Transcript When is “Too Much” Inequality Not Enough? The Selection of

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
Source: Brandolini and Smeeding (2008)
4
The Brain Drain from Israel
• Gould and Moav (2007): emigration rates
increase with education levels.
Figure 1a: Leaving Israel By Education
.05
All Jewish Israelis Between 30 and 40 Years Old
.03
.02
.019029
.012975
.009558
0
.01
Fraction Leaving Israel
.04
.047466
HS Dropouts
HS Graduates
BA Degree
MA Degree or More
5
The Brain Drain from Israel
• Gould and Moav (2007): emigration rates are
high for doctors, engineers, scientists, profs.
Figure 2a: Leaving Israel By Occupation
.08
All Men Between 30 and 40 Years Old
.078261
.06
.04
.036621
.032197
.02
.01596
.014073
0
Fraction Leaving Israel
.064854
Engineers
Lecturers
Other
Physicians
Scientists
Teachers
6
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.”
• 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))
9
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 countryspecific 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 )
Host
Wage
Home
S*
Stay
Skill
Emigrate
16
Borjas (1987) Model of Emigration
• Case 2: Negative Selection (β0 > β1 )
Home
Wage
Host
S*
Emigrate
Skill
Stay
17
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
Table 1: Descriptive Statistics for Male Workers from the 1995 Israel Census
Mean
Standard
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
28
Emigration increases with education
Figure 1a: Native Israelis Leaving Israel By Education
30 to 45 Year Old Israelis
.01
.015
.02
.025
.025915
.009029
.007169
0
.005
.006562
HS Dropouts
HS Graduates
BA Degree
MA Degree or More
29
Levels are higher for Non-Natives
Figure 1b: Non-Native Israelis Leaving Israel By Education
.06
30 to 45 Year Old Israelis
.04
.056843
.030564
.02
.021701
0
.01313
HS Dropouts
HS Graduates
BA Degree
MA Degree or More
30
Pattern is Similar for Earlier Ages
Figure 2a: Israelis Leaving Israel By Education Level
22 to 29 Year Old Israeli Males
.03
.030617
.025122
HS Dropouts
HS Graduates
0
.01
.02
.025104
HS Graduates +
31
Pattern is Similar for Earlier Ages
Figure 2b: Israelis Leaving Israel By Father's Education
.04
13 to 17 Year Old Israelis
.037037
.03
.031523
.027367
0
.01
.02
.022278
HS Dropouts
HS Graduates
BA Degree
MA Degree or More
32
No Selection on Returning Israelis
Figure 3: Returning to Israel from 2002-2004 by Education
.05
All Israelis
.01
.02
.03
.04
.044444
.02
.020833
BA Degree
MA Degree or More
0
.00641
HS Dropouts
HS Graduates
33
Emigration and Residual Wages: Inverse U-Shape
Figure 4: Fraction Leaving Israel by Residual Wages
Controlling for Education, Age, Ethnicity, and Native Status
.021125
.02
.019926
.01891 .018669
.017927
.015
.014988
.013019 .012776
.012039
0
.005
.01
.011542
Lowest 10%
10-20%
20-30%
30-40%
40-50%
50-60%
60-70%
70-80%
80-90% Highest 10%
34
Emigration and Residual Wages: Inverse U-Shape
Figure 5: Fraction Leaving Israel by Residual Wages
Controlling for Industry, Education, Age, Ethnicity, and Native Status
.02
.020874
.019165
.018423
.017199
.017191
.015
.016704
.014738
.013514
.012282
0
.005
.01
.010806
Lowest 10%
10-20%
20-30%
30-40%
40-50%
50-60%
60-70%
70-80%
80-90% Highest 10%
35
Emigration and Residual Wages: Inverse U-Shape
Figure 6: Fraction Leaving Israel by Residual Wages
Controlling for Occupation, Education, Age, Ethnicity, and Native Status
.02
.020388 .020147
.019406
.017927
.017436
.015
.015733
.014008
.013261
.01203
0
.005
.01
.010565
Lowest 10%
10-20%
20-30%
30-40%
40-50%
50-60%
60-70%
70-80%
80-90% Highest 10%
36
Table 2: Descriptive OLS Regressions for Male Workers in Israel and the US
Log Wage
US
(CPS Data)
Education
Native
Age Arrived in
Log Wage
Root MSE
Observations
Mover
2004
Returned
(to Israel)
2002-2004
