The Fertility of Migrants and Minorities in Europe: Fertility Intentions of Turkish Migrants in Germany and the Turkish Minority in Bulgaria Compared [1st ed. 2023] 9783658430986, 9783658430993, 3658430982

Table of contents :
Acknowledgements
Abstract
Contents
Abbreviations
List of Figures
List of Tables
1 Introduction
1.1 Ethnicity and Social Inequalities in Europe
1.2 Ethnicity, Fertility, and Assimilation in Europe
1.3 Shortcomings and Research Question(s)
1.4 Data and Case Selection
1.5 Proceeding of this Dissertation
2 Migrants, Minorities, and Assimilation
2.1 Defining Migrants and Minorities
2.1.1 Ethnic Group Formation
2.1.2 The Turkish Minority in Bulgaria
2.1.3 The Turkish Migrants in Germany
2.2 The Origins of Assimilation Theory
2.3 Segmented Assimilation Theory
2.4 Dimensions of Assimilation
2.5 Assimilation Theory in Europe
2.6 Minority Assimilation Outcomes
2.7 Berry’s Acculturation Model
2.8 Towards a Model of Migrant and Minority Assimilation
2.9 Summary
3 Explaining Migrant and Minority Fertility
3.1 Fertility in the Context of Social Demography
3.1.1 Classical Approaches Explaining Fertility
3.1.2 Fertility Decision-making
3.1.3 From Fertility Intentions to Fertility Behavior
3.2 Migrant Fertility
3.2.1 Theories Explaining Migrant Fertility
3.2.2 Migrant Fertility and Assimilation
3.3 Minority Fertility
3.3.1 Social Characteristics Hypothesis
3.3.2 Sub-culture Hypothesis
3.3.3 Minority Status Hypothesis
3.3.4 Shortcomings and Potentials
3.3.5 Minority Fertility and Assimilation
3.4 Summary and Research Hypotheses
4 Data Analytic Strategy
4.1 Data
4.1.1 GGS in Bulgaria
4.1.2 GGS in Germany
4.2 Sample Construction
4.3 Methods
4.3.1 Cluster Analyses
4.3.2 Descriptive Analyses
4.3.3 Structural Equation Modelling
4.3.4 Generalized Structural Equation Modelling
4.3.5 Robustness Checks
4.4 Variables
4.4.1 Variables for Ethnic Clusters
4.4.2 Endogenous Variable
4.4.3 Exogeneous Variables: Fertility
4.4.4 Exogeneous Variables: Assimilation
4.4.5 Control Variables
5 Assimilation Status of Turkish Migrants in Germany and Turkish Minority in Bulgaria
5.1 Bulgaria
5.1.1 Cluster analysis
5.1.2 Demographic Background
5.1.3 Structural Assimilation
5.1.4 Cultural Assimilation
5.1.5 Social Assimilation, Identity and Intergroup Relations
5.1.6 Summary
5.2 Germany
5.2.1 Cluster Analysis
5.2.2 Demographic Background
5.2.3 Structural Assimilation
5.2.4 Cultural Assimilation
5.2.5 Social Assimilation, Identity, and Intergroup Relations
5.2.6 Summary
5.3 Minority Sample
5.3.1 Cluster Analysis
5.3.2 Demographic Background
5.3.3 Structural Assimilation
5.3.4 Cultural Assimilation
5.4 Conclusion
6 Relationship Between Assimilation and Fertility Intentions
6.1 Turkish Minority in Bulgaria
6.1.1 Traits, Desires, and Intentions within Bulgaria
6.1.2 Influence of Assimilation on Fertility Differences
6.1.3 Robustness Checks for Bulgaria
6.2 Turkish Migrants in Germany
6.2.1 Traits, Desires and Intentions within Germany
6.2.2 Influence of Assimilation on Fertility Differences
6.2.3 Robustness Checks for Germany
6.3 Comparison of Minority and Migrant Group
6.3.1 Traits, Desires and Intentions within the Migrant and Minority Sample
6.3.2 Influence of Assimilation on Fertility Differences
6.3.3 Robustness Checks for Minority Sample
6.4 Summary
7 Discussion
7.1 Summary
7.1.1 Definition of Migrant and Minority from a Boundary Making Perspective
7.1.2 Assimilation Status of Turkish Migrant and Minority Groups
7.1.3 Fertility of Migrants and Minorities
7.1.4 Mediating Influence of Assimilation
7.2 Critical reflection
7.2.1 Theoretical Limitations
7.2.2 Methodological Limitations
7.3 Conclusion
References

Citation preview

Bianca Brünig

The Fertility of Migrants and Minorities in Europe Fertility Intentions of Turkish Migrants in Germany and the Turkish Minority in Bulgaria Compared

The Fertility of Migrants and Minorities in Europe

Bianca Brünig

The Fertility of Migrants and Minorities in Europe Fertility Intentions of Turkish Migrants in Germany and the Turkish Minority in Bulgaria Compared

Bianca Brünig Leibniz Universität Hannover Institute of Sociology Hannover, Germany Dissertation 2023, Leibniz Universität Hannover

ISBN 978-3-658-43098-6 ISBN 978-3-658-43099-3 (eBook) https://doi.org/10.1007/978-3-658-43099-3 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2023 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer VS imprint is published by the registered company Springer Fachmedien Wiesbaden GmbH, part of Springer Nature. The registered company address is: Abraham-Lincoln-Str. 46, 65189 Wiesbaden, Germany Paper in this product is recyclable.

Acknowledgements

This paper uses data from the GGS Wave 1 (DOIs: 10.17026/dans-z5z-xn8g), see Gauthier, A. H. et al. (2018) or visit the GGP website (https://www.ggp-i.org/) for methodological details.

v

Abstract

This thesis analyzes the relationship between assimilation and fertility intentions of migrants and minorities in Europe. Migrants are understood here as resulting out of (voluntary) processes of migration, while minorities on the European continent are defined as being the result of processes of annexation or redrawn political borders. Given that both groups often depict diverging reproductive behavior when compared to the native majority, their fertility could be a remedy to the ageing societies on the European continent. Yet, little is known about their fertility, especially as far as minority groups are concerned. Building upon assimilation theory, it is argued here that both migrants and minorities assimilate in the process of intercultural encounters. More concretely, assimilation is taken to signify a bi-dimensional process of home- and hostcountry inclusion which consists out of the stages marginalization, separation, integration, and assimilation. As will be argued for theoretically and shown empirically, especially separation and integration are meaningful alternatives for migrants and minorities in Europe. Given that fertility is part of the cultural dimension of assimilation, it is likely to be influenced by assimilation processes. While most theories on the fertility of migrants and minorities do not link fertility and assimilation theory directly, this thesis goes a step further by showing theoretically that both approaches can be merged to develop a holistic model to explain migrant and minority fertility. Using data from the Generations and Gender Survey, the empirical section builds upon a comparison of a migrant and minority group of same origin, namely Turkish migrants in Germany and the Turkish minority in Bulgaria. Throughout the analyses they are compared to the native majority within their countries of

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Abstract

residence, as well as to each other. The focus is thereby laid upon fertility intentions as they better capture the influence of circumstantial factors and mirror the reproductive decision-making process better than actual births of children. Using cluster analyses, the intersectionality of ethnic dividing lines within society along other socio-demographic characteristics such as education and residency is considered. Thus, migrants, minorities and majority are not taken to be a homogeneous group. Rather, the thesis acknowledges that diversity can exist and develops six clusters within Germany (three native, three Turkish) as well as five clusters in Bulgaria (four native, one Turkish) that provide the basis for further analyses. Comparing these clusters in terms of assimilation and fertility intentions it becomes clear that the Turkish minority can be considered separated from the native majority but does not differ in their fertility intentions from the majority society. Rather, differences are most prominent regarding structural factors such as unemployment or education. For Germany, Turkish migrants are found to be integrated but to varying degrees depending on their level of education. They differ from German natives regarding their fertility intentions. However, these differences can be explained by the assimilation status of Turkish respondents, especially by structural characteristics. Interestingly, it can be shown that Turkish respondents even outperform lower educated German respondents on several structural measures. When explicitly comparing migrant and minority, differences in fertility intentions exist and are accounted for by cultural dissimilarity. In conclusion, the thesis demonstrates that theoretical models explaining migrant and minority fertility can be merged and extended. Moreover, empirical analyses underline the necessity to better capture fertility decision-making of ethnic groups in Europe to fully understand their role in the demographic change. For prospective analyses, more data on the reproduction of these groups should be gathered to overcome the challenges encountered within this dissertation. These data should include high numbers of migrant and minority respondents and should entail extensive information on fertility and assimilation. Keywords: fertility · migrant · minority · Europe · assimilation · Germany · Bulgaria

Contents

1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1 Ethnicity and Social Inequalities in Europe . . . . . . . . . . . . . . . . . . . 1.2 Ethnicity, Fertility, and Assimilation in Europe . . . . . . . . . . . . . . . 1.3 Shortcomings and Research Question(s) . . . . . . . . . . . . . . . . . . . . . 1.4 Data and Case Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.5 Proceeding of this Dissertation . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1 2 5 7 8 10

2 Migrants, Minorities, and Assimilation . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1 Defining Migrants and Minorities . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1.1 Ethnic Group Formation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1.2 The Turkish Minority in Bulgaria . . . . . . . . . . . . . . . . . . . . 2.1.3 The Turkish Migrants in Germany . . . . . . . . . . . . . . . . . . . . 2.2 The Origins of Assimilation Theory . . . . . . . . . . . . . . . . . . . . . . . . . 2.3 Segmented Assimilation Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4 Dimensions of Assimilation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.5 Assimilation Theory in Europe . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.6 Minority Assimilation Outcomes . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.7 Berry’s Acculturation Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.8 Towards a Model of Migrant and Minority Assimilation . . . . . . . 2.9 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

13 13 15 17 19 20 23 25 29 36 38 41 45

3 Explaining Migrant and Minority Fertility . . . . . . . . . . . . . . . . . . . . . . 3.1 Fertility in the Context of Social Demography . . . . . . . . . . . . . . . . 3.1.1 Classical Approaches Explaining Fertility . . . . . . . . . . . . . 3.1.2 Fertility Decision-making . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1.3 From Fertility Intentions to Fertility Behavior . . . . . . . . . . 3.2 Migrant Fertility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

47 47 49 52 58 59

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3.2.1 Theories Explaining Migrant Fertility . . . . . . . . . . . . . . . . . 3.2.2 Migrant Fertility and Assimilation . . . . . . . . . . . . . . . . . . . . 3.3 Minority Fertility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3.1 Social Characteristics Hypothesis . . . . . . . . . . . . . . . . . . . . . 3.3.2 Sub-culture Hypothesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3.3 Minority Status Hypothesis . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3.4 Shortcomings and Potentials . . . . . . . . . . . . . . . . . . . . . . . . . 3.3.5 Minority Fertility and Assimilation . . . . . . . . . . . . . . . . . . . 3.4 Summary and Research Hypotheses . . . . . . . . . . . . . . . . . . . . . . . . .

60 65 67 68 69 70 71 75 81

4 Data Analytic Strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1.1 GGS in Bulgaria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1.2 GGS in Germany . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Sample Construction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3.1 Cluster Analyses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3.2 Descriptive Analyses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3.3 Structural Equation Modelling . . . . . . . . . . . . . . . . . . . . . . . 4.3.4 Generalized Structural Equation Modelling . . . . . . . . . . . . 4.3.5 Robustness Checks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4 Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4.1 Variables for Ethnic Clusters . . . . . . . . . . . . . . . . . . . . . . . . . 4.4.2 Endogenous Variable . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4.3 Exogeneous Variables: Fertility . . . . . . . . . . . . . . . . . . . . . . . 4.4.4 Exogeneous Variables: Assimilation . . . . . . . . . . . . . . . . . . 4.4.5 Control Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

87 87 88 89 90 92 92 94 95 97 98 100 100 101 101 103 107

5 Assimilation Status of Turkish Migrants in Germany and Turkish Minority in Bulgaria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1 Bulgaria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1.1 Cluster analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1.2 Demographic Background . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1.3 Structural Assimilation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1.4 Cultural Assimilation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1.5 Social Assimilation, Identity and Intergroup Relations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

109 109 109 110 115 117 121 123

Contents

5.2 Germany . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2.1 Cluster Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2.2 Demographic Background . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2.3 Structural Assimilation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2.4 Cultural Assimilation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2.5 Social Assimilation, Identity, and Intergroup Relations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3 Minority Sample . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3.1 Cluster Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3.2 Demographic Background . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3.3 Structural Assimilation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3.4 Cultural Assimilation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 Relationship Between Assimilation and Fertility Intentions . . . . . . . . 6.1 Turkish Minority in Bulgaria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1.1 Traits, Desires, and Intentions within Bulgaria . . . . . . . . . 6.1.2 Influence of Assimilation on Fertility Differences . . . . . . 6.1.3 Robustness Checks for Bulgaria . . . . . . . . . . . . . . . . . . . . . . 6.2 Turkish Migrants in Germany . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2.1 Traits, Desires and Intentions within Germany . . . . . . . . . 6.2.2 Influence of Assimilation on Fertility Differences . . . . . . 6.2.3 Robustness Checks for Germany . . . . . . . . . . . . . . . . . . . . . 6.3 Comparison of Minority and Migrant Group . . . . . . . . . . . . . . . . . 6.3.1 Traits, Desires and Intentions within the Migrant and Minority Sample . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3.2 Influence of Assimilation on Fertility Differences . . . . . . 6.3.3 Robustness Checks for Minority Sample . . . . . . . . . . . . . . 6.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.1 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.1.1 Definition of Migrant and Minority from a Boundary Making Perspective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.1.2 Assimilation Status of Turkish Migrant and Minority Groups . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.1.3 Fertility of Migrants and Minorities . . . . . . . . . . . . . . . . . . . 7.1.4 Mediating Influence of Assimilation . . . . . . . . . . . . . . . . . .

xi

124 124 125 129 132 135 137 137 138 138 140 141 143 147 147 147 149 158 160 161 163 170 173 173 175 179 182 185 185 186 187 189 191

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7.2 Critical reflection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2.1 Theoretical Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2.2 Methodological Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . 7.3 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

192 192 197 200

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

203

Abbreviations

ANOVA BU DE GGP GGS HI ISCED ML Resp RU SEM TDIB TFR TU UP URB USA VOC

Analysis of Variance Bulgaria/Bulgarian Germany/German Generations & Gender Programme Generations & Gender Survey High education International Standard Classification of Education Maximum Likelihood Respectively Rural Structural equation model Traits-desires-intentions-behavior Total fertility rate Turk/Turkish Upper-secondary education Urban United States of America Value of children

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List of Figures

Figure 2.1 Figure 3.1 Figure 3.2 Figure Figure Figure Figure Figure Figure Figure Figure

3.3 4.1 5.1 5.2 5.3 5.4 5.5 5.6

Figure 5.7 Figure Figure Figure Figure Figure Figure

5.8 5.9 5.10 5.11 5.12 5.13

Figure 5.14

Limited assimilation model for migrants and minorities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Schematic of the TDIB model (Miller & Pasta, 1995, p. 533) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Integration of migrant and minority fertility theories into an assimilation framework . . . . . . . . . . . . . . . . . . . . . . . Integrated model on migrant and minority fertility . . . . . . . Models estimated with help of regression analyses . . . . . . . Number of children by cluster for Bulgaria . . . . . . . . . . . . . Fertility intentions by cluster and gender for Bulgaria . . . . Use of contraceptives by cluster for Bulgaria . . . . . . . . . . . Activity status by cluster for Bulgaria . . . . . . . . . . . . . . . . . . Activity status by cluster and gender for Bulgaria . . . . . . . . Opinion towards marriage and family life (1) for Bulgaria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Opinion towards marriage and family life (2) for Bulgaria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Number of children by cluster for Germany . . . . . . . . . . . . Fertility intentions by cluster and gender for Germany . . . . Contraceptive use by cluster for Germany . . . . . . . . . . . . . . Activity status by cluster for Germany . . . . . . . . . . . . . . . . . Activity status by gender and cluster for Germany . . . . . . . Educational level by ethnic group and gender for Germany . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Language spoken at home by length of residence and cluster for Germany (Turkish clusters only) . . . . . . . . .

44 56 76 86 99 112 113 114 115 116 118 119 127 128 129 130 131 132 133

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Figure 5.15 Figure 5.16 Figure 5.17 Figure 5.18 Figure 5.19 Figure 6.1 Figure 6.2 Figure 6.3

Figure 6.4

Figure 6.5 Figure 6.6

Figure 6.7 Figure 6.8

List of Figures

Frequency of religious service attendance by cluster for Germany . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Number of children by minority status . . . . . . . . . . . . . . . . . Employment status by minority status . . . . . . . . . . . . . . . . . Employment status by educational background and minority status . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Attitude towards marriage and family life by minority status . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Predicted mean of positive fertility intentions by cluster and parity for Bulgaria . . . . . . . . . . . . . . . . . . . . . Predicted mean of positive fertility intentions by cluster, parity, and attitude for Bulgaria (n = 4,770) . . . Predicted mean of positive fertility intentions by cluster, parity, and employment status for Bulgaria (n = 4,770) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Predicted mean of positive fertility intentions by cluster, parity, and social support for Bulgaria (n = 4,770) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Predicted mean of positive fertility intentions by cluster and parity for Germany . . . . . . . . . . . . . . . . . . . . . Predicted mean of positive fertility intentions by cluster, parity, and employment status for Germany (n = 3,735) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Predicted mean of positive fertility intentions by cluster and parity for the minority sample . . . . . . . . . . . Predicted mean of positive fertility intentions by minority status, parity, and attitude . . . . . . . . . . . . . . . . .

134 139 140 141 142 151 152

153

154 165

166 175 179

List of Tables

Table 2.1 Table 2.2 Table 3.1 Table 4.1 Table 4.2 Table 4.3 Table Table Table Table Table Table Table

4.4 5.1 5.2 5.3 5.4 5.5 6.1

Table 6.2 Table 6.3

Acculturation Outcomes according to Berry (1974, 1980) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Match of Berry’s acculturation model with segmented assimilation theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Overview of publications on migrant and minority fertility (selection) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sample for Bulgaria and Germany . . . . . . . . . . . . . . . . . . . . . Factor loadings of motivational traits for Germany, Bulgaria and minority sample . . . . . . . . . . . . . . . . . . . . . . . . . Factor loadings of importance of religious ceremonies for Germany, Bulgaria and minority sample . . . . . . . . . . . . . Factor loadings of attitude for Germany . . . . . . . . . . . . . . . . . Cluster analysis GGS data Bulgaria . . . . . . . . . . . . . . . . . . . . Demographic characteristics by cluster for Bulgaria . . . . . . . Cluster analysis GGS data Germany . . . . . . . . . . . . . . . . . . . . Demographic characteristics by cluster for Germany . . . . . . Demographic characteristics by minority status . . . . . . . . . . . GSEM partial model for Bulgaria—Odds ratios (robust standard errors) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . GSEM results full model for Bulgaria—Odds ratios (robust standard errors) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Logistic regression models predicting desire and fertility intentions by fertility related variables for Bulgaria (Odds ratios) . . . . . . . . . . . . . . . . . . . . . . . . . . . .

38 41 77 91 103 104 105 110 111 124 126 139 148 155

158

xvii

xviii

Table 6.4

Table 6.5 Table 6.6 Table 6.7

Table 6.8

Table 6.9

Table 6.10 Table 6.11

Table 6.12

List of Tables

Logistic regression model for predicting fertility intentions by assimilation related variables for Bulgaria (Odds ratios) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . GSEM partial model for Germany—Odds ratios (robust standard errors) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . GSEM results full model for Germany—Odds ratios (robust standard errors) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Logistic regression models predicting desire and fertility intentions by fertility related variables for Germany (Odds ratios) . . . . . . . . . . . . . . . . . . . . . . . . . . . . Logistic regression model predicting fertility intentions by assimilation related variables for Germany (Odds ratios) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . GSEM partial model for migrants and minorities—Odds ratios (robust standard errors) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . GSEM full model for migrants and minorities—Odds ratios (robust standard errors) . . . . . . . . . . . . . . . . . . . . . . . . . Logistic regression models predicting desire and fertility intentions by fertility related variables for the minority sample (Odds ratios) . . . . . . . . . . . . . . . . . . . Logistic regression model predicting fertility intentions by assimilation related variables for the minority sample (Odds ratios) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

159 162 167

170

171

174 176

180

181

1

Introduction

The demographic change alters the nature of European societies fundamentally as low-fertility implies a decrease in population size across time (Vollset et al., 2020), a lower labor force participation rate (Lutz et al., 2019) and an increasing old-age dependency ratio (Jäger, 2019). Migrant and minority populations might be a remedy here as they can increase the participation rate in the labor force and at the same time contribute to the birth of more children (Lutz et al., 2019). In terms of the demographic change, minorities next to migrants are hence an important pillar and might counter the ageing of European societies. To understand the extent to which migration movements can influence the demographic change, more insight into long-term developments of the fertility of migrant groups is needed. A focus on minorities might help here given that these ethnic groups already reside for a long time within their host societies (Milewski, 2010). The fertility of migrants and minorities in Europe will therefore be focused upon within this dissertation. While there is research on the fertility of migrants in Europe, the investigation of indigenous minorities in Europe has long been a sensitive topic (Compton, 2000). Consequently, little is known about their demographic behavior and minority-majority relations. Though official statistics estimate the size of minority groups and their potential growth, Compton (2000) points at the tentativeness of these estimates due to limited insights into intercultural dynamics. What is more, the data availability is scarce as ethnic background is not necessarily assessed within national statistics. Even if ethnic categories are part of census data, the categories are often not presented neutrally meaning ethnic labels are often based on other- not on self-ascription (Compton, 2000). The few scientific studies on the reproductive behavior of minorities in Europe pinpoint towards higher fertility levels when compared to the majority (Katus et al., 2000; Szabo et al., 2021).

© The Author(s), under exclusive license to Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2023 B. Brünig, The Fertility of Migrants and Minorities in Europe, https://doi.org/10.1007/978-3-658-43099-3_1

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Introduction

Still, hardly any empirical study focuses on these minority groups and theories explaining their fertility are developed rudimentary (Milewski & Haug, 2022). Rather, migrant fertility remains the center of attention in Europe as several studies have underlined that migrants deviate from native fertility and often show higher fertility rates (Impicciatore et al., 2020; Schmid & Kohls, 2009). Their diverging fertility is often explained by socialization or processes of adaptation. Although these are closely tied to processes of assimilation, which is a prominent research topic within migration studies, no connection between the fertility of migrants and assimilative processes is made. This is interesting given that theories on fertility and assimilation build upon similar arguments and use equal measures to assess reproductive behavior. Therefore, this dissertation will look at the fertility intentions of a migrant and minority group of same origin in Europe by building upon assimilation theory as well as on theories on the fertility of migrants and minorities. A theoretical model to predict fertility intentions will be developed and tested empirically with help of quantitative data. To place this study within the context of scientific discourse, the next sections will introduce the topic of ethnicity within the context of social inequalities in Europe and will elaborate more carefully on the use of assimilation and fertility theories within a European context.

1.1

Ethnicity and Social Inequalities in Europe

To understand reproductive behavior of migrant and minority groups, it is essential to place them within a social context. This can be done by looking closer at ethnicity which is a concept that underlies definitions of both migrant and minority as well as majority groups. Ethnicity is a concept closely tied to migration studies, especially in Europe. It describes a societal line of distinction which is often applied to explain group differences and structure social relations (Bös, 2019; Siebers, 2017). While studies on ethnic groups are closely linked to racerelations within American literature, European scholars rather focus on ethnicity in the context of migration. This is not the only semantic difference when comparing US American and European migration scholars. Since migration is a phenomenon which arose in the USA much earlier than in Europe, American studies on migration, ethnicity and race already focus on third or fourth generation migrants (Milewski, 2010). The terms migrant and minority are part of American studies on social inequalities but are defined differently when compared to the European context (Sasse & Thielemann, 2005). In Europe, migrants and subsequent generations are summarized under the heading of migrant and

1.1 Ethnicity and Social Inequalities in Europe

3

can be differentiated from minorities who date back to historical processes of redrawn political boundaries and are hence much older, indigenous ethnic groups (Heckmann, 1983; Meyers, 1984; Schaefer, 2015). In America, however, the term minority either refers to ethnic minorities, which often signify subsequent migrant generations, or is used to adhere to a socio-economic underclass (Milewski, 2010; Wilkinson, 2015a). In many studies, migrants and minorities are focused upon without even defining what is understood by the terms. As a consequence, when focusing on migrants and minorities in Europe the applicability of theories on migrants and minorities that originate from US American contexts should be critically discussed and the ethnic definition underlying these studies should be questioned. This is also the case for theories on the fertility of migrants and minorities, which will be the focus within this dissertation. Given the ageing of European societies, theories on the fertility of migrants and minorities take a central role in predictions regarding population developments. Migration has been portrayed as one solution to ageing European societies given that many migrant groups come from high-fertility countries that have youthful age-structures (Coleman, 2009; Harper 2016; Milewski & Mussino 2018). Several studies have shown that the fertility levels of migrants are often above the native average (Fokkema et al., 2008; Impicciatore et al., 2020; Schmid & Kohls, 2009; Stonawski et al., 2016) and only in a few cases below national levels (Bagavos et al., 2008; Waller et al., 2014). Still, processes of migration and fertility remain understudied within Europe (Ediev, et al., 2014; Milewski & Mussino, 2018) and full insight into the childbearing patterns of migrant groups is lacking (Bagavos, 2019; Coleman, 2006). Regarding the fertility of European minority groups, some studies have equally found that reproductive patterns of established minorities differ from native fertility levels (Andorka, 1978; Katus et al., 2000; Lee & Lee, 1952). Yet, this subpopulation has received less attention and fewer theoretical explanations exist to account for their fertility (Milewski, 2010). This is surprising given that minorities, together with migrants, form an incremental part of European societies. Minorities are part of every European country (Pan, 2009), but differ in size, number, and their historical offspring. In terms of demographic change their keeping of cultural distinctiveness might also imply that they have preserved the fertility behavior of their countries of origin and might not have adapted to the low fertility level of their countries of living. When trying to counter ageing societies, minorities in Europe might hence play a pivotal role alongside migrants in keeping the total fertility rate at a constant or even increasing level. However, scientific research on the fertility of minorities is nearly non-existent (Milewski & Haug, 2022). Consequently, more research in Europe on the fertility behavior of

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Introduction

migrants and minorities is needed and the applicability of migrant and minority fertility theories—which mainly originate from a US American context—needs to be reviewed. Talking about ethnicity, independent of migrant or minority background, implies that individuals can be grouped into one ethnic group, which is defined by markers such as cultural habits, behavior, clothing style or phenotypical appearance. Yet, it is often neglected that ethnicity is socially constructed and that the attribute of belonging to a certain ethnic group does not necessarily match the self-identification of an individual (Bös, 2019). Moreover, most studies focusing on ethnicity in the context of social inequalities implicitly assume that ethnic groups are homogeneous. These shortcomings lead to an ethnicity bias (Bös, 2019) that too easily ascribes societal problems to ethnic categories. A different approach is taken by studies on the intersectionality of societal lines of distinction such as gender, age, educational background, or ethnicity. Here, it is assumed that social interactions are complex and that power relations are not only defined based upon one social dimension. However, few studies have followed this approach and even fewer have used an intersectionality perspective to explain societal phenomena instead of only discussing the meaningfulness of the concept (Collins, 2015). Among the few studies within European migration research that specifically point at intersecting group constructions, HerwartzEmden et al. (2010) highlight the intermingling of gender and ethnicity when focusing on the socialization and educational career of migrant youth. Their empirical studies highlight that social reality and upbringing differ between boys and girls of immigrant origin. In the context of fertility, Alarcao and colleagues (2019) as well as Eeckhaut (2020) belong to the few publications that consider intersecting lines of distinction within fertility research of ethnic groups. While the former looked at fertility care among males and females of migrant background in Portugal, the latter focused upon the intersection of ethnicity, education and gender when studying contraceptive use. Both studies confirm the relevance of treating ethnic groups as heterogenous units of analysis. While these studies broaden the perspective of ethnic studies on fertility, they still base their intersection of relevant societal categories on socially constructed groups. This means that it is assumed beforehand that gender, education, and ethnicity are important markers and people are attributed these ascriptions based on a pre-defined list of options within quantitative studies (see e.g. HerwartzEmden et al., 2010; Alarcao et al., 2019; Eeckhaut, 2020). This way little space for self-ascriptions is given and the interview situation might frame the salience of identities as only certain options are presented that respondents have to choose from (Bös, 2019). To overcome this short-coming and to consider the societal

1.2 Ethnicity, Fertility, and Assimilation in Europe

5

lines of distinction that might or might not entail ethnicity, more open-minded approaches to study ethnicity should be found. Moreover, the formation of ethnic migrant and minority groups should be looked upon in more detail entailing both a historical dimension as well as current intergroup settings within a country to understand how groups are shaped and constituted. Also, empirical studies would do well to include societal dividing lines along ethnicity within their studies to question the explanatory power of ethnic groupings and allow for intersecting lines of division.

1.2

Ethnicity, Fertility, and Assimilation in Europe

Connecting the formation of ethnic group processes to processes of fertility might help to understand why certain ethnic groups depict higher or lower fertility than the native majority. Most European countries experience sub-replacement fertility levels, which encourage the aging of European societies. Sub-replacement fertility is thereby defined as having a total fertility rate (TFR) that falls below 2.05 children (Lesthaeghe & Permanyer, 2014). Within the last two decades, fertility rates in German-speaking and Eastern European countries have even fallen to as low as 1.5 children per woman (Campisi et al., 2020; Goldstein et al., 2009; Kohler et al., 2006; Lesthaeghe & Permanyer, 2014), which implies that an increasing share of old people depends on a decreasing number of young people (Harper, 2011). The fertility level often depends on ethnic background. When studying migration to the UK, Robards and Berrington (2016) for instance highlight that Eastern European migrants tend to originate from low fertility settings, whereas migration from Pakistan generally shows higher fertility levels than UK averages. These results demonstrate that the setting and the ethnic background have to be considered when focusing on the fertility of migrants. One set of studies has therefore focused upon the comparison of different migrant and/or minority groups within one context of reception (Impicciatore et al., 2020 for Italy; Landschoot et al., 2017 for Belgium; Robards & Berrington, 2016 for the UK), while others have taken up the role of context by comparing two or more groups of the same origin within multiple destinations (Lelie et al., 2012 for Turkish and Moroccan in eight European countries; Mussino & Cantalini, 2022 for Romanians and Polish in Italy and the UK). To date, however, hardly any study has looked at migrant and minority groups of same origin as far as the reproductive behavior in Europe is concerned. One exception is Stonawski et al. (2016) who have looked at Muslims in Spain, Bulgaria, and Greece. While Muslims have mostly a migrant origin in

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Introduction

Spain and partly in Greece, they are part of minority groups within Bulgaria and partly also in Greece. That hardly any study has looked at migrant and minority fertility is interesting given that both ethnic minorities and migrants potentially deviate in their fertility behavior from the native majority. Ideally, a comparison of a minority and a migrant group within the same country of living would provide insights into the adaptation of fertility levels to the native majority as such a setting would imply that only the migrant/minority status distinguishes the two groups. If migrants and minorities are compared across different contexts of living, the influence of the context of reception must be considered in addition. An important predictor of reproductive behavior of migrants and minorities is the diversity in historical origin and length of residence of minority and migrant groups, which automatically directs the focus towards processes of assimilation. Here, the question arises to what extent these groups are integrated into European societies. Especially subsequent migrant generations are considered to assimilate, since they often take over the norms and values of their country of residence during socialization (Milewski, 2010; Stephen & Bean, 1992). While this argumentation implies that minorities, who have been born in their country of residence and whose families have lived in that country for decades, are part of the majority population and have (nearly) completed this process of assimilation, reality shows that minorities often keep their cultural distinctiveness and adjust only socio-structurally (Farley, 1966; Heckmann, 1992). Assimilation is thus not completed for minority groups—a fact which makes one wonder how the fertility behavior of these minority groups compares to native fertility levels and migrants of the same origin. Assimilation theory offers several social, cultural, and structural explanations for fertility decision-making and hence the opportunity to combine cultural with socio-structural explanations when looking into minority fertility (Chang, 2003). At the same time, assimilation theory has hardly been used to account for migrant and minority fertility. Only recently, some researchers have started analyzing migrant fertility under an assimilation framework (Milewski & Haug, 2022; Wilson & Kuha, 2017). For minority fertility, however, no such publications exist. Instead, several other theoretical approaches, which will be reviewed in chapter 3, have been employed to explain the reproductive decisions of migrants and minorities. These approaches stress socio-structural and cultural elements as well but do not reference assimilation in their theoretical arguments (Coleman, 2006). One clear lack is thus to discuss the extent to which theories on migrant and minority fertility can be linked to theories on assimilation.

1.3 Shortcomings and Research Question(s)

1.3

7

Shortcomings and Research Question(s)

The last sections have shown that the fertility of migrants and minorities is an important facet for understanding and countering demographic change. However, the topic has not sufficiently been studied so far, especially as far as older minority groups on the European continent are concerned. Therefore, more research is needed that looks at migrant and minority fertility from a European perspective. Theories on migrant and minority fertility originate from the American context, too, which implies that their applicability for the European context should be discussed before deriving hypotheses. Given that European migrant and minority groups are composed differently and originate from different countries of destination it cannot be taken for granted that American theoretical perspectives apply fully to European settings. Moreover, it is interesting that the fertility levels of migrants and minorities are usually explained by processes of adaptation and socialization, but that hardly any reference to assimilation theory can be found. As the assimilation framework is an important one to understand intercultural encounters, it is argued here that assimilation and fertility theories should be combined into an overall model including insights on assimilation. Alongside these aims, a further goal of this dissertation is to not only define ethnic minority and migrant groups, but to acknowledge that ethnicity might not be the only line of distinction that is relevant to study societal processes. By including cluster analyses as explorative method, it will be argued within this thesis that ethnic groups are not homogeneous and that further markers can contribute to understanding migrant and minority fertility. Lastly, a third objective of this dissertation is the focus on fertility intentions as a measure of reproductive behavior. Fertility intentions are chosen as a measure of reproductive behavior to focus on actual decision-making processes. Though intentions do not necessarily result in the actual birth of a child, they better capture the extent to which current living circumstances influence proceptive or contraceptive behavior as compared to actual births (Bühler & Fratczak, 2007; Kulu & Milewski, 2007; Huinink, 2016; Milewski & Mussino, 2018). Furthermore, Mussino and Milewski (2018) have stressed that fertility intentions better capture changes in norms, which makes them a suitable measure for reproduction especially in the context of migrant and minority integration. The dissertation will take up these potentials theoretically and empirically by focusing on the relationship between assimilation and fertility intentions of two selected groups, the Turkish migrants in Germany and the Turkish minority in Bulgaria.

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Introduction

The first research question will review and evaluate the assimilation of both groups regarding the different dimensions of assimilation. By comparing these groups with the majority society in their country of living, it is intended to conclude (1a) how the assimilation status of both groups can best be described and categorized. Moreover, the aim of the first research question is (1b) to assess to what extent both groups are comparable regarding their assimilation status. Especially for the Turkish minority in Bulgaria it will be of interest to investigate whether assimilation as such is a meaningful concept to describe their current living situation and if not, what possible alternative paths of intergroup relations with the majority could be. Secondly, the dissertation will analyze (2) whether ethnicity is an important line of distinction within Bulgarian and German society as well as when comparing migrant and minority. Following an intersectional perspective and the boundary making approach, it will be explored whether a mere distinction based on ethnic background suffices to explain variation in fertility intentions or whether a more heterogeneous approach that takes into account further societal characteristics is more suitable. Further, a third research question will ask (3a) whether the Turkish minority in Bulgaria and the Turkish migrants in Germany differ in fertility intentions from the native majority and (3b) from each other. These two research questions have to be answered before it is possible and meaningful to dig deeper the main research question, namely (4) to what extent differences in fertility intentions can be explained by the assimilation status of the groups under investigation. For this purpose, theories on assimilation will be merged with fertility theories to explain the reproductive intentions of minorities and migrants. This is of special importance given the lack of integrating assimilation into explaining migrant and minority fertility.

1.4

Data and Case Selection

The dissertation has deliberately chosen two groups—the Turkish minority in Bulgaria and the Turkish migrants in Germany—to investigate the relationship between assimilation and fertility. Ideally, the aim was to find one ethnic group of same origin that is both migrant and minority within the same country of residency. However, several demands had to be considered in addition to theoretical arguments and one year of intensive research on data availability showed that compromises are needed. When analyzing assimilation, it is important to (1) cover different dimensions of assimilation (EFFNATIS, 2001; Esser, 2001; Esser, 2002; Heckmann & Schnapper, 2003) such as working life, social contacts and cultural habits. (2)

1.4 Data and Case Selection

9

As the aim of this dissertation is the comparison of a migrant and a minority group, it is necessarily a precondition to find a minority and migrant group with the same country of origin. It would theoretically be possible to focus on a migrant and minority group of different origin. However, if the context of living of these groups varies, too, then it is hardly possible to track differences in fertility behavior to assimilation or migrant/minority status as too many variations prevail among the two groups. Therefore, it was aimed at finding a group of same origin to minimize variations. (3) Moreover, these groups must be settled within Europe as one further goal of this thesis is to apply assimilation and fertility theories to the less studied European context. (4) To place migrant and minority data within the context of their countries of reception, comparable data should exist, also for the majority. (5) Fifthly, the number of minority/migrant respondents within the data should be sufficient to conduct meaningful analyses. Here, the aim is a minimum of 200 cases per group. Similarly, to explain the fertility of different groups, (6) it is important to cover different measures of fertility such as fertility desires, intentions, and outcomes as well as use of contraception. These criteria guided the search for suitable data. (7) A seventh criterion, namely the availability of comparable data of the home country, Turkey, was considered, too. However, as the search for suitable data proved so difficult, the last criterion was considered less important. After screening comparative international data sets, European data collections and individual country level data, the Generations and Gender Survey (GGS) was found to best meet the criteria described. It should be noted though that no existing data set met every criterion. Therefore, a certain drawback in the data analyses has to be dealt with and will be discussed in more detail in chapter 7. The GGS is a large-scale household analysis, which includes both natives, minorities and migrants. It is a longitudinal study that has taken place in 19 countries, where respondents aged 18–79 years were interviewed about their fertility, economic activity, and attitudes. It covers topics such as household composition, demographic information, contraceptive use, as well as ethnic background and partnership information (GGS, 2022). It is thus suitable for this dissertation as it enables both the identification of migrant and minority respondents and provides relevant variables to answer the overall research question. Moreover, it was available for a migrant and minority of same origin, namely Turkish migrants in Germany and the Turkish minority in Bulgaria. For the upcoming analyses, three datasets from the GGS will be used: the first wave from Bulgaria collected in 2004, the first wave from Germany from 2005 and the Turkish subsample from Germany from 2006. These two Turkish groups are the only ones out of the GGS that provide a sufficient sample size

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Introduction

and comparability of minority and migrant group with same ethnic background. Therefore, they were chosen for the analysis. At the same time, the selection is also interesting from a theoretical point of view. The Turkish settlement in Bulgaria dates back to the Ottoman Empire, when Turkish was the ruling language of the Turkish elite. In 1866, the Turkish constituted 30—50 % of the Bulgarian population (Eminov, 1997). During the Russo-Turkish War in 1878 many Turks fled back to Turkey and interethnic relations changed. The Turkish migrants in Germany, on the other hand, form one of the biggest migrant groups in Germany and lots of research on their assimilation status (Diehl & Schnell, 2006; Griess et al., 2021; Hartmann, 2014; Haug, 2003) and fertility (Krapf & Wolf, 2015; Sobotka, 2012; Wolf, 2014) exists. They came to Germany as guest workers during the 1960 s and mostly stayed afterwards. More information on both groups and their historical origin as well as interethnic relations will be provided in section 2.1.

1.5

Proceeding of this Dissertation

The first chapter has placed the topic of this dissertation within the scientific and societal discourse on demographic change. Moreover, research questions guiding the analyses have been derived based on shortcomings in previous research. Chapter 2 will focus on migrants, minorities and assimilation. It will first of all define what is understood by migrant and minority within this dissertation. As there is little literature on the definition of a minority available, ethnic group formation processes are reviewed to distinguish migrants and minorities. Here, the boundary making approach will be attributed a central role in argumentation. Having established how boundaries and social categorizations have arisen within the two countries under study, the origins of the concept of assimilation will be focused on in section 2.2. Building upon the development of classical assimilation theory into more diverse forms of integration, the segmented assimilation theory (section 2.3) is focused upon next to highlighting that in multi-ethnic societies assimilation does not necessarily proceed linearly. Moreover, the concept can be differentiated even further by focusing on different dimensions of assimilation as discussed in section 2.4. Here, the economic, cultural, social and identificational dimension will be focused upon. Then, section 2.5 will discuss the extent to which assimilation is a concept useful to describe the living situation of ethnic groups in Europe. Having argued that assimilation as process of becoming alike can be applied within Europe, too, empirical and theoretical insights on the applicability of the concept to describe the living circumstances of minorities in

1.5 Proceeding of this Dissertation

11

the European continent is presented (section 2.6). To bring down these insights into a holistic framework to assess migrant and minority assimilation in Europe, the acculturation model of Berry is presented in section 2.7 as it provides a bidimensional understanding of assimilation that helps to structure the theoretical insights on assimilation presented up to this point. Finally, section 2.8 presents an overall model on migrant and minority assimilation applied within the thesis and summarizes the main findings in section 2.9 to develop research hypotheses. Having come to terms with a definition and a model of migrant and minority assimilation, chapter 3 then outlines the second theoretical approach, namely theories on the fertility of migrants and minorities. In section 3.1 classical approaches to explaining fertility are introduced. Concretely, economic, and social psychological theories are shortly reviewed. In this context, fertility decisionmaking with all its facets is discussed by building both upon the theory of planned behavior of Ajzen and Fishbein as well as the traits-desires-intentions framework of Miller and Pasta. Based on the model of Miller and Pasta the focus on fertility intentions as main outcome within this dissertation is argued for. As migrants and minorities have different theoretical frameworks to explain fertility patterns, the theories explaining their fertility behavior will be reviewed separately for migrants (section 3.2) and minorities (section 3.3). Next to a review and critical reflection on these theories, both chapters will also reflect upon the extent to which the theories can be merged with assimilation theory as presented within chapter 2. Then, own considerations will follow regarding the combination of theories on migrant and minority fertility, also regarding their integration into the assimilation model developed in section 2.8. Finally, section 3.4 summarizes the main conclusions and derives further hypotheses for the empirical part of this dissertation. Following, chapter 4 will introduce the data and the research strategy. The Generations & Gender Survey will serve as basis for all analyses and will be introduced for Germany and Bulgaria in section 4.1. All data sets comprise detailed information on the living situation, family composition and demographic as well as socio-structural characteristics. Having introduced all data sets, the sample will be constructed based on several criteria such as age of the respondents, ethnic background, or ability to have children (section 4.2). In a next step (section 4.3), the methods used for analyzing the data will be elaborated upon with cluster analyses, structural equation modelling and logistic regression models being the main type of statistical methods applied. Afterwards, section 4.4 operationalizes all variables used for the analyses. Chapter 5 presents descriptive analyses on the demographic background and assimilation status of minority and majority in Bulgaria (section 5.1) as well as

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Introduction

migrants and natives in Germany (section 5.2). These comparisons will show that both groups are not comparable in their assimilative state and that especially the Turkish migrants in Germany are a heterogeneous group. Moreover, cluster analyses are conducted to see whether societal lines of distinction run along ethnic categories or are mixed with other common distinctions such as place of living, gender, education or age. Results will show that both in Germany (section 5.2.1) and in Bulgaria (section 5.1.1) a distinction cannot only be made based on ethnicity, but on education and in Bulgaria based on place of residency, too. The resulting clusters serve as basis for further inferential statistics on the assimilation and fertility of the Turkish minority and the Turkish migrants. This way it is acknowledged that societal boundaries can intersect. In section 5.3 similar analyses are conducted when looking only on migrants and minorities without referencing to the majority society as comparison group. Lastly, section 5.4 summarizes the results of descriptive analyses and draws a first conclusion on the assimilation status of all groups under study. In chapter 6, inferential analyses testing the theoretical model developed within chapter 2 and chapter 3 are conducted. Here, structural relationships among the Bulgarian sample are focused upon first (section 6.1) to see whether ethnic clusters as constructed in chapter 5 differ regarding their fertility intentions. In a second step, it is checked whether the observed differences in intentions can be explained by the assimilation of the ethnic groups. Similarly, it is analyzed within the German context whether German and Turkish ethnic clusters differ in their fertility intentions and to what extent these differences can be explained by their integration status (section 6.2). While the analyses reveal no differences between Turkish minority and Bulgarian natives in terms of fertility intentions, there are differences between German and Turkish clusters in Germany, which can be explained by the employment status of the groups under study. In a last step, the minority sample consisting out of minority and migrant respondents only is looked at (section 6.3). Here, a difference in fertility intentions between Turkish minority and Turkish migrants is observed, which can be explained by cultural and structural differences between both groups. Section 6.4 summarizes these results. Chapter 7 starts with a summary of the aim of the dissertation and the main theoretical as well as empirical findings. Moreover, it draws conclusions regarding the research hypotheses and research questions (section 7.1). Then, the results are reflected upon critically and theoretical as well as methodological drawbacks are discussed (section 7.2). Finally, a conclusion presents an outlook for future research (section 7.3).

2

Migrants, Minorities, and Assimilation

As the first research question addresses the assimilation status of Turkish immigrants in Germany and the Turkish minority in Bulgaria, this chapter starts by defining the terms minority and migrant. Having done so, an overview of the theoretical background behind immigrant assimilation will follow. Assimilation is here understood as a general process of inclusion that is signified by the vanishing of ethnic differences. This chapter will dig deeper into the origins of the concept of assimilation, extensions of the theory as well as into the different dimensions of assimilation that exist. In a next step, it will be explicated to what extent the theory is applicable within the European context given that all theoretical frames of assimilation originate in the US American context. Finally, it will be worked out to what extent assimilation theory is useful to describe the living situation of migrant groups in Europe and minorities, too, before concluding by developing a model of migrant and minority assimilation.

