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Culture and gender allocation of tasks: source country characteristics and the division of non-market work among US immigrants


There is a well-known gender difference in time allocation within the household, which has important implications for gender differences in labor market outcomes. We ask how malleable this gender difference in time allocation is to culture. In particular, we ask if US immigrants allocate tasks differently depending upon the characteristics of the source countries from which they emigrated. Using data from the 2003–2017 waves of the American Time Use Survey (ATUS), we find that first-generation immigrants, both women and men, from source countries with more gender equality (as measured by the World Economic Forum’s Global Gender Gap Index) allocate tasks more equally, while those from less gender equal source countries allocate tasks more traditionally. These results are robust to controls for immigration cohort, years since migration, and other own and spouse characteristics. There is also some indication of an effect of parent source country gender equality for second-generation immigrants, particularly for second-generation men with children. Our findings suggest that broader cultural factors do influence the gender division of labor in the household.

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Fig. 1

Data availability

Data used for this paper are available online. American Time Use Survey data and matched Current Population Survey variables are available from IPUMS ATUS ( The Gender Gap Index comes from annual reports from the World Economic Forum ( Supplementary source country data come from the World Bank ( and the United Nations ( See the Data Appendix for additional information on variables used and sample selection. The analysis data set will be archived in a public access online repository upon publication.

Code availability

Stata programs are available from authors upon request and will be archived in a public access online repository upon publication.


  1. 1.

    Calculated from Blau and Winkler (2018), Table 4.1, p. 68.

  2. 2.

    For evidence that housework reduces wages, see, for example, Hersch (2009). We note that demand-side factors like discrimination against mothers may also play a role in the child penalty, see, Correll et al. (2007).

  3. 3.

    We follow Fernández and Fogli (2009) in defining culture in terms of beliefs and preferences.

  4. 4.

    The GGI has been used as an indicator of gender equality in a number of other studies. See, for example, Guiso et al. (2008); Zentner and Mitura (2012); Fryer and Levitt (2010); Nollenberger et al. (2016); and Marcén and Morales (2019a).

  5. 5.

    The GGI is intentionally designed to not measure overall levels of economic development.

  6. 6.

    Blau and Winkler (2018), Table 4.1, present similar results for 2014—with married women doing 63.2 percent of housework.

  7. 7.

    Interestingly, using data on 15 European countries, Ralsmark (2017) found that mandatory increases in education reduced agreement with the gender norm that men should be the breadwinner (“When jobs are scarce, men should have more right to a job than women”), but not the norm that women should be the homemaker (“A woman should be prepared to cut down on her paid work for the sake of her family”).

  8. 8.

    Research on other countries confirms the positive relationship between source country female labor supply and immigrant women’s labor supply in the host country. Using labor force participation of the source country as a proxy for norms about women’s roles, Bredtmann and Otten (2013) find that higher source country labor force participation increases immigrant women’s labor supply in their host country using immigrants from 26 European countries in the European Social Survey.

  9. 9.

    Hwang (2016) presented a graph of these relationships but did not study the effect in a regression context.

  10. 10.

    See Cislaghi and Heise (2020). As the authors note, gender norms are “embedded in formal and informal institutions”. For further discussion of social norms, see Young (2008).

  11. 11.

    We thank Claudia Olivetti for suggesting a model of the type we present. Note, that while the theoretical model considers wages, our empirical work is reduced form with respect to wages. The reason we do not include wages is to avoid well-known problems of estimating (imputing) a wage rate for non-labor force participants.

  12. 12.

    For more information on this data set, see and

  13. 13.

  14. 14.

    All means in this table are computed using sampling weights, which we discuss in more detail in the Methods section below. When we control for source country characteristics in the analyses restricted to first- or second-generation immigrants, the mean values are very similar to those shown in Table 1, with the sample size reduced only slightly due to missing data on country characteristics.

  15. 15.

    In the few instances where a GGI value is not available in 2006 but is available in a later year, we use the earliest year it was available. This affects about 6 percent of the immigrants in our sample. The latest year used is 2010, but most immigrants without a 2006 value matched to a 2007 value.

  16. 16.

    As noted, for this figure, we dropped source countries with less than 30 ATUS observations to avoid showing any possibly misleading country-level differentials. Including these dropped countries in the simple bivariate regression of the non-market work gender differential on GGI yields a slope coefficient of −62.6, significant at the 1 percent level. Note that the full set of countries is included in our regression analyses below.