0.002***
(0.000)
0.005***
(0.002)
0.001***
(0.000)
-0.001
(0.003)
-0.002
(0.002)
-0.016
(0.026)
-0.001
(0.001)
0.003
(0.014)
40,713
538
Israel
(Census)
37
Table 2: Descriptive OLS Regressions for Male Workers in Israel and the US
Log Wage
Education
US
(CPS Data)
Israel
(Census)
0.100***
(0.001)
0.071***
(0.001)
-0.099***
(0.008)
-0.019***
(0.000)
0.523
33,302
0.498
40,713
Native
Age Arrived in
Log Wage
Root MSE
Observations
Mover
2004
Returned
(to Israel)
2002-2004
0.002***
(0.000)
0.005***
(0.002)
0.001***
(0.000)
-0.001
(0.003)
-0.002
(0.002)
-0.016
(0.026)
-0.001
(0.001)
0.003
(0.014)
40,713
538
38
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 skills
40
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”:
• Wage at “Host”:
w0 = α0 + educ + g + s
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
44
A Model of Emigration with Country-Specific Skills
• A person decides to emigrate if: w1 > w0
β∙educ + γ∙g > a + s
Benefits of
Emigration
• where
β= β1-1
Costs of
Emigration
γ= γ1-1 a= α0- α1+f
45
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
Probability
to Emigrate
↑β1
Education
48
Positive and Convex Selection
Figure 1: Native Israelis Leaving Israel By Education
.01
.015
.02
.025
.025915
.009029
.007169
0
.005
.006562
HS Dropouts
HS Graduates
BA Degree
MA Degree or More
49
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
Probability
to Emigrate
↑γ1
Residual Wage (g+s)
51
Emigration and Residual Wages: Inverse U-Shape
Figure 4: Fraction Leaving Israel by Residual Wages
Controlling for Education, Age, Ethnicity, and Native Status
.021125
.02
.019926
.01891 .018669
.017927
.015
.014988
.013019 .012776
.012039
0
.005
.01
.011542
Lowest 10%
10-20%
20-30%
30-40%
40-50%
50-60%
60-70%
70-80%
80-90% Highest 10%
52
A Model of Emigration with Country-Specific Skills
• Intuition: Inverse U-shaped function
• A person emigrates if:
β∙educ + γ∙g > a + s
Benefits of
Emigration
Costs of
Emigration
• 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
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
Table 3: Industry Descriptive Statistics of the Israeli Sample with US CPS Variables
N
Mean
Mover 2004
ROR to Educ
in Israel
ROR to Educ
in US
Residual SD
in Israel
Residual SD
in US
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
Agriculture, Forestry, Fishing
57
Table 3: Industry Descriptive Statistics of the Israeli Sample with US CPS Variables
N
Mean
Mover 2004
ROR
to Educ
in 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
58
Table 4: Occupation Descriptive Statistics of the Israeli Sample with US CPS Variables
N
Mean
Mover 2004
ROR to Educ ROR to Educ
in Israel
in US
Residual SD
in Israel
Residual SD
in US
Academic Professionals
5624
0.027
0.016
0.067
0.516
0.489
Associate Professionals
and 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
59
Empirical Analysis of Selection on Education
The probability that person i in sector j moves is:
P rob( Moverij )   0   1 xi   2 educi   3 (residual wage) ij   4 (residual wage) ij2
  5 ( Israel ROR Educ) j   6 (US ROR Educ) j
 1 ( Israel ROR Educ) j  educi   2 (US ROR Educ) j  educi
 j  i
• αj = sector fixed-effect → γ5 and γ6 not identified
60
Empirical Analysis of Selection on Education
The probability that person i in sector j moves is:
P rob( Moverij )   0   1 xi   2 educi   3 (residual wage)ij   4 (residual wage)ij2
 1 ( Israel ROR Educ) j  educi   2 (US ROR Educ) j  educi
  j  i
• Theory: β1<0 and β2>0
61
Empirical Analysis of Selection on Education
The probability that person i in sector j moves is:
P rob( Moverij )   0   1 xi   2 educi   3 (residual wage)ij   4 (residual wage)ij2


  3 ( Israel ROR Educ) j  (US ROR Educ) j  educi
  j  i
• Theory: β3<0
62
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
