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Decomposing culture: an analysis of gender, language, and labor supply in the household


Despite broad progress in closing many dimensions of the gender gap around the globe, recent research has shown that traditional gender roles can still exert a large influence on female labor force participation, even in developed economies. This paper empirically analyzes the role of culture in determining the labor market engagement of women within the context of collective models of household decision making. In particular, we use the epidemiological approach to study the relationship between gender in language and labor market participation among married female immigrants to the U.S. We show that the presence of gender in language can act as a marker for culturally acquired gender roles and that these roles are important determinants of household labor allocations. Female immigrants who speak a language with sex-based grammatical rules exhibit lower labor force participation, hours worked, and weeks worked. Our strategy of isolating one component of culture reveals that roughly two thirds of this relationship can be explained by correlated cultural factors, including the role of bargaining power in the household, and the impact of ethnic enclaves and that at most one third is potentially explained by language having a causal impact.

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  1. 1.

    Gay et al. (2016) provides an in-depth discussion of the epidemiological approach in this context.

  2. 2.

    Theoretically language structures could influence preference formation, information processing, categorization of social reality, and the salience of certain categories of words. These impacts could alter a speaker’s decision making and behavior (Mavisakalyan and Weber 2016).

  3. 3.

    One of the challenges of the literature on culture and economic behavior has been to measure culture. An advantage of using language over existing alternatives is that language is defined at the individual level and is not an outcome measure. It allows us to control for country of origin characteristics including time varying ones.

  4. 4.

    This result is not solely attributable to selection since it holds for women married prior to migration— sometimes referred to as “tied women”.

  5. 5.

    See Everett (2013) for a review of the linguistics literature.

  6. 6.

    Formal checks suggest that the regression sample is not biased by the availability of the country of birth and the linguistic data. Online Appendix Table C.2 provides summary statistics comparable to those in Table 1 without dropping the respondents for which the country of birth or language are not precisely identifiable, which is about 6.78% of the uncorrected regression sample. The last column of this table demonstrates that data constraints— i.e., needing to know language spoken and country of birth—do not meaningfully bias the sample in any way along these observables.

  7. 7.

    This is the LABFORCE variable in the ACS (Ruggles et al. 2015). See Online Appendix A.2.1 for more details.

  8. 8.

    The yearly weeks worked correspond to the WKSWORK2 variable in the ACS (Ruggles et al. 2015). Because this variable is given in intervals, we define yearly weeks worked as the midpoint of those intervals. The usual weekly hours worked correspond to the UHRSWORK variable in the ACS (Ruggles et al. 2015). Online Appendix A.2.1 provides additional details.

  9. 9.

    The languages for which some variables were compiled are detailed in Online Appendix B. For robustness, we have also verified that the exclusion of our newly assigned languages in favor of the original WALS set of languages does not alter the main findings of the analysis.

  10. 10.

    More detailed definitions of these individual measures can be found in Hicks et al. (2015).

  11. 11.

    This measure should not be taken as measuring absolute intensity but rather as a ranking of relative intensity across languages grammar. The discussion of the measure is extended in Online Appendix discussion of Table C.7.

  12. 12.

    Indeed, as can be seen in the last column of Table 1, Asian immigrants are more likely to speak a non sex-based language. Since they have on average higher levels of labor force participation than other respondents, this explains part of the decline in magnitude.

  13. 13.

    To capture cultural variation in gender roles, existing studies have proxied culture with female outcomes in the country of origin. For instance, Fernández and Fogli (2009) use for country of origin female labor force participation to capture the culture of second generation immigrants to the U.S. Blau and Kahn (2015) additionally control for individual labor force participation prior to migration to separate culture from social capital. Oreffice (2014) create an index of gender roles in the country of origin as a function of several gender outcomes. This literature posits that immigrants carry with them some of the attitudes from their home country to the U.S., and, in the case of second generation immigrants, that immigrants transmit some of these attitudes to their children.

