It is demonstrated that family policies are an important aspect of the institutional context of earnings inequality among coupled households. Although seldom integrated into prominent analyses of economic inequality, women’s earnings are consistently found to reduce relative inequality among households. This means that family policies, as well-known determinants of women’s employment and earnings, are important contextual determinants of economic inequality. Using Luxembourg Income Study data from 18 OECD countries in the period 1981–2008, this study demonstrates that women have higher earnings, and that their earnings reduce inequality among coupled households more in institutional contexts with generous paid leave and public childcare. We found no sizeable association between financial support policies, such as family allowances and tax benefits to families with children, and the degree to which women’s earnings contribute to inequality among coupled households. Family policy arrangements that facilitate women’s employment and earnings are associated with less economic inequality among coupled households.

Data and method Person-level data Our hypotheses were tested using data from the Luxembourg Income Study (LIS, 2016). LIS provides representative country-comparative, household-level surveys and person-level surveys on income, organised in waves. We have used data from LIS Waves 1 through 6 for 18 OECD countries, covering the period 1981–2008. The countries covered by our data are listed in Table 1. In total, 116 LIS datasets were used, providing information on 1,726,700 individuals in 863,350 coupled households where both spouses were aged 18–59 at the time of interview. Sampling weights were applied. Table 1. Descriptive statistics on earnings inequality: reported values apply to the range and mean of the measurements across country-years, by country (N = 116). View larger version Our sample was limited to coupled households, and same-sex couples were removed from the dataset. These restrictions on the data were required to allow for the decomposition of earnings inequalities among households, and were necessary to determine the (influence of the changing) correlation between spouses’ earnings. These decisions correspond to those made in similar studies (e.g. Harkness, 2013), ensuring comparability of the results. The LIS data provide measurements of earnings, defined as monetary compensation for labour. Negative earnings were recoded to 0, and earnings were trimmed at the level of the 99th percentile. We measured individual earnings for both spouses. Household earnings were defined as the sum of the earnings of two spouses, even when either one or both spouses had no earnings. Based on these measurements, for each country year we aggregated the following measures. Contribution of women’s earnings to inequality among households This measure expresses as a percentage how much household inequality is lower than the counterfactual scenario in which women had no earnings at all. That is, the observed earnings inequality among households (measured by the squared coefficient of variation) is compared with the earnings inequality among men (representing the scenario in which all women in coupled households had zero earnings). This is a standard calculation (Lam, 1997) and is detailed by Harkness (2013). Earnings inequality among women Calculated as the squared coefficient of variation of women’s earnings. Women’s share in total household earnings Calculated as the women’s earnings as a proportion of total household earnings. Correlation between spouses’ earnings Pearson’s correlation coefficient between spouses’ earnings. Earnings inequality among men Calculated as the squared coefficient of variation of men’s earnings. This variable serves as a control variable. The measure of inequality on which this study is based is the squared coefficient of variation, which is defined as the variance of the earnings distribution divided by the square of its mean. This is a relative measure of inequality, which is insensitive to an overall increase or decrease in earnings over time. Hence, it is also comparable across countries with different levels of earnings. It is the typical measure of inequality used in studies of the contribution of women’s earnings to household inequality (Lam, 1997). Descriptive statistics of our measures of inequality are presented by country in Table 1. Income variables in LIS were reported as either net of taxes and social security contributions, or gross of taxes and social security contributions. In the absence of accounting for the fact that net and gross earnings are different constructs, these measures cannot be compared. We used earnings net of taxes and social security contributions where available, and when necessary net earnings were calculated by subtracting taxes and social security contributions from gross earnings (Nieuwenhuis et al., 2017). Although using net earnings data is necessary to allow for this large-scale comparison, an important caveat is that this may underestimate the share of women’s earnings in the household in countries with joint taxation, such as the Netherlands, compared with countries with individual taxation, such as Sweden. Nevertheless, because countries tend to be constant over time with respect to individual or joint taxation, this caveat can be partially addressed by using fixed effects (see below). Country-level data All variables describing family policies and contextual controls were measured at the country level and were measured for each year separately, so they represent the variation in contexts both across countries as well as within countries over time. Paid parental leave Our first indicator of reconciliation policies is an index of three leave policies: maternity leave, parental leave, and childcare leave. We calculated the total number of weeks of leave for which the replacement rate was at least 60%. In other words, the final measure represents the total number of weeks of leave during which at least 