Abstract
When immigrants enter the US they typically access a marriage market with a larger supply of educated spouses compared to the marriage market in their home countries. Absent any selectivity bias, this access should increase the likelihood that migrants ‘marry-up’ in terms of education. We combine survey data on British and German immigrants in the US with data on natives in Britain and Germany to estimate the causal effect of migration on educational mobility through cross-national marriage. To control for selective mating, we instrument educational attainment using government spending on education in the years each person was of school-age. To control for selective migration, we instrument the migration decision using inflows of immigrants to the US during puberty and early adulthood. We find strong selectivity effects that work against the positive prospects of the US marriage market. All migrants give up spousal education in exchange for US entry and assimilation. Migrant men also give up spousal education because they cannot compete with native men as bread-earners. Migrant women have some advantage in the US marriage market, as they can compete with native women in home production.
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Notes
i.e. that the immigrant has decided to migrate for reasons unrelated to spouse selection.
Because we focus on cross-national marriages, our results cannot relate to the ‘immigrant investment hypothesis’ which describes a form of delayed female household specialization in immigrant families. This hypothesis posits that, when spouses migrate together, the migrant wife may look for employment in the host country immediately after arrival in order to finance family consumption and allow the migrant husband to invest in human capital. After the husband completes his investment, the wife reduces her labor force commitment and resumes her traditional role as a home-keeper. The hypothesis finds support in data from Canada (Baker and Benjamin 1997) but US evidence points against it (Blau et al. 2003). For a wider discussion of previous literature, see the online Appendix.
In our data, marriage rates are around 93–95% and differences between migrants and non-migrants never exceed two percentage points. These rates are in line with the constancy of the marriage probability worldwide and the classification of marriage as a “cultural universal” (Brown 1991). Using data from the Demographic Yearbooks of the United Nations on 97 industrial and agricultural countries societies, Fisher (1989) reports that between 1972 and 1981, 93.1 % of women and 91.8 % of men were married by age 49.
Admittedly, this assumes away the possibility that the size of a given migrant community may impact views of educated US natives about that community (e.g. the educated US population may assimilate a migrant community more/less depending on its size). Although we acknowledge that possibility, we expect that it poses few challenges in our analysis. We are more inclined to believe that the educated US population may assimilate a migrant community more/less depending on its share of educated members rather than its size.
For example, researchers have documented that the War Brides, who probably form the majority of migrants in our sample who entered the US married, did not move into an existing immigrant population or settle in ethnic enclaves. Rather, they were welcomed by, and often moved in with, the families of their husbands. The war brides did not rely on co-ethic immigrant networks even for help with basic practicalities of their migration process. They received advice and assistance with paperwork by the American Red Cross and often their transportation to the US was arranged and paid by the US government (Virden 1996).
Note that in the German and British databases, which are collected annually, we allow individuals to appear multiple times but we treat each sample as a cross-section. In contrast, the CPS interviews individuals from every household for four months in a row, then ignores those individuals for eight months, and finally it interviews them again for another four months. Our CPS sample includes the responses of each immigrant during the first interview only; i.e. we do not allow multiple observations per person. The reason we do this is to avoid matching annual data in the BHPS and SOEP with multiple months within a year in the CPS data (as multiple monthly cross-sections would over-represent a given year).
Among all British and German migrants in our sample, about 68 % are married to US natives, 31 % are married to compatriots, and \(<\)1 % are married to migrants from other countries.
It is estimated that approximately 115,000 immigrants entered the US under the provisions of the War Brides Act of 1945; the Alien Fiancees and Fiances Act of 1946; and the Soldier Brides Acts of 1946 and 1947 (INSUS 1950). These brides entered the country within a narrow time window (in the decade following the end of WWII) and belonged to a narrow birth cohort. Based on a small survey of British war brides conducted in 1989, the average age of the brides at the time of marriage was twenty-three and the age of their US husbands was twenty-five (Virden 1996). These characteristics allow us to estimate an upper bound of the share of War Brides in our data. Migrant women who arrived in the US between 1945 and 1955 and were younger than 36 at the time of arrival comprise 15.8 % of the total British females and 21.6 % of the German females. More recent data from the UK International Passenger Survey (IPS) indicates that only twenty-five percent of British citizens leaving the UK between 1982 and 2012 said they were doing so primarily to join or accompany their family or (co-ethnic or other) partner. Most of them said that they migrated to work. While the IPS does not report primary reasons by country of destination, the relative strength of the US economy suggests that UK immigrants probably come to the US to work. The same logic applies to immigrants from Germany.
Ideally, one would like to use a more disaggregated measure, e.g. by level of education or geographic region/state. Long time-series on such disaggregated variables are not available. Snyder and Dillow (2011) provide separate data series on public and private education spending in the US by level of education from 1970 to 2010. Using those data we find that the correlation between total private and public spending and between spending in primary/secondary schools and post-secondary institutions exceed 0.9.
For each sex s, year t, and age-group k, we calculate population weights as: \((population_{stk}/population_{st})/(sample\,size_{stk}/sample\,size_{st})\).
For example, the German government has, until relatively recently, set welfare and public support policies according to a “male breadwinner” model. That is, the institutional structure in Germany provides strong financial and social incentives for couples to divide labor so that men specialize in market production and women specialize in home production. Starting around 2000, German social policymakers began to reform institutions using a different model (Meyer 1998). For information on the US, one can look at the Panel Study of Income Dynamics (PSID) - Child Development Supplement/Transition to Adulthood (CDS-TA) surveys. The CDS-TA surveys interviews the person in the PSID household who identifies herself/himself as the ’primary’ care-giver (PCG) of each child. Data from the 2002 and 2007 waves show that biological, adoptive, or step mothers comprise over 90 % of self-identified care-givers.
Available at the international online database of Integrated Public Use Microdata Series (IPUMS).
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We gratefully acknowledge research assistance from Jeffrey Han and Duk Gyoo Kim and funding from the Cornell Institute of the Social Sciences and the Cornell Population Program (small Grant award, fall 2010). We also wish to thank Daniel Lichter, Ahmed Jaber, and participants of the 10th IZA Annual Migration Meeting and the 11th International German Socio-Economic Panel User Conference for useful comments.
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Christopoulou, R., Lillard, D.R. Migration to the US and marital mobility. Rev Econ Household 14, 669–694 (2016). https://doi.org/10.1007/s11150-015-9308-7
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DOI: https://doi.org/10.1007/s11150-015-9308-7