2.1

Defining Migrants and Minorities

There is confusion about the concept of (a) minority, especially in comparison to migrant groups. Therefore, the first task is to untangle this confusion and derive a clear definition of what is meant by migrant and minority within this dissertation. In previous literature, the presence of four conditions is said to signify a minority group: (1) the group is numerically inferior, (2) its members experience a self-awareness of belonging to this group and want to be treated as separate group, (3) their members experience some form of discrimination by the majority,

© The Author(s), under exclusive license to Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2023 B. Brünig, The Fertility of Migrants and Minorities in Europe, https://doi.org/10.1007/978-3-658-43099-3_2

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Migrants, Minorities, and Assimilation

and (4) their members can be distinguished somehow from the majority (MeddaWindischer, 2007; Meyers, 1984; Milewski, 2010; Ramaga, 1992; Wirth, 1945). Still, there is disagreement to what extent the size of a minority constitutes a distinguishing feature. Meyers (1984) as well as Heckmann (1992) point out that it is not necessarily the numerical size of an ethnic group which makes it inferior to a majority, but rather its historic origin, its socio-structural position or its culture and sense of belonging. To determine whether a group is an ethnic minority, it is thus important to understand the origins of this group and to place its current situation within the context of this historic evolvement (Heckmann, 1983). This is even more important given the claim of several researchers that the term minority discounts people of their unique heritage and ethnic belonging (Wilkinson, 2015b). These critics argue that minority is a term too widely used, as it also applies to women as a minority group or homosexuals. In that sense, the term lacks precision if it is not observed within the context of the specific cultural, social and historic characteristics of the group under study (Wilkinson, 2015b). When it comes to the distinction between migrant and minority groups, Sasse and Thielemann (2005) illustrate that in some countries both concepts are used interchangeably. Most notably, the USA is one example for often signifying their migrant groups as ethnic minorities. Since the USA has experienced migration waves much earlier than Europe, American research is already focusing on third or fourth generation migrants. Often, these are summarized under the heading of minority groups (Milewski, 2010).1 This is not completely wrong given that minorities can be the result of earlier waves of immigration. Also, in the European context theories and concepts of migration are used to describe the living situation of minority groups (see e.g., Maggazini, 2020 for a discussion of assimilation of Roma in Europe). Nevertheless, Heckmann (1983) differentiates several forms of minority groups that go beyond the specific situation in the United States or a mere focus on minority groups based on migration processes. In his classification, only some minorities are the result of immigration, while others became a minority due to redrawn political boundaries. According to his typology, established minorities can be grouped into national and regional minorities. Both are a “consequence of centuries of conquests, of forced and voluntary migrations, of the

1

Besides subsequent migrant generations, the term is also used to refer to a socio-economic underclass in America, such as the African Americans, which experience discrimination and are distinguishable based on their outer appearance (see e.g. Raspberry, 1995, for such a use of the term minority). In the USA, the minority label is hence often given to visible groups (Wilkinson, 2015b) and based on a distinction along socio-economic lines (Meyers, 1984).

2.1 Defining Migrants and Minorities

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rise of cities with the advent of industrialization, and of redrawn political boundaries.” (Meyers, 1984: 2). Notwithstanding, they differ regarding their political intentions with the former aiming at a reunification with the mother country (Heckmann, 1983). New minority groups that Heckmann (1983) distinguishes are those which result out of modern colonialism and those who were generated through immigration. While the former is hardly present within the European context, the latter includes all forms of recent migration since the beginning of the 19th century. There are thus three developments out of which minorities originate: (1) migration, (2) annexation, and (3) colonialism (Schaefer, 2015). Minorities which have their origin in migration are referred to as migrants within this dissertation, while those who originate in processes of annexation are labelled minorities in the following. The third group of colonized minorities will not be considered within this dissertation. Based on this classification the question arises to what extent these historical origins of migrants and minorities are important to understand assimilation and fertility. As section 1.1 has already pointed at the relevance of cultural markers and the construction of belonging, the next section will dip deeper into the process of ethnicity.

2.1.1

Ethnic Group Formation

Heckmann’s (1983) classification of migrants and minorities underlines the need to consider the historical circumstances when talking about minority groups. It distinguishes several groups but stays rather general about the group formation processes. While several scholars have stressed the need to interpret processes of assimilation in the light of the specific historical and political circumstances of a certain ethnic group (Esser, 2004; Rodríguez-Garica, 2010; Schneider & Crul, 2010; Wilkinson, 2015b), few have been clear about how this task can be done. Therefore, this section will propose one way to work out the background of ethnic group formation more narrowly. Building upon the boundary making approach, first proposed by Barth (1969), migrant and minority terminology can be better placed within the context of evolvement. Boundary making signifies the process of drawing boundaries between ethnic groups. Wimmer (2009) highlights three characteristics of this boundary making process. First, ethnic groups are not seen as self-evident units of observation, but as the result of complex social processes of boundary making. In this process, both minority and majority are formed through the definition of markers that divide members of a society into different ethnic groups. Secondly, as Wimmer (2009) points out, these ethnic markers are not objective criteria that will be

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equally recognized and interpreted by outside observers. Rather, actors choose markers that they deem to be relevant, such as language, outer appearance, or cultural characteristics. Consequently, a third important fact is that (old and new) minority and majority groups, therefore, do not arise spontaneously but are the result of a deliberate act of social distancing that aims at categorizing society into an in- and out-group. Several things are implied in this approach. As certain members of a society select the characteristics that serve as markers in the process of boundary making, it follows that a minority or majority group does not necessarily share a specific culture, as is usually assumed within studies on migrants and minorities. Rather, members of a group might only share their common categorization into that specific group by another group. This stresses that the criteria introduced in section 2.1 are only relevant to a certain degree in that minorities and migrants might only share their distinction by and from the majority. Hence, a common fate can unite a minority or majority group, but not necessarily other characteristics (Barth 1969). Castles and Miller (1998) summarize this point by highlighting that an ethnic group can either become a group through the assignment of socialmarkers, or through a collective consciousness that is often based upon shared traditions and language. The important distinction is thus whether a minority or migrant group forms a group based on self- or other-definition (Castles & Miller, 1998). As the collection of minority characteristics in section 2.1 shows, it is often taken for granted that a minority shares a certain culture or language, while the boundary making approach stresses that a minority label can also be ascribed from outside. In Europe, nation state building constitutes an important act of boundary making. Nation states are the predominant form of political organization and often assume a certain degree of cultural consensus (Castles & Miller, 1998). Ideally, the nation, the state and the ethnic group have the same boundary, as the nation state derives its power from its people. It is therefore important to define the people of a nation state as well as their rights and obligations (Castles & Miller, 1998). In essence, assimilation and exclusion are often chosen strategies to unite the people of a nation in order to guarantee its functioning. How inclusive or exclusive the boundaries drawn through nation building are, depends upon the nation state model chosen. While ethnic models highlight common descent as basis for in-group and out-group definition, civic models stress the acceptance of common rules and easily include newcomers through citizenship (Castles & Miller, 1998). When studying the process of assimilation of migrants and minorities in Europe, one should thus bear in mind that any analysis should consider how

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the boundary between minority and majority, and migrant versus native majority respectively, has developed to fully understand processes of inclusion. Here, the definition and formation of the nation should be granted a special role. Therefore, the historical evolvement of the Turkish minority in Bulgaria and the Turkish migrants in Germany will shortly be reviewed at this point. This way, it is aimed at understanding more narrowly how these groups arose within a historical context and how group boundaries have arisen and been maintained. First, the evolvement of the Turkish minority in Bulgaria will be reviewed before the Turkish migrants in Germany are looked at in more detail.

2.1.2

The Turkish Minority in Bulgaria

Bulgaria is a multi-ethnic society with the Turkish and the Roma population being the two biggest minority groups. While Turks constitute about 9% of the total population, the share of the Roma population is estimated to equal 5% (Koytcheva & Philipov, 2008). Bulgarian-Turkish relations have a long history that dates back to the Middle Ages. The first Turkish settlements started during the Ottoman occupation of Sofia in 1386 (Kükcükcan, 1999). In the Ottoman Empire, it was a usual practice to settle Turkish descendants within the conquered territories to ‘Turkify’ them (Parla, 2009). During that time, Turkish became the ruling language, and most Bulgarians knew at least some Turkish. The Turkish inhabitants dominated Bulgarian public life socio-economically and politically from the fourteenth to the nineteenth century. They furthered trade, built streets along major trading routes and led administrative centers (Eminov, 1997). In 1866, the Turkish constituted 30 to 50% of the Bulgarian population (Eminov, 1997). During the Russo-Turkish War in 1878 many Turks fled back to Turkey and at the same time, many Turkish residents left Turkey for Bulgaria as they gradually lost their status and prestige within Turkey (Bates, 1994). With Bulgarian independence in 1878 inter-ethnic relations changed. Bulgarian politicians aimed at establishing a “territorially, culturally and linguistically unified nation-state” (Eminov, 1997, p. 4), in which all inhabitants are Bulgarian and expected to speak the Bulgarian language (Eminov, 1997; Lunt, 1986). Nation state building was hence an important turning point in Bulgarian-Turkish majority-minority relations and an important act of boundary drawing. Until the two World Wars, the Turkish residents maintained most of their privileges and cultural rights. The Turks were recognized as a minority and their rights were protected (Eminov, 1997). “Up to the Second World War, the Turkish community

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lived as a closed ethnic and religious group, mainly agrarian with only about 15 percent living in urban centers. By all social criteria the community was underdeveloped: illiteracy was almost the norm, reaching in the 1930s 81 percent among men and 91 percent among women” (Höpken, 1997, p. 56). But, during the 1950s, the existence of minorities was increasingly denied by Bulgarian authorities as they realized that their minority protection policy did not result in a common Bulgarian identity as envisaged (Eminov, 1997). What followed were thirty years of forced assimilation policies, which peaked in the forced name changes of 1989. Turkish schools became Bulgarian, Turkish literature was banned from the public sphere and Turks had to change their names into Bulgarian sounding ones (Eminov, 1997). The Bulgarian authorities justified their assimilation strategy by arguing that the Turkish minority had a Bulgarian origin. They claimed that all Turks were Bulgarians before the Ottoman invasion but were forced to take up Turkish names and values during the Ottoman occupation (Dimitrov, 2001). The Bulgarian political agenda was reversed in December 1989 and previous assimilation actions condemned. Islamic schools were re-opened, Turkish names restored, mosques built up and Turkish newspapers published again (Eminov, 1997). The new Bulgarian constitution of 1991 does not recognize the Turkish minority as an official minority, but acknowledges that every ethnic group has the right “to develop his own culture in accordance with his ethnic self-identification, which shall be recognized and guaranteed by the law” (Constitution of the Republic of Bulgaria, 1991, Article 54) and allows them to use “their own language alongside the compulsory study of the Bulgarian language” (Constitution of the Republic of Bulgaria, 1991: Article 36 (3)). Bulgarian-Turkish ethnic relations thus have a complex history, but despite forced assimilation attempts the Turkish minority in Bulgaria is nowadays often listed as example of successful integration (Genov, 2008; Petkova, 2002). In summary, nation building was an important process in Bulgaria as it transformed the previous Turkish elite into an inferior situation thereby enforcing the arising of a minority group. Though one first tried to achieve unity by granting the Turkish minority specific rights, this attempt was soon replaced by forced assimilation. Looking at this historical evolvement from a boundary making angle, Nitzova (1997) and Mylonas (2013) stress that religion was always related to specifying the “other” in Bulgarian society, which makes it hard to include Turks into the majority society. Already during Ottoman rule, being a Turk served as a label for all Muslims, while Bulgarians were related with Christianity. National and religious identity are therefore hard to entangle in Bulgaria, which implies that they form a clear boundary that is hard to cross as it is rooted in centuries of Bulgarian-Turkish relations. Liebhart (2012) amends that especially nowadays,

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the label of Christianity or Orthodoxy is strongly linked to civilization, while Muslims are often connected with backwardness. This aggravates a relocation of boundaries. When looking at the Bulgarian nation state model, access to citizenship and hence access to equal rights is rather hard to obtain. Five years of residency are a prerequisite to become a citizen and dual citizenship is not tolerated (Dumbrava, 2017). This underlines the ethnic nation state model, which supports state rule by the majority.

2.1.3

The Turkish Migrants in Germany

Turkish migration to Germany started in 1961 with Turkish residents migrating to Germany as guest workers. After the two World Wars, many Western European countries needed workers to rebuild their economies and ‘borrowed’ workforce from recruitment countries within the South of Europe (Münz & Ulrich, 1998). These guest workers were expected to stay only temporarily and were supposed to return once their manpower was no longer needed. Therefore, no efforts of incorporation were made. However, many guest workers decided to stay within their countries of reception, and migration to Germany reached its highpoint with 2.6 million in 1973 (Münz & Ulrich, 1998). Out of the guest workers, Turkish made up the biggest proportion with a share of approximately 600,000 people. Guest worker recruitment was stopped in 1973, on the one hand due to the oil crisis and on the other hand due to the limited job opportunities within the German economy (Münz et al., 1993). The intention of stopping labor migration was to decrease the number of foreigners in Germany. Yet, during the next years, many labor migrants stayed in Germany and brought their families over. Family reunification and the formation of new families on German territory increased the number of residents with migration background even further. During the 1970s, Turkish and Yugoslav migrants became the largest foreign-born groups in Germany (Münz & Ulrich, 1998). The Turkish migrants who arrived in Germany since 1960 were mainly from rural areas and had low educational levels as well as hardly any qualifications (von Gostomski, 2010). A report initiated by the German Federal Office for Migration and Refugees (BAMF) shows that although the initial reason for Turkish migration to Germany was employment, 70% of the Turkish residents who lived in Germany in 2006 arrived due to family reunification (von Gostomski, 2010). As Germany was not prepared to suddenly be a country of immigration, active integration policies only started during the 1990s (Bosswick, 2003). The

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topic of immigrant integration started evolving with major refugee waves arriving in Germany during the 1980s and 1990s. Their presence evoked xenophobic sentiments within the German population and underlined the demand to actively acknowledge and regulate the presence of foreigners within Germany (Bosswick, 2003). With a change in government in 1998, the incorporation of immigrants finally made it to the political agenda. Within the following years, naturalization has been facilitated and integration policies that further the participation in language classes have been released. Nevertheless, German integration and citizenship policy remains restrictive when compared to other European countries (Bosswick, 2003). Eight years of residency are required to become a German citizen, dual citizenship is not supported (Dumbrava, 2017). With these requirements, Germany qualifies as one of the strictest ethnic nation state models (Castles & Miller, 1998). This is also visible when looking at the acculturation attitudes of the majority. Compared to other European countries, the German population is slightly more in favor of assimilating or segregating its minority groups instead of aiming at integration (Berry, 1998). This of course, might exert significant influences on the incorporation strategies available to Turkish migrants. Zolberg and Woon (1999) stress that guest workers in Germany could easily live side by side with the native majority as long as they were located within their factories and lived out their religiosity on their own. However, when their families came over, Islam became a public issue as Turkish families suddenly were confronted with the need to educate their children about Islamic practices within a Christian country. Here, a clear boundary was drawn with religiosity being the primary marker of ethnic distinction (Zolberg & Woon, 1999; Alba, 2005). This boundary was furthered by public discourses and failing integration policies. Alba (2005) adds that boundaries in Germany are hard to cross as institutional burdens such as strict citizenship and naturalization policies aggravate any attempt to Germanize. This might force Turkish-born residents to stay within their Turkish group—even if they might not self-identify as belonging here. Nowadays, Turkish immigrants form the biggest immigrant group on German territory and make up 18% of the foreign population, followed by Poles and Russians (Zensus, 2011).

2.2

The Origins of Assimilation Theory

Assimilation theory dates back to the beginning of the 19th century, when the United States started becoming a nation of immigration. Early studies already applied the concept within the American scientific discourse in 1894 and 1901 (Mayo-Smith, 1894; Simons, 1901). These studies were interested in finding out

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how European immigrants who arrived in the United States since 1820 became part of the American society by losing their national distinctiveness. In one of the early definitions of the concept, Park and Burgess (1921, p. 736) define assimilation as “a process of interpenetration and fusion in which persons and groups acquire the memories, sentiments, and attitudes of other persons and groups and, by sharing their experience and history, are incorporated with them in a common cultural life.” While this understanding portrays assimilation as a two-sided process where minority and majority culture merge into a common lifestyle, assimilation is often associated with negative connotations as it is usually seen to imply the disappearance of the country-of-origin characteristics (Alba & Nee, 1997). Park was one of the first scholars to introduce assimilation into the study of interethnic relations. He considered assimilation as the irreversible end-state of ethnic mingling, which progresses inexorably with length of residence (Park, 1930). This idea was elaborated upon by Gordon (1964), who distinguished seven dimensions of incorporation. According to him, cultural, structural, marital, identificational, attitude-receptional, behavior-receptional, and civic assimilation are the stages that immigrants go through on their way to complete assimilation. Though these dimensions have been merged within later studies on immigrant integration, Gordon was a pioneer in differentiating between cultural and structural elements of assimilation. Furthermore, he suggested that acculturation is the first necessary step to further incorporation as the absorption of the host country culture is regarded the key to progress on all other integration dimensions (Gordon, 1964). Central to Gordon’s explication is the existence of a core culture within the host country. This core culture changes only slightly in the process of minority incorporation, making assimilation mainly a one-sided process (Gordon, 1964). Once this cultural inclusion has been initiated, structural assimilation is the second important step towards full assimilation. According to Gordon, the participation of immigrants in the labor market and institutions functions as a promoter for complete assimilation (Gordon, 1964). He underlined that “once structural assimilation has occurred […] all of the other types of assimilation will naturally follow” (Gordon, 1964, p. 80–81). In the upcoming decades, several scholars have acknowledged the relevance of Gordon’s theoretical approach, but have also revealed shortcomings (Alba & Nee, 1997; Gans, 1973; Hirschman, 2001). First, Gordon (1964) and Park (1930) developed their theory within the context of a two-group society, which raises the question to what extent it can be applied to a multi-cultural society with several different immigrant groups. Secondly, it is a theory developed on the micro-level that pays little attention to the role of macro-level factors such as employment rates or political settings. Thirdly, it addresses assimilation as incorporation into

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the American middle class, though one might argue that the American society is more diverse and consists of several classes. Finally, Gordon portrays assimilation as a static process since he considers the host country culture a stable one, which is hardly influenced by the cultural characteristics of immigrants (as opposed to Park (1930), who already acknowledged the bi-directional influence of assimilative processes). These criticisms have partly been addressed by scholars in the onward application of assimilation theory. Gans (1973) and Sandberg (1973) add a dynamic to assimilation theory by outlining that immigrant assimilation progresses not necessarily with length of residence, but with each generation as a straight-line process. Generations are hence the motor for assimilation—a finding which is supported by recent studies within Europe (Diehl & Schnell, 2006; Kalmijn & Kraaykamp, 2018; Krapf & Wolf, 2015) and America (Brown et al., 2008; South et al., 2005; Zhou & Gonzales, 2019). Still, some disagreement remains about the number of generations it takes for an ethnic group to be absorbed into its host society. Warner and Srole (1945) predicted that it takes six generations until a group is fully assimilated into the mainstream, though they left the opportunity for some ethnic groups to need even more time. This framework clearly pictures assimilation as straight-line process. Yet, others consider assimilation to be completed within three generations (Park, 1964). In this three-generation model, the first generation is the migrating one which takes up only few aspects of the receiving society. The second generation, however, grows up within the new context, is socialized into the host country norms and speaks both their home and host country language fluently. Finally, within the third generation, children are no longer attached to their mother tongue and culture, but are part of their destination country (Supper, 1999). Although this extension inserts vitality, it nevertheless still does not consider the importance of macro-sociological aspects such as the legal setting, the host country’s attitude and the integration policies present within the host country. Instead, this straight-line development is pictured as internal process which is inherent into an ethnic group itself (Gans, 1992). Furthermore, theoretical and empirical arguments have more recently argued against complete assimilation within three generations (Alba & Nee, 1997; Esser, 1990). In subsequent years, the theory has thus been further extended to address these shortcomings to a fuller extent.

2.3 Segmented Assimilation Theory

2.3

23

Segmented Assimilation Theory

During the 1990s, the theory of assimilation was already widespread within the study of immigrant integration. Yet, it was increasingly recognized that the straight-line process of successful assimilation did not capture the full scope of incorporation outcomes observed. Economic challenges and discrimination are often part of the everyday life of immigrants and aggravate their successful absorption into society (Portes & Manning, 2008). Eisenstadt (1970) pinpoints that assimilation depends, on the one hand, on the degree to which immigrants give up their home country culture and adhere to the norms and values of their context of reception, while on the other hand, it is also dependent upon the willingness of the host society to accept and include the minority. More concretely, it has to be considered (1) to what extent the majority within the host country is open-minded towards including newcomers into their society and (2) how the legal frames and opportunities for minorities are formulated. Furthermore, the developments of the 20th century facilitated cross-border movement and communication thereby supporting the establishment of transnational communities (Portes, 1997). These communities enable migrants to lead dual lives and encourage the maintenance of ethnic characteristics and mother tongues (Portes, 1997). Increasingly, these trends hindered successful adaptation, and instead opened the way towards new paths of assimilation. The theory of segmented assimilation recognizes these alternative outcomes by stating that assimilation can be associated with both desirable and undesirable results (Portes & Rumbaut, 2001; Portes & Zhou, 1993). It thus depends on the opportunity structures (Rumbaut, 2015, p. 10), how the adaptation process develops. The segmented assimilation approach hence detects that American society consists of diverse socio-economic classes into which immigrants might potentially be integrated. While those rich in human capital benefit from assimilation by climbing up the social hierarchy, those at the bottom face poverty and discrimination, which is why eventually they remain in the lower social layer (Piedra & Engstrom, 2009; Portes & Rumbaut, 2001). Consequently, segmented assimilation theory envisages three possible outcomes for assimilation: (1) Selective assimilation describes the bi-cultural outcome where immigrants are integrated into their ethnic community but are fluent in both their host and home country language and adhere to cultural norms of both societies (Warner, 2007). Often, this form of assimilation is associated with cultural perseverance and economic absorption into the host society (Portes and Zhou, 1993; Rumbaut, 1994). (2) Downward assimilation refers to acculturation into the lower social stratum, whereas (3) the final outcome would be straight-line assimilation into the middle class—as

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suggested by the original proposition of assimilation theory (Portes and Zhou, 1993; Rumbaut, 1994). Rumbaut (1997) moreover stresses that these different modes of incorporation often go hand in hand with diverse forms of identification. Instead of the linear assimilation of becoming and identifying as a member of the host society within subsequent generations, “reactive” ethnicities may arise, which make ethnicity salient within the children and grandchildren of migrants. In this perspective, the process of assimilation might take more than three generations and can result in diverse outcomes. An important determinant within this process is the access to structures of the host society, which imply primary contacts to the native majority. If these opportunity structures are not available, one only has access to secondary contacts, which cannot promote assimilation but rather accommodation to the current situation and in the long run potentially an integration into ethnic structures (Rumbaut, 2015). This theory of segmented assimilation hence moves assimilation theory away from picturing it as a process into one group of society with one potentially positive outcome only. Instead, it opens opportunities for immigrants to settle within the upper or lower class of society as well. It furthermore reveals that assimilation does not necessarily imply upwards mobility. Rather, becoming part of the majority might also be associated with downward mobility. Though the segmented assimilation theory recognizes that the majority is diverse, and that integration can happen into different strata of society, it considers assimilation not the outcome of interest, but rather a predictor of social mobility. Depending on the degree and form of assimilation chosen, one either climbs up socio-economically, ends up in the lower social segment or advances economically, but stays within one’s own group culturally. This is in opposition to classical understandings of assimilation theory, which consider assimilation the outcome to be explained and not the explanandum itself. The segmented assimilation theory though has to be interpreted in the context of migration to the US during the nineteenth century, where opportunity structures paved the way for socio-economic mobility (Alba & Nee, 1997). Consequently, assimilation and segmented assimilation theory aim at accounting for different things. Xie and Greenman (2011) have reformulated segmented assimilation as a process by stating that it is not the assimilation outcome itself which influences the subgroup one ends up in, but rather the subgroup one lives in which is detrimental for the final assimilation outcome. Concretely, Xie and Greenman (2011) argue that immigrants who live in poor neighborhoods are aware of the risk of assimilating downwards. Therefore, they try not to assimilate or assimilate only within certain spheres in order not to end up permanently within the lower segment of society. Instead, they may rely on the closeness of ethnic communities for social

2.4 Dimensions of Assimilation

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encounters and support (Portes & Rumbaut, 2001). The central idea is hence that assimilation outcomes vary by social context and that assimilation is a rational response to contextual elements (Esser, 2004). It also highlights that assimilation is not necessarily an unconscious process but can be a deliberate rational decision. This approach simultaneously addresses another shortcoming outlined above, namely the lacking acknowledgement of the social context within assimilation theory. When immigrant integration is analyzed within one context only, one usually ascribes differences to ethnic origin. Though, if one compares one group across several regions or countries one might more easily identify the degree to which assimilation outcomes are framed and influenced by labor market structures, educational systems or hostile attitudes of the majority (Crul & Schneider, 2010). To consider the context in the process of developing an assimilation model for this dissertation, the following sections will review the context of assimilation as well as different dimensions within assimilation.

2.4

Dimensions of Assimilation

Independent of whether migrants adhere to a straight-line form of integration or a segmented mode of incorporation, assimilation can be measured in terms of several sub-dimensions. Nowadays, most researchers distinguish between four facets of assimilation: structural, cultural, social and identificational assimilation (EFFNATIS, 2001; Esser, 2001; Heckmann & Schnapper, 2003). The structural dimension refers to the participation in host country structures and organizations (Esser, 2001). This includes partaking in the labor market, the educational and political system. Moreover, it signifies the degree to which one has equal opportunities for access and membership within these structures (Heckmann & Schnapper, 2003). As structural elements are incremental to nation states, it is hard to expect a two-sided assimilation process here. Rather, migrants have to accept the rules and regulations available within a country and adapt accordingly. To measure structural assimilation, authors usually focus on comparing national averages with migrant performance to be able to evaluate to what extent migrants are assimilating into the mainstream. Though structural inclusion is mainly onesided, one should keep in mind that labor market integration should distinguish between employment within an ethnic niche and employment within structures of the majority. Cultural incorporation, also known as acculturation, addresses the adjustment of immigrants towards cultural norms and values (Esser, 2001) but also the adjustment to specific cultural markers of the host society (Erel, 2010). While the former is more in line with Parson’s definition of culture who considers norms

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and values the normative components of a culture (Parsons, 1989), the latter perspective is chosen by Bourdieusian scholars (Erel, 2010). The latter considers “language skills, knowledge about customs and lifestyles, professional qualifications” (Erel, 2010, p. 649) important cultural elements. The reproductive behavior of immigrants belongs in the cultural sphere of assimilation as well (Coleman, 1994; Lesthaege, 1983; Milewski & Mussino, 2018). As fertility behavior is guided by norms and values, a change in fertility among immigrants is usually tied to ideational changes that occur in the process of inclusion into the majority (Lesthaege, 1983). Esser (1980) outlined that cultural assimilation entails a cognitive dimension as well, since immigrants have to acquire knowledge about norms, values and behavior during their process of socialization. Similarly, learning a new language requires both the cognitive dimension of knowing the language and the practical element of actually using it (Hans, 2010). Here, there is a clear link between cultural and structural integration. Without knowing the host country language, it is usually difficult to achieve structural integration. Language is often the key to labor market access and to educational success (Dustmann & Fabbri, 2003; Schmid, 2001). Moreover, depending on one’s definition of assimilation, the process of acculturation can be either two-sided with both the immigrants and the natives adapting towards each other, or one-sided with immigrants taking up the destination culture (Heckmann & Schnapper, 2003; Horenczyk et al., 2013). Compared to the structural dimension of assimilation, it is easier to integrate cultural elements such as food habits or religious festivities into the majority culture. One of the limits of assimilation theory outlined earlier is that assimilation theory was developed within a two-group scenario. However, migrants might be integrating into a mix of cultures, such that both the native majority, but also other minority groups shape this core culture, which Gordon (1964) talked about. Nevertheless, Yinger (1981) and Gordon (1975) stress that the extent to which migrants assimilate into the majority culture is dependent upon the proportional size of both groups as well as the brightness or blurriness of social boundaries (Alba, 2005). The more asymmetric group proportions and the brighter social boundaries, the more likely it is that cultural assimilation happens mainly on the side of the migrants. In equal terms, Zolberg and Woon (1999) argue that a twosided cultural assimilation becomes less likely, the more significant a cultural element is for the definition of “who we are and who we are not” (Zolberg & Woon, 1999, p. 28). Thus, migrants are more likely to adhere to destination country language, than, for example, to destination clothing or musical taste. The social dimension of inclusion relates to social relationships immigrants have with both natives and co-ethnics but also captures membership in associations and clubs (EFFNATIS, 2001; Heckmann & Schnapper, 2003). These

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relationships can be measured in terms of both the frequency of contact, but also by evaluating the closeness of these contacts. Most likely, many migrants will have contact to natives through the neighborhood, but it is more telling for assimilation whether one has close native friends. This distinction is represented in the difference between strong and weak social ties. While strong ties refer to close contacts such as family or friends, weak ties capture the network of acquaintances (Granovetter, 1973). Whereas native strong ties are usually more advantageous for social incorporation (Fokkema & de Haas, 2011), weak ties are assumed to be more dissimilar to oneself regarding several human capital characteristics and hence more useful for economic advancement (Granovetter, 1995; Lin, 1999; Wegener, 1991). One element of strong ties, which is particularly beneficial for social assimilation, is interethnic marriage (Hans, 2010). Intermarriage involves close contact to the out-group culture and demands compromises and tolerance towards the ideals and beliefs of the other, which in turn encourages the erosion of ethnic group solidarity (Qian & Lichter, 2001). It has been found that those migrants who are assimilated tend to be the same who have a partner from the native majority (Lievens, 1988). Finally, identificational assimilation covers the subjective dimension of belonging. It is frequently assessed in terms of national, regional and ethnic identification (Heckmann & Schnapper, 2003). A more objective approach towards measuring identificational integration is the often-used concept of citizenship. Here, it is assumed that being a citizen of the host country is essential for forming a sense of belonging (Koopmans et al., 2005). However, the emotional identification of immigrants is closely knit to a common history, common ancestry, or similar values (Yinger, 1981). To change one’s identity thus requires the internalization of symbols, celebrations, and practices. Accordingly, citizenship is only a vague indicator of belonging as some minorities and second-generation immigrants are citizens by birth, whereas others can only apply to become a citizen after having lived within their destination society for a certain number of years. Not surprisingly, Nimmerfeldt (2009, p. 26) therefore concludes that the “fuzziness of the concept of identity and the fact that identity aspects are less quantifiable and their interpretations are more grounded in the national context” make it hard to consider identity in comparative analyses. This is aggravated by the complexity of identity formation within subsequent generations. Heckmann and Schnapper (2003) as well as Bosswick and Heckmann (2006) underline that identity is a multi-faceted concept that comprises, alongside the parallel existence of host and home country identity, also sub-identities at the regional, local or even supra-national level.

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A two-fold conclusion can be drawn from this review of assimilation dimensions. First of all, despite many notions about the two-sidedness of assimilation, the process of immigrant incorporation is to a large extent one-sided since—as outlined above—the structures, institutions and language of a society are likely to be framed by the majority and hard to change due to foreign cultural influences of immigrants, independent of the number of ethnic groups present. Thus, especially structural assimilation takes places on the side of the minority and migrant groups. As for the other three dimensions, a degree of mutuality is conceivable as the native majority might take over certain migrant practices, such as food habits or musical taste. Equally, social incorporation depends upon the willingness of the majority to interact and is thus partly a two-sided process. Finally, identificational incorporation can be two-sided such that the identity of the host society becomes more multi-cultural and hence more open-minded towards foreign influences. Secondly, what can be taken from this review is that all four dimensions are intertwined though of different importance. Already Gordon (1964) suggested that these dimensions are not independent but build upon and influence each other. This idea is shared by several integration scholars such as Gordon (1964), Yinger, (1981), Esser (1980, 1990) and Hans (2010), although there is no clear consensus about the exact order of these assimilation aspects. While Yinger (1981) claimed structural assimilation to be the first and necessary step towards full inclusion, others regard cultural assimilation the starting point towards incorporation (Esser, 1980; Gordon, 1964). Gordon (1964) stressed that acculturation is the inevitable first step towards assimilation as immigrants automatically encounter the values and habits of natives upon arrival. Yet, crucial for complete assimilation is the structural side of incorporation as it sets the basis for primary social contacts and intermarriage (Gordon, 1964). The exact order, though, might also be dependent upon the reason for migration. Yinger (1981) for instance points out that labor migrants might be assimilated into the labor market first before progressing on any other dimension. This is supported by findings of Fokkema and de Haas (2011), who report that migrants who emigrate for economic reasons are worse integrated socio-culturally as opposed to those who emigrate out of persecution, political or ideological reasons. It thus seems that cultural and structural assimilation form important elements towards achieving successful incorporation into the host society. This theoretical expectation is supported empirically by Fokkema and de Haas (2001) among immigrants in Italy and Spain. They showed that being assimilated economically is (to a certain extent) beneficial for socio-cultural inclusion. Equally, Hans (2010) tested the interrelation of all four sub-dimensions empirically among different ethnic groups in Germany. Her analyses reveal that especially cultural, social and identificational integration influence

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each other reciprocally, while structural incorporation is slightly more decoupled from the other dimensions, most notably for the second generation. Empirically, there is thus evidence that all dimensions are interrelated and that progress on one dimension is associated with assimilation on the other dimensions as well. Reviewing existing findings, it seems likely that structural or cultural incorporation are the first steps towards inclusion, while emotional aspects of identity are usually the last steps towards full assimilation. To conclude, assimilation as a process progresses along the four dimensions of assimilation with full assimilation being reached once assimilation into the host country has taken place on all four dimensions. A last note concerns the focus of this dissertation on these four dimensions. Especially early scholars on assimilation (see also section 2.2) such as Gordon (1964) distinguished more than four dimensions by breaking up cultural assimilation into sub-dimensions such as marital, attitude-receptional and behavior-receptional stages. Others argue empirically for only three dimensions (Williams & Ortega, 1990), while yet others (Massey, 1981) have presented six dimensions (familism, fertility, residential segregation, political participation, intermarriage, social mobility). All approaches have in common that they assess and measure similar aspects of assimilation. They only differ in how they categorize and name these aspects. Recent scholarly work within the field of migration research seems to focus mainly on the four dimensions presented here (EFFNATIS, 2001; Hans, 2010) or on a subset of these dimensions (Aleksynska & Algan, 2010; Di Bartolomeo et. al., 2015; Penninx, 2005). Also, when looking for quantitative questionnaires and measures for assimilation, scientific research seems to rely on this fourfold distinction, which is why it will also be applied here to facilitate the testing and comparability of results. In a next step, the applicability of assimilation theory within Europe as well as for minorities will be critically assessed.

2.5

Assimilation Theory in Europe

The discussion around the concept of assimilation is fueled by cross-national specificities of ethnic relations and diverse contexts of reception. Thus, besides some basic agreement on how to assess assimilation (see section 2.4), RodríguezGarcia (2010) stresses that “no single model of integration or accommodation that is valid for all cases” can exist if one considers the differences that prevail between regions, countries and even local levels (Rodríguez-Garcia, 2010, p. 252). Existing models can hence be used to grasp the essence of assimilation but need to

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stay sensitive to cross-national specificities. Similarly, Schneider and Crul (2010) outline that assimilation theory and its applicability must be considered within national contexts. The major dividing line between America and Europe is that immigration is perceived as a rather new phenomenon within Europe and mainly associated with social costs and threats to national identity (Rodríguez-Garica, 2010). Many European societies constitute rather fixed entities that have their cultural core into which immigrants must be assimilated. In opposition, classic countries of immigration, such as the USA, Canada or Australia used to be in constant evolution and could in the past more easily integrate new cultural elements into their understanding of national identity (Rodríguez-Garcia, 2010). Here, migrants were historically perceived as being beneficial for the economy and social life. Consequently, it is easier for migrants to assimilate into this fluent mainstream culture as opposed to adapting towards a fixed national core culture. Europe, thus, has a narrower understanding of how integration paths should develop and considers ethnic communities and cultural traits indicators of failed assimilation (Morawska, 2008; Schneider & Crul, 2010). This restricted conception makes talk of “successful” assimilation rare within the European context and intensifies negative connotations towards the idea of assimilation. Interestingly, the scientific debate has mainly used assimilation as a concept of incorporation within the American context, whereas European scholars have preferred to talk about integration (Bade & Bommes, 2004; Favell, 2001; Morawska, 2008; Vermeulen, 2010). Esser is one of the few European scholars who clearly differentiates assimilation and integration from each other (Esser, 1999). He considers assimilation the only stable and desirable outcome of integrating newcomers into the receiving context as all other options entail the maintenance of home country characteristics, or, in the case of marginalization, a non-integration (Esser, 1999; Pries, 2015). This understanding clearly stresses assimilation as a one-sided concept that sees migrants and minorities as the groups that must try to be included. This is opposed to Berry and his colleagues (2002) who underline that integration as mutual achievement is more desirable and positively connotated given that it entails that both minority and majority have invested effort to achieve integration. To unite these diverse understandings of the concept of assimilation and integration, Brubaker (2001) as well as Löffler (2011) highlight that assimilation can be understood in two different ways. In a general and abstract sense, assimilation refers to a broad process of becoming alike, thereby suggesting that assimilation is not necessarily based solely on the effort of the minority group. However, in following its specific and organic meaning, it refers to the complete incorporation of a minority group into the majority society. Whereas the latter stresses

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assimilation as the outcome of the process of adaptation, the former understanding focuses on the process itself (Brubaker, 2001). The ultimate endpoint of this process can be complete disappearance of ethnic specificities (= assimilation in its specific meaning), yet the process itself can be characterized as the vanishing of group differences (Alba, 1999). As the former rather addresses an ideal type of complete homogeneity, Löffler (2011) illustrates that this normative option is not realistic and should hence rather be understood as a state that mirrors homogeneity to a certain extent. Consequently, the specific and organic understanding can only be judged in terms of success or failure, while the general terminology allows for an evaluation in terms of degree of assimilation. Following this understanding, integration can be a stage within the process of assimilating, while it can equally be the endpoint of this process given that not everyone needs to proceed through all stages of the assimilation process. At this point, there are two things that can be taken from the presented discussion. First, assimilation can be understood as a process or as a state. While assimilation as a state refers to an end state of the process of assimilation, the process itself stresses that various stages of assimilation are possible. These stages can be either stopovers in the process towards full assimilation or endpoints. This fluid understanding will also be employed within this dissertation and makes clear that there is not one route or endpoint of assimilating, but that assimilation paths vary individually thereby acknowledging that it is not possible to work out one assimilation model that works across all settings. Secondly, assimilation as outcome of the process of assimilating is hardly possible in Europe. While America has a culture and society in constant movement, which easily takes up different cultural influences, Europe is rather rigid in its process of integration. Assimilation is mainly a one-sided process in Europe, which in turn implies that assimilation as an endpoint is only reached if migrants adapt completely towards European culture, structure, social network and even identify with their host country. In terms of boundary making, this would hence demand individual boundary crossing. As this requires that individuals give up their home country characteristics, it seems unlikely to ever happen completely for an ethnic group. Along with the diverging understanding and preconditions of assimilation between the USA and Europe, the context in which assimilation theory evolved has to be considered, too. It was originally developed to explain European migration to the United States which evolved around 1820 and was stopped through regulations in 1920. This ending made sure that ethnic communities could not grow, but rather deplete (Alba & Nee, 1997). In turn, subsequent migrant generations grew up within a context in which ethnic communities played a minor role

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and assimilation through generative change thus implied a movement away from the origin culture and signified increasing distance towards the own past (MayoSmith, 1894). In comparison to recent migration waves, these early migrants were thus cut off from their countries of origin and more involved into their destination societies (Alba & Nee, 1997; Waters & Jiménez, 2005). Consequently, the original unidimensional scale of host country absorption was sufficient to study assimilation as it was a one-sided process and simultaneously the only option available. Only in 1965 were borders opened again and migration to the United States continued. As these new waves of migration had different origins and greater variance in skills than the early European migrants (Barry, 2006), new approaches such as the segmented assimilation theory gained prominence in explaining assimilation processes (Warner, 2007). These new migrants originated from Asia, Africa and Latin America, differed in skin color from the majority and depicted heterogeneous educational backgrounds (Gans, 1992; Portes & Zhou, 1993; Waldinger, 1996). They were more visible as minority groups and more likely to increasingly work within the low-skilled sector (Greenman & Xie, 2008). Furthermore, as for instance Waldinger (1996) and Massey et al. (1993) note, as migration to the US continues until the present day, these new groups live within an environment that is shaped by ethnic and transnational communities. These recent migrants thus differ in several regards from earlier migrants and assimilation is no longer the one eventual and straight-line outcome of their process of inclusion. Waters and Jiménez (2005) argue that assimilation theory can still explain the incorporation patterns of these new migrant groups, but the focus must be shifted. They claim that the geographical dispersion is different, and the region of settlement plays a more important role in framing assimilation opportunities than in earlier times. Whereas former immigrant groups settled in only a few regions, the new migrants are also found within small villages and are spread throughout the whole country. Hence, the theory as well as potential outcomes of assimilation have to be regarded within the geographical context of settlement. Secondly, the concept of migrant generation, Waters and Jiménez (2005) postulate, is no longer as important an explanatory variable of assimilation as it used to be during early migration to the US. This is the case, because these earlier migrants had no contact with subsequent migrant waves and did not develop ethnic communities to the same extent. In turn, generation was a powerful predictor of assimilation back then, and it seems reasonable that migrants were considered assimilated within the third generation. Nowadays, however, this straight forward generational integration process will be less applicable (Waters & Jiménez, 2005). This is even

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more the case given that migrant generations now consist of different age structures. They are born in different decades and have witnessed different historical events (Alba & Nee, 1997). As the boundary perspective stressed, when utilizing assimilation theory nowadays, one has to consider the historical origin of immigrant groups and has to pay special attention to the context and the role of ethnic communities. Additionally, several scholars have argued that it is not possible to judge based on observations of the first and second generation of the new migrant groups whether assimilation can be achieved by the third or fourth immigrant generation (Alba & Nee, 1997; Greenman & Xie, 2008; Perlmann & Waldinger, 1997). This implies that analyses of assimilation can only draw tentative conclusions as there are only few migrant generations that can be studied. That assimilation theory is still useful as general framework to explain the assimilative patterns of recent immigrant groups in the US is not only argued for theoretically, but also supported by several empirical studies which successfully applied the theory in studying the incorporation of recent migrant waves. LaLonde and Topel (1992) studied the economic assimilation of several migrant groups during the 1970s and 1980s. Though they note a declining overall quality in the human capital of these new migrants, they confirm that assimilation is taking place with increasing length of stay in the US being associated with higher earnings. Socio-culturally, assimilation theory has been confirmed for instance by Bleakley and Chin (2010) who demonstrate that English language proficiency is positively associated with higher intermarriage rates, higher earnings and better education. However, there are also several others who have confirmed assimilation theory within several spheres of immigrant life (Abramitzky et al., 2020; Alba, 2005; Allen & Turner, 1996; Ford, 1990; Kasinitz et al., 2008; South et al., 2005; Villarreal & Tamborini, 2018). A recent publication of Alba and Duyvendak (2019) even extends the assimilation framework to superdiverse contexts which no longer entail a clear (numeric) majority. Here, assimilation as such no longer applies, but is broken down to the neighborhood or school level (Crul, 2016). In Europe, migration as large-scale phenomenon started evolving within the 1950s and 60s. During that period, many Western countries received labor migrants, who were expected to stay only temporarily, through bilateral agreements. On these grounds it was permissible that migrants stayed in close contact with their home countries as these networks also facilitated the continuous flow of labor migrants (Geddes, 2003). However, when many foreign workers opted to remain within their host society permanently, integration policies were absent and migrant networks common practice. European countries did not consider

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themselves countries of immigration for several decades and mostly looked at migration suspiciously fearing a threat to their national identity and welfare state institutions (Rodríguez-Garica, 2010). Alba and Nee (1997) as well as Zolberg and Woon (1999) underline that there was and is much migration to the European continent as well, however, the perception of not being a continent of migration is important. The integration of immigrants is hence more restricted compared to the American context. More recently, the discussion has been re-framed to increasingly welcome highly skilled migrants as well as to consider migration a necessary tool to overcome the ageing problem of many European societies (Geddes, 2003). This prospect increasingly encourages European politicians to manage immigration and integration processes (Niessen & Schibel, 2003). This argument applies to migrants and minorities but does not extend towards refugees that still pose a challenge to European states. The reasons for migration, the ethnic origin of migrants and the context of reception are different within Europe and America. While the ethnic groups arriving in Europe are very diverse in terms of their national origin, they are rather homogenous regarding their human capital background. Most of them arrived as guest workers and come from rural areas with little schooling (Thomsen & Crul, 2007). This is in opposition to immigrants in the US who arrived since 1965 and who represent diverse educational levels (Alba & Nee, 1997; Thomsen & Crul, 2007). Besides differences in origin and skills of immigrants, Europe and America differ regarding the context of reception. Whereas Americans are used to immigration, the European population is only gradually adjusting to increasing waves of immigration and the thought of being a society of migration. The receiving societies in Europe are much less open towards immigration and many European countries had no integration policies until the 1990s. It is thus possible that assimilation as such will take much longer in Europe as the structures enabling inclusion are only rudimentarily developed and the receiving societies rather skeptical. Furthermore, Americans strongly identify with their nation and transfer this pride towards their newcomers (Coleman, 2006). This might function as a positive promoter of host country identification among ethnic groups. In Europe on the other hand, national pride is weakly developed, and some scholars claim that this can further slow the speed of assimilation (Coleman, 2006). Of even greater importance is that several scholars have highlighted that the underclass, which is a significant factor in segmented assimilation theory, is differently composed in Europe and the USA. While in America black people and undocumented migrants constitute the poor underclass and are considered a minority, who often resides in ghettos or suburbs, within the European context it is mainly Muslims who are

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at the bottom of the hierarchy (Croucher, 2013; Drouhot & Nee, 2019; Silberman et al., 2007; Vermeulen, 2010). Though Islam is perceived more negatively in the USA since 9/11, several comparative empirical studies have shown across time that anti-Muslim attitudes are relatively low in the USA compared to many Western European countries (Savelkoul et al., 2012; Strabac et al., 2014). This is, on the one hand, due to the strong perceived differences between Islam and Western culture and the boundary drawing along religious lines (Alba, 2005; Zolberg & Woon, 1999), but also due to the poor socio-economic standing of many Muslims (Lucassen et al., 2006). Moreover, as compared to other cultural markers, religion strongly persists even across migrant generations (Drouhot & Nee, 2019). Three possible scenarios can result out of this situation: Either Muslims are likely to integrate downwards as suggested by segmented assimilation theory (e.g., by staying within the lower economic segment or within ethnic professions), or, they could develop tendencies to segregate as they are stigmatized within the majority anyway. A third option would be the crossing of boundaries and the overcoming of stigmas. This is most likely possible through socio-economic advancement, but difficult to achieve (see section 2.1.1). Empirically, it has been shown that assimilation is still the trajectory chosen by most immigrant and minority groups within the US and Europe. By summarizing existing findings, Drouhot und Nee (2019) show that all groups advance socioeconomically and adapt towards the native structures and culture, though the main barriers to assimilation can be found in undocumented migration to the US and Muslim religiosity in Europe. Findings reveal that an undocumented status is associated with less favorable integration in the USA und that Muslim religiosity persists strongly across generations and is associated negatively by European majority societies (Drouhot & Nee, 2019). A discussion of the context of theory development and theory testing has proven to be essential before applying assimilation theory to migrants and minorities in Europe. First of all, the theory emerged and developed within a specific US context that cannot be transferred to the European situation without caution. European societies are much more closed and introverted when it comes to the inclusion of newcomers. This implicates that (1) assimilation as an end state of inclusion is rare in Europe and will rather occur on an individual than a group level with few individuals crossing existing boundaries, (2) assimilation as process takes much longer in Europe than in the US (and most likely much longer than three generations), and finally (3) assimilation is more one-sided in Europe than in the US with efforts of inclusion mainly originating from migrant and minority side. Moreover, a fourth conclusion regards the special standing of Muslims in European societies. They are stigmatized and often form the bottom

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of the social hierarchy. It is thus likely that (4) they will separate or integrate into the lower stratum of society. With these findings in mind, the next section will focus more narrowly on minorities in Europe and the relevance of assimilation to describe their living situation.

