Wealth and Relational Inequality: How Household Assets Shape Marital Stability by Family Type in Japan

Wealth and Relational Inequality: How Household Assets Shape Marital Stability by Family Type in Japan | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Wealth and Relational Inequality: How Household Assets Shape Marital Stability by Family Type in Japan Jiajie Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8004673/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study examines whether the protective effect of wealth on marital stability varies across family types in contemporary Japan. As women's economic participation rises and family structures diversify, scholars debate whether assets function similarly across different household configurations, yet empirical evidence remains limited, particularly in East Asian societies experiencing rapid family transformation. This study employs discrete-time event history models on panel data from the Japanese Panel Survey of Consumers spanning 1993 to 2021 to assess how assets influence divorce risk across family types. The analysis reveals that wealth provides significant protection against marital dissolution for all family configurations, though this effect varies by household type. The protective effect is strongest among traditional male-breadwinner families and weakest among female-breadwinner households, with dual-earner families falling between these extremes. Financial assets show substantially weaker protective effects in dual-earner families compared to traditional households, whereas illiquid assets such as housing maintain consistent protective effects across all family types. These patterns persist when using continuous measures of women's relative income or detailed employment classifications. The findings suggest that despite profound transformations in family structure, economic resources remain fundamental to marital stability. While the function of liquid assets varies with household power dynamics, illiquid assets maintain universal protective effects. These results illuminate how growing wealth inequality increasingly translates into disparities in relationship stability, contributing to the broader stratification of family life in contemporary Japan. Divorce Household Assets Family Structure Asset Type Stratification Japan Introduction The rise in economic inequality has brought new attention to the intersection of wealth and family life. Understanding how household assets shape marital stability has become significant to scholarship on social stratification. If families with assets experience greater stability while those without face heightened vulnerability, wealth inequality may be amplified through differential marital outcomes, creating cycles of cumulative disadvantage (Cherlin 2010 ; McLanahan 2004 ). Prior research investigated the link between assets and marital stability has established a relatively consistent narrative. Using SIPP data, Eads and Tach ( 2016 ) found that increases in family net worth significantly reduce the risk of union dissolution for both married and cohabiting couples. Killewald, Lee, and England ( 2023 ) further affirmed the robustness of this association. Drawing on UK data, Boertien and Härkönen ( 2019 ) demonstrated that homeowners face a substantially lower risk of divorce than renters. These studies draw on several theoretical perspectives to explain the protective function of assets. The economic stress perspective views assets as a crucial buffer, enabling families to smooth consumption during shocks like unemployment or illness and preventing economic hardship from escalating into marital conflict (Conger et al. 2010 ; Dew 2011a ). From a transaction cost perspective, joint assets, particularly illiquid ones, increase the economic threshold of separation (Becker 1991). Psychological frameworks suggest assets improve relationship quality by fostering a sense of control and reducing uncertainty (Dew 2007 , 2009 ). This seemingly settled relationship, however, faces a fundamental challenge rooted in the revolutionary changes to family economic structures over the past half-century. The large-scale entry of women into the labor market has profoundly altered intrahousehold resource allocation and power dynamics. Bargaining theories posit that a spouse's negotiating position is determined by their command over independent economic resources (Lundberg and Pollak 1996 ). When wives gain independent income, the nature of household assets may undergo a qualitative transformation. In traditional single-earner families, assets reinforce mutual dependence between spouses. In dual-earner families, however, assets may become tools that facilitate individual autonomy. Killewald ( 2016 ) found that male unemployment has a much greater impact on traditional families than on dual-earner families, suggesting the existence of such heterogeneity. Research by Schwartz and Gonalons-Pons ( 2016 ) also shows that increases in women's relative income have differential effects on divorce risk across different family backgrounds. Japan provides an ideal setting to examine these competing theoretical predictions. First, Japanese family structures have undergone a significant transformation. The number of dual-earner households surpassed that of male-breadwinner households in the late 2010s and continues to grow (Gender Equality Bureau 2022 ). Second, Japanese household portfolios are characterized by a high proportion of non-financial assets, such as housing and land, exceeding levels in the United States (Mori and Sugaya 2017 ). This high degree of asset illiquidity may be particularly salient for marital stability, consistent with prior work on the unique role of housing wealth (Lersch 2017 ). Third, Japan exhibits a persistent tension between rising female economic participation and enduring gendered norms and institutional incentives. While female employment rates are high among OECD countries, the gender wage gap remains one of the largest. Furthermore, institutional arrangements such as spousal tax deductions and the Category 3 dependent insurance system continue to bolster the traditional family model (Brinton and Nagase 2017 ; Yu and Kuo 2016 ). Category 3 dependent insurance system is designed for spouse, aged 20–59, dependent on an employee covered by an employees’ pension or mutual aid association. This tension between rapidly rising female economic status and the persistent traditional gender division system may amplify differences in asset functions across household types. In traditional households, assets may still serve their conventional function of reinforcing mutual dependence. In dual-earner households, particularly when wives possess relatively independent economic capacity, the meaning of assets may have already transformed. The extant research suffers from three key limitations. First, studies rarely differentiate between asset types. Financial assets and real estate diverge fundamentally in liquidity, division costs, and psychological meaning, yet most research relies on measures of total net worth. While Killewald et al. ( 2023 ) examined the symbolic value of home and vehicle ownership, they did not compare the heterogeneous effects of different asset types. Distinguishing these asset types is particularly important in East Asian societies where real estate wealth is dominant. Second, the moderating role of family type is often neglected. While theory predicts that asset effects should vary by family type, empirical tests remain limited. Existing studies either treat all families as equivalent or adopt overly simple classifications. Third, measurement strategies for family structure are often crude. Binary classifications based on employment status may mask important differences. Among dual-earner families, those where the wife contributes 20% versus 50% of income have vastly different power structures (Bertrand et al. 2015 ). Additionally, empirical research on East Asia remains severely lacking. Most East Asian studies focus on income rather than assets (Cheng 2016 ; Park and Raymo 2013 ), creating important gaps in both theoretical development and policy formulation. To fill these research gaps, this study uses 27 years of panel data from the Japanese Panel Survey of Consumers (1993–2021) to examine the relationship between assets and marital stability. We focus on three interrelated questions. First, in the East Asian context, how do different types of assets influence divorce risk? Do financial assets and real estate exhibit different effects? Second, does the protective association of assets vary by family type? We explore potential differences between male-breadwinner, dual-earner, and other household arrangements. Third, does this heterogeneity present differently when we use a continuous measure of women's relative economic position to capture intrahousehold power dynamics? By simultaneously employing employment-based classifications and relative-income-based continuous measures, this study seeks a more comprehensive understanding of the sources and mechanisms of asset effect heterogeneity. This study makes three important contributions to existing literature. First, by examining the relationship between assets and divorce in East Asian society, we fill a critical research gap in a region undergoing rapid family transformation. Unlike existing research that focuses primarily on income, this study incorporates assets and distinguishes the differential roles of financial and non-financial assets. Second, this study provides new empirical evidence for understanding the mechanisms by which assets operate in different family power structures by employing multiple measurement strategies to examine family heterogeneity. It also offers crucial empirical evidence on the relative effectiveness of specialization theory, economic stress theory, and bargaining theory in explaining asset effects. Third, the findings have important policy implications for understanding the socioeconomic differentiation of East Asian family transformation. If asset effects are universal, this means that in a context of growing wealth inequality, marital stability itself is becoming a new dimension of social stratification. Inclusive policies that promote family asset accumulation will be an effective choice. In an era of rising wealth inequality and diversifying family forms, understanding how economic resources shape the stability of different family types is crucial for developing effective social policies. Literature Review Assets and Marital Stability The impact of assets on marital stability has long been central to family sociology research. Existing literature offers three theoretical perspectives on why families with more assets face lower divorce risks. Economic stress theory treats assets as a buffer against external shocks. The Family Stress Model, developed from research on the Iowa farm crisis, posits that economic hardship erodes marital quality by increasing emotional distress and reducing positive spousal interaction. Assets function to interrupt this negative pathway (Conger et al. 1990 , 2010 ).This theory has received broad empirical support. In the United States, recent work confirms a negative association between wealth and divorce risk, with the protective effect strongest at lower levels of wealth. Even when controlling for net worth, visible assets such as homes and vehicles remain associated with lower divorce risk (Killewald et al. 2023 ). In the United Kingdom, research using the BHPS shows that homeownership acts as a barrier to dissolution and helps explain the greater marital stability observed among the highly educated (Boertien and Härkönen 2019 ). The life chances perspective in sociology emphasizes how assets alter the cost-benefit calculus of divorce. Classic family economics suggests that gender-based specialization grounded in comparative advantage leads to marriage-specific investments such as housing and joint savings (Becker 1991), thereby raising the economic costs of separation. Empirical research widely validates this mechanism. Homeownership is consistently linked to longer marital duration (Jalovaara 2002 ; South and Spitze 1986 ). Further studies in the US have identified a concurrent relationship between declining housing values and marital dissolution, suggesting that wealth effects influence divorce decisions (Farnham et al. 2011 ). Moreover, housing market constraints can create a "lock-in" effect, whereby falling prices or limited liquidity delay separations (Bram De Rock et al. 2023). These findings collectively illustrate that assets are not merely economic resources but also significant structural constraints on marital dissolution. The psychological perspective emphasizes how assets stabilize marriage through psychological mechanisms. Stress process theory suggests that economic resources enhance an individual's sense of control and self-efficacy, thereby mitigating stress and improving interpersonal interactions (Pearlin et al. 1981 ). Social psychological research finds significant positive associations between family wealth and subjective well-being and sense of control (D’Ambrosio et al. 2020 ; Mirowsky and Ross 1989 ). Within marital research, studies show a positive link between asset accumulation and relationship satisfaction, whereas consumer debt significantly increases conflict and dissolution risk (Dew 2007 , 2011b ). U.S. longitudinal studies find that debt burden has a more negative impact on marital satisfaction than income insufficiency (Dew and Xiao 2013 ). These findings reveal the psychological and emotional stabilizing functions of assets. Assets provide not only economic security but also foster marital maintenance by enhancing feelings of control and security. These three theoretical mechanisms are not mutually exclusive but rather reveal the complex pathways through which assets protect marriage from different angles. Notably, these studies have established a basic consensus that assets do reduce divorce risk after controlling for other factors. Despite this widely confirmed protective effect, the existing literature possesses two significant limitations. First, most research treats household assets as a monolithic category, neglecting the potentially divergent influences of different asset types. Financial assets and real estate differ fundamentally across multiple dimensions. Real estate is not only a store of wealth but also the spatial context of family life. Its illiquidity and high transaction costs likely enhance its lock-in effect (Lynn P. Cooke et al. 2013 ). Financial assets, while providing liquidity, may be more volatile and are more easily divisible upon dissolution (Dew 2011b ). Given that housing constitutes the predominant component of household portfolios globally, the "asset effect" identified in many studies may primarily reflect the role of real estate. The few studies that do differentiate asset types have found meaningful differences. Lersch’s ( 2017 ) analysis in Germany showed that homeownership had a stronger stabilizing effect on marriage than financial assets. Similarly, Killewald and Gough (2013) find that housing wealth shows the most significant negative association with divorce risk. Second, and more critically, existing research has rarely considered how the effects of assets might be heterogeneous across family types. In a traditional male-breadwinner household, assets serve as the material foundation of the husband's breadwinning role and the wife's primary source of economic security. When wives participate in the labor market and gain independent income, however, the meaning and function of household assets may shift. The extant literature has paid insufficient attention to this potential heterogeneity. Family Type and Asset Effects Rising female labor force participation has called into question whether the protective effect of assets remains uniform across all family types. The theoretical literature offers three competing predictions regarding this heterogeneity. Specialization theory predicts a stronger asset effect in traditional households. Becker's (1991) model posits that a gendered division of labor based on comparative advantage maximizes household utility. In this system, spouses make specialized, mutually dependent investments. Assets are not just wealth but the material foundation of this specialization. Greater asset accumulation signifies a more successful specialization, thereby increasing the gains to marriage and the opportunity cost of dissolution. In dual-earner couples, where both partners maintain market skills, mutual dependency is lower and the lock-in effect of assets should be correspondingly weaker. Bargaining theory predicts a diminished asset effect in dual-earner households. From this perspective, spousal power depends on their threat point or reservation utility, often defined by their post-divorce economic standing (Lundberg and Pollak 1996 ). In a traditional arrangement, a wife's lack of independent resources creates a strong incentive to preserve the marriage. When a wife possesses her own career and income, however, assets no longer constrain but may instead become capital for pursuing independence. Particularly when marital quality is low, assets may accelerate rather than prevent marital dissolution (Ono 1998 ). The universality hypothesis suggests the asset effect is consistent across family types. Neo institutional theories point to the standardizing pressures of modern institutions which promote behavioral convergence (DiMaggio and Powell 1983 ; Meyer and Rowan 1977 ). In the family domain, all households, regardless of their internal division of labor, must navigate standardized mortgage contracts, uniform educational fees, and formalized healthcare expenses. This homogeneity in external constraints may lead to a uniform function for assets. Furthermore, dual-earner families, while benefiting from income diversification, may also require assets as an essential buffer against risk (Oppenheimer 1997 ). The protective role of assets might therefore be equally important in these households. Existing empirical research provides limited and mixed evidence for this theoretical debate. Evidence supporting specialization theory comes mainly from historical comparisons and employment shock studies. Through analyzing half a century of U.S. divorce trends, Stevenson and Wolfers ( 2007 ) found that economic factors showed stronger predictive power for divorce during the pre-1970s period of clear gender specialization. This predictive power declined as female employment increased, suggesting that traditional specialization indeed reinforces the role of economic resources. Using PSID data, Killewald ( 2016 ) found that husband unemployment had 1.3 times the impact on divorce risk in traditional families compared to dual-earner families, indicating stronger dependence on male economic roles in traditional families. In a Germany-U.S. comparison, Cooke ( 2006 ) found that any deviation from traditional gender specialization in Germany, whether higher relative income for wives or greater housework by husbands, correlated with higher divorce risks. In the United States, more gender-equal arrangements correlated with lower divorce risks, highlighting how institutional and gender cultural contexts moderate the applicability of the specialization hypothesis. Gonalons-Pons and Gangl ( 2021 ) further showed in a cross-national study that the destabilizing effect of male unemployment is largest in countries with more traditional gender norms Support for the bargaining perspective centers on the association between women's economic resources and divorce. Sayer and Bianchi ( 2000 ) found that a wife's economic independence increased divorce risk only when marital dissatisfaction was high, consistent with the 'exit option' prediction. Using the exogenous shock of US divorce law reforms, Gray ( 1998 ) showed that wife's employment, which had previously been associated with lower divorce risk, became associated with higher risk after the adoption of unilateral divorce. Bertrand, Kamenica, and Pan ( 2015 ) notably found that divorce risk increases sharply when a wife's income begins to exceed her husband's, even controlling for total household income. Evidence for the universality hypothesis stems from cross-national research finding a consistent protective effect of economic resources. Lyngstad and Jalovaara ( 2010 ) noted in a systematic review that a positive association between economic resources and marital stability is found across diverse welfare regimes. Lersch and Vidal ( 2014 ), using German panel data, found that homeownership significantly reduced dissolution risk and that this effect was consistent across socioeconomic groups. Eads and Tach ( 2016 ) similarly argued that the protective effect of assets in the US likely reflects a more general social mechanism. These studies suggest the robustness of the resource-stability link across institutional settings. Although these studies provide valuable insights, important limitations remain in directly answering whether asset effects vary by family type. Most studies examine employment or income, not assets. Income flows are conceptually distinct from asset stocks. Income reflects current earning capacity, whereas assets represent accumulated wealth (Killewald and Bryan 2016 ). More importantly, asset distribution between spouses is more complex than income, involving property rights, inheritance, gifts, and multiple other factors. Simply inferring asset effects from income effects may be misleading. Furthermore, studies that do include assets seldom test for interaction effects directly. Most analyses either report main effects or offer stratified comparisons rather than formally modeling the interaction between assets and family type, a step necessary to rigorously test the heterogeneity hypotheses. Third, measures of family type are often too coarse. A simple traditional versus dual-earner dichotomy obscures vast in-group variance. A household where a wife contributes 10 percent of the income is structurally different from one where she contributes 50 percent (Tichenor 2005 ). Finally, there is a pronounced lack of empirical evidence from East Asian societies. The existing scholarship is overwhelmingly based on European and North American contexts. The unique institutional and cultural environments in East Asia, characterized by strong gender norms amid rapid economic change, may produce distinct patterns. Based on these theoretical debates and empirical gaps, the present study uses the case of Japan to test the differentiated role of assets across family types. The Japanese Context: Tension Between Tradition and Modernity Japanese society is a particularly valuable case for examining the heterogeneous effects of assets. Japan’s developmental trajectory and institutional features are distinct among advanced economies. In terms of asset structure, Japanese household wealth is highly concentrated in real estate. Physical assets such as land and housing account for 50 to 60 percent of total household assets (Mori and Sugaya 2017 ). This contrasts sharply with the United States, where real estate constitutes approximately 35 percent of household assets (Federal Reserve Board 2022 ). This concentration has deep historical and cultural roots, shaped by post-war land reforms, long-term expectations of rising land prices, and a cultural emphasis on the family home as an intergenerational asset (Hirayama 2021 ; Ronald and Hirayama 2009 ). The high proportion of real estate implies low portfolio liquidity and high division costs upon divorce, potentially strengthening the lock-in effect of assets. Regarding family structure, Japan is undergoing a rapid yet incomplete transition. The number of dual-earner households grew from 6.14 million in 1980 to 12.40 million in 2020, surpassing the number of traditional male-breadwinner households (Gender Equality Bureau 2022 ). However, this trend masks persistent gender inequality. Female non-regular employment rates remain high at 53–54 percent in 2023, far exceeding the male rate of 22 percent (OECD 2023 ; Statistics Bureau of Japan 2023 ). Even within dual-earner couples, the gender wage gap remains substantial, with women's average wages at 70 to 75 percent of men's (OECD 2023 ). Even in dual-earner families, because female wages are generally lower than male wages, with women earning approximately 70–75% of male wages (OECD 2023 ), wives' economic contributions typically remain far below those of husbands (Brinton and Oh 2019 ). The institutional environment continues to substantially support the traditional gender division of labor. The spousal deduction system in the tax code stipulates that when