Table 5: Selection on Education – Main Results for the Industry Level Analysis
Probit for being a Mover in 2004
Education*
Israel ROR Educ in Industry i
-0.0146
(0.018)
Education*
US ROR Educ in Industry i
-0.0930***
(0.027)
0.0202**
(0.0083)
0.0511***
(0.012)
-0.0427***
(0.011)
Education*
Diff between Israel and US in
ROR Educ in Industry i
Education
Industry Fixed Effects
Observations
0.00217*
(0.0013)
Yes
40,713
-0.000903
(0.00085)
Yes
40,713
0.00254*
(0.0014)
Yes
40,713
-0.000170
(0.00038)
Yes
40,713
65
Table 6: Selection on Education – Main Results for the Occupation Analysis
Probit for being a Mover in 2004
Education*
Israel ROR Educ in Occup i
-0.0297**
(0.012)
Education*
US ROR Educ in Occup i
-0.0298**
(0.012)
-0.0221**
(0.010)
-0.0219**
(0.0099)
0.00157
(0.0079)
Education*
Diff between Israel and US in
ROR Educ in Occup i
Education
Occupation Fixed Effects
Observations
0.00240***
(0.00058)
Yes
40,713
0.00269***
(0.00075)
Yes
40,713
0.00400***
(0.00093)
Yes
40,713
0.00113***
(0.00029)
Yes
40,713
66
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
Table 7: Selection on Education – Sensitivity to Sample Selection
Probit for being a Mover in 2004
Industry Level Analysis
-0.0427***
(0.011)
-0.0484***
(0.012)
-0.0321
(0.024)
-0.0426***
(0.011)
0.00157
(0.0079)
0.00509
(0.0096)
-0.00612
(0.016)
0.00147
(0.0079)
Sample Restriction
None
Natives
Non-Natives
Sectors with
N > 1000
Observations
40,713
25,011
15,702
40,197
Education*
Diff erence between Israel and US in
ROR Educ in Industry i
Occupation Level Analysis
Education*
Diff erence between Israel and US in
ROR Educ in Occupation i
68
Table 8: Selection on Education – Sensitivity to Definitions of a “Mover”
Mover 2004
Mover 2002
Mover 2002
and 2004
Mover 2004
since 2000
-0.0427***
(0.011)
-0.0321***
(0.010)
-0.0315***
(0.010)
-0.0269***
(0.0090)
0.00157
(0.0079)
0.00136
(0.0074)
0.00151
(0.0073)
-0.00103
(0.0064)
40,713
40,713
40,713
40,713
Industry Level Analysis
Education*
Diff between and US in
ROR Educ in Industry i
Occupation Level Analysis
Education*
Diff between and US in
ROR Educ in Occupation i
Observations
69
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:
P rob( Moverijk )   0   1 xi   2 educi   3 (residual wage) ij   4 (residual wage)ij2
 1 ( Israel residual SD) jk  (residual wage) ij
  2 (US residual SD) jk  (residual wage) ij
  jk   i
• αjk = cell fixed-effect
71
Empirical Analysis of Selection on Residuals
Prob that person i in sector j and educ group k moves is:
P rob( Moverijk )   0   1 xi   2 educi   3 (residual wage) ij   4 (residual wage)ij2
 1 ( Israel residual SD) jk  (residual wage) ij
  2 (US residual SD) jk  (residual wage) ij
  jk   i
• Theory: β1<0 and β2>0
72
Empirical Analysis of Selection on Residuals
Prob that person i in sector j and educ group k moves is:
Prob( Moverijk )   0   1 xi   2educi   3 (residual wage)ij   4 (residual wage)ij2


  3 ( Israel residual SD) jk  (US residual SD) jk  (residual wage)ij
  jk   i
• Theory: β3<0
73
Table 9: Selection on Unobservables – Main Industry Level Analysis
Probit for being a Mover in 2004
Industry Wage Residual*
Israel Residual SD
in Industry-Education Group i
-0.0212
(0.015)
Industry Wage Residual*
US Residual SD
in Industry-Education Group i
-0.0295*
(0.016)
0.0219
(0.021)
0.0357
(0.022)
Industry Wage Residual *
Difference between Israel and
US in Residual SD
in Industry-Education Group i
Industry-Education Group
Fixed Effects
Observations
-0.0311**
(0.015)
Yes
Yes
Yes
Yes
40,412
40,412
40,412
40,412
74
Table 10: Selection on Unobservables – Main Occupation Level Analysis
Probit for being a Mover in 2004
Occupation Wage Residual*
Israel Residual SD
in Occup-Education Group i
-0.0764***
(0.028)
Occupation Wage Residual*
US Residual SD
in Occup-Education Group i
-0.0785***
(0.028)
0.0245