2.6

Minority Assimilation Outcomes

As migrants are new minorities, one could wonder to what extent the assimilation progress of old minorities allows for conclusions regarding the potential future assimilation state of migrant groups. To find out whether one can learn about migrants from observing the minority situation, it still needs to be discussed to what extent assimilation is taking place among minorities. Schaefer (2015) argues that assimilation is only one out of seven options as to how the contact between majority and minority can be arranged. The first option is extermination as could be observed during WWII within the Holocaust. A second possibility is expulsion, which takes up the idea that the majority might expel the minority out of certain regions or even the whole country. Thirdly, the minority could form its own state and separate from their country of residence. A fourth alternative is segregation, so the physical separation of minority and majority in most spheres of living. Important to note is that segregation is hardly fully achieved. Rather, it seems likely that instrumental and occasional contact between both groups will persist. A fifth option outlined by Schaefer (2015) is the fusion of minority and majority into a new group, and the sixth possibility would then be assimilation as described so far. Finally, the seventh outcome is pluralism. By pluralism, he understands a society in which all groups live alongside each other without getting rid of their boundaries and specificities. A pluralist society is coined by mutual respect and tolerance, but still needs consensus about basic ideals and values. Cook (2003) groups most of these alternatives under the sole heading of segregation. He distinguishes segregation and assimilation only, as he argues that expulsion, extermination, and pluralism are all forms of segregation. They only differ regarding their intensity by which either the majority or the minority enforce their preferred strategy (Cook, 2003). Reviewing these potential outcomes in the light of previous conclusions, one essential aspect has received too little attention when listing potential assimilation outcomes for minorities. Every effort to assimilate depends on the legal structure and the willingness of the host society to take up new members into their society. If there are limited structures for assimilation, outcomes such as

2.6 Minority Assimilation Outcomes

37

fusion or assimilation seem very unrealistic scenarios. Fusion is only possible if there is no dominant culture in society (Bartlett, 1970). This is hardly the case in modern nation-states. Assimilation, on the other hand, is an unlikely outcome for minorities, because it implies the removal of boundaries and a total break with the past (Schaefer, 2015). For the end state of assimilation to be reached, a society would either need to remove or redefine boundaries. This would entail that both minority and majority are ready to alter existing boundaries. Alternatively, there can also occur individual boundary crossing which can take place if incentives to leave one’s own group and become part of the majority are high enough. As it seems unlikely that minorities will give up their roots, which they have maintained for generations, the presence of minorities might not so much signify that assimilation of ethnic groups can be lengthy but might rather suggest that ethnic minorities will live alongside the majority and never reach the state of assimilation. Important is here the distinction between the process of assimilation and the outcome of being assimilated. Concretely, it is suggested that minorities inevitably enter a process of assimilation, but do not reach the final outcome of being assimilated. This is facilitated given the fact that most minorities are granted the right to maintain their cultural and linguistic specificities within their countries of residence. As a consequence, minorities might choose to interfere as little as possible and could manage to live a separate life within their countries of residence. This would equal the segregation outcome outlined by Schaefer (2015) and would imply a separation within the occupational distribution, own political parties, specific territories, where the minority resides, and so on. As outlined before, complete segregation will hardly occur as there are certain aspects or occasions where minority and majority have to communicate or interact. Nevertheless, an instrumental mode of contact might suffice under this scenario. This separation option is empirically supported by Berry and Sam (1997) who observe that separation is the most likely outcome of minority assimilation, followed by integration. It follows that separation and integration are the most likely assimilation outcomes for minorities within Europe given their special legal standing in European societies (Thomson, 1989), especially when assimilation is discussed at the group level. It has to be acknowledged though, that there might still be individuals who manage to cross boundaries and do assimilate eventually. Before this conclusion will be used to develop an own model of migrant and minority assimilation, some thought will be dedicated to the situation of subsequent migrant generations. Subsequent migrant generations and minorities have in common that they are both citizens of their country of residence, which is in clear contrast to first generation migrants (Tanase, 2003). It is thus not unlikely that old minorities and subsequent migrant generations will follow similar paths of incorporation that

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differ from the assimilation experience of first-generation migrants. A crucial difference however, is, that old minorities in Europe are often officially recognized minorities that profit from special rights regarding their cultural perseverance and political claims. These rights significantly differentiate minorities from migrants and sustainably shape the assimilation strategy chosen. While minorities cannot fully assimilate without giving up their common ancestry for the sake of host country attachment, subsequent migrant generations are theoretically able to assimilate into the majority (though it is, in practice, a lengthy and hardly chosen process). In that sense, subsequent migrant generations are better studied under a migrant framework and should be distinguished from minorities.

2.7

Berry’s Acculturation Model

Taken together, the previous insights pose the question how exactly one should evaluate and talk about “final” assimilation outcomes for migrants and minorities. If both migrants and minorities are considered within one framework, what are potential outcomes of the assimilation process? And, can one call it inclusion if an individual is incorporated into the mainstream economically, but deviates from the majority in every other regard? Similarly, how does one appraise the assimilation status of an individual who is incorporated into the ethnic group culturally, socially and in terms of belonging, but is part of the host country labor market? Without claiming to be able to give a full answer to these questions, this section will use Berry’s (1974, 1980) acculturation model to elaborate on the potential outcomes that can result out of an assimilation process among migrants and minorities in Europe. Table 2.1 Acculturation Outcomes according to Berry (1974, 1980) Host country belonging Home country belonging

Yes

No

Yes

Integration

Separation

No

Assimiltion

Marginalization

Within section 2.4 one could see that acculturation usually refers to the cultural dimension of assimilative processes. Berry, on the other hand, defines acculturation as “the general processes and outcomes […] of cultural contact” (Berry & Sam, 1997, p. 294). This process entails biological, physical, political, economic and cultural changes (Berry, 1992) and is in essence very similar to the concept

2.7 Berry’s Acculturation Model

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of assimilation addressed so far. One may wonder why cultural contact suffices to describe outcomes of encounters between minority and majority, while the assimilation models presented so far deliberately address different dimensions of assimilation. Berry talks about acculturation as a process of cultural contacting. This process entails both private and public realms. Private realms capture identification, friendships, family, norms and habits, while the public aspect of acculturation refers to structural elements such as education, employment or legal regulations (Berry & Sam, 1997). Consequently, Berry uses the term acculturation synonymously to the assimilation concept introduced in sections 2.2 to 2.6 as he addresses both the structural, cultural, social and identificational dimension of cultural contact. Berry suggests that acculturation consists of two processes: adaptation into the host society and maintenance of home country characteristics. It is usually assumed that these processes proceed on one common scale: by giving up one’s own background, one becomes part of the majority society. Berry (1974, 1980) however, suggests that one can better think of these two aspects as being located on two independent scales. On the one hand, one can give up or maintain the own heritage to a certain degree, while on the other hand, one can adhere to the host country society to a fuller or lesser extent. For Berry (1992) it is important to note that becoming part of the host society does not necessarily imply giving up features brought over from the country of origin. This bi-dimensional thinking generates four possible outcomes of acculturation as depicted in Table 2.1. The combination of being part of both the host and the home society is what Berry labels integration, while the mere belonging to the host society for the sake of giving up the own cultural background, is labelled assimilation. When individuals identify only with the home society, separation is the outcome, whereas the association with neither the country of residence nor the home society results in marginalization. This last strategy, in particular, is hardly addressed by immigration scholars as it is commonly assumed that incorporation processes result in some form of host or origin country belonging. Berry, though, opened up the opportunity that the combination of cultural detachment from the home society and discrimination by the majority in the host society can result in processes of exclusion. Furthermore, it should be noted that Berry does not consider his four-fold typology as static outcomes of intercultural contact. Rather, assimilation, integration, separation and marginalization can be strategies and outcomes of the acculturation process at the same time. Thus, even if one pursues the strategy of assimilation, this stage is usually not reached immediately, but requires time. Meanwhile, one might thus be classified as integrated since acculturation has only been achieved on some dimensions so far. According to this logic,

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individuals can move along these four categories throughout their life cycle and throughout the process of assimilating. Assimilation can thereby be understood as end state, however, as argued before, this end state does not necessarily have to be reached by every individual. Although developed for the purpose of identifying the outcomes of acculturation, Berry’s model can equally be extended to picture the result and the process of assimilation. When assimilating into the majority, migrants can equally experience different outcomes depending on their degree of host country and home country incorporation. Yet, this four-fold outcome leaves unanswered the question how integration efforts are evaluated once the different dimensions introduced within section 2.4 are reached to a varying extent. Here, Berry and Sam (1997) give answers, too: “[W]hen structural assimilation is present […] combined with […] a high degree of cultural maintenance […], then an outcome similar to integration is likely.” (Berry & Sam, 1997: 297). Following this reasoning, one has to distinguish eight sub-dimensions of assimilation: economic, social, cultural and identificational assimilation towards the host society as well as economic, social, cultural and identificational maintenance of the country of origin. Complete incorporation on all four host country dimensions classifies as assimilation, while partial achievement on some dimensions classifies as integration. Consequently, if one only maintains elements from the country of origin, one is considered separated2 , while marginalization is present when neither home country nor host country features are observed. When thinking back to section 2.3, segmented assimilation theory suggests equally that assimilation can result in diverse outcomes, some of which are positive while others are rather disadvantageous for the immigrant. We can find parallels here in that the three assimilation outcomes outlined within the segmented assimilation theory can be equated with the acculturation strategies highlighted by Berry. Straight-line assimilation of the segmented approach is what Berry terms assimilation under the acculturation frame. Both outcomes consider the migrant incorporated on all dimensions with upwards mobility into the middle class being implied. Selective assimilation was defined in section 2.3 to mean a situation where migrants adhere culturally to their ethnic community, while integrating structurally into the middle class. As just outlined, Berry and Sam (1997) label this outcome integration. Finally, segmented assimilation theory pictures a third option of downward assimilation. Here, migrants are integrated into a certain stratum of society, namely the underclass. This outcome is often associated 2

Berry distinguishes between separation and segregation: Separation is a voluntary act of migrants to keep their heritage culture and live apart from the majority. Segregation, on the contrary, refers to the enforced separation that is initiated by the majority (Berry & Sam, 1997).

2.8 Towards a Model of Migrant and Minority Assimilation

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with ethnic cultural belonging (Xie & Greenman, 2011). How this third option fits into the frame of Berry depends on the position of the underclass within society. If the underclass is a fundamental part of the host society, then becoming part of this segment of society is equally a sort of inclusion as is the absorption into the middle class. Following this understanding, downward assimilation would be another form of integration within Berry’s framework. If, however, the underclass is a particular group within the host society, which is itself composed out of minorities who failed to access and succeed within the host country labor market, then one can rather talk about a form of separation as the migrant is not really part of any of the majority structures. The segmented assimilation theory can hence be embedded into the extended assimilation model developed out of Berry’s theories. Table 2.2 provides a short overview of this embedding. As becomes apparent, the concept of marginalization has no equivalent within the segmented assimilation theory. This is logical given the fact that segmented assimilation theory assumes that some form of assimilation results out of the process of assimilating, even if only minimally. Marginalization, however, claims that migrants and minorities, by entering the process of adjustment, eventually end up in a situation that parallels a scenario with no assimilation at all. Table 2.2 Match of Berry’s acculturation model with segmented assimilation theory Berry’s acculturation model Segmented assimilation theory Assimilation

Straight line assimilation

Integration

Selective assimilation Downward assimilation (if underclass part of society)

Separation

Downward assimilation (if underclass a specific sub-group)

Marginalization

–

2.8

Towards a Model of Migrant and Minority Assimilation

So far, it was possible to integrate assimilation theory, acculturation according to Berry and segmented assimilation theory into one common model. This classification clarifies some of the earlier specifications by making it possible to not only talk about “a certain degree” or the “extent” of assimilation, but by giving names to different stages of this degree and by providing a coherent framework to combine theoretical specifications on assimilation. Within the last section it was possible to combine the acculturation framework of Berry with segmented

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assimilation theory and assimilation in general. However, the different dimensions introduced in section 2.4 still need to be integrated. Furthermore, some critical thoughts are needed to come to terms with a final assimilation model that can be applied to migrants and minorities. Building upon Berry’s two-dimensional acculturation frame, four stages and outcomes of assimilation are possible. Once considering how these four stages are defined, it becomes obvious that assimilation, integration, separation, and marginalization are clearly distinct concepts. For someone to be classified as assimilated, the person should be incorporated into the mainstream on all four assimilation dimensions, while the home country characteristics have been clearly abandoned. This implies that migrants and minorities have similar access to structures of the host society, are dispersed across different social levels and do not exhibit any systematic differences (Löffler, 2011). As noted earlier, this ideal state can never fully be reached, but to a great extent. When the opposite is the case—adherence to the culture and structures of the home country only—somebody would be labelled separated. Separation is often a worthwhile option for migrants and minorities as climbing up socially is easier and less risky (in the short run) within the ethnic community than in the mainstream (Löffler, 2011). Yet, in the long run the options for meaningful and high positions are better within the majority society than within the ethnic community (Goebel & Pries, 2006; Wiley, 1967). If neither the host country nor the home country is adhered to on any dimension, someone is marginalized. This state describes migrants and minorities who have given up their home country characteristics, but (not yet) integrated into the majority of their country of reception (Hoffman-Nowotny, 1973; Löffler, 2011). The situation however is quite different when integration is considered. Here, there are plenty of outcomes that all signify as integration. Depending on the degree of host and home country assimilation, someone can be integrated into both cultures to a similar extent, can be closer to the host country or closer to the home country society. Correspondingly, there are integration outcomes that come closer to a situation of assimilation or separation, while other outcomes depict a balanced integration outcome. This fact is mirrored in Figure 2.1 and is empirically supported by Schwartz and Zamboanga (2008) who conducted a latent class analysis to verify Berry’s acculturation model among Hispanics in the USA. They found three types of integrated individuals: partial biculturals, American-oriented biculturals and full biculturals. American-oriented biculturals are integrated, but adhere strongly to American culture, while partial biculturals depict the highest levels of ethnic group belonging among these three types of integrated individuals and are hence a mixture of integration and separation (Schwartz & Zamboanga, 2008).

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Figure 2.1 forms the basis of assimilation within this dissertation; however, some critical remarks are needed at this stage of model development and earlier conclusions still need to be embedded. Berry was the first to note that the attempt to assimilate can also result in failure to become part of the host and home society. This marginalization option is appreciated to the extent that it broadens theoretical specifications of assimilation theory. However, one could wonder how such a scenario would look like in practice. First of all, an individual would hardly specify themself as being marginalized as the concept as such implies a passive standing of being labelled marginal from an outgroup perspective. In turn, marginalization can hardly be considered a strategy that an individual pursues consciously or had intended to end up in in the first place. Secondly, marginal refers to the failure to become part of the preferred group, but it does not necessarily mean that one is not part of any group. Rather, such a marginalized individual would choose to become part of a third group or a cultural sub-group (Rudmin & Ahmadzadeh, 2001), or is simply confused about its belonging (Schwartz & Zamboanga, 2008). As might become evident, this option of marginalization is hardly possible to detect in real life, which is why it is claimed that marginalization is not a real option in the study of assimilation processes. This conclusion is supported theoretically and empirically by several scholars (Del Pilar & Udasco, 2004; Rudmin & Ahmadzadeh, 2001; Lee et al., 2008; Sobal & Frongill, 2003). Moreover, thoughts on assimilation have been developed for (labor) migrants but have not yet been transferred to the situation of older minority groups. Section 2.6 already concluded that minorities are most likely to end up in a situation that equals separation in some form or the other; at best they might be integrated. However, it is very unlikely that they will reach assimilation as a final outcome of inclusion. Furthermore, even when migrant groups are considered that might assimilate eventually as time passes and generations replace each other, it is still questionable whether they will ever be completely assimilated in a modern world with transnational contacts and easy communication possibilities. Additionally, in Europe there are up to now only few subsequent migrant generations. At most, one might have data available on the third generation. This makes it hard to mirror long term processes and increases the likelihood to find assimilation outcomes that correspond to integration and separation only. These limitations are also summarized in Figure 2.1. The final assimilation model that is the foundation of this dissertation will hence assume that there are only two possible outcomes of assimilating: integration and separation. If an individual adheres (more or less) completely to its home society, they are separated. If, however, there is some form of adaptation

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Figure 2.1 Limited assimilation model for migrants and minorities

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towards the host society, the individual is automatically integrating in some form or the other. To also consider the different dimensions of assimilation processes, it is expected that there are different degrees or forms of integration. After all, an individual who is integrated structurally, culturally, and socially and only adheres to their home country in form of identity is much more integrated than an individual who is only integrated structurally into the host society. Following the discussion in section 2.4, cultural and structural integration will most likely be the first and most important steps in integrating into a receiving society.

2.9

Summary

This chapter has reviewed the history and development of the concept of assimilation. Starting from a straight-line understanding of assimilation, which inevitably leads towards full incorporation within three generations, the discussion around the concept has broadened towards more heterogeneous integration outcomes. Nowadays, many scholars stress that assimilation can take more than one form, as one can be included into different segments of society, can be part of the host as well as home society simultaneously and can integrate on selective dimensions only. As assimilation theory was developed within the American migration context of the early 20th century, the theory has to be modified to also suit the situation of migrants and minorities in Europe. Europe is much more restrictive regarding its integration policies and only recently started considering itself a region of migration. Consequently, societies are rather closed and do not yet consider migration enrichment, but rather a burden. This makes it hard for migrants and minorities to assimilate. It has therefore been argued that assimilation is a lengthy path for migrants in Europe. Rather, it seems likely that they will either remain completely within their own ethnic community or assimilate downwards into an underclass in society, or, even more likely, they will reach some form of integration by adapting towards the host society on some of the four dimensions of assimilation. Among minorities, the situation is slightly different. Old minorities in Europe are often recognized and hence, enjoy special rights within their country of residence. These rights suspend the possibility of assimilation as they grant minorities the option to have their own political representation, to preserve their language and culture, and often even to have their own schools. Accordingly, if such institutions exist, minorities have no incentive to assimilate as assimilation would imply a break with the past and the abandonment of this special standing. It thus

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seems likely that minorities will either integrate to a certain extent or stay within their group as separated individuals. Figure 2.1 therefore summarized that for the analysis of migrants and minorities within this dissertation, a limited assimilation model will be taken as a basis. Within this model, it is assumed that migrant and minority groups are more likely to be integrated or separated than assimilated or marginalized. Furthermore, it is expected that among groups being labelled integrated, there exist different forms of integration, some of which are closer to assimilation while others move more into the direction of separation. These assumptions focus on the group level and hence address general processes without denying that some individuals will fall into different categories. Accordingly, just because the group as such can be classified as integrated or segregated, everyone can still pursue an assimilation strategy that differs from the group trend. With this background regarding assimilation in mind, the next chapter will elaborate upon the fertility of migrants and minorities and will relate both theoretical standing to each other as well as derive hypotheses guiding the upcoming empirical study.

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Explaining Migrant and Minority Fertility

The previous chapter focused on the sociological perspective of assimilation theory, which is usually applied within migration research to explain differences between majority and minority and would hence also form a suitable basis to explain fertility differences from a sociological point of view. Demographers, however, so far have used specific fertility hypotheses to account for the reproductive behavior of migrants and minorities. This chapter will first of all review fertility theories from the angle of (social) demography, before exploring demographic theories on migrant and minority fertility in more detail. Then, the chapter aims at combining (1) migrant and minority fertility theories with each other, (2) as well as the presented fertility theories with assimilation theory to develop a comprehensive model of migrant and minority fertility.

3.1

Fertility in the Context of Social Demography

Besides mortality and migration, fertility is one of the major topics within demography. Around 1900, many European societies experienced changes within their fertility levels and reproductive behavior. This decline in fertility (and mortality) is described as the first demographic transition. This transition in fertility goes hand in hand with a development of societies from traditional to modernized states (Coale, 1989). As modes of production changed, medical possibilities advanced and life expectancy increased, values towards family life and the role of children underwent fundamental changes. Traditional families, which were characterized by a high number of children, were replaced by modern families that had few children only. This first transition is coined by traditional methods of

© The Author(s), under exclusive license to Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2023 B. Brünig, The Fertility of Migrants and Minorities in Europe, https://doi.org/10.1007/978-3-658-43099-3_3

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contraception such as refraining or coitus interruptus. In Germany, the transition started in 1890, while it commenced in 1912 in Bulgaria (Höpflinger, 2012). Though it was expected that this transition would even out with “replacement fertility (i.e., just over 2.1 children on average), zero population growth, and life expectancies higher than 70 y[ears]” (Lesthaeghe, 2014, p. 18112), the 1970s brought further changes, which Lesthaeghe and van de Kaa (1986) classified as the second demographic transition. These new changes brought sub-replacement fertility, a multitude of living arrangements, a decoupling of marriage from reproduction and increased mobility in terms of migration. Also, contraception became more modern with the pill and condoms replacing traditional modes of prevention. This enabled specific planning of fertility and thus resulted in a decoupling of reproduction from sexuality (Huinink, 2016). It was increasingly invested in the quality of children and individual needs, such as self-fulfillment and self-realization gained in importance (Lesthaeghe, 2014). The theoretical conceptualization of the two demographic transitions demonstrates that fertility cannot be studied without placing it within the wider societal context as fertility is not a phenomenon that can be investigated in isolation. Demography as scientific discipline mainly focuses upon describing statistically the population development (Hank & Kreyenfeld, 2015). Yet, without linking these figures with the societal and economic context of respective societies, it cannot explain the phenomena outlined. This combination of sociology and demography is known as social demography in American research but has not really found its way onto the European research agenda (Höpflinger, 2012). The most prominent approach combining sociological with demographic explanations within fertility research is the life course approach, which studies life events such as fertility and partnership within the context of birth cohort, age, mortality, family relationships and status achievements. This life course perspective highlights that fertility is interrelated to several circumstantial factors ranging from socialization, cohort specific events, and personal relationships to societal transformations (Huinink, 2000). It has thus been acknowledged by most researchers that there are several intervening variables that influence fertility behavior and have to be included into theoretical and empirical examinations of fertility. Next to fertility specific factors such as fecundity, use of contraceptives, and genetic conditions (Davis & Blake, 1956), the living situation of individuals is equally detrimental in determining fertility outcomes. Divorce, new partnerships, health and illness or unemployment can have sustainable effects on fertility behavior of individuals or couples (Höpflinger, 2012). It is thus not possible to explain any fertility behavior within a given society at a certain point in time without referencing the context in which

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this behavior occurred. After all, most intervening variables are influenced by societal norms and socialization. Correspondingly, they vary from society to society and from one ethnic group to another. This fact has been acknowledged in several theoretical approaches aiming at explaining the fertility decision-making process but has received less attention within empirical investigations.

3.1.1

Classical Approaches Explaining Fertility

Generally, one can distinguish two approaches that are drawn on to explain fertility. On the one hand, economic theories account for fertility through a cost-benefit analysis with children being the good “consumed”. On the other hand, social psychology as well as sociology have put the emphasis on social and cultural variables to account for fertility in modern societies. To fully understand the fertility behavior of migrants and minorities, both approaches will shortly be outlined to be able to place the model of this dissertation within the context of current demographic and sociological debates. In 1960, Becker developed his economic theory to explain fertility behavior. The basic assumption underlying his theory is that the birth of a child is comparable to the cost-benefit analysis one undertakes when buying goods. Children are hence viewed as consumables (Becker, 1960, 1981). Depending on whether one expects returns from having children, one either decides for or against the birth of a(nother) child. Returns can thereby be achieved by either investing in the number of children or in the quality per child. The more family income available, the more is usually invested in the quality of each child. This hypothesis was one of the first to explain why family size and family income are negatively correlated (Willis, 1987). When weighing the costs and benefits of childbearing and -rearing, Becker and his colleagues mainly consider monetary variables, while neglecting personal preferences and values (Huinink, 2002). Moreover, the approach has been criticized as it does not include uncertainties regarding fertility: The decision to have a child entails costs that can hardly be estimated beforehand. Furthermore, one never knows in advance the quality of the child (Höpflinger, 2012). Given these shortcomings, the economic approach to fertility has been advanced throughout the last decades. The new home economics already take a broader stance by including time, resources and both partners within the household into the decision-making process (Becker, 1981; Willis, 1974). This approach views households as production function. Each household produces and consumes commodities (e.g., health, esteem, children), which in turn define the well-being of the family (Berk &

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Berk, 1983). Depending on the human capital available within the family, it is decided whose time is best invested in e.g., taking care of the children or going to work. Overall, it is assumed that households aim at maximizing utility by finding the best possible allocation of time and resources. One point of discussion until today remains the extent to which families form a decision-making unit or take decisions individually (Grossbard, 2011). There is broad agreement, however, that opportunity costs are central within the fertility decision-making process. Opportunity costs cover the indirect costs of having and raising children, e.g., the surrender of other consumption goods or the time invested in children that can consequently not be invested in other goods (Oppenheimer, 1982). Hence, they arise from the “forgone opportunities that have been sacrificed” for the sake of doing something else (Samuelson, 1967, p. 443). As far as fertility is concerned, opportunity costs thus rise with income as well as with socio-economic status (Cette et al., 2005). Though more advanced, preferences, values and desires still take a minor role within this micro-economic reasoning. Furthermore, from a sociological point of view, the distribution of household chores within the family can equally result from processes of socialization instead of economic reasoning (Berk & Berk, 1983). To open-up micro-economic reasoning for sociology as well as demography, Easterlin (1969) designed a socio-economic model to explain fertility. It is based on Becker’s economic model of fertility as well as the new home economics but integrates variables relevant for explaining fertility within other disciplines, too. Novel to his modelling is the integration of preferences, which are subject to change depending on family income and prices of goods (such as children). Furthermore, he acknowledged that fertility preferences adjust to societal conditions, such as labor force participation, relative income and marriage rates (Easterlin, 1969; Macunovich, 1989). While economic approaches so far have neglected the role of values and preferences, Easterlin (1969) recognizes that norms, socialization and societal background are detrimental in the formulation of reproductive preferences. This model demonstrates that micro-economic reasoning on its own does not suffice to account for fertility behavior and builds a bridge towards sociological models that put the emphasis on socialization, upbringing and cultural background. From the angle of social-psychology and sociology, values and biography are important factors explaining fertility behavior. The value of children approach (VOC) uses the cost-benefit reasoning of economic theories to weigh the values children generate against the costs of having children. It is thereby assumed that the VOC depends upon social, cultural and economic characteristics as well as upon the social and economic state of the couple (Höpflinger, 2012). Hoffman

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and Hoffman (1973) refer to the VOC by addressing “the needs they fulfil for parents” (p. 20). Their empirical analyses thereby stress the multidimensionality of these needs and underline social esteem, comfort and affect to be the three major values of children (Nauck & Klaus, 2007). It is thereby taken into account that the actual importance of these needs varies across cultures, which in turn explains cultural variation in fertility behavior. Though adding to economic insights of fertility behavior, Nauck (2001) as well as Huinink (2000) point out that the values of children are inductively derived, but not grounded within a sound theoretical basis. Moreover, there is too little insight into the extent to which societal norms or individual preferences underlie the VOC. Therefore, he proposed to integrate the VOC into the theory of social production functions. This theory regards childbearing as part of human welfare production (Nauck, 2014). Physical wellbeing and social approval are the major goals individuals strive to achieve (Lindenberg, 1996); however, they cannot be achieved directly. Rather, a set of instrumental factors is decisive for reaching these goals. Generally, the theory of social production functions assumes that once a factor is deemed to enhance the likelihood of reaching social approval or physical wellbeing, it is likely that this factor will be chosen (Nauck, 2014). Under this scenario, the intention to have a child is one means to increase utility for reaching these major goals. In comparison to economic models who mainly stress the costs of having children, the value of children approach underlines the benefits derived from children. A different approach within the social psychological and sociological disciplines are biographical explanatory approaches. These approaches assume that previous life events such as socialization, education, work experiences or migration events influence generative behavior (Höpflinger, 2012). Biography is thereby understood to entail three layers: events belonging to the inner self, which cannot be seen by others; the outer life history that can be described by quantitative and qualitative measures (curriculum vitae); the history of possible alternatives that have been imagined but not pursued (Höpflinger, 2012). When taking a decision for or against children, one weighs all three layers against each other and considers the possible alternative decisions that will be barred by childbearing. The most important biographical levels are: socialization; reproduction & family life; working life; living & migration. Taken together, various theoretical approaches exist that aim at accounting for fertility behavior. These approaches stress that there are costs and benefits involved in the decision-making of generative behavior and that the perception of costs and benefits can be both individually and societally coined. What is missing though, is an overall model that combines explanatory factors of the different disciplines with each other. Furthermore, many fertility theories have been

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developed and advanced between the 1960s through the 1980s but have received little attention ever since. A further need is thus to update existing theories and integrate potential new factors. A last critical point concerns the applicability of existing models towards migrant and minority groups. There is nearly no consideration of these groups within theory specification. Rather, as will be shown later on, migration scholars have developed separate fertility theories that overlap only partly with the fertility theories presented here. Before these theories will be reviewed, a short outlook into fertility as decision-making process will be given to round up demographic theories on fertility.

3.1.2

Fertility Decision-making

Fertility can be assessed in terms of number of children (quantum) or timing of having children (tempo). The former is for instance central within economic analyses of fertility, which usually explain the number of children based on the costs and benefits involved in having and rearing children (Becker, 1960). Using a different starting point, one can assess fertility in terms of tempo, too. The focus of this latter perspective lies on the postponement of childbearing within developed societies and is closely linked to cultural change and demographic transitions (Balbo et al., 2013). As fertility is projectable, the number of children is often closely related to the timing of children: Once fertility is postponed, one often reduces the (total) intended number of children (Kohler & Ortega, 2002; Roustaei et al., 2019). Moreover, timing and postponement are tightly linked with biographical events such as education, migration and entry into working life (Billari et al., 2006; Lebano & Jamieson, 2020). When focusing on reproduction within developed countries, it is therefore important to study both quantum and tempo within the context of previously introduced fertility theories. Only when both aspects are covered, can fertility be fully understood. A problem in most analyses is the question of causality. Most theoretical approaches focus on one explanatory factor of fertility only and automatically link the presence or absence of this factor to fertility outcomes, namely the birth of a child (Höpflinger, 2012). It is thus neglected that fertility usually is a process of many interlinked variables. This proceeding furthermore prohibits an understanding of dynamics and does not allow for conclusions regarding the cause and effect of decisions and actions. Especially within demography, events have often been looked at as a single occurrence that are independent of past events (Kulu & Milewski, 2007). The focus of many demographers has in the past

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therefore rested on describing rather than explaining demographic events such as marriage or fertility. An alternative approach is offered by life course analyses as presented in section 3.1.1, which model fertility decisions under a life course perspective thereby considering timing of marriage, employment situation and earlier childbearing. These approaches provide for a more complex starting point of understanding cornerstones within the vita, such as marriage or childbearing, and grant a certain dynamic to fertility analyses (Kulu & Milewski, 2007). Life course analyses hence add to classic demographic analyses of fertility a more comprehensive analytical tool. Though these approaches acknowledge that fertility has to be placed within a wider context of the life course, they still assume that a certain event (e.g., marriage) is directly related to the presence (or absence) of a child. What is neglected though is that the decision to have a child is not solely influenced by partnership, education or occupation, but is equally determined by situational constraints. Given that there is a delay between taking the decision to have a child and the event of actually giving birth to a child, there is room for external events to influence the decision taken (Bühler & Fratczak, 2007). One might not become pregnant immediately and in the meanwhile, living circumstances might change which could alter the decision to have a child—e.g., losing work, breaking up of partnerships or getting diseases. Similarly, processes of contraception and fecundity can hinder or facilitate childbearing as can experiences of a stillbirth. Thus, even if an individual or a couple wants to have a child, this decision does not necessarily translate into the outcome of actually having one. Therefore, one should distinguish between the decision to have a child and the final outcome of having one. After all, the decision to form or extend a family might better reflect the extent to which situational circumstances are associated with fertility. Though of course, information about the decision to have a child does not necessarily deliver any information about the actual translation of this decision into family formation or extension. It is thus important to distinguish fertility as behavioral decision from fertility as actual outcome of a sequence of antecedent behaviors (Azjen & Klobas, 2013). Practically, this entails distinguishing between the intention or decision to have (or not to have) a child as opposed to the event of actually giving birth to a child. This distinction is especially important in modern societies, where sexuality and reproduction are decoupled from each other and entail a conscious decision-making (Huinink, 2016; Milewski & Mussino, 2018). Unfortunately, many demographic analyses still focus on the outcome of birth instead of studying the mechanisms leading towards the act of consciously aiming at having a child. After all, this latter aspect forms a precondition to finally having a child.

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The theory of reasoned action by Ajzen and Fishbein (Ajzen & Fishbein, 1980; Fishbein & Ajzen, 1975) considers reproductive decisions to be the outcome of a rational calculation of the costs and benefits involved in having a child, yet it places the final fertility outcome within a sequence of intentions, attitudes and subjective norms. The theory postulates that behavior is influenced by the intention to perform a certain action. The focus hence lies upon the intention to act not reach a certain goal. This intention in turn is formed based on the personal attitude towards the behavior and the perceived subjective norm (Ajzen & Fishbein, 1980; Fishbein & Ajzen, 1975). The latter thereby denominates the societal pressure to perform a certain action. Within a later publication, Fishbein and Ajzen (2010) have modified their theory of reasoned action by acknowledging that intentions do not directly translate into the behavior in question, but that the relationship between intentions and behavior depends also on the degree of control one has on exerting the action. Though one might intend to have a child, this intention can both result in success and failure depending on the degree of control over personal and contextual factors. Ajzen and Klobas (2013) stress that “people generally have greater control over performance of a behavior than they have over attaining a goal the behavior is intended to produce” (p. 207). This is the case given that to attain a certain goal, one needs to both ensure control over the performance of the behavior in question as well as ensuring the effectiveness of the performance. This is limited in the case of having a child as one cannot control whether one e.g., has a stillbirth. Therefore, it is important to note that having the intention to have a child does not automatically transform into the behavior of actually having one. Within the context of fertility research, it has already been empirically undermined that fertility intentions are preceded by attitudes, norms and the perceived level of control (Billari et al., 2009 for Bulgaria; Dommermuth et al., 2011 for Norway; Mencarini et al., 2015 for Italy). Mencarini et al. (2015) though stress that these antecedents are predictive only of fertility intentions, not of the actual fertility behavior. Similarly, Dommermuth et al. (2011) differentiate the theory of planned behavior/reasoned action by demonstrating empirically that both subjective norms and perceived behavioral control influence for parents and childless individuals likewise whether they intend to have a child within the next three years. Though a prominent explanation of fertility behavior, the theory of reasoned action rather serves as general heuristic to study reproductive processes. It was hence not designed in first place to explain fertility but can be adapted to do so. Given the general focus of the theory, it does not explicitly outline how unintended pregnancies can be explained by a theory that assumes rational behavior. One might argue that becoming pregnant without intention is related to a lack of

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control or a failed intention. Yet, this topic has received too little attention theoretically as well as empirically and needs more focus. Secondly, the model only indirectly considers the meso- or macro-level by including a subjective norm. Here, the model can be elaborated theoretically by making clear that the subjective norm both captures the intentions of the partner as well as opinions of family members and friends. The macro-level is not included in the model, however, fertility decision-making might be influenced by employment figures or family supporting policies, too. Thirdly, the model does not explicitly make clear that having a child is not the only behavior important to the fertility outcome. Equally, using contraceptives (or not), having sexual intercourse, going to work (or not) are equally behaviors that influence whether one ultimately has a child or not (Morgan & Bachrach, 2011). That these are captured by the intention to act for having a child in that the intention to have a child automatically leads to proceptive behavior such as having sexual intercourse or not using contraceptives is not clear based on the general framework of the model. Thus, explicitly including these facets of the intention to act for having a child might unravel some confusions about the applicability of the model within fertility research Ajzen (2011) commented on this criticism by outlining that the theory was not developed specifically to explain fertility behavior but has to be adjusted to the behavior under question. This is in line with Liefbroer’s (2011) conclusion that the theory of reasoned action serves rather as heuristic, a general frame to explain social behavior, than as specific fertility theory. An alternative theory that has received less attention so far has been developed by Miller and Pasta (1988, 1995), who suggest a traits-desires-intentions-behavior (TDIB) sequence. In their theoretical framework, latent motivational dispositions (traits) are the first unconscious step towards a certain action. They are partly learned during the process of socialization, while some dispositions have also biological or experiential origin. They include attitudes and beliefs as well. These translate into the conscious desire to perform an action, which in turn results in the intention to act. Desires here refer to the wishes, which do not directly lead to the desired result. Intentions, on the other hand, are defined as conscious commitments to act in a certain way. Finally, intentions unfold into the execution of a certain action. The framework can be divided into two parts: First, the formation of intentions and secondly, the implementation of intentions (Miller & Pasta, 1995). As depicted in Figure 3.1, desires and intentions can be further distinguished into the intended/desired number of children, the intended/desired timing of having children and childbearing desires/intentions. A combination of all three dimensions then results in the final outcome of having a child. For the decision to have a first child, all three dimensions are important, while for subsequent

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children, childbearing desires and intentions receive most importance (Miller & Pasta, 1995). Empirically, the framework of Miller and Pasta has received little attention. Most analyses using this model focus on the fertility within dyadic relationships. Here, it can be confirmed that traits, desires and intentions of both partners within a relationship are detrimental to influence behavior (Mynarska & Rytel, 2019 for Poland; Testa, 2012 for Austria). Moreover, Miller et al. (2010) have successfully applied the model to predict reproductive behavior among U.S. American youth. There is some discussion about the extent to which desires and intentions are distinct, with some empirical evidence supporting the notion that desires and intentions cannot be distinguished by individuals (Hagewen & Morgan, 2005; Langdridge et al., 2007; Westoff & Ryder, 1977). Miller and Pasta (1995) however distinguish these two by intentions taking into account the desires of others, such as the partner or family, as well as situational factors.

Figure 3.1 Schematic of the TDIB model (Miller & Pasta, 1995, p. 533)

Though the TDIB framework of Miller and Pasta has received less attention within academia, it better accounts for the social context, the role of the partner and the complexity within which decisions for or against children are formed (Miller, 2011). What is more, Mussino and Milewski (2018) point out that cultural influences such as norms and socialization are translated into fertility intentions rather than reproductive outcomes. Thus, when interested in studying the relationship between cultural predictors and fertility, intentions are a more suitable outcome as they better reflect a change in norms than behavior does. For the intention of this dissertation to illuminate fertility from a sociological point of view with assimilation variables included, this TDIB model seems more appropriate as it better describes sociological aspects of norms and upbringing. Huinink (2016) furthermore points out that the distinction between traits, desires and intention better captures the changing nature of fertility desires. As several

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empirical studies highlighted that increasing age, previous children or childbearing experiences can change desires for or against children (Buhr & Kuhnt, 2012; Heiland et al., 2008; Iacovou et al., 2011; Liefbroer, 2009), a stepwise model that distinguishes the actual outcome of having a child from the intentions and desires to have one is better able to capture changes (Hunink, 2016). As fertility research in European societies is mostly focused on explaining low fertility levels, the decisive advantage of Miller’s and Pasta’s model lies in its ability to capture the stage at which decisions for children are usually delayed. While most couples or individuals have the desire to have children, and the desire for a certain number of children at a certain point in their life, they often hesitate to concretely formulate an intention to act within the next years. Constraints, partnership situations, socialized norms or other biographical uncertainties exert significant influences on the process of forming desires into intentions (Hunink, 2016). This life course perspective is missing within the model of Ajzen and Fishbein. Miller (2011) even extends Miller’s and Pasta’s model by adding proceptive or contraceptive behavior as an additional step between the transformation of intentions into the outcome of having a child. Both the model of Miller and Pasta as well as Ajzen and Fishbein have hardly been applied to migrant and minority samples. Miller and Pasta (1995) have pointed out themselves that this is an open research topic. Within an earlier publication, Miller (1992) even mentions specifically that foreign born people were excluded from the empirical analyses due to their different upbringing. He thus acknowledges the role of socialization and cultural influences on the formation of traits, desires, and intentions. However, this is a point of particular interest as differences in intentions between minority and majority might be explained by differences in motivations, desires, situational constraints, and cultural roots. Adaptation as well as contact to individuals with other opinions and values can influence fertility related attitudes among migrant and minority individuals (Lesthaeghe & Moors, 2002). In conclusion, it might be interesting to find out whether ethnicity could be integrated into the relationships predicted by Miller and Pasta. Despite the meaningfulness to differentiate intentions from behavior within fertility research, there are hardly any studies on migrant or minority fertility, which have made use of fertility intentions. One exception that used fertility intentions within the study of fertility differentials between migrant and native samples found that socio-economic as well as demographic predictors impact differently on the intention to have a(nother) child soon (Mussino et. al., 2021). Concretely, it could be shown that differences in intentions are greatest when the living circumstances are not ideal (e.g. in case of unemployment). Under these

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circumstances, natives depict more negative intentions than migrants who seem to require less economic certainty for family formation/extension. Others have at least acknowledged the necessity to go beyond number of children born when looking at migrant resp. minority fertility. Kahn (1994) argued that fertility intentions are particularly relevant in studying reproduction in the context of migration as expectations regarding future fertility inform about the underlying norms and preferences of migrants. Hence, they offer valuable clues about migrants’ and minorities’ state of cultural assimilation (Kahn, 1994; Kulu et al., 2019; Milewski & Mussino, 2018). In line with this argumentation, this dissertation will use fertility intentions to account for the fertility of migrants and minorities. It is thereby argued that intentions better capture the underlying decision-making of reproduction and more suitably reveal how processes of assimilation influence family planning. To discuss in more detail how fertility intentions might predict actual fertility behavior, the next section will dip deeper into studies on the relationship between intentions and behavior.

3.1.3

From Fertility Intentions to Fertility Behavior

Although there are several studies which have used fertility intentions to account for reproductive behavior, one might wonder to what extent intentions translate into actual fertility behavior. Therefore, the degree to which intentions and behavior are related will be briefly discussed. Most empirical studies conclude that intentions predict behavior fairly well (Coombs, 1974; Monnier, 1989; Remez, 2000; Tan & Tey, 1994; see also Morgan, 2001 for a thorough overview of studies on fertility intentions) though Davidson and Beach (1981) as well as Rindfuss et al. (1988) state that intentions better predict who does not have a child as opposed to who does. Thus, they show that focusing upon the intention not to have a child is closer related to indeed not having a child. Moreover, one can distinguish short-term and long-term plans for having children. The former usually address a two- to three-year period of planning and is captured by the term intentions, whereas the latter refers to life-time plans and is often referred to as desires. Intentions are found to better associate with actual fertility outcomes as individuals are habitually more accurately able to plan their behavior for the upcoming years (Philipov, 2009). This is also logical when thinking back to how intentions are formed. Fishbein and Ajzen (1975) as well as Ajzen and Fishbein (1980) consider intentions the outcome of subjective norms and attitudes. Attitudes develop as one grows older and as one gains experience. Similarly, the perception of what constitutes the norm changes in the

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process of the life course (Morgan & Bachrach, 2011) and potentially in the process of assimilation. In turn, it is likely that fertility intentions are not constant over the life span but develop in reaction to living circumstances. As migration forms a major live event that is related to several changes, it seems more accurate to find changes due to migration or integration experiences mirrored in fertility intentions. Furthermore, several studies have analyzed fertility intentions across different situational contexts and have concluded that the extent to which fertility intentions relate to reproductive outcomes is also dependent upon the context. Amongst others, the marital status, age and parity matter in predicting fertility outcomes based on intentions. Married respondents are more likely to live up to their intentions when compared to singles (Schoen et al., 1999; Remez, 2000), and older people tend to be more certain about their plans (O’Connell & Rogers, 1983). Morgan (1982) outlines that most respondents do not have a fixed number of intended children, but rather a range of acceptable numbers. Once one has a number of children that is located within this range, it becomes more likely that one is unsure about one’s fertility intentions. Morgan (1982) demonstrates that especially older respondents at the end of their childbearing age or those facing insecure socio-economic conditions tend to be unsure about their intentions, given that they already have reached their possible range of children to some extent. Once one expresses intentions, there is a tendency for higher parities to eventually depict a better match between intentions and outcomes (Monnier, 1989; O’Connell & Rogers, 1983). Overall, one can conclude that fertility intentions can explain reproductive outcomes fairly well, though the degree to which intentions eventually match the reproductive outcome depends on several context characteristics. Given that the aim of this dissertation is to investigate how fertility intentions are related to the assimilative state of migrants and minorities, the focus lies on fertility intentions without testing to what extent the intention will transform into an actual childbearing. Nevertheless, it will of course be kept in mind that a sole intention to have or not to have a child will not necessarily result in the corresponding action to give birth.

