Integration Indexes of Third Country Nationals Methodological Contributions Gian Carlo Blangiardo Milano-Bicocca University / Ismu Foundation EUROPEAN FUND FOR THE INTEGRATION OF THIRD COUNTRY NATIONALS PAN-EUROPEAN CONFERENCE Work: A Tool for Inclusion or a Reason for Exclusion? Gian Carlo Blangiardo
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Integration Indexes of Third Country Nationals Methodological Contributions Gian Carlo Blangiardo Milano-Bicocca University / Ismu Foundation EUROPEAN.
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Integration Indexes of Third Country Nationals Methodological Contributions
Gian Carlo BlangiardoMilano-Bicocca University / Ismu Foundation
EUROPEAN FUND FOR THE INTEGRATION OF THIRD COUNTRY NATIONALSPAN-EUROPEAN CONFERENCE
Work: A Tool for Inclusion or a Reason for Exclusion?
Gian Carlo Blangiardo
to measure and compare integration level among migrant
populations (or sub-populations defined according to some specific features)
The ultimate purpose
Gian Carlo Blangiardo
The classical macro approach:by
statistical indicators of integration
Gian Carlo Blangiardo
• European Core Indicators of Migrant Integration• 1) Employment
• 2) Education• Highest educational attainment• Share of low-achieving 15-year-olds in reading, mathematics and science• Share of 30–34-year-olds with tertiary educational attainment • Share of early leavers from education and training
• 3) Social inclusion• Median disposable income • At-risk-of-poverty-or-social-exclusion rate (before and after social transfers)• Share of population perceiving their health status as good, fair, or poor• Ratio of property owners to non-property owners
• 4) Active citizenship• Share of immigrants that acquired citizenship• Share of immigrants with permanent or long-term residence, currently only EC long-term residence• Share of immigrants among elected representatives
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Unemployment rate of persons aged 25-54 by groups of country of birth, gender and highest level of educational attainment, EU-27, 2008 (%)
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Over-qualification rate differences between foreign-born and native-born tertiary educated persons aged 25-54, 2008 (%)
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Macro data from statistical sources duly processed to produce indicators
• Labour Force Survey• EU Statistics on Income and Living Conditions• Census data• OECD PISA Survey• Etc.
WE NEED
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Main sources
The alternative micro approach: by
individual scores of integration Very apt to investigate differential aspects of the integration corresponding
to local areas or to specific sub-populations& to control the effects of local or targeted policies
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Individual data-base from statistical surveys
duly processed• 1) Representative samples of the target
population• 2) A methodology able to assign an integration
score, according to a preliminary definition of integration, to every statistical unit of the sample
WE NEED
Gian Carlo Blangiardo
a representative samples of the target populations
AS REGARDS METHODS TO HAVE
see:
Baio G., Blangiardo G.C. and Blangiardo M., Centre Sampling Thecnique in Foreign Migration Surveys, Journal of Official Statistics, Vol.27, No.3, 2011, pp.451-465.
And for its numerous applications since early 90s
www.ismu.orgORIM – Regional Observatory for Integration and Multiethnicity
assign an integration score, according to a preliminary definition of integration, to every statistical unit of the sample ?
the following steps are required
REMARKIn this example we shall consider the sole topic of labor market integration of TCNs. Anyway a similar the procedure can be followed in order to assign individual integration scores regarding both other specific dimensions (education, social exclusion, etc.) and the integration level as a whole
Gian Carlo Blangiardo
A new approach to measure integration: by individual integration scores
Step 1
Step 2
Step 3
Step 4
Step 5
Step 6
• STEP1 Selection of a set of indicators according to a definition of integration in the labor market
• STEP2 Choice of the variables of sample dataset available to give the requested indicators
• STEP3 Identification of integration scores by processing the frequencies of the sample distribution of the variables selected
• STEP4 Assignment the scores to each statistical unit according to its modality of the variables under consideration
• STEP5 Attribution of the average score of integration at each statistical unit (additive variable to the sample dataset)
• STEP6 Processing the integration score together with structural data (personal features, education, social inclusion, etc.)
Gian Carlo Blangiardo
7Selection of a set of indicators according to a definition of integration in the labor market
4 dimensions
EmploymentStability &
job security
Net income from work
Over qualification
Source: PerLa Survey 2009 - Percorsi Lavorativi (Labor Path)13,006 sample units;Target population: migrants living in Italy who have or had a legal job since 12 months before the surveyMethodology: Centre sampling
Definition: “a migrant who is employed with a stable/secured job that gives good income and is adequate to his education level can be considered fully integrated into the labor market”
Example of the application of the procedure
Steps 1&2
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8
lowest highest
-1 0 1Employment integration index (score)
(for each of the 4 dimensions)
Identification of integration scores by processing the frequencies of the sample distribution of the variables selected
Step 3
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Step 3 (cont’d)
8Identification of integration scores by processing the frequencies of the sample distribution
For each modality the corresponding score is obtained through the difference between the sum of the previous frequencies (relative) less the sum of the following ones. It can be remarked that, for any variable, the mean score for the whole set of sample units will be zero.
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Set of scores(for each of the 4 dimensions)
Employment Unemployed -0.93
Employed +0.07
Stability & Low -0.99job security Medium -0.61
High +0.39< 500 € -0.97
Net income 500 – 800 € -0.65
from work 800 – 1200 € 0.141200 – 1500 € 0.781500 – 2000 € 0.952000 – 3000 € 0.99
> 3000 € 1.00
Over qualification Severely inadequate -0.95
(job compared Moderately inadequate -0.63
to education) Adequate 0.32
Gian Carlo Blangiardo
Assignment the scores to each statistical unit according to its modality of the variable under consideration (total 13,006 units) & Average of the 4 partial scores (Final Mean score)