Perceptions of Fairness in the Division of Housework and Childcare and Their Relationship with Fertility Intentions in Japan

Introduction

The decline in fertility rates accompanying economic growth has become a common challenge for many nations. Historically, improvements in women’s educational attainment and their expanding labour force participation have been viewed as primary drivers of fertility decline in developed countries (Brewster and Rindfuss, 2000). This argument, based on the observed negative correlation between fertility and female labour participation at the national level, was theoretically supported by Becker’s New Home Economics (1993). However, since the 1990s, this correlation has begun to reverse (Brewster and Rindfuss, 2000). Currently, the progress of gender equality—characterised by equal participation in the labour market and a balanced division of domestic labour—is associated with a reversal of fertility decline in many advanced nations (Goldscheider et al., 2013; Goldscheider et al., 2015). In particular, an increase in men’s contributions to housework and childcare fosters gender equity based on subjective agreement between spouses, which is expected to facilitate a recovery in fertility.

While research findings regarding the impact of paternal involvement in domestic labour on fertility are inconsistent in non-Western contexts (Nagase and Brinton, 2017; Nishioka and Hoshi, 2009), several commonalities have been observed between East Asian and Southern European countries (An and Peng, 2016). These similarities became even more pronounced moving into the 2000s (Gauthier, 2015). Based on these parallels, the gender equity framework is considered highly applicable to the study of Japanese fertility.

Furthermore, existing research on gender equity and fertility has primarily focused on the objective division of housework and childcare, while women’s subjective perceptions—namely, their sense of fairness—remain insufficiently examined. As McDonald (2013) points out, the perception of fairness is as crucial a dimension of gender equity as the actual division of labour. The lack of this subjective fairness may explain why the impact of gender equity on fertility remains limited in certain countries.

Accordingly, this study first outlines the theoretical framework regarding low fertility and distinguishes between the concepts of gender equality and gender equity. In this paper, gender equality is defined as a state where domestic labour is divided roughly equally (50/50) between partners. In contrast, gender equity is defined as a concept that incorporates both the objectively measurable division of labour and the couple's subjective evaluation of that division. Next, taking the current situation in Japan into account, this study aims empirically to verify the relationship between perceived fairness in the division of housework and childcare and fertility intentions.

This study contributes to the literature on low fertility and gender relations in several significant ways. First, it addresses the conceptual and empirical gap between gender equality (the objective division of labour) and gender equity (the subjective perception of fairness). While previous studies in Japan have yielded inconsistent results regarding the impact of paternal involvement, this study posits that the subjective assessment of fairness may be a more critical determinant of fertility than the actual hours spent on domestic tasks.

Second, by utilising the Traits-Desires-Intentions-Behaviour (TDIB) framework, this research distinguishes between fertility desires (immediate desire) and the planned number of children (concrete plans). This allows for a more nuanced understanding of how gender equity influences different stages of the fertility decision-making process.

Third, this study highlights the importance of sample representativeness. By comparing unweighted results with those weighted by prefectural population ratios, it uncovers how the relationship between fairness and fertility may vary across socio-geographic contexts—specifically between urban and non-urban areas—providing a cautionary empirical note for future research using non-probability samples in Japan.

By integrating these perspectives, the study aims to clarify why the gender revolution has yet to result in a significant fertility recovery in Japan, offering insights into the structural and normative barriers that persist in East Asian contexts.

Literature Review

Theories Explaining Low Fertility

The discourse surrounding low fertility often focuses on the gap between fertility intentions and actual fertility behaviour. The New Home Economics framework was a pioneering contribution to this field (Becker, 1993). According to this theory, the lower fertility rates observed among employed women—particularly those with higher education—are attributed to the high opportunity costs associated with childbirth. In this view, choosing to have a child may disadvantage a woman's career progression and promotion opportunities. If women perceive this as an opportunity cost and act rationally, they are expected to forgo or limit childbearing. In other words, as women’s educational attainment increased their earning capacity, the economic benefits of marriage based on the traditional male breadwinner model declined relatively. This led women to pursue professional careers, resulting in a decrease in births and a subsequent decline in total fertility rates. Under this framework, actual fertility intentions have not necessarily declined; rather, a divergence has emerged between intentions and behaviour because those intentions are not being realised.