a wife's annual income falls below 1.03 million yen, the husband receives a 380,000 yen income tax deduction. The Category 3 insured person system in social insurance allows spouses earning less than 1.30 million yen to be exempt from insurance premiums while receiving pension benefits. Many corporate family allowances also premise low spousal income. Nagase and Brinton ( 2017 ) demonstrate that these institutional designs create powerful incentives for many married women to rationally limit their work hours and income. Even as the government has promoted policies for women's active participation in recent years, these deep institutional arrangements have not fundamentally changed. Cultural attitudes have shifted even more slowly. While approval of the "husband as breadwinner, wife as homemaker" ideology has declined, it remains prevalent among middle-aged and older cohorts (Cabinet Office 2024 ). More subtly, even when superficially supporting gender equality, many people continue following traditional patterns in practice. Married women spend more than five times as much time on housework as their husbands, a ratio that has barely changed over the past 20 years (Statistics Bureau of Japan 2022 ). When women's economic roles conflict with these traditional expectations, additional marital stress may arise. Fukuda (2013) found that highly educated women face higher divorce risks, contrasting sharply with patterns in European and American societies where education positively correlates with marital stability (Schwartz and Han 2014 ). This tension between rapid economic change and slow institutional and cultural adjustment makes modern Japan society an ideal setting to test for the heterogeneous effects of assets. If the protective function of assets does indeed vary by family type, this difference should be particularly pronounced in the Japanese context, where traditional and modern arrangements coexist. Research Hypotheses Based on the theoretical and empirical literature reviewed, this study proposes the following hypotheses: H1a : Higher levels of household net worth will be associated with lower risk of divorce. H1b : Real estate assets will have a stronger protective association with marital stability than financial assets, while household debt will be associated with a higher risk of divorce. H2 : Traditional male-breadwinner households will exhibit a lower risk of divorce compared to dual-earner households and other family types. H3 : The protective effect of assets against divorce will be strongest among traditional male-breadwinner households and significantly weaker among dual-earner households. H4 : As the wife's share of the total household income increases, the protective effect of household assets on marital stability will progressively weaken. Methodology Data Source This study uses data from the Japanese Panel Survey of Consumers (JPSC) conducted by the Institute for Research on Household Economics. The JPSC, initiated in 1993, is one of Japan's preeminent longitudinal surveys, focusing specifically on the economic behavior and life course of women and their families. A key strength of the survey is its detailed collection of asset information, including specific amounts for financial assets, housing value, and various liabilities. The initial sample comprised 1,500 women aged 24–34. Two refreshment samples of 500 and 836 women in the same age cohort were added in 1997 and 2003, respectively, to maintain the sample's representativeness. The survey employs a drop-off and pick-up questionnaire method, achieving high response rates (over 90%) and panel retention (approximately 70%). We use data from 1993 to 2021, covering a 27-year observation window. This period spans multiple economic cycles, from Japan's Lost Decade following the asset bubble collapse to the era of Abenomics, providing an ideal setting to test the stability of asset effects. The analytic sample is restricted to women in their first marriage to avoid selection bias associated with prior divorce history. We further restrict the sample to women aged 25 to 55. The lower bound ensures most respondents have completed their education and entered a stable marital phase, while the upper bound avoids complications related to retirement and health. Missing data on assets are a common challenge in panel surveys. In our sample, asset variables have a missingness rate of approximately 40 percent, primarily due to respondent refusal, uncertainty about asset values, or items not being asked in certain waves. Analyses indicate that missingness on assets is related to age, education, and marital duration, but not significantly associated with the divorce event itself, suggesting the data are missing at random (MAR). To leverage the full dataset and reduce the bias and efficiency loss of complete-case analysis, we employ multiple imputation. We use chained equations (MICE) to generate 20 imputed datasets. The final analytic sample consists of 24,298 person-year observations from 1,794 women, including 211 observed divorce events. Although the number of divorce events is relatively small, it aligns with Japan's low divorce rate and provides sufficient statistical power for robust inference in event history analysis. Variable Measurement The dependent variable is the occurrence of divorce, coded as a binary indicator (1 = divorce occurred in that year, 0 = marriage continued). The timing of divorce is determined by self-reports of marital status changes. Because information on the husband is unavailable after the dissolution, we code the divorce event at year t-1 if the respondent reports a divorce in year t. This approach ensures that all information on spousal employment and household finances is measured prior to the event. The primary independent variables measure household assets. Net worth is defined as total assets minus total liabilities. Financial assets include liquid assets such as bank deposits, stocks, bonds, and insurance. Non-financial assets primarily consist of the market value of owner-occupied housing. Liabilities include housing loans and other consumer debt. All monetary variables are transformed using the inverse hyperbolic sine (IHS). The IHS transformation is similar to a logarithmic transformation but accommodates zero and negative values, and its coefficients are interpreted similarly. All monetary values are adjusted to 2020 constant yen to remove the effects of inflation. Family type is categorized based on the employment status of both spouses. Traditional male-breadwinner families are those where the husband is employed (regularly) and the wife is not in the labor force. Dual-earner families are those where both spouses are employed, regardless of regular or non-regular status. Other categories include female-breadwinner (wife only employed) and other arrangements (e.g., neither spouse employed). Female relative economic position is measured using a continuous indicator. Wife's income share is calculated as the wife's annual income divided by the couple's total annual income, ranging from 0 to 1. This variable is set to missing if the total household income is zero. Control variables are included at three levels. Individual-level controls include education for both wife and husband (college degree or higher vs. less than college) and age at first marriage. Household-level controls include the number of children, a categorical measure of marital duration (1–5, 6–10, 11–15, 16 + years) to capture the nonlinear risk profile, and co-residence with parents. Macro-level controls include the annual national GDP growth rate and unemployment rate to account for business cycles, as well as marriage cohort dummy variables to control for period effects. Analytical Strategy We use discrete-time event history analysis to estimate the risk of divorce(Allison 2014 ). This approach is well-suited for annual panel data, reformulating the survival problem as a binary logistic regression model using a person-year data structure. The baseline model is: $$\:\text{l}\text{o}\text{g}\text{i}\text{t}\left(\text{P}\right({Divorce}_{it}=1\left)\right)=\alpha\:+{\beta\:}_{1}{Assets}_{it}+{\beta\:}_{2}{FamilyType}_{it}+{\gamma\:X}_{it}+{\epsilon\:}_{it}$$ where i represents the individual, t represents time (year), Assets is household net worth, FamilyType is a vector of family type dummies, and X is a vector of control variables. To test whether the asset effect is heterogeneous by family type (H3), we introduce an interaction term: $$\:\text{l}\text{o}\text{g}\text{i}\text{t}\left(\text{P}\right({Divorce}_{it}=1\left)\right)=\alpha\:+{\beta\:}_{1}{Assets}_{it}+{\beta\:}_{2}{FamilyType}_{it}+{\beta\:}_{3}{(Assets\times\:\text{F}\text{a}\text{m}\text{i}\text{l}\text{y}\text{T}\text{y}\text{p}\text{e})}_{it}+{\gamma\:X}_{it}+{\epsilon\:}_{it}$$ If β₃ is significant, this indicates that asset effects vary by family type, supporting H3. If β₃ is not significant, this supports the consistency hypothesis. To test the moderating role of women's relative economic position (H4), we estimate a model interacting assets with the wife's income share: $$\:\text{l}\text{o}\text{g}\text{i}\text{t}\left(\text{P}\right({Divorce}_{it}=1\left)\right)=\alpha\:+{\beta\:}_{1}{Assets}_{it}+{\beta\:}_{2}{Wifeshare}_{it}+{\beta\:}_{3}{(Assets\times\:\text{W}\text{i}\text{f}\text{e}\text{S}\text{h}\text{a}\text{r}\text{e})}_{it}+{\gamma\:X}_{it}+{\epsilon\:}_{it}$$ where WifeShare includes wife's income share and wife's financial asset share. If β₃ is significantly positive, this supports the bargaining theory prediction in H4. If β₃ is not significant, this supports the universality hypothesis. All models use individual-level clustered robust standard errors to address the correlation of multiple observations from the same individual. Results Table 1. Descriptive Results Summary (N=24,298) Divorced 0.01 (0.09) Household Net Worth (IHS) 5.16 (5.45) Household Financial Assets (IHS) 6.09 (2.42) Household Fixed Assets (IHS) 5.81 (3.80) Household Debt (IHS) 4.37 (3.76) Household type Male-breadwinner (37.4%) Dual-earner (52.7%) Female-breadwinner (1.3%) Other (8.6%) Wife's income share 0.16 (0.18) Wife's education Below college (49.5%) College or above (50.5%) Husband's education Below college (51.4%) College or above (48.6%) Marriage Duration (years) 1-5years (10.4%) 6-10years (22.4%) 11-15years (22.1%) over 16 years (45.0%) Age at Marriage 23.89 (2.72) Number of Children 1.93 (0.94) Living with Parents or not 0.90 (0.29) Residence Size Major metropolitan areas (25.4%) Other cities (61.4%) Towns/Villages (13.2%) Annual GDP Growth Rate (%) 0.73 (2.02) Unemployment Rate (%) 3.91 (0.90) Birth cohort ≤1964 birth cohort (38.2%) 1965–1969 birth cohort (23.2%) 1970–1974 birth cohort (13.5%) 1975–1979 birth cohort (11.4%) 1980–1989 birth cohort (13.7%) 1.Values are percentage (%) for categorical variables and mean (SD) for continuous variables. Descriptive Statistics Table 1 presents the descriptive statistics for the analytic sample. Across 24,298 person-year observations, the annual divorce rate is 1.0 percent. This figure is consistent with Japan's low aggregate divorce rate and underscores the relative stability of marriage in this context. The mean age at first marriage for respondents is 23.89 years, and the average number of children is 1.93. Regarding educational attainment, 50.5 percent of wives and 48.6 percent of husbands hold a college degree or higher. The distribution of marital duration is skewed toward longer unions; 45 percent of observations are from marriages lasting 16 years or more, indicating the sample is predominantly composed of households in the middle and later stages of marriage. The household economic structure shows considerable diversity. Dual-earner households represent the most common arrangement at 52.7 percent of person-years, confirming this as the modal family type. Traditional male-breadwinner households account for 37.4 percent. While still a substantial portion, this figure shows they are no longer the dominant arrangement. Female-breadwinner households (1.3 percent) and other types (8.6 percent) make up the remainder. The mean for the wife's income share is 0.16 (SD = 0.18), reflecting significant variation in women's economic contributions. This distribution illustrates the historical transition in Japan from a male-breadwinner system toward a dual-earner model. In terms of assets, the mean of the IHS-transformed net worth is 5.16 with a large standard deviation (5.45), suggesting considerable wealth inequality. The mean for IHS-transformed financial assets is 6.09 (SD = 2.42), and the mean for non-financial assets is 5.81 (SD = 3.80). The mean for liabilities is 4.37 (SD = 3.76). The large standard deviations relative to the means indicate substantial heterogeneity in asset and liability holdings across the sample. The marriage cohort distribution reveals a distinct generational structure. Individuals born in or before 1964, representing the post-war baby boom generation, comprise 38.2 percent of the sample. Macroeconomic indicators reflect Japan's long period of economic stagnation. The average annual GDP growth rate during the observation period is 0.73 percent (SD = 2.02), and the average unemployment rate is 3.91 percent. Table 2 Discrete-Time Logistic Regression Models of Divorce Risk: Main Effects of Household Assets and Family Type Model 1 Model 2 Model 3 Model 4 Household Net Worth (IHS) -0.033 ** -0.034 ** (0.011) (0.011) Household Financial Assets (IHS) -0.123 *** -0.124 *** (0.026) (0.025) Household Fixed Assets (IHS) -0.111 *** -0.111 *** (0.031) (0.031) Mortgage Debt (IHS) 0.004 0.012 (0.031) (0.031) Other Loans/Debt (IHS) -0.020 -0.019 (0.029) (0.028) Wife's Income Share 2.847 *** 2.614 *** (0.311) (0.325) Family type: Male-breadwinner (ref.) Dual-earner 0.838 *** 0.884 *** (0.187) (0.191) Female-breadwinner 2.634 *** 2.386 *** (0.302) (0.331) Other 0.735 * 0.606 * (0.286) (0.293) Wife Edu: College or above -0.233 -0.128 -0.342 * -0.224 (0.171) (0.172) (0.171) (0.171) Husband Edu: College or above -0.254 -0.151 -0.220 -0.116 (0.167) (0.167) (0.167) (0.167) Marriage Duration (ref.1-5years) Marriage Duration (6-10years) -0.159 0.057 -0.001 0.222 (0.272) (0.272) (0.272) (0.271) Marriage Duration (11-15years) 0.029 0.374 0.216 0.554 + (0.280) (0.288) (0.277) (0.286) Marriage Duration (over16years) -0.384 0.055 -0.252 0.165 (0.298) (0.309) (0.289) (0.300) Age at Marriage -0.087 ** -0.053 -0.094 ** -0.060 + (0.034) (0.033) (0.033) (0.033) Number of Children -0.036 -0.071 0.008 -0.034 (0.100) (0.099) (0.098) (0.097) Living with Parents or Not 0.255 0.436 0.309 0.472 + (0.287) (0.288) (0.285) (0.286) Annual GDP Growth Rate (%) 0.047 0.046 0.046 0.043 (0.033) (0.033) (0.033) (0.033) Unemployment Rate (%) 0.108 0.086 0.125 0.103 (0.080) (0.081) (0.079) (0.080) Residence Size: Major metropolitan(ref.) Other cities 0.043 0.097 0.015 0.077 (0.166) (0.170) (0.167) (0.171) Towns/Villages -0.295 -0.174 -0.359 -0.233 (0.269) (0.276) (0.270) (0.276) Birth Cohort: ≤1964 (ref.) 1965–1969 birth cohort 0.673 ** 0.660 ** 0.682 ** 0.653 ** (0.210) (0.214) (0.213) (0.215) 1970–1974 birth cohort 0.632 * 0.585 * 0.631 * 0.562 * (0.247) (0.253) (0.251) (0.259) 1975–1979 birth cohort 0.940 *** 0.862 *** 0.956 *** 0.856 *** (0.247) (0.247) (0.250) (0.253) 1980–1989 birth cohort 0.675 ** 0.574 * 0.673 ** 0.553 * (0.256) (0.260) (0.255) (0.259) Constant -3.980 *** -4.127 *** -4.039 *** -4.100 *** (0.973) (0.976) (0.971) (0.977) N 24298 24298 24298 24298 Pseudo R-squared 0.065 0.090 0.071 0.095 Robust standard errors clustered at individual level in parentheses Reference categories: Male-breadwinner household, Below College education, ≤ 1964 birth cohort + p < 0.10, * p < 0.05, ** p < 0.01, *** p < 0.001 Table 3 Discrete-Time Logistic Regression Models of Divorce Risk: Interaction Effects between Assets and Family Type Model 5 Model 6 Model 7 Model 8 Household Net Worth (IHS) -0.050 * -0.025 (0.022) (0.016) Family type: Male-breadwinner (ref.) Dual-earner 0.764 *** 0.333 (0.198) (0.337) Female-breadwinner 2.616 *** 1.732 ** (0.332) (0.535) Other 0.661 * 0.154 (0.306) (0.459) Net Worth × Male-breadwinner (ref.) Net Worth × Dual-earner 0.023 (0.025) Net Worth × Female-breadwinner 0.005 (0.044) Net Worth × Other 0.024 (0.037) Household Financial Assets (IHS) -0.212 *** -0.143 *** (0.044) (0.034) Financial Assets × Male-breadwinner (ref.) Financial Assets × Dual-earner 0.107 * (0.054) Financial Assets × Female-breadwinner 0.173 + (0.095) Financial AssetsOther × Other 0.093 (0.083) Household Fixed Assets (IHS) -0.125 ** -0.119 ** (0.047) (0.038) Fixed Assets × Male-breadwinner (ref.) Fixed Assets × Dual-earner 0.017 (0.047) Fixed Assets × Female-breadwinner -0.022 (0.086) Fixed Assets × Other 0.012 (0.071) Mortgage Debt (IHS) 0.005 0.014 (0.031) (0.031) Other Loans/Debt (IHS) -0.021 -0.020 (0.028) (0.028) Wife's Income Share 2.956 *** 2.278 *** (0.343) (0.549) Net Worth × Wife's Income Share -0.036 (0.047) Financial Assets × Wife's Income Share 0.069 (0.103) Fixed Assets × Wife's Income Share 0.022 (0.084) Wife Edu: College or above -0.231 -0.137 -0.336 * -0.235 (0.171) (0.171) (0.171) (0.171) Husband Edu: College or above -0.254 -0.135 -0.225 -0.107 (0.166) (0.166) (0.167) (0.167) Marriage Duration (ref.1-5years) Marriage Duration (6-10years) -0.157 0.059 0.004 0.220 (0.272) (0.275) (0.272) (0.271) Marriage Duration (11-15years) 0.033 0.378 0.218 0.558 + (0.280) (0.292) (0.277) (0.285) Marriage Duration (over16years) -0.384 0.036 -0.246 0.159 (0.298) (0.310) (0.289) (0.300) Age at Marriage -0.086 * -0.054 + -0.094 ** -0.060 + (0.034) (0.032) (0.033) (0.032) Number of Children -0.035 -0.068 0.004 -0.032 (0.100) (0.099) (0.099) (0.096) Living with Parents or Not 0.250 0.456 0.314 0.484 + (0.288) (0.289) (0.285) (0.287) Annual GDP Growth Rate (%) 0.047 0.046 0.045 0.044 (0.033) (0.033) (0.033) (0.033) Unemployment Rate (%) 0.106 0.089 0.120 0.108 (0.080) (0.081) (0.079) (0.081) Residence Size: Major metropolitan(ref.) Other cities 0.041 0.094 0.017 0.077 (0.166) (0.170) (0.167) (0.171) Towns/Villages -0.301 -0.180 -0.358 -0.229 (0.270) (0.277) (0.270) (0.276) Birth Cohort: ≤1964 (ref.) 1965–1969 birth cohort 0.675 ** 0.665 ** 0.678 ** 0.660 ** (0.210) (0.213) (0.212) (0.215) 1970–1974 birth cohort 0.637 ** 0.614 * 0.627 * 0.584 * (0.246) (0.249) (0.251) (0.254) 1975–1979 birth cohort 0.943 *** 0.857 *** 0.953 *** 0.857 *** (0.246) (0.248) (0.249) (0.254) 1980–1989 birth cohort 0.679 ** 0.569 * 0.673 ** 0.551 * (0.255) (0.260) (0.255) (0.260) Constant -3.924 *** -3.684 *** -4.032 *** -4.021 *** (0.985) (1.005) (0.971) (0.970) N 24298 24298 24298 24298 Pseudo R-squared 0.065 0.092 0.071 0.095 Robust standard errors clustered at individual level in parentheses Reference categories: Male-breadwinner household, Below College education, ≤ 1964 birth cohort + p < 0.10, * p < 0.05, ** p < 0.01, *** p < 0.001 Main Effects of Assets and Family Type Table 2 presents the baseline models estimating the association between assets, family type, and divorce risk. Model 1 shows that household net worth is significantly associated with a lower risk of divorce (β = -0.033, p < 0.01), supporting Hypothesis H1a. This finding confirms the general protective function of economic resources. Family type also emerges as a strong predictor of marital stability, consistent with Hypothesis H2. Compared to traditional male-breadwinner households, dual-earner households face a significantly higher risk of divorce (β = 0.838, p < 0.001). The risk is highest for female-breadwinner households (β = 2.634, p < 0.001), and other household types also show elevated risk (β = 0.735, p < 0.05). These differences remain significant after controlling for economic resources, suggesting the traditional marital arrangement itself has an independent stabilizing association. Model 2 decomposes total assets into different types to test hypothesis H1b. Both financial assets (β = -0.123, p < 0.001) and nonfinancial assets, primarily housing (β = -0.111, p < 0.001), significantly reduce divorce risk. Although the coefficient for financial assets is slightly larger than that for nonfinancial assets (-0.123 vs -0.111), statistical testing reveals no significant difference in their protective effects (χ² = 0.08, p = 0.774). This finding suggests that in the Japanese context, liquid financial assets and illiquid real estate offer similar protective functions. The coefficients for housing loans and other loans are not significant. This pattern partially supports H1b. While different asset types are protective, the effect of real estate is not stronger than that of financial assets. The nonsignificant coefficient for mortgage debt likely reflects Japan's specific circumstances, where mortgages typically accompany homeownership and the protective effect of property offsets the pressure from debt. The nonsignificant effect of other debt may reflect heterogeneity in the sample, which includes both consumer debt and potentially educational loans whose opposing effects on marriage cancel out in the aggregate. Model 3 introduces the wife's income share as an alternative measure of household economic structure. The wife's income share positively predicts divorce risk (β = 2.847, p < 0.001). This result is consistent with findings from Western societies (Bertrand et al. 2015 ; Schwartz and Gonalons-Pons 2016 ), suggesting that a woman's increased relative economic contribution is associated with a higher risk of dissolution. This may reflect two mechanisms. On one hand, economic independence reduces women's dependence on marriage. On the other hand, in Japanese society with strong gender norms, deviation from traditional role divisions may generate additional marital strain. Notably, the protective effect of net worth remains robust (β = -0.034, p < 0.01) after controlling for relative economic contributions. Model 4 includes both disaggregated asset types and the wife's income share. Financial assets (β = -0.124, p < 0.001) and non-financial assets (β = -0.111, p < 0.001) retain their protective associations, while a higher wife's income share remains linked to higher divorce risk (β = 2.614, p < 0.001). The stability of these coefficients across models reinforces the main findings. Heterogeneity of the Asset Effect Table 3 tests whether the asset effect varies by family type (H3). Model 5 introduces interaction terms between net worth and family type. All interaction terms prove nonsignificant: assets × dual-earner (β = 0.023, ns), assets × female-breadwinner (β = 0.005, ns), and assets × other types (β = 0.024, ns). This indicates that the protective effect of assets remains stable across family types, failing to support specialization theory's prediction of heterogeneous asset effects. Model 6 explores whether different asset types exhibit heterogeneity across family structures. The interaction between financial assets and dual-earner families reaches significance (β = 0.107, p = 0.049), while the interaction with female-breadwinner families approaches marginal significance (β = 0.173, p = 0.070). These significant coefficients suggest that financial assets may function differently in nontraditional families. This finding partially supports bargaining theory's prediction that the liquidity of financial assets may alter their function in families where wives have independent income. However, the substantive impact of this heterogeneity remains limited. In dual-earner families, the combined effect of financial assets equals − 0.212 + 0.107 = -0.105, which still significantly reduces divorce risk. The protective effect merely weakens from − 0.212 in traditional families to -0.105, a reduction of approximately 50%. This demonstrates that even in dual-earner families, financial assets remain a protective factor for marital stability, albeit with somewhat diminished strength. In contrast, all interaction terms for non-financial assets (real estate) are non-significant, indicating the protective role of housing is highly consistent across all family types. This contrast highlights a key distinction. The role of liquid assets is conditional on the household employment structure, whereas the role of illiquid real estate as the foundation of family life remains uniformly protective. Models 7 and 8 test whether wife's income share moderates asset effects, examining hypotheses H4a and H4b. Model 7 introduces an interaction between net assets and wife's income share, which proves nonsignificant (β = -0.036, ns). Model 8 extends the analysis to specific asset types, finding that both financial assets × wife's income share (β = 0.069, ns) and nonfinancial assets × wife's income share (β = 0.022, ns) are nonsignificant. These results consistently support H4b rather than H4a, indicating that the protective effect of assets operates independently