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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.”.

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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

only two things israelis agree upon
(Only) Two Things Israelis Agree Upon
  • There is “too much” inequality in Israel.
  • Israel suffers from a “Brain Drain.”
too much inequality in israel
“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.
too much inequality in israel1
“Too Much” Inequality in Israel

Source: Brandolini and Smeeding (2008)

the brain drain from israel
The Brain Drain from Israel
  • Gould and Moav (2007): emigration rates increase with education levels.
the brain drain from israel1
The Brain Drain from Israel
  • Gould and Moav (2007): emigration rates are high for doctors, engineers, scientists, profs.
the brain drain from israel2
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.
only two things israelis agree upon1
(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))
goals of the paper
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)
unique data
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).
unique data1
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.
main contributions
Main Contributions
  • Empirical: analysis of emigrant selection based on observable and unobservable skill.
  • Theoretical: incorporate the notion of country-specific skills into the analysis.
outline of the talk
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.
borjas 1987 model of emigration
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
borjas 1987 model of emigration1
Borjas (1987) Model of Emigration
  • Case 1: Positive Selection (β0 < β1 )

Host

Wage

Home

S*

Skill

Stay

Emigrate

borjas 1987 model of emigration2
Borjas (1987) Model of Emigration
  • Case 2: Negative Selection (β0 > β1 )

Home

Wage

Host

S*

Skill

Emigrate

Stay

borjas 1987 model of emigration3
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.
evidence on the borjas 1987 model
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).
evidence on the borjas 1987 model1
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.
chiquiar and hanson jpe 2005
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.
chiquiar and hanson jpe 20051
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.
chiquiar and hanson jpe 20052
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.
slide24
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.
weaknesses in the data
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.”
strengths in the data
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.
our sample
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)
overall patterns in the data
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.
overall patterns in the data1
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
a model of emigration with country specific skills
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
a model of emigration with country specific skills1
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)
a model of emigration with country specific skills2
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.)
a model of emigration with country specific skills3
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
a model of emigration with country specific skills4
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

a model of emigration with country specific skills5
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.

a model of emigration with country specific skills6
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.
a model of emigration with country specific skills7
A Model of Emigration with Country-Specific Skills

Probability to Emigrate

↑β1

Education

a model of emigration with country specific skills8
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.
a model of emigration with country specific skills9
A Model of Emigration with Country-Specific Skills

Probability to Emigrate

↑γ1

Residual Wage (g+s)

a model of emigration with country specific skills10
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
  • sdecreases the probability of emigrating
  • Therefore, a higher g/s increases the chances to emigrate.

Benefits of Emigration

Costs of Emigration

a model of emigration with country specific skills11
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.
summary of our model s results
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.
empirical analysis of selection on education
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).
empirical analysis of selection on education1
Empirical Analysis of Selection on Education

The probability that person i in sector j moves is:

  • αj = sector fixed-effect → γ5 and γ6 not identified
empirical analysis of selection on education2
Empirical Analysis of Selection on Education

The probability that person i in sector j moves is:

  • Theory: β1<0 and β2>0
empirical analysis of selection on education3
Empirical Analysis of Selection on Education

The probability that person i in sector j moves is:

  • Theory: β3<0
comments on the empirical strategy
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.
comments on the empirical strategy1
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.
empirical analysis of selection on education4
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.
empirical analysis of selection on residuals
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.
empirical analysis of selection on residuals1
Empirical Analysis of Selection on Residuals

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

  • αjk = cell fixed-effect
empirical analysis of selection on residuals2
Empirical Analysis of Selection on Residuals

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

  • Theory: β1<0 and β2>0
empirical analysis of selection on residuals3
Empirical Analysis of Selection on Residuals

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

  • Theory: β3<0
empirical analysis of selection on residuals4
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.
further robustness checks in tables 13 and 14
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).
conclusion
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.
conclusion1
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.
implications
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.