3.2

Migrant Fertility

Research on migrant fertility originates in the United States, where especially the migration waves since 1965 fueled attention for fertility preferences and outcomes. In this context, several scholars have studied the fertility rates of Hispanic migrants in the US as these groups come from a home society with high

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average fertility. This high fertility was found to prevail among many Hispanics despite migration (Bean & Swicegood, 1985; Frank & Heuveline, 2005; Landale & Hauan, 1996) and similar high fertility rates have been found for other migrant groups in the United States (Anderton et al., 1997 for the Black and Asian population; Rumbaut & Weeks, 1986 for Indochinese migrants; Snipp, 1997 for the Indian population). When viewed in a long-term perspective that takes duration of residence into account, Ford (1990) as well as Mussino and colleagues (2021) note a tendency for migrants to increase fertility behavior after migration, but a convergence towards native reproduction with increasing length of stay. Still other scholars have highlighted the importance of socio-structural and cultural approximation in the development of migrant fertility within the host society (Kahn, 1988; Milewski & Mussino, 2018; Mussino et al., 2021). To capture the manifold influences that researchers have identified to explain migrant reproduction, several theories have been developed to account for the fertility outcomes of immigrants. The following summary will focus on the adaptation and socialization hypotheses but will also elaborate upon other existing approaches such as the legitimacy, selection, disruption and interrelation of events hypotheses.

3.2.1

Theories Explaining Migrant Fertility

The adaptation hypothesis puts strong emphasis on the structural and cultural influences of the receiving society. It posits that these factors quickly influence the values and habits of immigrants, thereby supporting reproduction to soon equal that of the native majority (Goldstein & Goldstein, 1983). The theory acknowledges that cultural socialization plays a role but regards the influence of the host country context more important than home country culture. The socialization hypothesis has often been portrayed as contrasting theoretical explanation of migrant fertility behavior. The socialization hypothesis stresses the influence of the country of origin on fertility patterns. It states that socialized norms and values from the origin culture are strongly embedded into migrants’ cultural ideals and change only slowly in the process of migration (Bean & Swicegood, 1985; Goldstein & Goldstein, 1983). Norms on reproductive preferences, childbearing and childrearing are highly inert (Thornton, 1995). The emphasis of this hypothesis lies on the importance of internalized value structures throughout the life course. This perspective implies that first generation migrants are unlikely to adapt to majority reproduction as they have been born and raised within their home society. Second generation migrants, however, have been born within their

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host societies and are likely to grow up with the childbearing values of the majority. Hence, the second generation is more likely to exhibit fertility levels similar to those of natives. By the same token, as early versions of the assimilation theory, this theoretical approach punctuates the relevance of generational change in processes of becoming alike (Stephen & Bean, 1992) and suggests that with subsequent generations, reproductive behavior will increasingly equal that of the majority. Empirically, both hypotheses have been confirmed and disconfirmed within several contexts. Rosenwaike (1973) as well as Stephen and Bean (1992) observe a change in reproductive behavior across generations. This finding is read to support the socialization hypothesis while at the same time disconfirming the adaptation approach. More recent findings on migrant fertility outline that the first generation often depicts similar fertility levels to the population in their country of origin and only adjusts gradually to host society standards. This pattern has for instance been found for Mexicans in the US (Kahn, 1994), Greek and Italian migrants in Australia (Abassi-Shavazi & Mc Donald, 2000), the Turkish 1.5th generation in Germany (Krapf & Wolf, 2015) and Moroccan migrants in Italy (Mussino & Strozza, 2012a). The socialization hypothesis is also confirmed within the Netherlands. De Valk (2006) as well as de Valk and Liefbroer (2007) demonstrated that parental preferences regarding childbearing are transmitted to children within migrant families. Yet, at the same time Schmid and Kohls (2009) as well as Mayer and Riphahn (2000) present findings in support for the adaptation hypothesis in the context of migration to Germany, as fertility rates adapt to native levels with increasing length of stay. In a similar vein, the adaptation hypothesis is supported by Bagavos et al. (2008) for Albanian migrants in Greece. Here, Albanians generally portray higher fertility levels than native Greek and other immigrant groups living in Greece. However, there is an adaptation towards native averages observable across time. Whether one finds support for the adaptation or the socialization hypothesis might also depend upon moderating influences. When comparing the 1.5th Turkish migrant generation with the second generation and native Germans, Krapf and Wolf (2015) concluded that the situation of the 1.5th generation, which was socialized within Turkey, fits better with predictions derived from the socialization hypothesis, while the second generation more clearly converges with native fertility levels. This conclusion however varies once the educational level of respondents is considered. Those Turkish migrants, who have medium education or higher, depict reproductive levels similar to native Germans. This, however, is not the case for the lowly educated. For them, strong differences between the 1.5th generation and native Germans prevail. Other results from the

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Italian context suggest that the cultural background of the migrant group matters for whether socialization or adaptation prevails. Impicciatore et al. (2020) demonstrate that Albanian migrants rather adapt their fertility levels which is explained by their more egalitarian culture and especially gender role attitudes. Other migrant groups within Italy (such as Moroccans) were found to keep their distinctive fertility patterns and norms much longer, which is attributed by the authors to the strong patriarchal values within North African countries. Hence, several factors have to be considered when drawing conclusions supporting one or the other hypothesis. (1) First of all, as a review of previous findings stressed, it is important to distinguish between migrant generations when drawing conclusions regarding these hypotheses. Socialization is likely to dominate the fertility behavior of first-generation migrants, while adaptation is likely to start taking place as of the second generation. For future applications of the hypotheses, one should keep in mind the discussion on the role of generations from section 2.2. (2) When analyzing pre- and post-migration fertility in Brazil, Hervitz (1985) points at the need to differentiate migrants by countries of origin when examining their fertility levels. His findings demonstrate that migrants from less developed regions are more in line with predictions derived from the socialization model, whereas adaptation was more likely to occur among migrants from developed regions. This conclusion is underlined by the findings of Impicciatore for Italy. Sobotka (2008) adds to this conclusion by not only considering the region of origin, but also the relationship between fertility at home and fertility in the receiving context. While migrants originating from a low fertility context might immediately adapt to a low fertility host society, migrants from high fertility settings are confronted with more all-embracing changes. In line with this argument, Woldemicael and Beaujot (2012) found that migrants from high fertility contexts have higher odds of having children in their country of reception. The context of origin and receiving society is thus important to consider in the study of migrant fertility. (3) The adaptation and socialization hypothesis were formulated during the 1960s to 1980s. Recent migration waves, however, are characterized by transnational communication, ethnic communities and more options for cultural maintenance, as outlined within the previous chapter. These developments are not included in the early theoretical specifications but are likely to exert significant influences on the degree and the tempo of fertility adjustment. Lastly, (4) the hypotheses originate in a US American context, but it remains to be discussed to what extent they are also applicable in Europe. The legitimacy hypothesis states that migrants give birth to children within their country of residence to gain legal rights for residency (Bledsoe, 2004). The

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hypothesis suggests that childbearing is likely to occur soon after migration. Furthermore, the hypothesis is only meaningful within destination countries where residency rights are connected to childbirth. Few studies have tested the hypothesis empirically. Mussino and Strozza (2012b) provide one of the few recent papers that refer to the legitimacy hypothesis when trying to understand fertility behavior among migrants in Italy. The selection hypothesis posits that migrants are a special kind of group already within their home countries that differ from the natives at origin along many fertility related dimensions such as age, education, or employment (Hervitz, 1985). Hence, they adapt quickly towards the fertility patterns of their country of destination. It is assumed that migrants already adhere to the fertility norms of their country of destination before even migrating, however, conscious, or unconscious constraints such as social mobility ambitions, education or opportunity structures prohibit the execution of these norms within their home countries (Milewski, 2007). This hypothesis thus does not aim to explain a change of behavior due to migration but takes for granted that migrants are a special group that self-selects into migration (Kulu, 2005). Due to limited data availability—after all, the hypothesis demands data of both home and host society—the hypothesis has been tested rarely. Recent results can be found in Chattopadhyay et al. (2006), who confirm the selection hypothesis for internal migrants in Ghana as well as in Milewski (2007) whose analyses support the selection hypothesis for migrants in Germany. The disruption hypothesis posits that migration is a stressful event that disrupts fertility in several ways (Majelantle & Navaneetham, 2013): “it may lead to separation of spouses, the move may be stressful so as to actually interfere with physiological capacity to bear children, and these and other factors may lead to a reduction in fertility of recent migrants” (Majelantle & Navaneetham, 2013, p. 102). The disruption is expected to be maintained temporarily as these stressful factors disappear over time. Fertility is thus likely to adjust to native standards in the long run. The disruption hypothesis has for instance been confirmed by Blau and Kahn (2007) for female Mexican migration to the US. They observed that females delay pregnancy after migration. This delay, however, resulted in higher fertility levels afterwards—an adjustment to fertility standards in their country of residence could thus not be observed. Kulu (2005) on the other hand noted a disruption of only a few months among internal migrants in Estonia, while Impicciatore et al. (2020) found a depressive effect of roughly two years among Ukrainian migrants in Italy. Wolf (2014), though, found no support for the disruption hypothesis among Turkish migrants in Germany.

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Based on life course approaches, the interrelation of events hypothesis postulates that migration, marriage and childbirth are interrelated events that occur in close sequence to each other. Andersson (2004) demonstrates for immigrants to Sweden that first-birth risks are elevated within the first two years after migration, while they even out to national averages five years after the migration event. In Germany, risks of a first birth are higher within the first year after migration as well as within the first year of marriage (Milewski, 2007). Especially if migration takes place for the purpose of union formation, migration, marriage and fertility are closely linked, as shown by Kulu (2005) for internal migrants in Estonia. The interrelation of events hypothesis is hence looking at a specific point within the life course and can only be applied once, namely around the event of migration (Milewski, 2007). When studying the event of migration in interrelation with fertility, partnership status is the most important covariate (Milewski, 2007). While a synchronization of events is likely for migrant couples, several researchers who focused on single migrants showed that longer searching times for a suitable partner and elevated marriage ages are likely (Carlson, 1985, for first-generation migrants to Australia and Milewski, 2003, for first-generation migrants to Germany). Here, marriage and fertility are hence decoupled from the migration event. To sum up, various theoretical approaches exist to account for migrant fertility. All theories have found some support within empirical studies, though there seems to be more support for the socialization, adaptation, interrelation of events and selection hypotheses than for the legitimacy and disruption hypotheses. When evaluating the suitability of one or the other hypothesis, one needs to keep in mind that the context of analysis is important. The legitimacy hypothesis for instance addresses only certain destination countries. Furthermore, it is often hard to disentangle the influence of the different theoretical approaches. If a woman moves to another country and gives birth to a child within the first year after migration, one might equally attribute this occurrence to the interrelation of events hypothesis as well as to the legitimacy hypothesis. Similarly, if a family or individual depicts fertility levels close to native averages, one might wonder whether adaptation or selectivity is going on. To study all hypotheses and to clearly disentangle the different effects, one needs data that address the situation in the country of origin before migration, that follow migrants after migration throughout some years within their country of destination and that take into account the legal situation at destination as well as the norms and values that individuals were socialized into within their country of origin. As these data requirements are extensive, one hardly succeeds in the attempt to integrate all theoretical approaches into one study. For

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this dissertation, data restrictions also hamper a full consideration of all migrant fertility approaches. Rather, the focus will be put on the adaptation and socialization hypotheses as these can best be covered by the available data and can be integrated into an overall model of migrant and minority assimilation.

3.2.2

Migrant Fertility and Assimilation

The latter suggestions can be incorporated into assimilation theories when linking those approaches with each other as well as with the limited assimilation model. It has already been suggested that the socialization and the adaptation hypothesis are not as contradictory as often claimed. Instead, they address similar factors and can be complemented to capture different sides of the assimilation process. An assimilation framework has been established in chapter 2, which distinguishes four assimilation outcomes that differentiate between home and host country belonging along four dimensions. The socialization hypothesis clearly puts the emphasis on the role of the country-of-origin culture. The familiarity of values and norms through upbringing has often a long-lasting and fundamental influence on an individual. Therefore, individuals socialized into a particular norm system within their host society, are likely to adjust slowly towards a new cultural setting while those who only spent a limited amount of time (or no time at all) in their country of origin are likely to take over host country norms more quickly. It follows that the prevalence of socialization can be expected to vary depending on the individual background and migrant generation, but for most individuals socialized norms are likely to persist across several generations and will only deteriorate in the long run. However, the hypothesis disregards that incorporation does not only take place on one dimension, but rather involves several parallel processes that impact upon each other. While cultural change is often perceived to be the most fundamental element of inclusion, as the acceptance of host country norms is essential to avoid a value clash (Hernandez, 2015), acculturation does not happen within a vacuum. Migrants encounter natives through work or school, and get to know about expectations, behavior and norms only through contact with the native majority. When assembled to the assimilation theory, the socialization hypothesis hence stresses the need for cultural assimilation for migrant fertility adjustment. Yet, as argued, it takes little consideration of the mutuality of different assimilation processes and this neglect might explain why some authors failed to support the socialization approach (Bagavos et al., 2008; Mayer & Riphahn, 2000; Schmid & Kohls, 2009). After all, integration processes

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have several dimensions and should consider both the influence of the country of residence as well as the impact of the country of origin. The adaptation hypothesis, on the other hand, is closely linked to several assimilation dimensions, but emphasizes mainly the role of the context of reception. It covers both the socio-structural dimension as well as cultural elements in the process of becoming alike. Here, the distinction between the mere adjustment to current circumstances in the short- or medium-term and full absorption of host country norms and values in the long run is important. Adaptation is easily achieved structurally, as especially labor migrants are likely to adapt quickly to the labor market situation and the bureaucracy of their host society. This is the case as their movement was primarily motivated by this aspect of integration. At the same time, this process of structural adaptation is likely to set in motion other developments of incorporation, with migrants achieving language skills and encountering natives. Gradually, they might therefore develop assimilative tendencies. The adaptation hypothesis is thus easier to integrate into the assimilation framework, as it in equal terms stresses the interrelatedness of assimilation processes. Yet, it puts too little focus on the maintenance of ethnic characteristics by neglecting that home and host country belonging can exist next to each other. Overall, both the adaptation and the socialization hypothesis can thus benefit by being considered in parallel to each other. When both hypotheses complement each other, they can capture assimilation as a bi-dimensional process in which home country belonging and host country assimilation can develop alongside each other. Furthermore, taken together they stress the main assimilation dimensions and hence provide a more thorough picture of the role of assimilation processes in the formation of fertility decisions. One last aspect, which should not be forgotten, is the role of ethnic communities. As argued before, these elements are not included in migrant fertility research but constitute important forces in migrant assimilation. If one understands assimilation as a process along the four assimilation dimensions and regards progress on each dimension a matter of degree, instead of fully achieved versus failed assimilation, it becomes easier to model the influence of such contacts. Interethnic contacts—whether within the same host society or still in the country of origin—are likely to hamper cultural, social and identificational assimilation within the host society. They help maintaining close ties to the norms and behaviors acquired and taught within the country of origin, and often make it harder to contemporaneously develop networks and identification with the host society. Accordingly, they help to preserve attachment with the country of origin in terms of cultural, social and identificational aspects. Structurally however, migrants face two options. They can either be integrated into their host society

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labor market and institutions, which would result in an integration outcome (structural integration in the host society, cultural, social and identificational belonging with the home society) or they can adhere to the structures of their ethnic community within their host society. This latter path would be labelled separation as migrants would then be incorporated into their home country structures regarding all assimilation dimensions. Generally, the presence of ethnic communities and transnational communication possibilities in modern societies makes complete assimilation less likely and prolongs the assimilation process. In contrast to studies originating in the 1960s to 80s, outcomes such as integration or separation are more likely nowadays and fertility levels are thus harder to adjust to national averages. These deliberations show that migrant fertility theories can be embedded into the broader frame of assimilation theory. Under the umbrella of assimilation theory, migrant fertility theories can partly be used to model the role of ethnic communities by acknowledging that one can adhere and be part of two cultural spheres at the same time. While previous research has only acknowledged the role of migrant fertility research for the understanding of cultural integration (Kulu et al., 2019; Milewski & Mussino, 2018), it could be highlighted here that fertility theories go beyond a sole focus on culture, but rather integrate into an overall framework of assimilation that covers several dimensions of adaptation. In addition, the socialization and adaptation hypothesis can also complement each other and thereby account for the diverse assimilation paths that an individual can choose. It remains to be discussed though, whether a similar merging is possible when it comes to minority fertility theories. As these are hardly developed within the context of migration research, it will most likely be more demanding to incorporate them into an overall model of migrant and minority fertility.

3.3

Minority Fertility

Theories accounting for the fertility behavior of minorities have developed independent of research on migrant fertility and are less prominent in scholarly discussion. Minority fertility theories evenly originate from the US American context and have mainly been used to study the fertility behavior of ethnic and religious minorities. However, only recently, the theories have found access to the study of European minorities. As will be shown later, theories on minority fertility stress similar elements as migrant fertility theories do. First, though the three main hypotheses will be introduced shortly.

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Explaining Migrant and Minority Fertility

Social Characteristics Hypothesis

The social characteristics hypothesis assumes socio-demographic variation to underlie fertility differences between the majority and minority. It is thereby assumed that both groups are differently composed regarding their age structures, marital status, education and occupation (Bean & Tienda, 1990). Early proponents of this theory have argued that ethnic differences simply reflect deviations in socio-economic status (Forste & Tienda, 1996; Johnson, 1979). Consequently, it was assumed that once these differences vanish, one would expect minority and majority fertility to become alike (Day, 1984; Goldscheider & Uhlenberg, 1969). There are some empirical findings that support the social characteristics hypothesis. Sly (1970) for instance concluded that the characteristics approach was helpful in explaining minority fertility variation in certain regions in the USA. Once the South was excluded from the variance analysis, education, occupation and income did exert significant influences on fertility. Yet, more recent findings highlight that despite similar socio-economic standings, minority and majority reproduction deviate (Chabé-Ferret & Ghidi, 2013). Especially large minority groups are likely to retain high fertility levels, because they perceive more uncertainty regarding their socio-economic mobility, while smaller minority groups rather give up childbearing for economic success. Johnson (1979) differentiates these findings into a strong and a weak form of the characteristics hypothesis. While the effect of education on fertility does not depend on ethnic background in the strong form of the characteristics hypothesis, the weak form presents a relationship between education and fertility that depends on the minority status. In this scenario, minority members compensate the lack of social status with increasing fertility levels, which is why one could expect differences in childbearing between minority and majority for the lowly educated, but similar levels among highly educated minority and majority members (Johnson, 1979). This distinction suggests that a mere decomposition into socio-economic and demographic characteristics—as suggested by the social characteristics hypothesis—might not suffice to account for the fertility behavior of minority individuals. Rather, a complex interaction of social and ethnic group characteristics seems to be at play. Empirical evidence on minority fertility is scarce. However, Szabó and colleagues (2021) have recently used the social characteristics hypothesis to investigate the fertility behavior of Roma in four different European countries. They could underline the weak version of the social characteristics approach by showing that highly educated Roma have similar fertility levels as the native majority with high educational levels.

3.3 Minority Fertility

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Sub-culture Hypothesis

The sub-culture hypothesis developed as an alternative explanation for diverging fertility levels between minority and majority. As social characteristics do not fully explain why minorities often portray higher or lower reproductive patterns than the majority, it was increasingly assumed that home country norms and values exert significant influences on minority fertility (Bean & Tienda, 1990). Minorities, which originate in countries with high fertility levels, are likely to cherish familism and adhere to pro-natalist norms. The sub-culture hypothesis hence states that fertility differences are grounded in different reproductive values. The more value expectations become alike, the more likely it seems that fertility differences between majority and minority will disappear. Massey (1981) presents findings which show that familism is generally strong among Japanese, Chinese and Mexican Americans in the US, but that there is also a trend observable that these traditional attitudes diminish across generations. Despite the familial attitudes among many minority groups, fertility levels differ. While Hispanic ethnic groups in the US depict high fertility levels, the reproduction of Asian groups tends to be below the native White average. Massey (1981) considers these group differences to be rooted in the late marriage of Japanese and Chinese couples. Yet, he also notes that fertility levels of highly educated minority groups are often below the reproductive average of similarly educated Whites. Thus, an interplay of cultural and socio-economic factors seems to be at hand. Empirical studies have usually used religiosity as measure of cultural norms. Bradshaw and Bean (1972) for instance used religiosity as measure of cultural background when studying the fertility behavior of Mexican American women. They concluded that even when socio-economic characteristics are controlled for, Mexican American women still depict higher birth rates than White-American and Black-American women. They concluded that this difference is best explained by culture and the varying fertility desires of Mexican American couples (Bradshaw & Bean, 1972). Similarly, Kennedy (1973) used religiosity to assess the influence of culture on the fertility of Catholics in Ireland. He claimed that sub-culture is particularly relevant when a minority (1) believes it can increase its political power by gaining in size and (2) when the group believes its chances for economic mobility are disadvantaged when compared to the remaining population. His findings support these expectations and underline the strong influence of religion on fertility outcomes (Kennedy, 1973). Recent studies applying the sub-culture hypothesis are scarce. Akonor and Biney (2021) applied the socialization, sub-culture and minority status hypotheses within the context of minority fertility in Ghana. They found that differences in fertility desires remained when ethnicity, socio-demographic

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factors and socio-economic status were controlled for, which was interpreted as support for the relevance of cultural characteristics.

3.3.3

Minority Status Hypothesis

The minority status hypothesis claims that it is not the socio-demographic or cultural differences which account for the fertility differentials between minority and majority, but other minority specific characteristics (Goldscheider & Uhlenberg, 1969). This theoretical explanation is often intermingled with the subculture hypothesis as it is often assumed that home country pro-natalist norms underlie the independent effect of the minority status on fertility. As Lopez and Sabagh (1978) note though, they should be treated as two separate explanatory approaches. Early specifications of the minority status hypothesis leave open what exactly explains the independent effect of the minority status. Blake and Davis (1956) as well as John (1982), point towards the age at first birth as the explanatory mechanism underlying the relationship between being in a minority position and fertility behavior. When comparing the number of children and age at childbearing among white and black US Americans, they find that the early motherhood of black women explains their higher number of children. Nevertheless, the authors have to conclude that there remains an independent effect of minority status on number of children. Several scholars have interpreted the independent effect of the minority status to represent the insecurities and discrimination associated with being in a minority position (Cooney et al., 1981; Goldscheider & Uhlenberg, 1969; Ritchey, 1975). This perspective interprets differences in reproduction between minority and majority to be caused by discrimination against minorities and has been empirically supported by Cooney et al. (1981) among Puerto Rican immigrants in the US. Having more children might be a strategy that provides security in times of discrimination and insecurity. Some scholars have argued that the minority status effect will be particularly pronounced among those individuals who are well integrated and highly educated (Johnson, 1979; Milewski, 2010). Minority individuals with good education often have to invest more effort into achieving a comparable degree of economic mobility and therefore sacrifice childbearing for the sake of social mobility (Duncan & Duncan, 1968). In that sense, the minority status effect might depend on education and social standing, an argument which is empirically supported by the Chinese minority in the US (Woldemicael & Beaujot, 2012). This finding implies a form of discrimination, as minorities cannot combine family planning and socio-economic mobility to the same extent as majority members can (Bean & Tienda, 1990; Goldscheider & Uhlenberg, 1969). The minority status hypothesis is thus closely connected to

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discrimination and social characteristics of the minority. Chabé-Ferret and Ghidi (2013) constructed an economic model for testing the minority status hypothesis. Their results show that minority status can be regarded as difference in the relationship between minority and majority education and fertility. Thus, while one would assume that those with higher education decrease fertility for the sake of social mobility, the authors see different relationships of education and fertility for minority and majority members. They explain these findings by uncertainties, which are more pronounced among minority group members. Simultaneously, their models reveal that this uncertainty varies in dependence of the minority group size. One last idea relates to the fact that the minority status hypothesis has been developed and tested in the USA. In Europe however, minorities often have a different historical background and other positions in society than those minorities residing in the USA (see discussion in chapter 1 and chapter 2). As many minority groups in Europe are recognized as minority group, they are able to keep their cultural specificities, have their own schools, preserve their language and often live within their ethnic communities. Accordingly, this legal status might be an explanation for the minority status effect in Europe.

3.3.4

Shortcomings and Potentials

Table 3.1 provides an overview of selected publications on migrant and minority fertility. When comparing the review of minority fertility theories with those approaches existing to account for the reproduction of migrants, it stands out that minority fertility hypotheses are less intensively developed and most findings date back to the 1970s and 80s. One clear necessity is thus to provide more recent empirical tests on minority fertility hypotheses. Furthermore, most findings on minority fertility are taken from a US American background, with the few empirical tests that focus on Europe being solely based on religious minorities or Roma. Some studies have applied minority fertility theories but have done so in the context of what is understood as a migrant group within this dissertation (Dubuc, 2017; Wilson, 2019). However, as pointed out in the previous chapter, migrant and minority groups in Europe and America differ substantially in their history, offspring, definition and characteristics. It is therefore important to also test the same set of theories among ethnic minorities in Europe. Despite these obvious shortcomings within the sphere of minority fertility, some more thoughts on the theoretical framework are worth pointing out. Sly (1970) was one of the first scholars to point at a fundamental fault in the argument of the social characteristics approach. It has usually been claimed that

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once socio-economic standings between majority and minority members become alike, their fertility will adjust accordingly. Yet, what has been overlooked is that cultural and socio-economic characteristics can vary and develop independently of each other (Sly, 1970; Van den Berghe, 1967). As has been argued in section 2.4, the four integration dimensions influence and impact upon each other. Nevertheless, they progress with different pace. Applied to minorities, this implies that even if minority and majority depict the same socio-economic status, their fertility does not have to be similar. Rather, integration can occur with minorities becoming alike the majority on selected assimilation dimensions only, while other aspects of the assimilation process continue to resemble the society within the country of origin. A similar criticism can be applied to the sub-culture hypothesis. Here, we find the argument that cultural similarity automatically results in comparable fertility levels. Yet—as just pointed out—processes of assimilation do not inevitably proceed congruently and host country adaptation can exist alongside home country maintenance. In both cases, the hypotheses would gain from being embedded in a more allembracing framework, which places cultural and socio-economic developments (1) within a group context by taking the specific situation of the minority group under study into consideration, (2) by linking cultural and socio-economic variables with each other and by acknowledging that these variables might interact and depend upon each other, but that assimilation on one dimension does not necessarily imply complete absorption on other dimensions of assimilation, and finally (3) by extending the scope of variables under consideration. For the latter purpose, minority fertility theories can gain from insights into migrant fertility. As has been argued in the introduction, migrants are new minorities and both groups share common experiences and often face similar obstacles and living circumstances. Minorities might not only be characterized by education, occupation, religion and their minority status. Instead, their values, norms, family background, community characteristics, rights and political engagement can be assessed as well and can be included into the social characteristics and sub-culture hypothesis. It is thereby expected that this extension will provide a better insight into the living situation of minorities and will allow for more careful conclusions regarding the relationship between fertility and assimilation. At the same time, this approach takes up the neglect of minority fertility theories to link cultural and structural explanations (Neyer & Andersson, 2004). So far, the hypotheses have mostly been considered in isolation from each other and most scholars have viewed them as competing rather than as complementary approaches. Regarding the minority status hypothesis, some work needs to be done as well. As outlined above, the hypothesis is not very specific about the indirect mechanisms at work. In its current form, basically any difference observed between

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minority and majority fertility can be attributed to the theory. Several scholars have suggested what might explain the independent effect of the minority status on fertility, yet empirical evidence is scarce and incomplete. The theory hence needs to be developed theoretically and empirically. From a theoretical point of view, the distinction between minority and majority only makes sense for individuals who are not yet assimilated into their country of residence. After all, assimilation as an outcome of the process of assimilating implies that differences between majority and minority have disappeared meaning a minority can no longer be recognized as such. Fertility differentials are thus only present if individuals are not assimilated (yet). Similarly, if one distinguishes minority and majority within a society, then inequalities or boundaries exist (Smith, 1991). In turn, this means that the minority status hypothesis can only apply to those who pursue an integrationist or separatist strategy. For those individuals, previous literature has suggested some explanations for the independent effect of the minority status (see also Section 3.3.3). Most scholars agree that the concept of a minority as such infers some degree of discrimination as a minority is not necessarily defined numerically, but on the basis of inferiority (Medda-Windischer, 2007; Meyers, 1984; Milewski, 2010; Ramaga, 1992; Wirth, 1945). Social-psychological theories on group and identity formation demonstrate that perceived discrimination makes group boundaries visible. Each individual has a self-identity and an identity prescribed by others. These identities do not necessarily match, which can cause insecurities and stress as highlighted also by the boundary making approach introduced in section 2.1 Often, however, one is not even aware of a mismatch between self- and other-identity. Here, perceived discrimination plays an important role as it often serves as marker of a mismatch between what is “in here” and “out there” (Rumbaut, 1994; Terry et al., 1999). This dissonance in identities can result in stress, insecurities and lower self-esteem (Terry et al., 1999). Consequently, experiences of discrimination can likely influence not-assimilated minorities and in turn also fertility behavior. This argumentation is extended by the findings of Chabé-Ferret and Ghidi (2013), who pinpoint uncertainty regarding the future and socio-economic standing as a predictor for fertility differentials between minority and majority. Their empirical study demonstrated that depending on group size, certain minority groups do perceive more stress and uncertainty which in turn influences their fertility decision making. Empirically, it needs to be tested whether being a minority per se relates to fertility, and if this relationship remains once socio-economic and cultural characteristics are controlled for. This first step takes into account that all three minority fertility approaches might be at work at the same time and tests the minority status hypothesis in the context of cultural and socio-economic assimilation. After

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all, control variables have been absent in most of the studies testing the minority status hypothesis empirically (Roberts & Lee, 1974). If an independent effect of minority status persists, one could include perceptions of discrimination as additional explanatory variable. However, available data often do not capture this dimension. A second idea to broaden the minority status hypothesis is to pay special attention to the group context, as suggested by many migration scholars (Lucassen et al., 2006; Thomsen & Crul, 2007; as well as chapter 2 for a discussion of the role of context in migrant integration). For migrant integration, the context of reception as well as the ethnic group context is important in framing options for incorporation and in creating opportunities. Policies in the country of residence in many places define what migrants and minorities can achieve and what they cannot. Similarly, the attitude of the majority is important in determining assimilation options. Simultaneously, the context often prescribes how minorities are perceived and composed (Roberts & Lee, 1974; Wimmer, 2008). Wimmer (2008) outlines how boundaries between ethnic groups are shaped by the institutional setting. Especially societies with high levels of inequality are likely to highlight group boundaries between the in-group and the out-group (Wimmer, 2008). Therefore, the context in which a minority resides can significantly influence the living conditions and chances of assimilation. In equal terms, the characteristics of the ethnic group one belongs to can potentially influence fertility outcomes. The degree to which one is embedded in this group, the extent to which an ethnic group has its own institutions, cherishes its own language, has its own schools and is territorially segregated can and will influence to what extent a minority wants to assimilate or to what extent it rather chooses to integrate into the local ethnic context instead of into the majority society (Marcum, 1980). After all, it should be remembered that assimilation is not the only possible path of how minority and majority can interact. Separation might be equally likely, either because the minority specifically opts for staying within their own cultural group, or because the majority favors a segregationist arrangement. In this case, it might not so much be forces of discrimination that create inequalities, but rather the inter-group context and the options available for a minority group. If ethnic integration is chosen, sub-cultural norms often prevail and promote high fertility (Lopez & Sabagh, 1978). However, little is known about the fertility of minority groups if segregation is not voluntarily chosen but enforced upon minorities by the majority. Finally, a last comment on how to build upon existing research is to acknowledge the dependencies of theoretical approaches. As Johnson (1979) has pointed out rightly, the manifold explanations towards minority fertility behavior interact and build upon each other. Therefore, the field would gain by working out to what

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extent certain relationships exist only among specific sub-groups of society or depend upon the presence of other characteristics. This implies that empirically, one should not neglect the possibility of indirect and interaction effects.

3.3.5

Minority Fertility and Assimilation

The shortcomings outlined above can be met by (1) merging the three minority fertility hypotheses into an overarching theory of minority fertility, and (2) by embedding minority fertility theory into a broader framework of migrant fertility, and ultimately the limited assimilation model presented in section 2.8. Looking at the three hypotheses presented, it is striking that they can complement each other to form a more comprehensive theoretical account of minority fertility. The social characteristics hypothesis stresses the diverging composition of minority and majority members in terms of structural, social and cultural characteristics. Once these differences are controlled for, fertility differentials are expected to disappear. It follows that once differences in composition diminish fertility differentials will equally vanish. The focus hence lies on the adjustment towards the host country socio-structurally and culturally, if one assumes that assimilation is mainly a process towards the standards and structures of the host society and not so much a process of mutual adjustment. The sub-culture hypothesis on the other hand puts the emphasis on the relevance of home country norms and values. Once one takes up the idea of assimilation theory that assimilation consists of both the home and host country dimension, the sub-culture theory can amend insights of the social characteristics approach by stressing the importance of home country belonging. Taken together, one thus obtains a similar framework as the one developed within the limited assimilation model presented in chapter 2. When considering the limited assimilation model presented in Figure 2.1, one can ascertain that the sub-culture and social characteristics hypothesis cover most of the dimensions included within this model. The sub-culture hypothesis addresses the cultural belonging towards the home society and acknowledges the gradual adjustment towards host country norms. Yet, as argued above, it should not be evaluated in isolation, but should ideally be accompanied by the social characteristics hypothesis. The social characteristics hypothesis then covers the structural, social and cultural dimension of assimilation. It is thereby mainly concerned with the assimilation towards the host society but is implicitly aware of the importance of home country characteristics. Finally, the minority status hypothesis is not that obviously represented within the model presented in Figure 2.1. It addresses the role of discrimination and feelings of inferiority, which are considered by Berry when it comes to processes of separation. If one perceives to

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be excluded by the majority and makes negative experiences while attempting to adjust one might draw back from the majority and rather orient towards the own ethnic group. Under this separation scenario, minorities would depict similar fertility levels as those observed within their country of origin. Another reading of the minority status hypothesis is that the relationship between minority status and fertility differs depending on educational status and group size. This argument comes close to the segmented assimilation theory, which considers assimilation a process which proceeds differently within different strata of society. Following this reasoning, the minority status effect would be visible once assimilation happens along socio-economic lines into different segments of society.

Figure 3.2 Integration of migrant and minority fertility theories into an assimilation framework

Thus, amending the limited assimilation model by feelings of insecurities and perceived discrimination, the model developed in chapter 2 can serve as a sound theoretical basis to integrate migrant and minority fertility theories into one common model that builds upon assimilation theory. As portrayed in Figure 3.2, this framework nicely highlights that all fertility hypotheses can be present at the same time as they address different dimensions and processes of assimilation. Moreover, as subsequent migrant generations are likely to be confronted with some aspects of the minority situation as well, their situation can be considered by testing migrant and minority fertility theories under a common assimilation framework. Table 3.1 furthermore summarizes the literature on the fertility of migrants and minorities. When comparing the empirical analyses, it becomes clear that empirically, the same measures are used to account for the fertility of old and new minorities. Hence, it seems logical from both a theoretical and an empirical point of view to combine all theoretical approaches into one common model.

Minorities in Ireland: – social characteristics – Catholics in Northern hypothesis Ireland versus Catholics – minority status hypothesis in Ireland

Migrants in the USA: – Italians versus Whites versus Non-Whites

Kennedy (1973)

Rosenwaike (1973)

– adaptation hypothesis – socialization hypothesis

– social characteristics hypothesis – minority status hypothesis

Minorities in the USA: – white versus non-white

Sly (1970)

Theoretical background – social characteristics hypothesis – minority status hypothesis – (sub-culture hypothesis)

Group(s) under study

Goldscheider Minorities in the USA: & Uhlenberg – Whites versus Blacks (1969) – Whites versus Japanese versus Chinese – Jews versus Protestants versus Catholics

Authors

size of minority mixed marriages social mobility cultural activities SES

education (husband’s) occupation residence income

– residence – religion

– – – – –

– – – –

– residence – education

Assimilation variables

Table 3.1 Overview of publications on migrant and minority fertility (selection)

– number of children ever born per 1000 ever married women (continued)

– number of children per 100 ever married women – number of children per 100 women aged 20–44 years – crude birth rate – legitimate birth rate

– number of children ever born per 1000 women

– number of children ever born per 1000 married women

Fertility variables

3.3 Minority Fertility 77

Minorities in the USA: – Whites versus Blacks

Minorities in the USA: – Whites versus Blacks

Migrants in the USA: – Mexicans versus non-Hispanic Whites

Johnson (1979)

John (1982)

Stephen & Bean (1992)

– adaptation hypothesis – socialization hypothesis – disruption hypothesis – selectivity hypothesis

education marriage income residence religion work status wife

education (husband’s) occupation residence income

– – – – – –

length of residence education residence income employment experience place of birth

– education – religion

– – – – – –

– – – –

Assimilation variables

(continued)

– current fertility – aggregated fertility – number of children ever born

– age at first birth – pace of subsequent births

– number of children ever born

– children ever born – average number of children under 5 per woman

Fertility variables

3

– social characteristics hypothesis – minority status hypothesis – (sub-culture hypothesis)

– social characteristics hypothesis – minority status hypothesis

Minorities in the USA: – social characteristics – Whites versus hypothesis – minority status Non-White – Hispanics versus Blacks hypothesis versus Whites – minority versus majority

Roberts & Lee (1974)

Theoretical background

Group(s) under study

Authors

Table 3.1 (continued)

78 Explaining Migrant and Minority Fertility

Minorities in the USA: – Hispanic versus Blacks versus Whites

Migrants in Germany: – 5 migrant groups

Forste & Tienda (1996)

Mayer & Riphahn (2000)

Schmid & Migrants in Germany: Kohls (2009) – 5 migrant groups

Group(s) under study

Authors

Table 3.1 (continued)

– socialization hypothesis – selection hypothesis – interrelation hypothesis – disruption hypothesis – adaptation hypothesis

– assimilation theory – disruption hypothesis – selectivity hypothesis

– social characteristics hypothesis – minority status hypothesis – sub-culture hypothesis

Theoretical background

– – – – –

– – – –

– – – – –

citizenship education religion belonging host/home language skills

education language skills religion years since migration

attitudes family background community environment education context of reception

Assimilation variables

(continued)

– completed fertility rate

– crude fertility rate – average completed fertility – number of births

– non-marital childbearing – timing of fertility – completed fertility

Fertility variables

3.3 Minority Fertility 79

– socialization hypothesis – adaptation hypothesis – selectivity hypothesis – disruption hypothesis – social characteristics hypothesis – minority status hypothesis – social characteristics hypothesis – sub-culture hypothesis – minority status hypothesis

Szabó et al. (2020)

Roma Minority in four European countries: – Roma in Hungary, Serbia, Slovakia and Romania

Theoretical background

Group(s) under study

Woldemicael Migrants in Canada: & Beaujot – first versus second (2012) versus subsequent generations

Authors

Table 3.1 (continued)

age at arrival visible minority status place of origin socio-demographic factors sense of belonging

– education – residential segregation

– – – – –

Assimilation variables

– number of children ever born

– average number of children per generation – age-specific fertility rates – current fertility

Fertility variables

80 3 Explaining Migrant and Minority Fertility

3.4 Summary and Research Hypotheses

3.4

81

Summary and Research Hypotheses

To sum up, there are several assumptions that can be drawn from the theoretical review of assimilation and fertility theories. As the arguments presented in chapter 2 underline, European societies are rather closed regarding migration movements and processes of integration. It can therefore be assumed that assimilation is a difficult and lengthy path for European migrant groups. Ethnic groups might become an attractive alternative to integrate into. Alternatively, integration might happen on some dimensions, e.g. functionally on the labor market, but full assimilation seems unlikely to happen at group level. Rather, diverse forms of integration can be thought of as potential assimilation outcomes as highlighted empirically by Schwartz and Zamboanga (2008). It is therefore assumed that: H1a: H1b:

The Turkish migrants in Germany can be classified as integrated. There exist different degrees of integration among the Turkish migrants in Germany.

As far as minority groups are concerned, the main factor aggravating full inclusion is their special legal standing which most groups enjoy within European societies. These rights make it more likely that minorities will only integrate on some dimensions or even separate fully into their ethnic community. Especially this last option seems likely given the official support and structures to preserve their culture and heritage. It is therefore assumed that: H2:

The Turkish minority in Bulgaria can be classified as separated.

When looking at the fertility of both Turkish groups, fertility intentions instead of actual fertility behavior will be focused upon given that the argumentation in section 3.1.2 underlined that intentions better capture the decision-making process underlying the act of finally giving birth to a child and are closer related to norms and values and hence assimilation. Once the focal point lies on fertility as sequence of actions, it becomes easier to understand the complexity of reproductive decision-making within the context of migrant and minority fertility. One clear conclusion drawn from the development of demographic and sociological approaches to fertility, is the importance of context. Fertility is not developed within a vacuum, but rather embedded into a complex set of individual and contextual characteristics. The approach of Miller and Pasta is chosen to embed fertility intentions within this interplay. They suggest that the formation

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of intentions is preceded by the definition of fertility desires, which in turn are derived from latent motivational dispositions. This sequence suggests that H3: H4:

The more pronatalist motivational dispositions, the more positive is the desire for children. The more positive the desire for children, the more likely it is that respondents hold positive fertility intentions.

As previous analyses give little insights into the suitability of Miller’s and Pasta’s framework for different ethnic groups and across different parities, it is assumed that the mechanisms specified in hypotheses three and four apply likewise across groups and independent of parity. The reason why desires do not necessarily translate into corresponding fertility intentions, can be found—according to Miller and Pasta—in the influence of partners and context characteristics on the formation of intentions to act. The focus on intentions thus acknowledges that there are further predictors of intentions than mere desires (Mogran & Bachrach, 2011). Here, assimilation theory as well as fertility theories on migrants and minorities can be included into the fertility model of Miller and Pasta. Interestingly, scholars have so far primarily paid attention to the role of the partner(‘s desires) in the formation of intentions, while being less specific about the role of contextual factors within this process. Here, chapter two as well as sections 3.2 and 3.3 within this chapter provide theoretical amendments. Specifically, within the minority/majority or migrant/native context respectively, ethnicity and the experiences connected to it can be powerful in influencing reproduction. Marriage, working life and working conditions, cultural habits and living circumstances often vary between ethnic groups. These aspects can be detrimental in the decision-making process of whether to have a(n additional) child or not. Theories on ethnicity, assimilation and migrant/minority fertility define these aspects and enable scholars of fertility to integrate these into an overall model of migrant and minority fertility. To account for the fact that ethnicity is often a group marker that does not necessarily correspond with one’s self-conception, the boundary making approach as summarized in section 2.1 will be referred to in order to investigate whether fertility intentions differ across ethnic groups. To account for the fact that ethnic groups are often socially constructed and hence not necessarily meaningful units of analyses (Wilkonson, 2015a), hypothesis five will focus on testing whether the distinction into Turkish minority/Turkish migrant is relevant when it comes to studying fertility intentions. Only when there are differences between minority/migrant and native majority, the last research question aiming at explaining

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83

differences in fertility intentions, is meaningful. Given that the Turkish minority is a recognized minority group within Bulgaria and Turkish migrants the biggest and best studied migrant group in Germany, it seems likely that these boundaries are visible and not only socially constructed, but also internalized and accepted by members of both groups. It is accordingly likely that a distinction into these groups is meaningful and that groups differ in their fertility intentions given their different socializing and their (partial) upholding of cultural values from their Turkish background. This argument also applies to an ethnic group comparison that contrasts the fertility intentions of Turkish migrant and Turkish minority respondents. Here, a common cultural base might unite the groups, yet the influence of the host society as well as the special standing of minorities might produce differences in fertility, too. Therefore, a fifth hypothesis states that H5a: H5b: H5c:

Fertility intentions differ between the Turkish minority and the Bulgarian majority. Fertility intentions differ between the Turkish migrants and the German natives. Fertility intentions differ between the Turkish migrants and the Turkish minority.

These hypotheses also mirror the reasoning of the limited assimilation model developed within chapter two as no differences in fertility intentions are expected if minority/migrants and majority are alike each other on all assimilation dimensions. Once fertility intentions differ across groups, the assumption that full assimilation can hardly be reached by migrants and minorities is underpinned. Given that fertility is considered one aspect of cultural assimilation (Mussino et al., 2021), complete assimilation would imply equal fertility intentions. Hypothesis one already predicted integration or separation as more likely assimilation statuses of migrant and minority groups. Accordingly, it seems likely that values and preferences related to reproduction do not yet equal the native majority. At the same time, hypotheses five captures a first argument of the minority status hypothesis. To test this hypothesis, one has to establish a relationship between ethnicity and fertility first, to then investigate whether this association is upheld once socio-economic characteristics are controlled for. If there are differences in fertility intentions that cluster along this ethnic group distinction, then assimilation and fertility theories suggest that these are likely to be influenced by a group’s incorporation into the host and home society. Here, one must distinguish a group’s standing with regard to structural, cultural, social

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and identificational assimilation and preferably acknowledge that each individual can adhere structurally, culturally, socially and in terms of identity towards its home and host society likewise. Separated individuals are assimilated into their home country or ethnic community within their country of residence. They have preserved their own cultural norms and values, identify with their country of origin and mostly uphold contacts to their own ethnic group. Structurally, they often work in an ethnic niche economy. At the same time, they have few points of contact to the structures and culture of their country of residence. Consequently, it seems likely that these separated individuals have kept the fertility norms of their home country, which is why it is predicted that H6a: H6b:

The more migrants and minorities are absorbed into their home country, the more difference in fertility intentions from the majority can be observed. The more migrants and minorities are absorbed into their home country, the less difference in fertility intentions from each other can be observed.

Integration suggests that migrants and minorities are partly incorporated into their host society while at the same time also stick to their country of origin with regard to some values or behavioral patterns. As these individuals are partly on their way to adjust to the host society, their fertility intentions might lie somewhere between those of the native majority in their home and host society. Consequently, it is hypothesized that H7a: H7b:

The more migrants and minority are absorbed into their host country, the less difference in fertility intentions from the majority can be observed. The more migrants and minority are absorbed into their host country, the more difference in fertility intentions from each other can be observed.

