014 0.058 0.079 0.418 0.407

Construction 2939 0.020 0.064 0.091 0.479 0.543

Wholesale and Retail 6270 0.014 0.072 0.094 0.513 0.535

Trans., Storage, Comm. 3331 0.011 0.072 0.088 0.510 0.531

Bank, Finance, Insurance 1627 0.010 0.068 0.108 0.467 0.496

Real Estate, Business 3776 0.022 0.069 0.124 0.533 0.535

Public Admin. 3216 0.008 0.067 0.067 0.417 0.439

Education 1488 0.018 0.052 0.073 0.484 0.440

Health, Welfare, Social Work 1693 0.028 0.073 0.122 0.605 0.543

Social Service 1179 0.015 0.061 0.066 0.531 0.567

Table 3: Industry Descriptive Statistics of the Israeli Sample with US CPS Variables

58

NMean

Mover 2004

ROR to Educin Israel

ROR to Educ

in US

Residual SD in Israel

Residual SD in US

Trans., Storage, Com. 3331 0.011 0.072 0.088 0.510 0.531

Real Estate, Business 3776 0.022 0.069 0.124 0.533 0.535

Table 3: Industry Descriptive Statistics of the Israeli Sample with US CPS Variables

59

Table 4: Occupation Descriptive Statistics of the Israeli Sample with US CPS Variables

NMean

Mover 2004ROR to Educ

in Israel ROR to Educ

in USResidual SD

in Israel Residual SD

in US

Academic Professionals 5624 0.027 0.016 0.067 0.516 0.489

Associate Professionalsand Technicians

3867 0.018 0.041 0.070 0.467 0.475

Managers 4452 0.012 0.047 0.098 0.511 0.507

Clerical 4395 0.008 0.063 0.054 0.452 0.521

Agents, Sales, and Service

4429 0.012 0.054 0.113 0.489 0.571

Skilled Agricultural 516 0.016 0.036 0.060 0.462 0.529

Skilled Workers 13835 0.017 0.045 0.070 0.438 0.509

Unskilled Workers 3595 0.014 0.063 0.054 0.473 0.532


The probability that person i in sector j moves is:

• αj = sector fixed-effect → γ5 and γ6 not identified60

ij

ijij

jj

ijijiiij

educEducRORUSeducEducRORIsrael

EducRORUSEducRORIsrael

wageresidualwageresidualeducxMover

) () (

) () (

) () ()(Prob

21

65

243210



• Theory: β1<0 and β2>0

61

ij

ijij

ijijiiij

educEducRORUSeducEducRORIsrael


) () (

) () ()(Prob

21

243210



• Theory: β3<0

62

ij

ijj

ijijiiij

educEducRORUSEducRORIsrael


) () (

) () ()(Prob

3

243210

Comments on the Empirical Strategy

• We do not assume that everyone moves to the US

• Although most of them do.• 123,000 in US (Global Migrant Origin Database)• Next highest (non-Muslim country) is Canada: 17,000

• We do not assume that individuals do not change sectors.

• We are checking to see if these factors are important.63

Comments on the Empirical Strategy

• If Israelis are not moving to the US or changing sectors, then the causal effects in our specification = 0.

• Also, sector fixed-effects control for unobserved heterogeneity in tastes across sectors for emigration.

• Identifying Assumption: the relative return to skill within a person’s sector is not correlated with tastes or policies that affect higher skilled people differentially more/less than less skilled people.

64

65

Probit for being a Mover in 2004

Education* -0.0146 -0.0930***

Israel ROR Educ in Industry i (0.018) (0.027)

Education* 0.0202** 0.0511***US ROR Educ in Industry i (0.0083) (0.012)

Education* -0.0427***

Diff between Israel and US in (0.011)ROR Educ in Industry i

Education 0.00217* -0.000903 0.00254* -0.000170(0.0013) (0.00085) (0.0014) (0.00038)

Industry Fixed Effects Yes Yes Yes YesObservations 40,713 40,713 40,713 40,713

Table 5: Selection on Education – Main Results for the Industry Level Analysis

66


Education* -0.0297** -0.0298**

Israel ROR Educ in Occup i (0.012) (0.012)

Education* -0.0221** -0.0219**US ROR Educ in Occup i (0.010) (0.0099)

Education* 0.00157

Diff between Israel and US in (0.0079)ROR Educ in Occup i

Education 0.00240*** 0.00269*** 0.00400*** 0.00113***(0.00058) (0.00075) (0.00093) (0.00029)

Occupation Fixed Effects Yes Yes Yes YesObservations 40,713 40,713 40,713 40,713

Table 6: Selection on Education – Main Results for the Occupation Analysis


• By Industry: both coefficients are consistent with theory

• By Occupation: one coefficient is consistent, one not

– maybe because occupation is already a proxy for education.

