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Migration and the Partner Market: How Gender-Selective Relocations Affect Regional Mating Chances in Germany

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Abstract

The study explores the consequences of gender-selective internal migration for regional mating chances in Germany, comparing different cohorts as well as different types of regions. Indicators of the partner market based on time series of the official German regional population statistics are combined with indicators of migration and on regional economic, educational, and settlement structures. Instead of the simple sex ratio, which is the standard measure for partner market conditions in previous research, the study at hand uses the availability ratio suggested by Goldman et al. (Popul Index 50(1):5–25, 1984). The availability ratio takes into account that partner markets are structured by age preferences. Like previous studies, results show that gender-selective migration has led to a strong deterioration of mating chances for men in most eastern districts of Germany. Exceptions are districts offering universities as well as a large tertiary sector. But, unlike previous research, results also show that migration-caused imbalances of the partner market in eastern German districts are not extraordinarily high. In the western part of Germany, there were at times even stronger partner market imbalances for some male cohorts as a consequence of oscillating birth rates.

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Fig. 1

Source: German Statistical Office: Statistisches Bundesamt (2015). Own figure

Fig. 2

Data: Own calculations based on the regional population statistics of the German Federal Statistical Office and the statistical offices of the German states

Fig. 3

Data: Own calculations based on the regional population statistics of the German Federal Statistical Office and the statistical offices of the German states

Fig. 4

Data: Own calculations based on the spatial statistics of the Federal Institute for Research on Building, Urban Affairs and Spatial Development and on statistics on population, employment, and higher education from the Federal Statistical Office and the statistical offices of the German states

Fig. 5

Data: Own calculations based on the spatial statistics of the Federal Institute for Research on Building, Urban Affairs and Spatial Development and on statistics on population, employment, and higher education from the Federal Statistical Office and the statistical offices of the German states

Fig. 6

Data: Own calculations based on statistics on population, and migration from the Federal Statistical Office and the statistical offices of the German states

Fig. 7

Data: Own calculations based on the spatial statistics of the Federal Institute for Research on Building, Urban Affairs and Spatial Development and on statistics on population, migration, employment, and higher education from the Federal Statistical Office and the statistical offices of the German states

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Notes

  1. Lampard (1993), Ní Bhrolcháin and Sigle-Rushton (2005), as well as Ní Bhrolcháin (2008) recommend a modified version of the availability ratio, which is called the iterated availability ratio (IAR). The IAR is calculated by summing up the number of only the unmarried persons of each sex. Hence, only unmarried persons are regarded as available partners and as competitors on the partner market. One problem of this measure is that women and men living together in non-marital relationships are considered to be available on the partner market to the same degree as single persons [see Stauder (2006) for an analysis of the availability on the partner market of married and cohabiting individuals]. Against the background of the rising acceptance of non-marital unions, it seems difficult to compare the outcomes of this measure between different cohorts or periods. Because non-marital unions are more common in the East of Germany compared to the West, the IAR seems also to be unsuitable for comparisons between the two parts of Germany. Another problem of the IAR concept is that the marriage rate is regarded at the same time as an influencing factor and as a consequence of the partner market. For these reasons, and as we focus on the consequences of internal migration on the partner market, we decided to use the standard version of the AR which makes no difference between persons in different types of couple relationships.

  2. For the German Family Survey see Bien and Marbach (2003), for the Generations and Gender Survey see Vikat et al. (2007) and Naderi et al. (2009), for the Panel Analyses of Intimate Relationships and Family Dynamics (pairfam) see Huinink et al. (2011), for the German Ageing Survey see Engstler and Schmiade (2013), for the German Socio-economic Panel see Haisken-DeNew and Frick (2005), for the Survey of Health, Ageing and Retirement in Europe see Börsch-Supan et al. (2013), and for the Heidelberg Partner Market Survey see Häring et al. (2014).

  3. These variations of age constellations over the life course are the same for younger cohorts and for older ones. We found no significant differences between cohorts.

  4. A more thorough discussion of our calculation of the availability ratio can be found in Eckhard, Stauder and Wiese (2015: 88–92). In Eckhard, Stauder and Wiese (2014), we provide a very detailed documentation about our methodic analyses, including robustness checks for different variants of the calculation of weights for age relevance.

  5. This is necessary because the applied migration data refer to the borders of the administrative districts as they are today. In contrast, our measures of the partner market and the socio-economic indicators refer to the borders as they were in each particular year of observation. Due to numerous reforms of district borders in the eastern part of Germany in the 1990s, harmonization of spaces of reference of the various indicators is possible only for the years since 2002. Between 2002 and 2010, the district’s geographical borders maintained nearly unchanged. Only a few districts changed between 2002 and 2010 and therefore were merged to joint areas of 2, 3, or 4 districts. The joint areas are (1) Potsdam/Potsdam-Mittelmark/Brandenburg an der Havel, (2) Cottbus/Spree-Neiße, (3) Südvorpommern/Mecklenburgische Seenplatte, (4) Leipzig (district)/Leipzig (City)/Northern Saxony, (5) Sächsische Schweiz-Osterzgebirge/Meißen, (6) Merseburg-Querfurt/Saalkreis, (7) Dessau-Roßlau/Anhalt-Bitterfeld/Wittenberg/Jerichower Land, (8) Harz/Salzland/Saale-Holzland, and (9) Rhein-Hunsrück/Cochem-Zell. For these joint areas, unemployment rates and the proportion of the tertiary sector were calculated using population weighted means. Other districts unified during the years since 2002. In these cases, our analyses (of Sects. 4, 5, and in online appendix) refer to the borders of the newly formed larger districts.

  6. Eckhard et al. (2015) illustrate the partner market conditions of older cohorts and also refer to the later life course.

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Acknowledgements

This study is part of a research project funded by the German Research Foundation, DFG.

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    Jan Eckhard & Johannes Stauder

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Eckhard, J., Stauder, J. Migration and the Partner Market: How Gender-Selective Relocations Affect Regional Mating Chances in Germany. Eur J Population 34, 59–86 (2018). https://doi.org/10.1007/s10680-017-9422-8

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