It has to be acknowledged, however, that this hypothesis might be too general to capture influences on fertility intentions. After all, individuals who would be classified as integrated might differ fundamentally with regard to their dimensions of integration. There might be respondents who are integrated structurally into their host society but stick to their home country norms on every other regard. Among those individuals, cultural adjustment towards the host society might not have started yet and consequently, fertility ideals and norms might be closer to standards of the home society. On the other hand, there might be individuals who are socially and culturally integrated into their country of residence, but who still identify with their country of origin and work within an ethnic niche. These

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85

individuals could have taken over the fertility ideals of their host society, as they are culturally similar to their native majority (Coleman, 1994; Lesthaege, 1983). Both the sub-culture hypothesis as well as the socialization theory suggest that cultural values are the key for fertility behavior. Cultural values are often inert and only change slightly. As fertility intentions are part of the cultural norms and habits of individuals, it seems likely that cultural adaptation is the key towards adjusting fertility to the majority within the country of residence. Hence, integrated individuals, who have taken over the cultural values of their host society (independent of how they are integrated on the other three dimensions), are more likely to depict fertility intentions close to the native majority. Hypothesis 7 can hence be refined to suggest that H8:

Those migrant and minority individuals, who are culturally integrated into their host society, are more likely to depict similar fertility intentions as the native majority in their host society than individuals, who differ culturally from the host society.

Summing up, hypotheses six to eight suggest that assimilation can explain why fertility intentions differ between majority and minority/migrant. A last hypothesis thus clarifies that H9:

The assimilation status of migrants and minorities mediates the relationship between ethnicity and fertility intentions.

Having derived hypotheses from the theoretical reflection, Figure 3.3 presents the hypotheses guiding the empirical analyses graphically. The dissertation proceeds by introducing the data used as well as the procedure to be followed to adequately test the presented hypotheses.

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Figure 3.3 Integrated model on migrant and minority fertility

4

Data Analytic Strategy

This chapter aims at providing a more detailed account of the data used for the empirical part of this dissertation. Therefore, a first step will be to describe the data sets used for analyses as well as to report on the methods to be used. Then, the variables that will form the basis of descriptive and inductive analyses will be introduced.

4.1

Data

The data used throughout this dissertation are taken from the Generations & Gender Survey (GGS), a representative large-scale database of individuals that focuses on relations between generations and partnerships. It is a longitudinal study that has taken place in 20 countries, where respondents aged 18–79 years were interviewed about their fertility. A new wave of data collection has started in 2020 but is not yet finished (GGS, 2023). The GGS covers topics such as household composition, demographic information, contraceptive use, as well as ethnic background and partnership information. It is thus suitable for this dissertation as it enables both the identification of minority respondents and provides the relevant variables to answer the overall research question (GGS, 2022). Moreover, it is comparable across all 20 participating countries thereby enabling comparability of results and analyses.

Supplementary Information The online version contains supplementary material available at https://doi.org/10.1007/978-3-658-43099-3_4.

© The Author(s), under exclusive license to Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2023 B. Brünig, The Fertility of Migrants and Minorities in Europe, https://doi.org/10.1007/978-3-658-43099-3_4

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Data Analytic Strategy

For the upcoming analyses, three cross-sectional datasets from the GGS will be used: the first wave from Bulgaria collected in 2004, the first wave from Germany from 2005 and the Turkish subsample from Germany as of 2006. These data will be looked upon in more detail within the next sections. For a comparison of minority and migrant respondents, a separate data file is constructed by merging the GGS data for Turkish minority and migrant respondents. Although the data can be criticized to be too old for meaningful contributions to scientific discourse, they are deemed suitable as (1) they enable testing of the research hypotheses better than any other data source and (2) since the purpose of this dissertation lies in establishing a new model and new testing methods. For the latter, the recency of data is of secondary importance.

4.1.1

GGS in Bulgaria

In Bulgaria, the GGS data were collected using a two-stage sampling procedure. Based on the Bulgarian Census from 2001, over 800 statistical units were identified, out of which eleven respondents per unit were randomly selected for participation in the GGS (GGS, 2015). It was thereby intended to cover the three major ethnic groups of Bulgaria with one-third each. The respondents were first contacted between November 2004 and January 2005 during the first wave of the Bulgarian GGS. During this interview period, no incentive for participation was given (Fokkema et al., 2016). Afterwards, participants were again interviewed in 2007 for the follow-up wave. While there were 12,858 participants in the first wave, 73 % of these took part in the second wave too. The questionnaire was available in Bulgarian, only, which resulted in some language difficulties among minority respondents (GGS, 2015). Although it was intended to also use the second GGS wave, the sample size of Turkish minority respondents became too small, which is why this dissertation will build upon the first wave from 2004 only. Turkish minority respondents are identified by their ethnic belonging. Through this self-identification n = 1,125 respondents for the minority sample could be identified. In similar terms, the Bulgarian majority was identified by selecting those who indicated to identify as Bulgarian (n = 11,733). Additionally, it was looked at how citizenship and identification match among the Bulgarian sample. Here, 14 participants indicated to identify as Bulgarian, but to have a non-Bulgarian citizenship. These participants were excluded from the upcoming analyses leaving n = 11,719 Bulgarian respondents.

4.1 Data

4.1.2

89

GGS in Germany

The German GGS includes a German sample as well as a separate subsample of Turkish migrants in Germany. The first wave of the German data sets was collected between February and May 2005, and between May and November 2006 for the Turkish data respectively (Ette et al., 2007; Fokkema et al., 2016). A follow-up wave was conducted in 2008/09 and for the Turkish sample in 2009/2010. Within the first wave, 10,017 Germans between age 18 and 79 were interviewed (Ruckdeschel et al., 2009) as well as 4,045 people with Turkish citizenship (Ette et al., 2007). An incentive of a 10e lottery voucher was used to motivate people to participate (Fokkema et al., 2016). Within the second wave, 3,227 respondents within the German GGS (Sauer et al., 2012) and 998 within the Turkish subsample were interviewed (Naderi et al., 2012). Within both data sets, a similar sampling procedure as for the Bulgarian data was used. Regions within Germany were identified and based on their size a proportional number of sampling points was pinpointed within each region. Out of these sampling points, 30 people per sampling point were selected for an interview through random sampling with help of data from residents’ registration offices (Ruckdeschel et al., 2009). Turkish participants were identified through data of the kommunale Ausländerbehörde, which provided regional information on Turkish citizens by gender. With help of these data, the sample size needed within each community could be estimated and participants could be randomly selected through registration offices (Ette et al., 2007). Among Turkish respondents, a translation of the questionnaire was available upon request. The interview was conducted personally using computer-assisted interviewing (CAPI) (Ette et al., 2007). The analyses of this dissertation will build upon the first GGS wave due to its higher sample size and for the sake of comparability with the Bulgarian data. Germans and Turks could be identified based on their own and parental place of birth. Those, whose parents and themselves are born in Germany (n = 7,381), are classified as native Germans. Moreover, a small proportion of participants (n = 80) who indicated to have German-born parents but are themselves not born in Germany were also assigned the group of native Germans. Out of those whose parents are not German-born (n = 5,489), n = 3,073 indicated that both they themselves as well as their parents were born in Turkey. These are classified as Turkish respondents within this dissertation. Additionally, n = 1,037 participants indicated that their parents are born in Turkey, but they themselves are born in Germany. These second-generation migrants are included into the group of Turkish respondents, too. Thus, for the German GGS data there are n = 4,110

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Turkish and n = 7,461 German respondents considered. As one’s own ethnic belonging was not asked to all participants in the German GGS, this variable could not be used for identifying ethnic groups.

4.2

Sample Construction

Before being able to conduct meaningful analyses, the sample has to be selected based on several criteria. A first concern regards the age of participants. Leridon and Slama (2008) showed that by age 45 more than half of all women are no longer able to conceive. Moreover, the risk for complications increases as women age. For males, though, both social and biological conditions differ. They often partner with younger women and their fertility starts declining later and at a slower pace (Eisenberg & Meldrum, 2017; Kovac et al., 2013). It can thus be assumed that especially females have completed their fertility at a certain age (Berrington, 2004; Dommermuth et al., 2015; Kapitány & Spéder, 2012; Spéder & Kapitány,2013; Pailhé & Régnier-Loilier, 2017). Accordingly, it is incremental to limit analyses on fertility intentions to participants who are of childbearing age. For Germany, recent statistics show that 66 % of all fathers were between 29 and 39 years old, while 6 % were aged 44 or older. For females, there are similarly 65 % who became a mother between age 29 and 39. However, only 0.30 % gave birth to a child at age 44 or older (Statistisches Bundesamt, 2020). Therefore, it was decided to include males between age 18 to 50 and for females all women between 18 and 45 years. Looking at Bulgaria, no similar recent statistics can be found. However, Koytcheva and Philipov (2008) demonstrate that most Bulgarian women do not give birth after the age of 42. For males, no data could be found. Accordingly, it was decided to apply equal age ranges for Bulgaria as for Germany to ensure comparability of the data for both countries. Though the mean age of giving birth for females tends to be lower in Bulgaria (Eurostat, 2021), several scholars have applied similar age restrictions for Bulgaria (see e.g., Testa et al., 2016 who have included Bulgarian females up to age 49 to study fertility intentions) and Germany (see e.g., Spath, 2018 who has included German males and females up to age 44) within existing empirical research. Accordingly, for Bulgaria males were included if they were aged 18 to 50 and females were considered if aged 18 to 45.

4.2 Sample Construction

91

Table 4.1 Sample for Bulgaria and Germany Bulgaria Sampling criteria

cases excluded

Germany remaining cases

cases excluded

remaining cases

Citizenship & Identification – Match identification & citizenship

14

12,844

– Missing data identification & citizenship

84

12,760

941

11,819

1,867

11,571

43

11,776

43

11,528

1,979

9,797

2,053

9,475

– females aged 18–45 years 2,324

7,473

2,664

6,811

– only Bulgarian/German & Turkish participants Age of participants – missing data age – males aged 18–50 years Fertility – self or partner definitely not possible

184

7,289

721

6,090

– self or partner currently pregnant

115

7,174

178

5,912

Given the focus on childbearing, a second filter to be applied concerns the ability to have children as well as the partner’s ability to conceive. Within the GGS data, there are two questions assessing whether it is physically possible for oneself and for the partner to have children. Across both countries, those who themselves or their partner indicate to be definitely not able to have children were excluded from the analyses. A further reason to exclude participants concerns current pregnancies. Intentions to have children can be estimated by those currently pregnant, but the analysis of fertility might be disturbed and influenced by their current experience. Preis et al. (2020) demonstrated that fertility intentions pre- and post-pregnancy are influenced especially by negative birth experiences. Negative experiences more often lead to an increase in estimated time between births. Therefore, it was decided to exclude currently pregnant women or partners from the analyses. Table 4.1 demonstrates the reduction in sample size for Bulgaria and Germany

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through the application of these filters. The final sample size thus consists of n = 7,174 for Bulgaria and n = 5,912 for Germany.

4.3

Methods

Following the assimilation model presented in chapter 2, this section will elaborate on the methods employed for testing the theoretical expectations. For this purpose, an exploratory analysis will be used first to construct clusters instead of purely using ethnicity as predictor. As will be shown in section 4.3.1 cluster analysis combines several socio-demographic measures to discover latent homogeneous groups within society. Then, descriptive analyses will describe the living situation of the constructed clusters more closely to explore in more detail the integration status of the groups under focus as well as to verify the heterogeneity between clusters. Following, inferential analyses using structural equation modelling will be applied to test the theoretical model. Throughout all analyses, minority and majority as well as migrant and majority respectively will be compared. Moreover, additional analyses will also focus on a comparison of minority and migrant groups with regard to assimilation and fertility. For a detailed overview, the proceeding will be reviewed step by step.

4.3.1

Cluster Analyses

To acknowledge that the term minority might be a concept too widely used within social science, pre-analyses are conducted to first identify whether all participants of this study naturally group into clusters equaling the minority-majority respectively migrant-native majority distinction. A cluster analysis explores whether a sample can be distributed into several clusters that are similar within but distinct from other clusters. This method can be used to discuss whether ethnicity is a meaningful concept of analysis or whether observed ethnic differences can rather be ascribed towards further distinguishing factors such as gender, socio-economic background, education, place of residence or even age as an intersectionality approach would suggest. After all, it has been argued in section 2.1.1 that ethnicity is socially constructed and should not be used self-evidently as an explanatory variable. To test for this eventuality, a cluster analysis among the GGS data is conducted aiming at distinguishing latent groups within society. A cluster analysis is an exploratory method of analysis that groups large data sets into groups that are similar within groups but heterogeneous between the

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clusters identified (Wiedenbeck & Züll, 2010). Thereby, it aims at seeing “if some natural groups or classes of individuals exist” (Härdle & Hlávka, 2007, p. 205). Statistically, clusters are built based on certain given criteria which are combined into groups that are as homogeneous as possible. Individuals within a cluster are hence “a group of individuals or objects that converge around [a] certain point, and are thus closely related in their position” (Cleff, 2014, p. 163). When conducting cluster analyses, pairs of variable combinations are compared to each other. Step by step, different combinations of the variables entered into the cluster analysis are compared and distances are evaluated. With each evaluation step, the cluster analysis merges outcomes to compare whether similarity within increases while distance between the aggregates becomes more pronounced (Cleff, 2014; Härdle & Hlávka, 2007; Norusis, 2011; Wiedenbeck & Züll, 2010). As cluster construction is not part of inferential statistics, there are few criteria to compare different cluster solutions and to evaluate the final clustering. Within the following analyses, a two-step cluster analysis in SPSS version 28 is performed. Two-step clustering was developed by Chiu, Fang, Chen, Wang and Jeris in 2001 and has hardly been used within the social sciences (Bacher et al., 2004). This method is chosen above other clustering methods as it can work with both continuous and categorical variables and bases cluster decisions on inferential statistics. In a first step, SPSS uses feature trees to develop pre-clusters. Then, hierarchical clustering is used to come to terms with the final number of clusters. For mixed models—entailing both continuous and categorical predictors—loglikelihood estimation is used to measure distances between clusters. The final decision on the number of clusters is based on Schwarz’s Bayesian information criterion (BIC). Different clustering solutions are compared based on their BIC values. Lower values thereby represent a better model fit. Compared to other clustering methods, two-step clustering offers the advantage that it uses statistical methods to decide on the number of clusters, while other methods leave this decision to the subjectivity of the researcher (Norusis, 2011). Furthermore, the relevance of the clustering variables is evaluated, too. For this purpose, χ2 tests are conducted for categorical variables and t-tests for continuous variables. If the absolute value of these statistics is higher than the critical value, then the corresponding variable is considered relevant for clustering (Norusis, 2011). One point of critique is that it must be noted that Bacher and colleagues (2004) used a simulation to test the two-step clustering in comparison to older clustering techniques. Though they support the conclusion that this method is novel and considers other methods, they also point towards the potential bias in

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results when including both continuous and categorical variables in the analyses. Due to the automatic standardization of continuous variables in the two-step procedure, differences in categorical outcomes are weighted three times higher than differences in continuous variables. This may cause a potential overestimation of the cluster solution along the distinctive categorical groups. Secondly, a further weakness of the method concerns potential biases in case of overlapping clusters, which are often found within social sciences. Besides those limitations, it is important for its practical application to note that all variables used to form clusters, cannot be used to explain differences between these clusters. Hence, when conducting path analyses later on to establish whether fertility intentions differ between the clusters and to investigate which factors can account for these differences, one has to exclude variables used for the construction of the clusters under investigation. This is plausible statistically given that regression techniques require prior check of multicollinearity and independence. As the clusters are constructed out of a certain set of predictors, these assumptions are likely to be violated when both predictors and clusters are included into a path analysis.

4.3.2

Descriptive Analyses

Depending on the results of the cluster analyses, additional information on the cluster groups is provided using SPSS version 28. These descriptive analyses aim at coming to terms with the assimilation status of all groups identified while at the same time providing certainty about the cluster distinction. Having seen that cluster results can be biased it is advisable to reassure that the solution identified in the previous clustering captures heterogeneity between the groups identified. Therefore, further description of the groups will warrant heterogeneity between groups and homogeneity within them. While clustering as such might already provide a hint towards the assimilation status of the groups under study—in that a clustering along ethnic lines implies that assimilation as end state has not been reached—further descriptive analyses are needed to understand the composition of the resulting clusters. This is even more relevant, given that cluster analysis is an exploratory analysis tool that is best underlined by additional data analyses (Cleff, 2014). The descriptive analyses will be divided into demographics, structural, cultural and social-identificational data. Along with percentages and distributions, T-Tests or Analyses of Variance (ANOVA) are applied when comparing two or more groups regarding a continuous measure. Here, Cohen’s d or Eta-squared are

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applied as effect size measures. For Eta-squared values starting from .01 signify a small, values from .06 a medium and values from .14 or above a large effect. As for Cohen’s d, 0.20 pinpoints a small, 0.50 a medium and values above 0.80 a large effect (Cohen, 1988). Where the data sets used provide too little information, additional literature is consulted to round up the conclusions and critically reflect on group differences or clusters respectively. At the end of the descriptive data and the clustering section, it is expected to be able to draw conclusions regarding the first two hypotheses.

4.3.3

Structural Equation Modelling

If one looks at multi-equation models, regression analyses are only able to test a certain range of relationships. Complex models that include inverse relationships or intertwined hypotheses could for a long time not be assessed. Therefore, Wright developed path models within the 1920s/30s that could account for an intertwined net of relationships. These models were made accessible for social sciences during the 1960s (Kaplan, 2009). Early approaches to path modelling assumed that relationships exist between manifest variables, which, however, did not meet the reality within social sciences. With advances in computer technology, path models have been complemented by factor analyses and hence the possibility to integrate latent and manifest variables within one structural equation model (SEM) (Arzheimer, 2016). Within these models, a variable can both be predictor and be predicted at the same time, which goes beyond the possibilities of regression analyses as SEM estimates several regression equations within one model. Within structural equation models one no longer differentiates independent and dependent variables but distinguishes between endogenous and exogeneous variables. Endogeneous variables can be considered the output of the model and while exogeneous variables serve as predictors (Bowen & Guo, 2012). Structural equation models estimate several single regression equations and combine these estimates within matrices to summarize the set of equations. These matrices are then transferred into a general path model that includes (1) an intercept term (alpha), (2) a structural coefficient from endogenous to endogeneous variables (beta), (3) a structural coefficient from exogeneous to endogenous variables (gamma) and (4) an error term (zeta) (Müller, 1996). Statistically, structural equation models estimate covariance structures which build on parameters that the estimation procedure chooses based on the model assumptions. These parameters are replaced within each iteration until the best fitting solution to the empirical data is found (Arzheimer, 2016). By estimating

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relationships between variables, it is also possible to assess the amount of variability explained by the solution. In turn, this implies that variance not accounted for can also be quantified. This unexplained variance is accounted for in SEMs by including the error term zeta. This inclusion of an error term makes latent variable analyses more realistic than scale construction, which is often used within regression analyses (Bowen & Guo, 2012). In structural equation models, the solution with the smallest error compared to other possible solutions provides the best fit to the data (Arzheimer, 2016). Several estimators can be chosen from within the process of model estimation. Maximum-Likelihood procedures are in most cases the best option as they present estimates that have the highest probability of having produced the sample under investigation. However, as it assumes multivariate normality, it is not applicable to all kinds of data. Other estimation options include weighed least squares or asymptotically distribution free estimation, which do not presume a certain distribution of the data. However, alternatives to ML require bigger sample sizes and are sensitive towards misspecifications of the model (Arzheimer, 2016). There is no agreed upon sample size that is required for structural equation modelling. Depending on the program used for analysis, 150 to 2000 cases are needed for meaningful analyses (Urban & Mayerl, 2003). Instead of using one formular to calculate the sample size needed, researchers tend to advise to rely on a number of considerations when deciding for a sample size (Muthén & Muthén, 2002). Generally speaking, the number of cases needed for reliable and robust analyses depends on (1) the number of items used for latent variables, (2) the distribution of the data (e.g., skewness and kurtosis) and the (3) estimation procedure chosen. While ML estimation sometimes requires only a few hundred cases, the biggest sample size is needed for weighted least square estimation. Depending on the kurtosis of the data, up to 250 cases per variable in the model is advised (Urban & Mayerl, 2003). Many scientists assume that structural equation models do prove causality, which is why they are also known as causal path models. However, as Arzheimer (2016) points out, structural equation models must meet the same criteria to presume causality as other statistical models. It is thus not the model per se, but rather the mode of data collection that determines the extent to which causal conclusions can be derived. For causality, three conditions have to be met: (1) cause needs to precede the outcome, (2) the influence of third variables has to be excluded, (3) there needs to be a relationship between cause and effect. A structural equation model helps in modelling potential causal relationships but cannot make up for the pre-conditions of data collection to ensure the fulfilment of these criteria (Arzheimer, 2016).

4.3 Methods

4.3.4

97

Generalized Structural Equation Modelling

To combine the estimation of categorical variables with structural equation models, recent developments have integrated generalized linear models into structural equation modelling. Generalized structural equation models (GSEM) can include continuous or categorical data and do not only build on linear regression models, but can estimate ordinal, binary or multinomial outcomes, too, using the logit or probit estimation. The estimation thereby builds upon maximum likelihood estimation and proceeds like the estimation outlined in section 4.3.3 (StataCorp., 2019). In comparison to SEM though, GSEM provides the advantage that categorial endogenous as well as exogeneous variables can be considered using generalized linear response functions. It is thereby assumed that categorical outcomes can be estimated via a link function, which depends on the data. For binary data for example, a Bernoulli function can be chosen (StataCorp., 2019). A drawback, however, is that practically GSEM presents less information than normal SEM analyses. No standardized estimates can be calculated, missing values cannot be considered and hardly any model fit criteria are estimated. Instead, models need to be evaluated in comparison to other potential explanations. Model comparison is possible using the Akaike’s Information Criterion (AIC) as well as the Bayesian Information Criterion (BIC). Generally, it can be said that the lower the AIC or BIC, the better the fit of the model as compared to any other model. The BIC and AIC deviate slightly as the BIC adjusts stronger for complex models. A further mode of comparison is possible via the Loglikelihood statistic. It is based on a χ2 distribution and by substracting the Loglikelihood values of two models it can be found out whether the more complex model fits the data significantly better (Arzheimer, 2016). Logit models as applied in case of categorial data predict the logit of the odds ratio of the outcome variable (Best & Wolf, 2010; Peng et al., 2001; Rohrlack, 2007). As this is a little intuitive and impractical, different modes of interpretation are applied in practice. While unstandardized regression coefficients are well suited to merely draw conclusions on positive or negative relationships, odds ratios give a first clue on the dimension of increase or decrease of the odds ratio towards the outcome in question (Behnke, 2015; Mayerl & Urban, 2010). An interpretation using the concrete change in probability ratios is thus more concrete than aiming at interpreting the logit of this ratio. Even better is working with probabilities that result out of the estimation of the regression model. The predicted probabilities can be calculated by dividing the odds through (1odds) (Mood, 2010). Using Stata, version 16, GSEM is used to test the model

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under investigation. As generally advised for binary outcomes, odds ratios as well as (mean) predicted probabilities will be estimated through the Bernoulli Logit function in Stata.

4.3.5

Robustness Checks

As this short introduction into SEM showed, both measurement as well as path models can be included into structural equation modelling. The model tested here entails both latent as well as manifest variables. However, several aspects make model estimation rather complex: – There are several latent variables – Nearly all variables are categorical and the outcome is binary – The sample size is rather low within some cluster groups (smallest group: n = 187) As a consequence, a full SEM with both measurement and path model could not be calculated. For parsimony, it was therefore decided to measure latent variables as scale instead of opting for the preferable solution of including a measurement part within the full structural equation model. The disadvantage of scale construction as opposed to latent modelling is that confirmatory factor analyses can include an error term into the modelling of a construct. This error term accounts for random measurement error meaning it acknowledges that the variance of the latent concept consists of explained as well as unexplained variance. The higher the explained variance, the more reliable the latent construct (Arzheimer, 2016). Consequently, latent variable modelling delivers more reliable estimates than scale construction does. However, in the present case the only alternative options to reduce model complexity is the shortening of the model (e.g., by leaving out motivational traits or by using ethnicity instead of clustering groups). As this reduction would eliminate novel aspects that the analysis adds to existing literature, it was decided to opt for a drawback in reliability to at least be able to add some first ideas to the explanation of fertility of migrants and minority. As the overall model had to be reduced drastically to enable estimation and all latent variables have been transformed into scales, regression analyses including indirect effects provide an alternative approach towards measuring the model. As these analyses are less complex, they are used to test the robustness of the results presented. Moreover, they offer more information on model fit, which is

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hardly provided by the GSEM model. A second aim of the additional regression analyses is thus to make an evaluation of the model possible, and the amount of explained variance explained. It has to be noted, though, that regression analyses cannot account for the full model. Therefore, the model is estimated within two steps as depicted in Figure 4.1.

Figure 4.1 Models estimated with help of regression analyses

The dotted lines in Figure 4.1 present relationships which are either not elaborated upon theoretically (e.g., relationship between motivational traits and fertility intentions) or relationships which are only tested indirectly (mediating role of assimilation for the direct relationship between ethnicity and fertility intentions). As fertility intentions form the outcome of interest, a binary logistic regression model is used for calculating the regression models. Once dichotomous outcomes (Y) are included into a regression model the assumptions of linear regression models are no longer met as the estimated values of the dependent variable are within linear models not limited to two outcomes. This problem of limited dependent variables is accounted for by logistic regression models, which estimate an S-shaped curve. They assume a non-linear relationship and the logistic regression curve converges towards 1 or 0 in its end (Behnke, 2015). Statistically, the logistic regression model transforms the influence of independent measures (X) into a likelihood of falling into either of the outcome categories. This is achieved by taking the logarithm of the odds of the outcome, which predicts a linearly increase or decrease in dependence of the independent variable. A change in the independent measure can hence be interpreted in terms of a change in the effect on the odds. If one places the predicted odds in relationship to the old odds, one gets a quotient called odds ratio. An odds ratio above 1 indicates an increase in the ratio of new to old values meaning, while a value below 1 signifies a decrease in the ratio. This odds ratio can be transformed into a likelihood of falling into

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category 1 of the dependent measure (Behnke, 2015). As the logistic regression model is only used for robustness checks here, the results will be reported in terms of odds ratios only. Nevertheless, the predicted probability resulting out of the estimation will be saved as well to enable a correlation of predicted probabilities from logistic regression and GSEM models.

4.4

Variables

In a first step, variables relevant for clustering are presented. Then, this section will elaborate upon dependent variables, independent variables, and control variables to test the model presented in Figure 3.3. All variables are constructed for the Bulgarian and the German sample. Moreover, a separate minority sample was constructed out of the Turkish minority and Turkish migrant respondents.

4.4.1

Variables for Ethnic Clusters

For clustering, age, ethnicity, gender, and residency are considered as potential dividing lines within society. Residence distributes respondents into whether they live rurally (1) or urbanely (0) within the Bulgarian sample. It is based on respondents’ own classification. All other variables are similarly coded for Bulgaria and Germany. Age is continuous ranging from 18 to 50 years for males and 18 to 45 for females respectively. Gender entails the value (1) for males and (0) for females. Education is collected within the GGS based on the International Standard Classification of Education (ISCED). It was recoded into four categories: (1) primary education or less comprising ISCED 0 and 1, (2) lower secondary education comprising ISCED 2, (3) upper secondary education covering ISCED 3 (4) post-secondary or tertiary education capturing ISCED 4 to 6. Finally, ethnicity is based on the self-identification for participants in the Bulgarian sample. They were asked: “Could you look at this card please and tell me which of these groups you consider you belong to?” and presented a list of the most sizeable ethnic groups within their country as well as an open answer option (GGP, 2020b). For Germany, ethnicity was based on place of birth as described in section 4.1.2. In this dissertation, the variable ethnicity thus distinguishes Bulgarians (1) from Turks (0) and separates Germans (1) from Turks (0).

4.4 Variables

4.4.2

101

Endogenous Variable

The dependent variable throughout the analyses are fertility intentions of respondents. These are measured asking participants “Do you intend to have a/another child during the next three years?”. Answers could be given on a four-point scale: (1) definitely not, (2) probably not, (3) probably yes, (4) definitely yes. The focus of this dissertation rests on a distinction between positive and negative fertility intentions. The certainty of the intention is thus of secondary interest as the theoretical foundation does not offer any differentiating expectation regarding the certainty of positive or negative intentions. Therefore, the variable is used dichotomous distinguishing (1) positive from (0) negative intentions. For this purpose, categories one and two are summarized as well as categories three and four. This variable has 285 missing values for Germany mainly resulting out of ‘don’t know’ answers (268 cases). For Bulgaria, there are only 12 missing values.

4.4.3

Exogeneous Variables: Fertility

As there are several independent variables, a distinction between fertility related and assimilation related variables will be made. Besides fertility intentions, fertility desires as well as motivational traits will be looked at. Desires are assessed using questions on personal and partner’s desire. It is asked whether “Do you yourself want to have a/another baby now?” and for the partner indirect information is given as respondents were asked “Couples do not always have the same feelings about the number or timing of children. Does your partner/spouse want to have a/another baby now?”. Both questions gave three answer possibilities: (1) yes, (2) no, (3) not sure/partner is not sure. Thus, desire could be indicated for both partners within a partnership independently. For the upcoming analyses only one variable measuring desire was included. For this purpose, the desires of both partners were matched. Those partners who indicated different desires, were classified within the group “not sure”. This label was also given if both partners indicated to be not sure. If both partners indicated a positive desire, they were attributed the category “positive desire”. If both indicated a negative desire, they were grouped into “negative desire”. This way, three categories for desire were constructed: (1) not sure, (2) positive desire and (3) negative desire. Throughout the analyses these will be included via dummy variables. Among Bulgarian participants there were 100 missing cases, among German respondents 253 cases were missing.

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Motivational traits are constructed through a factor analysis. According to Bühler (2008) motivational traits can be grouped into (structural) benefits of having children and (structural) costs from the birth of a child. While the former assesses the extent to which children are beneficial for e.g., old age security, closeness to partner and family and joy in life, the latter assesses the extent to which children influence the own economic situation, working opportunities as well as personal time and contact opportunities to others. From the GGS, eleven questions were looked at to construct this concept. All started with “If you were to have a/another child during the next three years, would it be better or worse for…” then, the following aspects were evaluated on a five-point Likert scale ranging from (1) much better to (5) much worse: (a) the possibility to do what you want, (b) your employment opportunities, (c) your financial situation, (d) your sexual life, (e) what people around you think of you, (f) the joy and satisfaction you get from life, (g) the closeness between you and your partner/spouse, (h) your partner’s/spouse’s employment opportunity, (i) the care and security you may get in old age, (j) certainty in your life and (k) closeness between you and your parents (GGS, 2020). Aspects a, b, c, d and h are taken to signify the costs of having children, while questions g, i, j, k, f and e represent the benefits of having children. To construct these two scales as measuring motivational traits for Germany, Cronbach’s alpha indicates an acceptable internal consistency (α = .84 for benefits and α = .79 for costs). Moreover, all items load upon one factor which explains 60.3 % of variance for costs (Eigenvalue = 3.01) and 58.4 % of variance for benefits (Eigenvalue = 3.50). As Table 4.2 demonstrates, all items load sufficiently upon these factors. Among Bulgarian respondents, a similar solution results. The internal consistency is high with α = .86 for the cost dimension and α = .93 for the benefit scale. All items load upon one factor which explains 67.9 % of variance for costs of having children (Eigenvalue = 3.40) and explains 74.1 % of variance for benefits of having children (Eigenvalue = 4.45). All items load high upon these two factors as summarized in Table 4.2. Lastly, a one factor solution with good reliability (α = .72) could also be found for the minority sample for the costs as well as for the benefits (α = .81). The cost factor has an Eigenvalue of 2.63 and explains 52.5 % variance. The benefit measure has an Eigenvalue of 3.22 and explains 53.7 % of variance. Therefore, for Germany, Bulgaria, and the minority sample two factors measuring the structural value as well as the benefits of children were constructed with help of the regression method. These final factors are standardized having a mean of 0 that represents the average attitude within the sample. Values above zero

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thus represent negative expected effects of having children while negative values represent pronatalist motivation traits. Table 4.2 Factor loadings of motivational traits for Germany, Bulgaria and minority sample Effects on having another child on:

Bulgaria

Germany

Minority sample

(F2) Possibility to do what you want

.89

.86

.82

(F2) Employment opportunities

.78

.86

.80

(F2) Financial situation

.90

.87

.81

(F2) Sexual life

.84

.69

.62

(F1) What people around you think of you

.84

.75

.72

(F1) Joy and satisfaction you get from life

.91

.81

.78

(F1) Closeness between you and your partner/ spouse

.85

.53

.54

(F2) Partner’s employment opportunities

.70

.56

.54

(F1) Care and security you may get in old age

.87

.82

.80

(F1) Certainty in your life

.92

.83

.82

(F1) Closeness between you and your parents

.77

.79

.71

Notes. F1 stands for benefits, F2 stands for costs.

4.4.4

Exogeneous Variables: Assimilation

Assimilation variables cover several measures to capture the four assimilation dimensions presented in section 2.4. Structural integration is the most widely used dimension of assimilation within empirical studies. It is assessed using activity status, educational background or unemployment (Beauftragte der Bundesregierung für Migration, Flüchtlinge und Integration 2014; Noll & Weick, 2011). Similar indicators will be used within this study. The activity status covers the structural dimension of assimilation. It is a categorical measure distinguishing (1) employed/self-employed, (2) unemployed, (3) students, (4) parental leave, (5) homemaker and (6) others. The employment status is included in form of dummies throughout the analyses. There is only one missing answer for Bulgaria and none for Germany. Cultural assimilation has been measured in different terms within existing literature. Language proficiency in both host and home country language have e.g., been applied by Noll and Weick (2011) as well as by Angelini and colleagues (2015). Moreover, religious and cultural practices as well as attitudes towards

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Table 4.3 Factor loadings of importance of religious ceremonies for Germany, Bulgaria and minority sample Item

Bulgaria

Germany

Minority sample

Opinion: Important for infant registered appropriate religious ceremony

.80

.64

.57

Opinion: Important for marrying people also have religious wedding

.83

.83

.78

Opinion: Important for funeral to include religious ceremony

.80

.80

.75

family life are typical concepts to measure cultural incorporation (Kalmijn & Kraaykamp, 2018). This dissertation will build on the latter approach and apply religious practices as well as attitude towards family life to measure cultural adaptation. The cultural dimension will be included by adding a variable on the importance of religious practices. All respondents independent of their religiosity were asked to rate the following statements on a scale from (1) strongly agree to (5) strongly disagree: “It is important for an infant to be registered in the appropriate religious ceremony.”, “It is important for people who marry in registry offices to have a religious wedding too.”, “It is important for a funeral to include a religious ceremony.”. Through factor analysis. a construct measuring the importance of religious ceremonies was constructed. Using principal axis factoring, all three items are important for measuring importance of religious practices. For Germany, a one factor solution is supported and underlines the construct validity of the concept. This factor explains 77.8 % of variance and has an Eigenvalue of 2.33. Reliability analysis outputs an alpha of .86. For Bulgaria one factor with an Eigenvalue of 2.29 was extracted, which explains 76.3 % of variance. Cronbach’s alpha supports that this measure is a of good quality (α = .85). For the minority sample, one factor with an Eigenvalue of 2.06 and 68.8 % explained variance was extracted. The corresponding Cronbach’s alpha indicates an acceptable internal consistency (α = .75). Higher values on this factor, which was constructed using the regression command, represents less importance of religious ceremonies. Factor loadings for all three samples are summarized in Table 4.3. Furthermore, the cultural aspect of assimilation will be accounted for using a scale measuring attitude towards partnership and family life. This will be assessed through agreement on a five-point Likert scale ranging from (1) strongly agree to (7) strongly disagree towards the following statements: marriage is an outdated

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institution, it is all right for an unmarried couple to live together even if they have no interest in marriage, marriage is a lifetime relationship and should never be ended (recoded), it is all right for a couple with an unhappy marriage to get a divorce even if they have children, a woman has to have children in order to be fulfilled (recoded), a man has to have children in order to be fulfilled (recoded), a child needs a home with both a father and a mother to grow up happily (recoded), a woman can have a child as a single parent even if she does not want to have a stable relationship with a man, when children turn 18– 20 years old they should start to live independently, homosexual couples should have the same rights as heterosexuals do (GGS, 2020). Using principal axis factor analysis for both Germany and Bulgaria separately, a factor measuring attitude was constructed. Table 4.4 Factor loadings of attitude for Germany Item

Factor 1

Marriage is an outdated institution.

.36

It is all right for an unmarried couple to live together.

.61

Marriage is a lifetime relationship and should never be ended (recoded).

.55

It’s all right for a couple to divorce even if they have children.

.39

A woman has to have children in order to be fulfilled (recoded).

.79

A man has to have children in order to be fulfilled (recoded).

.79

A child needs a home with father and mother to grow up happily (recoded).

.51

Woman can have child as single parent even without stable relationship.

.44

Homosexual couples should have same rights as heterosexual.

.47

For Germany, all items except number nine load upon one factor with an Eigenvalue of 2.87 and an explained variance of 91.0 %. Cronbach’s alpha also suggests an acceptable reliability (α = .79). Table 4.4 shows the factor loadings of all nine items on this factor. For the Bulgarian sample, no good reliability could be achieved when conducting factor analyses and reliability checks with all items. Therefore, an index instead of a factor was constructed by summing up all items. This yields an index for attitude ranging from 10 to 47 with a mean of M = 28.71 (SD = 4.53). Similarly, no sufficient one factor solution could be found for the minority sample, which is why a summative index was applied here, too. Among minorities, it ranges from 13 to 57 with a mean of M = 33.26 (SD = 6.28).

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As for social integration, previous studies have mainly built upon contacts to natives and the own ethnic community to assess this aspect of integration (Noll & Weick 2011). For the social dimension of assimilation there is no direct measurement included in the GGS data set. Indirect, social integration can be assessed using information regarding the personal network. These are partly measured within the GGS by asking (1) with whom one has exchanged personal experiences and feelings during the last twelve months and (2) whether anyone else has shared his or her personal experiences and feelings with the respondent. These variables are used to construct, whether one is embedded into a social support outside the own partnership and family. However, it is a drawback that no information on the set up or ethnicity of this network can be constructed. Specifically, a dummy was constructed assigning (1) if one has provided help or exchanged feelings with a person not living within one’s own household and not belonging to the own family or partnership, or a (0) if no help was provided or feelings exchanged, or if these were exchanged with family members or someone else living within the own household. Identificational assimilation is not considered within all studies focusing on assimilation. Those studies measuring identification have done so using citizenship or the intention to stay within the host society (Noll & Weick, 2011). A similar approach is opted for here though it has to be acknowledged that citizenship can also be understood as a structural component of the integration process given that it must not necessarily comply with identification, but with the gratification of rights. Citizenship status is used as an indicator of identificational integration and distinguishes (1) citizenship of country of residence from (0) other. It is only included in the German model. Among the Bulgarian respondents there are hardly any non-citizens. In Germany, citizenship is based on a jus sanguinis approach which implies that those born in Germany are assigned German citizenship independent of the citizenship of their parents (Bundesregierung, 2022). Moreover, migrants who have legally resided within Germany for at least eight years can apply for German citizenship (BMI, 2021). In Bulgaria, citizenship is granted if (1) one is born in Bulgaria, or (2) has Bulgarian parents, or (3) through naturalization or (4) restoration of citizenship. The last option applies for Turkish minority respondents who have migrated back to Turkey during the last decades but want to return to Bulgaria. As many Turkish left Bulgaria during times of forced assimilation (see section 2.1.2) and became Turkish citizens, Bulgaria is one of the few Eastern European countries that tolerates a dual citizenship (Smilov & Jileva, 2010). Natuaralization is possible if one has resided within Bulgaria for at least five years. After 2013, there were options

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107

to invest money in Bulgaria and in turn become Bulgarian citizen through a fasttrack procedure (Ilareva 2015). However, as the data analyzed here originate from 2004/05, this option is not relevant for this dissertation. Generally, it is much easier to become Bulgarian than to become German, which is probably why there are hardly any non-citizens within the Bulgarian data. Unfortunately, the GGS does not offer any measures that capture the home country dimension of assimilation. Thus, assimilation has to be assessed mainly based on the host country dimension of inclusion.

4.4.5

Control Variables

Throughout all analyses, age, parity, marital status and gender will be controlled for. Age and gender are only used in case they are not relevant for clustering participants. Parity distinguishes respondents who have (1) no children from those with (2) one child, (3) two children or (4) three or more children. Marital status is differentiating (1) married respondents from (2) partnerships without marriage and (3) singles. Gender differentiates males (1) from females (0). Age is a continuous variable that is assessed in years. In case any of the variables used for clustering turn out to be irrelevant for building the clusters, they will be included as control variables. Appendix A in the Electronic Supplementary Material summarizes descriptive statistics of all variables for Bulgaria, Germany and the minority sample.

5

Assimilation Status of Turkish Migrants in Germany and Turkish Minority in Bulgaria

Within this section, the results of clustering, descriptive analyses and structural equation modeling including regression analyses as robustness check will be presented. For this purpose, results are first presented separately for Bulgaria and Germany before a final step aims at comparing minority and migrant respondents more specifically.

5.1

Bulgaria

In a first step, results for Bulgaria are presented. A cluster analysis is conducted first, to check whether meaningful clusters can be constructed for further analyses. Once the results of this analysis have been presented, descriptive results are highlighted along the four assimilation dimensions as well as in terms of demographic characteristics. Thirdly, the structural equation model is presented, and additional regression analyses are reported to verify the presented results.

5.1.1

Cluster analysis

The cluster analysis reveals that age and gender are not important for clustering. Age is evaluated with a relative relevance of .04 (4 %), whereas sex is evaluated with a relevance of .03 (3 %). Therefore, these variables are excluded from the analysis. Ethnicity, education and residence, though, are deemed of importance and are attributed a relative relevance of 1.00. The final two-step clustering thus Supplementary Information The online version contains supplementary material available at https://doi.org/10.1007/978-3-658-43099-3_5. © The Author(s), under exclusive license to Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2023 B. Brünig, The Fertility of Migrants and Minorities in Europe, https://doi.org/10.1007/978-3-658-43099-3_5

109

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contains education, residence and ethnicity as clustering variables and is restricted to containing a maximum of six clusters. The corresponding BIC values can be found in Appendix B in the Electronic Supplementary Material. Table 5.1 summarizes the composition of the five resulting clusters for the Bulgarian data. Cluster one is comprised out of rural Bulgarians with upper secondary education, whereas cluster two contains both rural and urban Bulgarians with mainly lower secondary education. Cluster four and five summarize urban Bulgarians with tertiary and upper secondary education, while cluster three entails all Turkish minority participants, which are characterized by a rather rural location and mixed education though lower secondary degrees prevail. One can thus conclude that there is indeed a distinction between Bulgarians and Turks, though one should not treat the majority as one homogenous group and should consider more than ethnicity when dividing society into homogenous groups. As became already clear within the historical review of the evolvement of the Turkish minority in Bulgaria, next to ethnicity the place of residency is an important line of distinction within society. Table 5.1 Cluster analysis GGS data Bulgaria Variable

Cluster 1

Cluster 2

Cluster 3

Cluster 4

Cluster 5

Size (n)

884 (12.5 %)

816 (11.5 %)

730 (10.3 %)

1439 (20.3 %)

3230 (45.5 %)

Education

Upper secondary (100.0 %)

Lower secondary (86.5 %)

Lower Tertiary (100.0 Upper secondary (57.0 %) secondary %) (100.0 %)

Ethnicity

Bulgarian (100.0 %)

Bulgarian (100.0 %)

Turkish (100.0 %)

Residence

Rural (100.0 %)

Label

BU-UP-RU

5.1.2

Bulgarian (100.0 %)

Bugarian (100.0 %)

Urban (50.6 Rural (61.5 %) %)

Urban (100.0 %)

Urban (100.0 %)

BU-LOW

BU-HI-URB

BU-UP-URB

TU-LOW-RU

Demographic Background

Table 5.2 summarizes the demographic characteristics of the five clusters. The gender distribution shows an equal share of males and females within most clusters. However, the highly educated urban Bulgarians consist to 69 % of women. This group is also the oldest when the mean age of all clusters is compared. The youngest group are the rural, lowly educated Bulgarians. When looking at the number of children, one generally notes that Bulgarians and Turks have rather few children. The Turkish cluster has the highest number of children with 1.48

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on average. This is followed by 1.23 children in the rural Bulgarian cluster with upper secondary education. An analysis of variance with Welch correction shows that the clusters differ significantly in their number of children, F(4, 2231.716) = 47.598, p < .001, η2 = .048 Games-Howell post-hoc analyses underline, that all groups except BU-LOW and BU-HI-URB, BU-HI-URB and BU-UP-URB as well as BU-LOW and BU-UP-URB differ significantly from each in their mean number of children. As far as marital status is concerned (Table 5.2), the biggest share of respondents within all clusters is married. However, differences between clusters can be observed. The Turkish group depicts the highest rate of partnership without marriage, followed by the rural Bulgarians with lower secondary education. This corresponds with census data of Koytcheva and Philipov (2008) who show that

Table 5.2 Demographic characteristics by cluster for Bulgaria Variable

BU-UP-RU

BU-LOW

TU-LOW-RU

BU-HI-URB

BU-UP-URB

Male

473 (53.5 %)

419 (51.3 %)

352 (48.2 %)

442 (30.7 %)

1579 (48.9 %)

Female

411 (46.5 %)

397 (48.7 %)

378 (51.8 %)

997 (69.3 %)

1651 (51.1 %)

M (SD)

34.53 (7.89)

30.58 (9.51)

32.81 (7.95)

34.86 (6.72)

32.20 (8.50)

Range

18–50

18–50

18–50

19–50

18–50

M (SD)

1.23 (1.01)

1.06 (1.21) 1.48 (1.11)

0.99 (0.84)

0.94 (0.93)

Range

0–6

0–8

0–7

0–7

0–7

married

578 (66.4 %)

361 (45.8 %)

431 (59.9 %)

1020 (72.2 %)

1949 (61.5 %)

partner

63 (7.2 %)

90 (11.4 %)

115 (16.0 %)

75 (5.3 %)

172 (5.5 %)

single

230 (26.4 %)

338 (42.8 %)

173 (24.1 %)

318 (22.5 %)

1040 (33.0 %)

Gender

Age

Number of children

Marital status

Notes. M = mean, SD = standard deviation.

