1 Introduction

The housing phenomenon is recognized as one of the social determinants of health, with different effects according to gender, age, ethnicity, territory, social class, among other axes of inequality [1]. Despite its centrality in people’s lives, the right to adequate housing remains unguaranteed in our society. In Spain there is a brutal inequality in access to and maintenance of adequate housing, which affects a large part of the population [2, 3]; an example of this is housing insecurity, understood as those problems related to accessibility/affordability and stability of housing. Housing insecurity is mainly associated with economic and legal issues, such as eviction processes, renting without a contract or doubling-up [4, 5]. In Barcelona, 39.6% of renters and 14.3% of homeowners spend more than 40% of their income on housing expenses [6].

Housing difficulties are linked to other insecurities in life, for example, with the labor sphere and the work-life balance, especially in the context of the post-pandemic economic crisis [7]. In this case, productive work is understood as all paid work, while reproductive work is understood as unpaid care/domestic work [8, 9]. The importance of both paid productive work to pay for housing and unpaid reproductive work to maintain housing and the development of life in it is emphasized [10,11,12]. Unfortunately, work-life balance remains unresolved. In Spain, despite the decrease in the gap between women and men in the labor market, the inequality in the distribution of reproductive workload remains. In comparison with men, more than double of women perform reproductive work, and this difference increases in couples with children [13]. Moreover, in Barcelona, nearly half of women report being primarily responsible for childcare and domestic tasks, in comparison with only 9% of men [14].

Regarding housing studies, several have proposed both gender and social class as devices of socio-material stratification with effects on people’s health [1, 15,16,17]. Thus, a critical review that analyzed gender inequalities in the health effects of housing problems observed worse health conditions in women, transgender people and non-binary people [1]. Meanwhile, in terms of work-life balance, the evidence warns of the importance of not assuming gender as the only category of analysis of these aspects, finding differences, for example, of social class within each generic category [18, 19].

Some qualitative studies have gone further by linking work and housing conditions, proposing paid work [17] and productive and reproductive workloads [20] as possible mechanisms mediating the relationship between housing insecurity and unequal health effects. Nevertheless, to date there is no population-based evidence available that integrates the analysis of workloads and their balance as possible differentiating mechanisms in the health outcomes of people affected and not affected by housing insecurity, and whether the effects of these mediators vary according to social class and gender. Therefore, this study aimed to analyze the effects on health of reproductive and productive workload and their interaction, according to housing situation, gender, and social class.

2 Materials and methods

2.1 Design, study population, and data source

We conducted a cross-sectional study using a representative sample of the non-institutionalized population of Barcelona city. To assess the population's health status, disabilities, health-related behaviours, physical context, and socioeconomic context, we extracted data from the 2021 Barcelona Health Survey, developed periodically by the Barcelona Public Health Agency since 1983 [14]. Such survey was performed through household interviews by trained interviewers between February 2021 and February 2022. The survey was correctly answered by 4000 respondents, of whom 2005 identified as women, 1825 as men, 17 as non-binary or trans*, and 151 were under four years old. To reduce reverse causality bias, we included people in active working age between 25 and 65 years and excluded those with permanent disability. Our final sample consisted of 2241 participants (56.3% of the original sample; 1159 women, 1,077 men, and five trans*).

2.2 Variables

The dependent variable was health and we measured it through five parameters: mental health, self-perceived health, sleep quality, and the presence of chronic headaches and chronic back pain. We assessed mental health according to the GHQ-12 questionnaire [21], categorizing the variable into good health (zero to two points), or poor health (three points or more). We evaluated self-perceived health using the five-category question “How would you describe your overall health?”, and dichotomized it into good health (excellent, very good, good) and poor health (fair, poor) [22]. We assessed sleep quality using the single-item sleep quality scale (SQS) [23], dichotomizing it into good quality (excellent, good) and poor quality (fair, poor, very poor). Finally, we evaluated chronic headache and back pain on the basis of the answer to the question “Do you suffer, or have you ever suffered from the following chronic disorders?”, being it yes or no.

The main independent explanatory variables were productive and reproductive workloads. We constructed the productive workload variable from the answer to the question, “What is your current work situation?”, dichotomizing it into yes (paid work with less than three months leave in a year), and no (unemployed, students, or solely dedicated to household work). We constructed the reproductive workload variable from the answers to the questions on the number of hours of domestic and care work per week, dichotomizing it into moderate workload (up to 21 h) and high workload (more than 21 h).

