“IMMIGRANTS IN THE LABOR MARKET:

IMPACT, INTEGRATION AND METHODS”

By

Zvi Eckstein

Tel-Aviv University,

University of Minnesota and CEPR

and

Sarit Cohen

Bar-Ilan UniversityPrepared for the 2001 Annual Conference of the European Society for

the Population Economics, Athens, June 14-16, 2001

The Lecture Covers three topics of research on “IMMIGRANTS IN THE LABOR MARKET”

• Macroeconomic Implications and The Impact on Natives: Aggregate Data

• Wage Growth, The Value of Human Capital and Convergence: Cross-sectional Data

• Local Human Capital: Training, Occupational Choice and Experience: Panel Data

Definition of an Immigrant: An immigrant is an individual who moves from one society to another with the intention to stay permanently

Main Reason for Immigration: Improve standard of living

• Immigration Decision: Endogenous vs. Exogenous• The Process of Immigration:

Location (Housing), Language learning, Training, Job Search and Occupational Choice

• A case-study: Immigration of Jews from Former Soviet Union to Israel: 1990-1997 large growth in population

Macro Effects

• Large Growth of Labor Force• Response of GNP, Capital stock and

Consumption• Large change in the composition of the Labor

Force: Education, Age and Occupations

• Impact on Wages of Natives

0

5

10

15

20

25

30

35

1922-1932

1932-1947

1947-1950

1950-1951

1951-1964

1964-1972

1972-1982

1982-1989

1989-1993

1993-1997

years

pe

rce

nta

ge

population GNPper capita

Population and GNP per Capita (annual growth rates)

0

2

4

6

8

10

12

14

1951-1964

1964-1972

1972-1982

1982-1989

1989-1993

1993-1997

years

perc

enta

ge

Capital stock Consumption per capita

Capital Stock and Consumption per Capita (annual growth rates)

Immigrants 1990-1995

Native Israelis (male)

Average Education 13.6 12.5

Average Age 43.8 38.1

Occupation abroad

Immigrants 1990-1997

Occupation in 1991

(male)

High Skilled (Occ. 1) 34.7 18.5

White Collar (Occ. 2) 32.3 12.9

Blue Collar (Occ. 3) 33.0 68.6

Composition of Labor Force

0

50

100

150

200

250

1971 1976 1981 1986 1991 1996Year

Gross Capital Stock Inventory per Employee (in thousands of 1995 IS) Employment (in 10,000)

Aggregate Employment and Gross Capital Stock per Employee

Several Papers and Results:

• Eckstein and Weiss (2000), following Jorgenson and Griliches (1967), proposed a simple method for quality adjustment of employed natives and immigrants to show that capital labor ratio is relatively constant.

• Immigrants are transformed into equivalent Israelis by using relative wages predicted from regression as weights.

• The resulting capital labor ratio for 1990-95 is consistent with CRS aggregate production function.

Adjusted and Unadjusted Capital – Labor Ratio

178

180

182

184

186

188

190

192

194

196

1989 1990 1991 1992 1993 1994 1995Year

Unadjusted Adjusted

Females Males

Russian Immigrants

7.06 5.06

Native Israelis (Jews)

2.26 1.94

By Educational Attainment 9-12 Years of schooling

Russian Immigrants 5.62 3.82

Native Israelis (Jews) 1.88 1.34

13-15 Years of schooling

Russian Immigrants 7.48 5.32

Native Israelis (Jews) 0.56 0.98

>16 Years of schooling

Russian Immigrants 6.12 5.90

Native Israelis (Jews) 4.10 1.98

Average Annual Growth Rate of Real Wages of Natives and Immigrants 1991-1997

Note: The numbers are the log change in mean hourly wages x 100.Source: Cohen and Hsieh (2000)

Cohen and Hsieh (2000)“Macroeconomic and Labor Market Impact of Russian Immigration in Israel”

Calibration results on impulse response of immigration are consistent with the data, that is:

• Russians were quickly absorbed into the labor market.• Sharp initial fall in wages, small increase in unemployment.• But quick recovery due to capital accumulation (inflows of foreign capital)

Calibrated a one-sector macro model with labor and capital adjustment costs. Assume labor endowment increased between 1990-1997 due to immigration (using the actual growth rates)

• Hercowitz and Yashiv (1999) use an open-

economy neo-classical model to analyze the

impact of entry of immigrants into the labor and

goods markets on the dynamics labor demand.• They use aggregate data to estimate two reduced

form equations that relate employment on natives and immigrants. They find a negative effects of immigration on native employment a year and a half after arrival.