60% of wages was substituted. This variable was obtained from the Comparative Family Policy Database (Gauthier, 2010). Childcare expenditure This variable represents governmental expenditure on public childcare, expressed as the percentage of GDP divided by the total fertility rate. Expenditure covers day-care services and pre-primary education services for all children aged 0 to 5. Although data on the expenditure on governmental policies is commonly used in comparative welfare-state research, this is not without problems because such measures tend to be greater when (a) the policy is more generous and (b) when the demand for the policy is great (e.g. in the context of the current study, when the fertility rate is high). By dividing by the fertility rate of a country, we aim to partially correct for these problems. This variable was obtained from the OECD Family Database (OECD, 2016). Family allowances The average amount of family allowance that families are entitled to for their first, second, and third child. To ensure comparability across countries and over time, the nominal amounts originally reported were standardised by expressing them as a percentage of the average gross monthly earnings of a production worker. This variable was obtained from the Comparative Family Policy Database (Gauthier, 2010). Tax benefits to single-earner families with children The annual amount of tax benefits that a single-earner family with children receives more than a comparable family without children. To ensure comparability across countries and over time, the originally-reported nominal amounts were standardised by expressing them as a percentage of the average annual gross earnings of a production worker, following recommendations for use of the data obtained from the Comparative Family Policy Database (Gauthier, 2010). As outlined in the hypotheses above, FLFP and women’s share in household income may be directly related to a certain extent, but the women’s share is not only determined by the total number of women active on the labour market but also on the selection of which women. Hence, in our analyses, we differentiate between FLFP (at the country level) and women’s share of household earnings. Because the share of women’s earnings in total household earnings may further depend strongly on the gender–wage gap among those who are employed, we control for the female-to-male wage ratio at the country level. Finally, we control for the overall unemployment level as an indicator of the employment opportunities in an economy, an important determinant of wage-setting processes (Bernstein, 2016), and because unemployment is an important determinant of inequality. These three variables were obtained from the Comparative Family Policy Database (Gauthier, 2010). Female labour force participation rate The female labour force (employed and temporarily unemployed) as a percentage of the female population aged 15–64. Female-to-male wage ratio Calculated as the hourly wages in manufacturing for women divided by the hourly wages in manufacturing for men. Unemployment Unemployment rates measured as the number of unemployed persons as a percentage of the civilian labour force. Table 2 shows descriptive statistics on the four key policy variables. It shows average differences between countries, demonstrating familiar patterns of family policy arrangements (e.g. see Korpi, 2000). For instance, the Nordic countries – Denmark, Sweden and Finland – have quite generous work–family reconciliation policies (particularly with respect to childcare availability), whereas Australia, Canada, Greece, the UK and the USA do not have parental leave with a wage replacement of at least 60%. Financial support policies, on the other hand, are comparatively generous in continental European countries such as Germany, France and Luxembourg. Table 2. Descriptive statistics on family policies (N = 116). View larger version Statistical method: structural equation modelling Because our hypotheses refer to several mechanisms mediating the effect of family policies on the indicator of how women’s earnings affect inequality among households, we have used a structural equation model (SEM) to estimate all relevant effects simultaneously. Because all variables were measured at the interval level, and there were no latent variables, we could instead have estimated a series of OLS regression models (one for each dependent, or endogenous, variable), which would have produced the same results. Using SEM offers the advantage of convenience, the integrated calculation of indirect effects, and the maximum likelihood procedure that can account for three missing values on the childcare variable. Models were estimated in R using the Lavaan package (Rosseel, 2012), and inferences were based on (Huber-White) robust standard errors. A common challenge in country-comparative analyses like these is to account for unobserved heterogeneity. Fixed effects (at the country level) are often considered a strong approach to account for all time-invariant unobserved heterogeneity. Their inclusion restricts (the interpretation of) the regression parameters to variation within countries over time. Such variation is present in most variables, as demonstrated by Table 2. However, fixed effects have two disadvantages here. First, some countries did not show any variation in parental leave availability over time. With fixed effects, these countries do not contribute to the estimates. Second, even if countries show no or little variation over time, the differences between countries with respect to their policy generosity can still affect women’s employment and earnings. Hence, we present our main results both with and without fixed effects. The model with fixed effects does a better job of accounting for unobserved heterogeneity, while the model without fixed effects also makes use of variation between countries. Time-varying unobserved heterogeneity cannot be controlled for in these models, but we do account for unemployment and the female-to-male wage ratio.