  17. 17.

    See the Data Appendix for information on the sources for GDP per capita for the countries for which it is missing from the World Bank data.

  18. 18.

    See, and

  19. 19.

    Blau and Kahn (2015) are able to observe visa status using the New Immigrant Survey. They find that women are somewhat more likely than men to come on a family visa and somewhat less likely than men to have an employment visa. However, visa status itself could be an outcome of culture, with gender norms determining which spouse’s employment options are paramount. See the later discussion about possible selection biases on why we think this issue to be minimal.

  20. 20.

    For the initial immigrant-native comparisons, those born in US territories are included as immigrants. They are excluded from subsequent analyses that include source country characteristics since such variables are not available for them.

  21. 21.

    Regressions including this variable also include an indicator variable equal to 1 if the wife’s hours of market work vary from week to week in which case wife’s usual hours of market work are set to 0.

  22. 22.

    Specifically, we use data compiled by the OECD for countries in the OECD plus China and South Africa (we were unable to find such summary measures for other countries). The correlation between the GGI and the source country female-to-male non-market work ratio is −0.72, and a simple regression of that ratio on the GGI returns a coefficient of −32.63 that is significant at the 0.001 level.

  23. 23.

    Blau and Kahn (2015) used the New Immigrant Survey, which contains information on individual migrants’ pre-migration labor force activity. The CPS does not include such information.

  24. 24.

    As noted, our definition of total non-market work includes some items not in housework or childcare, such as caring for other adults in the household. These amounted to a very small portion of total non-market work time.

  25. 25.

    In her study which focused on housework, Hwang (2016) finds similar results for the first and second generations relative to natives.

  26. 26.

    We also ran these regressions including indicators for less than high school, high school diploma, and some college, leaving BA+ as the omitted category. All of these coefficients were significant and negative with two exceptions: the some college coefficient was very slightly positive but highly insignificant for all men and negative but insignificant for men with children.

  27. 27.

    These percentiles are computed using individual immigrant women as data points, weighted by sampling weights. Thus, larger sending countries implicitly receive larger weight in the calculations.

  28. 28.

    Recall that our definition of total non-market work includes some activities not included in housework or childcare; therefore, the sum of the GGI effects on housework and childcare need not be the same as the GGI effect on total non-market time.

  29. 29.

    Of course, like other analyses using independent cross-sections, our interpretation of the YSM coefficients must be qualified by admitting the possibility of selective return migration (Lubotsky 2007).

  30. 30.

    These simulations are based on Columns (2), (5), and (8) of Tables 3 and 4. Namely, these specifications include controls for YSM and YSM-squared for both the respondent and the partner. The simulation results simply sum these four relevant coefficients scaled by the respective YSM or YSM-squared value. The same YSM value is used for the respondent and spouse. Standard errors are computed accordingly.

  31. 31.

    The own GGI coefficients for women shown in Table 6 are much larger in magnitude than those for spouse’s GGI for Total Non-Market Work and Housework, perhaps reflecting a stronger influence of one’s own culture than one’s spouse’s culture; however, the effect of spouse’s GGI is larger than the own GGI effect for childcare, perhaps reflecting the difficulty in distinguishing the two effects because of their collinearity.

  32. 32.

    For men, the relative effects of own and spouse GGI in Table 6 are unstable. For example, among all immigrant men (married to immigrant women), the effect of spouse GGI on housework is about equal to the effect of own GGI; however, for married men with children, the own effect is much larger. The results for men give us further reason to be careful about making strong conclusions about the relative impacts of own and spouse GGI given the high level of collinearity.

  33. 33.

    The controls included a quadratic in age, race dummies, years since migration and its square, fixed effects for immigration cohort decade, state, day of the week, month and year of the survey, source country GDP per capita, and source country fertility.

  34. 34.

    These effects are almost identical and equally as significant when we control for the wife’s market work.

  35. 35.

    Marcén and Morales (2019a) also find among second-generation immigrants pooled with child immigrants (whom they treat as second-generation immigrants) that a higher GGI lowers the gender gap in nonmarket work.

  36. 36.

    In particular, the correlation coefficients are relatively high for the following: Health-Education (0.59), Economic-Education (0.33), and Political-Economic (0.21); but less so for the following: Political-Health (−0.07), Political-Education (−0.07), and Health-Economic (0.03).