However, in recent years, a macro-level shift has been observed where the correlation between female labour force participation and fertility has reversed from negative to positive (Esping-Andersen and Billari, 2015). Furthermore, some empirical evidence suggests a positive association between women's employment status and the number of children they have (Goldscheider et al., 2015).

To explain these shifts, the gender equity perspective was proposed (McDonald, 2000a; 2000b). McDonald noted a U-shaped relationship between gender equity and fertility, suggesting that the cause of this transformation lies in the evolution of societal gender norms and institutions.

Subsequently, the gender revolution theory expanded upon the gender equity framework by incorporating a micro-level focus on the division of housework (Goldscheider et al., 2015). This theory emphasizes the link between work and family more than macro-transformations, explaining the impact of women’s social advancement and men’s domestic participation in two stages. In the first stage, women’s participation in activities outside the home (specifically the labour market) increases significantly, while the burden of domestic labour remains unmitigated. This results in an extremely unequal division of housework and childcare, leading employed women to have fewer children than non-employed women. In the second stage, men’s domestic contributions increase, leading to a more equal division of labour. This reduces the burden on women, enabling them to balance work and family life while remaining employed, which tends to push fertility rates back toward replacement levels. This prediction has been confirmed in various countries, including Denmark (Brodmann et al., 2007), Norway (Dommermuth et al., 2017), and Germany (Cooke, 2004).

Gender Equity and Perceived Fairness

Despite these findings, certain phenomena regarding the equal division of housework and fertility remain unexplained. First is the heterogeneity between countries. In Italy, equal division and paternal involvement increase the probability of childbirth, whereas in Spain, paternal involvement fails to show the same effect (Cooke, 2009). In France, an increase in the male share of childcare has no positive impact on intentions for a second child or more (Mizuochi, 2010). Intergenerational differences have also been noted; in the United States, fertility rates are high not only in households with the most equitable division of housework but also in those with the most inequitable division (Torr and Short, 2004).

While evidence supporting the gender equity theory has primarily emerged from North America, Northern Europe, and Western Europe, proponents expect broader applicability (Goldscheider et al., 2015). From a gender equity perspective, the transformation of family systems in East Asian and Southern European countries aligns with the U-turn of gender equity and the first stage of the gender revolution. In Japan, however, research on the effects of gender equity on fertility has yielded inconsistent results. It is evident that the impact in Japan is not a simple linear causal relationship but appears conditionally through interactions with social structures such as household type, education level, and gender role norms.

Among studies supporting the gender equity hypothesis, Nishioka and Hoshi (2009) demonstrated that a higher frequency of paternal housework and childcare participation correlates with higher fertility desires and a higher desired number of children for women. Fukuda (2023) focused on highly educated women and found a positive association between improved gender equity and childbirth in Japan. Kan and Hertog (2017) stated that as paternal involvement increases, women’s ideal number of children also increases. Conversely, Yamaguchi (2005) showed that the ratio of housework and childcare shared by men had no effect on either fertility desires or behaviour. Mizuochi (2010) found that while increased paternal childcare had a positive effect on additional childbearing intentions in a combined sample of men and women, this effect disappeared when analysing the female-only sample. Nagase and Brinton (2017) pointed out that in dual-income households, an increase in the male share of domestic labour led to the birth of a second child, but this was not observed in single-income households; in fact, in households where the male is the sole breadwinner, his domestic participation significantly lowered the probability of a second birth. Kato et al. (2018) showed a non-linear relationship, where a moderate level of paternal involvement—rather than zero or high involvement—was associated with the highest probability of having a second or third child.