of women's relative economic position in marriage. The findings from the two tables exhibit remarkable consistency. Assets, both financial and real estate, are associated with lower divorce risk, with financial assets and housing showing statistically indistinguishable protective effects, suggesting that different forms of economic resources all contribute to marital stability. Traditional families have lower baseline divorce risk than other family types, while increases in women's economic contribution relate to higher divorce risk. Most importantly, the protective function of assets remains stable across different family structures and relative economic arrangements. These findings suggest that despite fundamental transformations in family structure, economic security as a basic need continues to provide universal value. Regardless of how families organize themselves and regardless of women's economic position within the household, assets protect marriage by alleviating economic stress, providing psychological security, and increasing the opportunity costs of divorce. This functional stability transcends differences in household power structures and divisions of labor, revealing the foundational role of economic resources in intimate relationships. Robustness check We conducted a series of supplementary analyses to test the robustness of the core findings. First, we examined a binary indicator of homeownership in place of continuous real estate value. As shown in Table A1 (Model 11) and A2 (Model 19), homeownership is associated with a 0.730 to 0.720 reduction in the log-odds of divorce (p < 0.001). This translates to an odds ratio of approximately 0.48, suggesting homeowners have roughly half the odds of divorcing as renters. Interaction analyses confirmed this strong protective effect is consistent across all family types and levels of wife's income share. We then restricted the sample to homeowners (Table A1, Models 13–14 and Table A2, Models 21–22). Among owners, higher housing value remains associated with lower divorce risk (β ≈ -0.25, p < 0.001), while the coefficient for housing loans is near zero and non-significant. Second, the main analysis categorizes family type into four groups, but treating all dual-earner families as a single category may mask internal heterogeneity. The dual structure of Japan's labor market creates significant differences between regular and nonregular employment in wages, stability, and benefits. Recent growth in dual-earner families stems primarily from expansion of women's nonregular employment, meaning that nominally dual-earner families may differ substantially in economic structure. Therefore, we refined family type into five categories (Table A3). The first type is traditional male-breadwinner families where husbands work in regular employment while wives do not participate in the labor force, comprising 37.4%. The second type consists of dual-earner families where husbands have regular employment and wives have nonregular employment, comprising 35.2% and representing the most common dual-earner pattern. The third type includes dual-earner families where both spouses engage in regular employment, comprising 17.5%. The fourth type is female-breadwinner families at 1.3%. The fifth type encompasses other arrangements at 8.6%. Analysis in Table A3 shows that the protective effect of assets remains significant across all five family types (Models 1–2). More importantly, interactions between financial assets and all three nontraditional family types reach marginal significance at the 10% level: with dual-earner families where wives have nonregular employment (β = 0.111, p < 0.10), with dual-earner families where both spouses have regular employment (β = 0.129, p < 0.10), and with female-breadwinner families (β = 0.174, p < 0.10). In contrast, interaction terms for nonfinancial assets prove entirely nonsignificant (Model 4), again confirming the universality of housing's protective effect. Interaction terms for net assets also show no significance (Model 2), indicating that when asset types are not distinguished, heterogeneous effects remain obscure and asset effect results remain robust. Third, to test for potential threshold effects, we replaced the continuous asset measures with quartiles (Table A4). Results show that all quartiles above the first (Q1) are associated with significantly lower divorce risk, though not in a perfectly linear pattern. For net worth, the protective effect peaks at the third quartile (Q3). Notably, the interaction between the highest quartile (Q4) of financial assets and both dual-earner and female-breadwinner households is significant at the p < 0.10 level. This finding echoes the results from Table A3, confirming that the heterogeneity in the financial asset effect is most pronounced at the top of the wealth distribution. Among the wealthiest 25 percent of families, the protective association of financial assets is significantly weaker for non-traditional households. All interactions for non-financial asset quartiles remain non-significant. Taken together, these robustness checks confirm three key conclusions: (1) The overall protective effect of assets is robust across specifications. (2) The protective effect of financial (liquid) assets is indeed weaker in non-traditional households, a distinction that becomes sharper when accounting for employment quality and wealth levels. (3) The protective effect of non-financial assets (housing) is highly universal and consistent. These findings not only verify the reliability of the main analysis but also deepen our understanding of the differentiated functions of liquid and illiquid assets during a period of family transition. Discussion and Conclusion This study used 27 years of Japanese panel data to examine whether the protective association between economic resources and marital stability is contingent on household economic structure. Against a backdrop of a sustained shift from male-breadwinner to dual-earner models, the findings show that assets, on the whole, are significantly associated with a lower divorce risk across all family types. This general pattern, however, masks a critical heterogeneity. While the protective effects of net worth and illiquid assets such as real estate are consistent, the protective association of financial assets is significantly weaker in dual-earner households compared to traditional ones. This result lends partial support to bargaining theories predicting a shifting function for liquid assets, while also affirming the economic stress perspective on the fundamental protective role of economic security. Furthermore, a wife's income share, while predictive of divorce risk itself, does not moderate the asset effect. This suggests the function of assets and intrahousehold power relations operate as two relatively independent dimensions. The former reflects a foundational need for economic security, while the latter captures shifts in gender relations and cultural norms. The most important contribution of this study lies in distinguishing heterogeneous patterns across different asset types. The protective association of illiquid assets, primarily real estate, is entirely consistent across all family structures. The effect of liquid financial assets, however, is demonstrably heterogeneous. This divergence stems from the fundamental properties of the assets themselves. The universality of the real estate effect is likely rooted in several overlapping mechanisms. As real estate constitutes a high proportion of Japanese household assets, the home is not just a financial instrument but the physical and emotional locus of family life. This illiquidity makes housing function less as capital to facilitate an exit and more as a marital-specific sunk cost. Its division involves complex legal procedures and high transaction costs, including the disruption of social networks and children's schooling. Moreover, within Japanese familistic traditions, the home carries symbolic meaning tied to intergenerational continuity, elevating its value beyond mere economic calculation. In contrast, the liquidity of financial assets makes their function more sensitive to intrahousehold power dynamics. In dual-earner households, a wife's independent income alters her relationship to the family's financial portfolio. The divisibility and transferability of these assets may allow for their strategic use during marital conflict. Yet, even in this context, financial assets remain a protective factor (net effect = -0.105). The association is merely attenuated, not reversed, indicating that the economic security function of liquid assets persists even as family power structures evolve. The predominance of this universal protective pattern, despite the heterogeneity of financial assets, suggests that standardized institutional features of modern society impose common economic challenges. Expenditures on housing, education, healthcare, and retirement are largely inelastic and are not adjusted based on a household's internal division of labor. The credit evaluation systems of financial institutions, the competitive mechanisms of the educational system, and the coverage rules of health insurance do not differentiate by family type. This institutional homogeneity compels a functional convergence in how families use assets to manage risk. As neo institutional theory suggests, while the external forms of the family may diversify, the core mechanisms for coping with economic uncertainty tend to converge (Meyer and Rowan 1977 ). In the East Asian context, familistic traditions imbue marriage with a social meaning that transcends the couple, framing asset accumulation as a core familial and intergenerational responsibility. Critically, given the dominance of real estate in household portfolios, the modest heterogeneity found in financial assets has a limited impact on the total asset effect. The study's primary implication is that marital stability is emerging as a mechanism for the reproduction of wealth inequality. Although the protective role of financial assets is attenuated in dual-earner households, the overall finding is that assets remain a crucial bulwark for marriage. This suggests that intimate relationships themselves are increasingly stratified by economic resources. This finding resonates with Cherlin’s ( 2010 ) insights on the deinstitutionalization of marriage but reveals a more complex pattern of class-based divergence. For affluent households, ample assets provide a robust buffer, securing marital stability even as family structures change. Stable marriage, in turn, functions as an institutional platform for further asset accumulation, educational investment in children, and the expansion of social capital. Conversely, for those lacking economic resources, marriage itself can become an unattainable goal rather than a starting point. A high economic threshold may lead individuals to delay or forgo marriage, and those who do marry face heightened economic vulnerability. This divergence constitutes a central paradox of contemporary family change. Marriage becomes more stable and valuable for those capable of sustaining it, while becoming inaccessible to those who most need its protective functions. This relational inequality has powerful intergenerational consequences. Stable marriage facilitate asset accumulation through economies of scale, risk pooling, and long-term investment horizons. These accumulated assets, in turn, further protect the union from dissolution, creating a virtuous cycle. Conversely, families without assets face higher dissolution risks due to economic stress, and divorce further erodes their economic foundation, leading to a cycle of cumulative disadvantage. Children raised in stable, high-asset households benefit from greater emotional support, educational investment, and social networks, advantages that translate into their own adult economic and marital success. As McLanahan ( 2004 ) argued, this divergence in marriage patterns is becoming a critical mechanism in the reproduction of social class. In the context of East Asia's low fertility rates and continuously delayed age at first marriage, this study's findings carry particular real-world significance. If marital stability depends heavily on economic foundations while younger generations face employment instability, declining housing accessibility, and stagnant wealth mobility, young people may rationally choose to postpone or forgo marriage. Japan's rising age at first marriage and climbing rates of lifelong singlehood may partly reflect this rational calculation. Economic inequality generates inequality in marital opportunities, which in turn exacerbates declining fertility and crises of social sustainability. This study has several limitations that offer directions for future research. First, the analysis does not establish causal relationships. Despite the use of longitudinal data, observational studies cannot fully overcome endogeneity. Marital stability and asset accumulation may be codetermined by unobserved factors such as risk preferences or relationship commitment. Future work could pursue causal identification through instrumental variable strategies or quasi-experimental designs. Second, this study used wife's income share to measure relative economic position but could not measure her actual control over household assets. While wives in Japan often manage household finances, it is unclear if this administrative role translates into bargaining power. Research with more direct measures of financial decision-making and asset control is needed. Third, our focus on dissolution as the outcome does not capture the multidimensional nature of marital quality, such as satisfaction or conflict. Bargaining theory predictions might find stronger support if applied to these more nuanced outcomes. The findings offer differentiated policy implications. The universal protective effect of real estate supports positioning housing policy as a core component of family policy. Enhancing housing affordability for young families is not only an economic goal but a social one. However, the heterogeneity of financial asset effects suggests that as dual-earner households become the norm, traditional savings incentives may require recalibration. The stability advantage of traditional families presents a policy dilemma. While existing institutions support this model, dual-earner families are now the majority yet face higher dissolution risks and institutional frictions. A potential path forward is to increase support for dual-earner households through expanded childcare, enhanced paternity leave, and work-life balance initiatives, thereby reducing the institutional gap between family types. The more fundamental challenge is to break the cycle of inequality reproduction. If assets are crucial for all families but are distributed unequally, policies promoting asset accumulation must prioritize low-wealth households. This requires integrating marital stability policy with broader wealth redistribution efforts. By differentiating between asset types, this study reveals the complex function of economic resources in an era of family transition. The key finding is that the protective association of liquid assets is conditional on family type, while the protection from illiquid assets is universal. This differentiated pattern lends partial support to bargaining theory while simultaneously confirming the foundational role of economic security. While family forms are in flux, the human need for security and stability remains a constant and constitutive element of intimate relationships. When marital stability itself becomes a product of economic stratification, intimacy ceases to be a purely private emotional bond and becomes an integral component of the social stratification system. This institutionalization of relational inequality marks a profound paradox of modernity. The process of individualization, while appearing to expand choice, may in fact be reproducing new forms of inequality through economic mechanisms. Understanding this paradox proves essential for grasping the nature of contemporary family change and provides crucial insights for formulating effective social policies. Declarations Author Contribution Jiajie, Zhang conducted the data analysis and interpretation independently. She wrote and reviewed the main manuscript text. References Allison, P. D. (2014). Event history and survival analysis (2nd ed.). Los Angeles: SAGE. https://doi.org/10.4135/9781452270029 Becker, G. S., 1930. (1991). A Treatise on the Family (Enl.). Cambridge, Mass: Harvard University Press. https://go.exlibris.link/qMbg6jtt Bertrand, M., Kamenica, E., & Pan, J. (2015). Gender Identity and Relative Income within Households. The Quarterly Journal of Economics , 130 (2), 571–614. https://doi.org/10.1093/qje/qjv001 Boertien, D., & Härkönen, J. (2019). Why Does Women’s Education Stabilize Marriages? The Role of Marital Attraction and Barriers to Divorce. Demographic research , 38 (Journal Article), 1241–1276. https://doi.org/10.4054/DemRes.2018.38.41 Bram De Rock, Mariia Kovaleva, & Tom Potoms. (2023). A Spouse and A House Are All We Need? Housing Demand, Labor Supply and Divorce over the Lifecycle. IFS Working Papers , (No. 23/35). https://ideas.repec.org/p/ifs/ifsewp/23-35.html Brinton, M. C., & Nagase, N. (2017). The Gender Division of Labor and Second Births: Labor Market Institutions and Fertility in Japan. Demographic Research , 36 (36, Art. 11), 339–370. https://doi.org/10.4054/DemRes.2017.36.11 Brinton, M. C., & Oh, E. (2019). Babies, Work, or Both? Highly Educated Women’s Employment and Fertility in East Asia. American Journal of Sociology , 125 (1), 105–140. https://doi.org/10.1086/704369 Cabinet Office, G. of J. (2024). Public Opinion Survey on Gender Equal Participation Society (Danjyo kyoudou sankaku shakai ni kansuru yoron chousa) . Tokyo: Cabinet Office. https://survey.gov-online.go.jp/202502/r06/r06-danjo/gairyaku.pdf Cheng, Y. A. (2016). More education, fewer divorces? Shifting education differentials of divorce in Taiwan from 1975 to 2010. Demographic Research , 34 , 927–942. Cherlin, A. J. (2010). Demographic Trends in the United States: A Review of Research in the 2000s. Journal of Marriage and Family , 72 (3), 403–419. https://doi.org/10.1111/j.1741-3737.2010.00710.x Conger, R. D., Conger, K. J., & Martin, M. J. (2010). Socioeconomic status, family processes, and individual development. Journal of marriage and family , 72 (3), 685–704. Conger, R. D., Elder, G. H. J., Lorenz, F. O., Conger, K. J., Simons, R. L., Whitbeck, L. B., et al. (1990). Linking economic hardship to marital quality and instability. Journal of Marriage and the Family , 52 (3), 643–656. https://doi.org/10.2307/352931 Cooke, Lynn P., Erola, J., Evertsson, M., Gähler, M., Härkönen, J., Hewitt, B., et al. (2013). Labor and Love: Wives’ Employment and Divorce Risk in its Socio-political Context. Social politics , 20 (4), 482–509. https://doi.org/10.1093/sp/jxt016 Cooke, Lynn Prince. (2006). “Doing” Gender in Context: Household Bargaining and Risk of Divorce in Germany and the United States. American Journal of Sociology , 112 (2), 442–472. https://doi.org/10.1086/506417 D’Ambrosio, C., Jäntti, M., & Lepinteur, A. (2020). Money and Happiness: Income, Wealth and Subjective Well-Being. Social indicators research , 148 (1), 47–66. https://doi.org/10.1007/s11205-019-02186-w Dew, J. (2007). Two Sides of the Same Coin? The Differing Roles of Assets and Consumer Debt in Marriage. Journal of Family and Economic Issues , 28 (1), 89–104. https://doi.org/10.1007/s10834-006-9051-6 Dew, J. (2009). The Gendered Meanings of Assets for Divorce. Journal of family and economic issues , 30 (1), 20–31. https://doi.org/10.1007/s10834-008-9138-3 Dew, J. (2011a). Financial Issues and Relationship Outcomes Among Cohabiting Individuals. Family relations , 60 (2), 178–190. https://doi.org/10.1111/j.1741-3729.2010.00641.x Dew, J. (2011b). The Association Between Consumer Debt and the Likelihood of Divorce. Journal of family and economic issues , 32 (4), 554–565. https://doi.org/10.1007/s10834-011-9274-z Dew, J., & Xiao, J. J. (2013). Financial Declines, Financial Behaviors, and Relationship Satisfaction during the Recession. Journal of Financial Therapy , 4 , 1–20. https://doi.org/10.4148/jft.v4i1.1723 DiMaggio, P. J., & Powell, W. W. (1983). The Iron Cage Revisited: Institutional Isomorphism and Collective Rationality in Organizational Fields. American sociological review , 48 (2), 147–160. https://doi.org/10.2307/2095101 Eads, A., & Tach, L. (2016). Wealth and Inequality in the Stability of Romantic Relationships. RSF : Russell Sage Foundation journal of the social sciences , 2 (6), 197–224. https://doi.org/10.7758/rsf.2016.2.6.10 Farnham, M., Schmidt, L., & Sevak, P. (2011). House Prices and Marital Stability. The American economic review , 101 (3), 615–619. https://doi.org/10.1257/aer.101.3.615 Federal Reserve Board. (2022). Survey of Consumer Finances, 2022. Washington, DC: Board of Governors of the Federal Reserve System. https://www.federalreserve.gov/econres/scfindex.htm Gender Equality Bureau. (2022). The White Paper on Gender Equality 2022 . Tokyo: Cabinet Office, Government of Japan. https://www.gender.go.jp/about_danjo/whitepaper/r04/gaiyou/pdf/r04_gaiyou_en.pdf Gonalons-Pons, P., & Gangl, M. (2021). Marriage and Masculinity: Male-Breadwinner Culture, Unemployment, and Separation Risk in 29 Countries. American Sociological Review , 86 (3), 465–502. https://doi.org/10.1177/00031224211012442 Gray, J. S. (1998). Divorce-Law Changes, Household Bargaining, and Married Women’s Labor Supply. The American economic review , 88 (3), 628–642. Hirayama, Y. (2021). Housing, Family, and Life‐Course in Post‐Growth Japan. Japan Architectural Review , 4 (2), 267–276. https://doi.org/10.1002/2475-8876.12216 Jalovaara, M. (2002). Socioeconomic Differentials in Divorce Risk by Duration of Marriage. Demographic research , 7 (Journal Article), 537–564. https://doi.org/10.4054/demres.2002.7.16 Killewald, A. (2016). Money, Work, and Marital Stability: Assessing Change in the Gendered Determinants of Divorce. American sociological review , 81 (4), 696–719. https://doi.org/10.1177/0003122416655340 Killewald, A., & Bryan, B. (2016). Does Your Home Make You Wealthy? RSF : Russell Sage Foundation journal of the social sciences , 2 (6), 110–128. https://doi.org/10.7758/rsf.2016.2.6.06 Killewald, A., Lee, A., & England, P. (2023). Wealth and Divorce. Demography , 60 (1), 147–171. https://doi.org/10.1215/00703370-10413021 Lersch, P. M. (2017). The Marriage Wealth Premium Revisited: Gender Disparities and Within-Individual Changes in Personal Wealth in Germany. Demography , 54 (3), 961–983. https://doi.org/10.1007/s13524-017-0572-4 Lersch, P. M., & VIdal, S. (2014). Falling Out of Love and Down the Housing Ladder: A Longitudinal Analysis of Marital Separation and Home Ownership. European sociological review , 30 (4), 512–524. https://doi.org/10.1093/esr/jcu055 Lundberg, S., & Pollak, R. A. (1996). Bargaining and Distribution in Marriage. Journal of Economic Perspectives , 10 (4), 139–158. https://doi.org/10.1257/jep.10.4.139 Lyngstad, T. H., & Jalovaara, M. (2010). A review of the antecedents of union dissolution. Demographic research , 23 (Journal Article), 257–292. https://doi.org/10.4054/DemRes.2010.23.10 McLanahan, S. (2004). Diverging Destinies: How Children are Faring under the Second Demographic Transition. Demography , 41 (4), 607–627. https://doi.org/10.1353/dem.2004.0033 Meyer, J. W., & Rowan, B. (1977). Institutionalized Organizations: Formal Structure as Myth and Ceremony. American Journal of Sociology , 83 (2), 340–363. https://doi.org/10.1086/226550 Mirowsky, J., & Ross, C. E. (1989). Social causes of psychological distress . New York: de Gruyter. https://go.exlibris.link/Z5XcJ4Yj Mori, S., & Sugaya, K. (2017). The Relationship between Financial Assets and Land/Housing Assets in Household Portfolios. Daiwa Institute of Research Quarterly , 26 , 66–79. OECD. (2023). OECD Employment Outlook 2023 . Paris: OECD Publishing. https://doi.org/10.1787/19991266 Ono, H. (1998). Husbands’ and Wives’ Resources and Marital Dissolution. Journal of Marriage and the Family , 60 (3), 674-. https://doi.org/10.2307/353537 Oppenheimer, V. K. (1997). Women’s Employment and the Gain to Marriage: The Specialization and Trading Model. Annual review of sociology , 431–453. Park, H., & Raymo, J. M. (2013). Divorce in Korea: Trends and Educational Differentials. Journal of marriage and family , 75 (1), 110–126. https://doi.org/10.1111/j.1741-3737.2012.01024.x Pearlin, L. I., Menaghan, E. G., Lieberman, M. A., & Mullan, J. T. (1981). The Stress Process. Journal of health and social behavior , 22 (4), 337–356. https://doi.org/10.2307/2136676 Ronald, R., & Hirayama, Y. (2009). Home Alone: The Individualization of Young, Urban Japanese Singles. Environment and Planning A: Economy and Space , 41 (12), 2836–2854. https://doi.org/10.1068/a41119 Sayer, L. C., & Bianchi, S. M. (2000). Women’s Economic Independence and the Probability of Divorce: A Review and Reexamination. Journal of family issues , 21 (7), 906–943. https://doi.org/10.1177/019251300021007005 Schwartz, C. R., & Gonalons-Pons, P. (2016). Trends in Relative Earnings and Marital Dissolution: Are Wives Who Outearn Their Husbands Still More Likely to Divorce? RSF : Russell Sage Foundation journal of the social sciences , 2 (4), 218–236. https://doi.org/10.7758/rsf.2016.2.4.08 Schwartz, C. R., & Han, H. (2014). The Reversal of the Gender Gap in Education and Trends in Marital Dissolution. American Sociological Review , 79 (4), 605–629. https://doi.org/10.1177/0003122414539682 South, S. J., & Spitze, G. (1986). Determinants of Divorce Over the Marital Life Course. American sociological review , 51 (5), 583. Statistics Bureau of Japan. (2022). Survey on Time Use and Leisure Activities 2021 . Tokyo: Ministry of Internal Affairs and Communications. Statistics Bureau of Japan. (2023). Labour Force Survey 2023 . Tokyo: Ministry of Internal Affairs and Communications. Stevenson, B., & Wolfers, J. (2007). Marriage and Divorce: Changes and Their Driving Forces. The Journal of economic perspectives , 21 (2), 27–52. https://doi.org/10.1257/jep.21.2.27 Tichenor, V. J. (2005). Earning More and Getting Less : Why Successful Wives Can’t Buy Equality . New Brunswick, N.J: Rutgers University Press. https://go.exlibris.link/8JgVRf29 Yu, W., & Kuo, J. C.