Results Figure 1 presents the main results of our SEM, as standardised coefficients. The effects of predictors on the contribution of women’s earnings to household inequality are controlled for the degree of men’s earnings inequality. In addition, all estimates are controlled for two labour-market variables (female-to-male wage ratio and unemployment) as well as fixed effects for countries (estimates not shown in Figure 1). The key outcome variable of interest in our analyses was the extent to which women’s earnings affect the inequality among households. Table 1 demonstrates that this contribution is negative in all countries, indicating that women’s earnings reduce inequality among households. Here, the results reveal that women’s earnings reduce inequality more when women’s share in household earnings is larger, as well as when the inequality of men’s earnings is greater. Women’s earnings reduce household inequality less when the inequality among women is great and when the correlation between spouses’ earnings is more strongly positive. Figure 1 shows that women’s share in household earnings not only has a direct effect, but also indirect effects by reducing the inequality among women and by increasing the correlation between spouses’ earnings. Therefore, in Table 3, we calculated for all independent variables the direct, indirect, and total effects on the contribution of women’s earnings on inequality among households. Table 3 shows that the indirect effect of women’s share in household earnings is 0.11 (not statistically significant), and the significant total effect is −0.54. More detailed specifications are possible for the three mechanisms through which women’s earnings affect inequality among households, including interactions and non-linear associations. However, these are not considered here because the focus is on the effect of FLFP and particularly the institutional context as shaped by family policy. Table 3. Total effects on the contribution of women’s earnings to inequality among households (based on Figure 1, direct and indirect effects). View larger version Moving farther left in Figure 1 shows the effect of FLFP on the three mechanisms through which women’s earnings affect inequality among households. Higher FLFP relates to a larger share of women’s earnings in total household earnings and lower inequality among women. This latter effect is reinforced by the indirect effect of FLFP on women’s share in household earnings, which is also associated with a lower level of inequality among women. A higher FLFP rate seems associated with a weaker (or more negative) correlation between spouses’ earnings, which is contrary to what we hypothesised. This suggests that, if a given level of women’s average earnings is achieved by a higher FLFP rate, these earnings tend to be contributed by a larger proportion of all women. In other words, as labour force participation becomes more universal, the correlation between spouses’ earnings is lower. However, as a larger women’s share in household earnings is associated with a higher correlation between spouses’ earnings, the total effect of FLFP does contribute to a higher correlation (but only weakly so). The total effect of FLFP on the contribution of women’s earnings to inequality among households, as shown in Table 3, is −0.58. This effect is explained by the associated rise in women’s share in household earnings and a reduction in the earnings inequality among women, and somewhat suppressed by the increase in the correlation between spouses’ earnings that is associated with the increase in women’s share in household earnings. Finally, on the far left of Figure 1, we turn to the role of family policies. The estimates indicate that both reconciliation policies are positively associated with higher FLFP rates. The total effects of these policies on the contribution of women’s earnings to inequality among households are −0.28 for paid leave and −0.17 for childcare expenditure (see Table 3). This corroborates our reconciliation policy hypothesis. We did not find support for our hypothesis that financial support policies reduce FLFP, nor that they affect how women’s earnings affect inequality among households. The financial support policy hypothesis therefore must be rejected. The results in Table 3 are presented both with fixed effects (as discussed above) and without. Without fixed effects, the results are very similar, although without the fixed effects the parameters tend to be somewhat greater. Overall, this suggests that the results are rather robust against unobserved time-invariant heterogeneity. Next, we test our alternative reconciliation policy hypothesis and examine whether the family policies have non-uniform outcomes on women’s employment and earnings. To do so, we have estimated the effects of the family policy variables on all endogenous variables in the model (with fixed effects included), presented in Table 4. The results show that neither of the reconciliation policies is directly associated with earnings inequality among women nor with the correlation between spouses’ earnings. So, the alternative reconciliation policy hypothesis must be rejected. Table 4. Alternative specification of structural equation model. Original and standardised coefficients (N = 116). View larger version In addition to testing our alternative reconciliation policy hypothesis, several results in Table 4 are worthy of discussion. First, we found that when keeping FLFP constant, women contribute a larger share to household earnings in the institutional context of generous public childcare and shorter periods of leave. This could result from longer periods of leave being associated with women having a weakened attachment to the labour force, for instance, as a result of mothers returning from leave to part-time jobs with lower wages (Pettit and Hook, 2009), while childcare provides opportunities to work longer hours in more demanding – and better paying – occupations. However, these differences in employment did not result in (substantive) differences in the level of earnings inequality among women. Second, family allowances were found to be associated with women contributing a larger share to household earnings – but it should be noted that this is after controlling for FLFP. This could mean that, in the context of generous family allowances – associated with the traditional breadwinner model –women with lower earnings in particular tend to reduce their labour force participation (cf. Korpi et al., 2013). Family allowances were also found to be associated with a reduced correlation between spouses’ earnings which, combined with the previous findings, could suggest that (in the context of generous family allowances) those women with higher earning spouses in particular reduce their labour force participation. However, our research design does not allow for observing which women remain in or opt out of the labour market, so this interpretation should be considered with caution. Finally, it should be noted that a greater expenditure on childcare is directly associated with the earnings of women further reducing inequality among households. This direct effect is relatively small and does not substantively affect the other estimates in the model.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Nieuwenhuis was supported by the Swedish Research Council for Health Working Life and Welfare (Forte), grant #2015-00921. ORCID iD

Rense Nieuwenhuis http://orcid.org/0000-0001-6138-0463