To explain these discrepancies, it is necessary to distinguish between gender equality and gender equity (McDonald, 2013). Gender equality is typically defined by the actual distribution of contributions in domestic labour. Equality is achieved when a 50/50 split is realised. In contrast, gender equity encompasses not only the objectively measurable division of labour but also the couple's subjective judgment of whether that division is fair (McDonald, 2013). This is because even if a partner’s domestic contribution is low, a sense of fairness may be maintained if it is balanced by high participation in paid labour (DeMaris and Mahoney, 2017). Thus, it is the gap between the ideal and actual division of labour that drives low fertility (Goldscheider et al., 2013). Consequently, we must consider the perception of fairness alongside the actual division. Indeed, instances where equal division promotes childbirth are often predicated on an increase in perceived fairness (Dommermuth et al., 2017; Zhang et al., 2024).

It is therefore necessary to examine the specific mechanisms through which perceived fairness links to fertility behaviour. While fertility intentions are not a perfect predictor of behaviour, they maintain a strong association, which has been confirmed in the Japanese context (Yamaguchi, 2005; Murakami, 2014). Regarding the operationalisation of fertility intentions, this study adopts both planned number of children and fertility desires within three years to ensure robustness (Han et al., 2024). Note that while fertility intentions and fertility desires are often used interchangeably in casual conversation, this study distinguishes them: fertility intentions refer to a comprehensive concept including desires and plans, while fertility desires refer specifically to the immediate desire for a child.

The Traits-Desires-Intentions-Behaviour (TDIB) model provides a framework for the relationship between abstract fertility desires and concrete intentions or plans (Miller and Pasta, 1995). According to TDIB, fertility desires are a critical antecedent to childbearing plans (i.e., the planned number of children), which in turn predict subsequent fertility behaviour. Empirical research supports this, confirming that fertility desires are a powerful predictor of the planned number of children, which mediates the effect of intentions on actual behaviour (Miller and Pasta, 1995; Brase, 2016). Both longitudinal and cross-sectional studies report that for most individuals, fertility desires and the planned number of children are largely consistent (Preis et al., 2020; Chen et al., 2017).

Based on the close relationship between intentions, plans, and behaviour, we propose the following hypotheses regarding the association between perceived fairness in the division of housework and childcare and fertility intentions:

Hypothesis 1: The more a woman perceives the division of housework and childcare to be fair, the higher her fertility desires.

Hypothesis 2: The more a woman perceives the division of housework and childcare to be fair, the higher her planned number of children.

Data

The data used in this study are based on the Survey of Childbirth Attitudes among Married Couples conducted in April 2023 by the 1 More Baby Ohendan. This nationwide internet-based survey employed quota sampling to recruit 2,961 married individuals. The eligibility criteria were as follows: females aged 20–39 and males aged 20–49, with the further condition for males that their wives be aged 39 or younger and their marriage duration be 14 years or less. The sampling strategy involved selecting 63 respondents from each of Japan's 47 prefectures. The sample was further stratified based on marital status and the current number of children (married with no children, married with one child, and married with two or more children), with 21 respondents selected for each stratum. After limiting the analysis to married women with at least one child and applying listwise deletion for cases with missing values among the variables used in the analysis, the final sample size for this study is 1,150.

This study employs multiple regression analysis (Ordinary Least Squares). While fertility intentions are technically ordinal categorical variables and would typically require an ordered logit model, we have adopted OLS for simplicity, as our preliminary analyses indicated that the results do not significantly differ between the two models.

Because the data were collected with an equal number of respondents (n = 63) from each of the 47 prefectures, smaller-population regions are over-represented while larger-population regions are under-represented. To correct this bias, we applied sample weights in the regression analysis. These weights were calculated based on the 2020 Population Census of Japan. Specifically, using Table 7-2 ‘Household members by Sex, Age (five-year groups), Marital status and Type of household - Japan, Prefectures, Municipalities’ (Statistics Bureau of Japan, 2021), we determined (1) the national female population aged 20–39 and (2) the female population aged 20–39 for each prefecture. The weights were then created by dividing (2) by (1) and applied to each case.

Measures

Dependent Variables

The dependent variable in this study is fertility intentions. Following previous literature (Han et al., 2024), we operationalise this using two separate variables: planned number of children and fertility desires within three years.

For the planned number of children, we utilised the question: ‘Considering the future of yourself/your household, please indicate the total number of children that you realistically expect to have.’ Responses ranging from 0 to 6 were coded as their respective numerical values, while ‘7 or more’ was coded as 7, forming a continuous variable.

