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

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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.”

“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 Israel

Source: Brandolini and Smeeding (2008)

The Brain Drain from Israel

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

The Brain Drain from Israel

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

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 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

- 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

- 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 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

- 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

- 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

- 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 Emigration

- Case 1: Positive Selection (β0 < β1 )

Host

Wage

Home

S*

Skill

Stay

Emigrate

Borjas (1987) Model of Emigration

- Case 2: Negative Selection (β0 > β1 )

Home

Wage

Host

S*

Skill

Emigrate

Stay

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

- 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) 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)

- 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, 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, 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.

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

- 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

- 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

- 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)

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

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

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.

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 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 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 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 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 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 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 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 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 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 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

- 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

- 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).

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

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

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

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 Education

The probability that person i in sector j moves is:

- Theory: β1<0 and β2>0

Empirical Analysis of Selection on Education

The probability that person i in sector j moves is:

- Theory: β3<0

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 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.

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

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.

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.

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 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 Residuals

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

- Theory: β3<0

Table 10: Selection on Unobservables – Main Occupation Level Analysis

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.

Table 11: Selection on Unobservables – Sensitivity to Sample Selection

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).

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.

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

- 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.

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