-L. (2016). Explaining the Effect of Parent-Child Coresidence on Marriage Formation: The Case of Japan. Demography , 53 (5), 1283–1318. https://doi.org/10.1007/s13524-016-0494-6 Additional Declarations No competing interests reported. Supplementary Files Appendix.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8004673","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":558417340,"identity":"4b44c279-d0c2-40c7-863b-25723bf953d7","order_by":0,"name":"Jiajie Zhang","email":"data:image/png;base64,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","orcid":"","institution":"The University of Tokyo","correspondingAuthor":true,"prefix":"","firstName":"Jiajie","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2025-11-01 09:53:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8004673/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8004673/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":97999067,"identity":"10f07cff-2489-46b5-930d-fb82b0df4743","added_by":"auto","created_at":"2025-12-11 15:59:05","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":75162,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.docx","url":"https://assets-eu.researchsquare.com/files/rs-8004673/v1/358d735b999e380657d847f8.docx"},{"id":97999064,"identity":"7b83b83e-23f4-47e3-914a-86a3770123cc","added_by":"auto","created_at":"2025-12-11 15:59:05","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":3643,"visible":true,"origin":"","legend":"","description":"","filename":"452bad3a02574d2eb51628b48f45eeec.json","url":"https://assets-eu.researchsquare.com/files/rs-8004673/v1/d2f610638e2f628074a9d8ec.json"},{"id":97999066,"identity":"57a6115f-f21d-4c7e-b9b2-0dad3c2ea234","added_by":"auto","created_at":"2025-12-11 15:59:05","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":55471,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix.docx","url":"https://assets-eu.researchsquare.com/files/rs-8004673/v1/31cbebb1e796bf2e4f6f3481.docx"},{"id":97999070,"identity":"ee8caa8d-f38e-4bff-a328-d02b1a326c94","added_by":"auto","created_at":"2025-12-11 15:59:06","extension":"xml","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":197907,"visible":true,"origin":"","legend":"","description":"","filename":"452bad3a02574d2eb51628b48f45eeec1enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-8004673/v1/45714024f5ab035f0109baec.xml"},{"id":97999068,"identity":"53cc020d-7b31-4da6-a261-0b5e68e901b8","added_by":"auto","created_at":"2025-12-11 15:59:05","extension":"xml","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":196147,"visible":true,"origin":"","legend":"","description":"","filename":"452bad3a02574d2eb51628b48f45eeec1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8004673/v1/c62be2280e10c62e55b3d1f2.xml"},{"id":97999069,"identity":"7002ff32-8155-45af-a36b-993859d600ae","added_by":"auto","created_at":"2025-12-11 15:59:06","extension":"html","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":202654,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8004673/v1/d7c4b541148941c4093a279b.html"},{"id":102748615,"identity":"f09b0879-7dee-4871-b3d2-95831daca293","added_by":"auto","created_at":"2026-02-16 09:11:17","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1582680,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8004673/v1/7c0a1db3-f7d8-4f95-a8f4-ff4af069f536.pdf"},{"id":97999065,"identity":"d264f2b1-8c40-4b9e-84a3-26bcef46fed7","added_by":"auto","created_at":"2025-12-11 15:59:05","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":55471,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix.docx","url":"https://assets-eu.researchsquare.com/files/rs-8004673/v1/3bd3b1917d0d57945916397d.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Wealth and Relational Inequality: How Household Assets Shape Marital Stability by Family Type in Japan","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe rise in economic inequality has brought new attention to the intersection of wealth and family life. Understanding how household assets shape marital stability has become significant to scholarship on social stratification. If families with assets experience greater stability while those without face heightened vulnerability, wealth inequality may be amplified through differential marital outcomes, creating cycles of cumulative disadvantage (Cherlin \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; McLanahan \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2004\u003c/span\u003e).\u003c/p\u003e\u003cp\u003ePrior research investigated the link between assets and marital stability has established a relatively consistent narrative. Using SIPP data, Eads and Tach (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) found that increases in family net worth significantly reduce the risk of union dissolution for both married and cohabiting couples. Killewald, Lee, and England (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) further affirmed the robustness of this association. Drawing on UK data, Boertien and H\u0026auml;rk\u0026ouml;nen (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) demonstrated that homeowners face a substantially lower risk of divorce than renters. These studies draw on several theoretical perspectives to explain the protective function of assets. The economic stress perspective views assets as a crucial buffer, enabling families to smooth consumption during shocks like unemployment or illness and preventing economic hardship from escalating into marital conflict (Conger et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Dew \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2011a\u003c/span\u003e). From a transaction cost perspective, joint assets, particularly illiquid ones, increase the economic threshold of separation (Becker 1991). Psychological frameworks suggest assets improve relationship quality by fostering a sense of control and reducing uncertainty (Dew \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2007\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThis seemingly settled relationship, however, faces a fundamental challenge rooted in the revolutionary changes to family economic structures over the past half-century. The large-scale entry of women into the labor market has profoundly altered intrahousehold resource allocation and power dynamics. Bargaining theories posit that a spouse's negotiating position is determined by their command over independent economic resources (Lundberg and Pollak \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). When wives gain independent income, the nature of household assets may undergo a qualitative transformation. In traditional single-earner families, assets reinforce mutual dependence between spouses. In dual-earner families, however, assets may become tools that facilitate individual autonomy. Killewald (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) found that male unemployment has a much greater impact on traditional families than on dual-earner families, suggesting the existence of such heterogeneity. Research by Schwartz and Gonalons-Pons (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) also shows that increases in women's relative income have differential effects on divorce risk across different family backgrounds.\u003c/p\u003e\u003cp\u003eJapan provides an ideal setting to examine these competing theoretical predictions. First, Japanese family structures have undergone a significant transformation. The number of dual-earner households surpassed that of male-breadwinner households in the late 2010s and continues to grow (Gender Equality Bureau \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Second, Japanese household portfolios are characterized by a high proportion of non-financial assets, such as housing and land, exceeding levels in the United States (Mori and Sugaya \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). This high degree of asset illiquidity may be particularly salient for marital stability, consistent with prior work on the unique role of housing wealth (Lersch \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Third, Japan exhibits a persistent tension between rising female economic participation and enduring gendered norms and institutional incentives. While female employment rates are high among OECD countries, the gender wage gap remains one of the largest. Furthermore, institutional arrangements such as spousal tax deductions and the Category 3 dependent insurance system continue to bolster the traditional family model (Brinton and Nagase \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Yu and Kuo \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Category 3 dependent insurance system is designed for spouse, aged 20\u0026ndash;59, dependent on an employee covered by an employees\u0026rsquo; pension or mutual aid association. This tension between rapidly rising female economic status and the persistent traditional gender division system may amplify differences in asset functions across household types. In traditional households, assets may still serve their conventional function of reinforcing mutual dependence. In dual-earner households, particularly when wives possess relatively independent economic capacity, the meaning of assets may have already transformed.\u003c/p\u003e\u003cp\u003eThe extant research suffers from three key limitations. First, studies rarely differentiate between asset types. Financial assets and real estate diverge fundamentally in liquidity, division costs, and psychological meaning, yet most research relies on measures of total net worth. While Killewald et al. (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) examined the symbolic value of home and vehicle ownership, they did not compare the heterogeneous effects of different asset types. Distinguishing these asset types is particularly important in East Asian societies where real estate wealth is dominant. Second, the moderating role of family type is often neglected. While theory predicts that asset effects should vary by family type, empirical tests remain limited. Existing studies either treat all families as equivalent or adopt overly simple classifications. Third, measurement strategies for family structure are often crude. Binary classifications based on employment status may mask important differences. Among dual-earner families, those where the wife contributes 20% versus 50% of income have vastly different power structures (Bertrand et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Additionally, empirical research on East Asia remains severely lacking. Most East Asian studies focus on income rather than assets (Cheng \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Park and Raymo \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), creating important gaps in both theoretical development and policy formulation.\u003c/p\u003e\u003cp\u003eTo fill these research gaps, this study uses 27 years of panel data from the Japanese Panel Survey of Consumers (1993\u0026ndash;2021) to examine the relationship between assets and marital stability. We focus on three interrelated questions. First, in the East Asian context, how do different types of assets influence divorce risk? Do financial assets and real estate exhibit different effects? Second, does the protective association of assets vary by family type? We explore potential differences between male-breadwinner, dual-earner, and other household arrangements. Third, does this heterogeneity present differently when we use a continuous measure of women's relative economic position to capture intrahousehold power dynamics? By simultaneously employing employment-based classifications and relative-income-based continuous measures, this study seeks a more comprehensive understanding of the sources and mechanisms of asset effect heterogeneity.\u003c/p\u003e\u003cp\u003eThis study makes three important contributions to existing literature. First, by examining the relationship between assets and divorce in East Asian society, we fill a critical research gap in a region undergoing rapid family transformation. Unlike existing research that focuses primarily on income, this study incorporates assets and distinguishes the differential roles of financial and non-financial assets. Second, this study provides new empirical evidence for understanding the mechanisms by which assets operate in different family power structures by employing multiple measurement strategies to examine family heterogeneity. It also offers crucial empirical evidence on the relative effectiveness of specialization theory, economic stress theory, and bargaining theory in explaining asset effects. Third, the findings have important policy implications for understanding the socioeconomic differentiation of East Asian family transformation. If asset effects are universal, this means that in a context of growing wealth inequality, marital stability itself is becoming a new dimension of social stratification. Inclusive policies that promote family asset accumulation will be an effective choice. In an era of rising wealth inequality and diversifying family forms, understanding how economic resources shape the stability of different family types is crucial for developing effective social policies.\u003c/p\u003e"},{"header":"Literature Review","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eAssets and Marital Stability\u003c/h2\u003e\u003cp\u003eThe impact of assets on marital stability has long been central to family sociology research. Existing literature offers three theoretical perspectives on why families with more assets face lower divorce risks.\u003c/p\u003e\u003cp\u003eEconomic stress theory treats assets as a buffer against external shocks. The Family Stress Model, developed from research on the Iowa farm crisis, posits that economic hardship erodes marital quality by increasing emotional distress and reducing positive spousal interaction. Assets function to interrupt this negative pathway (Conger et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1990\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).This theory has received broad empirical support. In the United States, recent work confirms a negative association between wealth and divorce risk, with the protective effect strongest at lower levels of wealth. Even when controlling for net worth, visible assets such as homes and vehicles remain associated with lower divorce risk (Killewald et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In the United Kingdom, research using the BHPS shows that homeownership acts as a barrier to dissolution and helps explain the greater marital stability observed among the highly educated (Boertien and H\u0026auml;rk\u0026ouml;nen \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe life chances perspective in sociology emphasizes how assets alter the cost-benefit calculus of divorce. Classic family economics suggests that gender-based specialization grounded in comparative advantage leads to marriage-specific investments such as housing and joint savings (Becker 1991), thereby raising the economic costs of separation. Empirical research widely validates this mechanism. Homeownership is consistently linked to longer marital duration (Jalovaara \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; South and Spitze \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e1986\u003c/span\u003e). Further studies in the US have identified a concurrent relationship between declining housing values and marital dissolution, suggesting that wealth effects influence divorce decisions (Farnham et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Moreover, housing market constraints can create a \"lock-in\" effect, whereby falling prices or limited liquidity delay separations (Bram De Rock et al. 2023). These findings collectively illustrate that assets are not merely economic resources but also significant structural constraints on marital dissolution.\u003c/p\u003e\u003cp\u003eThe psychological perspective emphasizes how assets stabilize marriage through psychological mechanisms. Stress process theory suggests that economic resources enhance an individual's sense of control and self-efficacy, thereby mitigating stress and improving interpersonal interactions (Pearlin et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e1981\u003c/span\u003e). Social psychological research finds significant positive associations between family wealth and subjective well-being and sense of control (D\u0026rsquo;Ambrosio et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Mirowsky and Ross \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e1989\u003c/span\u003e). Within marital research, studies show a positive link between asset accumulation and relationship satisfaction, whereas consumer debt significantly increases conflict and dissolution risk (Dew \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2007\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2011b\u003c/span\u003e). U.S. longitudinal studies find that debt burden has a more negative impact on marital satisfaction than income insufficiency (Dew and Xiao \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). These findings reveal the psychological and emotional stabilizing functions of assets. Assets provide not only economic security but also foster marital maintenance by enhancing feelings of control and security.\u003c/p\u003e\u003cp\u003eThese three theoretical mechanisms are not mutually exclusive but rather reveal the complex pathways through which assets protect marriage from different angles. Notably, these studies have established a basic consensus that assets do reduce divorce risk after controlling for other factors. Despite this widely confirmed protective effect, the existing literature possesses two significant limitations.\u003c/p\u003e\u003cp\u003eFirst, most research treats household assets as a monolithic category, neglecting the potentially divergent influences of different asset types. Financial assets and real estate differ fundamentally across multiple dimensions. Real estate is not only a store of wealth but also the spatial context of family life. Its illiquidity and high transaction costs likely enhance its lock-in effect (Lynn P. Cooke et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Financial assets, while providing liquidity, may be more volatile and are more easily divisible upon dissolution (Dew \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2011b\u003c/span\u003e). Given that housing constitutes the predominant component of household portfolios globally, the \"asset effect\" identified in many studies may primarily reflect the role of real estate. The few studies that do differentiate asset types have found meaningful differences. Lersch\u0026rsquo;s (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) analysis in Germany showed that homeownership had a stronger stabilizing effect on marriage than financial assets. Similarly, Killewald and Gough (2013) find that housing wealth shows the most significant negative association with divorce risk.\u003c/p\u003e\u003cp\u003eSecond, and more critically, existing research has rarely considered how the effects of assets might be heterogeneous across family types. In a traditional male-breadwinner household, assets serve as the material foundation of the husband's breadwinning role and the wife's primary source of economic security. When wives participate in the labor market and gain independent income, however, the meaning and function of household assets may shift. The extant literature has paid insufficient attention to this potential heterogeneity.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eFamily Type and Asset Effects\u003c/h3\u003e\n\u003cp\u003eRising female labor force participation has called into question whether the protective effect of assets remains uniform across all family types. The theoretical literature offers three competing predictions regarding this heterogeneity.\u003c/p\u003e\u003cp\u003eSpecialization theory predicts a stronger asset effect in traditional households. Becker's (1991) model posits that a gendered division of labor based on comparative advantage maximizes household utility. In this system, spouses make specialized, mutually dependent investments. Assets are not just wealth but the material foundation of this specialization. Greater asset accumulation signifies a more successful specialization, thereby increasing the gains to marriage and the opportunity cost of dissolution. In dual-earner couples, where both partners maintain market skills, mutual dependency is lower and the lock-in effect of assets should be correspondingly weaker.\u003c/p\u003e\u003cp\u003eBargaining theory predicts a diminished asset effect in dual-earner households. From this perspective, spousal power depends on their threat point or reservation utility, often defined by their post-divorce economic standing (Lundberg and Pollak \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). In a traditional arrangement, a wife's lack of independent resources creates a strong incentive to preserve the marriage. When a wife possesses her own career and income, however, assets no longer constrain but may instead become capital for pursuing independence. Particularly when marital quality is low, assets may accelerate rather than prevent marital dissolution (Ono \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e1998\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe universality hypothesis suggests the asset effect is consistent across family types. Neo institutional theories point to the standardizing pressures of modern institutions which promote behavioral convergence (DiMaggio and Powell \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e1983\u003c/span\u003e; Meyer and Rowan \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e1977\u003c/span\u003e). In the family domain, all households, regardless of their internal division of labor, must navigate standardized mortgage contracts, uniform educational fees, and formalized healthcare expenses. This homogeneity in external constraints may lead to a uniform function for assets. Furthermore, dual-earner families, while benefiting from income diversification, may also require assets as an essential buffer against risk (Oppenheimer \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). The protective role of assets might therefore be equally important in these households.