The variable fertility desires within three years is a composite continuous variable constructed from two items: the intention to have a child and the preferred timing. The decision to limit fertility desires to a three-year window is based on findings that the desire for children tends to decrease over time, and short-term intentions offer higher predictive accuracy for actual behaviour than long-term intentions (Murakami, 2014). Short-term intentions are more concrete and are influenced by various constraints, such as employment status, economic factors, lack of a suitable partner, social norms, or the desire to postpone motherhood (Mussino et al., 2021). In contrast, intentions expressed without a specific timeframe are more closely related to fertility ideals. Furthermore, maintaining a specific temporal range is considered crucial for international comparisons of the realisation of fertility intentions (Brzozowska and Beaujouan, 2020).

To operationalise fertility desires, responses to the question ‘Do you/your household intend to have children in the future?’ were coded as follows: ‘I will not have children’ was coded as 0, and ‘I don't know’ was coded as 1. The ‘I don't know’ response represents ambiguity or uncertainty on the continuum from ‘definitely will not’ to ‘definitely will’ and is thus positioned between the negative and positive intentions (Han et al., 2024). For those who responded ‘I will have children,’ the code was further refined based on timing: ‘Immediately’ and ‘In 1–2 years’ were coded as 2, while ‘In 3–5 years,’ ‘In 6 or more years,’ and ‘Eventually, but timing is undecided’ were coded as 1. Because this study focuses on the link between gender equity and fertility intentions—and given the high uncertainty of childbearing behaviour beyond a three-year horizon—these later-dated intentions were grouped with ‘I don't know’ for the analysis. The specific coding scheme for the combination of these items is detailed in Table 1.

Table 1: Coding of Fertility Desires within Three Years Based on Intention and Timing

	Preferred Timing
Intention to have children	Immediately	In 1–2 years	In 3–5 years	In 6+ years	Eventually
I will have children	2	2	1	1	1
I don't know	1	1	1	1	1
I will not have children	0	0	0	0	0

Independent Variables

The primary independent variable of this study is gender equity. Given the absence of a direct inquiry item in the survey, satisfaction with the division of housework and childcare was utilised as a proxy, following the precedent of Dommermuth et al. (2017). This operationalisation relies on the hypothesis that if the division of labour is perceived as inequitable, levels of satisfaction will be correspondingly low.

Regarding the measurement of perceived fairness, the analysis is restricted to women; thus, the variable was constructed based on the wives' perception and configured as a binary variable. Specifically, two items were used: ‘Are you and your spouse satisfied with your respective approaches to housework?’ and ‘Are you and your spouse satisfied with your respective approaches to childcare?’ Cases where the respondent reported being satisfied were coded as 1 (Fair), regardless of the husband's satisfaction level, while all other responses were coded as 0 (Unfair). The rationale for this approach is that because these items focus on concrete aspects of domestic labour, perceptions of unfairness are expected to manifest as low satisfaction; therefore, a significant discrepancy between satisfaction and perceived fairness is unlikely.

A strong correlation was observed between the variables for perceived fairness in housework and perceived fairness in childcare (Cramer’s V=0.6832, p<.001). To avoid potential issues of multicollinearity if both were included in the model simultaneously, these variables were summed up to create a new composite total fairness score variable.

The second independent variable is the male spouse's contribution to housework and childcare. The survey records the paternal involvement in housework and childcare at the start of cohabitation, as well as any subsequent increases in his contribution at key life stages: marriage, the first pregnancy, and the birth of the first child. These involvements consist of four housework items and five childcare items. To measure the increase in paternal involvement, we summed the number of tasks the husband was responsible for at the start of cohabitation and the number of additional tasks he undertook later. This total value was then log-transformed.