• However: the “industry” results are much larger.

• Evidence for the theory is pretty strong.67

68


Industry Level Analysis

Education* -0.0427*** -0.0484*** -0.0321 -0.0426***

Diff erence between Israel and US in ROR Educ in Industry i

(0.011) (0.012) (0.024) (0.011)

Occupation Level Analysis

Education* 0.00157 0.00509 -0.00612 0.00147

Diff erence between Israel and US in ROR Educ in Occupation i

(0.0079) (0.0096) (0.016) (0.0079)

Sample Restriction None Natives Non-Natives Sectors with N > 1000

Observations 40,713 25,011 15,702 40,197

Table 7: Selection on Education – Sensitivity to Sample Selection

69

Mover 2004 Mover 2002 Mover 2002 and 2004

Mover 2004 since 2000


Education* -0.0427*** -0.0321*** -0.0315*** -0.0269***Diff between and US in (0.011) (0.010) (0.010) (0.0090)

ROR Educ in Industry i


Education* 0.00157 0.00136 0.00151 -0.00103

Diff between and US in (0.0079) (0.0074) (0.0073) (0.0064)

ROR Educ in Occupation i

Observations 40,713 40,713 40,713 40,713

Table 8: Selection on Education – Sensitivity to Definitions of a “Mover”

Empirical Analysis of Selection on Residuals

• Strategy: exploit differences between Israel and the US in the residual variation (return to unobservables) across sectors (industries or occupations).

• Israeli and US Data: run regressions within each sector.

– Estimate “residual std” in each sector/educ group cell (both countries).

– Estimate each Israeli’s residual wage in his sector in Israel.

70


Prob that person i in sector j and educ group k moves is:

• αjk = cell fixed-effect71

ijk

ijjk

ijjk

ijijiiijk

wageresidualDresidual SUS

wageresidualDresidual SIsrael


) () (

) () (

) () ()(Prob

2

1

243210



• Theory: β1<0 and β2>072

ijk

ijjk

ijjk

ijijiiijk

wageresidualDresidual SUS

wageresidualDresidual SIsrael


) () (

) () (

) () ()(Prob

2

1

243210



• Theory: β3<073

ijk

ijjkjk

ijijiiijk

wageresidualDresidual SUSDresidual SIsrael


) () () (

) () ()(Prob

3

243210

74


Industry Wage Residual* -0.0212 -0.0295*Israel Residual SD (0.015) (0.016)in Industry-Education Group i

Industry Wage Residual* 0.0219 0.0357US Residual SD (0.021) (0.022)in Industry-Education Group i

Industry Wage Residual * -0.0311**Difference between Israel and (0.015)US in Residual SD in Industry-Education Group i

Industry-Education Group Yes Yes Yes YesFixed Effects

Observations 40,412 40,412 40,412 40,412

Table 9: Selection on Unobservables – Main Industry Level Analysis

75

Table 10: Selection on Unobservables – Main Occupation Level Analysis


Occupation Wage Residual* -0.0764*** -0.0785***

Israel Residual SD (0.028) (0.028)in Occup-Education Group i

Occupation Wage Residual* 0.0245 0.0303US Residual SD (0.029) (0.029)in Occup-Education Group i

Occupation Wage Residual * -0.0552***Difference between Israel and (0.021)

US in Residual SD in Occup-Education Group i

Occupation-Education Group Yes Yes Yes YesFixed Effects

Observations 40,621 40,621 40,621 40,621

• By Industry: results are consistent with theory

• By Occupation: results are consistent with theory

– does not suffer from the potential problem that occupation is already a proxy for education.

• However: the “occupation” results are now larger.

• Evidence for the theory is strong.