\u003c/p\u003e\u003cp\u003eExisting empirical research provides limited and mixed evidence for this theoretical debate. Evidence supporting specialization theory comes mainly from historical comparisons and employment shock studies. Through analyzing half a century of U.S. divorce trends, Stevenson and Wolfers (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) found that economic factors showed stronger predictive power for divorce during the pre-1970s period of clear gender specialization. This predictive power declined as female employment increased, suggesting that traditional specialization indeed reinforces the role of economic resources. Using PSID data, Killewald (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) found that husband unemployment had 1.3 times the impact on divorce risk in traditional families compared to dual-earner families, indicating stronger dependence on male economic roles in traditional families. In a Germany-U.S. comparison, Cooke (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) found that any deviation from traditional gender specialization in Germany, whether higher relative income for wives or greater housework by husbands, correlated with higher divorce risks. In the United States, more gender-equal arrangements correlated with lower divorce risks, highlighting how institutional and gender cultural contexts moderate the applicability of the specialization hypothesis. Gonalons-Pons and Gangl (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) further showed in a cross-national study that the destabilizing effect of male unemployment is largest in countries with more traditional gender norms\u003c/p\u003e\u003cp\u003eSupport for the bargaining perspective centers on the association between women's economic resources and divorce. Sayer and Bianchi (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2000\u003c/span\u003e) found that a wife's economic independence increased divorce risk only when marital dissatisfaction was high, consistent with the 'exit option' prediction. Using the exogenous shock of US divorce law reforms, Gray (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e1998\u003c/span\u003e) showed that wife's employment, which had previously been associated with lower divorce risk, became associated with higher risk after the adoption of unilateral divorce. Bertrand, Kamenica, and Pan (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) notably found that divorce risk increases sharply when a wife's income begins to exceed her husband's, even controlling for total household income.\u003c/p\u003e\u003cp\u003eEvidence for the universality hypothesis stems from cross-national research finding a consistent protective effect of economic resources. Lyngstad and Jalovaara (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) noted in a systematic review that a positive association between economic resources and marital stability is found across diverse welfare regimes. Lersch and Vidal (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), using German panel data, found that homeownership significantly reduced dissolution risk and that this effect was consistent across socioeconomic groups. Eads and Tach (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) similarly argued that the protective effect of assets in the US likely reflects a more general social mechanism. These studies suggest the robustness of the resource-stability link across institutional settings.\u003c/p\u003e\u003cp\u003eAlthough these studies provide valuable insights, important limitations remain in directly answering whether asset effects vary by family type. Most studies examine employment or income, not assets. Income flows are conceptually distinct from asset stocks. Income reflects current earning capacity, whereas assets represent accumulated wealth (Killewald and Bryan \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). More importantly, asset distribution between spouses is more complex than income, involving property rights, inheritance, gifts, and multiple other factors. Simply inferring asset effects from income effects may be misleading. Furthermore, studies that do include assets seldom test for interaction effects directly. Most analyses either report main effects or offer stratified comparisons rather than formally modeling the interaction between assets and family type, a step necessary to rigorously test the heterogeneity hypotheses. Third, measures of family type are often too coarse. A simple traditional versus dual-earner dichotomy obscures vast in-group variance. A household where a wife contributes 10 percent of the income is structurally different from one where she contributes 50 percent (Tichenor \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Finally, there is a pronounced lack of empirical evidence from East Asian societies. The existing scholarship is overwhelmingly based on European and North American contexts. The unique institutional and cultural environments in East Asia, characterized by strong gender norms amid rapid economic change, may produce distinct patterns. Based on these theoretical debates and empirical gaps, the present study uses the case of Japan to test the differentiated role of assets across family types.\u003c/p\u003e\n\u003ch3\u003eThe Japanese Context: Tension Between Tradition and Modernity\u003c/h3\u003e\n\u003cp\u003eJapanese society is a particularly valuable case for examining the heterogeneous effects of assets. Japan\u0026rsquo;s developmental trajectory and institutional features are distinct among advanced economies. In terms of asset structure, Japanese household wealth is highly concentrated in real estate. Physical assets such as land and housing account for 50 to 60 percent of total household assets (Mori and Sugaya \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). This contrasts sharply with the United States, where real estate constitutes approximately 35 percent of household assets (Federal Reserve Board \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This concentration has deep historical and cultural roots, shaped by post-war land reforms, long-term expectations of rising land prices, and a cultural emphasis on the family home as an intergenerational asset (Hirayama \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Ronald and Hirayama \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). The high proportion of real estate implies low portfolio liquidity and high division costs upon divorce, potentially strengthening the lock-in effect of assets.\u003c/p\u003e\u003cp\u003eRegarding family structure, Japan is undergoing a rapid yet incomplete transition. The number of dual-earner households grew from 6.14\u0026nbsp;million in 1980 to 12.40\u0026nbsp;million in 2020, surpassing the number of traditional male-breadwinner households (Gender Equality Bureau \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, this trend masks persistent gender inequality. Female non-regular employment rates remain high at 53\u0026ndash;54 percent in 2023, far exceeding the male rate of 22 percent (OECD \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Statistics Bureau of Japan \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Even within dual-earner couples, the gender wage gap remains substantial, with women's average wages at 70 to 75 percent of men's (OECD \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Even in dual-earner families, because female wages are generally lower than male wages, with women earning approximately 70\u0026ndash;75% of male wages (OECD \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), wives' economic contributions typically remain far below those of husbands (Brinton and Oh \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe institutional environment continues to substantially support the traditional gender division of labor. The spousal deduction system in the tax code stipulates that when a wife's annual income falls below 1.03\u0026nbsp;million yen, the husband receives a 380,000 yen income tax deduction. The Category 3 insured person system in social insurance allows spouses earning less than 1.30\u0026nbsp;million yen to be exempt from insurance premiums while receiving pension benefits. Many corporate family allowances also premise low spousal income. Nagase and Brinton (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) demonstrate that these institutional designs create powerful incentives for many married women to rationally limit their work hours and income. Even as the government has promoted policies for women's active participation in recent years, these deep institutional arrangements have not fundamentally changed.\u003c/p\u003e\u003cp\u003eCultural attitudes have shifted even more slowly. While approval of the \"husband as breadwinner, wife as homemaker\" ideology has declined, it remains prevalent among middle-aged and older cohorts (Cabinet Office \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). More subtly, even when superficially supporting gender equality, many people continue following traditional patterns in practice. Married women spend more than five times as much time on housework as their husbands, a ratio that has barely changed over the past 20 years (Statistics Bureau of Japan \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). When women's economic roles conflict with these traditional expectations, additional marital stress may arise. Fukuda (2013) found that highly educated women face higher divorce risks, contrasting sharply with patterns in European and American societies where education positively correlates with marital stability (Schwartz and Han \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThis tension between rapid economic change and slow institutional and cultural adjustment makes modern Japan society an ideal setting to test for the heterogeneous effects of assets. If the protective function of assets does indeed vary by family type, this difference should be particularly pronounced in the Japanese context, where traditional and modern arrangements coexist.\u003c/p\u003e\n\u003ch3\u003eResearch Hypotheses\u003c/h3\u003e\n\u003cp\u003eBased on the theoretical and empirical literature reviewed, this study proposes the following hypotheses:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eH1a\u003c/b\u003e: Higher levels of household net worth will be associated with lower risk of divorce.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eH1b\u003c/b\u003e: Real estate assets will have a stronger protective association with marital stability than financial assets, while household debt will be associated with a higher risk of divorce.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eH2\u003c/b\u003e: Traditional male-breadwinner households will exhibit a lower risk of divorce compared to dual-earner households and other family types.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eH3\u003c/b\u003e: The protective effect of assets against divorce will be strongest among traditional male-breadwinner households and significantly weaker among dual-earner households.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eH4\u003c/b\u003e: As the wife's share of the total household income increases, the protective effect of household assets on marital stability will progressively weaken.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e"},{"header":"Methodology","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eData Source\u003c/h2\u003e\u003cp\u003eThis study uses data from the Japanese Panel Survey of Consumers (JPSC) conducted by the Institute for Research on Household Economics. The JPSC, initiated in 1993, is one of Japan's preeminent longitudinal surveys, focusing specifically on the economic behavior and life course of women and their families. A key strength of the survey is its detailed collection of asset information, including specific amounts for financial assets, housing value, and various liabilities. The initial sample comprised 1,500 women aged 24\u0026ndash;34. Two refreshment samples of 500 and 836 women in the same age cohort were added in 1997 and 2003, respectively, to maintain the sample's representativeness. The survey employs a drop-off and pick-up questionnaire method, achieving high response rates (over 90%) and panel retention (approximately 70%).\u003c/p\u003e\u003cp\u003eWe use data from 1993 to 2021, covering a 27-year observation window. This period spans multiple economic cycles, from Japan's Lost Decade following the asset bubble collapse to the era of Abenomics, providing an ideal setting to test the stability of asset effects.\u003c/p\u003e\u003cp\u003eThe analytic sample is restricted to women in their first marriage to avoid selection bias associated with prior divorce history. We further restrict the sample to women aged 25 to 55. The lower bound ensures most respondents have completed their education and entered a stable marital phase, while the upper bound avoids complications related to retirement and health.\u003c/p\u003e\u003cp\u003eMissing data on assets are a common challenge in panel surveys. In our sample, asset variables have a missingness rate of approximately 40 percent, primarily due to respondent refusal, uncertainty about asset values, or items not being asked in certain waves. Analyses indicate that missingness on assets is related to age, education, and marital duration, but not significantly associated with the divorce event itself, suggesting the data are missing at random (MAR). To leverage the full dataset and reduce the bias and efficiency loss of complete-case analysis, we employ multiple imputation. We use chained equations (MICE) to generate 20 imputed datasets. The final analytic sample consists of 24,298 person-year observations from 1,794 women, including 211 observed divorce events. Although the number of divorce events is relatively small, it aligns with Japan's low divorce rate and provides sufficient statistical power for robust inference in event history analysis.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eVariable Measurement\u003c/h3\u003e\n\u003cp\u003eThe dependent variable is the occurrence of divorce, coded as a binary indicator (1\u0026thinsp;=\u0026thinsp;divorce occurred in that year, 0\u0026thinsp;=\u0026thinsp;marriage continued). The timing of divorce is determined by self-reports of marital status changes. Because information on the husband is unavailable after the dissolution, we code the divorce event at year t-1 if the respondent reports a divorce in year t. This approach ensures that all information on spousal employment and household finances is measured prior to the event.\u003c/p\u003e\u003cp\u003eThe primary independent variables measure household assets. Net worth is defined as total assets minus total liabilities. Financial assets include liquid assets such as bank deposits, stocks, bonds, and insurance. Non-financial assets primarily consist of the market value of owner-occupied housing. Liabilities include housing loans and other consumer debt. All monetary variables are transformed using the inverse hyperbolic sine (IHS). The IHS transformation is similar to a logarithmic transformation but accommodates zero and negative values, and its coefficients are interpreted similarly. All monetary values are adjusted to 2020 constant yen to remove the effects of inflation.\u003c/p\u003e\u003cp\u003eFamily type is categorized based on the employment status of both spouses. Traditional male-breadwinner families are those where the husband is employed (regularly) and the wife is not in the labor force. Dual-earner families are those where both spouses are employed, regardless of regular or non-regular status. Other categories include female-breadwinner (wife only employed) and other arrangements (e.g., neither spouse employed).\u003c/p\u003e\u003cp\u003eFemale relative economic position is measured using a continuous indicator. Wife's income share is calculated as the wife's annual income divided by the couple's total annual income, ranging from 0 to 1. This variable is set to missing if the total household income is zero.\u003c/p\u003e\u003cp\u003eControl variables are included at three levels. Individual-level controls include education for both wife and husband (college degree or higher vs. less than college) and age at first marriage. Household-level controls include the number of children, a categorical measure of marital duration (1\u0026ndash;5, 6\u0026ndash;10, 11\u0026ndash;15, 16\u0026thinsp;+\u0026thinsp;years) to capture the nonlinear risk profile, and co-residence with parents. Macro-level controls include the annual national GDP growth rate and unemployment rate to account for business cycles, as well as marriage cohort dummy variables to control for period effects.\u003c/p\u003e\n\u003ch3\u003eAnalytical Strategy\u003c/h3\u003e\n\u003cp\u003eWe use discrete-time event history analysis to estimate the risk of divorce(Allison \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). This approach is well-suited for annual panel data, reformulating the survival problem as a binary logistic regression model using a person-year data structure. The baseline model is:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\text{l}\\text{o}\\text{g}\\text{i}\\text{t}\\left(\\text{P}\\right({Divorce}_{it}=1\\left)\\right)=\\alpha\\:+{\\beta\\:}_{1}{Assets}_{it}+{\\beta\\:}_{2}{FamilyType}_{it}+{\\gamma\\:X}_{it}+{\\epsilon\\:}_{it}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ewhere i represents the individual, t represents time (year), \u003cem\u003eAssets\u003c/em\u003e is household net worth, \u003cem\u003eFamilyType\u003c/em\u003e is a vector of family type dummies, and \u003cb\u003eX\u003c/b\u003e is a vector of control variables.\u003c/p\u003e\u003cp\u003eTo test whether the asset effect is heterogeneous by family type (H3), we introduce an interaction term:\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:\\text{l}\\text{o}\\text{g}\\text{i}\\text{t}\\left(\\text{P}\\right({Divorce}_{it}=1\\left)\\right)=\\alpha\\:+{\\beta\\:}_{1}{Assets}_{it}+{\\beta\\:}_{2}{FamilyType}_{it}+{\\beta\\:}_{3}{(Assets\\times\\:\\text{F}\\text{a}\\text{m}\\text{i}\\text{l}\\text{y}\\text{T}\\text{y}\\text{p}\\text{e})}_{it}+{\\gamma\\:X}_{it}+{\\epsilon\\:}_{it}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eIf β₃ is significant, this indicates that asset effects vary by family type, supporting H3. If β₃ is not significant, this supports the consistency hypothesis.\u003c/p\u003e\u003cp\u003eTo test the moderating role of women's relative economic position (H4), we estimate a model interacting assets with the wife's income share:\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:\\text{l}\\text{o}\\text{g}\\text{i}\\text{t}\\left(\\text{P}\\right({Divorce}_{it}=1\\left)\\right)=\\alpha\\:+{\\beta\\:}_{1}{Assets}_{it}+{\\beta\\:}_{2}{Wifeshare}_{it}+{\\beta\\:}_{3}{(Assets\\times\\:\\text{W}\\text{i}\\text{f}\\text{e}\\text{S}\\text{h}\\text{a}\\text{r}\\text{e})}_{it}+{\\gamma\\:X}_{it}+{\\epsilon\\:}_{it}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ewhere \u003cem\u003eWifeShare\u003c/em\u003e includes wife's income share and wife's financial asset share. If β₃ is significantly positive, this supports the bargaining theory prediction in H4. If β₃ is not significant, this supports the universality hypothesis.\u003c/p\u003e\u003cp\u003eAll models use individual-level clustered robust standard errors to address the correlation of multiple observations from the same individual.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eTable 1. Descriptive Results\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"607\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 454px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 153px;\"\u003e\n \u003cp\u003eSummary\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 454px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 153px;\"\u003e\n \u003cp\u003e(N=24,298)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 454px;\"\u003e\n \u003cp\u003eDivorced\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 153px;\"\u003e\n \u003cp\u003e0.01 (0.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 454px;\"\u003e\n \u003cp\u003eHousehold Net Worth (IHS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 153px;\"\u003e\n \u003cp\u003e5.16 (5.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 454px;\"\u003e\n \u003cp\u003eHousehold Financial Assets (IHS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 153px;\"\u003e\n \u003cp\u003e6.09 (2.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 454px;\"\u003e\n \u003cp\u003eHousehold Fixed Assets (IHS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 153px;\"\u003e\n \u003cp\u003e5.81 (3.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 454px;\"\u003e\n \u003cp\u003eHousehold Debt (IHS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 153px;\"\u003e\n \u003cp\u003e4.37 (3.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 454px;\"\u003e\n \u003cp\u003eHousehold type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 153px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 454px;\"\u003e\n \u003cp\u003e\u0026nbsp; Male-breadwinner\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 153px;\"\u003e\n \u003cp\u003e(37.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 454px;\"\u003e\n \u003cp\u003e\u0026nbsp; Dual-earner\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 153px;\"\u003e\n \u003cp\u003e(52.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 454px;\"\u003e\n \u003cp\u003e\u0026nbsp; Female-breadwinner\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 153px;\"\u003e\n \u003cp\u003e(1.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 454px;\"\u003e\n \u003cp\u003e\u0026nbsp; Other\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 153px;\"\u003e\n \u003cp\u003e(8.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 454px;\"\u003e\n \u003cp\u003eWife\u0026apos;s income share\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 153px;\"\u003e\n \u003cp\u003e0.16 (0.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 454px;\"\u003e\n \u003cp\u003eWife\u0026apos;s education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 153px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 454px;\"\u003e\n \u003cp\u003e\u0026nbsp; Below college\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 153px;\"\u003e\n \u003cp\u003e(49.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 454px;\"\u003e\n \u003cp\u003e\u0026nbsp; College or above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 153px;\"\u003e\n \u003cp\u003e(50.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 454px;\"\u003e\n \u003cp\u003eHusband\u0026apos;s education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 153px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 454px;\"\u003e\n \u003cp\u003e\u0026nbsp; Below college\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 153px;\"\u003e\n \u003cp\u003e(51.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 454px;\"\u003e\n \u003cp\u003e\u0026nbsp; College or above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 153px;\"\u003e\n \u003cp\u003e(48.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 454px;\"\u003e\n \u003cp\u003eMarriage Duration (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 454px;\"\u003e\n \u003cp\u003e\u0026nbsp; 1-5years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 153px;\"\u003e\n \u003cp\u003e(10.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 454px;\"\u003e\n \u003cp\u003e\u0026nbsp; 6-10years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 153px;\"\u003e\n \u003cp\u003e(22.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 454px;\"\u003e\n \u003cp\u003e\u0026nbsp; 11-15years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 153px;\"\u003e\n \u003cp\u003e(22.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 454px;\"\u003e\n \u003cp\u003e\u0026nbsp; over 16 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 153px;\"\u003e\n \u003cp\u003e(45.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 454px;\"\u003e\n \u003cp\u003eAge at Marriage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 153px;\"\u003e\n \u003cp\u003e23.89 (2.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 454px;\"\u003e\n \u003cp\u003eNumber of Children\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 153px;\"\u003e\n \u003cp\u003e1.93 (0.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 454px;\"\u003e\n \u003cp\u003eLiving with Parents or not\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 153px;\"\u003e\n \u003cp\u003e0.90 (0.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 454px;\"\u003e\n \u003cp\u003eResidence Size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 153px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 454px;\"\u003e\n \u003cp\u003e\u0026nbsp; Major metropolitan areas\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 153px;\"\u003e\n \u003cp\u003e(25.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 454px;\"\u003e\n \u003cp\u003e\u0026nbsp; Other cities\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 153px;\"\u003e\n \u003cp\u003e(61.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 454px;\"\u003e\n \u003cp\u003e\u0026nbsp; Towns/Villages\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 153px;\"\u003e\n \u003cp\u003e(13.