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Bulgarians have higher rates of being unmarried than Turks, but Turks more often cohabitate without marriage. Tomova (1998) points out that the main aspect influencing the partnership status is age. There is a clear change observable among the younger generation with 55 % of Bulgarians aged 18–30 years and 52 % of Turks within the same age not being married. Vladov (2007) even concludes that cohabitation is the new preferred model of co-residence within Bulgaria. This fits the GGS data, as both clusters with higher levels of partnerships also have lower mean average age. Figure 5.1 demonstrates how many children respondents have. Clearly, respondents belonging to the Turkish cluster as well as the rural Bulgarian cluster with lower secondary education are most likely to have more than two children. Additionally, the Turkish cluster is least likely to have no children yet. When one compares the four Bulgarian clusters, the rural Bulgarians with upper secondary education are most likely to have no children yet. One child is more pronounced within the two urban Bulgarian clusters than in all other three clusters. Two children dominate within the rural Bulgarians with upper secondary education and the Turkish cluster. Vladov (2007) observes that the ideal family of most Bulgarians includes two children. Hristova and colleagues (2018) derive a similar conclusion from census data of 1995, 2000 and 2015. Roughly 65 to 70 % of Bulgarians still prefer a two-child family. In practice, most families fail to meet this ideal due to economic difficulties and uncertainties. Tomova (1998) stresses, that there is a clear tendency towards one-child families within Bulgarian society when looking at the real birth rates. However, the higher the family income and the higher the wives’ education, the higher the number of children (Hristova

Number of children by cluster (BU) (n = 7,099) 50%

percent

40% 30% 20% 10% 0% BU-UP-RU

BU-LOW 0

TU-LOW-RU 1

2

3

4

5 or more

Figure 5.1 Number of children by cluster for Bulgaria

BU-HI-URB

BU-UP-URB

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113

et al., 2018). Given that all clusters depict mean average numbers of children below two, these findings seem to correspond to the data presented here. Looking at fertility intentions by parity (Appendix C in the Electronic Supplementary Material), among all clusters, those who have two or more children are most likely to state that they definitely do not intend to have another child. As far as childless respondents are concerned, the highly educated urban Bulgarians are most positive about probably or definitely having a child within the next three years. This result could be influenced by the fact that there are mainly females within this group in combination with a high mean age within this cluster. Secondly, the rural Bulgarians with upper secondary education are rather positive about having a child soon, followed by the Turkish cluster. When it comes to respondents who already have one child, the rural Bulgarians seem slightly more negative about having another child as compared to the other three clusters. A comparison of fertility intentions by gender (Figure 5.2) shows that in all clusters males are slightly more likely than females to probably or definitely intend to have a(nother) child—the only exception here is the Bulgarian cluster with lower secondary education. Out of all clusters, the urban highly educated Bulgarians are most likely to probably or definitely intend to have a(nother) child within the next three years and least likely out of all clusters to definitely not plan to have a(nother) child.

Fetility intentions by cluster and gender (BU) (n = 6,597) 80%

percent

70% 60% 50% 40% 30% 20% 10% 0% male

female

BU-UP-RU

male

female

BU-LOW

definitely not

male

female

TU-LOW-RU

probably not

probably yes

male

female

BU-HI-URB definitely yes

Figure 5.2 Fertility intentions by cluster and gender for Bulgaria

male

female

BU-UP-URB

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Closely connected to fertility intentions is the use of contraceptives. Vladov (2007) highlights, that Bulgaria illustrates a low use of contraceptives in comparison to Western European standards. The lower the educational level, the more traditional the contraceptive used. This coincides with a high rate of abortions and an abortion-friendly norm within society. Looking at contraceptive use within the GGS, Figure 5.3 shows that contraception is clearly used differently by the five clusters. Both urban clusters most often use condoms followed by nothing or withdrawal. In contrast, the three rural clusters are most likely to either use nothing or the withdrawal method, followed by condoms. This is in line with previous findings of Carlson and Lamb (2003) who found rural-urban differences in contraceptive use with the urban Bulgarian population adhering more to modern contraceptives. Here, there is a difference between ethnic groups, too. While the two rural Bulgarian clusters are most likely to use withdrawal, nothing or condoms, the Turkish cluster is most often using nothing, followed by withdrawal. Ethnic differences in Bulgarian society in terms of contraception were also found by Carlson and Lamb (2003), who showed that ethnic minorities in Bulgaria more often use no contraception at all, thus supporting the results presented here.

Contraception by cluster (BU) (n = 4,502) 45% 40% 35% percent

30% 25% 20% 15% 10% 5% 0% BU-UP-RU nothing

condom

BU-LOW pills

TU-LOW-RU

intra-uterine device

withdrawal

Figure 5.3 Use of contraceptives by cluster for Bulgaria

BU-HI-URB

BU-UP-URB

safe period method

other

5.1 Bulgaria

5.1.3

115

Structural Assimilation

Literature on the structural situation of majority and minority in Bulgaria highlights the poor standing of the Turkish minority compared to, especially urban, Bulgarians (Tomova, 1998). As Bulgaria does not collect data based on ethnicity, most scholars have used indirect methods to estimate the share of unemployment among the Turkish minority. As Turkish minority residents primarily reside within three of the nine administrative regions (Rechel, 2012), figures are usually compared across these regions to draw conclusions regarding the situation of minority residents. In municipalities with high shares of Turks, unemployment in 1998 ranged between 30–47 %, while it was only 14 % among the municipalities with mainly Bulgarian population (Tomova, 1998). Even ten years later, Turks are four times more likely to be poor than Bulgarians (Abadjiva, 2008). Looking at the activity status of all five clusters within the GGS in (Figure 5.4), the urban highly educated Bulgarians have the highest employment rate (83.3 %) and only low levels of unemployment. The urban Bulgarians with upper secondary education have also high employment rates, but more than double the proportion of unemployed as compared to their highly educated counterparts. Within the Turkish cluster, the unemployment rate equals the employment rate.

Activity status by cluster (BU)

percent

(n = 7,095) 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% BU-UP-RU Employed

BU-LOW Unemployed

TU-LOW-RU Student

Parental leave

Figure 5.4 Activity status by cluster for Bulgaria

BU-HI-URB Homemaker

BU-UP-URB Other

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The activity status by cluster and gender in Figure 5.5 shows that within all clusters, males have higher employment rates than females. Among all clusters, employment varies between 93 % (highly educated urban males) and 38 % (Turkish females). The range across clusters is thus remarkably high. As far as the unemployment rate is concerned, variations are equally diverse. The lowest unemployment rates are found among males and females in the highly educated urban Bulgarian cluster (4 to 7 %), while the highest unemployment rate prevails among males (40 %) and females (44 %) of the Turkish cluster. This is in line with the literature that underlines the high unemployment rates of the Turkish minority in Bulgaria (Abadjieva, 2008; Tomova, 1998). In general, it is obvious that the rural clusters show higher unemployment levels than the two urban clusters. This underlines that unemployment is most notably a problem within the rural regions where agriculture and tobacco are the main forms of living and work contracts often temporary (Tomova, 1998). A further interesting note concerns the share of homemakers or those on parental leave. Here, there is no male within all clusters who (currently) stays at home. Out of all females, the percentage of women on parental leave ranges between 7 and 9 % with the highly educated urban Bulgarian cluster having the highest share of females on leave. When it comes to homemakers, Bulgarians with lower secondary education followed by the Turkish cluster have the highest rate of homemakers.

Activity status by cluster and gender (BU) (n = 7,095) 100% 90% 80%

percent

70% 60% 50% 40% 30% 20% 10% 0% male

female

BU-UP-RU employed

male

female

male

BU-LOW unemployed

female

TU-LOW-RU student

parental leave

male

female

BU-HI-URB homemaker

Figure 5.5 Activity status by cluster and gender for Bulgaria

male

female

BU-UP-URB other

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117

Since education is part of the clustering variables, it cannot be used to describe the clusters. To get an insight into the educational setup of the sample nevertheless, the distribution of educational levels by ethnicity is looked at. The Turkish minority clearly lags behind the Bulgarian majority. While Turkish respondents are mainly found within lower secondary education (47.6 %), followed by upper secondary education (34.0 %), Bulgarian participants in the GGS primarily indicate to have upper secondary education (64.3 %) or even first stage tertiary education (24.2 %). It is furthermore noteworthy that hardly any Bulgarian has primary or pre-primary education only (0.7 %), while there are roughly 11 % Turkish respondents who have primary education. When it comes to tertiary education, there are hardly any Turkish respondents who have reached such a high degree of education (3.2 %). Vladov (2007) amends that Turkish minority residents show higher rates of school dropout (9.6 %) and illiteracy (6.1 %) when compared to Bulgarians (4.7 and 0.7 % respectively). Furthermore, he highlights that 69.1 % of the rural population in Bulgaria completes primary education as opposed to 89.5 % of the urban population (Vladov, 2007). Again, the GGS data are in line with these reports. The structural comparison of all clusters and ethnic groups highlights, that Turkish respondents are disadvantaged when it comes to education. They are less highly educated than Bulgarians and have the highest unemployment rate within the population. However, one should also note that the cluster distribution underlines that the Bulgarian majority is diverse in structural terms, too. Unemployment rates are much lower in the urban centers than in the rural locations. Out of all clusters, the highly educated urban Bulgarians perform best when it comes to their activity status.

5.1.4

Cultural Assimilation

When it comes to cultural assimilation, little information exists within previous studies. Therefore, the following section will analyze own data from the GGS to derive conclusions regarding the cultural proximity or distance between Turks and Bulgarians in Bulgaria. Comparing the language use of all clusters, 86.6 % of the Turkish cluster use Turkish as primary language of communication, followed by Bulgarian as second language (58.3 %). All Bulgarian clusters indicate to use Bulgarian as primary language of communication at home. Another important aspect of cultural inclusion is religion and religious practice. Most respondents within the Bulgarian clusters are Orthodox (80 to 95 %), while most respondents within the Turkish cluster are Muslims (92.0 %). To

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investigate the religiosity of respondents further, the GGS offers the possibility to assess the importance one attaches to religious ceremonies such as having a religious wedding, having a religious ceremony for children/at birth or during a funeral. Within all clusters, 45 % (TU-LOW-RU) to 58 % (BU-HI-URB) of respondents considers religious ceremonies important. Roughly 12 % within all groups is neutral and correspondingly, there is a share of 28 % to 42 % who considers religious ceremonies not important. Generally, there is little variation between the clusters visible. Looking at the frequency of religious service attendance, there are differences between clusters, too. Most participants within all clusters visit religious services once a year (66 to 80 %), as compared to only 3 to 10 % who visit services on a weekly basis. Out of all clusters, Turkish respondents visit services most often weekly (10.6 %). All in all, the rural Bulgarian cluster as well as the lowly educated Bulgarian cluster seem less religious than their urban counterparts, the Turkish cluster ranks somewhere in between. In a next step, the attitude of GGS respondents to diverse statements within the range of marriage and family life was assessed. For this purpose, six out of ten available attitude measures were selected for discussion here. Figure 5.6 shows that most respondents disagree that marriage is an outdated institution. The strongest disagreement prevails in the highly educated Bulgarian cluster, the highest agreement can be found in the lowly educated Bulgarian cluster. When it comes to the statement that a couple can live together without

Option towards marriage and family (BU) (n = 7,065) 100%

percent

80% 60% 40% 20%

marriage outdated

live together unmarried agreement

neutral

divorce with children ok

disagreement

Figure 5.6 Opinion towards marriage and family life (1) for Bulgaria

BU-UP-URB

BU-HI-URB

TU-LOW-RU

BU-LOW

BU-UP-RU

BU-UP-URB

BU-HI-URB

TU-LOW-RU

BU-LOW

BU-UP-RU

BU-UP-URB

BU-HI-URB

TU-LOW-RU

BU-LOW

BU-UP-RU

0%

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119

getting married, the general tendency within all clusters is agreement. Roughly 75 to 85 % within the four Bulgarian clusters agree or strongly agree. The least agreement prevails within the Turkish cluster. That living together unmarried finds least agreement among Turkish respondents is interesting given that Table 5.2 showed that within the GGS, marriage rate is lowest within the Turkish cluster when compared to the four Bulgarian clusters. Looking at the statement whether divorce is ok even when children are present 76 to 91 % agree. The most positive are again the highly educated urban Bulgarians, while the Turkish cluster is again the most negative when compared to all other groups. Finally, Figure 5.7 presents the last three statements. Over 90 % of all respondents agree that children need father and mother to grow up happily. When asked whether women need children to be fulfilled, the highest agreement can be found within the Turkish cluster, followed by the rural Bulgarians with upper secondary education. The last statement argued that homosexual couples should have the same right as heterosexuals. Here, majority and minority disagree. Only 14 % of the Turkish cluster agree or strongly agree. Among the Bulgarians, agreement varies between 21 and 33 %. Again, one can observe that the rural Bulgarians display lower levels of agreement than their urban counterparts.

Opinion towards marriage and family (BU) (n = 7,057) 100%

percent

80% 60% 40% 20%

children need both parents

women fulfilled with children

agreement

neutral

BU-UP-URB

BU-HI-URB

TU-LOW-RU

BU-LOW

BU-UP-RU

BU-UP-URB

BU-HI-URB

TU-LOW-RU

BU-LOW

BU-UP-RU

BU-UP-URB

BU-HI-URB

TU-LOW-RU

BU-LOW

BU-UP-RU

0%

homosexuals same right

disagreement

Figure 5.7 Opinion towards marriage and family life (2) for Bulgaria

In a last step, the distribution of tasks within the household was looked at to learn more about cultural habits. Across most tasks, the highly educated urban

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Bulgarians seem the most egalitarian in their task distribution when compared to all other clusters. The only exception is the task “doing small repairs”, here the share of males within this cluster who indicate that they take over the task is highest out of all clusters (see Appendix D in the Electronic Supplementary Material). In general, though, one can observe a rather traditional task distribution. Females are mainly the ones preparing meals, doing the dishes, and vacuum-cleaning the house. Males, on the other hand, are responsible for small repairs. When it comes to paying bills and taking care of financial records, males and females seem to share this task. When contrasting the Turkish cluster with the four Bulgarian clusters, the most striking result is the high share of females who indicate that they themselves take over the ‘typical’ female tasks such as preparing meals, vacuum cleaning the house, shopping for food or doing the dishes. Across all clusters, the Turkish females show the highest percentages here. The task distribution is depicted graphically within Appendix D in the Electronic Supplementary Material. Summing up, a comparison of the cultural assimilation across the five clusters within Bulgaria demonstrates that differences between groups exist. Turks mainly speak Turkish at home and are Muslims, as compared to monolingual Bulgarians who are mostly Christian Orthodox. When analyzing the importance of religious ceremonies and the frequency of religious service attendance, the general impression is that religion is important for all respondents within the Bulgarian GGS. The Turkish cluster demonstrates the highest share of non-importance when it comes to religious ceremonies. This is in line with previous studies, which demonstrate that e.g., mosque attendance is not frequent within Muslim communities (Smits et al., 2010). On the other hand, they are the group who visits services most often on a weekly basis. Bulgarians, though, seem to attach more importance to religious ceremonies than to service attendance. This corresponds to B˘adic˘a’s (2013) statement that religiosity in Bulgaria is best described by “belonging without believing” (p. 44). According to B˘adic˘a (2013) Bulgarians attach quite some importance to having an Orthodox funeral, but other than that they do not practice their religiosity intensively. Additionally, among the Bulgarian clusters, a tendency can be observed that urban Bulgarians are more religious than their rural counterparts. In general, Kandaljieva (2008) describes Bulgarian majority religiosity by stating that “the stereotype that proper Bulgarians must be Orthodox” (p. 428) guides religious issues in Bulgaria. More interestingly, she points out that: “It seems that the Orthodox majority in postcommunist Bulgaria has no clear notion about the border between the secular and the religious. It regards Orthodoxy as a source of national identity, but turns into a guardian of secular principles when faced with the growing religiosity of some religious minorities.”

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121

(Kandaljivea, 2008, p. 429). This demonstrates that a mere description of religious practice hardly suffices to grasp the religious relationships underlying minority— majority relations in Bulgaria. It seems that religiosity gains in importance once religions clash. In terms of attitudes, the Turkish cluster is most conservative regarding family life. They attach a greater importance to the institution of marriage and are more strongly against homosexuals than the Bulgarian groups. This conservatism is confirmed when looking at the distribution of tasks within the household. Here, Turkish respondents seem to adhere most to a traditional distribution. The highly educated urban Bulgarians, however, seem the most liberal when attitudes and household task distribution is concerned. This is especially interesting given that this is the group who is most religious out of all clusters. In general, the Turkish cluster thus deviates culturally in several regards from the majority. This difference is visible in terms of religious denomination and practice, language use and traditionalism in attitude and task distribution. It should be noted, though, that cultural practices also vary among the Bulgarian clusters.

5.1.5

Social Assimilation, Identity and Intergroup Relations

The GGS covers no questions on intergroup or social relationships of respondents. The same is the case for the emotional dimension of assimilation. Therefore, the following review will be based on previous studies on the topic as well as proxy variables available within the GGS. Within the GGS, 95.7 % of the respondents within the Turkish cluster indicate to have a partner who has a Turkish ethnic background. The remaining respondents have a Bulgarian (3.9 %) or Roma (0.7 %) partner. Out of the four Bulgarian clusters, the respondents within the BU-UP-RU, BU-HI-URB and BU-UP-URB cluster have mostly Bulgarian partners (98.6 to 99.1 %). Only the BU-LOW cluster shows a higher percentage of heterogamy with 4.1 % having a Turkish partner, 4.1 % having a Roma partner and 3.1 % having a partner of another ethnic background. Homogamy thus prevails within all clusters and can be considered an indicator of weak social assimilation (Lichter & Qian, 2018). In terms of social contact, the GGS only offers insights into social support, without providing information on the ethnic background of the social environment. Within the first wave, most respondents within all clusters indicate to have no one outside the family to provide support. Within the Bulgarian clusters, perceived social support varies between 23.1 to 28.7 %. The Turkish cluster perceives

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less support with only 16.7 % saying that they have someone outside the family to talk to about their personal feelings. The social network of Turkish respondents thus seems more closed towards contacts from outside the family and one’s own household. Looking into the scientific literature, the term minority is associated with negative connotations in Bulgaria (Grekova, 1999). Generally, the word is ascribed two meanings, the pejorative and the political. While the first mainly applies to Roma within Bulgaria, the second is of importance when it comes to the Turkish minority (Grekova, 1999). As the status of a minority goes hand in hand with specific rights, the political connotation is perceived as threatening. Within qualitative interviews, Grekova (1999) analyzed that the term minority is associated with Roma, but less with Turks. Those respondents, who do describe Turks as minority, do so because of their cohesion and internal closure. By some Bulgarians, this is considered conspicuous. Turkish minority respondents, however, describe Bulgarians as ‘normal’ and do not see any reason why they differ from their own community. They do feel discriminated against, though, once they are stigmatized as minorities (Grekova, 1999). Among Turks, there is no clear tendency which country they belong to. Zhelyazkova (1999) identified within her analysis that there are both Turks within the minority who consider Bulgaria their motherland, while others strongly adhere to Turkey. Generally, the intergroup relations are stable and improved since the beginning of the 1990 s. A 1997 sociological survey found out that “There are more similarities and common things than differences between the two groups (Bulgarians and Turks) in many different spheres in social life” (Tomova, 1998, p. 39). The perceived difference between both groups has decreased and feelings of alienation or fear are diminishing. While there were 27 % of Bulgarians who indicated to have Turkish friend in 1994, it was 56 % of Bulgarians in 1997 (Tomova, 1998). Similarly, 61 % of Bulgarian citizens can imagine voting for a Pomak representative within elections (1994: 28 %) and 40 % would vote for a representative of the Turkish community (1994: 18 %). When interviewing the Turkish community, 86 % indicated to have Christian friends and 84 % can imagine voting for a Bulgarian representative (1994: 69 %). It was furthermore analyzed, how the decreased social distance varies between subgroups. Here, highly educated and younger respondents of both ethnic groups were most positive about the other (Tomova, 1998). All in all, existing studies point towards stable and mostly friendly relationships between the Turkish minority and the Bulgarian majority. Nevertheless, one should note that this does not imply that intergroup relations are uncomplicated, and that discrimination and intolerance does not occur in any

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123

form. The European Commission against Racism and Intolerance (ECRI) monitors ethnic relations in Bulgaria ever since Bulgaria’s desire to enter the European Union. In 2004, they pointed out that Turks are “financially and socially disadvantaged in comparison with the rest of the Bulgarian population” (ECRI, 2004, p. 14). Even ten years later, in 2014, the ECRI concludes that Islamophobia is still a problem, especially in election campaigns and equality between majority and minority groups is not yet achieved (ECRI, 2014).

5.1.6

Summary

All in all, the previous sections yield consistent results regarding the assimilation status of the clusters under study, and the Turkish versus the Bulgarian clusters in particular. Generally, the meaningfulness of distinguishing respondents not only based on ethnicity but to consider diverse intersecting lines of distinction proved to yield a more detailed picture of the living situation of ethnic minority and majority respondents within rural and urban location and of different educational background. This underlines that it is unprofitable to compare the Turkish minority to the Bulgarian majority. Though the Turkish population is mainly living within rural areas, the presented analyses still point out that Turkish residents are set apart from the Bulgarian rural population and that education plays an important role in comparisons, too. Specifically, the Turkish cluster presents the highest unemployment levels of all five clusters, and Turks have the lowest education when compared to both rural and urban Bulgarians. When looking at fertility behaviour, first descriptive analyses demonstrated that the rural population as such is slightly less positive about having another child and uses rather traditional contraceptives. Out of all clusters, the Turkish respondents use condoms or other modern contraceptives least often and they are, correspondingly, the group who has the most children on average. In terms of cultural assimilation, there are visible differences between the Turkish cluster and the remaining four Bulgarian clusters. Turks are most conservative regarding their attitude towards family life and partnership. Moreover, they have the most traditional household task distribution of all clusters, with females being mainly responsible for the household. In terms of religion, both Bulgarians and Turks are very religious. However, their denomination differs and correspondingly the importance of ceremonies and the frequency of service attendance differs also. Existing studies underline that for Bulgarians being Orthodox and being Bulgarian is tightly knit. This stresses that the clearest

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dividing lines between majority and minority are ethnicity and religion. Finally, a literature review on intergroup and social relations quarries that these have improved tremendously during the last decades. Yet, there remain differences between groups with economic exclusion being one of the most serious threats for Turks.

5.2

Germany

For the German sample it was similarly looked at the extent to which clusters can be found in society. Then, descriptive analyses of demographic data and assimilative states are conducted and placed within the context of existing findings.

5.2.1

Cluster Analysis

For Germany, the analysis contains 5,912 participants. For clustering, age, gender, education and ethnicity are considered. Gender and age are hardly relevant for clustering, as they are attributed a relative importance of .01 (1 %) for gender and .13 (13 %) for age. These two variables are thus excluded from cluster analysis. Therefore, the clustering procedure includes education and ethnicity, and is restricted to containing a maximum of six clusters. The resulting cluster solution is of good quality and both variables are important (100 %) for clustering. The corresponding BIC values are depicted graphically in Appendix E in the Electronic Supplementary Material. The resulting groups are named and described in Table 5.3. Table 5.3 Cluster analysis GGS data Germany Variables

Cluster 1

Cluster 2

Cluster 3

Cluster 4

Cluster 5

Cluster 6

Size (n)

1253 (24.0 %)

256 (4.9 %)

217 (4.2 %)

904 (17.3 %)

1589 (30.4 %)

1003 (19.2 %)

Education

Lower secondary (100.00%)

Tertiary (100.0 %)

Lower secondary (100.0 %)

Upper secondary (100.00%)

Upper secondary (100.00%)

Tertiary (100.00%)

Ethnicity

Turkish (100.0 %)

Turkish (100.0 %)

German (100.0 %)

Turkish (100.0 %)

German (100.0 %)

German (100.0 %)

Label

TU low

TU high

DE low

TU medium

DE medium

DE high

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125

The clusters are divided into three clusters with Turkish participants and three clusters with German participants that differ regarding their educational degree. The Turkish clusters are divided into one cluster with lower secondary education or less (Cluster 1: Turkish low), one with upper secondary education (Cluster 4: Turkish medium) and one that comprises participants with tertiary education (Cluster 2: Turkish high). The German clusters comprise one group with upper secondary education (Cluster 5: German medium), one with tertiary education (Cluster 6: German high), and one cluster with lower secondary education or less (Cluster 3: German low). In the following, these clusters will be looked at in terms of assimilation, fertility and demography to further explore their integration status as well as to investigate whether the clusters are homogeneous within but heterogeneous between.

5.2.2

Demographic Background

Table 5.4 summarizes the demographic characteristics of the six clusters identified. Most clusters are gender-balanced, however, the Turkish medium and high group have more men than women while the German low cluster contains more females. As far as age is concerned, differences can be found across the six clusters. While the Turkish clusters range between average ages of 32 to 35 years, the range is bigger in the three German groups. Here, the German high cluster is the oldest with 37 years, while the German low cluster is youngest with 30 years. In terms of marital status, marriage is preferred by all six groups (45 to 78 % are married). Generally, cohabitation is more pronounced within the German groups (11 to 18 %) as compared to the three Turkish clusters (2 to 4 %). The percentage of singles within the clusters ranges between 18 to 36 % with the German low cluster having the highest rate of singles. This might be related to the younger age structure of this group. When it comes to place of residence, all three German clusters are more likely to live in small places as compared to the three Turkish clusters. However, in general, most respondents live within places of 50,000 inhabitants or more. Regarding the number of children, the mean is higher in all Turkish clusters than in the German groups. While the Turkish low group displays the highest mean (1.86 children on average), the German low group has the fewest children (1.09 on average). Interestingly, the average number of children increases among German clusters with educational level, while it decreases with education in the Turkish clusters.

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Table 5.4 Demographic characteristics by cluster for Germany Variables

TU medium

TU low

TU high

DE high

DE medium

DE low

Males

585 (64.7 %)

562 (44.9 %)

171 (66.8 %)

515 (51.3 %)

790 (49.7 %)

84 (38.7 %)

Females

319 (35.3 %)

691 (55.1 %)

85 (33.2 %)

488 (48.7 %)

799 (50.3 %)

133 (61.3 %)

M (SD)

32.32 (7.23)

33.71 (6.87)

35.21 (5.98)

37.82 (6.96)

35.25 (7.93)

30.77 (8.69)

Range

18–50

18–50

18–50

20–50

18–50

18–50

Gender

Age

Place of residence < 50,000 inhabitants

23 (2.5 %) 47 (3.8 %)

7 (2.7 %)

193 (19.2 %)

434 (27.3 %)

49 (22.6 %)

> 49,999 inhabitants

881 (97.5 %)

1206 (96.2 %)

249 (97.3 %)

810 (80.8 %)

1155 (72.7 %)

168 (77.4 %)

M (SD)

1.40 (1.37)

1.86 (1.44)

1.28 (1.21)

1.18 (1.15)

1.12 (1.11)

1.09 (1.19)

Range

0–15

0–10

0–6

0–8

0–8

0–5

Married

663 (76.3 %)

954 (78.9 %)

190 (76.0 %)

674 (69.0 %)

969 (62.5 %)

95 (45.2 %)

Partner

32 (3.7 %) 30 (2.5 %)

10 (4.0 %) 115 (11.8 %)

189 (12.2 %)

38 (18.1 %)

Single

174 (20.0 %)

50 (20.0 %)

393 (25.3 %)

77 (36.7 %)

Number of children

Marital status

225 (18.6 %)

118 (19.2 %)

Notes. M = mean, SD = standard deviation.

A comparison of mean scores with the help of an analysis of variance with Welch correction shows that the average number of children differs significantly among clusters, F(5, 1168.918) = 58.283, p < .001, η2 = .053. Games-Howell post-hoc tests reveal the Turkish low group differs significantly from all other

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127

groups (p < .001). The Turkish medium cluster differs significantly from all German clusters (DE medium: p < .001, DE low: p = .012, DE high: p = .003). The German clusters do not differ significantly from each other. When looking at Figure 5.8, one can see that no children up to two children dominate clearly within the three German clusters. The highest percentage of childless respondents can be found within the German low group. The German high and medium group most often have two children.

Number of children by cluster (DE) (n = 5,222) 45% 40% 35% percent

30% 25% 20% 15% 10% 5% 0%

TU low

TU medium 0

TU high 1

2

3

DE low 4

DE medium

DE high

5 or more

Figure 5.8 Number of children by cluster for Germany

This partly reflects findings of Kreyenfeld and Konietzka (2008) who found childlessness to shift from highly educated German females to low educated groups. Looking at the Turkish groups, two children or no children are widely spread within all three clusters. However, especially the Turkish low group also shows a high percentage of respondents with four children. This corresponds to previous empirical research that showed decreasing birth rates among Turkish migrants in Germany with advanced educational levels (Krapf & Wolf, 2015). Given the differences in number of children, fertility intentions for the upcoming three years were also examined. As shown in Appendix F in the Electronic Supplementary Material, the share of German respondents who definitely do not want to have another child is higher than among Turkish respondents, especially if one has no children or one child. Furthermore, Turkish migrants are more likely

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than Germans to definitely or probably intend to have a(nother) child, particularly when they have one child or are childless. In general, it seems that Turkish respondents are more optimistic about having another child. Additionally, it was checked how fertility intentions look like for males and females of the six clusters. Looking at Figure 5.9, the three German clusters show rather equal intentions across males and females. Within the Turkish clusters, males and females differ in their intentions. Among the Turkish low cluster, males show higher rates of negative fertility intentions (definitely or probably not) as compared to males. Within the medium and highly educated Turkish groups, females seem more positive about having a(nother) child than males.

Fertility intentions by cluster and gender (DE)

percent

(n = 4,232) 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% male

female

TU low

male

female

TU medium definitely not

male

female

TU high probably not

male

female

DE low probably yes

male

female

DE medium

male

female

DE high

definitely yes

Figure 5.9 Fertility intentions by cluster and gender for Germany

As the previous comparison of the Bulgarian cluster groups demonstrated that use of contraception can deviate across groups, too, this aspect is also considered for Germany. Figure 5.10 therefore presents the contraceptive methods used by all clusters. Pills are the preferred method of contraception by all clusters, though the German medium and low clusters show the highest shares, followed by the Turkish high cluster. Condoms rank in second place for all groups. Their use is more expressed within the Turkish clusters. Other forms of prevention are hardly used in Germany. The findings are in line with reports of contraceptive use in Germany. Helfferich and colleagues (2011) report that pills and condoms are most often used as contraceptive methods independent of ethnic background.

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Once ethnic background is considered, too, native Germans use the pill more often than respondents of Turkish origin.

Contraceptive use by cluster (DE) (n = 2,382) 80% 70%

percent

60%

50% 40% 30% 20% 10% 0% TU low condom

pill

TU medium

TU high

intra-uterine device (coil, loop)

DE low withdrawal

DE medium

safe period method (rhythm)

DE high other

Figure 5.10 Contraceptive use by cluster for Germany

5.2.3

Structural Assimilation

Given that Turkish migrants are often found to be worse integrated than other immigrant groups on German territory (Hans, 2010; Woellert & Klingholz, 2014), plenty of research has focused on this ethnic group. Their structural incorporation is by far the most widely studied topic in German migration research and shows that Turkish migrants still deviate from the native majority population. Turkish migrants of both the first and the second generation have higher unemployment and lower employment rates than natives (Woellert & Klingholz, 2014), hold jobs with lower prestige (Euwals et al., 2007), and have lower educational levels (Kogan, 2004). Figure 5.11 shows the activity status of Germans and Turks within the GGS by cluster. On first sight, one can see that the employment rate increases within both ethnic groups by educational level. Similarly, the unemployment rate decreases with higher education. The highest unemployment (25.3 %) and lowest employment rate (38.7 %) can be found within the German low cluster. Moreover, the rate of homemakers is higher within lowly educated clusters than in higher educated groups.

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Activity status by cluster (DE) (n = 5,220) 90% 80% 70% percent

60% 50% 40% 30% 20% 10% 0%

TU low Employed

TU medium Unemployed

TU high Student

DE low Parental leave

DE medium Homemaker

DE high Other

Figure 5.11 Activity status by cluster for Germany

In a second step, it was also analyzed how the activity status differs across gender within the six clusters. Figure 5.12 demonstrates the results. There are big differences in employment rates among Turkish and German males of all clusters. Employment is highest within highly educated German and Turkish clusters and unemployment correspondingly low within these groups. For females within all clusters, the employment rate is lower than for their male counterparts. Moreover, especially Turkish females display high rates of homemakers. This share is generally higher the less educated the cluster. In comparison to previous findings that stress the disadvantages of Turkish migrants on the German labor market, it becomes clear that Turkish migrants are not a homogeneous group but rather differ along educational lines as does the German majority. The German labor market is highly segmented and perceived as meritocratic (Luetzelberger, 2015). Especially unskilled and lowly educated individuals are therefore faced with high (perceived) burdens to enter the labor market and to stay within it. This is also reflected within the data presented here. The BAMF showed that among Turkish immigrants, nearly 70 % have no vocational education, making it hard to gain ground within the German market economy (von Gostomski, 2010). Consequently, most Turkish immigrants work within low skilled or unskilled positions and are disproportionately hit by economic crises, seasonal employment and volatility (Kogan, 2004). In comparison to existing studies, the cluster distribution used here underlines that Turkish migrants are disadvantaged, but that the

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131

picture is much more nuanced than often reported. Turkish women are partly not included in unemployment statistics, as they often stay at home and care for children and their household. In that sense, it is hard to compare German and Turkish females regarding employment figures. What is more, some German natives (namely those within the German low cluster) even underperform Turkish migrants.

Activity status by gender and cluster (DE)

percent

(n = 5,220) 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% male

female

TU low Employed

male

female

TU medium Unemployed

male

female

TU high Student

male

female

DE low Parental leave

male

female

male

DE medium Homemaker

female

DE high Other

Figure 5.12 Activity status by gender and cluster for Germany

As education has been used to construct the cluster variables, this variable cannot be included into analyses differentiating clusters. To provide a short insight into the educational background of both Germans and Turks, nevertheless, Figure 5.13 shows the distribution of educational levels by ethnic belonging and gender. There are differences between Turkish migrants and German natives, as well as between females and males of both ethnic groups within the GGS. The share of Turkish migrants outweighs the number of Germans within primary and lower secondary education. Germans, on the other hand, outnumber Turks within upper secondary and higher levels of education. When comparing the educational background across gender, Turkish females seem particularly disadvantaged: Their share within primary or lower secondary education is even higher than the share of Turkish males.

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Educational level by gender and ethnicity (DE) (n = 5,222) 100%

percent

80% 60% 40% 20% 0% Male

Female

Male

Turkish lower secondary education or less

Female German

upper secondary education or less

tertiary education

Figure 5.13 Educational level by ethnic group and gender for Germany

Overall, one can conclude that Turkish migrants in Germany lag behind the native majority in terms of their structural inclusion, however, once clustering is used to typify both ethnic groups into smaller subgroups, the conclusion becomes more nuanced. While a comparison by ethnic group showed that Turkish migrants and especially Turkish women have a low educational background, an apportionment of ethnicity by education shows that Turks with high education even outperform Germans with low education. In that sense, one clearly has to conclude that structural inclusion is reached to a different extent by the Turkish cluster groups. What is more, when talking about inclusion one should differentiate which native average one is comparing to. In this study, it was already highlighted during the clustering procedure that even the majority can be grouped into different segments.

5.2.4

Cultural Assimilation

When it comes to cultural incorporation, there is evidence that cultural practices such as reading a Turkish newspaper, listening to Turkish music or cooking Turkish dishes are still common practice among most Turkish migrants in Germany. In comparison to other immigrant groups, they still cherish these cultural customs to a great extent, yet one can observe declines over time (Diehl & Schnell, 2006). This is supported by findings which reveal that 42 % of Turkish migrants in Germany use both languages when watching TV (von Gostomski, 2010). Within the

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133

GGS, about 46 % of all Turkish migrants report only speaking Turkish at home. When distinguishing these figures by clusters, one sees that highly educated Turks are more likely to speak German as first language (59.2 %) as compared to medium educated Turks (72.0 %) or lowly educated Turkish respondents (82.4 %). A comparison of language use with the German clusters is not possible, as the question of language use was only posed towards those indicated not to be born in Germany. Distinctive for language use seems to be the length of residence within Germany, as can be seen from Figure 5.14. Respondents who have resided within Germany for more than 25 years are more likely to use German at home and are least likely to use Turkish as language of communication at home.

Language use by cluster and length of residence (DE) (n = 1,536) 100%

percent

80% 60% 40% 20%

0% 0–10 years

11–25 years

25–46 years

0–10 years

TU low

11–25 years

25–46 years

TU medium German

Turkish

0–10 years

11–25 years

25–46 years

TU high

Other

Figure 5.14 Language spoken at home by length of residence and cluster for Germany (Turkish clusters only)

Religiosity is the one cultural element which is considered the biggest divide between Turkish immigrants and the majority (Foner & Alba, 2008). More than 90 % of all Turkish immigrants are Muslims and most of them agree that their belief is an important part of their lives. Still, Diehl and Schnell (2006) have underlined that the share of Turks who rarely attend religious services is increasing since 1984. Although the present data do not allow for testing this development, the GGS does offer the opportunity to compare the regularity of religious service attendance, the importance of religious ceremonies as well as whether one is religious or not. If one looks at whether respondents are religious,

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88 to 96 % of the respondents within the three Turkish clusters indicate to be Muslim. The lowest share of Muslim respondents is found within the highly educated Muslim cluster, where another 8.5 % indicate to be not religious. Looking at the three German clusters, the predominant domination indicated is Christianity (66 to 72 %) followed by no religion at all (27 to 32 %). Among German respondents, the share of religious respondents is highest within the highly educated cluster. Comparing the importance of religious ceremonies by cluster, the three Turkish clusters attach importance to religious ceremonies (54 to 58 % importance), while only 26 to 33 % of respondents within the three German clusters regard religious ceremonies important. Rather, half of all Germans considers religious ceremonies hardly important.

Religious service attendance by cluster (DE) (n = 5,222) 60% 50%

percent

40% 30% 20% 10% 0% TU low

TU medium

at least once a week

TU high

at least once a month

DE low at least once a year

DE medium rarely

DE high never

Figure 5.15 Frequency of religious service attendance by cluster for Germany

Figure 5.15 analyses the frequency of religious service attendance. The share of Germans who never or rarely attend religious services is high within the medium and low cluster (60 to 77 %). The corresponding percentage within the German high cluster, however, is comparable to the number of Turks within all three Turkish clusters who never or rarely attend religious services (39 to 50 %). The three Turkish groups, though, show higher levels of respondents who attend services at least weekly. Generally, it can be observed that Germans visit church never, rarely or a couple of times a year, while Turks are more often than Germans found within the categories weekly, monthly or a couple of times a year.

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135

Another interesting aspect to investigate is the attitude that Turkish migrants hold towards family life and partnership. Across the three German clusters, at least 44 to 56 % hold liberal attitudes and nearly one-third of all respondents within the German clusters indicates a neutral attitude. This is in clear contrast to the Turkish clusters. Here, 56 % (TU low) to 46 % (TU high) hold a conservative attitude. Only 12 to 22 % among respondents within the Turkish clusters can be classified as liberal. Generally, more highly educated respondents—independent of ethnic background—indicate more liberal attitudes. When looking at the division of household tasks, Appendix G in the Electronic Supplementary Material demonstrates that a traditional gender role distribution of household chores dominates across both ethnicities. Both German and Turkish females are more likely than men to indicate that they are usually responsible for preparing meals, doing the dishes or vacuuming the house. Repairs in the house, on the other hand, are rather a male domain among both Germans and Turks. Differences across gender and ethnicity can also be observed. Turkish respondents more often than Germans indicate that another person in the household does a certain task. One possible explanation might be that Turkish families live within and cherish extended family models (Citlak et al., 2008; Liversage & Jakobsen, 2019). Another ethnic difference can be found in the share of women who indicate to take over tasks such as repairs, financial responsibility. Here, German women are more likely than Turkish women to execute such a task. To conclude, culturally there are differences between German and Turkish respondents. Many Turks still speak Turkish as the first language at home, though one can see that the use of German increases with length of residency in Germany. Turkish respondents are more religious and slightly more likely to adhere to a traditional household task distribution. Moreover, as far as the attitude regarding marriage and family life are concerned, Turkish respondent are much more conservative than German respondents. Overall, one can thus conclude that there remain huge deviations in attitudes and religiosity across ethnic groups. Generally, higher educated Turkish respondents seem slightly more liberal and less religious than their less educated counterparts.

5.2.5

Social Assimilation, Identity, and Intergroup Relations

Socially, Turkish immigrants have improved their assimilation during the last decades. Nevertheless, clear differences between Turks and other immigrant groups in Germany remain. There is a share of approximately 30 % of the Turkish population who have only German friends, and 45 % who have one German

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friend among their three closest acquaintances (Diehl & Schnell, 2006; Haug, 2003). Still, there is also a proportion of 22 % who has never been in contact with Germans despite living in Germany (von Gostomski, 2010). In terms of partnership, co-ethnic unions dominate, but among the younger generation there is a tendency to opt for mixed-ethnic unions more often, especially when one looks at Turkish youngsters who live within a partnership without being married (Weidacher, 2000). Within the GGS data, 89 to 95 % of respondents within the three German clusters indicate that their partner is born in Germany. Among respondents within the Turkish clusters, 21 to 42 % of respondents within a partnership mention that their partner is born in Germany.1 The percentage of partners born in Germany increases with the educational background of Turkish respondents. When looking more specifically into the origin of the partner, 95 to 97 % of Turks with a foreign partner name a Turkish ethnic background of their partner. Homogamy is thus prevailing among Turkish migrants, especially when educational background is low. Looking at the social contacts of Turkish and German respondents, German respondents within the GGS are slightly more likely (27 to 30 %) than Turkish respondents (20 to 27 %) to indicate that they do have someone outside their family to talk with about their problems and concerns. As there is no information on the nature of these contacts, one cannot draw any concrete conclusion about social integration. As far as the attitude of the German majority towards the Turkish migrants is concerned, the GGS offers little insight. However, existing studies have looked at the acculturation attitude of German natives and at intergroup relations in Germany. A Eurobarometer from 1997 showed that 40 % of German natives favour integration over assimilation, 26 % prefer assimilation of migrant groups and 36 % disagrees with both forms of incorporation (Eurobarometer, 1997). Zick et al. (2001) summarise existing findings about the majority’s attitude towards acculturation and conclude that assimilation and separation are clearly favoured by Germans over integration of its minorities. Building upon these findings, Jasinskaja-Lahti et al. (2003) demonstrate that conflicting acculturation attitudes between majority and minority relate directly to perceived discrimination on the side of the minority respondents. In Germany, this clash is particularly likely to occur when natives favour assimilation as acculturation strategy. The attitude of the majority towards minorities thus has a clear influence on the assimilation process of migrants and minorities in Germany. 1

One should keep in mind that the remaining 28 % could have a partner who is born in Germany but is migrant of the second generation.

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137

In summary, social integration and intergroup relations point towards an assimilation status that has not yet reached the final stage of assimilation as Turkish respondents deviate strongly from the German majority. Again, it seems that those Turkish respondents with higher educational background have integrated further than those with lower educational levels.

5.2.6

Summary

In conclusion, one can say that Turkish immigrants in Germany are not yet fully incorporated. There are differences in fertility behaviour, as Turkish migrants have more children on average and tend to have more positive fertility intentions, especially if they have no children yet or only one child. Furthermore, they stay behind German natives culturally, structurally and socially. Turkish migrants are overrepresented among the un-/low skilled and lag behind the national majority in terms of their educational achievement. Diehl and Schnell (2006) also consider this the reason for the constant strong cultural link to Turkish traditions. Once migrants are noticing their disadvantaged labour market position, they might compensate this low social standing by adhering to familiar ethnic habits. There is, however, a clear difference between clusters visible: When comparing Germans and Turks by cluster, one notes that highly educated Turks are most advantaged compared to lower educated peers. In several analyses, highly educated Turks even outperform lowly educated Germans or behave equally to some German clusters. This is an important finding as it underlines the necessity to differentiate intergroup differences when talking about assimilation.

5.3

Minority Sample

Within section 2.1 it was pointed out that migrant groups are a minority, too. Though, the term minority is often applied to groups who have become a minority due to redrawn political boundaries, migrants are a minority numerically and, in some countries, also attributed the label minority. It is thus plausible to wonder to what extent migrant and minority groups who originate from the same home country deviate or equal each other in terms of culture and especially fertility. As both Turkish migrants in Germany as well as the Turkish minority in Bulgaria originate from Turkey, they will be compared in the following in more detail. For this purpose, a cluster analysis to identify meaningful dividing lines is conducted first before then describing the demographic background and assimilative dimensions.

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5.3.1

5

Assimilation Status of Turkish Migrants in Germany …

Cluster Analysis

As a new sample containing only minority and migrant respondents was constructed to concretely compare these groups, a new cluster analysis among all minority and migrant respondents was conducted to see, how dividing lines are dispersed. Gender, age, education, and ethnicity were included within this clustering procedure. Age is only attributed a relevance of 7 %, which is why it was excluded as first predictor. The remaining three predictors are attributed a relevance of 100 %, though no meaningful differentiation results and the cluster is of bad quality. As the resulting four clusters group along ethnic lines, education and gender were excluded, too, yielding a two-cluster solution with minority and migrant respondents only that has a very good cluster quality. This solution delivers a BIC of 2,817.534 for a one cluster solution and a minimized BIC of 15.981 for a two-cluster solution. The minority sample consists of 540 participants, the migrant sample is comprised out of 2,413 respondents.

5.3.2

Demographic Background

The demographic set up of the minority and migrant group is summarized in Table 5.5. One can see that the gender composition is balanced within both groups. The minority sample is slightly older than the migrant sample and has more children on average, while the migrant sample has a wider range when the number of children is concerned. A t-test with Welch correction shows that the difference in average number of children is significant, t(1126.364) = −3.904, p < .001, d = −0.147. As far as the marital status is concerned, roughly threefourth of all respondents within both groups are married, while the remaining share is mainly single within the minority group and mainly in a partnership within the migrant sample. The number of children by group is also presented graphically in Figure 5.16. Here, it is visible that minority respondents often have two children, followed by one child, while a similar share (roughly 30 %) of Turkish migrants has two children or no children. Turkish migrants, though, have more often three or more children than minority respondents. A comparison with average number of children in Turkey shows that Turkish residents generally have a high number of children. Turks residing in Turkey gave birth to 2.27 children on average in 2005 (Worldbank 2022). It thus seems that Turkish migrants are closer to the fertility rate of Turkey than Turkish minority respondents in Bulgaria.