Table 2 details the specific items used to calculate this contribution. Items marked with ‘Y’ existed and were included in the calculation, while items marked ‘N’ do not exist. Each applicable item was assigned one point, while non-applicable items received zero points. For example, if a husband performed no cooking, cleaning, laundry, or shopping at the start of cohabitation and did not increase his contribution after marriage, the first pregnancy, or childbirth, his housework contribution score would be 0. Conversely, if he performed all four tasks at the start of cohabitation and increased his contribution in all categories at every subsequent life stage, his score would be 16. Since childcare contributions are only observable after the birth of the first child, only the subsequent increase is calculated for that category.

Table 2: Items included in the calculation of the husband’s contribution

Items of increased paternal involvement		Key life stages
		Start of cohabitation	Marriage	First pregnancy	Birth of the first child
Housework items	Cooking	Y	Y	Y	Y
	Cleaning	Y	Y	Y	Y
	Laundry	Y	Y	Y	Y
	Shopping	Y	Y	Y	Y
Childcare items	Daily care	N	N	N	Y
	Playing with children	N	N	N	Y
	Gathering educational information	N	N	N	Y
	Transportation to/from school	N	N	N	Y
	Transportation to/from lessons	N	N	N	Y

For the control variables, annual household income is included. This variable is operationalised by taking the midpoint of each income bracket (the average of the upper and lower limits) and applying a logarithmic transformation. Employment status is categorised into regular employment, non-regular employment, self-employment, and unemployment. Age and the number of existing children (parity) are included as continuous variables using their raw values.

Furthermore, since time availability is a critical factor influencing both the division of domestic labour and the perception of fairness (Midgette, 2020), this study includes the average daily working hours of both the female respondent and her male spouse. Table 3 presents the results of the descriptive analysis for these variables.

Table 3: Descriptive Statistics (N = 1,150)

Variable	Mean / Proportion	SD	Min	Max
Planned number of children	2.152	0.731	1	7
Fertility desires within 3 years	1.028	0.871	0	2
Husband's contribution to housework	3.218	3.831	0	16
Husband's contribution to childcare	1.556	1.640	0	5
Housework fairness
Fair	0.521	0.500	0	1
Unfair	0.479	0.500	0	1
Childcare fairness
Fair	0.514	0.500	0	1
Unfair	0.486	0.500	0	1
Total fairness score	1.035	0.917	0	2
Age	32.236	3.995	21	39
Household annual income (100,000 JPY)	57.957	29.591	5	500
Number of existing children	1.594	0.679	1	4
Working hours	4.153	3.775	0	17
Husband's working hours	9.377	2.294	0	17
Employment status
Regular employment	0.328	0.470	0	1
Non-regular employment	0.257	0.437	0	1
Self-employment	0.027	0.162	0	1
Unemployment	0.388	0.487	0	1
Husband's employment status
Regular employment	0.895	0.307	0	1
Non-regular employment	0.025	0.157	0	1
Self-employment	0.077	0.266	0	1
Unemployment	0.003	0.059	0	1

Result

Table 4: Impact of Perceived Fairness on Fertility Desires (N = 1,150)

