“ LABOR MOBILITY OF IMMIGRANTS: TRAINING, EXPERIENCE, LANGUAGE AND OPPORTUNITIES ”
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Transcript of “ LABOR MOBILITY OF IMMIGRANTS: TRAINING, EXPERIENCE, LANGUAGE AND OPPORTUNITIES ”
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“LABOR MOBILITY OF IMMIGRANTS: TRAINING, EXPERIENCE, LANGUAGE AND
OPPORTUNITIES”By
Sarit Cohen
Bar-Ilan University
and
Zvi Eckstein
Tel-Aviv University,
University of Minnesota and CEPR
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Introduction
The transition pattern of immigrants to a new labor market is characterized by high wage growth, fast decrease in unemployment as immigrants first find blue-collar jobs, followed by a gradual movement to white-collar occupations.
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• Focus on - Acquisition of local human capital in: training, experience and local language.
• Data: quarterly labor mobility since arrival of high skilled male immigrants who moved from the former Soviet Union to Israel.
• Main macro facts.
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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
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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
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Formulate a dynamic choice model for: • blue and white-collar occupations• training related to these occupations• Unemployment
Labor market opportunities are random and
are affected by characteristics, past choices
and language knowledge.
Participation in training is affected by: the
mean wage return, the job offer probabilities,
preferences and lost of potential wages.
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Main Results• The estimated model fits well the main patterns of
the labor market mobility.• Return to training: white-collar 19%; blue-collar
13%, for 78% of population and zero for the rest.
• High return to local experience and language, but –conditional on local human capital - zero return to imported schooling.
• Main return to training is by the increase of 100% of white-collar offer probability.
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Main Results (cont.)
• Individual welfare gain at arrival from training programs is 1-1.5%.
• Aggregate growth rate of wages from the availability of the government provided vocational training programs is .85 percent.
• Main reasons: return to experience is high and utility from participating in training is low (liquidity constraint).
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Table 3: Multinomial-logit on Employment by Occupation and Unemployment
VariableWhite-Collar
Unemployed
constant-4.4424
)0.5034(
-0.4753 )0.4804 (
Hebrew 0.9612 )0.0761(
0.1342 )0.0701(
English0.6563 )0.0428 (
0.0205 )0.0052(
age at arrival0.0331 )0.0212 (
0.0332 )0.0190(
Schooling0.0031
)0.0212(
0.0332
)0.0190(
training in WC0.9421 )0.1153 (
0.8183 )0.1658 (
training in BC-0.2101 )0.1594 (
0.9586 )0.1815 (
experience-0.0046 )0.0100 (
- 0.6807 )0.0233 (
occupation in USSR
1.4837
)0.1417(
0.2156 )0.1137 (
Num. Of Obs .5536
Log likelihood-3558.40
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Table 4: OLS Wage RegressionDependent VariableLn hourly wage
white-collar occupation
Ln hourly wage Blue-collar occupation
Cons 1.091
) 0.407(
2.122
)0.120 (
Hebrew0.129
) 0.061(
0.050
)0.027(
English 0.132
) 0.036(
-0.011
)0.022 (
Age at arrival 0.013
)0.005(
-0.003
)0.002 (
Years of schooling 0.021
) 0.022(
0.008
) 0.006(
Training in WC 0.116
) 0.079(
-0.009
)0.062 (
Ttraining in BC-0.045
) 0.129 (
0.056
) 0.055(
Experience in Israel 0.017
) 0.009(
0.024
) 0.003(
Num. of Obs.132442
R20.2300.153
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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)
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Utility by Choice:
Wage Functions:
jit
jit
jit zKw ln
iSjiAjFiFj
HitHj
jitcjitejj0
jit edLLCEXK
00)( itit ueUUE 11)( itit wUWC
22)( itit wUBC 33)( itit trUWT
44)( itit trUBT
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Transition Probabilities are limited by job-offer probabilities and training-offer probabilities:
Individual state and characteristics: last period choice r, experience in Israel, occupation in the country of origin, knowledge of Hebrew and English and training.
)2,1j(,}Qexp{1
}Qexp{P
ijt
ijtrjit
: offunction linear ijtQ
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The Model
1.
UE
2.
UE
BC
3.
UE
BC
WC
BT
WT
20.
UE
BC
WC
BT
WT
Quarter SinceMigration:
Choices:
…….
