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.
This is a preview of subscription content, access via your institution.
Buy single article
Instant access to the full article PDF.
Tax calculation will be finalised during checkout.
Gay et al. (2016) provides an in-depth discussion of the epidemiological approach in this context.
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).
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.
This result is not solely attributable to selection since it holds for women married prior to migration— sometimes referred to as “tied women”.
See Everett (2013) for a review of the linguistics literature.
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.
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.
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.
More detailed definitions of these individual measures can be found in Hicks et al. (2015).
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.
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.
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.
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.
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.
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.
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.
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).
Online Appendix Table C.3 presents descriptive statistics for these husband characteristics.
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.
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.
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.
In all cases however, when a wife speaks a sex-based language, she exhibits on average lower female labor force participation.
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.
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.
See Online Appendix A.1.2 for more details on how we constructed these subsamples.
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.
Adsera, A., & Pytlikova, M. (2015). The role of language in shaping international migration. The Economic Journal, 125(586), F49–F81.
Alesina, A., Giuliano, P., & Nunn, N. (2013). On the origins of gender roles: Women and the plough. The Quarterly Journal of Economics, 128(2), 469–530.
Aronow, P. M., & Samii, C. (2016). Does regression produce representative estimates of causal effects? American Journal of Political Science, 60(1), 250–267.
Blau, F. D., & Kahn, L. M. (2015). Substitution between individual and source country characteristics: Social capital, culture, and us labor market outcomes among immigrant women. Journal of Human Capital, 9(4), 439–482.
Blau, F., Kahn, L., & Papps, K. (2011). Gender, source country characteristics, and labor market assimilation among immigrants. The Review of Economics and Statistics, 93(1), 43–58.
Blundell, R., Chiappori, P.-A., Magnac, T., & Meghir, C. (2007). Collective labour supply: Heterogeneity and non-participation. The Review of Economic Studies, 74(2), 417–445.
Chen, M. K. (2013). The effect of language on economic behavior: Evidence from savings rates, health behaviors, and retirement assets. American Economic Review, 103(2), 690–731.
Chiappori, P.-A., Fortin, B., & Lacroix, G. (2002). Marriage market, divorce legislation, and household labor supply. Journal of Political Economy, 110(1), 37–72.
Davis, L., & Reynolds, M. (2016). Gendered language and the educational gender gap. Available at SSRN: https://ssrn.com/abstract=2782540.
Dryer, M., & Haspelmath, M. (2011). The world atlas of language structures online. Leipzig: Max Planck Institute for Evolutionary Anthropology. (Available online at http://wals.info).
Edin, P.-A., Fredriksson, P., & Åslund, O. (2003). Ethnic enclaves and the economic success of immigrants evidence from a natural experiment. The Quarterly Journal of Economics, 118(1), 329–357.
Everett, C. (2013). Linguistic relativity: Evidence across languages and cognitive domains, Vol. 25, Berlin/Boston: Walter de Gruyter GmbH.
Farré, L., & Vella, F. (2013). The intergenerational transmission of gender role attitudes and its implications for female labour force participation. Economica, 80(318), 219–247.
Fernández, R. (2007). Alfred marshall lecture women, work, and culture. Journal of the European Economic Association, 5(2–3), 305–332.
Fernández, R. (2011). Chapter 11 - does culture matter?, Vol. 1 of Handbook of social economics (pp. 481–510). North Holland: Elsevier.
Fernández, R. (2013). Cultural change as learning: The evolution of female labor force participation over a century. The American Economic Review, 103(1), 472–500.
Fernández, R., & Fogli, A. (2009). Culture: An empirical investigation of beliefs, work, and fertility. American Economic Journal: Macroeconomics, 1(1), 146–177.
Field, E., Pande, R., Rigol, N., Schaner, S., & Moore, C. T. (2016). On her account: Can strengthening women’s financial control boost female labor supply?, Technical report.
Gay, V., Hicks, D., & Santacreu-Vasut, E. (2016). Migration as a window into the coevolution between language and behavior, In S. Roberts, C. Cuskley, L. McCrohon, L. Barceló-Coblijn, O. Fehér, & T. Verhoef (Eds.), The evolution of language: Proceedings of the 11th international conference (EVOLANGX11), Online at http://evolang.org/neworleans/papers/120.html.