Variable	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6
	Coef.	Coef.	Coef.	Coef.	Coef.	Coef.
	(S. E.)	(S. E.)	(S. E.)	(S. E.)	(S. E.)	(S. E.)
Housework fairness	0.107*	0.093
	(0.048)	(0.073)
Husband's contribution to housework	0.044	0.055			0.046	0.063
	(0.026)	(0.040)			(0.028)	(0.041)
Childcare fairness			0.124*	0.060
			(0.049)	(0.079)
Husband's contribution to housework			0.013	0.021	-0.014	-0.012
			(0.037)	(0.060)	(0.040)	(0.063)
Total fairness score					0.069**	0.042
					(0.027)	(0.042)
Age	-0.038***	-0.040***	-0.038***	-0.040***	-0.037***	-0.039***
	(0.006)	(0.009)	(0.006)	(0.009)	(0.006)	(0.009)
Household annual income (log)	-0.005	-0.059	-0.004	-0.050	-0.006	-0.057
	(0.048)	(0.077)	(0.048)	(0.078)	(0.048)	(0.076)
Employment status (Ref: Regular employment)
Non-regular employment	-0.159*	-0.186	-0.164*	-0.189	-0.158*	-0.187
	(0.072)	(0.111)	(0.072)	(0.112)	(0.072)	(0.111)
Self-employment	-0.115	0.158	-0.117	0.178	-0.114	0.155
	(0.154)	(0.300)	(0.154)	(0.297)	(0.154)	(0.299)
Unemployment	-0.141	0.032	-0.143	0.032	-0.142	0.031
	(0.129)	(0.204)	(0.129)	(0.205)	(0.129)	(0.204)
Husband’s employment status (Ref: Regular employment)
Non-regular employment	-0.422**	-0.403*	-0.432**	-0.417*	-0.424**	-0.404*
	(0.151)	(0.200)	(0.151)	(0.203)	(0.151)	(0.202)
Self-employment	0.048	0.235	0.053	0.237	0.051	0.236
	(0.089)	(0.133)	(0.090)	(0.137)	(0.090)	(0.134)
Unemployment	0.401	0.075	0.368	0.058	0.396	0.065
	(0.414)	(0.396)	(0.414)	(0.411)	(0.414)	(0.398)
Working hours	-0.000	0.015	0.000	0.015	-0.000	0.014
	(0.015)	(0.024)	(0.015)	(0.024)	(0.015)	(0.024)
Husband's working hours	0.019	0.016	0.019	0.016	0.019	0.015
	(0.011)	(0.015)	(0.011)	(0.015)	(0.011)	(0.015)
Existing children	-0.447***	-0.382***	-0.450***	-0.384***	-0.445***	-0.378***
	(0.035)	(0.052)	(0.035)	(0.054)	(0.035)	(0.053)
Intercept	2.798***	2.847***	2.826***	2.893***	2.765***	2.841***
	(0.300)	(0.479)	(0.300)	(0.491)	(0.301)	(0.484)
Adj R2	0.185	0.156	0.183	0.150	0.184	0.154

Note. Standard errors are in parentheses. *p < .05. **p < .01. ***p < .001.

In Table 4, the analysis of fertility desires yields the following results: in Model 1, the coefficient for perceived fairness in housework remains significant at 0.107 (p = .026, 95% CI [0.013, 0.201]), even after controlling for the male spouse's contribution to housework. This indicates that when women perceive the division of housework as fair, their fertility desires are significantly stronger, providing initial support for Hypothesis 1. Similarly, in Model 3, while the male spouse's childcare contribution does not directly influence fertility desires, the woman's perceived fairness in childcare remains significant when this contribution is controlled (b = 0.124, p = .011, 95% CI [0.029, 0.220]). This suggests that, regardless of the actual increase in paternal involvement, the subjective perception of fairness significantly bolsters fertility desires, further supporting Hypothesis 1. Furthermore, in Model 5, the effect of the composite fairness variable (which integrates housework and childcare) remains significant even when controlling for total paternal domestic contributions (b = 0.069, p = .010, 95% CI [0.017, 0.121]).

However, when sample weights were applied to the models using fertility desires as the dependent variable, the results changed substantially. In Models 2, 4, and 6 (the weighted models), all variables related to perceived fairness lost their statistical significance. Consequently, Hypothesis 1 is no longer supported when adjusting for sample representativeness.

Table 5: Impact of Perceived Fairness on the Planned Number of Children (N = 1,150)