  37. 37.

    Unlike Marcén and Morales (2019a) we do not pool child immigrants and the second generation because the circumstances of child immigrants’ upbringing may be affected by the early “dose” of source country culture received in their preschool years, and the possible disruption of parents’ employment and their own preschool education when they migrated. In addition, the parents of child immigrants are more likely to be recent arrivals than the parents of children born in the United States.

  38. 38.

    Similar to us, their cross-sectional analysis uses the ATUS.

  39. 39.

    We continue to use our sample restrictions and control variables from above whenever applicable. With the exception of our use of more recent years of data, the BKP sample restrictions were nearly identical to ours. Two small differences to note: they include 65 year olds, while we drop those older than 64, and we drop diary entries from holidays, while they do not. BKP also define their non-market work variable slightly differently than we do and use a slightly different set of control variables and fixed effects. Notably, our definition of non-market work differs due to our inclusion of care for household and non-household adults and our exclusion of shopping (apart from grocery shopping), obtaining professional services, and travel related to shopping or obtaining services. We believe that adult care is non-market “work”, while shopping and obtaining professional services could in many cases be considered self-care or leisure. Finally, BKP do not run separate male and female regressions, but instead interact all variables with a sex indicator variable. Our results are broadly similar when we adhere to the BKP sample and variable definitions as closely as possible.

  40. 40.

    Running the regressions separately for housework and childcare suggests that roughly two-thirds of the magnitude of the effect operates through housework and roughly one-third operates through childcare. Both regressions return a significant coefficient on Wife Earns More.

  41. 41.

    Comparative advantage depends on each partner’s value of non-market time relative to market time compared to the other partner. Thus, the expectation in the text implicitly assumes that the increase in the relative value of wife’s market time is not offset by a comparable rise in the value of her non-market time.

  42. 42.

    The result for GGI in Table 8, Column (2) for immigrants contrasts with the much larger, statistically significant effect of this variable in our main model shown in Table 3. This is likely due to the additional controls included in Table 8, including wife’s and husband income, wife’s relative income, and whether the wife earns more than her husband—controls that are likely to be affected by GGI.

  43. 43.

    However, when we estimate the regression using childcare as the outcome, we do find that immigrant men do 1.2 fewer hours of work when their wives earn more, significant at the 5 percent level.

  44. 44.

    Binder and Lam (2019) find that artificial features of earnings reporting—such as top-coding, rounding, and imputation—create a large mass point of couples with identical reported earnings. Relatedly, Zinovyeva and Tverdostup (2018) show that family businesses and co-working of spouses can contribute to a mass point of couples with identical reported earnings. We were able to reproduce this mass point in our data set.


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The authors thank the Editors, two anonymous referees, Claudia Olivetti and session participants at the American Economic Association meetings, Atlanta, Georgia, January 2019 for helpful comments and suggestions.

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Correspondence to Francine D. Blau.

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Theory appendix and additional results

Table 10 Effect of Years Since Migration (YSM) on non-market work for immigrant couples migrating together
Table 11 Effect of source country characteristics on non-market work for immigrants, GGI subindexes
Table 12 Effect of wife earning more than husband on total non-market work for women, dropping relative earnings from 0.489 to 0.50
Table 13 Effect of wife earning more than husband on total non-market work for men, dropping relative earnings from 0.489 to 0.50

Consider a stylized framework where a husband and wife each have \(t_g \in [0,1]\) units of time to allocate toward market production and \(1 - t_g\) units to allocate toward non-market production, for \(g \in \{ m,f\}\). The household has a joint utility function that is increasing and concave in total market good consumption c and household good consumption b. Utility also depends additively on a convex gender norm function \(\pi (t_f,t_m)\) that penalizes the wife’s market work relative to the husband’s market work. Market goods can be purchased with wages earned from market work, while household goods are produced via non-market production. The husband and wife can differ in their exogenous wage rate wg and productivity in home production.

Couples maximize utility subject to a market good consumption (income) constraint and their household non-market goods production function. Before putting any more structure on the model, note two main general results. First, couples will optimize their time at the point when the marginal utility gain from market work is equal to the marginal utility gain of non-market work. Second, the wife’s market work will decrease in the magnitude of the gender norm (and hence her non-market work will increase), while the husband’s market work will increase in the magnitude of the gender norm (and hence his non-market work will decrease). In the absence of income effects, market work will increase in the wage rate and non-market work will increase in home productivity for both men and women.