  14. 14.

    Alternatively, we checked the robustness of our results to measures of linguistic distance between languages from Adsera and Pytlikova (2015), including a Linguistic proximity index constructed using data from Ethnologue , and a Levenshtein distance measure developed by the Max Planck Institute for Evolutionary Anthropology . This data covers 42 languages out of the 63 in our regression sample. Even with a reduced sample size (435,899 observations instead of 480,619 observations), the results from the baseline regression the regression from Table 3 column (5) with country of birth fixed effects are essentially unchanged. Because of such a lower coverage of languages the use of linguistic distance as a control would entail, we do not include these measures throughout the analysis.

  15. 15.

    See Online Appendix A.2.6 for more details about the sources and the construction of the country-level variables. See also Online Appendix Table C.5, which reports summary statistics for these variables.

  16. 16.

    This is not surprising given the cross-country results in Gay et al. (2013), which show that country-level female labor participation rates are correlated with the linguistic structure of the majority language.

  17. 17.

    Field et al. (2016) implement a field experiment in India where traditional gender norms bound women away from the labor market, and investigate how a change in their bargaining power stemming from an increase in their control over their earnings can allow them to free themselves from the traditional gender norms.

  18. 18.

    We focus on the non-labor income gap because it is relatively less endogenous than the labor income gap (Lundberg et al. 1997). We do not include the education gap for the same reason. Online Appendix Table C.4 presents descriptive statistics for various gender gap measures within the household. Other variables, such as physical attributes, have been shown to influence female labor supply (Oreffice and Quintana-Domeque 2012). Unfortunately, they are not available in the ACS (Ruggles et al. 2015).

  19. 19.

    Online Appendix Table C.3 presents descriptive statistics for these husband characteristics.

  20. 20.

    This also consistent with findings in Hicks et al. (2015), wherein the division of household labor was shown to be heavily skewed against females in households coming from countries with a dominantly sex-based language, suggesting that languages could potentially constrain females to a traditional role within the household.

  21. 21.

    Throughout the table we sequentially add female country of birth characteristics and female country of birth fixed effects. We do not add the respondent’s husband country of birth fixed characteristics because only 20% of the sample features husbands and wives speaking different structure languages. In that case, our analysis does not have sufficient statistical power precisely identify the coefficients. While many non-English speaking couples share the same language in the household, we focus on the role of gender marking to learn about the impact of husbands and wives gender norms as embodied in the structure of their language.

  22. 22.

    Instead of simply comparing households where husbands and wives speak the same language vs. those without, this approach allows us both to include households with English speakers and to understand whether the observed mechanisms operate through grammatical gender.

  23. 23.

    In all cases however, when a wife speaks a sex-based language, she exhibits on average lower female labor force participation.

  24. 24.

    See Online Appendix A.2.3 for more details on the number of identifiable counties in the ACS. Although estimates on this subsample may not be comparable with those obtained with the full sample, the results column (7) of Table 3 being so similar to those in column (6) gives us confidence that selection into county does not drive the results.

  25. 25.

    In both measures, we use the total number of immigrants that are in the workforce because it is more relevant for networking and reducing information asymmetries regarding labor market opportunities. See Online Appendix A.2.7 for more details.

  26. 26.

    See Online Appendix A.1.2 for more details on how we constructed these subsamples.

  27. 27.

    We maintain the OLS throughout the paper, however, as it is computationally too intensive to run these models with the inclusion of hundreds of fixed effects in most of our specifications.


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Correspondence to Estefania Santacreu-Vasut.

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Gay, V., Hicks, D.L., Santacreu-Vasut, E. et al. Decomposing culture: an analysis of gender, language, and labor supply in the household. Rev Econ Household 16, 879–909 (2018).

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  • Language
  • Gender gap
  • Bargaining power in the household
  • Labor force participation
  • Immigrants

JEL Classification

  • F22
  • J16
  • Z13