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Live together: does culture matter?

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Abstract

This paper studies the role of culture in determining the decision to live together (as a married or unmarried couple). To examine this issue, we utilize data on first-generation immigrants who arrived in the United States at or before the age of 5. We follow the epidemiological approach, indicating that dissimilarities in the behavior of young-arrival immigrants originating from different countries, who grew up and live in the same country, can be interpreted as evidence of the existence of a cultural effect. Results show a positive and statistically significant relationship between the cultural proxy, that is, the proportion of individuals living together by country of origin, and the immigrant choice of living with a partner. We extend this analysis to an exploration of the formation of same- or different-origin couples, in addition to an examination of the effect of culture on other modes of household arrangement (such as living with an adult child, living with grandparents, same-gender couples, and family size, among others). In all cases, our findings suggest an important role of culture. The results are robust after controlling for several home-country, observable and unobservable characteristics, and to the use of different subsamples. Supplementary analysis shows a range of channels of transmission of culture.

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Notes

  1. In this analysis, the US native partners have also been considered.

  2. As in Furtado et al. (2013), we use a linear probability model for simplicity. Our conclusions are maintained when applying a probit model; see Table 9 in the Appendix.

  3. We have repeated the whole analysis with/without those individuals reporting being married with their partner absent. Our results do not vary. All our robustness checks are available upon request.

  4. The omitted race is other races. Those with more than one race have been eliminated from the analysis. The entire analysis has been re-estimated with/without race controls and results do not vary. We have also considered a sample of White individuals and our findings are unchanged, reinforcing that it is the cultural effect that we are observing. Our sample has been extended to incorporate those who report mixed race and results are similar, see Table 10 in the Appendix.

  5. The IPUMS USA and the IPUMS International define the head of household as any household member in whose name the property is owned or rented. We revisit the choice of the head of household below.

  6. The inclusion of the country of origin fixed effects is not possible in all specifications (see below). We have re-run the analysis replacing the state fixed effects with Metropolitan Statistical Areas (MSA) fixed effects, and we do not find substantial differences. Results do not change when including/excluding the country of origin fixed effects and the year of immigration fixed effects. In the same vein, our findings are invariant after the inclusion of interactions between the time-varying fixed effects and the country of origin fixed effects (see next section).

  7. All estimates have been repeated with/without weights and with/without clusters and we find no differences.

  8. With this dataset, we have enough observations for 38 countries of origin, which allows us to obtain reliable results with all our sample selection requirements.

  9. Countries of origin with less than 20 observations per country have been removed from our analysis, following the example of the existing literature, since with only a few observations it is difficult to assume that the immigrants represent the population composition of their country of origin. It is also worth noting that the analysis has been repeated by selecting a sample of immigrants who arrived in the US under age 8 and results are similar (see Table 10 in the Appendix).

  10. We only include heterosexual couples since same-gender couples were not allowed to marry in all countries during the period considered. There are only 84 heads of household who can be classified as having a same-gender partner.

  11. Among those household-head first-generation immigrants, we include those living in an identifiable statistical metropolitan area in order to maintain the same sample as in the cultural transmission analysis (see below).

  12. We use all the observations from countries where we have information on the cultural proxy in the IPUMS International. We include those immigrants originating from countries of origin where married and unmarried couples are identified in the Census data.

  13. As before, this has been calculated using a sample of heads of household aged 18 to 50.

  14. This strategy is followed in the rest of the literature. As Fernández (2007) explains, culture adjusts very slowly and our results do not vary when we measure the cultural proxy in different years.

  15. For those countries that do not distinguish between some college and more college, we have measured the cultural proxy for the tertiary level of education. Results are maintained without the immigrants originating from those particular countries of origin.

  16. Note that our sample only incorporates heads of household. The analysis by gender is described below.

  17. It is also possible that controlling for education may be over-controlling since we are also considering those same categories of education in our cultural proxy. For this reason, the effect of education on the living-together decision may be captured by the cultural proxy. To examine this issue, we have repeated the regressions without education controls and our findings do not change (see an example in Table 10 of the Appendix). Also, it could be that the choice of education level is affected by culture. Thus, there could be an indirect effect of culture on the living-together decision via education choice. To check this, we have redefined the dependent variable as a dummy variable that takes the value 1 if an individual has at least some college, and 0 otherwise. The explanatory variables are our first cultural proxy (without incorporating the education differences) in addition to the rest of the controls, excluding education. Estimates are presented in Table 10 of the Appendix. We find no effect of the cultural proxy on the education choice. Following the rest of the literature exploring the effect of culture on several variables, such as divorce and fertility (Furtado et al. 2013; Marcén et al. 2018), we maintain the education controls in the rest of the paper.

