Socio-Economic Disparities in the Integration Process of Immigrants in Western Europe

Name: Socio-Economic Disparities in the Integration Process of Immigrants in Western Europe
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Author: Erhan Özdemir
ISBN: 978-2-87574-440-1

Özdemir, Erhan

Socio-Economic Disparities in the Integration Process of Immigrants in Western Europe

A Comparative Study for Six EU Countries

by Erhan Özdemir (Author)

Science, Society & Culture

Series: Border Studies, Volume 4

Summary

International migration is one of the prominent facts in the contemporary world, which affects the political, socio-economic and cultural processes both in origin and destination countries. Historically, Western Europe has been one of the most attractive destinations for migrants because of the level of socio-economic development and political stability. However, there are many complex institutional, socio-economic and cultural issues to be addressed to achieve the integration of migrants and to eliminate social inequalities between the native populations and migrants in these host countries.
In this respect, this book examines some aspects of socio-economic disparities between native populations and the migrants in Belgium, Germany, France, the Netherlands, Sweden and the United Kingdom. Different migration histories, labour market features and welfare state characteristics of these countries are expected to provide insight about how the integration-related and inequality-related issues emerge in diverse social and institutional settings. The study covers the empirical analyses of the disparities in the labour market and accessing the social benefits between 2004 and 2016 by using comparable cross- country survey data. These analyses attempt to demonstrate the relationships between these two domains. The study has a comparative approach, which aims at providing comparable evidence both across the countries and over time in each of the selected countries.

Excerpt

Cover
Title
Copyright
About the author
About the book
This eBook can be cited
Acknowledgements
Data availability and disclaimer
Table of contents
List of tables
List of figures
Introduction
Purpose of the study
Why are these countries selected for the research?
Labour market regulations and welfare state regimes
Magnitude and composition of migrants
Change in migration policies over time
Literature review for the theoretical background of the study
Theories on social inequality
Theories of social mobility
Theories on social mobility of migrants
Theories on the labour market disparities between native population and migrants
Theoretical framework of the research
Data and multivariate analysis methods used in the study
The structure of the study
Chapter 1 Are immigrant employees disadvantaged in western Europe?
Introduction
Literature review
Wages
Segmentation
Data and methodology
Descriptive analysis findings
Multivariate analysis
Random intercept multilevel mixed effects linear regression models for the effect of various characteristics on gross employee income
Random intercept multilevel mixed effects ordinal logistic regression models for examining the effects of migration background on having low-earning jobs
Conclusion
Chapter 2 The differentiation in the permanency of the jobs between the migrants and the native population
Introduction
Literature review
Main factors affecting job permanency
Studies on the disparities between the native and migrant populations
The impact of welfare state and other policies
Data and methodology
Descriptive findings
Multivariate analysis
Discussion and conclusion
Chapter 3 The differentiation in having jobs with fixed-term contracts between migrants and the native-born employees: A comparative analysis for six European countries
Introduction
Literature review
Data and methodology
Descriptive findings
Multivariate analysis
Discussion and conclusion
Chapter 4 The variation in the social benefits receipts and return to employment across non-working adults
Introduction
A brief literature review on the theoretical approaches and empirical studies on welfare states in western countries
Differences in welfare regimes
Migrants’ access to welfare state facilities
Welfare system as a pull factor
Non-take up of benefits
Impact of benefits on labour market re-integration of the migrants
Data and methodology
Data
Defining the target population
Methods and presented results
Descriptive analysis
Multivariate analysis
Who are more likely to receive benefits?
Who are receiving higher amounts of social benefits
Who are more likely to return to employment among those, who receive social benefits?
Conclusion and discussion
Chapter 5 Context effects on socio-economic disparities between migrants and natives
Findings from Blinder-Oaxaca decomposition
At risk of poverty and material deprivation
Differences in integration policies
Is there a relation between inclusive social-cultural and political-cultural settings and level of socio-economic disparities between the migrants and the native population?
Conclusion and discussion
Conclusion and discussion
a) What do the findings say?
b) Methodological results
c) What has this research added new to the literature?
d) Further research topics
At risk of poverty and material deprivation
Analysis for specific migrant groups
Further longitudinal analysis with available data
Analysis for other social groups
e) Policy recommendations
Appendix: Description of the social benefits in study counties according to MISSOC data
Unemployment benefits
Sickness and disability benefits
Family and child benefits
Maternity and parental benefits
Other social exclusion benefits
Housing allowances
Annex tables
References
Index
Series Index

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List of tables

Tab. 1.1: Difference between mean full-time equivalent employed months of native-born and migrant employees aged 25–59 not in education by broad country of birth categories, 2004–2015 reference income years