Variable	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6
	Coef.	Coef.	Coef.	Coef.	Coef.	Coef.
	(S. E.)	(S. E.)	(S. E.)	(S. E.)	(S. E.)	(S. E.)
Housework fairness	0.061	0.064
	(0.038)	(0.050)
Husband's contribution to housework	-0.013	-0.015			-0.016	-0.004
	(0.020)	(0.031)			(0.022)	(0.030)
Childcare fairness			0.024	0.034
			(0.038)	(0.054)
Husband's contribution to housework			0.015	-0.031	0.020	-0.032
			(0.029)	(0.042)	(0.031)	(0.041)
Total fairness score					0.025	0.030
					(0.021)	(0.029)
Age	-0.032***	-0.023***	-0.032***	-0.023***	-0.032***	-0.023***
	(0.005)	(0.007)	(0.005)	(0.007)	(0.005)	(0.007)
Household annual income (log)	-0.011	-0.120	-0.010	-0.117	-0.011	-0.119
	(0.038)	(0.103)	(0.038)	(0.104)	(0.038)	(0.103)
Employment status (Ref: Regular employment)
Non-regular employment	-0.153**	-0.217**	-0.154**	-0.220**	-0.154**	-0.217**
	(0.056)	(0.081)	(0.056)	(0.081)	(0.056)	(0.080)
Self-employment	-0.174	-0.337**	-0.173	-0.341**	-0.174	-0.339**
	(0.121)	(0.122)	(0.121)	(0.118)	(0.121)	(0.121)
Unemployment	-0.197	-0.287	-0.195	-0.289	-0.196	-0.288
	(0.101)	(0.152)	(0.101)	(0.152)	(0.101)	(0.153)
Husband’s employment status (Ref: Regular employment)
Non-regular employment	-0.090	-0.041	-0.088	-0.041	-0.090	-0.039
	(0.118)	(0.180)	(0.118)	(0.181)	(0.118)	(0.181)
Self-employment	0.102	0.111	0.102	0.110	0.105	0.110
	(0.070)	(0.083)	(0.070)	(0.084)	(0.070)	(0.084)
Unemployment	0.471	0.128	0.451	0.111	0.459	0.118
	(0.324)	(0.701)	(0.324)	(0.711)	(0.324)	(0.709)
Working hours	-0.015	-0.031	-0.016	-0.032	-0.015	-0.032
	(0.012)	(0.019)	(0.012)	(0.019)	(0.012)	(0.019)
Husband's working hours	0.007	0.002	0.007	0.001	0.007	0.002
	(0.008)	(0.013)	(0.008)	(0.013)	(0.008)	(0.013)
Existing children	0.567***	0.544***	0.568***	0.550***	0.566***	0.549***
	(0.027)	(0.042)	(0.027)	(0.041)	(0.027)	(0.041)
Intercept	2.405***	2.790***	2.412***	2.800***	2.410***	2.787***
	(0.235)	(0.516)	(0.235)	(0.523)	(0.236)	(0.524)
Adj R2	0.291	0.256	0.290	0.255	0.290	0.255

Note. Standard errors are in parentheses. *p < .05. **p < .01. ***p < .001.

Table 5 displays the results for the planned number of children. Models 1, 3, and 5 represent the unweighted models. These results indicate that neither perceived fairness in housework nor perceived fairness in childcare significantly affects the planned number of children, even after controlling for various factors and the level of paternal domestic contribution.

These findings remain consistent in the weighted models (Models 2, 4, and 6). Consequently, in the models predicting the planned number of children, Hypothesis 2 is rejected, as no positive association between perceived fairness and the planned number of children was confirmed.

Conclusion and Discussion

This study investigated the relationship between perceived fairness and fertility intentions in Japan. The results yielded partial support for Hypothesis 1—observing a positive effect of perceived fairness on fertility desires—while Hypothesis 2 was rejected. Specifically, although the subjective perception of fairness regarding housework and childcare did not influence the planned number of children, it initially showed a positive association with the desire to have a child within three years. However, when the models were adjusted for sample representativeness based on prefectural population ratios, this effect on fertility desires also became non-significant.

In conclusion, the impact of perceived fairness on fertility intentions is only limitedly supported, and its effect on the planned number of children remains unconfirmed. These findings are consistent with the results of Mizuochi (2010), which focused on female-only samples, as well as the observations of Nagase and Brinton (2017) regarding single-income households. Several explanations are considered for the result.