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 454px;\"\u003e\n \u003cp\u003eAnnual GDP Growth Rate (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 153px;\"\u003e\n \u003cp\u003e0.73 (2.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 454px;\"\u003e\n \u003cp\u003eUnemployment Rate (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 153px;\"\u003e\n \u003cp\u003e3.91 (0.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 454px;\"\u003e\n \u003cp\u003eBirth cohort\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 153px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 454px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026le;1964 birth cohort\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 153px;\"\u003e\n \u003cp\u003e(38.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 454px;\"\u003e\n \u003cp\u003e\u0026nbsp; 1965\u0026ndash;1969 birth cohort\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 153px;\"\u003e\n \u003cp\u003e(23.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 454px;\"\u003e\n \u003cp\u003e\u0026nbsp; 1970\u0026ndash;1974 birth cohort\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 153px;\"\u003e\n \u003cp\u003e(13.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 454px;\"\u003e\n \u003cp\u003e\u0026nbsp; 1975\u0026ndash;1979 birth cohort\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 153px;\"\u003e\n \u003cp\u003e(11.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 454px;\"\u003e\n \u003cp\u003e\u0026nbsp; 1980\u0026ndash;1989 birth cohort\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 153px;\"\u003e\n \u003cp\u003e(13.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e1.Values are percentage (%) for categorical variables and mean (SD) for continuous variables.\u003c/p\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eDescriptive Statistics\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the descriptive statistics for the analytic sample. Across 24,298 person-year observations, the annual divorce rate is 1.0 percent. This figure is consistent with Japan's low aggregate divorce rate and underscores the relative stability of marriage in this context. The mean age at first marriage for respondents is 23.89 years, and the average number of children is 1.93. Regarding educational attainment, 50.5 percent of wives and 48.6 percent of husbands hold a college degree or higher. The distribution of marital duration is skewed toward longer unions; 45 percent of observations are from marriages lasting 16 years or more, indicating the sample is predominantly composed of households in the middle and later stages of marriage.\u003c/p\u003e\u003cp\u003eThe household economic structure shows considerable diversity. Dual-earner households represent the most common arrangement at 52.7 percent of person-years, confirming this as the modal family type. Traditional male-breadwinner households account for 37.4 percent. While still a substantial portion, this figure shows they are no longer the dominant arrangement. Female-breadwinner households (1.3 percent) and other types (8.6 percent) make up the remainder. The mean for the wife's income share is 0.16 (SD = 0.18), reflecting significant variation in women's economic contributions. This distribution illustrates the historical transition in Japan from a male-breadwinner system toward a dual-earner model.\u003c/p\u003e\u003cp\u003eIn terms of assets, the mean of the IHS-transformed net worth is 5.16 with a large standard deviation (5.45), suggesting considerable wealth inequality. The mean for IHS-transformed financial assets is 6.09 (SD = 2.42), and the mean for non-financial assets is 5.81 (SD = 3.80). The mean for liabilities is 4.37 (SD = 3.76). The large standard deviations relative to the means indicate substantial heterogeneity in asset and liability holdings across the sample.\u003c/p\u003e\u003cp\u003eThe marriage cohort distribution reveals a distinct generational structure. Individuals born in or before 1964, representing the post-war baby boom generation, comprise 38.2 percent of the sample. Macroeconomic indicators reflect Japan's long period of economic stagnation. The average annual GDP growth rate during the observation period is 0.73 percent (SD = 2.02), and the average unemployment rate is 3.91 percent.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDiscrete-Time Logistic Regression Models of Divorce Risk: Main Effects of Household Assets and Family Type\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eModel 1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eModel 2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eModel 3\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eModel 4\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHousehold Net Worth (IHS)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.033\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.034\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.011)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.011)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHousehold Financial Assets (IHS)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.123\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.124\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.026)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.025)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHousehold Fixed Assets (IHS)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.111\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.111\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.031)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.031)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMortgage Debt (IHS)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.012\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.031)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.031)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eOther Loans/Debt (IHS)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.019\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.029)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.028)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eWife's Income Share\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.847\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.614\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.311)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.325)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFamily type: Male-breadwinner (ref.)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDual-earner\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.838\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.884\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.187)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.191)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale-breadwinner\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.634\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.386\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.302)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.331)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.735\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.606\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.286)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.293)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eWife Edu: College or above\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.233\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.128\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.342\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.224\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.171)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.172)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.171)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.171)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHusband Edu: College or above\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.254\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.151\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.220\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.116\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.167)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.167)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.167)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.167)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMarriage Duration (ref.1-5years)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarriage Duration (6-10years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.159\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.057\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.222\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.272)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.272)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.272)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.271)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarriage Duration (11-15years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.029\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.374\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.216\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.554\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.280)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.288)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.277)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.286)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarriage Duration (over16years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.384\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.055\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.252\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.165\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.298)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.309)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.289)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.300)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge at Marriage\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.087\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.053\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.094\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.060\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.034)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.033)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.033)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.033)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNumber of Children\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.036\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.071\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.034\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.099)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.098)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.097)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLiving with Parents or Not\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.255\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.436\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.309\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.472\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.287)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.288)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.285)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.286)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAnnual GDP Growth Rate (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.047\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.046\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.046\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.043\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.033)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.033)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.033)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.033)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eUnemployment Rate (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.108\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.086\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.125\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.103\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.080)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.081)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.079)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.080)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eResidence Size: Major metropolitan(ref.)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther cities\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.043\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.097\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.077\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.166)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.170)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.167)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.171)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTowns/Villages\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.295\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.174\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.359\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.233\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.269)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.276)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.270)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.276)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBirth Cohort: ≤1964 (ref.)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1965–1969 birth cohort\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.673\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.660\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.682\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.653\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.210)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.214)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.213)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.215)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1970–1974 birth cohort\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.632\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.585\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.631\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.562\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.247)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.253)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.251)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.259)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1975–1979 birth cohort\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.940\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.862\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.956\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.856\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.247)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.247)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.250)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.253)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1980–1989 birth cohort\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.675\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.574\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.673\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.553\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.256)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.260)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.255)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.259)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-3.980\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-4.127\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-4.039\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-4.100\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.973)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.976)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.971)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.977)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24298\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24298\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e24298\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e24298\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePseudo R-squared\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.065\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.090\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.071\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.095\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eRobust standard errors clustered at individual level in parentheses\u003c/p\u003e\u003cp\u003eReference categories: Male-breadwinner household, Below College education, ≤ 1964 birth cohort\u003c/p\u003e\u003cp\u003e+ p \u0026lt; 0.10, * p \u0026lt; 0.05, ** p \u0026lt; 0.01, *** p \u0026lt; 0.001\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDiscrete-Time Logistic Regression Models of Divorce Risk: Interaction Effects between Assets and Family Type\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eModel 5\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eModel 6\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eModel 7\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eModel 8\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHousehold Net Worth (IHS)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.050\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.025\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.022)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.016)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFamily type: Male-breadwinner (ref.)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDual-earner\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.764\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.198)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.337)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale-breadwinner\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.616\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.732\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.332)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.535)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.661\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.154\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.306)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.459)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNet Worth × Male-breadwinner (ref.)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNet Worth × Dual-earner\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.025)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNet Worth × Female-breadwinner\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.044)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNet Worth × Other\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.037)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHousehold Financial Assets (IHS)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.212\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.143\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.044)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.034)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFinancial Assets × Male-breadwinner (ref.)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFinancial Assets × Dual-earner\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.107\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.054)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFinancial Assets × Female-breadwinner\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.173\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.095)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFinancial AssetsOther × Other\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.093\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.083)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHousehold Fixed Assets (IHS)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.125\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.119\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.047)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.038)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFixed Assets × Male-breadwinner (ref.)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFixed Assets × Dual-earner\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.047)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFixed Assets × Female-breadwinner\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.086)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFixed Assets × Other\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.071)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMortgage Debt (IHS)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.014\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.031)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.031)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eOther Loans/Debt (IHS)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.020\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.028)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.028)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eWife's Income Share\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.956\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.278\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.343)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.549)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNet Worth × Wife's Income Share\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.036\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.047)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFinancial Assets × Wife's Income Share\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.069\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.103)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFixed Assets × Wife's Income Share\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.022\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.084)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eWife Edu: College or above\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.231\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.137\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.336\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.235\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.171)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.171)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.171)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.171)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHusband Edu: College or above\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.254\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.135\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.225\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.107\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.166)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.166)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.167)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.167)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMarriage Duration (ref.1-5years)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarriage Duration (6-10years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.157\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.059\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.220\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.272)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.275)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.272)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.271)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarriage Duration (11-15years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.033\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.378\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.218\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.558\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.280)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.292)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.277)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.285)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarriage Duration (over16years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.384\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.036\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.246\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.159\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.298)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.310)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.289)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.300)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge at Marriage\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.086\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.054\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.094\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.060\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.034)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.032)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.033)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.032)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNumber of Children\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.035\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.068\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.032\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.099)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.099)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.096)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLiving with Parents or Not\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.250\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.456\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.314\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.484\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.288)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.289)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.285)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.287)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAnnual GDP Growth Rate (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.047\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.046\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.045\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.044\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.033)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.033)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.033)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.033)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eUnemployment Rate (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.106\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.089\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.120\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.108\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.080)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.081)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.079)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.081)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eResidence Size: Major metropolitan(ref.)