76


77



Industry Wage Residual * -0.0311** -0.0244* -0.0340 -0.0331**

Difference between Israel (0.015) (0.014) (0.033) (0.015)and US in Residual SD inIndustry-Education Group i


Occupation Wage Residual * -0.0552*** -0.0515** -0.0636 -0.0621***

Difference between Israel (0.021) (0.023) (0.044) (0.021)and US in Residual SD inOccup-Education Group iSample Restriction None Natives Non-Natives Sectors > 1000

Observations 40,621 24,573 15,673 40,105

Table 11: Selection on Unobservables – Sensitivity to Sample Selection

78

Mover 2004 Mover 2002 Mover 2002 and 2004

Mover 2004 since 2000


Industry Wage Residual * -0.0311** -0.0223 -0.0236* -0.0226*Difference between Israel (0.015) (0.014) (0.014) (0.012)

and US in Residual SD inIndustry-Education Group i


Occupation Wage Residual * -0.0552*** -0.0449** -0.0442** -0.0406**Difference between Israel (0.021) (0.019) (0.019) (0.017)

and US in Residual SD in

Occup-Education Group i

Observations 40,621 40,621 40,621 40,621

Table 12: Selection on Unobservables – Sensitivity to Definitions of a “Mover”

Further Robustness Checks in Tables 13 and 14

• Results are stronger using OLS instead of Probit

• Results are robust to including interaction between residual squared and difference in residual variation.

• Results are robust to using the residual rank (within each 5-year age group) instead of residuals (since residual variation increases with age).

• Results are robust to estimating selection on education and unobservables in one regression (Table 14).

79

Magnitude of the effects: Selection on Education

80

0.0

5.1

.15

Fra

ctio

n L

eavi

ng

Isra

el

5 10 15 20 25Years of Schooling

Actual Relative Return in Israel Decreased by 0.02

Actual versus Decrease in Relative Return to School in All Industries by 0.02Figure 9: Industry Analysis - Predicted Movers by Education

81

0.0

1.0

2.0

3.0

4F

ract

ion

Lea

vin

g Is

rael

5 10 15 20 25Years of Schooling

Actual Relative Return in Israel Increased by 0.03

Actual versus Increase in Relative Return to School in All Industries by 0.03Figure 10: Industry Analysis - Predicted Movers by Education

Magnitude of the effects: Selection on Education

82

.01

.012

.014

.016

.018

.02

Fra

ctio

n L

eavi

ng

Isra

el

1 2 3 4 5 6 7 8 9 10Industry Residual Wage Decile

Relative Residual SD = -0.05 Relative Residual SD = 0.00Relative Residual SD = 0.05

Under Various Levels of Relative Industry Inequality in Israel versus USFigure 11: Predicted Movers by Industry Residual Wages

Magnitude of the effects: Selection on Residuals

83

.01

.012

.014

.016

.018

.02

Fra

ctio

n L

eavi

ng

Isra

el


Actual Decrease Relative Residual SD by 0.04

Actual versus Decreasing Relative Inequality in all Industries in Israel by 0.04Figure 12: Predicted Movers by Industry Residual Wages


84

.01

.012

.014

.016

.018

.02

Fra

ctio

n L

eavi

ng

Isra

el


Actual Increase Relative Residual SD by 0.025

Actual versus Increase in Relative Inequality in all Industries in Israel by 0.025Figure 13: Predicted Movers by Industry Residual Wages


85

.01

.012

.014

.016

.018

.02

Fra

ctio

n L

eavi

ng

Isra

el

1 2 3 4 5 6 7 8 9 10Occupation Residual Wage Decile

Actual Decrease Relative Residual SD by 0.04

Actual versus Decreasing Relative Inequality in all Occupations in Israel by 0.04Figure 15: Predicted Movers by Occupation Residual Wages


86

.01

.012

.014

.016

.018

.02

Fra

ctio

n L

eavi

ng

Isra

el

1 2 3 4 5 6 7 8 9 10Occupation Residual Wage Decile

Actual Increase Relative Residual SD by 0.025

Actual versus Increase in Relative Inequality in all Occupations in Israel by 0.025Figure 16: Predicted Movers by Occupation Residual Wages


Conclusion

• Analyzed selection on observable and unobservable skill.

• Unique data (info on individuals before they move).

• Added “country-specific” skills to the Borjas Model.

• Theory is consistent with our results. – showing the importance of “country-specific” skills.

• Results: Inequality does affect emigrant selection.87

Conclusion

• Results are unlikely due to policy by US immigration.

• Policy cannot explain variation across sectors.

• Strongest evidence in favor of the Borjas model.

• Changes in inequality affect selection by shifting the curve.

88

Implications

• Not all inequality is “bad.”

• High inequality in the US is perceived in a negative light.

• But, this is how it attracts the best workers in the world.

• A country’s level of inequality – determines how it will compete for its best workers.

• Need to be careful about reducing inequality (by taxes) which will exacerbate the brain drain.

89

when is “too much” inequality not enough? the selection of israeli emigrants

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