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Do women face a glass ceiling at home? The division of household labor among dual-earner couples

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Abstract

In this paper, we use data on mixed-gender dual-earner couples in Southern and Western Europe to investigate how the division of unpaid household labor within mixed-gender couples varies depending on the ratio of the partners’ market wages. From analysis of the EU Statistics on Income and Living Conditions, we first show that married or cohabiting women do twice as much household work as single women with the same income. Furthermore, women’s time spent in home production does not vary in relation to the couple’s relative wages in Southern Europe. We find a positive elasticity of substitution between male and female labor in home production with respect to their relative within-couple wages in Western Europe. Our identification is based on predicting each country’s wage distributions within gender-specific cells defined by age group and education using distributions in all the other countries. We present a positive evidence for presence of a “second-shift” that women face especially in Southern Europe, which may stem from regional gender norms.

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Fig. 1: Ranking of all countries in the EVS according to the GVI.
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Notes

  1. Source: Authors’ computations based on EU-SILC data.

  2. Source: Authors’ computations based on EVS (2016).

  3. Examples of such research include Bisin & Verdier (2001), Alesina & Fuchs-Schündeln (2007), Doepke & Zilibotti (2008), Roland (2010), and Voigtländer & Voth (2012).

  4. Our definition of “wages” includes earned income from both employment and self-employment, divided by number of hours worked. We include the self-employment income in the numerator, because it can be viewed as primarily labor income rather than capital income. The data also contains separate information on capital income, which does not enter our definition.

  5. As discussed further in Section 3, we define marriage markets by age and education.

  6. We discuss pros and cons of our instrumental variable in Section 3.

  7. In this paper, we include childcare and caring for other dependent household or family members together with cleaning, shopping and similar activities in the definition of household work.

  8. These statistics approximately correspond to various European time use surveys (Charmes, 2015)

  9. We cannot use the HETUS as the main source in our analysis, because it contains only income from the main activity. Moreover, the income is interval-coded. Both of these factors increase the measurement error. Moreover, the first factor causes a downward systemic bias in the income estimates.

  10. The EVS is a research program that collects data on individuals’ attitudes, beliefs and opinions about various social, economic, and political issues.

  11. For an introduction to the principal component analysis, or PCA, see e.g. Jackson (2003).

  12. For instance, strong agreement with the first statement, “It is important to share household work in marriage”, which is coded as “1” changes the value of the index for a given answerer by 1 × − 0.05 = −0.05. On the other hand, strong disagreement with this statement (coded as “4”) contributes to the index by 4 × −0.05 = −0.2. Alternatively, strong agreement with the statement “Pre-school child suffers with working mother”, changes the value of the index for a given person by 1 × 0.553 = 0.553, while strong disagreement with the said statement modifies the index by 4 × 0.553 = 2.212.

  13. The ISCO (International Standard Classification of Occupations) is a classification system for jobs maintained by the International Labour Organization.

  14. The method of k-means clustering aims to partition observations into k clusters in which each observation belongs to the cluster with the nearest mean of the GVI, serving as a prototype of the cluster. In effect, it minimizes within-cluster variances, so it creates clusters based on closeness of the observations to each other. In our case k = 2. For a more detailed discussion of this method, see e.g. Hastie et al. (2009)

  15. These graphs do not control for the fact that married or cohabitating women are more likely than single women to have children. Their purpose is to illustrate differences between West and South.

  16. Nomenclature of Territorial Units for Statistics or NUTS (French: Nomenclature des unités territoriales statistiques) is a geocode standard for referencing the subdivisions of countries for statistical purposes developed and regulated by the European Union.

  17. The NUTS-1 level consists of five geographical units: Northwest Italy, Northeast Italy, Central Italy, South Italy, and Insular Italy. Each NUTS-1 unit is composed of groups of traditional Italian regions. For example, Northwest Italy contains regions Piemonte, Valle d’Aosta, Liguria, and Lombardia, which are classified as NUTS-2 level. For our purposes, we classified Northwest, Northeast and Central Italy as ‘North’, and South and Insular Italy as ‘South’.

  18. We thank an anonymous referee for this suggestion.

  19. Notably, we take a different modeling approach from Grossbard-Shechtman (1984) who also considers two types of work and allows for individual supply and demand schedules of the spouses, which leads to interesting interdependence. In contrast, even though a household is composed of two spouses in our model, it is ultimately a single economic unit. Because of our static empirical setting our simple model also abstracts from marriage and divorce choices.