Tab. 1.2: Ratio of mean full-time equivalent gross monthly employee earnings of the immigrant employees relative to the average of native-born employees aged 25–59 not in education by broad country of birth categories, 2004–2015 reference income years (%)

Tab. 1.3: Model summaries for empty and full models for random intercept multilevel mixed effects linear regression 2004–2015 reference income years

Tab. 1.4: Exponential values for random intercept multilevel mixed effects linear regression model coefficients for the migration background categories, 2004–2015 reference income years

Tab. 1.5: Distribution of employees aged 25–59 not in education by total number of disadvantageous job characteristics for lower monthly earnings at the time of the survey in selected years, 2005–2016 (%)

Tab. 1.6: Model summaries for empty and full models for the random intercept multilevel mixed effect ordinal logistic regression models, 2005–2016

Tab. 1.7: Cumulative odds ratios of the migration background categories for the random intercept multilevel mixed effects ordinal logistic regression models, 2005–2016

Tab. 2.1: Proportion of employed individuals one year prior to the survey and unemployed at the time of the survey, who lost job because of the termination of the contract among those, who were aged 25–59 and not in education by country of birth, 2006–2016 (%)

←17 | 18→

Tab. 2.2: Proportion of individuals aged 25–59 and not in education, who were employed one year prior to the survey and unemployed at the time of the survey, and who were the only adults in the household or none of the other 20–64 adults was working in the household, by country of birth for selected years, 2006–2016 (%)

Tab. 2.3: Multilevel mixed effects logistic regression model summaries for transition from employment to unemployment

Tab. 2.4: Odds ratios of the migration background variables for the multilevel mixed effects logistic regression models for transition from employment to unemployment (full model)

Tab. 3.1: Random intercept model summaries of the multilevel mixed effects logistic regression models for being employed in fixed-term jobs

Tab. 3.2: Odds ratios of the migration background variables for the random intercept multilevel mixed effects logistic regression models for being employed in fixed-term jobs, 2004–2016

Tab. 4.1: Ratio of average gross monthly benefits received per non-work during reference year of the immigrants relative to average of native-born non-working individuals aged 25–59 not in education by broad country of birth categories, 2004–2015 (%)

Tab. 4.2: Model summaries of the random intercept multilevel logistic regression models for accessing social benefits during non-work months, 2004–2015

Tab. 4.3: Odds ratios of the migration background variables for the multilevel logistic regression models for accessing social benefits during non-work months, 2004–2015

Tab. 4.4: Odds ratios of the migration background categories for the simple logistic regression models by pooled data for accessing social benefits during non-work months, 2009–2015

Tab. 4.5: Model summaries of the random intercept multilevel mixed effects linear regression models for average amount of gross social benefits received during non-work, 2004–2015

←18 | 19→

Tab. 4.6: Exponential values of coefficients for the migration background categories for the multilevel mixed effects linear regression models, 2004–2015

Tab. 4.7: Model summaries for the random intercept multilevel mixed effects logistic regression models for being employed at the time of the survey after receiving some social benefits during non-working months in the reference income year, 2005–2016

Tab. 4.8: Odds ratios of the migration background categories for the random intercept multilevel mixed effects logistic regression models for being employed at the time of the survey, 2005–2016

Tab. C.1: Evaluation of inequality between the native population and the migrants in selected domains

Tab. A 1: Random intercept multilevel mixed effects linear regression model coefficients for the average full-time equivalent gross monthly earnings, 2004–2015

Tab. A 2: Random intercept multilevel mixed effects ordinal logistic regression cumulative odds ratios in the models for the number of disadvantageous job characteristics for lower gross monthly earnings, 2005–2016

Tab. A 3: Description of 1-digit ISCO-08 occupational codes

Tab. A 4: Description of 1-digit NACE Rev. 2 codes for field of economic activity

Tab. A 5: Odd ratios for the multilevel mixed effects logistic regression models for transition from employment to unemployment for individuals aged 25–59, who were employed one year prior to the survey date (full model)

Tab. A 6: Pooled data simple logistic regression odds ratios of the occupation categories for the models for transition from employment to unemployment for individuals aged 25–59, who were employed one year prior to the survey date for the years with available data, France and the Netherlands

Tab. A 7: Sample size of the individuals in 25–59 age group, who were economically active at the time of the survey and who were employed one year prior to the survey date, 2004–2016

←19 | 20→

Tab. A 8: Random intercept multilevel mixed effects logistic regression odds ratios of the models for being hired by fixed-term contract for employees aged 25–59, who were employed at the time of the survey, 2004–2016

Tab. A 9: Odds ratios for the random intercept multivariate mixed effects logistic regression models for accessing social benefits (full model), 2004–2015

Tab. A 10: Odds ratios for the simple logistic regression models by pooled data for accessing social benefits, 2009–2015