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139

Table 5.5 Demographic characteristics by minority status

Variable

Minority

Migrant

Gender Males

254 (47.0 %)

1318 (54.6 %)

Females

286 (53.0 %)

1095 (45.4 %)

M ( SD)

34.54 (7.03)

32.91 (7.42)

Range

18–50

18–50

M (SD)

1.82 (0.96)

1.63 (1.41)

Range

0–7

0–15

Age

Number of children

Marital status Married

407 (76.4 %)

1807 (77.6 %)

Partner

72 (3.1 %)

111 (20.8 %)

Single

443 (19.3 %)

15 (2.8 %)

Notes. M = mean, SD = standard deviation.

Number of children by minority status (n = 2,952) 60%

percent

50% 40% 30% 20% 10% 0% 0

1

2 Minority

3

4

5 or more

Migrant

Figure 5.16 Number of children by minority status

When looking at fertility intentions, 65.9 % of migrant respondents definitely or probably do not intend to have another child within the upcoming three years,

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Assimilation Status of Turkish Migrants in Germany …

while 83.9 % of minority respondents do not intend a (further) child. Correspondingly, the share of positive fertility intentions is higher among migrant than among minority respondents.

5.3.3

Structural Assimilation

When focussing upon the comparison of Turkish minority and Turkish migrants in particular, Figure 5.17 compares the activity status of both groups. The employment level is comparable with about 50 % within both groups being employed. However, big differences can be observed in terms of the share of those who are unemployed and homemaker. While over 41.6 % of minority respondents indicate to be unemployed, only 16.5 % of Turkish migrants are classified as unemployed. Instead, many Turkish migrants (22.5 %) are homemaker and thus not listed within unemployment figures. When looking for comparable statistics from Turkey, Rivas and Ergun (2019) report that 29 % of Turks are a homemaker when using data from the 2015 Value Survey. It thus seems that there is a high share of homemakers in Turkey, too, implying that it can again be observed that the Turkish migrants in Germany resemble Turks in Turkey more closely than does the Turkish minority in Bulgaria.

Employment status by minority status (n = 2,953) 60%

percent

50% 40%

30% 20% 10% 0% employed

unemployed

student Migrant

leave

homemaker

other

Minority

Figure 5.17 Employment status by minority status

If one also considers the educational level of both groups, the Turkish minority seems to have lower degrees than migrant respondents. While there are only 8.7 % minority respondents with primary education as compared to 13.5 % of

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141

Turkish migrants, 48.5 % of minority respondents hold a lower secondary degree as compared to 38.5 % of Turkish migrants. Also, the share of tertiary education differs by minority status. Here, only 4.3 % of minority respondents hold a teratiry degree as opposed to 7.4 % of Turkish migrants. If one considers the employment status by educational degree, Figure 5.18 makes clear that especially minorities with tertiary education show a high level of employment. With lower educational levels, the share of unemployment, other employment, homemakers and those on parental leave rises.

percent

Employment status by education and minority status (n = 2,953) 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Primary

Lower secondary

Upper secondary

Tertiary

Primary

Migrant Employed

Unemployed

Lower secondary

Upper secondary

Tertiary

Minority Student

Leave

Homemaker

Other

Figure 5.18 Employment status by educational background and minority status

5.3.4

Cultural Assimilation

As cultural differences form major ethnic markers among all groups within both countries, some cultural aspects were picked out in the following to compare the Turkish minority in Bulgaria with Turkish migrants in Germany. For this purpose, three statements regarding marriage and family were compared across groups. Figure 5.19 presents respondents’ answers towards the statements, whether marriage is for lifetime, whether homosexual couples should have the same rights as heterosexual ones and whether a woman needs children to be fulfilled. On all three aspects, minority and migrant respondents differ in their opinion. As for

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Assimilation Status of Turkish Migrants in Germany …

marriage as lifetime relationship, migrants agree more than minority respondents. While nearly 70 % of migrants agree that divorce is not an option, 54.8 % of minorities disagree. When looking at data from Turkey to compare the presented results with data from the home country context, roughly 50 % of Turks agree that a marriage should never be ended (Engin et al., 2020). This is more in line with the attitude of the Turkish migrants in Germany than with the Turkish minority in Bulgaria. When looking at the attitude towards homosexuality, answers among both groups are closer to each other. Nevertheless, there is a greater share of Turkish migrants who agrees than among minority respondents. Instead, minority respondents are more often within the neutral category. Engin et al. (2020) showed based on data from the World Value Survey 2011 that Turkish residents in Turkey are mostly against homosexuality. Ranking the justification of homosexual relationships on a scale from 1 (never) to 10 (always justifiable), more than 77 % evaluated homosexual relationships as never justifiable. On this statement, the Turkish minority is thus closer to the opinion in Turkey.

Attitude by minority status (n = 2,953) 100%

percent

80% 60% 40% 20% 0% Minority

Migrant

Marriage for lifetime

Minority

Migrant

Homosexuals same rights agreement

neutral

Minority

Migrant

Children for fulfillment

disagreement

Figure 5.19 Attitude towards marriage and family life by minority status

Thirdly, Figure 5.19 reports the opinion towards children as fulfilment for women. Here, both groups agree (Minority: 76.3 %, Migrants: 66.5 %). Moreover, the share of disagreement is slightly higher among migrants (Minority: 6.7 %, Migrants: 17.5 %). No comparative numbers for Turkey could be found for this measure. Overall, all analyses demonstrate that minority and migrant groups are both influenced and coined by the attitude within their country of living as well

5.4 Conclusion

143

as by their home country Turkey. Generally, Turkish migrants seem more liberal towards family live than Turkish minority respondents. As there are hardly any data on social and identificational assimilation present within the GGS and as the topics were already discussed in section 5.1.5 for Bulgaria and section 5.2.5 for Germany, these dimensions will not be compared here. Instead, a conclusion will complete this chapter on descriptive statistics.

5.4

Conclusion

This review on the living situation and assimilation status of the Turkish minority in Bulgaria and the Turkish migrants in Germany as well as a comparison of migrant and minority respondents provided valuable insights regarding the first and second research questions of this dissertation. First of all, a clustering procedure was applied to check whether a (mere) distinction by ethnicity is meaningful to divide society into different groups. This analysis showed for both Germany and Bulgaria that their societies are more diverse than often assumed within the literature. While one could distinguish four majority clusters and one minority cluster within Bulgaria, the German GGS data could be divided into three migrant and three native clusters. Within both societies ethnicity and education are important dividing lines, while in Bulgaria the place of residence (rural versus urban) constitutes a further line of distinction. In Germany, ethnicity and education could even explain nearly all variance within the data during clustering thus underlining that these dividing lines seem to be essential to understand societal relationships. When only minority and migrant respondents were grouped into homogeneous clusters, no variable except minority status was relevant to draw societal dividing lines. This allows for a first conclusion, namely that ethnicity is an important marker within both countries and analyses along ethnic lines are meaningful but should not rest on ethnicity on its own but rather look at intersectional boundaries within society. Looking at the assimilation status of the Turkish minority in Bulgaria the results outlined within section 5.1 point towards segregationist tendencies. The Turks and Bulgarians do get along and uphold peaceful relations. Yet, the Turkish minority is overrepresented within three of nine districts within Bulgaria and forms the majority within many mountainous villages. Here, they live their separated lives, speaking Turkish, adhering to Turkish traditions and values. Also, the Turks are an endogamous group who builds up partnerships within the Turkish community (Tomova, 1998). When compared to the four Bulgarian clusters, the Turkish community seems closer to the rural Bulgarian clusters than to the urban

144

5

Assimilation Status of Turkish Migrants in Germany …

ones. However, a general conclusion that can be drawn from section 5.1 is that the Turkish minority differs in several regards from all Bulgarian clusters. The Turkish minority is rather conservative in their attitudes regarding family life and partnership. Moreover, analyses from the GGS showed that they uphold a more traditional distribution of household tasks than the Bulgarian majority. Linguistically, most of them speak Turkish at home. Eventually, religion is important to both Bulgarians and Turks and forms a dividing line in that Bulgarians adhere to Orthodoxy while Turks are Muslims. Besides the cultural dimension, the structural integration is a further significant difference between majority and minority. The Turkish cluster is outstanding regarding unemployment, even when compared to rurally living Bulgarians. Though unemployment and poverty are high within rural Bulgaria, the Turkish community is hit extremely hard as their educational levels are clearly below the educational averages of the Bulgarian majority. This disadvantages rural Turks even in comparison to rural Bulgarians. Duijzings (2014) even concludes that poverty and economic disadvantages are the driving forces behind self-imposed segregation and residential proximity of the Turkish community in Bulgaria. As far as the Turkish migrants in Germany are concerned, a more nuanced conclusion has to be drawn. Overall, the process of assimilation is taking place among this migrant group, however, the final state of assimilation is not reached (yet). The three Turkish clusters identified differ regarding their educational background. While Turks on average hold lower educational degrees than the German majority, the conclusion of section 5.2 is that highly educated Turks perform better than their lower educated peers. In some cases (e.g., employment rates), highly educated Turks even outperform lowly educated German natives. Hence, respecting the heterogeneity of the Turkish group is essential when drawing conclusions regarding their assimilation status. This is already one difference compared to the homogenous Turkish minority in Bulgaria. Looking at the different dimensions of assimilation, Turks differ structurally by having more children than Germans and by having higher unemployment rates and higher shares of homemakers, especially among Turkish females. One needs to highlight, though, that structurally highly educated Turks perform similar to Germans with medium or high education. However, culturally there remain huge differences between the German and the Turkish clusters. Turks are much more conservative in their attitude regarding family life and partnership. Also, religion is not that important for Germans and many Germans do not adhere to any religion or never visit services. Among all Turkish clusters, shares of believers are high and services as well as ceremonies are important. Linguistically, many Turkish respondents indicate to still speak Turkish at home. There is a tendency

5.4 Conclusion

145

observable that highly educated Turks more often speak (also) German at home and that the percentage of German speakers increases among all clusters with length of residence. Finally, socially both groups do get along. Yet, existing studies highlight that both ethnic groups prefer to stay within their communities when it comes to friendships. In conclusion, the Turkish migrants in Germany seem to have reached the state of integration, since they are incorporated to at least a certain extent on some dimensions. This state is reached by all three clusters as they have approached the German majority at some dimensions of integration. However, the Turkish respondents with high educational background are more advanced in their integrational state. The biggest divide in Germany is the cultural integration, while structural integration seems the most advanced, especially among Turks with good educational backgrounds. Finally, a comparison of Turkish migrants and minority with their peers in Turkey showed that demographic, structural and cultural differences exist and that both groups equal Turks in Turkey to some extent, but also differ from their home society. Turkish migrants have less children on average, but a broader range of number of children than minority respondents who mainly have two children. Also, Turkish migrants have more positive fertility intentions. Culturally, Turkish migrants seem more liberal in their attitudes towards marriage and family life. Structurally, especially the differences in unemployment rates and the share of homemakers divides the two groups. Unfortunately, the data sets that include a sufficient sample size of migrant and minority respondents while at the same time providing a thorough background on the living situation and assimilation status of these groups are limited. For this reason, it was not possible to analyze all aspects of assimilation sufficiently, as envisaged within the theoretical model in chapter 2. It was possible to capture the host country continuum of assimilation, mainly regarding structural and cultural assimilation. The home country assimilation, though, could only be assessed to a limited extent by looking at language use. To a certain extent, the conclusions derived from this section hence have to remain tentative and more research is needed to verify the assimilation status of the groups under study. Nevertheless, by embedding own research into existing findings, it was possible to track the assimilation process of Turkish migrants in Germany and the Turkish minority in Bulgaria, as well as to link their status to historical evolvements and the context of reception. Building upon these findings, the next chapter will focus on inferential analyses to find out whether the assimilation status of the migrants and minorities can explain differences in fertility intentions.

6

Relationship Between Assimilation and Fertility Intentions

Having seen that Turkish migrants in Germany and the Turkish minority in Bulgaria are not yet fully assimilated into their countries of residence, this chapter will focus on research question three by looking at the extent to which fertility intentions differ across ethnic groups and to what extent assimilation characteristics can explain differences in fertility intentions. For this purpose, the fertility intentions across clusters as well as the mediating role of assimilation related variables will be looked at first within the Bulgarian context before conducting the same analyses within the German context and lastly among minority and migrant respondents only.

6.1

Turkish Minority in Bulgaria

To analyze relationships between fertility and assimilation within the Bulgarian context, the relationship between traits, desires and intentions derived from the traits-desires-intentions-behavior sequence was looked at first. In this context, it was also analyzed, whether fertility intentions differ across the five identified clusters. Then, variables measuring the different aspects of assimilation were integrated to check, whether they can explain the relationship between the clusters and fertility intentions.

6.1.1

Traits, Desires, and Intentions within Bulgaria

When regressing motivational traits on fertility desire using generalized structural equation modelling in Stata, significant relationships within the Bulgarian sample can be observed. Table 6.1 presents the odds ratios resulting out of the structural equation model. As can be in the table, the more negative the anticipated costs © The Author(s), under exclusive license to Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2023 B. Brünig, The Fertility of Migrants and Minorities in Europe, https://doi.org/10.1007/978-3-658-43099-3_6

147

148

6

Relationship Between Assimilation and Fertility Intentions

Table 6.1 GSEM partial model for Bulgaria—Odds ratios (robust standard errors) Exogeneous variables

Endogeneous variables Desire no

Desire unsure

Positive intentions

Desire no

0.016***

Desire unsure

0.087***

BU-UP-RU

1.221

(0.003) (0.013) (0.212) BU-LOW

0.985

TU-LOW-RU

1.176

BU-HI-URB

2.147***

(0.224) (0.235) (0.288) Married

0.913

Age

0.935

Male

1.210

Parity three

0.153***

(0.137) (0.008) (0.141) (0.049) Parity two

0.065***

Parity one

0.566***

(0.014) (0.079) Traits—costs Traits—benefits n

1.863***

0.828*

(0.198)

(0.066)

2.187***

0.782**

(0.304)

(0.070)

4,770

df

19

Log-likelihood

−6,668.432 (continued)

6.1 Turkish Minority in Bulgaria

149

Table 6.1 (continued) Exogeneous variables

Endogeneous variables Desire no

AIC

13,374.86

BIC

13,497.80

Desire unsure

Positive intentions

Notes. Reference group: desire yes, BU_UP_URB, not married, female, parity zero. *** p < .001, ** p < .01, * p < .05

of having children, the more likely one is to indicate negative desires instead of positive ones. An even stronger effect on negative desires is exerted by the anticipated benefits of having children. Here, the worse the influence of children on these potential benefits, the higher the likelihood to hold negative instead of positive fertility desires. Unsure fertility desires are similarly influenced by both benefits and costs of having children. Worse anticipated effects in terms of both costs and benefits are related to a decreased likelihood of having unsure desires in comparison to positive desires. Out of both, benefits related to children have a stronger depressing influence. Desires in turn are significantly related to fertility intentions. Both unsure and negative desires are related to a decreased likelihood of indicating positive fertility intentions when compared to positive desires. These results are in line with the model of Miller and Pasta and confirm hypotheses three and four for the Bulgarian sample. When looking at the clusters, only the Bulgarian highly educated urban group differs significantly from the reference group—urban Bulgarians with upper secondary education. Compared to the reference group, the highly educated show a greater likelihood to intend to have a(nother) child within the next three years. The expected difference between the Turkish cluster and the Bulgarian reference group is not supported by the data thereby speaking against H5a. Looking at the control variables, parity has a strong, significant relationship to positive fertility intentions with parity two (OR = 0.065) having the strongest negative influence followed by parity three (OR = 0.153). Gender and age have no relevant influence on fertility intentions, neither does marital status.

6.1.2

Influence of Assimilation on Fertility Differences

In a second step, the assimilation related variables were taken up into the model as mediators of the relationship between the clustering variables and fertility intentions. All relationships described so far are still present within this full model (see Table 6.2). Additionally, students compared to those in employment are

150

6

Relationship Between Assimilation and Fertility Intentions

significantly less likely to intend to have a(nother) child soon. Importance of religious ceremonies, however, is not related to fertility intentions. Social support relates negatively to having positive intentions. Those who have little social support are thus less likely to indicate to intend to have a(nother) child soon. Also, attitude is a significant predictor of fertility intentions. The more conservative one’s attitude, the more likely one is to intend to have a(nother) child. Generally, assimilation related variables are thus related to fertility intentions but cannot explain differences between the Bulgarian clusters. Looking more closely at the indirect relationships, being in the Turkish cluster associates positively with being in other employment or unemployed when compared to those in employment. Also, being in the Turkish cluster is related negatively to being a student compared to being employed. As far as religion and attitude are concerned, being in the Turkish group increases the likelihood to show conservative values and to disagree that religious ceremonies are important. Moreover, there is a negative relationship of the Turkish cluster to social support meaning Turks are less likely to have a good social network. The rural Bulgarian cluster as well as the lowly educated Bulgarians—when compared to the urban Bulgarians with upper secondary education—are both more likely to be unemployed than in employment. Additionally, the rural Bulgarians with upper secondary education are less likely to be a student than in employment and have a significantly lower likelihood to experience good social support. In terms of attitude and religion, both Bulgarians in rural areas as well as lowly educated Bulgarians are more likely to hold conservative attitudes than the reference group. Bulgarians with lower education are also more likely than the urban Bulgarians with upper secondary education to cherish religious ceremonies. As far as the highly educated urban Bulgarians are concerned, significant negative relations with being a student and being unemployed as well as significant positive effects with being on parental leave can be observed. Moreover, they have a decreased likelihood for conservative attitudes. Looking at all predictors of positive fertility intentions, desire, parity and being a student exert the strongest negative influences on intentions, while the strongest positive influence has the cluster BU-HI-URB. To understand the found relationships in more detail, Figure 6.1 shows the predicted mean of positive fertility intentions for all five Bulgarian clusters grouped by parity. The distribution looks quite similar across all clusters with positive intentions decreasing as parity increases. Still, one can observe that urban Bulgarians with tertiary education hold the most positive intentions across all parities.

6.1 Turkish Minority in Bulgaria

151

Predicted mean of positive fertility intentions (BU) (n = 4,770) 1.0 0.9

predicted mean

0.8

0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0 BU-UP-RU No children

BU-LOW One child

TU-LOW-RU Two children

BU-HI-URB

BU-UP-URB

Three children or more

Figure 6.1 Predicted mean of positive fertility intentions by cluster and parity for Bulgaria

When the predicted mean of positive intentions is looked at by parity and attitude of respondents (Figure 6.2), it can be seen that across parity there is a tendency for conservative attitudes to be related to more positive intentions. This effect is least visible for respondents who already have two children. Moreover, it seems that attitude is more important to predict different fertility intentions within the Turkish and highly educated urban Bulgarian cluster. The predicted mean for positive intentions divided by cluster, parity and employment status is presented in Figure 6.3. There are visible differences across all clusters and parity by employment status. The smallest differences in the predicted fertility intentions can be found among those with two children. Here, urban Bulgarians with upper secondary education who are still studying stand out with a predicted mean of positive intentions of .50. Among those with no children yet those who are unemployed, employed or homemaker show very high levels of predicted positive intentions. Especially the Turkish cluster in combination with being student or homemaker shows a strong tendency to intend to have children soon. Focusing then on those who have one child already, the highest probability of positive intentions can be found across clusters among homemakers and those on parental leave. Among those respondents with three or more children, the predicted mean is highest among those who are studying or homemakers. The highest level of positive intentions can be found within the rural Bulgarians with upper secondary education who are homemaker.

predicted mean

BU-UP-RU

BU-UP-RU neutral

liberal

BU-LOW

BU-HI-URB

conservative

TU-LOW-RU neutral

BU-HI-URB

conservative

TU-LOW-RU

Two children (BU)

liberal

BU-LOW

No children (BU)

BU-UP-URB

BU-UP-URB

1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

BU-UP-RU

BU-UP-RU

BU-HI-URB conservative

TU-LOW-RU neutral

liberal

BU-LOW

neutral

conservative

TU-LOW-RU BU-HI-URB

Three children or more (BU)

liberal

BU-LOW

One child (BU)

Figure 6.2 Predicted mean of positive fertility intentions by cluster, parity, and attitude for Bulgaria (n = 4,770)

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

BU-UP-URB

BU-UP-URB

6

predicted mean

predicted mean

predicted mean

152 Relationship Between Assimilation and Fertility Intentions

predicted mean

student

TU-LOW-RU

BU-UP-URB

employed

unemployed

Student

leave

homemaker

other

0.0 BU-HI-URB

0.1

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0.0

0.0 TU-LOW-RU

BU-UP-URB

0.2

BU-LOW

other

BU-HI-URB homemaker

Two children (BU)

unemployed

BU-LOW

0.1

BU-UP-RU

employed

BU-UP-RU

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

No children (BU)

employed

leave

TU-LOW-RU student

BU-LOW unemployed

BU-UP-RU employed

leave

TU-LOW-RU student

homemaker

BU-HI-URB

homemaker

BU-HI-URB

Three children (BU)

BU-LOW unemployed

BU-UP-RU

One child (BU)

BU-UP-URB

other

BU-UP-URB

other

Figure 6.3 Predicted mean of positive fertility intentions by cluster, parity, and employment status for Bulgaria (n = 4,770)

predicted mean

predicted mean prediced mean

1.0

6.1 Turkish Minority in Bulgaria 153

154

6

Relationship Between Assimilation and Fertility Intentions

One child (BU)

predicted mean

predicted mean

No children (BU) 1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0 BU-UP-RU

BU-LOW

TU-LOW-RU

support

BU-HI-URB

1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0 BU-UP-RU

BU-UP-URB

Two children (BU)

TU-LOW-RU

BU-HI-URB

BU-UP-URB

no support

Three children (BU)

1.0

1.0

0.9

0.9

0.8

0.8

0.7

0.7

predicted mean

predicted meean

BU-LOW

support

no support

0.6 0.5 0.4 0.3 0.2

0.6 0.5 0.4 0.3 0.2

0.1

0.1

0.0

0.0 BU-UP-RU

BU-LOW

TU-LOW-RU

support

no support

BU-HI-URB

BU-UP-URB

BU-UP-RU

BU-LOW

TU-LOW-RU

support

BU-HI-URB

BU-UP-URB

no support

Figure 6.4 Predicted mean of positive fertility intentions by cluster, parity, and social support for Bulgaria (n = 4,770)

Finally, looking at the influence of social support on the predicted mean of having positive fertility intentions (Figure 6.4), one can see that the significant negative influence of social support on fertility intentions varies by cluster and parity. While there is hardly any difference in predicted fertility intentions in parity two and three, one can see a tendency of those having no social support to rather intend to have more children soon within the parities zero and one. Nevertheless, it is noteworthy that there is hardly any difference for the Turkish cluster in parity zero and an inverse relationship between social support and positive fertility intentions among the Bulgarian low and the urban Bulgarian cluster with upper secondary education in parity one.

Student

Unemployed

Employed other

BU-HI-URB

TU-LOW-RU

BU-LOW

BU-UP-RU

Desire unsure

Desire no

Exogeneous variables

Desire no

Desire unsure

(0.195)

(0.071)

0.233***

(0.166)

0.960

(0.510)

0.986

(0.319)

(0.474) 0.668

2.062***

(0.276)

(0.429) 1.768*

(0.176) 1.094

1.385

(0.371)

(0.217) 0.700

1.342

1.006

(0.017)

0.098***

(0.004)

0.019***

(0.056)

0.394***

(0.455)

4.222***

(0.335)

2.834***

(0.187)

1.628***

(0.247)

0.980

(0.206)

(0.213)

(0.085)

(0.235)

0.312*** 1.415*

(0.054)

0.076*** 0.864

(0.311)

1.439

(0.089)

(0.196)

0.591

(0.413)

1.276

(0.493)

1.519

(0.217)

0.491

(0.376)

1.604*

(0.549)

2.823***

Attitude

(0.065)

1.212***

(0.040)

1.028

Religiousness

(0.125)

1.181

(0.097)

(0.094)

0.576**

(3.858)

(continued)

(0.031)

0.980

(0.062)

0.523*** 17.129*** 1.252***

(0.140)

0.823

(0.103)

0.667**

Parental Homemaker Support leave

0.227*** 0.908

Positive Employed Unemployed Student intentions other

Endogeneous variables

Table 6.2 GSEM results full model for Bulgaria—Odds ratios (robust standard errors)

6.1 Turkish Minority in Bulgaria 155

Male

Age

Married

Religiousness

Attitude

Desire no

Desire unsure

(0.149)

1.097

(0.010)

0.921

(0.137)

0.744

(0.069)

0.891

(0.015)

1.035*

(0.115)

0.684*

(0.416)

0.819

(0.342)

0.992

Positive Employed Unemployed Student intentions other

Endogeneous variables Parental Homemaker Support leave

Attitude

(continued)

Religiousness

6

Support

Homemaker

Parental leave

Exogeneous variables

Table 6.2 (continued)

156 Relationship Between Assimilation and Fertility Intentions

Desire unsure

(0.198)

(0.066)

1.863*** 0.828*

Desire no

65,058.39

65,504.82

AIC

BIC

Parental Homemaker Support leave

Attitude

Notes. Reference group: desire yes, BU-UP-URB, employed, not married, no support, female, parity zero. *** p < .001, ** p < .01, * p < .05

69

−32,460.19

Log-likelihood

(0.070)

df

4,770

(0.304)

n

(0.064)

0.371***

(0.009)

0.034***

(0.038)

0.104***

Positive Employed Unemployed Student intentions other

Endogeneous variables

Traits—benefits 2.187*** 0.782**

Traits—costs

Parity one

Parity two

Parity three

Exogeneous variables

Table 6.2 (continued) Religiousness

6.1 Turkish Minority in Bulgaria 157

158

6.1.3

6

Relationship Between Assimilation and Fertility Intentions

Robustness Checks for Bulgaria

To verify the presented results, logistic regression analyses were carried out additionally. As presented in Figure 4.1, the overall model was splitted into two models for this purpose. The first part analyzed to what extent there is a relationship between motivational traits, fertility desire and fertility intentions. Table 6.3 summarizes the results. Table 6.3 Logistic regression models predicting desire and fertility intentions by fertility related variables for Bulgaria (Odds ratios) Variables

Desire yes Model 1

Positive intentions Model 2

Traits—costs

0.325***

0.411***

Traits—benefits

0.165***

0.377***

Desire yes

Model 1

Model 2

Model 3 0.400*** 0.422***

44.154***

25.928***

21.447***

Male

1.059

1.105

1.025

Married

1.308*

0.704**

0.656**

Age

0.952***

0.941***

0.949***

Parity one

0.884

0.469***

0.532***

Parity two

0.115***

0.037***

0.048***

Parity three

0.192***

0.074***

0.104***

n

4,770

Nagelkerke R2

.200

.333

.428

.621

4,770 .653

Chi2 (df)

579.246 (2)***

1,007.840 (8)***

1,521.204 (1)***

2,418.668 (7)***

2,590.396 (9)***

Notes. Refence group: not married, female, parity zero, desire no/unsure. *** p < .001, ** p < .01, * p < .05

As can be seen, there is a negative relationship between traits (both costs and benefits) and a positive fertility desire1 . Thus, the higher the expected burden of having (a)nother child, the less likely one is to desire further children. This relationship holds, once partnership status, parity, age and gender are controlled for. In a second step, Table 6.3 shows how traits, desire and the control variables impact upon positive fertility intentions. Here, one can observe a strong positive

1

Desire was limited to positive versus not positive desire for parsimony.

6.1 Turkish Minority in Bulgaria

159

influence of desiring children on the likelihood to express positive fertility intentions. This relationship persists, even when control variables and motivational traits are controlled for. These results are in line with those presented by the structural equation model. Moreover, they amend the theory of Miller and Pasta by showing that there is also a direct relationship between traits and intentions. Table 6.4 Logistic regression model for predicting fertility intentions by assimilation related variables for Bulgaria (Odds ratios) Variables

Positive intentions Model 1

Model 2

Model 3

BU-UP-RU

0.774

1.184

1.062

BU-LOW

0.709*

0.568*

0.659

TU-RU-LOW

0.630**

1.320

1.137

BU-HI-URB

1.656***

2.350***

2.231***

22.854***

21.478***

Desire: yes Male

1.212

1.171

Married

0.575***

0.588**

Age

0.931***

0.915***

Parity one

0.493***

0.335***

Parity two

0.032***

0.022***

Parity three

0.092***

0.065***

Employed other

1.066

Unemployed

0.983

Student

0.216***

Parental leave

1.082

Homemaker

0.850

Support

0.725*

Attitude

1.029*

Religiousness

0.880

n

4,770

Nagelkerke R2

.029

.642

.655

Chi2 (df)

65.123 (4)***

1,909.833 (11)***

1,961.839 (19)***

Notes. Refence group: not married, female, parity zero, desire no/unsure, employed, no support. *** p < .001, ** p < .01, * p < .05

160

6

Relationship Between Assimilation and Fertility Intentions

Within a second logistic regression model, it was looked at the influence of clustering and assimilation variables on fertility intentions. The results of these analyses are summarized in Table 6.4. When no control variables are considered, there are differences between clusters observable. However, some differences disappear once desire, parity, marital status, age and gender are controlled for. The two differences remaining are the one between the urban Bulgarians with higher education and the lowly educated Bulgarians compared to the urban Bulgarians with upper secondary education. These deviate slightly from the GSEM results presented in Table 6.1 given that there was only one group difference observed. When assimilation related variables are included within a third step, the difference between the lowly educated and the reference group disappears. Moreover, being a student is significantly negatively related to the likelihood of indicating positive fertility. Also, attitude and social support relate significantly to positive fertility intentions. These results resemble those presented beforehand. A comparison of Nagelkerke’s R2 across the logistic regression models in Table 6.3 and Table 6.4 shows that control variables (especially parity, age and partnership status) as well as fertility related variables (desire and motivational traits) can explain much more variance in fertility intentions than assimilation related variables do. The general conclusion that there are hardly any differences in fertility intentions between the Bulgarian clusters has to be refined, though. The logistic regression analyses suggest—unlike the structural equation models— that there are differences between the native Bulgarian clusters, which mostly disappear once fertility related variables and assimilation related variables are integrated. Yet, the conclusion that the Turkish cluster does not differ from the native reference group remains. Lastly, the predicted mean of having positive intentions of the GSEM and the predicted likelihood of the last logistic regression model (Model 3 in Table 6.4) were correlated. A Pearson correlation of r = .934 and p < .001 demonstrates that both predictions are close to being identical. The robustness checks thus showed that the overall conclusion of the structural equation model holds, though there are some differences between the models. It should be kept in mind, however, that the logistic regression models do not fully mirror the GSEM structure. The comparison of logistic regression and GSEM should hence be drawn with caution.

6.2

Turkish Migrants in Germany

After the influence of clustering and assimilation on fertility intentions has been analyzed for the Bulgarian sample, this section will look at the German data set. For this purpose, the relationship between fertility traits, desires and intentions

6.2 Turkish Migrants in Germany

161

is looked at first before the mediating influence of assimilation related variables is looked upon. Then, robustness checks will be carried out to see, whether they support the presented results.

6.2.1

Traits, Desires and Intentions within Germany

Conducting a generalized structural equation model with robust standard errors in Stata, first results show that motivational traits are significantly related to childbearing desire, which in turn is significantly related to the probability of having positive fertility intentions. As can be seen in Table 6.5, the worse the anticipated effects on costs and benefits of having children the more likely one is to have negative as opposed to positive fertility desires. Unsure desires, however, are only impacted negatively by worse anticipated costs. As far as the relationship between fertility desire and intentions is concerned, both negative and unsure desires (as opposed to positive desires) are negatively related to having positive fertility intentions. As can be observed in Table 6.5, the relationship between negative desires and positive intentions is stronger than the relationship between unsure desires and positive intentions. These results are in line with the hypotheses derived from the traits-desires-intentions model of Miller and Pasta. Focusing then on the relevance of the clusters, some group differences can be observed. In comparison to the German medium group, which was chosen as reference, all Turkish clusters are significantly more likely to have positive fertility intentions. The likelihood for intending (a)nother child increases most among the highly educated Turks. The German lowly educated cluster does not differ significantly from the German reference group, the highly educated Germans though, do. Highly educated Germans have a higher probability of intending to have a(nother) child than Germans with intermediate education. The first hypothesis that there are fertility differences is thus supported by the results: All Turkish clusters show an increased likelihood for positive fertility intentions when compared to the German reference group. When also considering the control variables, parity has a strong effect on the odds of having positive intentions. Compared to those who have no children yet, those who have two or more children are less likely to intend more children. Here, a comparison of odds ratios in Table 6.5 shows that the more children one already has, the stronger is the negative relationship to positive fertility intentions.

162

6

Relationship Between Assimilation and Fertility Intentions

Table 6.5 GSEM partial model for Germany—Odds ratios (robust standard errors) Exogeneous variables

Endogeneous variables Desire no

Desire unsure

Positive intentions

Desire no

0.012***

Desire unsure

0.100***

TU low

1.525*

(0.002) (0.017) (0.314) TU medium

1.682*

TU high

3.618***

DE high

1.672*

(0.353) (1.046) (0.349) DE low

0.941

Married

0.679

Age

0.947***

Male

1.302

(0.385) (0.149) (0.011) (0.183) Parity three

0.183***

Parity two

0.311***

Parity one

0.907

(0.045) (0.060) (0.165) Traits—costs Traits—benefits

1.661***

0.837*

(0.152)

(0.076)

1.392***

0.882 (continued)

6.2 Turkish Migrants in Germany

163

Table 6.5 (continued) Exogeneous variables

Endogeneous variables Desire no

Desire unsure

(0.123)

(0.079)

n

3,735

df

20

Log-likelihood

−5,097.181

AIC

10,234.36

BIC

10,358.87

Positive intentions

Notes. Reference group: desire yes, DE medium, not married, female, parity zero. *** p < .001, ** p < .01, * p < .05

Gender has no significant relationship to fertility intentions and neither does marital status. Age is significantly negatively related to positive fertility intentions. Hence, with increasing age, the likelihood to intend a(nother) child decreases.

6.2.2

Influence of Assimilation on Fertility Differences

Within the next step, a second model has been estimated that integrates assimilation variables as mediators into the relationship of clusters and fertility intentions. The results can be found in Table 6.6. The relationships between fertility intentions and marital status, age, desire, parity and gender stay constant within this model. Also, the relationship between traits and desire persists. Changes, however, are observed in the direct relationship between the cluster groups and fertility intentions. As expected, the differences in intentions between the German reference group and the Turkish clusters disappear. However, a significant difference between the highly educated and medium educated German natives remains. Out of the mediators added in this second model, being a student and being unemployed relate negatively to having positive intentions when compared to employed respondents. Religiosity, attitude, social support and citizenship are not significantly related to having positive fertility intentions. It thus seems that employment status explains the relationship between the clusters and positive fertility intentions.

164

6

Relationship Between Assimilation and Fertility Intentions

Looking at the associations between the clusters and the mediators, Table 6.6 highlights that Germans with low education have an increased risk of being in other employment, unemployed or a homemaker. Turkish clusters with low and medium education also show a greater likelihood of being unemployed, while Germans with high education have a significantly lower likelihood of being without employment. Moreover, the Turkish cluster with lower education is negatively related to being a student and on parental leave but relates positively to being a homemaker. All Turkish clusters have a decreased likelihood of indicating good social support, while both Germans with high and low education have significantly higher rates of social support than the German reference group with medium education. For attitude, all Turkish groups show an increasing likelihood of holding conservative attitudes, while German clusters are significantly more liberal. The three Turkish clusters are also more religious than the German reference group. When comparing the predicted mean of having positive intentions for the different cluster groups across parity in Figure 6.5, several things can be seen. First of all, the highest probability for positive intentions can be found in parity zero and one, while the probability to have further children goes down to around 10 to 18 % within parity two or higher. Secondly, independent of parity, Turkish groups display higher probabilities for positive intentions than all German groups. Among the latter, the German medium group has fewer positive intentions than their counterparts with high or low education. When it comes to the employment status, Figure 6.6 pictures its influence on positive intentions. Here, the only general tendency that can be observed is that the probability to have positive intentions is still noticeably lower once one already has two or more children and among German respondents. The influence of employment status within the parities and across the different clusters, however, varies. Among those with no children yet, highly educated Turks who are unemployed as well as lowly educated Turks who are homemakers show the highest probability to intend to have a child. Among the German clusters, a similar pattern is found. Highly educated Germans who are unemployed have a high predicted probability for intending to have a child. Also, medium educated Germans who are homemaker have a high probability of intending to have a child soon. Looking at parity one, highly educated Turks who are on parental leave, homemaker or in other employment have the highest probability for positive fertility intentions, followed by lowly educated Germans in other employment and

6.2 Turkish Migrants in Germany

165

Predicted mean of positive fertility intentions (DE) (n = 3,735) 1.0 0.9

predicted mean

0.8 0.7 0.6 0.5 0.4

0.3 0.2 0.1 0.0 TU low

TU medium No children

TU high

One child

DE low

Two children

DE medium

DE high

Three children or more

Figure 6.5 Predicted mean of positive fertility intentions by cluster and parity for Germany

lowly educated Turks who are students and medium educated Turks on parental leave. Within parity three, hardly any differences can be observed. Overall, the graphical analyses support the results of the structural equation modeling but go further by showing that some effects are very pronounced across all clusters and all parities, while most results have to be differentiated by parity and cluster in order to draw meaningful conclusions regarding their influence on positive fertility intentions. As far as the hypotheses are concerned, the results are supportive of the expectation that fertility traits influence fertility desires, which in turn impact upon the likelihood to name positive intentions. Moreover, they support the expectation that fertility differences exist within the clusters. All Turkish clusters were found to differ significantly in their intentions from Germans with medium education. The differences between Turkish and German clusters in their intentions could—as expected—be explained by the few assimilation variables in the model. Here, employment was the one relevant mediator.

1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0

TU medium

unemployed

TU low

employed

Student

TU high leave

DE low

Two children (DE)

DE high

homemaker

DE medium other

DE high

other

DE medium

homemaker

DE low

student

TU high

unemployed

TU medium

employed

TU low

No children (DE) 1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0

1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0

TU low

employed

TU low

employed

student

TU high

leave

DE low

unemployed

TU medium

student

TU high

leave

DE low

Three children (DE)

unemployed

TU medium

One child (DE)

DE medium

homemaker

DE medium

homemaker

other

DE high

other

DE high

6

predicted mean

predicted mean

Figure 6.6 Predicted mean of positive fertility intentions by cluster, parity, and employment status for Germany (n = 3,735)

1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0 predicted mean predicted mean

166 Relationship Between Assimilation and Fertility Intentions

Parental leave

Student

Unemployed

Employed other

DE low

DE high

TU high

TU medium

TU low

Desire unsure

Desire no

Exo-geneous

Desire no

Desire unsure

(0.281)

0.780

(0.105)

0.188**

(0.143)

0.591*

(0.300)

0.471

2.347** (0.765)

1.205 (0.497)

0.788 (0.213)

1.629* (0.357)

0.437 (0.263)

2.015 (0.840)

1.092 (0.276)

1.037 (0.375)

1.437 (0.310)

0.943

Employed other

(0.355)

(0.017)

0.094***

(0.017)

0.011***

Positive intentions

Endogeneous variables

(0.575)

2.708***

(0.055)

0.288***

(0.178)

0.750

(0.181)

1.489**

(0.199)

1.843***

Unemployed

(0.360)

1.171

(0.148)

0.744

(0.403)

1.577

(0.208)

1.146

(0.096)

0.436***

Student

(0.452)

1.497

(0.196)

0.996

(0.202)

0.432

(0.208)

1.032

(0.099)

0.406***

Parental leave

(0.501)

2.486***

(0.138)

0.892

(0.326)

1.496

(0.211)

1.508**

(0.635)

5.723***

Home-maker

Table 6.6 GSEM results full model for Germany—Odds ratios (robust standard errors)

(0.258)

1.608**

(0.118)

1.246*

(0.124)

0.689*

(0.077)

0.711**

(0.055)

0.523***

Support

(1.024)

0.956

(1.020)

1.475

(0.001)

0.001***

(0.001)

0.001***

(0.001)

0.001***

Citizen

(0.053)

0.854*

(0.028)

0.802***

(0.112)

1.834***

(0.069)

1.882***

(0.072)

2.286***

Attitude

(continued)

(0.069)

1.097

(0.035)

0.986

(0.036)

0.629***

(0.021)

0.589***

(0.019)

0.589***

Religiousness

6.2 Turkish Migrants in Germany 167

Parity one

Parity two

Parity three

Male

Age

Married

Religiousness

Desire unsure

0.781

(0.057)

0.274***

(0.048)

0.182***

(0.230)

1.303

(0.011)

0.934***

(0.160)

0.688

(0.091)

1.023

(0.093)

1.111

(0.221)

0.681

(0.220)

1.100

(0.213)

0.913

Positive intentions

Endogeneous variables

Desire no

Employed other

Unemployed

Student

Parental leave

Home-maker

Support

Citizen

Attitude

(continued)

Religiousness

6

Attitude

Citizen

Support

Homemaker

Exo-geneous

Table 6.6 (continued)

168 Relationship Between Assimilation and Fertility Intentions

55,260.38

BIC

(0.155)

Positive intentions

Employed other

Unemployed

Student

Parental leave

Home-maker

Support

Citizen

Attitude

Religiousness

Notes. Reference group: desire yes, non-native citizen, DE medium, employed, not married, no support, female, parity zero. *** p < .001, ** p < .01, * p < .05

−27,264.26

54,698.52

AIC

85

Log-likelihood

3,735

df

0.882

(0.079)

(0.123)

(0.076)

(0.152)

1.392***

0.837*

1.661***

Desire unsure

Endogeneous variables

Desire no

n

Traits—benefits

Traits—costs

Exo-geneous

Table 6.6 (continued)

6.2 Turkish Migrants in Germany 169

170

6

6.2.3

Relationship Between Assimilation and Fertility Intentions

Robustness Checks for Germany

In a next step, the presented results for Germany were also repeated with small adjustments within logistic regression models. First, the relationships between traits, desires and intentions were looked at. As can be seen in Table 6.7, traits are negatively related to positive fertility desire and this relationship persists once gender, partnership status, age and parity are controlled for. Within a second model, fertility intentions were predicted. Here, desire to have children relates strongly positively to positive fertility intentions. Moreover, having two or more children is negatively related to intending to have a(nother) child and being married as opposed to not being married relates also negatively to the likelihood of having positive intentions. Being male and having one child, though, exert a significant positive influence on fertility intentions. In a third step, traits are also included showing that both desire and traits in terms of costs show a direct, significant relationship to positive fertility intentions. These results are similar to

Table 6.7 Logistic regression models predicting desire and fertility intentions by fertility related variables for Germany (Odds ratios) Variables

Desire yes Model 1

Positve intentions Model 2

Model 1

Model 2

Model 3

Traits—costs

0.471***

0.326***

0.333***

Traits—benefits

0.556***

0.738**

0.978

Desire yes

46.791***

27.910***

24.257***

Male

0.748**

1.266*

1.059

Age

0.920***

0.934***

0.934***

Married

1.238

0.695*

0.689*

Parity one

1.191

1.415*

1.293

Parity two

0.229***

0.430***

0.369***

0.139***

0.266***

0.218***

Parity three n

3,735

Nagelkerke R2

.053

.304

.493

.566

.583

Chi2

120.675 (2)***

754.269 (8)***

1,335.805 (1)***

1,584.567 (7)***

1,646.926 (9)***

(df)

3,735

Notes. Refence group: not married, female, parity zero, desire no/unsure. *** p < .001, ** p < .01, * p < .05

6.2 Turkish Migrants in Germany

171

those presented within the partial GSEM model. The only difference is that gender and partnership status are partially significant within the logistic regression model. In Table 6.8, a logistic regression model predicting fertility intentions by assimilation related variables is presented. Here, all clusters show higher likelihoods of positive fertility intentions than the German reference group with intermediate education. Once fertility desire and the control variables are included, the significant differences between the German low and German medium cluster disappear. However, there are still differences between the three Turkish clusters and the German medium cluster as well as between the German highly educated group

Table 6.8 Logistic regression model predicting fertility intentions by assimilation related variables for Germany (Odds ratios) Variables

Positive intentions Model 1

Model 2

Model 3

TU low

1.410**

1.648*

1.226

TU medium

2.415***

1.760**

1.327

TU high

3.200***

3.320***

2.626*

DE low

1.813*

1.056

1.260

DE high

1.487**

1.842**

1.873**

Desire yes

30.650***

31.740***

Male

1.311*

1.338

Age

0.915***

0.906***

Married

0.639*

0.638*

Parity one

1.076

0.940

Parity two

0.322***

0.274***

Parity three

0.209***

0.182***

Employed other

0.482

Unemployed

0.785

Student

0.168***

Parental leave

0.820

Homemaker

0.952

Citizen

0.798

Support

1.095 (continued)

172

6

Relationship Between Assimilation and Fertility Intentions

Table 6.8 (continued) Variables

Positive intentions Model 1

Model 2

Model 3

Attitude

1.121

Religiousness

1.022

n Nagelkerke Chi2 (df)

3,735 R2

.032

.606

.615

62.558 (5)***

1,495.661 (12)***

1,524.025 (21)***

Notes. Refence group: not married, female, parity zero, desire no/unsure, DE medium, employed, no citizen, no social support. *** p < .001, ** p < .01, * p < .05

and Germans with intermediate education. When assimilation related variables such as employment, attitude, religiousness, social support, and citizenship are entered in a third step, the significant differences between the Turkish low and medium group with the German reference group disappear. However, the difference between the German medium and Turkish high group can only partially be explained by assimilation. Out of the assimilation variables, only being a student as compared to being in employment relates significantly to positive intentions. The difference between the German medium and German high cluster remains, too. Lastly, the predicted means of the last regression model presented (Model 3 in Table 6.8) and the full structural equation model were correlated. Here, a significant positive correlation with r = .948 and p < .001 resulted demonstrating that both predicted means are nearly identical. The results of the logistic regression models deviate slightly from the ones presented within the full GSEM model for Germany. In the GSEM model all differences between the Turkish clusters and the German reference group could be explained by assimilation related variables, whereas here a difference between the Turkish high and German medium group remains. Moreover, only being a student is a significant mediator here, whereas unemployment was significant within the structural equation model, too. Nevertheless, the result that employment status can explain some differences between fertility differences of the clusters remains.