The stratification variables were gender (women and men, except for the descriptive analysis that also included trans* people) and housing insecurity (with or without). The category with housing insecurity included people who have answered affirmatively to any of the questions, “Do you think that in the next six months you will be forced to change housing?”, “Have you made late payments because of economic difficulties of mortgages on the main residence?”, or “Have you delayed payment because of economic difficulties related to renting your main home?”. The remaining people were assigned the category of without housing insecurity. Within the category without housing insecurity, we performed a substratification by occupational social class (manual class, non-manual class) [24]. This allowed us to pick up some of the heterogeneity of living conditions among people not experiencing housing insecurity. We used age as an adjustment variable in the models.

2.3 Data analysis

Firstly, we made a descriptive analysis of the variables of the study by gender, to determine their distributions through absolute and relative frequencies. Trans* and non-binary people were only included in this part of the study (see Table 1). In the rest of the analysis, trans* and non-binary people were excluded because of their small sample size. Secondly, we performed a bivariate analysis between reproductive workload and the rest of the variables of the study, stratified by housing insecurity. We compared the distribution of variables between people with and without housing insecurity in both categories of reproductive work (moderate or high) by means of chi-square or exact Fischer tests. This analysis was performed separately for women and men. Lastly, we constructed robust age-adjusted Poisson regression models to analyse the association of workload (productive and reproductive) with health variables for the following groups: women with housing insecurity, women without housing insecurity of manual social class, women without housing insecurity of non-manual social class, men with housing insecurity, men without housing insecurity of manual social class, men without housing insecurity of non-manual social class (Tables 4 and 5). We calculated the adjusted prevalence of poor health in the different indicators and their respective confidence intervals (CI) using the margins command from the results of the models. For the analysis, we used the statistical software STATA15.

Table 1 Sociodemographic and health characteristics of the people in the sample
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3 Results

3.1 Sample characteristics

Women represented 51.7% of the sample, 48.1% were men, and 0.2% trans*. Most of the sample had Spanish nationality (69.7% women, 68.8% men). Regarding the balance between productive and reproductive workload and employment status, the vast majority of both men and women reported having paid jobs. On the contrary, there was a stark difference between women (6.8%) and men (0.2%) that were completely dedicated to unpaid housework. Moreover, women (40.7%) reported roughly twice the weekly high reproductive workload of men (24.3%). A percentage of 12.9% of women and 13.3% of men had housing insecurity, and both women and men were mostly of non-manual social class. Finally, women were more likely to report poor mental health than men (30.0% and 21.1%, respectively). Similar trends were observed for all the other health indicators, with a higher prevalence of poor self-perceived health, poor sleep quality, and chronic pain in women (Table 1).

3.2 Bivariate distribution of reproductive workload

Table 2 (women) and Table 3 (men) show the bivariate analysis of reproductive workload (moderate or high) and all the other variables, stratifying by housing insecurity.

Table 2 Bivariate analysis between reproductive workload (moderate or high) and all the other variables, stratifying by housing insecurity, in women
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Table 3 Bivariate analysis between reproductive workload (moderate or high) and all the other variables, stratifying by housing insecurity, in men
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For women with moderate reproductive workloads, we observed significant differences between those with housing insecurity and those without for age, nationality, and social class (Table 2).

In the case of women with high reproductive workloads, we observed significant differences between those with and without housing insecurity in all variables, except sleep quality. For example, regarding social class, 62.0% of the women with housing insecurity were from manual social class, and only 31.8% of the women without housing insecurity belonged to this social class; regarding employment status, 23.3% of the women with housing insecurity were unemployed, in comparison with 10.0% of the women without housing insecurity. Also, the prevalence of poor mental health, self-perceived health, chronic headaches, and chronic back pain were also higher in women with housing insecurity (46.8%, 26.2%, 38.3%, and 54.7%, respectively), in comparison with women without housing insecurity (31.6%, 14.2%, 24.5%, and 35.8%, respectively) (Table 2).

For men with moderate reproductive workloads, we observed significant differences between those with housing insecurity and those without for age, nationality, social class, employment status, and mental health (Table 3).