  18. The rest of the regressions shown in the paper only include the cultural proxy measured by age, education level, and country of origin, since this variable allows us to better capture the culture of each country of origin. As can be seen, the R2 is higher when we use this measure of culture rather than the rest of the cultural proxies. For consistency, we have repeated the analysis with the previous measures of culture and our conclusions on the impact of culture are maintained, although there are small variations in the magnitude of the effect.

  19. IPUMS USA defines a metropolitan area as a region formed by neighboring communities that have a high degree of economic and social integration with the urban core. The population threshold to identify an MSA is 100,000 inhabitants.

  20. We have repeated the analysis using the 2000 US Census and our results are the same.

  21. Because of the large number of interactions that this generates, to run this regression we had to redefine the time of migration fixed effects, using dummies for the decade in which the immigrants arrived in the US.

  22. Almost all the countries considered in this analysis have a greater proportion of individuals living together than that of the US, which is around 0.51. Then, the assimilation of the US living together culture, if this happens, in the case of immigrants would result in a lower preference for living together as married or as an unmarried couple. We explore this issue below.

  23. The total fertility rate is defined as the mean number of children that would be born alive to a woman during her lifetime if she were to pass through her childbearing years conforming to the fertility rates by age of a given year. Unemployment rate is the percentage of the total labor force, that is without work but available for and seeking employment. GDP per capita is gross domestic product divided by mid-year population. Crude marriage rates are the annual number of marriages per 1,000 mid-year population. Data are collected for the year 2015 (or for the closer year if no data is available for that year) and come from the World Bank Data and from the UN Demographic Yearbooks. The information to elaborate the religion variable came from The World Fact Book of the Central Intelligence Agency.

  24. We checked whether our estimates are similar if we exclude the variable controlling for religious practitioners, since the number of observations considerably decreases after its inclusion. Results are invariant.

  25. We re-run the analysis including each of these observable characteristics, one at a time, and our results do not change.

  26. Results are unchanged when the 2000 US Census is considered.

  27. The change in the number of observations is due to the fact that the non-heads-of-household are not all immigrants, so those who are native born have been excluded.

  28. We exclude those immigrants from countries where married and unmarried couples, separately, are not identified in the home-country Census data. Thus, India, China, Portugal, Vietnam, Pakistan, Thailand and Turkey have been removed from our sample.

  29. The same is observed after controlling for observable characteristics by country of origin, rather than the country of origin fixed effects.

  30. An adult child is a child older than 25.

  31. As before, we construct the cultural variable by age and education level. The variation in the sample size is due to the availability of information.

  32. With the exception of the gender analysis - although, in that case, we have considered a different analysis.

  33. As before, the cultural proxy is measured in this case by age, education level, and country of origin.

  34. There is no information on parents’ characteristics.

  35. The native concentration has been defined as the proportion of native individuals in each metropolitan area.

  36. The information on the language spoken by each immigrant is obtained from the ACS. This is used in the estimations presented in columns 6 and 7 of Table 8.

  37. The variation in the sample size is due to the availability of information for the gender-based system in WALS.

  38. Religion as a potential mechanism through which culture may operate is not considered here, since we do not have information on the religious persuasion of the immigrants. In any case, the incorporation of information of the home country relative to the religion presented above does not alter our findings.

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Acknowledgements

This research has been funded by the Regional Government of Aragon and the European Fund of Regional Development (Grant S32_17R).

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  1. Facultad de Economía y Empresa, Universidad de Zaragoza, Gran Vía 2, Zaragoza, 50005, Spain

    Miriam Marcén & Marina Morales

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  1. Miriam Marcén
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Correspondence to Miriam Marcén.

Appendix

Appendix

Table 9-11

Table 9 The effect of culture on the living-together decision using Probit Models
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Table 10 More robustness checks and Placebo tests
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Table 11 Home-country censuses from IPUMS international
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Marcén, M., Morales, M. Live together: does culture matter?. Rev Econ Household 17, 671–713 (2019). https://doi.org/10.1007/s11150-018-9431-3

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