(0.029)
0.0303
(0.029)
Occupation Wage Residual *
Difference between Israel and
US in Residual SD
in Occup-Education Group i
Occupation-Education Group
Fixed Effects
Observations
-0.0552***
(0.021)
Yes
Yes
Yes
Yes
40,621
40,621
40,621
40,621
75
Empirical Analysis of Selection on Residuals
• 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
Table 11: Selection on Unobservables – Sensitivity to Sample Selection
Probit for being a Mover in 2004
Industry Level Analysis
Industry Wage Residual *
Difference between Israel
and US in Residual SD in
Industry-Education Group i
-0.0311**
(0.015)
-0.0244*
(0.014)
-0.0340
(0.033)
-0.0331**
(0.015)
-0.0552***
(0.021)
-0.0515**
(0.023)
-0.0636
(0.044)
-0.0621***
(0.021)
None
Natives
Non-Natives
Sectors > 1000
40,621
24,573
15,673
40,105
Occupation Level Analysis
Occupation Wage Residual *
Difference between Israel
and US in Residual SD in
Occup-Education Group i
Sample Restriction
Observations
77
Table 12: Selection on Unobservables – Sensitivity to Definitions of a “Mover”
Mover 2004 Mover 2002 Mover 2002
and 2004
Mover 2004
since 2000
Industry Level Analysis
Industry Wage Residual *
Difference between Israel
and US in Residual SD in
Industry-Education Group i
-0.0311**
(0.015)
-0.0223
(0.014)
-0.0236*
(0.014)
-0.0226*
(0.012)
-0.0552***
(0.021)
-0.0449**
(0.019)
-0.0442**
(0.019)
-0.0406**
(0.017)
40,621
40,621
40,621
40,621
Occupation Level Analysis
Occupation Wage Residual *
Difference between Israel
and US in Residual SD in
Occup-Education Group i
Observations
78
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
Figure 9: Industry Analysis - Predicted Movers by Education
0
.05
.1
.15
Actual versus Decrease in Relative Return to School in All Industries by 0.02
5
10
Actual
15
Years of Schooling
20
25
Relative Return in Israel Decreased by 0.02
80
Magnitude of the effects: Selection on Education
Figure 10: Industry Analysis - Predicted Movers by Education
0
.01
.02
.03
.04
Actual versus Increase in Relative Return to School in All Industries by 0.03
5
10
Actual
15
Years of Schooling
20
25
Relative Return in Israel Increased by 0.03
81
Magnitude of the effects: Selection on Residuals
Figure 11: Predicted Movers by Industry Residual Wages
.01
.012
.014
.016
.018
.02
Under Various Levels of Relative Industry Inequality in Israel versus US
1
2
3
4
5
6
7
Industry Residual Wage Decile
Relative Residual SD = -0.05
Relative Residual SD = 0.05
8
9
10
Relative Residual SD = 0.00
82
Magnitude of the effects: Selection on Residuals
Figure 12: Predicted Movers by Industry Residual Wages
.01
.012
.014
.016
.018
.02
Actual versus Decreasing Relative Inequality in all Industries in Israel by 0.04
1
2
3
Actual
4
5
6
7
Industry Residual Wage Decile
8
9
10
Decrease Relative Residual SD by 0.04
83
Magnitude of the effects: Selection on Residuals
Figure 13: Predicted Movers by Industry Residual Wages
.01
.012
.014
.016
.018
.02
Actual versus Increase in Relative Inequality in all Industries in Israel by 0.025
1
2
3
Actual
4
5
6
7
Industry Residual Wage Decile
8
9
10
Increase Relative Residual SD by 0.025
84
Magnitude of the effects: Selection on Residuals
Figure 15: Predicted Movers by Occupation Residual Wages
.01
.012
.014
.016
.018
.02
Actual versus Decreasing Relative Inequality in all Occupations in Israel by 0.04
1
2
3
Actual
4
5
6
7
Occupation Residual Wage Decile
8
9
10
Decrease Relative Residual SD by 0.04
85
Magnitude of the effects: Selection on Residuals
Figure 16: Predicted Movers by Occupation Residual Wages
.01
.012
.014
.016
.018
.02
Actual versus Increase in Relative Inequality in all Occupations in Israel by 0.025
1
2
3
Actual
4
5
6
7
Occupation Residual Wage Decile
8
9
10
Increase Relative Residual SD by 0.025
86
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