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When is “Too Much” Inequality Not Enough? The Selection of Israeli Emigrants. Eric D. Gould Hebrew University Omer Moav Royal Holloway and Hebrew University. (Only) Two Things Israelis Agree Upon. There is “too much” inequality in Israel. Israel suffers from a “Brain Drain.”. - PowerPoint PPT Presentation
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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
The Brain Drain from Israel
• 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
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.
7
The Brain Drain from Israel
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
Borjas (1987) Model of Emigration
• Case 1: Positive Selection (β0 < β1 )
16
Host
Home
Skill
Wage
EmigrateStayS*
Borjas (1987) Model of Emigration
• Case 2: Negative Selection (β0 > β1 )
17
Host
Home
Skill
Wage
StayEmigrateS*
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.
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
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.22
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.
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
HS Dropouts HS Graduates BA Degree MA Degree or More
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
HS Dropouts HS Graduates BA Degree MA Degree or More
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
HS Dropouts HS Graduates BA Degree MA Degree or More
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
HS Dropouts HS Graduates BA Degree MA Degree or More
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
Emigration and Residual Wages: Inverse U-Shape
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
Emigration and Residual Wages: Inverse U-Shape
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
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)
42
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.)43
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+f44
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+f45
Benefits of Emigration
Costs of Emigration
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 and γ1>1 → Returns to skill are higher in host country
β1 and γ1 are not “too high” → most people do NOT move.
46
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 β1 increases.
47
A Model of Emigration with Country-Specific Skills
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
HS Dropouts HS Graduates BA Degree MA Degree or More
Figure 1: Native Israelis Leaving Israel By Education
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 γ1 increases.
– The curves shifts right, but u-shape remains intact.
50
A Model of Emigration with Country-Specific Skills
51
Probability to Emigrate
Residual Wage (g+s)
↑γ1
Emigration and Residual Wages: Inverse U-Shape
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
A Model of Emigration with Country-Specific Skills
• 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
A Model of Emigration with Country-Specific Skills
• 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
Empirical Analysis of Selection on Education
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
Empirical Analysis of Selection on Education
The probability that person i in sector j moves is:
• Theory: β1<0 and β2>0
61
ij
ijij
ijijiiij
educEducRORUSeducEducRORIsrael
wageresidualwageresidualeducxMover
) () (
) () ()(Prob
21
243210
Empirical Analysis of Selection on Education
The probability that person i in sector j moves is:
• Theory: β3<0
62
ij
ijj
ijijiiij
educEducRORUSEducRORIsrael
wageresidualwageresidualeducxMover
) () (
) () ()(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
Probit for being a Mover in 2004
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
Empirical Analysis of Selection on Education
• 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
Probit for being a Mover in 2004
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
Industry Level Analysis
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
Occupation Level Analysis
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
Empirical Analysis of Selection on Residuals
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
wageresidualwageresidualeducxMover
) () (
) () (
) () ()(Prob
2
1
243210
Empirical Analysis of Selection on Residuals
Prob that person i in sector j and educ group k moves is:
• Theory: β1<0 and β2>072
ijk
ijjk
ijjk
ijijiiijk
wageresidualDresidual SUS
wageresidualDresidual SIsrael
wageresidualwageresidualeducxMover
) () (
) () (
) () ()(Prob
2
1
243210
Empirical Analysis of Selection on Residuals
Prob that person i in sector j and educ group k moves is:
• Theory: β3<073
ijk
ijjkjk
ijijiiijk
wageresidualDresidual SUSDresidual SIsrael
wageresidualwageresidualeducxMover
) () () (
) () ()(Prob
3
243210
74
Probit for being a Mover in 2004
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
Probit for being a Mover in 2004
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
Empirical Analysis of Selection on Residuals
77
Probit for being a Mover in 2004
Industry Level Analysis
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 Level Analysis
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 Level Analysis
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 Level Analysis
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
1 2 3 4 5 6 7 8 9 10Industry Residual Wage Decile
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
Magnitude of the effects: Selection on Residuals
84
.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
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
Magnitude of the effects: Selection on Residuals
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
Magnitude of the effects: Selection on Residuals
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
Magnitude of the effects: Selection on Residuals
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
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