Study Hebrew
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Solution MethodThe value function
}.1d,t,S/4,...,0jfor),1t,S(Vmax{EU)t,S(V ritit1it
ji
ritit
ri
)}).1d,t,S/1t,S(V(max{E)g(PU)t,S(V jitit1it
a
1it
~A
1a
a1it
jitit
ji
ait
gait
P(g1
ofy probabilit lconditiona )1
outcomes feasible indicatesat vector tha1 a
itg
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• The model is solved using backward recursion with a finite linear approximated value at the 21’th quarter as function of Si21.
• We use Monte Carlo integration to numerically solve for the Value Functions and the probability of the choices jointly with the accepted wages.
• By simulations we show that the model can capture the main dynamic aspects of the labor market mobility as depicted by the figure.
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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. Wages are assumed to be measured with error. M=2.
mim
jo
mit
j
mit
jo
mi
j
mi
I
i
M
m
jo
mi
j
mi xmtypeSwdwdwdLii
),/,,....,,,,Pr()( 0221 1
11
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Results Order
• Fit of labor market states
• Fit of transitions and wages
• Estimated parameters
• Interpretation of types
• Policy Implications on training
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Actual and Predicted Proportions in Unemployment, Blue-Collar and White-
Collar*
0
10
20
30
40
50
60
70
80
90
100
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Quarter since Migration
%
UE - Actual UE - ML BC - Actual BC- ML WC- Actual WC - ML
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Actual and ML Proportions inWhite Collar Training
0
1
2
3
4
5
6
7
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
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Actual and ML Proportions inBlue Collar Training
0
1
2
3
4
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
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Fit results
• The estimated model fits well the pattern but a formal 2 test rejects the fit of the model.
• The 5’th year (20%)reduction in BC and increase in WC is explained by : Cohort and prior events (~10%); BC to WC transitions as unemployment reach minimum (~10%).
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Table 6: Actual and Simulated Accepted Wages by Tenure and Training
WC occupationBC occupation
ActualModelObservations
ActualModelObservations
By quarters in Israel
1-421.76614.215410.47510.96864
5-815.06215.5634610.96811.687139
9-1218.86417.3762911.86812.65873
13-1620.44918.7382512.49713.71797
17-2021.52120.0372815.23214.77569
By training
No training17.93216.8409611.98512.211402
After training19.98117.8463612.66013.66640
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Table 7: Estimated Wage Function ParametersWage parametersBCWC
Cons. type11.8799**1.6276
Deviation of type2
from type 1
*0.19300.1443-
Hebrew*0.1100*0.0964
English*0.0418-*0.1386
Age at arrival0.00008-0.0050
Years of schooling0.00900.0126
Accumulated experience*0.0187*0.0205
Trained in WC type1*0.1908
Trained in WC type 20.0004
Trained in BC type10.1275
Trained in BC type 20.00008
Proportion of type 1*0.781
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Wage Function Results
• Very large return to local human capital accumulation: Experience – 2% per quarter, Training- 13 to 19 % by Type; Hebrew – 15 to 19%.
• Conditional on local human capital – no return to imported human capital.
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Table 8: Estimated Job Offer Parameters
WC Offer Probability
J=1
BC Offer Probability
J=2
b01j1-worked in WC at t-1 type 1*15.9966*2.4980-
b01j2-worked in WC at t-1 deviation from type 1
0.0053-*1.7338
b02j1-worked in BC at t-1 type 1*2.9737-*14.0431
b02j2-worked in BC at t-1 deviation from type 1
1.1589-0.0082
b03j1- didn't worked at t-1
type 1
*1.7604-*0.4116-
b03j2- didn't worked at t-1
deviation from type 1
0.6392*1.3162
b11j-work experience in Israel 1-40.2761-*0.2421
b12j-work experience in Israel >5*0.8935-*0.2707-
b2j-training in occupation j*0.94240.2196
b3j – Age of arrival*0.0286-*0.0071-
b4j - Hebrew*0.0938-*0.1744-
b5 - English*0.2095
b6 – WC=1 in soviet union*0.5554
b7 - first period dummy*0.4881-
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Table 9: Training and Job offer Probabilities (weighted by types)
To/FromWCBCWT
Experience01-45+01-45+01-45+
WCAfter training0110.0840.1030.066000
No training1110.0690.0850.0540.0.370.0370.037
BCAfter training0.0680.0520.029111000
No training0.0280.0210.0121110.0370.0370.037
UEAfter training0.2540.2060.12400.3500.4030 295000
No training0.1180.0930.0520.3050.3550.2550.0370.0370.037
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Offer Probabilities
• Large positive effect of training on WC offers and on BC offers
• Very Low WT opportunities P=0.037
• Very low offers for WC from BC and higher , but low from UE.