  20. We recognize the endogeneity associated with joint income, so we instrument it analogously to the relative wages.

  21. Ventiles are points that divide an ordered distribution into 20 equally-sized parts, similarly to how a median divides it into two equally sized parts.

  22. NACE (French: Nomenclature statistique des activités économiques dans la Communauté européenne — The Statistical Classification of Economic Activities in the European Community) is the industry classification system used in the European Union. NACE uses four hierarchical levels: Level 1, which contains 21 sections identified by alphabetical letters A to U; Level 2 with 88 divisions identified by two-digit numerical codes (01 to 99), Level 3 containing 272 groups identified by three-digit numerical codes (01.1 to 99.0), and Level 4 that consists of 615 classes identified by four-digit numerical codes (01.11 to 99.00). Examples of Level 2 codes include “Crop and animal production, hunting and related service activities”, or “Manufacture of textiles”.

  23. As a robustness check we drop the joint earnings from the analysis. The omission does not significantly change the coefficients of interest. The results of this robustness check are available upon request.

  24. The results are robust to the inclusion of Bulgaria and Romania from the sample.

  25. See e.g. Hurst, Li & Pugsley (2014) or Lichard et al. (2019)

  26. This argument has been also made in the case of differences between macro and micro estimates of labor supply elasticities. See, for example, Chetty, Guren, Manoli & Weber (2011) or Peterman (2016).

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Acknowledgements

We offer our sincere thanks to David Card, Matias Cortes, Christian Dustmann, Hilary Hoynes, Andrea Ichino, Stěpán Jurajda, Klára Kalíšková, Shelly Lundberg, Vincenzo Merella, Nikolas Mittag, Barbara Pertold-Gębicka, and conference participants at the 2019 SOLE annual meeting for insightful comments and fruitful discussions. We are also grateful to the editor and three anonymous referees for suggestions that substantially improved the paper. Any remaining errors and omissions are the sole responsibility of the authors. TL gratefully acknowledges the support of the Czech Science Foundation, grant number 20-12023S. FP is thankful for the support of the Czech Science Foundation, grant number 17-09119Y. He worked on this research while being employed on Faculty of Social Sciences, Charles University.

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Authors and Affiliations

  1. Center for Economic Research and Graduate Education—Economics Institute (CERGE-EI), a joint workplace of Charles University in Prague and the Economics Institute of the Czech Academy of Sciences, Politickych veznu 7, 111 21, Prague, Czech Republic

    Tomáš Lichard & Filip Pertold

  2. Department of Economics, Prague University of Economics and Business, nám. W. Churchilla 1938/4, 130 67, Prague, Czech Republic

    Tomáš Lichard

  3. Department of Economics, University of Zurich, Schönberggase 1, 8001, Zurich, Switzerland

    Samuel Škoda

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  1. Tomáš Lichard
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Appendices

Gender values index

Fig. 9, Fig. 10, Table A.1

Fig. 9
figure 9

Distribution of GVI in the EVS data

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Fig. 10
figure 10

Distribution of GVI in the EU-SILC

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Table A.1 Determinants of the GVI
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Descriptive statistics

Table B.1, Table B.2.

Table B.1 Household work in hours per week of working and nonworking couples
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Table B.2 Descriptive statistics of the analyzed sample
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First stage and robustness check

The following tables show first stage regressions for 2SLS estimations in Table 5. We have three endogenous variables in each specification in Table 5 that need to be instrumented. For example, in the specification with the interaction with the progressive dummy, we instrument the hourly-wage gap, the hourly-wage gap interacted with the progressive dummy, and monthly household-wage relative to the median in a given country. We construct an instrument based on the methodology described in Section 3 for all these variables. The instrument for the hourly-wage gap is the difference between the logarithm of predicted hourly-wage of the man minus the logarithm of the predicted hourly-wage of the woman. The instrument for interaction terms are the predicted hourly-wage gaps times the respective dummy. Finally, monthly household wage relative to the median are instrumented by the sum of male and female predicted monthly-wage. Table C.1, Table C.2, Table C.3, Table C.4, Table C.5

Table C.1 First stage regression—progressive interaction
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Table C.2 First stage regression—college female interaction
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Table C.3 First stage regression—college male interaction
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Table C.4 First stage regression—South interaction
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Table C.5 Robustness check: elasticity of substitution in home production—wage and salary workers without self-employed
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Lichard, T., Pertold, F. & Škoda, S. Do women face a glass ceiling at home? The division of household labor among dual-earner couples. Rev Econ Household 19, 1209–1243 (2021). https://doi.org/10.1007/s11150-021-09558-7

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