Immigration, Diversity, Human
Capital and the Future Labor Force of Developed Countries: the
European Model
Guillaume Marois
1, Patrick Sabourin
1, Alain Bélanger
1,2and Wolfgang Lutz
11 World Population Program
International Institute for Applied Systems Analysis
2 Programme de démographie
Institut national de la recherche scientifique
6th World Congress of the International Microsimulation Association
June 21-23, 2017
Collegio Carlo Alberto, Moncalieri, Italy
Partnership between
• IIASA
• Joint Research Centre of the EC
Quantitative study of migration challenges arising from alternative scenarios in
1. the outmigration push factors in Africa and the Middle East,
2. the immigration pull factors in EU Member states and 3. the impact of the resulting migration streams into the
EU member states.
Center of Expertise in Population and
Migration (CEPAM)
CEPAM microsimulation model
• Core structure based on the LSD microsimulation model
– Microsimulation model for developed nations with similar purposes – Lead by Alain Bélanger and supported by the Social Sciences and
Humanities Research Council (Immigration, Education Ethnocultural Diversity and the Future of Labor Force Composition)
• Modgen language
– Statistics Canada’s programming language for microsimulation
• Dynamic / Continuous time / Case- and event-based / Open / Stochastic (Monte Carlo)
• Geography: 28 European Union member countries
• Time span: 2010 -
CEPAM microsimulation model
Variables:
• Demographic
– Age, Sex, Country of residence (28)
• Immigration and Ethnocultural variables
– Region of birth (Native + 11 regions) – Generation status (G1, G1.5, G2+) – Duration of residence (5)
– Religion (4)
– Language used at home (3)
• Socio-economic
– Education level + Education of the mother (3) – Labor force participation (2)
– Employment (2)
Data sources: European Social Survey, European Labor
Force Survey, Census 2011
CEPAM – Core Demographic Modules
• Mortality (Age, Sex, Education, Year)
• Fertility (Age, Sex, Education, Year)
• International migration (Composition, Size, Distribution)
• Internal Mobility (future)
Education module
Three-step modeling
Applied to individuals with incomplete education paths: newborns, immigrants arrived during childhood and members of the base population under 25 years old
1. Setting up an education level
When the individual is added to the population, the highest level of education that will be reached in his lifetime is set in a latent variable
– Probabilistically, according to individual characteristics
2. Schedule of education
Country-specific distribution by age from Eurostat (2013-2014)
3. Simulation of life course
If the individual survives until graduation age, the education state variable changes to reflect the appropriate educational attainment. A change in
education immediately affects other events (fertility, labor force participation, employment, mortality)
Education module
• Multinomial logit
Population (pooled data of ESS)
– Born before 1990
– Born in EU/Immigrated before age 25
– Stratified by gender and region (EU15/NMS13)
• Two purposes
1. To estimate differential for sociocultural variables in educational attainment
2. To estimate the net cohort/country-specific trend
• Assumptions on educational attainment of future cohorts
-1 -0.5 0 0.5 1 1.5 2 2.5 3
Effect of sociocultural variables on educational attainment (Low vs High), EU15
Male Female
• Education of the mother is the main determinant
• Most of the improvement in education for younger cohorts is explained by the education of the mother
<=1969 1970-1979 1980-1989
-1.5 -1 -0.5 0 0.5 1 1.5
Gross and net cohort trends for low education, Male
IT - Gross IT - Net HU - Gross HU - Net PT - Gross PT - Net
Labour Force Participation and Employment modules
Age 15-74?
Labor Force Participation
?
Employment
?
Employe d
Unemplo Non- yed
labor force labor Non-
force Yes
No
Yes
No
No Yes
Personal characteristics
• Specific effect by country and gender
• Age, education, age of youngest child, immigrant status, number of years since arrival, age at immigration
• Interaction: age*education / education*immigrant
22 27 32 37 42 47 52 57 62 67 72 0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Labour force participation by age and education, Native females, EU
L M H
Presence of a child age 0-4
• Steeper gradient of education for females
• There is an interaction between LFP and fertility
T
40%
50%
60%
70%
80%
90%
Labour force participation by immigrant status and education, Females, 30-34
100%L M H
• Effect of duration of residence
• Having a High education level has less effect for international immigrants
• Education gradient stronger for females
0%
5%
10%
15%
20%
25%
30%
35%
40%Unemployment by immigrant status and education, Female age 30-34
L M H
• International migrants have lower LFP than natives and higher unemployment
– Threshold effect in duration of residence
– Threshold effect in education level for international immigrants
Some preliminary results
Modules Scenario 1
Reference Scenario 2
Immigration+ Scenario 3
Selection and integration
Labor force participation
Constant parameters Constant parameters Immigrants = Natives
Employment Constant parameters Constant parameters Immigrants = Natives
Immigration level
Wittgenstein
reference scenario
50% increase Wittgenstein
reference scenario
Immigration composition
Recent trends Recent trends More educated Shift 50% L→M Shift 50% M→H
• Same assumptions for fertility, mortality, education and internal
migration for all scenarios
For the next decades, better integration or increasing
immigration have similar effect on the number of workers
2010 2015 2020 2025 2030 2035 2040 2045 2050 2055 2060
190000000 195000000 200000000 205000000 210000000 215000000 220000000 225000000 230000000