• K. Storesletten (JPE 2000) calibrated an OLG model for the US economy. He considered immigration policies that would help some of the current fiscal problems that are due to demographic changes.

Friedberg (1998) “The Impact of Mass Migration on the Israeli Labor Market”

• if the distribution of immigrants across occupations in Israel is not exogenous OLS estimate is biased

• Use the occupational distribution in the former Soviet-Union as an Instrument for rj

• IV estimates indicate that immigration did not have an adverse impact on wages of natives

Study the impact of immigration on wages of natives (cross sectional data)

W is the log earnings

OCCj is a set of J occupation dummies

rj is the ratio of immigrant to native workers in each occupation

Hypothesis: Immigrants’ presence in a certain occupation reduces specific occupational wage

jJ

j jj r OCCcontrolsW 10

0

“The Absorption of Highly Skilled Immigrants: Israel 1990-1995” by Eckstein and Weiss (1998)

Question 1: How does the wage growth of immigrants divide between: local experience, occupational transition and the “price” of imported human capital?

Method and application

Question 2: Convergence to Natives (Assimilation)?Definition and application

Literature: Chiswick(1978), Borjas(1985, 1994, 2000) and LaLond and Topel(1997)

Using repeated cross section data

Monthly Wages of Immigrants by Schooling and Years

since Arrival, Males aged 25-55

Year schooling12 Schooling 13-15 Schooling 16+

1 2661 2798 2707

2 2775 (4.3 ) 3188 (13.9) 3426 (26.6)

3 2901 ( 4.5) 3528 (10.7) 3654 (6.7)

4 3029 ( 4.4) 3748 (6.7) 4079 (11.6)

5 3264 ( 7.8) 4120 (9.9) 4621 (13.3)

Growth rates in parenthesisSource:1995 Census

Wages of Immigrants and Israelis by Work Experience in Israel, Males aged 25-55

All Workers Work Experience <= 5 Work Experience > 5

Israelis Immigrants Israelis Immigrants Israelis Immigrants

Years ofSchooling

0 - 12 3084 2095 2056 1782 3179 284113 - 15 4141 2401 2472 1954 4714 4322

16+ 5556 3066 3379 2342 6400 5461

Occupation inIsrael

1 5949 3945 3717 2978 6394 59032 4246 3264 3060 2571 4548 45183 3050 2018 2183 1749 3195 3073

Four Sources of Wage Growth for Immigrants

• Rising Prices of imported human capital

• Occupational upgrading

• Local accumulated human capital (experience –years since migration)

• Aggregate Wage growth

The Main Idea of Price change

ISISISIS XLnW ' :Natives of Wage

IMIMt

IMIMIS

IMISIM XdeXXLnW )0()0()( :Immigrants of Wage

NATIVES

IMMIGRANTS, T infinity

IMMIGRANTS, T = 0

SCHOOLING

LnW

Slope 0.073

Slope 0.03

• Use formal human capital theory to support the above model

• Estimate the two equations jointly imposing cross equations restrictions and non-linear price change on imported human capital

• Use repeated income surveys from 1990-95

• Estimate occupational transition regressions for natives and immigrants

Rates of Return for Natives and Immigrants (percent)

Israelis Immigrants At Arrival (T=0)

Immigrants (T Infinity)

Education

7.3

0.0

3.0

Occupation 1

27.2

39.7

63.4

Occupation 2

21.5

26.9

42.6

Predicted Proportion of Workers with 16+ Years of Schooling Employed in Occupation 1

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50

Age

Per

cen

tag

e

Natives* Immigrant-Engineers** All Immigrants*

* Natives-based on Logit estimation (CBS, income surveys 1991-95).* All Immigrants-based on Logit estimation (CBS, income surveys 1991-95).**Immigrant Engineers-based on the transition matrix (Brookdale).