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Interpretation of Types
• Type 2 have unobserved characteristics that fit well the Israeli labor market – easily receive offers and do not need training. (22%).
• Type 1 – need the training to adjust but the cost is high (utility ~ liquidity problem).
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Policy analysis by Counterfactual Simulations
Structural estimation enables to simulate the effect of alternative policy interventions on the choice distribution, wages, unemployment and the discounted expected utility (PV).
Policy Choices: Case 1: No training is available. Case 2: Only training in blue-collar (BT) is available.Case 3: Only training in white-collar (WT) is available.Case 4: Double the probability to participate in WT.
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Table 12: Predicted Policy Effects on Mean Accepted Wages and Unemployment (4’th and 5’th years)
Policy ChangeNo Training is AvailableDouble WT Offer Rate
ImmigrantAccepted wage) %( ((Change)Accepted wage) %( (Change)
WCBCUEWCBCUE
BC in USSR schooling=12-1.1-0.103.52.50
WC in USSR schooling=15-0.8-0.103.42.60
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Table 13: The Predicted Annual Effect of Training Availability on Mean Accepted Wages: Percent
Change Relative to an Economy without Training**Percent change of simulated mean accepted wages on the sample, comparing the training at the estimated model to a no
training economy.
AllWhite-Collar
Blue-Collar
Year 10.070.1460.035
Year 20.601.1720.239
Year 30.961.5590.318
Year 41.221.8830.396
Year 51.402.0290.492
All Years0.851.6050.261
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Aggregate Wage Growth (Social Rate of Return)
• Aggregate wage growth is increasing overtime due to the permanent affect on job offers to WC.
• The social rate of return is above 1% mainly due to type 1 accepting WC jobs and type 2 BC jobs. Better process of job sorting.
• Double WT opportunities has a high (above 3%) social rate of return.
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Table 14: Predicted Policy Effect on the Hourly Present Value (PV)
ExperimentBC in USSR, schooling=12
WC in USSR, schooling=15
age at arrival 30
age at arrival 45
age at arrival 30
age at arrival 45
Upon Arrival*
3,371.873,117.303,458.923,203.37
No Training-)1.11 (3,334.58
-)1.47 (3,071.45
-) 0.95 (3,425.98
-)1.35 (3,160.24
No WT-)1.11 (3,334.85
-)1.47 (3,071.45
-)0.95 (3,425.98
-)1.35 (3,160.24
No BT) 0.00 (3,371.87
)0.00 (3,117.30
)0.00 (3,458.92
)0.00 (3,203.37
Double WT offer
)0.96 (3,404.10
)1.24 (3,155.98
)0.84 (3,487.97
)1.16 (3,240.43
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Table 15: Partition of the Gain from Training by Sources
ExperimentBC in USSR, schooling=12WC in USSR, schooling=15
age at arrival 30
age at arrival 45
age at arrival 30
age at arrival 45
No training)3,334.58()3,071.45()3,425.98()3,160.24(
No return in all sources
)3,334.57(
0.00
)3,071.43 (
0.00
)3,425.97(
0.00
)3,160.23(
0.00
Return in utility only)3,335.17(
1.6
)3,072.23(
1.7
)3,426.49(
1.6
)3,160.94(
1.6
Return in utility and terminal
)3,361.53 (
72.3
)3,105.20(
73.6
)3,448.90(
69.6
)3,190.00 (
69.1
Return in utility, terminal, job offer
)3,371.20(
98.2
)3,116.63(
98.6
)3,458.10(
97.5
)3,202.49(
98.0
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Conclusions
• The model provided a way to estimate the social and the individual rate of return from alternative training programs.
• Most of the gain from training is due to increasing WC job opportunities over long time.
• Large fraction of wage growth is due to occupational mobility, experience and language learning.
• The return to imported imported human capital is zero conditional on the locally accumulated human capital.
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TableA1. Summary Statistics
ObservationsPercentMeanSD
Schooling41914.582.74
Age at arrival41938.059.15
White-collar USSR28467.78
Blue-collar USSR12730.31
Did not work in USSR
81.91
Married36386.63
English4191.760.94
Hebrew before migration
5011.9
Ulpan Attendance38692.3
Ulpan completion33279.2
Ulpan Length )months(
3874.61.34
Hebrew1 )first survey(
4192.710.82
Hebrew2 )second survey(
3162.980.83