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther cities\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.041\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.094\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.077\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.166)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.170)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.167)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.171)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTowns/Villages\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.301\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.180\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.358\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.229\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.270)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.277)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.270)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.276)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBirth Cohort: ≤1964 (ref.)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1965–1969 birth cohort\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.675\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.665\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.678\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.660\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.210)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.213)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.212)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.215)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1970–1974 birth cohort\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.637\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.614\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.627\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.584\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.246)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.249)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.251)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.254)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1975–1979 birth cohort\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.943\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.857\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.953\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.857\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.246)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.248)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.249)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.254)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1980–1989 birth cohort\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.679\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.569\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.673\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.551\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.255)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.260)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.255)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.260)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-3.924\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-3.684\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-4.032\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-4.021\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.985)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(1.005)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.971)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.970)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24298\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24298\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e24298\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e24298\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePseudo R-squared\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.065\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.092\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.071\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.095\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eRobust standard errors clustered at individual level in parentheses\u003c/p\u003e\u003cp\u003eReference categories: Male-breadwinner household, Below College education, ≤ 1964 birth cohort\u003c/p\u003e\u003cp\u003e+ p \u0026lt; 0.10, * p \u0026lt; 0.05, ** p \u0026lt; 0.01, *** p \u0026lt; 0.001\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eMain Effects of Assets and Family Type\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the baseline models estimating the association between assets, family type, and divorce risk. Model 1 shows that household net worth is significantly associated with a lower risk of divorce (β = -0.033, p \u0026lt; 0.01), supporting Hypothesis H1a. This finding confirms the general protective function of economic resources. Family type also emerges as a strong predictor of marital stability, consistent with Hypothesis H2. Compared to traditional male-breadwinner households, dual-earner households face a significantly higher risk of divorce (β = 0.838, p \u0026lt; 0.001). The risk is highest for female-breadwinner households (β = 2.634, p \u0026lt; 0.001), and other household types also show elevated risk (β = 0.735, p \u0026lt; 0.05). These differences remain significant after controlling for economic resources, suggesting the traditional marital arrangement itself has an independent stabilizing association.\u003c/p\u003e\u003cp\u003eModel 2 decomposes total assets into different types to test hypothesis H1b. Both financial assets (β = -0.123, p \u0026lt; 0.001) and nonfinancial assets, primarily housing (β = -0.111, p \u0026lt; 0.001), significantly reduce divorce risk. Although the coefficient for financial assets is slightly larger than that for nonfinancial assets (-0.123 vs -0.111), statistical testing reveals no significant difference in their protective effects (χ² = 0.08, p = 0.774). This finding suggests that in the Japanese context, liquid financial assets and illiquid real estate offer similar protective functions. The coefficients for housing loans and other loans are not significant. This pattern partially supports H1b. While different asset types are protective, the effect of real estate is not stronger than that of financial assets. The nonsignificant coefficient for mortgage debt likely reflects Japan's specific circumstances, where mortgages typically accompany homeownership and the protective effect of property offsets the pressure from debt. The nonsignificant effect of other debt may reflect heterogeneity in the sample, which includes both consumer debt and potentially educational loans whose opposing effects on marriage cancel out in the aggregate.\u003c/p\u003e\u003cp\u003eModel 3 introduces the wife's income share as an alternative measure of household economic structure. The wife's income share positively predicts divorce risk (β = 2.847, p \u0026lt; 0.001). This result is consistent with findings from Western societies (Bertrand et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Schwartz and Gonalons-Pons \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), suggesting that a woman's increased relative economic contribution is associated with a higher risk of dissolution. This may reflect two mechanisms. On one hand, economic independence reduces women's dependence on marriage. On the other hand, in Japanese society with strong gender norms, deviation from traditional role divisions may generate additional marital strain. Notably, the protective effect of net worth remains robust (β = -0.034, p \u0026lt; 0.01) after controlling for relative economic contributions.\u003c/p\u003e\u003cp\u003eModel 4 includes both disaggregated asset types and the wife's income share. Financial assets (β = -0.124, p \u0026lt; 0.001) and non-financial assets (β = -0.111, p \u0026lt; 0.001) retain their protective associations, while a higher wife's income share remains linked to higher divorce risk (β = 2.614, p \u0026lt; 0.001). The stability of these coefficients across models reinforces the main findings.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eHeterogeneity of the Asset Effect\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e tests whether the asset effect varies by family type (H3). Model 5 introduces interaction terms between net worth and family type. All interaction terms prove nonsignificant: assets × dual-earner (β = 0.023, ns), assets × female-breadwinner (β = 0.005, ns), and assets × other types (β = 0.024, ns). This indicates that the protective effect of assets remains stable across family types, failing to support specialization theory's prediction of heterogeneous asset effects.\u003c/p\u003e\u003cp\u003eModel 6 explores whether different asset types exhibit heterogeneity across family structures. The interaction between financial assets and dual-earner families reaches significance (β = 0.107, p = 0.049), while the interaction with female-breadwinner families approaches marginal significance (β = 0.173, p = 0.070). These significant coefficients suggest that financial assets may function differently in nontraditional families. This finding partially supports bargaining theory's prediction that the liquidity of financial assets may alter their function in families where wives have independent income. However, the substantive impact of this heterogeneity remains limited. In dual-earner families, the combined effect of financial assets equals − 0.212 + 0.107 = -0.105, which still significantly reduces divorce risk. The protective effect merely weakens from − 0.212 in traditional families to -0.105, a reduction of approximately 50%. This demonstrates that even in dual-earner families, financial assets remain a protective factor for marital stability, albeit with somewhat diminished strength. In contrast, all interaction terms for non-financial assets (real estate) are non-significant, indicating the protective role of housing is highly consistent across all family types. This contrast highlights a key distinction. The role of liquid assets is conditional on the household employment structure, whereas the role of illiquid real estate as the foundation of family life remains uniformly protective.\u003c/p\u003e\u003cp\u003eModels 7 and 8 test whether wife's income share moderates asset effects, examining hypotheses H4a and H4b. Model 7 introduces an interaction between net assets and wife's income share, which proves nonsignificant (β = -0.036, ns). Model 8 extends the analysis to specific asset types, finding that both financial assets × wife's income share (β = 0.069, ns) and nonfinancial assets × wife's income share (β = 0.022, ns) are nonsignificant. These results consistently support H4b rather than H4a, indicating that the protective effect of assets operates independently of women's relative economic position in marriage.\u003c/p\u003e\u003cp\u003eThe findings from the two tables exhibit remarkable consistency. Assets, both financial and real estate, are associated with lower divorce risk, with financial assets and housing showing statistically indistinguishable protective effects, suggesting that different forms of economic resources all contribute to marital stability. Traditional families have lower baseline divorce risk than other family types, while increases in women's economic contribution relate to higher divorce risk. Most importantly, the protective function of assets remains stable across different family structures and relative economic arrangements. These findings suggest that despite fundamental transformations in family structure, economic security as a basic need continues to provide universal value. Regardless of how families organize themselves and regardless of women's economic position within the household, assets protect marriage by alleviating economic stress, providing psychological security, and increasing the opportunity costs of divorce. This functional stability transcends differences in household power structures and divisions of labor, revealing the foundational role of economic resources in intimate relationships.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eRobustness check\u003c/h2\u003e\u003cp\u003eWe conducted a series of supplementary analyses to test the robustness of the core findings.\u003c/p\u003e\u003cp\u003eFirst, we examined a binary indicator of homeownership in place of continuous real estate value. As shown in Table A1 (Model 11) and A2 (Model 19), homeownership is associated with a 0.730 to 0.720 reduction in the log-odds of divorce (p \u0026lt; 0.001). This translates to an odds ratio of approximately 0.48, suggesting homeowners have roughly half the odds of divorcing as renters. Interaction analyses confirmed this strong protective effect is consistent across all family types and levels of wife's income share. We then restricted the sample to homeowners (Table A1, Models 13–14 and Table A2, Models 21–22). Among owners, higher housing value remains associated with lower divorce risk (β ≈ -0.25, p \u0026lt; 0.001), while the coefficient for housing loans is near zero and non-significant.\u003c/p\u003e\u003cp\u003eSecond, the main analysis categorizes family type into four groups, but treating all dual-earner families as a single category may mask internal heterogeneity. The dual structure of Japan's labor market creates significant differences between regular and nonregular employment in wages, stability, and benefits. Recent growth in dual-earner families stems primarily from expansion of women's nonregular employment, meaning that nominally dual-earner families may differ substantially in economic structure. Therefore, we refined family type into five categories (Table A3). The first type is traditional male-breadwinner families where husbands work in regular employment while wives do not participate in the labor force, comprising 37.4%. The second type consists of dual-earner families where husbands have regular employment and wives have nonregular employment, comprising 35.2% and representing the most common dual-earner pattern. The third type includes dual-earner families where both spouses engage in regular employment, comprising 17.5%. The fourth type is female-breadwinner families at 1.3%. The fifth type encompasses other arrangements at 8.6%. Analysis in Table A3 shows that the protective effect of assets remains significant across all five family types (Models 1–2). More importantly, interactions between financial assets and all three nontraditional family types reach marginal significance at the 10% level: with dual-earner families where wives have nonregular employment (β = 0.111, p \u0026lt; 0.10), with dual-earner families where both spouses have regular employment (β = 0.129, p \u0026lt; 0.10), and with female-breadwinner families (β = 0.174, p \u0026lt; 0.10). In contrast, interaction terms for nonfinancial assets prove entirely nonsignificant (Model 4), again confirming the universality of housing's protective effect. Interaction terms for net assets also show no significance (Model 2), indicating that when asset types are not distinguished, heterogeneous effects remain obscure and asset effect results remain robust.\u003c/p\u003e\u003cp\u003eThird, to test for potential threshold effects, we replaced the continuous asset measures with quartiles (Table A4). Results show that all quartiles above the first (Q1) are associated with significantly lower divorce risk, though not in a perfectly linear pattern. For net worth, the protective effect peaks at the third quartile (Q3). Notably, the interaction between the highest quartile (Q4) of financial assets and both dual-earner and female-breadwinner households is significant at the p \u0026lt; 0.10 level. This finding echoes the results from Table A3, confirming that the heterogeneity in the financial asset effect is most pronounced at the top of the wealth distribution. Among the wealthiest 25 percent of families, the protective association of financial assets is significantly weaker for non-traditional households. All interactions for non-financial asset quartiles remain non-significant.\u003c/p\u003e\u003cp\u003eTaken together, these robustness checks confirm three key conclusions: (1) The overall protective effect of assets is robust across specifications. (2) The protective effect of financial (liquid) assets is indeed weaker in non-traditional households, a distinction that becomes sharper when accounting for employment quality and wealth levels. (3) The protective effect of non-financial assets (housing) is highly universal and consistent. These findings not only verify the reliability of the main analysis but also deepen our understanding of the differentiated functions of liquid and illiquid assets during a period of family transition.\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion and Conclusion","content":"\u003cp\u003eThis study used 27 years of Japanese panel data to examine whether the protective association between economic resources and marital stability is contingent on household economic structure. Against a backdrop of a sustained shift from male-breadwinner to dual-earner models, the findings show that assets, on the whole, are significantly associated with a lower divorce risk across all family types. This general pattern, however, masks a critical heterogeneity. While the protective effects of net worth and illiquid assets such as real estate are consistent, the protective association of financial assets is significantly weaker in dual-earner households compared to traditional ones. This result lends partial support to bargaining theories predicting a shifting function for liquid assets, while also affirming the economic stress perspective on the fundamental protective role of economic security. Furthermore, a wife's income share, while predictive of divorce risk itself, does not moderate the asset effect. This suggests the function of assets and intrahousehold power relations operate as two relatively independent dimensions. The former reflects a foundational need for economic security, while the latter captures shifts in gender relations and cultural norms.\u003c/p\u003e\u003cp\u003eThe most important contribution of this study lies in distinguishing heterogeneous patterns across different asset types. The protective association of illiquid assets, primarily real estate, is entirely consistent across all family structures. The effect of liquid financial assets, however, is demonstrably heterogeneous. This divergence stems from the fundamental properties of the assets themselves. The universality of the real estate effect is likely rooted in several overlapping mechanisms. As real estate constitutes a high proportion of Japanese household assets, the home is not just a financial instrument but the physical and emotional locus of family life. This illiquidity makes housing function less as capital to facilitate an exit and more as a marital-specific sunk cost. Its division involves complex legal procedures and high transaction costs, including the disruption of social networks and children's schooling. Moreover, within Japanese familistic traditions, the home carries symbolic meaning tied to intergenerational continuity, elevating its value beyond mere economic calculation. In contrast, the liquidity of financial assets makes their function more sensitive to intrahousehold power dynamics. In dual-earner households, a wife's independent income alters her relationship to the family's financial portfolio. The divisibility and transferability of these assets may allow for their strategic use during marital conflict. Yet, even in this context, financial assets remain a protective factor (net effect = -0.105). The association is merely attenuated, not reversed, indicating that the economic security function of liquid assets persists even as family power structures evolve.\u003c/p\u003e\u003cp\u003eThe predominance of this universal protective pattern, despite the heterogeneity of financial assets, suggests that standardized institutional features of modern society impose common economic challenges. Expenditures on housing, education, healthcare, and retirement are largely inelastic and are not adjusted based on a household's internal division of labor. The credit evaluation systems of financial institutions, the competitive mechanisms of the educational system, and the coverage rules of health insurance do not differentiate by family type. This institutional homogeneity compels a functional convergence in how families use assets to manage risk. As neo institutional theory suggests, while the external forms of the family may diversify, the core mechanisms for coping with economic uncertainty tend to converge (Meyer and Rowan \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e1977\u003c/span\u003e). In the East Asian context, familistic traditions imbue marriage with a social meaning that transcends the couple, framing asset accumulation as a core familial and intergenerational responsibility. Critically, given the dominance of real estate in household portfolios, the modest heterogeneity found in financial assets has a limited impact on the \u003cem\u003etotal\u003c/em\u003e asset effect.