All Immigrants Schooling 16+ Actual 0.0641 0.0813 Predicted 0.0669 0.0822 TTiimmee 00..00111133 00..00111133 EExxppeerriieennccee 00..00112211 00..00112288 PPrriicceess 00..00332288 00..00444455 OOccccuuppaattiioonn 00..00110088 00..00113366 Sample size 1991 125 30 Sample size 1995 137 48

Components of Annual Wage Growth of Immigrants During 1991-1995 for the 1990 Cohort,

Males, Age at Arrival > 25

Simulated Wage-Age Profiles in Occupation 1 for an Israeli and an Immigrant, with and without Cohort Effects,

Schooling=16, Age at immigration=30*

0

5

10

15

20

25

30

35

30 35 40 45 50 55 60 65Age

Ho

urly

Wa

ge

Immigrant, 1990-1991 cohort, occ1Natives, occ1Immigrant, occ1

* Wage per hour in 1991 NIS.

0

5

10

15

20

25

30 35 40 45 50 55 60 65Age

Ho

urly

wa

ge

Immigrant, 1990-1991 cohort, occ3 Natives, occ3

Immigrant, occ3 Natives, occ3, schooling=12

* Wage per hour in 1991 NIS.

Simulated Wage-Age Profiles in Occupation 3 for an Israeli and an Immigrant, with and without Cohort Effects, Schooling=16, Age at immigration=30*

Simulated Wage-Age Profiles Based on Regression without Occupation Dummies for an Israeli Worker and

an Immigrant, with and without Cohort Effects (schooling=16, age at immigration=30)*

0

5

10

15

20

25

30

35

30 35 40 45 50 55 60 65Age

Hou

rly w

age

Immigrant, 1990-1991 cohort Natives Immigrant

Summary

• Occupational distribution of immigrants converges to that of natives

• Prices of imported human capital do not converge

Wages do not converge although growth is high during the first five years

The same conclusion was obtained by Weiss, Sauer and Gotlibovsky (2000) using panel data

“Training and Occupational Choice of Highly Skilled Immigrants” by Cohen and Eckstein (2000)

Using Panel data

The transition pattern of post-schooling individuals, displaced workers and immigrants to the labor market has similar characteristics.

Unemployment falls quickly as workers first find blue-collar jobs, followed by a gradual movement to white-collar occupations.

Actual Proportions in White Collar, Blue Collar and Unemployment

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20Quarter since Migration

%

Unemployment Blue Collar White Collar

Participation in White Collar andBlue Collar Training

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Quarter since Migration

%

Training in White CollarTraining in Blue Collar

• What is the impact of participation in training, job- search, occupational choice and language acquisition on the integration of immigrants in the labor market ?

• What is the impact of alternative motives for participation in training:

1. Increase the mean wage offer

2. Increase job offer probabilities

3. Provide direct utility

• The effect of training varies by the unobserved types of individuals (Heckman and Singer).

The Main Questions

A Dynamic Choice Model

Choice set:

•Work in a White-Collar job (WC)•Work in a Blue-Collar job (BC)•Training related to White-Collar jobs (WT)•Training related to Blue-Collar jobs (BT)•Unemployment (UE)

utilities:• (WC) • (BC)• (BT)• (WT)• (UE) tt

tt

tt

tt

tt

ueU

trU

trU

wU

wU

55

44

33

22

11

jtjjtjjtjtjtjjjt ScAgeCEngHebEXLnw 6543210

Wage Functions:

Transition Probabilities are limited by job-offer probabilities and training-offer probabilities:

)t,x,m(PP itjD

rjrjit ti

• represents time varying occupation specific demand indicators

• t indicates time in Israel

• xit represents individual characteristics, such as occupation in the country of origin, knowledge of Hebrew, training status etc.

jD ti

m

The Model

1.

UE

2.

UE

BC

3.

UE

BC

WC

BT

WT

20.