For men with high reproductive workloads, we observed significant differences between those with housing insecurity and those without for the following variables: social class (manual class performed by 63.8% of the first ones, in comparison with 35.3% of the second ones); employment status (23.2% of the first ones unemployed, in comparison with 12.2% of the second ones); mental health (poor in 21.8% of the first ones, in comparison with the 16.6% of the second ones); and chronic back pain (affecting 39.8% of the first ones, in comparison with 23.8% of the second ones) (Table 3).

3.3 Interactions between productive and reproductive workloads: health differences among women

Table 4 presents the association of productive and reproductive workloads with the different health variables adjusted for age, in three groups of women: with housing insecurity; without housing insecurity of manual social class; and without housing insecurity of non-manual social class. The results are presented as prevalence ratios (PRa), in comparison with women with productive workload and moderate reproductive workload.

Table 4 Association of productive and reproductive workloads with health variables in women according to housing insecurity and social class, Robust Poisson models fitted by age
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Among women with housing insecurity (Table 4, Fig. 1), those with no productive workload and moderate reproductive workload presented the highest prevalence of poor mental health [PRa 3.85 (CI95% 2.12–7.00)], followed by those with no productive workload and a high reproductive workload [PRa 2.96 (CI95% 1.68–5.23)]; finally, those with a productive workload and high reproductive workload also had a higher prevalence of poor mental health, although it was not statistically significant [PRa 1.82 (CI95% 0.98–3.39)].

Fig. 1
figure 1

Association of productive and reproductive workloads with health variables in women according to housing insecurity and social class. PMR: Productive and moderate reproductive workloads; PHR: Productive and high reproductive workloads; NPMR: No productive and moderate reproductive workloads; NPHR: No productive and high reproductive workloads

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Regarding poor self-perceived health and poor sleep quality, women with no productive workload and moderate reproductive workload have the highest prevalence [PRa 2.78 (CI95% 1.30–5.92) and PRa 2.79 (CI95% 1.70–4.57), respectively], for the other categories, the differences were not significant.

Finally, for chronic headaches, women with no productive workload and with high reproductive workload had the highest prevalence [PRa 2.73 (CI95% 1.34–5.57)]; for chronic back pain, those with no productive workload and with moderate reproductive workload had the highest prevalence [PRa 2.33 (CI95% 1.50–3.62)], followed by those with no productive workload and with high reproductive workload [PRa 1.89 (CI95% 1.21–2.94)].

Among women without housing insecurity of manual social class, no statistically significant associations were observed; however, in the case of sleep quality, a tendency to poorer sleep quality was observed in those with no productive workload. This association was almost significant in those with no productive workload and with a high reproductive workload [PRa 1.46 (CI95% 0.98–2.16)].

Among women without housing insecurity of non-manual social class, only those with productive workload and high reproductive workload had significantly higher prevalence of poor mental health and poor sleep quality [PRa 1.36 (CI95% 1.04–1.77); PRa 1.54 (CI95% 1.23–1.94), respectively]. On the contrary, women with no productive workload and high reproductive workload were less likely to have poor mental health [PRa 0.33 (CI95% 0.12–0.91)]. Finally, women with no productive workload and a moderate reproductive workload were less likely to have chronic headaches [PRa 0.49 (CI95% 0.24–0.98)] (Table 4, Fig. 1).

3.4 Interactions between productive and reproductive workloads: health differences among men

Table 5 presents the association of reproductive and productive workload with the different health variables adjusted for age, in three groups of men: with housing insecurity; without housing insecurity of manual social class; and without housing insecurity of non-manual social class. The results are presented as prevalence ratios (PRa), in comparison with men with productive workload and moderate reproductive workload.

Table 5 Association of productive and reproductive workloads with health variables in men according to housing insecurity and social class, Robust Poisson models fitted by age
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Among men (Table 5, Fig. 2) with housing insecurity, the health indicators with significant results were sleep quality, chronic headache and chronic back pain. Regarding sleep quality, those with no productive workload and high reproductive workload had the poorest outcomes [PRa 2.48 (CI95% 1.1–5.62)], followed by those with no productive workload and moderate reproductive workload [PRa 2.27 (CI95% 1.07–4.78)], and those with productive workload and high reproductive workload [PRa 2.2 (CI95% 1.12–4.31)].