Tab. A 11: Coefficients of random intercept multilevel mixed effects linear regression models for amount of monthly gross benefits receipts per non-working month, 2004–2015

Tab. A 12: Odds ratios for the random intercept multilevel mixed effects logistic regression models for being employed at the time of the survey, 2005–2016

Tab. A 13: Odds ratios for the simple logistic regression models by pooled data for being employed at the time of the survey, 2005–2016

Tab. A 14: Differences between self-reported and officially determined economic activity status for the selected categories, 2004–2016 (%)

Tab. A 15: Blinder-Oaxaca decomposition analysis coefficients for groups differences in the earnings and having at least three disadvantageous job characteristics resulting low earnings between native-born individuals and selected migrant groups

Tab. A 16: Blinder-Oaxaca decomposition analysis coefficients for groups differences in becoming unemployed and having fixed-term contracts between native-born individuals and selected migrant groups

Tab. A 17: Blinder-Oaxaca decomposition analysis coefficients for groups differences in access to social benefits, amount of benefits and return to work for the ones that received benefits between native-born individuals and selected migrant groups

←20 | 21→

Tab. A 18: Gini coefficients for equivalised disposable household income and full-time average monthly employee earnings, 2004–2015

←22 | 23→

List of figures

Fig. I.1: Cycle of sustaining socio-economic inequalities across generations

Fig. 1.1: Trends in the share of broad country of birth categories amongst employees aged 25–59 and not in education at the time of the survey, 2005–2016 (%)

Fig. 1.2: The proportion of employees not in education with no education/low educational attainment level and tertiary education at the time of the survey by broad country of birth categories in selected years, 2005–2016 (%)

Fig. 1.3: Proportion of employees not in education having ISCO-1, ISCO-2 and ISCO-3 occupations at the time of the survey by broad country of birth categories in selected years, 2005–2016 (%)

Fig. 1.4: Proportion of employees not in education having temporary work contacts at the time of the survey by broad country of birth categories in selected years, 2005–2016 (%)

Fig. 1.5: Proportion of employees not in education having supervisory position at work at the time of the survey by broad country of birth categories in selected years, 2005–2016 (%)

Fig. 1.6: Proportion of employees aged 25–59 not in education, who has at least two disadvantageous job characteristics for lower monthly earnings at the time of the survey by country of birth in selected years, 2005–2016 (%)

Fig. 2.1: Employment and unemployment rates of the individuals aged 25–59 and not in education in selected years by country of birth, 2006–2016 (%)

Fig. 2.2: Proportion of employed individuals one year prior to the survey and unemployed at the time of the survey among those, who were aged 25–59 and not in education by country of birth, 2004–2016 (%)

←23 | 24→

Fig. 2.3: Difference in the proportion of employed individuals, who became unemployed in the succeeding year, between the native-born individuals and immigrants in selected years by country of birth and years lived in the current country of residence, 2006–2016 (Percentage points)

Fig. 3.1: Proportion of employees at the time of the survey with temporary contracts among those, who were aged 25–59 and not in education by country of birth, 2004–2016 (%)

Fig. 3.2: Proportion of employees with temporary contracts at the time of the survey, who had temporary contract because of being unable to find permanent jobs, among those, who were aged 25–59 and not in education by country of birth, 2004–2016 (%)

Fig. 3.3: Proportion of employees at the time of the survey with temporary contracts to be ended in 12 months or less among those, who were working with temporary contracts and aged 25–59 and not in education by country of birth, 2004–2016 (%)

Fig. 3.4: Proportion of employees with temporary contracts at the time of the survey, who had temporary contract because of being in probationary period, among those who were aged 25–59 and not in education by country of birth, 2004–2016 (%)

Details

Pages: 380
Year: 2022
ISBN (PDF): 9782875744395
ISBN (ePUB): 9782875744401
ISBN (Softcover): 9782875744388
DOI: 10.3726/b19052
Language: English
Publication date: 2021 (December)
Published: Bruxelles, Berlin, Bern, New York, Oxford, Warszawa, Wien, 2022. 380 pp., 23 fig. b/w, 40 tables.

Biographical notes

Erhan Özdemir (Author)

Erhan Özdemir is a social researcher with a strong background in demographic and statistical analysis. He has specialized in the analysis of issues related to income inequality and living conditions as well as migration and other demographic processes. He has been a member of the research teams of the EU-funded projects such as "Social Situation Monitor" and "Study on the Adequacy and Sustainability of Social Protection Systems: Attitudes in the EU". He is currently a PhD candidate in Ghent University, Faculty of Political and Social Sciences, Department of Sociology.

Socio-Economic Disparities in the Integration Process of Immigrants in Western Europe

Summary

Excerpt

Table Of Contents

List of tables

List of figures

Details

Biographical notes

Key Subject Areas