6.3 Comparison of Minority and Migrant Group

6.3

173

Comparison of Minority and Migrant Group

To also come to terms with how fertility intentions differ between a migrant and minority group of same origin, the presented analyses from sections 6.1 and 6.2 were repeated with Turkish migrant and Turkish minority respondents only. For this purpose, the influence of fertility related variables on the intention to have a(nother) child was analyzed first, before including assimilation related variables in a second step. Lastly, logistic regression analyses were conducted to verify the models presented and amend model fit statistics.

6.3.1

Traits, Desires and Intentions within the Migrant and Minority Sample

Table 6.9 shows the results of the partial GSEM model including only the minority status and control variables as well as fertility related predictors. Fertility intentions are still negatively impacted by negative or unsure fertility desires. Moreover, the perceived costs of children still relate significantly positive to having a negative fertility desire, but significantly negative to having an unsure fertility desire. Hence, the worse the anticipated effect of the costly aspects of childbearing, the more likely one is to form a negative fertility desire. The likelihood to form an unsure fertility desire, however, decreases with more anticipated negative effects. As for the benefits of children, there is only a positive, significant relationship to having a negative fertility desire. The worse the anticipated effect of the beneficial aspects of having children, the more likely one is to not intend to have a(nother) child. Minorities as compared to migrants show significantly lower fertility intentions. Other than that, age and being married relate negatively to having positive intentions, while being male relates positively to having positive fertility intentions. Both parity two and parity three decrease the likelihood to intend to have a(nother) child when compared to those who have no children yet. A comparison of odds ratios reveals that the strongest negative effect is exerted by a negative fertility desire, followed by unsure desires and parity three.

174

6

Relationship Between Assimilation and Fertility Intentions

Table 6.9 GSEM partial model for migrants and minorities—Odds ratios (robust standard errors) Exogeneous variables

Endogeneous variables Desire no

Desire unsure

Positive intentions

Desire no

0.007***

Desire unsure

0.083***

(0.002) (0.018) Minority

0.599*

Married

0.549*

Age

0.961*

(0.136) (0.160) (0.015) Male

1.593*

Parity three

0.156***

Parity two

0.218***

(0.292) (0.050) (0.062) Parity one

1.032 (0.298)

Traits—costs

2.024*** (0.240)

(0.080)

Traits—benefits

1.490**

0.823

(0.178)

(0.090)

n

0.785*

2,192

df

16

Log Pseudolikelihood

−2,970.87

AIC

5,973.74

BIC

6,064.82

Notes. Reference group: desire yes, migrant, not married, female, parity zero. *** p < .001, ** p < .01, * p < .05

6.3 Comparison of Minority and Migrant Group

6.3.2

175

Influence of Assimilation on Fertility Differences

When including assimilation related variables into the model, the difference between Turkish migrant and minority respondents disappears. The only assimilation related variable that exerts a significant influence on fertility intentions is attitude. Here, a more conservative attitude is related to a higher likelihood of having positive intentions (see Table 6.10). As compared to the previous model, male is no longer a significant control variable. However, age, marital status and parity keep their significant influences. When looking at the relationship between minority status and the mediators, minorities have a higher unemployment rate than migrants and cherish religious ceremonies more. Migrants, on the other hand, are more often homemakers, have less social and a more conservative attitude than minority respondents. Out of all variables, desire has the strongest influence on fertility intentions, followed by parity and marital status.

Predicted mean of positive fertility intentions (Minority sample) (n = 2,192) 0.8

predicted mean

0.7 0.6 0.5

0.4 0.3 0.2 0.1 0 No children

One child Migrant

Two children

Three children or more

Minority

Figure 6.7 Predicted mean of positive fertility intentions by cluster and parity for the minority sample

Looking at the predicted mean by parity, one can see (Figure 6.7) that the differences between minority and migrant respondents is less pronounced in parity zero. Yet, once one already has one or two children, migrants seem more optimistic about their intentions than minority respondents. The differences are less obvious for those who have three children or more already. When looking at the influence of attitude within Figure 6.8, a more conservative attitude is related

Support

Homemaker

Parental leave

Student

Unemployed

Employed other

(0.301)

0.961

(0.308)

0.986

(0.443)

0.822

(0.262)

0.318

(0.160)

0.599

(0.274)

0.304

1.248 (0.314)

0.852 (0.237)

(0.021)

0.092***

(0.002)

0.008***

(0.371)

3.597*** (0.060)

(0.350)

(0.024)

0.085***

(0.124)

0.678*

Religiousness

(0.044)

(continued)

(0.045)

0.176*** 1.160***

Parental Home-maker Support Attitude leave

0.084** 1.379

Positive Employed Unemployed Student intentions other

Endogeneous variables

Desire no Desire unsure

6

Minority

Desire unsure

Desire no

Exogeneous variables

Table 6.10 GSEM full model for migrants and minorities—Odds ratios (robust standard errors)

176 Relationship Between Assimilation and Fertility Intentions

Parity one

Parity two

Parity three

Male

Age

Married

Religiousness

Attitude

Exogeneous variables

Desire no Desire unsure

(0.266)

0.836

(0.053)

0.171***

(0.043)

0.120***

(0.412)

1.612

(0.016)

0.958**

(0.161)

0.468*

(0.134)

1.013

(0.018)

1.039*

Positive Employed Unemployed Student intentions other

Endogeneous variables

Table 6.10 (continued) Parental Home-maker Support Attitude leave

(continued)

Religiousness

6.3 Comparison of Minority and Migrant Group 177

42

43,142.87

BIC

Parental Home-maker Support Attitude leave

Notes. Reference group: desire yes, migrant, employed, not married, no support, female, parity zero. *** p < .001, ** p < .01, * p < .05

42,891.27

AIC

Log-Pseudolikelihood −21,403.63

2,192

df

0.823 (0.090)

1.490**

(0.269)

0.785* (0.080)

2.023***

(0.240)

Positive Employed Unemployed Student intentions other

Endogeneous variables

Desire no Desire unsure

Religiousness

6

n

Traits—benefits

Traits—costs

Exogeneous variables

Table 6.10 (continued)

178 Relationship Between Assimilation and Fertility Intentions

6.3 Comparison of Minority and Migrant Group

179

to a higher predicted mean for having positive fertility intentions among minorities in parity zero but is related to less positive intentions among minorities in all other parities. Among migrants, attitude seems rather irrelevant if one has no children yet. However, once one already has children, a more conservative attitude is related to more positive fertility intentions.

Predicted mean of positive fertility intentions by attitude (Minority sample) (n = 2,192) 0.9 0.8 prdicted mean

0.7 0.6

0.5 0.4 0.3 0.2 0.1

No children

One child Migrant

Two children

Conservative

Neutral

Liberal

Conservative

Neutral

Liberal

Conservative

Neutral

Liberal

Conservative

Neutral

Liberal

0

Three children or more

Minority

Figure 6.8 Predicted mean of positive fertility intentions by minority status, parity, and attitude

6.3.3

Robustness Checks for Minority Sample

When conducting similar analyses with help of logistic regression analyses, motivational traits are still significantly related to fertility desires. Moreover, age decreases the likelihood for desiring a(nother) child significantly, as does parity two and parity three. When predicting positive fertility intentions, a positive fertility desire shows a strong positive relationship to having a positive fertility intention. This relationship persists when control variables and motivational traits are included. Out of the control variables, gender, age, and parity relate significantly to positive fertility intentions. Lastly, motivational traits measured in terms

180

6

Relationship Between Assimilation and Fertility Intentions

of costs are also significantly related to intending to have a(nother) child. The results are summarized in Table 6.11. Table 6.11 Logistic regression models predicting desire and fertility intentions by fertility related variables for the minority sample (Odds ratios) Variables

Desire yes Model 1

Positive intentions Model 2

Traits—costs

0.344***

0.340***

Traits—benefits

0.531***

0.768*

Desire: yes

Model 1

Model 2

Model 3 0.471*** 0.744

70.521***

39.745***

32.850***

Male

0.935

1.582**

1.416*

Age

0.932***

0.939***

0.943***

Married

1.338

0.709

0.720

Parity one

0.983

0.976

0.986

Parity two

0.147***

0.165***

0.158***

Parity three

0.098***

0.131***

0.113***

n

2,192

Nagelkerke R2

.109

.380

.559

2,192 .659

.676

Chi2 (df)

152.879 (2)***

583.523 (8)***

945.869 (1)***

1,174.756 (7)***

1,215.822 (9)***

Notes. Refence group: not married, female, parity zero, desire no/unsure. *** p < .001, ** p < .01, * p < .05

In a next step (Table 6.12), ethnicity and assimilation related variables were included to predict fertility intentions with the help of logistic regression. Here, model 1 demonstrates that minority respondents have significantly less positive fertility intentions than migrants. However, this difference disappears once control variables are included. Out of these, gender, age, being married and parity show significant relationships to fertility intentions with positive intentions being less likely the older one is and the more children one already has. The likelihood for positive intentions increases for males. In model 3, assimilation related variables are included. Desire, age, gender, marital status, and parity remain significant. Out of the assimilation variables, being a student relates negatively to intending a(nother) child. Furthermore, attitude relates positively to having positive intentions thereby supporting the GSEM results.

6.3 Comparison of Minority and Migrant Group

181

Table 6.12 Logistic regression model predicting fertility intentions by assimilation related variables for the minority sample (Odds ratios) Variables

Positive intentions Model 1

Model 2

Model 3

Minority

0.535***

0.591*

0.711

Desire yes

36.753***

36.787***

Male

1.547**

1.662*

Age

0.939***

0.938***

Married

0.541*

0.468**

Parity one

1.088

0.708

Parity two

0.176***

0.135***

Parity three

0.131***

0.096***

Employed other

0.379

Unemployed

0.634

Student

0.309*

Parental leave

0.965

Homemaker

1.065

Support

1.130

Attitude

1.044**

Religiousness

1.029

n

2,192

Nagelkerke R2

.017

.662

.669

Chi2 (df)

23.710 (1)***

1,179.428 (8)***

1,198.607 (17)***

Notes. Reference group: not married, female, parity zero, desire no/unsure, migrant, employed, no citizen, no social support. *** p < .001, ** p < .01, * p < .05

If one compares these results to those presented in section 6.3.1 and 6.3.2, some differences can be observed. While minority status was related to fertility intentions even when control variables were part of the structural equation model, this difference was explained by the socio-demographic variables within the logistic regression model. Out of the assimilation variables, attitude showed a similar effect as in the structural equation model, yet being a student was an additional predictor within the regression models. Overall, the predicted mean of the GSEM model correlates strongly (r = .956, p < .001) with the predicted

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Relationship Between Assimilation and Fertility Intentions

likelihoods resulting out of regression analysis showing that both models equal each other.

6.4

Summary

Having looked at how fertility decision-making is related to ethnicity and assimilation related predictors, this section will summarize the results before a more in-depth discussion and critical reflection of results follows in chapter 7. Focusing on Bulgaria, the structural equation model demonstrated that there are no differences between the Turkish cluster and the Bulgarian reference group. Rather, there were differences in fertility intentions between the Bulgarian groups showing that neither minority nor majority can be assumed to be homogeneous. The central assumption that fertility intentions differ between minority and majority is consequently not supported. When the assimilation related mediators were included within the full GSEM model, attitude and employment status were significantly related to fertility intentions. The difference between the two Bulgarian clusters in their fertility intentions could not be explained by the model, though. In conclusion, structural and cultural elements are relevant to explaining fertility decision-making, but there are no differences in fertility intentions between minority and majority in the first place making the mediators employed here general predictors of fertility independent of ethnicity. The logistic regression analyses as well as the cluster analyses underlined this conclusion by similarly showing that ethnicity does not matter for accounting for fertility within the Bulgarian context. For Germany, the results were slightly different and more in line with the postulated model. Here, there were significant differences within the GSEM model between the three Turkish clusters and the German reference group as far as fertility intentions are concerned. More concretely, all three Turkish groups were more likely to have positive fertility intentions than the German reference group. These results were confirmed by the robustness checks. Moreover, these differences disappeared once assimilation related variables were included into the model indicating that assimilation can explain the observed differences. Yet, one has to acknowledge that within logistic regression analyses the highly educated Turkish cluster still differed from the German reference group. Also, it should be noted that out of the assimilation related variables, only employment status was significantly related to fertility intentions. It is thus primarily the structural part of inclusion that accounts for differences.

6.4 Summary

183

For the minority sample, the same analyses were conducted showing that minority and migrant respondents differ significantly in their fertility intentions. These differences disappeared once assimilation related variables were integrated as mediators with attitude being the sole significant variable. The results were supported by the logistic regression analyses, though the difference between minority and migrant was already explained by control variables. Moreover, within the regression employment status was also related to fertility intentions. For all three samples, the traits-desires-intention framework of Miller and Pasta was supported by the data and showed strong relationships between motivational traits and fertility desires as well as between fertility desires and fertility intentions. These relationships remained even when control variables and assimilation measures were included. Moreover, the robustness checks could reveal that there is even a significant direct relationship between traits and fertility intentions even if this was not explicitly stated by Miller and Pasta themselves. With these findings in mind, the next section will place the results within the theoretical context discussed in chapter 2 and 3, before reflecting on limitations and possible amendments for future scientific studies.

7

Discussion

To round off the dissertation, this last chapter will summarize the results within the context of the theoretical model. The findings will thereby be discussed critically and reflected upon. Following the discussion of results, limitations will be brought forward to question the conclusions drawn from both a methodological as well as a theoretical point of view. Lastly, an outlook for future research will be provided.

7.1

Summary

This dissertation aimed at finding out how fertility intentions are related to assimilation for migrant and minority groups in Europe. To be able to compare a minority and a migrant group of same origin, the Turkish migrants in Germany and the Turkish minority in Bulgaria were selected as case studies. Specifically, a first research question asked (1a) how the assimilation status of both minority and migrant groups can be described and categorized, and (1b) to what extent both groups are comparable regarding their assimilation status. Within a second step, it was asked whether (2) ethnicity is an important line of distinction within Bulgarian and German society as well as when comparing migrant and minority. Having seen through explorative cluster analyses whether ethnicity divides society into diverse groups, it was furthermore looked at whether (3a) the Turkish minority in Bulgaria and the Turkish migrants in Germany differ in fertility intentions from the native majority and (3b) from each other. Once it was established whether differences in fertility intentions prevail, a last research question asked (4) to what extent differences in fertility intentions can be explained by the

© The Author(s), under exclusive license to Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2023 B. Brünig, The Fertility of Migrants and Minorities in Europe, https://doi.org/10.1007/978-3-658-43099-3_7

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assimilation status of the groups under investigation. The following sections will summarize the findings to answer these research questions.

7.1.1

Definition of Migrant and Minority from a Boundary Making Perspective

Assimilation theory is a classical approach within the study of immigrant integration. It can hence be applied to see how migrants integrate into their receiving society. To assess to what extent assimilation theory applies to minorities, too, the concept of a minority was discussed first as no clear definition could be found within the literature. Then, the applicability of an assimilation theory for migrants and minorities in Europe was revised. A minority was defined as an ethnic group that originates in processes of annexation and is hence the result of redrawn political boundaries (Heckmann, 1983; Schaefer, 2015). Migrants on the other hand are understood as resulting out of processes of migration. Subsequent migrant generations are hence termed migrants here, too, and not labelled minorities as is often the case in US American literature. When applying this distinction, it should be kept in mind though that ethnic groups are not seen as self-evident units of observation but are the result of a complex social process of boundary making. Terms like minority and majority are hence deliberate social constructions and do not necessarily match the self-identification of minority and majority groups. Rather, markers are defined that group society along ethnic lines. When talking about minorities, it is thus important to consider the possibility that a minority does not necessarily share a collective consciousness but can also be based on other-definition. Quantitative analyses as envisaged for this dissertation hardly recognize that boundaries do not necessarily run along ethnic lines but take variables that distinguish based on ethnic belonging for granted. To apply a more differentiated point of view that at least tries to move away from a purely ethnic-based approach, a cluster analysis was conducted before starting to look at assimilation and fertility of the Turkish minority and the Turkish migrants. This analysis included common societal lines of distinction such as gender, age, ethnicity, education, and residency (the latter for the Bulgarian society only). By acknowledging that ethnicity might not be a self-evident unit of distinction, this cluster analysis was integrated to see which boundaries are meaningful within the given societies. The cluster analysis showed that Bulgarian society is divided along education, residency, and ethnicity. Within this dissertation, five clusters for the Bulgarian data resulted with four of Bulgarian ethnicity and diverse educational levels and

7.1 Summary

187

places of residency, and one cluster of Turkish identity. Among the German sample, six clusters based on ethnicity and education resulted with three German and three Turkish clusters with low, medium and high education respectively. For a comparison of minority and migrants only, a third sample was constructed that was also looked at from a boundary making perspective. Here, a mere distinction based on minority/migrant status resulted out of clustering. Generally, these findings are in line with studies on intersectionality that have found that ethnicity in combination with gender and/or education meaningfully differentiate groups and can be applied to explain integration and fertility (Alarcao et al., 2019; Eeckhaut, 2020). Furthermore, the results underline the importance of ethnic markers, but show that ethnicity is only one common boundary. The upcoming analyses as well as the answers to the research questions are thus built upon the identified clusters.

7.1.2

Assimilation Status of Turkish Migrant and Minority Groups

To see how the Turkish migrants in Germany and the Turkish minority in Bulgaria are assimilated and comparable regarding their assimilation status, it was discussed from a theoretical point of view what is meant by assimilation, to what extent assimilation theory can be applied within a European context and whether assimilation is a useful context for describing the living circumstances of a minority group. When looking at the assimilation of migrants and minorities, a review of assimilation theory with its facets and developments revealed that assimilation outcomes nowadays are more diverse than the straight-line assimilation envisaged at the beginning of the 20th century. Especially within the European context, assimilation might be a lengthier process than in classic countries of assimilation such as Canada, Australia or the USA as European societies are only gradually adjusting to the thought of being countries of immigration. If one understands assimilation as the endpoint of the process of adaptation, it seems unlikely that migrants and even less likely that (officially recognized) minorities will reach a state of complete disappearance of ethnic specificities. However, a fluid understanding of assimilation that characterizes the process of vanishing group differences seems more likely to apply in interethnic relations. Therefore, assimilation is taken to signify this process within this dissertation. Connecting assimilation theory with the bi-dimensional acculturation theory of Berry (1974, 1980), a framework to typify the process of assimilation could

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be worked out. Here, assimilation takes place along the host- and home-country dimension with assimilation, integration, separation, and marginalization being potential outcomes or stages of this process. If one combines this setting with the commonly used dimensions of assimilation (structural, cultural, social, identificational) an assimilation model arises, that makes a categorization of the assimilation process possible (see Figure 2.1). Applying this model towards the Turkish minority and migrant group, it could empirically as well as with existing findings be worked out that the assimilation status of the two Turkish groups under study is not comparable. Turkish migrants in Germany seem no homogeneous group as cluster analyses revealed that they are stratified—as is the German majority—along educational lines. Depending upon the educational background, the assimilation of Turkish migrants is differently developed. Especially highly educated Turks show better integration along all assimilation dimensions than lowly educated Turks. Interestingly, a comparison of three German and three Turkish groups with diverse educational levels even revealed that on some assimilation dimensions, Germans with low education even underperform Turkish migrants. In conclusion, an integration status could be found here with Turkish being integrated in some dimensions, while lagging behind on others. The state of integration though varies with educational background highlighting that different forms of integration exist and can be found among Turkish migrants. This underlines the expectations derived from the theoretical background, which predicted diverse forms of integration among Turkish migrants in Germany. The Turkish minority in Bulgaria lags behind the national majority especially in structural terms. High unemployment rates and lower educational levels prevail among minority respondents in the GGS data. Moreover, Turks are mainly found within rural areas and deviate culturally in terms of religious denomination and practice, language use, and traditionalism. Especially the granting of specific rights to keep their culture makes it hard for the Turkish minority to fully assimilate into Bulgarian society. As the Turkish minority lives residentially segregated and speaks its own language, evidence points towards segregationist tendencies as expected. This conclusion contradicts studies that picture the Turkish minority in Bulgaria an example of successful integration (Genov, 2008; Petkova, 2002), but is in line with the conclusion of Duijzings (2014) who also sees self-imposed segregationist tendencies among Turks in Bulgaria. This comparison already makes clear that the process of assimilation among the two Turkish groups is not comparable. This was also highlighted within section 5.3 when specifically focusing on a descriptive comparison of minority and migrant respondents. Here, one could see that they differ structurally and

7.1 Summary

189

culturally. On some aspects they were closer to the natives of their country of residency while on other aspects (e.g., attitude towards marriage and family live) they mirrored the Turkish society in Turkey. In general, the process of assimilation can thus be considered an individual process that is guided by the context of reception, the structures available to integrate, the context of origin and the specific rights that are granted (or not) to minorities and migrants.

7.1.3

Fertility of Migrants and Minorities

Having reviewed theories on migrant and minority fertility in chapter 3, it could be shown that the theoretical approaches considered within this dissertation capture similar arguments and variables as assimilation theory. The adaptation and socialization hypotheses dominate research on migrant fertility. While some scholars treat these theoretical approaches as contrasting, it was argued here that they complement each other. Taking the bi-dimensional assimilation model as a basis, the adaptation hypothesis stresses the role of host country structural and cultural integration, while the socialization hypothesis highlights the role of upbringing and hence the influence of the sending context. Similarly, theories explaining the fertility of minorities stress the influence of the receiving context (social characteristics hypothesis) as well as the role of origin country culture (sub-culture hypothesis). By integrating these perspectives into a more holistic approach to explain fertility differences, they complement each other and specify both the home- and host-country dimension of assimilation that can take place in parallel to each other. Both fertility theories specifically focusing on migrants and minorities as well as the assimilation framework yield a set of predictors that are likely to influence the fertility of migrants and minorities. Yet, theory has so far failed to link these theoretical approaches and to integrate fertility theories into the assimilation framework. This step has been taken within this dissertation empirically as well as theoretically. Thereby, a focus on fertility intentions has been chosen. Instead of looking at actual fertility behavior, intentions have the advantage to better capture how a conscious decision to (not) have a child is formed. While the actual outcome of having a child can be influenced by experiences of stillbirth or the physical possibility to have a child, intentions better capture, which circumstances influence whether a decision for or against children is formed. Within the framework of Miller and Pasta (1995) applied here, motivational traits influence fertility desires, which in turn influence fertility intentions. As Hunink (2016) points out, though, the influence of fertility desires towards fertility intentions

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is influenced by several circumstantial constraints such as partnership situation, employment, or cultural attitudes. For this dissertation, the focus on intentions thus seemed more suitable to model the influence of fertility related variables (such as desire or motivational traits) as well as of assimilation related variables (such as structural, cultural or social predictors) on fertility intentions. Research question three aimed at finding out how these fertility intentions differ between migrants and natives, and minority and natives respectively, as well as when comparing migrant and minority. Descriptive analyses already revealed that differences in intentions can be observed with migrants being more positive than minority respondents when it comes to having a(nother) child. In comparison to natives, the results are less concrete as the clustering produced several native reference groups. In Germany, fertility intentions differed across educational levels, but Turkish respondents were generally more optimistic about their intentions. For Bulgaria, no clear difference between Turkish and Bulgarian respondents could be observed, rather, differences between the four Bulgarian clusters prevailed. These observations could be confirmed through inferential analyses in chapter 6. It could be shown that motivational traits influence fertility desires, which in turn form fertility intentions. When additionally linking the cluster variables to fertility intentions, no significant difference between Turkish minority and Bulgarian reference group could be found. This contradicts findings from Dimitrova (2021) who found ethnic Bulgarians to have significantly lower fertility intentions than ethnic groups within Bulgarian society. Yet, it has to be acknowledged that Dimitrova (2021) did not focus specifically on Turkish minority respondents but only distinguished ethnic background from native Bulgarian background. The results, however, are in line with findings from Stonawski and colleagues (2016) who compared Muslims and non-Muslims in Bulgaria and found no fertility differences once education and place of residency were controlled for. Differences, however, could be found in this dissertation between Bulgarian clusters in that highly educated urban Bulgarians showed significantly higher fertility intentions than urban Bulgarians with upper secondary education. These results are in line with findings from Dimitrova (2021) who found higher education in Bulgaria to be linked to more favorable fertility intentions. For Germany, though, fertility intentions of all three Turkish clusters differed from the native reference group and were more positive. These findings are underlined by other studies that have not focused on fertility intentions but have generally found Turkish migrants to have higher birth probabilities than native Germans (Krapf & Wolf, 2015). When focusing on migrants and minorities only, migrants showed a greater likelihood of indicating positive fertility intentions than minorities.

7.1 Summary

191

Generally, the results underline across all samples that pronatalist motivational traits have a positive impact upon fertility desires, which in turn are positively related to fertility intentions as stated by Miller and Pasta. Moreover, the results confirmed the expectation that Turkish migrants in Germany differ in their intentions from the native majority and from the Turkish minority. Overall, the Turkish minority cluster did not differ from the Bulgarian clusters concerning fertility intentions. In a next step, it was thus looked at how assimilation can explain these differences.

7.1.4

Mediating Influence of Assimilation

As it could be shown that fertility differences exist between (1) Turkish migrants and German natives as well as between (2) Turkish minority and Turkish migrants, a second round of analyses tested whether assimilation variables could explain the relationship between ethnicity and fertility intentions. Structural equation models as well as logistic regression analyses confirmed that assimilation variables can account for the observed differences to a certain extent. While the differences between Bulgarian clusters could not be explained by the assimilation related variables, the differences between Turkish and German clusters disappeared with employment status proving to be a relevant explanatory variable. Within the confirmatory regression analyses, a slight difference between the highly educated Turkish cluster and the German cluster with intermediate education remained. Nevertheless, in both logistic regression and GSEM the difference between the Turkish respondents with low and medium education and the German reference group with medium education in fertility intentions disappeared once assimilation was accounted for. For a comparison of minority and migrant groups, a similar observation could be made. The observed difference in fertility intentions disappeared when assimilation related variables were included. Here, attitude and, within the logistic regression model, also employment status were significant mediators. These results partly confirm the theoretical expectations, though the forecasts from hypotheses six to eight can only partly be assessed by the data. The results show that more similarity to the host society is related to less difference in fertility intentions. This could e.g., be observed for Germany by the three Turkish clusters that differ to different degrees from the German reference group. Looking at the odds ratio it could be shown that highly educated Turkish migrants differ more from the German reference group with medium educational level than the

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Discussion

less educated Turkish clusters. It furthermore showed within the minority sample that migrants and minority mirror their host society to a certain extent, but also adhere to their roots to a certain degree. As differences in fertility intentions could also be found among these two groups, it seems likely that the parallel influence of host and home country influence these observed differences. Once controlling for cultural attitude and structural integration, the differences could be explained. Generally, though, the home country dimension of integration could hardly be measured and integrated within this dissertation. Finally, culture measured in terms of attitude was relevant within the minority-migrant sample to explain fertility differences and within the Bulgarian sample, where no differences in fertility intentions could be observed. It thus is an important predictor that differs between migrant, minority, and majority groups. Yet, culture was only partly relevant to explain fertility differences. In Germany for instance, structural factors such as education and employment status seem much more relevant than culture showing that the context of study is important to consider. Furthermore, the central argument of this dissertation that assimilation can explain fertility differences could be supported by the data. A more critical reflection on these findings, however, is necessary to fully understand them.

7.2

Critical reflection

Section 7.1 summarized the most important results and drew first conclusions regarding the research questions and hypotheses. Nevertheless, it became clear that the hypotheses and research questions cannot be answered straightforwardly. Rather, a deeper discussion of the analyses conducted within the context of the theory developed is necessary to pinpoint the contribution of this dissertation to scientific discourse.

7.2.1

Theoretical Limitations

Most literature on minority and migrant assimilation does not define a minority but simply applies the term to a certain ethnic group, often to later migrant generations (Maggazzini, 2020; Milewski, 2010). A first step was taken here to develop a definition of minority groups within the European setting that focused on established minorities. These groups are a particularity of the European continent; the analyses presented here thus have to be understood within the context of this setting. The context of evolvement of both the Turkish migrants in Germany and the

7.2 Critical reflection

193

Turkish minority in Bulgaria were reviewed to integrate the statistical findings of the results section within a broader context of ethnic relations. Nevertheless, part of the findings presented here do not rest on new analyses but rely upon previous findings and analyses. Here, there were especially few publications for the Bulgarian context available. Given that data on ethnic background are not collected in Bulgaria (Büchsenschütz, 2004), studies on Bulgarian society and demographics hardly distinguish between Turkish and Bulgarian background. Since 1975 census data no longer capture the ethnic background of Bulgarian citizens directly, but only ask indirectly about mother tongue or religious characteristics (Büchsenschütz, 2004). The situation of Turks in Bulgaria could thus only be grasped rudimentarily with the available and often older data. Moreover, the amount of English literature on Bulgaria is limited, too. This task was easier for the German context, where migration background is assessed within official statistics and the topic of migrant integration discussed frequently. A further note on the definition and conceptualization of minority and migrant within this dissertation concerns the boundary making perspective. This perspective was applied here to provide a sounder basis for the definition of ethnic groups that does not solely rest on numerical superiority or inferiority. This way it enlarged the understanding of ethnicity often applied within quantitative research practice by not focusing on one characteristic only (namely ethnicity) that was seen as predictor of a certain action (in this case fertility intentions). To be more concrete, this dissertation sought to go beyond quantitative research practice by considering ethnicity not as a self-evident unit of analysis. By integrating the structural analyses within the context of historical background of the groups and existing findings on both ethnic groups, the aim was to provide a broader base to approach the topic of ethnic differences in fertility intentions. Moreover, the methodological approach to cluster common societal lines of division such as gender, age, education, ethnicity, or place of residency, it was aimed at showing that ethnicity might be one but not the only or most important boundary within society. Moreover, it was used to counter critical arguments that claim that studies based on ethnicity support the marginalization of migrant and minority groups by constructing them as homogeneous and “other” (Anthias & Pajnic, 2014). A literature review on methods applied within migration research could not find any similar approach to study migrant and minority assimilation processes and/or fertility (see Mahler & Brinkmann, 2016 on methods in migrant research). This approach is thus considered novel and an important contribution of this dissertation. It not only addresses different dividing lines in society, but also highlights that both minority and majority groups are not homogenous entities.

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Discussion

The methodological approach chosen here might hence contribute to the discussion around the normativity of the receiving society (Spencer-Charsley, 2021) and the question what migrants are actually integrating into. The analyses among the Bulgarian and German sample made clear that even natives differ across each other and that there is no clearly defined base to measure to what extent migrants/ minorities and natives are alike. Rather, in terms of segmented assimilation, different strata and groups exist within society making assimilation a multi-facetted option that can hardly be judged in terms of success or failure. Focusing more critically on the concept of assimilation, the presented dissertation opted to apply this concept to the study of intergroup differences. The concept was adopted under the broad definition as assimilation defining a process of becoming alike and not focusing on the mere outcome of being alike. The process was then described to depict the four possible stages and outcomes of assimilation, integration, separation, and marginalization. While it is hard to assess how an outcome of assimilation can look and whether it is ever reached, this dissertation used the four-fold distinction as stage that can be used to describe the current situation of interethnic relations. This understanding acknowledges that assimilation processes can be open ended and might only be grasped as “snapshots at any moment of time” (Spencer & Charsley, 2021, p. 6). Each of these stages can be reached depending on the degree of host and home country integration, which were understood to take place in parallel to each other. Building upon the critique of Entzinger and Biezeveld (2003, p. 6) that integration is a term often used, but hardly defined, this dissertation devoted quite some space to the discussion of the applicability of the concept, its usefulness for the current study, its facets, and its measurement. This way, an assimilation model could be developed that might also guide future studies and help to conceptualize and operationalize the concept of assimilation. Nevertheless, other researchers may still find that assimilation is too narrowly defined and operationalized within this dissertation as the fourfold distinction of assimilation stages might not leave enough space for other options of interethnic relations. Especially an assimilation stage equaling integration should have more time and research devoted to it in future studies as this dissertation as well as Schwartz and Zamboanga (2008) showed that different forms of integration prevail, though it was not possible within this dissertation to establish more specifically how these forms could look. Assimilation is often only applied to describe the circumstances of migrants and minorities. In this study, however, it was specifically operationalized through variables that measure the living situation of both migrants/minorities and majority. This perspective moves analyses away from the migrant/minority as “other”

7.2 Critical reflection

195

and “the research question loses its migration-specific focus while remaining sensitive to the role of migration and ethnicity in the phenomenon being investigated” (Dahinden, 2016, p. 2218). Moreover, it enables a comparison of minority, migrant and majority group. On the downside, though, it limits the number of measurements available to capture assimilation and excludes variables such as length of residency that are specific to the migrant situation and have no pendant among the native majority. A further critical note concerns the results regarding research question four. Here, it was asked whether assimilation can explain fertility differences. The results for Germany and the minority sample support the expectation that assimilation can at least partly account for fertility differences. In Germany, a strong focus on structural factors was found. This is in line with several recent studies, which have found knowledge (Milewski & Haug, 2022) as well as structural predictors such as education and employment status (Mussino et al., 2021) are important for fertility decision-making. Mussino et al. (2021) likewise focused on explaining fertility intentions among migrants, though focusing on the Italian context. Generally, their findings show that migrants have more positive fertility intentions than natives, yet a positive difference remained once controlling for age, parity, employment, and education. In the study presented here, only the logistic regression model revealed a small difference between highly educated Turkish migrants and German natives in fertility intentions after controlling for all variables, while the structural equation model explained all differences. Mussino and colleages (2021) conclude that fertility intentions differ more between natives and migrants if the situation is not ideal (e.g., in times of unemployment) with migrants favoring family building/formation above economic security. The results of this study are in line with this explanation to the extent that the impact of the economic status on fertility intentions differs across clusters. Unemployment, being a homemaker, being on parental leave and other employment relate more positively to intending to have (a)nother child among Turkish clusters—especially those with intermediate or lower education—than among Germans or higher educated groups. Thus, for those in precarious living situations, family formation/ extension might be a more promising option to find security than employment. The importance of structural factors, especially in the German context, however, raises the question whether it is indeed assimilation that explains the relationship between fertility intentions and ethnic boundaries. Rational choice approaches and especially the uncertainty reduction theory (Friedman et al., 1994) argue that individuals have basic human needs that they try to fulfill, among which certainty is one of them. Certainty can be achieved e.g., through a secure occupational career, a stable marriage or parenthood (Friedman et al.,

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1994). If a stable career is not possible, an alternative option for granting certainty in life might be chosen. Parenthood is—according to Friedman and colleagues—a promising option as parenthood is irreversible and lasts a lifetime. This uncertainty reduction might explain why especially unemployment is related to intending to have children and explains the more positive fertility intentions among lowly educated Turks in Germany. Here, future studies should look more carefully into why especially migrants in precarious situations favor having a(nother) child. Looking at fertility, the dissertation focused on a binary measure of fertility intentions that grouped participants into positive and negative intentions only without considering possible uncertainties regarding intentions. Bernadi et al. (2015) however show with the help of qualitative interviews that only certain individuals are sure about their intentions, while there remains a big range of answers that express uncertainty about childbearing for several reasons and some individuals even are indifferent. This abundance of opinions could not be considered within this dissertation as the statistical model would have become even more demanding making an estimation most likely impossible. Therefore, the drawback of differentiating two outcomes of intentions only was opted for. Several researchers have likewise focused on positive and negative intentions only (Ciritel et al., 2019; Testa et al., 2016) assuming that definitely not and probably not similarly reflect a negative intention while probably and definitely yes both mirror a positive intention. Mussino et al. (2021) for instance have contrasted definitely no fertility intentions to all other answers, as well as definitely yes answers to fertility intentions with all other answers thereby contrasting strong intentions from less strong ones. Future studies could verify the results presented here by focusing on uncertain intentions, too, and find out, whether assimilation and fertility related variables do relate to these intentions, too. A more holistic study on fertility intentions should moreover integrate the dyadic relationship between one’s own and the partner’s intentions as this might better reflect the extent to which intentions translate into actual behavior. Own intention as measured here can only reflect one part of the decision-making process (Philipov, 2009; Rosina & Testa, 2009). On the positive side, though, this dissertation integrated the framework of Miller and Pasta into the study of fertility among migrant and minority populations, thereby enriching the theoretical and empirical approaches on migrant and minority fertility. As Philipov (2011) highlights, the framework of Miller and Pasta has received too little focus within demographic research and there is need to research test it. Clearly, the traits-desires-intentions framework worked well among minority, migrant and majority groups within this dissertation and

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197

even could be extended to integrate assimilation specific predictors, too. Yet, it is of course clear that fertility intentions do not automatically translate into fertility behavior, which is why the results should be limited to intentions, which might or might not be transformed into the act of having or not having children (Philipov, 2011). Finally, a last topic to be discussed is the focus on migrant and minority groups. A clear contribution of this dissertation is the comparison of a longestablished ethnic minority against a more recently established migrant minority of same origin. To the knowledge of the author no such comparison exists so far. As elaborated on within the theoretical section of this paper, assimilation theory was chosen as a framework to compare migrant and minority groups regarding their living situation and fertility. As assimilation is taken to describe what happens when ethnic groups meet it was argued here that the framework is suitable to describe the living situation of migrants and minorities. At the same time however, it was already pointed out within this section that data availability for Bulgarian ethnic groups is scarce thereby aggravating the description of the assimilation of the Turkish group in Bulgaria. Hence, future research might add to this dissertation by challenging the conclusion drawn here that Turks in Bulgaria can be described in terms of the assimilation stages developed here and can indeed be labelled as separated. Here, qualitative research might provide interesting additional insights that go beyond the first results presented here.

7.2.2

Methodological Limitations

Having reviewed critically the conclusions derived from the dissertation, a reflection on the methodological path chosen is also necessary. A first methodological drawback concerns the data used for testing the relationship between fertility and assimilation. Ideally, representative data are desirable to transfer the conclusions drawn towards the overall society under study. As of the representativeness of the GGS data, Fokkema and colleagues showed that the GGS data deviate slightly from general population statistics in Germany as far as household and educational level is concerned. The bias in household size is made up by an underrepresentation of cohabiting couples without children, whereas the bias in education results out of an overrepresentation of highly educated people (Fokkema et al., 2016). When it comes to the representativeness of the Bulgarian GGS data, Fokkema and colleagues demonstrated for data of the first wave that those are mostly representative. However, deviations to general population characteristics can be found in the younger age structure of the GGS and the higher educational level

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of participants (Fokkema et al., 2016). These limitations are considered necessary drawbacks given that no other suitable data could be found. Moreover, the deviations are considered minor such that the conclusions drawn here are considered generalizable. The requirements for data to test the model envisaged here were demanding given that a sufficiently high number of minority and migrant respondents is needed in order to cluster into diverse societal groups and enable comparison along several categorical measures. Moreover, it was important to find data sets for at least two groups of the same origin, which live in different host societies and are comparable in terms of data collection and questions asked. The questionnaire thereby needed to capture assimilation measures, preferably for all assimilation dimensions, and measures of fertility that included fertility intentions. The GGS data fulfilled these criteria to the greatest extent, although they already are nearly 20 years old. Here, it would be interesting to repeat the study presented with more recent data and within other European contexts. This might be possible once the new data collection round of the GGP has finished. Furthermore, on a methodological note, one should keep in mind that only some assimilation variables were considered here and the models for Bulgaria and Germany are not fully comparable. While the clustering revealed that the cluster construction explained most variance in Germany, it was less suitable in Bulgaria. For Bulgaria though, the explained variance of the models presented was slightly higher than for Germany. The variables included are thus powerful to explain differences in fertility intentions. As just pointed out, this drawback results mainly out of data availability as well as the intention to find similar variables for majority and minority/migrant to account for their living situation. Nevertheless, it should be clear that a narrower description of the assimilation status of all groups under study might enable more nuanced conclusions than those presented here. An analysis planned but not carried out was the formation of assimilation clusters that are comprised of variables measuring the four dimensions of assimilation on both the home and host country dimension. Ideally, clusters could have resulted that represent different forms of assimilation that might resemble the four stages of assimilation, integration, separation, and marginalization, with different subgroups being possible for these four scenarios. This analysis would have provided a sounder base regarding research question one that asked how the assimilation status of the groups under investigation can best be described. While the answer to this question rests on few analyses and existing findings now, a classification would have yielded better insights. Practically, however, the measures available to assess assimilation were very limited leaving only a small

7.2 Critical reflection

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range of options to account for integration. Especially the social and identificational dimensions are hardly part of the studies conducted here. Furthermore, the available items did not capture the home country dimension of integration making the formation of a typology that builds upon a bi-dimensional understanding of assimilation impossible. Only this bi-dimensional set up can fully enable a researcher to evaluate how the process of integration is coming along. Therefore, the variables measuring assimilation have been included independently to assess the assimilation status. The home country dimension of integration could only be captured indirectly by referring to existing findings and descriptive statistics. Moreover, a challenge would have been on how to find corresponding measures of home country integration for majority members since the aim was to represent the situation of majority and minority likewise within the model. Moreover, the data available to account for integration were all categorical which made model estimation hard. A facilitation could have been reached by having more continuous variables that assess structural, cultural, social and identificational assimilation in terms of degree. One idea for future studies would be the development and validation of scales to measure a bi-dimensional assimilation that includes all four dimensions (structural, cultural, social and identificational). There are scales within literature that already capture Berry’s acculturation dimensions (see Demes & Geeraert 2014). However, these focus on cultural inclusion and cultural distance only. Ideally, scientific discourse should enlarge these measures to capture the other integration dimensions, too, thereby constructing a quantitative measure that validly and reliably captures the living situation of both minority and majority. This way, a better classification of assimilation stages would be possible. Moreover, it should also be emphasized that the study aimed at acknowledging that minority and migrant labels are socially constructed, and that minority and migrant respondents might not self-identify as belonging to a minority group. For Turkish minority respondents in Bulgaria, it was possible to build upon selfidentification as Turkish for ethnic grouping. For Turkish migrant respondents, though, the GGS data was already grouped into Turkish and German respondents. Here, the analyses rest on country of birth of oneself and/or parents. Out of those categorized as Turkish, not all self-identified as being Turk. The selection of data thus implies an imposed group ascription and does not control for the self-identification. Assessing whether one feels a belonging to a certain group of society is a difficult task within quantitative statistics given the limited scope of standardized answers. Rather, an ideal research set up should include both qualitative as well as quantitative assessments of belonging that do not frame the answers provided by respondents. This could enrich the findings presented here

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by building the clustering upon self- and other-ascription thereby acknowledging that the label one is attributed from the outside world does not necessarily match one’s own understanding of one’s position in society. Also, others might view the use of cluster analyses critically as the method is hardly applied within the context of migration studies. Accordingly, the results cannot be compared to existing ones and no standard on how to conduct and report this type of clustering exists. Upcoming studies should thus conduct similar analyses to verify the approach taken here and to widen this approach to different settings and possibly different interethnic contexts. Concretely, a comparison of methods (clustering versus integration of ethnicity) would be an interesting addition that could add to this dissertation an insight into how the results presented deviate once only ethnicity and no clustering along diverse societal lines of distinction is included to explain fertility intentions. Finally, ethnicity was still considered a predictor here and not—as Bös (2008) claimed—the outcome. However, it was at least considered that it might not be ethnicity alone that predicts fertility intentions. In line with recommendations of Herwartz-Emden and colleagues (2010) it was acknowledged within this dissertation that ethnicity is only one dividing line within society. Within upcoming studies, a mixed methods approach that interviews minority and migrant respondents to grasp the meaning and importance of ethnic dividing lines to respondents should be conducted that works out how minority and migrant respondents selfidentify before collecting quantitative data. This way, one might also get better insights into the meaningfulness of ethnic categorizations and possible other dividing lines. In the data available here, minority status could only be assessed using a question on ethnic identity that provided a standardized list of options. If Turkish minority respondents can only choose between the options of e.g., Bulgarian, Turkish and Roma identity, they are first of all forced to choose one of these options and are probably also more likely to self-identify as such given that the question and context of questioning frames the answers. A more open qualitative setting, that directs the focus towards the identity itself without providing frames of reference might yield different results and different classifications.

7.3

Conclusion

The dissertation could show that ethnicity, assimilation, and fertility are closely connected. While research has so far failed to clearly link theories on migrant and minority fertility to assimilation theory, a first step was provided here by developing an assimilation model that presents different stages of assimilation

7.3 Conclusion

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and their influence on fertility outcomes. Within this model, several theories on migrant and minority fertility could be integrated as they build on similar arguments and use similar predictors. Prospectively, this model should be applied within different contexts, should be operationalized with more assimilation variables, and should be applied to different (fertility) outcome variables. Moreover, it seems advisable to improve research on assimilation by developing continuous measures that enable the bi-dimensional capturing of host- and home-country assimilation. Over and above, cluster analysis was used within this dissertation to capture common dividing lines within society without purely focusing on ethnicity as common boundary. Here, future studies should apply a similar framework to see, whether this method can be a useful addition to migration studies in general and yield more differentiated results regarding societal boundaries and their influence on outcome variables. The biggest drawback within this study was the limited data availability. Two years of research was invested into the search for suitable data. The data should capture (1) fertility intentions, (2) assimilation in terms of bi-dimensional measures on structural, cultural, social and identificational aspects and (3) be available for both a minority and a migrant group of same origin with (4) sufficiently high sample sizes. The data used here were the only ones that came close to meeting these criteria. A big challenge for scientific discourse is hence the constant need to collect data on the fertility of migrants and minorities that enable a testing of fertility outcomes beyond actual number of children and at the same time provide detailed input on measures of integration. Ideally, these data should even entail validated scales assessing assimilation outcomes next to objective measures on the living situation of migrants and minorities. This is a huge task for future studies and will certainly take decades and funding. Besides these recommendations the presented dissertation should be taken as valuable addition to the present scientific discourse by connecting migration scholars with demographers within the European context. While social demography is an established discipline within the US scientific discourse, European scholars have just started the last years to look beyond their own discipline. A merging of theoretical insights as presented here is thus a valuable addition to all disciplines engaged in the study on fertility and assimilation. For the European context, migration studies were moreover enriched by the focus on a comparison of migrant and minority which is a scarce approach within ethnic studies. As could be shown, migrants and minorities differ in their living situation and the context of reception as well as the context of origin are important players in the formation of assimilation and fertility. This should be acknowledged within future studies and more comparisons of minority and migrants are desirable to come to terms with which factors differentiate and unite these groups.

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