Fig. 2
figure 2

Association of productive and reproductive workloads with health variables in men according to housing insecurity and social class. PMR: Productive and moderate reproductive workloads; PHR: Productive and high reproductive workloads; NPMR: No productive and moderate reproductive workloads; NPHR: No productive and high reproductive workloads

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Regarding chronic headaches, those with no productive workloads and with moderate reproductive workload were more likely to present them [PRa 6.05 (CI95% 1.70–21.58)]. For chronic back pain, those with no productive workload and with high reproductive workload were more likely to present it [PRa 2.25 (CI95% 1.22–4.12)].

Among men without housing insecurity of manual social class, no statistically significant associations were observed.

Finally, among men without housing insecurity of non-manual social class, those with no productive workload and with high reproductive workload had the greatest probability of presenting poor health indicators, except for chronic back pain: poor mental health [PRa 2.31 (CI95% 1.00–5.36)], poor self-perceived health [PRa 6.25 (CI95% 1.20–32.51)], poor sleep quality [PRa 2.21 (CI95% 0.94–5.21)], chronic headaches [PRa 4.5 (CI95% 1.83–11.09)] (Table 5, Fig. 2).

4 Discussion

This study broadens the understanding of the role of the life-work balance on population health, and how these effects vary according to housing insecurity, gender and social class. Women present the most significant effects of these workloads on health; moreover, this group is not homogeneous, important differences are observed according to housing insecurity and social class. In women living with housing insecurity, having a productive workload is related to improved health indicators. Reproductive workload intensity, however, does not seem to influence health indicators by itself, but intensifies the negative effects of the lack of productive workload on the indicators of mental health and chronic headaches. In women without housing insecurity of non-manual class, the interaction between reproductive and productive workload is associated with poorer health. In the case of men with housing insecurity, the absence of productive workload considerably worsens sleep quality, and is especially true for men with high reproductive workloads. For men without housing insecurity of non-manual class, high reproductive workload worsen all health outcomes, except for chronic back pain, if they do not have productive workload.

4.1 Work-life balance, differences between women and men

There are key differences in the distribution of productive and reproductive workload between women and men. Regarding productive workload, although most of women and men have productive workloads, the gap between the number of hours that both dedicate to this workload is evident: most men dedicate full time, while nearly half of women dedicate 30 h or less. Some studies reveal that the increase in the number of women in the labour market does not necessarily imply an improvement in their working conditions; conversely, vertical and horizontal segregation as well as employment conditions continue to be unequal between genders [8, 25, 26]. Moreover, this increase in women with productive work does not lead to a change in the balance between productive and reproductive workload, as pointed out by different reports and studies on this subject [9, 27,28,29]. This is linked to the artificial dichotomy that is made between the productive and reproductive spheres of life, making invisible the relationship between the logics of capitalist production and the patriarchal oppression of the gender binary [26, 30,31,32,33].

Regarding reproductive workload, many more women than men assume it as their main job. Also, there are differences among women according to their living conditions. Other studies have shown these differences, associated with negative effects on health [18, 34,35,36] The difference in hours dedicated between women and men is staggering: compared with men, nearly twice as many women have a high reproductive workload. Recent ILO reports point out that this gender gap in productive and reproductive workloads still exists today, and recommend the development of work-life balance policies with a gender perspective in order to avoid widening these differences [8, 9].

So, if most of both women and men have productive work, why twice as many women assume, also, the greatest burden of reproductive work? Tensions in the work-life balance between genders are still more present than ever. Diverse studies have observed how capitalism and patriarchy are enhanced in current practices of maintaining and developing everyday life: care-oriented actions are individualized and privatized, while they are assumed as inherent to non-hegemonic gender categories [35, 37,38,39,40].

However the categories of women and men are extremely heterogeneous, with different realities coexisting within these groups [25, 41]. Regarding intersectionality, as well as gender, we have also observed differences according to social class and housing insecurity. This is consistent with evidence on housing insecurity that shows how the most oppressed groups are more disadvantaged in terms of access to material conditions (e.g. housing or income from paid work) and of daily practices (e.g. triple workload related to coping with the housing insecurity and other precariousness, in addition to the daily productive and reproductive workloads) [20, 26, 37, 42,43,44,45].

4.2 Health implications for women

The results consistently show that women with housing insecurity have worse health indicators than those without. This is consistent with previous studies that have shown worse living and health conditions in people with housing insecurity [46, 47].