To see this in a concrete example, we assume that households solve the following maximization problem, where utility is additively separable with a linear gender norm penalty and a Cobb-Douglas household production function:

$$\mathop {{\max }}\limits_{t_m,t_f} c + \varphi \,\log \,b - \pi \left( {t_f - t_m} \right)$$
$${\rm{s}}.{\rm{t}}.\,{c} = w_mt_m + w_ft_f$$
$$b = A\left( {1 - t_m} \right)^\alpha \left( {1 - t_f} \right)^{1 - \alpha }$$

where \(\alpha \in (0,1)\) is the relative productivity of the husband in household good production and π is a positive scalar. First order conditions yield the following solution for optimal non-market work time:

$$1 - t_f^ \ast = \frac{{\varphi \left( {1 - \alpha } \right)}}{{w_f - \pi }}$$
$$1 - t_m^ \ast = \frac{{\varphi \alpha }}{{w_m + \pi }}$$

The general conclusions from above may be seen explicitly in these first order conditions. That is, non-market work is increasing in π for women and decreasing in π for men. Further, since this model does not have income effects, non-market work is increasing in relative productivity and decreasing in the wage rate for both men and women. For market work, the converse holds. Note that specific parameterizations must be checked for corner solutions (namely, if the penalty is too large, the wife will spend all her time in home production).

This framework can easily accommodate modeling the wage rate as an endogenous function of the gender norm penalty. For example, we may imagine the gender norm representing a general attitude about women’s accumulation of human capital and thus a stronger penalty decreases her market productivity and hence her wage rate. In this case, the first order conditions for optimization remain the same, though we need to explicitly note the dependence of the wife’s wage on π for the comparative statics with respect to the penalty. If we assume that the wife’s wage decreases in the gender norm, the direction of the effect on non-market allocation is the exact same but with a stronger magnitude.

While the theoretical model considers wages, our empirical work is reduced form with respect to wages in order to avoid well-known problems of estimating (imputing) a wage rate for labor force nonparticipants. This makes it important to control for variables related to human capital to isolate the effect of the penalty. However, the model discussed in this preceding paragraph suggests that those variables are themselves endogenous, motivating our empirical strategy of testing the effect of source country gender norms with and without the inclusion of human capital controls such as age and education.

Data appendix

Variable definitions

Demographic variables from the ATUS and CPS

Race and ethnicity

  • We control for race and ethnicity using a set of indicator variables for five mutually-exclusive categories: White non-Hispanic, Black non-Hispanic, Asian non-Hispanic, other non-Hispanic, and Hispanic.

  • Respondent is classified as Hispanic if the respondent reports being Hispanic or reports his/her ethnicity as Spanish, Portuguese, Mexican, Puerto Rican, Latin American Indian, South American Indian, or Mexican American Indian.

  • Respondent is classified as black non-Hispanic if the respondent reports being any detailed race that includes black and is not classified as Hispanic.

  • Respondent is classified as Asian non-Hispanic if the respondent is not classified as Hispanic or black non-Hispanic and reports race as Asian or any mixed race including Asian.

  • Respondent is classified as white non-Hispanic if the respondent is not classified as Hispanic, black non-Hispanic, or Asian non-Hispanic and reports race as white.

  • Respondent is classified as other non-Hispanic if none of the above classifications apply.

First- and second-generation immigration variables

  • Respondents are classified as first generation if they report their birthplace as outside the 50 states or the District of Columbia. Note that we count respondents born in US territories as immigrants, though they are not included in most immigrant analyses due to not having independent source country characteristics.

  • Respondents are classified as second generation if they were born in the fifty states or the District of Columbia and they report that either of their parents was born outside the United States.

    • Second-generation immigrant respondents are assigned the source country characteristics of their mother unless their mother’s source country characteristics are missing or their mother is US born. In that case, they are assigned their father’s source country characteristics. We follow this procedure because the high correlation between father’s and mother’s birthplace when both are foreign born makes estimating separate effects of father’s and mother’s source country difficult in a sample of this size. (Of second-generation immigrants where both parents are foreign born, both parents are from the same source country for 87.2 percent of our sample.) We compare our results to those that prioritize father source country characteristics and find them to be similar.

  • We use the term native to refer to those who were born in the United States, with both parents born in the United States. That is, natives may be considered third-plus-generation immigrants.