\u003c/p\u003e\u003cp\u003eThe study's primary implication is that marital stability is emerging as a mechanism for the reproduction of wealth inequality. Although the protective role of financial assets is attenuated in dual-earner households, the overall finding is that assets remain a crucial bulwark for marriage. This suggests that intimate relationships themselves are increasingly stratified by economic resources. This finding resonates with Cherlin’s (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) insights on the deinstitutionalization of marriage but reveals a more complex pattern of class-based divergence. For affluent households, ample assets provide a robust buffer, securing marital stability even as family structures change. Stable marriage, in turn, functions as an institutional platform for further asset accumulation, educational investment in children, and the expansion of social capital. Conversely, for those lacking economic resources, marriage itself can become an unattainable goal rather than a starting point. A high economic threshold may lead individuals to delay or forgo marriage, and those who do marry face heightened economic vulnerability. This divergence constitutes a central paradox of contemporary family change. Marriage becomes more stable and valuable for those capable of sustaining it, while becoming inaccessible to those who most need its protective functions.\u003c/p\u003e\u003cp\u003eThis relational inequality has powerful intergenerational consequences. Stable marriage facilitate asset accumulation through economies of scale, risk pooling, and long-term investment horizons. These accumulated assets, in turn, further protect the union from dissolution, creating a virtuous cycle. Conversely, families without assets face higher dissolution risks due to economic stress, and divorce further erodes their economic foundation, leading to a cycle of cumulative disadvantage. Children raised in stable, high-asset households benefit from greater emotional support, educational investment, and social networks, advantages that translate into their own adult economic and marital success. As McLanahan (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) argued, this divergence in marriage patterns is becoming a critical mechanism in the reproduction of social class.\u003c/p\u003e\u003cp\u003eIn the context of East Asia's low fertility rates and continuously delayed age at first marriage, this study's findings carry particular real-world significance. If marital stability depends heavily on economic foundations while younger generations face employment instability, declining housing accessibility, and stagnant wealth mobility, young people may rationally choose to postpone or forgo marriage. Japan's rising age at first marriage and climbing rates of lifelong singlehood may partly reflect this rational calculation. Economic inequality generates inequality in marital opportunities, which in turn exacerbates declining fertility and crises of social sustainability.\u003c/p\u003e\u003cp\u003eThis study has several limitations that offer directions for future research. First, the analysis does not establish causal relationships. Despite the use of longitudinal data, observational studies cannot fully overcome endogeneity. Marital stability and asset accumulation may be codetermined by unobserved factors such as risk preferences or relationship commitment. Future work could pursue causal identification through instrumental variable strategies or quasi-experimental designs. Second, this study used wife's income share to measure relative economic position but could not measure her actual control over household assets. While wives in Japan often manage household finances, it is unclear if this administrative role translates into bargaining power. Research with more direct measures of financial decision-making and asset control is needed. Third, our focus on dissolution as the outcome does not capture the multidimensional nature of marital quality, such as satisfaction or conflict. Bargaining theory predictions might find stronger support if applied to these more nuanced outcomes.\u003c/p\u003e\u003cp\u003eThe findings offer differentiated policy implications. The universal protective effect of real estate supports positioning housing policy as a core component of family policy. Enhancing housing affordability for young families is not only an economic goal but a social one. However, the heterogeneity of financial asset effects suggests that as dual-earner households become the norm, traditional savings incentives may require recalibration. The stability advantage of traditional families presents a policy dilemma. While existing institutions support this model, dual-earner families are now the majority yet face higher dissolution risks and institutional frictions. A potential path forward is to increase support for dual-earner households through expanded childcare, enhanced paternity leave, and work-life balance initiatives, thereby reducing the institutional gap between family types. The more fundamental challenge is to break the cycle of inequality reproduction. If assets are crucial for all families but are distributed unequally, policies promoting asset accumulation must prioritize low-wealth households. This requires integrating marital stability policy with broader wealth redistribution efforts.\u003c/p\u003e\u003cp\u003eBy differentiating between asset types, this study reveals the complex function of economic resources in an era of family transition. The key finding is that the protective association of liquid assets is conditional on family type, while the protection from illiquid assets is universal. This differentiated pattern lends partial support to bargaining theory while simultaneously confirming the foundational role of economic security. While family forms are in flux, the human need for security and stability remains a constant and constitutive element of intimate relationships. When marital stability itself becomes a product of economic stratification, intimacy ceases to be a purely private emotional bond and becomes an integral component of the social stratification system. This institutionalization of relational inequality marks a profound paradox of modernity. The process of individualization, while appearing to expand choice, may in fact be reproducing new forms of inequality through economic mechanisms. Understanding this paradox proves essential for grasping the nature of contemporary family change and provides crucial insights for formulating effective social policies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eJiajie, Zhang conducted the data analysis and interpretation independently. She wrote and reviewed the main manuscript text.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAllison, P. D. (2014). \u003cem\u003eEvent history and survival analysis\u003c/em\u003e (2nd ed.). Los Angeles: SAGE. https://doi.org/10.4135/9781452270029\u003c/li\u003e\n\u003cli\u003eBecker, G. S., 1930. (1991). \u003cem\u003eA Treatise on the Family\u003c/em\u003e (Enl.). Cambridge, Mass: Harvard University Press. https://go.exlibris.link/qMbg6jtt\u003c/li\u003e\n\u003cli\u003eBertrand, M., Kamenica, E., \u0026amp; Pan, J. (2015). Gender Identity and Relative Income within Households. \u003cem\u003eThe Quarterly Journal of Economics\u003c/em\u003e, \u003cem\u003e130\u003c/em\u003e(2), 571\u0026ndash;614. https://doi.org/10.1093/qje/qjv001\u003c/li\u003e\n\u003cli\u003eBoertien, D., \u0026amp; H\u0026auml;rk\u0026ouml;nen, J. (2019). Why Does Women\u0026rsquo;s Education Stabilize Marriages? The Role of Marital Attraction and Barriers to Divorce. \u003cem\u003eDemographic research\u003c/em\u003e, \u003cem\u003e38\u003c/em\u003e(Journal Article), 1241\u0026ndash;1276. https://doi.org/10.4054/DemRes.2018.38.41\u003c/li\u003e\n\u003cli\u003eBram De Rock, Mariia Kovaleva, \u0026amp; Tom Potoms. (2023). A Spouse and A House Are All We Need? Housing Demand, Labor Supply and Divorce over the Lifecycle. \u003cem\u003eIFS Working Papers\u003c/em\u003e, (No. 23/35). https://ideas.repec.org/p/ifs/ifsewp/23-35.html\u003c/li\u003e\n\u003cli\u003eBrinton, M. C., \u0026amp; Nagase, N. (2017). The Gender Division of Labor and Second Births: Labor Market Institutions and Fertility in Japan. \u003cem\u003eDemographic Research\u003c/em\u003e, \u003cem\u003e36\u003c/em\u003e(36, Art. 11), 339\u0026ndash;370. https://doi.org/10.4054/DemRes.2017.36.11\u003c/li\u003e\n\u003cli\u003eBrinton, M. C., \u0026amp; Oh, E. (2019). Babies, Work, or Both? Highly Educated Women\u0026rsquo;s Employment and Fertility in East Asia. \u003cem\u003eAmerican Journal of Sociology\u003c/em\u003e, \u003cem\u003e125\u003c/em\u003e(1), 105\u0026ndash;140. https://doi.org/10.1086/704369\u003c/li\u003e\n\u003cli\u003eCabinet Office, G. of J. (2024). \u003cem\u003ePublic Opinion Survey on Gender Equal Participation Society (Danjyo kyoudou sankaku shakai ni kansuru yoron chousa)\u003c/em\u003e. Tokyo: Cabinet Office. https://survey.gov-online.go.jp/202502/r06/r06-danjo/gairyaku.pdf\u003c/li\u003e\n\u003cli\u003eCheng, Y. A. (2016). More education, fewer divorces? Shifting education differentials of divorce in Taiwan from 1975 to 2010. \u003cem\u003eDemographic Research\u003c/em\u003e, \u003cem\u003e34\u003c/em\u003e, 927\u0026ndash;942.\u003c/li\u003e\n\u003cli\u003eCherlin, A. J. (2010). Demographic Trends in the United States: A Review of Research in the 2000s. \u003cem\u003eJournal of Marriage and Family\u003c/em\u003e, \u003cem\u003e72\u003c/em\u003e(3), 403\u0026ndash;419. https://doi.org/10.1111/j.1741-3737.2010.00710.x\u003c/li\u003e\n\u003cli\u003eConger, R. D., Conger, K. J., \u0026amp; Martin, M. J. (2010). Socioeconomic status, family processes, and individual development. \u003cem\u003eJournal of marriage and family\u003c/em\u003e, \u003cem\u003e72\u003c/em\u003e(3), 685\u0026ndash;704.\u003c/li\u003e\n\u003cli\u003eConger, R. D., Elder, G. H. J., Lorenz, F. O., Conger, K. J., Simons, R. L., Whitbeck, L. B., et al. (1990). Linking economic hardship to marital quality and instability. \u003cem\u003eJournal of Marriage and the Family\u003c/em\u003e, \u003cem\u003e52\u003c/em\u003e(3), 643\u0026ndash;656. https://doi.org/10.2307/352931\u003c/li\u003e\n\u003cli\u003eCooke, Lynn P., Erola, J., Evertsson, M., G\u0026auml;hler, M., H\u0026auml;rk\u0026ouml;nen, J., Hewitt, B., et al. (2013). Labor and Love: Wives\u0026rsquo; Employment and Divorce Risk in its Socio-political Context. \u003cem\u003eSocial politics\u003c/em\u003e, \u003cem\u003e20\u003c/em\u003e(4), 482\u0026ndash;509. https://doi.org/10.1093/sp/jxt016\u003c/li\u003e\n\u003cli\u003eCooke, Lynn Prince. (2006). \u0026ldquo;Doing\u0026rdquo; Gender in Context: Household Bargaining and Risk of Divorce in Germany and the United States. \u003cem\u003eAmerican Journal of Sociology\u003c/em\u003e, \u003cem\u003e112\u003c/em\u003e(2), 442\u0026ndash;472. https://doi.org/10.1086/506417\u003c/li\u003e\n\u003cli\u003eD\u0026rsquo;Ambrosio, C., J\u0026auml;ntti, M., \u0026amp; Lepinteur, A. (2020). Money and Happiness: Income, Wealth and Subjective Well-Being. \u003cem\u003eSocial indicators research\u003c/em\u003e, \u003cem\u003e148\u003c/em\u003e(1), 47\u0026ndash;66. https://doi.org/10.1007/s11205-019-02186-w\u003c/li\u003e\n\u003cli\u003eDew, J. (2007). Two Sides of the Same Coin? The Differing Roles of Assets and Consumer Debt in Marriage. \u003cem\u003eJournal of Family and Economic Issues\u003c/em\u003e, \u003cem\u003e28\u003c/em\u003e(1), 89\u0026ndash;104. https://doi.org/10.1007/s10834-006-9051-6\u003c/li\u003e\n\u003cli\u003eDew, J. (2009). The Gendered Meanings of Assets for Divorce. \u003cem\u003eJournal of family and economic issues\u003c/em\u003e, \u003cem\u003e30\u003c/em\u003e(1), 20\u0026ndash;31. https://doi.org/10.1007/s10834-008-9138-3\u003c/li\u003e\n\u003cli\u003eDew, J. (2011a). Financial Issues and Relationship Outcomes Among Cohabiting Individuals. \u003cem\u003eFamily relations\u003c/em\u003e, \u003cem\u003e60\u003c/em\u003e(2), 178\u0026ndash;190. https://doi.org/10.1111/j.1741-3729.2010.00641.x\u003c/li\u003e\n\u003cli\u003eDew, J. (2011b). The Association Between Consumer Debt and the Likelihood of Divorce. \u003cem\u003eJournal of family and economic issues\u003c/em\u003e, \u003cem\u003e32\u003c/em\u003e(4), 554\u0026ndash;565. https://doi.org/10.1007/s10834-011-9274-z\u003c/li\u003e\n\u003cli\u003eDew, J., \u0026amp; Xiao, J. J. (2013). Financial Declines, Financial Behaviors, and Relationship Satisfaction during the Recession. \u003cem\u003eJournal of Financial Therapy\u003c/em\u003e, \u003cem\u003e4\u003c/em\u003e, 1\u0026ndash;20. https://doi.org/10.4148/jft.v4i1.1723\u003c/li\u003e\n\u003cli\u003eDiMaggio, P. J., \u0026amp; Powell, W. W. (1983). The Iron Cage Revisited: Institutional Isomorphism and Collective Rationality in Organizational Fields. \u003cem\u003eAmerican sociological review\u003c/em\u003e, \u003cem\u003e48\u003c/em\u003e(2), 147\u0026ndash;160. https://doi.org/10.2307/2095101\u003c/li\u003e\n\u003cli\u003eEads, A., \u0026amp; Tach, L. (2016). Wealth and Inequality in the Stability of Romantic Relationships. \u003cem\u003eRSF : Russell Sage Foundation journal of the social sciences\u003c/em\u003e, \u003cem\u003e2\u003c/em\u003e(6), 197\u0026ndash;224. https://doi.org/10.7758/rsf.2016.2.6.10\u003c/li\u003e\n\u003cli\u003eFarnham, M., Schmidt, L., \u0026amp; Sevak, P. (2011). House Prices and Marital Stability. \u003cem\u003eThe American economic review\u003c/em\u003e, \u003cem\u003e101\u003c/em\u003e(3), 615\u0026ndash;619. https://doi.org/10.1257/aer.101.3.615\u003c/li\u003e\n\u003cli\u003eFederal Reserve Board. (2022). Survey of Consumer Finances, 2022. Washington, DC: Board of Governors of the Federal Reserve System. https://www.federalreserve.gov/econres/scfindex.htm\u003c/li\u003e\n\u003cli\u003eGender Equality Bureau. (2022). \u003cem\u003eThe White Paper on Gender Equality 2022\u003c/em\u003e. Tokyo: Cabinet Office, Government of Japan. https://www.gender.go.jp/about_danjo/whitepaper/r04/gaiyou/pdf/r04_gaiyou_en.pdf\u003c/li\u003e\n\u003cli\u003eGonalons-Pons, P., \u0026amp; Gangl, M. (2021). Marriage and Masculinity: Male-Breadwinner Culture, Unemployment, and Separation Risk in 29 Countries. \u003cem\u003eAmerican Sociological Review\u003c/em\u003e, \u003cem\u003e86\u003c/em\u003e(3), 465\u0026ndash;502. https://doi.org/10.1177/00031224211012442\u003c/li\u003e\n\u003cli\u003eGray, J. S. (1998). Divorce-Law Changes, Household Bargaining, and Married Women\u0026rsquo;s Labor Supply. \u003cem\u003eThe American economic review\u003c/em\u003e, \u003cem\u003e88\u003c/em\u003e(3), 628\u0026ndash;642.\u003c/li\u003e\n\u003cli\u003eHirayama, Y. (2021). Housing, Family, and Life‐Course in Post‐Growth Japan. \u003cem\u003eJapan Architectural Review\u003c/em\u003e, \u003cem\u003e4\u003c/em\u003e(2), 267\u0026ndash;276. https://doi.org/10.1002/2475-8876.12216\u003c/li\u003e\n\u003cli\u003eJalovaara, M. (2002). Socioeconomic Differentials in Divorce Risk by Duration of Marriage. \u003cem\u003eDemographic research\u003c/em\u003e, \u003cem\u003e7\u003c/em\u003e(Journal Article), 537\u0026ndash;564. https://doi.org/10.4054/demres.2002.7.16\u003c/li\u003e\n\u003cli\u003eKillewald, A. (2016). Money, Work, and Marital Stability: Assessing Change in the Gendered Determinants of Divorce. \u003cem\u003eAmerican sociological review\u003c/em\u003e, \u003cem\u003e81\u003c/em\u003e(4), 696\u0026ndash;719. https://doi.org/10.1177/0003122416655340\u003c/li\u003e\n\u003cli\u003eKillewald, A., \u0026amp; Bryan, B. (2016). Does Your Home Make You Wealthy? \u003cem\u003eRSF : Russell Sage Foundation journal of the social sciences\u003c/em\u003e, \u003cem\u003e2\u003c/em\u003e(6), 110\u0026ndash;128. https://doi.org/10.7758/rsf.2016.2.6.06\u003c/li\u003e\n\u003cli\u003eKillewald, A., Lee, A., \u0026amp; England, P. (2023). Wealth and Divorce. \u003cem\u003eDemography\u003c/em\u003e, \u003cem\u003e60\u003c/em\u003e(1), 147\u0026ndash;171. https://doi.org/10.1215/00703370-10413021\u003c/li\u003e\n\u003cli\u003eLersch, P. M. (2017). The Marriage Wealth Premium Revisited: Gender Disparities and Within-Individual Changes in Personal Wealth in Germany. \u003cem\u003eDemography\u003c/em\u003e, \u003cem\u003e54\u003c/em\u003e(3), 961\u0026ndash;983. https://doi.org/10.1007/s13524-017-0572-4\u003c/li\u003e\n\u003cli\u003eLersch, P. M., \u0026amp; VIdal, S. (2014). Falling Out of Love and Down the Housing Ladder: A Longitudinal Analysis of Marital Separation and Home Ownership. \u003cem\u003eEuropean sociological review\u003c/em\u003e, \u003cem\u003e30\u003c/em\u003e(4), 512\u0026ndash;524. https://doi.org/10.1093/esr/jcu055\u003c/li\u003e\n\u003cli\u003eLundberg, S., \u0026amp; Pollak, R. A. (1996). Bargaining and Distribution in Marriage. \u003cem\u003eJournal of Economic Perspectives\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e(4), 139\u0026ndash;158. https://doi.org/10.1257/jep.10.4.139\u003c/li\u003e\n\u003cli\u003eLyngstad, T. H., \u0026amp; Jalovaara, M. (2010). A review of the antecedents of union dissolution. \u003cem\u003eDemographic research\u003c/em\u003e, \u003cem\u003e23\u003c/em\u003e(Journal Article), 257\u0026ndash;292. https://doi.org/10.4054/DemRes.2010.23.10\u003c/li\u003e\n\u003cli\u003eMcLanahan, S. (2004). Diverging Destinies: How Children are Faring under the Second Demographic Transition. \u003cem\u003eDemography\u003c/em\u003e, \u003cem\u003e41\u003c/em\u003e(4), 607\u0026ndash;627. https://doi.org/10.1353/dem.2004.0033\u003c/li\u003e\n\u003cli\u003eMeyer, J. W., \u0026amp; Rowan, B. (1977). Institutionalized Organizations: Formal Structure as Myth and Ceremony. \u003cem\u003eAmerican Journal of Sociology\u003c/em\u003e, \u003cem\u003e83\u003c/em\u003e(2), 340\u0026ndash;363. https://doi.org/10.1086/226550\u003c/li\u003e\n\u003cli\u003eMirowsky, J., \u0026amp; Ross, C. E. (1989). \u003cem\u003eSocial causes of psychological distress\u003c/em\u003e. New York: de Gruyter. https://go.exlibris.link/Z5XcJ4Yj\u003c/li\u003e\n\u003cli\u003eMori, S., \u0026amp; Sugaya, K. (2017). The Relationship between Financial Assets and Land/Housing Assets in Household Portfolios. \u003cem\u003eDaiwa Institute of Research Quarterly\u003c/em\u003e, \u003cem\u003e26\u003c/em\u003e, 66\u0026ndash;79.\u003c/li\u003e\n\u003cli\u003eOECD. (2023). \u003cem\u003eOECD Employment Outlook 2023\u003c/em\u003e. Paris: OECD Publishing. https://doi.org/10.1787/19991266\u003c/li\u003e\n\u003cli\u003eOno, H. (1998). Husbands\u0026rsquo; and Wives\u0026rsquo; Resources and Marital Dissolution. \u003cem\u003eJournal of Marriage and the Family\u003c/em\u003e, \u003cem\u003e60\u003c/em\u003e(3), 674-. https://doi.org/10.2307/353537\u003c/li\u003e\n\u003cli\u003eOppenheimer, V. K. (1997). Women\u0026rsquo;s Employment and the Gain to Marriage: The Specialization and Trading Model. \u003cem\u003eAnnual review of sociology\u003c/em\u003e, 431\u0026ndash;453.\u003c/li\u003e\n\u003cli\u003ePark, H., \u0026amp; Raymo, J. M. (2013). Divorce in Korea: Trends and Educational Differentials. \u003cem\u003eJournal of marriage and family\u003c/em\u003e, \u003cem\u003e75\u003c/em\u003e(1), 110\u0026ndash;126. https://doi.org/10.1111/j.1741-3737.2012.01024.x\u003c/li\u003e\n\u003cli\u003ePearlin, L. I., Menaghan, E. G., Lieberman, M. A., \u0026amp; Mullan, J. T. (1981). The Stress Process. \u003cem\u003eJournal of health and social behavior\u003c/em\u003e, \u003cem\u003e22\u003c/em\u003e(4), 337\u0026ndash;356. https://doi.org/10.2307/2136676\u003c/li\u003e\n\u003cli\u003eRonald, R., \u0026amp; Hirayama, Y. (2009). Home Alone: The Individualization of Young, Urban Japanese Singles. \u003cem\u003eEnvironment and Planning A: Economy and Space\u003c/em\u003e, \u003cem\u003e41\u003c/em\u003e(12), 2836\u0026ndash;2854. https://doi.org/10.1068/a41119\u003c/li\u003e\n\u003cli\u003eSayer, L. C., \u0026amp; Bianchi, S. M. (2000). Women\u0026rsquo;s Economic Independence and the Probability of Divorce: A Review and Reexamination. \u003cem\u003eJournal of family issues\u003c/em\u003e, \u003cem\u003e21\u003c/em\u003e(7), 906\u0026ndash;943. https://doi.org/10.1177/019251300021007005\u003c/li\u003e\n\u003cli\u003eSchwartz, C. R., \u0026amp; Gonalons-Pons, P. (2016). Trends in Relative Earnings and Marital Dissolution: Are Wives Who Outearn Their Husbands Still More Likely to Divorce? \u003cem\u003eRSF : Russell Sage Foundation journal of the social sciences\u003c/em\u003e, \u003cem\u003e2\u003c/em\u003e(4), 218\u0026ndash;236. https://doi.org/10.7758/rsf.2016.2.4.08\u003c/li\u003e\n\u003cli\u003eSchwartz, C. R., \u0026amp; Han, H. (2014). The Reversal of the Gender Gap in Education and Trends in Marital Dissolution. \u003cem\u003eAmerican Sociological Review\u003c/em\u003e, \u003cem\u003e79\u003c/em\u003e(4), 605\u0026ndash;629. https://doi.org/10.1177/0003122414539682\u003c/li\u003e\n\u003cli\u003eSouth, S. J., \u0026amp; Spitze, G. (1986). Determinants of Divorce Over the Marital Life Course. \u003cem\u003eAmerican sociological review\u003c/em\u003e, \u003cem\u003e51\u003c/em\u003e(5), 583.\u003c/li\u003e\n\u003cli\u003eStatistics Bureau of Japan. (2022). \u003cem\u003eSurvey on Time Use and Leisure Activities 2021\u003c/em\u003e. Tokyo: Ministry of Internal Affairs and Communications.\u003c/li\u003e\n\u003cli\u003eStatistics Bureau of Japan. (2023). \u003cem\u003eLabour Force Survey 2023\u003c/em\u003e. Tokyo: Ministry of Internal Affairs and Communications.\u003c/li\u003e\n\u003cli\u003eStevenson, B., \u0026amp; Wolfers, J. (2007). Marriage and Divorce: Changes and Their Driving Forces. \u003cem\u003eThe Journal of economic perspectives\u003c/em\u003e, \u003cem\u003e21\u003c/em\u003e(2), 27\u0026ndash;52. https://doi.org/10.1257/jep.21.2.27\u003c/li\u003e\n\u003cli\u003eTichenor, V. J. (2005). \u003cem\u003eEarning More and Getting Less : Why Successful Wives Can\u0026rsquo;t Buy Equality\u003c/em\u003e. New Brunswick, N.J: Rutgers University Press. https://go.exlibris.link/8JgVRf29\u003c/li\u003e\n\u003cli\u003eYu, W., \u0026amp; Kuo, J. C.-L. (2016). Explaining the Effect of Parent-Child Coresidence on Marriage Formation: The Case of Japan. \u003cem\u003eDemography\u003c/em\u003e, \u003cem\u003e53\u003c/em\u003e(5), 1283\u0026ndash;1318. https://doi.org/10.1007/s13524-016-0494-6\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Divorce, Household Assets, Family Structure, Asset Type, Stratification, Japan","lastPublishedDoi":"10.21203/rs.3.rs-8004673/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8004673/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study examines whether the protective effect of wealth on marital stability varies across family types in contemporary Japan. As women's economic participation rises and family structures diversify, scholars debate whether assets function similarly across different household configurations, yet empirical evidence remains limited, particularly in East Asian societies experiencing rapid family transformation. This study employs discrete-time event history models on panel data from the Japanese Panel Survey of Consumers spanning 1993 to 2021 to assess how assets influence divorce risk across family types. The analysis reveals that wealth provides significant protection against marital dissolution for all family configurations, though this effect varies by household type. The protective effect is strongest among traditional male-breadwinner families and weakest among female-breadwinner households, with dual-earner families falling between these extremes. Financial assets show substantially weaker protective effects in dual-earner families compared to traditional households, whereas illiquid assets such as housing maintain consistent protective effects across all family types. These patterns persist when using continuous measures of women's relative income or detailed employment classifications. The findings suggest that despite profound transformations in family structure, economic resources remain fundamental to marital stability. While the function of liquid assets varies with household power dynamics, illiquid assets maintain universal protective effects. These results illuminate how growing wealth inequality increasingly translates into disparities in relationship stability, contributing to the broader stratification of family life in contemporary Japan.\u003c/p\u003e","manuscriptTitle":"Wealth and Relational Inequality: How Household Assets Shape Marital Stability by Family Type in Japan","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-11 15:59:01","doi":"10.21203/rs.3.rs-8004673/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"05473560-0e36-4cb3-a6bf-a4e67befb87c","owner":[],"postedDate":"December 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-15T22:23:56+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-11 15:59:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8004673","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8004673","identity":"rs-8004673","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}