UE

BC

WC

BT

WT

Quarter SinceMigration:

Choices:

…….

Study Hebrew

Solution Method

The model is solved using backward recursion with a finite approximated value at the 20’th quarter

We use Monte Carlo integration to numerically solve for the Value Functions and the probability of the choices jointly with the accepted wages

Value Functions:

}d,t,S|)t,S(Vmax{EU)t,S(V rtttJj

rtrttrt 111

Estimation Method

• The model is estimated using simulated maximum likelihood (SML) (McFadden(1989))

• Given data on choices and wage, the solution of the dynamic programming problem serves as input in the estimation procedure.

• All the parameters of the model enter to the likelihood through their effect on the choice probabilities and wages

Actual and ML Proportions in White Collar, Blue Collar and Unemployment

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Quarter since Migration

%

Unemployment - Actual Unemployment - ML Blue Collar - ActualBlue Collar - ML White Collar - Actual White Collar - ML

Actual and ML Proportions inWhite Collar Training

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Quarter since Migration

%

Training in White Collar - ActualTraining in White Collar - ML

Actual and ML Proportions inBlue Collar Training

0

0.01

0.02

0.03

0.04

0.05

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Quarters since Migration

%

Training in Blue Collar - ActualTraining in Blue Collar - ML

Estimated Wage Parameters BCWC

2.009*1.496*Constant-type 1

2.192*1.655*Constant-type 2

0.093*0.110*Hebrew

-0.0140.134*English

0.0110.018 Education

5.3 e-83.0 e-7Experience

0.168Training –type 1

0.001Training- type 2

0.0005Training –type 1

0.183*Training- type 2

-0.0020.005Age at arrival

• Results:• Initial WC job offer probabilities of an average

immigrant with no experience in Israel are very low: 0.0054-type 1, 0.028-type 2 (quarterly)

• Training increases these job offer probabilities by 70%

The impact of training on employment rates is mainlythrough its effect on job offer probabilities and not through the wage return to training.

Policy analysis by Counterfactual Simulations

Structural estimation enables to simulate the effect of alternative policy interventions on the choice distribution and on the discounted expected utility (PV).

Policy Choices: 1. Remove or add training opportunities2. Subsidies employment in WC occupations

Counterfactuals Experiments on TrainingIn parenthesis , percent of change compared to PV (first row)

BC in USSR,schooling = 12

WC in USSR,schooling = 15

Experimentage at

arrival 30age at

arrival 45age at

arrival 30age at

arrival 45Present ValueUpon Arrival*

-6642.78 -11557.49 -5276.35 -10184.66

No Training-7318.84(-10.18)

-12653.18(-9.48)

-5931.40(-12.41)

-11264.51(-10.60)

No WT-7128.68(-7.31)

-12180.67(-5.39)

-5807.78(-10. 07)

-10888.79(-6.91)

No BT-6737.63(-1.43)

-11793.20(-2.04)

-5338.04(-1.17)

-10372.01(-1.84)

WToffer+20%

-5874.87(11.56)

-10693.85(7.47)

-4326.71(18.00)

-9068.79(10.96)

*Per Hour, August 1995 prices

Partition of the Gain From Training by Sources (In parenthesis % of change compared to No Training)

ExperimentBC in USSR,

schooling = 12, Age=30WC in USSR,

schooling = 15 Age=30

No Training -7318.84 -5931.40

No return to training in allsources

-7303.85 (0.20) -5918.46 (0.22)

Return to training only inUtility

-7063.62 (3.49)-5749.08 (3.07)

Return to training in Utilityand Terminal value

-6738.09 (7.93)-5454.58 (8.04)

Return to training inUtility. Terminal value andJob-Offers

-6643.32 (9.32) -5277.49 (11.02)

Conclusions

• It is important to distinguish between the different occupation related training programs – WC and BC

• Training affects mainly job-offer probabilities while the return on the wage rate is type specific and has minor affect on participation

• The impact of WC-related training is positive and much greater than the effect of BC-related training

• Much of the participating in training is due to utility gain relative to the alternative of being UE