Gay V., Santacreu-Vasut E., & Shoham A. (2013). The grammatical origins of gender roles (Working Paper Series WP2013-03). Berkeley, CA: Berkeley Economic History Economic Laboratory.
Givati, Y., & Troiano, U. (2012). Law, economics, and culture: Theory of mandated benefits and evidence from maternity leave policies. Journal of Law and Economics, 55(2), 339–364.
Goldin, C. (2014). A grand gender convergence: Its last chapter. The American Economic Review, 104(4), 1091–1119.
Grossbard, S. (2015). The marriage motive: A price theory of marriage. How marriage markets affect employment, consumption and savings. Springer-Verlag New York Inc.
Hicks, D. L., Santacreu-Vasut, E., & Shoham, A. (2015). Does mother tongue make for women’s work? linguistics, household labor, and gender identity. Journal of Economic Behavior & Organization, 110, 19–44.
Inc., E. B. (2010), 2010 Britannica book of the year, Vol. 35, Encyclopaedia britannica. pp. 766–770.
Ladd, D. R., Roberts, S. G., & Dediu, D. (2015). Correlational studies in typological and historical linguistics. Annu. Rev. Linguist, 1(1), 221–241.
Lundberg, S. J., Pollak, R. A., & Wales, T. J. (1997). Do husbands and wives pool their resources? Evidence from the united kingdom child benefit. The Journal of Human Resources, 32(3), 463–480.
Lupyan G., & Dale R. (2010) Language structure is partly determined by social structure. PloS ONE, 5(1), e8559. doi:10.1371/journal.pone.0008559.
Mavisakalyan, A. (2015). Gender in language and gender in employment. Oxford Development Studies, 43(4), 403–424.
Mavisakalyan A., & Weber C. (2016) Linguistic Relativity and Economics. Bankwest Curtin Economics Centre Working Paper 16/05, Perth: Curtin University.
Munshi, K. (2003). Networks in the modern economy: Mexican migrants in the us labor market. The Quarterly Journal of Economics, 118(2), 549–599.
Oreffice, S. (2014). Culture and household decision making. balance of power and labor supply choices of us-born and foreign-born couples. Journal of Labor Research, 35(2), 162–184.
Oreffice, S., & Quintana-Domeque, C. (2012). Fat spouses and hours of work: Are body and pareto weights correlated? IZA Journal of Labor Economics, 1(1), 6.
Roberts S., Winters J., & Chen K. (2015) Future tense and economic decisions: Controlling for cultural evolution. PloS ONE, 10(7), e0132145. doi:10.1371/journal.pone.0132145.
Roberts S., & Winters J. (2013) Linguistic diversity and traffic accidents: lessons from statistical studies of cultural traits. PloS ONE, 8(8), e70902. doi:10.1371/journal.pone.0070902.
Ruggles, S., Genadek, K., Goeken, R., Grover, J., & Sobek, M. (2015). Integrated public use microdata series: Version 6.0. [dataset]. Minneapolis: University of Minnesota. doi:10.18128/D010.V6.0.
Santacreu-Vasut, E., Shenkar, O., & Shoham, A. (2014). Linguistic gender marking and its international business ramifications. Journal of International Business Studies, 45(9), 1170–1178.
Santacreu-Vasut, E., Shoham, A., & Gay, V. (2013). Do female/male distinctions in language matter? Evidence from gender political quotas. Applied Economics Letters, 20(5), 495–498.
Spolaore, E., & Wacziarg, R. (2009). The diffusion of development. The Quarterly Journal of Economics, 124(2), 469–529.
Spolaore, E., & Wacziarg, R. (2016), Ancestry and development: New evidence. Discussion Papers Series, Department of Economics, Tufts University 0820, Department of Economics, Tufts University.
van der Velde, L., Tyrowicz, J., & Siwinska, J. (2015). Language and (the estimates of) the gender wage gap. Economics Letters, 136, 165–170.
Conflict of interest
The authors declare that they have no competing interests.
Electronic supplementary material
About this article
Cite this article
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). https://doi.org/10.1007/s11150-017-9369-x
- Gender gap
- Bargaining power in the household
- Labor force participation