  • We compute years since migration as the difference between the survey year and the mid-point of the binned response to year of immigration. The mid-point of the binned response to year of immigration is also used to assign immigration cohorts.

Earnings variables in Section 8

  • We set earnings to 0 for those reporting to be unemployed or out of the labor force. We set earnings to missing if reported earnings are outside the ATUS defined range. We inflate top-coded earnings by 1.5. According to IPUMS: “[The individual earnings variable] was collected at the time of the ATUS interview. However, earnings information was only collected at that time for respondents who changed jobs or employers since the final CPS interview, or whose earnings were allocated in the final CPS interview. For other respondents, earnings information was carried forward from the final CPS interview” ( Further, spousal earnings are not updated at the time of the ATUS interview, but are always carried forward from the final CPS interview. The final CPS interview takes place two to five months before the ATUS interview.

  • Our measure of total household income comes from the CPS family income variable, which includes earned and unearned income of all household members. This variable is not updated at the time of the ATUS interview. Family income is reported in ranges, and we use the mid-point of reporting bins.

  • We set log earnings to zero for those with 0 earnings.

  • Relative earnings are defined as the wife’s earnings divided by the sum of the husband’s and wife’s earnings. If only one spouse is employed, relative earnings are set to 1 or 0 accordingly.

Country characteristics variables

Total fertility

GDP per capita

Global gender gap index

ATUS variables

We define housework as all activities that fall under the broad ATUS “Household Activities” category. These include housework, food and drink preparation and cleanup, home maintenance, lawn and garden care, pet care, appliance care, and household administrative tasks. We define primary childcare as care for children living in the household, including the “second-tier” ATUS categories of “Caring for & Helping HH Children”, “Activities Related to HH Children’s Education”, and “Activities Related to HH Children’s Health”. We define total non-market work as the sum of these housework and childcare variables, as well as time spent grocery shopping and all the activities included in the “second-tier” ATUS categories of “Caring for Household Adults”, “Helping Household Adults”, and “Caring for & Helping NonHH Members”.

Sample selection

We use data from the 2003–2017 waves of the American Time Use Survey (ATUS). For the main analysis, we focus on first- and second-generation immigrants as defined above. When natives are included, they are defined as those who are born in the United States with both parents born in the United States. Regressions are weighted by ATUS sampling weights that are normalized to provide equal weighting for each sample year.

We restrict our sample to married respondents in heterosexual partnerships where both the respondent and their spouse are between the ages of 18 and 64. To do this, we keep only respondents who report being married with a spouse present in the household. To collect partner characteristics, we match these respondents to the member of their respective households who lists the respondent as a spouse. If no household member lists the respondent as a spouse, we match the respondent to the member of the household who lists the respondent as an unmarried partner. We then drop any remaining respondents who do not match. We also exclude observations recorded on holidays, natives born abroad, and immigrants whose year of immigration is missing. While we are not able to observe the time allocation of the respondent and their partner, by enforcing these restrictions, we can estimate for the population how married men and women divide household labor. All analyses were repeated including those in heterosexual partnerships but not married. To do this, we do not impose the initial restriction of keeping only respondents claiming to be married with a spouse present. We follow the same partner match procedure outlined above. Our results are similar when partnered respondents are included (results available on request).

In some regressions, the sample is implicitly restricted due to missing data on control variables. Regressions that control for a range of spousal characteristics resulted in a few dropped observations that had missing values for any of these characteristics. Further, some immigrants did not match to source country characteristics and are therefore dropped from regressions including these characteristics. Namely, some source countries do not have GGI scores available, a list that includes any US territory, Taiwan, Haiti, Iraq, Hong Kong, and Laos.

We also recoded the source country of some immigrants to make matching possible. This included assigning individuals from England, Scotland, Wales, and Northern Ireland to the source country characteristics of the UK; assigning individuals from Azores to Portugal; assigning individuals from Kosovo to Albania; assigning individuals from Palestine to Israel; and assigning individuals from “USSR, n.s.” to Russia.

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Blau, F.D., Kahn, L.M., Comey, M. et al. Culture and gender allocation of tasks: source country characteristics and the division of non-market work among US immigrants. Rev Econ Household 18, 907–958 (2020).

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  • Housework
  • Childcare
  • Gender
  • Immigration
  • Time allocation

JEL codes

  • J13
  • J15
  • J16
  • J22