The first explanation for the lack of significance regarding the planned number of children lies in the persistence of male breadwinner model norms and traditional gender role attitudes. The reason fairness may not significantly sway childbearing plans is that many women in Japan tend to plan for two children even if the domestic burden falls almost entirely on them. Descriptive analysis reveals that while the regular employment rate for the husbands in this sample is approximately 90%, it remains just over 30% for the wives. In less urbanised regions where traditional perspectives are more entrenched and the concept of gender equity is less widespread, fertility decisions are likely driven by conventional customs rather than subjective fairness. Consequently, the influence of fairness may not manifest prominently. Furthermore, women in non-regular employment or with lower incomes are often reported to be less sensitive to gender equity compared to those in regular employment or with higher incomes (Kawamura and Brown, 2010). Additionally, in models where perceived fairness was significant, husbands’ working hours were marginally positively associated with wives’ fertility desires (p values ranged from .076 to .082). This aligns with Nagase and Brinton’s (2017) finding that under Japanese labour norms, men in large corporations who work long hours do not necessarily lower the probability of a second birth, even if their domestic participation is minimal.

A second explanation can be found in the Traits-Desires-Intentions-Behaviour (TDIB) framework (Miller and Pasta, 1995). According to this model, fertility desires and childbearing plans (planned number of children) are governed by different factors: the former reflects internal desires, while the latter incorporates external constraints and conditions. Heiland (2008) similarly notes that intentions, being rooted in internal desire, are often more stable than concrete plans. While gender equity may indeed bolster the simple desire for another child, its effect likely weakens when more pragmatic conditions of childbirth are considered.

A third explanation stems from the Theory of Conjunctural Action (TCA), which posits that fertility intentions are highly fluid and context-dependent variables. Consequently, it is difficult to capture their dynamic effects without the use of longitudinal data (Johnson-Hanks et al., 2011; Bachrach and Morgan, 2013). This is reflected in the diverging relationships between parity and our two dependent variables: parity is positively correlated with the planned number of children but negatively correlated with fertility desires (all p < .001). The former positive correlation can be explained by the ‘consistency between desires and behaviour’ within the TDIB framework (Miller and Pasta, 1995). In contrast, the latter negative correlation supports TCA, suggesting that as the number of children increases, the immediate desire for additional children tends to decline due to shifting situational contexts.

Furthermore, the loss of statistical significance for the effect of perceived fairness on fertility desires after weighting may be attributed to heterogeneity in fairness perceptions. In the unweighted data, where non-urban regions were overrepresented, perceived fairness showed a significant effect. In these regions, a division of labour that is not strictly equal may still be judged as ‘fair.’ In other words, individuals may maintain a high sense of fairness even within a traditional male breadwinner model. However, weighting the data increases the representativeness of major metropolitan areas because highly urbanised areas such as Tokyo get more weight, where acceptance of an unequal division of labour is likely lower, potentially leading to higher levels of perceived unfairness. Verifying this conjecture regarding regional differences remains a task for future research.

When examining whether perceptions of fairness are homogeneous, gender ideology and related educational attainment play crucial roles. Previous research has highlighted the association between gender role attitudes and both the division of labour (Kan, 2023; Inui, 2014) and perceived fairness (Hiekel and Ivanova, 2022). High adherence to traditional gender roles is consistently associated with lower sensitivity to objective inequity; thus, gender ideology is a key determinant of fairness and influences its effect on fertility intentions (Cheung, 2024). Unfortunately, this study was unable to control for these attitudinal variables. Given that only 30% of the female respondents are in regular employment, it is highly probable that traditional gender role attitudes exert a strong influence. Under such circumstances, fertility intentions may remain high even when objective inequity exists. According to the gender equity framework, in countries where a societal shift toward gender equality remains incomplete, progress in equality may result in a short-term fertility decline or have no observable impact (Esping-Andersen and Billari, 2015; Kan, 2023). This aligns with the results of this study and underscores the complexity of the gender equality effect.

Regarding future research, since the data used in this study were collected via non-probability sampling, these findings must be validated using probability-sampled data. Moreover, as this study is based on cross-sectional data, we could not definitively establish a causal relationship between perceived fairness and fertility intentions. Future studies should utilise panel data to assess causality more precisely. Additionally, incorporating items that more effectively measure gender role attitudes and urbanisation rates would allow for a more rigorous verification of these theoretical mechanisms.

Ethics Approval

This research relies exclusively on publicly available, de-identified secondary data. As no human subjects were directly involved and no identifiable private information was accessed, Institutional Review Board (IRB) approval was not required.

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Article copyright Guangyu Liang.

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