For women with housing insecurity, the fact of not having a productive workload is the key determining factor for poor health. Indeed, for all health indicators except for chronic headaches, those who do not have a productive workload and have a moderate reproductive workload perform worse. This can be linked to the critical situation of housing insecurity, where the paid work is crucial to ensure the minimum material conditions to maintain a roof overhead [48]. The function of productive work is mainly linked to the economic income, and, therefore, the material conditions are a necessary condition for the development of daily practices and work-life balance [49, 50].

Women without housing insecurity of manual social class generally presented worse health indicators than those without housing insecurity of non-manual social class, consistently with previous studies [25]. However, with respect to the interaction between productive and reproductive workloads, no significant effects were observed on health indicators. This may be due to the large heterogeneity of profiles and living conditions within this group, which leads to different effects of workload interactions. Other studies have shown that not only the distribution of productive/reproductive workload is important, but also what are the specific characteristics of these burdens and the role that is occupied within them (e.g. types of paid work, working and employment conditions, conditions and distribution of reproductive work, material conditions enabling the performance of work, etc.) [41, 51, 52]. Future studies are necessary to decipher this heterogeneity and to delve deeper into these other relevant aspects.

In agreement with other studies, in the case of women without housing insecurity of non-manual social class, we observe that the double workload negatively affected their health, being those with productive workload and high reproductive workload the ones with the worst health outcomes [19, 27].

Finally, our results coincide with the ones of a qualitative study showing that people with housing insecurity referred to productive work purely as subsistence to maintain housing conditions, whereas people without housing insecurity highlighted its importance for their professional and personal development, and housing as a facilitator to develop this area [20].

4.3 Health implications for men

As in the case of women, all health indicators for men with housing insecurity are worse than for men without. In addition, men without housing insecurity of manual social class have worse results than those without housing insecurity of non-manual social class, as has been shown in previous studies [46, 47].

Men with housing insecurity presented a similar tendency to women: those with no productive workload and with high reproductive workload were worse-off. However, this was only observed in sleep quality and chronic disorders.

Moreover, similarly to women, men without housing insecurity of manual social class were not significantly affected by workload distribution. However, they had worse results than those without housing insecurity of non-manual social class, as shown in previous studies [46, 47]. Future studies will have to explore the diversity of profiles inside this category.

Finally, among men without housing insecurity of non-manual social class, those who only have a high reproductive burden have consistently worse health indicators. This coincides with previous evidence pointing to the importance of productive work for men, not only on a material level [48], but also linked to the symbolic burden of being the main economic support of the household and the persistence of the gender bias associated with masculinity which excludes—explicitly or implicitly—their participation in reproductive work [19, 20].

4.4 Limitations and strengths

The study has two main limitations. The first one is the small sample size that did not allow us to carry out a more in-depth intersectional analysis, like not having been able to analyse people who do not fit within the binary gender classification (only five); however, not to contribute to their invisibility, we have included them in the description of the study. It is necessary to continue deepening population registries that go beyond the gender binary, in order to expand the scientific evidence of housing and health in these groups. A further limitation derives from the cross-sectional nature of the study that generates the possibility of reverse causality of the results; however, other studies have shown trends in poor health outcomes associated to material conditions and workloads. Moreover, to alleviate this limitation, people with permanent disability have been eliminated from the analysis. Finally, it is necessary to generate longitudinal studies that allow to fully correct for the possibility of reverse causality of the results.

A strength of the study is to have a representative sample of the city of Barcelona, which allowed us to understand the relationship among housing insecurity, health, and the inequality of the life-work balance at the population level. Another strength is to broaden the intersectional view of the balance of productive and reproductive workloads, and to analyse other realities and difficulties, such as housing insecurity, in both women and men, making the gender perspective in housing and health studies more complex.

5 Conclusions

The health effects of the work-life balance are not the same for all people and vary intersectionally due to the interaction of factors such as housing situation, social class, and gender. The differences in the balance of productive and reproductive workloads and the unequal effects on health according to different intersectional profiles, remain urgent problem to be addressed considering these factors.

The intersectional analysis—and the consequent multiplicative methodologies—of this problem is fundamental to address the diverse reality of life. This must not only consider gender, but also considering other axes of inequality and living conditions. Finally, a better understanding of this problem would serve to generate differentiated and specific solutions for different populational groups.