Are Children Barriers to the Gender Revolution? International Comparisons

Abstract

Children seem to present a barrier to the gender revolution in that parents are more likely to divide paid and domestic work along traditional gender lines than childless couples are. However, the extent to which this is so varies between countries and over time. We used data on 35 countries from the 2012 International Social Survey Programme to identify the contexts in which parents and non-parents differ the most in their division of labour. In Central/South America, Eastern Europe, Southern Europe, Asia, and South Africa, labour sharing configurations did not vary as much with the presence of children as in Australia, Western Europe, North America, and Northern Europe. Our multilevel models helped explain this pattern by showing that children seem to present a greater barrier to the gender revolution in richer and, surprisingly, more gender equal countries. However, the relationship between children and couples’ division of labour can be thought of as curvilinear, first increasing as societies progress, but then weakening if societies respond with policies that promote men’s involvement at home. In particular, having a portion of parental leave reserved for fathers reduces the extent to which children are associated with traditional labour sharing in the domestic sphere.

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Notes

  1. 1.

    Ideally, we would have imposed this age restriction on both partners in the couple, but the respondent’s partner’s age was not available in Austria, Hungary, the Philippines, Russia, or South Africa.

  2. 2.

    The respondent’s gender is known, but the respondent’s partner’s gender is not known. By assuming that all partners are opposite-sex partners, we might slightly underestimate the extent to which division of labour falls along gendered lines.

  3. 3.

    Number of children and their ages are considered in sensitivity analyses, Sect. 5.1. The sample includes biological parents as well as an unknown number of other families, e.g., step-parents and grandparents whose grandchildren live with them. We use the term “parents” for the sake of brevity to describe all those with a residential partner who also live with children.

  4. 4.

    This nonlinear relationship may reflect more follow through on fertility ideals among couples with modern labour sharing configurations, as described in Sect. 3.3.2: the least religious were the most likely to disagree with the statement “A man’s job is to earn money; a woman’s job is to look after the home and family”, and they were still more likely to have children than infrequent attenders.

  5. 5.

    We note that the Asian region is particularly heterogeneous (Japan, South Korea, India, The Philippines, and Israel).

  6. 6.

    The effects of the control variables are generally consistent with prior research (“Supplementary material” Table S2): age and frequent religious service attendance have negative effects on the odds of a modern work-family configuration; education and urban residence have positive effects; and men report a modern configuration more often than women. The effects are also generally similar across regions, with the exception of Asia, the most heterogeneous region.

  7. 7.

    Introducing fixed effects eliminated our ability to estimate the effects of national income and other contextual variables, because the country dummies used up all of the degrees of freedom available at the country-level. We nonetheless retained the ability to estimate the interaction between our contextual variables and individual-level variable for children in the household.

  8. 8.

    Because both the GGG and the GII incorporate measures of labour force participation, and are therefore potentially endogenous with couples’ division of labour, we also recalculated the GII using the UN’s methodology (UNDP 2013), but including only health and empowerment components. This did not affect the results.

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Acknowledgements

This work was sponsored by the Social Trends Institute (New York and Barcelona), the Institute for Family Studies, and the Eunice Kennedy Shriver National Center for Child Health and Human Development grant R24-HD041041, Maryland Population Research Center. Earlier work using some of the same conceptualization as in this paper is available from http://worldfamilymap.ifstudies.org/2015/articles/essay-2.

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Appendix: Implementation of Two-Stage Least Squares

Appendix: Implementation of Two-Stage Least Squares

We attempt to estimate the following model:

$${\text{modern}}_{i} = { \propto } + \beta \;{\text{haschildren}}_{i} + \gamma X_{i} + \mu_{i}$$

where \({\text{modern}}_{i}\) is an indicator of whether respondent i has a modern configuration of labour, \({\text{haschildren}}_{i}\) is an indicator of whether respondent i has children, and \(X_{i}\) represents a vector of controls of respondent i. This equation has a causal interpretation and is known as the structural equation.

The problem, however, is that \({\text{haschildren}}_{i}\) is most likely an endogenous regressor. That is, it may be correlated with the error term \(\mu_{i}\) due to the fact that couples’ labour configurations can affect fertility decisions. A solution to this is to find an exogenous regressor, which in our case is \({\text{wantchildren}}_{i}\), that is uncorrelated with \(\mu_{i}\) but correlated with the endogenous variable \({\text{haschildren}}_{i}\). More specifically, for this instrument to be valid it must be correlated with the endogenous variable and is exogenous in the structural equation, i.e. it does not influence labour configurations except through fertility decisions given preferences.

If this variable satisfies those requirements, then it can be used to “cleanse” the endogeneity by using predicted values based on the exogenous variables only. In other words, we are decomposing the variation of \({\text{haschildren}}_{i}\) into an exogenous and endogenous part. This is known as the first stage regression, which can be represented by the following equation:

$${\text{haschildren}}_{i} = \pi_{01} + \pi_{11} \;{\text{haschildren}}_{i} + \varphi X_{i} + \omega_{i}$$

The next step would be to estimate the structural equation using the predicted values of the first stage regression (\(\widehat{\text{haschildren}}\)). This is known as the second stage regression, which can be represented by the following equation:

$${\text{modern}}_{i} = \alpha + \beta\;\widehat{\text{haschildren}}_{i} + \gamma X_{i} + \mu_{i}$$

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DeRose, L.F., Goldscheider, F., Brito, J.R. et al. Are Children Barriers to the Gender Revolution? International Comparisons. Eur J Population 35, 987–1021 (2019). https://doi.org/10.1007/s10680-018-09515-8

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Keywords

  • Male role
  • Female role
  • Labour force
  • Housework
  • Child care
  • Family policy
  • Gender revolution